Random Forest Clustering Identifies Three Subgroups of β-Thalassemia with Distinct Clinical Severity

Vitrano, Angela; Musallam, Khaled M.; Meloni, Antonella; Pollina, Sebastiano Addario; Karimi, Mehran; El‐Beshlawy, Amal; Hajipour, Mahmoud; Marco, V. Di; Ansari, Saqib Hussain; Filosa, Aldo; Ricchi, Paolo; Ceci, Adriana; Daar, Shahina; Vlachaki, Efthymia; Singer, Sylvia T.; Naserullah, Zaki A.; Pepe, Alessia; Scondotto, Salvatore; Dardanoni, Gabriella; Bonifazi, Fedele; Sankaran, Vijay G.; Vichinsky, Elliott; Taher, Ali T.; Maggio, Aurelio

doi:10.3390/thalassrep12010004

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…The three phenogroups were comparable in terms of the frequency of regular transfusions. This finding recalls a recent study that applied unsupervised RF clustering to identify subgroups of clinical severity in a large cohort of β-thalassemia patients, including TM patients, regularly transfused TI patients, and non-transfused TI patients [73]. Nineteen indicators of phenotype severity, which did not include MRI data, were considered, and the presence of regular transfusions did not play a significant role in grouping the patients into phenogroups.…”

Section: Discussionsupporting

confidence: 65%

Phenotypic Clustering of Beta-Thalassemia Intermedia Patients Using Cardiovascular Magnetic Resonance

Meloni,

Parravano,

Pistoia

et al. 2023

JCM

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We employed an unsupervised clustering method that integrated demographic, clinical, and cardiac magnetic resonance (CMR) data to identify distinct phenogroups (PGs) of patients with beta-thalassemia intermedia (β-TI). We considered 138 β-TI patients consecutively enrolled in the Myocardial Iron Overload in Thalassemia (MIOT) Network who underwent MR for the quantification of hepatic and cardiac iron overload (T2* technique), the assessment of biventricular size and function and atrial dimensions (cine images), and the detection of replacement myocardial fibrosis (late gadolinium enhancement technique). Three mutually exclusive phenogroups were identified based on unsupervised hierarchical clustering of principal components: PG1, women; PG2, patients with replacement myocardial fibrosis, increased biventricular volumes and masses, and lower left ventricular ejection fraction; and PG3, men without replacement myocardial fibrosis, but with increased biventricular volumes and masses and lower left ventricular ejection fraction. The hematochemical parameters and the hepatic and cardiac iron levels did not contribute to the PG definition. PG2 exhibited a significantly higher risk of future cardiovascular events (heart failure, arrhythmias, and pulmonary hypertension) than PG1 (hazard ratio-HR = 10.5; p = 0.027) and PG3 (HR = 9.0; p = 0.038). Clustering emerged as a useful tool for risk stratification in TI, enabling the identification of three phenogroups with distinct clinical and prognostic characteristics.

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Section: Discussionsupporting

confidence: 65%

Phenotypic Clustering of Beta-Thalassemia Intermedia Patients Using Cardiovascular Magnetic Resonance

Meloni,

Parravano,

Pistoia

et al. 2023

JCM

Self Cite

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“…In comparison to PAM alone, RF clustering (RF-derived proximity measure combined with PAM clustering) can be used to handle nonlinear data with possible outliers and higher noise-signal ratio. RF clustering has been shown to be an effective method to determine the underlying structure of unlabeled data (38)(39)(40). RF was performed with 10,000 trees without resampling or replication.…”

Section: Discussionmentioning

confidence: 99%

Identification of Clinically Significant Cytokine Signature Clusters in Patients With Septic Shock

Zhao,

Patel,

Krishack

et al. 2023

Critical Care Medicine

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Objectives: To identify cytokine signature clusters in patients with septic shock. Design: Prospective observational cohort study. Setting: Single academic center in the United States. Patients: Adult (≥ 18 yr old) patients admitted to the medical ICU with septic shock requiring vasoactive medication support. Interventions: None. Measurements and Main Results: One hundred fourteen patients with septic shock completed cytokine measurement at time of enrollment (t1) and 24 hours later (t2). Unsupervised random forest analysis of the change in cytokines over time, defined as delta (t2–t1), identified three clusters with distinct cytokine profiles. Patients in cluster 1 had the lowest initial levels of circulating cytokines that decreased over time. Patients in cluster 2 and cluster 3 had higher initial levels that decreased over time in cluster 2 and increased in cluster 3. Patients in clusters 2 and 3 had higher mortality compared with cluster 1 (clusters 1–3: 11% vs 31%; odds ratio [OR], 3.56 [1.10–14.23] vs 54% OR, 9.23 [2.89–37.22]). Cluster 3 was independently associated with in-hospital mortality (hazard ratio, 5.24; p = 0.005) in multivariable analysis. There were no significant differences in initial clinical severity scoring or steroid use between the clusters. Analysis of either t1 or t2 cytokine measurements alone or in combination did not reveal clusters with clear clinical significance. Conclusions: Longitudinal measurement of cytokine profiles at initiation of vasoactive medications and 24 hours later revealed three distinct cytokine signature clusters that correlated with clinical outcomes.

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Inter-Provincial Electricity Trading and Its Effects on Carbon Emissions from the Power Industry

Huang

et al. 2022

Energies

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Electricity trading is an effective measure to minimize carbon emissions and alleviate the imbalance between reverse distribution of regional energy resources and power load. However, the effects of China’s electricity trading on carbon emissions have not been fully explored due to lack of complete and balanced inter-provincial power transmission data. Therefore, the electricity generation–consumption downscaling model, logarithmic mean Divisia index (LMDI) model, and random forest clustering algorithm within a general framework were used in the present study to explore the effect of electricity trading on level of carbon emissions. Comprehensive inter-provincial electricity transmission data were generated, driving factors including electricity imports and exports were decomposed at the national and provincial scales, and clustered provincial policy implications were evaluated. The results revealed that: (i) although economic activities were the main driving factor for increase in carbon emissions at the national level, 382.95 million tons carbon emissions were offset from 2005 to 2019 due to inter-provincial electricity importation, whereas electricity export increased carbon emission by 230.30 million tons; (ii) analysis at the provincial level showed that electricity exports from Sichuan and Yunnan provinces accounted for more than 20% of the nation’s total electricity flow. Notably, this high level of exports did not significantly increase carbon emissions in these provinces owing to the abundant hydropower resources; (iii) emission reductions were only observed at the national level if the carbon intensity of the exporting provinces was lower compared with that of importing provinces, or if the electricity trading was generated from renewable sources; (iv) the effect of electricity import on emissions reduction was markedly higher relative to the effect of electricity export in most provinces, which reflected the actual situation of sustaining optimization of electricity generation structure in provincial grids of China. These findings provide a basis for decision makers to understand the contributions of electricity trading to the changes in carbon emissions from electricity generation, as well as form a foundation to explore practicable carbon emission mitigation strategies in the power industry.

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Random Forest Clustering Identifies Three Subgroups of β-Thalassemia with Distinct Clinical Severity

Cited by 3 publications

References 26 publications

Phenotypic Clustering of Beta-Thalassemia Intermedia Patients Using Cardiovascular Magnetic Resonance

Phenotypic Clustering of Beta-Thalassemia Intermedia Patients Using Cardiovascular Magnetic Resonance

Identification of Clinically Significant Cytokine Signature Clusters in Patients With Septic Shock

Inter-Provincial Electricity Trading and Its Effects on Carbon Emissions from the Power Industry

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