JMIR Publications

ABSTRACT

Background:

Medical advances in managing chronic heart disease (HD) patients permit co-occurrence of other chronic diseases due to increased longevity, causing them to become multimorbid. Prior research on the effect of co-occurring diseases on mortality among HD patients often considers disease counts or clusters at the time of HD diagnosis, overlooking the dynamic development of patients’ disease portfolios over time where new chronic diseases are diagnosed prior to death. Further, these studies do not consider interactions among diseases and between diseases, biological and socioeconomic variables, which is essential for addressing health disparities among HD patients. Therefore, a mapping of the effect of combinations of these co-occurring diseases on mortality among HD patients that considers such interactions in a dynamic setting is warranted.

Objective:

This study aimed to examine the effect of HD patients’ co-occurring diseases on mortality, modelling their dynamically expanding disease portfolios while identifying interactions among the co-occurring diseases and socioeconomic and biological variables.

Methods:

The study utilized electronic health record data from the national Danish registries and algorithmic diagnoses of 15 chronic diseases to obtain a study population of all 766,596 adult HD patients in Denmark from the 1st of January 1995 until the 31st of December 2015. The time from HD diagnosis until death was modelled by an extended Cox model with time-varying covariates. We identified interactions among co-occurring diseases and socioeconomic and biological variables in a data-driven manner. We mapped the mortality impact of 1) the most common disease portfolios, 2) the disease portfolios subject to the highest level of interactions, and 3) the most severe disease portfolios. Estimates from interaction-based models were compared to an additive model.

Results:

Cancer had the highest impact on mortality (hazard ratio (HR) 6.72 males, 7.59 females). Excluding cancer revealed schizophrenia and dementia as those with the highest mortality impact (top five HRs in the range 11.72-13.37 males, 13.86-16.65 females, for combinations of four diseases). The additive model underestimated the effects up to a factor of 1.4 compared to the interaction model. Stroke, osteoporosis, COPD, dementia, and depression were identified as chronic diseases involved in the most complex interactions of the highest order.

Conclusions:

The findings of this study emphasize the importance of identifying and modelling disease interactions for a comprehensive understanding of mortality risk in heart disease patients.