Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Street, Chongqing, 400038, People's Republic of China.
Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of The Army Medical University, Chongqing, 400037, People's Republic of China.
BMC Pulm Med. 2021 Apr 14;21(1):120. doi: 10.1186/s12890-021-01487-6.
During outbreak of Coronavirus Disease 2019 (COVID-19), healthcare providers are facing critical clinical decisions based on the prognosis of patients. Decision support tools of risk stratification are needed to predict outcomes in patients with different clinical types of COVID-19.
This retrospective cohort study recruited 2425 patients with moderate or severe COVID-19. A logistic regression model was used to select and estimate the factors independently associated with outcomes. Simplified risk stratification score systems were constructed to predict outcomes in moderate and severe patients with COVID-19, and their performances were evaluated by discrimination and calibration.
We constructed two risk stratification score systems, named as STPCAL (including significant factors in the prediction model: number of clinical symptoms, the maximum body temperature during hospitalization, platelet count, C-reactive protein, albumin and lactate dehydrogenase) and TRPNCLP (including maximum body temperature during hospitalization, history of respiratory diseases, platelet count, neutrophil-to-lymphocyte ratio, creatinine, lactate dehydrogenase, and prothrombin time), to predict hospitalization duration for moderate patients and disease progression for severe patients, respectively. According to STPCAL score, moderate patients were classified into three risk categories for a longer hospital duration: low (Score 0-1, median = 8 days, with less than 20.0% probabilities), intermediate (Score 2-6, median = 13 days, with 30.0-78.9% probabilities), high (Score 7-9, median = 19 days, with more than 86.5% probabilities). Severe patients were stratified into three risk categories for disease progression: low risk (Score 0-5, with less than 12.7% probabilities), intermediate risk (Score 6-11, with 18.6-69.1% probabilities), and high risk (Score 12-16, with more than 77.9% probabilities) by TRPNCLP score. The two risk scores performed well with good discrimination and calibration.
Two easy-to-use risk stratification score systems were built to predict the outcomes in COVID-19 patients with different clinical types. Identifying high risk patients with longer stay or poor prognosis could assist healthcare providers in triaging patients when allocating limited healthcare during COVID-19 outbreak.
在 2019 年冠状病毒病（COVID-19）爆发期间，医疗保健提供者面临着基于患者预后的关键临床决策。需要风险分层的决策支持工具来预测不同临床类型 COVID-19 患者的结局。
本回顾性队列研究招募了 2425 例中重度 COVID-19 患者。使用逻辑回归模型选择并估计与结局独立相关的因素。构建简化的风险分层评分系统，以预测中重度 COVID-19 患者的结局，并通过区分度和校准度评估其性能。
我们构建了两个风险分层评分系统，命名为 STPCAL（包括预测模型中的显著因素：临床症状数量、住院期间最高体温、血小板计数、C 反应蛋白、白蛋白和乳酸脱氢酶）和 TRPNCLP（包括住院期间最高体温、呼吸疾病史、血小板计数、中性粒细胞与淋巴细胞比值、肌酐、乳酸脱氢酶和凝血酶原时间），分别预测中度患者的住院时间和重度患者的疾病进展。根据 STPCAL 评分，中度患者的住院时间分为三个风险类别：低（评分 0-1，中位数=8 天，概率小于 20.0%）、中（评分 2-6，中位数=13 天，概率 30.0%-78.9%）和高（评分 7-9，中位数=19 天，概率大于 86.5%）。严重患者的疾病进展分为三个风险类别：低危（评分 0-5，概率小于 12.7%）、中危（评分 6-11，概率 18.6%-69.1%）和高危（评分 12-16，概率大于 77.9%），TRPNCLP 评分。这两个风险评分具有良好的区分度和校准度。
构建了两个易于使用的风险分层评分系统，以预测不同临床类型 COVID-19 患者的结局。识别住院时间较长或预后较差的高危患者，有助于医疗保健提供者在 COVID-19 爆发期间分配有限的医疗资源时对患者进行分诊。
