Dataset Information

Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study).

ABSTRACT: OBJECTIVES:We aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19). METHODS:We performed a multicentre cohort study among hospitalized (>24 hours) patients diagnosed with COVID-19 from 22 February to 3 April 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospital discharge and was defined as: Spo2 <93% with 100% Fio2, respiratory rate >30 breaths/min or respiratory distress. Multivariable logistic regression models were built to identify predictors of SRF, ?-coefficients were used to develop a risk score. Trial Registration NCT04316949. RESULTS:We analysed 1113 patients (644 derivation, 469 validation cohort). Mean (±SD) age was 65.7 (±15) years, 704 (63.3%) were male. SRF occurred in 189/644 (29%) and 187/469 (40%) patients in the derivation and validation cohorts, respectively. At multivariate analysis, risk factors for SRF in the derivation cohort assessed at hospitalization were age ?70 years (OR 2.74; 95% CI 1.66-4.50), obesity (OR 4.62; 95% CI 2.78-7.70), body temperature ?38°C (OR 1.73; 95% CI 1.30-2.29), respiratory rate ?22 breaths/min (OR 3.75; 95% CI 2.01-7.01), lymphocytes ?900 cells/mm3 (OR 2.69; 95% CI 1.60-4.51), creatinine ?1 mg/dL (OR 2.38; 95% CI 1.59-3.56), C-reactive protein ?10 mg/dL (OR 5.91; 95% CI 4.88-7.17) and lactate dehydrogenase ?350 IU/L (OR 2.39; 95% CI 1.11-5.11). Assigning points to each variable, an individual risk score (PREDI-CO score) was obtained. Area under the receiver-operator curve was 0.89 (0.86-0.92). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 71.6% (65%-79%), 89.1% (86%-92%), 74% (67%-80%) and 89% (85%-91%), respectively. PREDI-CO score showed similar prognostic ability in the validation cohort: area under the receiver-operator curve 0.85 (0.81-0.88). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 80% (73%-85%), 76% (70%-81%), 69% (60%-74%) and 85% (80%-89%), respectively. CONCLUSION:PREDI-CO score can be useful to allocate resources and prioritize treatments during the COVID-19 pandemic.

SUBMITTER: Bartoletti M

PROVIDER: S-EPMC7414420 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study).

Bartoletti Michele M Giannella Maddalena M Scudeller Luigia L Tedeschi Sara S Rinaldi Matteo M Bussini Linda L Fornaro Giacomo G Pascale Renato R Pancaldi Livia L Pasquini Zeno Z Trapani Filippo F Badia Lorenzo L Campoli Caterina C Tadolini Marina M Attard Luciano L Puoti Massimo M Merli Marco M Mussini Cristina C Menozzi Marianna M Meschiari Marianna M Codeluppi Mauro M Barchiesi Francesco F Cristini Francesco F Saracino Annalisa A Licci Alberto A Rapuano Silvia S Tonetti Tommaso T Gaibani Paolo P Ranieri Vito M VM Viale Pierluigi P

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases 20200808 11

<h4>Objectives</h4>We aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19).<h4>Methods</h4>We performed a multicentre cohort study among hospitalized (>24 hours) patients diagnosed with COVID-19 from 22 February to 3 April 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospi ...[more]

PMID: 32781244

Similar Datasets

Project description:ObjectiveTo assess the efficacy of corticosteroids in patients with coronavirus disease 2019 (COVID-19).MethodsA multicentre observational study was performed from 22 February through 30 June 2020. We included consecutive adult patients with severe COVID-19, defined as respiratory rate ≥30 breath per minute, oxygen saturation ≤93% on ambient air or arterial partial pressure of oxygen to fraction of inspired oxygen ≤300 mm Hg. We excluded patients being treated with other immunomodulant drugs, receiving low-dose corticosteroids and receiving corticosteroids 72 hours after admission. The primary endpoint was 30-day mortality from hospital admission. The main exposure variable was corticosteroid therapy at a dose of ≥0.5 mg/kg of prednisone equivalents. It was introduced as binomial covariate in a logistic regression model for the primary endpoint and inverse probability of treatment weighting using the propensity score.ResultsOf 1717 patients with COVID-19 evaluated, 513 were included in the study, and of these, 170 (33%) were treated with corticosteroids. During hospitalization, 166 patients (34%) met the criteria of the primary outcome (60/170, 35% in the corticosteroid group and 106/343, 31% in the noncorticosteroid group). At multivariable analysis corticosteroid treatment was not associated with lower 30-day mortality rate (adjusted odds ratio, 0.59; 95% confidence interval (CI), 0.20-1.74; p 0.33). After inverse probability of treatment weighting, corticosteroids were not associated with lower 30-day mortality (average treatment effect, 0.05; 95% CI, -0.02 to 0.09; p 0.12). However, subgroup analysis revealed that in patients with PO2/FiO2 < 200 mm Hg at admission (135 patients, 52 (38%) treated with corticosteroids), corticosteroid treatment was associated with a lower risk of 30-day mortality (23/52, 44% vs. 45/83, 54%; adjusted odds ratio, 0.20; 95% CI, 0.04-0.90; p 0.036).ConclusionsThe effect of corticosteroid treatment on mortality might be limited to critically ill COVID-19 patients.

Project description:BackgroundThere is considerable variation in disease behavior among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19). Genomewide association analysis may allow for the identification of potential genetic factors involved in the development of Covid-19.MethodsWe conducted a genomewide association study involving 1980 patients with Covid-19 and severe disease (defined as respiratory failure) at seven hospitals in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe. After quality control and the exclusion of population outliers, 835 patients and 1255 control participants from Italy and 775 patients and 950 control participants from Spain were included in the final analysis. In total, we analyzed 8,582,968 single-nucleotide polymorphisms and conducted a meta-analysis of the two case-control panels.ResultsWe detected cross-replicating associations with rs11385942 at locus 3p21.31 and with rs657152 at locus 9q34.2, which were significant at the genomewide level (P<5×10-8) in the meta-analysis of the two case-control panels (odds ratio, 1.77; 95% confidence interval [CI], 1.48 to 2.11; P = 1.15×10-10; and odds ratio, 1.32; 95% CI, 1.20 to 1.47; P = 4.95×10-8, respectively). At locus 3p21.31, the association signal spanned the genes SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6 and XCR1. The association signal at locus 9q34.2 coincided with the ABO blood group locus; in this cohort, a blood-group-specific analysis showed a higher risk in blood group A than in other blood groups (odds ratio, 1.45; 95% CI, 1.20 to 1.75; P = 1.48×10-4) and a protective effect in blood group O as compared with other blood groups (odds ratio, 0.65; 95% CI, 0.53 to 0.79; P = 1.06×10-5).ConclusionsWe identified a 3p21.31 gene cluster as a genetic susceptibility locus in patients with Covid-19 with respiratory failure and confirmed a potential involvement of the ABO blood-group system. (Funded by Stein Erik Hagen and others.).

Project description:Children with severe pneumonia in low- and middle-income countries (LMICs) suffer from high rates of treatment failure despite appropriate World Health Organization (WHO)-directed antibiotic treatment. Developing a clinical prediction rule for treatment failure may allow early identification of high-risk patients and timely intervention to decrease mortality. We used data from two separate studies conducted at the Dhaka Hospital of the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) to derive and externally validate a clinical prediction rule for treatment failure of children hospitalized with severe pneumonia. The derivation dataset was from a randomized clinical trial conducted from 2018 to 2019, studying children aged 2 to 59 months hospitalized with severe pneumonia as defined by WHO. Treatment failure was defined by the persistence of danger signs at the end of 48 hours of antibiotic treatment or the appearance of any new danger signs within 24 hours of enrollment. We built a random forest model to identify the top predictors. The top six predictors were the presence of grunting, room air saturation, temperature, the presence of lower chest wall indrawing, the presence of respiratory distress, and central cyanosis. Using these six predictors, we created a parsimonious model with a discriminatory performance of 0.691, as measured by area under the receiving operating curve (AUC). We performed external validation using a temporally distinct dataset from a cohort study of 191 similarly aged children with severe acute malnutrition and pneumonia. In external validation, discriminatory performance was maintained with an improved AUC of 0.718. In conclusion, we developed and externally validated a parsimonious six-predictor model using random forest methods to predict treatment failure in young children with severe pneumonia in Bangladesh. These findings can be used to further develop and validate parsimonious and pragmatic prognostic clinical prediction rules for pediatric pneumonia, particularly in LMICs.

Dataset Information

Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study).

Publications

Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets