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Background There are limited U.S. data assessing adherence to surgical antimicrobial prophylaxis guidelines, particularly across a large, nationwide sample. Moreover, commonly prescribed inappropriate antimicrobial prophylaxis regimens remain unknown, hindering improvement initiatives. Methods We conducted a retrospective cohort study of adults who underwent elective craniotomy, hip replacement, knee replacement, spinal procedure, or hernia repair in 2019-2020 at hospitals in the PINC AI (Premier) Healthcare Database. We evaluated adherence of prophylaxis regimens, with respect to antimicrobial agents endorsed in the American Society of Health-System Pharmacist guidelines, accounting for patient antibiotic allergy and methicillin-resistant Staphylococcus aureus colonization status. We used multivariable logistic regression with random effects by hospital to evaluate associations between patient, procedural, and hospital characteristics and guideline adherence. Results Across 825 hospitals and 521,091 inpatient elective surgeries, 308,760 (59%) were adherent to prophylaxis guidelines. In adjusted analysis, adherence varied significantly by U.S. census division (adjusted OR [aOR] range: 0.61-1.61) and was significantly lower in 2020 compared to 2019 (aOR 0.92, 95% CI: 0.91-0.94, p < 0.001). The most common reason for nonadherence was unnecessary vancomycin use. In a post-hoc analysis, controlling for patient age, comorbidities, other nephrotoxic agent use, and patient and procedure characteristics, patients receiving cefazolin plus vancomycin had 19% higher odds of acute kidney injury (AKI) compared to patients receiving cefazolin alone (aOR 1.19; 95% CI: 1.11-1.27, p < 0.001). Conclusions Adherence to antimicrobial prophylaxis guidelines remains suboptimal, largely driven by unnecessary vancomycin use, which may increase the risk of AKI. Adherence decreased in the first year of the COVID-19 pandemic.

BackgroundPatients hospitalized with heart failure suffer the highest rates of 30-day readmission among other clinically defined patient populations in the United States. Investigation into the predictability of 30-day readmissions can lead to clinical decision support tools and targeted interventions that can help care providers to improve individual patient care and reduce readmission risk.ObjectiveThis study aimed to develop a dynamic readmission risk prediction model that yields daily predictions for patients hospitalized with heart failure toward identifying risk trajectories over time and identifying clinical predictors associated with different patterns in readmission risk trajectories.MethodsA two-stage predictive modeling approach combining logistic and beta regression was applied to electronic health record data accumulated daily to predict 30-day readmission for 534 hospital encounters of patients with heart failure over 2750 patient days. Unsupervised clustering was performed on predictions to uncover time-dependent trends in readmission risk over the patient’s hospital stay. We used data collected between September 1, 2013, and August 31, 2015, from a community hospital in Maryland (United States) for patients with a primary diagnosis of heart failure. Patients who died during the hospital stay or were transferred to other acute care hospitals or hospice care were excluded.ResultsReadmission occurred in 107 (107/534, 20.0%) encounters. The out-of-sample area under curve for the 2-stage predictive model was 0.73 (SD 0.08). Dynamic clinical predictors capturing laboratory results and vital signs had the highest predictive value compared with demographic, administrative, medical, and procedural data included. Unsupervised clustering identified four risk trajectory groups: decreasing risk (131/534, 24.5% encounters), high risk (113/534, 21.2%), moderate risk (177/534, 33.1%), and low risk (113/534, 21.2%). The decreasing risk group demonstrated change in average probability of readmission from admission (0.69) to discharge (0.30), whereas the high risk (0.75), moderate risk (0.61), and low risk (0.39) groups maintained consistency over the hospital course. A higher level of hemoglobin, larger decrease in potassium and diastolic blood pressure from admission to discharge, and smaller number of past hospitalizations are associated with decreasing readmission risk (P<.001).ConclusionsDynamically predicting readmission and quantifying trends over patients’ hospital stay illuminated differing risk trajectory groups. Identifying risk trajectory patterns and distinguishing predictors may shed new light on indicators of readmission and the isolated effects of the index hospitalization.