PDF(Pubmed)
Abstract：
BACKGROUND: 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.
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.
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.
摘要：
背景：二元分类模型经常用于预测临床恶化，然而，他们忽略了事件发生时间的信息。另一种方法是应用时间到事件模型，通过预测风险对患者进行排名来扩大临床工作流程。本研究探讨了生命体征数据的时间到事件建模如何以及为什么可以使用升力曲线来帮助确定恶化评估的优先级。并开发了一个预测模型，根据临床恶化的风险对急性护理住院患者进行分层。
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。
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。
