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Background Prognostic indices can enhance personalized predictions of health burdens. However, a simple, practical, and reproducible tool is lacking for clinical use. This study aimed to develop a machine learning-based prognostic index for predicting all-cause mortality in community-dwelling older individuals. Methods We utilized the Healthy Aging Longitudinal Study in Taiwan (HALST) cohort, encompassing data from 5 663 participants. Over the 5-year follow-up, 447 deaths were confirmed. A machine learning-based routine blood examination prognostic index (MARBE-PI) was developed using common laboratory tests based on machine learning techniques. Participants were grouped into multiple risk categories by stratum-specific likelihood ratio analysis based on their MARBE-PI scores. The MARBE-PI was subsequently externally validated with an independent population-based cohort from Japan. Results Beyond age, sex, education level, and BMI, 6 laboratory tests (low-density lipoprotein, albumin, aspartate aminotransferase, lymphocyte count, high-sensitivity C-reactive protein, and creatinine) emerged as pivotal predictors via stepwise logistic regression (LR) for 5-year mortality. The area under curves of MARBE-PI constructed by LR were 0.799 (95% confidence interval [95% CI]: 0.778–0.819) and 0.756 (95% CI: 0.694–0.814) for the internal and external validation data sets, and were 0.801 (95% CI: 0.790–0.811) and 0.809 (95% CI: 0.774–0.845) for the extended 10-year mortality in both data sets, respectively. Risk categories stratified by MARBE-PI showed a consistent dose–response association with mortality. The MARBE-PI also performed comparably with indices constructed with clinical health deficits and/or laboratory results. Conclusions The MARBE-PI is considered the most applicable measure for risk stratification in busy clinical settings. It holds potential to pinpoint older individuals at elevated mortality risk, thereby aiding clinical decision-making. [ABSTRACT FROM AUTHOR]

Purpose: Literature suggests that attention deficit hyperactivity disorder (ADHD) is associated with a high risk of unintentional injury. However, few studies have focused on whether risk of burn injury is relatively high among patients with ADHD. The aim of this study was to investigate whether ADHD affects the risk of burn injury. Materials and Methods: Individuals aged < 18 years with a current diagnosis of ADHD (N = 52,705) and age-, sex-, and other comorbidity-matched controls were selected from Taiwan's National Health Insurance Research Database for the period of January 1996 to December 2013. Burn injury was identified in both groups, and risk was evaluated using Cox proportional hazards models. We also explored the effects of age and sex on the association. Results: We determined that patients with ADHD had an increased probability of burn injury compared with the control group (ADHD vs controls, 4.6% vs 2.6%; adjusted hazard ratio [aHR] = 1.78; 95% confidence interval [CI] = 1.66– 1.90). The effect of ADHD on burn injury was more prominent among those aged < 6 years (aHR = 1.96; 95% CI = 1.75– 2.20) relative to those aged ≥ 6 years (aHR = 1.69; 95% CI = 1.56– 1.83). Both sexes had similar risk profiles. Conclusion: The study findings contribute to the increasing body of evidence that ADHD is associated with proneness to burn injury, particularly in children aged < 6 years. [ABSTRACT FROM AUTHOR]