P208 Differentiating intestinal tuberculosis from Crohn’s Disease: External validation of a new nomogram

Abstract Background Intestinal tuberculosis (IT) poses a real problem of differential diagnosis with Crohn’Disease (CD). Indeed, the distinction between these two pathologies represents a real challenge for clinicians because of their multiple similarities. Based on simple biological, endoscopic and radiological criteria, A new nomogram was developed by Yao He et al. who would differentiate between the two diseases. Objective To validate externally this new nomogram in a series of patients with IT and CD. Methods We have collected retrospectively patients diagnosed with CD and IT in our center for a period 11 years old. Patients whose medical file included the data used in the nomogram have been included. The discrimination performance of the nomogram was evaluated by calculating the area under the ROC curve. Results Of the 76 included patients, 16 had one IT and 60 had one CD. The average age of patients with IT was 44.76 ± 19 years old. The most frequent revealing symptoms were subocclusive syndromes (n = 10) followed by abdominal pain (n = 8). Endoscopic lesions were located in the majority of cases in the ileocecal region (n = 12) and were dominated by the retracted aspect of the cecum (n = 5), transverse colonic ulcerations (n = 4) and valvular stenosis (n = 2). A tumor appearance was noted in two patients.The radiological images of the lungs suggestive of pulmonary tuberculosis were found only in two patients. Intradermal reaction (IDR) to tuberculin was positive in all cases where it was practiced. (n = 14). In ten cases, the diagnosis of ITwas made following bowel resection whose indications were diagnostic uncertainty (n = 4), acute bowel obstruction (n = 4) and suspected CD ileocecal refractory to medical treatment (n = 2). The discrimination of the nomogram analyzed by the ROC curve was 0.956 (95% CI [0.875; 1]). A threshold of 0.5 was associated with a sensitivity of 98.3%; a specificity of 92.8% and a positive and negative predictive value for the diagnosis CD of 98.3 and 92.8 respectively. Conclusion Although Tunisia is a country of strong endemicity for tuberculosis, intestinal localization seems infrequent and misdiagnosed. The nomogram applied seems to have excellent performance diagnose what could prevent resections intestinal for diagnostic purposes. However, studies multicenter prospective studies remain necessary for a large-scale validation.

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Risk of Conversion to Arthroplasty After Hip Arthroscopy: Validation of a Published Risk Score Using an Independent, Prospectively Collected Database

The American Journal of Sports Medicine ◽

10.1177/0363546521993829 ◽

2021 ◽

pp. 036354652199382

Author(s):

Mario Hevesi ◽

Devin P. Leland ◽

Philip J. Rosinsky ◽

Ajay C. Lall ◽

Benjamin G. Domb ◽

...

Keyword(s):

Risk Score ◽

Roc Curve ◽

Hip Arthroscopy ◽

External Validation ◽

Area Under The Curve ◽

Harris Hip Score ◽

Brier Score ◽

Level Of Evidence ◽

Center Edge ◽

Modified Harris Hip Score

Background: Hip arthroscopy is rapidly advancing and increasingly commonly performed. The most common surgery after arthroscopy is total hip arthroplasty (THA), which unfortunately occurs within 2 years of arthroscopy in up to 10% of patients. Predictive models for conversion to THA, such as that proposed by Redmond et al, have potentially substantial value in perioperative counseling and decreasing early arthroscopy failures; however, these models need to be externally validated to demonstrate broad applicability. Purpose: To utilize an independent, prospectively collected database to externally validate a previously published risk calculator by determining its accuracy in predicting conversion of hip arthroscopy to THA at a minimum 2-year follow-up. Study Design: Cohort study (diagnosis); Level of evidence, 1. Methods: Hip arthroscopies performed at a single center between November 2015 and March 2017 were reviewed. Patients were assessed pre- and intraoperatively for components of the THA risk score studied—namely, age, modified Harris Hip Score, lateral center-edge angle, revision procedure, femoral version, and femoral and acetabular Outerbridge scores—and followed for a minimum of 2 years. Conversion to THA was determined along with the risk score’s receiver operating characteristic (ROC) curve and Brier score calibration characteristics. Results: A total of 187 patients (43 men, 144 women, mean age, 36.0 ± 12.4 years) underwent hip arthroscopy and were followed for a mean of 2.9 ± 0.85 years (range, 2.0-5.5 years), with 13 patients (7%) converting to THA at a mean of 1.6 ± 0.9 years. Patients who converted to THA had a mean predicted arthroplasty risk of 22.6% ± 12.0%, compared with patients who remained arthroplasty-free with a predicted risk of 4.6% ± 5.3% ( P < .01). The Brier score for the calculator was 0.04 ( P = .53), which was not statistically different from ideal calibration, and the calculator demonstrated a satisfactory area under the curve of 0.894 ( P < .001). Conclusion: This external validation study supported our hypothesis in that the THA risk score described by Redmond et al was found to accurately predict which patients undergoing hip arthroscopy were at risk for converting to subsequent arthroplasty, with satisfactory discriminatory, ROC curve, and Brier score calibration characteristics. These findings are important in that they provide surgeons with validated tools to identify the patients at greatest risk for failure after hip arthroscopy and assist in perioperative counseling and decision making.

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Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level

Social Sciences ◽

10.3390/socsci10060227 ◽

2021 ◽

Vol 10 (6) ◽

pp. 227

Author(s):

Yago Martín ◽

Zhenlong Li ◽

Yue Ge ◽

Xiao Huang

Keyword(s):

Large Scale ◽

Local Population ◽

External Validation ◽

Population Estimates ◽

County Level ◽

Internal Validation ◽

Population Movements ◽

Dynamic Component ◽

Home Location ◽

Non Local

The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of available data. Using geospatial digital trace data, the study of population movements can be much more precisely and dynamically measured. Our research seeks to develop a near real-time (one-day lag) Twitter census that gives a more temporally granular picture of local and non-local population at the county level. Internal validation reveals over 80% accuracy when compared with users’ self-reported home location. External validation results suggest these stocks correlate with available statistics of residents/non-residents at the county level and can accurately reflect regular (seasonal tourism) and non-regular events such as the Great American Solar Eclipse of 2017. The findings demonstrate that Twitter holds the potential to introduce the dynamic component often lacking in population estimates. This study could potentially benefit various fields such as demography, tourism, emergency management, and public health and create new opportunities for large-scale mobility analyses.

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Feasibility and Evaluation of a Large-Scale External Validation Approach for Patient-Level Prediction in an International Data Network: Validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation.

10.21203/rs.2.11750/v2 ◽

2020 ◽

Author(s):

Jenna Marie Reps ◽

Ross Williams ◽

Seng Chan You ◽

Thomas Falconer ◽

Evan Minty ◽

...

Keyword(s):

Atrial Fibrillation ◽

Large Scale ◽

Data Science ◽

Prediction Models ◽

External Validation ◽

Scale Up ◽

R Package ◽

Prognostic Models ◽

Healthcare Data ◽

Patient Level

Abstract Objective: To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets.Materials & Methods: Five previously published prognostic models (ATRIA, CHADS2, CHADS2VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites. Results: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation.Discussion: This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. Conclusion : In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.

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Development and validation of a prognostic model to predict clinical deterioration in hospitalized patients with COVID-19

10.21203/rs.3.rs-701172/v2 ◽

2021 ◽

Author(s):

Javid Azadbakht ◽

Sina Rashedi ◽

Soheil Kooraki ◽

Hamed Kowsari ◽

Elnaz Tabibian

Keyword(s):

Roc Curve ◽

Scoring System ◽

Prognostic Model ◽

Operating Characteristic ◽

Clinical Laboratory ◽

External Validation ◽

Clinical Deterioration ◽

Roc Curve Analysis ◽

Prospective Multicenter Study ◽

Development And Validation

Abstract Objectives We aimed to develop and validate a prognostic model to predict clinical deterioration defined as either death or intensive care unit admission of hospitalized COVID-19 patients.Methods This prospective, multicenter study investigated 172 consecutive hospitalized COVID-19 patients who underwent a chest computed tomography (CT) scan between March 20 and April 30, 2020 (development cohort), as well as an independent sample of 40 consecutive patients for external validation (validation cohort). The clinical, laboratory, and radiologic data were gathered, and logistic regression along with receiver operating characteristic (ROC) curve analysis was performed.Results The overall clinical deterioration rates of the development and validation cohorts were 28.4% (49 of 172) and 30% (12 of 40), respectively. Seven predictors were included in the scoring system with a total score of 15: CT severity score\(\ge\)15 (Odds Ratio (OR)=6.34, 4 points), pleural effusion (OR = 6.80, 2 points), symptom onset to admission ≤ 6 days (OR = 2.44, 2 points), age\(\ge\)70 years (OR = 2.44, 2 points), diabetes mellitus (OR = 2.24, 2 points), dyspnea (OR = 2.17, 1.5 points), and abnormal leukocyte count (OR = 1.89, 1.5 points). The area under the ROC curve for the scoring system in the development and validation cohorts was 0.823 (CI [0.751–0.895]) and 0.558 (CI [0.340–0.775]), respectively.Conclusion This study provided a new easy-to-calculate scoring system with external validation for hospitalized COVID-19 patients to predict clinical deterioration based on a combination of seven clinical, laboratory, and radiologic parameters.

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Feasibility and Evaluation of a Large-Scale External Validation Approach for Patient-Level Prediction in an International Data Network: Validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation.

10.21203/rs.2.11750/v3 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jenna Marie Reps ◽

Ross D Williams ◽

Seng Chan You ◽

Thomas Falconer ◽

Evan Minty ◽

...

Keyword(s):

Atrial Fibrillation ◽

Large Scale ◽

Data Science ◽

Prediction Models ◽

External Validation ◽

Scale Up ◽

R Package ◽

Prognostic Models ◽

Healthcare Data ◽

Patient Level

Abstract Background: To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets.Methods: Five previously published prognostic models (ATRIA, CHADS2, CHADS2VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites. Results: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation.Conclusion : This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.

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Abstract 210: Effects of Indication for Veno-Arterial Extracorporeal Membrane Oxygenation on Predictive Value of Save Score

Circulation ◽

10.1161/circ.140.suppl_2.210 ◽

2019 ◽

Vol 140 (Suppl_2) ◽

Author(s):

Marinos Kosmopoulos ◽

Jason A Bartos ◽

Demetris Yannopoulos

Keyword(s):

Extracorporeal Membrane Oxygenation ◽

Roc Curve ◽

Predictive Value ◽

Prediction Models ◽

External Validation ◽

Area Under The Curve ◽

Extracorporeal Cardiopulmonary Resuscitation ◽

Membrane Oxygenation ◽

Survival Estimation ◽

Va Ecmo

Introduction: Veno-Arterial Extracorporeal Membrane Oxygenation (VA ECMO) has emerged as a prominent tool for management of patients with Inability to Wean Off Cardiopulmonary Bypass (IWOCB), extracorporeal cardiopulmonary resuscitation (eCPR) or refractory cardiogenic shock (RCS). The high mortality that is still associated with these diseases urges for the development of reliable prediction models for mortality after cannulation. Survival After VA ECMO (SAVE) Score consists one of the most widely used prediction tools and the only model with external validation. However, its predictive value is still under debate. Hypothesis: Whether VA ECMO indication affects the predictive value of SAVE Score. Methods: 317 patients treated with VA ECMO in a quaternary center (n= 52 for IWOCB, n=179 for eCPR and n=86 for RCS) were retrospectively assessed for differences in SAVE Score and their primary outcomes. The Receiver Operating Characteristic (ROC) curve for SAVE Score and mortality was calculated separately for each VA ECMO indication. Results: The three groups had significant differences in SAVE Score (p<0.01) without significant differences in mortality (p=0.176). ROC Curve calculation indicated significant differences in predictive value of SAVE Score for survival among its different indications. (Area Under the Curve= 81.69% for IWOCB, 53.79% for eCPR and 69.46% for RCS). Conclusion: VA ECMO indication markedly affects the predictive value of SAVE Score. Prediction of primary outcome in IWOCB patients was reliable. On the contrary, routine application for survival estimation in eCPR patients is not supported from our results.

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Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study

Journal of Diabetes Research ◽

10.1155/2020/7261047 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Ruohui Mo ◽

Rong Shi ◽

Yuhong Hu ◽

Fan Hu

Keyword(s):

Diabetic Retinopathy ◽

Regression Analysis ◽

Roc Curve ◽

External Validation ◽

Curve Analysis ◽

Postprandial Blood Glucose ◽

Lasso Regression ◽

Prediction Ability ◽

Decision Curve Analysis ◽

Course Of Disease

Objectives. This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). Methods. A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation. Results. Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation. Conclusion. Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals.

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SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Complexity ◽

10.1155/2017/2713280 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Cheng Yan ◽

Jianxin Wang ◽

Wei Lan ◽

Fang-Xiang Wu ◽

Yi Pan

Keyword(s):

Computational Methods ◽

Drug Target ◽

Large Scale ◽

High Efficiency ◽

Drug Repositioning ◽

External Validation ◽

Complex Diseases ◽

Biological Databases ◽

Biomolecular Networks ◽

New Chemical Entities

It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

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An In Silico Model for Predicting Drug-Induced Hepatotoxicity

International Journal of Molecular Sciences ◽

10.3390/ijms20081897 ◽

2019 ◽

Vol 20 (8) ◽

pp. 1897 ◽

Cited By ~ 13

Author(s):

Shuaibing He ◽

Tianyuan Ye ◽

Ruiying Wang ◽

Chenyang Zhang ◽

Xuelian Zhang ◽

...

Keyword(s):

Large Scale ◽

Characteristic Curve ◽

External Validation ◽

Model Development ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

New Drugs ◽

Drug Induced ◽

Drug Candidates ◽

External Test

As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure–activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

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Interpretable Machine Learning Algorithm Reveals Novel Gut Microbiome Features in Predicting Type 2 Diabetes

Current Developments in Nutrition ◽

10.1093/cdn/nzaa062_016 ◽

2020 ◽

Vol 4 (Supplement_2) ◽

pp. 1559-1559

Author(s):

Wanglong Gou ◽

Chu-Wen Ling ◽

Yan He ◽

Zengliang Jiang ◽

Yuanqing Fu ◽

...

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Gut Microbiome ◽

Large Scale ◽

Validation Cohort ◽

Learning Algorithm ◽

External Validation ◽

Positive Association ◽

Interpretable Machine Learning

Abstract Objectives The gut microbiome-type 2 diabetes (T2D) relationship among human cohorts have been controversial. We hypothesized that this limitation could be addressed by integrating the cutting-edge interpretable machine learning framework and large-scale human cohort studies. Methods 3 independent cohorts with >9000 participants were included in this study. We proposed a new machine learning-based analytic framework — using LightGBM to infer the relationship between incorporated features and T2D, and SHapley Additive explanation(SHAP) to identified microbiome features associated with the risk of T2D. We then generated a microbiome risk score (MRS) integrating the threshold and direction of the identified microbiome features to predict T2D risk. Results We finally identified 15 microbiome features (two of them are indicators of microbial diversity, others are taxa-related features) associated with the risk of T2D. The identified T2D-related gut microbiome features showed superior T2D prediction accuracy compared to host genetics or traditional risk factors. Furthermore, we found that the MRS (per unit change in MRS) consistently showed positive association with T2D risk in the discovery cohort (RR 1.28, 95%CI 1.23-1.33), external validation cohort 1 (RR 1.23, 95%CI 1.13-1.34) and external validation cohort 2 (GGMP, RR 1.12, 95%CI 1.06-1.18). The MRS could also predict future glucose increment. We subsequently identified dietary and lifestyle factors which could prospectively modulate the microbiome features, and found that body fat distribution may be the key factor modulating the gut microbiome-T2D relationship. Conclusions Taken together, we proposed a new analytical framework for the investigation of microbiome-disease relationship. The identified microbiome features may serve as potential drug targets for T2D in future. Funding Sources This study was funded by National Natural Science Foundation of China (81903316, 81773416), Westlake University (101396021801) and the 5010 Program for Clinical Researches (2007032) of the Sun Yat-sen University (Guangzhou, China).

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