Tyler J Loftus1, Scott C Brakenridge1, Chasen A Croft2, Robert Stephen Smith2, Philip A Efron1, Frederick A Moore1, Alicia M Mohr1, Janeen R Jordan3. 1. Department of Surgery, University of Florida Health, Gainesville, Florida; Department of Sepsis and Critical Illness Research Center in Gainesville, University of Florida Health, Gainesville, Florida. 2. Department of Surgery, University of Florida Health, Gainesville, Florida. 3. Department of Surgery, University of Florida Health, Gainesville, Florida. Electronic address: Janeen.Jordan@surgery.ufl.edu.
Abstract
BACKGROUND: The prognosis for patients with severe acute lower intestinal bleeding (ALIB) may be assessed by complex artificial neural networks (ANNs) or user-friendly regression-based models. Comparisons between these modalities are limited, and predicting the need for surgical intervention remains elusive. We hypothesized that ANNs would outperform the Strate rule to predict severe bleeding and would also predict the need for surgical intervention. METHODS: We performed a 4-y retrospective analysis of 147 adult patients who underwent endoscopy, angiography, or surgery for ALIB. Baseline characteristics, Strate risk factors, management parameters, and outcomes were analyzed. The primary outcomes were severe bleeding and surgical intervention. ANNs were created in SPSS. Models were compared by area under the receiver operating characteristic curve (AUROC) with 95% confidence intervals. RESULTS: The number of Strate risk factors for each patient correlated significantly with the outcome of severe bleeding (r = 0.29, P < 0.001). However, the Strate model was less accurate than an ANN (AUROC 0.66 [0.57-0.75] versus 0.98 [0.95-1.00], respectively) which incorporated six variables present on admission: hemoglobin, systolic blood pressure, outpatient prescription for Aspirin 325 mg daily, Charlson comorbidity index, base deficit ≥5 mEq/L, and international normalized ratio ≥1.5. A similar ANN including hemoglobin nadir and the occurrence of a 20% decrease in hematocrit was effective in predicting the need for surgery (AUROC 0.95 [0.90-1.00]). CONCLUSIONS: The Strate prediction rule effectively stratified risk for severe ALIB, but was less accurate than an ANN. A separate ANN accurately predicted the need for surgery by combining risk factors for severe bleeding with parameters quantifying blood loss. Optimal prognostication may be achieved by integrating pragmatic regression-based calculators for quick decisions at the bedside and highly accurate ANNs when time and resources permit. Published by Elsevier Inc.
BACKGROUND: The prognosis for patients with severe acute lower intestinal bleeding (ALIB) may be assessed by complex artificial neural networks (ANNs) or user-friendly regression-based models. Comparisons between these modalities are limited, and predicting the need for surgical intervention remains elusive. We hypothesized that ANNs would outperform the Strate rule to predict severe bleeding and would also predict the need for surgical intervention. METHODS: We performed a 4-y retrospective analysis of 147 adult patients who underwent endoscopy, angiography, or surgery for ALIB. Baseline characteristics, Strate risk factors, management parameters, and outcomes were analyzed. The primary outcomes were severe bleeding and surgical intervention. ANNs were created in SPSS. Models were compared by area under the receiver operating characteristic curve (AUROC) with 95% confidence intervals. RESULTS: The number of Strate risk factors for each patient correlated significantly with the outcome of severe bleeding (r = 0.29, P < 0.001). However, the Strate model was less accurate than an ANN (AUROC 0.66 [0.57-0.75] versus 0.98 [0.95-1.00], respectively) which incorporated six variables present on admission: hemoglobin, systolic blood pressure, outpatient prescription for Aspirin 325 mg daily, Charlson comorbidity index, base deficit ≥5 mEq/L, and international normalized ratio ≥1.5. A similar ANN including hemoglobin nadir and the occurrence of a 20% decrease in hematocrit was effective in predicting the need for surgery (AUROC 0.95 [0.90-1.00]). CONCLUSIONS: The Strate prediction rule effectively stratified risk for severe ALIB, but was less accurate than an ANN. A separate ANN accurately predicted the need for surgery by combining risk factors for severe bleeding with parameters quantifying blood loss. Optimal prognostication may be achieved by integrating pragmatic regression-based calculators for quick decisions at the bedside and highly accurate ANNs when time and resources permit. Published by Elsevier Inc.
Entities:
Keywords:
Gastrointestinal bleeding; Neural network; Severe bleeding; Strate; Surgery; Transfusion
Authors: Ananya Das; Tamir Ben-Menachem; Gregory S Cooper; Amitabh Chak; Michael V Sivak; Judith A Gonet; Richard C K Wong Journal: Lancet Date: 2003-10-18 Impact factor: 79.321
Authors: Fernando S Velayos; Ann Williamson; Karen H Sousa; Edward Lung; Alan Bostrom; Ellen J Weber; James W Ostroff; Jonathan P Terdiman Journal: Clin Gastroenterol Hepatol Date: 2004-06 Impact factor: 11.382
Authors: Miguel C Soriano; Daniel Brunner; Miguel Escalona-Morán; Claudio R Mirasso; Ingo Fischer Journal: Front Comput Neurosci Date: 2015-06-02 Impact factor: 2.380
Authors: Tyler J Loftus; Patrick J Tighe; Amanda C Filiberto; Philip A Efron; Scott C Brakenridge; Alicia M Mohr; Parisa Rashidi; Gilbert R Upchurch; Azra Bihorac Journal: JAMA Surg Date: 2020-02-01 Impact factor: 14.766