Accuracy of Topometric Indices for Distinguishing between Keratoconic and Normal Corneas

ABSTRACT Purpose To evaluate the sensitivity and specificity of the Pentacam topometric indices derived from the corneal surface curvature to distinguish between normal and keratoconic corneas. Methods The study consisted of 226 normal corneas from 113 patients and 88 keratoconic eyes from 44 patients. Eyes were defined as keratoconus based on comprehensive ocular examination, including Placido-disk-based corneal topography (Atlas Corneal Topography System; Humphrey, San Leandro, California) and rotating Scheimpflug corneal tomography (Pentacam HR; Oculus, Wetzlar, Germany). Corneal Topometric indices ISV, IVA, KI, CKI, IHA and IHD, along with the TKC (Topometric Keratoconus Classification) score were calculated from the Pentacam HR exam. Statistical analysis were accomplished using BioEstat 5.0 (Instituto Mamiraua, Amazonas, Brazil) and MedCalc 12.0 (MedCalc Software, Mariakerke, Belgium) using unpaired nonparametric Mann Whitney test (Wilcoxon ranked-sum). ROC curves were calculated for each topometric parameter to determine the best cut off values from the significantly different parameters. A logistic regression analysis was performed to provide a combined parameter for optimizing accuracy. Results Statistical significant differences were found between keratoconic and normal corneas for all topometric indices (Mann Whitney, p < 0.05). There were four false negative cases among the keratoconic cases on the TKC classification (4.54%) and 16 false positive cases among normal (7.08%), so that the sensitivity and specificity of the TKC were 95.54 and 92.92% respectively. The areas under the ROC curves (AUC) for the individual topometric indices varied from 0.843 (CKI) and 0.992 (ISV). The sensitivity and specificity of the most accurate ISV were 97.7 and 96.5% respectively. The calculated parameter from logistic regression had AUC of 0.996, with sensitivity of 97.7% and specificity of 98.7%. Conclusion Pentacam topometric indices were useful for distinguishing between normal and keratoconic corneas. The TKC classification should be expected to have false positives and negatives and should not be considered alone. TKC had more false positives and false negatives than some individual topometric parameters. A novel combined parameter based on logistic regression analysis may improve accuracy for the diagnosis of keratoconus. Further studies are necessary to evaluate if adding other curvature derived indices is beneficial for the regression analysis, as well as for testing the sensitivity of such parameters for the diagnosis of milder forms of ectasia and for testing correlations with severity of the disease. How to cite this article Salomao MQ, Guerra FP, Ramos IC, Jordao LF, Canedo ALC, Valbon BF, Luz A, Correa R, Lopes B, Ambrósio Jr R. Accuracy of Topometric Indices for Distinguishing between Keratoconic and Normal Corneas. J Kerat Ect Cor Dis 2013;2(3):108-112.

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Multivariable Logistic Regression Equation to Predict Prostate Cancer

10.21203/rs.3.rs-923836/v1 ◽

2021 ◽

Author(s):

Lu Ma ◽

Dong Cheng ◽

Qinghua Li ◽

Jingbo Zhu ◽

Yu Wang ◽

...

Keyword(s):

Prostate Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Prostate Specific Antigen ◽

Sensitivity And Specificity ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Specific Antigen ◽

Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

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Performance of cytokine models in predicting SLE activity

Arthritis Research & Therapy ◽

10.1186/s13075-019-2029-1 ◽

2019 ◽

Vol 21 (1) ◽

Author(s):

Nopparat Ruchakorn ◽

Pintip Ngamjanyaporn ◽

Thanitta Suangtamai ◽

Thanuchporn Kafaksom ◽

Charin Polpanumas ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Disease Activity ◽

Sensitivity And Specificity ◽

Logistic Regression Analysis ◽

Lupus Erythematosus ◽

Activity Index ◽

Multiple Logistic Regression Analysis ◽

Negative Relationship ◽

Complement C3

Abstract Background Identification of universal biomarkers to predict systemic lupus erythematosus (SLE) flares is challenging due to the heterogeneity of the disease. Several biomarkers have been reported. However, the data of validated biomarkers to use as a predictor for lupus flares show variation. This study aimed to identify the biomarkers that are sensitive and specific to predict lupus flares. Methods One hundred and twenty-four SLE patients enrolled in this study and were prospectively followed up. The evaluation of disease activity achieved by the SLE disease activity index (SLEDAI-2K) and clinical SLEDAI (modified SLEDAI). Patients with active SLE were categorized into renal or non-renal flares. Serum cytokines were measured by multiplex bead-based flow cytometry. The correlation and logistic regression analysis were performed. Results Levels of IFN-α, MCP-1, IL-6, IL-8, and IL-18 significantly increased in active SLE and correlated with clinical SLEDAI. Complement C3 showed a weakly negative relationship with IFN-α and IL-18. IL-18 showed the highest positive likelihood ratios for active SLE. Multiple logistic regression analysis showed that IL-6, IL-8, and IL-18 significantly increased odds ratio (OR) for active SLE at baseline while complement C3 and IL-18 increased OR for active SLE at 12 weeks. IL-18 and IL-6 yielded higher sensitivity and specificity than anti-dsDNA and C3 to predict active renal and active non-renal, respectively. Conclusion The heterogeneity of SLE pathogenesis leads to different signaling mechanisms and mediates through several cytokines. The monitoring of cytokines increases the sensitivity and specificity to determine SLE disease activity. IL-18 predicts the risk of active renal SLE while IL-6 and IL-8 predict the risk of active non-renal. The sensitivity and specificity of these cytokines are higher than the anti-dsDNA or C3. We propose to use the serum level of IL-18, IL-6, and IL-8 to monitor SLE disease activity in clinical practice.

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Endoscopic ultrasound in diagnosis of solid pancreatic lesions: Elastography or contrast-enhanced harmonic alone versus the combination

Endoscopy International Open ◽

10.1055/s-0043-118829 ◽

2017 ◽

Vol 05 (11) ◽

pp. E1136-E1143 ◽

Cited By ~ 6

Author(s):

Tanyaporn Chantarojanasiri ◽

Yoshiki Hirooka ◽

Hiroki Kawashima ◽

Eizaburo Ohno ◽

Takamichi Kuwahara ◽

...

Keyword(s):

Logistic Regression ◽

Differential Diagnosis ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Endoscopic Ultrasound ◽

Inflammatory Pseudotumor ◽

Multivariate Logistic Regression Analysis ◽

Multivariate Logistic Regression ◽

Contrast Enhanced ◽

The Individual

Abstract Background and study aims Endoscopic ultrasound (EUS) elastography (EUS-E) and contrast-enhanced harmonic EUS (CH-EUS) are useful methods for the diagnosis of pancreatic lesions. This study aims to compare the accuracy of combined EUS-E and CH-EUS with that of EUS-E or CH-EUS alone in the differential diagnosis of pancreatic solid lesions. Patients and methods One hundred thirty-six patients with solid pancreatic lesions underwent EUS with both EUS-E and CH-EUS were included. Diagnoses were classified as adenocarcinoma, neuroendocrine tumor (NET), and inflammatory pseudotumor in 95, 22, and 19 patients, respectively. EUS records in each case were rearranged into 3 groups: EUS-E, CH-EUS, and combination. Each modality was randomly reviewed by 3 reviewers with different levels of clinical experience. Sensitivity, specificity, and accuracy of each modality according to each diagnosis group were evaluated. For the combined diagnosis populations, the proportions of correct diagnoses among the 3 modalities were compared by using the multivariate logistic regression analysis. Results The accuracies of EUS-E, CH-EUS, and the combination of them were 68.4 %, 65.4 %, and 75.7 %, respectively, for adenocarcinoma group; 83.8 %, 82.4 %, and 86.8 % for NET group; 80.1 %, 78.7 %, and 81.6 % for inflammatory pseudotumor group. The multivariate logistic regression analysis for the combined diagnosis populations showed that the proportion of correct diagnoses when EUS-E and CH-EUS were combined was slightly higher than with the other 2 modalities, although the significant differences among them were not observed. Conclusion EUS-E and CH-EUS combined may improve differential diagnosis of solid pancreatic lesions compared with use of the individual modalities.

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The Keratoconus screening using logistic regression analysis of Corneal topography

Journal of Vision ◽

10.1167/8.17.79 ◽

2010 ◽

Vol 8 (17) ◽

pp. 79-79

Author(s):

M. Saika ◽

N. Maeda ◽

T. Nakagawa ◽

Y. Hirohara ◽

T. Fujikado ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Corneal Topography

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ESM-1 Levels and ARDS Prediction Score for Predicting ARDS Occurrence in Sepsis Patients

10.21203/rs.3.rs-94380/v1 ◽

2020 ◽

Author(s):

Xuebin Wang ◽

Ting Dong ◽

Huan Yang ◽

Xuan Ju ◽

Haiyan Ye

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Pearson Correlation ◽

Characteristic Curve ◽

Roc Curves ◽

Prediction Score ◽

Predictive Score ◽

Retrospective Case ◽

Reactive Protein

Abstract Background: Acute respiratory distress syndrome (ARDS) development is overtly associated with elevated mortality. This study aimed to determine the parameters predicting ARDS in sepsis patients. Methods: This was a retrospective case control study. The sepsis patients admitted to the intensive care unit were divided into the ARDS and non-ARDS groups according to ARDS occurrence within 72 hours. Plasma endothelial cell specific molecule-1 (ESM-1), white blood cell (WBC), C-reactive protein (CRP), interleukin-6 (IL-6) and procalcitonin (PCT) were assessed on the first day. PaO2/FiO2 ratio was determined on the first two days. Pearson correlation analysis and logistic regression analysis were carried out. Results: The ARDS and non-ARDS groups included 12 and 42 patients respectively. ESM-1 levels in the ARDS group on the first day were significantly lower than those of the non-ARDS group (P=0.009). ESM-1 levels and PaO2/FiO2 ratio were positively correlated. Logistic regression analysis showed that ESM-1, CRP and IL-6 levels on the first day were associated with ARDS. The areas under the receiver operating characteristic curve (ROC) curves (AUCs) for ESM-1, CRP and IL-6 were 0.750, 0.736 and 0.736, respectively. A regression equation was established based on the coefficients of plasma ESM-1, CRP and IL-6 levels to derive an ARDS prediction score with an AUC for predicting ARDS of 0.895.Conclusion: Plasma ESM-1, CRP and IL-6 levels on the first day are associated with ARDS in sepsis. The novel ARDS predictive score is obviously better than ESM-1, CRP and IL-6 in predicting ARDS in sepsis patients.

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The True-Positive Rate of a Screening Questionnaire for Temporomandib-ular Disorders

The Open Dentistry Journal ◽

10.2174/1874210601408010236 ◽

2014 ◽

Vol 8 (1) ◽

pp. 236-240 ◽

Cited By ~ 1

Author(s):

Akira Nishiyama ◽

Natsuko Otomo ◽

Kaori Tsukagoshi ◽

Shoko Tobe ◽

Koji Kino

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

False Negative ◽

Mouth Opening ◽

True Positive Rate ◽

True Positive ◽

Vas Score ◽

Positive Rate ◽

The Mean

Background: Temporomandibular disorders (TMD) occur at an incidence of 5–12% in the general population. We aimed to investigate the rate of true-positives for a screening questionnaire for TMD (SQ-TMD) and differences in the characteristics between the true-positive and false-negative groups. Materials and Methods: Seventy-six individuals (16 men, 60 women; mean age, 41.1 ± 16.5 years) were selected from pa-tients with TMD who had visited the Temporomandibular Joint Clinic at Tokyo Medical and Dental University. The patients were assessed using a questionnaire that contained items on TMD screening (SQ-TMD); pain intensity (at rest, maximum mouth-opening, and chewing), as assessed using the visual analog scale (VAS); and TMD-related limitations of daily func-tion (LDF-TMD). A logistic regression analysis was performed to assess the factors potentially influencing the true-positive rate. Results: Of the 76 subjects, 62 (81.6%) were true-positive for the questionnaire based on the SQ-TMD scores. The mean VAS score for maximum mouth-opening and chewing and the mean LDF-TMD score were significantly greater in the true-positive group than those in the false-negative group. The results of the logistic regression analysis showed that only the VAS score for chewing was a statistically significant factor (P < 0.05). Conclusion: The true-positive rate of TMD using SQ-TMD was very high. The results indicate that SQ-TMD can be used to screen TMD in patients with moderate or severe pain and difficulty in living a healthy daily life.

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Identification of Patients Requiring Adrenalectomy for Unilateral Adrenal Tumors Through a Dual Approach: ceCT and [18F]FDG-PET

10.21203/rs.3.rs-927691/v1 ◽

2021 ◽

Author(s):

Andrea Farolfi ◽

Elisa Maietti ◽

Federica Piperno ◽

Pietro Coppolino ◽

Guido Di Dalmazi ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Sensitivity And Specificity ◽

Logistic Regression Analysis ◽

Fdg Pet ◽

Flow Chart ◽

Adrenal Tumors ◽

Optimal Management ◽

Diagnostic Score

Abstract PurposeHormonal assessment (HA) and contrast-enhanced CT (ceCT) show insufficient sensitivity and specificity when staging unilateral adrenal lesions (ALs). We aimed at: 1) developing an imaging-based, i.e. ceCT and FDG-PET, diagnostic score able to discriminate adrenal tumors entailing adrenalectomy from those who need clinical monitoring, and 2) identifying a diagnostic flow-chart that allows clinicians to avoid unneeded diagnostic procedures and to address patients to the optimal management.MethodsRetrospective single-center study assessing patients with unilateral AL and the following inclusion criteria: a) a four-phase ceCT; b) FDG-PET within one month of the ceCT; c) histopathology or a clinical follow-up of at least 24 months. Firstly, multivariate logistic regression analysis was employed to identify the predictors of adrenal tumors to treat surgically (AL-to-treat) and regression-based coefficients were used to develop a diagnostic score. Secondly, using cut-offs of predictor variables, a diagnostic flow-chart was defined.ResultsForty-eight patients were enrolled (mean age 61 years), of whom 21/48 (44%) had AL-to-treat. The remaining 27/48 (56%) presented with AL to follow-up only (i.e. benign). Maximum and minimum lesion diameter, Hounsfield units (HU) before contrast media injection and HU at the delayed phase (HUdelayed), relative and absolute washout, AL SUVmax, AL SUVmean, ratio SUVmax AL/SUVmax liver (adrenal-liver ratio) were associated with the presence of AL-to-treat (all p<0.05). In multiple logistic regression analysis, SUVmax and HUdelayed showed to be significant predictors of AL-to-treat and were used to create a diagnostic score. ceCT parameters’ cut-offs discriminating AL-to-treat surgically from AL-to-follow-up with 100% PPV and NPV were first identified, finding 4/48 AL-to-treat and 15/48 ALs to follow-up. Applying the adrenal-liver ratio cut-off of 1.7 to the 29/48 remaining patients with uncertain AL management, for adrenal tumors we found an overall accuracy, sensitivity and specificity of 83%, 76% and 89%, respectively, and a diagnostic flow-chart based on these results was proposed. ConclusionWe developed a two-parameter imaging-based score that may be applied to predict adrenal tumors requiring adrenalectomy. Furthermore, a diagnostic flow-chart to stratify patients on the basis of the optimal management was proposed, thus guiding undefined unilateral adrenal lesions to FDG-PET imaging.

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167 VOLATILE ORGANIC COMPOUND PROFILING FOR DETECTION OF ESOPHAGEAL CANCER IN EXHALED BREATH

Diseases of the Esophagus ◽

10.1093/dote/doaa087.32 ◽

2020 ◽

Vol 33 (Supplement_1) ◽

Author(s):

R Vissapragada ◽

N Dharmawardhana ◽

D Watson ◽

R Yazbek

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Real Time ◽

Logistic Regression Analysis ◽

Dimethyl Sulfide ◽

Roc Curves ◽

Exhaled Breath ◽

Non Invasive ◽

Volatile Organic ◽

Selected Ion Flow Tube

Abstract Endoscopic surveillance for Barrett’s esophagus (BE) is invasive but remains the standard modality for early diagnosis of esophageal adenocarcinoma (EAC) and intervention. Human breath contains an array of volatile organic compounds (VOC) that change in disease conditions. VOC detection provides a potential source of biomarkers for non-invasive, real-time identification of EAC. This study aimed to characterize a VOC-profile applicable to the detection of EAC and to provide pilot data to design a future validation trial. Methods Breath samples were collected in our endoscopy unit from BE, EAC, and control patients. Samples were collected in FlexFoil bags (SKC ltd) using previously standardized methods. Hydrogen, methane and other VOCs were quantified by QuinTron BreathTracker® and selected-ion flow tube mass spectrometry (SIFT-MS, Syft®) respectively. 250 reported cancer-related VOCs were selected for analysis. Non-parametric tests were used to identify candidate VOCs, and logistic regression analysis was then applied to determine the best predictors for EAC. Receiver Operating Characteristic (ROC) Curves were developed to determine the sensitivity and specificity of the model. Results 68 individuals were enrolled in the study (Controls, n = 37; BE, n = 21; EAC, n = 10). 8 VOCs were identified with significant concentration differences between the three groups: Trimethylbenzene (3 iso-forms), Dimethyl Sulfide, 4-isopropyl toluene, 1-butanol, trichloroethylene, hydrogen sulfide, methyl mercaptan, p-isopropenyl toluene. Logistic regression analysis of these 10 compounds demonstrated predictive probability of EAC from other groups with ROC curves calculating an area under the curve of 0.85. Conclusion Previous studies have supported the utility of VOCs in exhaled breath as non-invasive real-time tests for the identification of some other cancer types. This pilot study has identified potential VOCs which might identify individuals with EAC. A larger study will be needed to validate and confirm these findings.

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Decreased Serum microRNA-21, microRNA-25, microRNA-146a, and microRNA-181a in Autoimmune Diabetes: Potential Biomarkers for Diagnosis and Possible Involvement in Pathogenesis

International Journal of Endocrinology ◽

10.1155/2019/8406438 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Yiwen Liu ◽

Minglei Ma ◽

Jie Yu ◽

Fan Ping ◽

Huabing Zhang ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Bioinformatics Analysis ◽

Inflammatory Responses ◽

Autoimmune Diabetes ◽

Multiple Logistic Regression Analysis ◽

Roc Curves ◽

Serum Levels ◽

Multiple Logistic Regression

Objective. Previous studies have revealed dysregulated circulating microRNAs (miRNAs) in patients with type 1 diabetes (T1D). Here, we explored the serum levels of miR-21, miR-25, miR-146a, and miR-181a in patients with autoimmune diabetes (T1D and latent autoimmune diabetes of adults (LADA)) compared with type 2 diabetes (T2D) and nondiabetic individuals. Design, patients, and measurements. The serum levels of miR-21, miR-25, miR-146a, and miR-181a in patients with T1D (n = 29), LADA (n = 16), and T2D (n = 31) and in nondiabetic individuals (n = 19) were determined by quantitative real-time polymerase chain reaction, and receiver-operating characteristic (ROC) curves were evaluated to determine the discriminatory performances of these four miRNAs. Furthermore, target genes and pathways potentially modulated by these four miRNAs were predicted by bioinformatics analysis to investigate the possible functions of these miRNAs in autoimmune diabetes. Subsequently, multiple logistic regression analysis was performed to identify independent predictors for autoimmune diabetes, and a nomogram was established. Results. miR-21, miR-25, miR-146a, and miR-181a were significantly downregulated in the serum of patients with autoimmune diabetes compared with those in T2D patients and nondiabetic individuals (p<0.001). The areas under the ROC curves of these four miRNAs were greater than 0.80 (p<0.001). Bioinformatics analysis suggested that miR-21, miR-25, miR-146a, and miR-181a regulated multiple genes in pathways associated with immunity, inflammatory responses, hyperglycemia, and metabolism, which are involved in the pathogenesis of autoimmune diabetes. Multiple logistic regression analysis identified miR-25 (odds ratio (OR): 0.001, p<0.05), miR-146a (OR: 0.136, p<0.05), and fasting C-peptide levels (OR: 0.064, p<0.05) as independent predictors of autoimmune diabetes. Conclusions. miR-25 and miR-146a may serve as potential circulating biomarkers and provide insights into the pathogenesis of autoimmune diabetes.

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Intermediate Likelihood of Choledocholithiasis: Do All Need EUS or MRCP?

Journal of Digestive Endoscopy ◽

10.1055/s-0041-1728233 ◽

2021 ◽

Vol 12 (01) ◽

pp. 019-023

Author(s):

Nitin Jagtap ◽

Arun Karyampudi ◽

HS Yashavanth ◽

Mohan Ramchandani ◽

Sundeep Lakhtakia ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Sensitivity And Specificity ◽

Logistic Regression Analysis ◽

Validation Cohort ◽

Magnetic Resonance Cholangiopancreatography ◽

Composite Score ◽

Published Data ◽

Derivation Cohort ◽

Intermediate Likelihood

Abstract Background Recently updated guidelines for choledocholithiasis stratify suspected patients into high, intermediate, and low likelihood, with the aim to reduce risk of diagnostic endoscopic retrograde cholangiopancreatography. This approach has increased proportion of patients in intermediate likelihood making it heterogenous. We aim to substratify intermediate group so that diagnostic tests (endoscopic ultrasound/magnetic resonance cholangiopancreatography) are judicially used. Methods This is a single-center retrospective analysis of prospectively maintained data. We used subset of patients who met intermediate likelihood of American Society of Gastrointestinal Endoscopy (ASGE) criteria from previously published data (PMID:32106321) as derivation cohort. Binominal logistic regression analysis was used to define independent predictors of choledocholithiasis. A composite score was derived by allotting 1 point for presence of each independent predictor. The diagnostic performance of a composite score of ≥ 1 was evaluated in validation cohort. Results A total of 678 (mean age [standard deviation]: 47.0 [15.9] years; 48.1% men) and 162 (mean age 47.8 [14.8] years; 47.4% men) patients in ASGE intermediate-likelihood group were included as derivation cohort and validation cohort, respectively. Binominal logistic regression analysis showed that male gender (p = 0.024; odds ratio [OR] = 1.92), raised bilirubin (p = 0.001; OR = 2.40), and acute calculus cholecystitis (p = 0.010; OR = 2.04) were independent predictors for choledocholithiasis. A composite score was derived by allotting 1 point for presence of independent predictors Using ≥ 1 as cutoff, sensitivity and specificity for detection of choledocholithiasis were 80% (95% confidence interval [CI]: 68.2–88.9) and 36.2% (95% CI: 32.2–40.0), respectively, in derivation cohort. Applying composite score in independent validation cohort showed sensitivity and specificity of 73.3% (95% CI: 44.9–92.2) and 40.1% (95% CI: 30.1–48.5), respectively. Conclusion Substratification of intermediate-likelihood group of ASGE criteria is feasible. It may be useful in deciding in whom confirmatory tests should be performed with priority and in whom watchful waiting may be sufficient.

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