Systematic analysis of PANoptosis-related genes identifies XIAP as a functional oncogene in breast cancer

BACKGROUND

Breast cancer (BC) is the most prevalent malignant disease affecting women globally. PANoptosis, a novel form of cell death combining features of pyroptosis, apoptosis, and necroptosis, has recently gained attention. However, its precise function in BC and the predictive values of PANoptosis-related genes remain unclear.

METHODS

We used the expression data and clinical information of BC tissues or normal breast tissues from public databases, and then successfully developed and verified a BC PANoptosis-related risk model through a combination of univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and Kaplan-Meier (KM) analysis. A nomogram was constructed to estimate survival probability, and its accuracy was assessed using calibration curves.

RESULTS

Among 37 PANoptosis-related genes, we identified 4 differentially expressed genes related to overall survival (OS). Next, a risk model incorporating these four PANoptosis-related genes was established. Patients were stratified into low/high-risk groups based on the median risk score, with the low-risk group showing better prognoses and higher levels of immune infiltration. Utilizing the risk score and clinical features, we developed a nomogram to predict 1-, 3- and 5-year survival probability. X-linked inhibitor of apoptosis protein (XIAP) emerged as a potentially risky factor with the highest hazard ratio. In vitro experiments demonstrated that XIAP inhibition enhances the antitumor effect of doxorubicin through the PANoptosis pathway.

CONCLUSION

PANoptosis holds an important role in BC prognosis and treatment.

Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics

Purpose

The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC).

Methods

In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups.

Results

The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66.

Conclusion

The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning.

Development and validation of a nomogram model for predicting the risk of gallstone recurrence after gallbladder-preserving surgery

BACKGROUND

The high incidence of gallstone recurrence was a major concern for laparoscopic gallbladder-preserving surgery. This study aimed to investigate the risk factors for gallstone recurrence after gallbladder-preserving surgery and to establish an individualized nomogram model to predict the risk of gallstone recurrence.

METHODS

The clinicopathological and follow-up data of 183 patients who were initially diagnosed with gallstones and treated with gallbladder-preserving surgery at our hospital from January 2012 to January 2019 were retrospectively collected. The independent predictive factors for gallstone recurrence following gallbladder-preserving surgery were identified by multivariate logistic regression analysis. A nomogram model for the prediction of gallstone recurrence was constructed based on the selected variables. The C-index, receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive power of the nomogram model for gallstone recurrence.

RESULTS

During the follow-up period, a total of 65 patients experienced gallstone recurrence, and the recurrence rate was 35.5%. Multivariate logistic regression analysis revealed that the course of gallstones > 2 years [odds ratio (OR) = 2.567, 95% confidence interval (CI): 1.270-5.187, P = 0.009], symptomatic gallstones (OR = 2.589, 95% CI: 1.059-6.329, P = 0.037), multiple gallstones (OR = 2.436, 95% CI: 1.133-5.237, P = 0.023), history of acute cholecystitis (OR = 2.778, 95% CI: 1.178-6.549, P = 0.020) and a greasy diet (OR = 2.319, 95% CI: 1.186-4.535, P = 0.014) were independent risk factors for gallstone recurrence after gallbladder-preserving surgery. A nomogram model for predicting the recurrence of gallstones was established based on the above five variables. The results showed that the C-index of the nomogram model was 0.692, suggesting it was valuable to predict gallstone recurrence. Moreover, the calibration curve showed good consistency between the predicted probability and actual probability.

CONCLUSIONS

The nomogram model for the prediction of gallstone recurrence might help clinicians develop a proper treatment strategy for patients with gallstones. Gallbladder-preserving surgery should be cautiously considered for patients with high recurrence risks.

Development and validation of a risk score nomogram model to predict the risk of 5-year all-cause mortality in diabetic patients with hypertension: A study based on NHANES data

Background

The present study aimed to develop and validate a prediction nomogram model for 5-year all-cause mortality in diabetic patients with hypertension.

Methods

Data were extracted from the National Health and Nutrition Examination Survey (NHANES). A total of 3291 diabetic patients with hypertension in the NHANES cycles for 1999-2014 were selected and randomly assigned at a ratio of 8:2 to the training cohort (n = 2633) and validation cohort (n = 658). Multivariable Cox regression was conducted to establish a visual nomogram model for predicting the risk of 5-year all-cause mortality. Receiver operating characteristic curves and C-indexes were used to evaluate the discriminant ability of the prediction nomogram model for all-cause mortality. Survival curves were created using the Kaplan-Meier method and compared by the log-rank test.

Results

The nomogram model included eight independent predictors: age, sex, education status, marital status, smoking, serum albumin, blood urea nitrogen, and previous cardiovascular disease. The C-indexes for the model in the training and validation cohorts were 0.76 (95% confidence interval: 0.73-0.79, p < 0.001) and 0.75 (95% confidence interval: 0.69-0.81, p < 0.001), respectively. The calibration curves indicated that the model had satisfactory consistency in the two cohorts. The risk of all-cause mortality gradually increased as the tertiles of the nomogram model score increased (log-rank test, p < 0.001).

Conclusion

The newly developed nomogram model, a readily useable and efficient tool to predict the risk of 5-year all-cause mortality in diabetic patients with hypertension, provides a novel risk stratification method for individualized intervention.

Development and validation of a risk prediction model for valve regurgitation in Behçet's disease

BACKGROUND

In Behçet's disease (BD), mild-to-severe valvular regurgitation (VR) poses a serious complication that contributes significantly to heart failure and eventually death. The accurate prediction of VR is crucial in the early stages of BD subjects for improved prognosis. Accordingly, this study aimed to develop a nomogram that can detect VR early in the course of BD.

METHODS

One hundred seventy-two patients diagnosed with Behçet's disease (BD) were conducted to assess cardiac valve regurgitation as the primary outcome. The severity of regurgitation was classified as mild, moderate, or severe. The parameters related to the diagnostic criteria were used to develop model 1. The combination of stepAIC, best subset, and random forest approaches was employed to identify the independent predictors of VR and thus establish model 2 and create a nomogram for predicting the probability of VR in BD. Receiver operating characteristics (ROC) and decision curve analysis (DCA) were used to evaluate the model performance.

RESULTS

Thirty-four patients experienced mild-to-severe VR events. Model 2 was established using five variables, including arterial involvement, sex, age at hospitalization, mean arterial pressure, and skin lesions. In comparison with model 1 (0.635, 95% CI: 0.512-0.757), the ROC of model 2 (0.879, 95% CI: 0.793-0.966) was improved significantly. DCA suggested that model 2 was more feasible and clinically applicable than model 1.

CONCLUSION

A predictive model and a nomogram for predicting the VR of patients with Behçet's disease were developed. The good performance of this model can help us identify potential high-risk groups for heart failure. Key Points • In this study, the predictors of VR in BD were evaluated, and a risk prediction model was developed for the early prediction of the occurrence of VR in patients with BD. • The VR prediction model included the following indexes: arterial involvement, sex, age at hospitalization, mean arterial pressure, and skin lesions. • The risk model that we developed was better and more optimized than the models built with diagnostic criteria parameters, and visualizing and personalizing the model, a nomogram, provided clinicians with an easy and intuitive tool for practical prediction.

Prognostic nomogram for early-stage cervical cancer in the elderly: A SEER database analysis

Background

To identify key clinical factors affecting the survival of elderly patients with early-stage cervical cancer and to construct a nomogram for predicting their prognosis.

Methods

Patients (aged ≥ 65 years old) diagnosed with cervical cancer between 2004 and 2015 at clinical stages IA to IIA were included in this study. Diagnosis was confirmed via pathological examination, and the cases were randomly divided into a training or a validation group in a 7:3 ratio. Univariate and multivariable Cox regression analyses were performed to identify independent factors affecting the prognosis of elderly early-stage cervical cancer patients, based on which a nomogram was constructed to predict their 12-, 24- and 36-month overall survival (OS). The nomogram's performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) curves.

Results

A total of 686 patients were identified as eligible and assessed. Multivariable Cox proportional hazard regression analysis revealed that age, tumor diameter, marital status and surgical intervention were independent prognostic factors for elderly individuals with early-stage cervical cancer, which were then used to construct the nomogram. The calibration curves showed a strong correlation between predicted and observed survival rates, and Kaplan-Meier survival curves for different risk subgroups demonstrated significant survival differences (P < 0.001). DCA confirmed the nomogram's clinical utility in predicting the prognosis of elderly patients with early-stage cervical cancer.

Conclusion

The prognostic model developed in this study can accurately predict the OS of elderly patients with early-stage cervical cancer, showing high concordance with actual clinical outcomes.

Development of a microultrasound-based nomogram to predict extra-prostatic extension in patients with prostate cancer undergoing robot-assisted radical prostatectomy

OBJECTIVES

To develop a microultrasound-based nomogram including clinicopathological parameters and microultrasound findings to predict the presence of extra-prostatic extension and guide the grade of nerve-sparing.

MATERIAL AND METHODS

All patients underwent microultrasound the day before robot-assisted radical prostatectomy. Variables significantly associated with extra-prostatic extension at univariable analysis were used to build the multivariable logistic model, and the regression coefficients were used to develop the nomogram. The model was subjected to 1000 bootstrap resamples for internal validation. The performance of the microultrasound-based model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA).

RESULTS

Overall, 122/295 (41.4%) patients had a diagnosis of extra-prostatic extension on definitive pathology. Microultrasound correctly identify extra-prostatic extension in 84/122 (68.9%) cases showing a sensitivity and a specificity of 68.9% and 84.4%, with an AUC of 76.6%. After 1000 bootstrap resamples, the predictive accuracy of the microultrasound-based model was 85.9%. The calibration plot showed a satisfactory concordance between predicted probabilities and observed frequencies of extra-prostatic extension. The DCA showed a higher clinical net-benefit compared to the model including only clinical parameters. Considering a 4% cut-off, nerve-sparing was recommended in 173 (58.6%) patients and extra-prostatic extension was detected in 32 (18.5%) of them.

CONCLUSION

We developed a microultrasound-based nomogram for the prediction of extra-prostatic extension that could aid in the decision whether to preserve or not neurovascular bundles. External validation and a direct comparison with mpMRI-based nomogram is crucial to corroborate our results.

Development and validation of a diagnostic nomogram in pulmonary hypertension due to left heart disease

BACKGROUND

Pulmonary hypertension (pH) due to left heart disease (pH-LHD) is the most common form of pH in clinical practice.

OBJECTIVES

The purpose of the study is to develop a diagnostic nomogram predictive model combining conventional noninvasive examination and detection indicators.

METHODS

Our study retrospectively included 361 patients with left heart disease (LHD) who underwent right heart catheterization between 2013 and 2020. All patients were randomly divided into a training cohort (253, 70 %) and a validation cohort (108, 30 %). pH was defined as resting mean pulmonary arterial pressure (mPAP) ≥25 mmHg measured by RHC examination. Data dimension reduction and feature selection were used by Lasso regression model. The nomogram was constructed based on multivariable logistic regression.

RESULTS

A total of 175 patients with LHD were diagnosed with pH during their hospitalization, representing 48.5 % of the cohort. The mean age of the overall group was 55.6 years, with 76.7 % being male patients. Excessive resting heart rate, elevated New York Heart Association functional class, increased red blood cell distribution width, right ventricular end-diastolic diameter, and pulmonary artery systolic pressure measured by echocardiography were independently associated with the prevalence of pH-LHD. The inclusion of these 5 variables in the nomogram showed good discrimination (AUC = 0.866 [95 % CI, 0.820-0.911]) and optimal calibration (Hosmer-Lemeshow test, P = 0.791) for the validation cohort.

CONCLUSIONS

The noninvasive nomogram of pH-LHD developed in this study has excellent diagnostic value and clinical applicability, and can more accurately evaluate the presence risk of pH in patients with LHD.

Predicting the Survival Benefit of Radiotherapy in Elderly Breast Cancer Patients: A Population-Based Analysis

INTRODUCTION

This study aimed to establish two prediction tools predicting cancer-specific survival (CSS) and overall survival (OS) in elderly breast cancer patients with or without radiotherapy.

METHODS

Clinicopathological data of breast cancer patients aged more than 70 y from 2010 to 2018 were retrospectively collected from the Surveillance, Epidemiology, and End Results database. Patients were randomly divided into the training and validation cohorts at 7:3, and the Cox proportional risk model was used to construct the nomograms. The concordance index, the area under the receiver operating characteristic curve, and the calibration plot are used to evaluate the discrimination and accuracy of the nomograms.

RESULTS

One lakh twenty eight thousand two hundred twenty three elderly breast cancer patients were enrolled, including 57,915 who received radiotherapy. The Cox regression model was used to identify independent factors. These independent influencing factors are used to construct the prediction models. The calibration plots reflect the excellent consistency between the predicted and actual survival rates. The concordance index of nomograms for CSS and OS was more than 0.7 in both the radiotherapy group and the nonradiotherapy group, and similar results are also shown in area under the receiver operating characteristic curve. Decision curve analysis showed that the prognostication accuracy of the model was much higher than that of the traditional tumor, node, metastasis staging.

CONCLUSIONS

Radiotherapy can benefit elderly breast cancer patients significantly. The two prediction tools provide a personalized survival scale for evaluating the CSS and OS of elderly breast cancer patients, which can better provide clinicians with better-individualized management for these patients.

Non-invasive prediction model of histologic chorioamnionitis with preterm prelabour rupture of membranes

BACKGROUND

The aim of this study is to identify risk factors associated with histological chorioamnionitis (HCA) and develop a predictive model for antepartum assessment of the risk of PPROM with HCA.

METHODS

This study retrospectively analyzed pregnant women who experienced PPROM between 25 + 0 and 35 + 0 weeks of gestational age. The women were divided into two groups based on the presence or absence of HCA. Univariate and multivariate logistic regression analyses were conducted to identify maternal risk factors and develop a clinical prediction model for HCA. The model's discrimination and consistency were evaluated using receiver operating characteristic (ROC) and calibration curves.

RESULTS

Seventeen thousand one hundred forty-six (17,146) pregnant women were screened, and 726 (4.23 %) had PPROM. Out of the 286 subjects with PPROM, 160 developed HCA. The maternal age of these subjects ranged from 18 to 43 years (30.0 ± 5.4), while their gestational age (GA) ranged from 25 + 0 to 35 + 0 weeks (31.6 ± 2.0). The average GA at delivery was 32.2 ± 2.0 (weeks).Compared with the non-HCA group, the expectant time > 48 h, GA at delivery > 32 weeks, twin pregnancy, HGB (<110 g/Lg/L), degree of LGB (IIb-III), and WBC (>9.5 × 109 /L) were significantly more than in the PPROM with HCA group. The results show that the best model was obtained by leave-one-out logistic regression (AUC = 0.785, CA = 0.741, F1 = 0.739, Precision = 0.740, Recall = 0.741). In the validation set, logistic regression also achieved good results (AUC = 0.710, CA = 0.671, F1 = 0.654, Precision = 0.683, Recall = 0.671). Combining the previous analysis, we found that the prognostic model constructed using the core six features had the best predictive effect.

CONCLUSIONS

Six features were associated with the occurrence of chorioamnionitis. These features were used to construct a diagnostic model that can accurately predict the probability of chorioamnionitis occurrence and provide a beneficial tool for the prevention and management of PPROM with HCA.

A nomogram of preoperative indicators predicting lymph vascular space invasion in cervical cancer

PURPOSE

To develop predictive nomograms of lymph vascular space invasion (LVSI) in patients with early-stage cervical cancer.

METHODS

We identified 403 patients with cervical cancer from the Affiliated Hospital of Jiangnan University from January 2015 to December 2019. Patients were divided into the training set (n = 242) and the validation set (n = 161), with patients in the training set subdivided into LVSI (+) and LVSI (-) groups according to postoperative pathology. Preoperative hematologic indexes were compared between the two subgroups. Univariate and multivariate logistic regression analyses were used to analyze the independent risk factors for LVSI, from which a nomogram was constructed using the R package.

RESULTS

LVSI (+) was present in 94 out of 242 patients in the training set, accompanied by a significant increase in the preoperative squamous cell carcinoma antigen (SCC), white blood cells (WBC), neutrophil (NE), platelet (PLT), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammation index (SII), and tumor size (P < 0.05). Univariate analysis showed that SCC, WBC, NE, NLR, PLR, SII, and tumor size were correlated with LVSI (P < 0.05), and multivariate analysis showed that tumor size, SCC, WBC, and NLR were independent risk factors for LVSI (P < 0.05). A nomogram was correspondingly established with good performance in predicting LVSI [training: ROC-AUC = 0.845 (95% CI: 0.731-0.843) and external validation: ROC-AUC = 0.704 (95% CI: 0.683-0.835)] and high accuracy (training: C-index = 0.787; external validation: C-index = 0.759).

CONCLUSION

The nomogram based on preoperative tumor size, SCC, WBC, and NLR had excellent accuracy and discriminative capability to assess the risk of LVSI in early-stage cervical cancer patients.

A New Nephrometry Score for Predicting Positive Surgical Margin After Laparoscopic Partial Nephrectomy

PURPOSE

To introduce the KESKIN ratio as a novel predictor of positive surgical margin (PSM) after laparoscopic partial nephrectomy (PN) and to evaluate other clinical characteristics and nephrometry scores (including RENAL, PADUA, and C-index) for predicting PSM.

METHODS

We retrospectively analyzed 95 patients who underwent laparoscopic PN between June 2020 and April 2023. The KESKIN ratio was defined for all patients. The KESKIN ratio, tumor and patient-related paramaters, and nephrometry scores were analyzed to predict PSM.

RESULTS

Positive surgical margin was found in 12 of 95 patients (12.6%). There was no statistical difference between the PSM and negative surgical margin (NSM) groups in RENAL, PADUA, and C-index scores. Only the KESKIN ratio was found to be a statistically significant predictor of PSM in both univariate and multivariate regression analysis (p = 0.007 and p = 0.043, respectively). Mean endophytic diameter and endophytic percentage were found to be statistically significant predictors of PSM in only univariate analysis (p = 0.005 and p = 0.01, respectively). The value of 0.5 was determined as the cut-off value for the KESKIN ratio. Values higher than 0.5 indicate an increase in PSM.

CONCLUSIONS

The KESKIN ratio is a novel, easily measurable, and calculable image-based parameter that can be used to predict PSM after laparascopic PN. If externally validated in a larger patient population, the KESKIN ratio may be used in future versions of the current nephrometry scoring systems for predicting the PSM.

Development of a predictive nomogram for 28-day mortality risk in non-traumatic or post-traumatic subarachnoid hemorrhage patients

OBJECTIVE

Subarachnoid hemorrhage (SAH) is associated with high rates of mortality and permanent disability. At present, there are few definite clinical tools to predict prognosis in SAH patients. The current study aims to develop and assess a predictive nomogram model for estimating the 28-day mortality risk in both non-traumatic or post-traumatic SAH patients.

METHODS

The MIMIC-III database was searched to select patients with SAH based on ICD-9 codes. Patients were separated into non-traumatic and post-traumatic SAH groups. Using LASSO regression analysis, we identified independent risk factors associated with 28-day mortality and incorporated them into nomogram models. The performance of each nomogram was assessed by calculating various metrics, including the area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).

RESULTS

The study included 999 patients with SAH, with 631 in the non-traumatic group and 368 in the post-traumatic group. Logistic regression analysis revealed critical independent risk factors for 28-day mortality in non-traumatic SAH patients, including gender, age, glucose, platelet, sodium, BUN, WBC, PTT, urine output, SpO2, and heart rate and age, glucose, PTT, urine output, and body temperature for post-traumatic SAH patients. The prognostic nomograms outperformed the commonly used SAPSII and APSIII systems, as evidenced by superior AUC, NRI, IDI, and DCA results.

CONCLUSION

The study identified independent risk factors associated with the 28-day mortality risk and developed predictive nomogram models for both non-traumatic and post-traumatic SAH patients. The nomogram holds promise in guiding prognosis improvement strategies for patients with SAH.

Predictive progression outcomes and risk stratification in patients with recurrent or metastatic nasopharyngeal carcinoma who received first-line immunochemotherapy

PURPOSE

Progression after first-line immunochemotherapy (ICT) for recurrent or metastatic nasopharyngeal carcinoma (R/M NPC) is a clinical concern due to subsequent limited treatment options. This study firstly predicted the progress outcome.

METHODS

A cohort of 186 R/M NPC cases that received first-line ICT was included for developing a Cox regression model for progression-free survival (PFS) and risk stratification, which was verified by cross-validation. Discrimination and calibration were evaluated. Progression sites in risk groups was shown with a Sankey diagram.

RESULTS

Baseline predictors including liver metastasis, trend of plasma Epstein-Barr virus DNA copies, lymphocyte-to-monocyte ratio, and level of platelet and lactate dehydrogenase were identified for model construction, which stratify the cohort into low, middle, and high-risk groups. The overall concordance index (C-index) was 0.67 (95% CI 0.62-0.73). The area under the curve (AUC) was 0.68 (95% CI 0.60-0.76), 0.74 (95% CI 0.66-0.82), 0.75 (95% CI 0.65-0.84) at predicting 12, 18, and 24 months PFS, indicating a moderate accuracy. Cross-validation showed the model performance was robust. Compared with the low-risk group (median PFS: 24.4 months, 95% CI 18.4 months to not reached), the high-risk group (median PFS: 7.1 months, 95% CI 6.4-10.1 months; hazard risk: 7.4, 95% CI 4.4-12.4, p < 0.001) progressed with more liver metastasis after ICT resistance.

CONCLUSION

It was the first study that described the risk factors and progression characteristics in R/M NPC patients who received first-line ICT, investigating the progression patterns, which was helpful to identify patients with different risks and help guide personalized interventions.

Epidemiological features and a survival nomogram for primary lymphoma of the male genital tract

Primary lymphoma of the male genital tract (PLMGT) is rare, and data on its epidemiology and prognosis are lacking. Our study aimed to estimate the incidence and develop a predictive nomogram for PLMGT. We pooled the incidence and survival data of PLMGT over the last 20 years from the Surveillance, Epidemiology, and End Results (SEER) database. Incidence rates were calculated by year of diagnosis, age, race, and histology. Independent prognostic factors selected by Cox regression analysis were used to develop a nomogram for predicting overall survival (OS). Our study enrolled 1312 patients with PLMGT. The overall incidence rate of PLMGT was 0.437/1,000,000 during 2000-2019. OS was associated with age, marital status, histological subtype, Ann Arbor stage, and therapeutic strategy, which were used to construct nomograms to predict 1-, 3-, and 5-year OS rates. Receiver operating characteristic curves, calibration plots, and decision curve analysis showed good performance of the nomogram. Based on the total score of each patient from the nomogram, the patients were clustered into three risk groups, and the risk stratification model was more successful in predicting clinical outcomes than the traditional Ann Arbor staging system. The incidence rate of PLMGT has remained relatively stable over the past two decades. For the OS of patients with PLMGT, we established a novel predictive nomogram involving all independent risk factors obtained from the SEER database and developed a corresponding risk classification system that showed better predictive performance than the Ann Arbor staging system.

Nomogram for Predicting Remission of Metabolic Syndrome 1 Year after Sleeve Gastrectomy Surgery in Chinese Patients with Obesity

PURPOSE

Sleeve gastrectomy (SG) is a widely used and effective treatment for patients with obesity and comorbid metabolic abnormalities. No specialized tool is available to predict metabolic syndrome (MS) remission after SG. We presented a nomogram that evaluated the probability of MS remission in obese patients 1 year after SG.

MATERIALS AND METHODS

Patients with preoperative MS who underwent SG were enrolled in this retrospective study. They were divided into a training set and a validation set. Multivariate logistic regression analysis was performed to identify independent predictors of MS remission, and these predictors were included in the nomogram. Receiver operating characteristic curve was used to evaluate discrimination. Calibration was performed with the Hosmer-Lemeshow goodness-of-fit test. The net benefits of the nomogram were evaluated using decision curve analysis (DCA).

RESULTS

Three hundred and eighteen patients with a median age of 34.0 years were analyzed. A training set and a validation set with 159 individuals each were established. A combination of age, preoperative high-density lipoprotein cholesterol, elevated triglycerides and glycated hemoglobin level independently and accurately predicted MS remission. The nomogram included these factors. The discriminative ability was moderate in training and validation sets (Area under curve 0.800 and 0.727, respectively). The Hosmer-Lemeshow X2 value of the nomogram was 8.477 (P = 0.388) for the training set and 5.361 (P = 0.718) for the validation set, indicating good calibration. DCA showed the nomogram had clinical benefits in both datasets.

CONCLUSION

Our nomogram could accurately predict MS remission in Chinese patients with obesity 1 year after SG.

Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis

BACKGROUND

Liver cirrhosis patients admitted to intensive care unit (ICU) have a high mortality rate.

AIM

To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.

METHODS

We extracted demographic, etiological, vital sign, laboratory test, comorbidity, complication, treatment, and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU (eICU) collaborative research database (eICU-CRD). Predictor selection and model building were based on the MIMIC-IV dataset. The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors. The final predictors were included in the multivariate logistic regression model, which was used to construct a nomogram. Finally, we conducted external validation using the eICU-CRD. The area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were used to assess the efficacy of the models.

RESULTS

Risk factors, including the mean respiratory rate, mean systolic blood pressure, mean heart rate, white blood cells, international normalized ratio, total bilirubin, age, invasive ventilation, vasopressor use, maximum stage of acute kidney injury, and sequential organ failure assessment score, were included in the multivariate logistic regression. The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases, respectively. The calibration curve also confirmed the predictive ability of the model, while the decision curve confirmed its clinical value.

CONCLUSION

The nomogram has high accuracy in predicting in-hospital mortality. Improving the included predictors may help improve the prognosis of patients.

Prognostic and predictive value of tumor deposits in advanced signet ring cell colorectal cancer: SEER database analysis and multicenter validation

BACKGROUND

Colorectal signet-ring cell carcinoma (SRCC) is a rare cancer with a bleak prognosis. The relationship between its clinicopathological features and survival remains incompletely elucidated. Tumor deposits (TD) have been utilized to guide the N staging in the 8th edition of American Joint Committee on Cancer (AJCC) staging manual, but their prognostic significance remains to be established in colorectal SRCC.

PATIENTS AND METHODS

The subjects of this study were patients with stage III/IV colorectal SRCC who underwent surgical treatment. The research comprised two cohorts: a training cohort and a validation cohort. The training cohort consisted of 631 qualified patients from the SEER database, while the validation cohort included 135 eligible patients from four independent hospitals in China. The study assessed the impact of TD on Cancer-Specific Survival (CSS) and Overall Survival (OS) using Kaplan-Meier survival curves and Cox regression models. Additionally, a prognostic nomogram model was constructed for further evaluation.

RESULTS

In both cohorts, TD-positive patients were typically in the stage IV and exhibited the presence of perineural invasion (PNI) (P < 0.05). Compared to the TD-negative group, the TD-positive group showed significantly poorer CSS (the training cohort: HR, 1.87; 95% CI, 1.52-2.31; the validation cohort: HR, 2.43; 95% CI, 1.55-3.81; all P values < 0.001). This association was significant in stage III but not in stage IV. In the multivariate model, after adjusting for covariates, TD maintained an independent prognostic value (P < 0.05). A nomogram model including TD, N stage, T stage, TNM stage, CEA, and chemotherapy was constructed. Through internal and external validation, the model demonstrated good calibration and accuracy. Further survival curve analysis based on individual scores from the model showed good discrimination.

CONCLUSION

TD positivity is an independent factor of poor prognosis in colorectal SRCC patients, and it is more effective to predict the prognosis of colorectal SRCC by building a model with TD and other clinically related variables.

Designing a predictive Framework: Immune-Related Gene-Based nomogram and prognostic model for kidney renal papillary cell carcinoma

BACKGROUND

Kidney renal papillary cell carcinoma (KIRP) is frequently associated with an unfavorable prognosis for affected individuals. Unfortunately, there has been insufficient exploration in search for a reliable prognosis signature and predictive indicators to forecast outcomes for KIRP patients.

AIM

The aim of this study is to employ a comprehensive analysis of data for the identification of prognosis genes, leading to the development of a nomogram with strong predictive capabilities. The objective is to provide a valuable statistical tool that, when implemented in a clinical setting, can offer patients an early opportunity for treatment and enhance their chances of ultimate recovery from this life-threatening disease.

METHODS

Different packages in R were used to analyze RNA-seq data from the TCGA data portal. Multivariate Cox regression analysis and Kaplan-Meier analysis were also used to investigate the prognostic values of immune-related genes and construct the predictive model and nomogram. A p-value < 0.05 was considered to be significant.

RESULTS

A total of 368 immune-related genes and 60 TFs were identified as differentially expressed in KIRP tissues compared with normal tissues. Of the 368, 23 were found to be related to overall survival. GO and KEGG analysis suggested that these prognostic immune-related genes mainly participated in the ERK1 and ERK2 cascades, Rap1 signaling pathway, and the PI3K-Akt signaling pathway. 9 genes were identified from Cox regression to be statistically significant prognostic-related genes. Survival analysis showed that a model based on these 9 prognostic-related genes has high predictive performance. Immunohistochemistry results show that APOH, BIRC5, CCL19, and GRN were significantly increased in kidney cancer. B cells and CD4 + T cells were positively correlated with risk score model.

CONCLUSION

A prognostic model was successfully created based on 9 immune-related genes correlated with overall survival in KIRP. This work aims to provide some insight into therapeutic approaches and prognostic predictors of KIRP.

Prognostic performance of MRI LI-RADS version 2018 features and clinical-pathological factors in alpha-fetoprotein-negative hepatocellular carcinoma

PURPOSE

To evaluate the role of the magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) version 2018 features and clinical-pathological factors for predicting the prognosis of alpha-fetoprotein (AFP)-negative (≤ 20 ng/ml) hepatocellular carcinoma (HCC) patients, and to compare with other traditional staging systems.

METHODS

We retrospectively enrolled 169 patients with AFP-negative HCC who received preoperative MRI and hepatectomy between January 2015 and August 2020 (derivation dataset:validation dataset = 118:51). A prognostic model was constructed using the risk factors identified via Cox regression analysis. Predictive performance and discrimination capability were evaluated and compared with those of two traditional staging systems.

RESULTS

Six risk factors, namely the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade, were associated with recurrence-free survival. The prognostic model constructed using these factors achieved C-index of 0.705 and 0.674 in the derivation and validation datasets, respectively. Furthermore, the model performed better in predicting patient prognosis than traditional staging systems. The model effectively stratified patients with AFP-negative HCC into high- and low-risk groups with significantly different outcomes (p < 0.05).

CONCLUSION

A prognostic model integrating the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade may serve as a valuable tool for refining risk stratification in patients with AFP-negative HCC.

MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses

RATIONALE AND OBJECTIVES

Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses.

MATERIALS AND METHODS

A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05).

CONCLUSION

The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases.

Conditional survival analysis and real-time prognosis prediction in stage III T3-T4 colon cancer patients after surgical resection: a SEER database analysis

BACKGROUND

Conditional survival (CS) takes into consideration the duration of survival post-surgery and can provide valuable additional insights. The aim of this study was to investigate the risk factors associated with reduced one-year postoperative conditional survival in patients diagnosed with stage III T3-T4 colon cancer and real-time prognosis prediction. Furthermore, we aim to develop pertinent nomograms and predictive models.

METHODS

Clinical data and survival outcomes of patients diagnosed with stage III T3-T4 colon cancer were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2019. Patients were divided into training and validation cohorts at a ratio of 7:3. The training set consisted of a total of 11,386 patients for conditional overall survival (cOS) and 11,800 patients for conditional cancer-specific survival (cCSS), while the validation set comprised 4876 patients for cOS and 5055 patients for cCSS. Univariate and multivariate Cox regression analyses were employed to identify independent risk factors influencing one-year postoperative cOS and cCSS. Subsequently, predictive nomograms for cOS and cCSS at 2-year, 3-year, 4-year, and 5-year intervals were constructed based on the identified prognostic factors. The performance of these nomograms was rigorously assessed through metrics including the concordance index (C-index), calibration curves, and the area under curve (AUC) derived from the receiver operating characteristic (ROC) analysis. Clinical utility was further evaluated using decision curve analysis (DCA).

RESULTS

A total of 18,190 patients diagnosed with stage III T3-T4 colon cancer were included in this study. Independent risk factors for one-year postoperative cOS and cCSS included age, pT stage, pN stage, pretreatment carcinoembryonic antigen (CEA) levels, receipt of chemotherapy, perineural invasion (PNI), presence of tumor deposits, the number of harvested lymph nodes, and marital status. Sex and tumor site were significantly associated with one-year postoperative cOS, while radiation therapy was notably associated with one-year postoperative cCSS. In the training cohort, the developed nomogram demonstrated a C-index of 0.701 (95% CI, 0.711-0.691) for predicting one-year postoperative cOS and 0.701 (95% CI, 0.713-0.689) for one-year postoperative cCSS. Following validation, the C-index remained robust at 0.707 (95% CI, 0.721-0.693) for one-year postoperative cOS and 0.700 (95% CI, 0.716-0.684) for one-year postoperative cCSS. ROC and calibration curves provided evidence of the model's stability and reliability. Furthermore, DCA underscored the nomogram's superior clinical utility.

CONCLUSIONS

Our study developed nomograms and predictive models for postoperative stage III survival in T3-T4 colon cancer with the aim of accurately estimating conditional survival. Survival bias in our analyses may lead to overestimation of survival outcomes, which may limit the applicability of our findings.

Development and validation of a novel nomogram model for predicting delayed graft function in deceased donor kidney transplantation based on pre-transplant biopsies

BACKGROUND

Delayed graft function (DGF) is an important complication after kidney transplantation surgery. The present study aimed to develop and validate a nomogram for preoperative prediction of DGF on the basis of clinical and histological risk factors.

METHODS

The prediction model was constructed in a development cohort comprising 492 kidney transplant recipients from May 2018 to December 2019. Data regarding donor and recipient characteristics, pre-transplantation biopsy results, and machine perfusion parameters were collected, and univariate analysis was performed. The least absolute shrinkage and selection operator regression model was used for variable selection. The prediction model was developed by multivariate logistic regression analysis and presented as a nomogram. An external validation cohort comprising 105 transplantation cases from January 2020 to April 2020 was included in the analysis.

RESULTS

266 donors were included in the development cohort, 458 kidneys (93.1%) were preserved by hypothermic machine perfusion (HMP), 96 (19.51%) of 492 recipients developed DGF. Twenty-eight variables measured before transplantation surgery were included in the LASSO regression model. The nomogram consisted of 12 variables from donor characteristics, pre-transplantation biopsy results and machine perfusion parameters. Internal and external validation showed good discrimination and calibration of the nomogram, with Area Under Curve (AUC) 0.83 (95%CI, 0.78-0.88) and 0.87 (95%CI, 0.80-0.94). Decision curve analysis demonstrated that the nomogram was clinically useful.

CONCLUSION

A DGF predicting nomogram was developed that incorporated donor characteristics, pre-transplantation biopsy results, and machine perfusion parameters. This nomogram can be conveniently used for preoperative individualized prediction of DGF in kidney transplant recipients.

Development and validation of a nomogram to predict cancer-specific survival of elderly patients with unresected gastric cancer who received chemotherapy

This investigation aimed to explore the prognostic factors in elderly patients with unresected gastric cancer (GC) who have received chemotherapy and to develop a nomogram for predicting their cancer-specific survival (CSS). Elderly gastric cancer patients who have received chemotherapy but no surgery in the Surveillance, Epidemiology, and End Results Database between 2004 and 2015 were included in this study. Cox analyses were conducted to identify prognostic factors, leading to the formulation of a nomogram. The nomogram was validated using receiver operating characteristic (ROC) and calibration curves. The findings elucidated six prognostic factors encompassing grade, histology, M stage, radiotherapy, tumor size, and T stage, culminating in the development of a nomogram. The ROC curve indicated that the area under curve of the nomogram used to predict CSS for 3, 4, and 5 years in the training queue as 0.689, 0.708, and 0.731, and in the validation queue, as 0.666, 0.693, and 0.708. The calibration curve indicated a high degree of consistency between actual and predicted CSS for 3, 4, and 5 years. This nomogram created to predict the CSS of elderly patients with unresected GC who have received chemotherapy could significantly enhance treatment accuracy.

Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer

OBJECTIVE

Cancer-related fatigue (CRF) has been considered the biggest influencing factor for cancer patients after surgery. This study aimed to develop and validate a nomogram for severe cancer-related fatigue (CRF) patients with cervical cancer (CC).

METHODS

A cross-sectional study was conducted to develop and validate a nomogram (building set = 196; validation set = 88) in the Department of Obstetrics and Gynecology of a Class III hospital in Shenyang, Liaoning Province. We adopted the questionnaire method, including the Cancer Fatigue Scale (CFS), Medical Uncertainty in Illness Scale (MUIS), Medical Coping Modes Questionnaire (MCMQ), Multidimensional Scale of Perceived Social Support (MSPSS), and Sense of Coherence-13 (SOC-13). Binary logistic regression was used to test the risk factors of CRF. The R4.1.2 software was used to develop and validate the nomogram, including Bootstrap resampling method, the ability of Area Under Curve (AUC), Concordance Index (C-Index), Hosmer Lemeshow goodness of fit test, Receiver Operating Characteristic (ROC) curve, Calibration calibration curve, and Decision Curve Analysis curve (DCA).

RESULTS

The regression equation was Logit(P) = 1.276-0.947 Monthly income + 0.989 Long-term passive smoking - 0.952 Physical exercise + 1.512 Diagnosis type + 1.040 Coping style - 0.726 Perceived Social Support - 2.350 Sense of Coherence. The C-Index of the nomogram was 0.921 (95% CI: 0.877∼0.958). The ROC curve showed the sensitivity of the nomogram was 0.821, the specificity was 0.900, and the accuracy was 0.857. AUC was 0.916 (95% CI: 0.876∼0.957). The calibration showed that the predicted probability of the nomogram fitted well with the actual probability. The DCA curve showed when the prediction probability was greater than about 10%, the benefit of the nomogram was positive. The results in the validation group were similar.

CONCLUSION

This nomogram had good identifiability, accuracy and clinical practicality, and could be used as a prediction and evaluation tool for severe cases of clinical patients with CC.

Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms

Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan-Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61-11.28; P = 0.004), nonsphericity index (95%CI 4.10-25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00-1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan-Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.

Log odds of positive lymph nodes (LODDS)-based novel nomogram for survival estimation in patients with invasive micropapillary carcinoma of the breast

BACKGROUND

Invasive micropapillary carcinoma (IMPC) of the breast is known for its high propensity for lymph node (LN) invasion. Inadequate LN dissection may compromise the precision of prognostic assessments. This study introduces a log odds of positive lymph nodes (LODDS) method to address this issue and develops a novel LODDS-based nomogram to provide accurate prognostic information.

METHODS

The study analyzed data from 1,901 patients with breast IMPC from the Surveillance, Epidemiology, and End Results database. It assessed the relationships between LODDS and the number of excised LN (eLN), positive LN (pLN), and the pLN ratio (pLNR), identifying an optimal threshold value using a restricted cubic spline method. Predictive factors were identified by the Cox least absolute shrinkage and selection operator (Cox-LASSO) regression and validated through multivariate Cox regression to construct a nomogram. The model's accuracy, discrimination, and utility were assessed. The study also explored the consequences of excluding LODDS from the nomogram and compared its effectiveness with the tumor-node-metastasis (TNM) staging system.

RESULTS

LODDS improved N status classification by identifying heterogeneity in patients with pLN ratios of 0% (pLN =0) or 100% (pLN =eLN) and setting -1.08 as the ideal cutoff. Five independent prognostic factors for breast cancer-specific survival (BCSS) were identified: tumor size, N status, LODDS, progesterone receptor status, and histological grade. The LODDS-based nomogram achieved a strong concordance index of 0.802 (95% CI: 0.741-0.863), surpassing both the version without LODDS and the conventional TNM staging in all tests.

CONCLUSIONS

For breast IMPC, LODDS served as an independent prognostic factor, its effectiveness unaffected by the anatomical LN count, enhancing the accuracy of N staging. The LODDS-based nomogram showed promise in offering more personalized prognostic information.

Development and external validation of a nomogram for predicting short-term prognosis in patients with acute pulmonary embolism

BACKGROUND

Accurate assessment and timely intervention play a crucial role in ameliorating poor short-term prognosis of acute pulmonary embolism (APE) patients. The currently employed scoring models exhibit a degree of complexity, and some models may not comprehensively incorporate relevant indicators, thereby imposing limitations on the evaluative efficacy. Our study aimed to construct and externally validate a nomogram that predicts 30-day all-cause mortality risk in APE patients.

METHODS

Clinical data from APE patients in Intensive Care-IV database was included as a training cohort. Additionally, we utilized our hospital's APE database as an external validation cohort. The nomogram was developed, and its predictive ability was evaluated using receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis.

RESULTS

A collective of 1332 patients and 336 patients were respectively enrolled as the training cohort and the validation cohort in this study. Five variables including age, malignancy, oxygen saturation, blood glucose, and the use of vasopressor, were identified based on the results of the multivariate Cox regression model. The ROC value for the nomogram in the training cohort yielded 0.765, whereas in the validation group, it reached 0.907. Notably, these values surpassed the corresponding ROC values for the Pulmonary Embolism Severity Index, which were 0.713 in the training cohort and 0.754 in the validation cohort.

CONCLUSIONS

The nomogram including five indicators had a good performance in predicting short-term prognosis in patients with APE, which was easier to apply and provided better recommendations for clinical decision-making.

Venous thromboembolism in patients with acute myeloid leukemia: development of a predictive model

BACKGROUND

Patients with acute myeloid leukemia (AML) are at increased risk of venous thromboembolic events (VTE). However, thromboprophylaxis is largely underused.

OBJECTIVES

This study aimed to determine possible VTE development risk factors and to develop a novel predictive model.

METHODS

We conducted a retrospective cohort study of adult patients with newly diagnosed AML. We used univariate and multivariable logistic regression to estimate binary outcomes and identify potential predictors. Based on our final model, a dynamic nomogram was constructed with the goal of facilitating VTE probability calculation.

RESULTS

Out of 626 eligible patients with AML, 72 (11.5%) developed VTE during 6 months of follow-up. Six parameters were independent predictors: male sex (odds ratio [OR] 1.82, 95% confidence interval [CI]: 1.077-2.065), prior history of thrombotic events (OR 2.27, 95% CI: 1.4-4.96), international normalized ratio (OR 0.21, 95% CI: 0.05-0.95), Eastern Cooperative Oncology Group performance status (OR 0.71, 95% CI: 0.53-0.94), and intensive therapy (OR 2.05, 95% CI: 1.07-3.91). The C statistics for the model was 0.68. The model was adequately calibrated and internally validated. The decision-curve analysis suggested the use of thromboprophylaxis in patients with VTE risks between 8 and 20%.

CONCLUSION

We developed a novel and convenient tool that may assist clinicians in identifying patients whose VTE risk is high enough to warrant thromboprophylaxis.

Development and validation of a nomogram prediction model for ADHD in children based on individual, family, and social factors

OBJECTIVES

A reliable, user-friendly, and multidimensional prediction tool can help to identify children at high risk for ADHD and facilitate early recognition and family management of ADHD. We aimed to develop and validate a risk nomogram for ADHD in children aged 3-17 years in the United States based on clinical manifestations and complex environments.

METHODS

A total of 141,356 cases were collected for the prediction model. Another 54,444 cases from a new data set were utilized for performing independent external validation. The LASSO regression was used to control possible variables. A final risk nomogram for ADHD was established based on logistic regression, and the discrimination and calibration of the established nomogram were evaluated by bootstrapping with 1000 resamples.

RESULTS

A final risk nomogram for ADHD was established based on 13 independent predictors, including behavioral problems, learning disabilities, age, intellectual disabilities, anxiety symptoms, gender, premature birth, maternal age at childbirth, parent-child interaction patterns, etc. The C-index of this model was 0.887 in the training set, and 0.862 in the validation set. Internal and external validation proved that the model was reliable.

CONCLUSIONS

A nomogram, a statistical prediction tool that assesses individualized ADHD risk for children is helpful for the early identification of children at high risk for ADHD and the construction of a conceptual model of society-family-school collaborative diagnosis, treatment, and management of ADHD.

Construction of a nomogram for predicting compensated cirrhosis with Wilson's disease based on non-invasive indicators

BACKGROUND

Wilson's disease (WD) often leads to liver fibrosis and cirrhosis, and early diagnosis of WD cirrhosis is essential. Currently, there are few non-invasive prediction models for WD cirrhosis. The purpose of this study is to non-invasively predict the occurrence risk of compensated WD cirrhosis based on ultrasound imaging features and clinical characteristics.

METHODS

A retrospective analysis of the clinical characteristics and ultrasound examination data of 102 WD patients from November 2018 to November 2020 was conducted. According to the staging system for WD liver involvement, the patients were divided into a cirrhosis group (n = 43) and a non-cirrhosis group (n = 59). Multivariable logistic regression analysis was used to identify independent influencing factors for WD cirrhosis. A nomogram for predicting WD cirrhosis was constructed using R analysis software, and validation of the model's discrimination, calibration, and clinical applicability was completed. Due to the low incidence of WD and the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model.

RESULTS

Acoustic Radiation Force Impulse (ARFI), portal vein diameter (PVD), and serum albumin (ALB) are independent factors affecting WD cirrhosis. A nomogram for WD cirrhosis was constructed based on these factors. The area under the ROC curve (AUC) of the model's predictive ability is 0.927 (95% CI: 0.88-0.978). As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Clinical decision curve analysis shows that the model has high clinical practical value. ROC curve analysis of the model's rationality indicates that the model's AUC is greater than the AUC of using ALB, ARFI, and PVD alone.

CONCLUSION

The nomogram model constructed based on ARFI, PVD, and ALB can serve as a non-invasive tool to effectively predict the risk of developing WD cirrhosis.

Nomogram for predicting early hypophosphatemia in term infants

BACKGROUND

Physiological processes rely on phosphate, which is an essential component of adenosine triphosphate (ATP). Hypophosphatasia can affect nearly every organ system in the body. It is crucial to monitor newborns with risk factors for hypophosphatemia and provide them with the proper supplements. We aimed to evaluate the risk factors and develop a nomogram for early hypophosphatemia in term infants.

METHODS

We conducted a retrospective study involving 416 term infants measured serum phosphorus within three days of birth. The study included 82 term infants with hypophosphatemia (HP group) and 334 term infants without hypophosphatemia (NHP group). We collected data on the characteristics of mothers, newborn babies, and childbirth. Furthermore, univariate and multivariate logistic regression analyses were performed to identify independent risk factors for hypophosphatemia in term infants, and a nomogram was developed and validated based on the final independent risk factors.

RESULTS

According to our analysis, the multivariate logistic regression analysis showed that male, maternal diabetes, cesarean delivery, lower serum magnesium, and lower birth weight were independent risk factors for early hypophosphatemia in term infants. In addition, the C-index of the developed nomogram was 0.732 (95% CI = 0.668-0.796). Moreover, the calibration curve indicated good consistency between the hypophosphatemia diagnosis and the predicted probability, and a decision curve analysis (DCA) confirmed the clinical utility of the nomogram.

CONCLUSIONS

The analysis revealed that we successfully developed and validated a nomogram for predicting early hypophosphatemia in term infants.

The prognostic model and immune landscape based on cancer-associated fibroblast features for patients with locally advanced rectal cancer

Background

This study aimed to construct a nomogram based on CAF features to predict the cancer-specific survival (CSS) rates of locally advanced rectal cancer (LARC) patients.

Methods

The EPIC algorithm was employed to calculate the proportion of CAFs. based on the differentially expressed genes between the high and low CAF proportion subgroups, prognostic genes were identified via LASSO and Cox regression analyses. They were then used to construct a prognostic risk signature. Moreover, the GSE39582 and GGSE38832 datasets were used for external validation. Lastly, the level of immune infiltration was evaluated using ssGSEA, ESTIMATE, CIBERSORTx, and TIMER.

Results

A higher level of CAF infiltration was associated with a worse prognosis. Additionally, the number of metastasized lymph nodes and distant metastases, as well as the level of immune infiltration were higher in the high CAF proportion subgroup. Five prognostic genes (SMOC2, TUBAL3, C2CD4A, MAP1B, BMP8A) were identified and subsequently incorporated into the prognostic risk signature to predict the 1-, 3-, and 5-year CSS rates in the training and validation sets. Differences in survival rates were also determined in the external validation cohort. Furthermore, independent prognostic factors, including TNM stage and risk score, were combined to established a nomogram. Notably, our results revealed that the proportions of macrophages and neutrophils and the levels of cytokines secreted by M2 macrophages were higher in the high-risk subgroup. Finally, the prognostic genes were significantly associated with the level of immune cell infiltration.

Conclusion

Herein, a nomogram based on CAF features was developed to predict the CSS rate of LARC patients. The risk model was capable of reflecting differences in the level of immune cell infiltration.

An ion channel-based prognostic model identified TRPV2 and GJB3 as immunotherapy determinants in pancreatic cancer

Background

Less than 10% of people who have pancreatic ductal adenocarcinoma (PDAC) will survive the malignancy for five years. The ion channel genes-related biomarker and predictive model were needed for exploitation.

Methods

Differentially expressed ion channel genes (DEICGs) were detected in PDAC patients. GO and KEGG enrichment analysis was conducted on DEICGs. The prognostic genes were found using Cox regression analysis. After that, a risk model was created and examined. A nomogram was created based on independent predictive analysis. The molecular functions of two risk groups were explored. Immune checkpoint molecule expression was compared in two risk groups. We evaluated the possible cancer immunotherapy response in two risk groups using the TIDE method. We further examined how TRPV2 functions in PDAC as a potent oncogene and regulates the activity of macrophages by in vitro validation, including CCK8, EdU, and Transwell assays.

Results

A total of twenty-four DEICGs were found. Next, we discovered that two DEICGs (TRPV2 and GJB3) were connected to PDAC patients' overall survival (OS). The risk model was created and validated, and a nomogram was used to forecast the overall survival of PDAC patients. The high-risk group considerably accumulated oncogenic pathways. Furthermore, we discovered a correlation between the expression of critical immunological checkpoints and the risk score. Furthermore, patients in the high-risk category had a lower chance of benefiting from immune therapy. The HPA database confirmed that TRPV2 is expressed as a protein. Lastly, TRPV2 controls macrophage activity and acts as a potent oncogene in PDAC.

Conclusion

Altogether, this study suggested that two ion channel genes, TRPV2 and GJB3, were potential biomarkers for the prognosis of PDAC and immunotherapy targets, and the research will be crucial for creating novel PDAC treatment targets and predictive molecular indicators.

Identification of nomogram associated with durable clinical benefit gene for advanced non-small cell lung cancer with sensitivity to responsive to immunotherapy

Background

Immunotherapy has become the standard treatment for advanced non-small cell lung cancer (NSCLC). However, a subset of the most advanced NSCLC patients fails to respond adequately to Immune checkpoint inhibitors (ICIs). Developing new nomograms and integrating prognostic factors are crucial for improving the clinical predictability of NSCLC patients undergoing ICIs.

Methods

Clinical information and genomic data of NSCLC patients undergoing ICIs were retrieved from cBioPortal. Gene alterations associated with durable clinical benefit (DCB) were compared to those linked to no durable benefit (NDB). The Kaplan-Meier plot method was employed for survival analysis, and a novel nomogram was formulated by selecting pertinent clinical variables.

Results

For the NSCLC patients receiving immunotherapy, three subgroups were identified based on the treatment regimen, including anti-PD-1 monotherapy, anti-PD-1 combination with anti-CTLA-4, and first-line treatment. The mutation status of TP53, PGR, PTPRT, RELN, MUC19, LRP1B, and FAT3 was found to be associated with progression-free survival (PFS). Using the clinicopathological parameters and genomic data of the patients, we developed three novel nomograms to predict the prognosis of ICI treatment in different subgroups.

Conclusion

Our study revealed that PGR, PTPRD, RELN, MUC19, LRP1B, and FAT3 mutation could serve as predictive biomarkers. Our systematic nomograms demonstrate significant potential in predicting the prognosis for NSCLC patients with sensitivity to different ICI treatment strategies.

Multivariate analyses on male factors and construction of a nomogram for predicting low in vitro fertilization rate

Low fertilization rate (LFR) and total fertilization failure (TFF) are often encountered in routine in vitro fertilization (IVF) procedure. To solve this problem, multivariate analyses on the relationship between male factors and in vitro fertilization rate were performed, and a nomogram for prediction of LFR was constructed. This retrospective study contained 2011 couples who received IVF treatment from January 2017 to December 2021. Man factors and in vitro fertilization rate were collected. Among these couples, 1347 cases had in vitro fertilization rates ≥30 % (control group), and 664 cases had in vitro fertilization rates <30 % (LFR group). Univariate analyses of male factors found that between the two groups there were significant differences (p < 0.05) in sperm progressive motility (SPR), sperm concentration (SC), total sperm number, normal sperm morphology rate (NSMR), DNA fragmentation index (DFI), sperm acrosin activity (SAA) and the clinical diagnosis of primary or secondary infertility. Multivariate logistic regression analyses showed that SPR, SAA, and SC were independent risk factors for LFR. An algorithm and a correspondent nomogram for predicting high LFR risk were constructed using data from the training cohort. The LFR nomogram exhibited an excellent discrimination power and a high fitting degree in both the training cohort (AUC = 0.90, 95 % CI: 0.88-0.92), (H-L: x2 = 5.43, p = 0.71) and validation cohort (AUC = 0.89, 95 % CI:0.87-0.92), (H-L: x2 = 7.85, p = 0.45), respectively. The decision curve analysis (DCA) demonstrated a high efficiency of the LFR nomogram for clinical utility. SPR, SAA, and SC are independent risk factors for LFR. The LFR nomogram established based on these factors could be a useful tool to predict high risk of LFR, and patients with high risk of LFR can be guided to direct ICSI procedure. Clinical application of the LFR nomogram may increase the in vitro fertilization rate by facilitating the decision making in IVF service.

Predictive model for non-malignant portal vein thrombosis associated with cirrhosis based on inflammatory biomarkers

BACKGROUND

Portal vein thrombosis (PVT), a complication of liver cirrhosis, is a major public health concern. PVT prediction is the most effective method for PVT diagnosis and treatment.

AIM

To develop and validate a nomogram and network calculator based on clinical indicators to predict PVT in patients with cirrhosis.

METHODS

Patients with cirrhosis hospitalized between January 2016 and December 2021 at the First Hospital of Lanzhou University were screened and 643 patients with cirrhosis who met the eligibility criteria were retrieved. Following a 1:1 propensity score matching 572 patients with cirrhosis were screened, and relevant clinical data were collected. PVT risk factors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. Variance inflation factors and correlation matrix plots were used to analyze multicollinearity among the variables. A nomogram was constructed to predict the probability of PVT based on independent risk factors for PVT, and its predictive performance was verified using a receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Finally, a network calculator was constructed based on the nomograms.

RESULTS

This study enrolled 286 cirrhosis patients with PVT and 286 without PVT. LASSO analysis revealed 13 variables as strongly associated with PVT occurrence. Multivariate logistic regression analysis revealed nine indicators as independent PVT risk factors, including etiology, ascites, gastroesophageal varices, platelet count, D-dimer, portal vein diameter, portal vein velocity, aspartate transaminase to neutrophil ratio index, and platelet-to-lymphocyte ratio. LASSO and correlation matrix plot results revealed no significant multicollinearity or correlation among the variables. A nomogram was constructed based on the screened independent risk factors. The nomogram had excellent predictive performance, with an area under the ROC curve of 0.821 and 0.829 in the training and testing groups, respectively. Calibration curves and DCA revealed its good clinical performance. Finally, the optimal cutoff value for the total nomogram score was 0.513. The sensitivity and specificity of the optimal cutoff values were 0.822 and 0.706, respectively.

CONCLUSION

A nomogram for predicting PVT occurrence was successfully developed and validated, and a network calculator was constructed. This can enable clinicians to rapidly and easily identify high PVT risk groups.

Risk factors, prognostic factors, and nomograms for distant metastasis in patients with diagnosed duodenal cancer: A population-based study

BACKGROUND

Duodenal cancer is one of the most common subtypes of small intestinal cancer, and distant metastasis (DM) in this type of cancer still leads to poor prognosis. Although nomograms have recently been used in tumor areas, no studies have focused on the diagnostic and prognostic evaluation of DM in patients with primary duodenal cancer.

AIM

To develop and evaluate nomograms for predicting the risk of DM and personalized prognosis in patients with duodenal cancer.

METHODS

Data on duodenal cancer patients diagnosed between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for DM in patients with duodenal cancer, and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors in duodenal cancer patients with DM. Two novel nomograms were established, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

A total of 2603 patients with duodenal cancer were included, of whom 457 cases (17.56%) had DM at the time of diagnosis. Logistic analysis revealed independent risk factors for DM in duodenal cancer patients, including gender, grade, tumor size, T stage, and N stage (P < 0.05). Univariate and multivariate COX analyses further identified independent prognostic factors for duodenal cancer patients with DM, including age, histological type, T stage, tumor grade, tumor size, bone metastasis, chemotherapy, and surgery (P < 0.05). The accuracy of the nomograms was validated in the training set, validation set, and expanded testing set using ROC curves, calibration curves, and DCA curves. The results of Kaplan-Meier survival curves (P < 0.001) indicated that both nomograms accurately predicted the occurrence and prognosis of DM in patients with duodenal cancer.

CONCLUSION

The two nomograms are expected as effective tools for predicting DM risk in duodenal cancer patients and offering personalized prognosis predictions for those with DM, potentially enhancing clinical decision-making.

Construction and validation of a neovascular glaucoma nomogram in patients with diabetic retinopathy after pars plana vitrectomy

BACKGROUND

Neovascular glaucoma (NVG) is likely to occur after pars plana vitrectomy (PPV) for diabetic retinopathy (DR) in some patients, thus reducing the expected benefit. Understanding the risk factors for NVG occurrence and building effective risk prediction models are currently required for clinical research.

AIM

To develop a visual risk profile model to explore factors influencing DR after surgery.

METHODS

We retrospectively selected 151 patients with DR undergoing PPV. The patients were divided into the NVG (NVG occurrence) and No-NVG (No NVG occurrence) groups according to the occurrence of NVG within 6 months after surgery. Independent risk factors for postoperative NVG were screened by logistic regression. A nomogram prediction model was established using R software, and the model’s prediction accuracy was verified internally and externally, involving the receiver operator characteristic curve and correction curve.

RESULTS

After importing the data into a logistic regression model, we concluded that a posterior capsular defect, preoperative vascular endothelial growth factor ≥ 302.90 pg/mL, glycosylated hemoglobin ≥ 9.05%, aqueous fluid interleukin 6 (IL-6) ≥ 53.27 pg/mL, and aqueous fluid IL-10 ≥ 9.11 pg/mL were independent risk factors for postoperative NVG in patients with DR (P < 0.05). A nomogram model was established based on the aforementioned independent risk factors, and a computer simulation repeated sampling method was used to internally and externally verify the nomogram model. The area under the curve (AUC), sensitivity, and specificity of the model were 0.962 [95% confidence interval (95%CI): 0.932-0.991], 91.5%, and 82.3%, respectively. The AUC, sensitivity, and specificity of the external validation were 0.878 (95%CI: 0.746-0.982), 66.7%, and 95.7%, respectively.

CONCLUSION

A nomogram constructed based on the risk factors for postoperative NVG in patients with DR has a high prediction accuracy. This study can help formulate relevant preventive and treatment measures.

A nomogram model of spectral CT quantitative parameters and clinical characteristics predicting lymphovascular invasion of gastric cancer

Objective

The study established a nomogram based on quantitative parameters of spectral computed tomography (CT) and clinical characteristics, aiming to evaluate its predictive value for preoperative lymphovascular invasion (LVI) in gastric cancer (GC).

Methods

From December 2019 to December 2021, 171 patients with pathologically confirmed GC were retrospectively collected with corresponding clinical data and spectral CT quantitative data. Patients were divided into LVI-positive and LVI-negative groups based on their pathological results. The univariate and multivariate logistic regression analyses were used to identify the risk factors and construct a nomogram. The calibration curve and receiver operating characteristic (ROC) curve were adopted to evaluate the predictive accuracy of nomogram.

Results

Four clinical characteristics or spectral CT quantitative parameters, including Borrmann classification (P = 0.039), CA724 (P = 0.007), tumor thickness (P = 0.031), and iodine concentration in the venous phase (VIC) (P = 0.004) were identified as independent factors for LVI in GC patients. The nomogram was established based on the four factors, which had a potent predictive accuracy in the training, internal validation and external validation cohorts, with the area under the ROC curve (AUC) of 0.864 (95% CI, 0.798-0.930), 0.964 (95% CI, 0.903-1.000) and 0.877 (95% CI, 0.759-0.996), respectively.

Conclusion

This study constructed a comprehensive nomogram consisting spectral CT quantitative parameters and clinical characteristics of GC, which exhibited a robust efficiency in predicting LVI in GC patients.

Sarcopenia in peripheral arterial disease: Establishing and validating a predictive nomogram based on clinical and computed tomography angiography indicators

Purpose

To establish, validate, and clinically evaluate a nomogram for predicting the risk of sarcopenia in patients with peripheral arterial disease (PAD) based on clinical and lower extremity computed tomography angiography (LE-CTA) imaging characteristics.

Methods

Clinical data and CTA imaging features from 281 PAD patients treated between January 1, 2019, and May 1, 2023, at two hospitals were retrospectively analyzed using binary logistic regression to identify the independent risk factors for sarcopenia. These identified risk factors were used to develop a predictive nomogram. The nomogram's effectiveness was assessed through various metrics, including the receiver operating characteristic (ROC) curve, area under the curve (AUC), concordance index (C-index), Hosmer-Lemeshow (HL) test, and calibration curve. Its clinical utility was demonstrated using decision curve analysis (DCA).

Results

Several key independent risk factors for sarcopenia in PAD patients were identified, namely age, body mass index (BMI), history of coronary heart disease (CHD), and white blood cell (WBC) count, as well as the severity of luminal stenosis (P < 0.05). The discriminative ability of the nomogram was supported by the C-index and an AUC of 0.810 (95% confidence interval: 0.757-0.862). A robust concordance between predicted and observed outcomes was reflected by the calibration curve. The HL test further affirmed the model's calibration with a P-value of 0.40. The DCA curve validated the nomogram's favorable clinical utility. Lastly, the model underwent internal validation.

Conclusions

A simple nomogram based on five independent factors, namely age, BMI, history of CHD, WBC count, and the severity of luminal stenosis, was developed to assist clinicians in estimating sarcopenia risk among PAD patients. This tool boasts impressive predictive capabilities and broad utility, significantly aiding clinicians in identifying high-risk individuals and enhancing the prognosis of PAD patients.

Clinical characteristics and prognosis of Klebsiella pneumoniae meningitis in adults

Background

Klebsiella pneumoniae is a causative agent of bacterial meningitis in adults. However, there is little information regarding this infection. Therefore, this study comprehensively analyzed the clinical characteristics and prognosis of Klebsiella pneumoniae meningitis (KPM) patients.

Methods

The clinical data of adult hospitalized patients with KPM were retrospectively collected from January 2015 to December 2022. The clinical characteristics and antibiotic resistance of KPM were evaluated. Meanwhile, a set of logistic regression models was constructed to identify prognostic factors for death. These prognostic factors were subsequently combined to develop a nomogram for predicting the risk of in-hospital mortality in individual patients. Finally, the receiver operating characteristic curve and calibrate plot were utilized to verify the performance of the nomogram.

Results

This study included 80 adult patients with KPM, 58 (72.5%) of whom were males. The mortality rate was 45%. Among them, 74 (92.5%) were diagnosed with healthcare-associated meningitis. Thirty-seven carbapenem-resistant Klebsiella pneumoniae (CRKP) strains were susceptible to tigecycline, polymyxin, and ceftazidime/avibactam. CRKP (OR = 9.825, 95%CI = 2.757-35.011, P < 0.001), length of stay (OR = 0.953, 95%CI = 0.921-0.986, P = 0.005), and C-reactive protein-to-prealbumin ratio (CRP/PA, OR = 3.053, 95%CI = 1.329-7.016, P = 0.009) were identified as predictive factors for mortality using multivariate logistic regression. Finally, a nomogram for death prediction was established. The area under the curve of this nomogram was 0.900 (95% CI = 0.828-0.971).

Conclusions

KPM is a fatal disease associated with high incidence of healthcare-associated infections and carbapenem resistance. Moreover, CRKP, length of stay, and CRP/PA were found to be independent predictors of mortality.

Platelet-lymphocyte ratio as a predictor of lymph node metastasis in small bowel cancer

The purpose of this research was to investigate the potential predictive value of preoperative systemic inflammatory indexes in identifying lymph node metastasis among patients diagnosed with small bowel cancer. A retrospective analysis of clinical data was conducted on small bowel cancer patients who underwent surgical treatment at the gastrointestinal surgery department of our hospital between January 2010 and June 2021. Patients were divided into groups based on the presence or absence of lymph node metastasis as confirmed by postoperative pathological results. The study compared the differences in preoperative inflammatory indexes and clinical data between the two groups using single factor analysis and multifactorial Logistic regression analysis. Furthermore, a nomogram model for predicting lymph node metastasis in colorectal cancer was constructed using R software and internally validated. The study sample consisted of 140 small bowel cancer patients,postoperative pathology confirmed lymph node metastasis in 72 cases. Univariate analysis results indicated associations between preoperative inflammatory indexes and clinical data with lymph node metastasis in small bowel cancer. Multifactorial logistic regression analysis revealed that gender, PLR, number of lymph node dissection, and lymphovascular invasion independently influenced lymph node metastasis in small bowel cancer patients. The developed nomogram model demonstrated a C-index of 0.855 (95% CI 0.792-0.917), with a calibrated prediction curve closely resembling the ideal curve. An elevated PLR is an independent risk factor for LNM in patients with small bowel cancer.

Age and mean platelet volume-based nomogram for predicting the therapeutic efficacy of metoprolol in Chinese pediatric patients with vasovagal syncope

BACKGROUND

Vasovagal syncope (VVS) is the most common type of orthostatic intolerance in children. We investigated whether platelet-related factors related to treatment efficacy in children suffering from VVS treated with metoprolol.

METHODS

Metoprolol-treated VVS patients were recruited. The median duration of therapy was three months. Patients were followed and divided into two groups, treament-effective group and treatment-ineffective group. Logistic and least absolute shrinkage selection operator regressions were used to examine treatment outcome variables. Receiver-operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, and decision curve analyses were used to evaluate the nomogram model.

RESULTS

Among the 72 patients who complete the follow-up, treatment-effective group and treatment-ineffective group included 42 (58.3%) and 30 (41.7%) cases, respectively. The patients in the treatment-effective group exhibited higher mean platelet volume (MPV) [(11.0 ± 1.0) fl vs. (9.8 ± 1.0) fl, P < 0.01] and platelet distribution width [12.7% (12.3%, 14.3%) vs. 11.3% (10.2%, 12.2%), P < 0.01] than those in the treatment-ineffective group. The sex ratio was significantly different (P = 0.046). A fit model comprising age [odds ratio (OR) = 0.766, 95% confidence interval (CI) = 0.594-0.987] and MPV (OR = 5.613, 95% CI = 2.297-13.711) might predict therapeutic efficacy. The area under the curve of the ROC and PR curves was computed to be 0.85 and 0.9, respectively. The P value of the Hosmer-Lemeshow test was 0.27. The decision curve analysis confirmed that managing children with VVS based on the predictive model led to a net advantage ranging from 0.01 to 0.58. The nomogram is convenient for clinical applications.

CONCLUSION

A novel nomogram based on age and MPV can predict the therapeutic benefits of metoprolol in children with VVS.

A chest CT-based nomogram for predicting survival in acute myeloid leukemia

BACKGROUND

The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML).

METHODS

952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements.

RESULTS

Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011).

CONCLUSIONS

In summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.

A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

BACKGROUND

To predict pathological complete response (pCR) in patients receiving neoadjuvant immunochemotherapy (nICT) for esophageal squamous cell carcinoma (ESCC), we explored the factors that influence pCR after nICT and established a combined nomogram model.

METHODS

We retrospectively included 164 ESCC patients treated with nICT. The radiomics signature and hematology model were constructed utilizing least absolute shrinkage and selection operator (LASSO) regression, and the radiomics score (radScore) and hematology score (hemScore) were determined for each patient. Using the radScore, hemScore, and independent influencing factors obtained through univariate and multivariate analyses, a combined nomogram was established. The consistency and prediction ability of the nomogram were assessed utilizing calibration curve and the area under the receiver operating factor curve (AUC), and the clinical benefits were assessed utilizing decision curve analysis (DCA).

RESULTS

We constructed three predictive models.The AUC values of the radiomics signature and hematology model reached 0.874 (95% CI: 0.819-0.928) and 0.772 (95% CI: 0.699-0.845), respectively. Tumor length, cN stage, the radScore, and the hemScore were found to be independent factors influencing pCR according to univariate and multivariate analyses (P < 0.05). A combined nomogram was constructed from these factors, and AUC reached 0.934 (95% CI: 0.896-0.972). DCA demonstrated that the clinical benefits brought by the nomogram for patients across an extensive range were greater than those of other individual models.

CONCLUSIONS

By combining CT radiomics, hematological factors, and clinicopathological characteristics before treatment, we developed a nomogram model that effectively predicted whether ESCC patients would achieve pCR after nICT, thus identifying patients who are sensitive to nICT and assisting in clinical treatment decision-making.

Predicting surgical benefit of primary tumor resection in patients with stage IV colorectal cancer

BACKGROUND

There exists continuous controversy regarding the benefit of primary tumor resection (PTR) for stage IV colorectal cancer (CRC) patients. Little is known about how to predict the patients' benefit from PTR. This study aimed to develop a tool for surgical benefit prediction.

METHODS

Stage IV CRC patients diagnosed between 2010 and 2015 from the Surveillance, Epidemiology and End Results database were included. Patients receiving PTR who survived longer than the median cancer-specific survival (CSS) time of those who did not undergo PTR were considered to benefit from surgery. Logistic regression analysis identified prognostic factors influencing surgical benefit, based on which a nomogram was constructed. The data of patients who underwent PTR from our institution was used for external validation. A user-friendly webserver was then built for convenient clinical use.

RESULTS

The median CSS of the PTR group was 23 months, significantly longer than that of the non-PTR group (7 months, P < 0.001). In the PTR group, 23.3% of patients did not benefit from surgery. Logistic regression analysis identified age, marital status, tumor location, CEA level, chemotherapy, metastasectomy, tumor size, tumor deposits, number of examined lymph nodes, N stage, histological grade and number of distant metastases as independently associated with surgical benefit. The established prognostic nomogram demonstrated satisfactory performance in both the internal and external validation.

CONCLUSION

PTR was associated with prolonged CSS in stage IV CRC. The proposed nomogram could be used as an evidenced-based platform for risk-to-benefit assessment to select appropriate patients for undergoing PTR.

Development and validation of a nomogram for assessing comorbidity and frailty in triage: a multicentre observational study

Assessing patient frailty in the Emergency Department (ED) is crucial; however, triage frailty and comorbidity assessment scores developed in recent years are unsatisfactory. The underlying causes of this phenomenon could reside in the nature of the tools used, which were not designed specifically for the emergency context and, thus, are difficult to adapt to the emergency environment. The objective of this study was to create and internally validate a nomogram for identifying different levels of patient frailty during triage. Multicenter, prospective, observational exploratory study conducted in two ED. The study was conducted from April 1 to October 31, 2022. Following the triage assessment, the nurse collected variables related to the patient's comorbidities and chronic conditions using a predefined form. The primary outcome was the 90-day mortality rate. A total of 1345 patients were enrolled in this study; 6% died within 90 days. In the multivariate analysis, the Charlson Comorbidity Index, an altered motor condition, an altered cognitive condition, an autonomous chronic condition, arrival in an ambulance, and a previous hospitalization within 90 days were independently associated with death. The internal validation of the nomogram reported an area under the receiver operating characteristic of 0.91 (95% CI 0.884-0.937). A nomogram was created for assessing comorbidity and frailty during triage and was demonstrated to be capable of determining comorbidity and frailty in the ED setting. Integrating a tool capable of identifying frail patients at the first triage assessment could improve patient stratification.

Navigating the future of diabetes: innovative nomogram models for predicting all-cause mortality risk in diabetic nephropathy

OBJECTIVE

This study aims to establish and validate a nomogram model for the all-cause mortality rate in patients with diabetic nephropathy (DN).

METHODS

We analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2007 to 2016. A random split of 7:3 was performed between the training and validation sets. Utilizing follow-up data until December 31, 2019, we examined the all-cause mortality rate. Cox regression models and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were employed in the training cohort to develop a nomogram for predicting all-cause mortality in the studied population. Finally, various validation methods were employed to assess the predictive performance of the nomogram, and Decision Curve Analysis (DCA) was conducted to evaluate the clinical utility of the nomogram.

RESULTS

After the results of LASSO regression models and Cox multivariate analyses, a total of 8 variables were selected, gender, age, poverty income ratio, heart failure, body mass index, albumin, blood urea nitrogen and serum uric acid. A nomogram model was built based on these predictors. The C-index values in training cohort of 3-year, 5-year, 10-year mortality rates were 0.820, 0.807, and 0.798. In the validation cohort, the C-index values of 3-year, 5-year, 10-year mortality rates were 0.773, 0.788, and 0.817, respectively. The calibration curve demonstrates satisfactory consistency between the two cohorts.

CONCLUSION

The newly developed nomogram proves to be effective in predicting the all-cause mortality risk in patients with diabetic nephropathy, and it has undergone robust internal validation.

Preoperative nomogram for predicting spread through air spaces in clinical-stage IA non-small cell lung cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography

PURPOSE

This study aims to assess the predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiological features and the maximum standardized uptake value (SUVmax) in determining the presence of spread through air spaces (STAS) in clinical-stage IA non-small cell lung cancer (NSCLC).

METHODS

A retrospective analysis was conducted on 180 cases of NSCLC with postoperative pathological assessment of STAS status, spanning from September 2019 to September 2023. Of these, 116 cases from hospital one comprised the training set, while 64 cases from hospital two formed the testing set. The clinical information, tumor SUVmax, and 13 related CT features were analyzed. Subgroup analysis was carried out based on tumor density type. In the training set, univariable and multivariable logistic regression analyses were employed to identify the most significant variables. A multivariable logistic regression model was constructed and the corresponding nomogram was developed to predict STAS in NSCLC, and its diagnostic efficacy was evaluated in the testing set.

RESULTS

SUVmax, consolidation-to-tumor ratio (CTR), and lobulation sign emerged as the best combination of variables for predicting STAS in NSCLC. Among these, SUVmax and CTR were identified as independent predictors for STAS prediction. The constructed prediction model demonstrated area under the curve (AUC) values of 0.796 and 0.821 in the training and testing sets, respectively. Subgroup analysis revealed a 2.69 times higher STAS-positive rate in solid nodules compared to part-solid nodules. SUVmax was an independent predictor for predicting STAS in solid nodular NSCLC, while CTR and an emphysema background were independent predictors for STAS in part-solid nodular NSCLC.

CONCLUSION

Our nomogram based on preoperative 18F-FDG PET/CT radiological features and SUVmax effectively predicts STAS status in clinical-stage IA NSCLC. Furthermore, our study highlights that metabolic parameters and CT variables associated with STAS differ between solid and part-solid nodular NSCLC.