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1.
J Gen Intern Med ; 2022 Jan 13.
Artigo em Inglês | MEDLINE | ID: mdl-35028862

RESUMO

BACKGROUND: The US Veterans Affairs (VA) healthcare system began reporting risk-adjusted mortality for intensive care (ICU) admissions in 2005. However, while the VA's mortality model has been updated and adapted for risk-adjustment of all inpatient hospitalizations, recent model performance has not been published. We sought to assess the current performance of VA's 4 standardized mortality models: acute care 30-day mortality (acute care SMR-30); ICU 30-day mortality (ICU SMR-30); acute care in-hospital mortality (acute care SMR); and ICU in-hospital mortality (ICU SMR). METHODS: Retrospective cohort study with split derivation and validation samples. Standardized mortality models were fit using derivation data, with coefficients applied to the validation sample. Nationwide VA hospitalizations that met model inclusion criteria during fiscal years 2017-2018(derivation) and 2019 (validation) were included. Model performance was evaluated using c-statistics to assess discrimination and comparison of observed versus predicted deaths to assess calibration. RESULTS: Among 1,143,351 hospitalizations eligible for the acute care SMR-30 during 2017-2019, in-hospital mortality was 1.8%, and 30-day mortality was 4.3%. C-statistics for the SMR models in validation data were 0.870 (acute care SMR-30); 0.864 (ICU SMR-30); 0.914 (acute care SMR); and 0.887 (ICU SMR). There were 16,036 deaths (4.29% mortality) in the SMR-30 validation cohort versus 17,458 predicted deaths (4.67%), reflecting 0.38% over-prediction. Across deciles of predicted risk, the absolute difference in observed versus predicted percent mortality was a mean of 0.38%, with a maximum error of 1.81% seen in the highest-risk decile. CONCLUSIONS AND RELEVANCE: The VA's SMR models, which incorporate patient physiology on presentation, are highly predictive and demonstrate good calibration both overall and across risk deciles. The current SMR models perform similarly to the initial ICU SMR model, indicating appropriate adaption and re-calibration.

2.
Front Pediatr ; 9: 761994, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34869119

RESUMO

Sepsis, life-threatening organ dysfunction secondary to infection, hospitalizes nearly 75,000 children each year in the United States. Most children survive sepsis. However, there is increasing recognition of the longer-term consequences of pediatric sepsis hospitalization on both the child and their family, including medical, psychosocial, and financial impacts. Here, we describe family spillover effects (the impact of illness on caregivers) of pediatric sepsis, why measurement of family spillover effects is important, and the ways in which family spillover effects can be measured.

3.
JAMA Netw Open ; 4(12): e2140732, 2021 12 01.
Artigo em Inglês | MEDLINE | ID: mdl-34940862

RESUMO

Importance: Patients who survive pediatric critical illness and their caregivers commonly experience physical, emotional, and cognitive sequelae. However, the rate and duration of school absence among patients and work absence among their caregivers are unknown. Objective: To determine the rates and duration of school absence among children who survived hospitalization with acute respiratory failure and work absence among their caregivers. Design, Setting, and Participants: The Randomized Evaluation of Sedation Titration for Respiratory Failure (RESTORE) cluster randomized trial included 2449 children from 31 sites to protocolized sedation (intervention) vs usual care (control) from June 6, 2009, to December 2, 2013. In total, 1360 children survived hospitalization and were selected for follow-up at 6 months after pediatric intensive care unit (PICU) discharge, which was completed from January 12, 2010, to April 13, 2015. This secondary analysis was conducted from July 1, 2020, to September 30, 2021. Exposures: PICU hospitalization for acute respiratory failure, including invasive mechanical ventilation. Main Outcomes and Measures: Postdischarge assessments with caregivers of eligible participants at 6 months after PICU discharge, including questions about school and work absence. Risk factors associated with longer absence from school and work were identified. Results: Postdischarge assessments were completed for 960 children who survived treatment for acute respiratory failure, of whom 443 (46.1%) were girls and 517 (53.9%) were boys; 509 of 957 (53.2%) were non-Hispanic White. Median age was 1.8 years (IQR, 0.4-7.9 years). In total, 399 children (41.6%) were enrolled in school, of whom 279 (69.9%) missed school after discharge. Median duration of postdischarge absence was 9.1 days (IQR, 0-27.9 days) among all children enrolled in school and 16.9 days (IQR, 7.9-43.9 days) among the 279 children with postdischarge absence. Among 960 primary caregivers, 506 (52.7%) were employed outside the home, of whom 277 (54.7%) missed work. Median duration of postdischarge work absence was 2 days (IQR, 0-10 days) among all employed primary caregivers, and 8 days (IQR, 4-20 days) among the 277 caregivers who missed work after discharge. The odds of postdischarge school absence and greater duration of absence increased for children 5 years or older (compared with 0-4 years, odds ratios [ORs] for 5-8 years, 3.20 [95% CI, 1.69-6.05] and 2.09 [95% CI, 1.30-3.37], respectively; ORs for 9-12 years, 2.49 [95% CI, 1.17-5.27] and 2.32 [95% CI, 1.30-4.14], respectively; and ORs for 13-18 years, 2.37 [95% CI, 1.20-4.66] and 1.89 [95% CI, 1.11-3.24], respectively) and those with a preexisting comorbidity (ORs, 1.90 [95% CI, 1.10-3.29] and 1.76 [95% CI, 1.14-2.69], respectively). Conclusions and Relevance: In this secondary analysis of a cluster randomized trial, 2 in 3 children hospitalized for acute respiratory failure missed school after discharge, for a median duration of nearly 2 weeks. In addition, more than half of primary caregivers missed work after discharge. The magnitude of school absenteeism suggests that children may be at increased risk for lower educational achievement, economic hardship, and poor health outcomes in adulthood.


Assuntos
Absenteísmo , Cuidadores/estatística & dados numéricos , Criança Hospitalizada , Cuidados Críticos , Estado Terminal/terapia , Síndrome do Desconforto Respiratório/terapia , Feminino , Humanos , Lactente , Masculino , Fatores de Risco
5.
JAMA Netw Open ; 4(11): e2134290, 2021 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-34767025

RESUMO

Importance: Sepsis survivorship is associated with postsepsis morbidity, but epidemiological data from population-based cohorts are lacking. Objective: To quantify the frequency and co-occurrence of new diagnoses consistent with postsepsis morbidity and mortality as well as new nursing care dependency and total health care costs after sepsis. Design, Setting, and Participants: This retrospective cohort study based on nationwide health claims data included a population-based cohort of 23.0 million beneficiaries of a large German health insurance provider. Patients aged 15 years and older with incident hospital-treated sepsis in 2013 to 2014 were included. Data were analyzed from January 2009 to December 2017. Exposures: Sepsis, identified by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) hospital discharge codes. Main Outcomes and Measures: New medical, psychological, and cognitive diagnoses; long-term mortality; dependency on nursing care; and overall health care costs in survivors at 1 to 12, 13 to 24, and 25 to 36 months after hospital discharge. Results: Among 23.0 million eligible individuals, we identified 159 684 patients hospitalized with sepsis in 2013 to 2014. The mean (SD) age was 73.8 (12.8) years, and 75 809 (47.5%; 95% CI, 47.2%-47.7%) were female patients. In-hospital mortality was 27.0% (43 177 patients; 95% CI, 26.8%-27.3%). Among 116 507 hospital survivors, 86 578 (74.3%; 95% CI, 74.1%-74.6%) had a new diagnosis in the first year post sepsis; 28 405 (24.4%; 95% CI, 24.1%-24.6%) had diagnoses co-occurring in medical, psychological, or cognitive domains; and 23 572 of 74 878 survivors (31.5%; 95% CI, 31.1%-31.8%) without prior nursing care dependency were newly dependent on nursing care. In total, 35 765 survivors (30.7%; 95% CI, 30.4%-31.0%) died within the first year. In the second and third year, 53 089 (65.8%; 95% CI, 65.4%-66.1%) and 40 959 (59.4%; 95% CI, 59.0%-59.8%) had new diagnoses, respectively. Health care costs for sepsis hospital survivors for 3 years post sepsis totaled a mean of €29 088/patient ($32 868/patient) (SD, €44 195 [$49 938]). New postsepsis morbidity (>1 new diagnosis) was more common in survivors of severe sepsis (75.6% [95% CI, 75.1%-76.0%]) than nonsevere sepsis (73.7% [95% CI, 73.4%-74.0%]; P < .001) and more common in survivors treated in the intensive care unit (78.3% [95% CI, 77.8%-78.7%]) than in those not treated in the intensive care unit (72.8% [95% CI, 72.5%-73.1%]; P < .001). Postsepsis morbidity was 68.5% (95% CI, 67.5%-69.5%) among survivors without prior morbidity and 56.1% (95% CI, 54.2%-57.9%) in survivors younger than 40 years. Conclusions and Relevance: In this study, new medical, psychological, and cognitive diagnoses consistent with postsepsis morbidity were common after sepsis, including among patients with less severe sepsis, no prior diagnoses, and younger age. This calls for more efforts to elucidate the underlying mechanisms, define optimal screening for common new diagnoses, and test interventions to prevent and treat postsepsis morbidity.

6.
Am J Cardiol ; 2021 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-34794647

RESUMO

Atypical antipsychotics are used in cardiac intensive care units (CICU) to treat delirium despite limited data on safety in patients with acute cardiovascular conditions. Patients treated with these agents may be at higher risk for adverse events such as QTc prolongation and arrhythmias. We performed a retrospective cohort study of 144 adult patients who were not receiving antipsychotics before admission and received olanzapine (n = 50) or quetiapine (n = 94) in the Michigan Medicine CICU. Data on baseline characteristics, antipsychotic dose and duration, length of stay, and adverse events were collected. Adverse events included ventricular tachycardia (sustained ventricular tachycardia attributed to the medication), hypotension (systolic blood pressure <90 mm Hg attributed to the medication), and QTc prolongation (QTc increase by ≥60 ms or to an interval ≥500 ms). Twenty-six patients (18%) experienced an adverse event. Of those adverse events, 20 patients (14%) experienced QTc prolongation, 3 patients (2%) had ventricular tachycardia, and 3 patients (2%) had hypotension. Patients who received quetiapine had a higher rate of adverse events (25% vs 6%, p = 0.01) including QTc prolongation (18% vs 6%, p = 0.046). Intensive care unit length of stay was shorter in patients who received olanzapine (6.5 vs 9.5 days, p = 0.047). Eighteen patients (13%) had their antipsychotic continued at discharge from the hospital. In conclusion, QTc prolongation was more common in patients treated with quetiapine versus olanzapine although the number of events was relatively low with both agents in a CICU cohort.

8.
Crit Care Med ; 49(11): e1063-e1143, 2021 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-34605781
9.
Medicine (Baltimore) ; 100(40): e27422, 2021 Oct 08.
Artigo em Inglês | MEDLINE | ID: mdl-34622851

RESUMO

ABSTRACT: As severe acute respiratory syndrome coronavirus 2 continues to spread, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage. We aimed to develop a risk score model for in-hospital mortality in patients hospitalized with 2019 novel coronavirus (COVID-19) that was robust across hospitals and used clinical factors that are readily available and measured standardly across hospitals.In this retrospective observational study, we developed a risk score model using data collected by trained abstractors for patients in 20 diverse hospitals across the state of Michigan (Mi-COVID19) who were discharged between March 5, 2020 and August 14, 2020. Patients who tested positive for severe acute respiratory syndrome coronavirus 2 during hospitalization or were discharged with an ICD-10 code for COVID-19 (U07.1) were included. We employed an iterative forward selection approach to consider the inclusion of 145 potential risk factors available at hospital presentation. Model performance was externally validated with patients from 19 hospitals in the Mi-COVID19 registry not used in model development. We shared the model in an easy-to-use online application that allows the user to predict in-hospital mortality risk for a patient if they have any subset of the variables in the final model.Two thousand one hundred and ninety-three patients in the Mi-COVID19 registry met our inclusion criteria. The derivation and validation sets ultimately included 1690 and 398 patients, respectively, with mortality rates of 19.6% and 18.6%, respectively. The average age of participants in the study after exclusions was 64 years old, and the participants were 48% female, 49% Black, and 87% non-Hispanic. Our final model includes the patient's age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospital's COVID-19 mortality rate. No other factors showed sufficient incremental model improvement to warrant inclusion. The area under the receiver operating characteristics curve for the derivation and validation sets were .796 (95% confidence interval, .767-.826) and .829 (95% confidence interval, .782-.876) respectively.We conclude that the risk of in-hospital mortality in COVID-19 patients can be reliably estimated using a few factors, which are standardly measured and available to physicians very early in a hospital encounter.


Assuntos
COVID-19/mortalidade , Mortalidade Hospitalar/tendências , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , Índice de Massa Corporal , Comorbidade , Creatinina/sangue , Feminino , Comportamentos Relacionados com a Saúde , Humanos , Modelos Logísticos , Masculino , Michigan/epidemiologia , Pessoa de Meia-Idade , Oximetria , Prognóstico , Curva ROC , Estudos Retrospectivos , Medição de Risco , Fatores de Risco , SARS-CoV-2 , Índice de Gravidade de Doença , Fatores Sexuais , Fatores Socioeconômicos
11.
Crit Care Med ; 49(11): 1974-1982, 2021 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-34643578
12.
Ann Am Thorac Soc ; 2021 Oct 13.
Artigo em Inglês | MEDLINE | ID: mdl-34644242

RESUMO

RATIONALE: Extremity threat and amputation following sepsis is a well-publicized and devastating event. However, there is a paucity of data exists surrounding theabout the epidemiology of extmreityextremity threat following sepsis onset. OBJECTIVES: We aim toTo estimate the incidence of extremity threat with or without surgical amputation in community sepsis. METHODS: Multihospital Rretrospective cohort study of adults with Sepsis-3 hospitalized at XX14 academic and community sites from 2013 to 2017. Vasopressor-dependent sepsis was identified by administration epinephrine, norepinephrine, phenylephrine, vasopressin, or dopamine for >1 hour during the 48 hours before to 24 hours following sepsis onset. Outcomes included the incidence of extremity threat, defined as acute onset ischemia, with or without amputation in the 90 days following sepsis onset. The association between extremity threat, and demographics, comorbid conditions, and, and time-varying sepsis treatment factorss were evaluated using a Cox-proportional hazards model. RESULTS: Among 24,365 adults with sepsis, 12,060 (54%) were vasopressor-dependent (mean±standard deviation SD age, 64±16 years; male, 6,548 [54%]; sequential organ failure assessment (SOFA), 10±4). Of these, 231 (2%) patients had a threatened extremity with 26 undergoing 37 amputations, a risk of 2.2 (95% CI: 1.4-3.2) per 1,000, and 205 not undergoing amputation, a risk of 17.0 (95% CI: 14.8-19.5) per 1,000. 95% of the total 37Most amputations occurred in lower extremities (95%), a median (interquartile range) of 16 (6.3-4039.9) days after sepsis onset. Compared to patients with no extremity threat, patients with threat had a higher sequential organ failure assessmentSOFA score (11±4 vs 10±4; P < 0.001), serum lactate (4.6 mmol/L [2.4-8.7] vs 3.1 [1.7-6.0]; P < 0.001), and more bacteremia (n = 37 [37%??] vs n = 2,087 [26%]; P < 0.001) at sepsis onset. Peripheral vascular disease, congestive heart failure, sequential organ failure assessmentSOFA score, and norepinephrine equivalents were significantly associated with the risk of extremity threat. CONCLUSIONS: The evaluation of a threatened extremity resulting in surgical amputation occurred in 2 per 1,000 patients with vasopressor-dependent sepsis.

13.
Med Care ; 59(12): 1090-1098, 2021 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-34629424

RESUMO

BACKGROUND: Hospital-specific template matching is a newer method of hospital performance measurement that may be fairer than regression-based benchmarking. However, it has been tested in only limited research settings. OBJECTIVE: The objective of this study was to test the feasibility of hospital-specific template matching assessments in the Veterans Affairs (VA) health care system and determine power to detect greater-than-expected 30-day mortality. RESEARCH DESIGN: Observational cohort study with hospital-specific template matching assessment. For each VA hospital, the 30-day mortality of a representative subset of hospitalizations was compared with the pooled mortality from matched hospitalizations at a set of comparison VA hospitals treating sufficiently similar patients. The simulation was used to determine power to detect greater-than-expected mortality. SUBJECTS: A total of 556,266 hospitalizations at 122 VA hospitals in 2017. MEASURES: A number of comparison hospitals identified per hospital; 30-day mortality. RESULTS: Each hospital had a median of 38 comparison hospitals (interquartile range: 33, 44) identified, and 116 (95.1%) had at least 20 comparison hospitals. In total, 8 hospitals (6.6%) had a significantly lower 30-day mortality than their benchmark, 5 hospitals (4.1%) had a significantly higher 30-day mortality, and the remaining 109 hospitals (89.3%) were similar to their benchmark. Power to detect a standardized mortality ratio of 2.0 ranged from 72.5% to 79.4% for a hospital with the fewest (6) versus most (64) comparison hospitals. CONCLUSIONS: Hospital-specific template matching may be feasible for assessing hospital performance in the diverse VA health care system, but further refinements are needed to optimize the approach before operational use. Our findings are likely applicable to other large and diverse multihospital systems.

14.
Medicine (Baltimore) ; 100(37): e27265, 2021 Sep 17.
Artigo em Inglês | MEDLINE | ID: mdl-34664879

RESUMO

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.


Assuntos
COVID-19/complicações , Hospitalização/estatística & dados numéricos , Idoso , Estudos de Coortes , Feminino , Humanos , Masculino , Michigan , Pessoa de Meia-Idade , Índice de Gravidade de Doença , Fatores de Tempo
15.
JAMA Netw Open ; 4(9): e2123950, 2021 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-34491351

RESUMO

Importance: It is unclear whether antimicrobial timing for sepsis has changed outside of performance incentive initiatives. Objective: To examine temporal trends and variation in time-to-antibiotics for sepsis in the US Department of Veterans Affairs (VA) health care system. Design, Setting, and Participants: This observational cohort study included 130 VA hospitals from 2013 to 2018. Participants included all patients admitted to the hospital via the emergency department with sepsis from 2013 to 2018, using a definition adapted from the Centers for Disease Control and Prevention Adult Sepsis Event definition, which requires evidence of suspected infection, acute organ dysfunction, and systemic antimicrobial therapy within 12 hours of presentation. Data were analyzed from October 6, 2020, to July 1, 2021. Exposures: Time from presentation to antibiotic administration. Main Outcomes and Measures: The main outcome was differences in time-to-antibiotics across study periods, hospitals, and patient subgroups defined by presenting temperature and blood pressure. Temporal trends in time-to-antibiotics were measured overall and by subgroups. Hospital-level variation in time-to-antibiotics was quantified after adjusting for differences in patient characteristics using multilevel linear regression models. Results: A total of 111 385 hospitalizations for sepsis were identified, including 107 547 men (96.6%) men and 3838 women (3.4%) with a median (interquartile range [IQR]) age of 68 (62-77) years. A total of 7574 patients (6.8%) died in the hospital, and 13 855 patients (12.4%) died within 30 days. Median (IQR) time-to-antibiotics was 3.9 (2.4-6.5) hours but differed by presenting characteristics. Unadjusted median (IQR) time-to-antibiotics decreased over time, from 4.5 (2.7-7.1) hours during 2013 to 2014 to 3.5 (2.2-5.9) hours during 2017 to 2018 (P < .001). In multilevel models adjusted for patient characteristics, median time-to-antibiotics declined by 9.0 (95% CI, 8.8-9.2) minutes per calendar year. Temporal trends in time-to-antibiotics were similar across patient subgroups, but hospitals with faster baseline time-to-antibiotics had less change over time, with hospitals in the slowest tertile decreasing time-to-antibiotics by 16.6 minutes (23.1%) per year, while hospitals in the fastest tertile decreased time-to-antibiotics by 7.2 minutes (13.1%) per year. In the most recent years (2017-2018), median time-to-antibiotics ranged from 3.1 to 6.7 hours across hospitals (after adjustment for patient characteristics), 6.8% of variation in time-to-antibiotics was explained at the hospital level, and odds of receiving antibiotics within 3 hours increased by 65% (95% CI, 56%-77%) for the median patient if moving to a hospital with faster time-to-antibiotics. Conclusions and Relevance: This cohort study across nationwide VA hospitals found that time-to-antibiotics for sepsis has declined over time. However, there remains significant variability in time-to-antibiotics not explained by patient characteristics, suggesting potential unwarranted practice variation in sepsis treatment. Efforts to further accelerate time-to-antibiotics must be weighed against risks of overtreatment.

17.
Annu Rev Med ; 2021 Aug 20.
Artigo em Inglês | MEDLINE | ID: mdl-34416121

RESUMO

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created a global pandemic. Beyond the well-described respiratory manifestations, SARS-CoV-2 may cause a variety of neurologic complications, including headaches, alteration in taste and smell, encephalopathy, cerebrovascular disease, myopathy, psychiatric diseases, and ocular disorders. Herein we describe SARS-CoV-2's mechanism of neuroinvasion and the epidemiology, outcomes, and treatments for neurologic manifestations of COVID-19. Expected final online publication date for the Annual Review of Medicine, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

19.
Infect Control Hosp Epidemiol ; : 1-10, 2021 Jul 26.
Artigo em Inglês | MEDLINE | ID: mdl-34308805

RESUMO

BACKGROUND: We sought to determine the incidence of community-onset and hospital-acquired coinfection in patients hospitalized with coronavirus disease 2019 (COVID-19) and to evaluate associated predictors and outcomes. METHODS: In this multicenter retrospective cohort study of patients hospitalized for COVID-19 from March 2020 to August 2020 across 38 Michigan hospitals, we assessed prevalence, predictors, and outcomes of community-onset and hospital-acquired coinfections. In-hospital and 60-day mortality, readmission, discharge to long-term care facility (LTCF), and mechanical ventilation duration were assessed for patients with versus without coinfection. RESULTS: Of 2,205 patients with COVID-19, 141 (6.4%) had a coinfection: 3.0% community onset and 3.4% hospital acquired. Of patients without coinfection, 64.9% received antibiotics. Community-onset coinfection predictors included admission from an LTCF (OR, 3.98; 95% CI, 2.34-6.76; P < .001) and admission to intensive care (OR, 4.34; 95% CI, 2.87-6.55; P < .001). Hospital-acquired coinfection predictors included fever (OR, 2.46; 95% CI, 1.15-5.27; P = .02) and advanced respiratory support (OR, 40.72; 95% CI, 13.49-122.93; P < .001). Patients with (vs without) community-onset coinfection had longer mechanical ventilation (OR, 3.31; 95% CI, 1.67-6.56; P = .001) and higher in-hospital mortality (OR, 1.90; 95% CI, 1.06-3.40; P = .03) and 60-day mortality (OR, 1.86; 95% CI, 1.05-3.29; P = .03). Patients with (vs without) hospital-acquired coinfection had higher discharge to LTCF (OR, 8.48; 95% CI, 3.30-21.76; P < .001), in-hospital mortality (OR, 4.17; 95% CI, 2.37-7.33; P ≤ .001), and 60-day mortality (OR, 3.66; 95% CI, 2.11-6.33; P ≤ .001). CONCLUSION: Despite community-onset and hospital-acquired coinfection being uncommon, most patients hospitalized with COVID-19 received antibiotics. Admission from LTCF and to ICU were associated with increased risk of community-onset coinfection. Future studies should prospectively validate predictors of COVID-19 coinfection to facilitate the reduction of antibiotic use.

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