Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single-center cross-sectional study

BACKGROUND

To identify independent factors of cognitive frailty (CF) and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy.

METHODS

In this cross-sectional study, patients with lung cancer undergoing drug therapy from October 2022 to July 2023 were enrolled. The data collected includes general demographic characteristics, clinical data characteristics and assessment of tools for cognitive frailty and other factors. Logistic regression was harnessed to determine the influencing factors, R software was used to establish a nomogram model to predict the risk of cognitive frailty. The enhanced bootstrap method was employed for internal verification of the model. The performance of the nomogram was evaluated by using calibration curves, the area under the receiver operating characteristic curve, and decision curve analysis.

RESULTS

A total of 372 patients were recruited, with a cognitive frailty prevalence of 56.2%. Age, education background, diabetes mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors. Then, a nomogram model was constructed and patients were classified into high- and low-risk groups with a cutoff value of 0.552. The internal validation results revealed good concordance, calibration and discrimination. The decision curve analysis presented prominent clinical utility.

CONCLUSIONS

The prevalence of cognitive frailty was higher in lung cancer patients receiving drug therapy. The nomogram could identify the risk of cognitive frailty intuitively and simply in patients with lung cancer, so as to provide references for early screening and intervention for cognitive frailty at the early phases of drug treatment.

Prediction of liver metastasis and recommended optimal follow-up nursing in rectal cancer

We aimed to analyze and investigate the clinical factors that influence the occurrence of liver metastasis in locally advanced rectal cancer patients, with an attempt to assist patients in devising the optimal imaging-based follow-up nursing. Between June 2011 and May 2021, patients with rectal cancer at our hospital were retrospectively analyzed. A random survival forest model was developed to predict the probability of liver metastasis and provide a practical risk-based approach to surveillance. The results indicated that age, perineural invasion, and tumor deposit were significant factors associated with the liver metastasis and survival. The liver metastasis risk of the low-risk group was higher at 6-21 months, with a peak occurrence time in the 15th month. The liver metastasis risk of the high-risk group was higher at 0-24 months, with a peak occurrence time in the 8th month. In general, our clinical model could predict liver metastasis in rectal cancer patients. It provides a visualization tool that can aid physicians and nurses in making clinical decisions, by detecting the probability of liver metastasis.

Deep learning-assisted development and validation of an algorithm for predicting the growth of persistent pure ground-glass nodules

Background

The prediction of the persistent pure ground-glass nodule (pGGN) growth is challenging and limited by subjective assessment and variation across radiologists. A chest computed tomography (CT) image-based deep learning classification model (DLCM) may provide a more accurate growth prediction.

Methods

This retrospective study enrolled consecutive patients with pGGNs from January 2010 to December 2020 from two independent medical institutions. Four DLCM algorithms were built to predict the growth of pGGNs, which were extracted from the nodule areas of chest CT images annotated by two radiologists. All nodules were assigned to either the study, the inner validation, or the external validation cohort. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUROCs) were analyzed to evaluate our models.

Results

A total of 286 patients were included, with 419 pGGN. In total, 197 (68.9%) of the patients were female and the average age was 59.5±12.0 years. The number of pGGN assigned to the study, the inner validation, and the external validation cohort were 193, 130, and 96, respectively. The follow-up time of stable pGGNs for the primary and external validation cohorts were 3.66 (range, 2.01-10.08) and 4.63 (range, 2.00-9.91) years, respectively. Growth of the pGGN occurred in 166 nodules [83 (43%), 39 (30%), and 44 (45%) in the study, inner and external validation cohorts respectively]. The best-performing DLCM algorithm was DenseNet_DR, which achieved AUROCs of 0.79 [95% confidence interval (CI): 0.70, 0.86] in predicting pGGN growth in the inner validation cohort and 0.70 (95% CI: 0.60, 0.79) in the external validation cohort.

Conclusions

DLCM algorithms that use chest CT images can help predict the growth of pGGNs.

A PREDICTION MODEL FOR SEPSIS IN INFECTED PATIENTS: EARLY ASSESSMENT OF SEPSIS ENGAGEMENT

ABSTRACT

Purpose: To evaluate significant risk variables for sepsis incidence and develop a predictive model for rapid screening and diagnosis of sepsis in patients from the emergency department (ED). Methods: Sepsis-related risk variables were screened based on the PIRO (Predisposition, Insult, Response, Organ dysfunction) system. Training (n = 1,272) and external validation (n = 568) datasets were collected from Peking Union Medical College Hospital (PUMCH) and Beijing Tsinghua Changgung Hospital (BTCH), respectively. Variables were collected at the time of admission. Sepsis incidences were determined within 72 h after ED admissions. A predictive model, Early Assessment of Sepsis Engagement (EASE), was developed, and an EASE-based nomogram was generated for clinical applications. The predictive ability of EASE was evaluated and compared with the National Early Warning Score (NEWS) scoring system. In addition, internal and external validations were performed. Results: A total of 48 characteristics were identified. The EASE model, which consists of alcohol consumption, lung infection, temperature, respiration rate, heart rate, serum urea nitrogen, and white blood cell count, had an excellent predictive performance. The EASE-based nomogram showed a significantly higher area under curve (AUC) value of 86.5% (95% CI, 84.2%-88.8%) compared with the AUC value of 78.2% for the NEWS scoring system. The AUC of EASE in the external validation dataset was 72.2% (95% CI, 66.6%-77.7%). Both calibration curves of EASE in training and external validation datasets were close to the ideal model and were well-calibrated. Conclusions: The EASE model can predict and screen ED-admitted patients with sepsis. It demonstrated superior diagnostic performance and clinical application promise by external validation and in-parallel comparison with the NEWS scoring system.

The development and validation of a nomogram for predicting brain metastases after chemotherapy and radiotherapy in male small cell lung cancer patients with stage III

OBJECTIVE

The purpose of this research was to develop a model for brain metastasis (BM) in limited-stage small cell lung cancer (LS-SCLC) patients and to help in the early identification of high-risk patients and the selection of individualized therapies.

METHODS

Univariate and multivariate logic regression was applied to identify the independent risk factors of BM. A receiver operating curve (ROC) and nomogram for predicting the incidence of BM were then conducted based on the independent risk factors. The decision curve analysis (DCA) was performed to assess the clinical benefit of prediction model.

RESULTS

Univariate regression analysis showed that the CCRT, RT dose, PNI, LLR, and dNLR were the significant factors for the incidence of BM. Multivariate analysis showed that CCRT, RT dose, and PNI were independent risk factors of BM and were included in the nomogram model. The ROC curves revealed the area under the ROC (AUC) of the model was 0.764 (95% CI, 0.658-0.869), which was much higher than individual variable alone. The calibration curve revealed favorable consistency between the observed probability and predicted probability for BM in LS-SCLC patients. Finally, the DCA demonstrated that the nomogram provides a satisfactory positive net benefit across the majority of threshold probabilities.

CONCLUSIONS

In general, we established and verified a nomogram model that combines clinical variables and nutritional index characteristics to predict the incidence of BM in male SCLC patients with stage III. Since the model has high reliability and clinical applicability, it can provide clinicians with theoretical guidance and treatment strategy making.

Development and validation of a scoring system for the prediction of HIV drug resistance in Hubei province, China

Objective

The present study aimed to build and validate a new nomogram-based scoring system for the prediction of HIV drug resistance (HIVDR).

Design and methods

Totally 618 patients with HIV/AIDS were included. The predictive model was created using a retrospective set (N = 427) and internally validated with the remaining cases (N = 191). Multivariable logistic regression analysis was carried out to fit a model using candidate variables selected by Least absolute shrinkage and selection operator (LASSO) regression. The predictive model was first presented as a nomogram, then transformed into a simple and convenient scoring system and tested in the internal validation set.

Results

The developed scoring system consisted of age (2 points), duration of ART (5 points), treatment adherence (4 points), CD4 T cells (1 point) and HIV viral load (1 point). With a cutoff value of 7.5 points, the AUC, sensitivity, specificity, PLR and NLR values were 0.812, 82.13%, 64.55%, 2.32 and 0.28, respectively, in the training set. The novel scoring system exhibited a favorable diagnostic performance in both the training and validation sets.

Conclusion

The novel scoring system can be used for individualized prediction of HIVDR patients. It has satisfactory accuracy and good calibration, which is beneficial for clinical practice.

Development and validation of a preoperative nomogram for predicting the surgical difficulty of laparoscopic colectomy for right colon cancer: a retrospective analysis

BACKGROUND

In laparoscopic right hemicolectomy for right colon cancer, complete mesocolic excision is a standard procedure that involves extended lymphadenectomy and blood vessel ligation. This study aimed to establish a nomogram to facilitate evaluation of the surgical difficulty of laparoscopic right hemicolectomy based on preoperative parameters.

MATERIALS AND METHODS

The preoperative clinical and computed tomography-related parameters, operative details, and postoperative outcomes were analyzed. The difficulty of laparoscopic colectomy was defined using the scoring grade reported by Escal et al . with modifications. Multivariable logistic analysis was performed to identify parameters that increased the surgical difficulty. A preoperative nomogram to predict the surgical difficulty was established and validated.

RESULTS

A total of 418 consecutive patients with right colon cancer who underwent laparoscopic radical resection at a single tertiary medical center between January 2016 and May 2022 were retrospectively enrolled. The patients were randomly assigned to a training data set ( n =300, 71.8%) and an internal validation data set ( n =118, 28.2%). Meanwhile, an external validation data set with 150 consecutive eligible patients from another tertiary medical center was collected. In the training data set, 222 patients (74.0%) comprised the non-difficulty group and 78 (26.0%) comprised the difficulty group. Multivariable analysis demonstrated that adipose thickness at the ileocolic vessel drainage area, adipose area at the ileocolic vessel drainage area, adipose density at the ileocolic vessel drainage area, presence of the right colonic artery, presence of type III Henle's trunk, intra-abdominal adipose area, plasma triglyceride concentration, and tumor diameter at least 5 cm were independent risk factors for surgical difficulty; these factors were included in the nomogram. The nomogram incorporating seven independent predictors showed a high C-index of 0.922 and considerable reliability, accuracy, and net clinical benefit.

CONCLUSIONS

The study established and validated a reliable nomogram for predicting the surgical difficulty of laparoscopic colectomy for right colon cancer. The nomogram may assist surgeons in preoperatively evaluating risk and selecting appropriate patients.

New insights into a microvascular invasion prediction model in hepatocellular carcinoma: A retrospective study from the SEER database and China

Background and Aims

The prognosis of liver cancer is strongly influenced by microvascular infiltration (MVI). Accurate preoperative MVI prediction can aid clinicians in the selection of suitable treatment options. In this study, we constructed a novel, reliable, and adaptable nomogram for predicting MVI.

Methods

Using the Surveillance, Epidemiology, and End Results (SEER) database, we extracted the clinical data of 1,063 patients diagnosed with hepatocellular carcinoma (HCC) and divided it into either a training (n = 739) or an internal validation cohort (n = 326). Based on multivariate analysis, the training cohort data were analyzed and a nomogram was generated for MVI prediction. This was further verified using an internal validation cohort and an external validation cohort involving 293 Chinese patients. Furthermore, to evaluate the efficacy, accuracy, and clinical use of the nomogram, we used concordance index (C-index), calibration curve, and decision curve analysis (DCA) techniques.

Results

In accordance with the multivariate analysis, tumor size, tumor number, alpha-fetoprotein (AFP), and histological grade were independently associated with MVI. The established model exhibited satisfactory performance in predicting MVI. The C-indices were 0.719, 0.704, and 0.718 in the training, internal validation, and external validation cohorts, respectively. The calibration curves showed an excellent consistency between the predictions and actual observations. Finally, DCA demonstrated that the newly developed nomogram had favorable clinical utility.

Conclusions

We established and verified a novel preoperative MVI prediction model in HCC patients. This model can be a beneficial tool for clinicians in selecting an optimal treatment plan for HCC patients.

Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy

Background

Lenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.

Methods

304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.

Results

Multivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.

Conclusion

The PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients.

Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study

Introduction

Postoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients.

Methods

Patients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort.

Results

A total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application.

Conclusion

We developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients.

Constructing and validating of m6a-related genes prognostic signature for stomach adenocarcinoma and immune infiltration: Potential biomarkers for predicting the overall survival

Background

Stomach adenocarcinoma (STAD) arises from the mutations of stomach cells and has poor overall survival. Chemotherapy is commonly indicated for patients with stomach cancer following surgical resection. The most prevalent alteration that affects cancer growth is N6-methyladenosine methylation (m6A), although the possible function of m6A in STAD prognosis is not recognized.

Method

The research measured predictive FRGs in BLCA samples from the TCGA and GEO datasets. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from TCGA and GEO. STAD from TCGA and GEO at 24 m6A was investigated. Lasso regression was used to construct the prediction model to assess the m6A prognostic signals in STAD. In addition, the correlation between m6a and immune infiltration in STAD patients was discussed using GSVA and ssGSEA analysis. Based on these genes, GO and KEGG analyses were performed to identify key biological functions and key pathways.

Result

A significant relationship was discovered between numerous m6A clusters and the tumor immune microenvironment, as well as three m6A alteration patterns with different clinical outcomes. Furthermore, GSVA and ssGSEA showed that m6A clusters were significantly associated with immune infiltration in the STAD. The low-m6Ascore group had a lower immunotherapeutic response than the high-m6Ascore group. ICIs therapy was more effective in the group with a higher m6Ascore. Three writers (VIRMA, ZC3H13, and METTL3) showed significantly lower expression, whereas five authors (METTL14, METTL16, WTAP, RBM15, and RBM15B) showed considerably higher expression. Three readers (YTHDC2, YTHDF2, and LRPPRC) had higher levels of expression, whereas eleven readers (YTHDC1, YTHDF1, YTHDF3, HNRNPC, FMR1, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, and RBMX) had lower levels. As can be observed, the various types of m6 encoders have varied ramifications for STAD control.

Conclusion

STAD occurrence and progression are linked to m6A-genes. Corresponding prognostic models help forecast the prognosis of STAD patients. m6A-genes and associated immune cell infiltration in the tumor microenvironment (TME) may serve as potential therapeutic targets in STAD, which requires further trials. In addition, the m6a-related gene signature offers a viable alternative to predict bladder cancer, and these m6A-genes show a prospective research area for STAD targeted treatment in the future.

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning

Background

Bone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well.

Methods

The study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts.

Results

Our prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083-0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979-0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance.

Conclusion

Our developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

A predicting model of child-bearing-aged women' spontaneous abortion by co-infections of TORCH and reproductive tract

To develop a predicting model of child-bearing-aged women' spontaneous abortion (SA) by co-infections of TORCH and reproductive tract, in order to provide a reference tool for accurately predicting the risk of SA and guide the early prevention, diagnosis and treatment of SA. A prospective cohort study was designed based on 218 958 child-bearing-aged women following up in Hebei province in China from 2010 to 2017. Multivariable logistic regression analysis was used to select candidate predictive variables. Fisher's discriminant analysis was performed to build a predictive model, and the validity of the model was evaluated. The incidence rate of SA was 2.4%. Multivariable logistic regression analysis showed that age (OR = 3.507), adverse pregnancy history (OR = 1.509), co-infections status of Candida and HBsAg (OR_{Candida positive×HBsAg negative}  = 4.091, OR_{Candida negative×HBsAg positive}  = 3.327, and OR_{Candida positive×HBsAg positive}  = 13.762), and co-infections status of HBsAg, Rubella (IgG) and CMV (IgG) (OR_{HBs-Ag negative×Rubella (IgG) negative×CMV (IgG) positive}  = 1.789, OR_{HBs-Ag positive×Rubella (IgG) positive×CMV (IgG) negative}  = 3.809, and OR_{HBsAg positive×Rubella (IgG) positive×CMV (IgG) positive}  = 11.919) were the independent predictors of SA. The total discriminant rate reached 91%, with 82% of the sensitivity and 91% of the specificity. The predicting model of child-bearing-aged women' SA by co-infections status has a good performance. The co-infection status of TORCH and reproductive tract are suggested to be considered in pre-pregnancy physical examination.

New Insights Into a Classification-Based Microvascular Invasion Prediction Model in Hepatocellular Carcinoma: A Multicenter Study

Background and Aims

Most microvascular invasion (MVI)-predicting models have not considered MVI classification, and thus do not reflect true MVI effects on prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel MVI-predicting model focused on MVI classification, hoping to provide useful information for clinical treatment strategy decision-making.

Methods

A retrospective study was conducted with data from two Chinese medical centers for 800 consecutive patients with HCC (derivation cohort) and 250 matched patients (external validation cohort). MVI-associated variables were identified by ordinal logistic regression. Predictive models were constructed based on multivariate analysis results and validated internally and externally. The models' discriminative ability and calibration ability were examined.

Results

Four factors associated independently with MVI: tumor diameter, tumor number, serum lactate dehydrogenase (LDH) ≥ 176.58 U/L, and γ-glutamyl transpeptidase (γ-GGT). Area under the curve (AUC)s for our M2, M1, and M0 nomograms were 0.864, 0.648, and 0.782. Internal validation of all three models was confirmed with AUC analyses in D-sets (development datasets) and V-sets (validation datasets) and C-indices for each cohort. GiViTI calibration belt plots and Hosmer-Lemeshow (HL) chi-squared calibration values demonstrated good consistency between observed frequencies and predicted probabilities for the M2 and M0 nomograms. Although the M1 nomogram was well calibrated, its discrimination was poor.

Conclusion

We developed and validated MVI prediction models in patients with HCC that differentiate MVI classification and may provide useful guidance for treatment planning.

An Online Tool Using Basal or Activated Ovarian Reserve Markers to Predict the Number of Oocytes Retrieved Following Controlled Ovarian Stimulation: A Prospective Observational Cohort Study

BACKGROUND

Predicting the number of oocytes retrieved (NOR) following controlled ovarian stimulation (COS) is the only way to ensure effective and safe treatment in assisted reproductive technology (ART). To date, there have been limited studies about predicting specific NOR, which hinders the development of individualized treatment in ART.

OBJECTIVE

To establish an online tool for predicting NOR.

MATERIALS AND METHODS

In total, 621 prospective routine gonadotropin releasing hormone (GnRH) antagonist COS cycles were studied. Independent variables included age, body mass index, antral follicle counts, basal FSH, basal and increment of anti-mullerian hormone, Luteinizing hormon, estradiol, testosterone, androstenedione, and inhibin B. The outcome variable was NOR. The independent variables underwent appropriate transformation to achieve a better fit for a linear relationship with NOR. Pruned forward selection with holdback validation was then used to establish predictive models. Corrected Akaike's information criterion, Schwarz-Bayesian information criterion, scaled -log[likelihood], and the generalized coefficient of determination (R²) were used for model evaluation.

RESULTS

A multiple negative binomial regression model was used for predicting NOR because it fitted a negative binomial distribution. We established Model 1, using basal ovarian reserve markers, and Model 2, using both basal and early dynamic markers for predicting NOR following COS. The generalized R² values were 0.54 and 0.51 for Model 1 and 0.64 and 0.62 for Model 2 in the training and validation sets, respectively.

CONCLUSION

Models 1 and 2 could be applied to different scenarios. For directing the starting dose of recombinant follicle stimulation hormone (rFSH), Model 1 using basic predictors could be used prior to COS. Model 2 could be used for directing the adjustment of rFSH dosages during COS. An online tool (http://121.43.113.123:8002/) based on these two models is also developed. We anticipate that the clinical application of this tool could help the ART clinics to reduce iatrogenic ovarian under- or over-responses, and could reduce costs during COS for ART.

Predicting Cancer-Specific Survival Among Patients With Prostate Cancer After Radical Prostatectomy Based on the Competing Risk Model: Population-Based Study

Introduction: We aimed to develop an easy-to-use individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).

Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, pathological Gleason Score. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray's proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.

Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and pathological GS were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812–0.844), and the C-index was 0.838 (%95CI, 0.813–0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.

Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve post-operative management of patients.

Development and validation of a mortality predicting scoring system for severe aplastic anaemia patients receiving haploidentical allogeneic transplantation

Haploidentical allogeneic haematopoietic stem cell transplantation (haplo-HSCT) is a significant alternative treatment for severe aplastic anaemia (SAA). To improve this process by modifying the risk stratification system, we conducted a retrospective study using our database. 432 SAA patients who received haplo-HSCT between 2006 and 2020 were enrolled. These patients were divided into a training (n = 288) and a validation (n = 144) subset randomly. In the training cohort, longer time from diagnosis to transplantation, poorer Eastern Cooperative Oncology Group (ECOG) status and higher haematopoietic cell transplantation-specific comorbidity index (HCT-CI) score were independent risk factors for worse treatment-related mortality (TRM) in the final multivariable model. The haplo-HSCT scoring system was developed by these three parameters. Three-year TRM after haplo-HSCT were 6% [95% confidence interval (CI), 1-21%], 21% (95% CI, 7-40%), and 47% (95% CI, 20-70%) for the low-, intermediate-, and high-risk group, respectively (P < 0·0001). In the validation cohort, the haplo-HSCT scoring system also separated patients into three risk groups with increasing risk of TRM: intermediate-risk [hazard ratio (HR) 2·45, 95% CI, 0·92-6·53] and high-risk (HR 11·74, 95% CI, 3·07-44·89) compared with the low-risk group (P = 0·001). In conclusion, the haplo-HSCT scoring system could effectively predict TRM after transplantation.

Immune infiltration profiling in gastric cancer and their clinical implications

The abundance and type of immune cells in the tumor microenvironment (TME) significantly influence immunotherapy and tumor progression. However, the role of immune cells in the TME of gastric cancer (GC) is poorly understood. We studied the correlations, proportion, and infiltration of immune and stromal cells in GC tumors. Data analyses showed a significant association of infiltration levels of specific immune cells with the pathological characteristics and clinical outcomes of GC. Furthermore, based on the difference in infiltration levels of immune and stromal cells, GC patients were divided into two categories, those with "immunologically hot" (hot) tumors and those with "immunologically cold" (cold) tumors. The assay for transposase-accessible chromatin using sequencing and RNA sequencing analyses revealed that the hot and cold tumors had altered epigenomic and transcriptional profiles. Claudin-3 (CLDN3) was found to have high expression in the cold tumors and negatively correlated with CD8⁺ T cells in GC. Overexpression of CLDN3 in GC cells inhibited the expression of MHC-I and CXCL9. Finally, the differentially expressed genes between hot and cold tumors were utilized to generate a prognostic model, which predicted the overall survival of GC as well as patients with immunotherapy. Overall, we undertook a comprehensive analysis of the immune cell infiltration pattern in GC and provided an accurate model for predicting the prognosis of GC patients.

The development and validation of a nomogram for predicting brain metastases in lung squamous cell carcinoma patients: an analysis of the Surveillance, Epidemiology, and End Results (SEER) database

Background

The incidence of brain metastasis (BM) in patients suffering from lung squamous cell carcinoma (LUSC) is lower than that in patients suffering from non-squamous cell carcinoma (NSCC) and there are few studies on BM of LUSC. The purpose of this investigation was to ascertain the risk factors of LUSC, as well as to establish a nomogram prognostic model to predict the incidence of BM in patients with LUSC.

Methods

Patients diagnosed with LUSC between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and the patient data were collated. All patients diagnosed from 2010–2012 were allocated into the training cohort, and the remaining patients diagnosed from 2013–2015 formed the test cohort. Using factors that were screened out through logistic regression analyses, the nomogram in the training cohort was established. It was then evaluated for discrimination and calibration using the test cohort. The performance of the nomogram was assessed by quantifying the area under the receiver operating characteristic (ROC) curve and evaluating the calibration curve.

Results

A total of 26,154 LUSC patients were included in the study. The training cohort consisted of 16,543 patients and there were 8611 patients in the test cohort. Age, marital status, insurance status, histological grade, tumor location, laterality, stage of the cancer, number of metastatic organs, chemotherapy, surgery, and radiotherapy were highly correlated with the incidence of BM. The area under the ROC curve (AUC) of the nomogram for the training cohort and the test cohort were 0.810 [95% confidence interval (CI): 0.796 to 0.823] and 0.805 (95% CI: 0.784 to 0.825), respectively. The slope of the calibration curve was close to 1.

Conclusions

The nomogram was able to accurately predict the incidence of BM. This may be beneficial for the early identification of high-risk LUSC patients and the establishment of individualized treatments.

A nomogram to predict mechanical ventilation in Guillain-Barré syndrome patients

INTRODUCTION

Guillain-Barré syndrome (GBS) is one of the most common causes of acute flaccid paralysis, with up to 20%-30% of patients requiring mechanical ventilation. The aim of our study was to develop and validate a mechanical ventilation risk nomogram in a Chinese population of patients with GBS.

METHODS

A total of 312 GBS patients were recruited from January 1, 2015, to June 31, 2018, of whom 17% received mechanical ventilation. The least absolute shrinkage and selection operator (LASSO) regression model was used to select clinicodemographic characteristics and blood markers that were then incorporated, using multivariate logistic regression, into a risk model to predict the need for mechanical ventilation. The model was characterized and assessed using the C-index, calibration plot, and decision curve analysis. The model was validated using bootstrap resampling in a prospective study of 114 patients recruited from July 1, 2018, to July 10, 2019.

RESULTS

The predictive model included hospital stay, glossopharyngeal and vagal nerve deficits, Hughes functional grading scale scores at admission, and neutrophil/lymphocyte ratio (NLR). The model showed good discrimination with a C-index value of 0.938 and good calibration. A high C-index value of 0.856 was reached in the validation group. Decision curve analysis demonstrated the clinical utility of the mechanical ventilation nomogram.

CONCLUSIONS

A nomogram incorporating hospital stay, glossopharyngeal and vagal nerve deficits, Hughes functional grading scale scores at admission, and NLR may reliably predict the probability of requiring mechanical ventilation in GBS patients.

A New Model for Predicting Hypothyroidism After Intensity-Modulated Radiotherapy for Nasopharyngeal Carcinoma

Objectives

To develop a model that can predict the risk of hypothyroidism (HT) after intensity-modulated radiotherapy (IMRT) for nasopharyngeal carcinoma (NPC), and to accordingly recommend dose constraints.

Materials and Methods

NPC patients treated between 2011 and 2015 were retrospectively reviewed. HT was defined by an abnormally high level of thyrotropin. The dosimetry parameters V_x (percentage of thyroid volume receiving more than x Gy of radiation) and V_a,b (percentage of thyroid volume receiving >a Gy, while ≤b Gy radiation) were calculated. The primary endpoint was the development of HT within the first 2 years after IMRT. The least absolute shrinkage and selection operator and multivariate logistic regression were used to identify predictors of HT.

Results

A total of 545 patients were included in the analyses, with a median follow-up of 36 months. Of the 545 patients, 138 developed HT within 2 years, and the 2-year incidence of HT was 25.3%. In patients with thyroid volume >20 cm³, the 2-year incidence of HT was 11.7% (16/137); in patients with thyroid volume ≤20 cm³ and V₃₀,₆₀ ≤ 80%, the 2-year HT incidence was 19.9% (33/166); in patients with thyroid volume ≤20 cm³ and V₃₀,₆₀ > 80%, the 2-year incidence of HT was 36.8% (89/242).

Conclusion

Thyroid volume and V₃₀,₆₀ could be reliable predictors of HT after IMRT for NPC. For patients with thyroid volume ≤20 cm³, thyroid V₃₀,₆₀ ≤ 80% might be a useful dose constraint to adopt during IMRT planning.

Predicting Recurrence in Endometrial Cancer Based on a Combination of Classical Parameters and Immunohistochemical Markers

Objective

The aim of this study was to establish a nomogram to predict the recurrence of endometrial cancer (EC) by immunohistochemical markers and clinicopathological parameters and to evaluate the discriminative power of this model.

Methods

The data of 473 patients with stages I–III endometrial cancer who had received primary surgical treatment between October 2013 and May 2018 were randomly split into two sets: a training cohort and a validation cohort at a predefined ratio of 7:3. Univariate and multivariate Cox regression analysis of screening prognostic factors were performed in the training cohort (n=332) to develop a nomogram model for EC-recurrence prediction, which was further evaluated in the validation cohort (n=141).

Results

Univariate analysis found that FIGO stage, histological type, histological grade, myometrial invasion, cervical stromal invasion, postoperative adjuvant treatment, and four immunohistochemical markers (Ki67, ER, PR, and p53) were associated with recurrence in EC. Multivariate analysis showed that FIGO stage, histological type, ER, and p53 were superior parameters to generate the nomogram model for recurrence prediction in EC. Recurrence-free survival was better predicted by the proposed nomogram, with a C-index value of 0.79 (95% CI 0.66–0.92) in the validation cohort.

Conclusion

This nomogram model involving immunohistochemical markers can better predict recurrence in FIGO stages I–III EC.

Predicting the likelihood of early recurrence based on mRNA sequencing of pituitary adenomas

Background

There is no comprehensive and objective method existing for predicting early recurrence of pituitary adenomas (PAs). The most advanced gene sequencing technology can be applied to build a prognostic model that can effectively predict early recurrence of PAs.

Methods

In this study, using mRNA-Seq data, the corresponding postoperative early recurrence status, and other clinical features of 107 PA samples were obtained and randomly divided into the training and validation groups. Cox regression and receiver operating characteristic (ROC) analysis accompanied by the risk score method was used to build a seven-gene prediction model.

Results

Area under curve values was 0.857 in the training group, 0.936 in the validation group, and 0.848 in all patients. Patients with low-risk scores had a significantly lower probability of early postoperative recurrence compared to those acquiring high-risk scores in the training group, validation group, and all patient (P<0.0001) groups. In addition, 6 out of these 7 significant genes were highly correlated to the early recurrence of PAs.

Conclusions

This prediction model derived from mRNA-Seq data may help in identifying the early recurrence of PAs, consequently aiding in the classification of patients with PAs and the administration of the appropriate therapeutic and follow-up strategy for these patients.

Development and validation of a nomogram for survival benefit of lymphadenectomy in resected gallbladder cancer

Background

Due to absence of large, prospective, randomized, clinical trial data, the potential survival benefit of lymphadenectomy with different number of regional lymph nodes (LNs) remains controversial. We aim to create a predicting model to help estimate individualized potential survival benefit of lymphadenectomy with more regional LNs for patients with resected gallbladder cancer (GBC).

Methods

Patients with resected GBC were selected from the Surveillance, Epidemiology, and End Results database who were diagnosed between 2004 and 2014. Covariates included age, race, sex, grade, histological stage, tumor sizes and receipt of non-primary surgery. Two types of multivariate survival regression models were constructed and compared. The best model performance was tested by the external validation data from our hospital.

Results

A total of 1,669 patients met the inclusion criteria for this study. The lognormal survival model showed the best performance and was tested by the external validation data, including 193 patients with resected GBC from our hospital. Nomograms, which based on the accelerated failure time parametric survival model, were built to estimate individualized survival. C-index, was up to 0.754 and 0.710 in internal validation for more and less regional LNs removed, respectively. Both of internal and external calibration curves showed good agreement between predicted and observed outcomes in the 1-, 3-, and 5-year overall survival (OS).

Conclusions

A predicting model can be used as a decision model to predict which patients may obtain benefit from lymphadenectomy with more regional LNs.

Nomogram predicting survival of hepatocellular carcinoma with portal vein tumour thrombus after curative resection

BACKGROUND

The aim of this study was to combine clinicopathologic variables associated with overall survival and disease-free survival after curative resection for hepatocellular carcinoma (HCC) with portal vein tumour thrombus (PVTT) into a prediction nomogram.

METHODS

We retrospectively analysed 358 patients who underwent curative resection for HCC with PVTT at the Eastern Hepatobiliary Surgery Hospital, the Second Military Medical University, Shanghai, China. Two-thirds of the patients were randomly assigned to the training set (n = 252) and one-third were assigned to the validation set (n = 106). Multivariate analysis by Cox proportional hazards regression was performed using the training set, and the nomogram was constructed. Discrimination and calibration were performed using the validation set.

RESULTS

The multivariate Cox model identified alpha fetoprotein, hepatitis B surface antigen (HBsAg), tumour diameter, tumour capsule, PVTT type and TNM stage as covariates associated with 1-year survival, and alpha fetoprotein, HBsAg, tumour diameter, tumour capsule and PVTT type with half-year disease-free survival. In the validation set, the nomogram exhibited superior discrimination power (Harrell's C-index 0.78) compared with the American Joint Committe on Cancer TNM classification, the Cancer of the Liver Italian Program grade and the Japan Integrated Staging grade. Calibration of the nomogram-predicted survival corresponding closely with the actual survival, the predicted survival was within a 10% margin of ideal nomogram.

CONCLUSION

We developed a nomogram predicting 1-year overall survival and half-year disease-free survival after curative resection for HCC with PVTT. Validation data sets revealed good discrimination and calibration, suggesting good clinical utility. The nomogram improved individualized predictions of survival.

A predicting model of bone marrow malignant infiltration in 18F-FDG PET/CT images with increased diffuse bone marrow FDG uptake

Purpose: To demonstrate the relationship between the etiologies of increased diffuse bone marrow (BM) ¹⁸F-FDG uptake and PET/CT imaging/clinical features, as well as to explore a predicting model of BM malignant infiltration (MI) based on decision tree. Methods: 84 patients with increased diffuse BM uptake were retrospectively enrolled. Their complete case record and PET/CT images were reviewed, with the maximal standardized uptake values of bone marrow (SUVmaxBM) and other imaging/clinical features were noted. At the same time, the differences in imaging/clinical features between bone marrow MI and non-MI groups were compared. The decision tree for predicting MI was established by C5.0 component of SPSS Clementine. Results: In patients with homogenously increased BM uptake, 21 patients had MI resulted from leukemia, lymphoma and small cell lung cancer (SCLC). MI group had higher SUVmaxBM than non-MI group (6.7±3.1 vs 4.2±0.9, p=0.001). However, a considerable proportion of MI patients had similar SUVmaxBM to non-MI patients, which were mainly seen in lymphoplasmacytic lymphoma/Waldenström macroglobulinemia (LPL/WM), chronic myeloid leukemia (CML) and multiple myeloma (MM). There were significant differences in other factors between the two groups. MI patients were highly associated with SUVmaxAP/AX≥1 (the ratio of SUVmaxBM of appendicular skeleton to that of axial skeleton), hepatosplenomegaly, older age and lower rate of fever. The decision tree combining SUVmaxBM, SUVmaxAP/AX, fever and hepatosplenomegaly achieved a sensitivity of 81.0%, a specificity of 98.4% and an accuracy of 94.0% for predicting MI. Conclusion: Increased diffuse BM ¹⁸F-FDG uptake can be attributed to both bone marrow MI and benign etiologies. A decision tree based on C5.0 algorithm, combining PET/CT imaging and clinical features, is of potential use in discriminating BM malignant infiltration from patients with increased diffuse BM uptake.

Predicting the likelihood of postoperative seizure status based on mRNA sequencing in low-grade gliomas

AIM

No comprehensive and objective methods yet exist for predicting postoperative seizure.

PATIENTS & METHODS

mRNA-seq data and corresponding postoperative seizure status of 109 low-grade glioma samples were obtained from Chinese Glioma Genome Atlas database and divided into two sets randomly. Logistic regression and receiver operating characteristic analysis with risk score method were used to develop a ten-gene prediction model.

RESULTS

Considering gene number and area under the curve of receiver operating characteristic, a ten-gene model was generated which showed an area under the curve of 0.9965 in training set. Patients with high-risk scores had higher probability of postoperative seizure compared with those with low-risk scores.

CONCLUSION

This is the first prediction model for postoperative seizures in gliomas, integrating multiple genes. Clinical application may help patients with postoperative seizure control.

Factors associated with a prolonged length of stay after acute exacerbation of chronic obstructive pulmonary disease (AECOPD)

Background

Early identification of patients with a prolonged stay due to acute exacerbation of chronic obstructive pulmonary disease (COPD) may reduce risk of adverse event and treatment costs. This study aimed to identify predictors of prolonged stay after acute exacerbation of COPD based on variables on admission; the study also looked to establish a prediction model for length of stay (LOS).

Methods

We extracted demographic and clinical data from the medical records of 599 patients discharged after an acute exacerbation of COPD between March 2006 and December 2008 at Oslo University Hospital, Aker. We used logistic regression analyses to assess predictors of a length of stay above the 75th percentile and assessed the area under the receiving operating characteristic curve to evaluate the model’s performance.

Results

We included 590 patients (54% women) aged 73.2±10.8 years (mean ± standard deviation) in the analyses. Median LOS was 6.0 days (interquartile range [IQR] 3.5–11.0). In multivariate analysis, admission between Thursday and Saturday (odds ratio [OR] 2.24 [95% CI 1.60–3.51], P<0.001), heart failure (OR 2.26, 95% CI 1.34–3.80), diabetes (OR 1.90, 95% CI 1.07–3.37), stroke (OR 1.83, 95% CI 1.04–3.21), high arterial PCO₂ (OR 1.26 [95% CI 1.13–1.41], P<0.001), and low serum albumin level (OR 0.92 [95% CI 0.87–0.97], P=0.001) were associated with a LOS >11 days. The statistical model had an area under the receiver operating characteristic curve of 0.73.

Conclusion

Admission between Thursday and Saturday, heart failure, diabetes, stroke, high arterial PCO₂, and low serum albumin level were associated with a prolonged LOS. These findings may help physicians to identify patients that will need a prolonged LOS in the early stages of admission. However, the predictive model exhibited suboptimal performance and hence is not ready for clinical use.