PLOS ONE RESEARCH ARTICLE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity: MRisk-COVID study Marina Lleal ID1,2, Celia Corral-Vazquez3, Montserrat Baré4, Ricard Comet5, Susana Herranz5, Francisco Baigorri6, Antonio Gimeno-Miguel3,7, Maria Raurich8, Cristina Fortià6, Marta Navarro9, Beatriz Poblador-Plou ID3,7, Marisa Baré ID1,3,10,11* 1 Institutional Committee for the Improvement of Clinical Practice Adequacy, Clinical Epidemiology and Cancer Screening Department, Parc Taulı́ Hospital Universitari, Institut d’Investigació i Innovació Parc Taulı́ (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain, 2 Department of Paediatrics, a1111111111 Obstetrics and Gynaecology, Preventive Medicine and Public Health, Autonomous University of Barcelona (UAB), Bellaterra, Spain, 3 Research Network on Health Services in Chronic Patients (REDISSEC), Instituto a1111111111 de Salud Carlos III, Madrid, Spain, 4 Creu Alta Primary Care Centre, Institut Català de la Salut, Sabadell, a1111111111 Spain, 5 Acute Geriatric Unit, Centre Sociosanitari Albada, Parc Taulı́ Hospital Universitari, Institut a1111111111 d’Investigació i Innovació Parc Taulı́ (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain, a1111111111 6 Intensive Care Unit, Parc Taulı́ Hospital Universitari, Institut d’Investigació i Innovació Parc Taulı́ (I3PT- CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain, 7 EpiChron Research Group, Aragon Health Sciences Institute, IIS Aragón, Miguel Servet University Hospital, Zaragoza, Spain, 8 Health Record / Information Management, Parc Taulı́ Hospital Universitari, Institut d’Investigació i Innovació Parc Taulı́ (I3PT- CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain, 9 Infectious Diseases Department, Parc Taulı́ Hospital Universitari, Institut d’Investigació i Innovació Parc Taulı́ (I3PT-CERCA), Universitat Autònoma OPEN ACCESS de Barcelona, Sabadell, Spain, 10 Can Rull – Can Llong Primary Care Centre, Parc Taulı́ Hospital Citation: Lleal M, Corral-Vazquez C, Baré M, Comet Universitari, Institut d’Investigació i Innovació Parc Taulı́ (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain, 11 Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), R, Herranz S, Baigorri F, et al. (2023) Instituto de Salud Carlos III, Madrid, Spain Multimorbidity patterns in COVID-19 patients and their relationship with infection severity: MRisk- * mbare@tauli.cat COVID study. PLoS ONE 18(8): e0290969. https:// doi.org/10.1371/journal.pone.0290969 Editor: Ikechukwu Innocent Mbachu, Nnamdi Azikiwe University, NIGERIA Abstract Received: June 14, 2022 Background Accepted: August 19, 2023 Several chronic conditions have been identified as risk factors for severe COVID-19 infec- Published: August 31, 2023 tion, yet the implications of multimorbidity need to be explored. The objective of this study was to establish multimorbidity clusters from a cohort of COVID-19 patients and assess Copyright: © 2023 Lleal et al. This is an open access article distributed under the terms of the their relationship with infection severity/mortality. Creative Commons Attribution License, which permits unrestricted use, distribution, and Methods reproduction in any medium, provided the original author and source are credited. The MRisk-COVID Big Data study included 14 286 COVID-19 patients of the first wave in a Data Availability Statement: Data cannot be Spanish region. The cohort was stratified by age and sex. Multimorbid individuals were sub- shared publicly because of confidentiality. Data jected to a fuzzy c-means cluster analysis in order to identify multimorbidity clusters within used for this analysis are available from the each stratum. Bivariate analyses were performed to assess the relationship between sever- Government of Catalonia Institutional Data Access ity/mortality and age, sex, and multimorbidity clusters. (PADRIS Program) for researchers who meet the criteria for access to confidential data. Inquiries of access to aggregated data should be addressed to Results AQuAS director (direccio.aquas@gencat.cat), under the following conditions: http://aquas.gencat. Severe infection was reported in 9.5% (95% CI: 9.0–9.9) of the patients, and death occurred cat/ca/ambits/analitica-dades/padris/. in 3.9% (95% CI: 3.6–4.2). We identified multimorbidity clusters related to severity/mortality PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 1 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity Funding: MaB received funding from Institut in most age groups from 21 to 65 years. In males, the cluster with highest percentage of d’Investigació i Innovació Parc Taulı́ (http://www. severity/mortality was Heart-liver-gastrointestinal (81–90 years, 34.1% severity, 29.5% mor- tauli.cat/institut/) [Grant number: CIR2020/023] and Red de Investigación en Servicios de Salud en tality). In females, the clusters with the highest percentage of severity/mortality were Diabe- Enfermedades Crónicas (REDISSEC) (http://www. tes-cardiovascular (81–95 years, 22.5% severity) and Psychogeriatric (81–95 years, 16.0% redissec.com/) [Grant number: RD16/0001/0002]. mortality). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Conclusion Competing interests: The authors have declared This study characterized several multimorbidity clusters in COVID-19 patients based on sex that no competing interests exist. and age, some of which were found to be associated with higher rates of infection severity/ Abbreviations: AQuAS, Agency for Health Quality mortality, particularly in younger individuals. Further research is encouraged to ascertain and Assessment of Catalonia; CC, Chronic the role of specific multimorbidity patterns on infection prognosis and identify the most vul- condition; COPD, Chronic obstructive pulmonary disease; CV, Cardiovascular; DM, Diabetes mellitus; nerable morbidity profiles in the community. HC3, Shared Clinical History of Catalonia; ICD-10- CM, International Classification of Diseases, 10th edition, Clinical Modification; ICD-10-PCS, Trial registration International Classification of Diseases, 10th NCT04981249. Registered 4 August 2021 (retrospectively registered). edition, Procedure Coding System; MM, Multimorbidity; O/E, Observed/Expected; PADRIS, Public Data Analysis for Health Research and Innovation Program; SUVEC, Catalan Epidemiological Surveillance Emergency Service. Background The first wave of the COVID-19 pandemic caused high rates of death and critical infection. The first studies quickly identified a differential vulnerability depending on age and sex, so that higher rates of death and severity were detected in older and male individuals [1, 2]. The study of the relationship between isolated chronic diseases and COVID-19 infection has resulted in the identification of diverse morbidities as potential risk factors, such as diabe- tes mellitus (DM), obesity, hypertension, heart failure, chronic obstructive pulmonary disease (COPD), chronic kidney disease, and cardiovascular (CV) conditions [3–10]. Noticeably, many of these conditions share some pathophysiological pathways, suggesting that their inter- actions could increase the risk of a poor COVID-19 prognosis, and that their identification would help in the management of the disease. Nevertheless, few research works have examined the interaction between multiple diseases and its impact on COVID-19 outcomes [8, 11–13]. Multimorbidity (MM), which is traditionally defined as the concomitance of two or more chronic conditions (CC) in the same individual, is a highly prevalent issue that increases with age and society ageing [14–17]. Multimorbid patients may present complex profiles associated to pathophysiological interactions that could complicate medical treatment [18, 19]. However, there is no gold standard in the characterization of MM due to the novelty of the research field, and many studies analyse the mere presence or absence of MM, the sole number of CC, pre-established scores such as the Charlson Multimorbidity Index, or the aggregation of CC by factor analysis or hard hierarchical-clustering methods [20–22]. Remarkably, new strategies that more accurately reflect the complex, non-random associa- tion of CC in a patient-centred point of view are emerging, such as soft (or fuzzy) clustering analysis [23–25]. This technique has already provided relevant results in previous studies, so that several relationships between MM patterns and clinically relevant outcomes such as dis- ability, adverse drug reactions, hospitalization, or mortality have been identified [26–30]. In order to perform a solid profiling of the MM patterns, detailed medical information is needed. In this sense, the use of Big Data provides a quick and exhaustive covering of large datasets. Indeed, the access to public healthcare Big Data facilitated by several state organisms PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 2 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity enabled rapid analyses, yielding relevant results, and leading to a better handling of the pan- demic [13, 31–33]. Given the previous statements, the aim of the present study, encompassed in the MRisk- COVID Big Data project, was to provide an age- and sex-centred characterization of the MM clusters of an adult COVID-19 cohort and to assess the relationship between these clusters and the severity and mortality of the infection. Methods Study design and cohort The MRisk-COVID study is an observational Big Data research project based on a cohort of 14286 COVID-19 patients aged >20 years, residing in a healthcare zone of about 400000 inhabitants in the Northeast region of Spain (Vallès Occidental est). This healthcare zone is sit- uated within the autonomous community of Catalonia, specifically within the province of Bar- celona, and encompasses a single reference hospital, Parc Taulı́ Hospital Universitari. Data were provided by the Agency for Health Quality and Assessment of Catalonia (AQuAS) in the framework of the Public Data Analysis for Health Research and Innovation Program (PADRIS), under an extraordinary call that took place in April 2020 in order to provide health- care Big Data for COVID-19 research studies. The cohort of this study is composed by all COVID-19 cases registered between 27th Febru- ary and 15th June of 2020 by the Catalan Epidemiological Surveillance Emergency Service (SUVEC) as either confirmed cases (identified through positive PCR, ELISA or rapid antibody test), or suspected cases (no positive test result, but exhibited symptoms and were classified as potential cases by physicians). The study did not involve sampling, as it encompasses all indi- viduals who met the inclusion criteria. The International Classification of Diseases, 10th edi- tion, Clinical Modification (ICD-10-CM) codes considered as COVID-19 diagnoses are summarized in S1 Table. Ethics statement The study was approved by the Ethics Committee (CEIm) of the Parc Taulı́ University Hospi- tal (reference 2021/5067), which waived the requirement of informed consents due to the epi- demiological nature of the study and the use of anonymized data provided by PADRIS. This study was conducted according to the principles expressed in the Declaration of Helsinki. Big data processing, data linkage and study outcomes Demographic information and clinical records were compiled, integrated and analysed. Demographic data were obtained from the Shared Clinical History of Catalonia (HC3) and included age and sex. In order to prevent re-identification of the patients, age was provided by PADRIS in quinquennia, and groups with high risk of re-identification were not included (women aged >95 and men aged >90). Clinical records were obtained from any primary healthcare centre and hospital of Catalonia that the individuals attended. The provided records covered from 1923 to 2020 in primary healthcare centres, and from 2016 to 2020 in hospitals. These data encompassed all diagnoses and hospital procedures, including the index hospitali- zation episode of COVID-19 infection. Information about admission and discharge circum- stances was also provided (S1 Fig). Patients’ data were stratified by sex and age, thereby obtaining eight different datasets (groups of 21–45, 46–65, 66–80 and 81–95 years, separated into male and female individuals). PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 3 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity Infection severity 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 insuf- ficiency, 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 associated with COVID-19 episodes (either registered by epidemiological surveil- lance, primary care or hospital records). This definition was based on the COVID-19 Treat- ment Guidelines provided by National Institutes of Health [34], with some modifications under the research group physicians’ criteria. The ICD-10-CM and ICD-10 Procedure Coding System (PCS) codes considered as severity conditions are compiled in S1 Table. Besides, mor- tality was also assessed independently. Data compilation, processing and statistical analysis were performed using R [35] and RStu- dio [36]. Multimorbidity cluster analysis The ICD-10-CM codes of the complete diagnoses records were categorized into chronic or acute diseases by the Chronic Condition Indicator v.2021.1 [37], and only CC were selected. Then, in order to reduce the number of variables for the cluster analysis, diagnoses were grouped and classified by the Clinical Classification Software Refined v.2021.1 [37]. This soft- ware provides a hierarchical classification of the diagnoses into general CC categories (e.g. Asthma, or Chronic obstructive pulmonary disease and bronchiectasis), which, in turn, belong to disease families (e.g. Diseases of the respiratory system). The CC of the family Neoplasms were manually distributed into two general categories: Neoplasia (solid tumour), and Hemato- logic neoplasia. The clustering procedure was independently applied to each sex-age group. For each stra- tum, CC were filtered by prevalence (>2%) and only patients with MM, as defined by the pres- ence of 2 or more CC, were included in the analyses. The clustering methodology was adapted from the one described by Vetrano et al [28]. An initial dimension reduction of the CC data- sets was performed by a Multiple Correspondence Analysis, using the elbow criteria in the Scree plot for dimension selection. The resulting data were subjected to soft clustering analysis by the fuzzy c-means algorithm. This algorithm requires two main parameters: the number of desired clusters (C) and the fuzziness (m), which indicates the degree of overlapping member- ship of the patients to the clusters. This parameter can range from 1 (equivalent to hard, non- overlapping clustering) to infinite. Several values of C (4, 5, 6 and 7) and m (1.1, 1.2, 1.4, 1.5, 2, and 4) were tested. Since fuzzy c-means is a stochastic method, 100 iterations were performed for each combination of C and m in order to obtain mean and reproducible results. The opti- mal m was estimated by the mean calculation of five indexes: Calinski–Harabasz, Partition coefficient (both optimal at their highest value), Partition entropy, Fukuyama, and Xie-Beni (the three of them optimal at their lowest value). The final C for each sex and age stratum was determined through consensus among 11 pro- fessionals of the multidisciplinary research group. This agreement was achieved following a Delphi-like selection method that consisted on equal voting through two selection rounds. Consensus was defined when one of the options reached a majority of votes (�6); cases with lack of consensus or similar voting count were decided through a clinical debate session. Several indicators were calculated for the CC in each cluster in order to characterize the specificity and composition of the MM patterns: a) Prevalence within the cluster (%); b) Observed/Expected (O/E) ratio, estimated by dividing the prevalence in the cluster by the prev- alence in the corresponding age-sex group cohort; and c) Exclusivity (%), obtained by dividing PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 4 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity the number of patients that presented the CC in the cluster by the total number of patients affected by the CC in the corresponding age-sex group cohort. A generic label was assigned to each cluster in an attempt to summarize the most prevalent and overexpressed CC as well as to facilitate the clinical interpretation of the results. Regarding patients, membership percentages were calculated, indicating the degree of belonging of each patient to the selected clusters. Statistical analysis Statistical bivariate differences between severe and non-severe cases, as well as dead and survi- vor cases, were measured by chi-square tests in the case of categorical/dichotomous variables (age, sex, and MM) and Wilcoxon signed-rank tests in the case of continuous variables (num- ber of CC). The statistical relationship between mortality / severity and the MM clusters was assessed by weighted chi-square tests, where the weight of the variables was the percentage of member- ship of the patients to each cluster. Patients with only one or no CC were evaluated as an addi- tional “No multimorbidity” group. These groups were discarded from the analysis of the 66– 80 and 81–95 years strata, due to the scarce number of non-multimorbid cases. Results Characteristics of the study population, infection severity and mortality The study cohort comprised a total of 5411 males (37.9%) and 8875 females (62.1%). The age distribution revealed a mode of 41–45 years (n = 1438). Among the total cohort, 3140 patients had a confirmed COVID-19 infection (22.0%) and 11 146 were suspected cases (78.0%). The 78.9% of the cohort presented MM, and the mean number of CC was 6.0 (standard deviation of 5.4) (Table 1). According to the selected codes, 1352 of the patients (9.5%; 95% CI: 9.0–9.9) suffered a severe infection. The most frequent severity criteria were oxygen therapy (n = 891, 6.2%), and respira- tory failure (n = 880, 6.2%). Regarding isolated mortality, 555 patients (3.9%; 95% CI: 3.6–4.2) died during a COVID-19 associated episode, with 368 cases (66.3%) occurring at a hospital. The relationship between severity / mortality and the demographic and medical variables (age, sex, MM, and number of CC), identified by bivariate statistical analyses, is presented in Table 1. Descriptive statistics of severe COVID-19 infection stratified by confirmed or sus- pected cases are shown in S2 Table. Table 1. Demographic and clinical distribution of the study cohort in relation to severe COVID-19 infection/mortality. SD = standard deviation; OR = Odds Ratio; CI = Confidence Interval. Variable Total Severe infection p-value OR [95% CI] Mortality p-value OR [95% CI] Sex. N (%) Female 8875 621 (7.0) <0.001 Reference 267 (3.0) <0.001 Reference Male 5411 731 (13.5) 2.1 [1.8–2.3] 288 (5.3) 1.8 [1.5–2.1] Age. N (%) 21–45 5599 117 (2.1) <0.001 Reference 9 (0.2) <0.001 Reference 46–65 4699 390 (8.3) 4.2 [3.4–5.2] 67 (1.4) 8.8 [4.6–19.2] 66–80 2299 480 (20.9) 12.3 [10.0–15.3] 210 (9.1) 61.3 [33.4–129.4] 81–95 1689 365 (21.6) 12.9 [10.4–16.1] 269 (15.9) 115.6 [63.0–245.5] Multimorbidity. N (%) No 3014 50 (1.7) <0.001 Reference 2 (0.1) <0.001 Reference Yes 11 272 1302 (11.5) 7.7 [5.9–10.4] 553 (4.9) 72.1 [23.4–461.7] Number of chronic conditions. Mean [SD] 6.05 [5.4] 10.26 [5.7] <0.001 - 12.01 [5.2] <0.001 - https://doi.org/10.1371/journal.pone.0290969.t001 PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 5 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity Chronic conditions of the COVID-19 patients A total of 215 CC were identified, belonging to 20 different families of diseases. The most rep- resented families were Diseases of the circulatory system (which contained 33 CC), Diseases of the genitourinary system (22 CC), Mental, behavioural and neurodevelopmental disorders (22 CC), and Diseases of the musculoskeletal system and connective tissue (21 CC). The most preva- lent CC were Essential hypertension (30.3%), Anxiety and fear-related disorders (26.62%), Obe- sity (21.8%), and Disorders of lipid metabolism (21.3%). For each age-sex stratum, different sets of CC with prevalence equal to or greater than 2% were identified and are listed in S3 Table. Multimorbidity patterns Fuzzy c-means cluster analysis was performed with a m value of 1.1, and C values were set to 4 clusters (males of 21–45 years; females of 81–95 years), 5 clusters (males of 81–90 years; females of 21–45, 46–65, and 66–80 years), and 6 clusters (males of 46–65 and 66–80 years). The resulting MM clusters showed distinct CC distribution in each age-sex stratum. Detailed information on the prevalence, exclusivity, and O/E ratio of each CC per cluster is provided in S4 Table. S2 and S3 Figs illustrate the most representative CC per cluster. The dis- tribution of patients among the different clusters, estimated based on their membership per- centages, is summarized in Fig 1 and Table 2. Clusters involving DM or CV conditions / risk factors were found in all age-sex groups. Neurodevelopmental disorders clusters were present in both groups of 21–45 year-olds, and Neuropsychiatric patterns were found in the 46–65 year-olds’ groups. Clusters grouping Psy- chogeriatric diseases were identified in patients of >65 years, along with the Cardiorespiratory and DM—cardiorespiratory patterns. Some clusters were sex-specific, like Cognitive-motor dis- orders in males (46–65 and 66–80 years), and osteoinflammatory conditions in females, encompassing the clusters Osteoinflammatory—psychosomatic (46–65 years), Osteoinflamma- tory—psychosomatic—CV risk factors (66–80 years), and Osteoinflammatory—neoplasia (81– 95 years). Relationship between multimorbidity clusters and severe COVID-19 infection or mortality The distribution of severe cases and mortality among MM clusters is represented in Fig 1 and detailed in Table 2 along with the P-values and odds ratios. The weighted bivariate analysis showed that clusters of patients in the age ranges of 21–45 and 46–65 displayed P�0.001 in relation to severe infection, and clusters of males in the 66–80 cohort presented P = 0.016. Among male patients, the clusters with the highest percentage of severe cases were CV risk factors (21–45 years, 7.5%), Cognitive-motor disorders (46–65 years, 32.9%), Unspecific with COPD predominance (66–80 years, 30.4%), and Heart—liver—gastroin- testinal diseases (81–90 years, 34.1%). In female patients, the clusters associated with the high- est percentages of severity were DM and CV risk factors (21–45 years, 6.2%), DM—CV diseases (46–65 years, 21.2%), Neoplasia—haematologic (66–80 years, 19.7%), and DM—CV diseases (81–95 years, 22.5%). Regarding mortality, clusters of females aged 21–65 and males aged 46–65 obtained P<0.001, and males aged 66–80 presented P = 0.02. The clusters with the highest percentage of mortality among males were Substance abuse disorders (21–45 years, 1.7%), DM—liver diseases (46–65 years, 7.2%), Cognitive-motor disorders (66–80 years, 20.5%), and Heart—liver—gastro- intestinal diseases (81–90 years, 29.5%). Clusters with the highest percentage of mortality in PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 6 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity Fig 1. List of multimorbidity clusters per age and sex stratum. The size of the circles represents the population size and the color indicates the percentage of severe COVID-19 cases (upper figure) and mortality (lower figure). COPD = Chronic Obstructive Pulmonary Disease; CV = Cardiovascular. https://doi.org/10.1371/journal.pone.0290969.g001 PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 7 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity Table 2. Distribution of patients per cluster and infection severity or mortality. COPD = Chronic obstructive pulmonary disease; DM = Diabetes mellitus; OR = Odds ratio; CI = Confidence interval; (*) = not included in the bivariate analysis. Sex Cluster Total Severe p-value OR [95% CI] Mortality p-value OR [95% CI] infection 21– Male Substance abuse disorders 60 4 (6.7%) 0.001 3.5 [0.9–9.9] 1 (1.7%) 0.052 2.2 [0.1–86.7] 45 Neurodevelopmental disorders 126 7 (5.6%) 3.1 [1.2–7.2] 1 (0.8%) Reference Cardiovascular risk factors 186 14 (7.5%) 7.2 [3.5–15.0] 2 (1.1%) 2.1 [0.2–65.6] Trauma and anxiety 602 31 (5.1%) 2.2 [1.2–4.3] 0 (0.0%) - No multimorbidity 814 16 (2.0%) Reference 0 (0.0%) - Total 1788 72 (4.0%) 4 (0.2%) Female Neurodevelopmental disorders 141 3 (2.1%) <0.001 9.6 [1.6–56.5] 1 (0.7%) <0.001 1.2 [0.0–47.6] Gynecological disorders 174 3 (1.7%) 7.9 [1.3–46.3] 1 (0.6%) Reference DM and cardiovascular risk factors 209 13 (6.2%) 28.6 [9.0– 3 (1.4%) 2.3 [0.3–66.5] 131.5] Psychosomatic 532 7 (1.3%) 5.9 [1.6–28.9] 0 (0.0%) - Unspecific 1420 16 (1.1%) 4.8 [1.6–21.5] 0 (0.0%) - No multimorbidity 1335 3 (0.2%) Reference 0 (0.0%) - Total 3811 45 (1.2%) 5 (0.1%) 46– Male Cardiovascular diseases 65 14 (21.5%) <0.001 4.8 [2.2–10.0] 4 (6.1%) <0.001 3.6 [1.0–10.2] 65 Cognitive-motor disorders 79 26 (32.9%) 9.3 [4.9–18.1] 4 (5.1%) 3.0 [0.8–8.5] DM—liver diseases 97 18 (18.6%) 3.5 [1.7–7.2] 7 (7.2%) 4.3 [1.6–10.4] Neuropsychiatric 174 22 (12.6%) 2.6 [1.4–5.0] 10 (5.7%) 3.6 [1.6–7.8] Cardiovascular risk factors—COPD—Neoplasia 316 41 (13.0%) 2.5 [1.4-4-4] 7 (2.2%) 1.1 [0.4–2.7] Unspecific 876 106 (12.1%) 2.5 [1.5–4.2] 16 (1.8%) Reference No multimorbidity 376 20 (5.3%) Reference 0 (0.0%) - Total 1983 247 (12.5%) 48 (2.4%) Female DM—cardiovascular diseases 66 14 (21.2%) <0.001 16.3 [6.7–42.7] 6 (9.1%) <0.001 40.5 [6.5– 1060.7] Neuropsychiatric 120 10 (8.3%) 5.1 [1.9–13.8] 3 (2.5%) 10.7 [1.2– 309.5] Cardiovascular risk factors 304 26 (8.6%) 5.3 [2.5–12.8] 4 (1.3%) 5.4 [0.7–149.4] Osteoinflammatory—psychosomatic 456 41 (9.0%) 5.2 [2.5–12.2] 3 (0.6%) 2.8 [0.3–80.4] Gynecological disorders 1311 44 (3.4%) 1.9 [0.9–4.5] 2 (0.1%) 0.6 [0.1–20.7] No multimorbidity 459 8 (1.7%) Reference 1 (0.2%) Reference Total 2716 143 (5.3%) 19 (0.7%) (Continued ) PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 8 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity Table 2. (Continued) Sex Cluster Total Severe p-value OR [95% CI] Mortality p-value OR [95% CI] infection 66– Male Cardiorespiratory 72 20 (27.8%) 0.016 1.5 [0.8–2.6] 9 (12.5%) 0.020 1.6 [0.7–3.4] 80 Cognitive-motor disorders 73 20 (27.4%) 1.2 [0.6–2.2] 15 (20.5%) 3.1 [1.5–6.0] Cardiometabolic 145 29 (20.0%) 1.1 [0.6–1.7] 20 (13.8%) 1.8 [1.0–3.3] Psychogeriatric 160 34 (21.3%) 1.1 [0.7–1.7] 23 (14.4%) 1.9 [1.1–3.4] Unspecific with COPD predominance 276 84 (30.4%) 1.9 [1.4–2.8] 31 (11.2%) 1.4 [0.8–2.4] Unspecific with neoplasia predominance 412 81 (19.7%) Reference 32 (7.8%) Reference No multimorbidity (*) 15 3 (20.0) - 1 (6.7%) - Total 1153 271 (23.5%) 131 (11.4%) Female Cerebrovascular 64 12 (18.8%) 0.738 1.2 [0.5–2.7] 8 (12.5%) 0.215 2.2 [0.8–5.5] Psychogeriatric 146 21 (14.4%) Reference 14 (9.6%) 1.9 [0.9–4.2] DM—cardiorespiratory 160 30 (18.8%) 1.3 [0.7–2.5] 12 (7.5%) 1.5 [0.7–3.3] Osteoinflammatory—psychosomatic—cardiovascular 272 49 (18.0%) 1.2 [0.7–2.2] 15 (5.5%) Reference risk factors Neoplasia—haematologic 493 97 (19.7%) 1.3 [0.8–2.3] 30 (6.1%) 1.0 [0.6–2.1] No multimorbidity (*) 11 0 (0.0%) - 0 (0.0%) - Total 1146 209 (18.2%) 79 (6.9%) 81– Male Heart—liver—gastrointestinal diseases 44 15 (34.1%) 0.178 2.2 [1.0–4.7] 13 (29.5%) 0.084 2.8 [1.2–6.1] 95 Cardiorespiratory—COPD 59 20 (33.9%) 1.6 [0.8–3.2] 15 (25.4%) 1.7 [0.8–3.5] Psychogeriatric/cerebrovascular 106 31 (29.2%) 1.5 [0.9–2.7] 29 (27.3%) 2.3 [1.2–4.2] DM—cardiovascular risk factors 118 40 (33.9%) 1.7 [1.0–3.0] 23 (19.5%) 1.3 [0.7–2.4] Unspecific 160 35 (21.9%) Reference 25 (15.6%) Reference No multimorbidity (*) 0 0 (-) - 0 (-) - Total 487 141 (29.0%) 105 (21.6%) Female Cardiorespiratory 187 38 (20.3%) 0.379 1.4 [0.9–2.2] 20 (10.7%) 0.198 Reference DM—cardiovascular diseases 200 45 (22.5%) 1.4 [0.9–2.2] 27 (13.5%) 1.3 [0.7–2.5] Osteoinflammatory—neoplasia 310 52 (16.8%) Reference 37 (11.9%) 1.0 [0.6–1.8] Psychogeriatric 501 89 (17.8%) 1.2 [0.8–1.7] 80 (16.0%) 1.7 [1.1–3.0] No multimorbidity (*) 4 0 (0.0%) - 0 (0.0%) - Total 1202 224 (18.6%) 164 (13.6%) https://doi.org/10.1371/journal.pone.0290969.t002 females were DM and CV risk factors (21–45 years, 1.4%), DM—CV diseases (46–65 years, 9.1%), Cerebrovascular diseases (66–80 years, 12.5%), and Psychogeriatric (81–95 years, 16.0%). Discussion The present study assessed the MM patterns associated to the population affected by the first wave of COVID-19 in a Spanish area. The results revealed well-defined MM clusters taking into account age and sex, some of which showed a high severity of COVID-19 infection and mortality, with a stronger relationship in younger age groups. First of all, defining the study cohort was a vital aspect in order to establish reliable MM profiles. Many studies from the first wave of the pandemic focused only on PCR positive indi- viduals [1, 3, 9, 38]; nevertheless, the critical situation at that time resulted in a small fraction of patients undergoing confirmatory tests in the studied geographic area. The test positivity rate in Catalonia during the selected period peaked at 43.5% and did not drop below the PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 9 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity World Health Organization recommendation of 5% until mid-May, thereby showing the shortage of testing resources [39, 40]. This situation particularly affected older individuals, who suffered the highest mortality rates in nursing homes without testing nor hospital admis- sion [41]. Therefore, excluding these individuals from the cohort could introduce biases in the definition of multimorbidity patterns. Taking the whole situation into account, the inclusion of suspected cases in the study cohort allowed for a more accurate approximation of the actual affected population. Although this cri- terion may have resulted in some possible false positives, it helped avoid potential biases asso- ciated with factors like age, access to medical testing or socioeconomic situation. Using fuzzy c-means cluster analysis allowed for the simultaneous classification of patients into different clusters with a certain probability. This method is robust for pattern recognition in situations where clusters tend to overlap [42], such as in individuals with a high prevalence of co-existing conditions. It should be taken into account that the number of clusters C depends on the researchers’ subjective election, which could affect the reproducibility of the study. Nevertheless, a Delphi-like method was carried out in order to enhance the robustness to the cluster selection, taking advantage of the multidisciplinarity of the research team, which includes members with technical or clinical expertise [43]. In addition, another key methodological aspect of this work was the stratification of the cohort. Given that the accumulation of CC is more frequent at older ages [17, 44], and that males and females have differential tendencies to suffer certain pathologies [45], it was neces- sary to consider both age and sex perspectives. In fact, previous studies on COVID-19 data from different countries already revealed that men have a higher risk of admission to intensive treatment units (from 14.4% to 44.9%) [1] or adverse outcome (OR: 2.05, 95% CI: 1.39–3.04) [2], which was supported by our bivariate analysis of demographic data. Indeed, several sex- related differences were observed in the MM clusters: patterns related with psychosomatic conditions or osteoinflammatory diseases were more frequent in females, while cognitive- motor, digestive and respiratory disease patterns were more associated to males, consistent with previous findings [23, 46, 47]. Regarding age-related differences, previous works based on the fuzzy c-means algorithm already identified certain MM clusters in cohorts of older patients (�60 years) that were simi- lar to some patterns observed in the 66–80 and 81–95 age groups in the present study. For example, Vetrano et al identified clusters of cardiometabolic diseases, respiratory diseases, and cognitive / sensory impairment [28], and Akugizibwe et al found clusters of psychiatric condi- tions and sensory impairments/cancer [27]. Regarding the younger cohorts, a previous strati- fied study by Prados-Torres et al found some similar clusters by factor analysis, like psychiatric-substance abuse (males of 15–45 years), or cardio-metabolic (all strata) [48]. On the basis of these clusters, the results of the bivariate analyses suggest that certain MM patterns could be associated to higher COVID-19 infection severity or mortality rates in young-middle ages. The fact that this relationship was not strong in older patients may be explained by the inherent risk of old age itself [49, 50], as indicated by the bivariate analysis of demographic parameters. Additionally, other factors could also explain this mild association in older individuals, such as the simple accumulation of morbidities [17, 51, 52], difficulties in accessing healthcare services during the first wave of the pandemic [53], or the adequacy of therapeutic effort policy in age-related cases [54]. Furthermore, geriatric syndromes [55], which are barely coded in medical records, represent an important factor of vulnerability [25]. For example, frailty and disability are commonly detected in older patients and have been described as potential risk factors of critical COVID-19 infection [56, 57]. Nevertheless, in spite of the lack of strong statistical association, several MM clusters of patients in the 66–80 PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 10 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity and 81–95 age ranges showed elevated infection severity and mortality rates compared to other clusters. Therefore, the high percentages of infection severity / mortality displayed by certain MM clusters suggest that these patients may be more vulnerable to poor prognosis caused by COVID-19 infection. Previous pre-pandemic studies already showed some similar MM patterns associated with other adverse outcomes. For example, clusters of psychiatric disor- ders and CV diseases have been linked to a higher risk of unplanned hospitalization [27], while cognitive / sensory, complex cardiometabolic, respiratory, and age-associated chronic clusters showed increased risk of mortality [28, 58]. Taking into account other COVID-19 studies focusing on individual diseases, several CC that showed high prevalence and O/E ratio in the vulnerable clusters of our study were already identified as risk factors of adverse outcomes, such as COPD [4, 5, 8], liver disease, substance consumption [7], neoplasia [5, 33], and CV pathologies [31, 33]. Nevertheless, the present results indicate that beyond the occurrence of isolated pathologies, the accumulation of CC may represent an additional dimension to consider when facing patient-centered risk evaluations, as suggested by other authors [12]. In any case, the described results should always be interpreted considering the limitations of the study. Although the inclusion criteria took into account the characteristics of the first wave of the pandemic, there are inherent limitations related to this period (concerning data collection, codification and testing). Additionally, it should be considered that Big Data anal- yses imply some disadvantages, such as difficulties in ensuring data quality and homogeneity [59]. On the other hand, the study data corresponds to a specific region, which ensures a cer- tain uniformity in the characteristics of the population, as well as their clinical management, protocols and codification, mitigating the potential heterogeneity of Big Data. Lastly, it should be noted that the occurrence of COVID-19 severe infection or mortality, or even their relationship with MM clusters, might be influenced by additional factors that were not included in the analyses, such as healthcare-associated features, socioeconomic status, dependency or frailty. Therefore, these results provide insights into the relationships between MM clusters and severity or mortality, but do not provide a risk prediction. Our methodological approach allowed to explore if a comprehensive set of conditions forming multimorbidity clusters was related to the risk of COVID-19 infection severity or mortality. This approach may offer a complementary picture to the findings of possible further studies with multivariate analyses. Conclusions In summary, the present study provided a complete stratified profiling of MM clusters in COVID-19 patients from the first wave of the pandemic, revealing that males, older indi- viduals and patients with certain MM patterns displayed higher probabilities of suffering severe COVID-19 infection or mortality. These findings may contribute to enhance the current knowledge about MM definition as well as to identify the most vulnerable popula- tion. This might especially benefit the preventive care of high-risk patients or those with limited access to medical treatment, and might help optimize healthcare policies and future strategies. Supporting information S1 Table. List of codes for COVID-19 registration, primary care mortality and severe infection. ICD-10-CM = International Classification of Diseases, 10th edition, Clinical PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 11 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity Modification. ICD-10-PCS = International Classification of Diseases, 10th edition, Procedure Coding System. (PDF) S2 Table. Number of patients and prevalence of severe COVID-19 infection according to the type of case (confirmed or suspected). (PDF) S3 Table. Chronic conditions with >2% prevalence per age and sex stratum. (XLSX) S4 Table. Prevalence (%), Observed/Expected (O/E) ratio, and exclusivity (%) of the chronic conditions per multimorbidity clusters. (XLSX) S1 Fig. Source and distribution of the analysed data. The included patients were submitted to an anonymization process. A patient Id code was assigned to each individual, which was maintained through all the different datasets in order to enable data compilation. (PDF) S2 Fig. Representation of the Observed/Expected ratio (O/E) and exclusivity (%) of the chronic conditions composing the multimorbidity clusters of male patients. O/E >1 and exclusivity > 1/number of clusters are displayed. (PDF) S3 Fig. Representation of the Observed/Expected ratio (O/E) and exclusivity (%) of the chronic conditions composing the multimorbidity clusters of female patients. O/E >1 and exclusivity > 1/number of clusters are displayed. (PDF) Acknowledgments We would like to thank people working at PADRIS, from AQuAS, at the Department of Health of Catalonia, and specially Borja Velasco, for coordinating and performing the extraction of the data files that were used in this project. This work was carried out as part of the PhD pro- gramme in Biomedical Research Methodology and Public Health at the Autonomous Univer- sity of Barcelona. Author Contributions Conceptualization: Marina Lleal, Montserrat Baré, Ricard Comet, Susana Herranz, Francisco Baigorri, Maria Raurich, Cristina Fortià, Marta Navarro, Marisa Baré. Data curation: Celia Corral-Vazquez. Formal analysis: Marina Lleal, Celia Corral-Vazquez. Funding acquisition: Marisa Baré. Investigation: Marina Lleal, Marisa Baré. Methodology: Marina Lleal, Celia Corral-Vazquez, Antonio Gimeno-Miguel, Beatriz Pobla- dor-Plou, Marisa Baré. Project administration: Marisa Baré. Resources: Marisa Baré. PLOS ONE | https://doi.org/10.1371/journal.pone.0290969 August 31, 2023 12 / 16 PLOS ONE Multimorbidity patterns in COVID-19 patients and their relationship with infection severity Software: Marina Lleal, Celia Corral-Vazquez. Supervision: Marisa Baré. 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