ArticleDigestive diseases and sciences2023
A New Risk Score to Predict Intensive Care Unit Admission for Patients with Acute Pancreatitis 48 Hours After Admission: Multicenter Study.
Article in Digestive diseases and sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Application and impact of Lasso regression in gastroenterology: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2023Pooled it
- Early prediction of acute pancreatitis with acute kidney injury using abdominal contrast-enhanced CT features.iScience · 2024Article
- Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning.Proceedings (Baylor University. Medical Center) · 2024Article
- Letter Regarding "Predictive Model for Positive Video Capsule Endoscopy in Iron Deficiency Anemia".Digestive diseases and sciences · 2023Article
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThe objective of this study was to develop and validate an easy-to-use risk score (APRS) to predict which patients with acute pancreatitis (AP) will need intensive care unit (ICU) treatment within 48 h post-hospitalization on the basis of the ubiquitously available clinical records.
methodsPatients with acute pancreatitis were retrospectively included from three independent institutions (RM cohort, 5280; TJ cohort, 262; SN cohort, 196), with 56 candidate variables collected within 48 h post-hospitalization. The RM cohort was randomly divided into a training set (N = 4220) and a test set (N = 1060). The most predictive features were extracted by LASSO from the RM cohort and entered into multivariate analysis. APRS was constructed using the coefficients of the statistically significant variables weighted by the multivariable logistic regression model. The APRS was validated by RM, TJ, and SN cohorts. The C-statistic was employed to evaluate the APRS's discrimination. DeLong test was used to compare area under the receiver operating characteristic curve (AUC) differences.
resultsA total of 5738 patients with AP were enrolled. Eleven variables were selected by LASSO and entered into multivariate analysis. APRS was inferred using the above five factors (pleural effusion, ALT/AST, ALB/GLB, urea, and glucose) weighted by their regression coefficients in the multivariable logistic regression model. The C-statistics of APRS were 0.905 (95% CI 0.82-0.98) and 0.889 (95% CI 0.81-0.96) in RM and TJ validation. An online APRS web-based calculator was constructed to assist the clinician to earlier assess the clinical outcomes of patients with AP.
conclusionAPRS could effectively stratify patients with AP into high and low risk of ICU admission within 48 h post-hospitalization, offering clinical value in directing management and personalize therapeutic selection for patients with AP.
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