Modelling Key Population Attrition in the HIV and AIDS Programme in Kenya Using Random Survival Forests with Synthetic Minority Oversampling Technique-Nominal Continuous

HIV and AIDS has continued to be a major public health concern, and hence one of the epidemics that the world resolved to end by 2030 as highlighted in sustainable development goals (SDGs). A colossal amount of effort has been taken to reduce new HIV infections, but there are still a significant number of new infections reported. HIV prevalence is more skewed towards the key population who include female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID). The study design was retrospective and focused on key population enrolled in a comprehensive HIV and AIDS programme by the Kenya Red Cross Society from July 2019 to June 2021. Individuals who were either lost to follow up, defaulted (dropped out, transferred out, or relocated) or died were classified as attrition;while those who were active and alive by the end of the study were classified as retention. The study used density analysis to determine the spatial differences of key population attrition in the 19 targeted counties, and used Kilifi county as an example to map attrition cases in smaller administrative areas (sub-county level). The study used synthetic minority oversampling technique-nominal continuous (SMOTE-NC) to balance the datasets since the cases of attrition were much less than retention. The random survival forests model was then fitted to the balanced dataset. The model correctly identified attrition cases using the predicted ensemble mortality and their survival time using the estimated Kaplan-Meier survival function. The predictive performance of the model was strong and way better than random chance with concordance indices greater than 0.75.

Individual mortality risk predictive system of patients with acute-on-chronic liver failure based on a random survival forest model

Background:The basis of individualized treatment should be individualized mortality risk predictive information.The present study aimed to develop an online individual mortality risk predictive tool for acute-on-chronic liver failure(ACLF)patients based on a random survival forest(RSF)algorithm.Methods:The current study retrospectively enrolled ACLF patients from the Department of Infectious Diseases of The First People’s Hospital of Foshan,Shunde Hospital of Southern Medical University,and Jiangmen Central Hospital.Two hundred seventy-six consecutive ACLF patients were included in the present study as a model cohort(n=276).Then the current study constructed a validation cohort by drawing patients from the model dataset based on the resampling method(n=276).The RSF algorithm was used to develop an individual prognostic model for ACLF patients.The Brier score was used to evaluate the diagnostic accuracy of prognostic models.The weighted mean rank estimation method was used to compare the differences between the areas under the time-dependent ROC curves(AUROCs)of prognostic models.Results:Multivariate Cox regression identified hepatic encephalopathy(HE),age,serum sodium level,acute kidney injury(AKI),red cell distribution width(RDW),and international normalization index(INR)as independent risk factors for ACLF patients.A simplified RSF model was developed based on these previous risk factors.The AUROCs for predicting 3-,6-,and 12-month mortality were 0.916,0.916,and 0.905 for the RSF model and 0.872,0.866,and 0.848 for the Cox model in the model cohort,respectively.The Brier scores were 0.119,0.119,and 0.128 for the RSF model and 0.138,0.146,and 0.156 for the Cox model,respectively.The nonparametric comparison suggested that the RSF model was superior to the Cox model for predicting the prognosis of ACLF patients.Conclusions:The current study developed a novel online individual mortality risk predictive tool that could predict individual mortality risk predictive curves for individual patients.Additionally,the current online individual mortality risk predictive tool could further provide predicted mortality percentages and 95%confidence intervals at user-defined time points.

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)tumornode-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.

The Anzhen Risk Scoring System for Acute Type A Aortic Dissection:A Prospective Observational Study Protocol

Introduction:Acute type A aortic dissection(ATAAD)is a catastrophic disease with fatal outcomes.Malperfusion syndrome(MPS)is a serious complication of ATAAD,with an incidence of 20–40%.Many studies have shown that MPS is the main risk factor for poor ATAAD prognosis.However,a risk scoring system for ATAAD based on MPS is lacking.Here,we designed a risk scoring system for ATAAD to assess mortality through quantitative assessment of relevant organ malperfusion and subsequently develop rational treatment strategies.Methods and analysis:This was a prospective observational study.Patients’perioperative clinical data were col-lected to establish a database of ATAAD(N≥3000)and determine whether these patients had malperfusion complica-tions.The Anzhen risk scoring system was established on the basis of organ malperfusion by using a random forest survival model and a logistics model.The better method was then chosen to establish a revised risk scoring system.Ethics and dissemination:This study received ethical approval from the Ethics Committees of Beijing Anzhen Hospital,Capital Medical University(KS2019034-1).Patient consent was waived because biological samples were not collected,and no patient rights were violated.Findings will be disseminated at scientific conferences and in peer-reviewed publications.

Estimating prognosis of gastric neuroendocrine neoplasms using machine learning:A step towards precision medicine

Survival rates following radical surgery for gastric neuroendocrine neoplasms(g-NENs)are low,with high recurrence rates.This fact impacts patient prognosis and complicates postoperative management.Traditional prognostic models,including the Cox proportional hazards(CoxPH)model,have shown limited predictive power for postoperative survival in gastrointestinal neuroectodermal tumor patients.Machine learning methods offer a unique opportunity to analyze complex relationships within datasets,providing tools and methodologies to assess large volumes of high-dimensional,multimodal data generated by biological sciences.These methods show promise in predicting outcomes across various medical disciplines.In the context of g-NENs,utilizing machine learning to predict survival outcomes holds potential for personalized postoperative management strategies.This editorial reviews a study exploring the advantages and effectiveness of the random survival forest(RSF)model,using the lymph node ratio(LNR),in predicting disease-specific survival(DSS)in postoperative g-NEN patients stratified into low-risk and high-risk groups.The findings demonstrate that the RSF model,incorporating LNR,outperformed the CoxPH model in predicting DSS and constitutes an important step towards precision medicine.