Dataset Information

Clinical value of laboratory indicators for predicting disease progression and death in patients with COVID-19: a retrospective cohort study.

ABSTRACT:

Objectives

As early prediction of severe illness and death for patients with coronavirus disease 2019 (COVID-19) is important, we aim to explore the clinical value of laboratory indicators in evaluating the progression and prognosis of patients with COVID-19.

Design

Retrospective cohort study.

Setting

Hospital-based study in China.

Participants

Adult patients with COVID-19 from December 15, 2019 to March 15, 2020.

End point

Disease severity and mortality.

Methods

Clinical data of 638 patients with COVID-19 were collected and compared between severe and non-severe groups. The predictive ability of laboratory indicators in disease progression and prognosis of COVID-19 was analysed using the receiver operating characteristic curve. The survival differences of COVID-19 patients with different levels of laboratory indicators were analysed utilising Kaplan-Meier analysis.

Results

29.8% (190/638) of patients with COVID-19 progressed to severe. Compared with patients with no adverse events, C reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR) and D-dimer were significantly higher in severe patients with adverse events, such as acute myocardial injury, respiratory failure, acute kidney injury, mechanical ventilation, intensive care unit admission, multiple organ dysfunction syndromes and death (all p<0.05). The multivariate logistic analysis suggested that CRP, NLR and D-dimer were independent risk factors for the disease progression of COVID-19 (all p<0.05). The model combining all of them owned the highest area under the receiver operating characteristic curve (AUC) predicting disease progression and death of COVID-19, with AUC of 0.894 (95% CI 0.857 to 0.931) and 0.918 (95% CI 0.873 to 0.962), respectively. Survival analysis suggested that the patients with a high level of CRP, NLR or D-dimer performed shorter overall survival time (all p<0.05).

Conclusions

The combination of CRP, NLR and D-dimer could be an effective predictor for the aggravation and death in patients with COVID-19. The abnormal expression of these indicators might suggest a strong inflammatory response and multiple adverse events in patients with severe COVID-19.

SUBMITTER: Wang Q

PROVIDER: S-EPMC8488281 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

Clinical value of laboratory indicators for predicting disease progression and death in patients with COVID-19: a retrospective cohort study.

Wang Qian Q Cheng Jie J Shang Jian J Wang Ying Y Wan Jing J Yan You-Qin YQ Liu Wen-Bin WB Zhang Hai-Ping HP Wang Jian-Ping JP Wang Xiao-Yue XY Li Zi-Ang ZA Lin Jun J

BMJ open 20211001 10

<h4>Objectives</h4>As early prediction of severe illness and death for patients with coronavirus disease 2019 (COVID-19) is important, we aim to explore the clinical value of laboratory indicators in evaluating the progression and prognosis of patients with COVID-19.<h4>Design</h4>Retrospective cohort study.<h4>Setting</h4>Hospital-based study in China.<h4>Participants</h4>Adult patients with COVID-19 from December 15, 2019 to March 15, 2020.<h4>End point</h4>Disease severity and mortality.<h4>M ...[more]

PMID: 34598979

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Project description:BackgroundThe initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination.ObjectiveThe objective of our study was to construct an effective model for the early identification of COVID-19 that would also distinguish it from CAP.MethodsThe clinical laboratory indicators (CLIs) of 61 COVID-19 patients and 60 CAP patients were analyzed retrospectively. Random combinations of various CLIs (ie, CLI combinations) were utilized to establish COVID-19 versus CAP classifiers with machine learning algorithms, including random forest classifier (RFC), logistic regression classifier, and gradient boosting classifier (GBC). The performance of the classifiers was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and recall rate in COVID-19 prediction using the test data set.ResultsThe classifiers that were constructed with three algorithms from 43 CLI combinations showed high performance (recall rate >0.9 and AUROC >0.85) in COVID-19 prediction for the test data set. Among the high-performance classifiers, several CLIs showed a high usage rate; these included procalcitonin (PCT), mean corpuscular hemoglobin concentration (MCHC), uric acid, albumin, albumin to globulin ratio (AGR), neutrophil count, red blood cell (RBC) count, monocyte count, basophil count, and white blood cell (WBC) count. They also had high feature importance except for basophil count. The feature combination (FC) of PCT, AGR, uric acid, WBC count, neutrophil count, basophil count, RBC count, and MCHC was the representative one among the nine FCs used to construct the classifiers with an AUROC equal to 1.0 when using the RFC or GBC algorithms. Replacing any CLI in these FCs would lead to a significant reduction in the performance of the classifiers that were built with them.ConclusionsThe classifiers constructed with only a few specific CLIs could efficiently distinguish COVID-19 from CAP, which could help clinicians perform early isolation and centralized management of COVID-19 patients.

Project description:ImportanceImmunocompromised status is a risk factor for severe SARS-CoV-2 infection. Little is known about how systemic corticosteroid dose and concurrent use of immunosuppressants are associated with COVID-19 outcomes.ObjectiveTo assess the association between corticosteroid dose/duration and concurrent immunosuppressant use on COVID-19 hospitalisation and death in the era of COVID-19 vaccinations.DesignThis is a retrospective cohort study using a deidentified insurance claims database from 1 July 2020 to 30 June 30, 2022, with the risk period starting on 1 July 2021. Impact of corticosteroid exposures and concurrent use of other immunosuppressants was assessed with attributable risk analysis and Cox regression that included COVID-19 vaccination status and time-updated dichotomous immunosuppressive medication exposures.ParticipantsThere were 10 109 596 eligible patients enrolled during the risk period, each with at least 365 days of continuous enrolment prior to 1 July 2021.ExposuresSystemic corticosteroids, disease-modifying antirheumatic drugs (DMARDs), tumour necrosis factor-alpha inhibitors (TNFis) and other immunosuppressive drug categories.Main outcomesIncidence rate ratios and hazard ratios for COVID-19 hospitalisation and death.ResultsCorticosteroids were prescribed to 1 379 049 (13.6%) of 10 109 596 individuals. After adjustment, corticosteroids were associated with an increased risk of COVID-19 hospitalisation (HR: 5.40; 95% CI 5.27 to 5.53; p<0.0001) and death (HR: 5.90; 95% CI 5.59 to 6.22; p<0.0001). Among individuals exposed to corticosteroids without a record of COVID-19 vaccination, risks for COVID-19 hospitalisation and death were increased by 3- and 14.5-fold. The population attributable risk of corticosteroid use for COVID-19 hospitalisations was 13.9% (95% CI 13.5 to 14.3%). There was a significantly increased risk of COVID-19 hospitalisation associated with the use of corticosteroids plus DMARDs (HR: 1.55; 95% CI 1.42 to 1.70; p<0.0001) or plus TNFis (HR: 1.60; 95% CI 1.15 to 2.22; p=0.005).ConclusionsCorticosteroids are associated with greater risk of COVID-19 hospitalisation and death, especially among unvaccinated individuals. Concurrent use of DMARDs and TNFis with corticosteroids confers greater risk.

Dataset Information

Clinical value of laboratory indicators for predicting disease progression and death in patients with COVID-19: a retrospective cohort study.

Objectives

Design

Setting

Participants

End point

Methods

Results

Conclusions

Publications

Clinical value of laboratory indicators for predicting disease progression and death in patients with COVID-19: a retrospective cohort study.

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