The long-term prognostic value provided by Coronary CT angiography

Adult patients with an exacerbation of asthma and a higher risk for pulmonary embolism: a cluster analysis

OBJECTIVE

Current literature acknowledges the complexity of exacerbation triggers in patients with asthma. We studied the clinical heterogeneity of patients with asthma exacerbation suspected of having pulmonary embolism using cluster analysis and compared the clusters regarding of the risks for pulmonary embolism.

METHODS

In a secondary analysis of a dataset from the University of Florida, USA, individuals who experienced asthma exacerbation between June 2011 and October 2018 were included. All patients had undergone pulmonary CT angiography. Overall, 18 variables consisting of demographic, clinical, comorbidity, and therapeutic characteristics were used to cluster patients. The clusters were then profiled and compared in the percentages of pulmonary embolism.

RESULTS

In total, 758 patients (226; 29.8% men) with an exacerbation of asthma were included in the analysis. The frequency of a confirmed pulmonary embolism was 145 (19.1%). Two distinct clusters were identified with a statistically significant difference in pulmonary embolism [p < 0.001, odds ratio (95%CI)=2.24 (1.55, 3.24)]. We developed a high-performance classifier to profile the low- and high-risk clusters (area under the curve = 0.923, positive likelihood ratio = 20.2). The three top important variables discriminating the two clusters were age, heart rate, and body mass index. Older age, lower heart rate, higher body mass index, black race, and positive medical history (including atrial fibrillation) were more frequent in the high-risk group. Despite the higher percentage of women in the high-risk group, the sex ratios were not significantly different between the clusters.

CONCLUSION

There are two clusters in patients with an exacerbation of asthma with different prognoses percentages of pulmonary embolism. The clusters can be well identified based on patient characteristics.

Clustering Based on Laboratory Data in Patients With Heart Failure Admitted to the Intensive Care Unit

BACKGROUND

Heart failure (HF) is a common condition that imposes a significant burden on healthcare systems. We aimed to identify subgroups of patients with heart failure admitted to the ICU using routinely measured laboratory biomarkers.

METHODS

A large dataset (N = 1176) of patients with heart failure admitted to the ICU at the Beth Israel Deaconess Medical Center in Boston, USA, between June 1, 2001, and October 31, 2012, was analyzed. We clustered patients to identify laboratory phenotypes. Cluster profiling was then performed to characterize each cluster, using a binary logistic model.

RESULTS

Two distinct clusters of patients were identified (N = 679 and 497). There was a significant difference in the mortality rate between Clusters 1 and 2 (50 [7.4%] vs. 109 [21.9%], respectively, p < 0.001). Patients in the Cluster 2 were significantly older (mean [SD] age = 72.35 [14.40] and 76.37 [11.61] years, p < 0.001) with a higher percentage of chronic kidney disease (167 [24.6%] vs. 262 [52.7%], respectively, p < 0.001). The logistic model was significant (Log-likelihood ratio p < 0.001, pseudo R2 = 0.746) with an area under the curve of 0.905. The odds ratio for leucocyte count, mean corpuscular volume (MCV), red blood cell (RBC) distribution width, hematocrit (HcT), lactic acid, blood urea nitrogen (BUN), serum potassium, magnesium, and sodium were significant (all p < 0.05).

CONCLUSION

Laboratory data revealed two phenotypes of ICU-admitted patients with heart failure. The two phenotypes are of prognostic importance in terms of mortality rate. They can be differentiated using blood cell count, kidney function status, and serum electrolyte concentrations.

Clinical phenotypes of patients with acute stroke: a secondary analysis

INTRODUCTION

Stroke is a leading cause of mortality worldwide and a major cause of disability having a high burden on patients, society, and caregiving systems. This study was conducted to investigate the presence of clusters of in-hospital patients with acute stroke based on demographic and clinical data. Cluster analysis reveals patterns in patient characteristics without requiring knowledge of a predefined patient category or assumptions about likely groupings within the data.

METHODS

We performed a secondary analysis of open-access anonymized data from patients with acute stroke admitted to a hospital between December 2019 to June 2021. In total, 216 patients (78; 36.1% men) were included in the analytical dataset with a mean (SD) age of 60.3 (14.4). Many demographic and clinical features were included in the analysis and the Barthel Index on discharge was used for comparing the functional recovery of the identified clusters.

RESULTS

Hierarchical clustering based on the principal components identified two clusters of 109 and 107 patients. The clusters were different in the Barthel Index scores on discharge with the mean (SD) of 39.3 (29.3) versus 62.6 (29.4); t (213.87) = -5.818, P <0.001, Cohen's d (95%CI) = -0.80 (-1.07, -0.52). A logistic model showed that age, systolic blood pressure, pulse rate, D-dimer blood level, low-density lipoprotein, hemoglobin, creatinine concentration, the National Institute of Health Stroke Scale value, and the Barthel Index scores on admission were significant predictors of cluster profiles (all P ≤0.029).

CONCLUSION

There are two clusters in hospitalized patients with acute stroke with significantly different functional recovery. This allows prognostic grouping of hospitalized acute stroke patients for prioritization of care or resource allocation. The clusters can be recognized using easily measured demographic and clinical features.

Prognostic Clinical Phenotypes of Patients with Acute Decompensated Heart Failure

INTRODUCTION

Acute decompensated heart failure (AHF) is a clinical syndrome with a poor prognosis.

AIM

This study was conducted to identify clusters of inpatients with acute decompensated heart failure that shared similarities in their clinical features.

METHODS

We analyzed data from a cohort of patients with acute decompensated heart failure hospitalized between February 2013 and January 2017 in a Department of Cardiology. Patients were clustered using factorial analysis of mixed data. The clusters (phenotypes) were then compared using log-rank tests and profiled using a logistic model. In total, 458 patients (255; 55.7% male) with a mean (SD) age of 72.7 (11.1) years were included in the analytic dataset. The demographic, clinical, and laboratory features were included in the cluster analysis.

RESULTS

The two clusters were significantly different in terms of time to mortality and re-hospitalization (all P < 0.001). Cluster profiling yielded an accurate discriminating model (AUC = 0.934). Typically, high-risk patients were elderly females with a lower estimated glomerular filtration rate and hemoglobin on admission compared to the low-risk phenotype. Moreover, the high-risk phenotype had a higher likelihood of diabetes type 2, transient ischemic attack/cerebrovascular accident, previous heart failure or ischemic heart disease, and a higher serum potassium concentration on admission. Patients with the high-risk phenotype were of higher New York Heart Association functional classes and more positive in their medication history.

CONCLUSIONS

There are two phenotypes among patients with decompensated heart failure, high-risk and low-risk for mortality and re-hospitalization. They can be distinguished by easy-to-measure patients' characteristics.