Development of a machine learning model to predict bleed in esophageal varices in compensated advanced chronic liver disease: A proof of concept.

2021 ◽  
Author(s):  
Samagra Agarwal ◽  
Sanchit Sharma ◽  
Manoj Kumar ◽  
Shantan Venishetty ◽  
Ankit Bhardwaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Chronic Liver Disease ◽  
Esophageal Varices ◽  
Learning Model ◽  
Chronic Liver ◽  
Proof Of Concept ◽  
Machine Learning Model

scholarly journals A72 NON-INVASIVE PREDICTION OF ESOPHAGEAL VARICES BY TRANSIENT ELASTOGRAPHY AND PLATELET COUNT IN PATIENTS WITH HEPATITIS B AND ADVANCED CHRONIC LIVER DISEASE: VALIDATION OF BAVENO VI AND EXPANDED BAVENO VI CRITERIA

2021 ◽  
Vol 4 (Supplement_1) ◽  
pp. 37-38
Author(s):  
A Zoughlami ◽  
J Serero ◽  
G Sebastiani ◽  
M Deschenes ◽  
P Wong ◽  
...  
Keyword(s):  
Platelet Count ◽  
Liver Disease ◽  
Hepatitis B ◽  
Chronic Liver Disease ◽  
Esophageal Varices ◽  
Diagnostic Performance ◽  
Transient Elastography ◽  
Chronic Liver ◽  
Non Invasive ◽  
Chronic Hepatitis B Virus

Abstract Background Patients with compensated advanced chronic liver disease (cACLD) are at higher risk of developing complications from portal hypertension, including esophageal varices (EV). Baveno VI and expanded Baveno VI criteria, based on liver stiffness measurement (LSM) by transient elastography combined with platelet count, have been proposed to avoid unnecessary esophagogastroduodenoscopy (EGD) screening for large esophageal varices needing treatment (EVNT). This approach has not been validated in patients with chronic hepatitis B virus (HBV) infection, who have etiology-specific cut-off of LSM for liver fibrosis. Aims We aimed to validate the Baveno VI and expanded Baveno VI criteria for EVNT in HBV patients with cACLD. Methods We performed a retrospective analysis of HBV patients who underwent LSM in 2014–2020. Inclusion criteria were: a) diagnosis of cACLD, defined as LSM >9 kPa; b) availability of EGD and platelets within 1 year of LSM. Baveno VI (LSM <20 kPa and platelets >150,000) and expanded Baveno VI criteria (LSM <25 kPa and platelets >110,000) were tested for EGD sparing. Diagnostic performance of these criteria against gold standard (EGD) was computed and compared to patients with hepatitis C virus (HCV) infection and nonalcoholic steatohepatitis (NASH) etiologies, where these criteria have been widely validated. In these patients, the threshold for cACLD definition was >10 kPa. Results A total of 287 patients (mean age 56, 95% Child A) were included, comprising of 43 HBV (58% on antiviral therapy), 134 HCV and 110 NASH patients. The prevalence of any grade EV and EVNT was 25% and 8% in the whole cohort, with 19% and 5% in HBV patients, respectively. Table 1 reports diagnostic performance, spared EGD and missed EVNT according to non-invasive criteria and cACLD etiology. Both Baveno VI and expanded Baveno VI criteria performed well in patients with HBV-related cACLD. There was no significant difference on diagnostic performance of these non-invasive criteria across the cACLD etiologies. Conclusions These results support use of non-invasive criteria based on LSM and platelets to spare unnecessary EGD in patients with HBV and cACLD. Baveno VI and expanded Baveno VI criteria can improve resource utilization and avoid invasive testing in context of screening EGD for patients with HBV-related cACLD. Funding Agencies None

scholarly journals Liver stiffness-spleen size-to-platelet ratio risk score detects esophageal varices in chronic liver disease

SpringerPlus ◽  
2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Soichiro Shibata ◽  
Satoru Joshita ◽  
Takeji Umemura ◽  
Tomoo Yamazaki ◽  
Naoyuki Fujimori ◽  
...  
Keyword(s):  
Liver Disease ◽  
Chronic Liver Disease ◽  
Risk Score ◽  
Esophageal Varices ◽  
Liver Stiffness ◽  
Chronic Liver ◽  
Spleen Size

scholarly journals Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2102
Author(s):  
Eyal Klang ◽  
Robert Freeman ◽  
Matthew A. Levin ◽  
Shelly Soffer ◽  
Yiftach Barash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Acute Diverticulitis ◽  
External Validation ◽  
White Blood Cells ◽  
Learning Model ◽  
Complicated Diverticulitis ◽  
Gradient Boosting ◽  
Proof Of Concept ◽  
Machine Learning Model

Background & Aims: We aimed at identifying specific emergency department (ED) risk factors for developing complicated acute diverticulitis (AD) and evaluate a machine learning model (ML) for predicting complicated AD. Methods: We analyzed data retrieved from unselected consecutive large bowel AD patients from five hospitals from the Mount Sinai health system, NY. The study time frame was from January 2011 through March 2021. Data were used to train and evaluate a gradient-boosting machine learning model to identify patients with complicated diverticulitis, defined as a need for invasive intervention or in-hospital mortality. The model was trained and evaluated on data from four hospitals and externally validated on held-out data from the fifth hospital. Results: The final cohort included 4997 AD visits. Of them, 129 (2.9%) visits had complicated diverticulitis. Patients with complicated diverticulitis were more likely to be men, black, and arrive by ambulance. Regarding laboratory values, patients with complicated diverticulitis had higher levels of absolute neutrophils (AUC 0.73), higher white blood cells (AUC 0.70), platelet count (AUC 0.68) and lactate (AUC 0.61), and lower levels of albumin (AUC 0.69), chloride (AUC 0.64), and sodium (AUC 0.61). In the external validation cohort, the ML model showed AUC 0.85 (95% CI 0.78–0.91) for predicting complicated diverticulitis. For Youden’s index, the model showed a sensitivity of 88% with a false positive rate of 1:3.6. Conclusions: A ML model trained on clinical measures provides a proof of concept performance in predicting complications in patients presenting to the ED with AD. Clinically, it implies that a ML model may classify low-risk patients to be discharged from the ED for further treatment under an ambulatory setting.

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