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Development of a brief scoring system to predict any-cause mortality in patients hospitalized with COVID-19 infection.

ABSTRACT: Patients hospitalized with COVID-19 infection are at a high general risk for in-hospital mortality. A simple and easy-to-use model for predicting mortality based on data readily available to clinicians in the first 24 hours of hospital admission might be useful in directing scarce medical and personnel resources toward those patients at greater risk of dying. With this goal in mind, we evaluated factors predictive of in-hospital mortality in a random sample of 100 patients (derivation cohort) hospitalized for COVID-19 at our institution in April and May, 2020 and created potential models to test in a second random sample of 148 patients (validation cohort) hospitalized for the same disease over the same time period in the same institution. Two models (Model A: two variables, presence of pneumonia and ischemia); (Model B: three variables, age > 65 years, supplemental oxygen ≥ 4 L/min, and C-reactive protein (CRP) > 10 mg/L) were selected and tested in the validation cohort. Model B appeared the better of the two, with an AUC in receiver operating characteristic curve analysis of 0.74 versus 0.65 in Model A, but the AUC differences were not significant (p = 0.24. Model B also appeared to have a more robust separation of mortality between the lowest (none of the three variables present) and highest (all three variables present) scores at 0% and 71%, respectively. These brief scoring systems may prove to be useful to clinicians in assigning mortality risk in hospitalized patients.

SUBMITTER: Jiwa N

PROVIDER: S-EPMC8284608 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Early prediction of COVID-19 in-hospital mortality relies usually on patients' preexisting comorbidities and is rarely reproducible in independent cohorts. We wanted to compare the role of routinely measured biomarkers of immunity, inflammation, and cellular damage with preexisting comorbidities in eight different machine-learning models to predict mortality, and evaluate their performance in an independent population. We recruited and followed-up consecutive adult patients with SARS-Cov-2 infection in two different Italian hospitals. We predicted 60-day mortality in one cohort (development dataset, n = 299 patients, of which 80% was allocated to the development dataset and 20% to the training set) and retested the models in the second cohort (external validation dataset, n = 402). Demographic, clinical, and laboratory features at admission, treatments and disease outcomes were significantly different between the two cohorts. Notably, significant differences were observed for %lymphocytes (p < 0.05), international-normalized-ratio (p < 0.01), platelets, alanine-aminotransferase, creatinine (all p < 0.001). The primary outcome (60-day mortality) was 29.10% (n = 87) in the development dataset, and 39.55% (n = 159) in the external validation dataset. The performance of the 8 tested models on the external validation dataset were similar to that of the holdout test dataset, indicating that the models capture the key predictors of mortality. The shap analysis in both datasets showed that age, immune features (%lymphocytes, platelets) and LDH substantially impacted on all models' predictions, while creatinine and CRP varied among the different models. The model with the better performance was model 8 (60-day mortality AUROC 0.83 ± 0.06 in holdout test set, 0.79 ± 0.02 in external validation dataset). The features that had the greatest impact on this model's prediction were age, LDH, platelets, and %lymphocytes, more than comorbidities or inflammation markers, and these findings were highly consistent in both datasets, likely reflecting the virus effect at the very beginning of the disease.

Project description:BackgroundAge has been implicated as the main risk factor for COVID-19-related mortality. Our objective was to utilize administrative data to build an explanatory model accounting for geriatrics-focused indicators to predict mortality in hospitalized older adults with COVID-19.MethodsRetrospective cohort study of adults age 65 and older (N = 4783) hospitalized with COVID-19 in the greater New York metropolitan area between 3/1/20-4/20/20. Data included patient demographics and clinical presentation. Stepwise logistic regression with Akaike Information Criterion minimization was used.ResultsThe average age was 77.4 (SD = 8.4), 55.9% were male, 20.3% were African American, and 15.0% were Hispanic. In multivariable analysis, male sex (adjusted odds ration (adjOR) = 1.06, 95% CI:1.03-1.09); Asian race (adjOR = 1.08, CI:1.03-1.13); history of chronic kidney disease (adjOR = 1.05, CI:1.01-1.09) and interstitial lung disease (adjOR = 1.35, CI:1.28-1.42); low or normal body mass index (adjOR:1.03, CI:1.00-1.07); higher comorbidity index (adjOR = 1.01, CI:1.01-1.02); admission from a facility (adjOR = 1.14, CI:1.09-1.20); and mechanical ventilation (adjOR = 1.52, CI:1.43-1.62) were associated with mortality. While age was not an independent predictor of mortality, increasing age (centered at 65) interacted with hypertension (adjOR = 1.02, CI:0.98-1.07, reducing by a factor of 0.96 every 10 years); early Do-Not-Resuscitate (DNR, life-sustaining treatment preferences) (adjOR = 1.38, CI:1.22-1.57, reducing by a factor of 0.92 every 10 years); and severe illness on admission (at 65, adjOR = 1.47, CI:1.40-1.54, reducing by a factor of 0.96 every 10 years).ConclusionOur findings highlight that residence prior to admission, early DNR, and acute illness severity are important predictors of mortality in hospitalized older adults with COVID-19. Readily available administrative geriatrics-focused indicators that go beyond age can be utilized when considering prognosis.

Project description:The impact of the coronavirus disease 2019 (COVID-19) pandemic among patients with chronic liver disease is unknown. Given the high prevalence of nonalcoholic fatty liver disease (NAFLD), we determined the predictors of mortality and hospital resource use among patients with NAFLD admitted with COVID-19 by using electronic medical records data for adult patients with COVID-19 hospitalized in a multihospital health system who were discharged between March and December 2020. NAFLD was diagnosed by imaging or liver biopsy without other liver diseases. Charlson's comorbidity index (CCI) and Elixhauser comorbidity index (ECI) scores were calculated. In the study sample, among the 4,835 patients hospitalized for COVID-19, 553 had NAFLD (age: 55 ± 16 years, 51% male, 17% White, 11% Black, 58% Hispanic, 8% Asian, 5% from congregated living, 58% obese, 15% morbid obesity [body mass index ≥ 40], 51% type 2 diabetes, 63% hypertension, mean [SD] baseline CCI of 3.9 [3.2], and baseline ECI of 13.4 [11.3]). On admission, patients with NAFLD had more respiratory symptoms, higher body temperature and heart rate, higher alanine aminotransferase and aspartate aminotransferase than non-NAFLD controls (n = 2,736; P < 0.05). Of the patients with NAFLD infected with COVID-19, 3.9% experienced acute liver injury. The NAFLD group had significantly longer length of stay, intensive care unit use, and mechanical ventilation, with a crude inpatient mortality rate of 11%. In multivariate analysis, independent predictors of inpatient mortality among patients with NAFLD infected with COVID-19 were older age, morbid obesity, ECI score ≥ 11, higher Fibrosis-4 Index (FIB-4) score, and oxygen saturation <90% (all P < 0.05), but not sex, race/ethnicity, or any individual comorbidity (all P > 0.05). Conclusion: Patients with NAFLD infected with COVID-19 tend to be sicker on admission and require more hospital resource use. Independent predictors of mortality included higher FIB-4 and multimorbidity scores, morbid obesity, older age, and hypoxemia on admission.

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Development of a brief scoring system to predict any-cause mortality in patients hospitalized with COVID-19 infection.

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