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Hospital readmission is unsettling to patients and caregivers, costly to the healthcare system, and may leave patients at additional risk for hospital-acquired infections and other complications. We evaluated the association between comorbidities present during index coronavirus disease 2019 (COVID-19) hospitalization and the risk of 30-day readmission.
Design, setting, and participants:
We used the Premier Healthcare database to perform a retrospective cohort study of COVID-19 hospitalized patients discharged between April 2020 and March 2021 who were followed for 30 days after discharge to capture readmission to the same hospital.
Among the 331,136 unique patients in the index cohort, 36,827 (11.1%) had at least 1 all-cause readmission within 30 days. Of the readmitted patients, 11,382 (3.4%) were readmitted with COVID-19 as the primary diagnosis. In the multivariable model adjusted for demographics, hospital characteristics, coexisting comorbidities, and COVID-19 severity, each additional comorbidity category was associated with an 18% increase in the odds of all-cause readmission (adjusted odds ratio [aOR], 1.18; 95% confidence interval [CI], 1.17–1.19) and a 10% increase in the odds of readmission with COVID-19 as the primary readmission diagnosis (aOR, 1.10; 95% CI, 1.09–1.11). Lymphoma (aOR, 1.86; 95% CI, 1.58–2.19), renal failure (aOR, 1.32; 95% CI, 1.25–1.40), and chronic lung disease (aOR, 1.29; 95% CI, 1.24–1.34) were most associated with readmission for COVID-19.
Readmission within 30 days was common among COVID-19 survivors. A better understanding of comorbidities associated with readmission will aid hospital care teams in improving postdischarge care. Additionally, it will assist hospital epidemiologists and quality administrators in planning resources, allocating staff, and managing bed-flow issues to improve patient care and safety.
To determine which healthcare worker (HCW) roles and patient care activities are associated with acquisition of vancomycin-resistant Enterococcus (VRE) on HCW gloves or gowns after patient care, as a surrogate for transmission to other patients.
Prospective cohort study.
Medical and surgical intensive care units at a tertiary-care academic institution.
VRE-colonized patients on Contact Precautions and their HCWs.
Overall, 94 VRE-colonized patients and 469 HCW–patient interactions were observed. Research staff recorded patient care activities and cultured HCW gloves and gowns for VRE before doffing and exiting patient room.
VRE were isolated from 71 of 469 HCWs’ gloves or gowns (15%) following patient care. Occupational/physical therapists, patient care technicians, nurses, and physicians were more likely than environmental services workers and other HCWs to have contaminated gloves or gowns. Compared to touching the environment alone, the odds ratio (OR) for VRE contamination associated with touching both the patient (or objects in the immediate vicinity of the patient) and environment was 2.78 (95% confidence interval [CI], 0.99–0.77) and the OR associated with touching only the patient (or objects in the immediate vicinity) was 3.65 (95% CI, 1.17–11.41). Independent risk factors for transmission of VRE to HCWs were touching the patient’s skin (OR, 2.18; 95% CI, 1.15–4.13) and transferring the patient into or out of bed (OR, 2.66; 95% CI, 1.15–6.43).
Patient contact is a major risk factor for HCW contamination and subsequent transmission. Interventions should prioritize contact precautions and hand hygiene for HCWs whose activities involve touching the patient.
Risk adjustment is needed to fairly compare central-line–associated bloodstream infection (CLABSI) rates between hospitals. Until 2017, the Centers for Disease Control and Prevention (CDC) methodology adjusted CLABSI rates only by type of intensive care unit (ICU). The 2017 CDC models also adjust for hospital size and medical school affiliation. We hypothesized that risk adjustment would be improved by including patient demographics and comorbidities from electronically available hospital discharge codes.
Using a cohort design across 22 hospitals, we analyzed data from ICU patients admitted between January 2012 and December 2013. Demographics and International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) discharge codes were obtained for each patient, and CLABSIs were identified by trained infection preventionists. Models adjusting only for ICU type and for ICU type plus patient case mix were built and compared using discrimination and standardized infection ratio (SIR). Hospitals were ranked by SIR for each model to examine and compare the changes in rank.
Overall, 85,849 ICU patients were analyzed and 162 (0.2%) developed CLABSI. The significant variables added to the ICU model were coagulopathy, paralysis, renal failure, malnutrition, and age. The C statistics were 0.55 (95% CI, 0.51–0.59) for the ICU-type model and 0.64 (95% CI, 0.60–0.69) for the ICU-type plus patient case-mix model. When the hospitals were ranked by adjusted SIRs, 10 hospitals (45%) changed rank when comorbidity was added to the ICU-type model.
Our risk-adjustment model for CLABSI using electronically available comorbidities demonstrated better discrimination than did the CDC model. The CDC should strongly consider comorbidity-based risk adjustment to more accurately compare CLABSI rates across hospitals.
To identify comorbid conditions associated with surgical site infection (SSI) among patients undergoing renal transplantation and improve existing risk adjustment methodology used by the Centers for Disease Control and Prevention National Healthcare Safety Network (NHSN).
Patients (≥18 years) who underwent renal transplantation at University of Maryland Medical Center January 1, 2010-December 31, 2011.
Trained infection preventionists reviewed medical records to identify surgical site infections that developed within 30 days after transplantation, using NHSN criteria. Patient demographic characteristics and risk factors for surgical site infections were identified through a central data repository. International Statistical Classification of Disease, Ninth Revision, Clinical Modification codes were used to analyze individual component comorbid conditions and calculate the Charlson and Elixhauser comorbidity indices. These indices were compared with the current NHSN risk adjustment methodology.
A total of 441 patients were included in the final cohort. In bivariate analysis, the Charlson components of cerebrovascular disease, peripheral vascular disease, and rheumatologic disorders and Elixhauser components of obesity, rheumatoid arthritis, and weight loss were significantly associated with the outcome. A model utilizing the variables from the NHSN methodology had a c-statistic of 0.56 (95% CI, 0.48–0.63), whereas a model that also included comorbidities from the Charlson and Elixhauser indices had a c-statistic of 0.65 (95% CI, 0.58–0.73). The model with all 3 risk adjustment scores performed best and was statistically different from the NHSN model alone, demonstrated by improvement in the c statistic (0.65 vs 0.56).
Risk adjustment models should incorporate electronically available comorbid conditions.
Information on surges in critical care services including mechanical ventilator use during seasonal influenza outbreaks is limited. To potentially facilitate preparedness plans for future pandemics, we retrospectively quantitated surges in all-cause mechanical ventilator use during peak influenza for 12 consecutive years in all certified hospitals in Maryland.
Influenza testing data obtained for the Centers for Disease Control and Protection, Health and Human Services region 3, included defined peak influenza outbreak periods (PIOP), non-influenza time periods (non-ITP), and proportions of circulating influenza types for all study years. Procedure codes for mechanical ventilator use and diagnostic codes for medically attended acute respiratory illness (MAARI) were reviewed for every Maryland hospitalization. Daily counts of hospitalizations associated with ventilator use or MAARI during PIOP compared to non-ITP were analyzed using Poisson regression adjusted for month and year.
Ventilator use increased during PIOP by 7% (95% CI, 5-10) over non-ITP (P < .0001) for all study years. These annual surges correlated with influenza season intensity, as measured by MAARI-related hospitalizations (correlation coefficient = 0.91, P < .0001).
Surges in ventilator use were temporally associated with PIOP and were positively correlated with influenza season intensity, as measured by hospitalizations associated with acute respiratory illness. This information may assist resource planning for future pandemics. (Disaster Med Public Health Preparedness. 2014;x:1-7)
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