Remote Covid Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies
Remote Covid Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies
复制标题
初级保健远程新冠病毒评估 (RECAP) 风险预测工具：推导和现实世界验证研究
DOI：
10.1101/2021.12.23.21268279
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Espinosa-Gonzalez A
中科院分区：
文献类型：
--
作者：
Espinosa-Gonzalez A
背景准确评估社区COVID-19严重程度对患者护理至关重要，需要在社区环境中充分验证COVID-19特定风险预测评分。在识别体征、症状和风险因素的定性阶段之后，我们旨在开发和验证两种COVID-19特定风险预测评分。远程COVID-19评估在初级护理-全科实践评分（RECAP-GP;无外周血氧饱和度[SpO 2]）和RECAP-血氧饱和度评分（RECAP-O2;有SpO 2）.MethodsRECAP是一项前瞻性队列研究，使用多变量logistic回归。通过初级保健电子健康记录从社区疑似COVID-19患者中收集疾病体征和症状（预测因子）数据，并与症状发作28天内入院（结局）的二级数据相关联。RECAP-GP的数据来源是牛津-皇家全科医师研究与监测中心（RCGP-RSC）的初级保健实践（开发集）、西北伦敦初级保健实践（验证集）和NHS COVID-19临床评估服务（CCAS;验证集）。RECAP-O2的数据源是Doctaly Assist平台（后续样本中的开发集和验证集）。这两个概率风险预测模型是使用开发集通过向后消除建立的，并通过应用于验证数据集进行验证。估计每个模型的样本量，包括开发和验证集是2880 people.FindingsData可从8311个人。在伦敦西北部、RCGP-RSC和CCAS数据中，大多数观察结果（如SpO 2）缺失;但是，1948例使用Doctaly的患者中有1364例（70.0%）的SpO 2可用。在最终的预测模型中，RECAP-GP（n=1863）包括性别（男性和女性）、年龄（岁）、呼吸困难程度（三分量表），体温症状（两点量表）和高血压的存在（是或否）;曲线下面积为0·80（95% CI 0.76 - 0.85），经验证，低风险认定的阴性预测值为99%（95% CI 98.1 - 99.2; 1435/1453）。RECAP-O2包括年龄（岁）、呼吸困难程度（两点量表），疲劳（两点量表）和静息SpO 2（百分比）;曲线下面积为0·84（0.78 - 0.90），经验证，低风险认定的阴性预测值为99%（95%CI 98.9 - 99.7; 1176/1183）。解释两种RECAP模型都是评估社区COVID-19患者的有效工具。RECAP-GP最初可用于识别需要监测的患者，无需观察。如果患者接受监测且SpO 2可用，RECAP-O2可用于评估治疗升级的需求。资助Community Jameel和帝国理工学院校长卓越基金、经济和社会研究理事会、英国研究与创新以及英国健康数据研究。
BackgroundAccurate assessment of COVID-19 severity in the community is essential for patient care and requires COVID-19-specific risk prediction scores adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms, and risk factors, we aimed to develop and validate two COVID-19-specific risk prediction scores. Remote COVID-19 Assessment in Primary Care-General Practice score (RECAP-GP; without peripheral oxygen saturation [SpO2]) and RECAP-oxygen saturation score (RECAP-O2; with SpO2).MethodsRECAP was a prospective cohort study that used multivariable logistic regression. Data on signs and symptoms (predictors) of disease were collected from community-based patients with suspected COVID-19 via primary care electronic health records and linked with secondary data on hospital admission (outcome) within 28 days of symptom onset. Data sources for RECAP-GP were Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP-RSC) primary care practices (development set), northwest London primary care practices (validation set), and the NHS COVID-19 Clinical Assessment Service (CCAS; validation set). The data source for RECAP-O2 was the Doctaly Assist platform (development set and validation set in subsequent sample). The two probabilistic risk prediction models were built by backwards elimination using the development sets and validated by application to the validation datasets. Estimated sample size per model, including the development and validation sets was 2880 people.FindingsData were available from 8311 individuals. Observations, such as SpO2, were mostly missing in the northwest London, RCGP-RSC, and CCAS data; however, SpO2was available for 1364 (70·0%) of 1948 patients who used Doctaly. In the final predictive models, RECAP-GP (n=1863) included sex (male and female), age (years), degree of breathlessness (three point scale), temperature symptoms (two point scale), and presence of hypertension (yes or no); the area under the curve was 0·80 (95% CI 0·76–0·85) and on validation the negative predictive value of a low risk designation was 99% (95% CI 98·1–99·2; 1435 of 1453). RECAP-O2 included age (years), degree of breathlessness (two point scale), fatigue (two point scale), and SpO2at rest (as a percentage); the area under the curve was 0·84 (0·78–0·90) and on validation the negative predictive value of low risk designation was 99% (95% CI 98·9–99·7; 1176 of 1183).InterpretationBoth RECAP models are valid tools to assess COVID-19 patients in the community. RECAP-GP can be used initially, without need for observations, to identify patients who require monitoring. If the patient is monitored and SpO2is available, RECAP-O2 is useful to assess the need for treatment escalation.FundingCommunity Jameel and the Imperial College President's Excellence Fund, the Economic and Social Research Council, UK Research and Innovation, and Health Data Research UK.