Development and validation of nomograms to predict clinical outcomes of preeclampsia

Background

Preeclampsia (PE) is one of the most severe pregnancy-related diseases; however, there is still a lack of reliable biomarkers. In this study, we aimed to develop models for predicting early-onset PE, severe PE, and the gestation duration of patients with PE.

Methods

Eligible patients with PE were enrolled and divided into a training (n = 253) and a validation (n = 108) cohort. Multivariate logistic and Cox models were used to identify factors associated with early-onset PE, severe PE, and the gestation duration of patients with PE. Based on significant factors, nomograms were developed and evaluated using the area under the curve (AUC) and a calibration curve.

Results

In the training cohort, multiple gravidity experience (p = 0.005), lower albumin (ALB; p < 0.001), and higher lactate dehydrogenase (LDH; p < 0.001) were significantly associated with early-onset PE. Abortion history (p = 0.017), prolonged thrombin time (TT; p < 0.001), and higher aspartate aminotransferase (p = 0.002) and LDH (p = 0.003) were significantly associated with severe PE. Abortion history (p < 0.001), gemellary pregnancy (p < 0.001), prolonged TT (p < 0.001), higher mean platelet volume (p = 0.014) and LDH (p < 0.001), and lower ALB (p < 0.001) were significantly associated with shorter gestation duration. Three nomograms were developed and validated to predict the probability of early-onset PE, severe PE, and delivery time for each patient with PE. The AUC showed good predictive performance, and the calibration curve and decision curve analysis demonstrated clinical practicability.

Conclusion

Based on the clinical features and peripheral blood laboratory indicators, we identified significant factors and developed models to predict early-onset PE, severe PE, and the gestation duration of pregnant women with PE, which could help clinicians assess the clinical outcomes early and design appropriate strategies for patients.

Development of a Risk Prediction Model for Infection After Kidney Transplantation Transmitted from Bacterial Contaminated Preservation Solution

Background

The risk of transplant recipient infection is unknown when the preservation solution culture is positive.

Methods

We developed a prediction model to evaluate the infection in kidney transplant recipients within microbial contaminated preservation solution. Univariate logistic regression was utilized to identify risk factors for infection. Both stepwise selection with Akaike information criterion (AIC) was used to identify variables for multivariate logistic regression. Selected variables were incorporated in the nomograms to predict the probability of infection for kidney transplant recipients with microbial contaminated preservation solution.

Results

Age, preoperative creatinine, ESKAPE, PCT, hemofiltration, and sirolimus had a strongest association with infection risk, and a nomogram was established with an AUC value of 0.72 (95% confidence interval, 0.64-0.80) and Brier index 0.20 (95% confidence interval, 0.18-0.23). Finally, we found that when the infection probability was between 20% and 80%, the model oriented antibiotic strategy should have higher net benefits than the default strategy using decision curve analysis.

Conclusion

Our study developed and validated a risk prediction model for evaluating the infection of microbial contaminated preservation solutions in kidney transplant recipients and demonstrated good net benefits when the total infection probability was between 20% and 80%.

Risk Factor Analysis and Nomogram for Early Progression of COVID-19 Pneumonia in Older Adult Patients in the Omicron Era

Background and Objective

Timely recognition of risk factors for early progression in older adult patients with COVID-19 is of great significance to the following clinical management. This study aims to analyze the risk factors and create a nomogram for early progression in older adult patients with COVID-19 in the Omicron era.

Methods

A total of 272 older adults infected with COVID-19 admitted from December 2022 to February 2023 were retrospectively recruited. Risk factor selection was determined using the logistic and the least absolute shrinkage and selection operator (LASSO) regression. A nomogram was then created to predict early progression, followed by the internal validation and assessment of its performance through plotting the receiver operating characteristic (ROC), calibration, and decision curves.

Results

A total of 83 (30.5%) older adult patients presented an early progression on chest CT after 3-5 days of admission under standard initiate therapy. Six independent predictive factors were incorporated into the nomogram to predict the early progression, including CRP > 10 mg/L, IL-6 > 6.6 pg/mL, LDH > 245 U/L, CD4+ T-lymphocyte count <400/µL, the Activities of Daily Living (ADL) score ≤40 points, and the Mini Nutritional Assessment Scale-Short Form (MNA-SF) score ≤7 points. The area under the curve (AUC) of the nomogram in discriminating older adult patients who had risk factors in the training and validation cohort was 0.857 (95% CI 0.798, 0.916) and 0.774 (95% CI 0.667, 0.881), respectively. The calibration and decision curves demonstrated a high agreement in the predicted and observed risks, and the acceptable net benefit in predicting the early progression, respectively.

Conclusion

We created a nomogram incorporating highly available laboratory data and the Comprehensive Geriatric Assessment (CGA) findings that effectively predict early-stage progression in older adult patients with COVID-19 in the Omicron era.

Predictive nomograms of repeat intrahepatic recurrence and overall survival after radiofrequency ablation of recurrent colorectal liver metastases

OBJECTIVES

This study was conducted to develop nomograms for predicting repeat intrahepatic recurrence (rIHR) and overall survival (OS), after radiofrequency ablation (RFA), treatment in patients with recurrent colorectal liver metastases (CLMs) after hepatectomy based on clinicopathologic features.

METHODS

A total of 160 consecutive patients with recurrent CLMs after hepatectomy who were treated with ultrasound-guided percutaneous RFA from 2012 to 2022 were retrospectively included. Patients were randomly divided into a training cohort and a validation cohort, with a ratio of 8:2. Potential prognostic factors associated with rIHR and OS, after RFA, were identified by using the competing-risks and Cox proportional hazard models, respectively, and were used to construct the nomogram. The nomogram was evaluated by Harrell's C-index and a calibration curve.

RESULTS

The 1-, 2-, and 3-year rIHR rates after RFA were 58.8%, 70.2%, and 74.2%, respectively. The 1-, 3- and 5-year OS rates were 96.3%, 60.4%, and 38.5%, respectively. In the multivariate analysis, mutant RAS, interval from hepatectomy to intrahepatic recurrence ≤ 12 months, CEA level >5 ng/ml, and ablation margin <5 mm were the independent predictive factors for rIHR. Mutant RAS, largest CLM at hepatectomy >3 cm, CEA level >5 ng/ml, and extrahepatic disease were independent predictors of poor OS. Two nomograms for rIHR and OS were constructed using the respective significant variables. In both cohorts, the nomogram demonstrated good discrimination and calibration.

CONCLUSIONS

The established nomograms can predict individual risk of rIHR and OS after RFA for recurrent CLMs and contribute to improving individualized management.

Cost-effective prognostic evaluation of breast cancer: using a STAR nomogram model based on routine blood tests

Background

Breast cancer (BC) is the most common and prominent deadly disease among women. Predicting BC survival mainly relies on TNM staging, molecular profiling and imaging, hampered by subjectivity and expenses. This study aimed to establish an economical and reliable model using the most common preoperative routine blood tests (RT) data for survival and surveillance strategy management.

Methods

We examined 2863 BC patients, dividing them into training and validation cohorts (7:3). We collected demographic features, pathomics characteristics and preoperative 24-item RT data. BC risk factors were identified through Cox regression, and a predictive nomogram was established. Its performance was assessed using C-index, area under curves (AUC), calibration curve and decision curve analysis. Kaplan-Meier curves stratified patients into different risk groups. We further compared the STAR model (utilizing HE and RT methodologies) with alternative nomograms grounded in molecular profiling (employing second-generation short-read sequencing methodologies) and imaging (utilizing PET-CT methodologies).

Results

The STAR nomogram, incorporating subtype, TNM stage, age and preoperative RT data (LYM, LYM%, EOSO%, RDW-SD, P-LCR), achieved a C-index of 0.828 in the training cohort and impressive AUCs (0.847, 0.823 and 0.780) for 3-, 5- and 7-year OS rates, outperforming other nomograms. The validation cohort showed similar impressive results. The nomogram calculates a patient's total score by assigning values to each risk factor, higher scores indicating a poor prognosis. STAR promises potential cost savings by enabling less intensive surveillance in around 90% of BC patients. Compared to nomograms based on molecular profiling and imaging, STAR presents a more cost-effective, with potential savings of approximately $700-800 per breast cancer patient.

Conclusion

Combining appropriate RT parameters, STAR nomogram could help in the detection of patient anemia, coagulation function, inflammation and immune status. Practical implementation of the STAR nomogram in a clinical setting is feasible, and its potential clinical impact lies in its ability to provide an early, economical and reliable tool for survival prediction and surveillance strategy management. However, our model still has limitations and requires external data validation. In subsequent studies, we plan to mitigate the potential impact on model robustness by further updating and adjusting the data and model.

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model's performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients' average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses.

Development and validation of a novel model to predict pulmonary embolism in cardiology suspected patients: A 10-year retrospective analysis

As there are no predictive models for pulmonary embolism (PE) in patients with suspected PE at cardiology department. This study developed a predictive model for the probability of PE development in these patients. This retrospective analysis evaluated data from 995 patients with suspected PE at the cardiology department from January 2012 to December 2021. Patients were randomly divided into the training and validation cohorts (7:3 ratio). Using least absolute shrinkage and selection operator regression, optimal predictive features were selected, and the model was established using multivariate logistic regression. The features used in the final model included clinical and laboratory factors. A nomogram was developed, and its performance was assessed and validated by discrimination, calibration, and clinical utility. Our predictive model showed that six PE-associated variables (age, pulse, systolic pressure, syncope, D-dimer, and coronary heart disease). The area under the curve - receiver operating characteristic curves of the model were 0.721 and 0.709 (95% confidence interval: 0.676-0.766 and 0.633-0.784), respectively, in both cohorts. We also found good consistency between the predictions and real observations in both cohorts. In decision curve analysis, the numerical model had a good net clinical benefit. This novel model can predict the probability of PE development in patients with suspected PE at cardiology department.

Clinical characteristics and prognostic factors of male breast cancer in China

Purpose

This study aimed to explore the clinical characteristics of male breast cancer (MBC) patients and the factors influencing their prognosis.

Methods

We conducted a retrospective case series analysis of 117 MBC cases who were treated at Zhejiang Cancer Hospital from 2009 to 2022. Cox proportional hazard model was used to identify prognostic factors of MBC. Nomogram was constructed based on these factors, which was further evaluated by C-index and calibration curves.

Results

A total of 115 MBC cases were finally included in our analyses, with median diagnosis age of 59 years. Of these cases, 80.0% were estrogen receptor (ER) positive, 79.2% were progesterone receptor (PR) positive, 48.7% were human epidermal growth factor receptor 2 (HER2) negative, and 42.6% had Ki67 levels higher than 15%. 108 (93.9%) cases underwent radical mastectomy, while only 3 (2.6%) received breast-conserving surgery. The Logrank test suggested that lymphocyte-to-monocyte ratio (LMR) was negatively associated with both overall survival (OS) and disease-free survival (DFS) of MBC, while platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) were only positively associated with OS (all P-values < 0.05). Multivariate regression analysis showed that age (HR 1.08, 95% CI 1.03-1.13) was significant prognostic factors for OS. Meanwhile, age (HR 1.06, 95% CI 1.02-1.10), histological differentiation grade (poorly differentiated/undifferentiated vs. well-differentiated: HR 2.55, 95% CI 1.05-6.17), and TNM stage (IV vs. I: HR 31.59, 95% CI 6.01-165.93) were also significant prognostic factors for DFS. Nomograms were developed for DFS, with C-indexes of 0.782, indicating good predictive performance.

Conclusion

Increased age, bigger tumor size, higher TNM stage, and lower histological differentiation grade were associated with poor MBC prognosis, and LMR, PLR, and NLR might be potential predictors for MBC prognosis.

Nomogram model for predicting early recurrence for resectable pancreatic cancer: A multicenter study

Pancreatic cancer is a highly aggressive malignancy that is characterized by early metastasis, high recurrence, and therapy resistance. Early recurrence after surgery is one of the important reasons affecting the prognosis of pancreatic cancer. This study aimed to establish an accurate preoperative nomogram model for predicting early recurrence (ER) for resectable pancreatic adenocarcinoma. We retrospectively analyzed patients who underwent pancreatectomy for pancreatic ductal adenocarcinoma between January 2011 and December 2020. The training set consisted of 604 patients, while the validation set included 222 patients. Survival was estimated using Kaplan-Meier curves. The factors influencing early recurrence of resectable pancreatic cancer after surgery were investigated, then the predictive model for early recurrence was established, and subsequently the predictive model was validated based on the data of the validation group. The preoperative risk factors for ER included a Charlson age-comorbidity index ≥ 4 (odds ratio [OR]: 0.628), tumor size > 3.0 cm on computed tomography (OR: 0.628), presence of clinical symptoms (OR: 0.515), carbohydrate antigen 19-9 > 181.3 U/mL (OR 0.396), and carcinoembryonic antigen > 6.01 (OR: 0.440). The area under the curve (AUC) of the predictive model in the training group was 0.711 (95% confidence interval: 0.669-0.752), while it reached 0.730 (95% CI: 0.663-0.797) in the validation group. The predictive model may enable the prediction of the risk of postoperative ER in patients with resectable pancreatic ductal adenocarcinoma, thereby optimizing preoperative decision-making for effective treatment.

A Nomogram for Predicting Mortality in Patients with Pneumonia-Associated Acute Respiratory Distress Syndrome (ARDS)

Background

There is no predictive tool developed for pneumonia-associated acute respiratory distress syndrome (ARDS) specifically so far, and the clinical risk classification of these patients is not well defined. Our study aims to construct an early prediction model for hospital mortality in patients with pneumonia-associated ARDS.

Methods

In this single-center retrospective study, consecutive patients with pneumonia-associated ARDS admitted into intensive care units (ICUs) in West China Hospital of Sichuan University in China between January 2012 and December 2018 were enrolled. The least absolute shrinkage and selection operator (LASSO) regression and then multivariate logistic regression analysis were used to identify independent predictors which were used to develop a nomogram. We evaluated the performance of differentiation, calibration, and clinical utility of the nomogram.

Results

The included patients were divided into the training cohort (442 patients) and the testing cohort (190 patients) with comparable baseline characteristics. The independent predictors for hospital mortality included age (OR: 1.04; 95% CI: 1.02, 1.05), chronic cardiovascular diseases (OR: 2.62; 95% CI: 1.54, 4.45), chronic respiratory diseases (OR: 1.87; 95% CI: 1.02, 3.43), lymphocytes (OR: 0.56; 95% CI: 0.39, 0.81), albumin (OR: 0.94; 95% CI: 0.90, 1.00), creatinine (OR: 1.00; 95% CI: 1.00, 1.01), D-dimer (OR: 1.06; 95% CI: 1.03, 1.09) and procalcitonin (OR: 1.14; 95% CI: 1.07, 1.22). A web-based dynamic nomogram (https://h1234.shinyapps.io/dynnomapp/) was constructed based on these factors. The concordance index (C index) of the nomogram was 0.798 (95% CI: 0.756, 0.840) in the training cohort and 0.808 (95% CI: 0.747, 0.870) in testing cohort. The precision-recall (PR) curves, calibration curves, decision curve analyses (DCA) and clinical impact curves showed that the nomogram has good predictive value and clinical utility.

Conclusion

We developed and evaluated a convenient nomogram consisting of 8 clinical characteristics for predicting mortality in patients with pneumonia-associated ARDS.

Establishment and validation of a predictive nomogram for severe pleural effusion in liver cancer patients after hepatectomy

This study aims to develop and validate a predictive nomogram for severe postoperative pleural effusion (SPOPE) in patients undergoing hepatectomy for liver cancer. A total of 536 liver cancer patients who underwent hepatectomy at the Department of Hepatobiliary Surgery I of the Affiliated Hospital of North Sichuan Medical College from January 1, 2018, to December 31, 2022, were enrolled in a retrospective observational study and comprised the training dataset. Lasso regression and logistic regression analyses were employed to construct a predictive nomogram. The nomogram was internally validated using Bootstrapping and externally validated with a dataset of 203 patients who underwent liver cancer resection at the Department of General Surgery III of the same hospital from January 1, 2020, to December 31, 2022. We evaluated the nomogram using the receiver operating characteristic curve, calibration curve, and decision curve analysis. Variables such as drinking history, postoperative serum albumin, postoperative total bilirubin, right hepatectomy, diaphragm incision, and intraoperative blood loss were observed to be associated with SPOPE. These factors were integrated into our nomogram. The C-index of the nomogram was 0.736 (95% CI: 0.692-0.781) in the training set and 0.916 (95% CI: 0.872-0.961) in the validation set. The nomogram was then evaluated using sensitivity, specificity, positive predictive value, negative predictive value, calibration curve, and decision curve analysis. The nomogram demonstrates good discriminative ability, calibration, and clinical utility.

Nomogram prediction model for the risk of intracranial hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke

Background

Hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) might worsen the clinical outcomes, and a reliable predictive system is needed to identify the risk of hemorrhagic transformation after IVT.

Methods

Retrospective collection of patients with acute cerebral infarction treated with intravenous thrombolysis in our hospital from 2018 to 2022. 197 patients were included in the research study. Multivariate logistic regression analysis was used to screen the factors in the predictive nomogram. The performance of nomogram was assessed on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots and decision curve analysis (DCA).

Results

A total of 197 patients were recruited, of whom 24 (12.1%) developed HT. In multivariate logistic regression model National Institute of Health Stroke Scale (NIHSS) (OR, 1.362; 95% CI, 1.161-1.652; p = 0.001), N-terminal pro-brain natriuretic peptide (NT-pro BNP) (OR, 1.012; 95% CI, 1.004-1.020; p = 0.003), neutrophil to lymphocyte ratio (NLR) (OR, 3.430; 95% CI, 2.082-6.262; p < 0.001), systolic blood pressure (SBP) (OR, 1.039; 95% CI, 1.009-1.075; p = 0.016) were the independent predictors of HT which were used to generate nomogram. The nomogram showed good discrimination due to AUC-ROC values. Calibration plot showed good calibration. DCA showed that nomogram is clinically useful.

Conclusion

Nomogram consisting of NIHSS, NT-pro BNP, NLR, SBP scores predict the risk of HT in AIS patients treated with IVT.

SUMF1 overexpression promotes tumorous cell growth and migration and is correlated with the immune status of patients with glioma

BACKGROUND

Glioma is a prevalent type of malignant tumor. To date, there is a lack of literature reports that have examined the association between sulfatase modifying factor 1 (SUMF1) and glioma.

METHODS

The levels of SUMF1 were examined, and their relationships with the diagnosis, prognosis, and immune microenvironment of patients with glioma were investigated. Cox and Lasso regression analysis were employed to construct nomograms and risk models associated with SUMF1. The functions and mechanisms of SUMF1 were explored and verified using gene ontology, cell counting kit-8, wound healing, western blotting, and transwell experiments.

RESULTS

SUMF1 expression tended to increase in glioma tissues. SUMF1 overexpression was linked to the diagnosis of cancer, survival events, isocitrate dehydrogenase status, age, and histological subtype and was positively correlated with poor prognosis in patients with glioma. SUMF1 overexpression was an independent risk factor for poor prognosis. SUMF1-related nomograms and high-risk scores could predict the outcome of patients with glioma. SUMF1 co-expressed genes were involved in cytokine, T-cell activation, and lymphocyte proliferation. Inhibiting the expression of SUMF1 could deter the proliferation, migration, and invasion of glioma cells through epithelial mesenchymal transition. SUMF1 overexpression was significantly associated with the stromal score, immune cells (such as macrophages, neutrophils, activated dendritic cells), estimate score, immune score, and the expression of the programmed cell death 1, cytotoxic T-lymphocyte associated protein 4, CD79A and other immune cell marker.

CONCLUSION

SUMF1 overexpression was found to be correlated with adverse prognosis, cancer detection, and immune status in patients with glioma. Inhibiting the expression of SUMF1 was observed to deter the proliferation, migration, and invasion of cancer cells. The nomograms and risk models associated with SUMF1 could predict the prognosis of patients with glioma.

Machine Learning-Based Nomogram for Predicting Overall Survival in Elderly Patients with Cirrhotic Hepatocellular Carcinoma Undergoing Ablation Therapy

Purpose

The aim of the study is to identify and evaluate multifaceted factors impacting the survival of elderly cirrhotic HCC patients following ablation therapy, with the goal of constructing a nomogram to predict their 3-, 5-, and 8-year overall survival (OS).

Patients and Methods

A retrospective analysis was conducted on 736 elderly cirrhotic HCC patients who underwent ablation therapy between 2014 and 2022. LASSO regression, random survival forest (RSF), and multivariate Cox analyses were employed to identify independent prognostic factors for OS, followed by the development and validation of a predictive nomogram. Harrell's concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to assess the performance of the nomogram. The nomogram was finally utilized to stratify patients into low-, intermediate-, and high-risk groups, aiming to assess its efficacy in precisely discerning individuals with diverse overall survival outcomes.

Results

Alcohol drinking, tumor number, globulin (Glob) and prealbumin (Palb) were identified and integrated to establish a novel prognostic nomogram. The nomogram exhibited strong discriminative ability with C-indices of 0.723 (training cohort) and 0.693 (validation cohort), along with significant Area Under the Curve (AUC) values for 3-year, 5-year, and 8-year OS in both cohorts (0.758, 0.770, and 0.811 for training cohort; 0.744, 0.699 and 0.737 for validation cohort). Calibration plots substantiated its consistency, while DCA curves corroborated its clinical utility. The nomogram further demonstrated exceptional effectiveness in discerning distinct risk populations, highlighting its robust applicability for prognostic stratification.

Conclusion

Our study successfully developed and validated a robust nomogram model based on four key clinical parameters for predicting 3-, 5- and 8-year OS among elderly cirrhotic HCC patients following ablation therapy. The nomogram exhibited a remarkable capability in identifying high-risk patients, furnishing clinicians with invaluable insights for postoperative surveillance and tailored therapeutic interventions.

Development and Validation of a Novel Predictive Model for the Early Differentiation of Cardiac and Non-Cardiac Syncope

Background

The diagnosis of cardiac syncope remains a challenge. This study sought to develop and validate a diagnostic model for the early identification of individuals likely to have a cardiac cause.

Methods

877 syncope patients with a determined cause were retrospectively enrolled at a tertiary heart center. They were randomly divided into the training set and validation set at a 7:3 ratio. We analyzed the demographic information, medical history, laboratory tests, electrocardiogram, and echocardiogram by the least absolute shrinkage and selection operator (LASSO) regression for selection of key features. Then a multivariable logistic regression analysis was performed to identify independent predictors and construct a diagnostic model. The receiver operating characteristic curves, area under the curve (AUC), calibration curves, and decision curve analysis were used to evaluate the predictive accuracy and clinical value of this nomogram.

Results

Five independent predictors for cardiac syncope were selected: BMI (OR 1.088; 95% CI 1.022-1.158; P =0.008), chest symptoms preceding syncope (OR 5.251; 95% CI 3.326-8.288; P <0.001), logarithmic NT-proBNP (OR 1.463; 95% CI 1.240-1.727; P <0.001), left ventricular ejection fraction (OR 0.940; 95% CI 0.908-0.973; P <0.001), and abnormal electrocardiogram (OR 6.171; 95% CI 3.966-9.600; P <0.001). Subsequently, a nomogram based on a multivariate logistic regression model was developed and validated, yielding AUC of 0.873 (95% CI 0.845-0.902) and 0.856 (95% CI 0.809-0.903), respectively. The calibration curves showcased the nomogram's reasonable calibration, and the decision curve analysis demonstrated good clinical utility.

Conclusion

A diagnostic tool providing individualized probability predictions for cardiac syncope was developed and validated, which may potentially serve as an effective tool to facilitate early identification of such patients.

Risk stratification of stage II rectal mucinous adenocarcinoma to predict the benefit of adjuvant chemotherapy following neoadjuvant chemoradiation and surgery

Background

The treatment strategy for stage II rectal mucinous adenocarcinoma (RMA) recommends neoadjuvant chemoradiotherapy (NCR) followed by total mesorectal excision (TME). However, the necessity of adjuvant chemotherapy (AC) remains controversial.

Materials and methods

Chi-square test was used to assess the relationship between pathological classification, AC and clinicopathological characteristics. Kaplan-Meier (KM) curves and the log-rank test were utilized to analyze differences in overall survival (OS) and cancer-specific survival (CSS) among different groups. Cox regression identified prognostic factors. Nomogram was established utilizing the independent prognostic factors. X-tile divided patients into three risk subgroups.

Results

Compared to RMA, rectal adenocarcinoma (RA) demonstrates longer OS and CSS in all and non-AC stage II patients, with no difference in OS and CSS for AC stage II patients. Propensity score matching analyses yielded similar results. Stratified analysis found that AC both improve OS of RA and RMA patients. Age, gender, pathologic T stage, regional nodes examined, and tumor size were identified as independent prognostic factors for RMA patients without AC. A nomogram was constructed to generate risk scores and categorize RMA patients into three subgroups based on these scores. KM curves revealed AC benefits for moderate and high-risk groups but not for the low-risk group. The external validation cohort yielded similar results.

Conclusions

In summary, our study suggests that, compared to stage II RA patients, stage II RMA patients benefit more from AC after NCR. AC is recommended for moderate and high-risk stage II RMA patients after NCR, whereas low-risk patients do not require AC.

Prognostic nomogram for predicting the overall survival rate of patients with uterine clear-cell carcinoma: Based on SEER database

OBJECTIVE

To evaluate the risk factors for uterine clear-cell carcinoma (UCCC) and construct nomograms predicting 1-, 3-, and 5-year overall survival rates of patients with UCCC.

METHODS

The demographic and clinical information of 1674 patients diagnosed with UCCC between 2004 and 2015, including age, race, marital status, tumor size, American Joint Committee on Cancer (AJCC) stage, and details of surgery and radiotherapy/chemotherapy, was collected from the Surveillance, Epidemiology, and End Results (SEER) database. After excluding patients with unknown AJCC stage, race, marital status, or lymph node information, 1469 patients remained. Risk factors were determined using univariate and multivariate analyses, and nomograms were developed to predict 1-, 3-, and 5-year overall survival of UCCC. Various indicators were used to evaluate the performance of the nomogram, such as the C-index, net classification improvement (NRI) and decision curve analysis (DCA).

RESULTS

Age, log odds of positive lymph nodes, AJCC stage, surgery status, and chemotherapy status were independent risk factors for UCCC. The C-indexes of the training group and AJCC stage groups were 0.771 and 0.697, respectively. The results for the area under the receiver operating characteristics curve, NRI, and calibration curves indicated that the nomogram had good predictive ability. DCA revealed that the nomogram had greater clinical applicability than AJCC stage alone. Internal validation using the validation cohort also demonstrated that this nomogram had good predictive performance.

CONCLUSION

A new nomogram comprising a combination of demographic and clinical characteristics provided better survival predictions than the AJCC staging system alone, which will facilitate prognostic assessments and clinical decision-making.

Preoperative differentiation of gastric schwannomas and gastrointestinal stromal tumors based on computed tomography: a retrospective multicenter observational study

Introduction

Gastric schwannoma is a rare benign tumor accounting for only 1-2% of alimentary tract mesenchymal tumors. Owing to their low incidence rate, most cases are misdiagnosed as gastrointestinal stromal tumors (GISTs), especially tumors with a diameter of less than 5 cm. Therefore, this study aimed to develop and validate a diagnostic nomogram based on computed tomography (CT) imaging features for the preoperative prediction of gastric schwannomas and GISTs (diameters = 2-5 cm).

Methods

Gastric schwannomas in 47 patients and GISTs in 230 patients were confirmed by surgical pathology. Thirty-four patients with gastric schwannomas and 167 with GISTs admitted between June 2009 and August 2022 at Hospital 1 were retrospectively analyzed as the test and training sets, respectively. Seventy-six patients (13 with gastric schwannomas and 63 with GISTs) were included in the external validation set (June 2017 to September 2022 at Hospital 2). The independent factors for differentiating gastric schwannomas from GISTs were obtained by multivariate logistic regression analysis, and a corresponding nomogram model was established. The accuracy of the nomogram was evaluated using receiver operating characteristic and calibration curves.

Results

Logistic regression analysis showed that the growth pattern (odds ratio [OR] 3.626; 95% confidence interval [CI] 1.105-11.900), absence of necrosis (OR 4.752; 95% CI 1.464-15.424), presence of tumor-associated lymph nodes (OR 23.978; 95% CI 6.499-88.466), the difference between CT values during the portal and arterial phases (OR 1.117; 95% CI 1.042-1.198), and the difference between CT values during the delayed and portal phases (OR 1.159; 95% CI 1.080-1.245) were independent factors in differentiating gastric schwannoma from GIST. The resulting individualized prediction nomogram showed good discrimination in the training (area under the curve [AUC], 0.937; 95% CI, 0.900-0.973) and validation (AUC, 0.921; 95% CI, 0.830-1.000) datasets. The calibration curve showed that the probability of gastric schwannomas predicted using the nomogram agreed well with the actual value.

Conclusion

The proposed nomogram model based on CT imaging features can be used to differentiate gastric schwannoma from GIST before surgery.

Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit

Purpose

This study aims to develop and validate a nomogram for predicting the risk of bloodstream infections (BSI) in critically ill patients based on their admission status to the Intensive Care Unit (ICU).

Patients and methods

Patients' data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training set), the Beijing Friendship Hospital (BFH) database (validation set) and the eICU Collaborative Research Database (eICU-CRD) (validation set). Univariate logistic regression analyses were used to analyze the influencing factors, and lasso regression was used to select the predictive factors. Model performance was assessed using area under receiver operating characteristic curve (AUROC) and Presented as a Nomogram. Various aspects of the established predictive nomogram were evaluated, including discrimination, calibration, and clinical utility.

Results

The model dataset consisted of 14930 patients (1444 BSI patients) from the MIMIC-IV database, divided into the training and internal validation datasets in a 7:3 ratio. The eICU dataset included 2100 patients (100 with BSI) as the eICU validation dataset, and the BFH dataset included 419 patients (21 with BSI) as the BFH validation dataset. The nomogram was constructed based on Glasgow Coma Scale (GCS), sepsis related organ failure assessment (SOFA) score, temperature, heart rate, respiratory rate, white blood cell (WBC), red width of distribution (RDW), renal replacement therapy and presence of liver disease on their admission status to the ICU. The AUROCs were 0.83 (CI 95%:0.81-0.84) in the training dataset, 0.88 (CI 95%:0.88-0.96) in the BFH validation dataset, and 0.75 (95%CI 0.70-0.79) in the eICU validation dataset. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model has a certain clinical effectiveness.

Conclusion

The nomogram developed in this study provides a valuable tool for clinicians and nurses to assess individual risk, enabling them to identify patients at a high risk of bloodstream infections in the ICU.

Establishment and verification of a nomogram that predicts the risk for coronary slow flow

Background

Coronary slow flow (CSF) has gained significance as a chronic coronary artery disease, but few studies have integrated both biological and anatomical factors for CSF assessment. This study aimed to develop and validate a simple-to-use nomogram for predicting CSF risk by combining biological and anatomical factors.

Methods

In this retrospective case-control study, 1042 patients (614 CSF cases and 428 controls) were randomly assigned to the development and validation cohorts at a 7:3 ratio. Potential predictive factors were identified using least absolute shrinkage and selection operator regression and subsequently utilized in multivariate logistic regression to construct the nomogram. Validation of the nomogram was assessed by discrimination and calibration.

Results

N-terminal pro brain natriuretic peptide, high density lipoprotein cholesterol, hemoglobin, left anterior descending artery diameter, left circumflex artery diameter, and right coronary artery diameter were independent predictors of CSF. The model displayed high discrimination in the development and validation cohorts (C-index 0.771, 95% CI: 0.737-0.805 and 0.805, 95% CI: 0.757-0.853, respectively). The calibration curves for both cohorts showed close alignment between predicted and actual risk estimates, demonstrating improved model calibration. Decision curve analysis suggested high clinical utility for the predictive nomogram.

Conclusion

The constructed nomogram accurately and individually predicts the risk of CSF for patients with suspected CSF and may be considered for use in clinical care.

An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer

Introduction

We aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features.

Methods

Ultrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve.

Results

We found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value.

Discussion

The Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.

Development and validation of a prediction model for unexpected poor ovarian response during IVF/ICSI

Background

Identifying poor ovarian response (POR) among patients with good ovarian reserve poses a significant challenge within reproductive medicine. Currently, there is a lack of published data on the potential risk factors that could predict the occurrence of unexpected POR. The objective of this study was to develop a predictive model to assess the individual probability of unexpected POR during in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatments.

Methods

The development of the nomogram involved a cohort of 10,404 patients with normal ovarian reserve [age, ≤40 years; antral follicle count (AFC), ≥5; and anti-Müllerian hormone (AMH), ≥1.2 ng/ml] from January 2019 to December 2022. Univariate regression analyses and least absolute shrinkage and selection operator regression analysis were employed to ascertain the characteristics associated with POR. Subsequently, the selected variables were utilized to construct the nomogram.

Results

The predictors included in our model were body mass index, basal follicle-stimulating hormone, AMH, AFC, homeostasis model assessment of insulin resistance (HOMA-IR), protocol, and initial dose of gonadotropin. The area under the receiver operating characteristic curve (AUC) was 0.753 [95% confidence interval (CI) = 0.7257-0.7735]. The AUC, along with the Hosmer-Lemeshow test (p = 0.167), demonstrated a satisfactory level of congruence and discrimination ability of the developed model.

Conclusion

The nomogram can anticipate the probability of unexpected POR in IVF/ICSI treatment, thereby assisting professionals in making appropriate clinical judgments and in helping patients to effectively manage expectations.

Nomogram for predicting the risk of nonalcoholic fatty liver disease in older adults in Qingdao, China: A cross-sectional study

BACKGROUND AND OBJECTIVES

To explore the risk factors for non-alcoholic fatty liver disease (NAFLD) and to establish a non-invasive tool for the screening of NAFLD in an older adult population.

METHODS AND STUDY DESIGN

A total of 131,161 participants were included in this cross-sectional study. Participants were randomly divided into training and validation sets (7:3). The least absolute shrinkage and selection operator method was used to screen risk factors. Multivariate logistic regression was employed to develop a nomogram, which was made available online. Receiver operating characteristic curve analysis, calibration plots, and decision curve analysis were used to validate the discrimination, calibration, and clinical practicability of the nomogram. Sex and age subgroup analyses were conducted to further validate the reliability of the model.

RESULTS

Nine variables were identified for inclusion in the nomogram (age, sex, waist circumference, body mass index, exercise frequency, systolic blood pressure, fasting plasma glucose, alanine aminotransferase, and low-density lipoprotein cholesterol). The area under the receiver operating characteristic curve values were 0.793 and 0.790 for the training set and the validation set, respectively. The calibration plots and decision curve analyses showed good calibration and clinical utility. Subgroup analyses demonstrated consistent discriminatory ability in different sex and age subgroups.

CONCLUSIONS

This study established and validated a new nomogram model for evaluating the risk of NAFLD among older adults. The nomogram had good discriminatory performance and is a non-invasive and convenient tool for the screening of NAFLD in older adults.

Nomogram for predicting pathologic complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma

PURPOSE

A pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) is seen in up to 40% of the patients with esophageal squamous cell carcinoma (ESCC). No nomogram has been constructed for the prediction of pCR for patients whose primary chemotherapy was a taxane-based regimen. The aim is to identify characteristics associated with a pCR through analyzing multiple pre- and post-nCRT variables and to develop a nomogram for the prediction of pCR for these patients by integrating clinicopathological characteristics and hematological biomarkers.

MATERIALS AND METHODS

We analyzed 293 patients with ESCC who underwent nCRT followed by esophagectomy. Clinicopathological factors, hematological parameters before nCRT, and hematotoxicity during nCRT were collected. Univariate and multivariate logistic regression analyses were performed to identify predictive factors for pCR. A nomogram model was built and evaluated for both discrimination and calibration.

RESULTS

After surgery, 37.88% of the study patients achieved pCR. Six variables were included in the nomogram: sex, cN stage, chemotherapy regimen, duration of nCRT, pre-nCRT neutrophil-to-lymphocyte ratio (NLR), and pre-nCRT platelet-to-lymphocyte ratio (PLR). The nomogram indicated good accuracy and consistency in predicting pCR, with a C-index of 0.743 (95% confidence interval: 0.686, 0.800) and a p value of 0.600 (>0.05) in the Hosmer-Lemeshow goodness-of-fit test.

CONCLUSIONS

Female, earlier cN stage, duration of nCRT (< 62 days), chemotherapy regimen of taxane plus platinum, pre-nCRT NLR (≥2.199), and pre-nCRT PLR (≥99.302) were significantly associated with a higher pCR in ESCC patients whose primary chemotherapy was a taxane-based regimen for nCRT. A nomogram was developed and internally validated, showing good accuracy and consistency.

Nomogram for predicting pathological response to neoadjuvant treatment in patients with locally advanced gastric cancer: Data from a phase III clinical trial

PURPOSE

This study aimed to establish a nomogram using routinely available clinicopathological parameters to predict the pathological response in patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant treatment.

MATERIALS AND METHODS

We conducted this study based on the ongoing Neo-CRAG trial, a prospective study focused on preoperative treatment in patients with LAGC. A total of 221 patients who underwent surgery following neoadjuvant chemotherapy (nCT) or neoadjuvant chemoradiotherapy (nCRT) at Sun Yat-sen University Cancer Center between June 2013 and July 2022 were included in the analysis. We defined complete or near-complete pathological regression and ypN0 as good response (GR), and determined the prognostic value of GR by Kaplan-Meier survival analysis. Eventually, a nomogram for predicting GR was developed based on statistically identified predictors through multivariate logistic regression analysis and internally validated by the bootstrap method.

RESULTS

GR was confirmed in 54 patients (54/221, 24.4%). Patients who achieved GR had a longer progression-free survival and overall survival. Then, five independent factors, including pretreatment tumor differentiation, clinical T stage, monocyte count, CA724 level, and the use of nCRT, were identified. Based on these predictors, the nomogram was established with an area under the curve (AUC) of 0.777 (95% CI, 0.705-0.850) and a bias-corrected AUC of 0.752.

CONCLUSION

A good pathological response after neoadjuvant treatment was associated with an improved prognosis in LAGC patients. The nomogram we established exhibits a high predictive capability for GR, offering potential value in devising personalized and precise treatment strategies for LAGC patients.

Using MRI radiomics to predict the efficacy of immunotherapy for brain metastasis in patients with small cell lung cancer

BACKGROUND

Brain metastases (BMs) are common in small cell lung cancer (SCLC), and the efficacy of immune checkpoint inhibitors (ICIs) in these patients is uncertain. In this study we aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) for intracranial efficacy prediction of ICIs in patients with BMs from SCLC.

METHODS

The training and validation cohorts consisted of 101 patients from two centers. The interclass correlation coefficient (ICC), logistic univariate regression analysis, and random forest were applied to select the radiomic features, generating the radiomics score (Rad-score) through the formula. Using multivariable logistic regression analysis, a nomogram was created by the combined model. The discrimination, calibration, and clinical utility were used to assess the performance of the nomogram. Kaplan-Meier curves were plotted based on the nomogram scores.

RESULTS

Ten radiomic features were selected for calculating the Rad-score as they could differentiate the intracranial efficacy in the training (area under the curve [AUC], 0.759) and the validation cohort (AUC, 0.667). A nomogram was created by combining Rad-score, treatment lines, and neutrophil-to-lymphocyte ratio (NLR). The training cohort obtained an AUC of 0.878 for the combined model, verified in the validation cohort (AUC = 0.875). Kaplan-Meier analyses showed the nomogram was associated with progression-free survival (PFS) (p = 0.0152) and intracranial progression-free survival (iPFS) (p = 0.0052) but not overall survival (OS) (p = 0.4894).

CONCLUSION

A radiomics nomogram model for predicting the intracranial efficacy of ICIs in SCLC patients with BMs can provide suggestions for exploring individual-based treatments for patients.

Nomogram to predict recurrence risk factors in patients with non-valvular paroxysmal atrial fibrillation after catheter radiofrequency ablation

BACKGROUND

Radiofrequency catheter ablation (RFCA) is an effective method for controlling the heart rate of paroxysmal atrial fibrillation (PAF). However, recurrence is trouble under the RFCA. To gain a deeper understanding of the risk factors for recurrence in patients, we created a nomogram model to provide clinicians with treatment recommendations.

METHODS

A total of two hundred thirty-three patients with PAF treated with RFCA at Guizhou Medical University Hospital between January 2021 and December 2022 were consecutively included in this study, and after 1 year of follow-up coverage, 166 patients met the nadir inclusion criteria. Patients with AF were divided into an AF recurrence group and a non-recurrence group. The nomogram was constructed using univariate and multivariate logistic regression analyses. By calculating the area under the curve, we analyzed the predictive ability of the risk scores (AUC). In addition, the performance of the nomogram in terms of calibration, discrimination, and clinical utility was evaluated.

RESULTS

At the 12-month follow-up, 48 patients (28.92%) experienced a recurrence of AF after RFCA, while 118 patients (71.08%) maintained a sinus rhythm. In addition to age, sex, and TRV, LAD, and TTPG were independent predictors of recurrence of RFCA. The c-index of the nomogram predicted AF recurrence with an accuracy of .723, showing good decision curves and a calibrated nomogram, as determined by internal validation using a bootstrap sample size of 1000.

CONCLUSION

We created a nomogram based on multifactorial logistic regression analysis to estimate the probability of recurrence in patients with atrial fibrillation 1 year after catheter ablation. This plot can be utilized by clinicians to predict the likelihood of recurrence.

Predicting Marfan Syndrome in Children With Congenital Ectopia Lentis: Development and Validation of a Nomogram

Purpose

To derive an effective nomogram for predicting Marfan syndrome (MFS) in children with congenital ectopia lentis (CEL) using regularly collected data.

Methods

Diagnostic standards (Ghent nosology) and genetic test were applied in all patients with CEL to determine the presence or absence of MFS. Three potential MFS predictors were tested and chosen to build a prediction model using logistic regression. The predictive performance of the nomogram was validated internally through time-dependent receiver operating characteristic curves, calibration curves, and decision curve analysis.

Results

Eyes from 103 patients under 20 years old and with CEL were enrolled in this study. Z score of body mass index (odds ratio [OR] = 0.659; 95% confidence interval [CI], 0.453-0.958), corneal curvature radius (OR = 3.397; 95% CI, 1.829-6.307), and aortic root diameter (OR = 2.342; 95% CI, 1.403-3.911) were identified as predictors of MFS. The combination of the above predictors shows good predictive ability, as indicated by area under the curve of 0.889 (95% CI, 0.826-0.953). The calibration curves showed good agreement between the prediction of the nomogram and the actual observations. In addition, decision curve analysis showed that the nomogram was clinically useful and had better discriminatory power in identifying patients with MFS. For better individual prediction, an online MFS calculator was created.

Conclusions

The nomogram provides accurate and individualized prediction of MFS in children with CEL who cannot be identified with the Ghent criteria, enabling clinicians to personalize treatment plans and improve MFS outcomes.

Translational Relevance

The prediction model may help clinicians identify MFS in its early stages, which could reduce the likelihood of developing severe symptoms and improve MFS outcomes.

Circulating miR-129-3p in combination with clinical factors predicts vascular calcification in hemodialysis patients

Background

Vascular calcification (VC) commonly occurs and seriously increases the risk of cardiovascular events and mortality in patients with hemodialysis. For optimizing individual management, we will develop a diagnostic multivariable prediction model for evaluating the probability of VC.

Methods

The study was conducted in four steps. First, identification of miRNAs regulating osteogenic differentiation of vascular smooth muscle cells (VSMCs) in calcified condition. Second, observing the role of miR-129-3p on VC in vitro and the association between circulating miR-129-3p and VC in hemodialysis patients. Third, collecting all indicators related to VC as candidate variables, screening predictors from the candidate variables by Lasso regression, developing the prediction model by logistic regression and showing it as a nomogram in training cohort. Last, verifying predictive performance of the model in validation cohort.

Results

In cell experiments, miR-129-3p was found to attenuate vascular calcification, and in human, serum miR-129-3p exhibited a negative correlation with vascular calcification, suggesting that miR-129-3p could be one of the candidate predictor variables. Regression analysis demonstrated that miR-129-3p, age, dialysis duration and smoking were valid factors to establish the prediction model and nomogram for VC. The area under receiver operating characteristic curve of the model was 0.8698. The calibration curve showed that predicted probability of the model was in good agreement with actual probability and decision curve analysis indicated better net benefit of the model. Furthermore, internal validation through bootstrap process and external validation by another independent cohort confirmed the stability of the model.

Conclusion

We build a diagnostic prediction model and present it as an intuitive tool based on miR-129-3p and clinical indicators to evaluate the probability of VC in hemodialysis patients, facilitating risk stratification and effective decision, which may be of great importance for reducing the risk of serious cardiovascular events.

Based on PI-RADS v2.1 combining PHI and ADC values to guide prostate biopsy in patients with PSA 4-20 ng/mL

PURPOSE

The study aimed to investigate the diagnostic accuracy of prostate health index (PHI) and apparent diffusion coefficient (ADC) values in predicting prostate cancer (PCa) and construct a nomogram for the prediction of PCa and clinically significant PCa (CSPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) three lesions cohort.

METHODS

This study prospectively enrolled 301 patients who underwent multiparametric magnetic resonance (mpMRI) and were scheduled for prostate biopsy. The receiver operating characteristic curve (ROC) was performed to estimate the diagnostic accuracy of each predictor. Univariable and multivariable logistic regression analysis was conducted to ascertain hidden risk factors and constructed nomograms in PI-RADS three lesions cohort.

RESULTS

In the whole cohort, the area under the ROC curve (AUC) of PHI is relatively high, which is 0.779. As radiographic parameters, the AUC of PI-RADS and ADC values was 0.702 and 0.756, respectively. The utilization of PHI and ADC values either individually or in combination significantly improved the diagnostic accuracy of the basic model. In PI-RADS three lesions cohort, the AUC for PCa was 0.817 in the training cohort and 0.904 in the validation cohort. The AUC for CSPCa was 0.856 in the training cohort and 0.871 in the validation cohort. When applying the nomogram for predicting PCa, 50.0% of biopsies could be saved, supplemented by 6.9% of CSPCa being missed.

CONCLUSION

PHI and ADC values can be used as predictors of CSPCa. The nomogram included PHI, ADC values and other clinical predictors demonstrated an enhanced capability in detecting PCa and CSPCa within PI-RADS three lesions cohort.

Will synchronous esophageal and lung resection increase the incidence of anastomotic leaks? A multicenter retrospective study

BACKGROUND

Reports on combined resection for synchronous lung lesions and esophageal cancer (CRLE) cases are rare and mostly individual cases. Furthermore, the feasibility of CRLE has always been a controversial topic. In the current study, the authors retrospectively analyzed the feasibility of CRLE and established an individualized prediction model for esophageal anastomotic leaks after CRLE by performing a multicenter retrospective study.

METHODS

Patients who underwent esophagectomy between January 2009 and June 2021 were extracted from a four-center prospectively maintained database, and those with CRLE at the same setting were matched in a 1:2 propensity score-matched (PSM) ratio to esophagectomy alone (EA) patients. A nomogram was then established based on the variables involved in multivariate logistic regression analysis. Internal validation of the nomogram was conducted utilizing Bootstrap resampling. Decision and clinical impact curve analysis were computed to assess the practical clinical utility of the nomogram. A prognosis analysis for CRLE and EA patients by Kaplan-Meier curves was conducted.

RESULTS

Of the 7152 esophagectomies, 216 cases of CRLE were eligible, and 1:2 ratio propensity score-matched EA patients were matched. The incidence of anastomotic leaks following CRLE increased significantly ( P =0.035). The results of the multivariate analysis indicated the leaks varied according to the type of lung resection (anatomic>wedge resection, P =0.016) and site of resected lobe (upper>middle/low lobe; P =0.027), and a nomogram was established to predict the occurrence of leaks accurately (area under the curve=0.786). Although no statistically significant difference in overall survival (OS) was observed in the CRLE group ( P =0.070), a trend toward lower survival rates was noted. Further analysis revealed that combined upper lobe anatomic resection was significantly associated with reduced OS ( P =0.027).

CONCLUSION

Our study confirms that CRLE is feasible but comes with a significantly increased risk of anastomotic leaks and a concerning trend of reduced survival, particularly when upper lobe anatomic resections are performed. These findings highlight the need for careful patient selection and surgical planning when considering CRLE.

SP140 inhibitor suppressing TRIM22 expression regulates glioma progress through PI3K/AKT signaling pathway

BACKGROUND

SP gene family, consisting of SP100, SP110, SP140, and SP140L, has been implicated in the initiation and advancement of numerous malignancies. Nevertheless, their clinical significance in glioma remains incompletely understood.

METHOD

Expression levels and prognostic significance of SP family members were evaluated in the TCGA and CGGA datasets. Multifactorial analysis was used to identify SP gene family members that can independently impact the prognosis of glioma patients. A SP140-based predictive risk model/nomogram was developed in TCGA dataset and validated in CGGA dataset. The model's performance was evaluated through receiver operating characteristic (ROC) curves, calibration plots, and decision curve analyses. Phenotypic associations of SP140 and TRIM22 were examined through CancerSEA and TIMER. The effect of SP140 inhibitor in glioma progress and TRIM22/PI3K/AKT signaling pathway was confirmed in U251/U87 glioma cells.

RESULTS

The SP family members exhibited elevated expression in gliomas and were negatively correlated with prognosis. SP140 emerged as an independent prognostic factor, and a SP140-based nomogram/predictive risk model demonstrated high accuracy. SP140 inhibitor, GSK761, lead to the suppression of TRIM22 expression and the PI3K/AKT signaling pathway. GSK761 also restrain glioma proliferation, migration, and invasion. Furthermore, SP140 and TRIM22 coexpressed in glioma cells with high level of vascular proliferation, TRIM22 is closely associated with the immune cell infiltration.

CONCLUSION

SP140-based nomogram proved to be a practical tool for predicting the survival of glioma patients. SP140 inhibitor could suppress glioma progress via TRIM22/PI3K/AKT signaling pathway.

Construction and validation of a nomogram to predict left ventricular hypertrophy in low-risk patients with hypertension

Electrocardiography (ECG) is an accessible diagnostic tool for screening patients with hypertensive left ventricular hypertrophy (LVH). However, its diagnostic sensitivity is low, with a high probability of false-negatives. Thus, this study aimed to establish a clinically useful nomogram to supplement the assessment of LVH in patients with hypertension and without ECG-LVH based on Cornell product criteria (low-risk hypertensive population). A cross-sectional dataset was used for model construction and divided into development (n = 2906) and verification (n = 1447) datasets. A multivariable logistic regression risk model and nomogram were developed after screening for risk factors. Of the 4353 low-risk hypertensive patients, 673 (15.4%) had LVH diagnosed by echocardiography (Echo-LVH). Eleven risk factors were identified: hypertension awareness, duration of hypertension, age, sex, high waist-hip ratio, education level, tea consumption, hypochloremia, and other ECG-LVH diagnostic criteria (including Sokolow-Lyon, Sokolow-Lyon products, and Peguero-Lo Presti). For the development and validation datasets, the areas under the curve were 0.724 (sensitivity = 0.606) and 0.700 (sensitivity = 0.663), respectively. After including blood pressure, the areas under the curve were 0.735 (sensitivity = 0.734) and 0.716 (sensitivity = 0.718), respectively. This novel nomogram had a good predictive ability and may be used to assess the Echo-LVH risk in patients with hypertension and without ECG-LVH based on Cornell product criteria.

Development of a nomogram for predicting pharyngocutaneous fistula based on skeletal muscle mass and systemic inflammation indices

BACKGROUND

Laryngeal and hypopharyngeal cancers often require surgical treatment, which can lead to the development of pharyngocutaneous fistula (PCF). Our research aimed to assess the predictive value of skeletal muscle mass (SMM) and systemic inflammation indices for PCF and construct a clinically effective nomogram.

METHODS

A nested case-control study of 244 patients matched from 1171 patients with laryngeal or hypopharyngeal cancer was conducted. SMM was measured at the third cervical level based on CT scans. A PCF nomogram was developed based on the univariate and multivariate analyses.

RESULTS

Glucose, white blood cell count, platelet-to-lymphocyte ratio, and skeletal muscle index were independent risk factors for PCF. The area under the curve for the PCF nomogram was 0.841 (95% CI 0.786-0.897). The calibration and decision curves indicated that the nomogram was well-calibrated with good clinical utility.

CONCLUSIONS

The nomogram we constructed may help clinicians predict PCF risk early in the postoperative period, pending external validation.

Development and validation of survival nomograms in elder triple-negative invasive ductal breast carcinoma patients

BACKGROUND

We aimed to develop a nomogram to predict the overall survival of elderly patients with Triple-negative invasive ductal breast carcinoma (TNIDC).

RESEARCH DESIGN AND METHODS

12165 elderly patients with nonmetastatic TNIDC were retrieved from the SEER database from 2010 to 2019 and were randomly assigned to training and validation cohorts. Stepwise Cox regression analysis was used to select variables for the nomogram based on the training cohort. Univariate and multivariate Cox analyses were used to calculate the correlation between variables and prognosis of the patients. Survival analysis was performed for high- and low-risk subgroups based on risk score.

RESULTS

Eleven predictive factors were identified to construct our nomograms. Compared with the TNM stage, the discrimination of the nomogram revealed good prognostic accuracy and clinical applicability as indicated by C-index values of 0.741 (95% CI 0.728-0.754) against 0.708 (95% CI 0.694-0.721) and 0.765 (95% CI 0.747-0.783) against 0.725 (95% CI 0.705-0.744) for the training and validation cohorts, respectively. Differences in OS were also observed between the high- and low-risk groups (p < 0.001).

CONCLUSION

The proposed nomogram provides a convenient and reliable tool for individual evaluations for elderly patients with M0_stage TNIDC. However, the model may only for Americans.

Nomogram for predicting surgical site infections in elderly patients after open lumbar spine surgery: A retrospective study

The aim of this study is to develop a nomogram to assess the risk of surgical site infection in elderly patients undergoing open lumbar spine surgery and explore related risk factors. We reviewed the records of 578 elderly patients who had undergone open lumbar spine surgery. The clinical parameters were subjected to lasso regression and logistic regression analyses. Subsequently, a nomogram was constructed to predict the risk of postoperative surgical site infection and validated using bootstrap resampling. A total of 578 patients were included in the analysis, of which 17 were diagnosed as postoperative surgical site infection. Following the final logistic regression analysis, obesity, hypoalbuminemia and drinking history were identified as independent risk factors and subsequently incorporated into the nomogram. The nomogram demonstrated excellent discrimination, with an area under the receiver-operating characteristic curve of 0.879 (95% CI 0.769 ~ 0.989) after internal validation. The calibration curve exhibited a high level of consistency. Decision curve analysis revealed that this nomogram had greater clinical value when the risk threshold for surgical site infection occurrence was >1% and <89%. We had developed a nomogram for predicting the risk of postoperative surgical site infection in elderly patients who had undergone open lumbar spine surgery. Validation using bootstrap resampling demonstrated excellent discrimination and calibration, indicating that the nomogram may hold potential clinical utility as a simple predictive tool for healthcare professionals.

A nomogram based on clinical factors and CT radiomics for predicting anti-MDA5+ DM complicated by RP-ILD

OBJECTIVES

Anti-melanoma differentiation-associated gene 5 antibody-positive (anti-MDA5+) DM complicated by rapidly progressive interstitial lung disease (RP-ILD) has a high incidence and poor prognosis. The objective of this study was to establish a model for the prediction and early diagnosis of anti-MDA5+ DM-associated RP-ILD based on clinical manifestations and imaging features.

METHODS

A total of 103 patients with anti-MDA5+ DM were included. The patients were randomly split into training and testing sets of 72 and 31 patients, respectively. After image analysis, we collected clinical, imaging and radiomics features from each patient. Feature selection was performed first with the minimum redundancy and maximum relevance algorithm and then with the best subset selection method. The final remaining features comprised the radscore. A clinical model and imaging model were then constructed with the selected independent risk factors for the prediction of non-RP-ILD and RP-ILD. We also combined these models in different ways and compared their predictive abilities. A nomogram was also established. The predictive performances of the models were assessed based on receiver operating characteristics curves, calibration curves, discriminability and clinical utility.

RESULTS

The analyses showed that two clinical factors, dyspnoea (P = 0.000) and duration of illness in months (P = 0.001), and three radiomics features (P = 0.001, 0.044 and 0.008, separately) were independent predictors of non-RP-ILD and RP-ILD. However, no imaging features were significantly different between the two groups. The radiomics model built with the three radiomics features performed worse than the clinical model and showed areas under the curve (AUCs) of 0.805 and 0.754 in the training and test sets, respectively. The clinical model demonstrated a good predictive ability for RP-ILD in MDA5+ DM patients, with an AUC, sensitivity, specificity and accuracy of 0.954, 0.931, 0.837 and 0.847 in the training set and 0.890, 0.875, 0.800 and 0.774 in the testing set, respectively. The combination model built with clinical and radiomics features performed slightly better than the clinical model, with an AUC, sensitivity, specificity and accuracy of 0.994, 0.966, 0.977 and 0.931 in the training set and 0.890, 0.812, 1.000 and 0.839 in the testing set, respectively. The calibration curve and decision curve analyses showed satisfactory consistency and clinical utility of the nomogram.

CONCLUSION

Our results suggest that the combination model built with clinical and radiomics features could reliably predict the occurrence of RP-ILD in MDA5+ DM patients.

A nomogram prediction of implant apical non-coverage on bone-added transcrestal sinus floor elevation: A retrospective cohort study

OBJECTIVES

To identify the risk indicators and develop and validate a nomogram prediction model of implant apical non-coverage by comprehensively analyzing clinical and radiographic factors in bone-added transcrestal sinus floor elevation (TSFE).

MATERIAL AND METHODS

A total of 260 implants in 195 patients receiving bone-added TSFE were included in the study. The population was divided into a development (180 implants) and a validation (80 implants) cohort. According to 6 months post-surgery radiographic images, implants were categorized as "apical non-coverage" or "apical covered." The association of risk factors including clinical and radiographic parameters with implant apical non-coverage was assessed using regression analyses. A nomogram prediction model was developed, and its validation and discriminatory ability were analyzed.

RESULTS

The nomogram predicting bone-added TSFE's simultaneously placed implant's apex non-coverage after 6 months. This study revealed that sinus angle, endo-sinus bone gain, implant protrusion length, graft contact walls, and distal angle were predictors of implant apical non-coverage. The generated nomogram showed a strong predictive capability (area under the curve [AUC] = 0.845), confirmed by internal validation using 10-fold cross-validation (Median AUC of 0.870) and temporal validation (AUC = 0.854). The calibration curve and decision curve analysis demonstrated good performance and high net benefit of the nomogram, respectively.

CONCLUSIONS

The clinical implementation of the present nomogram is suitable for predicting the apex non-coverage of implants placed simultaneously with bone-added TSFE after 6 months.

Laparoscopic surgery for adrenocortical carcinoma: Estimating the risk of margin-positive resection

BACKGROUND

Over recent years, there has been increasing adoption of minimally invasive surgery (MIS) in the treatment of adrenocortical carcinoma (ACC). However, MIS has been associated with noncurative resection and locoregional recurrence. We aimed to identify risk factors for margin-positivity among patients who undergo MIS resection for ACC. We hypothesized that a simple nomogram can accurately identify patients most suitable for curative MIS resection.

METHODS

Curative-intent resections for ACC were identified through the National Cancer Database spanning 2010-2018. Trends in MIS utilization were reported using Pearson correlation coefficients. Factors associated with margin-positive resection were identified among preoperatively available variables using multivariable logistic regression, then incorporated into a predictive model. Model quality was cross validated using an 80% training data set and 20% test data set.

RESULTS

Among 1260 ACC cases, 38.6% (486) underwent MIS resection. MIS utilization increased over time at nonacademic centers (R = 0.818, p = 0.007), but not at academic centers (R = 0.009, p = 0.982). Factors associated with margin-positive MIS resection were increasing age, nonacademic center (odds ratio [OR]: 1.8, p = 0.006), cT3 (OR: 4.7, p < 0.001) or cT4 tumors (OR: 14.6, p < 0.001), and right-sided tumors (OR: 2.0, p = 0.006). A predictive model incorporating these four factors produced favorable c-statistics of 0.75 in the training data set and 0.72 in the test data set. A pragmatic nomogram was created to enable bedside risk stratification.

CONCLUSIONS

An increasing proportion of ACC are resected via minimally invasive operations, particularly at nonacademic centers. Patient selection based on a few key factors can minimize the risk of noncurative surgery.

Merkel cell carcinoma recurrence risk estimation is improved by integrating factors beyond cancer stage: A multivariable model and web-based calculator

BACKGROUND

Merkel cell carcinoma (MCC) recurs in 40% of patients. In addition to stage, factors known to affect recurrence risk include: sex, immunosuppression, unknown primary status, age, site of primary tumor, and time since diagnosis.

PURPOSE

Create a multivariable model and web-based calculator to predict MCC recurrence risk more accurately than stage alone.

METHODS

Data from 618 patients in a prospective cohort were used in a competing risk regression model to estimate recurrence risk using stage and other factors.

RESULTS

In this multivariable model, the most impactful recurrence risk factors were: American Joint Committee on Cancer stage (P < .001), immunosuppression (hazard ratio 2.05; P < .001), male sex (1.59; P = .003) and unknown primary (0.65; P = .064). Compared to stage alone, the model improved prognostic accuracy (concordance index for 2-year risk, 0.66 vs 0.70; P < .001), and modified estimated recurrence risk by up to 4-fold (18% for low-risk stage IIIA vs 78% for high-risk IIIA over 5 years).

LIMITATIONS

Lack of an external data set for model validation.

CONCLUSION/RELEVANCE

As demonstrated by this multivariable model, accurate recurrence risk prediction requires integration of factors beyond stage. An online calculator based on this model (at merkelcell.org/recur) integrates time since diagnosis and provides new data for optimizing surveillance for MCC patients.

Nomograms to predict long-term survival for patients with gallbladder carcinoma after resection

BACKGROUND

Surgical resection remains the primary treatment option for gallbladder carcinoma (GBC). However, there is a pressing demand for prognostic tools that can refine patients' treatment choices and tailor personalized therapies accordingly.

AIMS

The nomograms were constructed using the data of a training cohort (n = 378) of GBC patients at Eastern Hepatobiliary Surgery Hospital (EHBH) between 2008 and 2018. The model's performance was validated in GBC patients (n = 108) at Guangzhou Centre from 2007 to 2018.

METHODS AND RESULTS

The 5-year overall survival (OS) rate in the training cohort was 24.4%. Multivariate analyses were performed using preoperative and postoperative data to identify independent predictors of OS. These predictors were then incorporated into preoperative and postoperative nomograms, respectively. The C-index of the preoperative nomogram was 0.661 (95% CI, 0.627 to 0.694) for OS prediction and correctly delineated four subgroups (5-year OS rates: 48.1%, 19.0%, 15.6%, and 8.1%, p < 0.001). The C-index of the postoperative nomogram was 0.778 (95%CI, 0.756 -0.800). Furthermore, this nomogram was superior to the 8th TNM system in both C-index and the net benefit on decision curve analysis. The results were externally validated.

CONCLUSION

The two nomograms showed an optimally prognostic prediction in GBC patients after curative-intent resection.

A nomogram prediction model of pseudomyxoma peritonei established based on new prognostic factors of HE stained pathological images analysis

BACKGROUND

Pseudomyxoma peritonei (PMP) is a rare clinical malignant syndrome, and its rarity causes a lack of pathology research. This study aims to quantitatively analyze HE-stained pathological images (PIs), and develop a new predictive model integrating digital pathological parameters with clinical information.

METHODS

Ninety-two PMP patients with complete clinic-pathological information, were included. QuPath was used for PIs quantitative feature analysis at tissue-, cell-, and nucleus-level. The correlations between overall survival (OS) and general clinicopathological characteristics, and PIs features were analyzed. A nomogram was established based on independent prognostic factors and evaluated.

RESULTS

Among the 92 PMP patients, there were 34 (37.0%) females and 58 (63.0%) males, with a median age of 57 (range: 31-76). A total of 449 HE stained images were obtained for QuPath analysis, which extracted 40 pathological parameters at three levels. Kaplan-Meier survival analysis revealed eight clinicopathological characteristics and 20 PIs features significantly associated with OS (p < 0.05). Partial least squares regression was used to screen the multicollinearity features and synthesize four new features. Multivariate survival analysis identified the following five independent prognostic factors: preoperative CA199, completeness of cytoreduction, histopathological type, component one at tissue-level, and tumor nuclei circularity variance. A nomogram was established with internal validation C-index 0.795 and calibration plots indicating improved prediction performance.

CONCLUSIONS

The quantitative analysis of HE-stained PIs could extract the new prognostic information on PMP. A nomogram established by five independent prognosticators is the first model integrating digital pathological information with clinical data for improved clinical outcome prediction.

Nomogram for preoperative estimation of symptomatic subdural hygroma risk in pediatric intracranial arachnoid cysts

OBJECTIVE

The occurrence and predictors of symptomatic subdural hygroma (SSH) subsequent to the fenestration of pediatric intracranial arachnoid cysts (IACs) are unclear. In this study, the authors aimed to investigate the likelihood of an SSH following IAC fenestration and the impact on operative efficacy with the ultimate goal of constructing a nomogram.

METHODS

The medical records of 1782 consecutive patients who underwent surgical treatment at the Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were reviewed. Among these patients, a training cohort (n = 1214) underwent surgery during an earlier period and was used for the development of a nomogram. The remaining patients formed the validation cohort (n = 568) and were used to confirm the performance of the developed model. The development of the nomogram involved the use of potential predictors, while internal validation was conducted using a bootstrap-resampling approach.

RESULTS

SSH was detected in 13.2% (160 of 1214) of patients in the training cohort and in 11.1% (63 of 568) of patients in the validation cohort. Through multivariate analysis, several factors including Galassi type, IAC distance to the basal cisterns, temporal bulge, midline shift, IAC shape in the coronal view, area of the stoma, and artery location near the stoma were identified as independent predictors of SSH. These 7 predictors were used to construct a nomogram, which exhibited a concordance statistic (C-statistic) of 0.826 and demonstrated good calibration. Following internal validation, the nomogram maintained good calibration and discrimination with a C-statistic of 0.799 (95% CI 0.665-0.841). Patients who had nomogram scores < 30 or ≥ 30 were considered to be at low and high risk of SSH occurrence, respectively.

CONCLUSIONS

The predictive model and derived nomogram achieved satisfactory preoperative prediction of SSH. Using this nomogram, the risk for an individual patient can be estimated, and the appropriate surgery can be performed in high-risk patients.

Development of a Nomogram Model to Predict the Risk of Stricture Recurrence after Urethroplasty: A Retrospective Study

OBJECTIVE

A retrospective study was performed to analyse the influencing factors of stricture recurrence after urethroplasty and to establish a predictive nomogram model.

METHODS

The clinical data of patients who underwent urethroplasty in our hospital from January 2021 to June 2023 were retrospectively analysed. Depending on whether stenosis occurs six months after surgery, the patients were divided into recurrence and nonrecurrence groups. Logistic regression analysis was performed on the indicators with statistically significant differences between the two groups in single factor analysis to analyse the influencing factors of postoperative recurrence risk of stricture. X64.4.1.3 version R language and external source packages were used to build the nomogram model. The nomogram was internally validated through 10-fold cross-validation, and C-index was calculated. The area under the curve (AUC) of the receiver operating characteristic curve was employed to evaluate the results of the internal validation.

RESULTS

Amongst 105 patients who underwent urethroplasty in our hospital, 15 patients with recurrence were included in the recurrence group, and 90 patients without recurrence were included in the nonrecurrence group. The length of stricture segment, history of urethroplasty and smoking history within 3 months before surgery were risk factors for stricture recurrence, with odds ratio (OR) values of 1.874 (95% CI: 1.103-5.725), 1.670 (95% CI: 1.105-2.904) and 1.740 (95% CI: 1.456-5.785), respectively. The constructed nomogram obtained an average AUC of 0.842 and an average C-index of 0.794, calculated after 200 times of 10-fold cross-validation.

CONCLUSIONS

From the data of this study, it can be deduced that the influencing factors of stricture recurrence after urethroplasty include the length of stricture segment, history of urethroplasty and smoking history of 3 months before surgery. Using the above factors as a basis to construct a predictive nomogram model is helpful to screen high-risk patients with recurrence of stricture after urethroplasty.

Development and external validation of a novel model for predicting new clinically important atrial fibrillation after thoracoscopic anatomical lung cancer surgery: a multicenter retrospective cohort study

BACKGROUND

New clinically important postoperative atrial fibrillation (POAF) is the most common arrhythmia after thoracoscopic anatomical lung cancer surgery and is associated with increased morbidity and mortality. The full spectrum of predictors remains unclear, and effective assessment tools are lacking. This study aimed to develop and externally validate a novel model for predicting new clinically important POAF.

METHODS

This retrospective study included 14 074 consecutive patients who received thoracoscopic anatomical lung cancer surgery from January 2016 to December 2018 in Shanghai Chest Hospital. Based on the split date of 1 January 2018, we selected 8717 participants for the training cohort and 5357 participants for the testing cohort. For external validation, we pooled 2941 consecutive patients who received this surgical treatment from July 2016 to July 2021 in Shanghai Ruijin Hospital. Independent predictors were used to develop a model and internally validated using a bootstrap-resampling approach. The area under the receiver operating characteristic curves (AUROCs) and Brier score were performed to assess the model discrimination and calibration. The decision curve analysis (DCA) was used to evaluate clinical validity and net benefit. New clinically important POAF was defined as a new-onset of POAF that causes symptoms or requires treatment.

RESULTS

Multivariate analysis suggested that age, hypertension, preoperative treatment, clinical tumor stage, intraoperative arrhythmia and transfusion, and operative time were independent predictors of new clinically important POAF. These seven candidate predictors were used to develop a nomogram, which showed a concordance statistic (C-statistic) value of 0.740 and good calibration (Brier score; 0.025). Internal validation revealed similarly good discrimination (C-statistic, 0.736; 95% CI: 0.705-0.768) and calibration. The decision curve analysis showed positive net benefits with the threshold risk range of 0-100%. C-statistic value and Brier score were 0.717 and 0.028 in the testing cohort, and 0.768 and 0.012 in the external validation cohort, respectively.

CONCLUSIONS

This study identified seven predictors of new clinically important POAF, among which preoperative treatment, intraoperative arrhythmia, and operative time were rarely reported. The established and externally validated model has good performance and clinical usefulness, which may promote the application of prevention and treatment in high-risk patients, and reduce the development and related adverse outcomes of this event.

The prognostic value of age-adjusted Charlson comorbidity index in laparoscopic resection for hilar cholangiocarcinoma

The prognostic role of the Age-Adjusted Charlson Comorbidity Index (ACCI) in hilar cholangiocarcinoma patients undergoing laparoscopic resection is unclear. To evaluate ACCI's effect on overall survival (OS) and recurrence-free survival (RFS), we gathered data from 136 patients who underwent laparoscopic resection for hilar cholangiocarcinoma at Zhengzhou University People's Hospital between 1 June 2018 and 1 June 2022. ACCI scores were categorized into high ACCI (ACCI > 4.0) and low ACCI (ACCI ≤ 4.0) groups. We examined ACCI's association with OS and RFS using Cox regression analyses and developed an ACCI-based nomogram for survival prediction. Our analysis revealed that higher ACCI scores (ACCI > 4.0) (HR = 2.14, 95%CI: 1.37-3.34) were identified as an independent risk factor significantly affecting both OS and RFS in postoperative patients with hilar cholangiocarcinoma (p < 0.05). TNM stage III-IV (HR = 7.42, 95%CI: 3.11-17.68), not undergoing R0 resection (HR = 1.58, 95%CI: 1.01-2.46), hemorrhage quantity > 350 mL (HR = 1.92, 95%CI: 1.24-2.97), and not receiving chemotherapy (HR = 1.89, 95%CI: 1.21-2.95) were also independent risk factors for OS. The ACCI-based nomogram accurately predicted the 1-, 2-, and 3-year OS rates, with Area Under the Curve (AUC) values of 0.818, 0.844, and 0.924, respectively. Calibration curves confirmed the nomogram's accuracy, and decision curve analysis highlighted its superior predictive performance. These findings suggest that a higher ACCI is associated with a worse prognosis in patients undergoing laparoscopic resection for hilar cholangiocarcinoma. The ACCI-based nomogram could aid clinicians in making accurate predictions about patient survival and facilitate individualized treatment planning.

Nomogram for soiling prediction in postsurgery hirschsprung children: a retrospective study

PURPOSE

The aim of this study was to develop a nomogram for predicting the probability of postoperative soiling in patients aged greater than 1 year operated for Hirschsprung disease (HSCR).

MATERIALS AND METHODS

The authors retrospectively analyzed HSCR patients with surgical therapy over 1 year of age from January 2000 and December 2019 at our department. Eligible patients were randomly categorized into the training and validation set at a ratio of 7:3. By integrating the least absolute shrinkage and selection operator [LASSO] and multivariable logistic regression analysis, crucial variables were determined for establishment of the nomogram. And, the performance of nomogram was evaluated by C-index, area under the receiver operating characteristic curve, calibration curves, and decision curve analysis. Meanwhile, a validation set was used to further assess the model.

RESULTS

This study enrolled 601 cases, and 97 patients suffered from soiling. Three risk factors, including surgical history, length of removed bowel, and surgical procedures were identified as predictive factors for soiling occurrence. The C-index was 0.871 (95% CI: 0.821-0.921) in the training set and 0.878 (95% CI: 0.811-0.945) in the validation set, respectively. And, the AUC was found to be 0.896 (95% CI: 0.855-0.929) in the training set and 0.866 (95% CI: 0.767-0.920) in the validation set. Additionally, the calibration curves displayed a favorable agreement between the nomogram model and actual observations. The decision curve analysis revealed that employing the nomogram to predict the risk of soiling occurrence would be advantageous if the threshold was between 1 and 73% in the training set and 3-69% in the validation set.

CONCLUSION

This study represents the first efforts to develop and validate a model capable of predicting the postoperative risk of soiling in patients aged greater than 1 year operated for HSCR. This model may assist clinicians in determining the individual risk of soiling subsequent to HSCR surgery, aiding in personalized patient care and management.

Optimizing Refractive Outcomes of SMILE: Artificial Intelligence versus Conventional State-of-the-Art Nomograms

PURPOSE

AI (artificial intelligence)-based methodologies have become established tools for researchers and physicians in the entire field of ophthalmology. However, the potential of AI to optimize the refractive outcome of keratorefractive surgery by means of machine learning (ML)-based nomograms has not been exhausted yet. In this study, we wanted to comprehensively compare state-of-the-art conventional nomograms for Small-Incision-Lenticule-Extraction (SMILE) with a novel ML-based nomogram regarding both their spherical and astigmatic predictability.

METHODS

A total of 1,342 eyes were analyzed for creation of three different nomograms based on a linear model (LM), a generalized additive mixed model (GAMM) and an artificial-neuronal-network (ANN), respectively. A total of 16 patient- and treatment-related features were included. Each model was trained by 895 eyes and validated by the remaining 447 eyes. Predictability was assessed by the difference between attempted and achieved change in spherical equivalent (SE) and the difference between target induced astigmatism (TIA) and surgically induced astigmatism (SIA). The root mean squared error (RMSE) of each model was computed as a measure of overall model performance.

RESULTS

The RMSE of LM, GAMM and ANN were 0.355, 0.348 and 0.367 for the prediction of SE and 0.279, 0.278 and 0.290 for the astigmatic correction, respectively. By applying the created models, the theoretical yield of eyes within ±0.50 D of SE from target refraction improved from 82 to 83% (LM), 84% (GAMM) and 83% (ANN), respectively. Astigmatic outcomes showed an improvement of eyes within ±0.50 D from TIA from 90 to 93% (LM), 93% (GAMM) and 92% (ANN), respectively. Subjective manifest refraction was the single most influential covariate in all models.

CONCLUSION

Machine learning endorsed the validity of state-of-the-art linear and non-linear SMILE nomograms. However, improving the accuracy of subjective manifest refraction seems warranted for optimizing ±0.50 D SE predictability beyond an apparent methodological 90% limit.

Radiomics nomogram based on MRI water imaging identifying symptomatic nerves of patients with primary trigeminal neuralgia: A preliminary study

The study proposes a combined nomogram based on radiomics features from magnetic resonance neurohydrography and clinical features to identify symptomatic nerves in patients with primary trigeminal neuralgia. We retrospectively analyzed 140 patients with clinically confirmed trigeminal neuralgia. Out of these, 24 patients constituted the external validation set, while the remaining 116 patients contributed a total of 231 nerves, comprising 118 symptomatic nerves, and 113 normal nerves. Radiomics features were extracted from the MRI water imaging (t2-mix3d-tra-spair). Radiomics feature selection was performed using L1 regularization-based regression, while clinical feature selection utilized univariate analysis and multivariate logistic regression. Subsequently, radiomics, clinical, and combined models were developed by using multivariate logistic regression, and a nomogram of the combined model was drawn. The performance of nomogram in discriminating symptomatic nerves was assessed through the area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves. Clinical applications of the nomogram were further evaluated using decision curve analysis. Five clinical factors and 13 radiomics signatures were ultimately selected to establish predictive models. The AUCs in the training and validation cohorts were 0.77 (0.70-0.84) and 0.82 (0.72-0.92) with the radiomics model, 0.69 (0.61-0.77) and 0.66 (0.53-0.79) with the clinical model, 0.80 (0.74-0.87), and 0.85 (0.76-0.94) with the combined model, respectively. In the external validation set, the AUCs for the clinical, radiomics, and combined models were 0.70 (0.60-0.79), 0.78 (0.65-0.91), and 0.81 (0.70-0.93), respectively. The calibration curve demonstrated that the nomogram exhibited good predictive ability. Moreover, The decision curve analysis curve indicated shows that the combined model holds high clinical application value. The integrated model, combines radiomics features from magnetic resonance neurohydrography with clinical factors, proves to be effective in identify symptomatic nerves in trigeminal neuralgia. The diagnostic efficacy of the combined model was notably superior to that of the model constructed solely from conventional clinical features.

Identification of prognostic factors and construction of nomogram to predict cancer-specific survival for patients with ovarian granulosa cell tumors

BACKGROUND

Ovarian granulosa cell tumors (OGCTs) feature low incidence, indolent growth and late recurrence. Treatment for recurrent OGCTs is challenging.

METHODS

The present study was designed to explore the prognostic factors and establish a nomogram to predict cancer-specific survival (CSS) for OGCTs patients. Enrolled in the study were 1459 eligible patients in the Surveillance, Epidemiology, and End Results (SEER) database, who were randomized to the training (n = 1021) or testing set (n = 438) at a ratio of 7:3. Univariate and multivariate Cox regression analyses were employed to screen the prognostic factors. The predictors were determined by using the Least absolute shrinkage and selection operator (LASSO) regression analysis. The model was constructed via the Cox proportional hazards risk regression analysis. The performance and clinical value of the nomograms was assessed with C-index, calibration plots, and decision curve analysis.

RESULTS

Age, pTNM stage, tumor size, surgery of the primary tumor, surgery of regional lymph nodes (LNs), residual disease after surgery, and chemotherapy were considered as significant predictive factors for CSS in OGCTs patients. After screening, the prognostic factors except surgery of regional LNs and chemotherapy were employed to build the nomogram. With desirable discrimination and calibration, the nomogram was more powerful in predicting CSS than the American Joint Committee on Cancer staging system in clinical use.

CONCLUSION

This novel prognostic nomogram, which comprises a stationary nomogram and a web-based calculator, offers convenience for clinicians in personalized decision-making including optimal treatment plans and prognosis assessments for OGCTs patients.