Systematic Mapping of Methodologies Used for the Validation of Models and Scales Derived From e-SQ

Electronic Service Quality (e-SQ) is a topic that has been reviewed by several authors and, given the situation we live in, has become more relevant in the world; therefore, it is of vital importance to generate an effective measurement of the service being provided, which will allow companies to know the needs and expectations of their customers and how they evaluate the service received. This article considers an exploratory-descriptive research, which through a systematic literature mapping (MSL) reviews the methods and techniques that have been applied for the validity of the scales derived from e-SQ, thus generating a focused view of the methods applied in scientific research in the last five years. With the results obtained, a theoretical model for validating a scale is generated and proposed, which could be useful for researchers seeking to confirm and validate their scales or who are in the process of developing a research project.

Validation of models using basic parameters to differentiate intestinal tuberculosis from Crohn’s disease: A multicenter study from Asia

Background Data on external validation of models developed to distinguish Crohn’s disease (CD) from intestinal tuberculosis (ITB) are limited. This study aimed to validate and compare models using clinical, endoscopic, and/or pathology findings to differentiate CD from ITB. Methods Data from newly diagnosed ITB and CD patients were retrospectively collected from 5 centers located in Thailand or Hong Kong. The data was applied to Lee, et al., Makharia, et al., Jung, et al., and Limsrivilai, et al. model. Results Five hundred and thirty patients (383 CD, 147 ITB) with clinical and endoscopic data were included. The area under the receiver operating characteristic curve (AUROC) of Limsrivilai’s clinical-endoscopy (CE) model was 0.853, which was comparable to the value of 0.862 in Jung’s model (p = 0.52). Both models performed significantly better than Lee’s endoscopy model (AUROC: 0.713, p<0.01). Pathology was available for review in 199 patients (116 CD, 83 ITB). When 3 modalities were combined, Limsrivilai’s clinical-endoscopy-pathology (CEP) model performed significantly better (AUROC: 0.887) than Limsrivilai’s CE model (AUROC: 0.824, p = 0.01), Jung’s model (AUROC: 0.798, p = 0.005) and Makharia’s model (AUROC: 0.637, p<0.01). In 83 ITB patients, the rate of misdiagnosis with CD when used the proposed cutoff values in each original study was 9.6% for Limsrivilai’s CEP, 15.7% for Jung’s, and 66.3% for Makharia’s model. Conclusions Scoring systems with more parameters and diagnostic modalities performed better; however, application to clinical practice is still limited owing to high rate of misdiagnosis of ITB as CD. Models integrating more modalities such as imaging and serological tests are needed.

Abstract 16278: Discordant Lipid Phenotype and Other Determinants of Statin Response in the Pravastatin Inflammation/crp Evaluation (PRINCE) Trial

Introduction: Wide variability in LDL-C change is observed with statins, yet determinants of statin response are uncertain. Methods: Participants were selected from the primary prevention cohort of the Pravastatin Inflammation/CRP Evaluation double-blind trial that randomized participants to pravastatin 40 mg/d or placebo over 24 weeks. Baseline and 24-week levels of LDL-C and 15 other biomarkers were measured in 495 participants. We defined optimal statin response as >=30% LDL-C reduction and suboptimal response as <30% reduction. Sub-optimal hs-CRP response was defined as >=median (14%) decline in hs-CRP from baseline to 24 weeks and non-response as no decrease or an increase in hs-CRP. χ 2 , t-tests and ANOVA were used to compare variables across optimal statin response (N=166) and suboptimal response (N=287). Multivariable logistic regression models evaluated associations of determinants of statin response. Forward selection identified variables that associated with response. Xgboost was used to train and validate the models using 2/3 and 1/3 of the data respectively. Results: Significant determinants of optimal statin response included older age, and higher baseline levels of LDL-C and triglyceride-rich lipoproteins. By contrast, female sex, alcohol intake >=1 drink/day, diabetes, higher baseline levels of apo B and lipoprotein(a) were associated with decreased response, as was hs-CRP non-response (Table). Race, baseline hs-CRP and sub-optimal hs-CRP response, smoking, HDL-C and BMI had no significant effect on statin LDL-C response. Training and validation of models predicted suboptimal LDL-C response with an AUC of 0.71. Similarly, training and validation of models using Xgboost yielded an AUC of 0.85. Conclusion: This study identified discordant lipid phenotype and other determinants of moderate-intensity statin response and suggests other pathways of CVD risk beyond those addressed by statin treatment that require further investigation.