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2813 Clinical Prediction of Unsuccessful Endometrial Ablation: Random Forest vs Logistic Regression
S154
Abstracts / Journal of Minimally Invasive Gynecology 26 (2019) S98−S231
Video Objective: Although reliable knot tying is a one of the most important surgical concepts, few videos exist that illustrate how to tie a reproducible, reliable laparoscopic knot. The purpose of this video is to teach the basics of laparoscopic suturing, as well as to illustrate a reproducible method of tying reliable laparoscopic knots. Setting: The video setting uses a 3-D Med laparoscopic trainer box with both standard suture and thick, color-blocked string for better demonstration. Interventions: N/A Conclusion: Laparoscopic suturing and intracorporeal laparoscopic knot tying is one of the most difficult, yet important, skills to learn as a beginning minimally-invasive surgeon. There are few accessible videos which teach how to correctly insert a needle, suture, and tie a reliable knot using laparoscopic tools. This video aims to teach the basics of laparoscopic suturing with the aid of color-blocked string to aid in the viewer’s understanding of how to tie a proper knot.
Study Objective: To compare the performance of the prediction models of surgical re-intervention within 2 years after endometrial ablation (EA) by a multivariate random forest model vs the previously presented multivariate logistic regression model. Design: Retrospective cohort study, minimal follow-up time of 2 years. Setting: Data from Catharina Hospital, Eindhoven and Elkerliek Hospital, Helmond, both non-university teaching hospitals in the Netherlands, were used. Patients or Participants: Pre-menopausal women (18+) who have had an EA for heavy menstrual bleeding between January 2004 & April 2013. A total number of 446 patients were eligible for analysis. Interventions: Used ablation methods were Cavatherm (Veldana Medical SA, Morges, Switzerland), Gynecare Thermachoice (Ethicon, Sommerville, US.) and Thermablate EAS (Idoman, Ireland). Used interventions and other ablation techniques had the same outcomes according to previously published literature. Measurements and Main Results: Data-analysis was done by using IBM SPSS statistics software version 21.0 (IBM Corp., Armonk, NY, USA). The random forest model was trained in MATLAB (2018b) using the TreeBagger function in the Statistics and Machine Learning Toolbox. The prediction model based on a multivariate logistic regression analysis had an AUC of 0.71. The machine learning model had an AUC of 0.63 and an AUC of 0.65 after hyperparameter optimization. Conclusion: Based on the preliminary results, we can conclude that the random forest model in this case is not better than the logistic regression model to predict the outcome of surgical re-intervention within two years after EA. In summary, the performance of a random forest clinical prediction model is not necessarily superior to a logistic regression model. The performance of each model is influenced by the sample size, the number of predictors, hyperparameter tuning and the linearity of associations.
Virtual Poster Session 2: Basic Science/Research/Education (1:30 PM − 1:40 PM) 1:30 PM: STATION E 2080 Strategies for Difficult Hysterectomies Alvarez-Rosales A,1,* Garcia Rodriguez LF,2 Berlanga-Narro SM,3 Garza-Ayala M4. 12° year Fellow MIGS, Tecnologico de Monterrey, Monterrey, NL, Mexico; 2Fellowship Director MIGs, Tecnologico de Monterrey, Monterrey, NL, Mexico; 3Tecnologico de Monterrey, Monterrey, NL, Mexico; 41° year Fellow MIGS, Tecnologico de Monterrey, Monterrey, NL, Mexico *Corresponding author. Video Objective: Demonstrate alternative surgical approaches for difficult hysterectomies. Setting: Multiple Cases of Hysterectomies with Challenging circumstances. Interventions: Multiple Cases of Hysterectomies with Challenging circumstances with strategies for getting an alternative approach. 1. 30˚ angle Scope for better visualization 2. Decompression for vaginal extraction 3. Traction of the angles of vaginal cuff 4. Bladder Distention 5. Blunt Dissection 6. Cold Dissection 7. Power Morcellation 8. Identify Retroperitoneal Structures. Conclusion: These strategies are useful to perform complex surgery and reduce the risk of complications Virtual Poster Session 2: Basic Science/Research/Education (1:30 PM − 1:40 PM) 1:30 PM: STATION F 2813 Clinical Prediction of Unsuccessful Endometrial Ablation: Random Forest vs Logistic Regression Stevens KYR,1 Lagaert LVR,2,3,* Bakkes T,4 Van De Keere M,3 Houterman S,5 van Vliet H,6 Schoot BC6. 1Obsetrics and Gynaecology, Catharina ziekenhuis Eindhoven, Hulst, Netherlands; 2Women’s Clinic, Ghent University Hospital, Ghent, Belgium, Ghent, Belgium; 3Department of Obstetrics and Gynaecology, Catharina Hospital, Eindhoven, the Netherlands, Eindhoven, Netherlands; 4epartment of Electrical Engineering, Biomedical Diagnostics lab TU Eindhoven, The Netherlands, Eindhoven, Netherlands; 5Education and research, Catharina ziekenhuis Eindhoven, Eindhoven, Netherlands; 6Obsetrics and Gynaecology, Catharina ziekenhuis Eindhoven, Eindhoven, Netherlands *Corresponding author.
Virtual Poster Session 2: Basic Science/Research/Education (1:30 PM − 1:40 PM) 1:30 PM: STATION G 1282 Current Trends in Compensation for Minimally Invasive Gynecologic Sugery (MIGS) Graduates Adedayo P,1,* Dassel MW,2 Shiber L3. 1The Christ Hospital, Cincinnati, OH; 2The Cleveland Clinic, Cleveland; 3MetroHealth Medical Center, Cleveland, OH *Corresponding author. Study Objective: The Fellowship in Minimally Invasive Gynecology Surgery (FMIGS) is the most competitive fellowship in Obstetrics and Gynecology with 1.9 applicants per position and a 50% match rate in 2019. Yet, only one prior study, conducted in 2012- 2013, has examined trends in MIGS salaries and found widely variable levels of compensation. There is no current published data on the compensation and practice of this growing field. Here, we present updated information regarding compensation patterns for FMIGS physicians in the United States. Design: An online survey sent to FMIGS graduates between March -April 2019. We collected information on physicians’ demographics, compensation trend and physician attitudes towards fairness in compensation. Setting: Online Survey Patients or Participants: FMIGS Graduates practicing within the United States. Interventions: E-mail Survey Measurements and Main Results: Of 391 former FMIGS fellows surveyed, 204 responded (response rate =52%). 66.5% of respondents graduated from FMIGS fellowship in the last 5 years and 79.4% completed fellowship programs of 2 or 3 years duration. Median total salary in year 1 after fellowship was $230000 [range 130000-400000], increasing to a median salary of $260000 in current year for individuals at this job for more than 1 year [average years at job 1=3.6]. The majority of
Abstracts / Journal of Minimally Invasive Gynecology 26 (2019) S98−S231
Video Objective: Although reliable knot tying is a one of the most important surgical concepts, few videos exist that illustrate how to tie a reproducible, reliable laparoscopic knot. The purpose of this video is to teach the basics of laparoscopic suturing, as well as to illustrate a reproducible method of tying reliable laparoscopic knots. Setting: The video setting uses a 3-D Med laparoscopic trainer box with both standard suture and thick, color-blocked string for better demonstration. Interventions: N/A Conclusion: Laparoscopic suturing and intracorporeal laparoscopic knot tying is one of the most difficult, yet important, skills to learn as a beginning minimally-invasive surgeon. There are few accessible videos which teach how to correctly insert a needle, suture, and tie a reliable knot using laparoscopic tools. This video aims to teach the basics of laparoscopic suturing with the aid of color-blocked string to aid in the viewer’s understanding of how to tie a proper knot.
Study Objective: To compare the performance of the prediction models of surgical re-intervention within 2 years after endometrial ablation (EA) by a multivariate random forest model vs the previously presented multivariate logistic regression model. Design: Retrospective cohort study, minimal follow-up time of 2 years. Setting: Data from Catharina Hospital, Eindhoven and Elkerliek Hospital, Helmond, both non-university teaching hospitals in the Netherlands, were used. Patients or Participants: Pre-menopausal women (18+) who have had an EA for heavy menstrual bleeding between January 2004 & April 2013. A total number of 446 patients were eligible for analysis. Interventions: Used ablation methods were Cavatherm (Veldana Medical SA, Morges, Switzerland), Gynecare Thermachoice (Ethicon, Sommerville, US.) and Thermablate EAS (Idoman, Ireland). Used interventions and other ablation techniques had the same outcomes according to previously published literature. Measurements and Main Results: Data-analysis was done by using IBM SPSS statistics software version 21.0 (IBM Corp., Armonk, NY, USA). The random forest model was trained in MATLAB (2018b) using the TreeBagger function in the Statistics and Machine Learning Toolbox. The prediction model based on a multivariate logistic regression analysis had an AUC of 0.71. The machine learning model had an AUC of 0.63 and an AUC of 0.65 after hyperparameter optimization. Conclusion: Based on the preliminary results, we can conclude that the random forest model in this case is not better than the logistic regression model to predict the outcome of surgical re-intervention within two years after EA. In summary, the performance of a random forest clinical prediction model is not necessarily superior to a logistic regression model. The performance of each model is influenced by the sample size, the number of predictors, hyperparameter tuning and the linearity of associations.
Virtual Poster Session 2: Basic Science/Research/Education (1:30 PM − 1:40 PM) 1:30 PM: STATION E 2080 Strategies for Difficult Hysterectomies Alvarez-Rosales A,1,* Garcia Rodriguez LF,2 Berlanga-Narro SM,3 Garza-Ayala M4. 12° year Fellow MIGS, Tecnologico de Monterrey, Monterrey, NL, Mexico; 2Fellowship Director MIGs, Tecnologico de Monterrey, Monterrey, NL, Mexico; 3Tecnologico de Monterrey, Monterrey, NL, Mexico; 41° year Fellow MIGS, Tecnologico de Monterrey, Monterrey, NL, Mexico *Corresponding author. Video Objective: Demonstrate alternative surgical approaches for difficult hysterectomies. Setting: Multiple Cases of Hysterectomies with Challenging circumstances. Interventions: Multiple Cases of Hysterectomies with Challenging circumstances with strategies for getting an alternative approach. 1. 30˚ angle Scope for better visualization 2. Decompression for vaginal extraction 3. Traction of the angles of vaginal cuff 4. Bladder Distention 5. Blunt Dissection 6. Cold Dissection 7. Power Morcellation 8. Identify Retroperitoneal Structures. Conclusion: These strategies are useful to perform complex surgery and reduce the risk of complications Virtual Poster Session 2: Basic Science/Research/Education (1:30 PM − 1:40 PM) 1:30 PM: STATION F 2813 Clinical Prediction of Unsuccessful Endometrial Ablation: Random Forest vs Logistic Regression Stevens KYR,1 Lagaert LVR,2,3,* Bakkes T,4 Van De Keere M,3 Houterman S,5 van Vliet H,6 Schoot BC6. 1Obsetrics and Gynaecology, Catharina ziekenhuis Eindhoven, Hulst, Netherlands; 2Women’s Clinic, Ghent University Hospital, Ghent, Belgium, Ghent, Belgium; 3Department of Obstetrics and Gynaecology, Catharina Hospital, Eindhoven, the Netherlands, Eindhoven, Netherlands; 4epartment of Electrical Engineering, Biomedical Diagnostics lab TU Eindhoven, The Netherlands, Eindhoven, Netherlands; 5Education and research, Catharina ziekenhuis Eindhoven, Eindhoven, Netherlands; 6Obsetrics and Gynaecology, Catharina ziekenhuis Eindhoven, Eindhoven, Netherlands *Corresponding author.
Virtual Poster Session 2: Basic Science/Research/Education (1:30 PM − 1:40 PM) 1:30 PM: STATION G 1282 Current Trends in Compensation for Minimally Invasive Gynecologic Sugery (MIGS) Graduates Adedayo P,1,* Dassel MW,2 Shiber L3. 1The Christ Hospital, Cincinnati, OH; 2The Cleveland Clinic, Cleveland; 3MetroHealth Medical Center, Cleveland, OH *Corresponding author. Study Objective: The Fellowship in Minimally Invasive Gynecology Surgery (FMIGS) is the most competitive fellowship in Obstetrics and Gynecology with 1.9 applicants per position and a 50% match rate in 2019. Yet, only one prior study, conducted in 2012- 2013, has examined trends in MIGS salaries and found widely variable levels of compensation. There is no current published data on the compensation and practice of this growing field. Here, we present updated information regarding compensation patterns for FMIGS physicians in the United States. Design: An online survey sent to FMIGS graduates between March -April 2019. We collected information on physicians’ demographics, compensation trend and physician attitudes towards fairness in compensation. Setting: Online Survey Patients or Participants: FMIGS Graduates practicing within the United States. Interventions: E-mail Survey Measurements and Main Results: Of 391 former FMIGS fellows surveyed, 204 responded (response rate =52%). 66.5% of respondents graduated from FMIGS fellowship in the last 5 years and 79.4% completed fellowship programs of 2 or 3 years duration. Median total salary in year 1 after fellowship was $230000 [range 130000-400000], increasing to a median salary of $260000 in current year for individuals at this job for more than 1 year [average years at job 1=3.6]. The majority of