Browsing by Author "Escarate, Rodrigo"

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    An artificial intelligence-generated model predicts 90-day survival in alcohol-associated hepatitis: A global cohort study
    (2024) Dunn, Winston; Li, Yanming; Singal, Ashwani K.; Simonetto, Douglas A.; Díaz Piga, Luis Antonio; Idalsoaga Ferrer, Francisco Javier; Ayares, Gustavo; Arnold Alvaréz, Jorge Ignacio; Ayala-Valverde, Maria; Perez, Diego; Gomez, Jaime; Escarate, Rodrigo; Fuentes López, Eduardo; Ramirez-Cadiz, Carolina; Morales-Arraez, Dalia; Zhang, Wei; Qian, Steve; Ahn, Joseph C.; Buryska, Seth; Mehta, Heer; Dunn, Nicholas; Waleed, Muhammad; Stefanescu, Horia; Bumbu, Andreea; Horhat, Adelina; Attar, Bashar; Agrawal, Rohit; Cabezas, Joaquin; Echavaria, Victor; Cuyas, Berta; Poca, Maria; Soriano, German; Sarin, Shiv K.; Maiwall, Rakhi; Jalal, Prasun K.; Higuera-de-la-Tijera, Fatima; Kulkarni, Anand V.; Rao, P. Nagaraja; Guerra-Salazar, Patricia; Skladany, Lubomir; Kubanek, Natalia; Prado, Veronica; Clemente-Sanchez, Ana; Rincon, Diego; Haider, Tehseen; Chacko, Kristina R.; Romero, Gustavo A.; Pollarsky, Florencia D.; Restrepo, Juan C.; Toro, Luis G.; Yaquich, Pamela; Mendizabal, Manuel; Garrido, Maria L.; Marciano, Sebastian; Dirchwolf, Melisa; Vargas, Victor; Jimenez, Cesar; Hudson, David; Garcia-Tsao, Guadalupe; Ortiz, Guillermo; Abraldes, Juan G.; Kamath, Patrick S.; Arrese, Marco; Shah, Vijay H.; Bataller, Ramon; Arab, Juan P.
    Background and Aims: Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mortality. Utilizing artificial intelligence in a global cohort, we sought to derive and validate an enhanced prognostic model. Approach and Results: The Global AlcHep initiative, a retrospective study across 23 centers in 12 countries, enrolled patients with AH per National Institute for Alcohol Abuse and Alcoholism criteria. Centers were partitioned into derivation (11 centers, 860 patients) and validation cohorts (12 centers, 859 patients). Focusing on 30 and 90-day postadmission mortality, 3 artificial intelligence algorithms (Random Forest, Gradient Boosting Machines, and eXtreme Gradient Boosting) informed an ensemble model, subsequently refined through Bayesian updating, integrating the derivation cohort's average 90-day mortality with each center's approximate mortality rate to produce posttest probabilities. The ALCoholic Hepatitis Artificial INtelligence Ensemble score integrated age, gender, cirrhosis, and 9 laboratory values, with center-specific mortality rates. Mortality was 18.7% (30 d) and 27.9% (90 d) in the derivation cohort versus 21.7% and 32.5% in the validation cohort. Validation cohort 30 and 90-day AUCs were 0.811 (0.779-0.844) and 0.799 (0.769-0.830), significantly surpassing legacy models like Maddrey's Discriminant Function, Model for End-Stage Liver Disease variations, age-serum bilirubin-international normalized ratio-serum Creatinine score, Glasgow, and modified Glasgow Scores (p < 0.001). ALCoholic Hepatitis Artificial INtelligence Ensemble score also showcased superior calibration against MELD and its variants. Steroid use improved 30-day survival for those with an ALCoholic Hepatitis Artificial INtelligence Ensemble score > 0.20 in both derivation and validation cohorts. Conclusions: Harnessing artificial intelligence within a global consortium, we pioneered a scoring system excelling over traditional models for 30 and 90-day AH mortality predictions. Beneficial for clinical trials, steroid therapy, and transplant indications, it's accessible at: https://aihepatology.shinyapps.io/ALCHAIN/.
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    MELD 3.0 adequately predicts mortality and renal replacement therapy requirements in patients with alcohol-associated hepatitis
    (Elsevier B.V., 2023) Diaz Piga, Luis Antonio; Fuentes Lopez, Eduardo; Ayares Campos, Gustavo Ignacio; Idalsoaga Ferrer, Francisco Javier; Arnold Álvarez, Jorge Ignacio; Valverde, María Ayala; Perez, Diego; Gómez, Jaime; Escarate, Rodrigo; Villalon Friedrich, Alejandro Andrés; Ramírez, Carolina A.; Hernández-Tejero, María; Zhang, Wei; Qian, Steve; Simonetto, Douglas; Ahn, Joseph C.; Buryska, Seth; Dunn, Winston; Mehta, Heer; Agrawal, Rohit; Cabezas, Joaquín; Garcia Carrera, Inés; Cuyas, Berta; Poca, Maria; Soriano, German; Sarin, Shiv K.; Maiwall, Rakhi; Jalal, Prasun K.; Abdulsada, Saba; Higuera de la Tijera, Fátima; Kulkarni, Anand V.; Rao, P. Nagaraja; Guerra Salazar, Patricia; Skladany, Lubomir; Bystrianska, Natália; Clemente Sánchez, Ana; Villaseca Gómez, Clara; Haider, Tehseen; Chacko, Kristina R.; Romero, Gustavo A.; Pollarsky Florencia D.; Restrepo, Juan Carlos; Castro Sánchez, Susan; Toro, Luis G.; Yaquich, Pamela; Mendizabal, Manuel; Garrido, María Laura; Marciano, Sebastián; Dirchwolf, Melisa; Vargas, Víctor; Jimenez, César; Louvet, Alexandre; Garcia Tsao, Guadalupe; Roblero, Juan Pablo; Abraldes, Juan G.; Shah, Vijay H.; Kamath, Patrick S.; Arrese Jimenez, Marco Antonio; Singal, Ashwani K.; Bataller, Ramón; Arab Verdugo, Juan Pablo
    © 2023 The Author(s)Background & Aims: Model for End-Stage Liver Disease (MELD) score better predicts mortality in alcohol-associated hepatitis (AH) but could underestimate severity in women and malnourished patients. Using a global cohort, we assessed the ability of the MELD 3.0 score to predict short-term mortality in AH. Methods: This was a retrospective cohort study of patients admitted to hospital with AH from 2009 to 2019. The main outcome was all-cause 30-day mortality. We compared the AUC using DeLong's method and also performed a time-dependent AUC with competing risks analysis. Results: A total of 2,124 patients were included from 28 centres from 10 countries on three continents (median age 47.2 ± 11.2 years, 29.9% women, 71.3% with underlying cirrhosis). The median MELD 3.0 score at admission was 25 (20–33), with an estimated survival of 73.7% at 30 days. The MELD 3.0 score had a better performance in predicting 30-day mortality (AUC:0.761, 95%CI:0.732–0.791) compared with MELD sodium (MELD-Na; AUC: 0.744, 95% CI: 0.713–0.775; p = 0.042) and Maddrey's discriminant function (mDF) (AUC: 0.724, 95% CI: 0.691–0.757; p = 0.013). However, MELD 3.0 did not perform better than traditional MELD (AUC: 0.753, 95% CI: 0.723–0.783; p = 0.300) and Age-Bilirubin-International Normalised Ratio-Creatinine (ABIC) (AUC:0.757, 95% CI: 0.727–0.788; p = 0.765). These results were consistent in competing-risk analysis, where MELD 3.0 (AUC: 0.757, 95% CI: 0.724–0.790) predicted better 30-day mortality compared with MELD-Na (AUC: 0.739, 95% CI: 0.708–0.770; p = 0.028) and mDF (AUC:0.717, 95% CI: 0.687–0.748; p = 0.042). The MELD 3.0 score was significantly better in predicting renal replacement therapy requirements during admission compared with the other scores (AUC: 0.844, 95% CI: 0.805–0.883). Conclusions: MELD 3.0 demonstrated better performance compared with MELD-Na and mDF in predicting 30-day and 90-day mortality, and was the best predictor of renal replacement therapy requirements during admission for AH. However, further prospective studies are needed to validate its extensive use in AH. Impact and implications: Severe AH has high short-term mortality. The establishment of treatments and liver transplantation depends on mortality prediction. We evaluated the performance of the new MELD 3.0 score to predict short-term mortality in AH in a large global cohort. MELD 3.0 performed better in predicting 30- and 90-day mortality compared with MELD-Na and mDF, but was similar to MELD and ABIC scores. MELD 3.0 was the best predictor of renal replacement therapy requirements. Thus, further prospective studies are needed to support the wide use of MELD 3.0 in AH.