Enhancing the Prediction of 30-Day Readmission After Percutaneous Coronary Intervention Using Data Extracted by Querying of the Electronic Health Record

Abstract

A mong patients discharged from the hospital, those discharged after undergoing percutaneous coronary intervention (PCI) have among the highest rates of early readmission, accounting for an estimated $359 million 1 cost each year to Medicare alone. Because of their importance, hospital 30-day readmission rates are now publicly reported on a volunteer basis on the Hospital Compare website. Identifying patients at high risk for readmission is critical to targeted interventions that improve value. Editorial see p 463Nevertheless, despite a growing emphasis on reducing short-term hospital readmissions, predicting readmissions accurately has remained elusive.2 Risk prediction models derived from registry data have been developed, 3 but are limited by modest discrimination. The electronic health record (EHR) may be a repository of patient-level information, including both structured and unstructured data, that can improve the ability to risk-stratify patients based on their likelihood of readmission.Registry-derived risk models can benefit from large data sets and reflect broad ranges of patients. Nevertheless, registryderived risk models cannot account for unstructured data. In fact, Background-Early readmission after percutaneous coronary intervention is an important quality metric, but prediction models from registry data have only moderate discrimination. We aimed to improve ability to predict 30-day readmission after percutaneous coronary intervention from a previously validated registry-based model. Methods and Results-We matched readmitted to non-readmitted patients in a 1:2 ratio by risk of readmission, and extracted unstructured and unconventional structured data from the electronic medical record, including need for medical interpretation, albumin level, medical nonadherence, previous number of emergency department visits, atrial fibrillation/ flutter, syncope/presyncope, end-stage liver disease, malignancy, and anxiety. We assessed differences in rates of these conditions between cases/controls, and estimated their independent association with 30-day readmission using logistic regression conditional on matched groups. Among 9288 percutaneous coronary interventions, we matched 888 readmitted with 1776 non-readmitted patients. In univariate analysis, cases and controls were significantly different with respect to interpreter (7.9% for cases and 5.3% for controls; P=0.009), emergency department visits (

Imbalanced target prediction with pattern discovery on clinical data repositories

“…The challenge lies in the noises of practice data and imbalance of prediction targets of major clinical importance, such as bleeding after percutaneous coronary intervention (PCI) [4], or cardiac death [5]. Because data directly available from clinical data repositories is not subject to strict inclusion/exclusion criteria or sample matching to balance cases and controls [6], typical data mining methods for prediction (classification) are not designed to handle such challenges. The dilemma is that with rich existing data, domain users desire to generate initial data-driven hypotheses and get insights whether a specific clinical target of interest is predictable and what attributes (predictor variables) should be considered, before they take on the more involving way of a formal clinical study.…”

Should We Care About Short-Term Readmissions After Percutaneous Coronary Intervention?

“…Tripathi and colleagues demonstrate overall low-rates of PCI (13%), CABG (2%), and in-hospital mortality (3%) in individuals readmitted with PCI suggesting that chest pain after PCI is generally low risk (16). Despite this, however, the anxiety on behalf of patients and providers associated with chest pain after PCI frequently results in further diagnostic testing and readmission, which may in part drive the high costs for readmissions observed in the current study (14,23,25). …”

“…As social factors such as homelessness and psychological factors such as anxiety are associated with readmission after PCI (25), improved counseling and appropriately resourced discharge planning may reduce recurrent presentation to the hospital. Similarly, as low-risk chest pain is often the presentation leading to readmission after PCI (13,14,23,24), improved triage of post-PCI patients to identify the appropriate diagnostic and treatment pathway for these individuals may hasten evaluation and lead to safe discharge from the emergency department.…”

Future of the PCI Readmission Metric

“…21 Because anxiety is associated with PCI readmission, patient education and reassurance may also reduce readmission rates. 22 Many more may be preventable with improved triage mechanisms when patients return with low-risk chest discomfort. In a variety of settings throughout the United States and Europe, chest discomfort without myocardial infarction is the most common reason for early hospital readmission.…”