Correlation of the Boost Risk Stratification Tool as a Predictor of Unplanned 30-Day Readmission in Elderly Patients

Carol K. Sieck Loyola University Chicago CORRELATION OF THE BOOST RISK STRATIFICATION TOOL AS A PREDICTOR OF UNPLANNED 30-DAY REAMDISSION IN ELDERLY PATIENTS Risk stratification tools can identify patients at risk for 30-day readmission but available tools lack predictive strength. While physical, functional and social determinants of health have demonstrated an association with readmission, available risk stratification tools have been inconsistent in their use of variables to predict readmission. The Better Outcomes by Optimizing Safe Transitions (BOOST) 8 P\u27s tool is a risk stratification tool developed by the Society of Hospital Medicine but has no published validation studies. The theoretical foundation used for this study was Wagner\u27s Care Model that illustrates the interconnected nature of acute and preventive care needed by chronically ill patients over a lifetime. This quantitative study using secondary data to measure the degree to which the BOOST variables predict 30-day readmission. The sample included one year of hospitalized patients 65+ (n=6849) from a tertiary hospital in the Midwest. Univariate and multivariate logistic regression demonstrated that six of the eight variables in the BOOST risk stratification tool showed significant predictive strength, including the social variables of health literacy (p=.030), depression (p=.003) and isolation (p=.011). Other significant variables included problem medications (p=.001), physical limitations (p=\u3c.001) and prior hospitalization (p=\u3c.001). The BOOST risk stratification tool had limited predictive capability with a C-statistic of .631. This study was the first attempt to validate the BOOST 8 P\u27s tool and to utilize nursing documentation within an electronic medical record to capture social determinants of health. Implications for nursing practice include the need for nurses to gain skills in using risk stratification tools to identify patients at risk for readmission to target preventive interventions including care coordination efforts. Future research should target variables, especially social factors of depression, health literacy and isolation to predict 30-day readmission, especially for the growing population of elderly patients with chronic illness

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

Predictive risk models to identify people with chronic conditions at risk of hospitalisation

A disproportionately large percentage of health care costs and utilisation is spent on a small fraction of the population with complex and chronic conditions (Panattoni et al., 2011). It is widely agreed that effective and accessible primary health care (PHC) is central to reducing potentially avoidable hospitalisations (PAHs) associated with chronic disease. Predictive risk modelling is one method that is used to identify individuals who may be at risk of a hospitalisation event. The Predictive Risk Model (PRM) is a tool for identifying at-risk patients, so that appropriate preventive care can be provided, to avoid both exacerbation and complications of existing conditions, and acute events that may lead to hospitalisation. This Policy Issue Review identifies a selection of currently available PRMs, focusing on those applied in a PHC setting; and examines evidence of reliability in targeting patients with complex and chronic conditions

Generalized Representation of Electronic Health Records for Unplanned Hospital Readmission

Unplanned hospital readmissions soon after a person is discharged indicate the poor performance of the healthcare. Previous attempts of readmission prediction pose it as a binary classification problem and largely ignore the previous history. This study proposes a novel neural network architecture called Sequential Readmission Predictor with Multitasking (SRPM), to enhance the existing readmission prediction models. We retain the previous admission history of a patient by learning a latent representation for the patient, which could be used for every new admission by the same patient. Our proposed model uses a multitask neural network model that simultaneously models it as a binary classification problem and as a regression problem that predicts the exact days of readmission. By doing so, the error information from the regression task augments the classification task. The results show a promising improvement of up to 6.59% in AUCROC and 19% in F1 score over four benchmark methods

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Using structured pathology data to predict hospital-wide mortality at admission

Early prediction of in-hospital mortality can improve patient outcome. Current prediction models for in-hospital mortality focus mainly on specific pathologies. Structured pathology data is hospital-wide readily available and is primarily used for e.g. financing purposes. We aim to build a predictive model at admission using the International Classification of Diseases (ICD) codes as predictors and investigate the effect of the self-evident DNR ("Do Not Resuscitate") diagnosis codes and palliative care codes. We compare the models using ICD-10-CM codes with Risk of Mortality (RoM) and Charlson Comorbidity Index (CCI) as predictors using the Random Forests modeling approach. We use the Present on Admission flag to distinguish which diagnoses are present on admission. The study is performed in a single center (Ghent University Hospital) with the inclusion of 36 368 patients, all discharged in 2017. Our model at admission using ICD-10-CM codes (AUCROC = 0.9477) outperforms the model using RoM (AUCROC = 0.8797 and CCI (AUCROC = 0.7435). We confirmed that DNR and palliative care codes have a strong impact on the model resulting in a decrease of 7% for the ICD model (AUCROC = 0.8791) at admission. We therefore conclude that a model with a sufficient predictive performance can be derived from structured pathology data, and if real-time available, can serve as a prerequisite to develop a practical clinical decision support system for physicians