Methods Inf Med 2017; 56(05): 391-400
DOI: 10.3414/ME16-01-0135
Paper
Schattauer GmbH

Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions[*]

Adel Alaeddini
1  Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, USA
,
Carlos A. Jaramillo
2  South Texas Veterans Healthcare System, San Antonio, TX, USA
,
Syed H. A. Faruqui
1  Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, USA
,
Mary J. Pugh
2  South Texas Veterans Healthcare System, San Antonio, TX, USA
› Author Affiliations
Funding Supported by National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health under award number 1SC2GM118266-01, and VA Health Services Research and Development under award number VA HSR&D; DHI-09-237-2.
Further Information

Publication History

received: 22 November 2016

accepted in revised form: 15 August 2017

Publication Date:
24 January 2018 (online)

Summary

Objectives: Evolution of multiple chronic conditions (MCC) follows a complex stochastic process, influenced by several factors including the inter-relationship of existing conditions, and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are burdened with two or more (multiple) chronic conditions (MCC). Treatment for people living with MCC currently accounts for an estimated 66% of the Nation’s healthcare costs. However, it is still not known precisely how MCC emerge and accumulate among individuals or in the general population. This study investigates major patterns of MCC transitions in a diverse population of patients and identifies the risk factors affecting the transition process.

Methods: A Latent regression Markov clustering (LRMCL) algorithm is proposed to identify major transitions of four MCC that include hypertension (HTN), depression, Post- Traumatic Stress Disorder (PTSD), and back pain. A cohort of 601,805 individuals randomly selected from the population of Iraq and Afghanistan war Veterans (IAVs) who received VA care during three or more years between 2002-2015, is used for training the proposed LRMCL algorithm.

Results: Two major clusters of MCC transition patterns with 78% and 22% probability of membership respectively were identified. The primary cluster demonstrated the possibility of improvement when the number of MCC is small and an increase in probability of MCC accumulation as the number of co- morbidities increased. The second cluster showed stability (no change) of MCC overtime as the major pattern. Age was the most significant risk factor associated with the most probable cluster for each IAV.

Conclusions: These findings suggest that our proposed LRMCL algorithm can be used to describe and understand MCC transitions, which may ultimately allow healthcare systems to support optimal clinical decision- making. This method will be used to describe a broader range of MCC transitions in this and non-VA populations, and will add treatment information to see if models including treatments and MCC emergence can be used to support clinical decision-making in patient care.

* Supplementary material published on our website https://doi.org/10.3414/ME16-01-0135