Characterizing Patients Presenting on Hospital Admission With Central Line-Associated Bloodstream Infections: A Multicenter Study.

BACKGROUND: There are no systematic measures of central line-associated bloodstream infections (CLABSIs) in patients maintaining central venous catheters (CVCs) outside acute care hospitals. To clarify the burden of CLABSIs in these patients, we characterized patients with CLABSI present on hospital admission (POA). METHODS: Retrospective cross-sectional analysis of patients with CLABSI-POA in 3 health systems covering 11 hospitals across Maryland, Washington DC, and Missouri from November 2020 to October 2021. CLABSI-POA was defined using an adaptation of the acute care CLABSI definition. Patient demographics, clinical characteristics, and outcomes were collected via record review. Cox proportional hazard analysis was used to assess factors associated with the all-cause mortality rate within 30 days. RESULTS: A total of 461 patients were identified as having CLABSI-POA. CVCs were most commonly maintained in home infusion therapy (32.8%) or oncology clinics (31.2%). Enterobacterales were the most common etiologic agent (29.2%). Recurrent CLABSIs occurred in a quarter of patients (25%). Eleven percent of patients died during the hospital admission. Among patients with CLABSI-POA, mortality risk increased with age (hazard ratio vs age <20 years by age group: 20-44 years, 11.2 [95% confidence interval, 1.46-86.22]; 45-64 years, 20.88 [2.84-153.58]; ≥65 years, 22.50 [2.98-169.93]) and lack of insurance (2.46 [1.08-5.59]), and it decreased with CVC removal (0.57 [.39-.84]). CONCLUSIONS: CLABSI-POA is associated with significant in-hospital mortality risk. Surveillance is required to understand the burden of CLABSI in the community to identify targets for CLABSI prevention initiatives outside acute care settings.

Comorbidities associated with 30-day readmission following index coronavirus disease 2019 (COVID-19) hospitalization: A retrospective cohort study of 331,136 patients in the United States.

OBJECTIVE: 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. RESULTS: 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. CONCLUSIONS: 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.

Clinician Attitudes and Beliefs Associated with More Aggressive Diagnostic Testing.

BACKGROUND: Variation in clinicians' diagnostic test utilization is incompletely explained by demographics and likely relates to cognitive characteristics. We explored clinician factors associated with diagnostic test utilization. METHODS: We used a self-administered survey of attitudes, cognitive characteristics, and reported likelihood of test ordering in common scenarios; frequency of lipid and liver testing in patients on statin therapy. Participants were 552 primary care physicians, nurse practitioners, and physician assistants from practices in 8 US states across 3 regions, from June 1, 2018 to November 26, 2019. We measured Testing Likelihood Score: the mean of 4 responses to testing frequency and self-reported testing frequency in patients on statins. RESULTS: Respondents were 52.4% residents, 36.6% attendings, and 11.0% nurse practitioners/physician assistants; most were white (53.6%) or Asian (25.5%). Median age was 32 years; 53.1% were female. Participants reported ordering tests for a median of 20% (stress tests) to 90% (mammograms) of patients; Testing Likelihood Scores varied widely (median 54%, interquartile range 43%-69%). Higher scores were associated with geography, training type, low numeracy, high malpractice fear, high medical maximizer score, high stress from uncertainty, high concern about bad outcomes, and low acknowledgment of medical uncertainty. More frequent testing of lipids and liver tests was associated with low numeracy, high medical maximizer score, high malpractice fear, and low acknowledgment of uncertainty. CONCLUSIONS: Clinician variation in testing was common, with more aggressive testing consistently associated with low numeracy, being a medical maximizer, and low acknowledgment of uncertainty. Efforts to reduce undue variations in testing should consider clinician cognitive drivers.

Accuracy of Practitioner Estimates of Probability of Diagnosis Before and After Testing.

Importance: Accurate diagnosis is essential to proper patient care. Objective: To explore practitioner understanding of diagnostic reasoning. Design, Setting, and Participants: In this survey study, 723 practitioners at outpatient clinics in 8 US states were asked to estimate the probability of disease for 4 scenarios common in primary care (pneumonia, cardiac ischemia, breast cancer screening, and urinary tract infection) and the association of positive and negative test results with disease probability from June 1, 2018, to November 26, 2019. Of these practitioners, 585 responded to the survey, and 553 answered all of the questions. An expert panel developed the survey and determined correct responses based on literature review. Results: A total of 553 (290 resident physicians, 202 attending physicians, and 61 nurse practitioners and physician assistants) of 723 practitioners (76.5%) fully completed the survey (median age, 32 years; interquartile range, 29-44 years; 293 female [53.0%]; 296 [53.5%] White). Pretest probability was overestimated in all scenarios. Probabilities of disease after positive results were overestimated as follows: pneumonia after positive radiology results, 95% (evidence range, 46%-65%; comparison P < .001); breast cancer after positive mammography results, 50% (evidence range, 3%-9%; P < .001); cardiac ischemia after positive stress test result, 70% (evidence range, 2%-11%; P < .001); and urinary tract infection after positive urine culture result, 80% (evidence range, 0%-8.3%; P < .001). Overestimates of probability of disease with negative results were also observed as follows: pneumonia after negative radiography results, 50% (evidence range, 10%-19%; P < .001); breast cancer after negative mammography results, 5% (evidence range, <0.05%; P < .001); cardiac ischemia after negative stress test result, 5% (evidence range, 0.43%-2.5%; P < .001); and urinary tract infection after negative urine culture result, 5% (evidence range, 0%-0.11%; P < .001). Probability adjustments in response to test results varied from accurate to overestimates of risk by type of test (imputed median positive and negative likelihood ratios [LRs] for practitioners for chest radiography for pneumonia: positive LR, 4.8; evidence, 2.6; negative LR, 0.3; evidence, 0.3; mammography for breast cancer: positive LR, 44.3; evidence range, 13.0-33.0; negative LR, 1.0; evidence range, 0.05-0.24; exercise stress test for cardiac ischemia: positive LR, 21.0; evidence range, 2.0-2.7; negative LR, 0.6; evidence range, 0.5-0.6; urine culture for urinary tract infection: positive LR, 9.0; evidence, 9.0; negative LR, 0.1; evidence, 0.1). Conclusions and Relevance: This survey study suggests that for common diseases and tests, practitioners overestimate the probability of disease before and after testing. Pretest probability was overestimated in all scenarios, whereas adjustment in probability after a positive or negative result varied by test. Widespread overestimates of the probability of disease likely contribute to overdiagnosis and overuse.

Electronically Available Comorbidities Should Be Used in Surgical Site Infection Risk Adjustment.

BACKGROUND: Healthcare-associated infections such as surgical site infections (SSIs) are used by the Centers for Medicare and Medicaid Services (CMS) as pay-for-performance metrics. Risk adjustment allows a fairer comparison of SSI rates across hospitals. Until 2016, Centers for Disease Control and Prevention (CDC) risk adjustment models for pay-for-performance SSI did not adjust for patient comorbidities. New 2016 CDC models only adjust for body mass index and diabetes. METHODS: We performed a multicenter retrospective cohort study of patients undergoing surgical procedures at 28 US hospitals. Demographic data and International Classification of Diseases, Ninth Revision codes were obtained on patients undergoing colectomy, hysterectomy, and knee and hip replacement procedures. Complex SSIs were identified by infection preventionists at each hospital using CDC criteria. Model performance was evaluated using measures of discrimination and calibration. Hospitals were ranked by SSI proportion and risk-adjusted standardized infection ratios (SIR) to assess the impact of comorbidity adjustment on public reporting. RESULTS: Of 45394 patients at 28 hospitals, 573 (1.3%) developed a complex SSI. A model containing procedure type, age, race, smoking, diabetes, liver disease, obesity, renal failure, and malnutrition showed good discrimination (C-statistic, 0.73) and calibration. When comparing hospital rankings by crude proportion to risk-adjusted ranks, 24 of 28 (86%) hospitals changed ranks, 16 (57%) changed by ≥2 ranks, and 4 (14%) changed by >10 ranks. CONCLUSIONS: We developed a well-performing risk adjustment model for SSI using electronically available comorbidities. Comorbidity-based risk adjustment should be strongly considered by the CDC and CMS to adequately compare SSI rates across hospitals.

Frequency of Adverse Events Before, During, and After Hospital Admission.

OBJECTIVES: Adverse events (AEs) are unintended physical injuries resulting from or contributed to by medical or surgical care. We determined the frequency and type of AEs before, during, and after hospital admission. METHODS: We conducted a cohort study of 296 adult hospital patients. We used the standardized Institute for Healthcare Improvement Global Trigger Tool for Measuring Adverse Events to review the medical records of the hospital patients for occurrence, timing relative to hospital admission, severity, and preventability of AEs. We also identified the primary physiologic system affected by the AE. RESULTS: Among 296 patients, we identified 338 AEs. AEs occurred with similar frequency before (n = 148; 43.8%) and during hospital admission (n = 162; 47.9%). Fewer AEs occurred after discharge (n = 28; 8.3%). Half of all AEs (n = 169; 50.0%) were severe, whereas 47.9% (n = 162) were preventable. CONCLUSIONS: AEs occur with similar frequency before and during hospitalization and may contribute more to hospital admissions than previously recognized. These findings suggest that efforts to improve patient safety should include outpatient settings in addition to the more commonly targeted acute care settings.