Machine Learning Prediction for Prognosis of Patients With Aortic Stenosis.

Background: Aortic valve stenosis of any degree is associated with poor outcomes. Objectives: The authors aimed to develop a risk prediction model for aortic stenosis (AS) prognosis using machine learning techniques. Methods: A prognostic algorithm was developed using an AS registry of 10,407 patients undergoing echocardiography between 2008 and 2020. Clinical, echocardiographic, laboratory, and medication data were used to train and test a time-to-event model, the random survival forest (RSF), for AS patient's prognosis. The composite outcome included aortic valve replacement or mortality. The SHapley Additive exPlanations method attributed the importance of variables and provided personalized risk assessment. The algorithm was validated in 2 external cohorts of 11,738 and 954 patients with AS. Results: The median follow-up of the primary cohort was 48 (21-87) months. In this period, 1,116 patients underwent aortic valve replacement, and 5,069 patients died. RSF had an area under the curve (AUC) of 0.83 (95% CI: 0.80-0.86) and 0.83 (95% CI: 0.81-0.84) for outcomes prediction at 1 and 5 years, respectively. Using a cut-off of 50%, the RSF sensitivity and specificity for the composite outcome, were 0.80 and 0.73, respectively. Validation performance in the 2 external cohorts was similar, with AUCs of 0.73 (95% CI: 0.72-0.74) and 0.74 (95% CI: 0.72-0.76), respectively. AS severity, age, serum albumin, pulmonary artery pressure, and chronic kidney disease emerged as the top significant variables in the model. Conclusions: In patients with AS, a machine learning algorithm predicts outcomes with good accuracy, and prognostic characteristics were identified. The model can potentially guide risk factor modification and clinical decisions to improve patient prognosis.

Application of random survival forest to establish a nomogram combining clinlabomics-score and clinical data for predicting brain metastasis in primary lung cancer.

PURPOSE: To establish a nomogram for predicting brain metastasis (BM) in primary lung cancer at 12, 18, and 24 months after initial diagnosis. METHODS: In this study, we included 428 patients who were diagnosed with primary lung cancer at Harbin Medical University Cancer Hospital between January 2020 and January 2022. The endpoint event was BM. The patients were randomly categorized into two groups in a 7:3 ratio: training (n = 299) and validation (n = 129) sets. Least absolute shrinkage and selection operator was utilized to analyze the laboratory test results in the training set. Furthermore, clinlabomics-score was determined using regression coefficients. Then, clinlabomics-score was combined with clinical data to construct a nomogram using random survival forest (RSF) and Cox multivariate regression. Then, various methods were used to evaluate the performance of the nomogram. RESULTS: Five independent predictive factors (pathological type, diameter, lymph node metastasis, non-lymph node metastasis and clinlabomics-score) were used to construct the nomogram. In the validation set, the bootstrap C-index was 0.7672 (95% CI 0.7092-0.8037), 12-month AUC was 0.787 (95% CI 0.708-0.865), 18-month AUC was 0.809 (95% CI 0.735-0.884), and 24-month AUC was 0.858 (95% CI 0.792-0.924). In addition, the calibration curve, decision curve analysis and Kaplan-Meier curves revealed a good performance of the nomogram. CONCLUSIONS: Finally, we constructed and validated a nomogram to predict BM risk in primary lung cancer. Our nomogram can identify patients at high risk of BM and provide a reference for clinical decision-making at different disease time points.

Factors affecting the survival of prediabetic patients: comparison of Cox proportional hazards model and random survival forest method.

BACKGROUND: The worldwide prevalence of type 2 diabetes mellitus in adults is experiencing a rapid increase. This study aimed to identify the factors affecting the survival of prediabetic patients using a comparison of the Cox proportional hazards model (CPH) and the Random survival forest (RSF). METHOD: This prospective cohort study was performed on 746 prediabetics in southwest Iran. The demographic, lifestyle, and clinical data of the participants were recorded. The CPH and RSF models were used to determine the patients' survival. Furthermore, the concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curve were employed to compare the performance of the Cox proportional hazards (CPH) model and the random survival forest (RSF) model. RESULTS: The 5-year cumulative T2DM incidence was 12.73%. Based on the results of the CPH model, NAFLD (HR = 1.74, 95% CI: 1.06, 2.85), FBS (HR = 1.008, 95% CI: 1.005, 1.012) and increased abdominal fat (HR = 1.02, 95% CI: 1.01, 1.04) were directly associated with diabetes occurrence in prediabetic patients. The RSF model suggests that factors including FBS, waist circumference, depression, NAFLD, afternoon sleep, and female gender are the most important variables that predict diabetes. The C-index indicated that the RSF model has a higher percentage of agreement than the CPH model, and in the weighted Brier Score index, the RSF model had less error than the Kaplan-Meier and CPH model. CONCLUSION: Our findings show that the incidence of diabetes was alarmingly high in Iran. The results suggested that several demographic and clinical factors are associated with diabetes occurrence in prediabetic patients. The high-risk population needs special measures for screening and care programs.

The Impact of Starting Positions and Breathing Rhythms on Cardiopulmonary Stress and Post-Exercise Oxygen Consumption after High-Intensity Metabolic Training: A Randomized Crossover Prospective Study.

Background: The exploration of optimizing cardiopulmonary function and athletic performance through high-intensity metabolic exercises (HIMEs) is paramount in sports science. Despite the acknowledged efficacy of HIMEs in enhancing cardiopulmonary endurance, the high metabolic stress imposed on the cardiopulmonary system, especially for amateurs, necessitates a scaled approach to training. Objective: The aim of this study is to ascertain whether adjustments in the initiation posture and the adoption of an appropriate breathing strategy can effectively mitigate the cardiopulmonary stress induced by HIMEs without compromising training efficacy. Methods: Twenty-two subjects were recruited into this study. The post-exercise heart rate (PHR) and post-exercise oxygen consumption rate (POCR) were collected within 30 min after exercise. A two-way ANOVA, multi-variable Cox regression, and random survival forest machine learning algorithm were used to conduct the statistical analysis. Results: Under free breathing, only the maximum POCR differed significantly between standing and prone positions, with prone positions showing higher stress (mean difference = 3.15, p < 0.001). In contrast, the regulated breathing rhythm enhanced performance outcomes compared to free breathing regardless of the starting position. Specifically, exercises initiated from prone positions under regulated breathing recorded a significantly higher maximum and average PHR than those from standing positions (maximum PHR: mean difference = 13.40, p < 0.001; average PHR: mean difference = 6.45, p < 0.001). The multi-variable Cox regression highlighted the starting position as a critical factor influencing the PHR and breathing rhythm as a significant factor for the POCR, with respective variable importances confirmed by the random survival forest analysis. These results underscore the importance of controlled breathing and starting positions in optimizing HIME outcomes. Conclusions: Regulated breathing in high-intensity exercises enhances performance and physiological functions, emphasizing the importance of breathing rhythm over starting position. Effective training should balance exercise volume and technique to optimize performance and minimize stress, reducing overtraining and injury risks.

Development of a novel prognostic signature derived from super-enhancer-associated gene by machine learning in head and neck squamous cell carcinoma.

Dysregulated super-enhancer (SE) results in aberrant transcription that drives cancer initiation and progression. SEs have been demonstrated as novel promising diagnostic/prognostic biomarkers and therapeutic targets across multiple human cancers. Here, we sought to develop a novel prognostic signature derived from SE-associated genes for head and neck squamous cell carcinoma (HNSCC). SE was identified from H3K27ac ChIP-seq datasets in HNSCC cell lines by ROSE algorithm and SE-associated genes were further mapped and functionally annotated. A total number of 133 SE-associated genes with mRNA upregulation and prognostic significance was screened via differentially-expressed genes (DEGs) and Cox regression analyses. These candidates were subjected for prognostic model constructions by machine learning approaches using three independent HNSCC cohorts (TCGA-HNSC dataset as training cohort, GSE41613 and GSE42743 as validation cohorts). Among dozens of prognostic models, the random survival forest algorithm (RSF) stood out with the best performance as evidenced by the highest average concordance index (C-index). A prognostic nomogram integrating this SE-associated gene signature (SEAGS) plus tumor size demonstrated satisfactory predictive power and excellent calibration and discrimination. Moreover, WNT7A from SEARG was validated as a putative oncogene with transcriptional activation by SE to promote malignant phenotypes. Pharmacological disruption of SE functions by BRD4 or EP300 inhibitor significantly impaired tumor growth and diminished WNT7A expression in a HNSCC patient-derived xenograft model. Taken together, our results establish a novel, robust SE-derived prognostic model for HNSCC and suggest the translational potentials of SEs as promising therapeutic targets for HNSCC.

Evaluating the atherosclerosis cardiovascular disease risk score in patients with brain metastases: Associations with overall survival and high-value care outcomes.

OBJECTIVE: Brain metastases (BM) constitute the most common intracranial tumor in adults. Prior literature indicates the 10-year atherosclerotic cardiovascular disease (ASCVD) risk score is associated with increased risk of cancer, potentially attributable to shared risk factors. Understanding the role of ASCVD risk scores in BM may help optimize their care and inform clinical decision-making. Our aim was to explore associations between ASCVD risk score in BM patients and their overall survival, hospital charges, and non-routine discharge disposition. METHODS: Electronic medical records were reviewed to collect clinical data for BM patients undergoing surgery at a single institution (2017-2021). Regression analyses were performed accordingly and maximally selected rank statistics were employed to identify an optimal cutoff for ASCVD risk scores. The random survival forest (RSF) machine learning technique identified the most important variable associated with survival outcomes in BM patients. RESULTS: A total of 139 patients were included with average age 62.93±9.29 years, 48.2 % male, 25.2 % with high hospital charges, and 23.7 % experiencing non-routine discharge. Among these patients, 32.3 % had prior history of an ASCVD event, while 67.7 % did not. Overall, this cohort had an average 10-year ASCVD risk score of 12.51±12.98, indicating intermediate risk of ASCVD among all BM patients. On multivariate logistic regression, prior history of ASCVD was associated with higher odds of high hospital charges (OR=3.670, p=0.018), and higher ASCVD risk scores were associated with greater odds of non-routine discharge (OR=1.059, p=0.012). On the multivariate Cox regression model, higher ASCVD risk scores correlated with worse overall survival (HR=1.031, p=0.014). A threshold of 25.1 was identified for high-risk ASCVD scores. Patients with ASCVD scores >25.1 exhibited reduced overall survival in Kaplan-Meier analysis (p=0.015) and multivariate Cox regression (HR: 2.811, p=0.016). Notably, ASCVD risk scores were found to be the most important variable in predicting worse survival outcomes in BM patients compared to other established frailty indices. CONCLUSION: This study indicates higher ASCVD risk scores in BM patients are associated with worse overall survival. Integrating ASCVD assessment into clinical workflow may facilitate more informed risk-based decision-making.

Use of Machine Learning to Predict Onset of NAFLD in an All-Comers Cohort-Development and Validation in 2 Large Asian Cohorts.

Background and Aims: Nonalcoholic fatty liver disease (NAFLD) is one of the most common liver diseases. There are no universally accepted models that accurately predict time to onset of NAFLD. Machine learning (ML) models may allow prediction of such time-to-event (ie, survival) outcomes. This study aims to develop and independently validate ML-derived models to allow personalized prediction of time to onset of NAFLD in individuals who have no NAFLD at baseline. Methods: The development dataset comprised 25,599 individuals from a South Korean NAFLD registry. A random 70:30 split divided it into training and internal validation sets. ML survival models (random survival forest, extra survival trees) were fitted, with time to NAFLD diagnosis in months as the target variable and routine anthropometric and laboratory parameters as predictors. The independent validation dataset comprised 16,173 individuals from a Chinese open dataset. Models were evaluated using the concordance index (c-index) and Brier score on both the internal and independent validation sets. Results: The datasets (development vs independent validation) had 1,331,107 vs 543,874 person months of follow-up, NAFLD incidence of 25.7% (6584 individuals) vs 14.4% (2322 individuals), and median time to NAFLD onset of 60 (interquartile range 38-75) vs 24 (interquartile range 13-37) months, respectively. The ML models achieved a good c-index of >0.7 in the validation cohort-random survival forest 0.751 (95% confidence interval 0.742-0.759), extra survival trees 0.752 (95% confidence interval 0.744-0.762). Conclusion: ML models can predict time-to-onset of NAFLD based on routine patient data. They can be used by clinicians to deliver personalized predictions to patients, which may facilitate patient counseling and clinical decision making on interval imaging timing.

Combining lymph node ratio to develop prognostic models for postoperative gastric neuroendocrine neoplasm patients.

BACKGROUND: Lymph node ratio (LNR) was demonstrated to play a crucial role in the prognosis of many tumors. However, research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm (NEN) patients was limited. AIM: To explore the prognostic value of LNR in postoperative gastric NEN patients and to combine LNR to develop prognostic models. METHODS: A total of 286 patients from the Surveillance, Epidemiology, and End Results database were divided into the training set and validation set at a ratio of 8:2. 92 patients from the First Affiliated Hospital of Soochow University in China were designated as a test set. Cox regression analysis was used to explore the relationship between LNR and disease-specific survival (DSS) of gastric NEN patients. Random survival forest (RSF) algorithm and Cox proportional hazards (CoxPH) analysis were applied to develop models to predict DSS respectively, and compared with the 8th edition American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging. RESULTS: Multivariate analyses indicated that LNR was an independent prognostic factor for postoperative gastric NEN patients and a higher LNR was accompanied by a higher risk of death. The RSF model exhibited the best performance in predicting DSS, with the C-index in the test set being 0.769 [95% confidence interval (CI): 0.691-0.846] outperforming the CoxPH model (0.744, 95%CI: 0.665-0.822) and the 8th edition AJCC TNM staging (0.723, 95%CI: 0.613-0.833). The calibration curves and decision curve analysis (DCA) demonstrated the RSF model had good calibration and clinical benefits. Furthermore, the RSF model could perform risk stratification and individual prognosis prediction effectively. CONCLUSION: A higher LNR indicated a lower DSS in postoperative gastric NEN patients. The RSF model outperformed the CoxPH model and the 8th edition AJCC TNM staging in the test set, showing potential in clinical practice.

Risk factors for the time to development of retinopathy of prematurity in premature infants in Iran: a machine learning approach.

BACKGROUND: Retinopathy of prematurity (ROP), is a preventable leading cause of blindness in infants and is a condition in which the immature retina experiences abnormal blood vessel growth. The development of ROP is multifactorial; nevertheless, the risk factors are controversial. This study aimed to identify risk factors of time to development of ROP in Iran. METHODS: This historical cohort study utilized data from the hospital records of all newborns referred to the ROP department of Farabi Hospital (from 2017 to 2021) and the NICU records of infants referred from Mahdieh Hospital to Farabi Hospital. Preterm infants with birth weight (BW) ≤ 2000 g or gestational age (GA) < 34 wk, as well as selected infants with an unstable clinical course, as determined by their pediatricians or neonatologists, with BW > 2000 g or GA ≥ 34 wk. The outcome variable was the time to development of ROP (in weeks). Random survival forest was used to analyze the data. RESULTS: A total of 338 cases, including 676 eyes, were evaluated. The mean GA and BW of the study group were 31.59 ± 2.39 weeks and 1656.72 ± 453.80 g, respectively. According to the criteria of minimal depth and variable importance, the most significant predictors of the time to development of ROP were duration of ventilation, GA, duration of oxygen supplementation, bilirubin levels, duration of antibiotic administration, duration of Total Parenteral Nutrition (TPN), mother age, birth order, number of surfactant administration, and on time screening. The concordance index for predicting survival of the fitted model was 0.878. CONCLUSION: Our findings indicated that the duration of ventilation, GA, duration of oxygen supplementation, bilirubin levels, duration of antibiotic administration, duration of TPN, mother age, birth order, number of surfactant administrations, and on time screening are potential risk factors of prognosis of ROP. The associations between identified risk factors were mostly nonlinear. Therefore, it is recommended to consider the nature of these relationships in managing treatment and designing early interventions.

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period.

BACKGROUND: Severe heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period. METHODS: We extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values. RESULTS: A total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality. CONCLUSIONS: The survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction.

Breast cancer is most commonly faced with form of cancer amongst women worldwide. In spite of the fact that the breast cancer research and awareness have gained considerable momentum, there is still no one treatment due to disease heterogeneity. Survival data may be of specific interest in breast cancer studies to understand its dynamic and complex trajectories. This study copes with the most important covariates affecting the disease progression. The study utilizes the German Breast Cancer Study Group 2 (GBSG2) and the Molecular Taxonomy of Breast Cancer International Consortium dataset (METABRIC) datasets. In both datasets, interests lie in relapse of the disease and the time when the relapse happens. The three models, namely the Cox proportional hazards (PH) model, random survival forest (RSF) and conditional inference forest (Cforest) were employed to analyse the breast cancer datasets. The goal of this study is to apply these methods in prediction of breast cancer progression and compare their performances based on two different estimation methods: the bootstrap estimation and the bootstrap .632 estimation. The model performance was evaluated in concordance index (C-index) and prediction error curves (pec) for discrimination. The Cox PH model has a lower C-index and bigger prediction error compared to the RSF and the Cforest approach for both datasets. The analysis results of GBSG2 and METABRIC datasets reveal that the RSF and the Cforest algorithms provide non-parametric alternatives to Cox PH model for estimation of the survival probability of breast cancer patients.

Dendritic cell-related hub genes in head-and-neck squamous cell carcinoma: implications for prognosis and immunotherapy.

Background: In the context of head-and-neck squamous cell carcinoma (HNSCC), dendritic cells (DCs) assume pivotal responsibilities, acting as architects of antigen presentation and conductors of immune checkpoint modulation. In this study, we aimed to identify hub genes associated with DCs in HNSCC and explore their prognostic significance and implications for immunotherapy. Methods: Integrated clinical datasets from The Cancer Genome Atlas (TCGA)-HNSCC and GSE65858 cohorts underwent meticulous analysis. Employing weighted gene co-expression network analysis (WGCNA), we delineated candidate genes pertinent to DCs. Through the application of random survival forest and least absolute shrinkage and selection operator (LASSO) Cox's regression, we derived key genes of significance. Lisa (epigenetic Landscape In Silico deletion Analysis and the second descendent of MARGE) highlighted transcription factors, with Dual-luciferase assays confirming their regulatory role. Furthermore, immunotherapeutic sensitivity was assessed utilizing the Tumor Immune Dysfunction and Exclusion online tool. Results: This study illuminated the functional intricacies of HNSCC DC subsets to tailor innovative therapeutic strategies. We leveraged clinical data from the TCGA-HNSCC and GSE65858 cohorts. We subjected the data to advanced analysis, including WGCNA, which revealed 222 DC-related candidate genes. Following this, a discerning approach utilizing random survival forest analysis and LASSO Cox's regression unveiled seven genes associated with the prognostic impact of DCs, notably ACP2 and CPVL, associated with poor overall survival. Differential gene expression analysis between ACP2 + and ACP2 - DC cells revealed 208 differential expressed genes. Lisa analysis identified the top five significant transcription factors as STAT1, SPI1, SMAD1, CEBPB, and IRF1. The correlation between STAT1 and ACP2 was confirmed through quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Dual-luciferase assays in HEK293T cells. Additionally, TP53 and FAT1 mutations were more common in high-risk DC subgroups. Importantly, the sensitivity to immunotherapy differed among the risk clusters. The low-risk cohorts were anticipated to exhibit favorable responses to immunotherapy, marked by heightened expressions of immune system-related markers. In contrast, the high-risk group displayed augmented proportions of immunosuppressive cells, suggesting a less conducive environment for immunotherapeutic interventions. Conclusions: Our research may yield a robust DC-based prognostic system for HNSCC; this will aid personalized treatment and improve clinical outcomes as the battle against this challenging cancer continues.

Random Survival Forests With Competing Events: A Subdistribution-Based Imputation Approach.

Random survival forests (RSF) can be applied to many time-to-event research questions and are particularly useful in situations where the relationship between the independent variables and the event of interest is rather complex. However, in many clinical settings, the occurrence of the event of interest is affected by competing events, which means that a patient can experience an outcome other than the event of interest. Neglecting the competing event (i.e., regarding competing events as censoring) will typically result in biased estimates of the cumulative incidence function (CIF). A popular approach for competing events is Fine and Gray's subdistribution hazard model, which directly estimates the CIF by fitting a single-event model defined on a subdistribution timescale. Here, we integrate concepts from the subdistribution hazard modeling approach into the RSF. We develop several imputation strategies that use weights as in a discrete-time subdistribution hazard model to impute censoring times in cases where a competing event is observed. Our simulations show that the CIF is well estimated if the imputation already takes place outside the forest on the overall dataset. Especially in settings with a low rate of the event of interest or a high censoring rate, competing events must not be neglected, that is, treated as censoring. When applied to a real-world epidemiological dataset on chronic kidney disease, the imputation approach resulted in highly plausible predictor-response relationships and CIF estimates of renal events.

Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression.

Introduction: Lung cancer remains a significant global health burden, with non-small cell lung cancer (NSCLC) being the predominant subtype. Despite advancements in treatment, the prognosis for patients with advanced NSCLC remains unsatisfactory, underscoring the imperative for precise prognostic assessment models. This study aimed to develop and validate a survival prediction model specifically tailored for patients diagnosed with NSCLC. METHODS: A total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA). RESULTS: The LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved a bootstrap C-index of 0.668 (95% CI: 0.626-0.722) in the validation dataset, the highest among the three models tested. The model demonstrated good discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions, respectively. Calibration plots indicated good agreement between predicted and observed survival probabilities. Decision curve analysis demonstrated that the model provides clinical benefit at a range of decision thresholds. CONCLUSION: The LASSO regression model exhibited robust performance in the validation dataset, predicting survival outcomes for patients with advanced NSCLC effectively. This model can assist clinicians in making more informed treatment decisions and provide a valuable tool for patient risk stratification and personalized management.

Thoracic sarcopenia was a poor prognostic predictor in patients receiving immunotherapy retain-->for advanced non-small-cell retain-->lung cancer.

RATIONALE AND OBJECTIVES: Sarcopenia, as measured at the level of the third lumbar (L3) has been shown to predict the survival of cancer patients. However, many patients with advanced non-small cell lung cancer (NSCLC) do not undergo routine abdominal imaging. The objective of this study was to investigate the association of thoracic sarcopenia with survival outcomes among patients who underwent immunotherapy for NSCLC. MATERIALS AND METHODS: In this retrospective study, patients who initiated immunotherapy for advanced NSCLC from 2019 to 2022 were enrolled. and detailed patient data were collected. Cross sectional skeletal muscle area was calculated at the fifth thoracic vertebra (T5) on pretreatment chest computed tomography (CT) scan. Gender-specific lowest quartile values was used to define sarcopenia. The risk factors were analyzed using Cox analyses. The log-rank test and the random survival forest (RSF) were used to compare progression free survival (PFS). The model's performance was assessed using calibration curve and the receiver operating characteristic curve (ROC). RESULTS: A total of 242 patients was included (discovery cohort n = 194, validation cohort n = 48). In the discovery cohort, patients with sarcopenia exhibited significantly poorer PFS (p < 0.001) than patients without sarcopenia. Univariate cox regression revealed that sarcopenia, lung cancer stage, body mass index, smoking status, and neutrophil-to-lymphocyte ratio were predictors of poor PFS. A RSF model was constructed based on the aforementioned parameters, to evaluate the model's efficacy, the ROC curve was utilized. with an area under the curve for predicting 6-month PFS of 0.68 and for 12-month PFS of 0.69. The prediction models for survival outcomes built by the discovery cohort showed similar performance in the validation cohort. CONCLUSION: Sarcopenia at T5 is independent prognostic factors in patients who received immunotherapy for advanced NSCLC.

Comparison and use of explainable machine learning-based survival models for heart failure patients.

Objective: Explainable machine learning (XAI) was introduced in this study to improve the interpretability, explainability and transparency of the modelling results. The survex package in R was used to interpret and compare two survival models - the Cox proportional hazards regression (coxph) model and the random survival forest (rfsrc) model - and to estimate overall survival (OS) and its determinants in heart failure (HF) patients using these models. Methods: We selected 1159 HF patients hospitalised at the First Affiliated Hospital of Kunming Medical University. First, the performance of the two models was investigated using the C-index, the integrated C/D AUC, and the integrated Brier score. Second, a global explanation of the whole cohort was carried out using the time-dependent variable importance and the partial dependence survival profile. Finally, the SurvSHAP(t) and SurvLIME plots and the ceteris paribus survival profile were used to obtain a local explanation for each patient. Results: By comparing the C-index, the C/D AUC, and the Brier score, this study showed that the model performance of rfsrc was better than coxph. The global explanation of the whole cohort suggests that the C-reactive protein, lg BNP (brain natriuretic peptide), estimated glomerular filtration rate, albumin, age and blood chloride were significant unfavourable predictors of OS in HF patients in both the cxoph and the rfsrc models. By including individual patients in the model, we can provide a local explanation for each patient, which guides the clinician in individualising the patient's treatment. Conclusion: By comparison, we conclude that the model performance of rfsrc is better than that of coxph. These two predictive models, which address not only the whole population but also selected patients, can help clinicians personalise the treatment of each HF patient according to his or her specific situation.

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis.

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established. Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model. Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.

Identifying Factors Affecting the Survival of Patients with HIV-Associated B-Cell Lymphoma Using a Random Survival Forest Model.

Background: There have been no reports about the application of random survival forest (RSF) model to predict disease progression of HIV-associated B-cell lymphoma. Methods: A total of 44 patients with HIV-associated B-cell lymphoma who were referred to Nanjing Second Hospital from 2012 to 2019 were included. The RSF model was used to find predictors of survival, and the results of the RSF model were compared with those of the Cox model. The data were analyzed using R software (version 4.1.1). Results: One-, 2-, and 3-year survival rates were 74.5%, 57.7%, and 48.6%, respectively, and the median survival was 59.0 months. The first 3 most important predictors of survival included lactate dehydrogenase (LDH), absolute monocyte count (AMC), and white blood cells (WBCs) count. The median survival of high-risk patients was only 4.0 months. Areas under the curve (AUCs) of the RSF model remained at more than 0.90 at 1, 2, and 3 years. The RSF model displayed a lower prediction error rate (21.9%) than the Cox model (25.4%). Conclusions: Lactate dehydrogenase, AMC, and WBCs count are the most important prognostic predictors for patients with HIV-associated B-cell lymphoma. Much larger prospective and/or multicentre studies are required to validtae this RSF model.

Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.

Introduction: This study aimed to develop a prognostic nomogram for predicting the recurrence-free survival (RFS) of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) scores after transarterial chemoembolization (TACE) combined with local ablation treatment. Methods: We gathered clinical data from 632 HBV-related HCC patients who received the combination treatment at Beijing You'an Hospital, affiliated with Capital Medical University, from January 2014 to January 2020. The patients were divided into two groups based on their PALBI scores: low PALBI group (n=247) and high PALBI group (n=385). The low PALBI group was then divided into two cohorts: training cohort (n=172) and validation cohort (n=75). We utilized eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox analysis to pinpoint the risk factors for RFS. Then, we developed a nomogram based on the screened factors and assessed its risk stratification capabilities and predictive performance. Results: The study finally identified age, aspartate aminotransferase (AST), and prothrombin time activity (PTA) as key predictors. The three variables were included to develop the nomogram for predicting the 1-, 3-, and 5-year RFS of HCC patients. We confirmed the nomogram's ability to effectively discern high and low risk patients, as evidenced by Kaplan-Meier curves. We further corroborated the excellent discrimination, consistency, and clinical utility of the nomogram through assessments using the C-index, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Conclusion: Our study successfully constructed a robust nomogram, effectively predicting 1-, 3-, and 5-year RFS for HBV-related HCC patients with low preoperative PALBI scores after TACE combined with local ablation therapy.

Prognostication of colorectal cancer liver metastasis by CE-based radiomics and machine learning.

The liver is the most common organ for the formation of colorectal cancer metastasis. Non-invasive prognostication of colorectal cancer liver metastasis (CRLM) may better inform clinicians for decision-making. Contrast-enhanced computed tomography images of 180 CRLM cases were included in the final analyses. Radiomics features, including shape, first-order, wavelet, and texture, were extracted with Pyradiomics, followed by feature engineering by penalized Cox regression. Radiomics signatures were constructed for disease-free survival (DFS) by both elastic net (EN) and random survival forest (RSF) algorithms. The prognostic potential of the radiomics signatures was demonstrated by Kaplan-Meier curves and multivariate Cox regression. 11 radiomics features were selected for prognostic modelling for the EN algorithm, with 835 features for the RSF algorithm. Survival heatmap indicates a negative correlation between EN or RSF risk scores and DFS. Radiomics signature by EN algorithm successfully separates DFS of high-risk and low-risk cases in the training dataset (log-rank test: p < 0.01, hazard ratio: 1.45 (1.07-1.96), p < 0.01) and test dataset (hazard ratio: 1.89 (1.17-3.04), p < 0.05). RSF algorithm shows a better prognostic implication potential for DFS in the training dataset (log-rank test: p < 0.001, hazard ratio: 2.54 (1.80-3.61), p < 0.0001) and test dataset (log-rank test: p < 0.05, hazard ratio: 1.84 (1.15-2.96), p < 0.05). Radiomics features have the potential for the prediction of DFS in CRLM cases.