BACKGROUND: Critically ill patients requiring emergent colectomy have significant mortality risk. OBJECTIVE: A national administrative database was used to compose a simple scoring scheme for predicting in-hospital mortality risk. DESIGN: The 2007 to 2009 Nationwide Inpatient Sample was queried to identify patients requiring nonelective colectomy. Multivariable binary logistic regression analysis was used to identify predictors that increased mortality. Each predictor was given a point value, based on the corresponding logit, the sum of which constituted a risk score. The scoring system was tested by using k-partitions cross-validation. SETTINGS: This study is based on database analysis. PATIENTS: A total of 338,348 cases were identified. Mean age was 64, and 53% of the patients were women. MAIN OUTCOME MEASURES: The primary outcomes measured were mortality and risk score development. RESULTS: The overall mortality risk was 9%. Regression analysis identified the following risk factors and assigned points: acute renal failure (6), hemodialysis (6), age >65 (4), peripheral vascular disease (4), myocardial infarction (4), chronic obstructive pulmonary disease (2), cardiac arrhythmia (1), and congestive heart failure (1). The maximum score observed was 26 (of a possible 28), which corresponded to 100% mortality. Receiver operator characteristic analysis showed an area under the curve of 0.81. LIMITATIONS: This study was limited because of its retrospective nature, and because it used database data with variability in coding among participating institutions. CONCLUSIONS: With the use of a simple 8-variable scoring system, inpatient mortality estimates can be made for patients requiring emergent colectomy. When used judiciously, it can be used as a tool when counseling patients and family both before and after surgery.
BACKGROUND:Critically illpatients requiring emergent colectomy have significant mortality risk. OBJECTIVE: A national administrative database was used to compose a simple scoring scheme for predicting in-hospital mortality risk. DESIGN: The 2007 to 2009 Nationwide Inpatient Sample was queried to identify patients requiring nonelective colectomy. Multivariable binary logistic regression analysis was used to identify predictors that increased mortality. Each predictor was given a point value, based on the corresponding logit, the sum of which constituted a risk score. The scoring system was tested by using k-partitions cross-validation. SETTINGS: This study is based on database analysis. PATIENTS: A total of 338,348 cases were identified. Mean age was 64, and 53% of the patients were women. MAIN OUTCOME MEASURES: The primary outcomes measured were mortality and risk score development. RESULTS: The overall mortality risk was 9%. Regression analysis identified the following risk factors and assigned points: acute renal failure (6), hemodialysis (6), age >65 (4), peripheral vascular disease (4), myocardial infarction (4), chronic obstructive pulmonary disease (2), cardiac arrhythmia (1), and congestive heart failure (1). The maximum score observed was 26 (of a possible 28), which corresponded to 100% mortality. Receiver operator characteristic analysis showed an area under the curve of 0.81. LIMITATIONS: This study was limited because of its retrospective nature, and because it used database data with variability in coding among participating institutions. CONCLUSIONS: With the use of a simple 8-variable scoring system, inpatient mortality estimates can be made for patients requiring emergent colectomy. When used judiciously, it can be used as a tool when counseling patients and family both before and after surgery.
Authors: Zhaomin Xu; Adan Z Becerra; Christopher T Aquina; Bradley J Hensley; Carla F Justiniano; Courtney Boodry; Alex A Swanger; Reza Arsalanizadeh; Katia Noyes; John R Monson; Fergal J Fleming Journal: J Gastrointest Surg Date: 2017-01-12 Impact factor: 3.452
Authors: Gianluca Costa; Laura Lorenzon; Giulia Massa; Barbara Frezza; Mario Ferri; Pietro Fransvea; Paolo Mercantini; Maria Cristina Giustiniani; Genoveffa Balducci Journal: Int J Colorectal Dis Date: 2017-07-28 Impact factor: 2.571
Authors: Sandra R DiBrito; Yewande Alimi; Israel O Olorundare; Courtenay M Holscher; Christine E Haugen; Dorry L Segev; Jacqueline Garonzik-Wang Journal: J Gastrointest Surg Date: 2018-05-07 Impact factor: 3.452