Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning.
Acute pancreatitis (AP) is a complex and life-threatening disease. Early recognition of factors predicting morbidity and mortality is crucial. We aimed to develop and validate a pragmatic model to predict the individualized risk of early intensive care unit (ICU) admission for patients with AP. The...
| Publicado en: | Baylor University Medical Center Proceedings Vol. 37; no. 3; pp. 437 - 448 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
May2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176582729&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176582729 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08998280 1XEP jtl: Baylor University Medical Center Proceedings issn: 08998280 maglogo: N pubinfo: dt: May2024 vid: 37 iid: 3 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 176582729 176582729 176582729 10.1080/08998280.2024.2326371 176582729 ppf: 437 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning. aug: au: Ali, Hassam Inayat, Faisal Dhillon, Rubaid Patel, Pratik Afzal, Arslan Wilkinson, Christin Rehman, Attiq Ur Anwar, Muhammad Sajeel Nawaz, Gul Chaudhry, Ahtshamullah Awan, Junaid Rasul Afzal, Muhammad Sohaib Samanta, Jayanta Adler, Douglas G. Mohan, Babu P. affil: Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, North Carolina, USA sug: subj: Patient Admission United States Intensive Care Units Transfer, Intrahospital Risk Assessment Individualized Medicine Hospitalization Pancreatitis Machine Learning Decision Support Systems, Clinical Human Inpatients Validation Studies Prediction Models United States Prospective Studies Regression Probability Clinical Assessment Tools Adult Middle Age Anoxia Complications Kidney Failure, Acute Complications Arrhythmia Complications Descriptive Statistics Confidence Intervals Sensitivity and Specificity Adult: 19-44 years Middle Aged: 45-64 years ab: Acute pancreatitis (AP) is a complex and life-threatening disease. Early recognition of factors predicting morbidity and mortality is crucial. We aimed to develop and validate a pragmatic model to predict the individualized risk of early intensive care unit (ICU) admission for patients with AP. The 2019 Nationwide Readmission Database was used to identify patients hospitalized with a primary diagnosis of AP without ICU admission. A matched comparison cohort of AP patients with ICU admission within 7 days of hospitalization was identified from the National Inpatient Sample after 1:N propensity score matching. The least absolute shrinkage and selection operator (LASSO) regression was used to select predictors and develop an ICU acute pancreatitis risk (IAPR) score validated by 10-fold cross-validation. A total of 1513 patients hospitalized for AP were included. The median age was 50.0 years (interquartile range: 39.0–63.0). The three predictors that were selected included hypoxia (area under the curve [AUC] 0.78), acute kidney injury (AUC 0.72), and cardiac arrhythmia (AUC 0.61). These variables were used to develop a nomogram that displayed excellent discrimination (AUC 0.874) (bootstrap bias-corrected 95% confidence interval 0.824–0.876). There was no evidence of miscalibration (test statistic = 2.88; P = 0.09). For high-risk patients (total score >6 points), the sensitivity was 68.94% and the specificity was 92.66%. This supervised machine learning-based model can help recognize high-risk AP hospitalizations. Clinicians may use the IAPR score to identify patients with AP at high risk of ICU admission within the first week of hospitalization. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|