Dataset Information

Multimorbidity patterns in patients with heart failure: an observational Spanish study based on electronic health records.

ABSTRACT:

Objectives

To characterise the comorbidities of heart failure (HF) in men and women, to explore their clustering into multimorbidity patterns, and to measure the impact of such patterns on the risk of hospitalisation and mortality.

Design

Observational retrospective population study based on electronic health records.

Setting

EpiChron Cohort (Aragón, Spain).

Participants

All the primary and hospital care patients of the EpiChron Cohort with a diagnosis of HF on 1 January 2011 (ie, 8488 women and 6182 men). We analysed all the chronic diseases registered in patients' electronic health records until 31 December 2011.

Primary outcome

We performed an exploratory factor analysis to identify the multimorbidity patterns in men and women, and logistic and Cox proportional-hazards regressions to investigate the association between the patterns and the risk of hospitalisation in 2012, and of 3-year mortality.

Results

Almost all HF patients (98%) had multimorbidity, with an average of 7.8 chronic diseases per patient. We identified six different multimorbidity patterns, named cardiovascular, neurovascular, coronary, metabolic, degenerative and respiratory. The most prevalent were the degenerative (64.0%) and cardiovascular (29.9%) patterns in women, and the metabolic (49.3%) and cardiovascular (43.2%) patterns in men. Every pattern was associated with higher hospitalisation risks; and the cardiovascular, neurovascular and respiratory patterns significantly increased the likelihood of 3-year mortality.

Conclusions

Multimorbidity is the norm rather than the exception in patients with heart failure, whose comorbidities tend to cluster together beyond simple chance in the form of multimorbidity patterns that have different impact on health outcomes. This knowledge could be useful to better understand common pathophysiological pathways underlying this condition and its comorbidities, and the factors influencing the prognosis of men and women with HF. Further large scale longitudinal studies are encouraged to confirm the existence of these patterns as well as their differential impact on health outcomes.

SUBMITTER: Gimeno-Miguel A

PROVIDER: S-EPMC7008407 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Multimorbidity patterns in patients with heart failure: an observational Spanish study based on electronic health records.

Gimeno-Miguel Antonio A Gracia Gutiérrez Anyuli A Poblador-Plou Beatriz B Coscollar-Santaliestra Carlos C Pérez-Calvo J Ignacio JI Divo Miguel J MJ Calderón-Larrañaga Amaia A Prados-Torres Alexandra A Ruiz-Laiglesia Fernando J FJ

BMJ open 20191223 12

<h4>Objectives</h4>To characterise the comorbidities of heart failure (HF) in men and women, to explore their clustering into multimorbidity patterns, and to measure the impact of such patterns on the risk of hospitalisation and mortality.<h4>Design</h4>Observational retrospective population study based on electronic health records.<h4>Setting</h4>EpiChron Cohort (Aragón, Spain).<h4>Participants</h4>All the primary and hospital care patients of the EpiChron Cohort with a diagnosis of HF on 1 Jan ...[more]

PMID: 31874886

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Project description:BackgroundMost available quality indicators for hospitals are represented by simple ratios or proportions, and are limited to specific events. A generalized method that can be applied to diverse clinical events has not been developed. The aim of this study was to develop a simple method of evaluating physicians' prescription patterns for diverse events and their level of awareness of clinical practice guidelines.Methods and findingsWe developed a quantitative method called Prescription pattern Around Clinical Event (PACE), which is applicable to electronic health records (EHRs). Three discrete prescription patterns (intervention, maintenance, and discontinuation) were determined based on the prescription change index (PCI), which was calculated by means of the increase or decrease in the prescription rate after a clinical event. Hyperkalemia and Clostridium difficile-associated diarrhea (CDAD) were used as example cases. We calculated the PCIs of 10 drugs related to hyperkalemia, categorized them into prescription patterns, and then compared the resulting prescription patterns with the known standards for hyperkalemia treatment. The hyperkalemia knowledge of physicians was estimated using a questionnaire and compared to the prescription pattern. Prescriptions for CDAD were also determined and compared to clinical knowledge. Clinical data of 1698, 348, and 1288 patients were collected from EHR data. The physicians prescribing behaviors for hyperkalemia and CDAD were concordant with the standard knowledge. Prescription patterns were well correlated with individual physicians' knowledge of hyperkalemia (κ = 0.714). Prescribing behaviors according to event severity or clinical condition were plotted as a simple summary graph.ConclusionThe algorithm successfully assessed the prescribing patterns from the EHR data. The prescription patterns were well correlated with physicians' knowledge. We expect that this algorithm will enable quantification of prescribers' adherence to clinical guidelines and be used to facilitate improved prescribing practices.

Dataset Information

Multimorbidity patterns in patients with heart failure: an observational Spanish study based on electronic health records.

Objectives

Design

Setting

Participants

Primary outcome

Results

Conclusions

Publications

Multimorbidity patterns in patients with heart failure: an observational Spanish study based on electronic health records.

Similar Datasets

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