Dataset Information

Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use.

ABSTRACT:

Backgrounds

Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19.

Methods and findings

We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07-1.09), male sex (HR 1.62, 95%CI 1.30-2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39-2.12), diabetes (HR 1.21, 95%CI 1.02-1.45), coronary heart disease (HR 1.40 95% CI 1.09-1.80), chronic liver disease (HR 1.78, 95%CI 1.16-2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002-1.0005). The AUC was 0.822 (95%CI 0.722-0.922) in the derivation cohort and 0.820 (95%CI 0.724-0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp).

Conclusions

A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.

SUBMITTER: Magro B

PROVIDER: S-EPMC7808616 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use.

Magro Bianca B Zuccaro Valentina V Novelli Luca L Zileri Lorenzo L Celsa Ciro C Raimondi Federico F Gori Mauro M Cammà Giulia G Battaglia Salvatore S Genova Vincenzo Giuseppe VG Paris Laura L Tacelli Matteo M Mancarella Francesco Antonio FA Enea Marco M Attanasio Massimo M Senni Michele M Di Marco Fabiano F Lorini Luca Ferdinando LF Fagiuoli Stefano S Bruno Raffaele R Cammà Calogero C Gasbarrini Antonio A Gasbarrini Antonio A

PloS one 20210114 1

<h4>Backgrounds</h4>Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19.<h4>Methods and findings</h4>We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patie ...[more]

PMID: 33444411

Similar Datasets

Project description:ObjectiveTo evaluate whether pregnancy is an independent risk factor for in-hospital mortality among patients of reproductive age hospitalized with coronavirus disease 2019 (COVID-19) viral pneumonia.MethodsWe conducted a retrospective cohort study (April 2020-May 2021) of 23,574 female inpatients aged 15-45 years with an International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code for COVID-19 discharged from 749 U.S. hospitals in the Premier Healthcare Database. We used a viral pneumonia diagnosis to select for patients with symptomatic COVID-19. The associations between pregnancy and in-hospital mortality, intensive care unit (ICU) admission, and mechanical ventilation were analyzed using propensity score-matched conditional logistic regression. Models were matched for age, marital status, race and ethnicity, Elixhauser comorbidity score, payer, hospital number of beds, season of discharge, hospital region, obesity, hypertension, diabetes mellitus, chronic pulmonary disease, deficiency anemias, depression, hypothyroidism, and liver disease.ResultsIn-hospital mortality occurred in 1.1% of pregnant patients and 3.5% of nonpregnant patients hospitalized with COVID-19 and viral pneumonia (propensity score-matched odds ratio [OR] 0.39, 95% CI 0.25-0.63). The frequency of ICU admission for pregnant and nonpregnant patients was 22.0% and 17.7%, respectively (OR 1.34, 95% CI 1.15-1.55). Mechanical ventilation was used in 8.7% of both pregnant and nonpregnant patients (OR 1.05, 95% CI 0.86-1.29). Among patients who were admitted to an ICU, mortality was lower for pregnant compared with nonpregnant patients (OR 0.33, 95% CI 0.20-0.57), though mechanical ventilation rates were similar (35.7% vs 38.3%, OR 0.90, 95% CI 0.70-1.16). Among patients with mechanical ventilation, pregnant patients had a reduced risk of in-hospital mortality compared with nonpregnant patients (0.26, 95% CI 0.15-0.46).ConclusionDespite a higher frequency of ICU admission, in-hospital mortality was lower among pregnant patients compared with nonpregnant patients with COVID-19 viral pneumonia, and these findings persisted after propensity score matching.

Project description:BackgroundSome academic organizations recommended that physicians intubate patients with COVID-19 with a relatively lower threshold of oxygen usage particularly in the early phase of pandemic. We aimed to elucidate whether early intubation is associated with decreased in-hospital mortality among patients with novel coronavirus disease 2019 (COVID-19) who required intubation.MethodsA multicenter, retrospective, observational study was conducted at 66 hospitals in Japan where patients with moderate-to-severe COVID-19 were treated between January and September 2020. Patients who were diagnosed as COVID-19 with a positive reverse-transcription polymerase chain reaction test and intubated during admission were included. Early intubation was defined as intubation conducted in the setting of ≤ 6 L/min of oxygen usage. In-hospital mortality was compared between patients with early and non-early intubation. Inverse probability weighting analyses with propensity scores were performed to adjust patient demographics, comorbidities, hemodynamic status on admission and time at intubation, medications before intubation, severity of COVID-19, and institution characteristics. Subgroup analyses were conducted on the basis of age, severity of hypoxemia at intubation, and days from admission to intubation.ResultsAmong 412 patients eligible for the study, 110 underwent early intubation. In-hospital mortality was lower in patients with early intubation than those with non-early intubation (18 [16.4%] vs. 88 [29.1%]; odds ratio, 0.48 [95% confidence interval 0.27-0.84]; p = 0.009, and adjusted odds ratio, 0.28 [95% confidence interval 0.19-0.42]; p < 0.001). The beneficial effects of early intubation were observed regardless of age and severity of hypoxemia at time of intubation; however, early intubation was associated with lower in-hospital mortality only among patients who were intubated later than 2 days after admission.ConclusionsEarly intubation in the setting of ≤ 6 L/min of oxygen usage was associated with decreased in-hospital mortality among patients with COVID-19 who required intubation. Trial Registration None.

Dataset Information

Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use.

Backgrounds

Methods and findings

Conclusions

Publications

Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets