Multimorbidity patterns with K-means nonhierarchical cluster analysis

This is the final version. Available on open access from BMC via the DOI in this recordAvailability of data and materials: The datasets are not available because researchers have signed an agreement with the Information System for the Development of Research in Primary Care (SIDIAP) concerning confidentiality and security of the dataset that forbids providing data to third parties. This organization is subject to periodic audits to ensure the validity and quality of the data.BACKGROUND: The purpose of this study was to ascertain multimorbidity patterns using a non-hierarchical cluster analysis in adult primary patients with multimorbidity attended in primary care centers in Catalonia. METHODS: Cross-sectional study using electronic health records from 523,656 patients, aged 45-64 years in 274 primary health care teams in 2010 in Catalonia, Spain. Data were provided by the Information System for the Development of Research in Primary Care (SIDIAP), a population database. Diagnoses were extracted using 241 blocks of diseases (International Classification of Diseases, version 10). Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex. RESULTS: The 408,994 patients who met multimorbidity criteria were included in the analysis (mean age, 54.2 years [Standard deviation, SD: 5.8], 53.3% women). Six multimorbidity patterns were obtained for each sex; the three most prevalent included 68% of the women and 66% of the men, respectively. The top cluster included coincident diseases in both men and women: Metabolic disorders, Hypertensive diseases, Mental and behavioural disorders due to psychoactive substance use, Other dorsopathies, and Other soft tissue disorders. CONCLUSION: Non-hierarchical cluster analysis identified multimorbidity patterns consistent with clinical practice, identifying phenotypic subgroups of patients.The project has been funded by the Instituto de Salud Carlos III of the Ministry of Economy and Competitiveness (Spain) through the Network for Prevention and Health Promotion in Primary Health Care (redIAPP, RD12/0005), by a grant for research projects on health from ISCiii (PI12/00427) and co-financed with European Union ERDF funds). Jose M. Valderas was supported by the National Institute for Health Research Clinician Scientist Award NIHR/CS/010/024

Five-year trajectories of multimorbidity patterns in an elderly Mediterranean population using Hidden Markov Models

This is the final version. Available on open access from Nature Research via the DOI in this recordThis study aimed to analyse the trajectories and mortality of multimorbidity patterns in patients aged 65 to 99 years in Catalonia (Spain). Five year (2012–2016) data of 916,619 participants from a primary care, population-based electronic health record database (Information System for Research in Primary Care, SIDIAP) were included in this retrospective cohort study. Individual longitudinal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns. We computed the mortality hazard using Cox regression models to estimate survival in multimorbidity patterns. Ten multimorbidity patterns were originally identified and two more states (death and drop-outs) were subsequently added. At baseline, the most frequent cluster was the Non-Specific Pattern (42%), and the least frequent the Multisystem Pattern (1.6%). Most participants stayed in the same cluster over the 5 year follow-up period, from 92.1% in the Nervous, Musculoskeletal pattern to 59.2% in the Cardio-Circulatory and Renal pattern. The highest mortality rates were observed for patterns that included cardio-circulatory diseases: Cardio-Circulatory and Renal (37.1%); Nervous, Digestive and Circulatory (31.8%); and Cardio-Circulatory, Mental, Respiratory and Genitourinary (28.8%). This study demonstrates the feasibility of characterizing multimorbidity patterns along time. Multimorbidity trajectories were generally stable, although changes in specific multimorbidity patterns were observed. The Hidden Markov Model is useful for modelling transitions across multimorbidity patterns and mortality risk. Our findings suggest that health interventions targeting specific multimorbidity patterns may reduce mortality in patients with multimorbidity.Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain)European Regional Development FundDepartment of Health of the Catalan GovernmentCatalan Governmen

Integrating Mobile Devices with Cohort Analysis into Personalised Weather-Based Healthcare

Mobile healthcare applications can empower users to self-monitor their health conditions without the need to visit any medical centre. However, the lack of attention on engagement aspects of mobile healthcare applications often result in users choosing to uninstall the application after the first usage experience. This results in failure of effective prolonged personalised healthcare, especially for users with chronic disease related to weather conditions such as asthma and eczema which require long-term monitoring and self-care. Therefore, this paper aims to identify the pattern of application user engagement with a weather-based mobile healthcare application through cohort retention analysis. Enhancement features for improving the engagement of personalised healthcare can provide meaningful insight. The proposed application allows the patient to conduct disease control tests to check the severity of their condition on a daily basis. To measure the application engagement, we distribute the mobile application designed for primary testing over a period of ten days. Based on the primary testing, data related to retention rate and the number of control test reported were collected via Firebase Analytic to determine the application engagement. Subsequently, we apply cohort analysis using a machine learning clustering technique implemented in Python to identify the pattern of the engagement by application users. Finally, useful insights were analysed and implemented as enhancement features within the application for improving the personalised weather-based mobile healthcare. The findings in this paper can assist machine learning facilitators design effective use policies for weather-based mobile healthcare with fundamental knowledge enhanced with personalisation and user engagement

Twelve-year clinical trajectories of multimorbidity in a population of older adults

Multimorbidity—the co-occurrence of multiple diseases—is associated to poor prognosis, but the scarce knowledge of its development over time hampers the effectiveness of clinical interventions. Here we identify multimorbidity clusters, trace their evolution in older adults, and detect the clinical trajectories and mortality of single individuals as they move among clusters over 12 years. By means of a fuzzy c-means cluster algorithm, we group 2931 people ≥60 years in five clinically meaningful multimorbidity clusters (52%). The remaining 48% are part of an unspecific cluster (i.e. none of the diseases are overrepresented), which greatly fuels other clusters at follow-ups. Clusters contribute differentially to the longitudinal development of other clusters and to mortality. We report that multimorbidity clusters and their trajectories may help identifying homogeneous groups of people with similar needs and prognosis, and assisting clinicians and health care systems in the personalization of clinical interventions and preventive strategies