Methods:
This retrospective two-centred cohort study, performed in two non-university teaching hospitals in the Netherlands (Catharina Hospital, Eindhoven; Elkerliek Hospital Helmond), included 446 patients who have had an EA for complaints of abnormal uterine bleeding (1). Both hospitals used similar ablation techniques between 2004 and 2013, being Cavatherm® (Veldana Medical SA, Morges, Switzerland), Gynecare Thermachoice® (Ethicon, Sommerville, US) and Thermablate® EAS (Idoman, Ireland). Recent publications have shown that these ablation techniques were equally effective (15,28). Local medical ethical review boards approved the study. All patients gave informed consent.
Patients were identified in the Electronic Patient care System by using specified search terms related to endometrial ablation. Exclusion criteria were a postmenopausal status at time of EA; (suspicion of) endometrial malignancy or uterine cavity deformations (adenomyosis; anomalies; fibroids; or a polyp). Follow-up period after treatment was at least two years. This time-interval was chosen because previous literature stated that most re-interventions were done within two years. Follow-up ended on the day of hysterectomy, in case of death or on April 15, 2015 (10,16,18,28–30).
Data were extracted from individual patient files by two researchers. Next, patients were asked to fill in a questionnaire regarding follow-up information. In case of non-response, patients were contacted by letter and ultimately by telephone. The questionnaire contained questions based on significant variables predicting surgical re-intervention after EA that were previously published (2,5,8,11–16,18,31,32).
The entire dataset consists of 446 patients with different categorical and continuous variables. For the machine learning algorithms all features were extracted from the original dataset and a total of five pre-operative variables were used to develop the machine learning model. This were the preoperative variables that were significant predictors in the final multivariate prediction model of Stevens et al. (age, duration of menstruation, dysmenorrhea, parity and previous caesarean section) (1). The continuous data were not discretized into categories as was done in the development of the previously published logistic regression model(1).