Literature DB >> 32247844

Risk of Complication at the Time of Laparoscopic Hysterectomy; A Prediction Model Built from The National Surgical Quality Improvement Program Database.

Kristen J Pepin1, E Francis Cook2, Sarah L Cohen3.   

Abstract

BACKGROUND: While laparoscopic hysterectomy is well-established as a favorable mode of hysterectomy due to decreased perioperative complications, there is still room for improvement in quality of care. Though previous studies have described laparoscopic hysterectomy risk factors, there is currently no tool for predicting risk of complication at the time of laparoscopic hysterectomy.
OBJECTIVE: Create a prediction model for complications at the time of laparoscopic hysterectomy for benign conditions. STUDY
DESIGN: Retrospective cohort study including patients undergoing laparoscopic hysterectomy for benign indications between 2014 and 2017 at United States hospitals contributing to American College of Surgeons- National Surgical Quality Improvement Program Database. Data about patient baseline characteristics, perioperative complications (intraoperative complications, readmission, reoperation, need for transfusion, operative time greater than 4 hour or postoperative medical complication) and uterine weight at the time of pathologic examination were collected retrospectively. Postoperative uterine weight was used as a proxy for preoperative uterine weight estimate. The sample was randomly split to create two patient populations, one for deriving the model and the other to validate the model.
RESULTS: A total of 33,123 women met inclusion criteria. The rate of composite complication was 14.1%. Complication rates were similar in the derivation and validation cohorts (14.1% [2,306/14,051] vs 13.9% [2,289/14,107], p=0.7207). The logistic regression risk-prediction tool for hysterectomy complication identified seven variables predictive of complication; history of prior laparotomy (increases odds of complication by 21%), age (2% increase odds of complication per year of life), BMI (0.2% increase odds of complication per each unit increase in BMI), parity (7% increased odds of complication per delivery), race (when compared to white women, black women had a 34% increased odds and women of other races had a 18% increased odds of complication) and American Society of Anesthesiologists score (when compared to an score = 1, score = 2 had a 31% increased odds of complication, score = 3 had a 62% increased odds and score =4 had a 172% increased odds). Predicted preoperative uterine weight also had a statistically significant non-linear relationship with odds of complication. The c statistics for the derivation and validation cohorts were 0.62 and 0.62, respectively. The model is well-calibrated for women at all levels of risk.
CONCLUSION: The laparoscopic hysterectomy complication predictor model is a tool for predicting complications in patients planning hysterectomy.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Laparoscopic Hysterectomy; NSQIP; Risk Prediction

Year:  2020        PMID: 32247844     DOI: 10.1016/j.ajog.2020.03.023

Source DB:  PubMed          Journal:  Am J Obstet Gynecol        ISSN: 0002-9378            Impact factor:   8.661


  3 in total

1.  Racial/Ethnic Differences in the Risk of Surgical Complications and Posthysterectomy Hospitalization among Women Undergoing Hysterectomy for Benign Conditions.

Authors:  Lisa M Pollack; Jerry L Lowder; Matt Keller; Su-Hsin Chang; Sarah J Gehlert; Margaret A Olsen
Journal:  J Minim Invasive Gynecol       Date:  2021-01-01       Impact factor: 4.137

2.  Machine learning approaches for the prediction of postoperative complication risk in liver resection patients.

Authors:  Siyu Zeng; Lele Li; Yanjie Hu; Li Luo; Yuanchen Fang
Journal:  BMC Med Inform Decis Mak       Date:  2021-12-30       Impact factor: 2.796

3.  Predicting major complications in patients undergoing laparoscopic and open hysterectomy for benign indications.

Authors:  Krupa Madhvani; Silvia Fernandez Garcia; Borja M Fernandez-Felix; Javier Zamora; Tyrone Carpenter; Khalid S Khan
Journal:  CMAJ       Date:  2022-10-03       Impact factor: 16.859

  3 in total

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