Mertens Elly, Serrien Ben, Vandromme Mathil, Peñalvo José L
Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine Antwerp, Antwerp, Belgium.
Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
Front Med (Lausanne). 2022 Nov 23;9:1027674. doi: 10.3389/fmed.2022.1027674. eCollection 2022.
To adopt a multi-state risk prediction model for critical disease/mortality outcomes among hospitalised COVID-19 patients using nationwide COVID-19 hospital surveillance data in Belgium.
Information on 44,659 COVID-19 patients hospitalised between March 2020 and June 2021 with complete data on disease outcomes and candidate predictors was used to adopt a multi-state, multivariate Cox model to predict patients' probability of recovery, critical [transfer to intensive care units (ICU)] or fatal outcomes during hospital stay.
Median length of hospital stay was 9 days (interquartile range: 5-14). After admission, approximately 82% of the COVID-19 patients were discharged alive, 15% of patients were admitted to ICU, and 15% died in the hospital. The main predictors of an increased probability for recovery were younger age, and to a lesser extent, a lower number of prevalent comorbidities. A patient's transition to ICU or in-hospital death had in common the following predictors: high levels of c-reactive protein (CRP) and lactate dehydrogenase (LDH), reporting lower respiratory complaints and male sex. Additionally predictors for a transfer to ICU included middle-age, obesity and reporting loss of appetite and staying at a university hospital, while advanced age and a higher number of prevalent comorbidities for in-hospital death. After ICU, younger age and low levels of CRP and LDH were the main predictors for recovery, while in-hospital death was predicted by advanced age and concurrent comorbidities.
As one of the very few, a multi-state model was adopted to identify key factors predicting COVID-19 progression to critical disease, and recovery or death.
利用比利时全国范围内的新冠病毒住院监测数据，采用多状态风险预测模型来预测新冠病毒住院患者的重症疾病/死亡结局。
收集了2020年3月至2021年6月期间住院的44659例新冠病毒患者的信息，这些患者具有完整的疾病结局数据和候选预测指标，采用多状态多变量Cox模型来预测患者在住院期间康复、重症（转入重症监护病房）或死亡的概率。
住院时间中位数为9天（四分位间距：5 - 14天）。入院后，约82%的新冠病毒患者存活出院，15%的患者转入重症监护病房，15%的患者在医院死亡。康复概率增加的主要预测因素是年龄较小，其次是并存疾病数量较少。患者转入重症监护病房或院内死亡的共同预测因素如下：高水平的C反应蛋白（CRP）和乳酸脱氢酶（LDH）、有下呼吸道症状以及男性。此外，转入重症监护病房的预测因素还包括中年、肥胖、有食欲减退症状以及在大学医院就诊，而高龄和并存疾病数量较多则是院内死亡的预测因素。在转入重症监护病房后，年龄较小以及CRP和LDH水平较低是康复的主要预测因素，而高龄和并存疾病则是院内死亡的预测因素。
作为极少数研究之一，采用了多状态模型来确定预测新冠病毒进展为重症疾病、康复或死亡的关键因素。
