Background: Although multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, only a few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable.
Objective: We aimed to develop a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization.
Methods: Patients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (3601/5145, 70.0%) and validation (1544/5145, 30.0%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and association measures in the validation cohort.
Results: Of 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher with the addition of predictive factors, attaining the best performance with an AUROC of 0.818 (95% CI 0.792-0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were 0.744 (95% CI 0.716-0.773) and 0.745 (95% CI 0.709-0.776), respectively.
Conclusions: For patients transferred to EDs, we developed a well-performing hospital admission prediction model based on routinely collected prehospital information including chief complaints.
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http://dx.doi.org/10.2196/20324 | DOI Listing |
JAMA Surg
December 2024
Department of Surgery, Stanford University School of Medicine, Stanford, California.
Importance: Surgical quality improvement efforts have largely focused on 30-day outcomes, such as readmissions and complications. Surgery may have a sustained impact on the health and quality of life of patients considered frail, yet data are lacking on the long-term health care utilization of patients with frailty following surgery.
Objective: To examine the independent association of preoperative frailty on long-term health care utilization (up to 24 months) following surgery.
JAMA Netw Open
December 2024
Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Importance: Access to appropriate postpartum care is essential for improving maternal health outcomes and promoting maternal health equity.
Objective: To analyze the impact of the Nurse-Family Partnership (NFP) home visiting program on use of routine and emergency postpartum care.
Design, Setting, And Participants: This study was a secondary analysis of a randomized clinical trial that enrolled eligible participants between 2016 and 2020 to receive NFP or usual care from a South Carolina Medicaid program.
JAMA Netw Open
December 2024
School of Pharmacy, University of Maryland, Baltimore.
Importance: Initiating effective therapy early is associated with improved survival among patients hospitalized with gram-negative bloodstream infections; furthermore, providing early phenotype-desirable antimicrobial therapy (PDAT; defined as receipt of a β-lactam antibiotic with the narrowest spectrum of activity to effectively treat the pathogen's phenotype) is crucial for antimicrobial stewardship. However, the timing of targeted therapy among patients hospitalized with gram-negative bloodstream infections is not well understood.
Objective: To compare the clinical outcomes between patients who were hospitalized with Enterobacterales bloodstream infections receiving early vs delayed PDAT.
JAMA Netw Open
December 2024
Center for Advancing Health Services, Policy & Economics Research, Institute for Public Health, Washington University, St Louis, Missouri.
Importance: Hospital participation in the Bundled Payments for Care Improvement-Advanced (BPCI-A) initiative has been associated with modest savings and stable clinical outcomes overall, but it is unknown whether the program performs differently for medical and surgical or procedural (henceforth, surgical) episodes.
Objective: To assess the association of BPCI-A participation with Medicare spending and clinical outcomes for medical and surgical episodes.
Design, Setting, And Participants: This retrospective difference-in-differences cohort study utilized 100% Medicare fee-for-service inpatient claims for episodes initiated between January 1, 2017, and September 30, 2019, and included 90 days of follow-up.
JAMA Intern Med
December 2024
Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, California.
Importance: An emergency department (ED) physician's decision to admit a patient to the hospital plays a pivotal role in determining the type and intensity of care that patient will receive. ED physicians vary widely in their propensity to admit patients to the hospital, but it is unknown whether higher admission propensities result in lower subsequent mortality rates.
Objective: To measure the variation in ED physicians' admission propensities and estimate their association with patients' subsequent mortality rates.
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