To develop and user-test a patient decision aid for people diagnosed with degenerative cervical myelopathy and who are considering surgery.

Mixed-methods study describing the development of a patient decision aid.

A draft decision aid was developed by a multidisciplinary steering group (including study authors with degenerative cervical myelopathy, health professionals and researchers) informed by the best available evidence, authorship consensus and existing patient decision aids.

Patient-participants and health professional-participants who manage people with degenerative cervical myelopathy were recruited through social media and the steering group’s research and practice network. Quantitative questionnaires were used to gather baseline data, descriptive feedback, refine the decision aid and assess its acceptability. Qualitative semi-structured interviews were conducted online to gather feedback on the decision aid and were analysed using reflexive thematic analysis.

We conducted 32 interviews: 19 patient-participants and 13 health professional-participants who manage people with degenerative cervical myelopathy (neurosurgeons, neurologists, physiotherapists, orthopaedic surgeons, general practitioners, rehabilitation and pain specialists and consultant occupational physicians and chiropractors). Participants were from 10 countries (Australia, Canada, Cyprus, Germany, Ireland, New Zealand, Sweden, Switzerland, United Kingdom and USA). Most participants rated the decision aid’s acceptability as good-to-excellent and agreed with most aspects of the decision aid (eg, defining degenerative cervical myelopathy, management recommendations, potential benefits and harms, questions to consider asking a health professional).

Our patient decision aid was rated as an acceptable tool by both health professional-participants who treat degenerative cervical myelopathy and patient-participants with lived experience of degenerative cervical myelopathy. This decision aid can be used by clinicians and people with degenerative cervical myelopathy to help with shared decision making following a diagnosis of degenerative cervical myelopathy. A study testing the potential benefits of this decision aid in a clinical setting is recommended.

Digital therapeutics (DTx) show promise in bridging mental healthcare gaps. However, treatment selection often relies on availability and trial-and-error, prolonging suffering and increasing costs. Personalised prediction models could help identify individuals benefiting most from specific DTx.

The aim of this secondary analysis was to establish a machine learning-based prediction model for positive treatment outcomes in patients with depressive or anxiety symptoms after 8 weeks of internet-delivered cognitive behavioural therapy (iCBT).

We analysed a large real-world dataset of patients from the online therapy unit iCBT programme in Saskatchewan, Canada (2013–2021). Clinically significant changes in depressive symptoms or anxiety were measured using the Patient Health Questionnaire-9 (PHQ-9) and the Generalised Anxiety Disorder-7 (GAD-7). We trained six prediction models using sociodemographic and mental health-related factors at baseline, compared model performances and calculated Shapley values for feature importance.

Data from 4175 patients using 34 features for prediction, identified by least absolute shrinkage and selection operator regression, showed the Gradient Boosted Model (gbm) and logistic regression (log) performed best, with balanced accuracies of 0.76, 95% CI (0.70 to 0.83) and 0.70, 95% CI (0.63 to 0.77). Shapley values indicated GAD-7 scores at baseline as the most important predictor of clinically significant improvement, along with mental health history and sociodemographic variables.

The gbm and log models achieved comparable accuracy in predicting clinically significant improvement after iCBT, supporting the use of simpler, interpretable methods in clinical practice.

These findings could help improve mental health treatment selection, iCBT assignment, enhance effectiveness and optimise treatment for patients.

NCT05758285.