EASY‐APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

Kui, Balázs; Pintér, József; Molontay, Roland; Nagy, Marcell; Farkas, Nelli; Gede, Noémi; Vincze, Áron; Bajor, Judit; Gódi, Szilárd; Czimmer, József; Szabó, Imre; Illés, Anita; Patrícia, Silvia; Hágendorn, Roland; Pár, Gabriella; Papp, Mária; Vitális, Zsuzsanna; Kovács, György; Fehér, Eszter; Földi, Ildikó; Izbéki, Ferenc; Gajdán, László; Fejes, R; Németh, Balázs Csaba; Török, Imola; Farkas, Hunor; Mickevičius, Artautas; Sallinen, Ville; Galeev, Shamil; Ramírez-Maldonado, Elena; Párniczky, Andrea; Erőss, Bálint; Korbonits, M; Váncsa, Szilárd; Szatmary, Peter; Latawiec, Diane; Halloran, Chris; de-Madaria, Enriqué; Pando, Elizabeth; Alberti, Piero; Gómez-Jurado, María José; Tanțău, Alina; Szentesi, Andrea; Hegyi, Péter

doi:10.1002/ctm2.842

Cited by 52 publications

(40 citation statements)

References 53 publications

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“…Meanwhile, it has better performance in diagnostic efficacy and clinical decision-making for patients with AHTGP. It is worth mentioning that an artificial intelligence model-EASY prediction score consisting of 23 parameters was developed recently based on a multicenter, multinational, prospective and observational study[ 17 ]. We applied this model to our research population.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, several new clinical prediction models have been developed to predict the severity of AP[ 15 , 16 ]. The early achievable severity index (EASY) prediction score as an artificial intelligence model was recently developed based on machine learning for the early and easy prediction of severity in AP[ 17 ]. However, almost all of them were developed for all etiologies of pancreatitis and not for HTG-induced pancreatitis separately.…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of a risk prediction score for the severity of acute hypertriglyceridemic pancreatitis in Chinese patients

Liu¹,

Liu²,

Sun³

et al. 2022

WJG

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BACKGROUND The frequency of acute hypertriglyceridemic pancreatitis (AHTGP) is increasing worldwide. AHTGP may be associated with a more severe clinical course and greater mortality than pancreatitis caused by other causes. Early identification of patients with severe inclination is essential for clinical decision-making and improving prognosis. Therefore, we first developed and validated a risk prediction score for the severity of AHTGP in Chinese patients. AIM To develop and validate a risk prediction score for the severity of AHTGP in Chinese patients. METHODS We performed a retrospective study including 243 patients with AHTGP. Patients were randomly divided into a development cohort ( n = 170) and a validation cohort ( n = 73). Least absolute shrinkage and selection operator and logistic regression were used to screen 42 potential predictive variables to construct a risk score for the severity of AHTGP. We evaluated the performance of the nomogram and compared it with existing scoring systems. Last, we used the best cutoff value (88.16) for severe acute pancreatitis (SAP) to determine the risk stratification classification. RESULTS Age, the reduction in apolipoprotein A1 and the presence of pleural effusion were independent risk factors for SAP and were used to construct the nomogram (risk prediction score referred to as AAP). The concordance index of the nomogram in the development and validation groups was 0.930 and 0.928, respectively. Calibration plots demonstrate excellent agreement between the predicted and actual probabilities in SAP patients. The area under the curve of the nomogram (0.929) was better than those of the Bedside Index of Severity in AP (BISAP), Ranson, Acute Physiology and Chronic Health Evaluation (APACHE II), modified computed tomography severity index (MCTSI), and early achievable severity index scores (0.852, 0.825, 0.807, 0.831 and 0.807, respectively). In comparison with these scores, the integrated discrimination improvement and decision curve analysis showed improved accuracy in predicting SAP and better net benefits for clinical decisions. Receiver operating characteristic curve analysis was used to determine risk stratification classification for AHTGP by dividing patients into high-risk and low-risk groups according to the best cutoff value (88.16). The high-risk group (> 88.16) was closely related to the appearance of local and systemic complications, Ranson score ≥ 3, BISAP score ≥ 3, MCTSI score ≥ 4, APACHE II score ≥ 8, C-reactive protein level ≥ 190, and length of hospital stay. CONCLUSION The nomogram could help identify AHTGP patients who are likely to develop SAP at an early stage, which is of great value in guiding clinical decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and validation of a risk prediction score for the severity of acute hypertriglyceridemic pancreatitis in Chinese patients

Liu¹,

Liu²,

Sun³

et al. 2022

WJG

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“…The top six most influential features regarding all cohorts were creatinine, glucose, respiratory rate, urea nitrogen, white blood cell count, and gender. The web application is available for clinicians and contributes to the improvement of the model [ 91 ].…”

Section: Assessment Of Severity Of Acute Pancreatitismentioning

confidence: 99%

Diagnosis and Treatment of Acute Pancreatitis

et al. 2022

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The pancreas is a glandular organ that is responsible for the proper functioning of the digestive and endocrine systems, and therefore, it affects the condition of the entire body. Consequently, it is important to effectively diagnose and treat diseases of this organ. According to clinicians, pancreatitis—a common disease affecting the pancreas—is one of the most complicated and demanding diseases of the abdomen. The classification of pancreatitis is based on clinical, morphologic, and histologic criteria. Medical doctors distinguish, inter alia, acute pancreatitis (AP), the most common causes of which are gallstone migration and alcohol abuse. Effective diagnostic methods and the correct assessment of the severity of acute pancreatitis determine the selection of an appropriate treatment strategy and the prediction of the clinical course of the disease, thus preventing life-threatening complications and organ dysfunction or failure. This review collects and organizes recommendations and guidelines for the management of patients suffering from acute pancreatitis.

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“…Kui et al. have presented the results of a large prospective multicentre study on the role of AI in predicting severe acute pancreatitis (EASY, early achievable severity index) in the current issue of Clinical and Translational Medicine 13 . Their work aimed to overcome some of the limitations of the earlier studies cited above.…”

Section: Study Reference Ai Technique Predictors* Sample Size (N) Out...mentioning

confidence: 99%

“…These include artificial neural network, logistic regression, support vector machine, classification and regression tree, naive Bayes algorithm, quadratic discriminant analysis, adaptive boosting and back propagation network. These 13 Their work aimed to overcome some of the limitations of the earlier studies cited above. The first phase of the study aimed at developing a machine learning model for predicting severe disease.…”

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confidence: 99%

Predicting severity of acute pancreatitis: Emerging role of artificial intelligence

Dutta

2022

Clinical and Translational Dis

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EASY‐APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

Cited by 52 publications

References 53 publications

Development and validation of a risk prediction score for the severity of acute hypertriglyceridemic pancreatitis in Chinese patients

Development and validation of a risk prediction score for the severity of acute hypertriglyceridemic pancreatitis in Chinese patients

Diagnosis and Treatment of Acute Pancreatitis

Predicting severity of acute pancreatitis: Emerging role of artificial intelligence

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