Development and validation of QMortality risk prediction algorithm to estimate short term risk of death and assess frailty: cohort study.
Development and validation of QMortality risk prediction algorithm to estimate short term risk of death and assess frailty: cohort study.
复制标题
DOI：
10.1136/bmj.j4208
复制
发表时间：
2017-09-20
期刊：
影响因子：
--
通讯作者：
Coupland C
中科院分区：
文献类型：
--
作者：
Hippisley-Cox J;Coupland C
目标 推导并验证风险预测方程以估计短期死亡风险，并开发基于死亡风险和计划外入院风险的衰弱分类方法。 设计前瞻性开放队列研究。 参与者定期从 2012 年至 2016 年间向英国 QResearch 提供数据的 1436 个常规实践中收集数据。其中 1079 个实践用于制定分数，并使用一组单独的 357 个实践来验证分数。推导队列中有 147 万名年龄在 65-100 岁之间的患者，验证队列中有 50 万名患者。 方法 使用推导队列中的 Cox 比例风险模型推导出男性和女性的单独风险方程，以评估一年内的死亡风险。考虑的危险因素包括年龄、性别、种族、贫困、吸烟状况、酒精摄入量、体重指数、医疗状况、特定药物、社会因素和最近的调查结果。校准和歧视的测量是在验证队列中分别针对男性和女性以及每个年龄和种族群体确定的。新的死亡率方程与现有的 QAdmissions 方程（预测计划外入院的风险）结合使用，将患者分为虚弱组。 主要结局指标 主要结局是全因死亡率。 结果 在随访期间，根据 439 万人年的观察，在推导队列中发现了 180-132 例死亡。最终模型包括年龄、体重指数、汤森评分、种族、吸烟状况、饮酒、过去 12 个月内计划外住院、心房颤动、抗精神病药物、癌症、哮喘或慢性阻塞性肺病、住在疗养院、充血性心力衰竭、皮质类固醇、心血管疾病、痴呆、癫痫、学习障碍、腿部溃疡、慢性肝病或胰腺炎、帕金森病、行动不便、类风湿性关节炎、慢性肾病、1型糖尿病、2型糖尿病、静脉血栓栓塞、贫血、肝功能检查结果异常、血小板计数高、过去一年因食欲不振、体重意外减轻或呼吸困难而就诊。该模型具有良好的校准和高水平的解释变异和区分度。在女性中，该方程解释了 55.6% 的死亡时间变异 (R2)，并且具有很好的区分度——D 统计值为 2.29，Harrell 的 C 统计值为 0.85。男性的相应值为 53.1%、2.18 和 0.84。通过结合死亡和意外入院的预测风险，2.7% 的患者 (n=13~665) 被分类为严重虚弱，9.4% (n=46~770) 被分类为中度虚弱，43.1% (n=215~253) 为轻度虚弱，44.8% (n=223~790) 为健康。 结论我们考虑了人口、社会和临床变量，开发了新的方程来预测 65 岁或以上男性和女性的短期死亡风险。这些方程在单独的验证队列中具有良好的性能。 QMortality 方程可与 QAdmissions 方程结合使用，将患者分为四个虚弱组（称为 QFrailty 类别），以便能够识别患者以进行进一步评估或干预。
Objectives To derive and validate a risk prediction equation to estimate the short term risk of death, and to develop a classification method for frailty based on risk of death and risk of unplanned hospital admission. Design Prospective open cohort study. Participants Routinely collected data from 1436 general practices contributing data to QResearch in England between 2012 and 2016. 1079 practices were used to develop the scores and a separate set of 357 practices to validate the scores. 1.47 million patients aged 65-100 years were in the derivation cohort and 0.50 million patients in the validation cohort. Methods Cox proportional hazards models in the derivation cohort were used to derive separate risk equations in men and women for evaluation of the risk of death at one year. Risk factors considered were age, sex, ethnicity, deprivation, smoking status, alcohol intake, body mass index, medical conditions, specific drugs, social factors, and results of recent investigations. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for each age and ethnic group. The new mortality equation was used in conjunction with the existing QAdmissions equation (which predicts risk of unplanned hospital admission) to classify patients into frailty groups. Main outcome measure The primary outcome was all cause mortality. Results During follow-up 180 132 deaths were identified in the derivation cohort arising from 4.39 million person years of observation. The final model included terms for age, body mass index, Townsend score, ethnic group, smoking status, alcohol intake, unplanned hospital admissions in the past 12 months, atrial fibrillation, antipsychotics, cancer, asthma or chronic obstructive pulmonary disease, living in a care home, congestive heart failure, corticosteroids, cardiovascular disease, dementia, epilepsy, learning disability, leg ulcer, chronic liver disease or pancreatitis, Parkinson’s disease, poor mobility, rheumatoid arthritis, chronic kidney disease, type 1 diabetes, type 2 diabetes, venous thromboembolism, anaemia, abnormal liver function test result, high platelet count, visited doctor in the past year with either appetite loss, unexpected weight loss, or breathlessness. The model had good calibration and high levels of explained variation and discrimination. In women, the equation explained 55.6% of the variation in time to death (R2), and had very good discrimination—the D statistic was 2.29, and Harrell’s C statistic value was 0.85. The corresponding values for men were 53.1%, 2.18, and 0.84. By combining predicted risks of mortality and unplanned hospital admissions, 2.7% of patients (n=13 665) were classified as severely frail, 9.4% (n=46 770) as moderately frail, 43.1% (n=215 253) as mildly frail, and 44.8% (n=223 790) as fit. Conclusions We have developed new equations to predict the short term risk of death in men and women aged 65 or more, taking account of demographic, social, and clinical variables. The equations had good performance on a separate validation cohort. The QMortality equations can be used in conjunction with the QAdmissions equations, to classify patients into four frailty groups (known as QFrailty categories) to enable patients to be identified for further assessment or interventions.
登录
查看更多内容
DOI：
10.1136/bmj.b4229
发表时间：
2009-11-19
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Hippisley-Cox J;Coupland C
通讯作者：
Coupland C
DOI：
10.1136/bmj.d3651
发表时间：
2011-06-22
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Collins GS;Mallett S;Altman DG
通讯作者：
Altman DG
影响因子：
2.9
作者：
Hippisley-Cox, Julia;Coupland, Carol
通讯作者：
Coupland, Carol
影响因子：
2.9
作者：
Freund, Tobias;Gondan, Matthias;Szecsenyi, Joachim
通讯作者：
Szecsenyi, Joachim
影响因子：
105.7
作者：
Hippisley-Cox, Julia;Coupland, Carol;Brindle, Peter
通讯作者：
Brindle, Peter