Steinmeyer Zara, Piau Antoine, Thomazeau Joséphine, Kai Samantha Huo Yung, Nourhashemi Fati
Geriatrics, CHU, Toulouse, France
UMR 1295, Paul Sabatier University Toulouse III, INSERM, Toulouse, France.
BMJ Support Palliat Care. 2021 Nov 25. doi: 10.1136/bmjspcare-2021-003258.
To develop and validate the WHALES screening tool predicting short-term mortality (3 months) in older patients hospitalised in an acute geriatric unit.
Older patients transferred to an acute geriatric ward from June 2017 to December 2018 were included. The cohort was divided into two groups: derivation (n=664) and validation (n=332) cohorts. Cause for admission in emergency room, hospitalisation history within the previous year, ongoing medical conditions, cognitive impairment, frailty status, living conditions, presence of proteinuria on a urine strip or urine albumin-to-creatinine ratio and abnormalities on an ECG were collected at baseline. Multiple logistic regressions were performed to identify independent variables associated with mortality at 3 months in the derivation cohort. The prediction score was then validated in the validation cohort.
Five independent variables available from medical history and clinical data were strongly predictive of short-term mortality in older adults including age, sex, living in a nursing home, unintentional weight loss and self-reported exhaustion. The screening tool was discriminative (C-statistic=0.74 (95% CI: 0.67 to 0.82)) and had a good fit (Hosmer-Lemeshow goodness-of-fit test (X (3)=0.55, p=0.908)). The area under the curve value for the final model was 0.74 (95% CI: 0.67 to 0.82).
The WHALES screening tool is a short and rapid tool predicting 3-month mortality among hospitalised older patients. Early identification of end of life may help appropriate timing and implementation of palliative care.
开发并验证用于预测急性老年病科住院老年患者短期死亡率（3个月）的WHALES筛查工具。
纳入2017年6月至2018年12月转入急性老年病病房的老年患者。该队列分为两组：推导队列（n = 664）和验证队列（n = 332）。在基线时收集急诊室入院原因、前一年的住院史、现有医疗状况、认知障碍、虚弱状态、生活条件、尿试纸检测蛋白尿或尿白蛋白与肌酐比值以及心电图异常情况。在推导队列中进行多因素逻辑回归分析，以确定与3个月死亡率相关的独立变量。然后在验证队列中对预测评分进行验证。
从病史和临床数据中获得的五个独立变量可强烈预测老年人的短期死亡率，包括年龄、性别、住在养老院、非故意体重减轻和自我报告的疲惫感。该筛查工具具有鉴别力（C统计量 = 0.74（95%可信区间：0.67至0.82））且拟合良好（Hosmer-Lemeshow拟合优度检验（X²(3)=0.55，p = 0.908））。最终模型的曲线下面积值为0.74（95%可信区间：0.67至0.82）。
WHALES筛查工具是一种简短快速的工具，可预测住院老年患者的3个月死亡率。早期识别生命末期可能有助于适时且恰当地实施姑息治疗。
