Background Platelets play a critical role in hemostasis and inflammatory diseases. Low platelet count and activity have been reported to be associated with unfavorable prognosis. This study aims to explore the relationship between dynamics in platelet count and in-hospital morality among septic patients and to provide real-time updates on mortality risk to achieve dynamic prediction. Methods We conducted a multi-cohort, retrospective, observational study that encompasses data on septic patients in the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The joint latent class model (JLCM) was utilized to identify heterogenous platelet count trajectories over time among septic patients. We assessed the association between different trajectory patterns and 28-day in-hospital mortality using a piecewise Cox hazard model within each trajectory. We evaluated the performance of our dynamic prediction model through area under the receiver operating characteristic curve, concordance index (C-index), accuracy, sensitivity, and specificity calculated at predefined time points. Results Four subgroups of platelet count trajectories were identified that correspond to distinct in-hospital mortality risk. Including platelet count did not significantly enhance prediction accuracy at early stages (day 1 C-indexDynamic vs C-indexWeibull: 0.713 vs 0.714). However, our model showed superior performance to the static survival model over time (day 14 C-indexDynamic vs C-indexWeibull: 0.644 vs 0.617). Conclusions For septic patients in an intensive care unit, the rapid decline in platelet counts is a critical prognostic factor, and serial platelet measures are associated with prognosis.

中文翻译：

---

连续血小板计数作为脓毒症患者医院死亡率的动态预测指标


背景 血小板在止血和炎症性疾病中发挥着关键作用。据报道，低血小板计数和活性与不良预后相关。本研究旨在探讨脓毒症患者血小板计数动态与院内道德之间的关系，并提供死亡风险的实时更新以实现动态预测。方法 我们进行了一项多队列、回顾性、观察性研究，其中包括 eICU 合作研究数据库 (eICU-CRD) 和重症监护 IV 医疗信息集市 (MIMIC-IV) 数据库中脓毒症患者的数据。利用联合潜在类别模型 (JLCM) 来识别脓毒症患者随时间的异质性血小板计数轨迹。我们使用每个轨迹内的分段 Cox 风险模型评估了不同轨迹模式与 28 天院内死亡率之间的关联。我们通过在预定义时间点计算的接收者操作特征曲线下面积、一致性指数（C 指数）、准确性、灵敏度和特异性来评估动态预测模型的性能。结果 确定了与不同的院内死亡风险相对应的血小板计数轨迹的四个亚组。包括血小板计数并没有显着提高早期预测的准确性（第 1 天 C-indexDynamic 与 C-indexWeibull：0.713 vs 0.714）。然而，随着时间的推移，我们的模型显示出优于静态生存模型的性能（第 14 天的 C-indexDynamic 与 C-indexWeibull：0.644 vs 0.617）。结论 对于重症监护病房的脓毒症患者，血小板计数快速下降是一个关键的预后因素，连续血小板检测与预后相关。