Binary classification models are frequently used to predict clinical deterioration, however they ignore information on the timing of events. An alternative is to apply time-to-event models, augmenting clinical workflows by ranking patients by predicted risks. This study examines how and why time-to-event modelling of vital signs data can help prioritise deterioration assessments using lift curves, and develops a prediction model to stratify acute care inpatients by risk of clinical deterioration. We developed and validated a Cox regression for time to in-hospital mortality. The model used time-varying covariates to estimate the risk of clinical deterioration. Adult inpatient medical records from 5 Australian hospitals between 1 January 2019 and 31 December 2020 were used for model development and validation. Model discrimination and calibration were assessed using internal–external cross validation. A discrete-time logistic regression model predicting death within 24 h with the same covariates was used as a comparator to the Cox regression model to estimate differences in predictive performance between the binary and time-to-event outcome modelling approaches. Our data contained 150,342 admissions and 1016 deaths. Model discrimination was higher for Cox regression than for discrete-time logistic regression, with cross-validated AUCs of 0.96 and 0.93, respectively, for mortality predictions within 24 h, declining to 0.93 and 0.88, respectively, for mortality predictions within 1 week. Calibration plots showed that calibration varied by hospital, but this can be mitigated by ranking patients by predicted risks. Time-varying covariate Cox models can be powerful tools for triaging patients, which may lead to more efficient and effective care in time-poor environments when the times between observations are highly variable.

中文翻译：

---

使用事件时间分析优先考虑恶化的患者：预测模型开发和内部外部验证


二元分类模型经常用于预测临床恶化，但它们忽略了事件发生时间的信息。另一种方法是应用事件发生时间模型，通过按预测风险对患者进行排名来增强临床工作流程。本研究探讨了生命体征数据的事件发生时间建模如何以及为何能够使用提升曲线帮助优先考虑恶化评估，并开发了一个预测模型，根据临床恶化风险对急症护理住院患者进行分层。我们开发并验证了院内死亡率时间的 Cox 回归。该模型使用时变协变量来估计临床恶化的风险。 2019年1月1日至2020年12月31日期间来自5家澳大利亚医院的成人住院病历用于模型开发和验证。使用内部-外部交叉验证评估模型辨别力和校准。使用具有相同协变量的预测 24 小时内死亡的离散时间逻辑回归模型作为 Cox 回归模型的比较器，以估计二元模型和事件时间结果模型方法之间预测性能的差异。我们的数据包含 150,342 例入院病例和 1016 例死亡病例。 Cox 回归的模型辨别力高于离散时间 Logistic 回归，24 小时内死亡率预测的交叉验证 AUC 分别为 0.96 和 0.93，1 周内死亡率预测分别下降至 0.93 和 0.88。校准图显示校准因医院而异，但可以通过按预测风险对患者进行排名来缓解这种情况。 时变协变量 Cox 模型可以成为对患者进行分类的强大工具，当观察之间的时间变化很大时，这可能会在时间紧迫的环境中带来更高效和有效的护理。