Analysis of T-lymphocyte subsets and risk factors in children with tuberculosis

BACKGROUND

Tuberculosis (TB) is not only related to infection but also involves immune factors. This study explores the changes in T-lymphocyte subsets in children with TB who are human immunodeficiency virus (HIV)-negative and examines their relationship using chest computed tomography (CT) scans. Additionally, the study identifies risk factors for severe TB (STB) in children and establishes relevant risk prediction models.

METHODS

We recruited 235 participants between 2018 and 2022, comprising 176 paediatric patients with TB who were HIV-negative and 59 age-matched children with bacterial community-acquired pneumonia (CAP). We quantitatively analysed and compared T-lymphocyte subsets between the two groups and among different types of TB infection. Both univariate and multivariate analyses of clinical and laboratory characteristics were conducted to identify independent risk factors for STB in children and to establish a risk prediction model.

RESULTS

The absolute counts of CD3, CD4 and CD8 T-cells in children with TB infection decreased significantly compared with bacterial CAP. The percentage of CD8 T-cells increased, whereas the percentage of CD4 T-cells did not change significantly. The absolute count of CD3, CD4 and CD8 T-cells in extrapulmonary TB (EPTB) was significantly higher than in extra-respiratory TB, with unchanged subset percentages. According to chest CT lesion classification, CD4 T-cell counts decreased significantly in S3 compared with S1 or S2, with no significant change in CD3 and CD8 T-cell counts and percentages. No significant differences were observed in lymphocyte subset counts and percentages between S1 and S2. Univariate analyses indicated that factors such as age, symptom duration, white blood cell count, platelet count, neutrophil-to-lymphocyte ratio (NLR), erythrocyte sedimentation rate, prealbumin level, albumin level, globulin level, albumin/globulin (A/G) ratio, high-sensitivity C-reactive protein (Hs-CRP) level and CD4 and CD8 T-cell counts are associated with STB. Multivariate logistic regression analysis revealed that age, Hs-CRP level, NLR, symptom duration and A/G ratio are independent risk factors for STB in children. Increased age, Hs-CRP levels and NLR, along with decreased A/G, correlate with increased susceptibility to STB. A nomogram model, based on these independent risk factors, demonstrated an area under the receiver operating characteristics curve of 0.867 (95% CI: 0.813-0.921). Internal verification confirmed the model's accuracy, with the calibration curve approaching the ideal and the Hosmer-Lemeshow goodness-of-fit test showing consistent results (χ2 = 12.212, p = 0.142).

CONCLUSION

In paediatric patients with TB, the absolute counts of all lymphocyte subsets were considerably reduced compared with those in patients with bacterial CAP. Clinicians should consider the possibility of EPTB infection in addition to respiratory infections in children with TB who have higher CD3, CD4 and CD8 T-cell counts than the ERTB group. Furthermore, CD4 T-cell counts correlated closely with the severity of chest CT lesions. Age, symptom duration, A/G ratio, Hs-CRP level and NLR were established as independent risk factors for STB. The nomogram model, based on these factors, offers effective discrimination and calibration in predicting STB in children.

Analysis of factors affecting intraoperative hemorrhage during percutaneous nephrolithotomy and establishment of nomogram model

Intraoperative hemorrhage is an important factor affecting intraoperative safety and postoperative patient recovery in percutaneous nephrolithotomy (PCNL). This study aimed to identify the factors that influence intraoperative hemorrhage during PCNL and develop a predictive nomogram model based on these factors.A total of 118 patients who underwent PCNL at the Department of Urology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University from January 2021 to September 2023 was included in this study. The patients were divided into a hemorrhage group (58 cases) and a control group (60 cases) based on the decrease in hemoglobin levels after surgery. The clinical data of all patients were collected, and both univariate analysis and multivariate logistic regression analysis were conducted to identify the independent risk factors for intraoperative hemorrhage during PCNL. The independent risk factors were used to construct a nomogram model using R software. Additionally, receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were utilized to evaluate the model.Multivariate logistic regression analysis revealed that diabetes, long operation time and low psoas muscle mass index (PMI) were independent risk factors for intraoperative hemorrhage during PCNL (P < 0.05). A nomogram model was developed incorporating these factors, and the areas under the ROC curve (AUCs) in the training set and validation set were 0.740 (95% CI: 0.637-0.843) and 0.742 (95% CI: 0.554-0.931), respectively. The calibration curve and Hosmer-Lemeshow test (P = 0.719) of the model proved that the model was well fitted and calibrated. The results of the DCA showed that the model had high value for clinical application.Diabetes, long operation time and low PMI were found to be independent risk factors for intraoperative hemorrhage during PCNL. The nomogram model based on these factors can be used to predict the risk of intraoperative hemorrhage, which is beneficial for perioperative intervention in high-risk groups to improve the safety of surgery and reduce the incidence of postoperative complications.

Development and validation of a nomogram to predict the risk of residual low back pain after tubular microdiskectomy of lumbar disk herniation

OBJECTIVE

Tubular microdiskectomy (tMD) is one of the most commonly used for treating lumbar disk herniation. However, there still patients still complain of persistent postoperative residual low back pain (rLBP) postoperatively. This study attempts to develop a nomogram to predict the risk of rLBP after tMD.

METHODS

The patients were divided into non-rLBP (LBP VAS score < 2) and rLBP (LBP VAS score ≥ 2) group. The correlation between rLBP and these factors were analyzed by multivariate logistic analysis. Then, a nomogram prediction model of rLBP was developed based on the risk factors screened by multivariate analysis. The samples in the model are randomly divided into training and validation sets in a 7:3 ratio. The Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the diskrimination, calibration and clinical value of the model, respectively.

RESULTS

A total of 14.3% (47/329) of patients have persistent rLBP. The multivariate analysis suggests that higher preoperative LBP visual analog scale (VAS) score, lower facet orientation (FO), grade 2-3 facet joint degeneration (FJD) and moderate-severe multifidus fat atrophy (MFA) are risk factors for postoperative rLBP. In the training and validation sets, the ROC curves, calibration curves, and DCAs suggested the good diskrimination, predictive accuracy between the predicted probability and actual probability, and clinical value of the model, respectively.

CONCLUSION

This nomogram including preoperative LBP VAS score, FO, FJD and MFA can serve a promising prediction model, which will provide a reference for clinicians to predict the rLBP after tMD.

Genetic Susceptibility, Mendelian randomization and Nomogram Model Construction of Gestational Diabetes Mellitus

CONTEXT

Gestational diabetes mellitus (GDM) is a pregnancy complicated disease that poses a risk to maternal and infant health. However, the etiology of the disease has been not yet elucidated.

OBJECTIVE

To detect the genetic susceptibility and construct a nomogram model with significantly associated polymorphisms and key clinical indicators for early prediction of gestational diabetes mellitus (GDM).

METHODS

11 functional single nucleotide polymorphisms (SNPs) screened by genome-wide association study (GWAS) were genotyped in 554 GDM cases and 641 healthy controls. Functional analysis of GDM positively associated SNPs, Multivariate mendelian randomization (MVMR) and a GDM early predictive nomogram model construction were performed.

RESULT

rs1965211, rs3760675 and rs7814359 were significantly associated with genetic susceptibility to GDM after adjusting age and pre-pregnancy BMI (pre-BMI). It seems that GDM associated SNPs have effects on regulating target gene transcription factor binding, post transcriptional splicing, and translation product structure. Besides, rs3760675 can be expression quantitative trait locis (eQTLs) and increase the XAB2 mRNA expression level (P = 0.047). The MVMR analysis showed that the increase of clinical variables of BMI, HbA1c and FPG had significant causal effects on GDM (BMI-ORMVMR = 1.52, HbA1c-ORMVMR = 1.32, FPG-ORMVMR = 1.78), P <0.05. A nomogram model constructed with pre-BMI, FPG, HbA1c, and genotypes of rs1965211, rs3760675 and rs7814359 showed an area under the ROC curve of 0.824.

CONCLUSION

Functional polymorphisms can change women's susceptibility to GDM and the predictive nomogram model based on genetic and environmental factors can effectively distinguish individuals with different GDM risks in early stages of pregnancy.

Prognostic analysis of related factors of adverse reactions to immunotherapy in advanced gastric cancer and establishment of a nomogram model

BACKGROUND

Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years. However, the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study. In order to determine the association between adverse reaction factors and prognosis, the aim of this study was to conduct a systematic prognostic analysis. By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy, a nomogram model will be established to predict the survival status of patients more accurately.

AIM

To explore the characteristics and predictors of immune-related adverse reactions (irAEs) in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1 (PD-1) inhibitors and to analyze the correlation between irAEs and patient prognosis.

METHODS

A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected. Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred. Clinical features, manifestations, and prognosis of irAEs in the two groups were collected and analyzed. A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs, and the prediction model of irAEs was established. The receiver operating characteristic (ROC) curve was used to evaluate the ability of different indicators to predict irAEs. A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis. The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients.

RESULTS

A total of 132 patients were followed up, of whom 63 (47.7%) developed irAEs. We looked at the two groups' clinical features and found that the two groups were statistically different in age ≥ 65 years, Ki-67 index, white blood cell count, neutrophil count, and regulatory T cell (Treg) count (all P < 0.05). Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence (P = 0.030). The ROC curve indicated that Treg + Ki-67 + age (≥ 65 years) combined could predict irAEs well (area under the curve = 0.753, 95% confidence interval: 0.623-0.848, P = 0.001). Results of the Kaplan-Meier survival curve showed that progression-free survival (PFS) was longer in the irAEs group than in the non-irAEs group (P = 0.001). Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS (P = 0.006).

CONCLUSION

The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy. irAEs can affect the patients' PFS and result in longer PFS. Treg + Ki-67 + age (≥ 65 years old) combined can better predict the occurrence of adverse reactions.

PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage.

METHODS

We first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis.

RESULTS

The datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation.

CONCLUSION

The new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients.

Tenascin-C as a potential biomarker and therapeutic target for esophageal squamous cell carcinoma

PURPOSE

To establish a prognostic model of esophageal squamous cell carcinoma (ESCC) patients based on tenascin-C (TNC) expression level and clinicopathological characteristics, and to explore the therapeutic potential of TNC inhibition.

METHODS

The expression of TNC was detected using immunohistochemistry (IHC) in 326 ESCC specimens and 50 normal esophageal tissues. Prognostic factors were determined by Cox regression analyses and were incorporated to establish the nomogram. The effects of TNC knockdown on ESCC cells were assessed in vitro and in vivo. Transcriptome sequencing (RNA-seq) and gene set enrichment analysis (GSEA) were performed to reveal signaling pathways regulated by TNC knockdown. The therapeutic significance of TNC knockdown combined with small-molecule inhibitors on cell proliferation was examined.

RESULTS

TNC protein was highly expressed in 48.77 % of ESCC tissues compared to only 2 % in normal esophageal epithelia (p < 0.001). The established nomogram model, based on TNC expression, pT stage, and lymph node metastasis, showed good performance on prognosis evaluation. More importantly, the reduction of TNC expression inhibited tumor cell proliferation and xenograft growth, and mainly down-regulated signaling pathways involved in tumor growth, hypoxia signaling transduction, metabolism, infection, etc. Knockdown of TNC enhanced the inhibitory effect of inhibitors targeting ErbB, PI3K-Akt, Ras and MAPK signaling pathways.

CONCLUSION

The established nomogram may be a promising model for survival prediction in ESCC. Reducing TNC expression enhanced the sensitivity of ESCC cells to inhibitors of Epidermal Growth Factor Receptor (EGFR) and downstream signaling pathways, providing a novel combination therapy strategy.

Differential diagnosis of cervical lymphadenopathy: Integration of postvascular phase of contrast-enhanced ultrasound and predictive nomogram model

BACKGROUND

Distinguishing benign from malignant cervical lymph nodes is critical yet challenging. This study evaluates the postvascular phase of contrast-enhanced ultrasound (CEUS) and develops a user-friendly nomogram integrating demographic, conventional ultrasound, and CEUS features for accurate differentiation.

METHODS

We retrospectively analyzed 395 cervical lymph nodes from 395 patients between January 2020 and December 2022. The cohort was divided into training and validation sets using stratified random sampling. A predictive model, based on demographic, ultrasound, and CEUS features, was created and internally validated.

RESULTS

The training set included 280 patients (130 benign, 150 malignant nodes) and the validation set 115 patients (46 benign, 69 malignant). Relative hypoenhancement in the postvascular phase emerged as a promising indicator for MLN, with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 96.7 %,52.3 %, 70.0 %, 93.2 %, and 76.1 %, respectively in the training set and 95.7 %, 52.2 %, 75.0 %, 88.9 %, and 74.8 % in the validation set. Age over 50 years, history of malignancy, short-axis diameter greater than 1.00 cm, focal hyperechogenicity, ill-defined borders, and centripetal perfusion were also identified as independent MLN indicators. The nomogram prediction model showed outstanding accuracy, with an area under the curve (AUC) of 0.922 (95 % CI: 0.892-0.953) in the training set and 0.914 (95 % CI: 0.864-0.963) in the validation set.

CONCLUSION

Relative hypoenhancement in the postvascular phase of CEUS, combined with demographics and ultrasound features, is effective for identifying MLNs. The developed prediction model, with a user-friendly nomogram, can facilitate clinical decision-making.

[Clinical study of constructing nomogram model based on multi-dimensional clinical indicators to predict prognosis of knee osteoarthritis]

OBJECTIVE

To analyze the factors affecting the prognosis of patients with knee osteoarthritis, and to construct a nomogram prediction model in conjunction with multi-dimensional clinical indicators.

METHODS

The clinical data of 234 patients with knee osteoarthritis who were treated in our hospital from January 2015 to June 2021 were retrospectively analyzed, including 126 males and 108 females；age more than 60 years old for 135 cases, age less than 60 years old for 99 cases. Lysholm knee function score was used to evaluate the prognosis of the patients, and the patients were divided into good prognosis group for 155 patients and poor prognosis group for 79 patients according to the prognosis. The clinical data of the subjects in the experimental cohort were analyzed by single factor and multiple factors. The patients were divided into experimental cohort and verification cohort, the results of the multiple factor analysis were visualized to obtain a nomogram prediction model, the receiver operating characteristic curve（ROC）, calibration curve and decision curve were used to evaluate the model's discrimination, accuracy and clinical benefit rate.

RESULTS

The results of multivariate analysis showed that smoking, pre-treatment K-L grades of Ⅲ to Ⅳ, and high levels of interleukin 6 （IL-6） and matrix metallo proteinase-3 （MMP-3） were risk factors for the prognosis of patients with knee osteoarthritis. ROC test results showed that the area under the curve of the nomogram model in the experimental cohort and validation cohort was 0.806[95%CI（0.742, 0.866）] and 0.786[（95%CI（0.678, 0.893）], respectively. The results of the calibration curve showed that the Brier values of the experimental cohort and verification cohort were 0.151 points and 0.134 points, respectively. When the threshold probability value in the decision curve was set to 31%, the clinical benefit rates of the experimental cohort and validation cohort were 51% and 56%, respectively.

CONCLUSION

The prognostic model of patients with knee osteoarthritis constructed based on multi-dimensional clinical data has both theoretical and practical significance, and can provide a reference for taking targeted measures to improve the prognosis of patients.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a serious threat worldwide. Although early screening is suggested to be the most effective method to prevent and control CRC, the current situation of early screening for CRC is still not optimistic. In China, the incidence of CRC in the Yangtze River Delta region is increasing dramatically, but few studies have been conducted. Therefore, it is necessary to develop a simple and efficient early screening model for CRC.

AIM

To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.

METHODS

Data of 64448 participants obtained from Ningbo Hospital, China between 2014 and 2017 were retrospectively analyzed. The cohort comprised 64448 individuals, of which, 530 were excluded due to missing or incorrect data. Of 63918, 7607 (11.9%) individuals were considered to be high risk for CRC, and 56311 (88.1%) were not. The participants were randomly allocated to a training set (44743) or validation set (19175). The discriminatory ability, predictive accuracy, and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic (ROC) curves and calibration curves and by decision curve analysis. Finally, the model was validated internally using a bootstrap resampling technique.

RESULTS

Seven variables, including demographic, lifestyle, and family history information, were examined. Multifactorial logistic regression analysis revealed that age [odds ratio (OR): 1.03, 95% confidence interval (CI): 1.02-1.03, P < 0.001], body mass index (BMI) (OR: 1.07, 95%CI: 1.06-1.08, P < 0.001), waist circumference (WC) (OR: 1.03, 95%CI: 1.02-1.03 P < 0.001), lifestyle (OR: 0.45, 95%CI: 0.42-0.48, P < 0.001), and family history (OR: 4.28, 95%CI: 4.04-4.54, P < 0.001) were the most significant predictors of high-risk CRC. Healthy lifestyle was a protective factor, whereas family history was the most significant risk factor. The area under the curve was 0.734 (95%CI: 0.723-0.745) for the final validation set ROC curve and 0.735 (95%CI: 0.728-0.742) for the training set ROC curve. The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.

CONCLUSION

The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age, BMI, WC, lifestyle, and family history exhibited high accuracy.

Establishment and validation of a multivariate predictive model for the efficacy of oral rehydration salts in children with postural tachycardia syndrome

BACKGROUND

The therapeutic effectiveness of the empirical and unselected use of oral rehydration salts (ORS) on postural tachycardia syndrome (POTS) is not satisfactory in children. Therefore, looking for suitable predictors of the therapeutic effects of ORS before treatment is extremely necessary to implement individualised treatment for paediatric patients with POTS.

METHODS

A retrospective case-control analysis of 130 patients (aged 5-18 years) who suffered from POTS with a 3-month treatment of ORS was conducted. A nomogram model was developed in the training set (n = 87) to predict the therapeutic response to ORS. Univariate analysis and logistic regression were applied to select the most useful predictors. ROC curves were applied to evaluate the discriminative performance of the nomogram model. The nomogram was then evaluated by calibration curves and the Hosmer-Lemeshow (H-L) test. The results were further validated using 1000 bootstrap resamples. External validation was performed in an independent validation set (n = 43).

FINDINGS

Among the ten variables with significant differences between the responders and non-responders in univariate analysis, five variables were found to be independently associated factors for ORS therapeutic efficacy among POTS children in the further logistic regression, including mean corpuscular haemoglobin concentration (MCHC), mean corpuscular volume (MCV), mean arterial pressure (MAP) at the first minute of the upright position, urine specific gravity (SG), and P-wave voltage peaking ratio (PWP). The nomogram model was established in the training set (AUC 0.926 [95% CI: 0.865-0.988], yielding a sensitivity of 87.8% and a specificity of 86.8%). The calibration curves showed good agreement between the prediction of the nomogram and actual observation in both the training and validation sets. The nomogram also effectively predicted the external validation set (sensitivity 82.1%, specificity 73.3%, and accuracy 79.1%).

INTERPRETATION

We established a feasible and high-precision nomogram model to predict the efficacy of ORS, which would help implement individualised treatment for children with POTS.

FUNDING

This study was supported by National High-Level Hospital Clinical Research Funding (Multi-centre Clinical Research Project of Peking University First Hospital) (2022CR59).

A Nomogram Model to Predict Deep Vein Thrombosis Risk After Surgery in Patients with Hip Fractures

Aims

This study aimed to establish a nomogram model for predicting the probability of postoperative deep vein thrombosis (DVT) risk in patients with hip fractures.

Methods

504 patients were randomly assigned to the training set and validation set, and then divided into a DVT group and a non-DVT group. The study analysed the risk factors for DVT using univariate and multivariate analyses. Based on these parameters, a nomogram model was constructed and validated. The predicting performance of nomogram was evaluated by discrimination, calibration, and clinical usefulness.

Results

The predictors contained in the nomogram model included age, surgical approach, 1-day postoperative D-dimer value and admission ultrasound diagnosis of the lower limb vein. Furthermore, the area under the ROC curve (AUC) for the specific DVT risk-stratification nomogram model (0.815; 95% CI 0.746-0.884) was significantly higher than the current model (Caprini) (0.659; 95% CI 0.572-0.746, P < 0.05). According to the calibration plots, the prediction and actual observation were in good agreement. In the range of threshold probabilities of 0.2-0.8, the predictive performance of the model on DVT risk could be maximized.

Conclusions

The current predictive model could serve as a reliable tool to quantify the possibility of postoperative DVT in hip fractures patients.

Prognosis of carcinoembryonic antigen (CEA) in stage I colorectal adenocarcinoma and development of a prediction model: a retrospective study based on the SEER database

BACKGROUND

To investigate the prognostic significance of preoperative carcinoembryonic antigen (CEA) status in stage I colorectal classical adenocarcinoma (CCA) and mucinous adenocarcinoma (MUC), and to construct a nomogram model of stage I CCA.

METHODS

The SEER database was used to collect 14,226 patients diagnosed with stage I colorectal adenocarcinoma (CA) from 2010 to 2015. The prognostic significance of preoperative CEA status in stage I CA and MUC was examined by propensity-matching score (PSM). We analyzed the factors affecting the prognosis of patients with stage I CCA, and constructed and verified the prognostic model.

RESULTS

After PSM, the cancer-specific survival rate (CCS) of CEA-positive patients in stage T1 and T2 CCA was significantly lower than that of CEA-negative patients in stage T1 and T2 [HR = 0.37 (0.29-0.48), P < 0.001], [HR = 0.52 (0.41-0.65), P < 0.001]. However, there was no significant difference in CSS between CEA-positive and CEA-negative patients in T1 and T2 MUC [HR = 0.58 (0.43-0.79), P = 0.096], [HR = 0.76 (0.36-1.62), P = 0.477]. A nomogram was constructed based on the results of the multivariate COX regression model. Based on the AUC of ROC analysis, calibration plot and decision curve analysis (DCA), we concluded that the risk and prognosis model of CCA showed excellent performance.

CONCLUSION

Elevated CEA is a risk factor for stage I CCA, but not for MUC. And the nomogram is accurate enough to predict the risk and prognostic factors of CCA.

Prognostic value of the systemic inflammation response index in patients with aneurismal subarachnoid hemorrhage and a Nomogram model construction

OBJECTIVE

To investigate the prognostic value of inflammatory markers, including neutrophil/lymphocyte ratio (NLR), derived neutrophil/lymphocyte ratio (dNLR), platelet/lymphocyte ratio (PLR), monocyte/lymphocyte ratio (MLR), prognostic nutritional index (PNI), and systemic inflammation response index (SIRI) in patients with aneurismal subarachnoid hemorrhage (aSAH), and then develop a Nomogram prognostic model.

METHODS

We analysed 178 aSAH patients who underwent surgery at Subei People's Hospital of Jiangsu province from January 2015 to December 2017. Patients were divided into two groups according to Glasgow outcome scale (GOS) score at 3 months. Univariate and multivariate analysis were used to identify the association between inflammatory markers and prognosis. Subsequently, we identified the best cutoff of SIRI for unfavorable outcome using receiver operating characteristic (ROC) curve analysis and compared the clinical data between high and low SIRI levels. We further evaluated the additive value of SIRI by comparing prognostic nomogram models with and without it.

RESULTS

A total of 47 (26.4%) patients had a poor outcome. Multivariate logistic regression analysis showed that SIRI was an independent risk factor of poor outcome. The SIRI of 4.105 × 109/L was identified as the optimal cutoff value, patients with high SIRI levels had worse clinical status and higher rates of unfavorable outcome. ROC analysis showed that a nomogram model combining the SIRI and other conventional factors showed more favorable predictive ability than the model without the SIRI.

CONCLUSIONS

SIRI was independently correlated with unfavorable outcome in SAH patients, and the nomogram model combining the SIRI had more favorable discrimination ability.

Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China

This study comprehensively incorporates pathological parameters and novel clinical prognostic factors from the international prognostic index (IPI) to develop a nomogram prognostic model for overall survival in patients with diffuse large B-cell lymphoma (DLBCL). The aim is to facilitate personalized treatment and management strategies. This study enrolled a total of 783 cases for analysis. LASSO regression and stepwise multivariate COX regression were employed to identify significant variables and build a nomogram model. The calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were utilized to assess the model's performance and effectiveness. Additionally, the time-dependent concordance index (C-index) and time-dependent area under the ROC curve (AUC) were computed to validate the model's stability across different time points. The study utilized 8 selected clinical features as predictors to develop a nomogram model for predicting the overall survival of DLBCL patients. The model exhibited robust generalization ability with an AUC exceeding 0.7 at 1, 3, and 5 years. The calibration curve displayed evenly distributed points on both sides of the diagonal, and the slopes of the three calibration curves were close to 1 and statistically significant, indicating high prediction accuracy of the model. Furthermore, the model demonstrated valuable clinical significance and holds the potential for widespread adoption in clinical practice. The novel prognostic model developed for DLBCL patients incorporates readily accessible clinical parameters, resulting in significantly enhanced prediction accuracy and performance. Moreover, the study's use of a continuous general cohort, as opposed to clinical trials, makes it more representative of the broader lymphoma patient population, thus increasing its applicability in routine clinical care.

A nomogram prediction model for refracture in elderly patients with osteoporotic vertebral compression fractures after percutaneous vertebroplasty

BACKGROUND

This study aims to evaluate the risk factors of refracture in elderly patients with osteoporotic vertebral compression fracture (OVCF) patients after percutaneous vertebroplasty (PVP) and construct a predictive nomogram model.

METHODS

Elderly symptomatic OVCF patients undergoing PVP were enrolled and grouped based on the development of refracture within 1 year postoperatively. Univariate and multivariate logistic regression analyses were performed to identify risk factors. Subsequently, a nomogram prediction model was constructed and evaluated based on these risk factors.

RESULTS

A total of 264 elderly OVCF patients were enrolled in the final cohort. Among these, 48 (18.2%) patients had suffered refracture within 1 year after surgery. Older age, lower mean spinal BMD, multiple vertebral fracture, lower albumin/fibrinogen ratio (AFR), no postoperative regular anti-osteoporosis, and exercise were six independent risk factors identified for postoperative refracture. The AUC of the constructed nomogram model based on these six factors was 0.812 with a specificity and sensitivity of 0.787 and 0.750, respectively.

CONCLUSIONS

In summary, the nomogram model based on the six risk factors had clinical efficacy for refracture prediction.

The prediction model of operative link on gastric intestinal metaplasia stage III-IV: A multicenter study

Purpose

Intestinal metaplasia plays a crucial role in the risk stratification of gastric cancer development. The objective of the study was to develop a prediction model for Operative Link on Gastric Intestinal Metaplasia (OLGIM) Stage III-IV.

Methods

We analyzed 7945 high-risk gastric cancer individuals from 115 hospitals who underwent questionnaires and gastroscope. The participants were assigned to either the development or validation cohort randomly. Demographics and clinical characteristics were obtained. The outcome measurement was OLGIM III-IV. Univariate logistic regression was used for feature selection and multivariate logistic analysis was performed to develop the nomogram. Area under the curves, calibration plots, decision curve and clinical impact analysis were used to assess the performance of the nomogram.

Results

4600 individuals and 3345 individuals were included in the development and validation cohort, of which 124 and 86 individuals were diagnosed with OLGIM III-IV, respectively. Parameters in the training validation cohort matched well and there was no significant difference between two cohorts. A nomogram model for predicting OLGIM Stage III-IV consisted of 4 significantly associated variables, including age, gender, PG I and G-17 (AUC 0.723 and 0.700 for the 2 cohorts). The nomogram demonstrated excellent performance in the calibration curve. Decision curve and clinical impact analysis suggested clinical benefit of the prediction model.

Conclusions

This reliable individualized nomogram might contribute to more accurate management for patients with OLGIM III-IV. Therefore, we suggest that this study be used as an incentive to promote the application.

Chromosome 1p36 candidate gene ZNF436 predicts the prognosis of neuroblastoma: a bioinformatic analysis

BACKGROUND

Genetic 1p deletion is reported in 30% of all neuroblastoma and is associated with the unfavorable prognosis of neuroblastoma. The expressions and prognosis of 1p candidate genes in neuroblastoma are unclear.

METHODS

Public neuroblastoma cohorts were obtained for secondary analysis. The prognosis of 1p candidate genes in neuroblastoma was determined using Kaplan-Meier and cox regression analysis. The prediction of the nomogram model was determined using timeROC.

RESULTS

First, we confirmed the bad prognosis of 1p deletion in neuroblastoma. Moreover, zinc finger protein 436 (ZNF436) located at 1p36 region was down-regulated in 1p deleted neuroblastoma and higher ZNF436 expression was associated with the longer event free survival and overall survival of neuroblastoma. The expression levels of ZNF436 were lower in neuroblastoma patients with MYCN amplification or age at diagnosis ≥ 18months, or with stage 4 neuroblastoma. ZNF436 had robust predictive values of MYCN amplification and overall survival of neuroblastoma. Furthermore, the prognostic significance of ZNF436 in neuroblastoma was independent of MYCN amplification and age of diagnosis. Combinations of ZNF436 with MYCN amplification or age of diagnosis achieved better prognosis. At last, we constructed a nomogram risk model based on age, MYCN amplification and ZNF436. The nomogram model could predict the overall survival of neuroblastoma with high specificity and sensitivity.

CONCLUSIONS

Chromosome 1p36 candidate gene ZNF436 was a prognostic maker of neuroblastoma.

Optimization strategy for the early timing of bronchoalveolar lavage treatment for children with severe mycoplasma pneumoniae pneumonia

BACKGROUND

Early evaluation of severe mycoplasma pneumoniae pneumonia (SMPP) and the prompt utilization of fiberoptic bronchoscopic manipulation can effectively alleviate complications and restrict the progression of sequelae. This study aim to establish a nomogram forecasting model for SMPP in children and explore an optimal early therapeutic bronchoalveolar lavage (TBAL) treatment strategy.

METHODS

This retrospective study included children with mycoplasma pneumoniae pneumonia (MPP) from January 2019 to December 2021. Multivariate logistic regression analysis was used to screen independent risk factors for SMPP and establish a nomogram model. The bootstrap method was employed and a receiver operator characteristic (ROC) curve was drawn to evaluate the accuracy and robustness of the model. Kaplan-Meier analysis was used to assess the effect of lavage and hospitalization times.

RESULTS

A total of 244 cases were enrolled in the study, among whom 68 with SMPP and 176 with non-SMPP (NSMPP). A prediction model with five independent risk factors: left upper lobe computed tomography (CT) score, sequential organ failure assessment (SOFA) score, acute physiology and chronic health assessment (APACHE) II score, bronchitis score (BS), and c-reactive protein (CRP) was established based on the multivariate logistic regression analysis. The ROC curve of the prediction model showed the area under ROC curve (AUC) was 0.985 (95% confidence interval (CI) 0.972-0.997). The Hosmer-Lemeshow goodness-of-fit test results showed that the nomogram model predicted the risk of SMPP well (χ2 = 2.127, P = 0.977). The log-rank result suggested that an early BAL treatment could shorten MPP hospitalization time (P = 0.0057).

CONCLUSION

This nomogram model, based on the left upper lobe CT score, SOFA score, APACHE II score, BS, and CRP level, represents a valuable tool to predict the risk of SMPP in children and optimize the timing of TBAL.

A nomogram model for predicting lower extremity deep vein thrombosis after gynecologic laparoscopic surgery: a retrospective cohort study

Objective

To investigate the risk factors associated with lower extremity deep vein thrombosis (LEDVT) and to establish a predictive model for patients who undergo gynecologic laparoscopic surgery.

Methods

A review of clinical data was conducted on patients who underwent gynecologic laparoscopic surgery between November 1, 2020, and January 31, 2022. Patients who developed LEDVT after surgery were included as the observation group, while the control group comprised patients who did not experience complications. Multivariate forward stepwise logistic regression models were used to identify independent risk factors associated with LEDVT. A nomogram model was then developed based on these risk factors.

Results

A total of 659 patients underwent gynecologic laparoscopic surgery during the study period, and 52 (7.89%) of these patients developed postoperative LEDVT. Multivariate logistic regression analysis showed that older age (adjusted OR, 1.085; 95% CI [1.034-1.138]; P < 0.05), longer operation duration (adjusted OR, 1.014; 95% CI [1.009-1.020]; P < 0.05), shorter activated partial thromboplastin time (APTT) (adjusted OR, 0.749; 95% CI [0.635-0.884]; P < 0.05), higher D-dimer (adjusted OR, 4.929; 95% CI [2.369-10.255]; P < 0.05), higher Human Epididymis Protein 4 (HE4) (adjusted OR, 1.007; 95% CI [1.001-1.012]; P < 0.05), and history of hypertension (adjusted OR, 3.732; 95% CI [1.405-9.915]; P < 0.05) were all independent risk factors for LEDVT in patients who underwent gynecologic laparoscopic surgery. A nomogram model was then created, which had an area under the curve of 0.927 (95% CI [0.893-0.961]; P < 0.05), a sensitivity of 96.1%, and a specificity of 79.5%.

Conclusions

A nomogram model that incorporates information on age, operation duration, APTT, D-dimer, history of hypertension, and HE4 could effectively predict the risk of LEDVT in patients undergoing gynecologic laparoscopic surgery, potentially helping to prevent the development of this complication.

Establishment of a nomogram for predicting prolonged mechanical ventilation in cardiovascular surgery patients

AIMS

This study aimed to develop a nomogram model for predicting prolonged mechanical ventilation (PMV) in patients undergoing cardiovascular surgery.

METHODS AND RESULTS

In total, 693 patients undergoing cardiovascular surgery at an Affiliated Hospital of Nantong University between January 2018 and June 2020 were studied. Postoperative PMV was required in 147 patients (21.2%). Logistic regression analysis showed that delirium [odds ratio (OR), 3.063; 95% confidence interval (CI), 1.991-4.713; P < 0.001], intraoperative blood transfusion (OR, 2.489; 95% CI, 1.565-3.960; P < 0.001), obesity (OR, 2.789; 95% CI, 1.543-5.040; P = 0.001), postoperative serum creatinine level (mmol/L; OR, 1.012; 95% CI, 1.007-1.017; P < 0.001), postoperative serum albumin level (g/L; OR, 0.937; 95% CI, 0.902-0.973; P = 0.001), and postoperative total bilirubin level (μmol/L; OR, 1.020; 95% CI, 1.005-1.034; P = 0.008) were independent risk factors for PMV. The area under the receiver operating characteristic curve for our nomogram was found to be 0.770 (95% CI, 0.727-0.813). The goodness-of-fit test indicated that the model fitted the data well (χ2 = 12.480, P = 0.131). After the model was internally validated, the calibration plot demonstrated good performance of the nomogram, as supported by the Harrell concordance index of 0.760. Decision curve analysis demonstrated that the nomogram was clinically useful in identifying patients at risk for PMV.

CONCLUSION

We established a new nomogram model that may provide an individual prediction of PMV. This model may provide nurses, social workers, physicians, and administrators with an accurate and objective assessment tool to identify patients at high risk for PMV after cardiovascular surgery.

Identification of a distinct tumor endothelial cell-related gene expression signature associated with patient prognosis and immunotherapy response in multiple cancers

BACKGROUND

Tumor endothelial cells (TECs) play a significant role in regulating the tumor microenvironment, drug response, and immune cell activities in various cancers. However, the association between TEC gene expression signature and patient prognosis or therapeutic response remains poorly understood.

METHODS

We analyzed transcriptomics data of normal and tumor endothelial cells obtained from the GEO database to identify differentially expressed genes (DEGs) associated with TECs. We then compared these DEGs with those commonly found across five different tumor types from the TCGA database to determine their prognostic relevance. Using these genes, we constructed a prognostic risk model integrated with clinical features to develop a nomogram model, which we validated through biological experiments.

RESULTS

We identified 12 TEC-related prognostic genes across multiple tumor types, of which five genes were sufficient to construct a prognostic risk model with an AUC of 0.682. The risk scores effectively predicted patient prognosis and immunotherapeutic response. Our newly developed nomogram model provided more accurate prognostic estimates of cancer patients than the TNM staging method (AUC = 0.735) and was validated using external patient cohorts. Finally, RT-PCR and immunohistochemical analyses indicated that the expression of these 5 TEC-related prognostic genes was up-regulated in both patient-derived tumors and cancer cell lines, while depletion of the hub genes reduced cancer cell growth, migration and invasion, and enhanced their sensitivity to gemcitabine or cytarabine.

CONCLUSIONS

Our study discovered the first TEC-related gene expression signature that can be used to construct a prognostic risk model for guiding treatment options in multiple cancers.

Establishment of a nomogram model for prediction of postoperative heterochronous liver metastasis in young and middle-aged patients with rectal cancer

BACKGROUND

The incidence of rectal cancer is increasing year by year. Radical surgery is often used for the treatment of rectal cancer in clinical practice, but postoperative liver metastasis has become an important reason for the increase in mortality. Therefore, establishing a model to predict the trend of metachronous liver metastasis has become a research focus. Nomogram model has been widely used in the medical field, but there has been no widely accepted nomogram model available for prediction of metachronous liver metastasis after rectal cancer surgery.

AIM

To constuct a nomogram model based on the risk factors for postoperative metachronous liver metastasis in young and middle-aged patients with rectal cancer, and to evaluate the performance of the model for predicting the risk of postoperative metachronous liver metastasis, so as to provide some guidance for clinical prevention and treatment.

METHODS

A total of 120 young and middle-aged patients with rectal cancer admitted to our hospital from March 2019 to February 2022 were selected as research subjects to observe the incidence of postoperative heterochronous liver metastasis. Univariate and multivariate Logistic regression analyses were performed to identify the risk factors for postoperative heterochronous liver metastasis and to construct a nomogram model. ROC curve, decision curve, and correction curve analyses were used to verify the value of nomogram model for the prediction of postoperative heterochronous liver metastasis.

RESULTS

The incidence of anomalous liver metastasis 1 year after surgery was 23.33% in 120 young and middle-aged patients with rectal cancer. Low differentiation, lymph node metastasis, depth of invasion (T3/T4), margin width of primary cancer < 2 cm, high expression of peripheral blood telomerase reverse transcriptase (hTERT), and elevated serum levels of carcinoembryonic antigen (CEA), vascular endothelial growth factor (VEGF), lemur tyrosine kinase-3 (LMTK3), squamous cell carcinoma-associated antigen (SCC-Ag), and axon-guided factor-1 (Netrin-1) were identified to be risk factor for postoperative hetero-chronic liver metastasis (P < 0.05). The C-index and area under the curve of the nomogram model were 0.860 and 0.957, respectively, and the net benefit value was high (P < 0.05).

CONCLUSION

Low differentiation, lymph node metastasis, depth of invasion (T3/T4), margin width of primary cancer < 2 cm, high expression of hTERT in peripheral blood, and elevated levels of serum CEA, VEGF, LMTK3, SC-AG and Netrin-1 are risk factors for postoperative xenotemporal liver metastasis in young and middle-aged patients with rectal cancer. Based on the above risk factors, a nomogram model has been established to predict postoperative heterochronous liver metastasis in such patients.

A nomogram to predict postoperative deep vein thrombosis in patients with femoral fracture: a retrospective study

OBJECTIVE

The implementation of more active anticoagulant prevention and treatment measures has indeed led to a significant reduction in the incidence of perioperative deep vein thrombosis (DVT) among patients with bone trauma. However, it is important to note that despite these efforts, the incidence of DVT still remains relatively high. According to the Caprini score, all patients undergoing major orthopedic surgery were defined as the high-risk group for DVT. Stratifying the risk further within high-risk groups for DVT continues to present challenges. As a result, the commonly used Caprini score during the perioperative period is not applicable to orthopedic patients. We attempt to establish a specialized model to predict postoperative DVT risk in patients with femoral fracture.

METHODS

We collected the clinical data of 513 patients undergoing femoral fracture surgery in our hospital from May 2018 to December 2019. According to the independent risk factors of DVT obtained by univariate and multivariate logistic regression analysis, the corresponding nomogram model was established and verified internally. The discriminative capacity of nomogram was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). The calibration curve used to verify model consistency was the fitted line between predicted and actual incidences. The clinical validity of the nomogram model was assessed using decision curve analysis (DCA) which could quantify the net benefit of different risk threshold probabilities. Bootstrap method was applied to the internal validation of the nomogram model. Furthermore, a comparison was made between the Caprini score and the developed nomogram model.

RESULTS

The Caprini scores of subjects ranged from 5 to 17 points. The incidence of DVT was not positively correlated with the Caprini score. The predictors of the nomogram model included 10 risk factors such as age, hypoalbuminemia, multiple trauma, perioperative red blood cell infusion, etc. Compared with the Caprini scale (AUC = 0.571, 95% CI 0.479-0.623), the calibration accuracy and identification ability of nomogram were higher (AUC = 0.865,95% CI 0.780-0.935). The decision curve analysis (DCA) indicated the clinical effectiveness of nomogram was higher than the Caprini score.

CONCLUSIONS

The nomogram was established to effectively predict postoperative DVT in patients with femoral fracture. To further reduce the incidence, more specialized risk assessment models for DVT should take into account the unique risk factors and characteristics associated with specific patient populations.

Risk prediction of gestational diabetes mellitus in women with polycystic ovary syndrome based on a nomogram model

Women with polycystic ovary syndrome are prone to develop gestational diabetes mellitus, a disease which may have significant impact on the postpartum health of both mother and infant. We performed a retrospective cohort study to develop and test a model that could predict gestational diabetes mellitus in the first trimester in women with polycystic ovary syndrome. Our study included 434 pregnant women who were referred to the obstetrics department between December 2017 and March 2020 with a diagnosis of polycystic ovary syndrome. Of these women, 104 were diagnosed with gestational diabetes mellitus in the second trimester. Univariate analysis revealed that in the first trimester, Hemoglobin A1c (HbA1C), age, total cholesterol(TC), low-density lipoprotein cholesterol (LDL-C), SBP (systolic blood pressure), family history, body mass index (BMI), and testosterone were predictive factors of gestational diabetes mellitus (P < 0.05). Logistic regression revealed that TC, age, HbA1C, BMI and family history were independent risk factors for gestational diabetes mellitus. The area under the ROC curve of the gestational diabetes mellitus risk prediction model was 0.937 in this retrospective analysis, demonstrating a great discriminatory ability. The sensitivity and specificity of the prediction model were 0.833 and 0.923, respectively. The Hosmer-Lemeshow test also showed that the model was well calibrated.

Lung cancer risk and exposure to air pollution: a multicenter North China case-control study involving 14604 subjects

BACKGROUND

For North Chinese lung cancer patients, there is limited study on the distribution of air pollution and smoking related features based on analyses of large-scale, high-quality population datasets. The aim of the study was to fully analyze risk factors for 14604 Subjects.

METHODS

Participants and controls were recruited in 11 cities of North China. Participants' basic information (sex, age, marital status, occupation, height, and weight), blood type, smoking history, alcohol consumption, history of lung-related diseases and family history of cancer were collected. PM2.5 concentration data for each year in each city of the study area from 2005 to 2018 were extracted based on geocoding of each person's residential address at the time of diagnosis. Demographic variables and risk factors were compared between cases and matched controls using a univariate conditional logistic regression model. Multivariate conditional logistic regression models were applied to estimate the odds ratio (OR) and 95% confidence interval (CI) for risk factors in univariate analysis. The nomogram model and the calibration curve were developed to predict lung cancer probability for the probability of lung cancer.

RESULTS

There was a total of 14604 subjects, comprising 7124 lung cancer cases and 7480 healthy controls included in the study. Marital status of unmarried persons, people with a history of lung-related disease, corporate personnel and production /service personnel were protective factors for lung cancer. People younger than 50 years old, people who were smoking and quit smoking, people who had been drinking consistently, people with family history of cancer and PM2.5 exposure were proven to be a risk factor for lung cancer. The risk of lung cancer varied with sex, smoking status and air pollution. Consistent alcohol consumption, persistent smoking and smoking quit were risk factors for lung cancer in men. By smoking status, male was risk factor for lung cancer in never smokers. Consistent alcohol consumption added risk for lung cancer in never smokers. The combined effects of PM2.5 pollution exposure and ever smoking aggravated the incidence of lung cancer. According to air pollution, lung cancer risk factors are completely different in lightly and heavily polluted areas. In lightly polluted areas, a history of lung-related disease was a risk factor for lung cancer. In heavily polluted areas, male, consistent alcohol consumption, a family history of cancer, ever smokers and smoking quit were all risk factors for lung cancer. A nomogram was plotted and the results showed that PM2.5 was the main factor affecting the occurrence of lung cancer.

CONCLUSIONS

The large-scale accurate analysis of multiple risk factors in different air quality environments and various populations, provide clear directions and guidance for lung cancer prevention and precise treatment.

[Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma]

Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.

Development and validation of a nomogram model for medication non-adherence in patients with chronic kidney disease

OBJECTIVE

The high prevalence of medication non-adherence in patients with chronic kidney disease places a tremendous burden on healthcare resources. The study was designed to develop and validate a nomogram model of medication non-adherence in patients with chronic kidney disease in China.

METHODS

A multicenter cross-sectional study was conducted. 1206 chronic kidney disease patients were consecutively enrolled from Be Resilient to Chronic Kidney Disease (registration number: ChiCTR2200062288) between September 2021 and October 2022 in four tertiary hospitals in China. The Chinese version of four-item Morisky Medication Adherence Scale was used to assess the medication adherence of the patients and associated factors consisted of socio-demographic information, self-designed medication knowledge questionnaire, the 10-item Connor-Davidson Resilience Scale, the Beliefs about Medicine questionnaire, the Acceptance Illness Scale, and the Family Adaptation Partnership Growth and Resolve Index. Least Absolute Shrinkage and Selection Operator regression was performed to select significant factors. Concordance index, Hosmer-Lemeshow test and decision curve analysis were estimated.

RESULTS

The prevalence of medication non-adherence was 63.8%. Area under the curves ranged from 0.72 to 0.96 in internal and external validation sets. The predicted probabilities of the model were consistent with those of the actual observations by Hosmer-Lemeshow test (all P > .05). The final model included educational level, occupational status, duration of chronic kidney disease, medication beliefs (perceptions of the need to take medications and concerns about adverse effects), and illness acceptance (adaptation and acceptance of the disease).

CONCLUSIONS

There is a high prevalence of medication non-adherence among Chinese patients with chronic kidney disease. A nomogram model based on five factors has been successfully developed and validated and could be incorporated into long-term medication management.

Construction and validation of a nomogram model to predict symptomatic intracranial hemorrhage after intravenous thrombolysis in severe white matter lesions

Cerebral white matter lesions (WMLs) increase the risk of bleeding after intravenous thrombolysis (IVT) but are also considered to require IVT. Its risk factors and predictive models are still poorly studied. The aim of this study is to develop a clinically applicable model for post-IVT haemorrhage. It offers the possibility to prevent symptomatic intracranial hemorrhage (sICH) in patients with IVT in severe WMLs. A large single-center observational study conducted a retrospective analysis of IVT in patients with severe WMLs from January 2018 to December 2022. Univariate and multi-factor logistic regression results were used to construct nomogram model, and a series of validations were performed on the model. More than 2,000 patients with IVT were screened for inclusion in this study after cranial magnetic resonance imaging evaluation of 180 patients with severe WMLs, 28 of whom developed sICH. In univariate analysis, history of hypertension (OR 3.505 CI 2.257-4.752, p = 0.049), hyperlipidemia (OR 4.622 CI 3.761- 5.483, p < 0.001), the NIHSS score before IVT (OR 41.250 CI 39.212-43.288, p < 0.001), low-density lipoprotein levels (OR 1.995 CI 1.448-2.543, p = 0.013), cholesterol levels (OR 1.668 CI 1.246-2.090, p = 0.017), platelet count (OR 0.992 CI 0.985-0.999, p = 0.028), systolic blood pressure (OR 1.044 CI 1.022-1.066, p < 0.001), diastolic blood pressure (OR 1.047 CI 1.024-1.070, p < 0.001) were significantly associated with sICH. In a multifactorial analysis, the NIHSS score before IVT (OR 94.743 CI 92.311-97.175, p < 0.001), and diastolic blood pressure (OR 1.051 CI 1.005-1.097, p = 0.033) were considered to be significantly associated with sICH after IVT as risk factors for the occurrence of sICH. The four most significant factors from logistic regression are subsequently fitted to create a predictive model. The accuracy was verified using ROC curves, calibration curves, decision curves, and clinical impact curves, and the model was considered to have high accuracy (AUC 0.932, 95% 0.888-0.976). The NHISS score before IVT and diastolic blood pressure are independent risk factors for sICH after IVT in patients with severe WMLs. The models based on hyperlipidemia, the NIHSS score before IVT, low-density lipoprotein and diastolic blood pressure are highly accurate and can be applied clinically to provide a reliable predictive basis for IVT in patients with severe WMLs.

Predicative factors and development of a nomogram for postoperative delayed neurocognitive recovery in elderly patients with gastric cancer

BACKGROUND

Delayed neurocognitive recovery (DNR) is a common complication after radical gastrectomy and closely associated with poor outcomes. This study aimed to investigate predictors and develop a nomogram prediction model for DNR.

METHODS

Elderly gastric cancer (GC) patients (≥ 65 years) undergoing elective laparoscopic radical gastrectomy between 2018 and 2022 were prospectively included in this study. DNR was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V, 2013). Independent risk factors for DNR were screened by the multivariate logistic regression analysis. Based on these factors, the nomogram model was established and validated by R.

RESULTS

A total of 312 elderly GC patients were enrolled in the training set, with an incidence of DNR within postoperative 1 month of 23.4% (73/312). Multivariate logistic regression analysis indicated that age (OR: 1.207, 95%CI: 1.113-1.309, P < 0.001), nutritional risk screening 2002 (NRS2002) score (OR: 1.716, 95%CI: 1.211-2.433, P = 0.002), neutrophil-to-lymphocyte ratio (NLR) (OR: 1.976, 95%CI: 1.099-3.552, P = 0.023), albumin-to-fibrinogen ratio (AFR) (OR: 0.774, 95%CI: 0.620-0.966, P = 0.024), and prognostic nutritional index (PNI) (OR: 0.768, 95%CI: 0.706-0.835, P < 0.001) were five independent factors for DNR in elderly GC patients. The constructed nomogram model based on these five factors has a good predictive value for DNR with an area under the curve (AUC) of 0.863.

CONCLUSIONS

In conclusions, the established nomogram model based on age, NRS-2002, NLR, AFR, and PNI has a well predictive value for postoperative DNR in elderly GC patients.

[Establishment and Analysis of Risk Prediction Model for Metabolic Dysfunction-Associated Fatty Liver Disease in Physical Examination Population]

Objective

To analyze the risk factors of metabolic dysfunction-associated fatty liver disease (MAFLD) in the physical examination population, to establish a risk prediction model for the occurrence of MAFLD, and to provide management strategies for the prevention and occurrence of the disease.

Methods

A total of 14664 people who underwent physical examination at the Physical Examination Center, West China Hospital, Sichuan University between January 2018 and December 2021 were selected as research subjects. The subjects were divided into a MAFLD group ( n=4013) and a non-MAFLD group ( n=10651) according to whether they had MAFLD. The differences in biochemical indices, for example, glycolipid metabolism levels, were compared and logistic regression was conducted to analyze the risk factors for MAFLD, thereby establishing a nomogram prediction model. The prediction effect of the model was validated and evaluated with the consistency index (C-index) and the calibration curve.

Results

Among the 14664 subjects who underwent physical examination, 4013 were MAFLD patients, presenting an overall prevalence of 27.37%, with significantly higher prevalence in men than that in women (38.99% vs. 10.06%, P<0.001). Compared with those of the non-MAFLD group, the levels of glucose (GLU), total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transpeptidase (GGT) and uric acid (UA) were increased ( P<0.05), while the high density lipoprotein cholesterol (HDL-C) level was decreased ( P<0.05) in the MAFLD group. The results of logistic regression analysis showed that male sex, age, body mass index, GLU, TG and hypertension were all independent risk factors of MAFLD, while HDL-C was a protective factor of MAFLD. The risk factors were used to establish a nomogram risk prediction model and the C-index and calibration curve showed that the nomogram model produced good predictive performance. The receiver operating characteristic (ROC) curve showed that the nomogram model had good predictive value for the risk of MAFLD.

Conclusion

We found a relatively high prevalence of MAFLD in the physical examination population, and the nomogram model established with routine physical examination screening can provide indications for the clinical screening and analysis of high-risk patients, which has an early warning effect on the high-risk population.

A nomogram model to predict the acute venous thromboembolism risk after surgery in patients with glioma

INTRODUCTION

Postoperative venous thromboembolism (VTE) is a common complication for glioma patients, with an incidence rate of about 20 %. The purpose of this study was to explore the risk factors of acute VTE after glioma surgery, which may provide an essential reference for clinical guidance on the prevention of acute VTE.

MATERIALS AND METHODS

A total of 435 patients who underwent glioma surgery from 2012 to 2021 were included in this study. Duplex ultrasonography was performed routinely 3-5 days after the surgery to define VTE. Univariate and multivariate logistic regression analyses were performed to explore the independent predictor of acute VTE after glioma surgery and use these selected risk factors to construct and validate a nomogram.

RESULTS

Several risk factors for predicting acute VTE after glioma surgery were identified and used to build the nomogram: age, operation time, systemic immune-inflammation index (SII), hypertension, and diabetes mellitus. The area under the curve of the nomogram was 0.834, indicating good discrimination. Hosmer-Lemeshow of the calibration curve was 3.05 (P = 0.98), showing a high degree of agreement between the prediction and actual outcome. Decision curve analysis indicated that the nomogram model was helpful when the incidence of VTE was 5-80 %.

CONCLUSIONS

A nomogram to predict acute VTE after glioma surgery was constructed and validated. Clinicians can use this predictive model to achieve risk assessment and take different treatment measures to prevent acute postoperative VTE and improve patients' quality of life effectively.

Development of a nomogram for predicting pelvic lymph node metastasis in cervical squamous cell carcinoma

OBJECTIVE

To develop and validate a nomogram for predicting pelvic lymph node metastasis (LNM) in cervical squamous cell carcinoma (SCC).

METHODS

This was a retrospective study that included 715 patients with cervical SCC who underwent radical hysterectomy and bilateral pelvic lymphadenectomy between 2009 and 2018. Logistic regression analysis was used to identify independent risk factors for pelvic LNM. Based on these risk factors, a nomogram predicting LNM risk was constructed and internally validated using the bootstrapping resampling method.

RESULTS

The rate of LNM in FIGO (the International Federation of Gynecology & Obstetrics) Stage IA2-IIA2 cervical SCC was 24.2%. In multivariate analysis, FIGO Stage II, moderately differentiated or poorly differentiated histology, abnormally elevated serum SCC-antigen, and triglyceride were identified as independent risk factors for LNM. Tumor size greater than 2 cm and parametrial involvement had borderline significance. Ultimately, the nomogram contained the six variables mentioned above, showing positive calibration and positive discrimination. The area under the receiver operating characteristic curvewas 0.827 and the bootstrap-validated C-index was 0.827. The Youden index of this paper was 0.540.

CONCLUSIONS

We developed and validated a nomogram to predict pelvic LNM in SCC based on clinical data, which can help physicians develop an optimal treatment strategy.

Clinical features and risk factors of plastic bronchitis caused by refractory Mycoplasma pneumoniae pneumonia in children: a practical nomogram prediction model

Early assessment of refractory Mycoplasma pneumoniae pneumonia (RMPP) with plastic bronchitis (PB) allows timely removal of casts using fiberoptic bronchoscopic manipulation, which relieves airway obstruction and limit sequelae development. This study aimed to analyze clinical data for risk factors and develop a nomogram for early predictive evaluation of RMPP with PB. The clinical data of 1-14 year-old patients with RMPP were retrospectively analyzed. Patients were classified into a PB or non-PB group. The general characteristics, clinical symptoms, laboratory test results, imaging findings, and microscopic changes of the two groups were compared. A statistical analysis of the risk factors for developing PB was performed, and a nomogram model of risk factors was constructed. Of 120 patients with RMPP included, 68 and 52 were in the non-PB and PB groups, respectively. Using multivariate logistic regression analysis, fever before bronchoscopy, extrapulmonary complications, pleural effusion, cough duration, and lactate dehydrogenase (LDH) levels were identified as risk factors. A nomogram was constructed based on the results of the multivariate analysis. The area under the receiver operating characteristic curve value of the nomogram was 0.944 (95% confidence interval: 0.779-0.962). The Hosmer-Lemeshow test displayed good calibration of the nomogram (p = 0.376, R2 = 0.723).

CONCLUSION

The nomogram model constructed in this study based on five risk factors (persistent fever before bronchoscopy, extrapulmonary complications, pleural effusion, cough duration, and LDH levels) prior to bronchoscopy can be used for the early identification of RMPP-induced PB.

WHAT IS KNOWN

• Refractory Mycoplasma pneumoniae pneumonia (RMPP) in children has been increasingly reported and recognized, which often leads to serious complications. • Plastic bronchitis (PB) is considered to be one of the causes of RMPP, and bronchoscopic treatment should be improved as soon as possible to remove plastic sputum thrombus in bronchus.

WHAT IS NEW

• This study determined the risk factors for RMPP-induced PB. • The nomogram model constructed in this study prior to bronchoscopy can be used for the early identification of RMPP-induced PB, which facilitate the early bronchoscopic removal of casts, thereby promoting recovery and reducing cases with poor RMPP prognosis.

惊厥患儿镇静后谵妄发生的危险因素及风险列线图预测模型的建立

OBJECTIVES

To investigate the risk factors for delirium after sedation in children with convulsion, and to establish a nomogram model for predicting the risk of delirium.

METHODS

A total of 373 children with convulsion who were hospitalized in the pediatric ward of the Second Affiliated Hospital of Air Force Medical University from August 2020 to January 2022 were prospectively enrolled. There were 245 children in the modeling group and 128 children in the validation group. A multivariate logistic regression analysis was used to identify independent predictive factors for delirium after sedation and establish a nomogram model for predicting the risk of this disorder based on these factors. The calibration curve, the receiver operating characteristic curve, and the decision curve analysis were used to evaluate the accuracy, discriminatory ability, and clinical application value of this model, respectively.

RESULTS

The incidence of delirium after sedation was 22.3% (83/373) in the children with convulsion. The multivariate logistic regression analysis showed that age>5 years (OR=0.401, P<0.05) was a protective factor against delirium after sedation in these children, while presence of infection (OR=3.020, P<0.05), admission to the pediatric intensive care unit (OR=3.126, P<0.05), use of benzodiazepines (OR=5.219, P<0.05), history of status convulsion (OR=2.623, P<0.05), and history of delirium episodes (OR=3.119, P<0.05) were risk factors for delirium. The H-L deviation test of the nomogram prediction model showed a good degree of fit (χ²=9.494, P=0.302). Internal and external validation showed that the mean absolute errors between the actual and predicted values of the calibration curve were 0.030 and 0.018, respectively, and the areas under the receiver operating characteristic curve were 0.777 and 0.775, respectively. The decision curve analysis showed that the model provided significant net clinical benefit when the predicted risk threshold was >0.01.

CONCLUSIONS

Age, presence of infection, admission to the pediatric intensive care unit, use of benzodiazepines, history of status convulsion, and history of delirium episodes are closely associated with the development of delirium after sedation in children with convulsion. The nomogram model for predicting this disorder that is established based on these factors has relatively high accuracy, discriminatory ability, and clinical application value.

STING pathway contributes to the prognosis of hepatocellular carcinoma and identification of prognostic gene signatures correlated to tumor microenvironment

Background

Hepatocellular carcinoma (HCC) is one of the most malignant solid tumors worldwide. Recent evidence shows that the stimulator of interferon genes (STING) pathway is essential for anti-tumor immunity via inducing the production of downstream inflammatory cytokines. However, its impact on the prognosis and tumor microenvironment of HCC was still limited known.

Methods

We obtained gene expression profiles of HCC from GEO, TCGA, and ICGC databases, and immune-related genes (IRGs) from the ImmPort database. Multivariate Cox regression was performed to identify independent prognostic factors. Nomogram was established to predict survival probability for individual patients. Kaplan–Meier curve was used to evaluate the survival difference. Afterward, ESTIMATE, TISCH, and TIMER databases were combined to assess the immune cell infiltration. Furthermore, the qPCR, western blotting, and immunohistochemistry were done to evaluate gene expression, and in vitro cell models were built to determine cell migratory ability.

Results

We found that gene markers of NLRC3, STING1, TBK1, TRIM21, and XRCC6 within STING pathway were independent prognostic factors in HCC patients. Underlying the finding, a predictive nomogram was constructed in TCGA-training cohort and further validated in TCGA-all and ICGC datasets, showing credible performance. Experimentally, up-regulated TBK1 promotes the ability of HCC cell migration. Next, the survival-related immune-related co-expressed gene signatures (IRCGS) (VAV1, RHOA, and ZC3HAV1) were determined in HCC cohorts and their expression was verified in human HCC cells and clinical samples. Furthermore, survival-related IRCGS was associated with the infiltration of various immune cell subtypes in HCC, the transcriptional expression of prominent immune checkpoints, and immunotherapeutic response.

Conclusion

Collectively, we constructed a novel prognostic nomogram model for predicting the survival probability of individual HCC patients. Moreover, an immune-related prognostic gene signature was determined. Both might function as potential therapeutic targets for HCC treatment in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02734-4.

Prediction model for delirium in patients with cardiovascular surgery: development and validation

BACKGROUND

The aim of this study was to construct a nomogram model for discriminating the risk of delirium in patients undergoing cardiovascular surgery.

METHODS

From January 2017 to June 2020, we collected data from 838 patients who underwent cardiovascular surgery at the Affiliated Hospital of Nantong University. Patients were randomly divided into a training set and a validation set at a 5:5 ratio. A nomogram model was established based on logistic regression. Discrimination and calibration were used to evaluate the predictive performance of the model.

RESULTS

The incidence of delirium was 48.3%. A total of 389 patients were in the modelling group, and 449 patients were in the verification group. Logistic regression analysis showed that CPB duration (OR [Formula: see text] 1.004, 95% CI: 1.001-1.008, [Formula: see text] 0.018), postoperative serum sodium (OR [Formula: see text] 1.112, 95% CI: 1.049-1.178, [Formula: see text] 0.001), age (OR [Formula: see text] 1.027, 95% CI: 1.006-1.048, [Formula: see text] 0.011), and postoperative MV (OR [Formula: see text] 1.019, 95% CI: 1.008-1.030, [Formula: see text] 0.001) were independent risk factors. The results showed that AUC[Formula: see text] was 0.712 and that the 95% CI was 0.661-0.762. The Hosmer-Lemeshow goodness of fit test showed that the predicted results of the model were in good agreement with the actual situation ([Formula: see text] 6.200, [Formula: see text] 0.625). The results of verification showed that the AUC[Formula: see text] was 0.705, and the 95% CI was 0.657-0.752. The Hosmer-Lemeshow goodness of fit test results were [Formula: see text] 8.653 and [Formula: see text] 0.372, indicating that the predictive effect of the model is good.

CONCLUSIONS

The establishment of the model provides accurate and objective assessment tools for medical staff to start preventing postoperative delirium in a purposeful and focused manner when a patient enters the CSICU after surgery.

Risk factors and a nomogram model for postoperative delirium in elderly gastric cancer patients after laparoscopic gastrectomy

Background

To evaluate the risk factors of postoperative delirium (POD) in elderly gastric cancer (GC) patients after laparoscopic gastrectomy and construct a predictive model.

Methods

Elderly GC patients undergoing laparoscopic gastrectomy were enrolled and grouped based on the status of POD development within postoperative 7 days. Independent risk factors were selected out by univariate and multivariate logistic regression analyses and then enrolled in the nomogram prediction model.

Results

A total of 270 elderly GC patients were enrolled, and POD occurred in 74 (27.4%) patients within postoperative 7 days. The results of multivariate regression analysis indicated that age (OR: 3.30, 95% CI: 1.41–6.85, P < 0.001), sleeping pills (OR: 1.87, 95% CI: 1.12–3.09, P = 0.012), duration of ICU stay (OR: 1.55, 95% CI: 1.02–2.37, P = 0.029), albumin/fibrinogen ratio (AFR) (OR: 1.74, 95% CI: 1.03–2.76, P = 0.019), and neutrophils/lymphocytes ratio (NLR) (OR: 2.12, 95% CI: 1.11–4.01, P = 0.016) were five independent risk factors for POD in elderly GC patients. The AUC of the constructed nomogram model based on these five factors was 0.807.

Conclusions

This study highlighted that age, AFR, NLR, sleeping pills taking, and duration of ICU stay were independent risk factors for POD, and the nomogram model based on these factors could effectively predict POD in elderly GC patients.

A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

Purpose

To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification.

Methods

A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves.

Results

Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit.

Conclusions

A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00483-1.

Development and validation of a predictive model for adverse left ventricular remodeling in NSTEMI patients after primary percutaneous coronary intervention

Introduction

To develop and validate clinical evaluators that predict adverse left ventricular remodeling (ALVR) in non-ST-elevation myocardial infarction (NSTEMI) patients after primary percutaneous coronary intervention (PCI).

Methods

The retrospective study analyzed the clinical data of 507 NSTEMI patients who were treated with primary PCI from the Affiliated Hospital of Xuzhou Medical University and the Second Affiliated Hospital of Xuzhou Medical University, between January 1, 2019 and September 31, 2021. The training cohort consisted of patients admitted before June 2020 (n = 287), and the remaining patients (n = 220) were assigned to an external validation cohort. The endpoint event was the occurrence of ALVR, which was described as an increase ≥ 20% in left ventricular end-diastolic volume (LVEDV) at 3–4 months follow-up CMR compared with baseline measurements. The occurrence probability of ALVR stemmed from the final model, which embodied independent predictors recommended by logistic regression analysis. The area under the receiver operating characteristic curve (AUC), Calibration plot, Hosmer–Lemeshow method, and decision curve analysis (DCA) were applied to quantify the performance.

Results

Independent predictors for ALVR included age (odds ratio (OR): 1.040; 95% confidence interval (CI): 1.009–1.073), the level of neutrophil to lymphocyte ratio (OR: 4.492; 95% CI: 1.906–10.582), the cardiac microvascular obstruction (OR: 3.416; 95% CI: 1.170–9.970), peak global longitudinal strain (OR: 1.131; 95% CI: 1.026–1.246), infarct size (OR: 1.082; 95% CI: 1.042–1.125) and left ventricular ejection fraction (OR: 0.925; 95% CI: 0.872–0.980), which were screened by regression analysis then merged into the nomogram model. Both internal validation (AUC: 0.805) and external validation (AUC: 0.867) revealed that the prediction model was capable of good discrimination. Calibration plot and Hosmer–Lemeshow method showed high consistency between the probabilities predicted by the nomogram (P = 0.514) and the validation set (P = 0.762) and the probabilities of actual occurrence. DCA corroborated the clinical utility of the nomogram.

Conclusions

In this study, the proposed nomogram model enabled individualized prediction of ALVR in NSTEMI patients after reperfusion and conduced to guide clinical therapeutic schedules.

Development of a nomogram to predict surgical site infection after closed comminuted calcaneal fracture

Background

Compared with open comminuted calcaneal fractures, less emphasis is placed on postoperative surgical site infection (SSI) of closed comminuted calcaneal fractures. This study aimed to identify the risk factors associated with SSI and build a nomogram model to visualize the risk factors for postoperative SSI.

Methods

We retrospectively collected patients with closed comminuted calcaneal fractures from the Second Affiliated Hospital of Wenzhou Medical University database from 2017 to 2020. Risk factors were identified by logistics regression analysis, and the predictive value of risk factors was evaluated by ROC (receiver operating characteristic curve). Besides, the final risk factors were incorporated into R4.1.2 software to establish a visual nomogram prediction model.

Results

The high-fall injury, operative time, prealbumin, aspartate aminotransferase (AST), and cystatin-C were independent predictors of SSI in calcaneal fracture patients, with OR values of 5.565 (95%CI 2.220–13.951), 1.044 (95%CI 1.023–1.064), 0.988 (95%CI 0.980–0.995), 1.035 (95%CI 1.004–1.067) and 0.010 (95%CI 0.001–0.185) (P_s < 0.05). Furthermore, ROC curve analysis showed that the AUC values of high-fall injury, operation time, prealbumin, AST, cystatin-C, and their composite indicator for predicting SSI were 0.680 (95%CI 0.593–0.766), 0.756 (95%CI 0.672–939), 0.331 (95%CI 0.243–0.419), 0.605 (95%CI 0.512–0.698), 0.319 (95%CI 0.226–0.413) and 0.860 (95%CI 0.794–0.926), respectively (P_s < 0.05). Moreover, the accuracy of the nomogram to predict SSI risk was 0.860.

Conclusions

Our study findings suggest that clinicians should pay more attention to the preoperative prealbumin, AST, cystatin C, high-fall injury, and operative time for patients with closed comminuting calcaneal fractures to avoid the occurrence of postoperative SSI. Furthermore, our established nomogram to assess the risk of SSI in calcaneal fracture patients yielded good accuracy and can assist clinicians in taking appropriate measures to prevent SSI.

A nomogram model predicting the risk of postpartum stress urinary incontinence in primiparas: A multicenter study

Stress urinary incontinence (SUI) is a common gynecological urinary system disease, and globally, 200 million or more people suffer from it. However, the existing literature mostly focuses on postpartum urinary incontinence (UI) or UI in middle-aged and elderly people, with little focus on primiparas. To analyse urinary incontinence prevalence and its risk factors in primiparas and establish a nomogram prediction model, 360 parturients were recruited from three hospitals between April and September 2021. A homemade electronic questionnaire was used to investigate the general demographic and perinatal characteristics of primiparas. The SUI was diagnosed by the physicians. Logistic regression analysis of independent risk factors for SUI and a nomogram prediction model were established. Ninety people were diagnosed as SUI. The number of pregnancies (OR = 3.322, 95% CI = 1.473-7.492), residence (OR = 5.451, 95% CI = 2.725-10.903), occupation (OR = 3.393, 95% CI = 1.144-10.064), education level (OR = 3.551, 95% CI = 1.223-10.308), delivery method (OR = 10.270, 95% CI = 4.090-25.789), and oxytocin use (OR = 2.166, 95% CI = 1.142-4.109) were independent risk factors for SUI. The C-index of the nomogram prediction model was 0.798 (95% CI = 0.749-0.846). The POPDI score, CRADI score, UDI score, and PFDI scores of women with SUI were significantly higher than those of non-SUI women, while I-QOL scores were significantly lower than those of non-SUI women. In conclusion, the prevalence of SUI among primiparas in Fuyang, China, was 25.00%, which exhibited a large impact on the quality of life of puerperae. The present study successfully established an individualized nomogram prediction model of SUI for primiparas with good discrimination and diagnostic efficiency, which was helpful for the early clinical identification of high-risk primiparas with SUI.

The prognostic value of preoperative peripheral blood inflammatory indicators for squamous cell carcinoma of tongue

OBJECTIVES

A study was conducted to investigate the value of preoperative peripheral blood inflammatory indicators in the prediction of tongue squamous cell carcinoma (TSCC) prognosis.

METHODS

This retrospective analysis included 210 patients who underwent radical resection for TSCC in the Department of Oral and Maxillofacial Surgery of The First Affiliated Hospital of Zhengzhou University from January 2010 to December 2017. Receiver operating characteristic curve was conducted to determine the best cut-off values of platelet/lymphocyte ratio (PLR) and neutrophil/lymphocyte ratio (NLR). The Kaplan-Meier method and Log-rank test were conducted for univariate analysis, and the Cox proportional hazard regression model was conducted for multivariate analysis. A Nomogram model was established based on the independent risk factors, which were screened by Cox regression model.

RESULTS

The univariate analysis showed that PLR, NLR, tumor differentiation, and T, N, and TNM stages were TSCC's prognostic factors (P<0.05). Multivariate analysis showed that PLR and N and TNM stages were TSCC's independent risk factors (P<0.05). The C-index of the Nomogram was 0.701 (95%CI: 0.651-0.752). The calibration curve shows that the predicted survival rate of the nomogram was in good agreement with the relative survival rate.

CONCLUSIONS

Preoperative peripheral blood inflammatory indicators can potentially be used to predict TSCC prognosis.

A Nomogram-based Model to Predict Neoplastic Risk for Patients with Gallbladder Polyps

BACKGROUND AND AIMS

Gallbladder polyp (GBP) assessment aims to identify the early stages of gallbladder carcinoma. Many studies have analyzed the risk factors for malignant GBPs. In this retrospective study, we aimed to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs.

METHODS

We developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood test findings were analyzed. Mann-Whitney U test and multivariate logistic regression analyses were used to identify independent predictors and establish the nomogram model. An internal validation was conducted in 225 consecutive patients. Performance and clinical benefit of the model were evaluated using receiver operating characteristic curves and decision curve analysis (DCA), respectively.

RESULTS

Age, cholelithiasis, carcinoembryonic antigen, polyp size, and sessile shape were confirmed as independent predictors of GBP neoplastic potential in the training group. Compared with five other proposed prediction methods, the established nomogram model presented better discrimination of neoplastic GBPs in the training cohort (area under the curve [AUC]: 0.846) and the validation cohort (AUC: 0.835). DCA demonstrated that the greatest clinical benefit was provided by the nomogram compared with the other five methods.

CONCLUSIONS

Our developed preoperative nomogram model can successfully be used to evaluate the neoplastic potential of GBPs based on simple clinical variables that maybe useful for clinical decision-making.

An immune-related nomogram model that predicts the overall survival of patients with lung adenocarcinoma

Background

Lung adenocarcinoma accounts for approximately 40% of all primary lung cancers; however, the mortality rates remain high. Successfully predicting progression and overall (OS) time will provide clinicians with more options to manage this disease.

Methods

We analyzed RNA sequencing data from 510 cases of lung adenocarcinoma from The Cancer Genome Atlas database using CIBERSORT, ImmuCellAI, and ESTIMATE algorithms. Through these data we constructed 6 immune subtypes and then compared the difference of OS, immune infiltration level and gene expression between these immune subtypes. Also, all the subtypes and immune cells infiltration level were used to evaluate the relationship with prognosis and we introduced lasso-cox method to constructe an immune-related prognosis model. Finally we validated this model in another independent cohort.

Results

The C3 immune subtype of lung adenocarcinoma exhibited longer survival, whereas the C1 subtype was associated with a higher mutation rate of MUC17 and FLG genes compared with other subtypes. A multifactorial correlation analysis revealed that immune cell infiltration was closely associated with overall survival. Using data from 510 cases, we constructed a nomogram prediction model composed of clinicopathologic factors and immune signatures. This model produced a C-index of 0.73 and achieved a C-index of 0.844 using a validation set.

Conclusions

Through this study we constructed an immune related prognosis model to instruct lung adenocarcinoma’s OS and validated its value in another independent cohost. These results will be useful in guiding treatment for lung adenocarcinoma based on tumor immune profiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01902-6.

Prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model

Background

We aimed to develop and validate a nomogram model for predicting CKD after orthotopic liver transplantation (OLT).

Methods

The retrospective data of 399 patients who underwent transplantation and were followed in our centre were collected. They were randomly assigned to the training set (n = 293) and validation set (n = 106). Multivariable Cox regression analysis was performed in the training set to identify predictors of CKD. According to the Cox regression analysis results, a nomogram model was developed and validated. The renal function of recipients was monitored, and the long-term survival prognosis was assessed.

Results

The incidence of CKD at 5 years after OLT was 25.6%. Cox regression analysis identified several predictors of post-OLT CKD, including recipient age at surgery (HR 1.036, 95% CI 1.006-1.068; p = 0.018), female sex (HR 2.867, 95% CI 1.709-4.810; p < 0.001), preoperative hypertension (HR 1.670, 95% CI 0.962-2.898; p = 0.068), preoperative eGFR (HR 0.996, 95% CI 0.991-1.001; p = 0.143), uric acid at 3 months (HR 1.002, 95% CI 1.001-1.004; p = 0.028), haemoglobin at 3 months (HR 0.970, 95% CI 0.956-0.983; p < 0.001), and average concentration of cyclosporine A at 3 months (HR 1.002, 95% CI 1.001-1.003; p < 0.001). According to these parameters, a nomogram model for predicting CKD after OLT was constructed and validated. The C-indices were 0.75 and 0.80 in the training and validation sets. The calibration curve of the nomogram showed that the CKD probabilities predicted by the nomogram agreed with the observed probabilities at 1, 3, and 5 years after OLT (p > 0.05). Renal function declined slowly year by year, and there were significant differences between patients divided by these predictors. Kaplan-Meier survival analysis showed that the survival prognosis of recipients decreased significantly with the progression of renal function.

Conclusions

With excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for CKD and poor long-term prognosis after OLT.

Nomogram model for predicting postoperative survival of patients with stage IB-IIA cervical cancer

To establish a prediction model based on clinical and pathological information for the long-term survival of patients with cervical cancer, we retrospectively analyzed the clinical data of patients pathologically diagnosed with stage IB-IIA cervical cancer between July 2007 and September 2017 in the Chinese Academy of Medical Sciences Cancer Hospital. Factors affecting the overall survival of the patients were analyzed using a Cox model, and a cervical cancer patient prediction nomogram model was established. A total of 2,319 patients were included in the study. According to the multivariate Cox regression analysis, number of complications, surgical methods, neoadjuvant treatment, lymph node metastasis, postoperative treatment, lymphovascular space invasion (LVSI), and other independent factors affecting prognosis were included to establish a nomogram. The nomogram consistency index in the training and validation cohorts was 0.691 and 0.615, respectively. The study established a highly accurate predictive model for the postoperative survival of cervical cancer patients.

One-year renal outcome in lupus nephritis patients with acute kidney injury: a nomogram model

OBJECTIVE

To develop a short-term renal outcome prediction model for acute kidney injury (AKI) in patients with lupus nephritis.

METHODS

Two lupus AKI cohorts from 2 independent centers during 2013-2019 were included, i.e., a derivation cohort from a rheumatology center and a validation cohort from a nephrology center. Clinical characteristics and renal histologic features were obtained. The outcome measurement was the recovery of kidney function within 12-month. Lasso regression was used for feature selection. Prediction models with or without pathology were built and nomogram was plotted. Model evaluation including calibration curve and decision curve analysis was performed.

RESULTS

130 patients and 96 patients were included in the derivation and validation cohorts, of which 82 and 73 patients received renal biopsy, respectively. The prognostic nomogram model without pathology included determinants of SLE duration, days from AKI onset to treatment and baseline creatinine level (C-index 0.85 (95%CI 0.78∼0.91) and 0.79 (95%CI 0.70∼0.88) for the 2 cohorts). Combination of histologic interstitial tubular fibrosis in the nomogram gave an incremental predictive performance (C-index 0.93 vs 0.85, p = 0.039) in the derivation cohort, but failed to improve the performance in the validation cohort (C-index 0.81 vs 0.79, p = 0.78). Decision curve analysis suggested clinical benefit of the prediction models.

CONCLUSION

The predictive nomogram models might facilitate more accurate management for lupus patients with AKI.

Clinical features and risk factors analysis of bronchitis obliterans due to refractory Mycoplasma pneumoniae pneumonia in children: a nomogram prediction model

Background

Early prediction of bronchitis obliterans (BO) is of great significance to the improvement of the long-term prognosis of children caused by refractory Mycoplasma pneumoniae pneumonia (RMPP). This study aimed to establish a nomogram model to predict the risk of BO in children due to RMPP.

Methods

A retrospective observation was conducted to study the clinical data of children with RMPP (1–14 years old) during acute infection. According to whether there is BO observed in the bronchoscope, children were divided into BO and the non-BO groups. The multivariate logistic regression model was used to construct the nomogram model.

Results

One hundred and forty-one children with RMPP were finally included, of which 65 (46.0%) children with RMPP were complicated by BO. According to the multivariate logistic regression analysis, WBC count, ALB level, consolidation range exceeding 2/3 of lung lobes, timing of macrolides, glucocorticoids or fiber bronchoscopy and plastic bronchitis were independent influencing factors for the occurrence of BO and were incorporated into the nomogram. The area under the receiver operating characteristic curve (AUC-ROC) value of nomogram was 0.899 (95% confidence interval [CI] 0.848–0.950). The Hosmer–Lemeshow test showed good calibration of the nomogram (p = 0.692).

Conclusion

A nomogram model found by seven risk factor was successfully constructed and can use to early prediction of children with BO due to RMPP.

[Establishment of model to predict lateral neck recurrence of central lymph node metastasis in papillary thyroid carcinoma]

Objective: To explore the risk factors for lateral neck recurrence of central lymph node metastasis (CLMN) in papillary thyroid cancer (PTC), and to construct a model to predict the recurrence. Methods: The records of 245 consecutive PTC patients with CLMN underwent surgical treatment from 1996 to 2009 in our department were retrospectively reviewed. The threshold value of CLNM number is determined by ROC curve. The risk factors for lateral neck recurrence were determined by using Cox regression model. The identified risk factors were incorporated into a nomogram model to predict the risk of lateral neck recurrence. Results: A total of 245 patients were enrolled in the study, among them, 32 cases occurred lateral neck lymph node recurrence and 4 cases were dead of thyroid carcinoma. Multivariate analysis revealed that primary tumor size, extrathyroidal extension, the number of metastatic CLNM >3 were independent risk factors of lateral neck recurrence (P<0.05), lateral neck recurrence was a risk factor of disease-free survival(P<0.05). The nomogram model of predicting the lateral neck recurrence was further established based on the above 3 independent risk factors, the area under the receiver operating characteristic (ROC) curve of which was 0.790. Conclusions: The nomogram model based on the independent risk factors of LN recurrence can be helpful to screen the papillary thyroid carcinoma patients with high risk of lateral neck recurrence, and provide more guidance for clinical treatment.