Applying latent class analysis to risk stratification of incident diabetes among Chinese adults

Wu, Yang; Hu, Haofei; Chen, Runtian; Zuo, Xi‐Nian; Cheng, Huilin; Yan, Dewen

doi:10.1016/j.diabres.2021.108742

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…Given comorbidity data inputs, these latent clusters represent patient subgroups with similar comorbidity profiles. LCA has been successfully implemented to describe endotypes in ARDS 12 , sepsis 15 , diabetes 24 , and obesity 25 .…”

Section: Introductionmentioning

confidence: 99%

Identification of clinically relevant patient endotypes in traumatic brain injury using latent class analysis

Qiu,

Zador,

Lannon

et al. 2024

Sci Rep

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Traumatic brain injury (TBI) is a complex condition where heterogeneity impedes the advancement of care. Understanding the diverse presentations of TBI is crucial for personalized medicine. Our study aimed to identify clinically relevant patient endotypes in TBI using latent class analysis based on comorbidity data. We used the Medical Information Mart for Intensive Care III database, which includes 2,629 adult TBI patients. We identified five stable endotypes characterized by specific comorbidity profiles: Heart Failure and Arrhythmia, Healthy, Renal Failure with Hypertension, Alcohol Abuse, and Hypertension. Each endotype had distinct clinical characteristics and outcomes: The Heart Failure and Arrhythmia endotype had lower survival rates than the Renal Failure with Hypertension despite featuring fewer comorbidities overall. Patients in the Hypertension endotype had higher rates of neurosurgical intervention but shorter stays in contrast to the Alcohol Abuse endotype which had lower rates of neurosurgical intervention but significantly longer hospital stays. Both endotypes had high overall survival rates comparable to the Healthy endotype. Logistic regression models showed that endotypes improved the predictability of survival compared to individual comorbidities alone. This study validates clinical endotypes as an approach to addressing heterogeneity in TBI and demonstrates the potential of this methodology in other complex conditions.

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Section: Introductionmentioning

confidence: 99%

Identification of clinically relevant patient endotypes in traumatic brain injury using latent class analysis

Qiu,

Zador,

Lannon

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Given comorbidity data inputs, these latent clusters represent patient subgroups with similar comorbidity pro les. LCA has been successfully implemented to describe endotypes in ARDS 12 , sepsis 15 , diabetes 24 , and obesity 25 .…”

Section: Introductionmentioning

confidence: 99%

Identification of Clinically Relevant Patient Endotypes in Traumatic Brain Injury Using Latent Class Analysis

Qiu

Zádor

Lannon

et al. 2023

Preprint

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Traumatic brain injury (TBI) is a complex condition where heterogeneity impedes the advancement of care. Understanding the diverse presentations of TBI is crucial for personalized medicine. Our study aimed to identify clinically relevant patient endotypes in TBI using latent class analysis based on comorbidity data. We used the Medical Information Mart for Intensive Care III database, which includes 2,629 adult TBI patients. We identified five stable endotypes characterized by specific comorbidity profiles: Heart Failure and Arrhythmia, Healthy, Renal Failure with Hypertension, Alcohol Abuse, and Hypertension. Each endotype had distinct clinical characteristics and outcomes: The Heart Failure and Arrhythmia endotype had lower survival rates than the Renal Failure with Hypertension despite featuring less comorbidities overall. Patients in the Hypertension endotype had higher rates of neurosurgical intervention but shorter stays in contrast to the Alcohol Abuse endotype which had lower rates of neurosurgical intervention but significantly longer hospital stays. Both endotypes had high overall survival rates comparable to the Healthy endotype. Logistic regression models showed that endotypes improved the predictability of survival compared to individual comorbidities alone. This study validates clinical endotypes as an approach to addressing heterogeneity in TBI, and demonstrates the potential of this methodology in other complex conditions.

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“…Latent class analysis (LCA) is a robust probabilistic approach which bases on the characteristics of the data. LCA can provide a more sensitive and effective classification to identify the subpopulation of individuals with a potential for depression episodes whose PHQ-9 scores are under 10 points cut-off ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

A study on differences about the influencing factors of depressive symptoms between medical staff and residents during 2022 city-wide temporary static management period to fighting against COVID-19 pandemic in Shanghai

Zhao

Tao²,

Bao³

et al. 2023

Front. Public Health

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ObjectivesOur study aimed to identify the latent class of depressive symptoms in the Shanghai population during the city-wide temporary static management period and compare differences in the factors influencing depressive symptoms between medical staff and residents.MethodsAn online cross-sectional survey was conducted with 840 participants using questionnaires, including Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Pittsburgh Sleep Quality Index (PSQI), and self-compiled questionnaire (demographic characteristics and internet usage time). Latent class analysis (LCA) was performed based on participants' depressive symptoms. The latent class subgroups were compared using the chi-square test and t-test. Logistic regression was used in our study to analyze the factors influencing depressive symptoms within the medical staff group and residents group and then compare their differences.ResultsTwo distinct subgroups were identified based on the LCA: the group with low-depressive symptoms and the group with high-depressive symptoms. There were significant differences between the two groups (P < 0.05) on age, education level, marital status, internet usage time, identity characteristics (medical staff or residents), family income level, living style, overall quality of sleep, and anxiety levels. Furthermore, logistic regression analysis results showed that compared with the residents group, the participants in the group of medical staff with “increasing internet usage time” and the “daytime dysfunction” would have nearly two times the possibility of getting serious depressive symptoms.ConclusionsThere are differences in the factors influencing depression symptoms between medical staff and residents during the 2022 city-wide temporary static management period to fighting against the COVID-19 pandemic in Shanghai. We should pay special attention to those with increasing internet usage time and daytime dysfunction in medical staff working in a special environment such as the COVID-19 pandemic.

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Applying latent class analysis to risk stratification of incident diabetes among Chinese adults

Cited by 6 publications

References 48 publications

Identification of clinically relevant patient endotypes in traumatic brain injury using latent class analysis

Identification of clinically relevant patient endotypes in traumatic brain injury using latent class analysis

Identification of Clinically Relevant Patient Endotypes in Traumatic Brain Injury Using Latent Class Analysis

A study on differences about the influencing factors of depressive symptoms between medical staff and residents during 2022 city-wide temporary static management period to fighting against COVID-19 pandemic in Shanghai

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