An artificial intelligence-generated model predicts 90-day survival in alcohol-associated hepatitis: A global cohort study

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dc.catalogadordfo
dc.contributor.authorDunn, Winston
dc.contributor.authorLi, Yanming
dc.contributor.authorSingal, Ashwani K.
dc.contributor.authorSimonetto, Douglas A.
dc.contributor.authorDíaz Piga, Luis Antonio
dc.contributor.authorIdalsoaga Ferrer, Francisco Javier
dc.contributor.authorAyares, Gustavo
dc.contributor.authorArnold Alvaréz, Jorge Ignacio
dc.contributor.authorAyala-Valverde, Maria
dc.contributor.authorPerez, Diego
dc.contributor.authorGomez, Jaime
dc.contributor.authorEscarate, Rodrigo
dc.contributor.authorFuentes López, Eduardo
dc.contributor.authorRamirez-Cadiz, Carolina
dc.contributor.authorMorales-Arraez, Dalia
dc.contributor.authorZhang, Wei
dc.contributor.authorQian, Steve
dc.contributor.authorAhn, Joseph C.
dc.contributor.authorBuryska, Seth
dc.contributor.authorMehta, Heer
dc.contributor.authorDunn, Nicholas
dc.contributor.authorWaleed, Muhammad
dc.contributor.authorStefanescu, Horia
dc.contributor.authorBumbu, Andreea
dc.contributor.authorHorhat, Adelina
dc.contributor.authorAttar, Bashar
dc.contributor.authorAgrawal, Rohit
dc.contributor.authorCabezas, Joaquin
dc.contributor.authorEchavaria, Victor
dc.contributor.authorCuyas, Berta
dc.contributor.authorPoca, Maria
dc.contributor.authorSoriano, German
dc.contributor.authorSarin, Shiv K.
dc.contributor.authorMaiwall, Rakhi
dc.contributor.authorJalal, Prasun K.
dc.contributor.authorHiguera-de-la-Tijera, Fatima
dc.contributor.authorKulkarni, Anand V.
dc.contributor.authorRao, P. Nagaraja
dc.contributor.authorGuerra-Salazar, Patricia
dc.contributor.authorSkladany, Lubomir
dc.contributor.authorKubanek, Natalia
dc.contributor.authorPrado, Veronica
dc.contributor.authorClemente-Sanchez, Ana
dc.contributor.authorRincon, Diego
dc.contributor.authorHaider, Tehseen
dc.contributor.authorChacko, Kristina R.
dc.contributor.authorRomero, Gustavo A.
dc.contributor.authorPollarsky, Florencia D.
dc.contributor.authorRestrepo, Juan C.
dc.contributor.authorToro, Luis G.
dc.contributor.authorYaquich, Pamela
dc.contributor.authorMendizabal, Manuel
dc.contributor.authorGarrido, Maria L.
dc.contributor.authorMarciano, Sebastian
dc.contributor.authorDirchwolf, Melisa
dc.contributor.authorVargas, Victor
dc.contributor.authorJimenez, Cesar
dc.contributor.authorHudson, David
dc.contributor.authorGarcia-Tsao, Guadalupe
dc.contributor.authorOrtiz, Guillermo
dc.contributor.authorAbraldes, Juan G.
dc.contributor.authorKamath, Patrick S.
dc.contributor.authorArrese, Marco
dc.contributor.authorShah, Vijay H.
dc.contributor.authorBataller, Ramon
dc.contributor.authorArab, Juan P.
dc.date.accessioned2024-06-24T20:04:06Z
dc.date.available2024-06-24T20:04:06Z
dc.date.issued2024
dc.description.abstractBackground 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/.
dc.fuente.origenWOS
dc.identifier.doi10.1097/HEP.0000000000000883
dc.identifier.eissn1527-3350
dc.identifier.issn0270-9139
dc.identifier.urihttps://doi.org/10.1097/HEP.0000000000000883
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86835
dc.identifier.wosidWOS:001235952000001
dc.information.autorucEscuela de Medicina; Diaz Piga Luis Antonio; 0000-0002-8540-4930; 179253
dc.information.autorucEscuela de Medicina; Idalsoaga Ferrer Francisco Javier; 0000-0001-5607-0698; 1017394
dc.information.autorucEscuela de Medicina; Arnold Alvarez Jorge Ignacio; 0000-0003-2139-5120; 1148388
dc.information.autorucEscuela de Medicina; Fuentes Lopez Eduardo; 0000-0002-0141-0226; 1013849
dc.language.isoen
dc.nota.accesoContenido parcial
dc.revistaHepatology
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.titleAn artificial intelligence-generated model predicts 90-day survival in alcohol-associated hepatitis: A global cohort study
dc.typepreprint
sipa.codpersvinculados179253
sipa.codpersvinculados1017394
sipa.codpersvinculados1148388
sipa.codpersvinculados1013849
sipa.trazabilidadWOS;2024-06-15
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