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Predictive model for bacterial co-infection in patients hospitalized for COVID-19: a multicenter observational cohort study.

ABSTRACT: The aim of our study was to build a predictive model able to stratify the risk of bacterial co-infection at hospitalization in patients with COVID-19. Multicenter observational study of adult patients hospitalized from February to December 2020 with confirmed COVID-19 diagnosis. Endpoint was microbiologically documented bacterial co-infection diagnosed within 72 h from hospitalization. The cohort was randomly split into derivation and validation cohort. To investigate risk factors for co-infection univariable and multivariable logistic regression analyses were performed. Predictive risk score was obtained assigning a point value corresponding to β-coefficients to the variables in the multivariable model. ROC analysis in the validation cohort was used to estimate prediction accuracy. Overall, 1733 patients were analyzed: 61.4% males, median age 69 years (IQR 57-80), median Charlson 3 (IQR 2-6). Co-infection was diagnosed in 110 (6.3%) patients. Empirical antibiotics were started in 64.2 and 59.5% of patients with and without co-infection (p = 0.35). At multivariable analysis in the derivation cohort: WBC ≥ 7.7/mm3, PCT ≥ 0.2 ng/mL, and Charlson index ≥ 5 were risk factors for bacterial co-infection. A point was assigned to each variable obtaining a predictive score ranging from 0 to 5. In the validation cohort, ROC analysis showed AUC of 0.83 (95%CI 0.75-0.90). The optimal cut-point was ≥2 with sensitivity 70.0%, specificity 75.9%, positive predictive value 16.0% and negative predictive value 97.5%. According to individual risk score, patients were classified at low (point 0), intermediate (point 1), and high risk (point ≥ 2). CURB-65 ≥ 2 was further proposed to identify patients at intermediate risk who would benefit from early antibiotic coverage. Our score may be useful in stratifying bacterial co-infection risk in COVID-19 hospitalized patients, optimizing diagnostic testing and antibiotic use.

SUBMITTER: Giannella M

PROVIDER: S-EPMC9053127 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Introduction. During previous viral pandemics, reported co-infection rates and implicated pathogens have varied. In the 1918 influenza pandemic, a large proportion of severe illness and death was complicated by bacterial co-infection, predominantly Streptococcus pneumoniae and Staphylococcus aureus.Gap statement. A better understanding of the incidence of co-infection in patients with COVID-19 infection and the pathogens involved is necessary for effective antimicrobial stewardship.Aim. To describe the incidence and nature of co-infection in critically ill adults with COVID-19 infection in England.Methodology. A retrospective cohort study of adults with COVID-19 admitted to seven intensive care units (ICUs) in England up to 18 May 2020, was performed. Patients with completed ICU stays were included. The proportion and type of organisms were determined at <48 and >48 h following hospital admission, corresponding to community and hospital-acquired co-infections.Results. Of 254 patients studied (median age 59 years (IQR 49-69); 64.6 % male), 139 clinically significant organisms were identified from 83 (32.7 %) patients. Bacterial co-infections/ co-colonisation were identified within 48 h of admission in 14 (5.5 %) patients; the commonest pathogens were Staphylococcus aureus (four patients) and Streptococcus pneumoniae (two patients). The proportion of pathogens detected increased with duration of ICU stay, consisting largely of Gram-negative bacteria, particularly Klebsiella pneumoniae and Escherichia coli. The co-infection/ co-colonisation rate >48 h after admission was 27/1000 person-days (95 % CI 21.3-34.1). Patients with co-infections/ co-colonisation were more likely to die in ICU (crude OR 1.78,95 % CI 1.03-3.08, P=0.04) compared to those without co-infections/ co-colonisation.Conclusion. We found limited evidence for community-acquired bacterial co-infection in hospitalised adults with COVID-19, but a high rate of Gram-negative infection acquired during ICU stay.

Project description:Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection results in a spectrum of clinical presentations. Evidence from Africa indicates that significantly less COVID-19 patients suffer from serious symptoms than in the industrialized world. We and others previously postulated a partial explanation for this phenomenon, being a different, more activated immune system due to parasite infections. Here, we aimed to test this hypothesis by investigating a potential correlation of co-infection with parasites with COVID-19 severity in an endemic area in Africa. Methods: Ethiopian COVID-19 patients were enrolled and screened for intestinal parasites, between July 2020 and March 2021. The primary outcome was the proportion of patients with severe COVID-19. Ordinal logistic regression models were used to estimate the association between parasite infection, and COVID-19 severity. Models were adjusted for sex, age, residence, education level, occupation, body mass index, and comorbidities. Findings: 751 SARS-CoV-2 infected patients were enrolled, of whom 284 (37.8%) had intestinal parasitic infection. Only 27/255 (10.6%) severe COVID-19 patients were co-infected with intestinal parasites, while 257/496 (51.8%) non-severe COVID-19 patients were parasite positive (p<0.0001). Patients co-infected with parasites had lower odds of developing severe COVID-19, with an adjusted odds ratio (aOR) of 0.23 (95% CI 0.17-0.30; p<0.0001) for all parasites, aOR 0.37 ([95% CI 0.26-0.51]; p<0.0001) for protozoa, and aOR 0.26 ([95% CI 0.19-0.35]; p<0.0001) for helminths. When stratified by species, co-infection with Entamoeba spp., Hymenolepis nana, Schistosoma mansoni, and Trichuris trichiura implied lower probability of developing severe COVID-19. There were 11 deaths (1.5%), and all were among patients without parasites (p = 0.009). Interpretation: Parasite co-infection is associated with a reduced risk of severe COVID-19 in African patients. Parasite-driven immunomodulatory responses may mute hyper-inflammation associated with severe COVID-19. Funding: European and Developing Countries Clinical Trials Partnership (EDCTP) - European Union, and Joep Lange Institute (JLI), The Netherlands. Trial registration: Clinicaltrials.gov: NCT04473365.

Project description:BackgroundThe novel coronavirus SARS-CoV-2 and its associated disease, COVID-19, have caused worldwide disruption, leading countries to take drastic measures to address the progression of the disease. As SARS-CoV-2 continues to spread, hospitals are struggling to allocate resources to patients who are most at risk. In this context, it has become important to develop models that can accurately predict the severity of infection of hospitalized patients to help guide triage, planning, and resource allocation.ObjectiveThe aim of this study was to develop accurate models to predict the mortality of hospitalized patients with COVID-19 using basic demographics and easily obtainable laboratory data.MethodsWe performed a retrospective study of 375 hospitalized patients with COVID-19 in Wuhan, China. The patients were randomly split into derivation and validation cohorts. Regularized logistic regression and support vector machine classifiers were trained on the derivation cohort, and accuracy metrics (F1 scores) were computed on the validation cohort. Two types of models were developed: the first type used laboratory findings from the entire length of the patient's hospital stay, and the second type used laboratory findings that were obtained no later than 12 hours after admission. The models were further validated on a multicenter external cohort of 542 patients.ResultsOf the 375 patients with COVID-19, 174 (46.4%) died of the infection. The study cohort was composed of 224/375 men (59.7%) and 151/375 women (40.3%), with a mean age of 58.83 years (SD 16.46). The models developed using data from throughout the patients' length of stay demonstrated accuracies as high as 97%, whereas the models with admission laboratory variables possessed accuracies of up to 93%. The latter models predicted patient outcomes an average of 11.5 days in advance. Key variables such as lactate dehydrogenase, high-sensitivity C-reactive protein, and percentage of lymphocytes in the blood were indicated by the models. In line with previous studies, age was also found to be an important variable in predicting mortality. In particular, the mean age of patients who survived COVID-19 infection (50.23 years, SD 15.02) was significantly lower than the mean age of patients who died of the infection (68.75 years, SD 11.83; P<.001).ConclusionsMachine learning models can be successfully employed to accurately predict outcomes of patients with COVID-19. Our models achieved high accuracies and could predict outcomes more than one week in advance; this promising result suggests that these models can be highly useful for resource allocation in hospitals.

Project description:BackgroundSeveral anti-cytokine therapies were tested in the randomized trials in hospitalized patients with severe acute respiratory syndrome coronavirus 2 infection (COVID-19). Previously, dexamethasone demonstrated a reduction of case-fatality rate in hospitalized patients with respiratory failure. In this matched control study we compared dexamethasone to a Janus kinase inhibitor, ruxolitinib.MethodsThe matched cohort study included 146 hospitalized patients with COVID-19 and oxygen support requirement. The control group was selected 1:1 from 1355 dexamethasone-treated patients and was matched by main clinical and laboratory parameters predicting survival. Recruitment period was April 7, 2020 through September 9, 2020.ResultsRuxolitinib treatment in the general cohort of patients was associated with case-fatality rate similar to dexamethasone treatment: 9.6% (95% CI [4.6-14.6%]) vs 13.0% (95% CI [7.5-18.5%]) respectively (p = 0.35, OR = 0.71, 95% CI [0.31-1.57]). Median time to discharge without oxygen support requirement was also not different between these groups: 13 vs. 11 days (p = 0.13). Subgroup analysis without adjustment for multiple comparisons demonstrated a reduced case-fatality rate in ruxolitnib-treated patients with a high fever (≥ 38.5 °C) (OR 0.33, 95% CI [0.11-1.00]). Except higher incidence of grade 1 thrombocytopenia (37% vs 23%, p = 0.042), ruxolitinib therapy was associated with a better safety profile due to a reduced rate of severe cardiovascular adverse events (6.8% vs 15%, p = 0.025). For 32 patients from ruxolitinib group (21.9%) with ongoing progression of respiratory failure after 72 h of treatment, additional anti-cytokine therapy was prescribed (8-16 mg dexamethasone).ConclusionsRuxolitinib may be an alternative initial anti-cytokine therapy with comparable effectiveness in patients with potential risks of steroid administration. Patients with a high fever (≥ 38.5 °C) at admission may potentially benefit from ruxolitinib administration. Trial registration The Ruxolitinib Managed Access Program (MAP) for Patients Diagnosed With Severe/Very Severe COVID-19 Illness NCT04337359, CINC424A2001M, registered April, 7, 2020. First participant was recruited after registration date.

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Predictive model for bacterial co-infection in patients hospitalized for COVID-19: a multicenter observational cohort study.

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