Cluster analysis based clinical profiling of Idiopathic Pulmonary Fibrosis patients according to comorbidities evident prior to diagnosis: a single-center observational study

Coronary artery calcium score is a prognostic factor for mortality in idiopathic pulmonary fibrosis

BACKGROUND

Cardiovascular diseases are frequent in idiopathic pulmonary fibrosis (IPF) and impact on survival. We investigated the association of coronary artery calcium (CAC) score at IPF diagnosis and during mid-term follow-up, with adverse cardiovascular events and all-cause mortality.

METHODS

Consecutive patients with IPF were retrospectively analyzed. Demographic data, smoking history, comorbidities and pulmonary function tests (PFTs) were recorded. All patients had at least two chest high resolution computed tomography (HRCT) performed 2 years apart. The total CAC score and visual fibrotic score were calculated, and all clinically significant cardiovascular events and deaths were reported.

RESULTS

The population consisted of 79 patients (57 males, mean age: 74.4±7.6 years); 67% of patients had a history of smoking, 48% of hypertension, 37% of dyslipidemia and 22.8% of diabetes. The visual score was 21.28±7.99% at T0 and 26.54±9.34% at T1, respectively (T1-T0 5.26±6.13%, P<0.001). CAC score at T0 and at T1 was 537.93±839.94 and 759.98±1027.6, respectively (T1-T0 224.66±406.87, P<0.001). Mean follow-up time was 2.47±1.1 years. On multivariate analysis, male sex (HR=3.58, 95% CI: 1.14-11.2) and CAC score at T0 (HR=1.04, 95% CI: 1.01-1.07) correlated with mortality and cardiovascular events. CAC score at T0≥405 showed 82% sensitivity and 100% specificity for predicting mortality and adverse cardiovascular events.

CONCLUSIONS

IPF patients with a CAC score at diagnosis ≥405 have a poor prognosis over a mid-term follow-up. A higher CAC score is associated with mortality and cardiovascular events.

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.

Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data

Introduction

The aim of this study was to cluster patients with chronic complications of type 2 diabetes mellitus (T2DM) by cluster analysis in Dalian, China, and examine the variance in risk of different chronic complications and metabolic levels among the various subclusters.

Methods

2267 hospitalized patients were included in the K-means cluster analysis based on 11 variables [Body Mass Index (BMI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Glucose, Triglycerides (TG), Total Cholesterol (TC), Uric Acid (UA), microalbuminuria (mAlb), Insulin, Insulin Sensitivity Index (ISI) and Homa Insulin-Resistance (Homa-IR)]. The risk of various chronic complications of T2DM in different subclusters was analyzed by multivariate logistic regression, and the Kruskal-Wallis H test and the Nemenyi test examined the differences in metabolites among different subclusters.

Results

Four subclusters were identified by clustering analysis, and each subcluster had significant features and was labeled with a different level of risk. Cluster 1 contained 1112 inpatients (49.05%), labeled as "Low-Risk"; cluster 2 included 859 (37.89%) inpatients, the label characteristics as "Medium-Low-Risk"; cluster 3 included 134 (5.91%) inpatients, labeled "Medium-Risk"; cluster 4 included 162 (7.15%) inpatients, and the label feature was "High-Risk". Additionally, in different subclusters, the proportion of patients with multiple chronic complications was different, and the risk of the same chronic complication also had significant differences. Compared to the "Low-Risk" cluster, the other three clusters exhibit a higher risk of microangiopathy. After additional adjustment for 20 covariates, the odds ratios (ORs) and 95% confidence intervals (95%CI) of the "Medium-Low-Risk" cluster, the "Medium-Risk" cluster, and the"High-Risk" cluster are 1.369 (1.042, 1.799), 2.188 (1.496, 3.201), and 9.644 (5.851, 15.896) (all p<0.05). Representatively, the "High-Risk" cluster had the highest risk of DN [OR (95%CI): 11.510(7.139,18.557), (p<0.05)] and DR [OR (95%CI): 3.917(2.526,6.075), (p<0.05)] after 20 variables adjusted. Four metabolites with statistically significant distribution differences when compared with other subclusters [Threonine (Thr), Tyrosine (Tyr), Glutaryl carnitine (C5DC), and Butyryl carnitine (C4)].

Conclusion

Patients with chronic complications of T2DM had significant clustering characteristics, and the risk of target organ damage in different subclusters was significantly different, as were the levels of metabolites. Which may become a new idea for the prevention and treatment of chronic complications of T2DM.

A Promising Approach: Artificial Intelligence Applied to Small Intestinal Bacterial Overgrowth (SIBO) Diagnosis Using Cluster Analysis

Small intestinal bacterial overgrowth (SIBO) is characterized by abnormal and excessive amounts of bacteria in the small intestine. Since symptoms and lab tests are non-specific, the diagnosis of SIBO is highly dependent on breath testing. There is a lack of a universally accepted cut-off point for breath testing to diagnose SIBO, and the dilemma of defining "SIBO patients" has made it more difficult to explore the gold standard for SIBO diagnosis. How to validate the gold standard for breath testing without defining "SIBO patients" has become an imperious demand in clinic. Breath-testing datasets from 1071 patients were collected from Xiangya Hospital in the past 3 years and analyzed with an artificial intelligence method using cluster analysis. K-means and DBSCAN algorithms were applied to the dataset after the clustering tendency was confirmed with Hopkins Statistic. Satisfying the clustering effect was evaluated with a Silhouette score, and patterns of each group were described. Advantages of artificial intelligence application in adaptive breath-testing diagnosis criteria with SIBO were discussed from the aspects of high dimensional analysis, and data-driven and regional specific dietary influence. This research work implied a promising application of artificial intelligence for SIBO diagnosis, which would benefit clinical practice and scientific research.

Clusters of comorbidities in idiopathic pulmonary fibrosis

INTRODUCTION

Comorbidities are common in patients with idiopathic pulmonary fibrosis (IPF) and negatively impact health-related quality of life, health-care costs and mortality. Retrospective studies have focused on individual comorbidities, but clusters of multiple comorbidities have rarely been analysed. This study aimed to comprehensively and prospectively assess comorbidities in a multicentre, real-world cohort of patients with IPF, including prespecified conditions of special interest and to analyse clusters of comorbidities and examine characteristics, disease course and mortality of the clusters.

METHODS

Several measurements, questionnaires, medications and medical history were combined to assess comorbidities. Using self-organizing maps, clusters of comorbidities were identified and phenotypes characterized. Disease course was assessed using mixed effects models and mortality using Cox regression.

RESULTS

One-hundred and fifty IPF patients were included prospectively. All except one patient suffered from at least one comorbidity and multimorbidity was common. Arterial hypertension, gastro-oesophageal reflux disease, hypercholesterolemia, emphysema and obstructive sleep apnea were most prevalent. Four comorbidity clusters were identified. Each cluster had distinct comorbidity profiles, patient characteristics, symptom burden and disease severity. Patients with fewer comorbidities had better exercise capacity and less dyspnea at baseline, but a trend towards faster deterioration was observed. Mortality analyses showed no significant differences between clusters.

CONCLUSIONS

Multimorbidity is prevalent in patients with IPF. Four specific clusters of comorbidities may represent phenotypes in IPF. A trend towards faster decline in exercise capacity and dyspnea was observed in patients with fewer comorbidities. Increased knowledge of comorbidities facilitates prevention and treatment of comorbidities in patients with IPF.