Development and validation of a risk score using complete blood count to predict in-hospital mortality in COVID-19 patients.
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ABSTRACT: Background
To develop a sensitive risk score predicting the risk of mortality in patients with coronavirus disease 2019 (COVID-19) using complete blood count (CBC).Methods
We performed a retrospective cohort study from a total of 13,138 inpatients with COVID-19 in Hubei, China, and Milan, Italy. Among them, 9,810 patients with ≥2 CBC records from Hubei were assigned to the training cohort. CBC parameters were analyzed as potential predictors for all-cause mortality and were selected by the generalized linear mixed model (GLMM).Findings
Five risk factors were derived to construct a composite score (PAWNN score) using the Cox regression model, including platelet counts, age, white blood cell counts, neutrophil counts, and neutrophil:lymphocyte ratio. The PAWNN score showed good accuracy for predicting mortality in 10-fold cross-validation (AUROCs 0.92-0.93) and subsets with different quartile intervals of follow-up and preexisting diseases. The performance of the score was further validated in 2,949 patients with only 1 CBC record from the Hubei cohort (AUROC 0.97) and 227 patients from the Italian cohort (AUROC 0.80). The latent Markov model (LMM) demonstrated that the PAWNN score has good prediction power for transition probabilities between different latent conditions.Conclusions
The PAWNN score is a simple and accurate risk assessment tool that can predict the mortality for COVID-19 patients during their entire hospitalization. This tool can assist clinicians in prioritizing medical treatment of COVID-19 patients.Funding
This work was supported by National Key R&D Program of China (2016YFF0101504, 2016YFF0101505, 2020YFC2004702, 2020YFC0845500), the Key R&D Program of Guangdong Province (2020B1111330003), and the medical flight plan of Wuhan University (TFJH2018006).
SUBMITTER: Liu H
PROVIDER: S-EPMC7831644 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundEarly identification of patients with coronavirus disease 2019 (COVID-19) who are at high risk of mortality is of vital importance for appropriate clinical decision making and delivering optimal treatment. We aimed to develop and validate a clinical risk score for predicting mortality at the time of admission of patients hospitalized with COVID-19.MethodsCollaborating with the Korea Centers for Disease Control and Prevention (KCDC), we established a prospective consecutive cohort of 5,628 patients with confirmed COVID-19 infection who were admitted to 120 hospitals in Korea between January 20, 2020, and April 30, 2020. The cohort was randomly divided using a 7:3 ratio into a development (n = 3,940) and validation (n = 1,688) set. Clinical information and complete blood count (CBC) detected at admission were investigated using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-Mortality Score). The discriminative power of the risk model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic curves.ResultsThe incidence of mortality was 4.3% in both the development and validation set. A COVID-Mortality Score consisting of age, sex, body mass index, combined comorbidity, clinical symptoms, and CBC was developed. AUCs of the scoring system were 0.96 (95% confidence interval [CI], 0.85-0.91) and 0.97 (95% CI, 0.84-0.93) in the development and validation set, respectively. If the model was optimized for > 90% sensitivity, accuracies were 81.0% and 80.2% with sensitivities of 91.7% and 86.1% in the development and validation set, respectively. The optimized scoring system has been applied to the public online risk calculator (https://www.diseaseriskscore.com).ConclusionThis clinically developed and validated COVID-Mortality Score, using clinical data available at the time of admission, will aid clinicians in predicting in-hospital mortality.
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Project description:AbstractAs severe acute respiratory syndrome coronavirus 2 continues to spread, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage. We aimed to develop a risk score model for in-hospital mortality in patients hospitalized with 2019 novel coronavirus (COVID-19) that was robust across hospitals and used clinical factors that are readily available and measured standardly across hospitals.In this retrospective observational study, we developed a risk score model using data collected by trained abstractors for patients in 20 diverse hospitals across the state of Michigan (Mi-COVID19) who were discharged between March 5, 2020 and August 14, 2020. Patients who tested positive for severe acute respiratory syndrome coronavirus 2 during hospitalization or were discharged with an ICD-10 code for COVID-19 (U07.1) were included. We employed an iterative forward selection approach to consider the inclusion of 145 potential risk factors available at hospital presentation. Model performance was externally validated with patients from 19 hospitals in the Mi-COVID19 registry not used in model development. We shared the model in an easy-to-use online application that allows the user to predict in-hospital mortality risk for a patient if they have any subset of the variables in the final model.Two thousand one hundred and ninety-three patients in the Mi-COVID19 registry met our inclusion criteria. The derivation and validation sets ultimately included 1690 and 398 patients, respectively, with mortality rates of 19.6% and 18.6%, respectively. The average age of participants in the study after exclusions was 64 years old, and the participants were 48% female, 49% Black, and 87% non-Hispanic. Our final model includes the patient's age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospital's COVID-19 mortality rate. No other factors showed sufficient incremental model improvement to warrant inclusion. The area under the receiver operating characteristics curve for the derivation and validation sets were .796 (95% confidence interval, .767-.826) and .829 (95% confidence interval, .782-.876) respectively.We conclude that the risk of in-hospital mortality in COVID-19 patients can be reliably estimated using a few factors, which are standardly measured and available to physicians very early in a hospital encounter.
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Project description:ObjectiveSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is protean in its manifestations, affecting nearly every organ system. However, nervous system involvement and its effect on disease outcome are poorly characterized. The objective of this study was to determine whether neurologic syndromes are associated with increased risk of inpatient mortality.MethodsA total of 581 hospitalized patients with confirmed SARS-CoV-2 infection, neurologic involvement, and brain imaging were compared to hospitalized non-neurologic patients with coronavirus disease 2019 (COVID-19). Four patterns of neurologic manifestations were identified: acute stroke, new or recrudescent seizures, altered mentation with normal imaging, and neuro-COVID-19 complex. Factors present on admission were analyzed as potential predictors of in-hospital mortality, including sociodemographic variables, preexisting comorbidities, vital signs, laboratory values, and pattern of neurologic manifestations. Significant predictors were incorporated into a disease severity score. Patients with neurologic manifestations were matched with patients of the same age and disease severity to assess the risk of death.ResultsA total of 4,711 patients with confirmed SARS-CoV-2 infection were admitted to one medical system in New York City during a 6-week period. Of these, 581 (12%) had neurologic issues of sufficient concern to warrant neuroimaging. These patients were compared to 1,743 non-neurologic patients with COVID-19 matched for age and disease severity admitted during the same period. Patients with altered mentation (n = 258, p = 0.04, odds ratio [OR] 1.39, confidence interval [CI] 1.04-1.86) or radiologically confirmed stroke (n = 55, p = 0.001, OR 3.1, CI 1.65-5.92) had a higher risk of mortality than age- and severity-matched controls.ConclusionsThe incidence of altered mentation or stroke on admission predicts a modest but significantly higher risk of in-hospital mortality independent of disease severity. While other biomarker factors also predict mortality, measures to identify and treat such patients may be important in reducing overall mortality of COVID-19.
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Project description:BackgroundPredicting mortality from COVID-19 using information available when patients present to the emergency department can inform goals-of-care decisions and assist with ethical allocation of critical care resources. The study objective was to develop and validate a clinical score to predict emergency department and in-hospital mortality among consecutive nonpalliative patients with COVID-19; in this study, we define palliative patients as those who do not want resuscitative measures, such as intubation, intensive care unit care or cardiopulmonary resuscitation.MethodsThis derivation and validation study used observational cohort data recruited from 46 hospitals in 8 Canadian provinces participating in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN). We included adult (age ≥ 18 yr) nonpalliative patients with confirmed COVID-19 who presented to the emergency department of a participating site between Mar. 1, 2020, and Jan. 31, 2021. We randomly assigned hospitals to derivation or validation, and prespecified clinical variables as candidate predictors. We used logistic regression to develop the score in a derivation cohort and examined its performance in predicting emergency department and in-hospital mortality in a validation cohort.ResultsOf 8761 eligible patients, 618 (7.0%) died. The CCEDRRN COVID-19 Mortality Score included age, sex, type of residence, arrival mode, chest pain, severe liver disease, respiratory rate and level of respiratory support. The area under the curve was 0.92 (95% confidence interval [CI] 0.90-0.93) in derivation and 0.92 (95% CI 0.90-0.93) in validation. The score had excellent calibration. These results suggest that scores of 6 or less would categorize patients as being at low risk for in-hospital death, with a negative predictive value of 99.9%. Patients in the low-risk group had an in-hospital mortality rate of 0.1%. Patients with a score of 15 or higher had an observed mortality rate of 81.0%.InterpretationThe CCEDRRN COVID-19 Mortality Score is a simple score that can be used for level-of-care discussions with patients and in situations of critical care resource constraints to accurately predict death using variables available on emergency department arrival. The score was derived and validated mostly in unvaccinated patients, and before variants of concern were circulating widely and newer treatment regimens implemented in Canada.Study registrationClinicalTrials.gov, no. NCT04702945.
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