Prediction of first acute exacerbation using COPD subtypes identified by cluster analysis

A Delphi Consensus Project to Capture Greek Experts' Opinion on the Position of Triple Therapies in COPD: Why, When and to Whom

Background

In recent years, COPD treatment has become more personalized considering specific patient's characteristics.

Aim and Methods

We have performed a DELPHI consensus project to assess the level of consensus among Greek experts on the use of triple therapy in COPD as an initial and follow-up treatment. A three-round Delphi online survey was developed. The questionnaire was developed by a 6-member steering committee, included 54 statements, and divided into 3 domains: (A) triple therapy as initial treatment (divided into subdomains examining the impact of exacerbations based on lung function, bronchodilation reversibility and/or blood eosinophil count, smoking, symptoms, and comorbidities), (B) escalation to triple therapy from dual bronchodilation and (C) de-escalation from triple therapy to dual bronchodilation. The survey was funded by AstraZeneca and was hosted and analysed by an independent external company.

Results

Consensus was reached in 84.8%, 63% and 80% of statements for domains A, B and C, respectively. Experts agreed that initial treatment with triple therapy is a reasonable option for specific patients, while escalation from dual bronchodilation to triple therapy could be considered, besides frequent exacerbators, also in patients with a history of one moderate exacerbation, mainly in the presence of marked bronchodilator reversibility or high blood eosinophil count. Finally, there was a consensus that de-escalation from triple therapy to dual bronchodilation was inappropriate in patients who had experienced one moderate exacerbation in the previous year.

Conclusion

Although consensus was generated in several statements, panelists failed to reach consensus in many aspects of the use of triple therapy, identifying areas for further research.

Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review

Introduction

Computational sciences have significantly contributed to characterizing airway disease phenotypes, complementing medical expertise. However, comparing studies that derive phenotypes is challenging due to varying decisions made during phenotyping. We conducted a systematic review to describe studies that utilized unsupervised computational approaches for phenotyping obstructive airway diseases in children and adults.

Methods

We searched for relevant papers published between 2010 and 2020 in PubMed, EMBASE, Scopus, Web of Science, and Google Scholar. Additional sources included conference proceedings, reference lists, and expert recommendations. Two reviewers independently screened studies for eligibility, extracted data, and assessed study quality. Disagreements were resolved by a third reviewer. An in-house quality appraisal tool was used. Evidence was synthesized, focusing on populations, variables, and computational approaches used for deriving phenotypes.

Results

Of 120 studies included in the review, 60 focused on asthma, 19 on severe asthma, 28 on COPD, 4 on asthma-COPD overlap (ACO), and 9 on rhinitis. Among asthma studies, 31 focused on adults and 9 on children, with phenotypes related to atopy, age at onset, and disease severity. Severe asthma phenotypes were characterized by symptomatology, atopy, and age at onset. COPD phenotypes involved lung function, emphysematous changes, smoking, comorbidities, and daily life impairment. ACO and rhinitis phenotypes were mostly defined by symptoms, lung function, and sensitization, respectively. Most studies used hierarchical clustering, with some employing latent class modeling, mixture models, and factor analysis. The comprehensiveness of variable reporting was the best quality indicator, while reproducibility measures were often lacking.

Conclusion

Variations in phenotyping methods, study settings, participant profiles, and variables contribute to significant differences in characterizing asthma, severe asthma, COPD, ACO, and rhinitis phenotypes across studies. Lack of reproducibility measures limits the evaluation of computational phenotyping in airway diseases, underscoring the need for consistent approaches to defining outcomes and selecting variables to ensure reliable phenotyping.

Clinical and Paraclinical Characteristics and Predictive Factors of Chronic Obstructive Pulmonary Exacerbation

Background

Chronic obstructive pulmonary disease (COPD) is a common global condition, yet real-world data on exacerbations in Vietnamese patients remains limited. This highlights the need for further exploration of clinical complexities in this population.

Objective

The aim of this study was to characterize the clinical and paraclinical features of COPD and identify predictors of exacerbation.

Methods

A cross-sectional, prospective study was conducted on 180 inpatients at Vietnam National Lung Hospital from January 2016 to June 2021. Clinical and paraclinical data were collected.

Results

The mean patient age was 69.38 ± 9.40 years, with 92.8% male. Common symptoms included dyspnea (97.8%), cough (85.6%), and expectoration (80.0%). GOLD stage distribution was: GOLD III (53.7%), GOLD IV (29.3%), and GOLD II (17.0%). Significant predictors of exacerbation included smoking (OR=2.79), comorbidities (OR=3.95), increased dyspnea (OR=14.83), increased sputum (OR=3.13), decreased alveolar murmur (OR=4.11), wheezing (OR=2.70), white blood cell count ≥10 G/L (OR=4.79), GOLD group D (OR=9.75), and FEV1 <30% (GOLD IV) (OR=7.51) (p < 0.05).

Conclusion

Clinical and paraclinical predictors can aid in forecasting and mitigating COPD exacerbations.

Comparison of STAR and GOLD in Assessing Disease Severity Among High-Risk and COPD Patients: Evidence from Enjoying Breathing Program in China

Background

The STAR staging standard has been demonstrated to have good performance in distinguishing mortality among patients at different stages. However, the effectiveness of STAR and GOLD staging in distinguishing disease severity in high-risk and COPD patients remained unclear.

Methods

Based on Enjoying Breathing Program data through June 2023, a total of 7.924 high-risk and COPD patients were included. STAR and GOLD severity stages were based on FEV1/FVC (0.6-0.7, 0.5-0.6, 0.4-0.5, and <0.4 for stage 1 to 4 in STAR) and the proportion of predicted FEV1 value (≥80%, 50%-80%, 30%-50%, and <30% for stage 1 to 4 in GOLD), respectively. The cox regression model was used to assess the risk of medical visit due to severe respiratory symptoms according to STAR and GOLD.

Results

The current study included 1603 high-risk individuals and 6321 COPD patients. The proportions of STAR 1-4 in COPD patients were 37.1%, 33.2%, 20.5%, and 9.2%, respectively. In COPD patients only, GOLD stage distinguished disease severity well, but there was no difference in the risk of exacerbation between the different STAR stage groups. In addition, in COPD patients, by considering of GOLD and STAR together, GOLD 3 and 4 can provide more information about the exacerbation based on each STAR level, and STAR 1 and 2 can provide more information about the exacerbation in GOLD 2-4. COPD patients with GOLD 4 and STAR 2 (HR=4.08, 95% CI: 2.75-6.04) had the highest risk of exacerbation, followed by COPD patients with GOLD 4 and STAR 1 (HR=3.94, 95% CI: 2.49-6.23).

Conclusion

In COPD patients, GOLD performs better than STAR in predicting exacerbation risk. In addition, the combination of GOLD and STAR can provide more information, especially for COPD patients with GOLD 4 and STAR 1-2, which should be paid more attention in treatment and disease management.

Gastroesophageal reflux disease in chronic obstructive pulmonary disease

Gastroesophageal reflux disease (GERD) is one of the most common comorbidities of chronic obstructive pulmonary disease (COPD). Decreased lower and upper esophageal sphincter pressures, esophageal dysmotility, high transdiaphragmatic pressure, and decreased saliva secretion have been implicated as mechanisms leading to the development of GERD in COPD. Clinically, comorbid GERD in COPD is reportedly associated with worse symptoms, quality of life, and lung function, as well as a high risk of exacerbations. Aspiration of regurgitation and the cholinergic-mediated esophagobronchial reflex play a significant role in the pathophysiology. Abnormal swallowing reflexes and discoordination of swallowing can worsen aspiration. The diagnosis of GERD is not based on a single criterion; however, various approaches, including questionnaires and endoscopic evaluations, can be widely applied in clinical settings. Due to the increased risk of esophageal and gastric cancers in patients with COPD, the threshold for endoscopic examination should be low. Acid inhibitory agents, such as proton pump inhibitors and histamine H2 receptor antagonists, and prokinetic agents, including mosapride and itopride, are clinically used to treat GERD. Endoscopic fundoplication can be performed in patients with GERD refractory to medical treatment. There is still insufficient evidence, but an increasing number of studies have suggested the clinical efficacy of treatment in patients with COPD and GERD. As GERD is an evaluative and treatable common disease, and access to evaluation and treatment is relatively easy, clinicians should provide adequate care for GERD in the management of COPD.

Clinical Characteristics and Frequency of Chronic Obstructive Pulmonary Disease Exacerbations in Korean Patients: Findings From the KOCOSS Cohort 2012-2021

BACKGROUND

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) exert a substantial burden on patients and healthcare systems; however, data related to the frequency of AECOPD in the Korean population are limited. Therefore, this study aimed to describe the frequency of severe, and moderate or severe AECOPD, as well as clinical and demographic characteristics of patients with chronic obstructive pulmonary disease (COPD) in South Korea.

METHODS

Data from patients aged > 40 years with post-bronchodilator forced expiratory volume in 1 second (FEV1)/forced vital capacity ≤ 70% of the normal predicted value from the Korea COPD Subgroup Study database were analyzed (April 2012 to 2021). The protocol was based on the EXAcerbations of COPD and their OutcomeS International study. Data were collected retrospectively for year 0 (0-12 months before study enrollment) based on patient recall, and prospectively during years 1, 2, and 3 (0-12, 13-24, and 25-36 months after study enrollment, respectively). The data were summarized using descriptive statistics.

RESULTS

Data from 3,477 Korean patients (mean age, 68.5 years) with COPD were analyzed. Overall, most patients were male (92.3%), former or current smokers (90.8%), had a modified Medical Research Council dyspnea scale score ≥ 1 (83.3%), and had moderate airflow limitation (54.4%). The mean body mass index (BMI) of the study population was 23.1 kg/m², and 27.6% were obese or overweight. Hypertension was the most common comorbidity (37.6%). The mean blood eosinophil count was 226.8 cells/μL, with 21.9% of patients having ≥ 300 cells/μL. A clinically insignificant change in FEV1 (+1.4%) was observed a year after enrollment. Overall, patients experienced a mean of 0.2 severe annual AECOPD and approximately 1.1 mean moderate or severe AECOPD. Notably, the rates of severe AECOPD remained generally consistent over time. Compared with patients with no exacerbations, patients who experienced severe exacerbations had a lower mean BMI (21.7 vs. 23.1 kg/m²; P < 0.001) and lower lung function parameters (all P values < 0.001), but reported high rates of depression (25.5% vs. 15.1%; P = 0.044) and anxiety (37.3% vs. 16.7%; P < 0.001) as a comorbidity.

CONCLUSION

Findings from this Korean cohort of patients with COPD indicated a high exacerbation burden, which may be attributable to the unique characteristics of the study population and suboptimal disease management. This highlights the need to align clinical practices with the latest treatment recommendations to alleviate AECOPD burden in Korea.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05750810.

Prediction value of neutrophil and eosinophil count at risk of COPD exacerbation

INTRODUCTION

Predicting acute exacerbations (AEs) in chronic obstructive pulmonary disease (COPD) is crucial. This study aimed to identify blood biomarkers for predicting COPD exacerbations by inflammatory phenotypes.

MATERIALS AND METHODS

We analyzed blood cell counts and clinical outcomes in 340 COPD patients aged 20-90 years. Patients were categorized into eosinophilic inflammation (EOCOPD) and non-eosinophilic inflammation (N-EOCOPD) groups. Blood cell counts, eosinophil-to-lymphocyte ratio (ELR), neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-eosinophil ratio (NER) were calculated. Linear and logistic regression models assessed relationships between health outcomes and blood cell counts.

RESULTS

EOCOPD patients had distinct characteristics compared to N-EOCOPD patients. Increased neutrophil % and decreased lymphocyte % were associated with reduced pulmonary function, worse quality of life and more exacerbations, but they did not show statistical significance after adjusting by age, sex, BMI, smoking status, FEV1% and patient's medication. Subgroup analysis revealed a 1.372-fold increase in the OR of AE for every 1 unit increase in NLR in EOCOPD patients (p < .05). In N-EOCOPD patients, every 1% increase in blood eosinophil decreased the risk of exacerbation by 59.6%.

CONCLUSIONS

Our study indicates that distinct white blood cell profiles in COPD patients, with or without eosinophilic inflammation, can help assess the risk of AE in clinical settings.

Cluster features in fibrosing interstitial lung disease and associations with prognosis

BACKGROUND

Clustering is helpful in identifying subtypes in complex fibrosing interstitial lung disease (F-ILD) and associating them with prognosis at an early stage of the disease to improve treatment management. We aimed to identify associations between clinical characteristics and outcomes in patients with F-ILD.

METHODS

Retrospectively, 575 out of 926 patients with F-ILD were eligible for analysis. Four clusters were identified based on baseline data using cluster analysis. The clinical characteristics and outcomes were compared among the groups.

RESULTS

Cluster 1 was characterized by a high prevalence of comorbidities and hypoxemia at rest, with the worst lung function at baseline; Cluster 2 by young female patients with less or no smoking history; Cluster 3 by male patients with highest smoking history, the most noticeable signs of velcro crackles and clubbing of fingers, and the severe lung involvement on chest image; Cluster 4 by male patients with a high percentage of occupational or environmental exposure. Clusters 1 (median overall survival [OS] = 7.0 years) and 3 (OS = 5.9 years) had shorter OS than Clusters 2 (OS = not reached, Cluster 1: p < 0.001, Cluster 3: p < 0.001) and 4 (OS = not reached, Cluster 1: p = 0.004, Cluster 3: p < 0.001). Clusters 1 and 3 had a higher cumulative incidence of acute exacerbation than Clusters 2 (Cluster 1: p < 0.001, Cluster 3: p = 0.014) and 4 (Cluster 1: p < 0.001, Cluster 3: p = 0.006). Stratification by using clusters also independently predicted acute exacerbation (p < 0.001) and overall survival (p < 0.001).

CONCLUSIONS

The high degree of disease heterogeneity of F-ILD can be underscored by four clusters based on clinical characteristics, which may be helpful in predicting the risk of fibrosis progression, acute exacerbation and overall survival.

Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity

BACKGROUND

The ever-growing size, breadth, and availability of patient data allows for a wide variety of clinical features to serve as inputs for phenotype discovery using cluster analysis. Data of mixed types in particular are not straightforward to combine into a single feature vector, and techniques used to address this can be biased towards certain data types in ways that are not immediately obvious or intended. In this context, the process of constructing clinically meaningful patient representations from complex datasets has not been systematically evaluated.

AIMS

Our aim was to a) outline and b) implement an analytical framework to evaluate distinct methods of constructing patient representations from routine electronic health record data for the purpose of measuring patient similarity. We applied the analysis on a patient cohort diagnosed with chronic obstructive pulmonary disease.

METHODS

Using data from the CALIBER data resource, we extracted clinically relevant features for a cohort of patients diagnosed with chronic obstructive pulmonary disease. We used four different data processing pipelines to construct lower dimensional patient representations from which we calculated patient similarity scores. We described the resulting representations, ranked the influence of each individual feature on patient similarity and evaluated the effect of different pipelines on clustering outcomes. Experts evaluated the resulting representations by rating the clinical relevance of similar patient suggestions with regard to a reference patient.

RESULTS

Each of the four pipelines resulted in similarity scores primarily driven by a unique set of features. It was demonstrated that data transformations according to each pipeline prior to clustering can result in a variation of clustering results of over 40%. The most appropriate pipeline was selected on the basis of feature ranking and clinical expertise. There was moderate agreement between clinicians as measured by Cohen's kappa coefficient.

CONCLUSIONS

Data transformation has downstream and unforeseen consequences in cluster analysis. Rather than viewing this process as a black box, we have shown ways to quantitatively and qualitatively evaluate and select the appropriate preprocessing pipeline.

Prognostic risk factors for moderate-to-severe exacerbations in patients with chronic obstructive pulmonary disease: a systematic literature review

Background

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. COPD exacerbations are associated with a worsening of lung function, increased disease burden, and mortality, and, therefore, preventing their occurrence is an important goal of COPD management. This review was conducted to identify the evidence base regarding risk factors and predictors of moderate-to-severe exacerbations in patients with COPD.

Methods

A literature review was performed in Embase, MEDLINE, MEDLINE In-Process, and the Cochrane Central Register of Controlled Trials (CENTRAL). Searches were conducted from January 2015 to July 2019. Eligible publications were peer-reviewed journal articles, published in English, that reported risk factors or predictors for the occurrence of moderate-to-severe exacerbations in adults age ≥ 40 years with a diagnosis of COPD.

Results

The literature review identified 5112 references, of which 113 publications (reporting results for 76 studies) met the eligibility criteria and were included in the review. Among the 76 studies included, 61 were observational and 15 were randomized controlled clinical trials. Exacerbation history was the strongest predictor of future exacerbations, with 34 studies reporting a significant association between history of exacerbations and risk of future moderate or severe exacerbations. Other significant risk factors identified in multiple studies included disease severity or bronchodilator reversibility (39 studies), comorbidities (34 studies), higher symptom burden (17 studies), and higher blood eosinophil count (16 studies).

Conclusions

This systematic literature review identified several demographic and clinical characteristics that predict the future risk of COPD exacerbations. Prior exacerbation history was confirmed as the most important predictor of future exacerbations. These prognostic factors may help clinicians identify patients at high risk of exacerbations, which are a major driver of the global burden of COPD, including morbidity and mortality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02123-5.

Stability of distinct symptom experiences in patients with chronic obstructive pulmonary disease (COPD)

PURPOSE

This study aimed to examine reclassification rates among classes of chronic obstructive pulmonary disease (COPD) patients based on their distinct symptom experiences and to assess how these subgroups differed in symptom scores and health-related quality of life (HRQoL) outcomes over one year. Moreover, we wished to assess how these subgroups differed in demographic and clinical characteristics at 12 months.

PATIENTS AND METHODS

This is a follow-up study of 267 patients with moderate, severe, and very severe COPD. Based on their distinct symptom experiences using the Memorial Symptom Assessment Scale (MSAS), three subgroups (i.e., "high", "intermediate", and "low") were identified at baseline. In the present study, transitions between the subgroups at three, six, nine, and 12 months were investigated and calculated as reclassification rates. Differences among the subgroups in symptom scores and HRQoL at each time point and demographic and clinical characteristics at 12 months were evaluated using analysis of variance with post hoc comparisons.

RESULTS

Almost 65% were still in the "high" class after 12 months. At 12 months, pairwise comparisons for respiratory function measurements were not significantly different. Compared to the "intermediate" and "low" class, patients in the "high" class were more likely to be women and had significantly more comorbidities, reported a significantly higher number of symptoms at all time points, and worse HRQoL scores.

CONCLUSION

Our findings suggest that the pattern of a high symptom burden in COPD is consistent over time. The patients' individual symptom experiences should be the primary focus of treatment.

Stratification of COPD patients towards personalized medicine: reproduction and formation of clusters

BACKGROUND

The global initiative for chronic obstructive lung disease (GOLD) 2020 emphasizes that there is only a weak correlation between FEV₁, symptoms and impairment of the health status of patients with chronic obstructive pulmonary disease (COPD). Various studies aimed to identify COPD phenotypes by cluster analyses, but behavioral aspects besides smoking were rarely included.

METHODS

The aims of the study were to investigate whether (i) clustering analyses are in line with the classification into GOLD ABCD groups; (ii) clustering according to Burgel et al. (Eur Respir J. 36(3):531-9, 2010) can be reproduced in a real-world COPD cohort; and (iii) addition of new behavioral variables alters the clustering outcome. Principal component and hierarchical cluster analyses were applied to real-world clinical data of COPD patients newly referred to secondary care (n = 155). We investigated if the obtained clusters paralleled GOLD ABCD subgroups and determined the impact of adding several variables, including quality of life (QOL), fatigue, satisfaction relationship, air trapping, steps per day and activities of daily living, on clustering.

RESULTS

Using the appropriate corresponding variables, we identified clusters that largely reflected the GOLD ABCD groups, but we could not reproduce Burgel's clinical phenotypes. Adding six new variables resulted in the formation of four new clusters that mainly differed from each other in the following parameters: number of steps per day, activities of daily living and QOL.

CONCLUSIONS

We could not reproduce previously identified clinical COPD phenotypes in an independent population of COPD patients. Our findings therefore indicate that COPD phenotypes based on cluster analysis may not be a suitable basis for treatment strategies for individual patients.

Neurological and Psychiatric Comorbidities in Chronic Obstructive Pulmonary Disease

Background and Purpose

Chronic obstructive pulmonary disease (COPD) is often accompanied by different neurological and psychiatric comorbidities. The purpose of this study was to examine which of them are the most frequent and to explore whether their manifestation can be explained by underlying latent variables.

Methods

Data about patients with COPD and their neurological and psychiatric comorbidities were extracted from an electronic database of the National Health Insurance Fund of Lithuania for the period between January 1, 2012, and June 30, 2014. Exploratory factor analysis (EFA) was used to investigate comorbidity patterns.

Results

A study sample of 4834 patients with COPD was obtained from the database, 3338 (69.1%) of who were male. The most frequent neurological and psychiatric comorbidities were nerve, nerve root and plexus disorders (n=1439, 29.8%), sleep disorders (n=666, 13.8%), transient ischemic attack (n=545, 11.3%), depression (n=364, 7.5%) and ischemic stroke (n=349, 7.2%). The prevalence of ischemic stroke, transient ischemic attack, Parkinson’s disease, dementia and sleep disorders increased with age. One latent variable outlined during EFA grouped neurological disorders, namely ischemic stroke, transient ischemic attack, epilepsy, dementia and Parkinson’s disease. The second encompassed depression, anxiety, somatoform and sleep disorders. While similar patterns emerged in data from male patients, no clear comorbidity profiles among women with COPD were obtained.

Conclusion

Our study provides novel insights into the neurological and psychiatric comorbidities in COPD by outlining an association among cerebrovascular, neurodegenerative disorders and epilepsy, and psychiatric and sleep disorders. Future studies could substantiate the discrete pathological mechanism that underlie these comorbidity groups.

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research.

Cluster Analysis: A New Approach for Identification of Underlying Risk Factors and Demographic Features of First Trimester Pregnancy Women

Thyroid pathology is reported internationally in 5-10% of all pregnancies. The overall aim of this research was to determine the prevalence of hypothyroidism and risk factors during the first trimester screening in a Mexican patients sample. We included the records of 306 patients who attended a prenatal control consultation between January 2016 and December 2017 at the Women's Institute in Monterrey, Mexico. The studied sample had homogeneous demographic characteristics in terms of age, weight, height, BMI (body mass index) and number of pregnancies. The presence of at least one of the risk factors for thyroid disease was observed in 39.2% of the sample. Two and three clusters were identified, in which patients varied considerably among risk factors, symptoms and pregnancy complications. Compared to Cluster 0, one or more symptoms or signs of hypothyroidism occurred, while Cluster 1 was characterized by healthier patients. When three clusters were used, Cluster 2 had a higher TSH (thyroid stimulating hormone) value and pregnancy complications. There were no significant differences in perinatal variables. In addition, high TSH levels in first trimester pregnancy are characterized by pregnancy complications and decreased newborn weight. Our findings underline the high degree of disease heterogeneity with existing pregnant hypothyroid patients and the need to improve the phenotyping of the syndrome in the Mexican population.