Could clustering of comorbidities be useful for better defining the internal medicine patients’ complexity?

Tangianu, Flavio; Gnerre, Paola; Colombo, Fabrizio; Frediani, Roberto; Pinna, Giuliano; Berti, Franco; Mathieu, Giovanni; Regina, Micaela La; Orlandini, Francesco; Mazzone, Antonino; Canale, Clelia; Borioni, D; Nardi, Roberto

doi:10.4081/itjm.2018.940

Cited by 8 publications

(11 citation statements)

References 26 publications

(29 reference statements)

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“…The analysis of multimorbidity patterns constitutes an effective and appropriate tool to model and assess the complexity of patients in terms of the diversity and statistical co-occurrences of their health conditions [15]. The literature on multimorbidity patterns was reviewed in 2014 by Pradros-Torres et al and in 2019 by Busija et al, and several methods and population contexts were identified [16,17].…”

Section: Introductionmentioning

confidence: 99%

Clustering of a Health Dataset Using Diagnosis Co-Occurrences

et al. 2021

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Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the healthcare needs of their population. This paper proposes a novel hierarchical agglomerative clustering algorithm based on multimorbidity analysis. The proposed approach constructs the clustering dendrogram by introducing new quality indicators based on the relative risk of co-occurrences of patient diagnoses. This algorithm enables the detection of multimorbidity patterns by merging similar patient profiles according to their common diagnoses. The multimorbidity approach has been applied to the data of the largest ED of the Aube Department (Eastern France) to cluster its patient visits. Among the 120,718 visits identified during a 24-month period, 16 clusters were identified, accounting for 94.8% of the visits, with the five most prevalent clusters representing 63.0% of them. The new quality indicators show a coherent and good clustering solution with a cluster membership of 1.81 based on a cluster compactness of 1.40 and a cluster separation of 0.77. Compared to the literature, the proposed approach is appropriate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

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Section: Introductionmentioning

confidence: 99%

Clustering of a Health Dataset Using Diagnosis Co-Occurrences

et al. 2021

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“…Our study applied a novel method of analysing multimorbities by clustering the general population of ED patients. To the best of our knowledge; and based on the review of Padros-Torres et al [22], where most studies focus on complex patient and chronic diseases [21][22][23][24][25], this is the first time that analysis of multimorbidity patterns has been used to cluster healthcare visits in a temporal analysis, and in this specific population . With our novel method, we extend the usage of theses methods to a global population with a low multimorbidity context and with a comprehensive clustering that was able to classify the vast majority of ED visits (92.93%).…”

Section: Discussionmentioning

confidence: 99%

“…In this regard, the analysis of multimorbidity patterns is an appropriate tool to model the complexity of patients in terms of the diversity and statistical co-occurrences of their health conditions [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Multimorbidity Clustering of the Emergency Department Patient Flow: Impact Analysis of New Unscheduled Care Clinics

Wartelle

Mourad-Chehade

yalaoui

et al. 2020

Preprint

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Background: In France, the number of admissions to emergency departments doubled between 1996 and 2016, leading to overcrowding. To cope with the resultant overcrowding, redirecting patients to new healthcare services is a viable solution, to spread demand more evenly across available healthcare delivery points, and render care more efficient. The goal of this study was to analyse the impact of opening new unscheduled care services on variations in patient attendance at a large emergency department. Methods: We performed a before-and-after study investigating the use of unscheduled care services in the Aube Department (Eastern France), focusing on emergency department attendance of Troyes Hospital. We applied a hierarchical clustering based on co-occurrence of diagnoses, to divide the population into different multimorbidity profiles and study their temporal trends. A multivariate logistic regression model was constructed to adjust the period effect for appropriate confounders. Results: In total, 120,718 visits to the emergency department were recorded over a 24-month period (2018-2019), and 14 clusters were identified accounting for 94.76% of all visits. The before-and-after analysis showed a decrease of 57.95 visits per week in 7 specific clusters, while the consumption of unscheduled health care services increased by 328.12 visits per week.Conclusions: Using an innovative and reliable methodology to evaluate changes in patient flow through the emergency department, our results could help to inform public health policy regarding the implementation of unscheduled care services, to ease pressure on emergency departments.

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“…It is, therefore, worth considering a larger number of principal components in the analysis to explain a larger part of the data variability. Almost all the studies which have examined specific comorbidities start from a specific disease rather than examining all the co-occurring clinical and medical conditions [27,28]. Nosology clusters groups of diseases, disorders, or syndromes with meaningful associations into a type of classification, so that diseases, for example, within a cluster, are very similar to one another, but are dissimilar to diseases in other clusters [29].…”

Section: Overviewmentioning

confidence: 99%

“…To thoroughly analyze the data and identify the MRPs leading to adverse health outcomes-such as rehospitalization, nonreturn home, and early death [40,41]-among older adult inpatients, a literature review was conducted [27].…”

Section: Two-step Clustering Frameworkmentioning

confidence: 99%

Transforming a Patient Registry Into a Customized Data Set for the Advanced Statistical Analysis of Health Risk Factors and for Medication-Related Hospitalization Research: Retrospective Hospital Patient Registry Study

Taushanov¹,

Verloo²,

Wernli³

et al. 2021

JMIR Med Inform

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Background Hospital patient registries provide substantial longitudinal data sets describing the clinical and medical health statuses of inpatients and their pharmacological prescriptions. Despite the multiple advantages of routinely collecting multidimensional longitudinal data, those data sets are rarely suitable for advanced statistical analysis and they require customization and synthesis. Objective The aim of this study was to describe the methods used to transform and synthesize a raw, multidimensional, hospital patient registry data set into an exploitable database for the further investigation of risk profiles and predictive and survival health outcomes among polymorbid, polymedicated, older inpatients in relation to their medicine prescriptions at hospital discharge. Methods A raw, multidimensional data set from a public hospital was extracted from the hospital registry in a CSV (.csv) file and imported into the R statistical package for cleaning, customization, and synthesis. Patients fulfilling the criteria for inclusion were home-dwelling, polymedicated, older adults with multiple chronic conditions aged ≥65 who became hospitalized. The patient data set covered 140 variables from 20,422 hospitalizations of polymedicated, home-dwelling older adults from 2015 to 2018. Each variable, according to type, was explored and computed to describe distributions, missing values, and associations. Different clustering methods, expert opinion, recoding, and missing-value techniques were used to customize and synthesize these multidimensional data sets. Results Sociodemographic data showed no missing values. Average age, hospital length of stay, and frequency of hospitalization were computed. Discharge details were recoded and summarized. Clinical data were cleaned up and best practices for managing missing values were applied. Seven clusters of medical diagnoses, surgical interventions, somatic, cognitive, and medicines data were extracted using empirical and statistical best practices, with each presenting the health status of the patients included in it as accurately as possible. Medical, comorbidity, and drug data were recoded and summarized. Conclusions A cleaner, better-structured data set was obtained, combining empirical and best-practice statistical approaches. The overall strategy delivered an exploitable, population-based database suitable for an advanced analysis of the descriptive, predictive, and survival statistics relating to polymedicated, home-dwelling older adults admitted as inpatients. More research is needed to develop best practices for customizing and synthesizing large, multidimensional, population-based registries. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2019-030030

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Could clustering of comorbidities be useful for better defining the internal medicine patients’ complexity?

Cited by 8 publications

References 26 publications

Clustering of a Health Dataset Using Diagnosis Co-Occurrences

Clustering of a Health Dataset Using Diagnosis Co-Occurrences

Multimorbidity Clustering of the Emergency Department Patient Flow: Impact Analysis of New Unscheduled Care Clinics

Transforming a Patient Registry Into a Customized Data Set for the Advanced Statistical Analysis of Health Risk Factors and for Medication-Related Hospitalization Research: Retrospective Hospital Patient Registry Study

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