A risk prediction model for medication safety: assessing cardiopulmonary adverse outcomes in 11,252 patients treated with ulinastatin - Hua, Rutong; Zhang, Ying; Deng, Jianxiong; Wen, Qiqi; Li, Guozheng; Fang, Rende; Chen, Zhuoyu; Cao, Yuxuan; Yu, Xi-Yong; Li, Jin; Lin, Zhongxiao (Frontiers in Pharmacology, 2026)
BackgroundEnsuring medication safety is a critical public health issue, particularly for widely used drugs like ulinastatin in critical care. Proactively identifying patients at high risk for adverse drug events is key to promoting safer medication practices and improving patient outcomes. This study focuses on developing a practical tool to stratify the risk of cardiopulmonary adverse outcomes associated with ulinastatin use.MethodsA multicenter, retrospective cohort study was conducted using data from 11,252 patients treated with ulinastatin between 2014 and 2017, 34 were excluded from the final statistical analysis due to missing critical data. Consequently, the cohort for all subsequent analyzes comprised 11,218 patients. The outcome of interest was the occurrence of a cardiopulmonary adverse event. We employed logistic regression to identify independent clinical risk factors and used these to construct a simple, points-based risk scoring system. The model’s performance in discriminating between high-risk and low-risk patients was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsAmong the cohort, 152 (1.35%) patients experienced a cardiopulmonary adverse outcome. Four factors were identified as independent predictors and incorporated into the risk model: low Ulinastatin dosage <300,000 U (OR (odds ratios) = 5.570, 95% CI (confidence intervals): 3.670–8.454, p < 0.001), duration of medication >1 day (OR = 2.165, 95% CI: 1.480–3.166, p < 0.001), concomitant medications (OR = 2.088, 95% CI: 1.414–3.083, p < 0.001), and treatment in the Intensive Care Unit (ICU) (OR = 3.737, 95% CI: 2.487–5.615, p < 0.001). The composite risk score demonstrated good predictive accuracy, with an AUC of 0.779 (95% CI: 0.741–0.817), significantly outperforming any single predictor.ConclusionWe developed and validated a simple, clinically actionable risk stratification tool for cardiopulmonary adverse events in patients receiving ulinastatin. This model can help clinicians and healthcare systems identify high-risk individuals before treatment initiation, facilitating targeted monitoring, informed decision-making, and personalized dosing strategies. The implementation of such a tool represents a tangible step towards enhancing medication safety protocols and promoting safer prescribing behaviors in clinical practice.Clinical Trial Registrationhttps://www.chictr.org.cn/showproj.html?proj=11439.