TY - JOUR
T1 - Incorporating patient-centered factors into heart failure readmission risk prediction
T2 - A mixed-methods study
AU - Ahmad, Faraz S.
AU - French, Benjamin
AU - Bowles, Kathryn H.
AU - Sevilla-Cazes, Jonathan
AU - Jaskowiak-Barr, Anne
AU - Gallagher, Thomas R.
AU - Kangovi, Shreya
AU - Goldberg, Lee R.
AU - Barg, Frances K.
AU - Kimmel, Stephen E.
N1 - Funding Information:
The primary extramural funding source for this study was the Patient-Centered Outcomes Research Institute (grant number 1IP2PI000186–02). The National Heart, Lung and Blood Institute of the National Institutes of Health (grant number T32HL069771) in part supported one of the investigators (FSA). The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the paper and its final contents.
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/6
Y1 - 2018/6
N2 - 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 30 days 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.
AB - 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 30 days 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.
UR - http://www.scopus.com/inward/record.url?scp=85045464254&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045464254&partnerID=8YFLogxK
U2 - 10.1016/j.ahj.2018.03.002
DO - 10.1016/j.ahj.2018.03.002
M3 - Article
C2 - 29898852
AN - SCOPUS:85045464254
VL - 200
SP - 75
EP - 82
JO - American Heart Journal
JF - American Heart Journal
SN - 0002-8703
ER -