Statistics and Data Science Department, Hospital Authority, Hospital Authority Building, 147B Argyle Street, Kowloon, Hong Kong.
Strategy and Planning Division, Hospital Authority, Hospital Authority Building, 147B Argyle Street, Kowloon, Hong Kong.
BMC Med Inform Decis Mak. 2020 Dec 7;20(1):323. doi: 10.1186/s12911-020-01338-0.
This is the first study on prognostication in an entire cohort of laboratory-confirmed COVID-19 patients in the city of Hong Kong. Prognostic tool is essential in the contingency response for the next wave of outbreak. This study aims to develop prognostic models to predict COVID-19 patients' clinical outcome on day 1 and day 5 of hospital admission.
We did a retrospective analysis of a complete cohort of 1037 COVID-19 laboratory-confirmed patients in Hong Kong as of 30 April 2020, who were admitted to 16 public hospitals with their data sourced from an integrated electronic health records system. It covered demographic information, chronic disease(s) history, presenting symptoms as well as the worst clinical condition status, biomarkers' readings and Ct value of PCR tests on Day-1 and Day-5 of admission. The study subjects were randomly split into training and testing datasets in a 8:2 ratio. Extreme Gradient Boosting (XGBoost) model was used to classify the training data into three disease severity groups on Day-1 and Day-5.
The 1037 patients had a mean age of 37.8 (SD ± 17.8), 53.8% of them were male. They were grouped under three disease outcome: 4.8% critical/serious, 46.8% stable and 48.4% satisfactory. Under the full models, 30 indicators on Day-1 and Day-5 were used to predict the patients' disease outcome and achieved an accuracy rate of 92.3% and 99.5%. With a trade-off between practical application and predictive accuracy, the full models were reduced into simpler models with seven common specific predictors, including the worst clinical condition status (4-level), age group, and five biomarkers, namely, CRP, LDH, platelet, neutrophil/lymphocyte ratio and albumin/globulin ratio. Day-1 model's accuracy rate, macro-/micro-averaged sensitivity and specificity were 91.3%, 84.9%/91.3% and 96.0%/95.7% respectively, as compared to 94.2%, 95.9%/94.2% and 97.8%/97.1% under Day-5 model.
Both Day-1 and Day-5 models can accurately predict the disease severity. Relevant clinical management could be planned according to the predicted patients' outcome. The model is transformed into a simple online calculator to provide convenient clinical reference tools at the point of care, with an aim to inform clinical decision on triage and step-down care.
这是首例针对香港确诊 COVID-19 患者整个人群进行预后预测的研究。在应对下一波疫情时，预后工具至关重要。本研究旨在建立预测模型，以预测 COVID-19 患者入院第 1 天和第 5 天的临床结局。
我们对截至 2020 年 4 月 30 日香港 1037 例经实验室确诊的 COVID-19 患者进行了回顾性分析，这些患者被收治在 16 家公立医院，数据来源于综合电子健康记录系统。该系统涵盖了人口统计学信息、慢性病史、入院时的症状以及最严重的临床状况、生物标志物读数和入院第 1 天和第 5 天的 PCR 检测 Ct 值。研究对象按照 8:2 的比例随机分为训练数据集和测试数据集。使用极端梯度提升（XGBoost）模型将训练数据分为第 1 天和第 5 天的三个疾病严重程度组。
1037 例患者的平均年龄为 37.8（标准差±17.8），其中 53.8%为男性。他们被分为三种疾病结局：4.8%为危急/严重，46.8%为稳定，48.4%为满意。在全模型下，第 1 天和第 5 天使用 30 个指标预测患者的疾病结局，准确率分别为 92.3%和 99.5%。为了在实际应用和预测精度之间取得平衡，将全模型简化为包含七个常见特定预测因子的模型，包括最严重的临床状况（4 级）、年龄组和五种生物标志物，即 C 反应蛋白（CRP）、乳酸脱氢酶（LDH）、血小板、中性粒细胞/淋巴细胞比值和白蛋白/球蛋白比值。第 1 天模型的准确率、宏观/微观平均敏感度和特异度分别为 91.3%、84.9%/91.3%和 96.0%/95.7%，而第 5 天模型的准确率、宏观/微观平均敏感度和特异度分别为 94.2%、95.9%/94.2%和 97.8%/97.1%。
第 1 天和第 5 天模型均能准确预测疾病严重程度。可根据预测的患者结局进行相关临床管理。该模型已转化为一个简单的在线计算器，以便在护理点提供方便的临床参考工具，旨在为分诊和降阶梯护理提供临床决策依据。
