Patrick Stegeman

Objective: This study aimed to examine the test–retest reliability and responsiveness of the Self-Regulation Assessment, a recently developed patient-reported outcome measure designed to evaluate self-regulation in a multi-diagnostic rehabilitation population. Design: A prospective cohort study following COSMIN guidelines. Participants completed the Self-Regulation Assessment and other measurements at start of rehabilitation (T0), 6 months later (T1), and 2 weeks after T1 (T2). Test–retest reliability (T1-T2) was quantified by intraclass correlation coefficient values, Bland-Altman plots, and the smallest detectable change. Responsiveness (T0-T1) was quantified by hypothesis testing, effect size, area under the curve, and minimal important change based on the anchor Global Rating of Change scale of self-regulation. Setting and participants: Inpatients and outpatients of 14 Dutch rehabilitation institutions with various diagnosis. Main measure: The Self-Regulation Assessment. Results: In total, 555 patients completed the Self-Regulation Assessment at T0 and T1 and 167 patients at T1 and T2. In inpatients and outpatients, the Self-Regulation Assessment showed adequate to good reliability, with smallest detectable changes ranging from 16.0 to 22.9 points at individual level and 1.5 to 3.3 at group level for outpatients and inpatients, respectively. Significant changes (T0-T1) were observed in both groups, with a small effect size for inpatients and large for outpatients. Hypothesis testing indicated near sufficient responsiveness in both groups (67% confirmed). Minimal important change values ranged from 6.25 to 9.8 points for outpatients. Conclusion: The Self-Regulation Assessment demonstrated sufficient reliability and detected changes at group level, but was not suitable for detecting changes at individual level in outpatient rehabilitation.

Background: Decision support systems (DSSs) for suggesting optimal treatments for individual patients with low back pain (LBP) are currently insufficiently accurate for clinical application. Most of the input provided to train these systems is based on patient-reported outcome measures. However, with the appearance of electronic health records (EHRs), additional qualitative data on reasons for referrals and patients’ goals become available for DSSs. Currently, no decision support tools cover a wide range of biopsychosocial factors, including referral letter information to help clinicians triage patients to the optimal LBP treatment. Objective: The objective of this study was to investigate the added value of including qualitative data from EHRs and referral letters to the accuracy of a quantitative DSS for patients with LBP. Methods: A retrospective study was conducted in a clinical cohort of Dutch patients with LBP. Patients filled out a baseline questionnaire about demographics, pain, disability, work status, quality of life, medication, psychosocial functioning, comorbidity, history, and duration of pain. Referral reasons and patient requests for help (patient goals) were extracted via natural language processing (NLP) and enriched in the data set. For decision support, these data were considered independent factors for triage to neurosurgery, anesthesiology, rehabilitation, or minimal intervention. Support vector machine, k-nearest neighbor, and multilayer perceptron models were trained for 2 conditions: with and without consideration of the referral letter content. The models’ accuracies were evaluated via F1-scores, and confusion matrices were used to predict the treatment path (out of 4 paths) with and without additional referral parameters. Results: Data from 1608 patients were evaluated. The evaluation indicated that 2 referral reasons from the referral letters (for anesthesiology and rehabilitation intervention) increased the F1-score accuracy by up to 19.5% for triaging. The confusion matrices confirmed the results. Conclusions: This study indicates that data enriching by adding NLP-based extraction of the content of referral letters increases the model accuracy of DSSs in suggesting optimal treatments for individual patients with LBP. Overall model accuracies were considered low and insufficient for clinical application.

BACKGROUND Decision support systems (DSSs) for suggesting optimal treatments for individual patients with low back pain (LBP) are currently insufficiently accurate for clinical application. Most of the input provided to train these systems is based on patient-reported outcome measures. However, with the appearance of electronic health records (EHRs), additional qualitative data on reasons for referrals and patients’ goals become available for DSSs. Currently, no decision support tools cover a wide range of biopsychosocial factors, including referral letter information to help clinicians triage patients to the optimal LBP treatment. OBJECTIVE The objective of this study was to investigate the added value of including qualitative data from EHRs and referral letters to the accuracy of a quantitative DSS for patients with LBP. METHODS A retrospective study was conducted in a clinical cohort of Dutch patients with LBP. Patients filled out a baseline questionnaire about demographics, pain, disability, work status, quality of life, medication, psychosocial functioning, comorbidity, history, and duration of pain. Referral reasons and patient requests for help (patient goals) were extracted via natural language processing (NLP) and enriched in the data set. For decision support, these data were considered independent factors for triage to neurosurgery, anesthesiology, rehabilitation, or minimal intervention. Support vector machine, k-nearest neighbor, and multilayer perceptron models were trained for 2 conditions: with and without consideration of the referral letter content. The models’ accuracies were evaluated via F1-scores, and confusion matrices were used to predict the treatment path (out of 4 paths) with and without additional referral parameters. RESULTS Data from 1608 patients were evaluated. The evaluation indicated that 2 referral reasons from the referral letters (for anesthesiology and rehabilitation intervention) increased the F1-score accuracy by up to 19.5% for triaging. The confusion matrices confirmed the results. CONCLUSIONS This study indicates that data enriching by adding NLP-based extraction of the content of referral letters increases the model accuracy of DSSs in suggesting optimal treatments for individual patients with LBP. Overall model accuracies were considered low and insufficient for clinical application.

A cross-sectional study design was performed. To validate the pain disability index (PDI) extensively in 3 groups of patients with musculoskeletal pain. The PDI is a widely used and studied instrument for disability related to various pain syndromes, although there is conflicting evidence concerning factor structure, test-retest reliability, and missing items. Additionally, an official translation of the Dutch language version has never been performed. For reliability, internal consistency, factor structure, test-retest reliability and measurement error were calculated. Validity was tested with hypothesized correlations with pain intensity, kinesiophobia, Rand-36 subscales, Depression, Roland-Morris Disability Questionnaire, Quality of Life, and Work Status. Structural validity was tested with independent backward translation and approval from the original authors. One hundred seventy-eight patients with acute back pain, 425 patients with chronic low back pain and 365 with widespread pain were included. Internal consistency of the PDI was good. One factor was identified with factor analyses. Test-retest reliability was good for the PDI (intraclass correlation coefficient, 0.76). Standard error of measurement was 6.5 points and smallest detectable change was 17.9 points. Little correlations between the PDI were observed with kinesiophobia and depression, fair correlations with pain intensity, work status, and vitality and moderate correlations with the Rand-36 subscales and the Roland-Morris Disability Questionnaire. The PDI-Dutch language version is internally consistent as a 1-factor structure, and test-retest reliable. Missing items seem high in sexual and professional items. Using the PDI as a 2-factor questionnaire has no additional value and is unreliable.

Study Design. Prospective cohort study. Objective. The objective of this study was to test the responsiveness and minimal clinically important change (MCIC) of the Pain Disability Index (PDI) in patients with chronic back pain (CBP). Summary of Background Data. Treatment of patients with CBP is primarily focused on reduction of disability. For disability measurement, the PDI is a widely used questionnaire. There are, however, no data available on responsiveness and MCIC. Methods. Two hundred forty-two patients with CBP were included in this study. Patients filled in the PDI at baseline and at discharge. The PDI consists of 2 subscales: 1 measuring voluntary activities and 1 measuring obligatory activities. PDI was anchored at 2 self-reported global perceived effect (GPE) scales for complaints and self-care, respectively. Responsiveness was considered sufficient when Area Under the Receiver Operating Characteristics (ROC) Curve (AUC) was higher than 0.70. To test interpretability, change scores and MCIC were calculated. MCIC was tested by determination of optimal cut-off point of the ROC curve and determination of specificity and sensitivity of the optimal cut-off point. Results. AUCs were 0.76 and 0.77 depending on the external criterion. The subscale obligatory activities did not meet the criteria for responsiveness (AUC: 0.63–0.69). MCIC of the PDI was 9.5 points for GPE “complaints” and 8.5 for GPE “self-care.” Conclusion. The total score of the PDI as well as the subscale of voluntary activities is responsive. Partly because of floor effects, the subscale obligatory activities are not sufficiently responsive in patients with CBP. However, the responsiveness of this subscale in other patient groups should be further tested. In patients with CBP, change can be considered clinically important when PDI score has decreased 8.5 to 9.5 points.