Multidimensional prognostic index domain phenotypes identified using latent class analysis and mortality in the European Study of Older Subjects With Atrial Fibrillation (EUROSAF)
The multidimensional prognostic index (MPI), an established tool to predict adverse outcomes, classifies frailty using an aggregate-weighted tripartite scoring system based on 8 domains (low, moderate, or severe risk). However, this approach may fail to capture specific patient phenotypes that can b...
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Published in | Heart rhythm |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Elsevier Inc
12.05.2025
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ISSN | 1547-5271 1556-3871 1556-3871 |
DOI | 10.1016/j.hrthm.2025.05.012 |
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Abstract | The multidimensional prognostic index (MPI), an established tool to predict adverse outcomes, classifies frailty using an aggregate-weighted tripartite scoring system based on 8 domains (low, moderate, or severe risk). However, this approach may fail to capture specific patient phenotypes that can be characterized by separate MPI domains and for whom health outcome risk also differs.
We sought to identify latent patient phenotypes based on MPI domain data and to determine their association with mortality in older patients with atrial fibrillation (AF).
Using data from the European Study of Older Subjects With Atrial Fibrillation, we used latent class analysis to identify phenotypes using individual MPI domains and Cox regression models to examine their association with 12-month mortality.
Four MPI domain phenotypes were identified in 2019 patients with AF (mean age 82.9 years [standard deviation, 7.5]; 57% females): phenotype 1 (relatively fit, few comorbidities; n = 672, 33%), phenotype 2 (functionally impaired, polypharmacy, comorbidities; n = 685, 34%), phenotype 3 (multidimensional frailty, comorbidities; n = 161, 8%), and phenotype 4 (relatively fit, polypharmacy, comorbidities; n = 501, 25%). Compared with phenotype 1, 12-month mortality was higher in phenotype 3 (adjusted hazard ratio [aHR], 4.68; 95% confidence interval [CI], 3.41–6.43), phenotype 2 (aHR, 1.98; 95% CI, 1.53–2.57), and phenotype 4 (aHR, 1.44; 95% CI, 1.07–1.94).
In a cohort of older patients with AF, latent class analysis identified 4 MPI domain phenotypes with different risks of mortality. Pending confirmatory studies, the identified subgroups might allow more targeted interventions to improve outcomes in this population. |
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AbstractList | The multidimensional prognostic index (MPI), an established tool to predict adverse outcomes, classifies frailty using an aggregate-weighted tripartite scoring system based on 8 domains (low, moderate, or severe risk). However, this approach may fail to capture specific patient phenotypes that can be characterized by separate MPI domains and for whom health outcome risk also differs.
We sought to identify latent patient phenotypes based on MPI domain data and to determine their association with mortality in older patients with atrial fibrillation (AF).
Using data from the European Study of Older Subjects With Atrial Fibrillation, we used latent class analysis to identify phenotypes using individual MPI domains and Cox regression models to examine their association with 12-month mortality.
Four MPI domain phenotypes were identified in 2019 patients with AF (mean age 82.9 years [standard deviation, 7.5]; 57% females): phenotype 1 (relatively fit, few comorbidities; n = 672, 33%), phenotype 2 (functionally impaired, polypharmacy, comorbidities; n = 685, 34%), phenotype 3 (multidimensional frailty, comorbidities; n = 161, 8%), and phenotype 4 (relatively fit, polypharmacy, comorbidities; n = 501, 25%). Compared with phenotype 1, 12-month mortality was higher in phenotype 3 (adjusted hazard ratio [aHR], 4.68; 95% confidence interval [CI], 3.41-6.43), phenotype 2 (aHR, 1.98; 95% CI, 1.53-2.57), and phenotype 4 (aHR, 1.44; 95% CI, 1.07-1.94).
In a cohort of older patients with AF, latent class analysis identified 4 MPI domain phenotypes with different risks of mortality. Pending confirmatory studies, the identified subgroups might allow more targeted interventions to improve outcomes in this population. The Multidimensional Prognostic Index (MPI), an established tool to predict adverse outcomes, classifies frailty using an aggregate-weighted tripartite scoring system based on eight domains (low-, moderate-, or severe-risk). However, this approach may fail to capture specific patient phenotypes that can be characterised by separate MPI domains and for whom health outcome risk also differs.BACKGROUNDThe Multidimensional Prognostic Index (MPI), an established tool to predict adverse outcomes, classifies frailty using an aggregate-weighted tripartite scoring system based on eight domains (low-, moderate-, or severe-risk). However, this approach may fail to capture specific patient phenotypes that can be characterised by separate MPI domains and for whom health outcome risk also differs.We sought to identify latent patient phenotypes based on MPI domain data and to determine their association with mortality in older patients with atrial fibrillation (AF).OBJECTIVEWe sought to identify latent patient phenotypes based on MPI domain data and to determine their association with mortality in older patients with atrial fibrillation (AF).Using data from the EURopean study of Older Subjects with Atrial Fibrillation (EUROSAF), we used latent class analysis (LCA) to identify phenotypes using individual MPI domains and Cox regression models to examine their association with 12-month mortality.METHODSUsing data from the EURopean study of Older Subjects with Atrial Fibrillation (EUROSAF), we used latent class analysis (LCA) to identify phenotypes using individual MPI domains and Cox regression models to examine their association with 12-month mortality.Four MPI domain phenotypes were identified in N=2,019 AF patients (mean (SD) age=82.9 (7.5) years; 57% females): phenotype 1 (relatively fit, few comorbidities; n=672, 33%), phenotype 2 (functionally impaired, polypharmacy, comorbidities; n=685, 34%), phenotype 3 (multidimensional frailty, comorbidities; n=161, 8%), and phenotype 4 (relatively fit, polypharmacy, comorbidities; n=501, 25%). Compared to phenotype 1, 12-month mortality was higher in phenotype 3 (adjusted hazard ratio (aHR):4.68, 95%CI=3.41-6.43), phenotype 2 (aHR:1.98, 95%CI=1.53-2.57), and phenotype 4 (aHR:1.44, 95%CI=1.07-1.94).RESULTSFour MPI domain phenotypes were identified in N=2,019 AF patients (mean (SD) age=82.9 (7.5) years; 57% females): phenotype 1 (relatively fit, few comorbidities; n=672, 33%), phenotype 2 (functionally impaired, polypharmacy, comorbidities; n=685, 34%), phenotype 3 (multidimensional frailty, comorbidities; n=161, 8%), and phenotype 4 (relatively fit, polypharmacy, comorbidities; n=501, 25%). Compared to phenotype 1, 12-month mortality was higher in phenotype 3 (adjusted hazard ratio (aHR):4.68, 95%CI=3.41-6.43), phenotype 2 (aHR:1.98, 95%CI=1.53-2.57), and phenotype 4 (aHR:1.44, 95%CI=1.07-1.94).In a cohort of older AF patients, LCA identified four MPI domain phenotypes with different risk of mortality. Pending confirmatory studies, the identified sub-groups might allow more targeted interventions to improve outcomes in this population.CONCLUSIONIn a cohort of older AF patients, LCA identified four MPI domain phenotypes with different risk of mortality. Pending confirmatory studies, the identified sub-groups might allow more targeted interventions to improve outcomes in this population. |
Author | Ng, Huah Shin Veronese, Nicola Pilotto, Alberto Mangoni, Arduino A. Woodman, Richard |
Author_xml | – sequence: 1 givenname: Huah Shin orcidid: 0000-0001-8381-5253 surname: Ng fullname: Ng, Huah Shin organization: Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, Australia – sequence: 2 givenname: Richard surname: Woodman fullname: Woodman, Richard organization: Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, Australia – sequence: 3 givenname: Nicola surname: Veronese fullname: Veronese, Nicola organization: Saint Camillus International University of Health Sciences, Rome, Italy – sequence: 4 givenname: Alberto surname: Pilotto fullname: Pilotto, Alberto organization: Geriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genova, Italy – sequence: 5 givenname: Arduino A. orcidid: 0000-0001-8699-1412 surname: Mangoni fullname: Mangoni, Arduino A. email: arduino.mangoni@flinders.edu.au organization: Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, Australia |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40368294$$D View this record in MEDLINE/PubMed |
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Keywords | MNA IADL ESS AF Atrial fibrillation Mortality MPI ADL aHR LCA Latent class analysis CIRS-CI Frail older adults Multidimensional prognostic index SPMSQ |
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Snippet | The multidimensional prognostic index (MPI), an established tool to predict adverse outcomes, classifies frailty using an aggregate-weighted tripartite scoring... The Multidimensional Prognostic Index (MPI), an established tool to predict adverse outcomes, classifies frailty using an aggregate-weighted tripartite scoring... |
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Title | Multidimensional prognostic index domain phenotypes identified using latent class analysis and mortality in the European Study of Older Subjects With Atrial Fibrillation (EUROSAF) |
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