A Novel Model for Enhanced Prediction and Understanding of Unplanned 30-Day Pediatric Readmission

Ehwerhemuepha, Louis; Finn, Stacey; Rothman, Michael; Rakovski, Cyril; Feaster, William

doi:10.1542/hpeds.2017-0220

Cited by 29 publications

(46 citation statements)

References 37 publications

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“…We provide an assessment of model performance with recommendations on the potential implementation of the corresponding predictive models in the ED. The new variables we considered include several measures of current and past healthcare resource utilization that have been found to be associated with the related problem of hospital readmission [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A multicenter mixed-effects model for inference and prediction of 72-h return visits to the emergency department for adult patients with trauma-related diagnoses

et al. 2020

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Objective: Emergency department (ED) return visits within 72 h may be a sign of poor quality of care and entail unnecessary use of healthcare resources. In this study, we compare the performance of two leading statistical and machine learning classification algorithms, and we use the best performing approach to identify novel risk factors of ED return visits. Methods: We analyzed 3.2 million ED encounters with at least one diagnosis under "injury, poisoning and certain other consequences of external causes" and "external causes of morbidity." These encounters included patients 18 years or older from across 128 emergency room facilities in the USA. For each encounter, we calculated the 72-h ED return status and retrieved 57 features from demographics, diagnoses, procedures, and medications administered during the process of administration of medical care. We implemented a mixed-effects model to assess the effects of the covariates while accounting for the hierarchical structure of the data. Additionally, we investigated the predictive accuracy of the extreme gradient boosting tree ensemble approach and compared the performance of the two methods. Results: The mixed-effects model indicates that certain blunt force and non-blunt trauma inflates the risk of a return visit. Notably, patients with trauma to the head and patients with burns and corrosions have elevated risks. This is in addition to 11 other classes of both blunt force and non-blunt force traumas. In addition, prior healthcare resource utilization, patients who have had one or more prior return visits within the last 6 months, prior ED visits, and the number of hospitalizations within the 6 months are associated with increased risk of returning to the ED after discharge. On the one hand, the area under the receiver characteristic curve (AUROC) of the mixed-effects model was 0.710 (0.707, 0.712). On the other hand, the gradient boosting tree ensemble had a lower AUROC of 0.698 CI (0.696, 0.700) on the independent test model.

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Section: Introductionmentioning

confidence: 99%

A multicenter mixed-effects model for inference and prediction of 72-h return visits to the emergency department for adult patients with trauma-related diagnoses

et al. 2020

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“…7 However, a recent study on a pediatric population presented a promising prediction model with an area under the receiver operating characteristic curve (AUC) of 0.79. 8 There has been a growing interest in measuring gait speed for assessing motor function and long-term mortality. 9 It is thought to be a surrogate marker for frailty, and has even been called a new vital sign.…”

Section: Introductionmentioning

confidence: 99%

<p>Gait Speed at Discharge and Risk for Readmission or Death: A Prospective Study of an Emergency Ward Population</p>

Hertzberg

Luksha

Kus

et al. 2020

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“…It is reasonable to assume that patients at greater risk for short-term readmission may also be at increased risk for hospitalization over a longer time frame [22]. The type and extent of surgery is known to affect readmission rate [5, 7], as is length of stay during a hospitalization [14, 23]. Auger and Davis found that patients admitted on a weekend were more likely to be readmitted within 30 days [10].…”

Section: Discussionmentioning

confidence: 99%

Prediction of 30-day pediatric unplanned hospitalizations using the Johns Hopkins Adjusted Clinical Groups risk adjustment system

2019

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Background The Johns Hopkins ACG System is widely used to predict patient healthcare service use and costs. Most applications have focused on adult populations. In this study, we evaluated the use of the ACG software to predict pediatric unplanned hospital admission in a given month, based on the past year’s clinical information captured by electronic health records (EHRs). Methods and findings EHR data from a multi-state pediatric integrated delivery system were obtained for 920,051 patients with at least one physician visit during January 2009 to December 2016. Over this interval an average of 0.36% of patients each month had an unplanned hospitalization. In a 70% training sample, we used the generalized linear mixed model (GLMM) to generate regression coefficients for demographic, clinical predictors derived from the ACG system, and prior year hospitalizations. Applying these coefficients to a 30% test sample to generate risk scores, we found that the area under the receiver operator characteristic curve (AUC) was 0.82. Omitting prior hospitalizations decreased the AUC from 0.82 to 0.80, and increased under-estimation of hospitalizations at the greater risk levels. Patients in the top 5% of risk scores accounted for 43% and the top 1% of risk scores accounted for 20% of all unplanned hospitalizations. Conclusions A predictive model based on 12-months of demographic and clinical data using the ACG system has excellent predictive performance for 30-day pediatric unplanned hospitalization. This model may be useful in population health and care management applications targeting patients likely to be hospitalized. External validation at other institutions should be done to confirm our results.

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A Novel Model for Enhanced Prediction and Understanding of Unplanned 30-Day Pediatric Readmission

Cited by 29 publications

References 37 publications

A multicenter mixed-effects model for inference and prediction of 72-h return visits to the emergency department for adult patients with trauma-related diagnoses

A multicenter mixed-effects model for inference and prediction of 72-h return visits to the emergency department for adult patients with trauma-related diagnoses

<p>Gait Speed at Discharge and Risk for Readmission or Death: A Prospective Study of an Emergency Ward Population</p>

Prediction of 30-day pediatric unplanned hospitalizations using the Johns Hopkins Adjusted Clinical Groups risk adjustment system

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