Initial classification of joint data in EM estimation of latent class joint model

The purpose of the chapter is to elucidate the key issues regarding the classification of eating disorders. To this end, a review of nosological research in the area of eating disorders is presented, with a particular focus on empirically based techniques such as taxometric and latent class analysis. This is followed by a section outlining areas of overlap between the current Diagnostic and Statistical Manual of Mental Disorders – Fourth Edition, Text Revision (DSM-IV-TR; American Psychiatric Association, 2000) eating disorder categories and their symptoms. Next, eating disorder classification models that are alternatives to the DSM-IV-TR are described and critically examined in light of available empirical data. Finally, areas of controversy and considerations for change in next version of the DSM (i.e., the applicability of DSM criteria to minority groups, children, males; the question of whether clinical categories should be differentiated from research categories) are discussed.

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ST-Elevation Myocardial Infarction and COVID-19 Pneumonia: The Importance of Risk-Stratification of Myocardial Injury in COVID-19 Era

Indian Journal of Clinical Cardiology ◽

10.1177/2632463621992389 ◽

2021 ◽

pp. 263246362199238

Author(s):

Julio C. Sauza-Sosa ◽

Oscar Millan-Iturbe ◽

Jorge Mendoza-Ramirez ◽

Carlos N. Velazquez-Gutierrez ◽

Erika Lizeth De la Cruz Reyna ◽

...

Keyword(s):

Risk Stratification ◽

Injury Risk ◽

Myocardial Injury ◽

Cardiovascular Complications ◽

Adverse Outcomes ◽

St Elevation ◽

Diagnostic Coronary Angiography ◽

Serial Measurements ◽

Initial Classification

Background: Myocardial injury is a common manifestation in patients with coronavirus disease (COVID-19), and the correlation with adverse outcomes has been demonstrated; therefore, adequate monitoring of myocardial injury markers is very important. Case Summary: A patient with COVID-19 was hospitalized in our hospital with an initial classification of intermediate risk for myocardial injury, after serial measurements of myocardial injury markers, risk was readjusted to high, as shown later by electrocardiographic abnormalities. The patient underwent emergency diagnostic coronary angiography and successful angioplasty. The patient was discharged to home. Discussion: Myocardial injury risk-stratification is essential in patients with COVID-19, since it is essential in the recognition of patients who are susceptible to cardiovascular complications.

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Software systems All swept up: An initial classification of NSA surveillance technology

Safety and Reliability: Methodology and Applications ◽

10.1201/b17399-93 ◽

2014 ◽

pp. 679-686

Keyword(s):

Software Systems ◽

Surveillance Technology ◽

Initial Classification

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Voluntary Climate Change Mitigation Actions of Young Adults: A Classification of Mitigators through Latent Class Analysis

PLoS ONE ◽

10.1371/journal.pone.0102072 ◽

2014 ◽

Vol 9 (7) ◽

pp. e102072 ◽

Cited By ~ 4

Author(s):

Essi A. E. Korkala ◽

Timo T. Hugg ◽

Jouni J. K. Jaakkola

Keyword(s):

Climate Change ◽

Young Adults ◽

Latent Class Analysis ◽

Climate Change Mitigation ◽

Latent Class ◽

Class Analysis ◽

Change Mitigation

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Classification of multiple sclerosis patients by latent class analysis of magnetic resonance imaging characteristics

Multiple Sclerosis Journal ◽

10.1177/1352458506070759 ◽

2006 ◽

Vol 12 (5) ◽

pp. 565-572 ◽

Cited By ~ 6

Author(s):

J NP Zwemmer ◽

J Berkhof ◽

J A Castelijns ◽

F Barkhof ◽

C H Polman ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Latent Class Analysis ◽

Latent Class ◽

Class Analysis ◽

Resonance Imaging ◽

Disease Characteristics ◽

Mri Characteristics ◽

The Brain ◽

Laboratory Measures

Background Disease heterogeneity is a major issue in multiple sclerosis (MS). Classification of MS patients is usually based on clinical characteristics. More recently, a pathological classification has been presented. While clinical subtypes differ by magnetic resonance imaging (MRI) signature on a group level, a classification of individual MS patients based purely on MRI characteristics has not been presented so far. Objectives To investigate whether a restricted classification of MS patients can be made based on a combination of quantitative and qualitative MRI characteristics and to test whether the resulting subgroups are associated with clinical and laboratory characteristics. Methods MRI examinations of the brain and spinal cord of 50 patients were scored for 21 quantitative and qualitative characteristics. Using latent class analysis, subgroups were identified, for whom disease characteristics and laboratory measures were compared. Results Latent class analysis revealed two subgroups that mainly differed in the extent of lesion confluency and MRI correlates of neuronal loss in the brain. Demographics and disease characteristics were comparable except for cognitive deficits. No correlations with laboratory measures were found. Conclusions Latent class analysis offers a feasible approach for classifying subgroups of MS patients based on the presence of MRI characteristics. The reproducibility, longitudinal evolution and further clinical or prognostic relevance of the observed classification will have to be explored in a larger and independent sample of patients.

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A Latent Classification of Male Batterers

Violence and Victims ◽

10.1891/0886-6708.24.4.419 ◽

2009 ◽

Vol 24 (4) ◽

pp. 419-438 ◽

Cited By ~ 21

Author(s):

Anne M. Mauricio ◽

Frederick G. Lopez

Keyword(s):

Personality Disorders ◽

Adult Attachment ◽

Latent Class ◽

Borderline Personality ◽

Anxious Attachment ◽

Attachment Orientations ◽

Attachment Orientation ◽

High Level ◽

Latent Classification

Regression latent class analysis was used to identify batterer subgroups with distinct violence patterns and to examine associations between class membership and adult attachment orientations as well as antisocial and borderline personality disorders. Results supported three batterer subgroups, with classes varying on frequency and severity of violence. The high-level violence class represented 40% of batterers, and both anxious and avoidant adult attachment orientations as well as borderline personality characteristics predicted membership in this class. The moderate-level violence class represented 35% of the batterers, and adult anxious attachment orientation was associated with membership in this class. The low-level violence class represented 25% of the sample and reported significantly less violence than other classes. Neither adult attachment orientations nor personality disorders predicted membership in this class.

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Integrating latent classes in the Bayesian shared parameter joint model of longitudinal and survival outcomes

Statistical Methods in Medical Research ◽

10.1177/0962280220924680 ◽

2020 ◽

Vol 29 (11) ◽

pp. 3294-3307

Author(s):

Eleni-Rosalina Andrinopoulou ◽

Kazem Nasserinejad ◽

Rhonda Szczesniak ◽

Dimitris Rizopoulos

Keyword(s):

Cystic Fibrosis ◽

Lung Function ◽

Latent Class ◽

Joint Model ◽

Forced Expiratory Volume ◽

Optimal Number ◽

Latent Classes ◽

Joint Models ◽

Number Of Classes ◽

Strength Of Association

Cystic fibrosis is a chronic lung disease requiring frequent lung-function monitoring to track acute respiratory events (pulmonary exacerbations). The association between lung-function trajectory and time-to-first exacerbation can be characterized using joint longitudinal-survival modeling. Joint models specified through the shared parameter framework quantify the strength of association between such outcomes but do not incorporate latent sub-populations reflective of heterogeneous disease progression. Conversely, latent class joint models explicitly postulate the existence of sub-populations but do not directly quantify the strength of association. Furthermore, choosing the optimal number of classes using established metrics like deviance information criterion is computationally intensive in complex models. To overcome these limitations, we integrate latent classes in the shared parameter joint model through a fully Bayesian approach. To choose the optimal number of classes, we construct a mixture model assuming more latent classes than present in the data, thereby asymptotically “emptying” superfluous latent classes, provided the Dirichlet prior on class proportions is sufficiently uninformative. Model properties are evaluated in simulation studies. Application to data from the US Cystic Fibrosis Registry supports the existence of three sub-populations corresponding to lung-function trajectories with high initial forced expiratory volume in 1 s ( FEV1), rapid FEV1 decline, and low but steady FEV1 progression. The association between FEV1 and hazard of exacerbation was negative in each class, but magnitude varied.

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Sensitivity, Specificity and Predictive Values of Molecular and Serological Tests for COVID-19: A Longitudinal Study in Emergency Room

Diagnostics ◽

10.3390/diagnostics10090669 ◽

2020 ◽

Vol 10 (9) ◽

pp. 669 ◽

Cited By ~ 4

Author(s):

Zeno Bisoffi ◽

Elena Pomari ◽

Michela Deiana ◽

Chiara Piubelli ◽

Niccolò Ronzoni ◽

...

Keyword(s):

Emergency Room ◽

Latent Class ◽

Single Gene ◽

Molecular Test ◽

Clinical Context ◽

Serological Tests ◽

Predictive Values ◽

Molecular Tests ◽

Sensitivity Specificity

Background: We assessed the sensitivity, specificity and positive and negative predictive value (PPV and NPV) of molecular and serological tests for the diagnosis of SARS-CoV-2 infection. Methods: A total of 346 patients were enrolled in the emergency room. We evaluated three Reverse Transcriptase-real time PCRs (RT-PCRs) including six different gene targets, five serologic rapid diagnostic tests (RDT) and one ELISA. The final classification of infected/non-infected patients was performed using Latent Class Analysis combined with clinical re-assessment of incongruous cases. Results: Out of these, 24.6% of patients were classified as infected. The molecular test RQ-SARS-nCoV-2 showed the highest performance with 91.8% sensitivity, 100% specificity, 100.0% PPV and 97.4% NPV respectively. Considering the single gene targets, S and RdRp of RQ-SARS-nCoV-2 had the highest sensitivity (94.1%). The in-house RdRp presented the lowest sensitivity (62.4%). The specificity ranged from 99.2% for in-house RdRp and N2 to 95.0% for E. The PPV ranged from 97.1% of N2 to 85.4% of E and the NPV from 98.1% of S to 89.0% of in-house RdRp. All serological tests had < 50% sensitivity and low PPV and NPV. VivaDiag IgM (RDT) had 98.5% specificity, with 84.0% PPV, but 24.7% sensitivity. Conclusion: Molecular tests for SARS-CoV-2 infection showed excellent specificity, but significant differences in sensitivity. Serological tests have limited utility in a clinical context.

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The vulnerability analysis of catchment areas using rapid assessment method (case study in Universitas Indonesia, Depok)

MATEC Web of Conferences ◽

10.1051/matecconf/201819202033 ◽

2018 ◽

Vol 192 ◽

pp. 02033

Author(s):

Eva Rolia ◽

Dwita Sutjiningsih ◽

Fitria Fitria

Keyword(s):

Catchment Area ◽

Rapid Assessment ◽

Buffer Zone ◽

Assessment Method ◽

Rapid Assessment Method ◽

Catchment Areas ◽

High Level ◽

Initial Classification

Catchment area of aquatic system in Universitas Indonesia (UI) campus has a function as groundwater recharge for Depok area, which is the buffer zone of Jakarta. This catchment area has high level of imperviousness as a consequence of increasing population. The high imperviousness implicates in the degradation of the catchment area health. For that reason, the vulnerability assessment of UI catchment area is needed to arrange of restoration recommendation. This research applies Rapid Assessment Method which implements eco-hydrological concepts including aquatic and riparian condition as well as imperviousness of catchment area. Locations of the research are Kenanga and Agathis catchments area. The result of initial classification based imperviousness both catchment areas are categorised as Non-Supporting, where the imperviousness of Kenanga and Agathis is 31,0% and 77,18% respectivelly. Based on the assessment on aquatic and riparian condition as well as future land use, the final classification of both catchment areas is Restorable Non-Supporting catchment area.

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