Khan Sikandar H, Perkins Anthony J, Fuchita Mikita, Holler Emma, Ortiz Damaris, Boustani Malaz, Khan Babar A, Gao Sujuan
Division of Pulmonary, Critical Care Sleep and Occupational Medicine Indianapolis Indiana USA.
Regenstrief Institute Indiana University Center for Aging Research Indianapolis Indiana USA.
Health Sci Rep. 2023 Oct 19;6(10):e1634. doi: 10.1002/hsr2.1634. eCollection 2023 Oct.
Given the growing utilization of critical care services by an aging population, development of population-level risk models which predict intensive care unit (ICU) survivorship and mortality may offer advantages for researchers and health systems. Our objective was to develop a risk model for ICU survivorship and mortality among community dwelling older adults.
This was a population-based cohort study of 48,127 patients who were 50 years and older with at least one primary care visit between January 1, 2017, and December 31, 2017. We used electronic health record (EHR) data to identify variables predictive of ICU survivorship.
ICU admission and mortality within 2 years after index primary care visit date were used to divide patients into three groups of "alive without ICU admission", "ICU survivors," and "death." Multinomial logistic regression was used to identify EHR predictive variables for the three patient outcomes. Cross-validation by randomly splitting the data into derivation and validation data sets (60:40 split) was used to identify predictor variables and validate model performance using area under the receiver operating characteristics (AUC) curve. In our overall sample, 92.2% of patients were alive without ICU admission, 6.2% were admitted to the ICU at least once and survived, and 1.6% died. Greater deciles of age over 50 years, diagnoses of chronic obstructive pulmonary disorder or chronic heart failure, and laboratory abnormalities in alkaline phosphatase, hematocrit, and albumin contributed highest risk score weights for mortality. Risk scores derived from the model discriminated between patients that died versus remained alive without ICU admission (AUC = 0.858), and between ICU survivors versus alive without ICU admission (AUC = 0.765).
Our risk scores provide a feasible and scalable tool for researchers and health systems to identify patient cohorts at increased risk for ICU admission and survivorship. Further studies are needed to prospectively validate the risk scores in other patient populations.
鉴于老年人口对重症监护服务的使用日益增加，开发能够预测重症监护病房（ICU）生存率和死亡率的人群水平风险模型，可能会给研究人员和卫生系统带来益处。我们的目标是开发一种针对社区居住的老年人ICU生存率和死亡率的风险模型。
这是一项基于人群的队列研究，研究对象为48127名年龄在50岁及以上的患者，他们在2017年1月1日至2017年12月31日期间至少有一次初级保健就诊。我们使用电子健康记录（EHR）数据来识别预测ICU生存率的变量。
在索引初级保健就诊日期后的2年内，ICU入院情况和死亡率被用于将患者分为三组：“未入住ICU存活”、“ICU幸存者”和“死亡”。多项逻辑回归用于识别这三种患者结局的EHR预测变量。通过将数据随机分为推导数据集和验证数据集（60:40分割）进行交叉验证，以识别预测变量，并使用受试者工作特征（AUC）曲线下面积来验证模型性能。在我们的总体样本中，92.2%的患者未入住ICU存活，6.2%的患者至少入住一次ICU并存活，1.6%的患者死亡。50岁以上年龄的更高十分位数、慢性阻塞性肺疾病或慢性心力衰竭的诊断，以及碱性磷酸酶、血细胞比容和白蛋白的实验室异常对死亡风险评分权重贡献最高。该模型得出的风险评分能够区分死亡患者与未入住ICU存活的患者（AUC = 0.858），以及ICU幸存者与未入住ICU存活的患者（AUC = 0.765）。
我们的风险评分可为研究人员和卫生系统提供一种可行且可扩展的工具，以识别ICU入院和生存风险增加的患者队列。需要进一步的研究来前瞻性地验证其他患者群体中的风险评分。
