1
|
Ito Y, Iwagami M, Komiyama J, Hamasaki Y, Kuroda N, Suzuki A, Ito T, Goto T, Wan EYF, Lai FTT, Tamiya N. Clinical subtypes of older adults starting long-term care in Japan and their association with prognoses: a data-driven cluster analysis. Sci Rep 2024; 14:14911. [PMID: 38942898 PMCID: PMC11213852 DOI: 10.1038/s41598-024-65699-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 06/24/2024] [Indexed: 06/30/2024] Open
Abstract
We aimed to identify the clinical subtypes in individuals starting long-term care in Japan and examined their association with prognoses. Using linked medical insurance claims data and survey data for care-need certification in a large city, we identified participants who started long-term care. Grouping them based on 22 diseases recorded in the past 6 months using fuzzy c-means clustering, we examined the longitudinal association between clusters and death or care-need level deterioration within 2 years. We analyzed 4,648 participants (median age 83 [interquartile range 78-88] years, female 60.4%) between October 2014 and March 2019 and categorized them into (i) musculoskeletal and sensory, (ii) cardiac, (iii) neurological, (iv) respiratory and cancer, (v) insulin-dependent diabetes, and (vi) unspecified subtypes. The results of clustering were replicated in another city. Compared with the musculoskeletal and sensory subtype, the adjusted hazard ratio (95% confidence interval) for death was 1.22 (1.05-1.42), 1.81 (1.54-2.13), and 1.21 (1.00-1.46) for the cardiac, respiratory and cancer, and insulin-dependent diabetes subtypes, respectively. The care-need levels more likely worsened in the cardiac, respiratory and cancer, and unspecified subtypes than in the musculoskeletal and sensory subtype. In conclusion, distinct clinical subtypes exist among individuals initiating long-term care.
Collapse
Affiliation(s)
- Yuji Ito
- Department of General Internal Medicine, Chutoen General Medical Center, Kakegawa, Shizuoka, Japan
- Health Services Research and Development Center, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Masao Iwagami
- Health Services Research and Development Center, University of Tsukuba, Tsukuba, Ibaraki, Japan.
- Department of Health Services Research, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan.
| | - Jun Komiyama
- Health Services Research and Development Center, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Department of Health Services Research, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yoko Hamasaki
- Health Services Research and Development Center, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Department of Health Services Research, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Naoaki Kuroda
- Health Services Research and Development Center, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Health Department, Tsukuba City, Ibaraki, Japan
- Department of Public Mental Health Research, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Ai Suzuki
- Department of Health Services Research, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Health Department, Tsukuba City, Ibaraki, Japan
| | - Tomoko Ito
- Department of Nursing, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | | | - Eric Y F Wan
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
- Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong
| | - Francisco T T Lai
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
- Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong
| | - Nanako Tamiya
- Health Services Research and Development Center, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Department of Health Services Research, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| |
Collapse
|
2
|
Ioakeim-Skoufa I, González-Rubio F, Aza-Pascual-Salcedo M, Laguna-Berna C, Poblador-Plou B, Vicente-Romero J, Coelho H, Santos-Mejías A, Prados-Torres A, Moreno-Juste A, Gimeno-Miguel A. Multimorbidity patterns and trajectories in young and middle-aged adults: a large-scale population-based cohort study. Front Public Health 2024; 12:1349723. [PMID: 38818448 PMCID: PMC11137269 DOI: 10.3389/fpubh.2024.1349723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 05/06/2024] [Indexed: 06/01/2024] Open
Abstract
Introduction The presence of multiple chronic conditions, also referred to as multimorbidity, is a common finding in adults. Epidemiologic research can help identify groups of individuals with similar clinical profiles who could benefit from similar interventions. Many cross-sectional studies have revealed the existence of different multimorbidity patterns. Most of these studies were focused on the older population. However, multimorbidity patterns begin to form at a young age and can evolve over time following distinct multimorbidity trajectories with different impact on health. In this study, we aimed to identify multimorbidity patterns and trajectories in adults 18-65 years old. Methods We conducted a retrospective longitudinal epidemiologic study in the EpiChron Cohort, which includes all inhabitants of Aragón (Spain) registered as users of the Spanish National Health System, linking, at the patient level, information from electronic health records from both primary and specialised care. We included all 293,923 patients 18-65 years old with multimorbidity in 2011. We used cluster analysis at baseline (2011) and in 2015 and 2019 to identify multimorbidity patterns at four and eight years of follow-up, and we then created alluvial plots to visualise multimorbidity trajectories. We performed age- and sex-adjusted logistic regression analysis to study the association of each pattern with four- and eight-year mortality. Results We identified three multimorbidity patterns at baseline, named dyslipidaemia & endocrine-metabolic, hypertension & obesity, and unspecific. The hypertension & obesity pattern, found in one out of every four patients was associated with a higher likelihood of four- and eight-year mortality (age- and sex-adjusted odds ratio 1.11 and 1.16, respectively) compared to the unspecific pattern. Baseline patterns evolved into different patterns during the follow-up. Discussion Well-known preventable cardiovascular risk factors were key elements in most patterns, highlighting the role of hypertension and obesity as risk factors for higher mortality. Two out of every three patients had a cardiovascular profile with chronic conditions like diabetes and obesity that are linked to low-grade systemic chronic inflammation. More studies are encouraged to better characterise the relatively large portion of the population with an unspecific disease pattern and to help design and implement effective and comprehensive strategies towards healthier ageing.
Collapse
Affiliation(s)
- Ignatios Ioakeim-Skoufa
- Department of Drug Statistics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway
- Emerging Technologies Advisory Group, ISACA, Chicago, IL, United States
- EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute (IACS), Aragon Health Research Institute (IIS Aragón), Miguel Servet University Hospital, Zaragoza, Spain
- Drug Utilisation Work Group, Spanish Society of Family and Community Medicine (semFYC), Barcelona, Spain
- Research Network on Chronicity, Primary Care, and Health Promotion (RICAPPS), Institute of Health Carlos III (ISCIII), Madrid, Spain
- Department of Pharmacology, Physiology, and Legal and Forensic Medicine, Faculty of Medicine, University of Zaragoza, Zaragoza, Spain
| | - Francisca González-Rubio
- EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute (IACS), Aragon Health Research Institute (IIS Aragón), Miguel Servet University Hospital, Zaragoza, Spain
- Drug Utilisation Work Group, Spanish Society of Family and Community Medicine (semFYC), Barcelona, Spain
| | - Mercedes Aza-Pascual-Salcedo
- EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute (IACS), Aragon Health Research Institute (IIS Aragón), Miguel Servet University Hospital, Zaragoza, Spain
- Research Network on Chronicity, Primary Care, and Health Promotion (RICAPPS), Institute of Health Carlos III (ISCIII), Madrid, Spain
- Primary Care Pharmacy Service Zaragoza III, Aragon Health Service (SALUD), Zaragoza, Spain
| | - Clara Laguna-Berna
- EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute (IACS), Aragon Health Research Institute (IIS Aragón), Miguel Servet University Hospital, Zaragoza, Spain
| | - Beatriz Poblador-Plou
- EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute (IACS), Aragon Health Research Institute (IIS Aragón), Miguel Servet University Hospital, Zaragoza, Spain
- Research Network on Chronicity, Primary Care, and Health Promotion (RICAPPS), Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Jorge Vicente-Romero
- Department of Pharmacology, Physiology, and Legal and Forensic Medicine, Faculty of Medicine, University of Zaragoza, Zaragoza, Spain
| | - Helena Coelho
- Tondela-Viseu Hospital Centre, Viseu, Portugal
- Specialised Section for Regulatory Affairs & Quality, Portuguese Society of Health Care Pharmacists (SPFCS), Coimbra, Portugal
| | - Alejandro Santos-Mejías
- EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute (IACS), Aragon Health Research Institute (IIS Aragón), Miguel Servet University Hospital, Zaragoza, Spain
| | - Alexandra Prados-Torres
- EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute (IACS), Aragon Health Research Institute (IIS Aragón), Miguel Servet University Hospital, Zaragoza, Spain
- Research Network on Chronicity, Primary Care, and Health Promotion (RICAPPS), Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Aida Moreno-Juste
- EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute (IACS), Aragon Health Research Institute (IIS Aragón), Miguel Servet University Hospital, Zaragoza, Spain
- Research Network on Chronicity, Primary Care, and Health Promotion (RICAPPS), Institute of Health Carlos III (ISCIII), Madrid, Spain
- Aragon Health Service (SALUD), Zaragoza, Spain
| | - Antonio Gimeno-Miguel
- EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute (IACS), Aragon Health Research Institute (IIS Aragón), Miguel Servet University Hospital, Zaragoza, Spain
- Research Network on Chronicity, Primary Care, and Health Promotion (RICAPPS), Institute of Health Carlos III (ISCIII), Madrid, Spain
| |
Collapse
|
3
|
Sánchez-Valle J, Correia RB, Camacho-Artacho M, Lepore R, Mattos MM, Rocha LM, Valencia A. Prevalence and differences in the co-administration of drugs known to interact: an analysis of three distinct and large populations. BMC Med 2024; 22:166. [PMID: 38637816 PMCID: PMC11027217 DOI: 10.1186/s12916-024-03384-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 04/08/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND The co-administration of drugs known to interact greatly impacts morbidity, mortality, and health economics. This study aims to examine the drug-drug interaction (DDI) phenomenon with a large-scale longitudinal analysis of age and gender differences found in drug administration data from three distinct healthcare systems. METHODS This study analyzes drug administrations from population-wide electronic health records in Blumenau (Brazil; 133 K individuals), Catalonia (Spain; 5.5 M individuals), and Indianapolis (USA; 264 K individuals). The stratified prevalences of DDI for multiple severity levels per patient gender and age at the time of administration are computed, and null models are used to estimate the expected impact of polypharmacy on DDI prevalence. Finally, to study actionable strategies to reduce DDI prevalence, alternative polypharmacy regimens using drugs with fewer known interactions are simulated. RESULTS A large prevalence of co-administration of drugs known to interact is found in all populations, affecting 12.51%, 12.12%, and 10.06% of individuals in Blumenau, Indianapolis, and Catalonia, respectively. Despite very different healthcare systems and drug availability, the increasing prevalence of DDI as patients age is very similar across all three populations and is not explained solely by higher co-administration rates in the elderly. In general, the prevalence of DDI is significantly higher in women - with the exception of men over 50 years old in Indianapolis. Finally, we show that using proton pump inhibitor alternatives to omeprazole (the drug involved in more co-administrations in Catalonia and Blumenau), the proportion of patients that are administered known DDI can be reduced by up to 21% in both Blumenau and Catalonia and 2% in Indianapolis. CONCLUSIONS DDI administration has a high incidence in society, regardless of geographic, population, and healthcare management differences. Although DDI prevalence increases with age, our analysis points to a complex phenomenon that is much more prevalent than expected, suggesting comorbidities as key drivers of the increase. Furthermore, the gender differences observed in most age groups across populations are concerning in regard to gender equity in healthcare. Finally, our study exemplifies how electronic health records' analysis can lead to actionable interventions that significantly reduce the administration of known DDI and its associated human and economic costs.
Collapse
Affiliation(s)
- Jon Sánchez-Valle
- Life Sciences Department, Barcelona Supercomputing Center, 08034, Barcelona, Spain.
| | | | | | - Rosalba Lepore
- Life Sciences Department, Barcelona Supercomputing Center, 08034, Barcelona, Spain
- Department of Biomedicine, Basel University Hospital and University of Basel, CH-4031, Basel, Switzerland
| | - Mauro M Mattos
- Universidade Regional de Blumenau, Blumenau, 89030-903, Brazil
| | - Luis M Rocha
- Instituto Gulbenkian de Ciência, 2780-156, Street, Oeiras, Portugal.
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, 13902, USA.
| | - Alfonso Valencia
- Life Sciences Department, Barcelona Supercomputing Center, 08034, Barcelona, Spain.
- ICREA, 08010, Barcelona, Spain.
| |
Collapse
|
4
|
Abbad-Gomez D, Domingo L, Comas M, Santiá P, Jansana A, Poblador B, Sanz T, Del Cura I, Ibañez B, Padilla M, Redondo M, Castells X, Sala M. Effect of comorbidity and multimorbidity on adherence to follow-up recommendations among long-term breast cancer survivors. Maturitas 2024; 182:107918. [PMID: 38280353 DOI: 10.1016/j.maturitas.2024.107918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/22/2023] [Accepted: 01/14/2024] [Indexed: 01/29/2024]
Abstract
OBJECTIVES To study the impact of comorbidities, multimorbidity, and multimorbidity clusters on adherence to recommended follow-up guidelines among long-term breast cancer survivors. STUDY DESIGN Retrospective cohort study based on 2078 women diagnosed with breast cancer from 2000 to 2006 and followed up from 2012 to 2016. MAIN OUTCOME MEASURES Adherence to breast cancer follow-up recommendations (annual medical visit and imaging) was determined. Comorbidities were classified as acute/chronic. Multimorbidity was defined as the presence of two or more chronic comorbidities aside from breast cancer. Five multimorbidity clusters were considered. Multivariate logistic regression models were fitted to determine the relationship between adherence to recommendations and the presence of comorbidities and multimorbidity, considering both sociodemographic and clinical characteristics. RESULTS Overall adherence to recommendations was 79.5 %. Adherence was lower among long-term breast cancer survivors with no comorbidities (75.8 %). Among multimorbidity clusters, adherence was highest in the anxiety and fractures cluster (84.3 %) and was lowest in the musculoskeletal and cardiovascular cluster (76.4 %). In adjusted multivariate models, multimorbidity was associated with higher levels of adherence (OR = 1.52 95 %CI 1.16-1.99), and adherence was highest in the metabolic and degenerative cluster (OR = 2.2 95 %CI 1.4-3.5). CONCLUSION Adherence to follow-up recommendations was higher among long-term breast cancer survivors with multimorbidity than among those without. Adherence also differed by multimorbidity cluster. These results suggest suboptimal adherence to the current follow-up recommendations in certain groups, suggesting the need to adapt clinical practice guidelines to reflect patients' comorbidities and different characteristics.
Collapse
Affiliation(s)
- David Abbad-Gomez
- Department of Epidemiology and Evaluation, Hospital del Mar Research Institute, Barcelona, Spain; PhD Program in Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain
| | - Laia Domingo
- Department of Epidemiology and Evaluation, Hospital del Mar Research Institute, Barcelona, Spain; Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain.
| | - Mercè Comas
- Department of Epidemiology and Evaluation, Hospital del Mar Research Institute, Barcelona, Spain; Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain
| | - Paula Santiá
- Department of Epidemiology and Evaluation, Hospital del Mar Research Institute, Barcelona, Spain
| | - Anna Jansana
- Department of Epidemiology and Evaluation, Hospital del Mar Research Institute, Barcelona, Spain; Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain; Nutrition and Metabolism Branch, International Agency for Research On Cancer (IARC-WHO), 25 Avenue Tony Garnier, Lyon, France
| | - Beatriz Poblador
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain; EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, Spain
| | - Teresa Sanz
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain; Madrid Health Service, Primary Care Research Unit, Madrid, Spain
| | - Isabel Del Cura
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain; Madrid Health Service, Primary Care Research Unit, Madrid, Spain
| | - Berta Ibañez
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain; Navarrabiomed, HUN, UPNA, IdISNA. Pamplona, Spain
| | - Maria Padilla
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain; Research Unit, Costa del Sol Hospital, University of Málaga, Instituto de Investigación Biomédica de Málaga (IBIMA), Marbella, Spain
| | - Maximino Redondo
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain; Research Unit, Costa del Sol Hospital, University of Málaga, Instituto de Investigación Biomédica de Málaga (IBIMA), Marbella, Spain
| | - Xavier Castells
- Department of Epidemiology and Evaluation, Hospital del Mar Research Institute, Barcelona, Spain; Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain; Autonomous University of Barcelona (UAB), Barcelona, Spain
| | - Maria Sala
- Department of Epidemiology and Evaluation, Hospital del Mar Research Institute, Barcelona, Spain; Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, 28029 Madrid, Spain
| |
Collapse
|
5
|
Torrell G, Puente D, Jacques-Aviñó C, Carrasco-Ribelles LA, Violán C, López-Jiménez T, Royano V, Cantón AM, Medina-Perucha L, Rodríguez-Giralt I, Berenguera A. Characterisation, symptom pattern and symptom clusters from a retrospective cohort of Long COVID patients in primary care in Catalonia. BMC Infect Dis 2024; 24:82. [PMID: 38225587 PMCID: PMC10789045 DOI: 10.1186/s12879-023-08954-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 12/27/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Around 10% of people infected by SARS-COV-2 report symptoms that persist longer than 3 months. Little has been reported about sex differences in symptoms and clustering over time of non-hospitalised patients in primary care settings. METHODS This is a descriptive study of a cohort of mainly non-hospitalized patients with a persistence of symptoms longer than 3 months from the clinical onset in co-creation with the Long Covid Catalan affected group using an online survey. Recruitment was from March 2020 to June 2021. Exclusion criteria were being admitted to an ICU, < 18 years of age and not living in Catalonia. We focused on 117 symptoms gathered in 18 groups and performed cluster analysis over the first 21 days of infection, at 22-60 days, and ≥ 3 months. RESULTS We analysed responses of 905 participants (80.3% women). Median time between symptom onset and the questionnaire response date was 8.7 months. General symptoms (as fatigue) were the most prevalent with no differences by sex, age, or wave although its frequency decreased over time (from 91.8 to 78.3%). Dermatological (52.1% in women, 28.5% in men), olfactory (34.9% women, 20.9% men) and neurocognitive symptoms (70.1% women, 55.8% men) showed the greatest differences by sex. Cluster analysis showed five clusters with a predominance of Taste & smell (24.9%) and Multisystemic clusters (26.5%) at baseline and _Multisystemic (34.59%) and Heterogeneous (24.0%) at ≥3 months. The Multisystemic cluster was more prevalent in men. The Menstrual cluster was the most stable over time, while most transitions occurred from the Heterogeneous cluster to the Multisystemic cluster and from Taste & smell to Heterogeneous. CONCLUSIONS General symptoms were the most prevalent in both sexes at three-time cut-off points. Major sex differences were observed in dermatological, olfactory and neurocognitive symptoms. The increase of the Heterogeneous cluster might suggest an adaptation to symptoms or a non-specific evolution of the condition which can hinder its detection at medical appointments. A carefully symptom collection and patients' participation in research may generate useful knowledge about Long Covid presentation in primary care settings.
Collapse
Affiliation(s)
- Gemma Torrell
- Centre d'Atenció Primària Les Indianes, Gerència Territorial de Barcelona, Institut Català de la Salut, Barcelona, Spain
| | - Diana Puente
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
- Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029) Insitituto de Salud Carlos III, Madrid, Spain.
| | - Constanza Jacques-Aviñó
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Lucia A Carrasco-Ribelles
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029) Insitituto de Salud Carlos III, Madrid, Spain
- Unitat de Suport a la Recerca Metropolitana Nord, Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Mataró, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra), (2021 SGR 01537), Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAPJGol), 08008, Barcelona, Spain
| | - Concepció Violán
- Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029) Insitituto de Salud Carlos III, Madrid, Spain
- Unitat de Suport a la Recerca Metropolitana Nord, Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Mataró, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra), (2021 SGR 01537), Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAPJGol), 08008, Barcelona, Spain
- Direcció d'Atenció Primària Metropolitana Nord Institut Català de Salut, Barcelona, Spain
| | - Tomás López-Jiménez
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | - Veronica Royano
- Universitat Politècnica de Catalunya BarcelonaTech (UPC), Barcelona, Spain
- Members of the Col·lectiu d'Afectades i Afectats Persistents per COVID-19 a Catalunya, Barcelona, Spain
| | - Alba Molina Cantón
- Members of the Col·lectiu d'Afectades i Afectats Persistents per COVID-19 a Catalunya, Barcelona, Spain
| | - Laura Medina-Perucha
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Israel Rodríguez-Giralt
- Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Barcelona, Spain
| | - Anna Berenguera
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029) Insitituto de Salud Carlos III, Madrid, Spain
| |
Collapse
|
6
|
Beridze G, Abbadi A, Ars J, Remelli F, Vetrano DL, Trevisan C, Pérez LM, López-Rodríguez JA, Calderón-Larrañaga A. Patterns of multimorbidity in primary care electronic health records: A systematic review. JOURNAL OF MULTIMORBIDITY AND COMORBIDITY 2024; 14:26335565231223350. [PMID: 38298757 PMCID: PMC10829499 DOI: 10.1177/26335565231223350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/12/2023] [Indexed: 02/02/2024]
Abstract
Background Multimorbidity, the coexistence of multiple chronic conditions in an individual, is a complex phenomenon that is highly prevalent in primary care settings, particularly in older individuals. This systematic review summarises the current evidence on multimorbidity patterns identified in primary care electronic health record (EHR) data. Methods Three databases were searched from inception to April 2022 to identify studies that derived original multimorbidity patterns from primary care EHR data. The quality of the included studies was assessed using a modified version of the Newcastle-Ottawa Quality Assessment Scale. Results Sixteen studies were included in this systematic review, none of which was of low quality. Most studies were conducted in Spain, and only one study was conducted outside of Europe. The prevalence of multimorbidity (i.e. two or more conditions) ranged from 14.0% to 93.9%. The most common stratification variable in disease clustering models was sex, followed by age and calendar year. Despite significant heterogeneity in clustering methods and disease classification tools, consistent patterns of multimorbidity emerged. Mental health and cardiovascular patterns were identified in all studies, often in combination with diseases of other organ systems (e.g. neurological, endocrine). Discussion These findings emphasise the frequent coexistence of physical and mental health conditions in primary care, and provide useful information for the development of targeted preventive and management strategies. Future research should explore mechanisms underlying multimorbidity patterns, prioritise methodological harmonisation to facilitate the comparability of findings, and promote the use of EHR data globally to enhance our understanding of multimorbidity in more diverse populations.
Collapse
Affiliation(s)
- Giorgi Beridze
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
| | - Ahmad Abbadi
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
| | - Joan Ars
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- RE-FiT Barcelona Research group, Vall d'Hebron Institute of Research (VHIR) and Parc Sanitari Pere Virgili, Barcelona, Spain
- Medicine Department, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Francesca Remelli
- Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Davide L Vetrano
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Caterina Trevisan
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Laura-Mónica Pérez
- RE-FiT Barcelona Research group, Vall d'Hebron Institute of Research (VHIR) and Parc Sanitari Pere Virgili, Barcelona, Spain
| | - Juan A López-Rodríguez
- Research Unit, Primary Health Care Management, Madrid, Spain
- Department of Medical Specialties and Public Health, Faculty of Health Sciences Rey Juan Carlos University, Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), Carlos III Health Institute, Madrid, Spain
| | - Amaia Calderón-Larrañaga
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- Stockholm Gerontology Research Center, Stockholm, Sweden
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), Carlos III Health Institute, Madrid, Spain
| |
Collapse
|
7
|
Dhafari TB, Pate A, Azadbakht N, Bailey R, Rafferty J, Jalali-Najafabadi F, Martin GP, Hassaine A, Akbari A, Lyons J, Watkins A, Lyons RA, Peek N. A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods. J Clin Epidemiol 2024; 165:111214. [PMID: 37952700 DOI: 10.1016/j.jclinepi.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/14/2023] [Accepted: 11/05/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns. STUDY DESIGN AND SETTING We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns. RESULTS Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation. CONCLUSION The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.
Collapse
Affiliation(s)
- Thamer Ba Dhafari
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Alexander Pate
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Narges Azadbakht
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Rowena Bailey
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - James Rafferty
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Farideh Jalali-Najafabadi
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, M13 9PL Manchester, UK
| | - Glen P Martin
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Abdelaali Hassaine
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Jane Lyons
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Alan Watkins
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Niels Peek
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
| |
Collapse
|
8
|
Garg M, Liu X, Vassilaki M, Petersen RC, St Sauver J, Sohn S. Navigating Sex-Specific Disease Dynamics in Incident Dementia. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2023; 2023:4065-4072. [PMID: 38404695 PMCID: PMC10883293 DOI: 10.1109/bibm58861.2023.10385324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Dementia is among the leading causes of cognitive and functional loss and disability in older adults. Past studies suggested sex differences in health conditions and progression of cognitive decline. Existing studies on the temporal trajectory of health conditions for patient characterization after dementia diagnosis are scarce and ambiguous. Thus, there's limited and unclear research on how health conditions change over time after a dementia diagnosis. To this end, we aim to analyze the shift in medical conditions and examine sex-specific changes in patterns of chronic health conditions after dementia diagnosis. We centered our analysis on a 15-year window around the point of dementia diagnosis, encompassing the 5 years leading up to the diagnosis and the 10 years following it. We introduce (i) MedMet, a network metric to quantify the contribution of each medical condition, and (ii) growth and decay function for temporal trajectory analysis of medical conditions. Our experiments demonstrate that certain health conditions are more prevalent among females than males. Thus, our findings underscore the pressing need to examine differences between men and women, which could be important for healthcare utilization after a dementia diagnosis.
Collapse
Affiliation(s)
- Muskan Garg
- Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Xingyi Liu
- Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Maria Vassilaki
- Quantitative Health Science Research, Mayo Clinic, Rochester, MN, USA
| | | | | | - Sunghwan Sohn
- Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
9
|
Owen RK, Lyons J, Akbari A, Guthrie B, Agrawal U, Alexander DC, Azcoaga-Lorenzo A, Brookes AJ, Denaxas S, Dezateux C, Fagbamigbe AF, Harper G, Kirk PDW, Özyiğit EB, Richardson S, Staniszewska S, McCowan C, Lyons RA, Abrams KR. Effect on life expectancy of temporal sequence in a multimorbidity cluster of psychosis, diabetes, and congestive heart failure among 1·7 million individuals in Wales with 20-year follow-up: a retrospective cohort study using linked data. Lancet Public Health 2023; 8:e535-e545. [PMID: 37393092 DOI: 10.1016/s2468-2667(23)00098-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 07/03/2023]
Abstract
BACKGROUND To inform targeted public health strategies, it is crucial to understand how coexisting diseases develop over time and their associated impacts on patient outcomes and health-care resources. This study aimed to examine how psychosis, diabetes, and congestive heart failure, in a cluster of physical-mental health multimorbidity, develop and coexist over time, and to assess the associated effects of different temporal sequences of these diseases on life expectancy in Wales. METHODS In this retrospective cohort study, we used population-scale, individual-level, anonymised, linked, demographic, administrative, and electronic health record data from the Wales Multimorbidity e-Cohort. We included data on all individuals aged 25 years and older who were living in Wales on Jan 1, 2000 (the start of follow-up), with follow-up continuing until Dec 31, 2019, first break in Welsh residency, or death. Multistate models were applied to these data to model trajectories of disease in multimorbidity and their associated effect on all-cause mortality, accounting for competing risks. Life expectancy was calculated as the restricted mean survival time (bound by the maximum follow-up of 20 years) for each of the transitions from the health states to death. Cox regression models were used to estimate baseline hazards for transitions between health states, adjusted for sex, age, and area-level deprivation (Welsh Index of Multiple Deprivation [WIMD] quintile). FINDINGS Our analyses included data for 1 675 585 individuals (811 393 [48·4%] men and 864 192 [51·6%] women) with a median age of 51·0 years (IQR 37·0-65·0) at cohort entry. The order of disease acquisition in cases of multimorbidity had an important and complex association with patient life expectancy. Individuals who developed diabetes, psychosis, and congestive heart failure, in that order (DPC), had reduced life expectancy compared with people who developed the same three conditions in a different order: for a 50-year-old man in the third quintile of the WIMD (on which we based our main analyses to allow comparability), DPC was associated with a loss in life expectancy of 13·23 years (SD 0·80) compared with the general otherwise healthy or otherwise diseased population. Congestive heart failure as a single condition was associated with mean a loss in life expectancy of 12·38 years (0·00), and with a loss of 12·95 years (0·06) when preceded by psychosis and 13·45 years (0·13) when followed by psychosis. Findings were robust in people of older ages, more deprived populations, and women, except that the trajectory of psychosis, congestive heart failure, and diabetes was associated with higher mortality in women than men. Within 5 years of an initial diagnosis of diabetes, the risk of developing psychosis or congestive heart failure, or both, was increased. INTERPRETATION The order in which individuals develop psychosis, diabetes, and congestive heart failure as combinations of conditions can substantially affect life expectancy. Multistate models offer a flexible framework to assess temporal sequences of diseases and allow identification of periods of increased risk of developing subsequent conditions and death. FUNDING Health Data Research UK.
Collapse
Affiliation(s)
- Rhiannon K Owen
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK.
| | - Jane Lyons
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Ashley Akbari
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Bruce Guthrie
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Utkarsh Agrawal
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, Faculty of Engineering Sciences, University College London, London, UK
| | - Amaya Azcoaga-Lorenzo
- School of Medicine, University of St Andrews, St Andrews, UK; Hospital Rey Juan Carlos, Instituto de Investigación Sanitaria Fundación Jimenez Diaz, Madrid, Spain
| | | | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | - Carol Dezateux
- Clinical Effectiveness Group, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | | | - Gill Harper
- Clinical Effectiveness Group, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Paul D W Kirk
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
| | - Eda Bilici Özyiğit
- Centre for Medical Image Computing, Department of Computer Science, Faculty of Engineering Sciences, University College London, London, UK
| | | | - Sophie Staniszewska
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Colin McCowan
- School of Medicine, University of St Andrews, St Andrews, UK
| | - Ronan A Lyons
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Keith R Abrams
- Department of Statistics, University of Warwick, Coventry, UK; Centre for Health Economics, University of York, York, UK
| |
Collapse
|
10
|
Carrasco-Ribelles LA, Cabrera-Bean M, Danés-Castells M, Zabaleta-Del-Olmo E, Roso-Llorach A, Violán C. Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People. JMIR Public Health Surveill 2023; 9:e45848. [PMID: 37368462 PMCID: PMC10365626 DOI: 10.2196/45848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/02/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people. OBJECTIVE This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older. METHODS Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d'Informació pel Desenvolupament de la Investigació a l'Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period. RESULTS The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern. CONCLUSIONS Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.
Collapse
Affiliation(s)
- Lucía A Carrasco-Ribelles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Signal Processing and Communications Group (SPCOM), Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTRA) (2021 SGR 01537), Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029), Instituto de Salud Carlos III, Madrid, Spain
| | - Margarita Cabrera-Bean
- Signal Processing and Communications Group (SPCOM), Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Marc Danés-Castells
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, Spain
| | - Edurne Zabaleta-Del-Olmo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTRA) (2021 SGR 01537), Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029), Instituto de Salud Carlos III, Madrid, Spain
- Gerència Territorial de Barcelona, Institut Català de la Salut, Barcelona, Spain
- Nursing Department, Faculty of Nursing, Universitat de Girona, Girona, Spain
| | - Albert Roso-Llorach
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTRA) (2021 SGR 01537), Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029), Instituto de Salud Carlos III, Madrid, Spain
- Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | - Concepción Violán
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTRA) (2021 SGR 01537), Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029), Instituto de Salud Carlos III, Madrid, Spain
- Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
- Fundació Institut d'Investigació en ciències de la Salut Germans Trias i Pujol (IGTP), Badalona, Spain
| |
Collapse
|
11
|
Roso-Llorach A, Vetrano DL, Trevisan C, Fernández S, Guisado-Clavero M, Carrasco-Ribelles LA, Fratiglioni L, Violán C, Calderón-Larrañaga A. 12-year evolution of multimorbidity patterns among older adults based on Hidden Markov Models. Aging (Albany NY) 2022; 14:9805-9817. [PMID: 36435509 PMCID: PMC9831736 DOI: 10.18632/aging.204395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/14/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND The evolution of multimorbidity patterns during aging is still an under-researched area. We lack evidence concerning the time spent by older adults within one same multimorbidity pattern, and their transitional probability across different patterns when further chronic diseases arise. The aim of this study is to fill this gap by exploring multimorbidity patterns across decades of age in older adults, and longitudinal dynamics among these patterns. METHODS Longitudinal study based on the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) on adults ≥60 years (N=3,363). Hidden Markov Models were applied to model the temporal evolution of both multimorbidity patterns and individuals' transitions over a 12-year follow-up. FINDINGS Within the study population (mean age 76.1 years, 66.6% female), 87.2% had ≥2 chronic conditions at baseline. Four longitudinal multimorbidity patterns were identified for each decade. Individuals in all decades showed the shortest permanence time in an Unspecific pattern lacking any overrepresented diseases (range: 4.6-10.9 years), but the pattern with the longest permanence time varied by age. Sexagenarians remained longest in the Psychiatric-endocrine and sensorial pattern (15.4 years); septuagenarians in the Neuro-vascular and skin-sensorial pattern (11.0 years); and octogenarians and beyond in the Neuro-sensorial pattern (8.9 years). Transition probabilities varied across decades, sexagenarians showing the highest levels of stability. INTERPRETATION Our findings highlight the dynamism and heterogeneity underlying multimorbidity by quantifying the varying permanence times and transition probabilities across patterns in different decades. With increasing age, older adults experience decreasing stability and progressively shorter permanence time within one same multimorbidity pattern.
Collapse
Affiliation(s)
- Albert Roso-Llorach
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola de Vallès), Spain,Programa de Doctorat en Metodologia de la Recerca Biomèdica i Salut Pública, Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
| | - Davide L. Vetrano
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Caterina Trevisan
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Sergio Fernández
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain,Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola de Vallès), Spain
| | - Marina Guisado-Clavero
- Unidad Docente Multiprofesional de Atención Familiar y Comunitaria Norte, Gerencia Asistencial Atención Primaria, Madrid Health Service, Madrid, Spain
| | - Lucía A. Carrasco-Ribelles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain,Signal Theory and Communications Department, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Laura Fratiglioni
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Concepción Violán
- Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola de Vallès), Spain,Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitaria per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Mataró, Barcelona, Spain
| | - Amaia Calderón-Larrañaga
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Stockholm Gerontology Research Center, Stockholm, Sweden
| |
Collapse
|
12
|
Athreya AP, Vande Voort JL, Shekunov J, Rackley SJ, Leffler JM, McKean AJ, Romanowicz M, Kennard BD, Emslie GJ, Mayes T, Trivedi M, Wang L, Weinshilboum RM, Bobo WV, Croarkin PE. Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants. J Child Psychol Psychiatry 2022; 63:1347-1358. [PMID: 35288932 PMCID: PMC9475486 DOI: 10.1111/jcpp.13580] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/17/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.
Collapse
Affiliation(s)
- Arjun P. Athreya
- Department of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMNUSA
| | | | - Julia Shekunov
- Department of Psychiatry and PsychologyMayo ClinicRochesterMNUSA
| | | | | | | | | | - Betsy D. Kennard
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Graham J. Emslie
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA,Children’s HealthChildren’s Medical CenterDallasTXUSA
| | - Taryn Mayes
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Madhukar Trivedi
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMNUSA
| | | | - William V. Bobo
- Department of Psychiatry and PsychologyMayo ClinicJacksonvilleFLUSA
| | - Paul E. Croarkin
- Department of Psychiatry and PsychologyMayo ClinicRochesterMNUSA
| |
Collapse
|
13
|
Ioakeim-Skoufa I, Clerencia-Sierra M, Moreno-Juste A, Elías de Molins Peña C, Poblador-Plou B, Aza-Pascual-Salcedo M, González-Rubio F, Prados-Torres A, Gimeno-Miguel A. Multimorbidity Clusters in the Oldest Old: Results from the EpiChron Cohort. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10180. [PMID: 36011814 PMCID: PMC9408216 DOI: 10.3390/ijerph191610180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/15/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Multimorbidity is challenging for both patients and healthcare systems due to its increasing prevalence and high impact on people's health and well-being. The risk of multimorbidity increases with age, but there is still more to discover regarding the clinical profile of the oldest old. In this study, we used information from the EpiChron Cohort Study to identify multimorbidity patterns in individuals who died during the period 2010-2019 at the ages of 80-89, 90-99, and ≥100. This cohort links the demographic, clinical, and drug dispensation information of public health system users in Aragón, Spain. We saw a significantly lower number of chronic diseases and drugs and a lower prevalence of polypharmacy in centenarians compared to those aged 80-99. K-means clustering revealed different multimorbidity clusters by sex and age group. We observed clusters of cardiovascular and metabolic diseases, obstructive pulmonary conditions, and neoplasms, amongst other profiles. One in three octogenarian women had a metabolic pattern (diabetes, dyslipidaemia, and other endocrine-metabolic disorders) with the highest number of diseases (up to seven) and prevalence of polypharmacy (64%). We observed clusters of dementia and genitourinary disorders in individuals on medication with anticholinergic activity. Our study offers an opportunity to better understand the urgency of adequately addressing multimorbidity in our older adults.
Collapse
Affiliation(s)
- Ignatios Ioakeim-Skoufa
- WHO Collaborating Centre for Drug Statistics Methodology, Department of Drug Statistics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, NO-0213 Oslo, Norway
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, ES-50009 Zaragoza, Spain
- Drug Utilization Work Group, Spanish Society of Family and Community Medicine (semFYC), ES-08009 Barcelona, Spain
| | - Mercedes Clerencia-Sierra
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, ES-50009 Zaragoza, Spain
- Aragon Health Service (SALUD), Miguel Servet University Hospital, ES-50009 Zaragoza, Spain
- Research Network on Health Services in Chronic Patients (REDISSEC), ISCIII, ES-28029 Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, ES-28029 Madrid, Spain
| | - Aida Moreno-Juste
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, ES-50009 Zaragoza, Spain
- Aragon Health Service (SALUD), Miguel Servet University Hospital, ES-50009 Zaragoza, Spain
- Research Network on Health Services in Chronic Patients (REDISSEC), ISCIII, ES-28029 Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, ES-28029 Madrid, Spain
| | | | - Beatriz Poblador-Plou
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, ES-50009 Zaragoza, Spain
- Research Network on Health Services in Chronic Patients (REDISSEC), ISCIII, ES-28029 Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, ES-28029 Madrid, Spain
| | - Mercedes Aza-Pascual-Salcedo
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, ES-50009 Zaragoza, Spain
- Research Network on Health Services in Chronic Patients (REDISSEC), ISCIII, ES-28029 Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, ES-28029 Madrid, Spain
- Primary Care Pharmacy Service Zaragoza III, Aragon Health Service (SALUD), ES-50017 Zaragoza, Spain
| | - Francisca González-Rubio
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, ES-50009 Zaragoza, Spain
- Drug Utilization Work Group, Spanish Society of Family and Community Medicine (semFYC), ES-08009 Barcelona, Spain
- Research Network on Health Services in Chronic Patients (REDISSEC), ISCIII, ES-28029 Madrid, Spain
| | - Alexandra Prados-Torres
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, ES-50009 Zaragoza, Spain
- Research Network on Health Services in Chronic Patients (REDISSEC), ISCIII, ES-28029 Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, ES-28029 Madrid, Spain
| | - Antonio Gimeno-Miguel
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, ES-50009 Zaragoza, Spain
- Research Network on Health Services in Chronic Patients (REDISSEC), ISCIII, ES-28029 Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), ISCIII, ES-28029 Madrid, Spain
| |
Collapse
|
14
|
Identifying multimorbidity clusters among Brazilian older adults using network analysis: Findings and perspectives. PLoS One 2022; 17:e0271639. [PMID: 35857809 PMCID: PMC9299350 DOI: 10.1371/journal.pone.0271639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 07/05/2022] [Indexed: 11/19/2022] Open
Abstract
In aging populations, multimorbidity (MM) is a significant challenge for health systems, however there are scarce evidence available in Low- and Middle-Income Countries, particularly in Brazil. A national cross-sectional study was conducted with 11,177 Brazilian older adults to evaluate the occurrence of MM and related clusters in Brazilians aged ≥ 60 years old. MM was assessed by a list of 16 physical and mental morbidities and it was defined considering ≥ 2 morbidities. The frequencies of MM and its associated factors were analyzed. After this initial approach, a network analysis was performed to verify the occurrence of clusters of MM and the network of interactions between coexisting morbidities. The occurrence of MM was 58.6% (95% confidence interval [CI]: 57.0–60.2). Hypertension (50.6%) was the most frequent morbidity and it was present all combinations of morbidities. Network analysis has demonstrated 4 MM clusters: 1) cardiometabolic; 2) respiratory + cancer; 3) musculoskeletal; and 4) a mixed mental illness + other diseases. Depression was the most central morbidity in the model according to nodes’ centrality measures (strength, closeness, and betweenness) followed by heart disease, and low back pain. Similarity in male and female networks was observed with a conformation of four clusters of MM and cancer as an isolated morbidity. The prevalence of MM in the older Brazilians was high, especially in female sex and persons living in the South region of Brazil. Use of network analysis could be an important tool for identifying MM clusters and address the appropriate health care, research, and medical education for older adults in Brazil.
Collapse
|
15
|
Vetrano DL, Damiano C, Tazzeo C, Zucchelli A, Marengoni A, Luo H, Zazzara MB, van Hout H, Onder G. Multimorbidity Patterns and 5-Year Mortality in Institutionalized Older Adults. J Am Med Dir Assoc 2022; 23:1389-1395.e4. [PMID: 35218731 DOI: 10.1016/j.jamda.2022.01.067] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/12/2022] [Accepted: 01/22/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVES The aim was to characterize multimorbidity patterns in a large sample of older individuals living in nursing homes (NHs) and to investigate their association with mortality, also considering the effect of functional status. DESIGN Observational and retrospective study. SETTING AND PARTICIPANTS We analyzed data on 4131 NH residents in Italy, aged 60 years and older, assessed through the interRAI long-term care facility instrument. Entry date was between 2014 and 2018, and participants were followed until 2019. METHODS Multimorbidity patterns were identified through principal component analysis; for the identified components, subjects were stratified in quintiles (Q) with respect to their loading values, with the higher quantiles indicating greater expression of the component's pattern. Their association [hazard ratio (HR) and 95% CI] with mortality was tested in Cox regression models. Analyses were stratified by disability status. RESULTS Four patterns of multimorbidity were identified: (1) heart diseases; (2) dementia and sensory impairments; (3) heart, respiratory, and psychiatric diseases; and (4) diabetes, musculoskeletal, and vascular diseases. For the heart diseases pattern [HR Q5 vs Q1 = 1.83 (1.53-2.20)] and the dementia and sensory impairments pattern [HR Q5 vs Q1 = 1.23 (1.06-1.42)], as the specific multimorbidity expression increases, the risk of mortality increases. On stratifying by disability status, the association between the multimorbidity patterns and mortality was not always present. CONCLUSIONS AND IMPLICATIONS Different multimorbidity patterns are differentially associated with mortality in older residents of NHs, confirming that multimorbidity's prognosis is strictly dependent on the underlying disease combinations. This knowledge may be useful to implement personalized preventive and therapeutic care pathways for institutionalized older adults, which respond to individuals' health needs.
Collapse
Affiliation(s)
- Davide L Vetrano
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Cecilia Damiano
- Università Cattolica del Sacro Cuore, Department of Geriatric and Orthopaedic Sciences, Rome, Italy.
| | - Clare Tazzeo
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Alberto Zucchelli
- Department of Informatic Engineering, University of Brescia, Brescia, Italy
| | - Alessandra Marengoni
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Hao Luo
- Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong, China
| | - Maria Beatrice Zazzara
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Department of Gerontology, Neuroscience and Orthopedics, Rome, Italy
| | - Hein van Hout
- Departments of General Practice and Medicine of Older Persons, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Graziano Onder
- Department of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy
| |
Collapse
|
16
|
Jansana A, Poblador-Plou B, Gimeno-Miguel A, Lanzuela M, Prados-Torres A, Domingo L, Comas M, Sanz-Cuesta T, Del Cura-Gonzalez I, Ibañez B, Abizanda M, Duarte-Salles T, Padilla-Ruiz M, Redondo M, Castells X, Sala M. Multimorbidity clusters among long-term breast cancer survivors in Spain: Results of the SURBCAN study. Int J Cancer 2021; 149:1755-1767. [PMID: 34255861 DOI: 10.1002/ijc.33736] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 11/07/2022]
Abstract
The disease management of long-term breast cancer survivors (BCS) is hampered by the scarce knowledge of multimorbidity patterns. The aim of our study was to identify multimorbidity clusters among long-term BCS and assess their impact on mortality and health services use. We conducted a retrospective study using electronic health records of 6512 BCS from Spain surviving at least 5 years. Hierarchical cluster analysis was used to identify groups of similar patients based on their chronic diagnoses, which were assessed using the Clinical Classifications Software. As a result, multimorbidity clusters were obtained, clinically defined and named according to the comorbidities with higher observed/expected prevalence ratios. Multivariable Cox and negative binomial regression models were fitted to estimate overall mortality risk and probability of contacting health services according to the clusters identified. 83.7% of BCS presented multimorbidity, essential hypertension (34.5%) and obesity and other metabolic disorders (27.4%) being the most prevalent chronic diseases at the beginning of follow-up. Five multimorbidity clusters were identified: C1-unspecific (29.9%), C2-metabolic and neurodegenerative (28.3%), C3-anxiety and fractures (9.7%), C4-musculoskeletal and cardiovascular (9.6%) and C5-thyroid disorders (5.3%). All clusters except C5-thyroid disorders were associated with higher mortality compared to BCS without comorbidities. The risk of mortality in C4 was increased by 64% (adjusted hazard ratio 1.64, 95% confidence interval 1.52-2.07). Stratified analysis showed an increased risk of death among BCS with 5 to 10 years of survival in all clusters. These results help to identify subgroups of long-term BCS with specific needs and mortality risks and to guide BCS clinical practice regarding multimorbidity.
Collapse
Affiliation(s)
- Anna Jansana
- Department of Epidemiology and Evaluation, Hospital del Mar Institute for Medical Research, Barcelona, Spain.,European Higher Education Area Doctoral Program in Methodology of Biomedical Research and Public Health in Department of Pediatrics, Obstetrics and Gynecology, Preventive Medicine and Public Health, Universitat Autónoma de Barcelona (UAB), Barcelona, Spain.,Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Beatriz Poblador-Plou
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, Spain
| | - Antonio Gimeno-Miguel
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, Spain
| | - Manuela Lanzuela
- Radiotherapy Department, Miguel Servet University Hospital, Zaragoza, Spain
| | - Alexandra Prados-Torres
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, Spain
| | - Laia Domingo
- Department of Epidemiology and Evaluation, Hospital del Mar Institute for Medical Research, Barcelona, Spain.,Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Mercè Comas
- Department of Epidemiology and Evaluation, Hospital del Mar Institute for Medical Research, Barcelona, Spain.,Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Teresa Sanz-Cuesta
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Madrid Health Service, Primary Care Research Unit, Calle San Martín de Porres, Madrid, Spain
| | - Isabel Del Cura-Gonzalez
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Madrid Health Service, Primary Care Research Unit, Calle San Martín de Porres, Madrid, Spain
| | - Berta Ibañez
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Navarrabiomed-Complejo Hospitalario de Navarra-Universidad Pública de Navarra, IdiSNA, Pamplona, Spain
| | - Mercè Abizanda
- Department of Organization and Communication, Parc Sanitari Pere Virgili, Barcelona, Spain
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Maria Padilla-Ruiz
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Research Unit, Costa del Sol Hospital, University of Málaga, Instituto de Investigación Biomédica de Málaga (IBIMA), Marbella, Spain
| | - Maximino Redondo
- Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Research Unit, Costa del Sol Hospital, University of Málaga, Instituto de Investigación Biomédica de Málaga (IBIMA), Marbella, Spain
| | - Xavier Castells
- Department of Epidemiology and Evaluation, Hospital del Mar Institute for Medical Research, Barcelona, Spain.,Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain.,Autonomous University of Barcelona (UAB), Barcelona, Spain
| | - Maria Sala
- Department of Epidemiology and Evaluation, Hospital del Mar Institute for Medical Research, Barcelona, Spain.,Health Services Research on Chronic Patients Network (REDISSEC), Instituto de Salud Carlos III, Madrid, Spain
| | | |
Collapse
|
17
|
Cesario A, D’Oria M, Calvani R, Picca A, Pietragalla A, Lorusso D, Daniele G, Lohmeyer FM, Boldrini L, Valentini V, Bernabei R, Auffray C, Scambia G. The Role of Artificial Intelligence in Managing Multimorbidity and Cancer. J Pers Med 2021; 11:jpm11040314. [PMID: 33921621 PMCID: PMC8074144 DOI: 10.3390/jpm11040314] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/13/2021] [Accepted: 04/16/2021] [Indexed: 02/07/2023] Open
Abstract
Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.
Collapse
Affiliation(s)
- Alfredo Cesario
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (A.C.); (A.P.); (D.L.); (G.D.); (F.M.L.); (G.S.)
| | - Marika D’Oria
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (A.C.); (A.P.); (D.L.); (G.D.); (F.M.L.); (G.S.)
- Correspondence:
| | - Riccardo Calvani
- Department of Ageing, Neurosciences, Head-Neck and Orthopaedics Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (R.C.); (A.P.); (R.B.)
| | - Anna Picca
- Department of Ageing, Neurosciences, Head-Neck and Orthopaedics Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (R.C.); (A.P.); (R.B.)
| | - Antonella Pietragalla
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (A.C.); (A.P.); (D.L.); (G.D.); (F.M.L.); (G.S.)
- Gynecological Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Domenica Lorusso
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (A.C.); (A.P.); (D.L.); (G.D.); (F.M.L.); (G.S.)
- Gynecological Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Department of Life Sciences and Public Health, Faculty of Medicine and Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Gennaro Daniele
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (A.C.); (A.P.); (D.L.); (G.D.); (F.M.L.); (G.S.)
| | - Franziska Michaela Lohmeyer
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (A.C.); (A.P.); (D.L.); (G.D.); (F.M.L.); (G.S.)
| | - Luca Boldrini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (L.B.); (V.V.)
| | - Vincenzo Valentini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (L.B.); (V.V.)
| | - Roberto Bernabei
- Department of Ageing, Neurosciences, Head-Neck and Orthopaedics Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (R.C.); (A.P.); (R.B.)
| | - Charles Auffray
- European Institute for Systems Biology and Medicine (EISBM), 69390 Vourles, France;
| | - Giovanni Scambia
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (A.C.); (A.P.); (D.L.); (G.D.); (F.M.L.); (G.S.)
- Gynecological Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Department of Life Sciences and Public Health, Faculty of Medicine and Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| |
Collapse
|
18
|
Majnarić LT, Babič F, O’Sullivan S, Holzinger A. AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity. J Clin Med 2021; 10:jcm10040766. [PMID: 33672914 PMCID: PMC7918668 DOI: 10.3390/jcm10040766] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/02/2021] [Accepted: 02/11/2021] [Indexed: 12/11/2022] Open
Abstract
Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.
Collapse
Affiliation(s)
- Ljiljana Trtica Majnarić
- Department of Internal Medicine, Family Medicine and the History of Medicine, Faculty of Medicine, University Josip Juraj Strossmayer, 31000 Osijek, Croatia;
- Department of Public Health, Faculty of Dental Medicine, University Josip Juraj Strossmayer, 31000 Osijek, Croatia
| | - František Babič
- Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 066 01 Košice, Slovakia
- Correspondence: ; Tel.: +421-55-602-4220
| | - Shane O’Sullivan
- Department of Pathology, Faculdade de Medicina, Universidade de São Paulo, 05508-220 São Paulo, Brazil;
| | - Andreas Holzinger
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria;
| |
Collapse
|