A multi-center validation of the electronic health record admission source and discharge location fields against the clinical notes for identifying inpatients with long-term care facility exposure.

Identifying long-term care facility (LTCF)-exposed inpatients is important for infection control research and practice, but ascertaining LTCF exposure is challenging. Across a large validation study, electronic health record data fields identified 76% of LTCF-exposed patients compared to manual chart review. OBJECTIVE: Residence or recent stay in a long-term care facility (LTCF) is an important risk factor for antibiotic-resistant bacterial colonization. However, absent dedicated intake questionnaires or resource-intensive chart review, ascertaining LTCF exposure in inpatients is challenging. We aimed to validate the electronic health record (EHR) admission and discharge location fields against the clinical notes for identifying LTCF-exposed inpatients. METHODS: We conducted a retrospective study of 1020 randomly sampled adult admissions between 2016 and 2021 across 12 University of Maryland Medical System hospitals. Using study-developed guidelines, we categorized the following data for LTCF exposure: each admission's history & physical (H&P) note, each admission's EHR-extracted "Admission Source," and (3) the EHR-extracted admission and discharge locations for previous admissions (≤90 days). We estimated sensitivities, with 95% CIs, of H&P notes and of EHR admission/discharge location fields for detecting "current" and "any recent" (≤90 days, including current) LTCF exposure. RESULTS: For detecting current LTCF exposure, the sensitivity of the index admission's EHR-extracted "Admission Source" was 46% (95% CI: 35%­58%) and of the H&P note was 92% (83%­97%). For detecting any recent LTCF exposure, the sensitivity of "Admission Source" across the index and previous admissions was 32% (24%­41%), "Discharge Location" across previous admission(s) was 57% (47%­66%), and of the H&P note was 68% (59%­76%). The combined sensitivity of admission source and discharge location for detecting any recent LTCF exposure was 76% (67%­83%). CONCLUSIONS: The EHR-obtained admission source and discharge location fields identified 76% of LTCF-exposed patients compared to chart review but disproportionately missed currently exposed patients.

Clinical yield of multiple testing with respiratory pathogen panels.

Though multiplex respiratory pathogen panels (RPP) have high sensitivity, multiple tests are occasionally performed simultaneously or in rapid succession in an attempt to increase the yield. The purpose of this study was to assess the impact of this practice. METHODS: "Multiple testing" was defined as >1 RPP performed within 12 hours on the same patient and specimen type. All cases of multiple testing for adults at two hospitals over a 5-year period were included. Chart review was performed to determine whether discordant results led to a clinical diagnosis or change in clinical management. RESULTS: Of 18,779 RPPs, 462 (2.5%) represented cases of multiple testing. Twenty-six of 462 cases (5.6%) produced discordant results. Five discordant results (1.1% of 462 multiple testing episodes) were associated with a clinical diagnosis, and 4 (0.9%) influenced clinical management. CONCLUSION: Multiple RPP testing facilitates clinical management in <1% of cases. Medical centers may consider de-implementing this practice.

A Multicenter Evaluation of Probiotic Use for the Primary Prevention of Clostridioides difficile Infection.

BACKGROUND: Primary prevention of Clostridioides difficile infection (CDI) is a priority for hospitals. Probiotics have the potential to interfere with colonization and CDI. In this study, we evaluated the impact of a computerized clinical decision support (CCDS) tool to prescribe probiotics for primary prevention of CDI among adult hospitalized patients. METHODS: A CCDS tool was implemented into the electronic medical record at 4 hospitals to prompt prescription of a probiotic preparation at the time of antibiotic prescription in high-risk patients in May 2019. Interrupted time series using segmented regression analysis was conducted to evaluate hospital-wide CDI incidence for the year pre- and post-CCDS implementation. In addition, multivariable logistic regression was used to evaluate CDI incidence in patients who qualified for probiotics in the pre- vs post-intervention periods, adjusting for potential confounders. To adjust for potential differences in patients who received probiotics in the post-intervention period, propensity score-matched pairs were developed to evaluate CDI risk by receipt of probiotics. RESULTS: Quarterly CDI incidence increased over time post-intervention relative to baseline trends (slope change, 1.4; 95% confidence interval [CI], .9-1.9). The odds ratio (OR) of CDI was 1.41 in eligible patients post-intervention compared with pre-intervention (adjusted OR, 1.41; 95% CI, 1.11-1.79). Propensity score-matched analysis showed that patients who received probiotics did not have lower rates of CDI compared with those who did not receive probiotics (OR, 1.46; 95% CI, .87-2.45). CONCLUSIONS: Use of probiotics for primary prevention of CDI among adult inpatients receiving antibiotics is not supported.

Follow-up blood cultures in Pseudomonas aeruginosa bacteremia: A potential target for diagnostic stewardship.

Objectives: Evidence supporting collection of follow-up blood cultures for Gram-negative bacteremia is mixed. We sought to understand why providers order follow-up blood cultures when managing P. aeruginosa bacteremia and whether follow-up blood cultures in this context are associated with short- and long-term survival. Methods: We conducted a retrospective cohort study of adult inpatients with P. aeruginosa bacteremia at the University of Maryland Medical Center in 2015-2020. Kaplan-Meier survival curves and Cox regression with time-varying covariates were used to evaluate the association between follow-up blood cultures and time to mortality within 30 days of first positive blood culture. Provider justifications for follow-up blood cultures were identified through chart review. Results: Of 159 eligible patients, 127 (80%) had follow-up blood cultures, including 9 (7%) that were positive for P. aeruginosa and 10 (8%) that were positive for other organisms. Follow-up blood cultures were typically collected "to ensure clearance" or "to guide antibiotic therapy." Overall, 30-day mortality was 25.2%. After risk adjustment for patient characteristics, follow-up blood cultures were associated with a nonsignificant reduction in mortality risk (hazard ratio, 0.43; 95% confidence interval, 1.08; P = .071). In exploratory analyses, the potential mortality reduction from follow-up blood cultures was driven by their use in patients with Pitt bacteremia scores >0. Conclusions: Follow-up blood cultures are commonly collected for P. aeruginosa bacteremia but infrequently identify persistent bacteremia. Targeted use of follow-up blood cultures based on severity of illness may reduce unnecessary culturing.

Newborn Screening Long Term Follow-Up in the Medical Home.

This demonstration project explored the feasibility of utilizing data from pediatric primary care providers to evaluate the long-term outcomes of children with disorders identified by newborn screening (NBS). Compliance with national guidelines for care and the morbidity for this population was also examined. Primary care practices were recruited and patients with sickle cell disease or who were deaf/hard of hearing were given the opportunity to enroll in the study. Data were collected on the quality of the medical home with practice data compared to family responses. Clinical outcomes for each patient were assessed by review of medical records and patient surveys. These data sources were compared to determine accuracy of primary care data, morbidity, and receipt of preventive care. Electronic data sharing was explored through transmission of Clinical Document Architecture (CDA) files. Care coordination was a challenge, even in highly accredited medical homes. Providers did not have complete information regarding clinical outcomes and children were not consistently receiving recommended preventive care. Electronic data sharing with public health departments encountered interface challenges. Primary care providers in the USA should not currently be used as a sole source to evaluate long-term outcomes of children with disorders identified by NBS.