Dataset Information

Categorization of COVID-19 severity to determine mortality risk.

ABSTRACT:

Purpose

Algorithms for classification of inpatient COVID-19 severity are necessary for confounding control in studies using real-world data.

Methods

Using Healthverity chargemaster and claims data, we selected patients hospitalized with COVID-19 between April 2020 and February 2021, and classified them by severity at admission using an algorithm we developed based on respiratory support requirements (supplemental oxygen or non-invasive ventilation, O2/NIV, invasive mechanical ventilation, IMV, or NEITHER). To evaluate the utility of the algorithm, patients were followed from admission until death, discharge, or a 28-day maximum to report mortality risks and rates overall and by stratified by severity. Trends for heterogeneity in mortality risk and rate across severity classifications were evaluated using Cochran-Armitage and Logrank trend tests, respectively.

Results

Among 118 117 patients, the algorithm categorized patients in increasing severity as NEITHER (36.7%), O2/NIV (54.3%), and IMV (9.0%). Associated mortality risk (and 95% CI) was 11.8% (11.6-12.0%) overall and increased with severity [3.4% (3.2-3.5%), 11.5% (11.3-11.8%), 47.3% (46.3-48.2%); p < 0.001]. Mortality rate per 1000 person-days (and 95% CI) was 15.1 (14.9-15.4) overall and increased with severity [5.7 (5.4-6.0), 14.5 (14.2-14.9), 32.7 (31.8-33.6); p < 0.001].

Conclusion

As expected, we observed a positive association between the algorithm-defined severity on admission and 28-day mortality risk and rate. Although performance remains to be validated, this provides some assurance that this algorithm may be used for confounding control or stratification in treatment effect studies.

SUBMITTER: Garry EM

PROVIDER: S-EPMC9088650 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionCoronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 virus, is a major public health concern spanning from healthy carriers to patients with life-threatening conditions. Although most of COVID-19 patients have mild-to-moderate clinical symptoms, some patients have severe pneumonia leading to death. Therefore, the early prediction of disease prognosis and severity is crucial in COVID-19 patients. The main objective of this study is to evaluate the haemocytometric parameters and identify severity score associated with SARS-CoV-2 infection.MethodsClinical and laboratory records were retrospectively reviewed from 97 cases of COVID-19 admitted to hospitals in Istanbul, Turkey. The patient groups were subdivided into three major groups: Group 1 (Non-critical): 59 patients, Group 2 (Critical-Survivors): 23 patients and Group 3 (Critical-Non-survivors):15 patients. These data was tested for correlation, including with derived haemocytometric parameters. The blood analyses were performed the Sysmex XN-series automated hematology analyser using standard laboratory protocols. All statistical testing was undertaken using Analyse-it software.Results97 patients with COVID-19 disease and 935 sequential complete blood count (CBC-Diff) measurements (days 0-30) were included in the final analyses. Multivariate analysis demonstrated that red cell distribution width (RDW) (>13.7), neutrophil to lymphocyte ratio (NLR) (4.4), Hemoglobin (Hgb) (<11.4 gr/dL) and monocyte to neutrophil ratio (MNR) (0.084) had the highest area under curve (AUC) values, respectively in discrimination critical patients than non-critical patients. In determining Group 3, MNR (<0.095), NLR (>5.2), Plateletcount (PLT) (>142 x103/L) and RDW (>14) were important haemocytometric parameters, and the mortality risk value created by their combination had the highest AUC value (AUC = 0.911, 95% CI, 0886-0.931). Trend analysis of CBC-Diff parameters over 30 days of hospitalization, NLR on day 2, MNR on day 4, RDW on day 6 and PLT on day 7 of admission were found to be the best time related parameters in discrimination non-critical (mild-moderate) patient group from critical (severe and non-survivor) patient group.ConclusionNLR is a strong predictor for the prognosis for severe COVID-19 patients when the cut-off chosen was 4.4, the combined mortality risk factor COVID-19 disease generated from RDW-CV, NLR, MNR and PLT is best as a mortality haematocytometric index.