A nomogram based on a patient-reported outcomes measure: predicting the risk of readmission for patients with chronic heart failure

Abstract Background Health-related quality of life, as evaluated by a patient-reported outcomes measure (PROM), is an important prognostic marker in patients with chronic heart failure. This study aimed to use PROM to establish an effective readmission nomogram for chronic heart failure. Methods Usi...

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Bibliographic Details
Main Authors: Qiang Han, Jia Ren, Jing Tian, Hong Yang, Qing Zhang, Ruoya Wang, Jinghua Zhao, Linai Han, Chenhao Li, Jingjing Yan, Ke Wang, Chu Zheng, Qinghua Han, Yanbo Zhang
Format: Article
Language:English
Published: BMC 2020-08-01
Series:Health and Quality of Life Outcomes
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12955-020-01534-6
Description
Summary:Abstract Background Health-related quality of life, as evaluated by a patient-reported outcomes measure (PROM), is an important prognostic marker in patients with chronic heart failure. This study aimed to use PROM to establish an effective readmission nomogram for chronic heart failure. Methods Using a PROM as a measurement tool, we conducted a readmission nomogram for chronic heart failure on a prospective observational study comprising of 454 patients with chronic heart failure hospitalized between May 2017 to January 2020. A Concordance index and calibration curve were used to evaluate the discriminative ability and predictive accuracy of the nomogram. A bootstrap resampling method was used for internal validation of results. Results The median follow-up period in the study was 372 days. After a final COX regression analysis, the gender, income, health care, appetite-sleep, anxiety, depression, paranoia, support, and independence were identified and included in the nomogram. The nomogram showed moderate discrimination, with a concordance index of 0.737 (95% CI 0.673–0.800). The calibration curves for the probability of readmission for patients with chronic heart failure showed high consistency between the probability, as predicted, and the actual probability. Conclusions This model offers a platform to assess the risk of readmission for different populations with CHF and can assist clinicians with personalized treatment recommendations.
ISSN:1477-7525