scholarly journals Correction to: Identifying high risk subgroups of MSM: a latent class analysis using two samples

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
M. Kumi Smith ◽  
Gabriella Stein ◽  
Weibin Cheng ◽  
William C. Miller ◽  
Joseph D. Tucker
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
M. Kumi Smith ◽  
Gabriella Stein ◽  
Weibin Cheng ◽  
William C. Miller ◽  
Joseph D. Tucker

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 694.3-695
Author(s):  
K. Wójcik ◽  
A. Ćmiel ◽  
A. Masiak ◽  
Z. Zdrojewski ◽  
R. Jeleniewicz ◽  
...  

Background:ANCA associated vasculitides (AAV) are a heterogeneous group of rare diseases with unknown etiology and the clinical spectrum ranging from life-threatening systemic disease, through single organ involvement to minor isolated skin changes. Thus there is an unmet need for phenotype identification especially among patients with granulomatosis with polyangiitis GPA, patients with microscopic polyangiitis MPA group seems to be more uniform. Recently, based on previous clustering analysis and clinical, histopathological, serological and prognostic aspects three subcategories of AAV have been proposed and named as: non-severe AAV, severe PR3-AAV and severe MPO-AAV [1].Objectives:In line with these attempts to subcategorize AAV we decided to use latent class analysis (LCA) on a large multicenter cohort of polish AAV patients from POLVAS [2] registry to identify potential new subphenotypes or confirm already proposed ones.Methods:Latent Class Analysis (LCA) approach was used as a model based clustering method of objects described by dichotomous (e.g., gender; ANCA status – cANCA, pANCA; organ involvement - skin, eye, ENT, respiratory, heart, GI, renal, urinary, CNS, peripheral nerves) and polytomous (number of relapses) variables supported by quantitative covariates (e.g., age at diagnosis, CRP at diagnosis, maximal serum creatinine concentration ever).Results:Results of LCA on our AAV group returned four class model of AAV subphenotypes, confirming existence of the previously proposed by Mahr at al. [1] and revealed fourth – previously not described clinically relevant subphenotype. To this fourth class - belong patients only with GPA, diagnosed at young age, with multiorgan involvement, high relapse rate and relatively high risk of death.Table 1.AAV subcategorization – summary of clinical characteristics and ANCA specificityLCA Class 1LCA Class 2LCA Class 3LCA Class 4No of patients13019410297AAV typeMainly GPAMainly GPAmainly MPAOnly GPAAge at diagnosisMiddle ageMiddle ageOldYoungMale/female ratio1:22:11:11:1Main organ involvementENT, respiratory, eyeRenal, respiratory, ENTRenal, respiratory, skinMultiorgan involvementRelapse rateintermediateintermediatelowhighModified class description (based on ref. [1])Non severe AAVSevere PR3 AAVSevere MPO AAVSevere non-renalPR3 AAVConclusion:Based on multiple clinical and serological variables LCA methodology identified 4-class subphenotypes model of AAV. Fourth-class is a new clinically important subphenotype including exclusively PR3-positive young AAV patients with multiorgan involvement, high risk of relapse and distinct mortality.References:[1]Mahr A, Specks U, Jayne D. Subclassifying ANCA-associated vasculitis: a unifying view of disease spectrum. Rheumatol Oxf Engl 2019;58:1707–9.https://doi.org/10.1093/rheumatology/kez148.[2]Wójcik K, Wawrzycka-Adamczyk K, Włudarczyk A, Sznajd J, Zdrojewski Z, Masiak A, i in. Clinical characteristics of Polish patients with ANCA-associated vasculitides—retrospective analysis of POLVAS registry. Clinical Rheumatology. 1 wrzesień 2019;38(9):2553–63.Disclosure of Interests:Krzysztof Wójcik: None declared, Adam Ćmiel: None declared, Anna Masiak: None declared, Zbigniew Zdrojewski: None declared, Radoslaw Jeleniewicz: None declared, Maria Majdan Consultant of: Roche, Amgen, Speakers bureau: Roche, Amgen, Iwona Brzosko: None declared, Marek Brzosko: None declared, Piotr Głuszko: None declared, Małgorzata Stasiek: None declared, Małgorzata Wisłowska: None declared, Joanna Kur-Zalewska: None declared, Marta Madej: None declared, Anna Hawrot-Kawecka: None declared, Hanna Storoniak: None declared, Barbara Bułło-Piontecka: None declared, Alicja Dębska-Ślizień: None declared, Eugeniusz Kucharz: None declared, Katarzyna Jakuszko: None declared, Jacek Musiał: None declared


2020 ◽  
Author(s):  
Fei Wang

BACKGROUND The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. OBJECTIVE This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. METHODS A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. RESULTS In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. CONCLUSIONS Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.


2014 ◽  
Vol 33 (4) ◽  
pp. S107-S108
Author(s):  
R. Shah ◽  
S. Bellamy ◽  
J. Diamond ◽  
E. Cantu ◽  
J. Flesch ◽  
...  

2021 ◽  
Author(s):  
Zhuang Liu ◽  
Yue Zhang ◽  
Ran Zhang ◽  
Rongxun Liu ◽  
Lijuan Liang ◽  
...  

Abstract Background: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. Methods: A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. Results: In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. Conclusions: Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.


2021 ◽  
Vol 8 ◽  
Author(s):  
Ming Fu ◽  
Xiangming Hu ◽  
Shixin Yi ◽  
Shuo Sun ◽  
Ying Zhang ◽  
...  

Background: There is controversy whether masked hypertension (MHT) requires additional intervention. The aim of this study is to evaluate whether MHT accompanied with high-risk metabolic syndrome (MetS), as the subphenotype, will have a different prognosis from low-risk MetS.Methods: We applied latent class analysis to identify subphenotypes of MHT, using the clinical and biological information collected from High-risk Cardiovascular Factor Screening and Chronic Disease Management Programme. We modeled the data, examined the relationship between subphenotypes and clinical outcomes, and further explored the impact of antihypertensive medication.Results: We included a total of 140 patients with MHT for analysis. The latent class model showed that the two-class (high/low-risk MetS) model was most suitable for MHT classification. The high-risk MetS subphenotype was characterized by larger waist circumference, lower HDL-C, higher fasting blood glucose and triglycerides, and prevalence of diabetes. After four years of follow-up, participants in subphenotype 1 had a higher non-major adverse cardiovascular event (MACE) survival probability than those in subphenotype 2 (P = 0.016). There was no interaction between different subphenotypes and the use of antihypertensive medications affecting the occurrence of MACE.Conclusions: We have identified two subphenotypes in MHT that have different metabolic characteristics and prognosis, which could give a clue to the importance of tracing the clinical correlation between MHT and metabolic risk factors. For patients with MHT and high-risk MetS, antihypertensive therapy may be insufficient.


2020 ◽  
Vol 111 (2) ◽  
pp. 174-199 ◽  
Author(s):  
Robin L. J. Lines ◽  
Monique Crane ◽  
Kagan J. Ducker ◽  
Nikos Ntoumanis ◽  
Cecilie Thøgersen‐Ntoumani ◽  
...  

2020 ◽  
pp. 004947552097159
Author(s):  
Mojtaba Salimi ◽  
Ebrahim Shakiba ◽  
Eslam Moradi-Asl ◽  
Abbas Abbasi-Ghahramanloo ◽  
Keyvan Khassi

Cutaneous leishmaniasis is one of the main health-economic problems around the world. Data were collected from all patients with cutaneous leishmaniasis referred to the health centres of Kermanshah province between 2013 and 2019. Latent class analysis was conducted by PROC LCA in SAS 9.2 and a significant level was set at 0.05. Four latent classes were identified: low (33.8%), moderate (9.8%), high (22.4%) and very high risk (34.0%). The probability of having a travel history was high in the third class. Our study indicated that having history of an eschar has no role in the classification of patients. On the other hand, a positive smear test is important in classifying subjects. Our results indicate that more than half of all patients fell under high risk or very high-risk class. This emphasises the importance of planning preventive intervention by considering different risk factors of cutaneous leishmaniasis simultaneously.


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