Risk Stratification Scores for Hospitalization Duration and Disease Progression in Moderate and Severe Patients With COVID-19

Jiaqi Huang; Yu Xu; Bin Wang; Ying Xiang; Na Wu; Wenjing Zhang; Tingting Xia; Zhiquan Yuan; Chengying Li; Xiaoyue Jia; Yifan Shan; Menglei Chen; Qi Li; Li Bai; Yafei Li


BMC Pulm Med. 2021;21(120) 

In This Article


Coronavirus disease 2019 (COVID-19), a newly emerged respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently become the most important global public health emergency. Because of the coronavirus's novel nature, it remains difficult to come up with specific remedies that will allow us to prevail over COVID-19. It is now widely recognized that a large-scale epidemic of COVID-19 can cause many deaths and more emergency patients, which presents a severe challenge to regional healthcare systems.[1] Rational medical resource allocation and efficiency of emergency rescue, which will be key measures to reduce the mortality of disease, depend on early prediction for length of hospital stay and disease progression.

Among the COVID-19 cases, about 81% are in mild or moderate condition, and 19% are severe or critical cases.[2] Mild patients do not need hospitalization, while some moderate patients may need. The length of hospital stay means the amount of time patients spend on medical resources. Thus, identifying factors affecting hospitalization duration to assess the risk stratification of patients will help to shorten hospital stay to the briefest amount of time possible and alleviate the burden of medical resources. Compared with moderate patients, severe and critical patients are more likely to progress rapidly and have adverse outcomes.[3] Predicting patients at high risk of progression, who often require more care and precise treatment, will improve the prognosis.

A recent systematic review critically appraised published and preprint reports of prediction models for prognosis of patients with COVID-19.[4] The most reported predictors of severe prognosis included age, sex, C-reactive protein (CRP), lactate dehydrogenase (LDH) and lymphocyte count. However, all included studies were rated at high risk of bias, mostly because of small sample sizes (ranging from 26 to 577 patients) and high risk of model overfitting. Reporting quality varied substantially between studies, and calibration of predictions was rarely assessed. In addition, findings from previous studies are inconsistent. For example, although several studies reported older patients had longer length of hospital stay, other studies have shown that demographic variables including age may not be good indicators for length of stay.[5–7] Therefore, sharing data with large sample sizes and updating of COVID-19 prognosis related prediction models are urgently needed.

Here, we performed a retrospective cohort study in 2425 cases from one of the largest special hospital of COVID-19 in Wuhan, China. Our aims were to construct two risk stratification scoring systems for predicting length of hospital stay and disease progression in moderate and severe patients, respectively. We present the following article in accordance with the STROBE reporting checklist.