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Variation in COVID-19 disease severity at hospital admission over time and across hospitals : A multi-institution cohort of Michigan hospitals

ABSTRACT: Abstract During the spring 2020 COVID-19 surge, hospitals in Southeast Michigan were overwhelmed, and hospital beds were limited. However, it is unknown whether threshold for hospital admission varied across hospitals or over time. Using a statewide registry, we performed a retrospective cohort study. We identified adult patients hospitalized with COVID-19 in Southeast Michigan (3/1/2020-6/1/2020). We classified disease severity on admission using the World Health Organization (WHO) ordinal scale. Our primary measure of interest was the proportion of patients admitted on room air. We also determined the proportion without acute organ dysfunction on admission or any point during hospitalization. We quantified variation across hospitals and over time by half-month epochs. Among 1315 hospitalizations across 22 hospitals, 57.3% (754/1,315) were admitted on room air, and 26.1% (343/1,315) remained on room air for the duration of hospitalization. Across hospitals, the proportion of COVID-19 hospitalizations admitted on room air varied from 32.3% to 80.0%. Across half-month epochs, the proportion ranged from 49.4% to 69.4% and nadired in early April 2020. Among patients admitted on room air, 75.1% (566/754) had no acute organ dysfunction on admission, and 35.3% (266/754) never developed acute organ dysfunction at any point during hospitalization; there was marked variation in both proportions across hospitals. In-hospital mortality was 13.7% for patients admitted on room air vs 26.3% for patients requiring nasal cannula oxygen. Among patients hospitalized with COVID-19 during the spring 2020 surge in Southeast Michigan, more than half were on room air and a third had no acute organ dysfunction upon admission, but experienced high rates of disease progression and in-hospital mortality.

SUBMITTER: Wayne M

PROVIDER: S-EPMC8448065 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundFluoroquinolones increase the risk of Clostridioides difficile infection and antibiotic resistance. Hospitals often use pre-prescription approval or prospective audit and feedback to target fluoroquinolone prescribing. Whether these strategies impact aggregate fluoroquinolone use is unknown.MethodsThis study is a 48-hospital, retrospective cohort of general-care, medical patients hospitalized with pneumonia or positive urine culture between December 2015-September 2017. Hospitals were surveyed on their use of pre-prescription approval and/or prospective audit and feedback to target fluoroquinolone prescribing during hospitalization (fluoroquinolone stewardship). After controlling for hospital clustering and patient factors, aggregate (inpatient and post-discharge) fluoroquinolone (ciprofloxacin, levofloxacin, moxifloxacin) exposure was compared between hospitals with and without fluoroquinolone stewardship.ResultsThere were 11 748 patients (6820 pneumonia; 4928 positive urine culture) included at 48 hospitals. All hospitals responded to the survey: 29.2% (14/48) reported using pre-prescription approval and/or prospective audit and feedback to target fluoroquinolone prescribing. After adjustment, fluoroquinolone stewardship was associated with fewer patients receiving a fluoroquinolone (37.1% vs 48.2%; P = .01) and fewer fluoroquinolone treatment days per 1000 patients (2282 vs 3096 days/1000 patients; P = .01), driven by lower inpatient prescribing. However, most (66.6%) fluoroquinolone treatment days occurred after discharge, and hospitals with fluoroquinolone stewardship had twice as many new fluoroquinolone starts after discharge as hospitals without (15.6% vs 8.4%; P = .003).ConclusionsHospital-based stewardship interventions targeting fluoroquinolone prescribing were associated with less fluoroquinolone prescribing during hospitalization, but not at discharge. To limit aggregate fluoroquinolone exposure, stewardship programs should target both inpatient and discharge prescribing.

Project description:BackgroundAlthough multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, only a few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable.ObjectiveWe aimed to develop a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization.MethodsPatients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (3601/5145, 70.0%) and validation (1544/5145, 30.0%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and association measures in the validation cohort.ResultsOf 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher with the addition of predictive factors, attaining the best performance with an AUROC of 0.818 (95% CI 0.792-0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were 0.744 (95% CI 0.716-0.773) and 0.745 (95% CI 0.709-0.776), respectively.ConclusionsFor patients transferred to EDs, we developed a well-performing hospital admission prediction model based on routinely collected prehospital information including chief complaints.

Project description:ObjectiveTo describe patient characteristics, symptoms, patterns of care and outcomes for patients hospitalised with COVID-19 in Michigan.DesignMulticentre retrospective cohort study.Setting32 acute care hospitals in the state of Michigan.ParticipantsPatients discharged (16 March-11 May 2020) with suspected or confirmed COVID-19 were identified. Trained abstractors collected demographic information on all patients and detailed clinical data on a subset of COVID-19-positive patients.Primary outcome measurementsPatient characteristics, treatment and outcomes including cardiopulmonary resuscitation, mortality and venous thromboembolism within and across hospitals.ResultsDemographic-only data from 1593 COVID-19-positive and 1259 persons under investigation discharges were collected. Among 1024 cases with detailed data, the median age was 63 years; median body mass index was 30.6; and 51.4% were black. Cough, fever and shortness of breath were the top symptoms. 37.2% reported a known COVID-19 contact; 7.0% were healthcare workers; and 16.1% presented from congregated living facilities.During hospitalisation, 232 (22.7%) patients were treated in an intensive care unit (ICU); 558 (54.9%) in a 'cohorted' unit; 161 (15.7%) received mechanical ventilation; and 90 (8.8%) received high-flow nasal cannula. ICU patients more often received hydroxychloroquine (66% vs 46%), corticosteroids (34% vs 18%) and antibiotic therapy (92% vs 71%) than general ward patients (p<0.05 for all). Overall, 219 (21.4%) patients died, with in-hospital mortality ranging from 7.9% to 45.7% across hospitals. 73% received at least one COVID-19-specific treatment, ranging from 32% to 96% across sites.Across 14 hospitals, the proportion of patients admitted directly to an ICU ranged from 0% to 43.8%; mechanical ventilation on admission from 0% to 12.8%; mortality from 7.9% to 45.7%. Use of at least one COVID-19-specific therapy varied from 32% to 96.3% across sites.ConclusionsDuring the early days of the Michigan outbreak of COVID-19, patient characteristics, treatment and outcomes varied widely within and across hospitals.

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Variation in COVID-19 disease severity at hospital admission over time and across hospitals : A multi-institution cohort of Michigan hospitals

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