CONSORT flow diagram of the study.

<p>CONSORT flow diagram of the study.</p

Impact of a non-return-to-work prognostic model (WORRK) on allocation to rehabilitation clinical pathways: A single centre parallel group randomised trial

<div><p>Introduction</p><p>Stratified medicine might allow improvement of patient outcomes while keeping costs stable or even diminishing them. Our objective was to measure if a prediction model, developed to predict non-return to work (nRTW) after orthopaedic trauma, improves the allocation to various vocational pathways for use in clinical practice.</p><p>Material and methods</p><p>Randomised-controlled trial on vocational inpatients after orthopaedic trauma (n = 280). In the intervention group, nRTW risk (estimated using the WORRK tool) was given to the clinician team before allocation of vocational pathways, while in the control group it was not. Three pathways were available: simple, coaching and evaluation (EP). Accompanying indications for interpretation of the nRTW risk were given. The primary outcome was the proportion of patients allocated to the EP. The secondary outcome was patients’ and clinicians’ satisfaction.</p><p>Results</p><p>450 patients were assessed for eligibility, 280 included, 139 randomized to the control group (mean age 42.3years) and 141 to the intervention group (43.2years). The two groups had a similar risk profile. The patients in the intervention group were more often referred to the EP compared to the control group, but not statistically significantly more (risk ratio 1.31 [95% CI 0.70–2.46]). The number needed to treat was 30. When considering patients transferred to different pathways during rehabilitation, more patients from the intervention group were transferred to the EP over the course of the rehabilitation, increasing the risk ratio to 1.57 [95% CI 0.89 to 2.74].</p><p>Discussion</p><p>The knowledge of the risk of nRTW has an influence, that is not however statistically significant and is without clinical importance as previously defined by our own power calculations (based on a 15% increase in referral to EP in the intervention group compared to the control group), on clinical decision making with regards to the allocation of patients to different physical and vocational rehabilitation programs after orthopaedic trauma. This influence is less than what was expected, possibly due to insufficient directive guidelines accompanying the WORRK model, or because clinicians associate less hours of therapy (as with certain rehabilitation programs) to disadvantaging the patient. These findings do, however, support the multi-factorial aspect of clinician decision-making.</p></div

Characteristics of study participants.

<p>Characteristics of study participants.</p

Point estimate and 95% confidence interval for the risk ratio for the referral to the “Evaluation Pathway” in the intervention group compared to the control group.

<p>The upper part shows the risk ratio for the primary analysis; the lower part shows the analysis taking into account the patients who were transferred into the “Evaluation Pathway over the course of the stay.</p

Point estimate and 95% confidence interval for the risk ratio for the patients being satisfied with the rehabilitation in the intervention group compared to the control group.

<p>Point estimate and 95% confidence interval for the risk ratio for the patients being satisfied with the rehabilitation in the intervention group compared to the control group.</p

Flow chart of patients through the study.

<p>Flow chart of patients through the study.</p

Non-return to work: Odds ratios for the univariable, multivariable and the reduced model after random forest selection process.

<p>Odds Ratios of the different models in the development sample, with corresponding 95% confidence intervals (CI).</p

Allocation to the different treatment pathways.

<p>Allocation to the different treatment pathways.</p

Comparison predictive Values in the development and the validation sample.

<p>Compares diagnostic properties in the development sample with the validation sample. Threshold = Chosen cut-off for the dichotomizing in test negatives (i.e. return to work, below thresholds; non return to work, equal or above threshold).</p

Decision curve analysis.

<p>Decision curve analysis of the Full Model (dashed line black line) and Reduced Model (blue solid line) in the development sample (Panel A) and the Reduced Model in the temporal validation sample (Panel B). The y-Axis represents the net benefit, which is the probability of true positives minus the probability of false-positives weighted for the threshold probability. With threshold probability (or risk thresholds) we mean the threshold above which a patient is declared at risk to not return to work at two years. The dashed red curve shows net benefit of considering all patients as positive (i.e. classified as being not returning to work). The benefit of considering all patients as returning to work was set as reference (solid grey horizontal line). In the left Panel (A) we see that the net benefits for both models are quite similar. The Full Modell would show advantages if a threshold would be set between 15% to 82%. The right Panel (B) shows that that the net benefit in the temporal validation sample is only little lower than in the development sample. Clear benefits are seen from risks thresholds from about 20 to 75%. The net benefit is calculated as (proportion of true positives) – (proportion of false positives)*pt/(1−pt), where pt is the threshold probability.</p