Min Hua, Avramovic Sanja, Wojtusiak Janusz, Khosla Rahul, Fletcher Ross D, Alemi Farrokh, Kheirbek Raya
1 Department of Health Administration and Policy, George Mason University , Fairfax, Virginia.
2 Veterans Affairs Medical Center , Washington, DC.
J Palliat Med. 2017 Jan;20(1):35-41. doi: 10.1089/jpm.2015.0392. Epub 2016 Dec 7.
Accurate prediction of mortality for patients admitted to the intensive care units (ICUs) is an important component of medical care. However, little is known about the role of multimorbidity in predicting end of life for high-risk and vulnerable patients.
The aim of the study was to derive and validate a multimorbidity risk model in an attempt to predict all-cause mortality at 6 and 12 months posthospital discharge.
This is a retrospective, observational, clinical cohort study. Data were collected on 442,692 ICU patients who received care through the Veterans Administration between January 2003 and December 2013. The primary outcome was all-cause mortality at 6 and 12 months posthospital discharge. We divided the data into derivation (80%) and validation (20%) sets. Using multivariable logistic regression models, we compared prognostic models based on age, principal diagnosis groups, physiological markers, immunosuppressants, comorbidity categories, and a newly developed multimorbidity index (MMI) based on 5695 comorbidities. The cross-validated area under the receiver operating characteristic curve (AUC) was used to report the accuracy of predicting all-cause mortality at 6 and 12 months of hospital discharge.
The average age of patients was 68.87 years (standard deviation = 12.1), 95.9% were males, 44.9% were widowed, divorced, or separated. The relative order of accuracy in predicting mortality was the MMI (AUC = 0.84, CI = 0.83-0.84), VA Inpatient Evaluation Center index (AUC = 0.80, CI = 0.79-0.81), principal diagnosis groups (AUC = 0.74, CI = 0.73-0.74), comorbidities (AUC = 0.69, CI = 0.68-0.70), physiological markers (AUC = 0.65, CI = 0.64-0.65), age (AUC = 0.60, CI = 0.60-0.61),and immunosuppressant use (AUC = 0.59, CI = 0.58-0.59).
The MMI improved the accuracy of predicting short- and long-term all-cause mortality for ICU patients. Further prospective studies are needed to validate the index in different clinical settings and test generalizability of results in patients outside the VA system of care.
准确预测重症监护病房(ICU)患者的死亡率是医疗护理的重要组成部分。然而,对于多种疾病并存情况在预测高危和脆弱患者生命终结方面的作用,我们知之甚少。
本研究旨在推导并验证一个多种疾病并存风险模型,以尝试预测出院后6个月和12个月的全因死亡率。
这是一项回顾性、观察性临床队列研究。收集了2003年1月至2013年12月期间通过退伍军人管理局接受治疗的442,692例ICU患者的数据。主要结局是出院后6个月和12个月的全因死亡率。我们将数据分为推导集(80%)和验证集(20%)。使用多变量逻辑回归模型,我们比较了基于年龄、主要诊断组、生理指标、免疫抑制剂、合并症类别以及基于5695种合并症新开发的多种疾病并存指数(MMI)的预后模型。采用交叉验证的受试者工作特征曲线下面积(AUC)来报告预测出院后6个月和12个月全因死亡率的准确性。
患者的平均年龄为68.87岁(标准差 = 12.1),95.9%为男性,44.9%为丧偶、离异或分居状态。预测死亡率准确性的相对顺序为MMI(AUC = 0.84,CI = 0.83 - 0.84)、退伍军人事务部住院评估中心指数(AUC = 0.80,CI = 0.79 - 0.81)、主要诊断组(AUC = 0.74,CI = 0.73 - 0.74)、合并症(AUC = 0.69,CI = 0.68 - 0.70)、生理指标(AUC = 0.65,CI = 0.64 - 0.65)、年龄(AUC = 0.60,CI = 0.60 - 0.61)以及免疫抑制剂使用情况(AUC = 0.59,CI = 0.58 - 0.59)。
MMI提高了预测ICU患者短期和长期全因死亡率的准确性。需要进一步的前瞻性研究在不同临床环境中验证该指数,并测试在退伍军人事务部医疗系统以外的患者中结果的可推广性。