METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
方法:我们开发并验证了住院死亡率时间的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医疗记录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,以估计二元和时间到事件结果建模方法之间的预测性能差异。
结果:我们的数据包含150,342例入院和1016例死亡。Cox回归的模型判别高于离散时间逻辑回归,交叉验证的AUC分别为0.96和0.93,对于24小时内的死亡率预测,分别下降到0.93和0.88,1周内的死亡率预测。校准图显示校准因医院而异,但这可以通过预测风险对患者进行排名来缓解。
结论:时变协变量Cox模型可以成为分类患者的有力工具,当观察之间的时间高度可变时,这可能会导致在时间贫乏的环境中更有效和有效的护理。