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Traction force data was collected from 277 vacuum extraction deliveries at the delivery ward, Karolinska University Hospital from June 2012 to February 2015. The department is a tertiary unit, with nearly 5000 deliveries every year. All term, singleton, low and mid cavity metal cup extractions were eligible, including aborted attempts followed by CS or, on rare occasions, by forceps. A total of 855 vacuum extraction deliveries were performed during this period. Of these, approximately 60% were low or mid high vacuum extractions, and 40% outlet. The total number of vacuum extractions was 8% of all deliveries at the hospital, which is similar to the Swedish national rate. [9] As a result of risk group identification, we excluded outlet extractions, since our previous observational study on traction force levels showed no strong extractions within this group[8]. High extractions (vertex above the ischiatic spines) are not common practice in Swedish obstetrics. Plastic cup extractions were excluded by default, since the handle device for force measurement and documentation requires attachment to a metal cup. Obstetricians were recommended to use metal cup for all vacuum extractions during the study period. An MD (first author) collected maternal, intrapartal and neonatal data from the medical charts. Ethical approval was given by the local ethics committee of Stockholm, Sweden (D:nr 2012/1553-31/1, 2016/211-32). To measure the traction force employed during vacuum extraction, we used an intelligent handle attached to the chain of a regular metal cup (Bird 50 mm, 80 kPa) as described in a previous study[8]. The handle contains a load cell, a well established type of force sensor[10]. The intelligent handle also encapsulates the necessary instrumentation (e.g. battery, signal conditioning, processor and Bluetooth transceiver) to enable force measurements from the load cell to be wirelessly transmitted to a computer. The computer in turn, records the force measurements which are then retrieved and utilized to compute traction force variables.
The force sensor (load cell) was calibrated using standard force-transfer methods [11](using a pre-calibrated material testing machine (Instron E3000®, Instron, Norwood, MA, US). Regular inspection, calibration and maintenance of the intelligent handle during the course of this study depicted no deviation in its performance and good accuracy of force measurements.
In every vacuum extraction delivery, the highest momentary peak force (Newton, N) during each individual pull, as well as the total force (Newton minutes, Nmin), area under the curve, during each pull was measured. One pull corresponds to one uterine contraction. The differences in peak and total force between the first and subsequent pulls within each extraction were also calculated. We chose not to include other clinical data as possible predictors when developing our test, since we aimed for simplicity in the clinical situation. The candidate predictors (i.e. variables included in the statistical analysis) were peak and total force during each individual pull one through three, and the difference in force between the second and first pull, and the third and first pull. Accumulated total force after the second and third pulls. In the analyses, the outcome was binary: strong or non-strong extraction as subjectively classified by the obstetrician following the procedure. Many countries and clinics use a similar three grade scale for perceived difficulty or required traction effort (e.g. easy, moderate, strong), and we have chosen the term “strong” as it is exemplified in RCOG guidelines [5]. For analyses, we clustered easy and moderate as non-strong. Paired traction force data (strong vs non-strong) were analysed using logistic regression with shrinkage and selection, resulting in two receiver operating curves (ROC): 1–2 is based on traction forces in pull one and two, while 1–3 additionally includes traction forces from the third pull. Per definition, there was no missing item data regarding traction force measurement, since the included cases were collected from the traction force documentation software. Seven units (cases) were excluded by default because there was no documentation of subjective category: no outcome data available. However, not all eligible cases during the study period underwent extraction with the traction force measuring handle; a total of 546 mid or low extractions were eligible. In 269 of these, the measurement device was not used. The most likely explanations for this unintended exclusion are: technical (equipment not available due to maintenance) or compliance factors (assistant nurses or doctors not comfortable or sufficiently trained to use the equipment).
The binary outcome variable was strong or non-strong extraction category, while the continuous predictor variables included in the analyses were the different traction force data described above. Descriptive statistics were computed with Statistica, and are presented as mean±SD or median (min–max) as appropriate. The Student’s t-test was used to test mean values of normally distributed data and the Mann–Whitney U-test for data with a skewed distribution. The chi-square test was used for dichotomous data. A value of P < 0.05 was considered statistically significant. A prediction model was developed using a logistic regression model based on the shrinkage and selection method lasso (Least Absolute Shrinkage and Selection Operator)[12]. The method involves penalizing the absolute size of the coefficients of a regression model, based on the value of a tuning parameter λ. The larger the applied penalty, the further estimates are shrunk towards zero. This makes the coefficient of irrelevant variables zero: an automatic variable selection procedure. Cross validation is used to choose λ and to assess the predictive accuracy of the model, which protects against overfitting. Calculations were carried out using Stata v13.1 [13]and the R-library glmnet[14]
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