Centre for Digital Innovations in Health & Social Care, Faculty of Health Studies, University of Bradford, Bradford, UK.
Wolfson Centre for Applied Health Research, Bradford, UK.
BMC Res Notes. 2024 Apr 18;17(1):109. doi: 10.1186/s13104-024-06773-0.
In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic.
Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code 'U071' which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically).
The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)).
The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.
在英国国民保健制度（NHS）中，患者的生命体征被监测并汇总为国家早期预警评分（NEWS）评分。已经开发并验证了一组计算机辅助风险评分系统（CARSS），用于使用 NEWS 和常规血液检查结果预测计划外入院的院内死亡率和败血症。我们试图评估这些模型在大流行的第一阶段预测计划外入院 COVID-19 风险的准确性。
2020 年 3 月 11 日至 6 月 13 日期间，从两家急性医院出院的（存活/死亡）成年（≥18 岁）非择期入院患者，入院后 24 小时内电子记录了指数 NEWS。我们根据 ICD-10 代码 'U071' 确定 COVID-19 入院，该代码是通过 COVID-19 拭子检测结果（医院或社区）确定的。我们评估了 CARSS（CARS_N、CARS_NB、CARM_N、CARM_NB）在预测 COVID-19 风险方面的表现，包括区分度（c 统计量）和校准（图形）。
急诊入院后院内死亡率的风险为 8.4%（500/6444），有 9.6%（620/6444）被诊断为 COVID-19。对于预测 COVID-19 入院，CARS_N 模型具有最高的区分度 0.73（0.71 至 0.75）和校准斜率 0.81（0.72 至 0.89），而其他 CARSS 模型则为：CARM_N（区分度：0.68（0.66 至 0.70）和校准斜率 0.47（0.41 至 0.54））、CARM_NB（区分度：0.68（0.65 至 0.70）和校准斜率 0.37（0.31 至 0.43））和 CARS_NB（区分度：0.68（0.66 至 0.70）和校准斜率 0.56（0.47 至 0.64））。
CARS_N 模型在预测 COVID-19 风险方面具有合理的准确性。它可能具有临床实用性，作为入院时的早期预警系统，特别是在分诊大量计划外入院患者时，因为它不需要额外的数据收集并且易于自动化。
