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Incorporating patient-centered factors into heart failure readmission risk prediction: A mixed-methods study.

ABSTRACT: BACKGROUND:Capturing and incorporating patient-centered factors into 30-day readmission risk prediction after hospitalized heart failure (HF) could improve the modest performance of current models. METHODS:Using a mixed-methods approach, we developed a patient-centered survey and evaluated the additional predictive utility of the survey compared to a traditional readmission risk model (the Krumholz et al. model). Area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit statistic quantified the performance of both models. We measured the amount of model improvement with the addition of patient-centered factors to the Krumholz et al. model with the integrated discrimination improvement (IDI). In an exploratory analysis, we used hierarchical clustering algorithms to identify groups with similar survey responses and tested for differences between clusters using standard descriptive statistics. RESULTS:From 3/24/2014 to 3/12/2015, 183 patients hospitalized with HF were enrolled from an urban, academic health system and followed for 30days after discharge. The Krumholz et al. plus patient-centered factors model had similar-to-slightly lower performance (AUC [95%CI]:0.62 [0.52, 0.71]; goodness-of-fit P=.10) than the Krumholz et al. model (AUC [95%CI]:0.66 [0.57, 0.76]; goodness-of-fit P=.19). The IDI (95%CI) was 0.003 (-0.014,0.020). We identified three patient clusters based on patient-centered survey responses. The clusters differed with respect to gender, self-rated health, employment status, and prior hospitalization frequency (all P<.05). CONCLUSIONS:The addition of patient-centered factors did not improve 30-day readmission model performance. Rather than designing interventions based on predicted readmission risk, tailoring interventions to all patients, based on their characteristics, could inform the design of targeted, readmission reduction strategies.

SUBMITTER: Ahmad FS

PROVIDER: S-EPMC6004826 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Incorporating patient-centered factors into heart failure readmission risk prediction: A mixed-methods study.

Ahmad Faraz S FS French Benjamin B Bowles Kathryn H KH Sevilla-Cazes Jonathan J Jaskowiak-Barr Anne A Gallagher Thomas R TR Kangovi Shreya S Goldberg Lee R LR Barg Frances K FK Kimmel Stephen E SE

American heart journal 20180309

<h4>Background</h4>Capturing and incorporating patient-centered factors into 30-day readmission risk prediction after hospitalized heart failure (HF) could improve the modest performance of current models.<h4>Methods</h4>Using a mixed-methods approach, we developed a patient-centered survey and evaluated the additional predictive utility of the survey compared to a traditional readmission risk model (the Krumholz et al. model). Area under the receiver operating characteristic curve (AUC) and the ...[more]

PMID: 29898852

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Project description:ObjectivesThis study sought to assess the prevalence of frailty, its associations with physical function, quality of life (QoL), cognition, and depression and to investigate more efficient methods of detection in older patients hospitalized with acute decompensated heart failure (ADHF).BackgroundIn contrast to the outpatient population with chronic HF, much less is known regarding frailty in older, hospitalized patients with ADHF.MethodsOlder hospitalized patients (N = 202) with ADHF underwent assessment of frailty (using Fried criteria), short physical performance battery (SPPB), 6-min walk test (6-MWT) distance, quality of life (QoL using the Kansas City Cardiomyopathy Questionnaire), cognition (using the Montreal Cognition Assessment), and depression (using the Geriatric Depression Screen [GDS]). The associations of frailty with these patient-centered outcomes were assessed by using adjusted linear regression models. Novel strategies to identify frailty were examined.ResultsA total of 50% of older, hospitalized patients with ADHF were frail, 48% were pre-frail, and 2% were non-frail. Female sex, burden of comorbidity, and prior HF hospitalization were significantly associated with higher likelihood of frailty. Frailty (vs. pre-frail status) was associated with a significantly worse SPPB score (5 ± 2.2 vs. 7 ± 2.4, respectively), 6-MWT distance (143 ± 79 m vs. 221 ± 99 m, respectively), QoL (35 ± 19 vs. 46 ± 21, respectively), and more depression (GDS score: 5.5 ± 3.5 vs. 4.2 ± 3.3, respectively) but similar cognition. These associations were unchanged after adjustment for age, sex, race, total comorbidities, and body mass index. Slow gait speed plus low physical activity signaled frailty status as well (C-statistic = 0.85).ConclusionsNinety-eight percent of older, hospitalized patients with ADHF are frail or pre-frail. Frailty (vs. pre-frail status) is associated with worse physical function, QoL, comorbidity, and depression. The simple 4-m walk test combined with self-reported physical activity may quickly and efficiently identify frailty in older patients with ADHF.

Project description:BackgroundEffective self-care is an important factor in the successful management of patients with heart failure (HF). Despite the importance of self-care, most patients with HF are not adequately taught the wide range of skills required to become proficient in self-care. Digital health technology (DHT) may provide a novel solution to support patients at home in effective self-care, with the view to enhancing the quality of life and ultimately improving patient outcomes. However, many of the solutions developed to date have failed to consider users' perspectives at the point of design, resulting in poor effectiveness. Leveraging a human-centered design (HCD) approach to the development of DHTs may lead to the successful promotion of self-care behaviors in patients with HF.ObjectiveThis study aimed to outline the HCD, development, and evaluation process of a DHT designed to promote effective self-care in patients with HF.MethodsA design thinking approach within the HCD framework was undertaken, as described in the International Organization for Standardization 9241-210:2019 regulations, using a 5-step process: empathize, ideate, design, develop, and test. Patients with HF were involved throughout the design and evaluation of the system. The designed system was grounded in behavior change theory using the Theoretical Domains Framework and included behavior change techniques. Mixed methods were used to evaluate the DHT during the testing phase.ResultsSteps 1 to 3 of the process resulted in a set of evidence- and user-informed design requirements that were carried forward into the iterative development of a version 1 system. A cross-platform (iOS and Android) mobile app integrated with Fitbit activity trackers and smart scales was developed. A 2-week user testing phase highlighted the ease of use of the system, with patients demonstrating excellent adherence. Qualitative analysis of semistructured interviews identified the early potential for the system to positively influence self-care. Specifically, users perceived that the system increased their confidence and motivation to engage in key self-care behaviors, provided them with skills and knowledge that made them more aware of the importance of self-care behaviors, and might facilitate timely help seeking.ConclusionsThe use of an HCD methodology in this research has resulted in the development of a DHT that may engage patients with HF and potentially affect their self-care behaviors. This comprehensive work lays the groundwork for further development and evaluation of this solution before its implementation in health care systems. A detailed description of the HCD process used in this research will help guide the development and evaluation of future DHTs across a range of disease use cases.

Project description:BACKGROUND:Patients hospitalized with heart failure suffer the highest rates of 30-day readmission among other clinically defined patient populations in the United States. Investigation into the predictability of 30-day readmissions can lead to clinical decision support tools and targeted interventions that can help care providers to improve individual patient care and reduce readmission risk. OBJECTIVE:This study aimed to develop a dynamic readmission risk prediction model that yields daily predictions for patients hospitalized with heart failure toward identifying risk trajectories over time and identifying clinical predictors associated with different patterns in readmission risk trajectories. METHODS:A two-stage predictive modeling approach combining logistic and beta regression was applied to electronic health record data accumulated daily to predict 30-day readmission for 534 hospital encounters of patients with heart failure over 2750 patient days. Unsupervised clustering was performed on predictions to uncover time-dependent trends in readmission risk over the patient's hospital stay. We used data collected between September 1, 2013, and August 31, 2015, from a community hospital in Maryland (United States) for patients with a primary diagnosis of heart failure. Patients who died during the hospital stay or were transferred to other acute care hospitals or hospice care were excluded. RESULTS:Readmission occurred in 107 (107/534, 20.0%) encounters. The out-of-sample area under curve for the 2-stage predictive model was 0.73 (SD 0.08). Dynamic clinical predictors capturing laboratory results and vital signs had the highest predictive value compared with demographic, administrative, medical, and procedural data included. Unsupervised clustering identified four risk trajectory groups: decreasing risk (131/534, 24.5% encounters), high risk (113/534, 21.2%), moderate risk (177/534, 33.1%), and low risk (113/534, 21.2%). The decreasing risk group demonstrated change in average probability of readmission from admission (0.69) to discharge (0.30), whereas the high risk (0.75), moderate risk (0.61), and low risk (0.39) groups maintained consistency over the hospital course. A higher level of hemoglobin, larger decrease in potassium and diastolic blood pressure from admission to discharge, and smaller number of past hospitalizations are associated with decreasing readmission risk (P<.001). CONCLUSIONS:Dynamically predicting readmission and quantifying trends over patients' hospital stay illuminated differing risk trajectory groups. Identifying risk trajectory patterns and distinguishing predictors may shed new light on indicators of readmission and the isolated effects of the index hospitalization.

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Incorporating patient-centered factors into heart failure readmission risk prediction: A mixed-methods study.

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Incorporating patient-centered factors into heart failure readmission risk prediction: A mixed-methods study.

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