Dataset Information

Development and validation of the HNC-LL score for predicting the severity of coronavirus disease 2019.

ABSTRACT:

Background

Information regarding risk factors associated with severe coronavirus disease (COVID-19) is limited. This study aimed to develop a model for predicting COVID-19 severity.

Methods

Overall, 690 patients with confirmed COVID-19 were recruited between 1 January and 18 March 2020 from hospitals in Honghu and Nanchang; finally, 442 patients were assessed. Data were categorised into the training and test sets to develop and validate the model, respectively.

Findings

A predictive HNC-LL (Hypertension, Neutrophil count, C-reactive protein, Lymphocyte count, Lactate dehydrogenase) score was established using multivariate logistic regression analysis. The HNC-LL score accurately predicted disease severity in the Honghu training cohort (area under the curve [AUC]=0.861, 95% confidence interval [CI]: 0.800-0.922; P<0.001); Honghu internal validation cohort (AUC=0.871, 95% CI: 0.769-0.972; P<0.001); and Nanchang external validation cohort (AUC=0.826, 95% CI: 0.746-0.907; P<0.001) and outperformed other models, including CURB-65 (confusion, uraemia, respiratory rate, BP, age ≥65 years) score model, MuLBSTA (multilobular infiltration, hypo-lymphocytosis, bacterial coinfection, smoking history, hypertension, and age) score model, and neutrophil-to-lymphocyte ratio model. The clinical significance of HNC-LL in accurately predicting the risk of future development of severe COVID-19 was confirmed.

Interpretation

We developed an accurate tool for predicting disease severity among COVID-19 patients. This model can potentially be used to identify patients at risks of developing severe disease in the early stage and therefore guide treatment decisions.

Funding

This work was supported by the National Nature Science Foundation of China (grant no. 81972897) and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2015).

SUBMITTER: Xiao LS

PROVIDER: S-EPMC7338276 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Development and validation of the HNC-LL score for predicting the severity of coronavirus disease 2019.

Xiao Lu-Shan LS Zhang Wen-Feng WF Gong Meng-Chun MC Zhang Yan-Pei YP Chen Li-Ya LY Zhu Hong-Bo HB Hu Chen-Yi CY Kang Pei P Liu Li L Zhu Hong H

EBioMedicine 20200707

<h4>Background</h4>Information regarding risk factors associated with severe coronavirus disease (COVID-19) is limited. This study aimed to develop a model for predicting COVID-19 severity.<h4>Methods</h4>Overall, 690 patients with confirmed COVID-19 were recruited between 1 January and 18 March 2020 from hospitals in Honghu and Nanchang; finally, 442 patients were assessed. Data were categorised into the training and test sets to develop and validate the model, respectively.<h4>Findings</h4>A p ...[more]

PMID: 32645614

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Project description:Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis. We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran's Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1). The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts. The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.

Project description:SubjectsEarly detection of coronavirus disease 2019 in patients likely to develop severe manifestations enables appropriate interventions, including rapid ICU admission. This study was conducted to determine whether noninvasive urine biomarkers can predict the clinical severity of coronavirus disease 2019.InterventionsNot applicable.Measurements and main resultsThis is single-center study, national center hospital designated for infectious disease. Fifty-eight patients who tested positive for severe acute respiratory syndrome coronavirus 2 in respiratory specimens through real-time reverse transcription-polymerase chain reaction were retrospectively studied. Urinary β2-microglobulin, liver-type fatty acid-binding protein were serially measured. Serum interferon-γ and monocyte chemotactic protein-1 were also evaluated. The 58 patients were assigned into three groups. Patients requiring intensive care were assigned to the severe group (n = 12). Patients treated with oxygen were assigned to the moderate group (n = 13). Other patients were assigned to the mild group (n = 33). Urine tests revealed that low β2-microglobulin and liver-type fatty acid-binding protein levels were associated with mild disease, whereas high levels were associated with severe disease. In severe cases, liver-type fatty acid-binding protein tended to be persistently high. The resulting cutoff values were β2-microglobulin; severe versus moderate + mild: 2,457 μg/dL (specificity 76.9% and sensitivity 90.0%, area under the receiver operating characteristic curve 85.9%), liver-type fatty acid-binding protein; severe versus moderate + mild: 22.0 μg/gCre (specificity 84.6% and sensitivity 90%, area under the receiver operating characteristic curve 91.8%). Urinary β2-microglobulin and serum interferon-γ/monocyte chemotactic protein-1 showed a similar trend.ConclusionsEvaluating urinary biomarkers such as β2-microglobulin and liver-type fatty acid-binding protein may allow determination of coronavirus disease 2019 patients with active cytokines and recognition of patients likely to become critically ill and requiring careful observation and early intervention.

Dataset Information

Development and validation of the HNC-LL score for predicting the severity of coronavirus disease 2019.

Background

Methods

Findings

Interpretation

Funding

Publications

Development and validation of the HNC-LL score for predicting the severity of coronavirus disease 2019.

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