London Health Sciences Centre - Victoria Hospital, 800 Commissioner's Rd E, London, ON, Canada, N6A 5W9.
Division of Critical Care, Department of Medicine, Schulich School of Dentistry and Medicine, Western University, London, ON, Canada.
Can J Anaesth. 2020 Aug;67(8):981-991. doi: 10.1007/s12630-020-01686-5. Epub 2020 May 7.
To externally validate an intensive care unit (ICU) mortality prediction model that was created using the Ontario Critical Care Information System (CCIS), which includes the Multiple Organ Dysfunction Score (MODS).
We applied the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) recommendations to a prospective longitudinal cohort of patients discharged between 1 July 2015 and 31 December 31 2016 from 90 adult level-3 critical care units in Ontario. We used multivariable logistic regression with measures of discrimination, calibration-in-the-large, calibration slope, and flexible calibration plots to compare prediction model performance of the entire data set and for each ICU subtype.
Among 121,201 CCIS records with ICU mortality of 11.3%, the C-statistic for the validation data set was 0.805. The C-statistic ranged from 0.775 to 0.846 among the ICU subtypes. After intercept recalibration to adjust the baseline risk, the mean predicted risk of death matched actual ICU mortality. The calibration slope was close to 1 with all CCIS data and ICU subtypes of cardiovascular and community hospitals with low ventilation rates. Calibration slopes significantly less than 1 were found for ICUs in teaching hospitals and community hospitals with high ventilation rates whereas coronary care units had a calibration slope significantly higher than 1. Calibration plots revealed over-prediction in high risk groups to a varying degree across all cohorts.
A risk prediction model primarily based on the MODS shows reproducibility and transportability after intercept recalibration. Risk adjusting models that use existing and feasible data collection can support performance measurement at the individual ICU level.
验证一个使用安大略省重症监护信息系统（CCIS）创建的重症监护病房（ICU）死亡率预测模型，该模型包含多器官功能障碍评分（MODS）。
我们应用了透明报告多变量预测模型个体预后或诊断（TRIPOD）建议，对 2015 年 7 月 1 日至 2016 年 12 月 31 日期间从安大略省 90 个成人三级重症监护病房出院的前瞻性纵向队列进行了分析。我们使用多变量逻辑回归以及区分度、大校准、校准斜率和灵活校准图等指标来比较整个数据集和每个 ICU 亚型的预测模型性能。
在 121201 份 CCIS 记录中，ICU 死亡率为 11.3%，验证数据集的 C 统计量为 0.805。ICU 亚型的 C 统计量范围为 0.775 至 0.846。在对截距进行重新校准以调整基线风险后，死亡的平均预测风险与 ICU 实际死亡率相匹配。校准斜率与所有 CCIS 数据和心血管和社区医院低通气率的 ICU 亚型接近 1。在教学医院和社区医院高通气率的 ICU 中，校准斜率显著小于 1，而冠心病监护病房的校准斜率显著高于 1。校准图显示，在所有队列中，高危组都存在不同程度的过度预测。
经过截距重新校准，主要基于 MODS 的风险预测模型具有可重复性和可转移性。使用现有和可行的数据收集来调整风险的模型可以支持个体 ICU 水平的绩效测量。
