geriatrics Article Polypharmacy Patterns in Multimorbid Older People with Cardiovascular Disease: Longitudinal Study Noemí Villén 1,2 , Albert Roso-Llorach 2,3 , Carlos Gallego-Moll 3 , Marc Danes-Castells 4,5 , Sergio Fernández-Bertolin 3 , Amelia Troncoso-Mariño 1 , Monica Monteagudo 3 , Ester Amado 1 and Concepción Violán 2,4,6,7, * 1 Medicines Area and Pharmacy Service, Barcelona Territorial Management, Institut Català de la Salut, 08015 Barcelona, Spain 2 Department of Pediatrics, Obstetrics, Gynecology, and Preventive Medicine, Universitat Autònoma de Barcelona, Bellaterra, 08193 Cerdanyola del Vallès, Spain 3 IDIAP Research Institute, 08007 Barcelona, Spain 4 Germans Trias i Pujol Research Institute (IGTP), Camí de les Escoles, s/n, 08916 Badalona, Spain 5 Sant Quirze del Vallès Primary Health Care Center Av. d0 Ègara, s/n, Sant Quirze del Vallès, 08192 Barcelona, Spain 6 North Metropolitan Research Support Unit, IDIAP Research Institute, Mataró, 08303 Barcelona, Spain 7 North Metropolitan Primary Health Care Administration, Institut Català de Salut, Ctra. de Barcelona, 473, Sabadell, 08204 Barcelona, Spain * Correspondence: cviolanf.mn.ics@gencat.cat Abstract: (1) Introduction: Cardiovascular disease is associated with high mortality, especially in older people. This study aimed to characterize the evolution of combined multimorbidity and polypharmacy patterns in older people with different cardiovascular disease profiles. (2) Material and Citation: Villén, N.; Roso-Llorach, A.; methods: This longitudinal study drew data from the Information System for Research in Primary Gallego-Moll, C.; Danes-Castells, M.; Care in people aged 65 to 99 years with profiles of cardiovascular multimorbidity. Combined patterns Fernández-Bertolin, S.; of multimorbidity and polypharmacy were analysed using fuzzy c-means clustering techniques and Troncoso-Mariño, A.; Monteagudo, hidden Markov models. The prevalence, observed/expected ratio, and exclusivity of chronic diseases M.; Amado, E.; Violán, C. Polypharmacy Patterns in and/or groups of these with the corresponding medication were described. (3) Results: The study Multimorbid Older People with included 114,516 people, mostly men (59.6%) with a mean age of 78.8 years and a high prevalence Cardiovascular Disease: of polypharmacy (83.5%). The following patterns were identified: Mental, behavioural, digestive and Longitudinal Study. Geriatrics 2022, 7, cerebrovascular; Neuropathy, autoimmune and musculoskeletal; Musculoskeletal, mental, behavioural, geni- 141. https://doi.org/10.3390/ tourinary, digestive and dermatological; Non-specific; Multisystemic; Respiratory, cardiovascular, behavioural geriatrics7060141 and genitourinary; Diabetes and ischemic cardiopathy; and Cardiac. The prevalence of overrepresented Academic Editor: Phyo Kyaw Myint health problems and drugs remained stable over the years, although by study end, cohort survivors had more polypharmacy and multimorbidity. Most people followed the same pattern over time; the Received: 10 November 2022 most frequent transitions were from Non-specific to Mental, behavioural, digestive and cerebrovascular Accepted: 9 December 2022 and from Musculoskeletal, mental, behavioural, genitourinary, digestive and dermatological to Non-specific. Published: 13 December 2022 (4) Conclusions: Eight combined multimorbidity and polypharmacy patterns, differentiated by sex, Publisher’s Note: MDPI stays neutral remained stable over follow-up. Understanding the behaviour of different diseases and drugs can with regard to jurisdictional claims in help design individualised interventions in populations with clinical complexity. published maps and institutional affil- iations. Keywords: polypharmacy; multimorbidity; elderly; cardiovascular disease; chronic disease; combined patterns; clustering; primary healthcare Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. 1. Introduction This article is an open access article distributed under the terms and The ageing population has emerged as a major and growing challenge for public conditions of the Creative Commons healthcare systems, with the population aged over 60 years expected to double between Attribution (CC BY) license (https:// 2020 and 2050, and the population over 80 years of age expected to triple [1]. creativecommons.org/licenses/by/ Cardiovascular disease (CVD) is one of the primary causes of morbidity and mortality 4.0/). and mainly affects people over 70 years of age [2,3]. It was responsible for 17.9 million Geriatrics 2022, 7, 141. https://doi.org/10.3390/geriatrics7060141 https://www.mdpi.com/journal/geriatrics Geriatrics 2022, 7, 141 2 of 19 deaths worldwide in 2019 (32% of global mortality), representing the number one cause of death in both men and women [2–4]. CVD is associated with multimorbidity that leads to polypharmacy (that is, actively using five or more prescription drugs), especially in people over 65 years of age, who are also more susceptible to drug-related safety problems [5–7]. The increased use of medication has an impact on medication-related outcomes [8] as well as on morbidity, hospitalizations, mortality, and health system expenditure [6,9–11]. In addition, the pharmacokinetics and pharmacodynamics of drugs are different in older compared to younger adults due to the accumulation of cellular and molecular damage, which can make organs such as the kidneys, liver, heart, or lungs more vulnerable [12,13]. Thus, polypharmacy has been identified as a priority for health systems, especially in older people [14]. At the same time, numerous studies have identified consistent patterns of cardiovas- cular multimorbidity, regardless of population characteristics and the analytical technique used [15–23]. Our group previously found that among the main patterns of multimorbidity, those dominated by CVD were associated with more advanced age along with greater mortality, polypharmacy, comorbidity, and alterations in kidney and liver function [19]. The cases that fit into these patterns were also the ones associated with the most visits to primary health care (PHC) [19,20]. Therefore, substantial health system resources are required to meet the needs of people with patterns of cardiovascular multimorbidity. Interventions for preventing and treating the main cardiovascular risk factors have contributed to a decrease in cardiovascular mortality in Europe, although this is still considered insufficient [5,24]. However, people with CVD also have other comorbidities requiring pharmacological management. The joint prescription of drugs indicated for CVD and other comorbidities hinders optimal disease management in these people and favors side effects produced by polypharmacy [25]. The literature mainly analyses polypharmacy descriptively, in terms of drugs used in different multimorbidity patterns, and researchers focus almost exclusively on either multimorbidity or polypharmacy patterns, without simultaneously considering both. Med- ication is considered a proxy variable for the disease, so analysing multimorbidity and polypharmacy together can lead to overestimation errors. However, analysing multimor- bidity according to the drugs indicated for treating each disease can help identify subgroups of patients who need personalized clinical management. Therefore, a joint approach to managing both disease and polypharmacy is needed; this must be personalized and appro- priate for each multimorbidity and polypharmacy pattern in the subgroup of older people with CVD. CVDs are chronic diseases, so they need to be analysed longitudinally, using contin- uous or repeated measurements that help characterize groups of individuals over time, according to the diseases they have, and the drugs indicated for their management. Lon- gitudinal designs are thus especially useful for evaluating the temporal relationship that risk factors have with disease development and treatment outcomes [26], including, for example, the relationship(s) between different diseases and drugs in people with CVD. Although there are some clinical practice guidelines with recommendations on mul- timorbidity and polypharmacy, they do not consider the previous medication prescribed over time and how it can influence patient management [10,25,27]. Knowing how diseases cluster in this population and the specific medications that are used continuously and together can contribute to better health care. This study aims to identify and analyse the evolution of combined multimorbidity and polypharmacy patterns in people aged 65 years and older with CVD in Catalonia (Spain), between 2012 and 2016. 2. Materials and Methods 2.1. Design, Setting, and Population This longitudinal study was performed in Catalonia (Spain), a Mediterranean region with an estimated population of 7,515,398 inhabitants in 2012 (study baseline) [28]. Univer- and polypharmacy patterns in people aged 65 years and older with CVD in Catalonia (Spain), between 2012 and 2016. 2. Materials and Methods Geriatrics 2022, 7, 141 2.1. Design, Setting, and Population 3 of 19 This longitudinal study was performed in Catalonia (Spain), a Mediterranean region with an estimated population of 7,515,398 inhabitants in 2012 (study baseline) [28]. Uni- versal sal healthhealth coverage coverage is organized is organized throughthrough the Spanish the Spanish National National HealthHealth ServiceService and fi- and financed nanced primarily by tax revenue. In Catalonia, the Catalan Health Institute primarily by tax revenue. In Catalonia, the Catalan Health Institute is the main public is the main public healthcare healthcare provider provider and managesand285manages PHC centres,285 which PHC centres, which 5,501,784 covered around covered people around 5,501,784 (77.2% people population) of Catalan (77.2% of Catalan at studypopulation) at study baseline [29]. baseline [29]. Inclusioncriteria Inclusion criteriawere werepeople peopleaged aged65 65toto99 99years yearsininDecember December20112011who whowerewerealive alive ininDecember December 2012, 2012, had at last last one one visit visittotopublic publicPHC PHCduring duringthe five-year the five-yearstudy studyperiod pe- (2012–2016), were registered in the Information System for Research in Primary riod (2012–2016), were registered in the Information System for Research in Primary Care Care (SID- IAP), andand (SIDIAP), had had beenbeen identified as belonging identified to two to as belonging baseline cardiovascular two baseline multimorbidity cardiovascular multi- patterns inpatterns morbidity a previous in a study [19].study previous No new [19].entries No new were allowed entries wereinallowed the study. in theDropouts study. Dropouts were duewere due to mortality to mortality or transferor totransfer another to another provider. healthcare healthcareAprovider. A totalindi- total of 114,516 of 114,516 vidualsindividuals wereatincluded were included baseline,atand baseline, 72,091and 72,091 completed completed follow-up follow-up (Figure 1). (Figure 1). Figure1.1.Estimated Figure Estimatedpopulation populationstudy studyaccording accordingtotoselection selectioncriteria. criteria. 2.2. 2.2.Data DataSource Source The The SIDIAPdatabase SIDIAP databasehashasheld anonymized held anonymized electronic electronichealth records health (EHR), records including (EHR), includ- from public PHC centres, since 2006 and was the source for all study variables. ing from public PHC centres, since 2006 and was the source for all study variables. The database The includes databaseEHRs fromEHRs includes primaryfrom and secondary primary and health secondarycare,health with data care,on demographics, with data on de- socioeconomic status, symptoms, mographics, socioeconomic diagnoses, status, blood symptoms, test results, diagnoses, drug blood testprescriptions, results, drug and pre- drug invoice information [30]. scriptions, and drug invoice information [30]. 2.3. Variables 2.3. Variables 2.3.1. Chronic Diseases and Multimorbidity 2.3.1. Chronic Diseases and Multimorbidity Diseases in the SIDIAP database are coded according to the International Classification Diseases of Diseases, in the 10th SIDIAP revision databaseAn (ICD-10). are operational coded according to theof definition International Classifica- multimorbidity was tion of Diseases, 10th revision (ICD-10). An operational definition of multimorbidity adopted, as described by the Swedish National study of Ageing and Care in Kungsholmen was adopted, as described by the Swedish National study of Ageing and Care in Kungsholmen (SNAC-K) [31]. Multimorbidity was defined as the presence of two or more chronic diseases (SNAC-K) included [31].60Multimorbidity in the selected chronicwas defined disease as the presence of two or more chronic dis- categories. eases included in the 60 selected chronic disease categories. 2.3.2. Drugs and Classification Drugs registered in the SIDIAP database included medicines dispensed in community pharmacies and invoiced through the National Health Service. Drugs received in hospital and/or dispensed by a hospital pharmacy, together with all other drugs not publicly subsidized, were excluded from the study. Drugs were coded according to the Anatomical Therapeutic Chemical (ATC) Classification System [32], which categorizes drugs into increasingly specific groups according to the targeted organ/system and their chemical, pharmacological, and therapeutic properties. Altogether, 89 drug categories used in at least 1% of the study population at baseline were included. Chronic use was defined Geriatrics 2022, 7, 141 4 of 19 as recorded invoices for three or more packages of the same drug category during the study period. Each drug category was included as an individual dichotomous variable, and polypharmacy was likewise defined via a dichotomous variable as chronic use by the same individual of five or more different drug categories (ATC 4th level) out of the 89 drug categories. 2.3.3. Drug Groups and Chronic Disease Mapping All 89 drug categories were mapped onto the 60 SNAC-K chronic disease categories for which they are prescribed to treat. Chronic disease-drug categories were then created in the form of dichotomous variables for individuals who were diagnosed with SNAC-K chronic disease categories [33]. Fourteen categories of chronic disease require non-pharmacological treatments or treatment with drugs excluded from this study, so these could not be mapped, leaving 46 SNAC-K chronic disease categories mapped to drug categories, plus 14 chronic disease groups. Only 41 of these with a median prevalence of at least 2% over follow-up were analysed (see Supplementary Table S1). 2.3.4. Analytical Variables Over the five years of follow-up, EHRs were reviewed to identify alterations in blood test results, based on the reference values defined by Catalan Health Institute labora- tories. The variables analysed were related to: kidney function—creatinine, estimated glomerular filtration rate (eGFR); liver function—aspartate aminotransferase (AST or GOT), alanine aminotransferase (ALT or GPT), gamma-glutamyltransferase (GGT), alkaline phos- phatase (AP); cardiovascular risk factors—total cholesterol, blood glucose, glycosylated haemoglobin (HbA1c); proteins such as alpha-1-antitrypsin (AAT) or albumin; and vari- ables such as haemoglobin (Supplementary Table S2). 2.3.5. Demographic and Socioeconomic Variables Demographic data, such as data on chronic drugs and diseases, were collected at baseline and monitored over follow-up. The following variables were analysed: mean age, sex (dichotomized as men or women), number of visits to PHC, and socioeconomic status. This last variable was categorized according to the Spanish MEDEA index, which classifies people into quintiles according to the level of socioeconomic deprivation in their urban area of residence (higher score = more deprivation), with a specific category for rural areas [34]. 2.4. Statistical Analysis The characteristics of included people were described at baseline and at each year of follow-up. Quantitative variables were described as means (standard deviation, SD) or medians (interquartile range, IQR), as appropriate, and categorical variables as frequency and percentage. The combined multimorbidity and polypharmacy patterns were identified using a two-step approach. First, the dataset was pre-processed through a principal component analysis for categorical and continuous data (PCAmix), reducing the size and maintaining the original longitudinal data complexity [35]. To prevent statistical noise and spurious findings, only groups of diseases/drugs that achieved a median prevalence of 2% across all follow-up waves were included (Supplementary Table S1). To select the optimal number of dimensions, the Karlis–Saporta–Spinaki rule was used [36]. For the initial clustering of participants, combined patterns based on dis- ease/drug groups, age and sex were initially characterized through fuzzy c-means cluster- ing [37]. Next, a hidden Markov model (HMM) was applied to characterize the temporal evolution of individuals and clusters. Sequential individual observations were assumed to follow a dynamic random process, and each cluster was associated with a hidden state. The set of HMM parameters was estimated by applying the Baum-Welch algorithm, and the best cluster was defined by means of the Viterbi algorithm [38]. The best mathematical ease/drug groups, age and sex were initially characterized through fuzzy c-means clus- tering [37]. Next, a hidden Markov model (HMM) was applied to characterize the temporal evo- lution of individuals and clusters. Sequential individual observations were assumed to follow a dynamic random process, and each cluster was associated with a hidden state. Geriatrics 2022, 7, 141 5 of 19 The set of HMM parameters was estimated by applying the Baum-Welch algorithm, and the best cluster was defined by means of the Viterbi algorithm [38]. The best mathematical model was selected after 100 iterations with different initializations for each number of model was clusters. Theselected researchafter team100 iterations decided on thewith different final numberinitializations of clusters byfor each number making a compro- of clusters. The research team decided on the final number of mise between the best log likelihood and the clinical significance. clusters by making a compromise between the best Patterns werelog likelihood described and the in terms of clinical significance. prevalence, observed/expected ratios (O/E ratios) and exclusivity of chronic diseases. O/E ratios wereobserved/expected Patterns were described in terms of prevalence, ratiosdisease/drug calculated by dividing (O/E ratios) and exclusivity of chronic diseases. O/E ratios were calculated by dividing group prevalence in the specific cluster by the disease prevalence in the total study pop-disease/drug group prevalence ulation. Exclusivityin the wasspecific cluster by determined by the diseasethe dividing prevalence number in ofthe total study patients withpopula- a dis- tion. Exclusivity was determined by dividing the number of patients with a disease/drug ease/drug group in a specific cluster by the overall population with the same disease. Dis- group in agroups ease/drug specificwere cluster by the overall and overrepresented population with the characterizing same the disease. cluster whenDisease/drug the O/E-ratio was over 2. Exclusivity values were considered to be a complementary approachwas groups were overrepresented and characterizing the cluster when the O/E-ratio [19].over 2. Exclusivity values were considered to be a complementary approach [19]. Patterns were compared according to the distribution of sociodemographic, clinical, Patterns were compared according to the distribution of sociodemographic, clinical, and health services variables at baseline and study end. Furthermore, laboratory test val- and health services variables at baseline and study end. Furthermore, laboratory test values ues were also compared by patterns, using the chi-square test. were also compared by patterns, using the chi-square test. Analyses were carried out using R (version 4.2.1, R Foundation for Statistical Com- Analyses were carried out using R (version 4.2.1, R Foundation for Statistical Comput- puting, Vienna, Austria). The significance level was set at α = 0.05. ing, Vienna, Austria). The significance level was set at α = 0.05. 3. 3. Results Results A total of A total of 72,091 72,091 people people (63%) (63%) completed completed follow-up, follow-up, and 39,365 people and 39,365 people (34%) (34%) died died (Figure 2). (Figure 2). Figure 2. Longitudinal flow chart of study period (year 2012–2016; N = 114,516 persons). At baseline, baseline, the thepeople peopleincluded includedwere were mostly mostly menmen (59.6%), (59.6%), withwith a mean a mean age78.8 age of of 78.8 years (SD 7.4) and a high prevalence (83.5%) of polypharmacy. At baseline years (SD 7.4) and a high prevalence (83.5%) of polypharmacy. At baseline versus study versus studyrespectively, end, end, respectively, participants participants had a median had a median of 7-11) of 9 (IQR 9 (IQR 7-11)10 versus versus (IQR 10 (IQR 9-13) 9-13) chronic chronic diseases, diseases, wereataking were taking median a median of 8 (IQRof6-11) 8 (IQR 6-11) medications medications (stable (stable over theover studythe study period) period) andPHC and visited visited PHC annually annually a medianaof median 17 (IQRof9-28) 17 (IQR 9-28) versus 18versus 18 (IQR (IQR 10-30) 10-30) times times (Table 1). (Table 1). The chronic disease groups with the highest prevalence were those related to risk factors and CVD itself, such as hypertension, dyslipidaemia, obesity, diabetes, and heart failure. The prevalence of these diseases increased each year (Supplementary Table S1). With respect to laboratory variables, the ones most frequently requested by physicians were total cholesterol, creatinine, and glycaemia. The percentage of people showing alterations in these indicators increased over follow-up, with glycaemia standing out as the variable with the highest number of people with one or more altered results (Table 2). Included patients were grouped into 8 combined multimorbidity and polypharmacy patterns. The prevalence, O/E-ratio and exclusivity of the diseases, drugs and patterns for each study year are presented in Supplementary Table S3. Patterns dominated by women included Mental, behavioural, digestive and cerebrovascular (62.4% women). Mental disorders, including dementia (almost exclusive to this pattern) were overrepresented (O/E-ratio > 2). Patients in this group were older and somewhat Geriatrics 2022, 7, 141 6 of 19 deprived. Likewise, 61.1% of the patients in the Neuropathy, autoimmune and musculoskeletal pattern were women. This pattern was characterized by a high prevalence of obesity and musculoskeletal disease, autoimmune diseases, and peripheral neuropathy. It was the pattern with the most polymedicated people and the second-youngest population. Finally, the Musculoskeletal, mental, behavioural, genitourinary, digestive and dermatological pattern comprised 58.6% women. This group was made up of patients with mainly rheumatic and psychiatric disorders and was characterized by a high number of diseases and medications (Tables 1 and 3; Supplementary Table S4). Most patients belonging to other patterns were men. In the Non-specific pattern (57.6% men), no disease, drug, or association was overrepresented with respect to the other patterns; it mainly includes chronic diseases that do not require pharmacological treatment, such as diseases related to the organs and senses, associated with senility and a slow disease course. This pattern was characterized by younger participants with less polypharmacy. The Multisystemic pattern was made up of 67.7% men. This group contained people with very varied diseases, especially digestive and chronic inflammatory diseases, and showed the highest levels of eGFR alterations. The Respiratory, cardiovascular, behavioural and genitourinary pattern was almost exclusively (99.2%) made up of men. It presents a high O/E-ratio and exclusivity of respiratory disease, tobacco use, and prostate disease, with the youngest population out of all the patterns. In addition, the Diabetes and ischemic cardiopathy pattern (66.1% men) is characterized by diabetes and ischemic heart disease. Key features include a high prevalence of obesity as well as blood glucose and Hb1A1c abnormalities (Table 2). Finally, the Cardiac pattern, made up of people mostly affected by CVD, showed a similar distribution between sexes. Vascular diseases were found almost exclusively in this group, which generated the most visits to PHC out of all the patterns. Over follow-up, the diseases and drug treatments characterizing the people grouped in each pattern remained stable, with a similar or slightly lower O/E-ratio value. Supplementary Table S4 shows the transition probability matrix between patterns. The transition matrix diagonal shows that a high proportion of patients remained in their baseline pattern throughout the follow-up period. Permanence in the same pattern was highest in the Cardiac pattern (67.27%) and lowest in the Mental, behavioural, digestive and cerebrovascular pattern (33.3%). The highest mortality was observed in Mental, behavioural, digestive, and cerebrovascular (60.2%) and Multisystemic (42.08%). Figure 3 shows transitions and trajectories in the individual longitudinal sequences, sorted according to the initial pattern and the number of patients in each. Most trajectories consisted of a single transition between two clusters. The most frequent transitions were to the death and dropout clusters (Supplementary Table S4). The most frequent transitions were from Musculoskeletal, mental, behaviour, genitourinary, digestive and dermatological to Non-specific (4.5%) and from Non- specific to Mental, behavioural, digestive and cerebrovascular (3.9%) (Supplementary Table S4). Geriatrics 2022, 7, 141 7 of 19 Table 1. Variables characterizing each cluster in the study at baseline and at the end of the study (N = 114,516). Musculoskeletal, Mental, Respiratory, Mental, Neuropathy, Diabetes and Behavioural, Cardiovascular, Behavioural, Cardiac Non-Specific Autoimmune and Multisystemic Ischemic Overall Digestive and Behavioural and Genitourinary, Musculoskeletal Cardiopathy Variables Cerebrovascular Genitourinary Digestive and Dermatological N2012 = 14,533 N2012 = 39,530 N2012 = 8937 N2012 = 5002 N2012 = 5929 N2012 = 12,270 N2012 = 19,601 N2012 = 8714 N2012 = (12.7%)–N2016 = (34.5%)–N2016 = (7.8%)–N2016 = (4.4%)–N2016 = (5.2%)–N2016 = (10.7%)–N2016 = (17.1%)–N2016 = (7.6%)–N2016 = 114,516–N2016 = 11,145 (15.5%) 17,749 (24.6%) 4757 (6.6%) 4393 (6.1%) 5031 (7.0%) 10,024 (13.9%) 12,122 (16.8%) 6870 (9.5%) 72,091 Sex: 16,751 46,256 7116 (49.0%)–5524 5574 (62.4%)–2925 3054 (61.1%)–2548 1914 (32.3%)–1554 95 (0.77%)–96 6644 (33.9%)–3637 5108 (58.6%)–3896 Female (42.4%)–6917 (40.4%)–27,097 (49.6%) (61.5%) (58.0%) (30.9%) (0.96%) (30.0%) (56.7%) (39.0%) (37.6%) 22,779 12,175 68,260 7417 (51.0%)–5621 3363 (37.6%)–1832 1948 (38.9%)–1845 4015 (67.7%)–3477 12,957 (66.1%)–8485 3606 (41.4%)–2974 Male (57.6%)–10,832 (99.2%)–9928 (59.6%)–44,994 (50.4%) (38.5%) (42.0%) (69.1%) (70.0%) (43.3%) (61.0%) (99.0%) (62.4%) 14,533 39,524 122,70 114,510 Patients with 8937 (100%)–4757 5002 (100%)–4393 5929 (100%)–5031 19,601 (100%)–12,122 8714 (100%)–6870 (100%)–11,145 (100.0%)–17,734 (100%)–10,024 (100.0%)–72,076 Multimorbidity: (100%) (100%) (100%) (100%) (100%) (100%) (99.9%) (100%) (100.0%) Chronic 8.00 [7.00; 7.00 [6.00; 10.0 [8.00; 11.0 [9.00; 11.0 [9.00; 9.00 [7.00; 11.0 [9.00; 9.00 [7.00; 9.00 [8.00; 11.0]–11.0 diseases number 10.0]–10.0 [8.00; 9.00]–9.00 [7.00; 11.0]–11.0 [9.00; 13.0]–13.0 [11.0; 13.0]–13.0 [11.0; 11.0]–11.0 [9.00; 12.0]–12.0 [10.0; 11.0]–10.0 [9.00; [9.00; 12.0] (median, IQR): 12.0] 10.0] 13.0] 15.0] 15.0] 13.0] 14.0] 13.0] 12,961 25,588 10,840 19,013 95,588 Patients with 8546 (95.6%)–4516 4877 (97.5%)–4284 5646 (95.2%)–4848 8117 (93.1%)–6425 (89.2%)–10,003 (64.7%)–11,785 (88.3%)–9043 (97.0%)–11,812 (83.5%)–62,716 Polypharmacy: (94.9%) (97.5%) (96.4%) (93.5%) (89.8%) (66.4%) (90.2%) (97.4%) (87.0%) 8.00 [6.00; 6.00 [3.00; 10.0 [7.00; 11.0 [8.00; 10.0 [8.00; 8.00 [6.00; 9.00 [7.00; 8.00 [6.00; Drugs number 9.00 [7.00; 12.0]–10.0 10.0]–8.00 [6.00; 8.00]–6.00 [4.00; 12.0]–9.00 [7.00; 13.0]–11.0 [9.00; 13.0]–10.0 [8.00; 11.0]–9.00 [6.00; 12.0]–9.00 [7.00; 11.0]–8.00 [6.00; (median, IQR): [8.00; 12.0] 10.0] 8.00] 11.0] 13.0] 13.0] 11.0] 12.0] 11.0] Age (Mean, SD): 76.3 (6.4)–75.2 (6.0) 80.6 (7.5)–78.4 (7.2) 84.3 (6.1)–82.1 (5.8) 76.0 (6.2)–75.1 (5.9) 80.6 (6.4)–78.8 (6.1) 74.5 (6.5)–73.5 (6.0) 77.0 (6.8)–75.6 (6.36) 79.2 (6.7)–77.8 (6.4) 78.8 (7.4)–76.8 (6.8) Age (n, %): 15,617 2678 (18.4%)–2417 3931 (9.94%)–2615 150 (1.68%)–127 935 (18.7%)–983 352 (5.94%)– 429 3408 (27.8%)–3165 3304 (16.9%)–2571 859 (9.86%)–845 [65,70) (13.6%)–13,152 (21.7%) (14.7%) (2.67%) (22.4%) (8.53%) (31.6%) (21.2%) (12.3%) (18.2%) 12,539 44,564 7123 (49.0%)–5945 1714 (19.2%)–1382 2540 (50.8%)–2319 2138 (36.1%)–2237 6000 (48.9%)–5097 9046 (46.2%)–6113 3464 (39.8%)–3171 [70,80) (31.7%)–6947 (38.9%)–33,211 (53.3%) (29.1%) (52.8%) (44.5%) (50.8%) (50.4%) (46.2%) (39.1%) (46.1%) Geriatrics 2022, 7, 141 8 of 19 Table 1. Cont. Musculoskeletal, Mental, Respiratory, Mental, Neuropathy, Diabetes and Behavioural, Cardiovascular, Behavioural, Cardiac Non-Specific Autoimmune and Multisystemic Ischemic Overall Digestive and Behavioural and Genitourinary, Musculoskeletal Cardiopathy Variables Cerebrovascular Genitourinary Digestive and Dermatological N2012 = 14,533 N2012 = 39,530 N2012 = 8937 N2012 = 5002 N2012 = 5929 N2012 = 12,270 N2012 = 19,601 N2012 = 8714 N2012 = (12.7%)–N2016 = (34.5%)–N2016 = (7.8%)–N2016 = (4.4%)–N2016 = (5.2%)–N2016 = (10.7%)–N2016 = (17.1%)–N2016 = (7.6%)–N2016 = 114,516–N2016 = 11,145 (15.5%) 17,749 (24.6%) 4757 (6.6%) 4393 (6.1%) 5031 (7.0%) 10,024 (13.9%) 12,122 (16.8%) 6870 (9.5%) 72,091 18,578 46,195 4498 (31.0%)–2710 5356 (59.9%)–2830 1480 (29.6%)–1073 3011 (50.8%)–2195 2685 (21.9%)–1713 6690 (34.1%)–3289 3897 (44.7%)–2671 [80,90) (47.0%)–7212 (40.3%)–23,693 (24.3%) (59.5%) (24.4%) (43.6%) (17.1%) (27.1%) (38.9%) (40.6%) (32.9%) 234 (1.61%)–73 4482 (11.3%)–975 1717 (19.2%)–418 47 (0.94%)–18 428 (7.22%)–170 177 (1.44%)–49 561 (2.86%)–149 494 (5.67%)–183 8140 (7.11%)–2035 [90,99] (0.66%) (5.49%) (8.79%) (0.41%) (3.38%) (0.49%) (1.23%) (2.66%) (2.82%) MEDEA *: 21,558 2961 (22.6%)–2228 7996 (23.0%)–3406 1955 (27.3%)– 980 764 (17.0%)–636 1141 (22.7%)–946 1866 (16.9%)–1461 3368 (19.1%)–2035 1507 (19.7%)–1164 R (21.4%)–12,856 (20.6%) (19.9%) (21.6%) (15.0%) (19.3%) (15.1%) (17.3%) (17.5%) (18.4%) 15,391 1974 (15.1%)–1706 5718 (16.5%)–2786 1255 (17.5%)–824 489 (10.9%)–490 722 (14.4%)–744 1558 (14.1%)–1409 2487 (14.1%)–1690 1188 (15.5%)–1066 U1 (15.3%)–10,715 (15.8%) (16.3%) (18.2%) (11.5%) (15.2%) (14.5%) (14.3%) (16.1%) (15.4%) 15,490 2100 (16.0%)–1797 5412 (15.6%)–2793 1093 (15.3%)–710 656 (14.6%)–633 701 (13.9%)–761 1714 (15.5%)–1565 2638 (15.0%)–1811 1176 (15.4%)–1089 U2 (15.4%)–11,159 (16.6%) (16.3%) (15.7%) (14.9%) (15.6%) (16.1%) (15.4%) (16.4%) (16.0%) 1983 (15.1%)–1668 5638 (16.2%)–2879 1048 (14.6%)–727 748 (16.7%)–738 761 (15.1%)–818 1797 (16.3%)–1609 2953 (16.8%)–2009 1225 (16.0%)–1092 16,153 (16.0%)– U3 (15.4%) (16.8%) (16.1%) (17.4%) (16.7%) (16.6%) (17.0%) (16.5%) 11,540 (16.6%) 16,193 2087 (15.9%)–1772 5203 (15.0%)–2774 948 (13.2%)–657 846 (18.8%)–797 833 (16.6%)–801 1978 (17.9%)–1783 3050 (17.3%)–2113 1248 (16.3%)–1105 U4 (16.1%)–11,802 (16.4%) (16.2%) (14.5%) (18.8%) (16.4%) (18.4%) (17.9%) (16.6%) (16.9%) 16,011 1999 (15.3%)–1631 4751 (13.7%)–2471 866 (12.1%)–631 989 (22.0%)–955 871 (17.3%)–823 2117 (19.2%)–1871 3107 (17.7%)–2133 1311 (17.1%)–1121 U5 (15.9%)–11,636 (15.1%) (14.4%) (13.9%) (22.5%) (16.8%) (19.3%) (18.1%) (16.9%) (16.7%) 25.0 [15.0; 13.0 [7.00; 17.0 [10.0; 21.0 [13.0; 24.0 [14.0; 14.0 [9.00; 19.0 [11.0; 17.0 [9.00; N. of visits 16.0 [10.0; 26.0]–16.0 35.0]–27.0 [18.0; 23.0]–13.0 [7.00; 29.0]–17.0 [8.00; 33.0]–22.0 [13.0; 37.0]–25.0 [14.0; 22.0]–15.0 [9.00; 31.8]–20.0 [12.0; 28.0]–18.0 [10.0; (median, IQR): [10.0; 26.0] 36.0] 23.0] 29.0] 35.0] 39.0] 23.0] 33.0] 30.0] * MEDEA index starts with U1 (urban setting, least deprived) and ends with U5 (urban setting, most deprived). Individuals in rural settings are classified in the variable R. Geriatrics 2022, 7, 141 9 of 19 Table 2. Average of analytical determinations and patients by pattern with at least one adverse result. Musculoskeletal, Mental, Respiratory, Mental, Neuropathy, Diabetes and Behavioural, Cardiovascular, Behavioural, Cardiac Non-Specific Autoimmune and Multisystemic Ischemic All Population Digestive and Behavioural and Genitourinary, (N = 14,533) (N = 39,530) Musculoskeletal (N = 5929) Cardiopathy (N = 114,516) Cerebrovascular Genitourinary Digestive and (N = 5002) (N = 19,601) (N = 8937) (N = 12,270) Dermatological Year of Analy- (N = 8714) Follow- tical Vari- Patients Patients Patients Patients Patients Patients Patients Patients Patients up Patients with Patients with Patients with Patients with Patients with Patients with Patients with Patients with Patients with ables with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- Deter- tered Deter- tered Deter- tered Deter- tered Deter- tered Deter- tered Deter- tered Deter- tered Deter- tered mina- Deter- mina- Deter- mina- Deter- mina- Deter- mina- Deter- mina- Deter- mina- Deter- mina- Deter- mina- Deter- tion mina- tion mina- tion mina- tion mina- tion mina- tion mina- tion mina- tion mina- tion mina- (%) tion (%) tion (%) tion (%) tion (%) tion (%) tion (%) tion (%) tion (%) tion (N, %) (N, %) (N, %) (N, %) (N, %) (N, %) (N, %) (N, %) (N, %) 549 1288 340 222 295 511 856 315 4376 ALB_ser 11.3% 10.9% 19.3% 12.5% 16.1% 10.3% 12.4% 12.0% 12.2% (3.78%) (3.26%) (3.80%) (4.44%) (4.98%) (4.16%) (4.37%) (3.61%) (3.82%) 841 2267 582 381 315 568 972 711 6637 TCHOL 70.8% 64.8% 74.2% 81.1% 74.6% 73.0% 80.4% 73.0% 71.7% (5.79%) (5.73%) (6.51%) (7.62%) (5.31%) (4.63%) (4.96%) (8.16%) (5.80%) 1557 3848 1562 699 1268 1046 3331 833 14,144 CREAT 71.7% 66.0% 75.3% 81.3% 76.9% 73.7% 80.5% 74.1% 72.6% (10.7%) (9.73%) (17.5%) (14.0%) (21.4%) (8.52%) (17.0%) (9.56%) (12.4%) 1034 1963 582 300 499 558 1124 508 6568 ALP 26.2% 21.1% 24.3% 26.7% 29.0% 24.1% 24.4% 25.5% 23.9% (7.11%) (4.97%) (6.51%) (6.00%) (8.42%) (4.55%) (5.73%) (5.83%) (5.74%) 2070 3053 699 550 887 1174 1839 782 11,054 GGT 47.5% 43.3% 49.2% 51.4% 51.9% 49.2% 51.0% 49.6% 47.5% 2012 (14.2%) (7.72%) (7.82%) (11.0%) (15.0%) (9.57%) (9.38%) (8.97%) (9.65%) 6000 13,501 3718 3023 2711 5501 12,696 2953 50,103 GLYC 72.0% 66.5% 75.9% 82.6% 76.9% 74.6% 82.0% 74.3% 73.3% (41.3%) (34.2%) (41.6%) (60.4%) (45.7%) (44.8%) (64.8%) (33.9%) (43.8%) AST 323 599 152 79 157 248 381 121 2060 (or 27.1% 23.6% 26.1% 29.1% 30.2% 27.0% 27.7% 27.2% 26.2% (2.22%) (1.52%) (1.70%) (1.58%) (2.65%) (2.02%) (1.94%) (1.39%) (1.80%) SGOT) ALT 631 1254 235 219 294 634 951 261 4479 (or 63.9% 59.3% 67.9% 72.3% 68.6% 67.4% 70.8% 67.6% 65.1% (4.34%) (3.17%) (2.63%) (4.38%) (4.96%) (5.17%) (4.85%) (3.00%) (3.91%) SGPT) 2745 6298 2304 2200 1422 2936 9714 1104 28,723 HbA1c 33.5% 29.1% 42.6% 59.6% 40.9% 39.2% 64.8% 28.8% 39.8% (18.9%) (15.9%) (25.8%) (44.0%) (24.0%) (23.9%) (49.6%) (12.7%) (25.1%) 3536 8358 3530 1613 2377 1874 7042 2077 30,407 eGFR 58.7% 52.9% 64.0% 69.5% 64.8% 62.2% 68.1% 62.3% 60.1% (24.3%) (21.1%) (39.5%) (32.2%) (40.1%) (15.3%) (35.9%) (23.8%) (26.6%) Geriatrics 2022, 7, 141 10 of 19 Table 2. Cont. Musculoskeletal, Mental, Respiratory, Mental, Neuropathy, Diabetes and Behavioural, Cardiovascular, Behavioural, Cardiac Non-Specific Autoimmune and Multisystemic Ischemic All Population Digestive and Behavioural and Genitourinary, (N = 14,533) (N = 39,530) Musculoskeletal (N = 5929) Cardiopathy (N = 114,516) Cerebrovascular Genitourinary Digestive and (N = 5002) (N = 19,601) (N = 8937) (N = 12,270) Dermatological Year of Analy- (N = 8714) Follow- tical Vari- Patients Patients Patients Patients Patients Patients Patients Patients Patients up Patients with Patients with Patients with Patients with Patients with Patients with Patients with Patients with Patients with ables with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- with ≥1 Al- Deter- tered Deter- tered Deter- tered Deter- tered Deter- tered Deter- tered Deter- tered Deter- tered Deter- tered mina- Deter- mina- Deter- mina- Deter- mina- Deter- mina- Deter- mina- Deter- mina- Deter- mina- Deter- mina- Deter- tion mina- tion mina- tion mina- tion mina- tion mina- tion mina- tion mina- tion mina- tion mina- (%) tion (%) tion (%) tion (%) tion (%) tion (%) tion (%) tion (%) tion (%) tion (N, %) (N, %) (N, %) (N, %) (N, %) (N, %) (N, %) (N, %) (N, %) 713 776 214 323 349 708 815 395 4293 ALB_ser 18.6% 15.7% 25.9% 23.3% 24.2% 19.4% 21.3% 19.4% 19.7% (6.40%) (4.37%) (4.50%) (7.35%) (6.94%) (7.06%) (6.72%) (5.75%) (5.95%) 451 821 261 227 194 321 393 412 3080 TCHOL 75.0% 68.7% 73.8% 82.7% 77.1% 77.0% 81.0% 75.9% 75.4% (4.05%) (4.63%) (5.49%) (5.17%) (3.86%) (3.20%) (3.24%) (6.00%) (4.27%) 1562 1897 830 823 1305 1245 2724 792 11,178 CREAT 77.7% 71.3% 77.0% 84.5% 80.5% 79.7% 82.8% 78.9% 77.9% (14.0%) (10.7%) (17.4%) (18.7%) (25.9%) (12.4%) (22.5%) (11.5%) (15.5%) 861 879 316 372 537 582 845 428 4820 ALP 30.0% 23.2% 23.8% 32.6% 33.9% 29.9% 29.5% 28.1% 28.1% (7.73%) (4.95%) (6.64%) (8.47%) (10.7%) (5.81%) (6.97%) (6.23%) (6.69%) 1883 1524 435 613 905 1116 1298 805 8579 GGT 53.6% 47.1% 49.2% 56.8% 56.9% 56.3% 55.0% 53.1% 52.7% 2016 (16.9%) (8.59%) (9.14%) (14.0%) (18.0%) (11.1%) (10.7%) (11.7%) (11.9%) 4818 6527 1990 2714 2493 4975 8089 2618 34,224 GLYC 77.4% 71.3% 77.0% 84.8% 80.1% 80.1% 83.5% 78.6% 78.0% (43.2%) (36.8%) (41.8%) (61.8%) (49.6%) (49.6%) (66.7%) (38.1%) (47.5%) AST 294 297 85 112 153 252 273 120 1586 (or 26.8% 22.5% 24.0% 28.8% 30.5% 28.6% 28.5% 25.8% 26.4% (2.64%) (1.67%) (1.79%) (2.55%) (3.04%) (2.51%) (2.25%) (1.75%) (2.20%) SGOT) ALT 367 430 117 158 186 410 460 166 2294 (or 69.1% 63.9% 68.4% 74.8% 72.3% 72.3% 72.9% 71.1% 69.6% (3.29%) (2.42%) (2.46%) (3.60%) (3.70%) (4.09%) (3.79%) (2.42%) (3.18%) SGPT) 1956 2568 1156 1742 1183 2509 5698 870 17,682 HbA1c 36.3% 30.4% 42.1% 60.1% 42.8% 43.5% 66.7% 30.5% 42.7% (17.6%) (14.5%) (24.3%) (39.7%) (23.5%) (25.0%) (47.0%) (12.7%) (24.5%) 3951 5288 2301 1975 2706 2635 5888 2346 27,090 eGFR 71.9% 65.3% 72.5% 80.3% 76.3% 74.0% 78.4% 73.3% 72.6% (35.5%) (29.8%) (48.4%) (45.0%) (53.8%) (26.3%) (48.6%) (34.1%) (37.6%) All results have p < 0.05. Analytical variables with ≥ 30% altered determinations are shaded in blue. Abbreviations: AAT: Alpha-1 antitrypsin, ALB_ser: Serum Albumin, TCHOL: Total Cholesterol, CREAT: Cretinine, ALP: Alkaline Phosphatase, GGT: Gamma-Glutamyl Transpeptidase, GLYC: Glycaemia, AST (or SGOT): Aspartate Aminotransferase (serum glutamic-oxaloacetic), ALT (or SGPT): Alanine Transaminase (serum glutamic-pyruvic transaminase), HbA1c: Glycosylated Haemoglobin, eGFR: estimated Glomerular Filtration Rate. Geriatrics 2022, 7, 141 11 of 19 Table 3. Evolution of combined multimorbidity and polypharmacy patterns associated with more mortality. Baseline (2012) End of Follow-Up (2016) Disease or Disease-Drug Disease or Disease-Drug Pattern Prev C1 OE C1 Exc C1 Pattern Prev C1 OE C1 Exc C1 Group Group Dementia 48.96 8.67 67.69 Dementia 57.70 8.02 52.95 Depression mood 48.20 3.54 27.64 Other digestive (D) 17.87 4.10 27.07 Other digestive (D) 10.43 3.42 26.66 Depression mood 49.91 3.12 20.61 Other psychiatric and Other psychiatric and 14.05 2.71 21.17 24.24 2.73 18.03 behavioural behavioural Neurotic, stress and Neurotic, stress and 28.43 2.64 20.61 29.58 2.07 13.66 somatoform somatoform Anaemia 33.21 2.54 19.85 Anaemia 30.29 2.07 13.65 Cerebrovascular 30.18 2.04 15.92 Cerebrovascular 27.50 2.00 13.18 Chronic pancreas, Colitis related diseases 30.97 1.83 14.31 8.66 1.54 10.16 biliary-gallbladder (D) Chronic pancreas, 6.99 1.83 14.25 Colitis related diseases 23.50 1.32 8.71 biliary-gallbladder (D) Mental, Autoimmune 3.70 1.66 12.98 Mental, Chronic kidney 48.48 1.27 8.37 behavioural, behavioural, digestive and Chronic kidney 39.45 1.51 11.79 digestive and Glaucoma 10.51 1.26 8.30 cerebrovascular Sleep 10.53 1.45 11.30 cerebrovascular Autoimmune 3.51 1.17 7.71 Glaucoma 10.09 1.37 10.68 Sleep 14.23 1.13 7.45 Diabetes 39.52 1.19 9.25 Diabetes 38.28 1.09 7.21 Osteoarthritis, degenerative 23.93 1.08 8.42 Thyroid 7.21 1.03 6.83 joint Hypertension 80.13 1.08 8.40 Hypertension 79.40 1.01 6.68 Osteoarthritis, degenerative Osteoporosis 7.65 1.07 8.36 27.10 1.01 6.67 joint Thyroid 5.96 1.06 8.25 Solid neoplasms (D) 23.10 0.97 6.39 Solid neoplasms (D) 19.66 1.04 8.09 Osteoporosis 4.88 0.93 6.15 Cataract lens (D) 25.09 1.02 7.93 Cataract lens (D) 29.68 0.92 6.09 Geriatrics 2022, 7, 141 12 of 19 Table 3. Cont. Baseline (2012) End of Follow-Up (2016) Disease or Disease-Drug Disease or Disease-Drug Pattern Prev C1 OE C1 Exc C1 Pattern Prev C1 OE C1 Exc C1 Group Group Chronic pancreas, Chronic pancreas, 27.74 7.24 37.50 31.01 5.51 38.45 biliary-gallbladder (D) biliary-gallbladder (D) Inflammatory arthropathies 25.48 6.71 34.75 Inflammatory arthropathies 27.83 4.94 34.47 Other respiratory (D) 11.01 4.76 24.63 Other respiratory (D) 13.52 3.37 23.51 Other cardiovascular 17.22 3.94 20.39 Autoimmune 8.75 2.91 20.31 diseases (D) Other cardiovascular Autoimmune 8.58 3.85 19.95 17.21 2.89 20.17 diseases (D) Other digestive (D) 9.36 3.07 15.88 Other digestive (D) 12.03 2.76 19.27 Bradycardias conduction (D) 31.57 2.69 13.95 Bradycardias conduction (D) 38.54 2.33 16.27 Peripheral neuropathy 10.58 2.19 11.32 Dorsopathies 23.63 1.79 12.47 Osteoarthritis, Dorsopathies 19.67 2.12 10.98 46.95 1.75 12.22 degenerative joint Other genitourinary 6.53 2.03 10.48 Peripheral neuropathy 12.72 1.74 12.16 Osteoarthritis, 43.11 1.94 10.06 Other genitourinary 6.98 1.73 12.05 Multisystemic degenerative joint Multisystemic COPD, emphysema, chronic COPD, emphysema, chronic 38.66 1.86 9.64 38.96 1.65 11.53 bronchitis bronchitis Other musculoskeletal joint 15.37 1.85 9.59 Heart failure 68.79 1.63 11.35 Chronic kidney 47.06 1.80 9.33 Anaemia 23.79 1.62 11.34 Allergy 2.78 1.76 9.13 Other musculoskeletal joint 18.64 1.62 11.31 Anaemia 22.43 1.72 8.89 Allergy 4.75 1.61 11.22 Heart failure 62.35 1.71 8.83 Chronic kidney 61.30 1.60 11.20 Other skin (D) 3.17 1.61 8.33 Sleep 18.58 1.47 10.28 Sleep 11.62 1.60 8.28 Colitis related diseases 26.24 1.47 10.28 Colitis related diseases 25.22 1.49 7.73 Atrial fibrillation 47.31 1.34 9.37 Categories highlighted reach the O/E-ratio threshold of two or exclusivity ≥ 30%. Abbreviations: Prev: disease prevalence in the cluster; O/E-ratio: observed/expected ratio; Exc: exclusivity; COPD: chronic obstructive pulmonary; D: Disease category (all other groups correspond to disease-drug). pattern and the number of patients in each. Most trajectories consisted of a single transi- tion between two clusters. The most frequent transitions were to the death and dropout clusters (Supplementary Table S4). The most frequent transitions were from Musculoskel- etal, mental, behaviour, genitourinary, digestive and dermatological to Non-specific (4.5%) and Geriatrics 2022, 7, 141 from Non-specific to Mental, behavioural, digestive and cerebrovascular (3.9%) (Supplementary 13 of 19 Table S4). Figure3.3.Patterns Figure Patternslongitudinal longitudinalsequences sequencesof ofindividuals individualssorted sortedby byinitial initialpattern. pattern. 4. Discussion 4. Discussion This study describes how diseases and medication are grouped in people identified in This study describes how diseases and medication are grouped in people identified previous patterns of cardiovascular multimorbidity. Medication indicated for each disease in previous patterns of cardiovascular multimorbidity. Medication indicated for each dis- was used as a proxy indicator for the presence of that disease, as detailed in a previous ease was used as a proxy indicator for the presence of that disease, as detailed in a previ- study [19]. Fuzzy c-means clustering techniques and hidden Markov models were applied ous study [19]. Fuzzy c-means clustering techniques and hidden Markov models were in a Mediterranean population over a period of five years. applied in a Mediterranean population over a period of five years. The main results of this study, which had a baseline cohort of 72,091 people aged 65 years or older, are as follows: (1) Eight multimorbidity and polypharmacy patterns were identified; (2) people with a history of CVD are subgrouped into different patterns of polypharmacy, depending on the other diseases they have; (3) multimorbidity and polypharmacy patterns are stable over time; most people remained in their baseline cluster, although some patterns were more likely to lead to death. The multimorbidity and polypharmacy patterns identified reflect the medication most frequently administered, remaining stable over five years of follow-up. Chronic diseases such as obesity, kidney disease, hypertension, or dyslipidaemia were highly prevalent among all patterns, but they were not overrepresented in any of them. This is because all the included patients presented cardiovascular risk factors [5]. The most prevalent patterns were those mainly comprising CVDs, with a low preva- lence of other diseases. These patterns were Non-specific, Cardiac, and Diabetes and ischemic cardiopathy. The Cardiac pattern brings together two closely related diseases: atrial fibrillation and heart valve disease. Valve involvement increases the risk of thromboembolism and stroke, especially in older people and in those with associated comorbidities. The medication associated with people who have both diseases is vitamin K antagonist anticoagulants [39], which requires frequent visits to health care services to monitor medication use. The high mortality observed in the Mental, behavioural, digestive and cerebrovascular pattern may be due to patients’ older age and greater multimorbidity and polypharmacy. These factors are interrelated, but also independently increase medication-related problems, worsen health outcomes, and increase mortality [6,9,10,40]. The results obtained can be interpreted in different ways. First, anaemia occurs as a complication of chronic kidney disease, caused by inadequate production of erythropoietin, associated with increased morbidity and mortality [41]. Second, chronic kidney disease has been related to an increase in dementia [42], although the exact mechanism of this relationship is unknown. Third, the overrepresented groups of diseases and medications in this cluster expose its members to various medication groups that act at the level of the central nervous system, such as antidepressants, anxiolytics, hypnotics, antipsychotics, gabapentinoids, and opioid analgesics. The concomitant use of these drugs, especially in older people, increases the Geriatrics 2022, 7, 141 14 of 19 risk of safety problems such as falls, respiratory depression, and death, so it is necessary to identify these people and periodically review their prescriptions to reduce their exposure to these drugs [43,44]. The Neuropathy, autoimmune and musculoskeletal pattern reflects the association be- tween osteoporosis, osteoarthritis, and obesity. Specifically, obesity produces metabolic and inflammatory alterations that contribute to the development of osteoarthritis and osteoporosis, although these mechanisms are complex and not fully understood. Whether osteoporosis is associated with low or high body mass index is still under debate, but it is quite clear that the latter condition increases the risk of developing osteoarthritis. Bone marrow adipocytes have the same precursors as osteoblasts, which are the primary cells involved in bone formation, and the number of bone marrow adipocytes appears to be in- versely related to bone mineral density. Although adipokines released by these adipocytes influence the progression of bone loss, their exact role remains controversial [45]. Phar- macological treatment for autoimmune and musculoskeletal diseases includes systemic corticosteroids, whose side effects include an increase in diabetes, especially in older and obese people [46]. This pattern, together with Diabetes and ischemic cardiopathy, are the ones in which more people present glycaemic alterations. Diabetes secondary to corticosteroids explains why one of the most frequent transitions is from Neuropathy, autoimmune and musculoskeletal to Diabetes and ischemic cardiopathy. Diabetes is associated with more atheroma and increased macrophage infiltration and intraplaque thrombi [47]. It doubles the risk of cardiovascular disease and increases chronic kidney disease [5] helping explain the diseases overrepresented in the Diabetes and ischemic cardiopathy pattern. In the Respiratory, cardiovascular, behavioural and genitourinary pattern, tobacco use is overrepresented (Other psychiatric and behavioural disorders). This risk factor provokes an inflammatory response and causes dysfunction in the cilia and oxidative injury in the pulmonary alveoli. It is responsible for 40% to 70% of cases of chronic obstructive pulmonary disease (COPD), which is more prevalent in men, and its association with increased prostate-specific antigen values and prostatic hyperplasia has been studied [48]. The laboratory parameters presenting the most alterations were those related to dia- betes and kidney disease, two cardiovascular risk factors that figure among the 10 main causes of death [3,5,24]. Among the causes of impaired kidney function are ageing [13], diabetes [5] and the use of anti-inflammatory drugs used for musculoskeletal and inflam- matory diseases. When these are administered together with drugs targeting the renin angiotensin system and diuretics used for hypertension and other CVDs, they can cause acute kidney injury (triple whammy) [49]. Consequently, we observed that the patterns in which these diseases or drugs are overrepresented (Mental, behavioural, digestive and cerebrovascular; Diabetes and ischemic cardiopathy; and Multisystemic) show more alterations in kidney function and higher mortality. The trajectory of the Non-specific pattern presented the most changes over the study period. This result can be explained by the patient profile for this pattern, with younger individuals and fewer overrepresented diseases. As the people included in this cluster age, they acquire a greater burden of disease and more polypharmacy, moving on to more complex patterns. This results in an increase in drugs and diseases. To the best of our knowledge, there are no similar studies to ours, as multimorbidity patterns are mainly studied independently of polypharmacy, so it is not possible to compare our results with those in the literature. Moreover, we did not find any longitudinal studies analysing polypharmacy [50]. Despite the novel approach, the same non-specific and mul- tisystemic patterns identified by other authors, who used different clustering techniques, were replicated here [16,51,52]. This paper analysed people with high mortality who had previously been grouped into multimorbidity patterns dominated by different CVDs, unlike other studies focusing on one cardiovascular disease or type of disease, precluding comparison [53]. Geriatrics 2022, 7, 141 15 of 19 This study has both strengths and limitations. Among the strengths is the use of a large, high-quality database containing primary care records that are representative of the population with multimorbidity and polypharmacy in Catalonia [54]. In addition, we used a clinically oriented and validated methodology to measure chronic diseases, which allows a standardized assessment of chronic diseases in the European Union [31]. The pre-selection of people clustered in CVD patterns with a high mortality risk is a novel aspect that enables a better characterization of disease and polypharmacy subclusters. Finally, the longitudinal design was apt for evaluating the relationship between cardiovascular risk factors, the development of disease, and treatment outcomes over time [26]. Regarding the statistical analysis, different initializations can be considered in the HMM, and there is no guarantee of reaching a global optimal solution, since the HMM obtains a local optimum instead. To minimize this effect and ultimately use the model with the highest probability, we performed 100 Baum-Welch iterations with different initializations. In addition, the HMM performs better over longer periods of time. To address this limitation and increase the reliability of the model, data from all patients for each time interval were used [55]. Among the limitations, the SIDIAP database contains billing information for drugs prescribed in primary and specialized care, but not drugs used or dispensed in hospital, nor those not financed by the public healthcare system. There is also no information on people who transferred to private healthcare providers, although these would represent a minority of patients, and moreover, people receiving private healthcare services often also receive care through the public system. Furthermore, relatively few variables could be analysed, although those of the most interest for studying CVD were available. It is understood that when doctors do not request certain laboratory tests, it is because the patient0 s clinical situation does not demand it, so the absence of laboratory data should not be considered a bias. The results of the study are applicable to the study period. However, recent studies carried out in Catalonia [56,57] and other countries [58] have shown that the incidence in the diagnosis of chronic diseases and cancer has decreased after the pandemic. Knowing how diseases and medications are grouped and evolve in older people with cardiovascular pathology has implications for clinical practice. The results of the study will allow the design of more specific clinical practice guidelines and prevention strategies, leading to a more individualised management of pathologies and safer and more appropriate use of medications. Our work exposes some topics that would be worth to investigate further in the future, suggesting that using fuzzy c-means clustering techniques and Hidden Markov models identified multimorbidity patterns and phenotypes of certain subgroups of patients that were more consistent with clinical practice. First, these results open the door to further research in subgroups of women and in younger patients. Second, our method can be applied to other prevalent diseases to find out how multimorbidity and polypharmacy patterns evolve. 5. Conclusions Using HMM to obtain longitudinal patterns of multimorbidity in people with CVD al- lowed a characterization of this population according to their diseases and pharmacological treatments. This approach was useful for classifying patients into specific subgroups with both CVD and other prevalent diseases. Altogether, eight clinically meaningful clusters were identified based on the diseases and medications included in each. The trajectories of these multimorbidity and polypharmacy patterns were stable over time, with most people remaining in their baseline pattern, although some patterns presented a more frequent evolution towards death. An understanding of the characteristics of these patterns and how they evolve can inform the development and implementation of strategies that contribute to providing these patients with better and more personalized health care. This study opens the door to further research in subgroups of people with high mortality. Moreover, it contributes to Geriatrics 2022, 7, 141 16 of 19 a deeper clinical understanding of multimorbid older people with CVD. The complexity in these patients entails a higher risk of hospital admissions, functional impairments, and mortality, and they could benefit from tailored health and social care [59]. If the associations between diseases and drugs described in this study are considered in clinical practice guidelines and in the management of older patients, its results can contribute to planning and individualising interventions in this population group. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/geriatrics7060141/s1, Table S1: Disease or disease-drug group prevalence in all population; Table S2: Values of laboratory variables determination considered altered; Table S3: Evolution of combined multimorbidity and polypharmacy patterns identified, people included and diseases or diseases-drug group; Table S4: Transition probabilities for Viterbi decoding from start to end of study between patterns. Author Contributions: Conceptualization, N.V. and C.V.; methodology, A.R.-L., S.F.-B., C.G.-M., N.V., A.T.-M. and C.V.; software, S.F.-B., C.G.-M. and A.R.-L.; validation, N.V., A.T.-M., A.R.-L. and C.V.; formal analysis, S.F.-B., C.G.-M. and A.R.-L.; investigation, N.V., A.R.-L., A.T.-M., M.D.-C., M.M., E.A. and C.V.; resources, A.R.-L. and C.V.; data curation, S.F.-B., C.G.-M. and A.R.-L.; writing—original draft preparation, N.V., A.R.-L., M.D.-C. and C.V.; writing—review and editing, N.V., A.R.-L., M.D.-C., A.T.-M., M.M., E.A. and C.V.; visualization, S.F.-B., C.G.-M. and A.R.-L.; supervision, A.R.-L. and C.V.; project administration, C.V.; funding acquisition, C.V. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by a research grant from the Instituto de Salud Carlos III, under the auspices of the Ministry of Science, Innovation and Universities, awarded in the 2016 call under the Health Strategy Action 2013–2016, within the National Research Program oriented to Societal Challenges, within the Technical, Scientific and Innovation Research National Plan 2013–2016 (Grant Number PI16/00639), co-funded with the EU European Regional Development Fund and the Department of Health of the Catalan Government, in the 2017 call that awarded subsidies for the Strategic Plan for Research in Health (Pla Estratègic de Recerca i Innovació en Salut, PERIS) 2016–2020, modality research oriented to primary care (Grant Number SLT002/16/00058) and by the Catalan Government (Grant Number AGAUR 2017 SGR 578). This project has been partially funded by the Carlos III Health Institute (Ministry of Economy and Competitiveness, Spain) through the Network for Prevention and Health Promotion in Primary Care (redIAPP, RD16/0007/0001) and by European Union European Regional Development Fund (ERDF) funds (European Regional Development Fund). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Clinical Research Ethics Committee, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol) (Protocol No: P17/080). All data were anonymized in agreement with national and international law. Informed Consent Statement: Not applicable. Data Availability Statement: The datasets are not available, since researchers signed an agreement with the Information System for the Development of Research in Primary Care (SIDIAP) concerning confidentiality and security of the dataset, which forbids providing data to third parties. The SIDIAP is subject to periodic audits. Acknowledgments: We thank the Catalan Institute of Health and the SIDIAP, which provided the database for the study. Conflicts of Interest: The authors declare no conflict of interest. References 1. World Health Organization. Ageing and Health. Available online: https://www.who.int/news-room/fact-sheets/detail/ageing- and-health (accessed on 15 September 2022). 2. Khan, T. WHO Health Topics: Cardiovascular Diseases; World Health Organization: Geneva, Switzerland. Available online: https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1 (accessed on 15 September 2022). 3. World Health Organization. The Global Health Observatory: Global Health Estimates: Leading Causes of Death; World Health Organization: Geneva, Switzerland, 2022; pp. 1–4. Geriatrics 2022, 7, 141 17 of 19 4. Bots, S.H.; Peters, S.A.E.; Woodward, M. Sex differences in coronary heart disease and stroke mortality: A global assessment of the effect of ageing between 1980 and 2010. BMJ Glob. Health 2017, 2, e000298. [CrossRef] [PubMed] 5. Visseren, F.L.J.; Mach, F.; Smulders, Y.M.; Carballo, D.; Koskinas, K.C.; Bäck, M.; Benetos, A.; Biffi, A.; Boavida, J.-M.; Capodanno, D.; et al. 2021 ESC guidelines on cardiovascular disease prevention in clinical practice. Eur. Heart J. 2021, 42, 3227–3337. [CrossRef] 6. World Health Organization. Medication Safety in Polypharmacy; World Health Organization: Geneva, Switzerland, 2019. 7. Hoel, R.W.; Connolly, R.M.G.; Takahashi, P.Y. Polypharmacy management in older patients. Mayo Clin. Proc. 2021, 96, 242–256. [CrossRef] [PubMed] 8. Troncoso-Mariño, A.; Roso-Llorach, A.; López-Jiménez, T.; Villen, N.; Amado-Guirado, E.; Fernández-Bertolin, S.; Carrasco- Ribelles, L.A.; Borras, J.M.; Violán, C.; Vetrano, L. Medication-related problems in older people with multimorbidity in catalonia: A real-world data study with 5 years’ follow-up. J. Clin. Med. 2021, 10, 709. [CrossRef] [PubMed] 9. Chang, I.T.; Park, H.; Kim, D.W.; Jeon, E.K.; Rhee, C.M.; Kalantar-Zadeh, K.; Kang, E.W.; Kang, S.-W.; Han, S.H. Polypharmacy, hospitalization, and mortality risk: A nationwide cohort study. Sci. Rep. 2020, 10, 18964. [CrossRef] 10. Tamargo, J.; Kjeldsen, K.P.; Delpón, E.; Semb, A.G.; Cerbai, E.; Dobrev, D.; Savarese, G.; Sulzgruber, P.; Rosano, G.; Borghi, C.; et al. Facing the challenge of polypharmacy when prescribing for older people with cardiovascular disease. A review by the European society of cardiology working group on cardiovascular pharmacotherapy. Eur. Heart J. Cardiovasc. Pharm. 2022, 8, 406–419. [CrossRef] 11. Leelakanok, N.; Holcombe, A.L.; Lund, B.C.; Gu, X.; Schweizer, M.L. Association between polypharmacy and death: A systematic review and meta-analysis. J. Am. Pharm. Assoc. 2017, 57, 729.e10–738.e10. [CrossRef] 12. Corsonello, A.; Pedone, C.; Incalzi, R. Age-related pharmacokinetic and pharmacodynamic changes and related risk of adverse drug reactions. Curr. Med. Chem. 2010, 17, 571–584. [CrossRef] 13. Poulose, N.; Raju, R. Aging and injury: Alterations in cellular energetics and organ function. Aging Dis. 2014, 5, 101–108. [CrossRef] 14. Mair, A.; Fernandez-Llimos, F.; Alonso, A.; Harrison, C.; Hurding, S.; Kempen, T.; Kinnear, M.; Michael, N.; McIntosh, J.; Wilson, M. Polypharmacy a Patient Safety by 2030: Management Challenge; SIMPATHY Consortium: New Delhi, India, 2017. 15. Violan, C.; Foguet-Boreu, Q.; Flores-Mateo, G.; Salisbury, C.; Blom, J.; Freitag, M.; Glynn, L.; Muth, C.; Valderas, J.M. Prevalence, determinants and patterns of multimorbidity in primary care: A systematic review of observational studies. PLoS ONE 2014, 9, e102149. [CrossRef] [PubMed] 16. Prados-Torres, A.; Calderón-Larrañaga, A.; Hancco-Saavedra, J.; Poblador-Plou, B.; van den Akker, M. Multimorbidity patterns: A systematic review. J. Clin. Epidemiol. 2014, 67, 254–266. [CrossRef] [PubMed] 17. Rajoo, S.S.; Wee, Z.J.; Lee, P.S.S.; Wong, F.Y.; Lee, E.S. A systematic review of the patterns of associative multimorbidity in Asia. Biomed. Res. Int. 2021, 2021, 6621785. [CrossRef] 18. Busija, L.; Lim, K.; Szoeke, C.; Sanders, K.M.; McCabe, M.P. Do replicable profiles of multimorbidity exist? Systematic review and synthesis. Eur. J. Epidemiol. 2019, 34, 1025–1053. [CrossRef] 19. Villén, N.; Guisado-Clavero, M.; Guisado-Clavero, M.; Fernández-Bertolín, S.; Fernández-Bertolín, S.; Troncoso-Mariño, A.; Foguet-Boreu, Q.; Foguet-Boreu, Q.; Foguet-Boreu, Q.; Amado, E.; et al. Multimorbidity patterns, polypharmacy and their association with liver and kidney abnormalities in people over 65 years of age: A longitudinal study. BMC Geriatr. 2020, 20, 206. [CrossRef] 20. Marengoni, A.; Roso-Llorach, A.; Vetrano, D.L.; Fernández-Bertolín, S.; Guisado-Clavero, M.; Violán, C.; Calderón-Larrañaga, A. Patterns of multimorbidity in a population-based cohort of older people: Sociodemographic, lifestyle, clinical, and functional differences. J. Gerontol. A Biol. Sci. Med. Sci. 2020, 75, 798–805. [CrossRef] 21. Ho, I.S.S.; Azcoaga-Lorenzo, A.; Akbari, A.; Black, C.; Davies, J.; Hodgins, P.; Khunti, K.; Kadam, U.; Lyons, R.A.; McCowan, C.; et al. Examining variation in the measurement of multimorbidity in research: A systematic review of 566 studies. Lancet Public Health 2021, 6, e587–e597. [CrossRef] 22. Ng, S.K.; Tawiah, R.; Sawyer, M.; Scuffham, P. Patterns of multimorbid health conditions: A systematic review of analytical methods and comparison analysis. Int. J. Epidemiol. 2018, 47, 1687–1704. [CrossRef] 23. Asogwa, O.A.; Boateng, D.; Marzà-Florensa, A.; Peters, S.; Levitt, N.; van Olmen, J.; Klipstein-Grobusch, K. Multimorbidity of non-communicable diseases in low-income and middle-income countries: A systematic review and meta-analysis. BMJ Open 2022, 12, 49133. [CrossRef] [PubMed] 24. Wilson, P.W.; Elmore, J.G.; Cannon, C.P.; Givens, J.; Parikh, N. Overview of Established Risk Factors for Cardiovascular Disease; UpToDate: Waltham, MA, USA. Available online: https://www.uptodate.com/contents/overview-of-established-risk-factors- for-cardiovascular-disease?search=OverviewofRiskFactorsforCardiovascularDisease&source=search_result&selectedTitle=1~ {}150&usage_type=default&display_rank=1 (accessed on 15 September 2022). 25. Scottish Government Polypharmacy Model of Care Group. Polypharmacy Guidance, Realistic Prescribing; Scottish Government: Edinburgh, Scotland, 2018. 26. Caruana, E.J.; Roman, M.; Hernández-Sánchez, J.; Solli, P. Longitudinal studies. J. Thorac. Dis. 2015, 7, E537–E540. [CrossRef] [PubMed] 27. Onder, G.; Vetrano, D.L.; Palmer, K.; Trevisan, C.; Amato, L.; Berti, F.; Campomori, A.; Catalano, L.; Corsonello, A.; Kruger, P.; et al. Italian guidelines on management of persons with multimorbidity and polypharmacy. Aging Clin. Exp. Res. 2022, 34, 989–996. [CrossRef] [PubMed] Geriatrics 2022, 7, 141 18 of 19 28. Statistical Institute of Catalonia. Idescat. Statistical Yearbook of Catalonia. Available online: https://www.idescat.cat/pub/?id= aec&lang=en (accessed on 15 September 2022). 29. Institut Català de la Salut. Memòria d’activitats 2012. Available online: http://ics.gencat.cat/ca/detall/publicacio/memoria_20 12-00007 (accessed on 17 October 2022). 30. García-Gil, M.M.; Hermosilla, E.; Prieto-Alhambra, D.; Fina, F.; Rosell, M.; Ramos, R.; Rodriguez, J.; Williams, T.; van Staa, T.; Bolíbar, B. Construction and validation of a scoring syst.tem for the selection of high-quality data in a Spanish population primary care database (SIDIAP). Inform. Prim. Care 2011, 19, 135–145. 31. World Health Organization Collaborating Centre for Drug Statistics Methodology. Guidelines for ATC Classification and DDD Assignment. 2022. Available online: https://www.whocc.no/atc_ddd_index_and_guidelines/guidelines/ (accessed on 17 October 2022). 32. Stafford, G.; Villén, N.; Roso-Llorach, A.; Troncoso-Mariño, A.; Monteagudo, M.; Violán, C. Combined multimorbidity and polypharmacy patterns in the elderly: A cross-sectional study in primary health care. Int. J. Environ. Res. Public Health 2021, 18, 9216. [CrossRef] [PubMed] 33. Domínguez-Berjón, M.F.; Borrell, C.; Cano-Serral, G.; Esnaola, S.; Nolasco, A.; Pasarín, M.I.; Ramis, R.; Saurina, C.; Escolar-Pujolar, A. Construcción de un índice de privación a partir de datos censales en grandes ciudades españolas (proyecto MEDEA). Gac Sanit 2008, 22, 179–187. [CrossRef] 34. Chavent, M.; Kuentz-Simonet, V.; Labenne, A.; Saracco, J. Multivariate analysis of mixed data: The R package PCAmixdata. arXiv 2017, arXiv:1411.4911. 35. Karlis, D.; Saporta, G.; Spinakis, A. A simple rule for the selection of principal components. Commun. Stat. Theory Methods 2003, 32, 643–666. [CrossRef] 36. Bezdek, J.C. Pattern Recognition with Fuzzy Objective Function Algorithms, 1st ed.; Springer: Berlin/Heidelberg, Germany, 1981. 37. Rabinier, L.R. A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 1989, 77, 257–286. [CrossRef] 38. Arbelo, E.; Bax, J.J.; Blomströ m-Lundqvist, C.; Boriani, G.; Castella, M.; Dan, G.-A.; Dilaveris, P.E.; Fauchier, L.; Filippatos, G.; Kalman, J.M.; et al. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European association for cardio-thoracic surgery (EACTS). Eur. Heart J. 2021, 42, 373–498. [CrossRef] 39. Nunes, B.P.; Flores, T.R.; Mielke, G.I.; Thumé, E.; Facchini, L.A. Multimorbidity and mortality in older adults: A systematic review and meta-analysis. Arch. Gerontol. Geriatr. 2016, 67, 130–138. [CrossRef] [PubMed] 40. Shiferaw, W.S.; Akalu, T.Y.; Aynalem, Y.A. Risk factors for anemia in patients with chronic renal failure: A systematic review and meta-analysis. Ethiop. J. Health Sci. 2020, 30, 829–842. [CrossRef] 41. Kung, W.-M.; Yuan, S.-P.; Lin, M.-S.; Wu, C.-C.; Atique, S.; Touray, M.; Huang, C.-Y.; Wang, Y.-C. Anemia and the risk of cognitive impairment: An updated systematic review and meta-analysis. Brain Sci. 2021, 11, 777. [CrossRef] [PubMed] 42. Gomes, T.; Greaves, S.; van den Brink, W.; Antoniou, T.; Mamdani, M.M.; Paterson, J.M.; Martins, D.; Juurlink, D.N. Pregabalin and the risk for opioid-related death: A nested case-control study. Ann. Intern. Med. 2018, 169, 732–734. [CrossRef] [PubMed] 43. Portenoy, R.K.; Mehta, Z.; Ahmed, E. Prevention and Management of Side Effects in Patients Receiving Opioids for Chronic Pain; UpToDate: Waltham, MA, USA. Available online: www.uptodate.com (accessed on 15 September 2022). 44. Oliveira, M.C.; Vullings, J.; van de Loo, F.A.J. Osteoporosis and osteoarthritis are two sides of the same coin paid for obesity. Nutrition 2020, 70, 110486. [CrossRef] 45. Suh, S.; Park, M.K. Glucocorticoid-induced diabetes mellitus: An important but overlooked problem. Endocrinol. Metab. 2017, 32, 180–189. [CrossRef] [PubMed] 46. Grant, P.J.; Cosentino, F.; Marx, N. Diabetes and coronary artery disease: Not just a risk factor. Heart 2020, 106, 1357–1364. [CrossRef] 47. BMJ Publishing Group. Chronic obstructive pulmonary disease (COPD). In BMJ Best Practice; BMJ Publishing Group: London, UK, 2022. 48. Har˛eżlak, T.; Religioni, U.; Szymański, F.M.; Hering, D.; Barańska, A.; Neumann-Podczaska, A.; Allan, M.; Merks, P. Drug interactions affecting kidney function: Beware of health threats from triple whammy. Adv. Ther. 2022, 39, 140–147. [CrossRef] 49. Nguyen, H.; Manolova, G.; Daskalopoulou, C.; Vitoratou, S.; Prince, M.; Prina, A.M. Prevalence of multimorbidity in community settings: A systematic review and meta-analysis of observational studies. J. Comorb. 2019, 9, 2235042X19870934. [CrossRef] 50. Guisado-Clavero, M.; Roso-Llorach, A.; López-Jimenez, T.; Pons-Vigués, M.; Foguet-Boreu, Q.; Angel Muñoz, M.; Violán, C. Multimorbidity patterns in the elderly: A prospective cohort study with cluster analysis. BMC Geriatr. 2019, 18, 16. [CrossRef] 51. Violán, C.; Foguet-Boreu, Q.; Fernández-Bertolín, S.; Guisado-Clavero, M.; Cabrera-Bean, M.; Formiga, F.; Valderas, J.M.; Roso- Llorach, A. Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: Cross-sectional study in a mediterranean population. BMJ Open 2019, 9, e029594. [CrossRef] 52. Gimeno-Miguel, A.; Gracia Gutiérrez, A.; Poblador-Plou, B.; Coscollar-Santaliestra, C.; Pérez-Calvo, I.; Divo, M.J.; Calderón- Larrañaga, A.; Prados-Torres, A.; Ruiz-Laiglesia, F.J. Multimorbidity patterns in patients with heart failure: An observational spanish study based on electronic health records. BMJ Open 2019, 9, e033174. [CrossRef] 53. Recalde, M.; Manzano-Salgado, C.B.; Díaz, Y.; Puente, D.; Garcia-Gil, M.D.M.; Marcos-Gragera, R.; Ribes-Puig, J.; Galceran, J.; Posso, M.; Macià, F.; et al. Validation of cancer diagnoses in electronic health records: Results from the information system for research in primary care (SIDIAP) in Northeast Spain. Clin. Epidemiol. 2019, 11, 1015–1024. [CrossRef] [PubMed] Geriatrics 2022, 7, 141 19 of 19 54. Calderón-Larrañaga, A.; Vetrano, D.L.; Onder, G.; Gimeno-Feliu, L.A.; Coscollar-Santaliestra, C.; Carfí, A.; Pisciotta, M.S.; Angleman, S.; Melis, R.J.F.; Santoni, G.; et al. Assessing and measuring chronic multimorbidity in the older population: A proposal for its operationalization. J. Gerontol.-Ser. A Biol. Sci. Med. Sci. 2017, 72, 1417–1423. [CrossRef] [PubMed] 55. Knuuti, J.; Wijns, W.; Saraste, A.; Capodanno, D.; Barbato, E.; Funck-Brentano, C.; Prescott, E.; Storey, R.; Deaton, C.; Cuisset, T.; et al. 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur. Heart J. 2020, 41, 407–477. [CrossRef] [PubMed] 56. Amador, M.; Matias-Guiu, X.; Sancho-Pardo, G.; Contreras Martinez, J.; de la Torre-Montero, J.C.; Peñuelas Saiz, A.; Garrido, P.; García-Sanz, R.; Rodríguez-Lescure; Paz-Ares, L. Impact of the COVID-19 pandemic on the care of cancer patients in Spain. ESMO Open 2021, 6, 100157. [CrossRef] [PubMed] 57. Mora, N.; Fina, F.; Méndez-Boo, L.; Cantenys, R.; Benítez, M.; Moreno, N.; Balló, E.; Hermosilla, E.; Fàbregas, M.; Guiriguet, C.C.X.; et al. Decline and uneven recovery from 7 common long-term conditions managed in the catalan primary care after two pandemic years: An observational retrospective population-based study using primary care electronic health records. Res. Sq. Preprint 2022. [CrossRef] 58. Duffy, E.; Chilazi, M.; Cainzos-Achirica, M.; Michos, E.D. Cardiovascular disease prevention during the COVID-19 pandemic: Lessons learned and future opportunities. Methodist DeBakey Cardiovasc. J. 2021, 17, 68–78. [CrossRef] 59. Feachem, R.G.A.; Sekhri, N.K.; White, K.L. Papers getting more for their dollar: A comparison of the NHS with California’s kaiser permanente. BMJ 2002, 324, 135–143. [CrossRef]