Search

Background: To develop and evaluate the prognostic value of a comprehensive inflammatory biomarker for postoperative colorectal cancer (CRC) patients. Methods: A total of 646 CRC patients were recruited between August 2017 and December 2019 from Fujian Medical University Union Hospital, with follow-up data up to 2021. The least absolute shrinkage and selection operator method (LASSO) was used to select inflammation indicators in order to construct a comprehensive biomarker (named NSAP). The Cox regression model was utilized to analyze the association between the NSAP and the disease-free survival (DFS) of CRC. Predictive performance and clinical utility of prognostic models were evaluated by area under the curve (AUC) and decision curve analyses (DCAs). Results: During a median follow-up of 23 months, 95 clinical outcomes were observed, with a 1-year survival rate is 89.47%. A comprehensive inflammatory biomarker (NSAP) was established based on four blood indicators (including neutrophil-to-lymphocyte ratio (NLR), neutrophil×monocyte-to-lymphocyte ratio (SIRI), albumin-to-globulin ratio (AGR), and platelet-to-lymphocytes ratio (PLR)). Patients with a lower NSAP had significantly associated with better DFS of CRC (HR=0.53, 95%CI 0.32–0.89). Moreover, compared to a previously established model, the traditional TNM staging system or/and tumor markers, the nomogram based on NSAP displayed more excellent predictive ability (0.752 vs 0.597, 0.711 and 0.735, P < 0.05). DCAs also demonstrated that the established nomogram had better utility for decision making. Conclusions: Our study suggests that NSAP may be a useful comprehensive prognostic biomarker for predicting the DFS of CRC patients. The nomogram based on NSAP can be considered a valuable tool to estimate the prognosis of patients with CRC. [ABSTRACT FROM AUTHOR]

Background: The study aimed to build and validate practical nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with synovial sarcoma (SyS). Methods: A total of 893 eligible patients confirmed to have SyS between 2007 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training cohort (n = 448) and validation cohort (n = 445). Clinically independent prognostic and important factors were determined according to the Akaike information criterion in multivariate Cox regression models when developing the nomograms with the training cohort. The predictive accuracy of nomograms was bootstrapped validated internally and externally with the concordance index (C-index) and calibration curve. Decision curve analysis (DCA) was performed to compare the clinical usefulness between nomograms and American Joint Commission on Cancer (AJCC) staging system. Results: Two nomograms shared common indicators including age, insurance status, tumor site, tumor size, SEER stage, surgery, and radiation, while marital status and tumor site were only included into the OS nomogram. The C-index of nomograms for predicting OS and CSS was 0.819 (0.873–0.764) and 0.821 (0.876–0.766), respectively, suggesting satisfactory predictive performance. Internal and external calibration curves exhibited optimal agreement between the nomogram prediction and the actual survival. Additionally, DCA demonstrated that our nomograms had obvious superiority over the AJCC staging system with more clinical net benefits. Conclusions: Two nomograms predicting 3- and 5-year OS and CSS of SyS patients were successfully constructed and validated for the first time, with higher predictive accuracy and clinical values than the AJCC staging system regarding OS and CSS. [ABSTRACT FROM AUTHOR]

Objective To find out the risk genes related to lymph node metastasis of colon cancer and construct a nomogram model to predict lymph node metastasis. Methods Genome sequencing data were downloaded from TCGA and GEO databases, and candidate genes were screened by differential expressed gene analysis and LASSO regression. AIC was used to determine the optimal nomogram model. ROC curve, calibration curve and Hosmer-Lemeshow test were used to evaluate the accuracy of the model. Decision curve analysis was used to evaluate the clinical utility. Results Eleven genes which could effectively predict lymph node metastasis of colon cancer were obtained through LASSO regression. According to the results of stepwise regression, the model composed of age, pathological T stage, TH, CDH4, PNMA6A, TNNC1, KIR2DL4, STUM and SFTA2 had the minimum AIC value (440.4). The AUC value of internal evaluation was 0.800, and that of external verification was 0.664. In model evaluation, the calibration and Hosmer-Lemeshow test showed favorable performance. Decision curve analysis showed nomogram model could bring clinical benefits for predicting lymph nodes metastasis. Conclusion Eleven risk genes of lymph node metastasis of colon cancer are selected and a nomogram model is constructed. The model has favorable performance in discriminative and calibration abilities to help evaluate the status of lymph node metastasis of colon cancer patients.

BackgroundVestibular schwannoma (VS) is the most common benign tumor of the posterior fossa. The recurrence of VS has always received widespread attention. This study aimed to develop a nomogram to predict Recurrence-free survival (RFS) following resection of VS.MethodsA total of 425 patients with VS who underwent resection at the Department of Neurosurgery in Chinese PLA General Hospital between January 2014 and December 2020 were enrolled in this retrospective study. The medical records and follow-up data were collected. Cox regression analysis was used to screen prognostic factors and construct the nomogram. The predictive accuracy and clinical benefits of the nomogram were validated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).ResultsThe Cox regression analysis revealed that age (HR = 0.96; 95% CI 0.94 - 0.99; p < 0.01), EOR (HR = 4.65; 95% CI 2.22 - 9.74; p < 0.001), and Ki-67 (HR = 1.16; 95% CI 1.09 - 1.23; p < 0.001) were all significantly correlated with recurrence, and they were finally included in the nomogram model. The concordance index of the nomogram was 0.86. The areas under the curve (AUCs) of the nomogram model of 3-, 4- and 5-year were 0.912, 0.865, and 0.809, respectively. A well-fitted calibration curve was also generated for the nomogram model. The DCA curves also indicated that the nomogram model had satisfactory clinical utility compared to the single indicators.ConclusionsWe developed a nomogram that has high accuracy in predicting RFS in patients after resection of VS. All of the included prognostic factors are easy to obtain. The nomogram can improve the postoperative management of patients and assist clinicians in individualized clinical treatment. Furthermore, we generated a web-based calculator to facilitate clinical application: https://abc123-123.shinyapps.io/VS-RFS/.

BackgroundThe study aimed to build and validate practical nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with synovial sarcoma (SyS).MethodsA total of 893 eligible patients confirmed to have SyS between 2007 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training cohort (n = 448) and validation cohort (n = 445). Clinically independent prognostic and important factors were determined according to the Akaike information criterion in multivariate Cox regression models when developing the nomograms with the training cohort. The predictive accuracy of nomograms was bootstrapped validated internally and externally with the concordance index (C-index) and calibration curve. Decision curve analysis (DCA) was performed to compare the clinical usefulness between nomograms and American Joint Commission on Cancer (AJCC) staging system.ResultsTwo nomograms shared common indicators including age, insurance status, tumor site, tumor size, SEER stage, surgery, and radiation, while marital status and tumor site were only included into the OS nomogram. The C-index of nomograms for predicting OS and CSS was 0.819 (0.873–0.764) and 0.821 (0.876–0.766), respectively, suggesting satisfactory predictive performance. Internal and external calibration curves exhibited optimal agreement between the nomogram prediction and the actual survival. Additionally, DCA demonstrated that our nomograms had obvious superiority over the AJCC staging system with more clinical net benefits.ConclusionsTwo nomograms predicting 3- and 5-year OS and CSS of SyS patients were successfully constructed and validated for the first time, with higher predictive accuracy and clinical values than the AJCC staging system regarding OS and CSS.

Background and aims: Liver stiffness measurement (LSM) alone or its combination with platelet counts [Baveno-VI criteria (B6C)] is an important non-invasive tool to predict risk of clinical decompensation in patients with advanced compensated cirrhosis. We compared its utility to decide need for beta-blockers in this regard compared with hepatic venous pressure gradient (HVPG) estimation. Methods: Patients with compensated cirrhosis with available baseline HVPG and LSM were followed up over a median follow-up of 26 (IQR 7–55) months to determine onset of clinical decompensations. Optimal cut-off for LSM for predicting decompensations was identified and compared with HVPG of 10 mmHg and B6C. Feasibility of initiating beta-blockers based on these strategies was assessed using decision curve analysis (DCA) at different threshold probabilities of rates of clinical decompensation. Results: 626 patients (mean age: 50.8 ± 12.4 years, 72.4% males) related to alcohol (30.3%), non-alcoholic steatohepatitis (26.4%), hepatitis C (16.6%), hepatitis B (10.2%) were included. Altogether, 132 (21.0%) patients developed clinical decompensation. The time-dependant area under curve over 5 years for HVPG and LSM for predicting clinical decompensation was 0.716–0.742 and 0.709–0.784, respectively. A LSM of 22 kPa (Sensitivity-88.6%/Specificity-51.8%) had a similar negative predictive value but a higher positive predictive value [37.9% (LSM) vs.30.9% (HVPG)vs.30.6% (B6C), p = 0.026 at 3 years] for incident decompensations. On DCA, LSM-based strategy emerged superior for deciding beta-blocker initiation with 150, 47, and 18 additional patients meriting treatment identified over and above B6C and HVPG based strategy at 5%, 10%, 20% annual rates of clinical decompensation, respectively. Conclusion: Measurement of liver stiffness alone can be a useful alternative to B6C and HVPG in deciding need for beta-blockers to prevent decompensations in compensated cirrhosis. [ABSTRACT FROM AUTHOR]

To develop and validate a nomogram for predicting the risk of venous thromboembolism in critically ill patients with primary intracerebral hemorrhage. Patients ≥18 years old with primary intracerebral hemorrhage were screened within 24 hours of onset from January 2019 to April 2021. Univariate and multivariate logistic regression analyses were performed to screen out independent predictors that were significantly associated with venous thromboembolism. A nomogram was constructed based on the results of a multivariate regression analysis. Discrimination and calibration were used to evaluate performance of the nomogram. A decision curve analysis was used to assess its clinical utility. This study enrolled 369 patients. The nomogram included 3 predictors from the regression analysis: D-dimer, National Institutes of Health Stroke Scale score, and Glasgow Coma Scale score on admission. The area under the receiver operating characteristic curve was 0.794, indicating good discrimination of the nomogram. The nomogram demonstrated calibration curves with slight deviation from the ideal predictions. Decision curve analysis showed that the prediction nomogram was clinically useful. This nomogram comprising D-dimer, National Institutes of Health Stroke Scale score and Glasgow Coma Scale score on admission can accurately predict the risk of venous thromboembolism in critically ill patients with intracerebral hemorrhage. [ABSTRACT FROM AUTHOR]

Abstract Background Pelvic fractures are common among blunt trauma patients, and timely and accurate diagnosis can improve patient outcomes. However, it remains unclear whether physical examinations are sufficient in this context. This study aims to perform a systematic review and meta-analysis of studies on the diagnostic accuracy and clinical utility of physical examination for pelvic fracture among blunt trauma patients. Methods Studies were identified using the MEDLINE, EMBASE, and CENTRAL databases starting from the creation of the database to January 2020. A total of 20 studies (49,043 patients with 8300 cases [16.9%] of pelvic fracture) were included in the quality assessment and meta-analysis. Two investigators extracted the data and evaluated the risk of bias in each study. The meta-analysis involved a hierarchical summary receiver operating curve (ROC) model to calculate the diagnostic accuracy of the physical exam. Subgroup analysis assessed the extent of between-study heterogeneity. Clinical utility was assessed using decision curve analysis. Results The median prevalence of pelvic fracture was 10.5% (interquartile range, 5.1–16.5). The pooled sensitivity (and corresponding 95% confidence interval) of the hierarchical summary ROC parameters was 0.859 (0.761–0.952) at a given specificity of 0.920, which was the median value among the included studies. Subgroup analysis revealed that the pooled sensitivity among patients with a Glasgow Coma Scale score ≥ 13 was 0.933 (0.847–0.998) at a given specificity of 0.920. The corresponding value for patients with scores ≤ 13 was 0.761 (0.560–0.932). For threshold probability

Abstract Background Cervical cancer has long been a common malignance troubling women. However, there are few studies developing nomogram with comprehensive factors for the prognosis of cervical cancer. Hence, we aimed to build a nomogram to calculate the overall survival (OS) probability in patients with cervical cancer. Methods Data of 9876 female patients in SEER database and diagnosed as cervical cancer during 2010–2015, was retrospectively analyzed. Univariate and multivariate Cox proportional hazard regression model were applied to select predicted factors and a nomogram was developed to visualize the prediction model. The nomogram was compared with the FIGO stage prediction model. Harrell’s C-index, receiver operating curve, calibration plot and decision curve analysis were used to assess the discrimination, accuracy, calibration and clinical utility of the prediction models. Result Eleven independent prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, primary site surgery, regional lymph node surgery, radiotherapy and chemotherapy, were used to build the nomogram. The C-index of the nomogram was 0.826 (95% CI: 0.818 to 0.834), which was better than that of the FIGO stage prediction model (C-index: 0.785, 95% CI: 0.776 to 0.793). Calibration plot of the nomogram was well fitted in 3-year overall OS prediction, but overfitting in 5-year OS prediction. The net benefit of the nomogram was higher than the FIGO prediction model. Conclusion A clinical useful nomogram for calculating the overall survival probability in cervical cancer patients was developed. It performed better than the FIGO stage prediction model and could help clinicians to choose optimal treatments and precisely predict prognosis in clinical care and research.

Abstract Background/Aims Pancreatic ductal adenocarcinoma (PDAC) is associated with high mortality, even after surgical resection. The existing predictive models for survival have limitations. This study aimed to develop better nomograms for predicting overall survival (OS) and cancer‐specific survival (CSS) in PDAC patients after surgery. Methods A total of 6323 PDAC patients were retrospectively recruited from the Surveillance, Epidemiology, and End Results (SEER) database and randomly allocated into training, validation, and test cohorts. Multivariate Cox regression analysis was conducted to identify significant independent factors for OS and CSS, which were used for construction of nomograms. The performance was evaluated, validated, and compared with that of the 8th edition AJCC staging system. Results Ten independent factors were significantly correlated with OS and CSS. The 1‐, 3‐, and 5‐year OS rates were 40%, 20%, and 15%, and 1‐, 3‐, and 5‐year CSS rates were 45%, 24%, and 19%, respectively. The nomograms were calibrated well, with c‐indexes of 0.640 for OS and 0.643 for CSS, respectively. Notably, relative to the 8th edition AJCC staging system, the nomograms were able to stratify each AJCC stage into three prognostic subgroups for more robust risk stratification. Furthermore, the nomograms achieved significant clinical validity, exhibiting wide threshold probabilities and high net benefit. Performance assessment also showed high predictive accuracy and reliability. Conclusions The predictive ability and reliability of the established nomograms have been validated, and therefore, these nomograms hold potential as novel approaches to predicting survival and assessing survival risks for PDAC patients after surgery.

Abstract Background This study aims to develop functional nomograms to predict specific distant metastatic sites and overall survival (OS) of colorectal cancer (CRC) patients. Methods CRC case data were retrospectively recruited from a large population‐based public dataset. Nomograms were developed to predict the probabilities of specific distant metastatic sites and OS of CRC patients. The performance of nomogram was evaluated with the concordance index (C‐index), calibration curves, area under the curve (AUC), and decision curve analysis (DCA). Results A total of 142 343 cases were included in the current study. On the basis of univariate and multivariate analyses, clinicopathological features were correlated with specific distant metastatic sites and survival outcomes and were used to establish nomograms. The nomograms showed excellent accuracy in predicting specific distant metastatic sites. The C‐indexes for the prediction of liver, lung, bone, and brain metastases were 0.82 (95% confidence interval (CI), 0.81‐0.83), 0.80 (95% CI, 0.78‐0.81), 0.83 (95% CI, 0.79‐0.86), and 0.73 (95% CI, 0.72‐0.84), respectively. Then, a prognostic nomogram integrating clinicopathological features and specific distant metastatic sites was established to predict 1‐, 3‐, and 5‐year OS of CRC, with AUCs of 0.764 (95% CI, 0.741‐0.783), 0.762 (95% CI, 0.745‐0.781), and 0.745 (95% CI, 0.730‐0.761), respectively. DCA showed that the prognostic nomogram had a better clinical application value than current TNM staging system. Conclusions Based on clinicopathological features, original nomograms were constructed for clinicians to predict specific distant metastatic sites and OS of CRC patients. These models could help to support the postoperative personalized assessment.

Abstract Background Currently used performance measures for discrimination were not informative to determine the clinical benefit of predictor variables. The purpose was to evaluate if a former relevant predictor, kinesiophobia, remained clinically relevant to predict chronic occupational low back pain (LBP) in the light of a novel discriminative performance measure, Decision Curve Analysis (DCA), using the Net Benefit (NB). Methods Prospective cohort data (n = 170) of two merged randomized trials with workers with LBP on sickleave, treated with Usual Care (UC) were used for the analyses. An existing prediction model for chronic LBP with the variables ‘a clinically relevant change in pain intensity and disability status in the first 3 months’, ‘baseline measured pain intensity’ and ‘kinesiophobia’ was compared with the same model without the variable ‘kinesiophobia’ using the NB and DCA. Results Both prediction models showed an equal performance according to the DCA and NB. Between 10 and 95% probability thresholds of chronic LBP risk, both models were of clinically benefit. There were virtually no differences between both models in the improved classification of true positive (TP) patients. Conclusions This study showed that the variable kinesiophobia, which was originally included in a prediction model for chronic LBP, was not informative to predict chronic LBP by using DCA. DCA and NB have to be used more often to develop clinically beneficial prediction models in workers because they are more sensitive to evaluate the discriminate ability of prediction models.

Abstract Background The purpose of this study was to build functional nomograms based on significant clinicopathological features to predict cause-specific survival (CSS) and overall survival (OS) in patients with stage I–III colon cancer. Methods Data on patients diagnosed with stage I–III colon cancer between 2010 and 2015 were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were used to identify independent prognostic factors, which were used to construct nomograms to predict the probabilities of CSS and OS. The performance of the nomogram was assessed by C-indexes, receiver operating characteristic (ROC) curves and calibration curves. Decision curve analysis (DCA) was used to compare clinical usage between the nomogram and the tumor–node–metastasis (TNM) staging system. Results Based on the univariate and multivariate analyses, features that correlated with survival outcomes were used to establish nomograms for CSS and OS prediction. The nomograms showed favorable sensitivity at predicting 1-, 3-, and 5-year CSS and OS, with a C-index of 0.78 (95% confidence interval (CI) 0.77–0.80) for CSS and 0.74 (95% CI 0.73–0.75) for OS. Calibration curves and ROC curves revealed excellent predictive accuracy. The clinically and statistically significant prognostic performance of the nomogram generated with the entire group of patients and risk scores was validated by a stratified analysis. DCA showed that the nomograms were more clinically useful than TNM stage. Conclusion Novel nomograms based on significant clinicopathological characteristics were developed and can be used as a tool for clinicians to predict CSS and OS in stage I–III colon cancer patients. These models could help facilitate a personalized postoperative evaluation.

Background: The incidence and mortality rates of primary hepatocellular carcinoma (HCC) are high, and the conventional treatment is radiofrequency ablation (RFA) with transcatheter arterial chemoembolization (TACE); however, the 3-year survival rate is still low. Further, there are no visual methods to effectively predict their prognosis., Aim: To explore the factors influencing the prognosis of HCC after RFA and TACE and develop a nomogram prediction model., Methods: Clinical and follow-up information of 150 patients with HCC treated using RFA and TACE in the Hangzhou Linping Hospital of Traditional Chinese Medicine from May 2020 to December 2022 was retrospectively collected and recorded. We examined their prognostic factors using multivariate logistic regression and created a nomogram prognosis prediction model using the R software (version 4.1.2). Internal verification was performed using the bootstrapping technique. The prognostic efficacy of the nomogram prediction model was evaluated using the concordance index (CI), calibration curve, and receiver operating characteristic curve., Results: Of the 150 patients treated with RFA and TACE, 92 (61.33%) developed recurrence and metastasis. Logistic regression analysis identified six variables, and a predictive model was created. The internal validation results of the model showed a CI of 0.882. The correction curve trend of the prognosis prediction model was always near the diagonal, and the mean absolute error before and after internal validation was 0.021. The area under the curve of the prediction model after internal verification was 0.882 [95% confidence interval (95%CI): 0.820-0.945], with a specificity of 0.828 and sensitivity of 0.656. According to the Hosmer-Lemeshow test, χ 2 = 0.895. The predictive model demonstrated a satisfactory calibration, and the decision curve analysis demonstrated its clinical applicability.P = 0.895. The predictive model demonstrated a satisfactory calibration, and the decision curve analysis demonstrated its clinical applicability., Conclusion: The prognosis of patients with HCC after RFA and TACE is affected by several factors. The developed prediction model based on the influencing parameters shows a good prognosis predictive efficacy., Competing Interests: Conflict-of-interest statement: The authors declare no conflicts of interest., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background: Data regarding risk factors for premature coronary artery disease (PCAD) is scarce given that few research focus on it. This study aimed to develop and validate a clinical nomogram for PCAD patients in Guangzhou., Methods: We recruited 108 PCAD patients (female ≤65 years old and male ≤55 years old) and 96 healthy controls from Sun Yat-sen Memorial Hospital of Sun Yat-sen University between 01/01/2021 and 31/12/2022. Twenty potentially relevant indicators of PCAD were extracted. Next, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. The nomogram was developed based on the selected variables visually., Results: Independent risk factors, including body mass index (BMI), history of PCAD, glucose, Apolipoprotein A1(ApoA1), high density lipoprotein 2-cholesterol (HDL2-C), total cholesterol and triglyceride, were identified by LASSO and logistic regression analysis. The nomogram showed accurate discrimination (area under the receiver operator characteristic curve, ROC, 87.45 %, 95 % CI: 82.58 %-92.32 %). Decision curve analysis (DCA) suggested that the nomogram was clinical beneficial. HDL2, one risk factor, was isolated by a two-step discontinuous density-gradient ultracentrifugation method. And HDL2 from PCAD patients exhibited less 3 H-cholesterol efflux (22.17 % vs 26.64 %, P  < 0.05) and less delivery of NBD-cholesterol detecting by confocal microscope compared with healthy controls., Conclusions: In conclusion, the seven-factor nomogram can achieve a reasonable relationship with PCAD, and a large cohort were needed to enhance the credibility and effectiveness of our model in future., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Mild cognitive impairment (MCI) is the prodromal stage and an important risk factor of Alzheimer’s disease (AD). Interventions at the MCI stage are significant in reducing the occurrence of AD. However, there are still many obstacles to the screening of MCI, resulting in a large number of patients going undetected. Given the strong correlation between gastrointestinal function and neuropsychiatric disorders, the aim of this study is to develop a risk prediction model for MCI based on gastrointestinal myoelectrical activity. The Mini-Mental State Examination and electrogastroenterography were applied to 886 participants in western China. All participants were randomly assigned to the training and validation sets in a ratio of 7:3. In the training set, risk variables were screened using LASSO regression and logistic regression, and risk prediction models were built based on nomogram and decision curve analysis, then validation was performed. Eight predictors were selected in the training set, including four electrogastroenterography parameters (rhythm disturbance, dominant frequency and dominant power ratio of gastric channel after meal, and time difference of intestinal channel after meal). The area under the ROC curve for the prediction model was 0.74 in the training set and 0.75 in the validation set, both of which exhibited great prediction ability. Furthermore, decision curve analysis displayed that the net benefit was more desirable when the risk thresholds ranged from 15% to 35%, indicating that the nomogram was clinically usable. The model based on gastrointestinal myoelectrical activity has great significance in predicting the risk of MCI and is expected to be an alternative to scales assessment.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most prevalent gynecologic malignancies globally. The prognosis is abysmal once cervical cancer progresses to lymphatic metastasis. Anoikis, a specialized form of apoptosis induced by loss of cell adhesion to the extracellular matrix, plays a critical role. The prediction model based on anoikis-related genes (ARGs) expression and clinical data could greatly aid clinical decision-making. However, the relationship between ARGs and CESC remains unclear., Methods: ARGs curated from the GeneCards and Harmonizome portals were instrumental in delineating CESC subtypes and in developing a prognostic framework for patients afflicted with this condition. We further delved into the intricacies of the immune microenvironment and pathway enrichment across the identified subtypes. Finally, our efforts culminated in the creation of an innovative nomogram that integrates ARGs. The utility of this prognostic tool was underscored by Decision Curve Analysis (DCA), which illuminate its prospective benefits in guiding clinical interventions., Results: In our study, We discerned a set of 17 survival-pertinent, anoikis-related differentially expressed genes (DEGs) in CESC, from which nine were meticulously selected for the construction of prognostic models. The derived prognostic risk score was subsequently validated as an autonomous prognostic determinant. Through comprehensive functional analyses, we observed distinct immune profiles and drug response patterns among divergent prognostic stratifications. Further, we integrated the risk scores with the clinicopathological characteristics of CESC to develop a robust nomogram. DCA corroborated the utility of our model, demonstrating its potential to enhance patient outcomes through tailored clinical treatment strategies., Conclusion: The predictive signature, encompassing nine pivotal genes, alongside the meticulously constructed nomogram developed in this research, furnishes clinicians with a sophisticated tool for tailoring treatment strategies to individual patients diagnosed with CESC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Ying, Ding, Xing, Wang, Pan, Pan, Xiang and Liu.)

There is increased interest in the use of prediction models to guide clinical decision-making in orthopedics. Prediction models are typically evaluated in terms of their accuracy: discrimination (area-under-the-curve [AUC] or concordance index) and calibration (a plot of predicted vs. observed risk). But it can be hard to know how high an AUC has to be in order to be "high enough" to warrant use of a prediction model, or how much miscalibration would be disqualifying. Decision curve analysis was developed as a method to determine whether use of a prediction model in the clinic to inform decision-making would do more good than harm. Here we give a brief introduction to decision curve analysis, explaining the critical concepts of net benefit and threshold probability. We briefly review some prediction models reported in the orthopedic literature, demonstrating how use of decision curves has allowed conclusions as to the clinical value of a prediction model. Conversely, papers without decision curves were unable to address questions of clinical value. We recommend increased use of decision curve analysis to evaluate prediction models in the orthopedics literature. [ABSTRACT FROM AUTHOR]

Objective: Radical hysterectomy (RH) is the surgical standard for the treatment of the early-stage cervical cancer (CC). However, this procedure is associated with a high rate of adverse impact on the quality of the life of the patient. Since the rate of parametrial involvement (PI) is low for the patients with the early-stage CC, some authors believe that the patients with the early-stage CC may benefit from the less radical surgery. This study aims to estimate the incidence of the PI in the patients with the early-stage CC and establish a simple nomogram to identify a cohort of the patients with low risk of the PI who may benefit from the less radical surgery.Methods: All the patients who underwent the RH and pelvic lymphadenectomy were included from 2013 to 2018. The significant independent predictors were identified through the Cox regression analysis and then incorporated into a nomogram to predicate the PI. The calibration plots and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy of the nomogram.Results: A total of 4,533 patients met the inclusion criteria and 441 women (9.7%) had the PI. The positive PI rate in the ≤2 cm group (1.2%) was significantly lower compared to >2– ≤4 cm (6.2%) or >4 cm (22.4%) groups. The multivariate analyses revealed that tumor size (p = 0.002), lymphovascular space invasion (LVSI) (p = 0.001), vaginal involvement (VI) (p < 0.001), status of the pelvic lymph nodes (PLNs) (p = 0.001), and depth of stromal invasion (DSI) (p < 0.001) were the independent prognostic factors of the PI. Finally, the five variables were combined to construct the nomogram model. The concordance indexes (C-indexes) of the PI were 0.756 (95% CI 0.726–0.786) for the internal validation and 0.729 (95% CI 0.678–0.780) for the external validation. The calibration plots further showed good consistency between the nomogram prediction and the actual observation.Conclusion: This study confirmed that the patients with tumor size 2 cm or smaller were at very low risk for the PI. If other variables such as negative LVSI, DSI

BackgroundClinically relevant postoperative pancreatic fistula (CR-POPF) remains a severe and challenging complication of pancreaticoduodenectomy (PD). This study aimed to establish a novel postoperative nomogram-based diagnostic model for the early detection of CR-POPF in patients subjected to PD.MethodsConsecutive patients who underwent PD in Zhongshan Hospital, Fudan University from December 2018 to October 2020 were retrospectively enrolled. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for CR-POPF. Then, a novel predictive nomogram was established accordingly.ResultsAmong the consecutive 176 patients who underwent PD, 37 (21.1%) patients developed CR-POPF. Through univariate and multivariate analyses, the drain amylase (P = 0.002), serum creatinine (P = 0.009), and serum C reactive protein (P = 0.045) at postoperative day 1 (POD1) as well as the neutrophil count (P = 0.025) and temperature (P = 0.025) at POD3 were identified as independent risk factors for CR-POPF. Based on this, a novel predictive nomogram containing these factors was constructed to predict the probability of CR-POPF after PD. The formulated nomogram showed better performance to detect CR-POPF after PD with a sensitivity of 0.784, specificity of 0.770, positive predictive value of 0.475, and negative predictive value of 0.930 when compared to other predictors. In addition, the predictive value of the nomogram was assessed by a concordance index of 0.814 (95% CI, 0.736–0.892), which was significantly higher than indicators alone. This was further validated and depicted by decision curve analysis and clinical impact curve.ConclusionThis study established a diagnostic nomogram of postoperative objective parameters that can predict the development of CR-POPF after PD with a good discriminative ability and predictive accuracy.

PurposeOur study aimed to establish and validate prognostic nomograms based on gross tumor volume (GTV) and cervical nodal volume (CNV) for nasopharyngeal carcinoma (NPC) patients treated with two cycles of concurrent chemoradiotherapy (CCRT).MethodsFrom 2012 to 2015, 620 eligible patients who received radical treatment at the Cancer Hospital of Shantou University Medical College were recruited for a nomogram study. Variables were determined in a training set of 463 patients from 2012 to 2014 by X-tile analysis, univariate and multivariate Cox proportional hazard analyses, and the least absolute shrinkage and selection operator (LASSO). Another cohort of 157 patients in 2015 was validated with bootstrap resampling. The concordance index (C-index) and calibration curves were applied to assess its predictive discriminative and accuracy ability, while decision curve analysis (DCA), X-tile analysis and Kaplan–Meier curve for clinical application.ResultsIndependent prognostic variables for overall survival (OS) were age, GTV, CNV, cranial nerve, positive cervical lymph node laterality below the caudal border of cricoid cartilage (LNBC), and were selected for the nomogram. Optimal prognostic factors including Karnofsky performance status (KPS), age, GTV, CNV, LNBC were incorporated in the nomogram for progression-free survival (PFS). In the training set, the C-index of our nomograms for OS and PFS were 0.755 (95% CI, 0.704 to 0.807) and 0.698 (95% CI, 0.652 to 0.744). The calibration curve showed good agreement between nomogram-predicted and actual survival. DCA indicated that our nomograms were of clinical benefit.ConclusionOur nomograms are capable of effective prognostic prediction for patients with NPC.

Background and aims: Highly accurate noninvasive methods for predicting gastroesophageal varices needing treatment (VNT) are desired. Radiomics is a newly emerging technology of image analysis. This study aims to develop and validate a novel noninvasive method based on radiomics for predicting VNT in cirrhosis. Methods: In this retrospective–prospective study, a total of 245 cirrhotic patients were divided as the training set, internal validation set and external validation set. Radiomics features were extracted from portal-phase computed tomography (CT) images of each patient. A radiomics signature (Rad score) was constructed with the least absolute shrinkage and selection operator algorithm and tenfold cross-validation in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. Results: The Rad score, consisting of 14 features from the gastroesophageal region and 5 from the splenic hilum region, was effective for VNT classification. The diagnostic performance was further improved by combining the Rad score with platelet counts, achieving an AUC of 0.987 (95% CI 0.969–1.00), 0.973 (95% CI 0.939–1.00) and 0.947 (95% CI 0.876–1.00) in the training set, internal validation set and external validation set, respectively. In efficacy and safety assessment, the radiomics nomogram could spare more than 40% of endoscopic examinations with a low risk of missing VNT (< 5%), and no more than 8.3% of unnecessary endoscopic examinations still be performed. Conclusions: In this study, we developed and validated a novel, diagnostic radiomics-based nomogram which is a reliable and noninvasive method to predict VNT in cirrhotic patients. Clinical trials registration: NCT04210297. [ABSTRACT FROM AUTHOR]

Background: Clinically relevant postoperative pancreatic fistula (CR-POPF) remains a severe and challenging complication of pancreaticoduodenectomy (PD). This study aimed to establish a novel postoperative nomogram-based diagnostic model for the early detection of CR-POPF in patients subjected to PD. Methods: Consecutive patients who underwent PD in Zhongshan Hospital, Fudan University from December 2018 to October 2020 were retrospectively enrolled. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for CR-POPF. Then, a novel predictive nomogram was established accordingly. Results: Among the consecutive 176 patients who underwent PD, 37 (21.1%) patients developed CR-POPF. Through univariate and multivariate analyses, the drain amylase (P = 0.002), serum creatinine (P = 0.009), and serum C reactive protein (P = 0.045) at postoperative day 1 (POD1) as well as the neutrophil count (P = 0.025) and temperature (P = 0.025) at POD3 were identified as independent risk factors for CR-POPF. Based on this, a novel predictive nomogram containing these factors was constructed to predict the probability of CR-POPF after PD. The formulated nomogram showed better performance to detect CR-POPF after PD with a sensitivity of 0.784, specificity of 0.770, positive predictive value of 0.475, and negative predictive value of 0.930 when compared to other predictors. In addition, the predictive value of the nomogram was assessed by a concordance index of 0.814 (95% CI, 0.736–0.892), which was significantly higher than indicators alone. This was further validated and depicted by decision curve analysis and clinical impact curve. Conclusion: This study established a diagnostic nomogram of postoperative objective parameters that can predict the development of CR-POPF after PD with a good discriminative ability and predictive accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Our study aimed to establish and validate prognostic nomograms based on gross tumor volume (GTV) and cervical nodal volume (CNV) for nasopharyngeal carcinoma (NPC) patients treated with two cycles of concurrent chemoradiotherapy (CCRT). Methods: From 2012 to 2015, 620 eligible patients who received radical treatment at the Cancer Hospital of Shantou University Medical College were recruited for a nomogram study. Variables were determined in a training set of 463 patients from 2012 to 2014 by X-tile analysis, univariate and multivariate Cox proportional hazard analyses, and the least absolute shrinkage and selection operator (LASSO). Another cohort of 157 patients in 2015 was validated with bootstrap resampling. The concordance index (C-index) and calibration curves were applied to assess its predictive discriminative and accuracy ability, while decision curve analysis (DCA), X-tile analysis and Kaplan–Meier curve for clinical application. Results: Independent prognostic variables for overall survival (OS) were age, GTV, CNV, cranial nerve, positive cervical lymph node laterality below the caudal border of cricoid cartilage (LNBC), and were selected for the nomogram. Optimal prognostic factors including Karnofsky performance status (KPS), age, GTV, CNV, LNBC were incorporated in the nomogram for progression-free survival (PFS). In the training set, the C-index of our nomograms for OS and PFS were 0.755 (95% CI, 0.704 to 0.807) and 0.698 (95% CI, 0.652 to 0.744). The calibration curve showed good agreement between nomogram-predicted and actual survival. DCA indicated that our nomograms were of clinical benefit. Conclusion: Our nomograms are capable of effective prognostic prediction for patients with NPC. [ABSTRACT FROM AUTHOR]

Abstract Background Clinical outcome of adrenocortical carcinoma (ACC) varies because of its heterogeneous nature and reliable prognostic prediction model for adult ACC patients is limited. The objective of this study was to develop and externally validate a nomogram for overall survival (OS) prediction in adult patients with ACC after surgery. Methods Based on the data from the Surveillance Epidemiology, and End Results (SEER) database, adults patients diagnosed with ACC between January 1988 and December 2015 were identified and classified into a training set, comprised of 404 patients diagnosed between January 2007 and December 2015, and an internal validation set, comprised of 318 patients diagnosed between January 1988 and December 2006. The endpoint of this study was OS. The nomogram was developed using a multivariate Cox proportional hazards regression algorithm in the training set and its performance was evaluated in terms of its discriminative ability, calibration, and clinical usefulness. The nomogram was then validated using the internal SEER validation, also externally validated using the Cancer Genome Atlas set (TCGA, 82 patients diagnosed between 1998 and 2012) and a Chinese multicenter cohort dataset (82 patients diagnosed between December 2002 and May 2018), respectively. Results Age at diagnosis, T stage, N stage, and M stage were identified as independent predictors for OS. A nomogram incorporating these four predictors was constructed using the training set and demonstrated good calibration and discrimination (C-index 95% confidence interval [CI], 0.715 [0.679–0.751]), which was validated in the internal validation set (C-index [95% CI], 0.672 [0.637–0.707]), the TCGA set (C-index [95% CI], 0.810 [0.732–0.888]) and the Chinese multicenter set (C-index [95% CI], 0.726 [0.633–0.819]), respectively. Encouragingly, the nomogram was able to successfully distinguished patients with a high-risk of mortality in all enrolled patients and in the subgroup analyses. Decision curve analysis indicated that the nomogram was clinically useful and applicable. Conclusions The study presents a nomogram that incorporates clinicopathological predictors, which can accurately predict the OS of adult ACC patients after surgery. This model and the corresponding risk classification system have the potential to guide therapy decisions after surgery.

Abstract Background Decision curve analysis is a method to evaluate prediction models and diagnostic tests that was introduced in a 2006 publication. Decision curves are now commonly reported in the literature, but there remains widespread misunderstanding of and confusion about what they mean. Summary of commentary In this paper, we present a didactic, step-by-step introduction to interpreting a decision curve analysis and answer some common questions about the method. We argue that many of the difficulties with interpreting decision curves can be solved by relabeling the y-axis as “benefit” and the x-axis as “preference.” A model or test can be recommended for clinical use if it has the highest level of benefit across a range of clinically reasonable preferences. Conclusion Decision curves are readily interpretable if readers and authors follow a few simple guidelines.

Abstract Background Lymph node status and liver metastasis (LIM) are important in determining the prognosis of early colon carcinoma. We attempted to develop and validate nomograms to predict lymph node metastasis (LNM) and LIM in patients with early colon carcinoma. Methods A total of 32,819 patients who underwent surgery for pT1 or pT2 colon carcinoma were enrolled in the study based on their records in the SEER database. Risk factors for LNM and LIM were assessed based on univariate and multivariate binary logistic regression. The C-index and calibration plots were used to evaluate LNM and LIM model discrimination. The predictive accuracy and clinical values of the nomograms were measured by decision curve analysis. The predictive nomograms were further validated in the internal testing set. Results The LNM nomogram, consisting of seven features, achieved the same favorable prediction efficacy as the five-feature LIM nomogram. The calibration curves showed perfect agreement between nomogram predictions and actual observations. The decision curves indicated the clinical usefulness of the prediction nomograms. Receiver operating characteristic curves indicated good discrimination in the training set (area under the curve [AUC] = 0.667, 95% CI 0.661–0.673) and the testing set (AUC = 0.658, 95% CI 0.649–0.667) for the LNM nomogram and encouraging performance in the training set (AUC = 0.766, 95% CI 0.760–0.771) and the testing set (AUC = 0.825, 95% CI 0.818–0.832) for the LIM nomogram. Conclusion Novel validated nomograms for patients with early colon carcinoma can effectively predict the individualized risk of LNM and LIM, and this predictive power may help doctors formulate suitable individual treatments.

Zhiyu Xiao1,2,*, Yongcong Yan1,2,*, Qianlei Zhou1,2,*, Haohan Liu1,2, Pinbo Huang1,2, Qiming Zhou1,2, Changliang Lai1,2, Jianlong Zhang1,2, Jie Wang1,2, Kai Mao1,21Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, People’s Republic of China; 2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, People’s Republic of China*These authors contributed equally to this work Background: We attempted to construct and validate novel nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in patients with hepatocellular carcinoma (HCC).Methods: Models were established using a discovery set (n=10,262) obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Based on univariate and multivariate Cox regression analyses, we identified independent risk factors for OS and CSS. Concordance indexes (c-indexes) and calibration plots were used to evaluate model discrimination. The predictive accuracy and clinical values of the nomograms were measured by decision curve analysis (DCA).Results: Our OS nomogram with a c-index of 0.753 (95% confidence interval (CI), 0.745–0.761) was based on age, sex, race, marital status, histological grade, TNM stage, tumor size, and surgery performed, and it performed better than TNM stage. Our CSS nomogram had a c-index of 0.748 (95% CI, 0.740–0.756). The calibration curves fit well. DCA showed that the two nomograms provided substantial clinical value. Internal validation produced c-indexes of 0.758 and 0.752 for OS and CSS, respectively, while external validation in the Sun Yat-sen Memorial Hospital (SYMH) cohort produced a c-indexes of 0.702 and 0.686 for OS and CSS, respectively.Conclusions: We have developed nomograms that enable more accurate individualized predictions of OS and CSS to help doctors better formulate individual treatment and follow-up management strategies.Keywords: surveillance, epidemiology and end results, overall survival, cancer-specific survival, decision curve analysis

Shi-Long Zhang,1,* Zhi-Ming Wang,2,3,* Wen-Rong Wang,4 Xin Wang,5 Yu-Hong Zhou21Institute of Fudan-Minhang Academic Health System, Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, 201199, People’s Republic of China; 2Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China; 3Department of Medical Oncology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, 361000, People’s Republic of China; 4Faculty of Physical Education, Shandong Normal University, Jinan, 250014, People’s Republic of China; 5Department of Acupuncture and Moxibustion, Central Hospital of Shanghai, Xuhui District, Shanghai, 200031, People’s Republic of China*These authors contributed equally to this workBackground: The aim of the study was to build and validate practical nomograms to better predict the overall survival (OS) and cancer-specific survival (CSS) of the patients with soft tissue sarcomas (STS) who underwent surgery.Methods: Patient data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified 8804 patients who underwent surgery with STS between 2007 and 2015, and randomly divided them into the training (n=6164) and validation (n=2640) cohorts. The Cox regression analysis and cumulative incidence function were performed to identify the independent prognostic factors associated with OS and CSS, respectively. The performance of the nomograms was evaluated using Harrell’s concordance index (C-index) and the calibration curves. Decision curve analysis (DCA) was introduced to compare the clinical practicality between the nomograms and the AJCC staging system.Results: Eight independent prognostic factors for OS and seven for CSS were determined and then used to build the nomograms for 3- and 5-year OS and CSS, respectively. The C-indexes of the nomograms for predicting OS were 0.788 in the internal validation and 0.823 in external validation, significantly higher than C-index of the AJCC staging system (P

Background/AimsPancreatic ductal adenocarcinoma (PDAC) can occur in different parts of the pancreas. This study aimed to identify clinicopathological characteristics independently correlated with the prognosis of PDAC of the pancreatic head/uncinate (PHC) or body-tail (PBTC), and to develop novel nomograms for predicting cancer-specific survival (CSS) according to different primary cancer locations.Methods1160 PDAC patients were retrospectively enrolled and assigned to training and test sets with each set divided into PHC and PBTC groups. Comparative analysis of clinicopathologic characteristics, survival analysis, and multivariate analysis were performed. Independent factors were identified and used for constructing nomograms. The performance of the nomograms was validated in the test set.ResultsPrimary tumor location was an independent risk factor for prognosis of PDAC after surgery. Specially, gender, fasting blood glucose, and preoperative cancer antigen 19-9 were significantly associated with prognosis of PHC, whereas age, body mass index, and lymph nodes were significantly correlated with the prognosis of PBTC. A significant difference in prognosis was found between PHC and PBTC in stage Ia and stage III. Three nomograms were established for predicting the prognosis for PDAC, PHC, and PBTC. Notably, these nomograms were calibrated modestly (c-indexes of 0.690 for PDAC, 0.669 for PHC, and 0.704 for PBTC), presented better accuracy and reliability than the 8th AJCC staging system, and achieved clinical validity.ConclusionsPHC and PBTC share the differential clinical-pathological characteristics and survival. The nomograms show good performance for predicting prognosis in PHC and PBTC. Therefore, these nomograms hold potential as novel approaches for predicting survival of PHC and PBTC patients after surgery.

BackgroundSubchorionic hematoma (SCH) is common in early pregnancy achieved by in vitro fertilization-embryo transfer/frozen embryo transfer (IVF-ET/FET), and is associated with adverse obstetric outcomes. However, there are no methods known to accurately predict the occurrence of SCH.ObjectiveTo establish a nomogram prediction model for predicting the risk of SCH in early pregnancy with IVF-ET/FET and to analyze pregnancy outcomes of patients with SCH.MethodsPatients who underwent IVF-ET/FET treatment and were diagnosed with clinical pregnancy were enrolled in our study. A total of 256 patients with SCH were enrolled in the SCH group, and 526 patients without SCH in the control group. Logistic regression was used to screen risk factors for SCH, and the nomogram was developed according to the regression coefficient of relevant variables. Discrimination, effect, calibration, and the predictive model’s clinical usefulness were assessed using the C-index, the area under the receiver operating characteristic standard curve, calibration plot, and decision curve analysis. Internal validation was assessed using bootstrapping validation. The effects of SCH on pregnancy outcomes were analyzed.ResultsA multivariate logistic regression analysis showed that fresh embryo transfer, polycystic ovary syndrome, hydrosalpinx, and thin endometrium were risk factors affecting the occurrence of SCH. Based on the above factors, a predictive model for the risk of SCH was created. The model displayed good discrimination, with a C-index of 0.783 (95% confidence interval: 0.750–0.816), area under the receiver operating characteristic standard curve of 0.783, and good calibration. A high C-index value of 0.765 could still be reached in the interval validation. Decision curve analysis showed that the nomogram was clinically useful when the intervention was decided at the SCH possibility threshold of 4%–87%. For patients with successful deliveries, the occurrence of SCH did not influence the gestational weeks of delivery, mode of delivery, preterm birth, height, and weight of the newborn.ConclusionWe screened the risk factors for SCH in patients who underwent IVF-ET/FET treatment. Successful establishment of a nomogram can effectively predict the occurrence of SCH. Furthermore, the incidence of miscarriage is higher in patients with SCH.

Background: The Kidney Failure Risk Equation (KFRE) predicts the 2- and 5-year risk of end-stage renal disease (ESRD) in patients with chronic kidney disease (CKD) stages 3a-5. Its predictive performance in advanced CKD and in specific disease aetiologies requires further exploration. This study validates the 4- and 8-variable KFREs in an advanced CKD population in the United Kingdom by evaluating discrimination, calibration and clinical utility.Methods: Patients enrolled in the Salford Kidney Study who were referred to the Advanced Kidney Care Service (AKCS) clinic at Salford Royal NHS Foundation Trust between 2011 and 2018 were included. The 4- and 8-variable KFREs were calculated on the first AKCS visit and the observed events of ESRD (dialysis or pre-emptive transplantation) within 2- and 5-years were the primary outcome. The area under the receiver operator characteristic curve (AUC) and calibration plots were used to evaluate discrimination and calibration respectively in the whole cohort and in specific disease aetiologies: diabetic nephropathy, hypertensive nephropathy, glomerulonephritis, autosomal dominant polycystic kidney disease (ADPKD) and other diseases. Clinical utility was assessed with decision curve analyses, comparing the net benefit of using the KFREs against estimated glomerular filtration rate (eGFR) cut-offs of < 20 ml/min/1.73m2 and < 15 ml/min/1.73m2 to guide further treatment.Results: A total of 743 patients comprised the 2-year analysis and 613 patients were in the 5-year analysis. Discrimination was good in the whole cohort: the 4-variable KFRE had an AUC of 0.796 (95% confidence interval [CI] 0.762-0.831) for predicting ESRD at 2-years and 0.773 (95% CI 0.736-0.810) at 5-years, and there was good-to-excellent discrimination across disease aetiologies. Calibration plots revealed underestimation of risk at 2-years and overestimation of risk at 5-years, especially in high-risk patients. There was, however, underestimation of risk in patients with ADPKD for all KFRE calculations. The predictive accuracy was similar between the 4- and 8-variable KFREs. Finally, compared to eGFR-based thresholds, the KFRE was the optimal tool to guide further care based on decision curve analyses.Conclusions: The 4- and 8-variable KFREs demonstrate adequate discrimination and calibration for predicting ESRD in an advanced CKD population and, importantly, can provide better clinical utility than using an eGFR-based strategy to inform decision-making. [ABSTRACT FROM AUTHOR]

Background: The incidence of endometrial cancer has tended to increase in recent years. However, competing risk nomogram combining comprehensive factors for endometrial cancer patients treated with hysterectomy is still scarce. Therefore, we aimed to build a competing risk nomogram predicting cancer‐specific mortality for endometrial cancer patients treated with hysterectomy. Methods: Patients diagnosed with endometrial cancer between 2010 and 2012 were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database. Competing risk model was performed to select prognostic variables to build the competing risk nomogram to predict the cumulative 3‐ and 5‐year incidences of endometrial cancer‐specific mortality. Harrell's C‐index, receiver operating characteristic (ROC) curve, and calibration plot were used in the internal validation. And decision curve analysis was applied to evaluate clinical utility. Results: A total of 10,447 patients were selected for analysis. The competing risk nomogram identified eight prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, and number of regional nodes positive. The C‐index of the competing risk nomogram was 0.857 (95% confidence interval [CI]: 0.854–0.859), and the calibration plots were adequately fitted. When the threshold probabilities were between 1% and 57% for 3‐year prediction and between 2% and 67% for 5‐year prediction, the competing risk nomogram was of good clinical utility. Conclusions: A competing risk nomogram for endometrial cancer patients treated with hysterectomy was successfully built and internally validated. It was an accurately predicted and clinical useful tool, which could play an important role in consulting and health care management of endometrial cancer patients. [ABSTRACT FROM AUTHOR]

Surgical risk calculators (SRCs) have been developed for estimation of postoperative complications but do not directly inform decision-making. Decision curve analysis (DCA) is a method for evaluating prediction models, measuring their utility in guiding decisions. We aimed to analyze the utility of SRCs to guide both preoperative and postoperative management of patients undergoing hepatopancreaticobiliary surgery by using DCA. A single-institution, retrospective review of patients undergoing hepatopancreaticobiliary operations between 2015 and 2017 was performed. Estimation of postoperative complications was conducted using the American College of Surgeons SRC [ACS-SRC] and the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator; risks were compared with observed outcomes. DCA was used to model optimal patient selection for risk prevention strategies and to compare the relative performance of the ACS-SRC and POTTER calculators. A total of 994 patients were included in the analysis. C-statistics for the ACS-SRC prediction of 12 postoperative complications ranged from 0.546 to 0.782. DCA revealed that an ACS-SRC–guided readmission prevention intervention, when compared with an all-or-none approach, yielded a superior net benefit for patients with estimated risk between 5% and 20%. Comparison of SRCs for venous thromboembolism intervention demonstrated superiority of the ACS-SRC for thresholds for intervention between 2% and 4% with the POTTER calculator performing superiorly between 4% and 8% estimated risk. SRCs can be used not only to predict complication risk but also to guide risk prevention strategies. This methodology should be incorporated into external validations of future risk calculators and can be applied for institution-specific quality improvement initiatives to improve patient outcomes. • Surgical risk calculators predict complications but do not inform clinical practice • Decision curve analysis (DCA) utilizes risk to explore benefits of intervention • We applied DCA to understand the net benefit of a risk-guided intervention • DCA identifies a range of risk in which intervention yields a net benefit • DCA expands the clinical utility of calculators and may inform quality initiatives [ABSTRACT FROM AUTHOR]

Background: The established clinical risk factors for post-traumatic intracranial bleeding have not been evaluated in patients receiving DOACs yet.Aim: Evaluating the association between clinic and patient characteristics and post-traumatic intracranial bleeding (ICH) in patients with mild traumatic brain injury (MTBI) and DOACs.Methods: This is a retrospective observational study conducted on three Emergency Departments. Multivariate analysis provided association in terms of OR with the risk of ICH. The performance of the multivariate model, described in a nomogram, has been tested with discrimination and decision curve analysis.Results: Of 473 DOACs patients with MTBI, 8.5% had post-traumatic ICH. On multivariable analysis, major dynamics (odds ratio [OR] 6.255), post-traumatic amnesia (OR 3.961), post-traumatic loss of consciousness (LOC, OR 7.353), Glasgow Coma Scale (GCS) score < 15 (OR 3.315), post-traumatic headache (OR 4.168) and visible trauma above the clavicles (OR 3.378) were associated with a higher likelihood of ICH. The multivariate model, used for the nomogram construction, showed a good performance (AUC bias corrected with 5000 bootstraps resample 0.78). The DCAs showed a net clinical benefit of the prognostic model.Conclusions: Clinical risk factors can be used in DOACs patients to better define the risk of post-traumatic ICH. [ABSTRACT FROM AUTHOR]

Background/Aims: Pancreatic ductal adenocarcinoma (PDAC) can occur in different parts of the pancreas. This study aimed to identify clinicopathological characteristics independently correlated with the prognosis of PDAC of the pancreatic head/uncinate (PHC) or body-tail (PBTC), and to develop novel nomograms for predicting cancer-specific survival (CSS) according to different primary cancer locations. Methods: 1160 PDAC patients were retrospectively enrolled and assigned to training and test sets with each set divided into PHC and PBTC groups. Comparative analysis of clinicopathologic characteristics, survival analysis, and multivariate analysis were performed. Independent factors were identified and used for constructing nomograms. The performance of the nomograms was validated in the test set. Results: Primary tumor location was an independent risk factor for prognosis of PDAC after surgery. Specially, gender, fasting blood glucose, and preoperative cancer antigen 19-9 were significantly associated with prognosis of PHC, whereas age, body mass index, and lymph nodes were significantly correlated with the prognosis of PBTC. A significant difference in prognosis was found between PHC and PBTC in stage Ia and stage III. Three nomograms were established for predicting the prognosis for PDAC, PHC, and PBTC. Notably, these nomograms were calibrated modestly (c-indexes of 0.690 for PDAC, 0.669 for PHC, and 0.704 for PBTC), presented better accuracy and reliability than the 8th AJCC staging system, and achieved clinical validity. Conclusions: PHC and PBTC share the differential clinical-pathological characteristics and survival. The nomograms show good performance for predicting prognosis in PHC and PBTC. Therefore, these nomograms hold potential as novel approaches for predicting survival of PHC and PBTC patients after surgery. [ABSTRACT FROM AUTHOR]

Background and aim: Most of patients with erosive esophagitis (EE) are of LA grade A&B with low reflux burden, therefore require further esophageal function tests (EFTs). One-third of them respond poorly to pump proton inhibitor (PPI) treatment. The aim was to establish and validate a physiologic nomogram to discriminate symptom outcome to PPI treatment in patients with EE. Methods: A total of 79 EE patients with heartburn who underwent EFTs and received PPI therapy were randomly assigned into a training set (n = 55) and a validation set (n = 24). Clinical data including physiologic parameters from EFTs were collected. Significant factors for the positive symptomatic outcome were identified using logistic regression analysis. Physiologic signature was developed using the least absolute shrinkage and selection operator algorithm. The nomogram was established by combining significant factors and physiologic signature, and its performance was evaluated and validated in the training and validation set. The clinical value of the nomogram was measured by decision curve analysis. Results: Significant factors for positive symptomatic response to PPI treatment were identified as follows: acid exposure time, total number of reflux episodes, and two novel metrics including mean nocturnal baseline impedance and post-reflux swallow-induced peristaltic wave index. The nomogram which incorporated both significant factors and physiologic signature demonstrated good performance in the training and validation sets [C-index: 0.938 (95% CI 0.882–0.995); 0.839 (95% CI 0.678–0.995), respectively]. Decision curves showed significant clinical usefulness. Conclusion: The first physiologic nomogram was developed to discriminate the individualized response to PPI therapy among EE patients. [ABSTRACT FROM AUTHOR]

Objectives: To compare the ability of Frailty Phenotype (FP), FRAIL and Frailty Index (FI) to predict adverse outcomes. Design: A prospective cohort study. Setting: A senior community in Beijing, China. Participants: A total of 188 older adults aged 65 years or older (mean age 84.0 ± 4.4 years, 58.5% female). Measurements: Frailty was evaluated by FP, FRAIL and FI. The agreement between scales was assessed by Cohen kappa coefficient. The predictive value of the three scales for adverse outcomes during one-year follow-up period were analyzed using decision curve analysis(DCA) and receiver operating characteristic curve (ROC) analysis. Results: Frailty ranged from 25% (FRAIL) to 42.6% (FI). The agreement between scales was moderate to good (Cohen's kappa coefficient 0.44∼0.61). DCA showed though the curves of the scales overlapped across all relevant risk thresholds, clinical treating had a higher net benefit than "treat all" and "treat none" when risk of unplanned hospital visits ≥30%, risk of functional decline or falls ≥15%. The three scales had similar predictive value for unplanned hospital visits (area under ROC, AUC 0.63, 0.64 and 0.69). FRAIL and FI had similar predictive value for functional decline (AUC 0.63,0.65). FI had predictive value for falls (AUC 0.65). Conclusions: All three scales showed clinical utility but FRAIL may be best in practice because it is simple. Multidimensional measures of frailty are better than unidimensional for prediction of adverse outcomes among older adults. [ABSTRACT FROM AUTHOR]

Aim: To develop and internally validate nomograms to predict the overall survival (OS) and the cancer-specific survival (CSS) of patients with epithelial ovarian cancer (EOC). Methods: A total of 9001 EOC patients diagnosed between 2010 and 2013 were randomly divided into the training (n = 6301) and validation (n = 2700) cohorts. Nomogram and bootstrap validation were used to assess the predictive values of the models, including discrimination, calibration and clinical benefit. Results: In the validation cohort, the concordance statistic values were 0.733 for OS and 0.747 for CSS. Calibration plots and decision curve analyses demonstrated moderate accuracy and clinical applicability. Conclusion: Nomograms were user-friendly tools for guiding clinical treatment and estimating prognosis. [ABSTRACT FROM AUTHOR]

Background: Subchorionic hematoma (SCH) is common in early pregnancy achieved by in vitro fertilization-embryo transfer/frozen embryo transfer (IVF-ET/FET), and is associated with adverse obstetric outcomes. However, there are no methods known to accurately predict the occurrence of SCH. Objective: To establish a nomogram prediction model for predicting the risk of SCH in early pregnancy with IVF-ET/FET and to analyze pregnancy outcomes of patients with SCH. Methods: Patients who underwent IVF-ET/FET treatment and were diagnosed with clinical pregnancy were enrolled in our study. A total of 256 patients with SCH were enrolled in the SCH group, and 526 patients without SCH in the control group. Logistic regression was used to screen risk factors for SCH, and the nomogram was developed according to the regression coefficient of relevant variables. Discrimination, effect, calibration, and the predictive model's clinical usefulness were assessed using the C-index, the area under the receiver operating characteristic standard curve, calibration plot, and decision curve analysis. Internal validation was assessed using bootstrapping validation. The effects of SCH on pregnancy outcomes were analyzed. Results: A multivariate logistic regression analysis showed that fresh embryo transfer, polycystic ovary syndrome, hydrosalpinx, and thin endometrium were risk factors affecting the occurrence of SCH. Based on the above factors, a predictive model for the risk of SCH was created. The model displayed good discrimination, with a C-index of 0.783 (95% confidence interval: 0.750–0.816), area under the receiver operating characteristic standard curve of 0.783, and good calibration. A high C-index value of 0.765 could still be reached in the interval validation. Decision curve analysis showed that the nomogram was clinically useful when the intervention was decided at the SCH possibility threshold of 4%–87%. For patients with successful deliveries, the occurrence of SCH did not influence the gestational weeks of delivery, mode of delivery, preterm birth, height, and weight of the newborn. Conclusion: We screened the risk factors for SCH in patients who underwent IVF-ET/FET treatment. Successful establishment of a nomogram can effectively predict the occurrence of SCH. Furthermore, the incidence of miscarriage is higher in patients with SCH. [ABSTRACT FROM AUTHOR]

BackgroundEuropean Association of Urology (EAU) guidelines recommend using risk-calculators (RCs), imaging or additional biomarkers in asymptomatic men at risk of prostate cancer (PCa).ObjectivesTo compare the performance of mpMRI, a RC we recently developed and two commonly used RC not including mpMRI in predicting the risk of PCa, as well as the added value of mpMRI to each RC.Design, Setting, and ParticipantsSingle-center retrospective study evaluating 221 biopsy-naïve patients who underwent prebiopsy mpMRI.Outcome Measurements and Statistical AnalysisPatients’ probabilities of any PCa and clinically significant PCa (csPC, defined as Gleason-Score ≥3 + 4) were computed according to mpMRI, European Randomized Study of Screening for Prostate Cancer RC (ERSPC-RC), the Prostate Biopsy Collaborative Group RC (PBCG-RC) and the Foggia Prostate Cancer RC (FPC-RC). Logistic regression, AUC, and Decision curve analysis (DCA) were used to assess the accuracy of tested models.Results and LimitationThe FPC-RC outperformed mpMRI in diagnosing both any PCa (AUC 0.76 vs 0.69) and csPCa (AUC 0.80 vs 0.75). Conversely mpMRI showed a higher accuracy in predicting any PCa compared to the PBCG-RC and the ERSPC-RC but similar performances in predicting csPCa. At multivariable analysis predicting csPCa and any PCa, the addition of mpMRI findings improved the accuracy of each calculator. DCA showed that the FPC-RC provided a greater net benefit than mpMRI and the other RCs. The addition of mpMRI findings improved the net benefit provided by each calculator.ConclusionsmpMRI was outperformed by the novel FPC-RC and showed similar performances compared to the PBCG and ERSPC RCs in predicting csPCa. The addition of mpMRI findings improved the diagnostic accuracy of each of these calculators

Summary: Using decision curve analysis on 2188 women and 1324 men, we found that an osteogenomic profile constructed from 62 genetic variants improved the clinical net benefit of fracture risk prediction over and above that of clinical risk factors and BMD. Introduction: Genetic profiling is a promising tool for assessing fracture risk. This study sought to use the decision curve analysis (DCA), a novel approach to determine the impact of genetic profiling on fracture risk prediction. Methods: The study involved 2188 women and 1324 men, aged 60 years and above, who were followed for up to 23 years. Bone mineral density (BMD) and clinical risk factors were obtained at baseline. The incidence of fracture and mortality were recorded. A weighted individual genetic risk score (GRS) was constructed from 62 BMD-associated genetic variants. Four models were considered: CRF (clinical risk factors); CRF + GRS; Garvan model (GFRC) including CRF and femoral neck BMD; and GFRC + GRS. The DCA was used to evaluate the clinical net benefit of predictive models at a range of clinically reasonable risk thresholds. Results: In both women and men, the full model GFRC + GRS achieved the highest net benefits. For 10-year risk threshold > 18% for women and > 15% for men, the GRS provided net benefit above those of the CRF models. At 20% risk threshold, adding the GRS could help to avoid 1 additional treatment per 81 women or 1 per 24 men compared with the Garvan model. At lower risk thresholds, there was no significant difference between the four models. Conclusions: The addition of genetic profiling into the clinical risk factors can improve the net clinical benefit at higher risk thresholds of fracture. Although the contribution of genetic profiling was modest in the presence of BMD + CRF, it appeared to be able to replace BMD for fracture prediction. [ABSTRACT FROM AUTHOR]

Background: European Association of Urology (EAU) guidelines recommend using risk-calculators (RCs), imaging or additional biomarkers in asymptomatic men at risk of prostate cancer (PCa). Objectives: To compare the performance of mpMRI, a RC we recently developed and two commonly used RC not including mpMRI in predicting the risk of PCa, as well as the added value of mpMRI to each RC. Design, Setting, and Participants: Single-center retrospective study evaluating 221 biopsy-naïve patients who underwent prebiopsy mpMRI. Outcome Measurements and Statistical Analysis: Patients' probabilities of any PCa and clinically significant PCa (csPC, defined as Gleason-Score ≥3 + 4) were computed according to mpMRI, European Randomized Study of Screening for Prostate Cancer RC (ERSPC-RC), the Prostate Biopsy Collaborative Group RC (PBCG-RC) and the Foggia Prostate Cancer RC (FPC-RC). Logistic regression, AUC, and Decision curve analysis (DCA) were used to assess the accuracy of tested models. Results and Limitation: The FPC-RC outperformed mpMRI in diagnosing both any PCa (AUC 0.76 vs 0.69) and csPCa (AUC 0.80 vs 0.75). Conversely mpMRI showed a higher accuracy in predicting any PCa compared to the PBCG-RC and the ERSPC-RC but similar performances in predicting csPCa. At multivariable analysis predicting csPCa and any PCa, the addition of mpMRI findings improved the accuracy of each calculator. DCA showed that the FPC-RC provided a greater net benefit than mpMRI and the other RCs. The addition of mpMRI findings improved the net benefit provided by each calculator. Conclusions: mpMRI was outperformed by the novel FPC-RC and showed similar performances compared to the PBCG and ERSPC RCs in predicting csPCa. The addition of mpMRI findings improved the diagnostic accuracy of each of these calculators [ABSTRACT FROM AUTHOR]

Reflecting the increasing risk in elderly patients with diffuse large B cell lymphoma (DLBCL), prognostic predictors other than the International Prognostic Index have attracted more attention. This study presents the first analysis of the prognostic utility of the Geriatric Nutritional Risk Index (GNRI) in combination with the Charlson Comorbidity Index (CCI) for overall survival (OS) in elderly DLBCL patients. A multicentre retrospective was conducted on a cohort of 451 patients (≥65 years). The GNRI and CCI were independent predictors in a multivariate Cox proportional hazard model. There was a nonlinear correlation between the GNRI and OS in a Cox model with restricted cubic spline. Multivariate receiver operating characteristic curves showed a significant improvement in prediction accuracy when the GNRI was added to CCI. Adding the GNRI to CCI yielded a significant category‐free net reclassification improvement (0·556; 95% CI: 0·378–0·736, P < 0·001) and integrated discrimination improvement (0·094; 95% CI: 0·067–0·122, P < 0·001). The decision curve analysis demonstrated the clinical net benefit associated with the adoption of the GNRI. The GNRI was not only a predictor of OS but also remarkably improved the prognosis prediction accuracy when incorporated with the CCI, having the ability to stratify the prognosis of elderly DLBCL patients. [ABSTRACT FROM AUTHOR]

The overall response rate (ORR) 28 days after treatment has been adopted as the primary endpoint for clinical trials of acute graft versus host disease (GVHD). However, physicians often need to modify immunosuppression earlier than day (D) 28, and non-relapse mortality (NRM) does not always correlate with ORR at D28. We studied 1144 patients that received systemic treatment for GVHD in the Mount Sinai Acute GVHD International Consortium (MAGIC) and divided them into a training set (n=764) and a validation set (n=380). We used a recursive partitioning algorithm to create a Mount Sinai model that classifies patients into favorable or unfavorable groups that predicted 12 month NRM according to overall GVHD grade at both onset and D14. In the Mount Sinai model grade II GVHD at D14 was unfavorable for grade III/IV GVHD at onset and predicted NRM as well as the D28 standard response model. The MAGIC algorithm probability (MAP) is a validated score that combines the serum concentrations of suppression of tumorigenicity 2 (ST2) and regenerating islet-derived 3-alpha (REG3α) to predict NRM. Inclusion of the D14 MAP biomarker score with the D14 Mount Sinai model created three distinct groups (good, intermediate, poor) with strikingly different NRM (8%, 35%, 76% respectively). This D14 MAGIC model displayed better AUC, sensitivity, positive and negative predictive value, and net benefit in decision curve analysis compared to the D28 standard response model. We conclude that this D14 MAGIC model could be useful in therapeutic decisions and may offer an improved endpoint for clinical trials of acute GVHD treatment., (Copyright © 2024 The American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc. All rights reserved.)

Background: Surgical resection remains the primary treatment option for gallbladder carcinoma (GBC). However, there is a pressing demand for prognostic tools that can refine patients' treatment choices and tailor personalized therapies accordingly., Aims: The nomograms were constructed using the data of a training cohort (n = 378) of GBC patients at Eastern Hepatobiliary Surgery Hospital (EHBH) between 2008 and 2018. The model's performance was validated in GBC patients (n = 108) at Guangzhou Centre from 2007 to 2018., Methods and Results: The 5-year overall survival (OS) rate in the training cohort was 24.4%. Multivariate analyses were performed using preoperative and postoperative data to identify independent predictors of OS. These predictors were then incorporated into preoperative and postoperative nomograms, respectively. The C-index of the preoperative nomogram was 0.661 (95% CI, 0.627 to 0.694) for OS prediction and correctly delineated four subgroups (5-year OS rates: 48.1%, 19.0%, 15.6%, and 8.1%, p < 0.001). The C-index of the postoperative nomogram was 0.778 (95%CI, 0.756 -0.800). Furthermore, this nomogram was superior to the 8th TNM system in both C-index and the net benefit on decision curve analysis. The results were externally validated., Conclusion: The two nomograms showed an optimally prognostic prediction in GBC patients after curative-intent resection., (© 2024 The Authors. Cancer Reports published by Wiley Periodicals LLC.)

Background: To realize the potential of precision medicine, predictive models should be integrated within the framework of decision analysis, such as the decision curve analysis (DCA). To date, its application has required individual patient data (IPD) that are often unavailable. Performing DCA using aggregate data without requiring IPD may advance the goals of precision medicine., Methods: We present a statistical framework demonstrating that DCA can be conducted by using only the mean and standard deviation (SD) from the raw probabilities of the predictive model. We tested our theoretical framework by performing extensive simulations and comparing the aggregate-based DCA with IPD DCA. The latter was conducted using IPD from four predictive models that employed logistic regression, Cox or competing risk time-to-event modeling including (a) statins for primary prevention of cardiovascular disease (n = 4859), (b) hospice referral for terminally ill patients (n = 9104), (c) use of thromboprophylaxis for preventing venous thromboembolism in patients with cancer (n = 425) and (d) prevention of sinusoidal obstruction syndrome after hematopoietic cell transplantation (SCT) (n = 80)., Results: Simulations assuming perfect calibration showed that regardless of which probability distributions informed the predictive models, the differences in DCA were negligible. Similarly, for the adequately powered models, the results of DCA based on the summary data were similar to IPD-derived DCA. The inherent instability of the predictive models, based on the smaller sample sizes, resulted in a somewhat larger discrepancy between aggregate and IPD-based DCA., Conclusions: DCA informed by adequately powered and well-calibrated models using only summary statistical estimates (mean and SD) approximates well models using IPD. Use of aggregate data will facilitate broader integration of predictive with decision modeling toward the goals of individualized decision-making., (© 2023 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

Study Design: Retrospective cohort study., Objectives: Development, validation, and decision curve analysis of a novel tool (NZSpine) for modelling risk of complications within 30 days of spine surgery., Methods: Data was gathered retrospectively from medical records of patients who underwent spine surgery at a single tertiary centre between January 2019 and December 2020 (n = 488). Postoperative adverse events were classified objectively using the Comprehensive Complication Index (CCI). The model was derived using backward stepwise logistic regression. Validation was undertaken using bootstrap resampling. Discrimination was determined by calculating the area under the receiver operating characteristic (AUC). Calibration was assessed graphically. Clinical utility of the model was assessed using decision curve analysis (DCA). Performance measures were compared to an existing tool, SpineSage., Results: Overall complication rate was 34%. Modelling showed higher age, surgical invasiveness and preoperative anemia were most strongly predictive of any complication (OR = 1.03, 1.09, 2.1 respectively, P < .001), whereas the occurrence of a major complication (CCI >26) was most strongly associated with the presence of respiratory disease (OR = 2.82, P < .001). At validation, the model showed good discrimination with an AUC of .73 (.71 - .75) and excellent calibration. SpineSage had an AUC of .71, while DCA showed the novel model had greater expected benefit at all risk thresholds., Conclusion: NZSpine is a novel risk assessment tool for patients undergoing acute and elective spine surgery and may help inform clinicians and patients of their perioperative risk., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.