New App for Monitoring Postoperative Pain in Total Ankle Replacement

Background:

The management of postoperative pain in patients undergoing total ankle replacement is routinely based on one of the two following drugs: tapentadol and oxycodone/naloxone (Nett, 2010). The drugs are used with similar frequencies. Indeed, previous studies compared the two drugs in terms of efficacy in reducing postoperative pain and frequency of side effects, in the field of orthopaedic and trauma surgery (Haeseler, 2017, Hartrick, 2009). The studies demonstrated that there are no substantial differences.

With regard to pain monitoring, the gold standard is the visual-analogue scale (VAS), which allows the patient to indicate the pain level as a score ranging from 0 to 10 (from minimum to maximum) (Campbell, 1990). Recently, a pain monitoring web-based application has been developed and distributed: 'NavigatePain' (NavigatePain, Aglance Solutions, Denmark). The app can be used in a simple and intuitive manner by PC, smartphone or tablet devices (Boudreau, 2016; Matthews, 2018). Moreover, physicians can easily schedule postoperative self-monitoring sessions for the patients (exploiting automatic notification messages sent by e-mail). The app allows the user to draw the pain areas in the frontal, posterior and lateral body planes, providing an index that quantifies the percentage of pain area with respect to the total body area in the considered plane. VAS score and notes reporting side effects can be reported as well in the app. Compared to VAS score, the pain area index is differentiated in the body planes and can represent a complementary and more specific source of information in monitoring postoperative pain.

Aim of the study:

The study evaluates the NavigatePain app for monitoring the postoperative pain in patients undergoing total ankle replacement, from preoperative condition to 30 days follow-up. The indexes provided by the app in terms of percentages of pain areas are compared to VAS reference score. Moreover, the relation between the percentage of pain area and the presence of side effects of the intervention are compared as well.

Methods:

A sample of 70 subjects undergoing total ankle replacement surgery will be consecutively enrolled. Age range will be 18 to 60 years. A user account will be created in the app for every subject, allowing to acquire data immediately visible to the physician. Pain areas, VAS score and presence of side effects will be acquired using the app at the following times: i) the day before surgery, without drug administration; ii) day 1 after surgery; iii) day 2; iv) day of hospital discharge (corresponding to day 3); v) day 7; vi) day 15; vii) day 30. Monitorings from i) to iv) will be acquired during hospitalization using a dedicated tablet device. In v), vi) and vii), the monitoring sessions will be notified to patients by the automatic notification service of the app (via e-mail); in these cases, patients will use own devices (e.g. PC, tablet or smartphone) to connect to account and perform drawing.

Outcomes:

The mean value of pain area index, and of VAS score will be compared over time and tested according to one way ANOVA for repeated measurements.

The relation between pain area index and VAS will be assessed according to Pearson correlation coefficient, or Spearman rank correlation in case of not normal distribution. Statistical significance of the coefficients will be tested according to two-tailed t-test or permutation distribution test assessing Pearson and Spearman coefficients, respectively. Linear regression analysis will be performed as well.

The relation between pain area index and presence of side effects (expressed as dichotomic variable: 1 in case of reported effects, 0 otherwise) will be tested according to point biserial correlation coefficient.

Correlation coefficient and linear regression will be evaluated in each time session. The results will be compared among the consecutive times in order to assess the potential dependence on the follow-up time.

The sample size of 70 subjects has been verified to guarantee identifying statistically significant differences in the mean value along the follow-up times (ANOVA test, one group, seven repetitions, 0.05 level alpha, power 95%, expected correlation 0.5, effect size 0.15). Moreover, the sample size guarantees identifying as significantly different from zero a correlation value larger than 0.7 (i.e. strong correlation), by t-test at 0.05 level alpha and power 95% (G*Power software, Universitat Dusseldorf, Germany).