Dataset Information

Early identification of patients with severe COVID-19 at increased risk of in-hospital death: a multicenter case-control study in Wuhan.

ABSTRACT:

Background

Most evidence regarding the risk factors for early in-hospital mortality in patients with severe COVID-19 focused on laboratory data at the time of hospital admission without adequate adjustment for confounding variables. A multicenter, age-matched, case-control study was therefore designed to explore the dynamic changes in laboratory parameters during the first 10 days after admission and identify early risk indicators for in-hospital mortality in this patient cohort.

Methods

Demographics and clinical data were extracted from the medical records of 93 pairs of patients who had been admitted to hospital with severe COVID-19. These patients had either been discharged or were deceased by March 3, 2020. Data from days 1, 4, 7, and 10 of hospital admission were compared between survivors and non-survivors. Univariate and multivariate conditional logistic regression analyses were employed to identify early risk indicators of in-hospital death in this cohort.

Results

On admission, in-hospital mortality was associated with five risk indicators (ORs in descending order): aspartate aminotransferase (AST, >32 U/L) 43.20 (95% CI: 2.63, 710.04); C-reactive protein (CRP) greater than 100 mg/L 13.61 (1.78, 103.941); lymphocyte count lower than 0.6×10⁹/L 9.95 (1.30, 76.42); oxygen index (OI) less than 200 8.23 (1.04, 65.15); and D-dimer over 1 mg/L 8.16 (1.23, 54.34). Sharp increases in D-dimer at day 4, accompanied by decreasing lymphocyte counts, deteriorating OI, and persistent remarkably high CRP concentration were observed among non-survivors during the early stages of hospital admission.

Conclusions

The potential risk factors of high D-dimer, CRP, AST, low lymphocyte count and OI could help clinicians identify patients at high risk of death early in the hospital admission. This might assist with rationalization of health care resources.

SUBMITTER: Zhou W

PROVIDER: S-EPMC8024856 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Early identification of patients with severe COVID-19 at increased risk of in-hospital death: a multicenter case-control study in Wuhan.

Zhou Wei W Liu Yisi Y Xu Beibei B Wang Sa S Li Shusheng S Liu Hong H Huang Ziting Z Luo Yan Y Hu Ming M Wu Wenjuan W Zhang Zhanguo Z Long Xin X Zou Wenbin W Bian Yi Y Zou Xiaojing X Elliott Malcolm M Yue Lanxin L Deng Huifang H Chen Hairong H Gao Xueli X Wu Ying Y Fang Minghao M Zhang Boli B Gao Yue Y

Journal of thoracic disease 20210301 3

<h4>Background</h4>Most evidence regarding the risk factors for early in-hospital mortality in patients with severe COVID-19 focused on laboratory data at the time of hospital admission without adequate adjustment for confounding variables. A multicenter, age-matched, case-control study was therefore designed to explore the dynamic changes in laboratory parameters during the first 10 days after admission and identify early risk indicators for in-hospital mortality in this patient cohort.<h4>Meth ...[more]

PMID: 33841931

Similar Datasets

Project description:To determine if early CNS symptoms are associated with severe coronavirus disease 2019.DesignA retrospective, observational case series study design.SettingElectronic health records were reviewed for patients from five healthcare systems across the state of Florida, United States.PatientsA clinical sample (n = 36,615) of patients with confirmed diagnosis of coronavirus disease 2019 were included. Twelve percent (n = 4,417) of the sample developed severe coronavirus disease 2019, defined as requiring critical care, mechanical ventilation, or diagnosis of acute respiratory distress syndrome, sepsis, or severe inflammatory response syndrome.InterventionsNone.Measurement and main resultsWe reviewed the electronic health record for diagnosis of early CNS symptoms (encephalopathy, headache, ageusia, anosmia, dizziness, acute cerebrovascular disease) between 14 days before the diagnosis of coronavirus disease 2019 and 8 days after the diagnosis of coronavirus disease 2019, or before the date of severe coronavirus disease 2019 diagnosis, whichever came first. Hierarchal logistic regression models were used to examine the odds of developing severe coronavirus disease 2019 based on diagnosis of early CNS symptoms. Severe coronavirus disease 2019 patients were significantly more likely to have early CNS symptoms (32.8%) compared with nonsevere patients (6.11%; χ2[1] = 3,266.08, p < 0.0001, φ = 0.29). After adjusting for demographic variables and pertinent comorbidities, early CNS symptoms were significantly associated with severe coronavirus disease 2019 (odds ratio = 3.21). Diagnosis of encephalopathy (odds ratio = 14.38) was associated with greater odds of severe coronavirus disease 2019; whereas diagnosis of anosmia (odds ratio = 0.45), ageusia (odds ratio = 0.46), and headache (odds ratio = 0.63) were associated with reduced odds of severe coronavirus disease 2019.ConclusionsEarly CNS symptoms, and specifically encephalopathy, are differentially associated with risk of severe coronavirus disease 2019 and may serve as an early marker for differences in clinical disease course. Therapies for early coronavirus disease 2019 are scarce, and further identification of subgroups at risk may help to advance understanding of the severity trajectories and enable focused treatment.

Project description:BackgroundAcute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model-Accurate Prediction of Prolonged Ventilation (APPROVE)-to identify patients at risk of death or respiratory failure requiring >= 48 h of MV.MethodsThis was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort.ResultsThere were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85-0.88) in 2013 and 0.90 (95% CI 0.84-0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%).ConclusionsAn automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification.Trial registrationClinicalTrials.gov, NCT02488174 . Registered on 18 March 2015.

Dataset Information

Early identification of patients with severe COVID-19 at increased risk of in-hospital death: a multicenter case-control study in Wuhan.

Background

Methods

Results

Conclusions

Publications

Early identification of patients with severe COVID-19 at increased risk of in-hospital death: a multicenter case-control study in Wuhan.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets