Trajectories of Chronic Multimorbidity Patterns in Older Patients

Study Overview
=================
Brief Summary
-----------------
Following the MRisk-COVID project, MTOP (Multimorbidity Trajectories in Older Patients) study was developed. It is a retrospective observational study using Real World Data that aims to identify patterns of chronic multimorbidity in patients aged ≥65 years and their evolution and trajectories in the previous 10 years. The secondary objective is to identify the relationship between the trajectories of multimorbidity patterns in the previous 10 years and the severity of the infection by COVID-19.

Detailed Description
-----------------
Multimorbidity is associated with negative results and presents difficulties in clinical management. Recently, new methodologies are emerging based on the hypothesis that chronic conditions are associated in a non-random way forming multimorbidity patterns. However, there are few studies that study the temporal evolution and trajectories of these multimorbidity patterns, which could be associated with different prognoses and could allow better forecasting and planning. The primary objective of this analysis is to identify patterns of chronic multimorbidity in patients aged ≥65 years and their evolution and trajectories in the previous 10 years, using part of the MRisk-COVID project data. As a secondary objective, the investigators want to identify the relationship between the trajectories of multimorbidity patterns in the previous 10 years and the severity of the COVID-19 infection. This retrospective observational study has a historical cohort of 3958 patients ≥65 years of age suspected and confirmed of COVID-19 infection from February 1 to June 15, 2020 in the reference area of Parc Taulí University Hospital. The available data (real-world data) are socio-demographic and diagnostic variables provided by the Data Analytics Program for Research and Innovation in Health (PADRIS), which include sex, age and primary care diagnoses. To identify patterns of multimorbidity, the Clinical Classification Software, Chronic Condition Indicator, multiple correspondence analysis and cluster analysis using the fuzzy c-means algorithm will be used. Then, a clinical consensus process (Delphi-like) will be made of the clusters obtained. To identify the most probable trajectories along the three time points, each patient will be assigned to the cluster with the highest probability of membership. Descriptive and bivariate statistics will be performed.

Official Title
-----------------
Trajectories of Chronic Multimorbidity Patterns in Patients >65 Years Old. MTOP

Conditions
-----------------
Chronic Disease

Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: Positive results of COVID-19 laboratory tests COVID-19 related clinical profile verified by healthcare professionals Exclusion Criteria: Males >90 years (re-identification risk)

Ages Eligible for Study
-----------------
Minimum Age: 65 Years
Maximum Age: 94 Years

Sexes Eligible for Study
-----------------
All

Study Plan
=================
How is the study designed?
-----------------
What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Chronic multimorbidity patterns - Time Frame 1 | Obtained using fuzzy c-means cluster analysis | Baseline (2020) | 
| Chronic multimorbidity patterns - Time Frame 2 | Obtained using fuzzy c-means cluster analysis | 5 years before (2015) | 
| Chronic multimorbidity patterns - Time Frame 3 | Obtained using fuzzy c-means cluster analysis | 10 years before (2010) | 
| Trajectories of chronic multimorbidity patterns | Obtained by assigning the highest probable cluster at each time point | Change over 10 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Severe COVID-19 infection | Severe COVID-19 infection was defined as the occurrence of at least one of the following conditions during any of the registered COVID-19 episodes: severe respiratory affection (including insufficiency, failure, or distress); use of respiratory support (including mechanical ventilation or oxygen therapy); septic shock; multiple organ failure (the combination of respiratory failure and any other organ failure); inflammatory response; admission to intensive care unit; and mortality | From 27-February-2020 to 15-June-2020 | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Multimorbidity, Cluster analysis, Trajectories, Older patient, COVID-19