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).