One-year follow up of cardiac anxiety after a myocardial infarction: A latent class analysis

2012 ◽  
Vol 73 (5) ◽  
pp. 362-368 ◽  
Author(s):  
M.H.C.T. van Beek ◽  
M. Mingels ◽  
R.C. Oude Voshaar ◽  
A.J.L.M. van Balkom ◽  
M. Lappenschaar ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Cardiac Anxiety ◽  
One Year

scholarly journals Recovery Trajectories Over Six Weeks in Patients Selected for A High-Intensity Physiotherapy Program After Total Knee Arthroplasty: A Latent Class Analysis

2020 ◽  
Author(s):  
K.E.M. Harmelink ◽  
R. Dandis ◽  
P.J. Wees ◽  
A.V.C.M. Zeegers ◽  
M.W. Nijhuis-van der Sanden ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Physical Functioning ◽  
Latent Class ◽  
Class Analysis ◽  
Timed Up And Go ◽  
Surgery Outcome ◽  
Post Surgery ◽  
Multivariable Regression ◽  
The Difference ◽  
One Year

Abstract Background: Recovery trajectories differ between individual patients and it is hypothesizes that they can be used to predict if an individual patient is likely to recover earlier or later. Primary aim of this study was to determine if it is possible to identify recovery trajectories for physical functioning and pain during the first six weeks in patients after TKA. Secondary aim was to explore the association of these trajectories with one-year outcomes. Methods: Prospective cohort study of 218 patients with the following measurement time points: preoperative, and at three days, two weeks, six weeks, and one year post-surgery. Outcome measures were performance-based physical functioning (Timed Up and Go [TUG]), self-reported physical functioning (Knee injury and Osteoarthritis Outcome Score-Activities of Daily Living [KOOS-ADL]), and pain (Visual Analogue Scale [VAS]). Latent Class Analysis was used to distinguish classes based on recovery trajectories over the first six weeks postoperatively. Multivariable regression analyses were used to identify associations between classes and one year outcomes.Results: TUG showed three classes: “gain group” (n=203), “moderate gain group” (n=8) and “slow gain group” (n=7), KOOS showed two classes: “gain group” (n=86) and “moderate gain group” (n=132), and VAS-pain three classes: “no/very little pain” (n=151), “normal decrease of pain” (n=48) and “sustained pain” (n=19). The ”low gain group” scored 3.31 [95% CI 1.52, 5.09] seconds less on the TUG than the “moderate gain group” group, and the KOOS “gain group” scored 11.97 [95% CI 8.62, 15.33] points better than the “moderate gain group” after one year.Patients who had an early trajectory of “sustained pain” had less chance to become free of pain at one year than those who reported “no or little pain” (odds ratio 0.11 [95% CI 0.03,0.42]. Conclusion: The findings of this study indicate that different recovery trajectories can be detected. Especially the difference in TUG and KOOS-ADL between the “gain” and “moderate gain” group was considered clinically relevant, while for VAS scores differences between “no/very little pain,” and “sustained pain” were statistically significant but small.

Course of Depressive Symptoms After Myocardial Infarction and Cardiac Prognosis: A Latent Class Analysis

2006 ◽  
Vol 68 (5) ◽  
pp. 662-668 ◽  
Author(s):  
Kirsten I. Kaptein ◽  
Peter de Jonge ◽  
Rob H. S. van den Brink ◽  
Jakob Korf
Keyword(s):  
Myocardial Infarction ◽  
Depressive Symptoms ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis

Adherence styles of schizophrenia patients identified by a latent class analysis of the Medication Adherence Rating Scale (MARS): A six-month follow-up study

2012 ◽  
Vol 200 (2-3) ◽  
pp. 83-88 ◽  
Author(s):  
Susanne Jaeger ◽  
Carmen Pfiffner ◽  
Prisca Weiser ◽  
Reinhold Kilian ◽  
Thomas Becker ◽  
...  
Keyword(s):  
Medication Adherence ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Rating Scale ◽  
Class Analysis ◽  
Follow Up Study

scholarly journals Sleep disturbance subtypes in Parkinson’s disease based on the cross-culturally validated Korean version of the Parkinson’s Disease Sleep Scale-2

2019 ◽  
Author(s):  
Hui-Jun Yang ◽  
Han-Joon Kim ◽  
Seong-Beom Koh ◽  
Joong-Seok Kim ◽  
Tae-Beom Ahn ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Latent Class Analysis ◽  
Sleep Disturbance ◽  
Latent Class ◽  
Intraclass Correlation ◽  
Class Analysis ◽  
Korean Version ◽  
The Cross

Abstract Background: Sleep-related problems in Parkinson’s disease (PD) have received greater attention in recent years due to their clinical influence on morbidity, disability, and the health-related quality of life (HRQoL) of patients. This study aimed to evaluate the clinimetric properties of the Korean version of the Parkinson’s Disease Sleep Scale-2 (K-PDSS-2), and to analyze whether distinct sleep disturbance subtypes could be empirically identified in patients with PD based on the cross-culturally validated K-PDSS-2. Methods: The internal consistency, test-retest reliability, scale precision, and convergent validity of the K-PDSS-2 were assessed in a nationwide, multicenter study of 122 patients with PD. Latent class analysis (LCA) was used to derive subgroups of patients who experienced similar patterns of sleep-related problems and nocturnal disabilities. Results: The mean total K-PDSS-2 scores were 11.67 ± 9.87 (mean ± standard deviation) at baseline, and 12.61 ± 11.17 upon follow up testing. The Cronbach’s α coefficients of the total K-PDSS-2 score at baseline and at follow up testing were 0.851 and 0.880 respectively. Intraclass correlation coefficient over the 2-week period ranged from 0.672 to 0.848. The total K-PDSS-2 score was strongly correlated to HRQoL measures and other corresponding nonmotor scales. LCA indicated three distinct sleep disturbance classes in the study patients, namely “less troubled sleepers”, “PD-related nocturnal difficulties”, and “disturbed sleepers”. Conclusions: The K-PDSS-2 showed good clinimetric attributes in accordance with prior studies that were using the original version of the PDSS-2, therefore confirming the cross-cultural usefulness of the scale. Further, this study documents the first application of an LCA approach for identifying sleep disturbance subtypes in patients with PD. Keywords: Parkinson’s disease; sleep; PDSS-2; validity; reliability; Korean version; latent class analysis.

A Latent Class Analysis Approach to Extradyadic Involvement

2018 ◽  
Vol 9 ◽  
Author(s):  
Lindsey M. Rodriguez ◽  
Angelo M. DiBello ◽  
Camilla S. Øverup ◽  
Helen Lee Lin
Keyword(s):  
Latent Class Analysis ◽  
Undergraduate Students ◽  
Romantic Relationship ◽  
Latent Class ◽  
Investment Model ◽  
Relationship Functioning ◽  
Class Analysis ◽  
Sexual Involvement ◽  
Two Samples

Extradyadic involvement — emotional, romantic, or sexual involvement with another person outside of one's romantic relationship — may have serious personal and relational consequences. The current research examines extradyadic involvement in two samples of individuals in relationships and identifies subgroups of people based on their engagement in different types of extradyadic behaviour. To assess involvement in such behaviour, we created a new behavioural inventory intended to broaden the conceptualisation of types of extradyadic behaviours. Subgroups of individuals who engage in these behaviours were extracted using latent class analysis. Study 1 assessed undergraduate students in relationships (N = 339), and results revealed four classes of individuals: loyal, confiding, deceptive, and unfaithful. Follow-up tests demonstrated that these classes of individuals differed significantly in ways that are consistent with the investment model and attachment theory. Study 2 (N = 202) replicated the four-class solution, as well as the group differences in relationship functioning and attachment orientations. Results suggest theoretically consistent typologies of extradyadic behaviour that may be useful in differentiating deceptive behaviour in close relationships in a more precise way.

scholarly journals Identification of three classes of acute respiratory distress syndrome using latent class analysis

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4592 ◽  
Author(s):  
Zhongheng Zhang
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Latent Class Analysis ◽  
Fluid Balance ◽  
Latent Class ◽  
Distress Syndrome ◽  
Class Analysis ◽  
Class 1

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous syndrome that can exhibit significant differences in the underlying causes, leading to different responses to treatment. It is required to identify subtypes of ARDS to guideline clinical treatment and trial design. The study aimed to identify subtypes of ARDS using latent class analysis (LCA). The study was a secondary analysis of the EDEN study, which was a randomized, controlled, multicenter trial conducted from January 2, 2008 to April 12, 2011. The primary study endpoint was death through 90-day follow up. LCA was performed incorporating variables on day 0 before randomization. The number of classes was chosen by a bootstrapped likelihood ratio test, Bayesian information criterion and the number of patients in each class. A total of 943 patients were enrolled in the study, including 219 (23.2%) non-survivors and 724 (76.8%) survivors. The LCA identified three classes of ARDS. Class 1 (hemodynamically unstable type) had significantly higher mortality rate (p = 0.003) than class 2 (intermediate type) and 3 (stable type) through 90 days follow up. There was significant interaction between cumulative fluid balance and the class (p = 0.02). While more fluid balance was beneficial for class 1, it was harmful for class 2 and 3. In conclusion, the study identified three classes of ARDS, which showed different clinical presentations, responses to fluid therapy and prognosis. The classification system used simple clinical variables and could help to design ARDS trials in the future.

scholarly journals Latent class analysis of 216 patients with adult-onset Still’s disease

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
Takahiro Sugiyama ◽  
Shunsuke Furuta ◽  
Masaki Hiraguri ◽  
Kei Ikeda ◽  
Yosuke Inaba ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Disease Onset ◽  
The Elderly ◽  
Event Analysis ◽  
Class Analysis ◽  
Time To Event ◽  
Class 1 ◽  
Elderly Onset

Abstract Background Adult-onset Still’s disease (AOSD) is a rare systemic autoinflammatory disease which encompasses patients with heterogenous presentation and a wide range of clinical courses. In this study, we aimed to identify potential subgroups of AOSD and reveal risk factors for relapse. Methods We included a total of 216 AOSD patients who received treatment in nine hospitals between 2000 and 2019. All patients fulfilled the Yamaguchi classification criteria. We retrospectively collected information about baseline characteristics, laboratory tests, treatment, relapse, and death. We performed latent class analysis and time-to-event analysis for relapse using the Cox proportional hazard model. Results The median age at disease onset was 51.6 years. The median follow-up period was 36.8 months. At disease onset, 22.3% of the patients had macrophage activation syndrome. The median white blood cell count was 12,600/μL, and the median serum ferritin level was 7230 ng/mL. Systemic corticosteroids were administered in all but three patients (98.6%) and the median initial dosage of prednisolone was 40mg/day. Ninety-six patients (44.4%) were treated with concomitant immunosuppressants, and 22 (10.2%) were treated with biologics. Latent class analysis revealed that AOSD patients were divided into two subgroups: the typical group (Class 1: 71.8%) and the elderly-onset group (Class 2: 28.2%). During the follow-up period, 13 of 216 patients (6.0%) died (12 infections and one senility), and 76 of 216 patients (35.1%) experienced relapses. Overall and relapse-free survival rates at 5 years were 94.9% and 57.3%, respectively, and those rates were not significantly different between Class 1 and 2 (p=0.30 and p=0.19). Time-to-event analysis suggested higher neutrophil count, lower hemoglobin, and age ≥65 years at disease onset as risk factors for death and age ≥65 years at disease onset as a risk factor for relapse. Conclusions AOSD patients were divided into two subgroups: the typical group and the elderly-onset group. Although the survival of patients with AOSD was generally good, the patients often experienced relapses. Age ≥65 years at disease onset was the risk factor for relapse.

scholarly journals Attitudes and Behaviors of Patients With Rheumatic Diseases During the Early Stages of the COVID-19 Outbreak

2020 ◽  
Vol 48 (1) ◽  
pp. 35-39 ◽  
Author(s):  
Margaret H.Y. Ma ◽  
Sen Hee Tay ◽  
Peter P.M. Cheung ◽  
Amelia Santosa ◽  
Yiong Huak Chan ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Rheumatic Diseases ◽  
Online Survey ◽  
Latent Class ◽  
Text Message ◽  
Class Analysis ◽  
Attitudes And Behaviors ◽  
Early Stages ◽  
And Behaviors

Objective.To evaluate attitudes and behaviors of patients with rheumatic diseases during the coronavirus disease 2019 (COVID-19) pandemic.Methods.An online survey delivered by text message to 4695 patients on follow-up at a tertiary rheumatology center. Latent class analysis was performed on the survey variables.Results.There were 2239 (47.7%) who responded to the survey and 3 clusters were identified. Cluster 3 (C3) was defined by patients who were most worried about COVID-19, more likely to wear face masks, and more likely to alter or stop their medications. Patients in C3 were more likely to be female, Malay, and unemployed.Conclusion.We identified 3 clusters with different healthcare beliefs and distinct sociodemographics.

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