Nomogram predicting the prognosis of primary liver cancer after radiofrequency ablation combined with transcatheter arterial chemoembolization.

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 = 3.552 and 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.

Development and validation of a nomogram for premature coronary artery disease patients in Guangzhou.

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 3H-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.

Internal validation of modified Mirels' scoring system for pathologic femur fractures.

BACKGROUND: The proximal femur is a common site of bone metastasis. The Mirels' score is a frequently utilized system to identify patients at risk for pathologic fracture and while it has consistently demonstrated strong sensitivity, specificity has been relatively poor. Our group previously developed a Modified Mirels' scoring system which demonstrated improved ability to predict cases at risk of fracture in this patient population through modification of the Mirels' location score. The purpose of the present study is to internally validate this newly developed scoring system on an independent patient series. METHODS: Retrospective review was performed to identify patients who were evaluated for proximal femoral bone lesions. Patients were stratified into one of two groups: 1) those who went on to fracture within 4 months after initial evaluation (Fracture Group) and 2) those who did not fracture within 4 months of initial evaluation (No Fracture Group). Retrospective chart review was performed to assign an Original Mirels' (OM) Score and Modified Mirels' (MM) score to each patient at the time of initial evaluation. Descriptive statistics, logistic regression, receiver operating curve, and net benefit analyses were performed to determine the predictability of fractures when utilizing both scoring systems. RESULTS: The use of the MM scoring improved fracture prediction over OM scoring for patients observed over a 4 month follow up based on logistic regression. Decision curve analysis showed that there was a net benefit using the MM score over the OM scoring for a full range of fracture threshold probabilities. Fracture prevalence was similar for current internal validation dataset when compared to the dataset of our index study with a comparable reduction in misclassification of fracture prediction when utilizing the modified scoring system versus the original. CONCLUSIONS: Use of MM scoring was found to improve fracture prediction over OM scoring when tested on an internal validation set of patients with disseminated metastatic lesions to the proximal femur. The improvement in fracture prediction demonstrated in the present study mirrored the results of our index study during which the MM system was developed.

Factors Associated with Nonalcoholic Fatty Liver Disease in a Non-Overweight/Obese and Overweight/Obese Chinese Population at Risk for Metabolic Syndrome: A Cross-Sectional Multicenter Study.

Background: To investigate the association of demographic, clinical, and metabolic factors with nonalcoholic fatty liver disease (NAFLD) in a non-overweight/obese and overweight/obese Chinese population at risk for metabolic syndrome. Patients and Method: A cross-sectional multicenter study was conducted using convenience sampling from eight selected counties/cities in Zhejiang, China, between May 2021 and September 2022. Demographics, epidemiological, anthropometric, and clinical characteristics were obtained from a questionnaire. Least absolute shrinkage and selection operator (LASSO)-logistic regression analysis was used to identify the variables associated with NAFLD. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to evaluate the diagnostic value and clinical utility of the variables and models. Results: A total of 1739 patients were enrolled in the final analysis, 345 (19.8%) were non-overweight/obese and 1394 (80.2%) were overweight/obese participants. There were 114 (33.0%) and 1094 (78.5%) patients who met the criteria for NAFLD in the non-overweight/obese participants and the overweight/obese participants respectively. Older age, current smoking, higher triglyceride (TG) levels, higher AST levels, higher albumin levels, lower insulin levels, and higher controlled attenuation parameter (CAP) scores were associated with NAFLD in both non-overweight/obese and overweight/obese participants. The combination of TG+CAP scores had strong predictive values for NAFLD, especially in non-overweight/obese (Area Under Curve = 0.812, 95% confidence interval: 0.764-0.863). DCA showed a superior net benefit of the TG+CAP score over other variables or models, suggesting a better clinical utility in identifying NAFLD. Conclusions: More stringent lipid management strategies remain essential, and the convenience and efficacy of transient elastography for liver steatosis should be recognized, especially in the non-overweight/obese population.

Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.

Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to have recurrent postoperative seizures. Results from preclinical models and intracranial EEG in humans suggest that the window of time immediately before and after a seizure ("peri-ictal") represents a unique brain state with implications for clinical outcome prediction. Using a dataset of 294 patients who underwent temporal lobe resection for seizures, we show that machine learning classifiers can make accurate predictions of postoperative seizure outcome using 5 min of peri-ictal scalp EEG data that is part of universal presurgical evaluation (AUC 0.98, out-of-group testing accuracy > 90%). This is the first approach to seizure outcome prediction that employs a routine non-invasive preoperative study (scalp EEG) with accuracy range likely to translate into a clinical tool. Decision curve analysis (DCA) shows that compared to the prevalent clinical-variable based nomogram, use of the EEG-augmented approach could decrease the rate of unsuccessful brain resections by 20%.

Incorporating body mass index into esophageal manometry metrics and mean nocturnal baseline impedance for the evaluation of gastro-esophageal reflux disease.

This study aims to enhance the effectiveness of high resolution manometry (HRM) and pH-impedance monitoring metrics in distinguishing between gastro-esophageal reflux disease (GERD) and non-GERD. A retrospective propensity score matching (PSM) study was conducted on 643 patients with GERD symptoms. PSM matched 134 GERD patients with 134 non-GERD controls. Body mass index (BMI), intra-esophageal pressure (IEP) and intra-gastric pressure (IGP) were significantly higher in the GERD group compared to the non-GERD group. BMI was correlated with IEP and IGP positively. IGP was positively correlated with esophagogastric (EGJ) pressure (EGJ-P) in participants with EGJ type 1 and 2, but not in participants with EGJ type 3. BMI was correlated with distal MNBI negatively. Logistic regression showed BMI as an independent risk factor for GERD. Receiver operating characteristic curve (ROC) and decision curve analysis (DCA) showed that BMI adjusted EGJ contractile integral (EGJ-CI) and BMI adjusted MNBI were superior to the corresponding original ones in predicting GERD susceptibility. According to the findings, BMI and IGP are the main factors contributing to the development of GERD. BMI affects IEP through the adaptive response of EGJ-P to IGP. Incorporating BMI into the calculations of EGJ-CI and MNBI can improve their ability in predicting GERD susceptibility.

Elucidating prognosis in cervical squamous cell carcinoma and endocervical adenocarcinoma: a novel anoikis-related gene signature model.

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.

Optimising the use of colonoscopy to improve risk stratification for colorectal cancer in symptomatic patients: A decision-curve analysis.

OBJECTIVES: Pressured healthcare resources make risk stratification and patient prioritisation fundamental issues for the investigation of colorectal cancer (CRC) in symptomatic patients. The present study uses machine learning algorithms and decision strategies to improve the appropriate use of colonoscopy. DESIGN: All symptomatic patients in a single health board (2018-2021) proceeding to colonoscopy to investigate for CRC were included. Machine learning algorithms (NeuralNetwork, randomForest, Logistic regression, Naïve-Bayes and Adaboost) were used to risk-stratify patients for CRC using demographics, symptoms, quantitative faecal immunochemical test (qFIT) and haematological tests. Decision curve analyses were performed to determine the optimal decision strategies. RESULTS: 3776 patients were included (median age, 65; M:F,0.9:1.0) and CRC was identified in 217 patients (5.7%). qFIT > 400 µg Hb/g was the most important variable (%IncMSE = 78.5). RandomForrest had the highest area under curve (0.91) and accuracy (0.80) for CRC. When utilising decision curve analysis (DCA), 30%, 46% and 54% of colonoscopies were saved at accepted CRC probabilities of 1%, 2% and 3%, respectively. RandomForrest modelling had superior net clinical benefit compared to default colonoscopy strategies. CONCLUSIONS: MLA-derived decision strategies that account for patient and referrer risk preference reduce colonoscopy demand and carry net clinical benefit compared to default colonoscopy strategies.

Establishment and validation of a predictive nomogram for polyuria during general anesthesia in thoracic surgery.

BACKGROUND: To develop and evaluate a predictive nomogram for polyuria during general anesthesia in thoracic surgery. METHODS: A retrospective study was designed and performed. The whole dataset was used to develop the predictive nomogram and used a stepwise algorithm to screen variables. The stepwise algorithm was based on Akaike's information criterion (AIC). Multivariable logistic regression analysis was used to develop the nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the model's discrimination ability. The Hosmer-Lemeshow (HL) test was performed to check if the model was well calibrated. Decision curve analysis (DCA) was performed to measure the nomogram's clinical usefulness and net benefits. P < 0.05 was considered to indicate statistical significance. RESULTS: The sample included 529 subjects who had undergone thoracic surgery. Fentanyl use, gender, the difference between mean arterial pressure at admission and before the operation, operation type, total amount of fluids and blood products transfused, blood loss, vasopressor, and cisatracurium use were identified as predictors and incorporated into the nomogram. The nomogram showed good discrimination ability on the receiver operating characteristic curve (0.6937) and is well calibrated using the Hosmer-Lemeshow test. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS: Individualized and precise prediction of intraoperative polyuria allows for better anesthesia management and early prevention optimization.

NEWS2, S/F-ratio and ROX-index at emergency department for the prediction of adverse outcomes in COVID-19 patients: An external validation study.

BACKGROUND: In the context of the COVID-19 pandemic, the early and accurate identification of patients at risk of deterioration was crucial in overcrowded and resource-limited emergency departments. This study conducts an external validation for the evaluation of the performance of the National Early Warning Score 2 (NEWS2), the S/F ratio, and the ROX index at ED admission in a large cohort of COVID-19 patients from Colombia, South America, assessing the net clinical benefit with decision curve analysis. METHODS: A prospective cohort study was conducted on 6907 adult patients with confirmed COVID-19 admitted to a tertiary care ED in Colombia. The study evaluated the diagnostic performance of NEWS2, S/F ratio, and ROX index scores at ED admission using the area under the receiver operating characteristic curve (AUROC) for discrimination, calibration, and decision curve analysis for the prediction of intensive care unit admission, invasive mechanical ventilation, and in-hospital mortality. RESULTS: We included 6907 patients who presented to the ED with confirmed SARS-CoV-2 infection from March 2020 to November 2021. Mean age was 51 (35-65) years and 50.4% of patients were males. The rate of intensive care unit admission was 28%, and in-hospital death was 9.8%. All three scores have good discriminatory performance for the three outcomes based on the AUROC. S/F ratio showed miscalibration at low predicted probabilities and decision curve analysis indicated that the NEWS2 score provided a greater net benefit compared to other scores across at a 10% threshold to decide ED admission at a high-level of care facility. CONCLUSIONS: The NEWS2, S/F ratio, and ROX index at ED admission have good discriminatory performances in COVID-19 patients for the prediction of adverse outcomes, but the NEWS2 score has a higher net benefit underscoring its clinical utility in optimizing patient management and resource allocation in emergency settings.

Deciphering AKI in Burn Patients: Correlations between Clinical Clusters and Biomarkers.

Acute kidney injury (AKI) is a significant complication in burn patients, impacting outcomes substantially. This study explores the heterogeneity of AKI in burn patients by analyzing creatinine time-series data to identify distinct AKI clusters and evaluating routine biomarkers' predictive values. A retrospective cohort analysis was performed on 2608 adult burn patients admitted to Hangang Sacred Heart Hospital's Burn Intensive Care Unit (BICU) from July 2010 to December 2022. Patients were divided into four clusters based on creatinine trajectories, ranging from high-risk, severe cases to lower-risk, short-term care cases. Cluster A, characterized by high-risk, severe cases, showed the highest mortality and severity, with significant predictors being PT and TB. Cluster B, representing intermediate recovery cases, highlighted PT and albumin as useful predictors. Cluster C, a low-risk, high-resilience group, demonstrated predictive values for cystatin C and eGFR cys. Cluster D, comprising lower-risk, short-term care patients, indicated the importance of PT and lactate. Key biomarkers, including albumin, prothrombin time (PT), cystatin C, eGFR cys, and total bilirubin (TB), were identified as significant predictors of AKI development, varying across clusters. Diagnostic accuracy was assessed using area under the curve (AUC) metrics, reclassification metrics (NRI and IDI), and decision curve analysis. Cystatin C and eGFR cys consistently provided significant predictive value over creatinine, with AUC values significantly higher (p < 0.05) in each cluster. This study highlights the need for a tailored, biomarker-driven approach to AKI management in burn patients, advocating for the integration of diverse biomarkers in clinical practice to facilitate personalized treatment strategies. Future research should validate these biomarkers prospectively to confirm their clinical utility.

External validation and updating of the Rossi nomogram for predicting cesarean delivery following induction: is the Bishop score valuable?

OBJECTIVE: This study sought to validate the Rossi nomogram in a Chinese population and then to include the Bishop score to see if it has an effect on the accuracy of the nomogram. MATERIALS AND METHODS: The Rossi predictive model was applied and externally validated in a retrospective cohort from August 2017 and July 2023 in a Chinese tertiary-level medical center. For the revision and updating of the models, the regression coefficients of all the predictors (except race) were re-estimated and then the cervical Bishop score at the time of induction was added. Each model's performance was measured using the receiver-operating characteristic and calibration plots. Decision curve analysis determined the range of the probability threshold for each prediction model that would be of clinical value. RESULTS: A total of 721 women met the inclusion criteria, of whom 183 (25.4%) underwent a cesarean delivery. The calibration demonstrated the underestimation of the original model, with an area under the curve (AUC) of 0.789 (95% confidence interval [CI] 0.753-0.825, p < 0.001). After recalibrating the original model, the discriminative performance was improved from 0.789 to 0.803. Moreover, the discriminatory power of the updated model was further improved when the Bishop score at the time of induction was added to the recalibrated multivariable model. Indeed, the updated model demonstrated good calibration and discriminatory power, with an AUC of 0.811. The decision curve analysis indicated that all the models (original, recalibrated, and updated) provided higher net benefits of between 0 and 60% of the probability threshold, which indicates the benefits of using the models to make decisions concerning patients who fall within the identified range of the probability threshold. The net benefits of the updated model were higher than those of the original model and the recalibrated model. CONCLUSION: The nomogram used to predict cesarean delivery following induction developed by Rossi et al. has been validated in a Chinese population in this study. More specifically, adaptation to a Chinese population by excluding ethnicity and including the Bishop score prior to induction gave rise to better performance. The three models (original, recalibrated, and updated) offer higher net benefits when the probability threshold is between 0 and 60%.

Clinical Decision Analysis of Genetic Evaluation and Testing in 1013 Intensive Care Unit Infants with Congenital Heart Defects Supports Universal Genetic Testing.

Extracardiac anomalies (ECAs) are strong predictors of genetic disorders in infants with congenital heart disease (CHD), but there are no prior studies assessing performance of ECA status as a screen for genetic diagnoses in CHD patients. This retrospective cohort study assessed this in our comprehensive inpatient CHD genetics service focusing on neonates and infants admitted to the intensive care unit (ICU). The performance and diagnostic utility of using ECA status to screen for genetic disorders was assessed using decision curve analysis, a statistical tool to assess clinical utility, determining the threshold of phenotypic screening by ECA versus a Test-All approach. Over 24% of infants had genetic diagnoses identified (n = 244/1013), and ECA-positive status indicated a 4-fold increased risk of having a genetic disorder. However, ECA status had low-moderate screening performance based on predictive summary index, a compositive measure of positive and negative predictive values. For those with genetic diagnoses, nearly one-third (32%, 78/244) were ECA-negative but had cytogenetic and/or monogenic disorders identified by genetic testing. Thus, if the presence of multiple congenital anomalies is the phenotypic driver to initiate genetic testing, 13.4% (78/580) of infants with isolated CHD with identifiable genetic causes will be missed. Given the prevalence of genetic disorders and limited screening performance of ECA status, this analysis supports genetic testing in all CHD infants in intensive care settings rather than screening based on ECA.

Understanding decision curve analysis in clinical prediction model research.

BACKGROUND: Many medical graduate students lack a thorough understanding of decision curve analysis (DCA), a valuable tool in clinical research for evaluating diagnostic models. METHODS: This article elucidates the concept and process of DCA through the lens of clinical research practices, exemplified by its application in diagnosing liver cancer using serum alpha-fetoprotein levels and radiomics indices. It covers the calculation of probability thresholds, computation of net benefits for each threshold, construction of decision curves, and comparison of decision curves from different models to identify the one offering the highest net benefit. RESULTS: The paper provides a detailed explanation of DCA, including the creation and comparison of decision curves, and discusses the relationship and differences between decision curves and receiver operating characteristic curves. It highlights the superiority of decision curves in supporting clinical decision-making processes. CONCLUSION: By clarifying the concept of DCA and highlighting its benefits in clinical decisionmaking, this article has improved researchers' comprehension of how DCA is applied and interpreted, thereby enhancing the quality of research in the medical field.

Nomogram to Predict the Outcome of Ventriculoperitoneal Shunt Among Patients with Non-HIV Cryptococcal Meningitis.

BACKGROUND: The ventriculoperitoneal (VP) shunt is widely acknowledged as a treatment option for managing intracranial hypertension resulting from non-human immunodeficiency virus (HIV) cryptococcal meningitis (CM). Nonetheless, there is currently no consensus on the appropriate surgical indications for this procedure. Therefore, it is crucial to conduct a preoperative evaluation of patient characteristics and predict the outcome of the VP shunt to guide clinical treatment effectively. METHODS: A retrospective analysis was conducted on data from 85 patients with non-HIV CM who underwent VP shunt surgery at our hospital. The analysis involved studying demographic data, preoperative clinical manifestations, cerebrospinal fluid (CSF) characteristics, and surgical outcomes and comparisons between before and after surgery. A nomogram was developed and evaluated. RESULTS: The therapy outcomes of 71 patients improved, whereas 14 cases had worse outcomes. Age, preoperative cryptococcus count, and preoperative CSF protein levels were found to influence the surgical outcome. The nomogram exhibited exceptional predictive performance (area under the curve = 0.896, 95% confidence interval: 0.8292-0.9635). Internal validation confirmed the nomogram's excellent predictive capabilities. Moreover, decision curve analysis demonstrated the nomogram's practical clinical utility. CONCLUSIONS: The surgical outcome of VP shunt procedures patients with non-HIV CM was associated with age, preoperative cryptococcal count, and preoperative CSF protein levels. We developed a nomogram that can be used to predict surgical outcomes in patients with non-HIV CM.

Development and validation of a prognostic model for predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI).

BACKGROUND: Accurately predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) remains a complex and critical challenge. The primary objective of this study was to develop and validate a robust risk prediction model to assess the 12-month and 24-month mortality risk in STEMI patients after hospital discharge. METHODS: A retrospective study was conducted on 664 STEMI patients who underwent PPCI at Xiangtan Central Hospital Chest Pain Center between 2020 and 2022. The dataset was randomly divided into a training cohort (n = 464) and a validation cohort (n = 200) using a 7:3 ratio. The primary outcome was all-cause mortality following hospital discharge. The least absolute shrinkage and selection operator (LASSO) regression model was employed to identify the optimal predictive variables. Based on these variables, a regression model was constructed to determine the significant predictors of mortality. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). RESULTS: The prognostic model was developed based on the LASSO regression results and further validated using the independent validation cohort. LASSO regression identified five important predictors: age, Killip classification, B-type natriuretic peptide precursor (NTpro-BNP), left ventricular ejection fraction (LVEF), and the usage of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors (ACEI/ARB/ARNI). The Harrell's concordance index (C-index) for the training and validation cohorts were 0.863 (95% CI: 0.792-0.934) and 0.888 (95% CI: 0.821-0.955), respectively. The area under the curve (AUC) for the training cohort at 12 months and 24 months was 0.785 (95% CI: 0.771-0.948) and 0.812 (95% CI: 0.772-0.940), respectively, while the corresponding values for the validation cohort were 0.864 (95% CI: 0.604-0.965) and 0.845 (95% CI: 0.705-0.951). These results confirm the stability and predictive accuracy of our model, demonstrating its reliable discriminative ability for post-discharge all-cause mortality risk. DCA analysis exhibited favorable net benefit of the nomogram. CONCLUSION: The developed nomogram shows potential as a tool for predicting post-discharge mortality in STEMI patients undergoing PPCI. However, its full utility awaits confirmation through broader external and temporal validation.

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

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

Development of a nomogram for prediction of central lymph node metastasis of papillary thyroid microcarcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the most frequent malignant tumor in thyroid carcinoma. The aim of this study was to explore the risk factors associated with central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) and establish a nomogram model that can assess the probability of central lymph node metastasis (CLNM). METHODS: The clinicopathological data of 377 patients with cN0 PTMC were collected and analyzed from The Second Affiliated Hospital of Fujian Medical University from July 1st, 2019 to December 30th, 2021. All patients were examined by underwent ultrasound (US), found without metastasis to central lymph nodes, and diagnosed with PTMC through pathologic examination. All patients received thyroid lobectomy or total thyroidectomy with therapeutic or prophylactic central lymph node dissection (CLND). R software (Version 4.1.0) was employed to conduct a series of statistical analyses and establish the nomogram. RESULTS: A total of 119 patients with PTMC had central lymph node metastases (31.56%). After that, age (P < 0.05), gender (P < 0.05), tumor size (P < 0.05), tumor multifocality (P < 0.05), and ultrasound imaging-suggested tumor boundaries (P < 0.05) were identified as the risk factors associated with CLNM. Subsequently, multivariate logistic regression analysis indicated that the area under the receiver operating characteristic (ROC) curve (AUC) of the training cohort was 0.703 and that of the validation cohort was 0.656, demonstrating that the prediction ability of this model is relatively good compared to existing models. The calibration curves indicated a good fit for the nomogram model. Finally, the decision curve analysis (DCA) showed that a probability threshold of 0.15-0.50 could benefit patients clinically. The probability threshold used in DCA captures the relative value the patient places on receiving treatment for the disease, if present, compared to the value of avoiding treatment if the disease is not present. CONCLUSION: CLNM is associated with many risk factors, including age, gender, tumor size, tumor multifocality, and ultrasound imaging-suggested tumor boundaries. The nomogram established in our study has moderate predictive ability for CLNM and can be applied to the clinical management of patients with PTMC. Our findings will provide a better preoperative assessment and treatment strategies for patients with PTMC whether to undergo central lymph node dissection.

A Day 14 Endpoint for Acute GVHD Clinical Trials.

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.

[Establishment of a Predictive Model for Chronic Cough after Pulmonary Resection].

BACKGROUND: Chronic cough after pulmonary resection is one of the most common complications, which seriously affects the quality of life of patients after surgery. Therefore, the aim of this study is to explore the risk factors of chronic cough after pulmonary resection and construct a prediction model. METHODS: The clinical data and postoperative cough of 499 patients who underwent pneumonectomy or pulmonary resection in The First Affiliated Hospital of University of Science and Technology of China from January 2021 to June 2023 were retrospectively analyzed. The patients were randomly divided into training set (n=348) and validation set (n=151) according to the principle of 7:3 randomization. According to whether the patients in the training set had chronic cough after surgery, they were divided into cough group and non-cough group. The Mandarin Chinese version of Leicester cough questionnare (LCQ-MC) was used to assess the severity of cough and its impact on patients' quality of life before and after surgery. The visual analog scale (VAS) and the self-designed numerical rating scale (NRS) were used to evaluate the postoperative chronic cough. Univariate and multivariate Logistic regression analysis were used to analyze the independent risk factors and construct a model. Receiver operator characteristic (ROC) curve was used to evaluate the discrimination of the model, and calibration curve was used to evaluate the consistency of the model. The clinical application value of the model was evaluated by decision curve analysis (DCA). RESULTS: Multivariate Logistic analysis screened out that preoperative forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), surgical procedure, upper mediastinal lymph node dissection, subcarinal lymph node dissection, and postoperative closed thoracic drainage time were independent risk factors for postoperative chronic cough. Based on the results of multivariate analysis, a Nomogram prediction model was constructed. The area under the ROC curve was 0.954 (95%CI: 0.930-0.978), and the cut-off value corresponding to the maximum Youden index was 0.171, with a sensitivity of 94.7% and a specificity of 86.6%. With a Bootstrap sample of 1000 times, the predicted risk of chronic cough after pulmonary resection by the calibration curve was highly consistent with the actual risk. DCA showed that when the preprobability of the prediction model probability was between 0.1 and 0.9, patients showed a positive net benefit. CONCLUSIONS: Chronic cough after pulmonary resection seriously affects the quality of life of patients. The visual presentation form of the Nomogram is helpful to accurately predict chronic cough after pulmonary resection and provide support for clinical decision-making.