Identification of COVID-19 Clinical Phenotypes by Principal Component Analysis-Based Cluster Analysis

Ye, Wenjing; Lu, Weiwei; Tang, Yanping; Chen, Guoxi; Li, Xiaopan; Ji, Chen; Huang, Min; Zeng, Guangwang; Xing, Lidan; Wang, Yaling; Deng, Xiaoqin; Cai, Yuyang; Hai, Huang; Yang, Ling

doi:10.2139/ssrn.3582711

Cited by 9 publications

(17 citation statements)

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“…Moreover, survival analyses showed an almost four-fold increase of incident in-hospital mortality for the high risk compared to the low risk cluster. Although a previous study attempted to identify subtypes of Covid-19 patients, associating them with disease severity [37], this represents the first attempt to use clustering in disentangling the effect of HCQ on different types of patients, by testing associations with incident inhospital mortality risk. Specifically, we tested and observed both additive and interactive associations of HCQ and Covid-19 subtypes.…”

Section: Discussionmentioning

confidence: 99%

Disentangling the Association of Hydroxychloroquine Treatment with Mortality in Covid-19 Hospitalized Patients through Hierarchical Clustering

Castelnuovo

Gialluisi

Antinori

et al. 2021

Journal of Healthcare Engineering

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The efficacy of hydroxychloroquine (HCQ) in treating SARS-CoV-2 infection is harshly debated, with observational and experimental studies reporting contrasting results. To clarify the role of HCQ in Covid-19 patients, we carried out a retrospective observational study of 4,396 unselected patients hospitalized for Covid-19 in Italy (February–May 2020). Patients’ characteristics were collected at entry, including age, sex, obesity, smoking status, blood parameters, history of diabetes, cancer, cardiovascular and chronic pulmonary diseases, and medications in use. These were used to identify subtypes of patients with similar characteristics through hierarchical clustering based on Gower distance. Using multivariable Cox regressions, these clusters were then tested for association with mortality and modification of effect by treatment with HCQ. We identified two clusters, one of 3,913 younger patients with lower circulating inflammation levels and better renal function, and one of 483 generally older and more comorbid subjects, more prevalently men and smokers. The latter group was at increased death risk adjusted by HCQ (HR[CI95%] = 3.80[3.08-4.67]), while HCQ showed an independent inverse association (0.51[0.43-0.61]), as well as a significant influence of cluster∗HCQ interaction ( p < 0.001 ). This was driven by a differential association of HCQ with mortality between the high (0.89[0.65-1.22]) and the low risk cluster (0.46[0.39-0.54]). These effects survived adjustments for additional medications in use and were concordant with associations with disease severity and outcome. These findings suggest a particularly beneficial effect of HCQ within low risk Covid-19 patients and may contribute to clarifying the current controversy on HCQ efficacy in Covid-19 treatment.

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Section: Discussionmentioning

confidence: 99%

Disentangling the Association of Hydroxychloroquine Treatment with Mortality in Covid-19 Hospitalized Patients through Hierarchical Clustering

Castelnuovo

Gialluisi

Antinori

et al. 2021

Journal of Healthcare Engineering

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“…To date, several reports used cluster analysis to describe heterogeneity or characterization in COVID-19 patient-level epidemiological data 17,18,19,20,21,22,23,24 . To our knowledge, none characterized populationbased data (778,692 patients) and neither analyzed the Mexican population to find potential subphenotypes through cluster analysis on age-gender controlled patient strata.…”

Section: Discussionmentioning

confidence: 99%

“…Other studies proposed unsupervised ML methods for aggregated population data 12 , CT image analyses 13,14 , molecular-level clustering 15 , or coronavirus-related scientific texts 16 . Several studies provided to date results from unsupervised ML on patient-level epidemiological data 17,18,19,20,21,22,23,24 . To our knowledge, however, none characterized agegender subphenotypes, nor aimed to a population-based study with solely the phenotypical information available at pre-admission towards automated risk stratification, and neither characterized the Mexican population that is generally more vulnerable due to its particularity in a high prevalence of comorbidities.…”

Section: Introductionmentioning

confidence: 99%

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-gender unbiased meta-clustering technique

Zhou¹,

Romero²,

Martı́nez-Miranda

et al. 2021

Preprint

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Objective To describe COVID-19 subphenotypes regarding severity patterns including prognostic, ICU and morbimortality outcomes, through stratification based on gender and age groups, as described by inter-patient variability patterns in clinical phenotypes and demographic features. Materials and methods We used the COVID-19 open data from the Mexican Government including patient-level epidemiological and clinical data from 778 692 SARS-CoV-2 patients from January 13, 2020 to September 30, 2020. Inter-patient variability was analyzed by combining dimensionality reduction and hierarchical clustering methods. We produced cluster analyses for all combinations of gender and age groups (<18, 18-49, 50-64, and >64). For each group, the optimum number of clusters was selected combining a quantitative approach using the Silhouette coefficient, and a qualitative approach through a subgroup expert inspection via visual analytics. Using the features of the resultant age-gender clusters, we performed a meta-clustering analysis to provide an overall description of the population. Results We observed a total of 56 age-gender clusters, grouped in 11 clinically distinguishable meta-clusters with different outcomes. Meta-clusters 1 to 3 showed the highest recovery rates (90.27-95.22%). These clusters include: (1) healthy patients of all ages, (2) children with comorbidities who had priority in medical resources, (3) young patients with obesity and smoking habit. Meta-clusters 4 and 5 showed moderate recovery rates (81.3-82.81%): (4) patients with hypertension or diabetes of all ages, (5) typical obese patients with three highly correlated conditions, namely, pneumonia, hypertension and diabetes. Meta-clusters 6 to 11 had very low recovery rates (53.96-66.94%) which include: (6) immunosuppressed patients with the highest comorbidity rate in many diseases, (7) CKD patients with the worse survival length and recovery, (8) elderly smoker with mild COPD, (9) severe diabetic elderly with hypertension, (10, 11) oldest obese smokers with severe COPD and mild cardiovascular disease with the latter (11) showing a relatively higher age and smoke rate, severe COPD and shorter survival length, reinforcing a high correlation between smoking habit and COPD among elderly. Additionally, the source Mexican state and type of clinical institution proved to be an important factor for heterogeneity in severity. Discussion The proposed unsupervised learning approach successfully uncovered discriminative COVID-19 severity patterns for both genders and all age groups from clinical phenotypes and demographic features. A careful read of group outcomes showed consistent results regarding recent literature. Regarding the Mexican population, our results suggest that habits and comorbidities may play a key role in predicting mortality in older patients. Centenarians tended to fall in the groups with better outcomes repeatedly. Additionally, immunosuppression was not found as a relevant factor for severity alone but did when present along with chronic kidney disease. Further useful correlations could be found by evaluating the duration of unhealthy habits, demographic features, comorbidities, the time since diagnosis, recovery progress, readmission record, and the effect of source variability. Conclusion The resultant eleven meta-clusters provide bases to comprehend the classification of patients with COVID-19 based on comorbidities, habits, demographic characteristics, geographic data and type of clinical institutions, as well as revealing the correlations between the above characteristics thereby help to anticipate the possible clinical outcomes for every specifically characterized patient. These subphenotypes can establish target groups for automated stratification or triage systems to provide personalized therapies or treatments. Code available at: https://github.com/bdslab-upv/covid19-metaclustering Dynamic results visualization at: http://covid19sdetool.upv.es/?tab=mexicoGov

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“…Moreover, survival analyses showed an almost four-fold increase of incident in-hospital mortality for the high risk compared to the low risk cluster. Although a previous study attempted to identify subtypes of Covid-19 patients, associating them with disease severity [33], this represents the first attempt to use clustering in disentangling the effect of HCQ on different types of patients, by testing associations with incident in-hospital mortality risk. Specifically, we tested and observed both additive and interactive associations of HCQ and Covid-19 subtypes.…”

Section: Discussionmentioning

confidence: 99%

“…The copyright holder for this preprint this version posted January 29, 2021. ; https://doi.org/10.1101/2021.01.27.21250238 doi: medRxiv preprint [33], this represents the first attempt to use clustering in disentangling the effect of HCQ on different types of patients, by testing associations with incident in-hospital mortality risk. Specifically, we tested and observed both additive and interactive associations of HCQ and Covid-19 subtypes.…”

Section: Discussionmentioning

confidence: 99%

Disentangling the association of hydroxychloroquine treatment with mortality in Covid-19 hospitalized patients through Hierarchical Clustering

Castelnuovo¹,

Gialluisi²,

Iacoviello³

2021

Preprint

View full text Add to dashboard Cite

The efficacy of hydroxychloroquine (HCQ) in treating SARS-CoV-2 infection is harshly debated, with observational and intervention studies reporting contrasting results.To clarify the role of HCQ in Covid-19 patients, we carried out a retrospective observational study of 4,396 unselected patients hospitalized for Covid-19 in Italy (February-May 2020). Patients’ characteristics were collected at entry, including age, sex, obesity, smoking status, blood parameters, history of diabetes, cancer, cardiovascular and chronic pulmonary diseases and medications in use. These were used to identify subtypes of patients with similar characteristics through hierarchical clustering based on Gower distance. Using multivariable Cox regressions, these clusters were then tested for association with mortality and modification of effect by treatment with HCQ.We identified two clusters, one of 3,913 younger patients with lower circulating inflammation levels and better renal function, and one of 483 generally older and more comorbid subjects, more prevalently men and smokers. The latter group was at increased death risk adjusted by HCQ (HR[CI95%] = 3.80[3.08-4.67]), while HCQ showed an independent inverse association (0.51[0.43-0.61]), as well as a significant influence of cluster*HCQ interaction (p<0.001). This was driven by a differential association of HCQ with mortality between the high (0.89[0.65-1.22]) and the low risk cluster (0.46[0.39-0.54]). These effects survived adjustments for additional medications in use and were concordant with associations with disease severity and outcome.These findings suggest a particularly beneficial effect of HCQ within low risk Covid-19 patients and may contribute clarifying the current controversy on HCQ efficacy in Covid-19 treatment.

show abstract

Identification of COVID-19 Clinical Phenotypes by Principal Component Analysis-Based Cluster Analysis

Cited by 9 publications

References 0 publications

Disentangling the Association of Hydroxychloroquine Treatment with Mortality in Covid-19 Hospitalized Patients through Hierarchical Clustering

Disentangling the Association of Hydroxychloroquine Treatment with Mortality in Covid-19 Hospitalized Patients through Hierarchical Clustering

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-gender unbiased meta-clustering technique

Disentangling the association of hydroxychloroquine treatment with mortality in Covid-19 hospitalized patients through Hierarchical Clustering

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