scholarly journals Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

10.2196/30022 ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. e30022
Author(s):  
Ann Corneille Monahan ◽  
Sue S Feldman

Background Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

2021 ◽  
Author(s):  
Ann Corneille Monahan ◽  
Sue S Feldman

BACKGROUND Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. OBJECTIVE The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. METHODS We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. RESULTS Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. CONCLUSIONS There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.


2020 ◽  
Author(s):  
Ben J. Brintz ◽  
Benjamin Haaland ◽  
Joel Howard ◽  
Dennis L. Chao ◽  
Joshua L. Proctor ◽  
...  

AbstractTraditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where “pre-test” epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.


2020 ◽  
Vol 38 (4) ◽  
pp. 774-779 ◽  
Author(s):  
Wanyi Chen ◽  
Benjamin Linthicum ◽  
Nilay Tanik Argon ◽  
Thomas Bohrmann ◽  
Kenneth Lopiano ◽  
...  

2009 ◽  
Vol 16 (3) ◽  
pp. 338-345 ◽  
Author(s):  
N. R. Hoot ◽  
L. J. LeBlanc ◽  
I. Jones ◽  
S. R. Levin ◽  
C. Zhou ◽  
...  

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Patrick Rockenschaub ◽  
Martin J. Gill ◽  
David McNulty ◽  
Orlagh Carroll ◽  
Nick Freemantle ◽  
...  

Abstract Background Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 h, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions. Methods Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimate the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/2019. Discussion Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.


2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S351-S352
Author(s):  
Thomas P Lodise Jr. ◽  
Nicole G Bonine ◽  
J Michael Ye ◽  
Henry J Folse ◽  
Patrick Gillard

Abstract Background Identification of infections caused by antimicrobial-resistant microorganisms is critical to administration of early appropriate antibiotic therapy. We developed a clinical bedside tool to estimate the probability of carbapenem-resistant Enterobacteriaceae (CRE), extended spectrum β-lactamase-producing Enterobacteriaceae (ESBL), and multidrug-resistant Pseudomonas aeruginosa (MDRP) among hospitalized adult patients with Gram-negative infections. Methods A retrospective observational study of the Premier Hospital Database (PHD) was conducted. The study included adult hospitalized patients with complicated urinary tract infection (cUTI), complicated intraabdominal infection (cIAI), bloodstream infections (BSI), or hospital-acquired/ventilator-associated pneumonia (HAP/VAP) with a culture-confirmed Gram-negative infection in PHD from 2011 to 2015. Model development steps are shown in Figure 1. The study population was split into training and test cohorts. Prediction models were developed using logistic regression in the training cohort (Figure 1). For each resistant phenotype (CRE, ESBL, and MDRP), a separate model was developed for community-acquired (index culture ≤3 days of admission) and hospital-acquired (index culture >3 days of admission) infections (six models in total). The predictive performance of the models was assessed in the training and test cohorts. Models were converted to a singular user-friendly interface for use at the bedside. Results The most important predictors of antibiotic-resistant Gram-negative bacterial infection were prior number of antibiotics, infection site, prior infection in the last 3 months, hospital prevalence of each resistant pathogen (CRE, ESBL, and MDRP), and age (Figure 2). The predictive performance was highly acceptable for all six models (Figure 3). Conclusion We developed a clinical prediction tool to estimate the probability of CRE, ESBL, and MDRP among hospitalized adult patients with community- and hospital-acquired Gram-negative infections. Our predictive model has been implemented as a user-friendly bedside tool for use by clinicians to predict the probability of resistant infections in individual patients, to guide early appropriate therapy. Disclosures T. P. Lodise Jr., Motif BioSciences: Board Member, Consulting fee. N. G. Bonine, Allergan: Employee, Salary. J. M. Ye, Allergan: Employee, Salary. H. J. Folse, Evidera: Employee, Salary. P. Gillard, Allergan: Employee, Salary.


2020 ◽  
Author(s):  
Patrick Rockenschaub ◽  
Martin J Gill ◽  
David McNulty ◽  
Orlagh Carroll ◽  
Nick Freemantle ◽  
...  

Abstract Background: Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 hours, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions.Methods: Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimates the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected urinary tract infection syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/19.Discussion: Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.


2020 ◽  
Author(s):  
Patrick Rockenschaub ◽  
Martin J Gill ◽  
David McNulty ◽  
Orlagh Carroll ◽  
Nick Freemantle ◽  
...  

Abstract Background:Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 hours, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions.Methods:Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimates the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/19. Discussion:Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.


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