Literature DB >> 35431735

Can ACS-NSQIP score be used to predict postoperative mortality in Saudi population?

Anwar U Huda1, Mohammad Yasir1, Nasrullah Sheikh1, Asad Z Khan1.   

Abstract

Background: Various scoring systems help in classifying the patient's risk preoperatively and hence to decide the best available treatment option. ACS-NSQIP score has been introduced in clinical practice for few years. This study was done to find out whether there is any difference between predicted mortality from ACS-NSQIP score and observed mortality in Saudi population.
Methods: This prospective observational study was conducted at Security Forces Hospital, Riyadh, Kingdom of Saudi Arabia. We included patients undergoing elective and emergency surgical procedures in our hospital. Thirty days mortality data was collected and then observed to expected (O/E) mortality ratio was calculated. The sample size for our study was nine hundred and three (903) patients.
Results: The mean ACS-NSQIP mortality risk score (%) for the study was 0.49. Expected number of mortalities was 4.42 while observed mortalities were 11, yielding an O/E ratio of 2.48 (p-value 0.000). We did not find a significant difference between expected and observed mortalities except for ASA class 3 and 4 patients where expected numbers of mortalities were lower than observed (p-value < 0.05).
Conclusion: ACS-NSQIP can be reliably used for postoperative mortality prediction especially in lower risk groups. Copyright:
© 2022 Saudi Journal of Anesthesia.

Entities:  

Keywords:  ACS-NSQIP; postoperative mortality; risk assessment; scoring system

Year:  2022        PMID: 35431735      PMCID: PMC9009561          DOI: 10.4103/sja.sja_734_21

Source DB:  PubMed          Journal:  Saudi J Anaesth


  24 in total

1.  Comparison of observed to predicted outcomes using the ACS NSQIP risk calculator in patients undergoing pancreaticoduodenectomy.

Authors:  Harveshp D Mogal; Nora Fino; Clancy Clark; Perry Shen
Journal:  J Surg Oncol       Date:  2016-05-04       Impact factor: 3.454

2.  The ACS NSQIP Risk Calculator Is a Fair Predictor of Acute Periprosthetic Joint Infection.

Authors:  Nathaniel C Wingert; James Gotoff; Edgardo Parrilla; Robert Gotoff; Laura Hou; Elie Ghanem
Journal:  Clin Orthop Relat Res       Date:  2016-07       Impact factor: 4.176

Review 3.  Biomarkers to guide perioperative management.

Authors:  Mark Edwards; John Whittle; Gareth L Ackland
Journal:  Postgrad Med J       Date:  2011-01-17       Impact factor: 2.401

4.  Accuracy of American College of Surgeons National Surgical Quality Improvement Program Universal Surgical Risk Calculator in Predicting Complications Following Robot-Assisted Radical Cystectomy at a National Comprehensive Cancer Center.

Authors:  Zaeem Lone; Shelby Hall; Tomoaki Terakawa; Youssef E Ahmed; Ahmed S Elsayed; Naif Aldhaam; Paul R May; Austin Miller; Zhe Jing; Luciano Nunez Bragayrac; Hijab Khan; Jared Cohen; Adam Cole; Omer Rana; Renuka Kanapan; Christian Prechtl; Ahmed A Hussein; Khurshid A Guru
Journal:  J Endourol       Date:  2019-04-22       Impact factor: 2.942

5.  Risk Prediction Accuracy Differs for Emergency Versus Elective Cases in the ACS-NSQIP.

Authors:  Joseph A Hyder; Gally Reznor; Elliot Wakeam; Louis L Nguyen; Stuart R Lipsitz; Joaquim M Havens
Journal:  Ann Surg       Date:  2016-12       Impact factor: 12.969

6.  Evaluation of the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator in Gynecologic Oncology Patients Undergoing Minimally Invasive Surgery.

Authors:  Deanna Teoh; Rebi Nahum Halloway; Jennifer Heim; Rachel Isaksson Vogel; Colleen Rivard
Journal:  J Minim Invasive Gynecol       Date:  2016-10-24       Impact factor: 4.137

7.  Predictive performance of the American College of Surgeons universal risk calculator in neurosurgical patients.

Authors:  Sasha Vaziri; Jacob Wilson; Joseph Abbatematteo; Paul Kubilis; Saptarshi Chakraborty; Khare Kshitij; Daniel J Hoh
Journal:  J Neurosurg       Date:  2017-04-28       Impact factor: 5.115

Review 8.  Risk stratification tools for predicting morbidity and mortality in adult patients undergoing major surgery: qualitative systematic review.

Authors:  Suneetha Ramani Moonesinghe; Michael G Mythen; Priya Das; Kathryn M Rowan; Michael P W Grocott
Journal:  Anesthesiology       Date:  2013-10       Impact factor: 7.892

9.  Preoperative APACHE II and GCS scores as predictors of outcomes in patients with malignant MCA infarction after decompressive hemicraniectomy.

Authors:  Chia-Lin Tsai; Hsin Chu; Giia-Sheun Peng; Hsin-I Ma; Chun-An Cheng; Dueng-Yuan Hueng
Journal:  Neurol India       Date:  2012 Nov-Dec       Impact factor: 2.117

10.  Comparison of postoperative complication risk prediction approaches based on factors known preoperatively to surgeons versus patients.

Authors:  Allison R Dahlke; Ryan P Merkow; Jeanette W Chung; Christine V Kinnier; Mark E Cohen; Min-Woong Sohn; Jennifer Paruch; Jane L Holl; Karl Y Bilimoria
Journal:  Surgery       Date:  2014-03-15       Impact factor: 3.982

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