Comorbidity Clusters Research Articles

Clusters of Comorbidities in the Short-Term Prognosis of Acute Heart Failure among Elderly Patients: A Retrospective Cohort Study.

Background and Objectives: Elderly patients affected by acute heart failure (AHF) often show different patterns of comorbidities. In this paper, we aimed to evaluate how chronic comorbidities cluster and which pattern of comorbidities is more strongly related to in-hospital death in AHF. Materials and Methods: All patients admitted for AHF to an Internal Medicine Department (01/2015–01/2019) were retrospectively evaluated; the main outcome of this study was in-hospital death during an admission for AHF; age, sex, the Charlson comorbidity index (CCI), and 17 different chronic pathologies were investigated; the association between the comorbidities was studied with Pearson’s bivariate test, considering a level of p ≤ 0.10 significant, and considering p < 0.05 strongly significant. Thus, we identified the clusters of comorbidities associated with the main outcome and tested the CCI and each cluster against in-hospital death with logistic regression analysis, assessing the accuracy of the prediction with ROC curve analysis. Results: A total of 459 consecutive patients (age: 83.9 ± 8.02 years; males: 56.6%). A total of 55 (12%) subjects reached the main outcome; the CCI and 16 clusters of comorbidities emerged as being associated with in-hospital death from AHF. Of these, CCI and six clusters showed an accurate prediction of in-hospital death. Conclusions: Both the CCI and specific clusters of comorbidities are associated with in-hospital death from AHF among elderly patients. Specific phenotypes show a greater association with a worse short-term prognosis than a more generic scale, such as the CCI.

Using Unsupervised Machine Learning Methods to Cluster Comorbidities in a Population-Based Cohort of Patients With Rheumatoid Arthritis.

To identify clusters of comorbidities in patients with rheumatoid arthritis (RA) using 4 methods and to compare to patients without RA. In this retrospective, population-based study, residents of 8 Minnesota counties with prevalent RA as of January 1, 2015 were identified. Age-, sex-, and county-matched non-RA comparators were selected from the same underlying population. Diagnostic codes were retrieved for 5 years before January 1, 2015. Using 2 codes ≥30 days apart, 44 previously defined morbidities and 11 nonoverlapping chronic disease categories based on Clinical Classifications Software were defined. Unsupervised machine learning methods of interest included hierarchical clustering, factor analysis, K-means clustering, and network analysis. Two groups of 1,643 patients with and without RA (72% female; mean age 63.1 years in both groups) were studied. Clustering of comorbidities revealed strong associations among mental/behavioral comorbidities and among cardiovascular risk factors and diseases. The clusters were associated with age and sex. Differences between the 4 clustering methods were driven by comorbidities that are rare and those that were weakly associated with other comorbidities. Common comorbidities tended to group together consistently across approaches. The instability of clusters when using different random seeds or bootstrap sampling impugns the usefulness and reliability of these methods. Clusters of common comorbidities between RA and non-RA cohorts were similar. Despite the higher comorbidity burden in patients with RA compared to the general population, clustering comorbidities did not identify substantial differences in comorbidity patterns between the RA and non-RA cohorts. The instability of clustering methods suggests caution when interpreting clustering using 1 method.

34889 Comorbidity clusters and drug consumption patterns of patients with atopic dermatitis

Introduction: Patients with atopic dermatitis (AD) suffer from an intensely itchy chronic inflammatory skin disease. AD has been associated, not only with atopic diseases as asthma or rhino-conjunctivitis, also with neuropsychiatric, ophthalmological, infectious, metabolic or autoimmune comorbidities. This probably leads to a different consumption of certain drugs than the general population.

Cluster B personality disorders and its associated factors among psychiatric outpatients in Southwest Ethiopia: institutional-based cross-sectional study

BackgroundDiagnosis of co-occurring personality disorders, particularly the most comorbid cluster B personality disorders in psychiatric patients is clinically important because of their association with the duration, recurrence, and outcome of the comorbid disorders. The study aimed to assess the prevalence of cluster B personality disorders and associated factors among psychiatric outpatients in Jimma Medical Center.MethodsAn institution-based cross-sectional study was conducted among 404 patients with mental illnesses at Jimma Medical Center from July 15 to September 14, 2021. A systematic random sampling method was used to recruit the participants. Personality disorder questionnaire four (PDQ-4) was used to assess the prevalence of cluster B personality disorders through a face-to-face interview. Data were entered into Epi Data Version 4.6 and exported to SPSS Version 26 for analysis. Logistic regression analysis was done and variables with a p-value less than 0.05 with a 95% confidence interval in the final fitting model were declared as independent predictors of cluster B personality disorders.ResultAmongst 401 respondents with response rate of 99.3%, slightly less than one-fourth (23.19%, N = 93) were found to have cluster B personality disorders. Unable to read and write(AOR = 3.28, 95%CI = 1.43—7.51), unemployment(AOR = 2.32, 95%CI = 1.19—4.49), diagnosis of depressive (AOR = 3.72, 95%CI = 1.52–9.10) and bipolar-I disorders (AOR = 2.94, 95%CI = 1.37—6.29), longer duration of illness (AOR = 2.44, 95%CI = 1.33—4.47), multiple relapses (AOR = 2.21, 95%CI = 1.18–4.15)), history of family mental illnesses (AOR = 2.05, 95%CI = 1.17—3.62), recent cannabis use (AOR = 4.38, 95%CI = 1.61—11.95), recent use of alcohol (AOR = 2.86, 95%CI = 1.34—6.10), starting to use substance at earlier age (AOR = 4.42, 95%CI = 1.51 -12.96), and suicidal attempt (AOR = 2.24, 95%CI = 1.01—4.96), were the factors significantly associated with cluster B personality disorders.ConclusionThe prevalence of cluster B personality disorders was high among mentally ill outpatients and found to be important for mental health professionals working in the outpatient departments to screen for cluster B personality disorders as part of their routine activities, particularly those who have mood disorders, longer duration of illness, multiple relapses, history of family mental illnesses, suicidal attempt and are a current user of alcohol and cannabis.

Comorbidity patterns, family history and breast cancer risk: a latent class analysis

BackgroundLimited evidence exists on how the presence of multiple conditions affects breast cancer (BC) risk.MethodsWe used data from a network hospital-based case–control study conducted in Italy and Switzerland, including 3034...

Multimorbidity, guideline‐directed medical therapies, and associated outcomes among hospitalized heart failure patients

AimsMultimorbidity is common among heart failure (HF) patients and may attenuate guideline‐directed medical therapy (GDMT). Multimorbid patients are under‐represented in clinical trials; therefore, the effect of multimorbidity clustering on the prognosis of HF patients remains unknown. We evaluated the prevalence of multimorbidity clusters among consecutively registered hospitalized HF patients and assessed whether GDMT attenuated outcomes.Methods and resultsWe examined 1924 hospitalized HF patients with reduced left ventricular ejection fraction (<50%) in a multicentre registry (West Tokyo HF Registry: WET‐HF). Ten comorbid conditions in the WET‐HF were abstracted: coronary artery disease, atrial fibrillation, stroke, anaemia, chronic obstructive pulmonary disease, renal dysfunction, obesity, hypertension, dyslipidaemia, and diabetes. Patients were divided into three groups (0–2: n = 451; 3–4: n = 787; and ≥5: n = 686) based on the number of comorbid conditions. The primary composite endpoint was all‐cause mortality and HF rehospitalization. The most prevalent comorbidities were renal dysfunction (67.9%), hypertension (66.0%), and anaemia (53.8%). Increased comorbidity was associated with increased adverse outcomes [3–4: hazard ratio (HR) 1.42, 95% confidence interval (CI) 1.13–1.77, P = 0.003; ≥5: HR 2.12, 95%CI 1.69–2.65, P < 0.001; and reference: 0–2] and lower GDMT prescription rate (0–2: 69.2%; 3–4: 57.7%; and ≥5: 57.6%). GDMT was associated with decreased adverse outcomes; this association was maintained even as the comorbidity burden increased but tended to weaken (0–2: HR 0.53, 95%CI 0.35–0.78; P = 0.001; 3–4: HR 0.82, 95%CI 0.65–1.04, P = 0.095; and ≥5: HR 0.81, 95%CI 0.65–1.00, P = 0.053; P for interaction = 0.156).ConclusionsComorbidity clusters were prevalent and associated with poorer outcomes. GDMT remained beneficial regardless of the comorbidity burden but tended to weaken with increasing comorbidity burden. Further research is required to optimize medical care in these patients.

Comorbidities, Rather Than Older Age, Are Strongly Associated With Higher Utilization of Healthcare in Colorectal Cancer Survivors.

Colorectal cancer (CRC) survivors generally have a higher healthcare utilization (HCU) than the general population due to cancer burden. However, it is unclear which factors are associated with this increased uptake. Our study aimed to (1) compare CRC-related and non-CRC visits to general practitioners (GPs) and medical specialists (MSs) by comorbidities, and (2) assess whether HCU differs by demographic, clinical, and psychological factors. We used data from a German population-based cohort of 1,718 survivors of stage I-III CRC diagnosed in 2003 through 2010 who provided information on HCU at 5-year follow-up. Multivariable linear regression was used to calculate least-square means of CRC-related and non-CRC HCU according to the Charlson comorbidity index and comorbidity cluster, adjusting for relevant demographic, clinical, and psychological characteristics. A higher comorbidity level was associated with more CRC-related MS visits and non-CRC GP visits. In addition to being strongly associated with non-CRC GP visits, comorbidity clusters were associated with CRC-related GP and MS visits, but their association varied by specific cardiometabolic comorbidities. HCU was less dependent on prognostic factors for CRC, such as age and tumor stage, but was strongly associated with disease recurrence, depression, and emotional functioning. Comorbidities, rather than age or tumor stage, were related to HCU, suggesting that CRC survivors use healthcare mainly for reasons other than cancer 5 years postdiagnosis. Improved communication between primary and tertiary healthcare providers could enhance the medical care of cancer survivors with complex health needs and thereby also reduce healthcare costs.

The psychological features of distinct somatic syndromes: A cluster analysis according to population-based somatic symptom profiles in Taiwan

Functional somatic syndromes (such as chronic fatigue syndrome, fibromyalgia, irritable bowel syndrome) are often comorbid. Whether these syndromes are distinct constructs and whether they have different psychological features are interesting questions. We perform a cluster analysis based on a nationwide survey in Taiwan to answer these questions. A score of at least 5 on the Patient Health Questionnaire-15 (PHQ-15, measuring somatic symptoms) indicated somatic syndromes and the data of 550 subjects were included. According to the gastrointestinal, pain-fatigue and cardiovascular subdimension scores of the PHQ-15, we performed a two-step cluster analysis. The demographic data and the cluster scores of the Health Anxiety Questionnaire and the Patient Health Questionnaire-4 (measuring depression and anxiety) were compared. Multinomial logistic and multiple linear regression analyses were used to clarify the associations between clusters/somatic symptoms and demographics/psychological features. Four clusters were generated and named according to their somatic features: "high gastrointestinal symptoms", "high pain-fatigue and comorbid somatic symptoms", "middle to high pain-fatigue symptoms" and "high cardiovascular symptoms". The high pain-fatigue and comorbid somatic symptom cluster had the highest levels of extent to which symptoms interfere with a person's life, depression and anxiety. The high cardiovascular symptom cluster was featured by high excessive worry over health and illness and low educational level. The high gastrointestinal symptom cluster had relatively low psychopathologies. The results of this population-based analysis supported the existence of distinct somatic syndromes that are not parts of a single whole somatic syndrome and have different psychological features.

Comorbidities in unclassifiable interstitial lung disease

BackgroundComorbidities are common in interstitial lung diseases (ILD) and have an important association with survival, but the frequency and prognostic impact of comorbidities in unclassifiable interstitial lung disease (uILD) remains elusive. We aimed to describe the prevalence of comorbidities and assess the impact on survival in patients with uILD. Furthermore, we aimed to identify and characterize potential phenotypes based on clusters of comorbidities and examine their association with disease progression and survival.MethodsIncident patients diagnosed with uILD were identified at two ILD referral centers in Denmark and Germany from 2003 to 2018. The diagnosis uILD was based on multidisciplinary team meetings. Clinical characteristics and comorbidities were extracted from ILD registries and patient case files. Survival analyses were performed using Cox regression analyses, disease progression was analyzed by linear mixed effects models, and clusters of comorbidities were analyzed using self-organizing maps.ResultsA total of 249 patients with uILD were identified. The cohort was dominated by males (60%), former (49%) or current (15%) smokers, median age was 70 years, mean FVC was 75.9% predicted, and mean DLCO was 49.9% predicted. One-year survival was 89% and three-year survival was 73%. Eighty-five percent of the patients had ≥ 1 comorbidities, 33% had ≥ 3 comorbidities and 9% had ≥ 5 comorbidities. The only comorbidity associated with excess mortality was dyslipidemia. No association between survival and number of comorbidities or the Charlson comorbidity index was observed. Three clusters with different comorbidities profiles and clinical characteristics were identified. A significant annual decline in FVC and DLCO % predicted was observed in cluster 1 and 2, but not in cluster 3. No difference in mortality was observed between the clusters.ConclusionsThe comorbidity burden in uILD is lower than reported in other types of ILD and the impact of comorbidities on mortality needs further clarification. Three clusters with distinct comorbidity profiles were identified and could represent specific phenotypes. No difference in mortality was observed between clusters, but slower disease progression was observed in cluster 3. Better understanding of disease behavior and mortality will require further studies of subgroups of uILD with longer observation time.

Direct Healthcare Costs by Level of Adherence of a Real-World Population of Statin Users in Italy.

PurposeThis real-world study investigates the direct healthcare costs from the perspective of the Italian Healthcare National Service of experienced statin users according to their level of adherence to therapy and to their cardiovascular (CV) profile in Italian settings of outpatients clinical practice.Patients and MethodsA retrospective observational analysis was performed based on administrative databases covering approximately 6 million health-assisted individuals. Adult patients with statins prescription between January 2014 and December 2016 were screened, and first prescription within this period was the index date. Follow-up lasted 1 year after index date. Only patients receiving statins prior index date (experienced statin users) were included and distributed in clusters based on their CV profile. Adherence was calculated during follow-up as proportion of days covered (PDC) and classified in low adherence (PDC<40%), partial adherence (PDC=40–79%) and adherence (PDC≥80%). Mean direct healthcare costs of drugs, hospitalizations, and outpatient services were evaluated during follow-up.ResultsA total of 436,623 experienced statin users were included and distributed as follows: 5.5% in the previous CV events, 22.6% in diabetes, 55.7% in CV treatments and 16.2% in the no comorbidity cluster. Total mean annual cost/patient decreased from low adherent to adherent patients from €4826 to €3497 in previous CV events, from €2815 to €2360 in diabetes cluster, from €2077 to €1863 for patients with CV treatments. Same trend was reported for the cost item related to hospitalizations, which was the major determinant of the total costs. In previous CV event cluster, adherence was associated to a saving of €879 on total costs.ConclusionThe study highlighted a decrease in overall mean costs as adherence levels increase, particularly for patients with previous CV events, showing how improving adherence could be associated to cost savings and suggesting suited strategy based on CV profile should be undertaken for adherence optimization.

The HUNT study: Association of comorbidity clusters with long-term survival and incidence of exacerbation in a population-based Norwegian COPD cohort.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease often viewed as part of a multimorbidity complex. There is a need for better phenotyping of the disease, characterization of its interplay with other comorbidities and its association with long-term outcomes. This study aims to examine how clusters of comorbidities are associated with severe exacerbations and mortality in COPD. Participants with potential COPD were recruited from the second (1995-1997) and third (2006-2008) survey of the HUNT Study and followed up until April 2020. Ten objectively identified comorbidities were clustered using self-organizing maps. Severe COPD exacerbations requiring hospitalization were assessed using hospital data. All-cause mortality was collected from national registries. Multivariable Cox regression was used to calculate hazard ratios (HRs) with 95% CIs for the association between comorbidity clusters and all-cause mortality. Poisson regression was used to calculate incidence rate ratios (IRRs) with 95% CI for the cumulative number of severe exacerbations for each cluster. Five distinct clusters were identified, including 'less comorbidity', 'psychological', 'cardiovascular', 'metabolic' and 'cachectic' clusters. Using the less comorbidity cluster as reference, the psychological and cachectic clusters were associated with all-cause mortality (HR 1.23 [1.04-1.45] and HR 1.83 [1.52-2.20], adjusted for age and sex). The same clusters also had increased risk of exacerbations (unadjusted IRR of 1.24 [95% CI 1.04-1.48] and 1.50 [95% CI 1.23-1.83], respectively). During 25 years of follow-up, individuals in the psychological and cachectic clusters had increased mortality. Furthermore, these clusters were associated with increased risk of severe COPD exacerbations.

Clustering of comorbidities and associated outcomes in people with osteoarthritis - A UK Clinical Practice Research Datalink study

To examine the clusters of chronic conditions present in people with osteoarthritis and the associated risk factors and health outcomes. Clinical Practice Research Datalink (CPRD) GOLD was used to identify people diagnosed with incident osteoarthritis (n=221,807) between 1997 and 2017 and age (±2 years), gender, and practice matched controls (no osteoarthritis, n=221,807) from UK primary care. Clustering of people was examined for 49 conditions using latent class analysis. The associations between cluster membership and covariates were quantified by odds ratios (OR) using multinomial logistic regression. General practice (GP) consultations, hospitalisations, and all-cause mortality rates were compared across the clusters identified at the time of first diagnosis of osteoarthritis (index date). In both groups, conditions largely grouped around five clusters: relatively healthy; cardiovascular (CV), musculoskeletal-mental health (MSK-MH), CV-musculoskeletal (CV-MSK) and metabolic (MB). In the osteoarthritis group, compared to the relatively healthy cluster, strong associations were seen for 1) age with all clusters; 2) women with the MB cluster (OR 5.55: 5.14-5.99); 3) obesity with the CV-MSK (OR 2.11: 2.03-2.20) and CV clusters (OR 2.03: 1.97-2.09). The CV-MSK cluster in the osteoarthritis group had the highest number of GP consultations and hospitalisations, and the mortality risk was 2.45 (2.33-2.58) times higher compared to the relatively healthy cluster. Of the five identified clusters, CV-MSK, CV, and MSK-MH are more common in OA and CV-MSK cluster had higher health utilisation. Further research is warranted to better understand the mechanistic pathways and clinical implications.

Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) Comorbidities in Lithuanian National Database: A Cluster Analysis.

Various comorbidities and multimorbidity frequently occur in chronic obstructive pulmonary disease (COPD), leading to the overload of health care systems and increased mortality. We aimed to assess the impact of COPD on the probability and clustering of comorbidities. The cross-sectional analysis of the nationwide Lithuanian database was performed based on the entries of the codes of chronic diseases. COPD was defined on the code J44.8 entry and six-month consumption of bronchodilators. Descriptive statistics and odds ratios (ORs) for associations and agglomerative hierarchical clustering were carried out. 321,297 patients aged 40–79 years were included; 4834 of them had COPD. A significantly higher prevalence of cardiovascular diseases (CVD), lung cancer, kidney diseases, and the association of COPD with six-fold higher odds of lung cancer (OR 6.66; p < 0.0001), a two-fold of heart failure (OR 2.61; p < 0.0001), and CVD (OR 1.83; p < 0.0001) was found. Six clusters in COPD males and five in females were pointed out, in patients without COPD—five and four clusters accordingly. The most prevalent cardiovascular cluster had no significant difference according to sex or COPD presence, but a different linkage of dyslipidemia was found. The study raises the need to elaborate adjusted multimorbidity case management and screening tools enabling better outcomes.

Comorbidity phenotypes and risk of mortality in patients with osteoarthritis in the UK: a latent class analysis

BackgroundOsteoarthritis (OA) is a common chronic condition but its association with other chronic conditions and mortality is largely unknown. This study aimed to use latent class analysis (LCA) of 30 comorbidities in patients with OA and matched controls without OA to identify clusters of comorbidities and examine the associations between the clusters, opioid use, and mortality.MethodsA matched cohort analysis of patients derived from the IQVIA Medical Research Data (IMRD-UK) database between 2000 and 2019. 418,329 patients with newly diagnosed OA were matched to 243,170 patients without OA to identify comorbidity phenotypes. Further analysis investigated the effect of opioid use on mortality in individuals with OA and their matched controls.ResultsThe median (interquartile range (IQR)) number of comorbidities was 2 (1–4) and 1 (0–3) in the OA and control groups respectively. LCA identified six comorbidity phenotypes in individuals with and without OA. Clusters with a high prevalence of comorbidities were characterised by hypertension, circulatory, and metabolic diseases. We identified a comorbidity cluster with the aforementioned comorbidities plus a high prevalence of chronic kidney disease, which was associated with twice the hazard of mortality in hand OA with a hazard ratio (HR) (95% CI) of 2.53 (2.05–3.13) compared to the hazard observed in hip/knee OA subtype 1.33 (1.24–1.42). The impact of opioid use in the first 12 months on hazards of mortality was significantly greater for weak opioids and strong opioids across all groups HR (95% CI) ranging from 1.11 (1.07–11.6) to 1.80 (1.69–1.92)). There was however no evidence of association between NSAID use and altered risk of mortality.ConclusionThis study identified six comorbidity clusters in individuals with OA and matched controls within this cohort. Opioid use and comorbidity clusters were differentially associated with the risk of mortality. The analyses may help shape the development of future interventions or health services that take into account the impact of these comorbidity clusters.

Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity.

AimsIt is increasingly recognized that the presence of comorbidities substantially contributes to the disease burden in patients with heart failure (HF). Several reports have suggested that clustering of comorbidities can lead to improved characterization of the disease phenotypes, which may influence management of the individual patient. Therefore, we aimed to cluster patients with HF based on medical comorbidities and their treatment and, subsequently, compare the clinical characteristics between these clusters.Methods and resultsA total of 603 patients with HF entering an outpatient HF rehabilitation programme were included [median age 65 years (interquartile range 56–71), 57% ischaemic origin of cardiomyopathy, and left ventricular ejection fraction 35% (26–45)]. Exercise performance, daily life activities, disease‐specific health status, coping styles, and personality traits were assessed. In addition, the presence of 12 clinically relevant comorbidities was recorded, based on targeted diagnostics combined with applicable pharmacotherapies. Self‐organizing maps (SOMs; www.viscovery.net) were used to visualize clusters, generated by using a hybrid algorithm that applies the classical hierarchical cluster method of Ward on top of the SOM topology. Five clusters were identified: (1) a least comorbidities cluster; (2) a cachectic/implosive cluster; (3) a metabolic diabetes cluster; (4) a metabolic renal cluster; and (5) a psychologic cluster. Exercise performance, daily life activities, disease‐specific health status, coping styles, personality traits, and number of comorbidities were significantly different between these clusters.ConclusionsDistinct combinations of comorbidities could be identified in patients with HF. Therapy may be tailored based on these clusters as next step towards precision medicine. The effect of such an approach needs to be prospectively tested.

Empirical support for the use and further study of the countertransference construct in the clinical care of patients with bipolar disorder.

We thank Andrew Shaw and colleagues for their recent paper1 inBipolar Disorders, highlightingthat theeffectivetreatment of patients with bipolar disorder requires a good therapeutic alliance,which is related to the clinicians' countertransference reactions and their management. The authorssuggestthat the integration of a psychodynamic approach into the biological paradigm in psychiatry might be helpful to increase countertransference awareness in clinicians, andpotentially improvingdyadic communication,resolution ofalliance ruptures, and treatment adherence. Theyalsomention that although the theoretical literature on countertransference is well developed, no good empirical studies exist that investigate the countertransferential responses experienced by mental health clinicians involved in the care of individualsduring acute phases of bipolar disorder. We would, however, like to add some examples of empirical evidence for the use (and further study) of the countertransference in the clinical care of these patients.

Clustering of asthma and related comorbidities and their association with maternal health during pregnancy: evidence from an Australian birth cohort

BackgroundThe population-based classification of asthma severity is varied and needs further classification. This study identified clusters of asthma and related comorbidities of Australian children aged 12–13 years; determined health outcome differences among clusters; and investigated the associations between maternal asthma and other health conditions during pregnancy and the children’s clustered groups.MethodsParticipants were 1777 children in the birth cohort of the Longitudinal Study of Australian Children (LSAC) who participated in the Health CheckPoint survey and the LSAC 7th Wave. A latent class analysis (LCA) was conducted to identify clusters of children afflicted with eight diseases, such as asthma (ever diagnosed or current), wheezing, eczema, sleep problem/snoring/breathing problem, general health status, having any health condition and food allergy. Multinomial logistic regression was used to investigate the association between maternal asthma or other health conditions and LCA clusters.ResultsThe study identified four clusters: (i) had asthma – currently healthy (11.0%), (ii) never asthmatic & healthy (64.9%), (iii) early-onset asthmatic or allergic (10.7%), and (iv) asthmatic unhealthy (13.4%). The asthmatic unhealthy cluster was in poor health in terms of health-related quality of life, general wellbeing and lung functions compared to other clusters. Children whose mothers had asthma during pregnancy were 3.31 times (OR 3.31, 95% CI: 2.06–5.30) more likely to be in the asthmatic unhealthy cluster than children whose mothers were non-asthmatic during pregnancy.ConclusionUsing LCA analysis, this study improved a classification strategy for children with asthma and related morbidities to identify the most vulnerable groups within a population-based sample.

Clinical phenotypes of patients with non-valvular atrial fibrillation as defined by a cluster analysis: A report from the J-RHYTHM registry.

BackgroundAtrial fibrillation (AF) is a heterogeneous condition caused by various underlying disorders and comorbidities. A cluster analysis is a statistical technique that attempts to group populations by shared traits. Applied to AF, it could be useful in classifying the variables and complex presentations of AF into phenotypes of coherent, more tractable subpopulations.ObjectivesThis study aimed to characterize the clinical phenotypes of AF using a national AF patient registry using a cluster analysis.MethodsWe used data of an observational cohort that included 7406 patients with non-valvular AF enrolled from 158 sites participating in a nationwide AF registry (J-RHYTHM). The endpoints analyzed were all-cause mortality, thromboembolisms, and major bleeding.ResultsThe optimal number of clusters was found to be 4 based on 40 characteristics. They were those with (1) a younger age and low rate of comorbidities (n = 1876), (2) a high rate of hypertension (n = 4579), (3) high bleeding risk (n = 302), and (4) prior coronary artery disease and other atherosclerotic comorbidities (n = 649). The patients in the younger/low comorbidity cluster demonstrated the lowest risk for all 3 endpoints. The atherosclerotic comorbidity cluster had significantly higher adjusted risks of total mortality (odds ratio [OR], 3.70; 95% confidence interval [CI], 2.37–5.80) and major bleeding (OR, 5.19; 95% CI, 2.58–10.9) than the younger/low comorbidity cluster.ConclusionsA cluster analysis identified 4 distinct groups of non-valvular AF patients with different clinical characteristics and outcomes. Awareness of these groupings may lead to a differentiated patient management for AF.

Comorbidity Trajectories Associated With Alzheimer's Disease: A Matched Case-Control Study in a United States Claims Database.

Background: Trajectories of comorbidities among individuals at risk of Alzheimer’s disease (AD) may differ from those aging without AD clinical syndrome. Therefore, characterizing the comorbidity burden and pattern associated with AD risk may facilitate earlier detection, enable timely intervention, and help slow the rate of cognitive and functional decline in AD. This case-control study was performed to compare the prevalence of comorbidities between AD cases and controls during the 5 years prior to diagnosis (or index date for controls); and to identify comorbidities with a differential time-dependent prevalence trajectory during the 5 years prior to AD diagnosis.Methods: Incident AD cases and individually matched controls were identified in a United States claims database between January 1, 2000 and December 31, 2016. AD status and comorbidities were defined based on the presence of diagnosis codes in administrative claims records. Generalized estimating equations were used to assess evidence of changes over time and between AD and controls. A principal component analysis and hierarchical clustering was performed to identify groups of AD-related comorbidities with respect to prevalence changes over time (or trajectory), and differences between AD and controls.Results: Data from 186,064 individuals in the IBM MarketScan Commercial Claims and Medicare Supplementary databases were analyzed (93,032 AD cases and 93,032 non-AD controls). In total, there were 177 comorbidities with a ≥ 5% prevalence. Five main clusters of comorbidities were identified. Clusters differed between AD cases and controls in the overall magnitude of association with AD, in their diverging time trajectories, and in comorbidity prevalence. Three clusters contained comorbidities that notably increased in frequency over time in AD cases but not in controls during the 5-year period before AD diagnosis. Comorbidities in these clusters were related to the early signs and/or symptoms of AD, psychiatric and mood disorders, cerebrovascular disease, history of hazard and injuries, and metabolic, cardiovascular, and respiratory complaints.Conclusion: We demonstrated a greater comorbidity burden among those who later developed AD vs. controls, and identified comorbidity clusters that could distinguish these two groups. Further investigation of comorbidity burden is warranted to facilitate early detection of individuals at risk of developing AD.

Galcanezumab effectiveness on comorbid cluster headache and chronic migraine: a prospective case series.

Cluster headache (CH) and migraine are recurrent painful primary cephalalgies, typically with different clinical appearance and some shared features, such as unilateral pain, common triggers and response to triptans and/or monoclonal antibodies against the calcitonin gene-related peptide (CGRP) pathway. Common pathophysiological mechanisms are proposed in CH and migraine, including the role of the trigeminal-vascular system with its most representative neuropeptide, CGRP. Very few case series have been conducted so far investigating anti-CGRP treatments in patients with comorbid CH and migraine, and no cases have been reported which assess both CH and chronic migraine outcomes. We describe 4 patients with both chronic migraine and cluster headache, with or without failure to preventive medications. Galcanezumab (240mg loading dose, followed by 120mg monthly) was used for at least a 3-month treatment, demonstrating improvement in both migraine and CH outcomes (i.e. migraine days, CH attacks, Headache Impact Test -6 item score, acute medications use), achieving sustained clinical benefit. No adverse events were reported. Taking into account the role of CGRP in migraine and CH pathophysiology, a usually well-tolerated treatment with CGRP blockade could be a rationale-based option to treat patients with coexisting chronic migraine and cluster headache. Additional studies are needed to assess the role of anti-CGRP drugs in episodic and chronic CH treatment, as well as to establish correct timing and patient prerequisites to begin therapy.