Testing longitudinal data for prognostication in ambulatory heart failure patients with reduced ejection fraction. A proof of principle from the GISSI-HF database

Clinical Predictive Modeling of Heart Failure: Domain Description, Models' Characteristics and Literature Review

This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of syndromes with subsequent hospitalizations, which are called acute decompensation of heart failure (ADHF). These decompensations are the subject of diagnostic and prognostic predictions. The primary purpose of ADHF predictions is to clarify the current and future health status of patients and subsequently optimize therapeutic responses. We proposed a simplified discrete-state disease model as an attempt at a typical summarization of a medical subject before starting predictive modeling. The study tries also to structure the essential common characteristics of quantitative models in order to understand the issue in an application context. The last part provides an overview of prediction works in the field of CHFS. These three parts provide the reader with a comprehensive view of quantitative clinical predictive modeling in heart failure telemedicine with an emphasis on several key general aspects. The target community is medical researchers seeking to align their clinical studies with prognostic or diagnostic predictive modeling, as well as other predictive researchers. The study was written by a non-medical expert.

Individual Trajectories of Health Status During the First Year of Discharge From Hospitalization for Heart Failure and Their Associations With Death in the Following Years

Background Improving health status is one of the major goals in the management of heart failure (HF). However, little is known about the long-term individual trajectories of health status in patients with acute HF after discharge. Methods and Results We enrolled 2328 patients hospitalized for HF from 51 hospitals prospectively and measured their health status via the Kansas City Cardiomyopathy Questionnaire-12 at admission and 1, 6, and 12 months after discharge, respectively. The median age of the patients included was 66 years, and 63.3% were men. Six patterns of Kansas City Cardiomyopathy Questionnaire-12 trajectories were identified by a latent class trajectory model: persistently good (34.0%), rapidly improving (35.5%), slowly improving (10.4%), moderately regressing (7.4%), severely regressing (7.5%), and persistently poor (5.3%). Advanced age, decompensated chronic HF, HF with mildly reduced ejection fraction, HF with preserved ejection fraction, depression symptoms, cognitive impairment, and each additional HF rehospitalization within 1 year of discharge were associated with unfavorable health status (moderately regressing, severely regressing, and persistently poor) (P<0.05). Compared with the pattern of persistently good, slowly improving (hazard ratio [HR], 1.50 [95% CI, 1.06-2.12]), moderately regressing (HR, 1.92 [1.43-2.58]), severely regressing (HR, 2.26 [1.54-3.31]), and persistently poor (HR, 2.34 [1.55-3.53]) were associated with increased risks of all-cause death. Conclusions One-fifth of 1-year survivors after hospitalization for HF experienced unfavorable health status trajectories and had a substantially increased risk of death during the following years. Our findings help inform the understanding of disease progression from a patient perception perspective and its relationship with long-term survival. Registration URL: https://www.clinicaltrials.gov; unique identifier: NCT02878811.

Joint modelling of longitudinal processes and time-to-event outcomes in heart failure: systematic review and exemplar examining the relationship between serum digoxin levels and mortality

BACKGROUND

Joint modelling combines two or more statistical models to reduce bias and increase efficiency. As the use of joint modelling increases it is important to understand how and why it is being applied to heart failure research.

METHODS

A systematic review of major medical databases of studies which used joint modelling within heart failure alongside an exemplar; joint modelling repeat measurements of serum digoxin with all-cause mortality using data from the Effect of Digoxin on Mortality and Morbidity in Patients with Heart Failure (DIG) trial.

RESULTS

Overall, 28 studies were included that used joint models, 25 (89%) used data from cohort studies, the remaining 3 (11%) using data from clinical trials. 21 (75%) of the studies used biomarkers and the remaining studies used imaging parameters and functional parameters. The exemplar findings show that a per unit increase of square root serum digoxin is associated with the hazard of all-cause mortality increasing by 1.77 (1.34-2.33) times when adjusting for clinically relevant covariates.

CONCLUSION

Recently, there has been a rise in publications of joint modelling being applied to heart failure. Where appropriate, joint models should be preferred over traditional models allowing for the inclusion of repeated measures while accounting for the biological nature of biomarkers and measurement error.