Dataset Information

Assessment of chronic disease self-management in patients with chronic heart failure based on the MCID of patient-reported outcomes by the multilevel model.

ABSTRACT:

Purpose

The minimal clinically important difference (MCID) of a patient-reported outcome (PRO) represents the threshold value of the change in the score for that PRO. It is deemed to have an important implication in clinical management. This study was performed to evaluate the clinical significance of chronic disease self-management (CDSM) for patients with chronic heart failure based on the MCID of the chronic heart failure-PRO measure (CHF-PROM).

Methods

A multicenter, prospective cohort study of 555 patients with heart failure were enrolled from July 2018. Advice of CDSM was provided in written form at discharge to all patients. Information regarding CHF-PROM and CDSM were collected during follow-up. Multilevel models were applied to dynamically evaluate the effects of CDSM for CHF-PROM scores, as well as its physical and psychological domains. MCID changes of the PRO were introduced and compared with ? values of CDSM obtained from the multi-level models to further evaluate the clinical significance. The STROBE checklist is shown in Additional file 1.

Results

Scores for CHF-PROM improved significantly after discharge. The multilevel models showed that a regular schedule, avoidance of over-eating, a low-sodium diet and exercise increased scores on CHF-PROM. Compared with the MCID, avoidance of over-eating (12.39 vs. 9.75) and maintenance of a regular schedule often (10.98 vs. 9.75), and exercise almost every day (11.36 vs. 9.75) reached clinical significance for the overall summary. Avoidance of over-eating (5.88 vs. 4.79) and a regular schedule almost every day (4.96 vs. 4.79) reached clinical significance for the physical scores. Avoidance of over-eating half of the time (5.26 vs. 4.87) and a regular schedule almost every day (5.84 vs. 4.87) demonstrated clinical significance for the psychological scores.

Conclusions

This study observed an association of avoidance of over-eating and maintenance of a regular schedule with the improvement of CHF-PROM. It provides further evidence for management of heart failure.

Trial registration

Current Prospective Trials NCT02878811; registered August 25, 2016; https://clinicaltrials.gov/ct2/show/NCT02878811?term=NCT02878811&draw=2&rank=1 .

SUBMITTER: Tian J

PROVIDER: S-EPMC7847136 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessment of chronic disease self-management in patients with chronic heart failure based on the MCID of patient-reported outcomes by the multilevel model.

Tian Jing J Zhao Jinghua J Zhang Qing Q Ren Jia J Han Linai L Li Jing J Zhang Yanbo Y Han Qinghua Q

BMC cardiovascular disorders 20210130 1

<h4>Purpose</h4>The minimal clinically important difference (MCID) of a patient-reported outcome (PRO) represents the threshold value of the change in the score for that PRO. It is deemed to have an important implication in clinical management. This study was performed to evaluate the clinical significance of chronic disease self-management (CDSM) for patients with chronic heart failure based on the MCID of the chronic heart failure-PRO measure (CHF-PROM).<h4>Methods</h4>A multicenter, prospecti ...[more]

PMID: 33516189

Similar Datasets

Project description:BackgroundPatient-reported outcomes (PROs) can be obtained outside hospitals and are of great significance for evaluation of patients with chronic heart failure (CHF). The aim of this study was to establish a prediction model using PROs for out-of-hospital patients.MethodsCHF-PRO were collected in 941 patients with CHF from a prospective cohort. Primary endpoints were all-cause mortality, HF hospitalization, and major adverse cardiovascular events (MACEs). To establish prognosis models during the two years follow-up, six machine learning methods were used, including logistic regression, random forest classifier, extreme gradient boosting (XGBoost), light gradient boosting machine, naive bayes, and multilayer perceptron. Models were established in four steps, namely, using general information as predictors, using four domains of CHF-PRO, using both of them and adjusting the parameters. The discrimination and calibration were then estimated. Further analyze were performed for the best model. The top prediction variables were further assessed. The Shapley additive explanations (SHAP) method was used to explain black boxes of the models. Moreover, a self-made web-based risk calculator was established to facilitate the clinical application.ResultsCHF-PRO showed strong prediction value and improved the performance of the models. Among the approaches, XGBoost of the parameter adjustment model had the highest prediction performance with an area under the curve of 0.754 (95% CI: 0.737 to 0.761) for death, 0.718 (95% CI: 0.717 to 0.721) for HF rehospitalization and 0.670 (95% CI: 0.595 to 0.710) for MACEs. The four domains of CHF-PRO, especially the physical domain, showed the most significant impact on the prediction of outcomes.ConclusionCHF-PRO showed strong prediction value in the models. The XGBoost models using variables based on CHF-PRO and the patient's general information provide prognostic assessment for patients with CHF. The self-made web-based risk calculator can be conveniently used to predict the prognosis for patients after discharge.Clinical trial registrationURL: http://www.chictr.org.cn/index.aspx ; Unique identifier: ChiCTR2100043337.

Project description:Background: Adherence to a complex, yet effective medication regimen improves clinical outcomes in patients with chronic heart failure (CHF). However, patient adherence to an agreed upon plan for medication-taking is sub-optimal and continues to hover at 50% in developed countries. Studies to improve medication-taking have focused on interventions to improve adherence to guideline-directed medication therapy, yet few of these studies have integrated patients' perceptions of what constitutes effective strategies for improved medication-taking and self-care in everyday life. The purpose of this formative study was to explore patient perceived facilitators of selfcare and medication-taking in South Asian CHF patients. Methods: We conducted in-depth interviews of patients with long standing heart failure admitted to the cardiology and internal medicine wards of a South Indian tertiary care hospital. We purposively sampled using the following criteria: sex, socio-economic status, health literacy and patient reported medication adherence in the month prior to hospitalization. We employed inductive coding to identify facilitators. At the end of 15 interviews (eight patients and seven caregivers; seven patient-caregiver dyads), we arrived at theoretical saturation for facilitators. Results: Facilitators could be classified into intrinsic (patient traits - situational awareness, self-efficacy, gratitude, resilience, spiritual invocation and support seeking behavior) and extrinsic (shaped by the environment - financial security and caregiver support, company of children, ease of healthcare access, trust in provider/hospital, supportive environment and recognizing the importance of knowledge). Conclusions: We identified and classified a set of key patient and caregiver reported self-care facilitators among Indian CHF patients. The learnings from this study will be incorporated into an intervention package to improve patient engagement, overall self-care and patient-caregiver-provider dynamics.

Dataset Information

Assessment of chronic disease self-management in patients with chronic heart failure based on the MCID of patient-reported outcomes by the multilevel model.

Purpose

Methods

Results

Conclusions

Trial registration

Publications

Assessment of chronic disease self-management in patients with chronic heart failure based on the MCID of patient-reported outcomes by the multilevel model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets