Study Design and Participants
DECISIONS-SMA is a non-interventional, cross-sectional, web-based pilot study in collaboration with the Spanish Society of Pediatric Neurology (SENEP). The selection criteria included (i) pediatric neurologists (with or without specialization in neuromuscular disorders) and (ii) active practice either in an academic or non-academic setting. Participants were recruited by receiving an invitation by SENEP from June 3 to November 2, 2021. The study was approved by the ethics committee of Hospital Clínico San Carlos, Madrid, Spain (reference 21/313-E), and performed in accordance with the 1964 Helsinki Declaration and its later amendments. Participants provided written informed consent. Further details of the study protocol were described in a previous publication .
The primary objective of DECISIONS-SMA was to assess preferences and expectations of pediatric neurologists regarding treatment choices for SMA. We also assessed treatment initiation and escalation when warranted by contemporary recommendations (see definitions in the next section). Participants were exposed to 11 simulated case scenarios or case vignettes (Supplementary Material; answers in bold were considered suboptimal treatment decisions). Case scenarios were designed by a research team led by GS and IM and based on the most common situations experienced by pediatric neurologists in clinical practice and after reviewing SMA clinical trials and patients’ and caregivers’ preferences from the literature [5, 10, 13,14,15,16].
Outcome Measures and Definitions
The primary outcome of interest was therapeutic inertia (TI), defined as the absence of treatment initiation or intensification when treatment goals are unmet . As in our previous research, we created a TI score to represent the number of case scenarios where treatment initiation or escalation was warranted over the 11 presented case scenarios [18, 19]. This score may range from 0 to 11, where higher values represent a higher degree of TI. Participants with a TI score ≥ 1 (i.e., therapeutic inertia in at least one case scenario) were considered to calculate the prevalence of therapeutic inertia. An appropriate treatment switch was defined by the number of case scenarios where the initial treatment was changed given the clinical evidence provided of disease progression (i.e., a decrease in baseline scale score greater than the scale’s minimal clinically important difference) over the total of five case scenarios (nos. 2, 3, 7, 8, and 9; Supplementary Material) assessing this strategy according to contemporary treatment recommendations [9, 20,21,22,23,24].
Given the complexity of analyzing treatment effects observed in randomized clinical trials in SMA, we also assessed participants’ expectation of treatment benefit using four simulated case scenarios (e.g., a 5-month-old patient with SMA type 1, a 1-year-old patient with SMA type 2, a 16-year-old patient with advanced SMA type 2 and delayed diagnosis, and a 15-year-old stable patient with SMA type 2 diagnosed at 3 years of age; these correspond to cases 1, 6, 10, and 11, respectively—Supplementary Material). Participants were asked: “On a scale from 1% to 100%, what are your expectations of improvement in 2 years for this patient with any of the treatments currently available?” We reported participants’ expectation of improvement for each case scenario and a global metric by combining all four cases.
Clinical stabilization is a success in the context of a progressive disease like SMA. However, when creating simulated case scenarios we wanted to pose the questions in a broader and more open way by including improvement. According to behavioral economics, participants made decisions on the basis of their perception of benefits (instead of clinically defined or proven motor or respiratory metrics).
We applied concepts from behavioral economics that were previously associated with suboptimal therapeutic decisions or TI [18, 19, 25]. Physicians’ tolerance to uncertainty was assessed using the standardized physician’s reaction to an uncertainty test [26, 27]. Participants rated their level of agreement with each question from 0 (strongly disagree) to 5 (strongly agree), and a total score was calculated . Higher values indicate lower tolerance to uncertainty . In the present study, a score of 12 or higher indicated low tolerance to uncertainty. Ambiguity aversion is defined as dislike for events with unknown probability over events with known probability. As in our previous studies, participants were asked to choose between a visual option represented by bars with known 50/50 probability of winning €400 (blue bar) or €0 (red bar) and an option with an unknown probability of the same outcomes in one of the following degrees of uncertainty representing a 10%, 30%, 50%, 70%, and 90% of probability of winning, illustrated by a gray area covering in the bar—Supplementary Material, concepts from behavioral economics) . There was no cutoff point. The degree of aversion to ambiguity was defined as the proportion of times participants chose the 50/50 option over the ambiguous option combining all five uncertainty options.
We used descriptive statistics to report frequency distributions of qualitative variables, measures of central tendency, and dispersion of quantitative variables using non-parametric tests, and 95% confidence intervals. Wilcoxon’s sign test was used to compare participant’s expectations with treatment. Factors associated with TI, treatment initiation, and intensification (switches) were analyzed using linear regression analysis with backward selection. We included the following explanatory variables: age, gender, specialization in neuromuscular disorders, years of experience as a pediatric neurologist and also seeing patients with SMA, number of patients seen per week, practice setting (academic vs. non-academic), proportion of time devoted to clinical care, co-author of a peer-reviewed publication within the last 3 years (yes/no), aversion to ambiguity, physicians’ reaction to uncertainty.
We also assessed participants’ expectations of improvement with treatment for different SMA scenarios (only for cases 1, 6, 10, and 11). Participants could select the expectation of improvement with treatment ranging from 0 to 100%. Results are presented as mean percentage of expected improvement (and standard deviation, SD), and illustrated by box plots. All tests were two-tailed, and p values less than 0.05 were considered significant. Unavailable data was described as missing, without any imputation/allocation. The statistical analysis will be performed using Stata Statistical Software 17.0 (StataCorp., College Station, TX, USA) and considering a significant level of 0.05.