Validation of the Spanish Center for Epidemiological Studies Depression and Zung Self-Rating Depression Scales: A Comparative Validation Study

Abstract Background This study proposes a prediction model for the automatic assessment of lung cancer risk based on an artificial neural network (ANN) with a data-driven approach to the low-dose computed tomography (LDCT) standardized structure report. Methods This comparative validation study analysed a prospective cohort from Chiayi Chang Gung Memorial Hospital, Taiwan. In total, 836 asymptomatic patients who had undergone LDCT scans between February 2017 and August 2018 were included, comprising 27 lung cancer cases and 809 controls. A derivation cohort of 602 participants (19 lung cancer cases and 583 controls) was collected to construct the ANN prediction model. A comparative validation of the ANN and Lung-RADS was conducted with a prospective cohort of 234 participants (8 lung cancer cases and 226 controls). The areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were used to compare the prediction models. Results At the cut-off of category 3, the Lung-RADS had a sensitivity of 12.5%, specificity of 96.0%, positive predictive value of 10.0%, and negative predictive value of 96.9%. At its optimal cut-off value, the ANN had a sensitivity of 75.0%, specificity of 85.0%, positive predictive value of 15.0%, and negative predictive value of 99.0%. The area under the ROC curve was 0.764 for the Lung-RADS and 0.873 for the ANN (P = 0.01). The two most important predictors used by the ANN for predicting lung cancer were the documented sizes of partially solid nodules and ground-glass nodules. Conclusions Compared to the Lung-RADS, the ANN provided better sensitivity for the detection of lung cancer in an Asian population. In addition, the ANN provided a more refined discriminative ability than the Lung-RADS for lung cancer risk stratification with population-specific demographic characteristics. When lung nodules are detected and documented in a standardized structured report, ANNs may better provide important insights for lung cancer prediction than conventional rule-based criteria.

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Relationship and Differential Validity of Alexithymia and Depression: A Comparison of the Toronto Alexithymia and Self-Rating Depression Scales

Psychopathology ◽

10.1159/000070361 ◽

2003 ◽

Vol 36 (2) ◽

pp. 71-77 ◽

Cited By ~ 27

Author(s):

Jochen Müller ◽

Markus Bühner ◽

Heiner Ellgring

Keyword(s):

Differential Validity ◽

Self Rating ◽

Depression Scales

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A brief questionnaire is able to measure population physical activity levels accurately: A comparative validation study

Journal of Clinical Gerontology and Geriatrics ◽

10.1016/j.jcgg.2011.06.003 ◽

2011 ◽

Vol 2 (3) ◽

pp. 83-87 ◽

Cited By ~ 4

Author(s):

Stephanie Webster ◽

Asad Khan ◽

Jennifer Catherine Nitz

Keyword(s):

Physical Activity ◽

Validation Study ◽

Activity Levels ◽

Physical Activity Levels ◽

Comparative Validation

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Preoperative Grading System versus Intraoperative Grading System as Predictors for Difficult Laparoscopic Cholecystectomy: A Comparative Validation Study

Journal of Clinical and Basic Research ◽

10.29252/jcbr.2.1.39 ◽

2018 ◽

Vol 2 (1) ◽

pp. 39-47

Author(s):

Sudhir Sudhir M ◽

Pruthvi Raj ◽

◽

Keyword(s):

Laparoscopic Cholecystectomy ◽

Validation Study ◽

Grading System ◽

System Versus ◽

Comparative Validation

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Artificial neural networks improve LDCT lung cancer screening: A comparative validation study

10.21203/rs.3.rs-24642/v2 ◽

2020 ◽

Author(s):

Yin-Chen Hsu ◽

Yuan-Hsiung Tsai ◽

Hsu-Huei Weng ◽

Li-Sheng Hsu ◽

Ying-Huang Tsai ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Positive Predictive Value ◽

Prediction Model ◽

Negative Predictive Value ◽

Validation Study ◽

Predictive Value ◽

Prospective Cohort ◽

Lung Cancer Risk ◽

Comparative Validation

Abstract Background: This study proposes a prediction model for the automatic assessment of lung cancer risk based on an artificial neural network (ANN) with a data-driven approach to the low-dose computed tomography (LDCT) standardized structure report.Methods: This comparative validation study analysed a prospective cohort from Chiayi Chang Gung Memorial Hospital, Taiwan. In total, 836 asymptomatic patients who had undergone LDCT scans between February 2017 and August 2018 were included, comprising 27 lung cancer cases and 809 controls. A derivation cohort of 602 participants (19 lung cancer cases and 583 controls) was collected to construct the ANN prediction model. A comparative validation of the ANN and Lung-RADS was conducted with a prospective cohort of 234 participants (8 lung cancer cases and 226 controls). The areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were used to compare the prediction models.Results: At the cut-off of category 3, the Lung-RADS had a sensitivity of 12.5%, specificity of 96.0%, positive predictive value of 10.0%, and negative predictive value of 96.9%. At its optimal cut-off value, the ANN had a sensitivity of 75.0%, specificity of 85.0%, positive predictive value of 15.0%, and negative predictive value of 99.0%. The area under the ROC curve was 0.764 for the Lung-RADS and 0.873 for the ANN (P=0.01). The heatmap plot demonstrates the leading items, i.e., solid nodules, partially solid nodules, and ground-glass nodules, as the significant predictors of malignant outcomes.Conclusions: Compared to the Lung-RADS, the ANN provided better sensitivity for the detection of lung cancer in an Asian population. In addition, the ANN provided a more refined discriminative ability than the Lung-RADS for lung cancer risk stratification with population-specific demographic characteristics. When lung nodules are detected and documented in a standardized structured report, ANNs may better provide important insights for lung cancer prediction than conventional rule-based criteria.Trial registrationNot applicable.

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Online Mental Health Survey for Addressing Psychosocial Conditions During the COVID-19 Pandemic in Indonesia: Instrument Evaluation

10.21203/rs.3.rs-140796/v1 ◽

2021 ◽

Author(s):

Deni Sunjaya ◽

Bambang Sumintono ◽

Elvine Gunawan ◽

Dewi Herawati ◽

Teddy Hidayat

Keyword(s):

Rating Scale ◽

Virus Disease ◽

Depression Scale ◽

Epidemiological Studies ◽

Online Assessment ◽

Validity And Reliability ◽

Post Traumatic Stress ◽

Chi Square ◽

Self Rating ◽

Psychosocial Conditions

Abstract Background: Regular monitoring of the pandemic’s psychosocial impact could be conducted among the community but is limited through online media. This study aims to evaluate the self-rating questionnaire commonly used for online monitoring of the psychosocial implications of the corona virus disease 2019 (COVID-19) pandemic. Methods: The data was taken from the online assessment results of two groups, with a total of 765 participants. The instruments studied were: Self-Rating Questionnaire (SRQ-20), post-traumatic stress disorder (PTSD), and Center for Epidemiological Studies Depression Scale-10 (CESD-10), used in the online assessment. Data analysis used Rasch modeling and Winsteps applications. Validity and reliability were tested, data were fit with the model, rating scale, and item fit analysis.Results: All the scales for outfit mean square (MnSq) were very close to the ideal value of 1.0, and the Chi-square test was significant. Item reliability was greater than 0.67, item separation was greater than 3, and Cronbach’s alpha was greater than 0.60; all the instruments were considered very good. The raw variance explained by measures for the SRQ-20, PTSD, and CESD-10 was 30.7%, 41.6%, and 47.6%, respectively. The unexplained Eigen-value variances in the first contrast were 2.3, 1.6, and 2.0 for the SRQ-20, PTSD, and CESD-10, respectively. All items had positive point-measure correlations. Conclusions: The internal consistency of all the instruments was reliable. Data were fit to the model as the items were productive for measurement and had a reasonable prediction. All the scales are functionally one-dimensional.

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Identification and Reproducibility of Plasma Metabolomic Biomarkers of Habitual Food Intake in a US Diet Validation Study

Metabolites ◽

10.3390/metabo10100382 ◽

2020 ◽

Vol 10 (10) ◽

pp. 382

Author(s):

Ying Wang ◽

Rebecca A. Hodge ◽

Victoria L. Stevens ◽

Terryl J. Hartman ◽

Marjorie L. McCullough

Keyword(s):

Validation Study ◽

Intraclass Correlation ◽

Correlation Coefficients ◽

Ethnically Diverse ◽

Epidemiological Studies ◽

Food Groups ◽

Intraclass Correlation Coefficients ◽

Bonferroni Adjustment ◽

Absolute Correlation ◽

Potential Use

Previous metabolomic studies have identified putative blood biomarkers of dietary intake. These biomarkers need to be replicated in other populations and tested for reproducibility over time for the potential use in future epidemiological studies. We conducted a metabolomics analysis among 671 racially/ethnically diverse men and women included in a diet validation study to examine the correlation between >100 food groups/items (101 by a food frequency questionnaire (FFQ), 105 by 24-h diet recalls (24HRs)) with 1141 metabolites measured in fasting plasma sample replicates, six months apart. Diet–metabolite associations were examined by Pearson’s partial correlation analysis. Biomarker reproducibility was assessed using intraclass correlation coefficients (ICCs). A total of 677 diet–metabolite associations were identified after Bonferroni adjustment for multiple comparisons and restricting absolute correlation coefficients to greater than 0.2 (601 associations using the FFQ and 395 using 24HRs). The median ICCs of the 238 putative biomarkers was 0.56 (interquartile range 0.46–0.68). In this study, with repeated FFQs, 24HRs and plasma metabolic profiles, we identified several potentially novel food biomarkers and replicated others found in our previous study. Our findings contribute to the growing literature on food-based biomarkers and provide important information on biomarker reproducibility which could facilitate their utilization in future nutritional epidemiological studies.

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