Polypharmacy Patterns in Multimorbid Older People with Cardiovascular Disease: Longitudinal Study

Villén, Noemí; Roso-Llorach, Albert; Gallego-Moll, Carlos; Danes-Castells, Marc; Fernández-Bertolín, Sergio; Troncoso-Mariño, Amelia; Monteagudo, Mónica; Amado, Ester; Violán, Concepción

doi:10.3390/geriatrics7060141

Cited by 3 publications

(3 citation statements)

References 49 publications

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“…All in all, these clusters may encompass those most burdensome, age-dependent disorders in which the potential to reduce disease burden is proposed to come from primary, secondary and tertiary prevention targeting older people and not only middle-aged adults [39]. Thus, it may also be important to explore the inclusion of further clinical variables that might impact patient management such as geriatric syndromes, frailty or drug prescriptions to the analysis and definition of multimorbidity patterns [40][41][42].…”

Section: Clinical Interpretation Of the Resultsmentioning

confidence: 99%

Trajectories of chronic multimorbidity patterns in older patients: MTOP study

Lleal,

Baré,

Herranz

et al. 2024

BMC Geriatr

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Background Multimorbidity is associated with negative results and poses difficulties in clinical management. New methodological approaches are emerging based on the hypothesis that chronic conditions are non-randomly associated forming multimorbidity patterns. However, there are few longitudinal studies of these patterns, which could allow for better preventive strategies and healthcare planning. The objective of the MTOP (Multimorbidity Trajectories in Older Patients) study is to identify patterns of chronic multimorbidity in a cohort of older patients and their progression and trajectories in the previous 10 years. Methods A retrospective, observational study with a cohort of 3988 patients aged > 65 was conducted, including suspected and confirmed COVID-19 patients in the reference area of Parc Taulí University Hospital. Real-world data on socio-demographic and diagnostic variables were retrieved. Multimorbidity patterns of chronic conditions were identified with fuzzy c-means cluster analysis. Trajectories of each patient were established along three time points (baseline, 5 years before, 10 years before). Descriptive statistics were performed together with a stratification by sex and age group. Results 3988 patients aged over 65 were included (58.9% females). Patients with ≥ 2 chronic conditions changed from 73.6 to 98.3% in the 10-year range of the study. Six clusters of chronic multimorbidity were identified 10 years before baseline, whereas five clusters were identified at both 5 years before and at baseline. Three clusters were consistently identified in all time points (Metabolic and vascular disease, Musculoskeletal and chronic pain syndrome, Unspecific); three clusters were only present at the earliest time point (Male-predominant diseases, Minor conditions and sensory impairment, Lipid metabolism disorders) and two clusters emerged 5 years before baseline and remained (Heart diseases and Neurocognitive). Sex and age stratification showed different distribution in cluster prevalence and trajectories. Conclusions In a cohort of older patients, we were able to identify multimorbidity patterns of chronic conditions and describe their individual trajectories in the previous 10 years. Our results suggest that taking these trajectories into consideration might improve decisions in clinical management and healthcare planning. Trial registration number NCT05717309.

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Section: Clinical Interpretation Of the Resultsmentioning

confidence: 99%

Trajectories of chronic multimorbidity patterns in older patients: MTOP study

Lleal,

Baré,

Herranz

et al. 2024

BMC Geriatr

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“…CLARA (good for scalability due to use of data sampling.) K-means: Use of LCA and K-means to Identify complex patient profiles [ 187 ] K-medoids: Plasma biomarkers identified adults at risk of Alzheimer's disease and related dementias [ 188 ] PAM: Identifying subgroups among Home Health patients with heart failure [ 189 ] CLARA: Identifying ED patient subgroups [ 190 ] Fuzzy C-Means: Polypharmacy patterns in multimorbid older persons with CVD [ 191 ] Fuzzy Compactness and Separation (FCS): Clustering of fMRI data [ 192 ] LCA: Clustering of multimorbidity patterns to examine risk of developing dementia [ 193 ] LDA: Examining trends in Alzheimer's Disease Research using PubMed abstracts [ 194 ] Combinatorial k-means: Clustering of T2DM [ 195 ] Hierarchical Algorithms: Hierarchical, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Clustering Using REpresentatives (CURE), STatistical INformation Grid-based (STING), Spectral, Affinity propagation Specific features: Hierarchical: Each subject is initially considered as being a cluster. Based on similarity (e.g.…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…CLARA: Identifying ED patient subgroups [ 190 ] Fuzzy C-Means: Polypharmacy patterns in multimorbid older persons with CVD [ 191 ]…”

Section: Unsupervised Learningmentioning

confidence: 99%

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

Woodman,

Mangoni

2023

Aging Clin Exp Res

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The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.

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Simultaneous Pharmacokinetic Evaluation of Pantoprazole and Vitamin B Complex for Assessing Drug–Drug Interactions in Healthy Bangladeshi Adults by a Newly Developed and Validated HPLC Method

2023

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The present study has been designed to evaluate the impact of the co-administration of pantoprazole (PNT) with vitamin B (VTB) complex (VTB comprising VTB1, VTB6, and VTB12 in this study) on pharmacokinetic behavior. In this study, HPLC-based sensitive and efficient methods for simultaneous determination in human plasma were developed per US-FDA bioanalytical standards. The pharmacokinetic parameters of PNT, VTB1, VTB6, and VTB12 were also evaluated when the medicines were administered alone and co-administered. Following linearity, it was observed that the plasma PNT, VTB1, VTB6, and VTB12 retention times were 6.8 ± 0.2, 2.7 ± 0.1, 5.5 ± 0.2, and 3.8 ± 0.1 min, respectively, over the range of 1−100 μg/mL. For all analytes at the lower limit of quantification and all other values, intra-assay and inter-assay bias were within 15% and 13.5%, respectively. They barely interacted when PNT and VTB samples were evaluated in physical combinations through in vitro tests. Moreover, in the pharmacokinetics study, treatment with VTB did not significantly alter the pharmacokinetic characteristics of PNT. Therefore, the current work’s results might help assess drug–drug interactions that may be applied to bioequivalence studies and therapeutic drug monitoring.

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Polypharmacy Patterns in Multimorbid Older People with Cardiovascular Disease: Longitudinal Study

Cited by 3 publications

References 49 publications

Trajectories of chronic multimorbidity patterns in older patients: MTOP study

Trajectories of chronic multimorbidity patterns in older patients: MTOP study

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

Simultaneous Pharmacokinetic Evaluation of Pantoprazole and Vitamin B Complex for Assessing Drug–Drug Interactions in Healthy Bangladeshi Adults by a Newly Developed and Validated HPLC Method

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