Department of Human Genetics, Section Oncogenetics, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands j.delange1@amsterdamumc.nl.
Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, Netherlands.
Cancer Center Amsterdam, Department of Clinical Genetics, section Oncogenetics, Amsterdam University Medical Centers, Amsterdam, the Netherlands. Electronic address: r.wolthuis@amsterdamumc.nl.
Life Sciences Institute, State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital of Zhejiang University School of Medicine, MOE Laboratory of Biosystems Homeostasis and Protection, Zhejiang University, Hangzhou, 310058, China. fwwang@zju.edu.cn.
Department of Gynecological Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China. fwwang@zju.edu.cn.
Cancer Center, Zhejiang University, Hangzhou, 310058, China. fwwang@zju.edu.cn.
Zhejiang Provincial Key Laboratory of Geriatrics and Geriatrics Institute of Zhejiang Province, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China. fwwang@zju.edu.cn.
Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Guangdong Key Laboratory for Genome Stability & Disease Prevention, School of Basic Medical Sciences, Shenzhen University Medical School, South China Hospital, Shenzhen 518116. State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Guangdong Key Laboratory for Genome Stability & Disease Prevention, School of Basic Medical Sciences, Shenzhen University Medical School, South China Hospital, Shenzhen 518116. State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Guangdong Key Laboratory for Genome Stability & Disease Prevention, School of Basic Medical Sciences, Shenzhen University Medical School, South China Hospital, Shenzhen 518116. State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
The prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrom...
In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third pha...
Based on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and t...
Based on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed be...
Latent class analysis (LCA) offers a powerful analytical approach for categorizing groups (or "classes") within a heterogenous population. LCA identifies these hidden classes by a set of predefined fe...
Borderline personality disorder (BPD) is characterized by instability in interpersonal, affective, cognitive, self-identity, and behavioral domains. For a BPD diagnosis, individuals must present at le...
South Africa has the largest burden of HIV worldwide and has a growing burden of non-communicable diseases; the combination of which may lead to diseases clustering in ways that are not seen in other ...
Data were analyzed from the South African Demographic and Health Survey 2016. A latent class analysis (LCA) was conducted using nine disease conditions. Sociodemographic and behavioral factors associa...
Multimorbid participants were included (...
This study affirmed that integrated care is urgently needed, evidenced by the largest disease class being an overlap of chronic infectious diseases and non-communicable diseases. This study also highl...
We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates a...
Neonatal risk factors, such as preterm birth and low birth weight, have been robustly linked to neurodevelopmental deficits, yet it is still unclear why some infants born preterm and/or low birth weig...
Neonates who received neonatal care at an academic public hospital during an almost 10-year period (n = 19,951) were included in the latent class analysis, and 21 neonatal indicators of health were us...
The best fitting model included five infant classes: healthy, hypoxic, critically ill, minorly ill, and complicated delivery. Scores on the parent-rated neurodevelopmental measure differed by class su...
The current study extends the understanding of risk factors in neurodevelopment by including several neonatal medical conditions that are often overlooked and by using a person-centered, as opposed to...
An intersectionality framework recognizes individuals as simultaneously inhabiting multiple intersecting social identities embedded within systems of disadvantage and privilege. Previous research link...
We analyzed data from a cohort of 2,286 pregnant participants (Black, n = 933; Hispanic, n = 471; White, n = 853; and Other, n = 29) from the Centering and Racial Disparities trial. Perceived discrimi...
Four discrimination subgroups were identified: no discrimination, general discrimination, discrimination attributed to one or several social identities, and discrimination attributed to most or all so...
Perceived discrimination may play an influential role in shaping perinatal health. More research applying an intersectional lens to the study of discrimination and perinatal health outcomes is needed....
Recent studies have shown that anticoagulant therapy has heterogeneous treatment effects on patients with sepsis-induced coagulopathy (SIC)....
To identify the latent phenotypes of patients with SIC....
Retrospective cohort study....
We obtained data of patients with SIC from the Medical Information Mart for Intensive Care IV database. SIC subphenotypes were identified by latent class analysis (LCA) and K-means clustering. Clinica...
We identified 4,993 patients with SIC. The LCA and K-means clustering analysis robustly identified three subphenotypes of SIC. Class 1 patients (n = 1,808) had the lowest blood cell counts (leukocytes...
Three SIC subphenotypes were defined using clinical findings and laboratory variables. The effects of heparin treatment differ between the subphenotypes. This finding will facilitate the identificatio...
Despite evidence demonstrating the effectiveness of the COVID-19 vaccine, vaccine hesitancy has emerged as a major challenge for vaccine uptake. The objective of this study was to classify latent typo...
We employed a cross-sectional household survey among 1,112 individuals aged 18 and above who were partially vaccinated (one dose) or not vaccinated at the time of the survey. Data was collected in Aug...
Using latent class analysis we found a four-class solution for vaccine hesitancy typologies. The identified classes were strong vaccine acceptors (30%); vaccine acceptors with some concerns (7%); vacc...
Half of the study participants were in the vaccine rejectors class. Individuals in the vaccine sceptics and rejector classes evidenced lower vaccine knowledge and worse COVD-19 prevention practices an...
General practitioners (GPs) were on the front line of the COVID-19 outbreak. Identifying clinical profiles in COVID-19 might improve patient care and enable closer monitoring of at-risk profiles....
To identify COVID-19 profiles in a population of adult primary care patients, and to determine whether the profiles were associated with negative outcomes and persistent symptoms....
In a prospective multicentre study, 44 GPs from multiprofessional primary care practices in the Paris area of France recruited 340 consecutive adult patients (median age: 47 years) with a confirmed di...
A latent class (LC) analysis with 11 indicators (clinical signs and symptoms) was performed. The resulting profiles were characterised by a 3-month composite outcome (COVID-19-related hospital admissi...
We identified six profiles: 'paucisymptomatic' (LC1, 9%), 'anosmia and/or ageusia' (LC2, 12.9%), 'influenza-like syndrome with anosmia and ageusia' (LC3, 15.5%), 'influenza-like syndrome without anosm...
Our findings might help GPs to identify patients at risk of persistent COVID-19 symptoms and hospital admission and then set up procedures for closer monitoring....
