Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study

Amarasingham, Ruben; Patel, Parag C.; Toto, Kathleen H.; Nelson, Lauren L.; Swanson, Timothy S.; Moore, Billy J.; Xie, Bin; Zhang, Song; Alvarez, Kristin; Ma, Ying; Drazner, Mark H.; Kollipara, Usha K.; Halm, Ethan A.

doi:10.1136/bmjqs-2013-001901

Cited by 110 publications

(84 citation statements)

References 20 publications

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“…Three models were developed from administrative claims data with the intent of estimating riskstandardized readmission rates for hospital profiling and benchmarking purposes, including the CMS Pneumonia Administrative Model. Eight models were derived from EHR data or Veterans Affairs administrative data (which included more clinical detail than traditional claims data), with the goal of identifying patients at high risk for 30-day readmission for whom realtime identification and enrollment in a transitional care intervention may improve outcomes (18). Most models had poor to modest predictive ability (median C statistic of 0.63).…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, hospitals and health systems can currently use available pneumonia risk prediction models to help identify a subset of patients at highest risk and enroll these patients in resource-intensive transitional care interventions to potentially prevent readmissions (18). This is essential to the sustainability and durability of interventions aimed at lowering readmission rates, because most hospitals do not have the resources to enroll every patient hospitalized for pneumonia for a transitional care intervention, nor would such an approach be cost effective.…”

Section: Systematic Reviewmentioning

confidence: 99%

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Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance

Weinreich¹,

Nguyen²,

Wang³

et al. 2016

Annals ATS

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Rationale: Predicting which patients are at highest risk for readmission after hospitalization for pneumonia could enable hospitals to proactively reallocate scarce resources to reduce 30-day readmissions.Objectives: To synthesize the available literature on readmission risk prediction models for adults who are hospitalized because of pneumonia and describe their performance. Methods:We systematically searched Ovid MEDLINE, Embase, The Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature databases from inception through July 2015. We included studies of adults discharged with pneumonia that developed or validated a model that predicted hospital readmission. Two independent reviewers abstracted data and assessed the risk of bias.Measurements and Main Results: Of 992 citations reviewed, 7 studies met inclusion criteria, which included 11 unique risk prediction models. All-cause 30-day readmission rates ranged from 11.8 to 20.8% (median, 17.3%). Model discrimination (C statistic) ranged from 0.59 to 0.77 (median, 0.63) with the highest-quality, best-validated model, the Centers for Medicare and Medicaid Services Pneumonia Administrative Model performing modestly (C Statistic of 0.63 in 4 separate multicenter cohorts). The best performing model (C statistic of 0.77) was a single-site study that lacked internal validation. The models had adequate calibration, with patients predicted as high risk for readmission having a higher average observed readmission rate than those predicted to be low risk. None of the studies included pneumonia illness severity scores, and only one included measures of in-hospital clinical trajectory and stability on discharge, robust predictors of readmission. Conclusions:We found a limited number of validated pneumoniaspecific readmission models, and their predictive ability was modest. To improve predictive accuracy, future models should include measures of pneumonia illness severity, hospital complications, and stability on discharge.

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Section: Discussionmentioning

confidence: 99%

Section: Systematic Reviewmentioning

confidence: 99%

Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance

Weinreich¹,

Nguyen²,

Wang³

et al. 2016

Annals ATS

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show abstract

“…Internationally, the literature on HF readmissions is dominated by studies predicting a single readmission, usually 30-day readmissions, 54,55 though some used longer follow-up periods of 90 days 56 and 12 months. 57 Braunstein et al 58 modelled the number of all-cause and HF ambulatory care-sensitive conditions (ACSCs) and all-cause ACSC hospitalisations during 12 months.…”

Section: First Stepmentioning

confidence: 99%

Can valid and practical risk-prediction or casemix adjustment models, including adjustment for comorbidity, be generated from English hospital administrative data (Hospital Episode Statistics)? A national observational study

Bottle

Gaudoin

Goudie

et al. 2014

Health Services and Delivery Research

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Background NHS hospitals collect a wealth of administrative data covering accident and emergency (A&E) department attendances, inpatient and day case activity, and outpatient appointments. Such data are increasingly being used to compare units and services, but adjusting for risk is difficult. Objectives To derive robust risk-adjustment models for various patient groups, including those admitted for heart failure (HF), acute myocardial infarction, colorectal and orthopaedic surgery, and outcomes adjusting for available patient factors such as comorbidity, using England’s Hospital Episode Statistics (HES) data. To assess if more sophisticated statistical methods based on machine learning such as artificial neural networks (ANNs) outperform traditional logistic regression (LR) for risk prediction. To update and assess for the NHS the Charlson index for comorbidity. To assess the usefulness of outpatient data for these models. Main outcome measures Mortality, readmission, return to theatre, outpatient non-attendance. For HF patients we considered various readmission measures such as diagnosis-specific and total within a year. Methods We systematically reviewed studies comparing two or more comorbidity indices. Logistic regression, ANNs, support vector machines and random forests were compared for mortality and readmission. Models were assessed using discrimination and calibration statistics. Competing risks proportional hazards regression and various count models were used for future admissions and bed-days. Results Our systematic review and empirical analysis suggested that for general purposes comorbidity is currently best described by the set of 30 Elixhauser comorbidities plus dementia. Model discrimination was often high for mortality and poor, or at best moderate, for other outcomes, for example c = 0.62 for readmission and c = 0.73 for death following stroke. Calibration was often good for procedure groups but poorer for diagnosis groups, with overprediction of low risk a common cause. The machine learning methods we investigated offered little beyond LR for their greater complexity and implementation difficulties. For HF, some patient-level predictors differed by primary diagnosis of readmission but not by length of follow-up. Prior non-attendance at outpatient appointments was a useful, strong predictor of readmission. Hospital-level readmission rates for HF did not correlate with readmission rates for non-HF; hospital performance on national audit process measures largely correlated only with HF readmission rates. Conclusions Many practical risk-prediction or casemix adjustment models can be generated from HES data using LR, though an extra step is often required for accurate calibration. Including outpatient data in readmission models is useful. The three machine learning methods we assessed added little with these data. Readmission rates for HF patients should be divided by diagnosis on readmission when used for quality improvement. Future work As HES data continue to develop and improve in scop...

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“…There have been efforts made to predict 30-day hospital readmission by the Centers for Medicare and Medicaid Services [3] and other studies [3,[6][7][8][9], some of which include data on prior hospital admissions. However, most of these studies were conducted with Medicare patients, and the predictive accuracy of such models may be suboptimal in other cohorts.…”

Section: Introductionmentioning

confidence: 99%

Prior hospital admission predicts thirty-day hospital readmission for heart failure patients

et al. 2016

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Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study

Cited by 110 publications

References 20 publications

Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance

Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance

Can valid and practical risk-prediction or casemix adjustment models, including adjustment for comorbidity, be generated from English hospital administrative data (Hospital Episode Statistics)? A national observational study

Prior hospital admission predicts thirty-day hospital readmission for heart failure patients

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