Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets.

Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via "trial and error". Given the varied presentation of MDD and heterogeneity of treatment response, the use of machine learning to understand complex, non-linear relationships in data may be key for treatment personalization. Well-organized, structured data from clinical trials with standardized outcome measures is useful for training machine learning models; however, combining data across trials poses numerous challenges. There is also persistent concern that machine learning models can propagate harmful biases. We have created a methodology for organizing and preprocessing depression clinical trial data such that transformed variables harmonized across disparate datasets can be used as input for feature selection. Using Bayesian optimization, we identified an optimal multi-layer dense neural network that used data from 21 clinical and sociodemographic features as input in order to perform differential treatment benefit prediction. With this combined dataset of 5032 individuals and 6 drugs, we created a differential treatment benefit prediction model. Our model generalized well to the held-out test set and produced similar accuracy metrics in the test and validation set with an AUC of 0.7 when predicting binary remission. To address the potential for bias propagation, we used a bias testing performance metric to evaluate the model for harmful biases related to ethnicity, age, or sex. We present a full pipeline from data preprocessing to model validation that was employed to create the first differential treatment benefit prediction model for MDD containing 6 treatment options.

Correction: Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.

[This corrects the article DOI: 10.2196/31862.].

Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies.

BACKGROUND: Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological and behavioral substrates are associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning (ML) shows promise in predicting treatment response in MDD, but its application is limited by challenges to the clinical interpretability of ML models, and clinicians often lack confidence in model results. In order to improve the interpretability of ML models in clinical practice, our goal was to demonstrate the derivation of treatment-relevant patient profiles comprised of clinical and demographic information using a novel ML approach. METHODS: We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a ML model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Prototypes were evaluated for interpretability by assessing differences in feature distributions (e.g. age, sex, symptom severity) and treatment-specific outcomes. RESULTS: A 3-prototype model achieved an area under the receiver operating curve of 0.66 and an expected absolute improvement in remission rate for those receiving the best predicted treatment of 6.5% (relative improvement of 15.6%) compared to the population remission rate. We identified three treatment-relevant patient clusters. Cluster A patients tended to be younger, to have increased levels of fatigue, and more severe symptoms. Cluster B patients tended to be older, female, have less severe symptoms, and the highest remission rates. Cluster C patients had more severe symptoms, lower remission rates, more psychomotor agitation, more intense suicidal ideation, and more somatic genital symptoms. CONCLUSION: It is possible to produce novel treatment-relevant patient profiles using ML models; doing so may improve interpretability of ML models and the quality of precision medicine treatments for MDD.

A systematic meta-review of patient-level predictors of psychological therapy outcome in major depressive disorder.

BACKGROUND: Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors. METHODS: EMBASE, Medline, PubMed, PsycINFO were searched for systematic reviews with or without meta-analysis published until June 2020 to identify individual patient-level predictors of response to psychological treatments. 3113 abstracts were identified and 300 articles assessed. We qualitatively synthesized our findings by predictor category (sociodemographic; symptom profile; social support; personality features; affective, cognitive, and behavioural; comorbidities; neuroimaging; genetics) and treatment type. We used the AMSTAR 2 to evaluate the quality of included reviews. RESULTS: Following screening and full-text assessment, 27 systematic reviews including 12 meta-analyses were eligible for inclusion. 74 predictors emerged for various psychological treatments, primarily cognitive behavioural therapy, interpersonal therapy, and mindfulness-based cognitive therapy. LIMITATIONS: A paucity of studies examining predictors of psychological treatment outcome, as well as methodological heterogeneities and publication biases limit the strength of the identified predictors. CONCLUSIONS: The synthesized predictors could be used to supplement clinical decision-making in selecting psychological therapies based on individual patient characteristics. These predictors could also be used as a priori input features for machine learning models aimed at predicting a given patient's likelihood of response to different treatment options for depression, and may contribute toward the development of patient-specific treatment recommendations in clinical guidelines.

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.

BACKGROUND: Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. OBJECTIVE: This study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction. METHODS: Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. RESULTS: Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change. CONCLUSIONS: Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies. TRIAL REGISTRATION: ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.

National Athletic Trainers' Association Position Statement: Prevention of Anterior Cruciate Ligament Injury.

OBJECTIVE: To provide certified athletic trainers, physicians, and other health care and fitness professionals with recommendations based on current evidence regarding the prevention of noncontact and indirect-contact anterior cruciate ligament (ACL) injuries in athletes and physically active individuals. BACKGROUND: Preventing ACL injuries during sport and physical activity may dramatically decrease medical costs and long-term disability. Implementing ACL injury-prevention training programs may improve an individual's neuromuscular control and lower extremity biomechanics and thereby reduce the risk of injury. Recent evidence indicates that ACL injuries may be prevented through the use of multicomponent neuromuscular-training programs. RECOMMENDATIONS: Multicomponent injury-prevention training programs are recommended for reducing noncontact and indirect-contact ACL injuries and strongly recommended for reducing noncontact and indirect-contact knee injuries during physical activity. These programs are advocated for improving balance, lower extremity biomechanics, muscle activation, functional performance, strength, and power, as well as decreasing landing impact forces. A multicomponent injury-prevention training program should, at minimum, provide feedback on movement technique in at least 3 of the following exercise categories: strength, plyometrics, agility, balance, and flexibility. Further guidance on training dosage, intensity, and implementation recommendations is offered in this statement.

Effects of decision making on landing mechanics as a function of task and sex.

BACKGROUND: Factors that contribute to sex-differences in the incidence of anterior cruciate ligament injuries among athletes are not well understood. Of interest is whether decision making during landing influences biomechanical factors associated with anterior cruciate ligament injury. This study examined the effects of decision making on the mechanics of two-footed landing tasks in women and men. METHODS: Twenty-nine healthy young adults (13 women, 16 men) completed drop landings and drop-jumps under preplanned and decision-making conditions. Biomechanical data were collected and effects of decision making on lower extremity kinematics and kinetics were examined as a function of task and sex. FINDINGS: Landing mechanics were influenced by decision-making condition, task, and sex. During drop-jumps, participants exhibited lesser hip flexion (-3.3°), lesser knee flexion (-5.1°), and greater knee abduction (+1.0°) at initial contact under decision-making conditions. Under decision-making conditions, no differences were observed in these variables between tasks or with respect to preplanned drop landings. Across tasks and sexes, participants exhibited greater ankle plantarflexion at initial contact (+1.6°), greater peak knee external rotation (+1.5°), lesser peak knee internal rotation (-1.0°), and smaller hip adduction moments (-0.2% body weight×height) under decision-making conditions. Women but not men exhibited smaller ankle inversion moments (-0.1% body weight×height) under decision-making conditions. INTERPRETATION: Modifications in landing mechanics suggest a default towards the preplanned drop landing strategy under decision-making conditions. Across sexes, drop landings and drop-jumps may be no more dangerous under decision-making conditions, with respect to anterior cruciate ligament loading, than preplanned drop landings.

Knee joint kinematics and kinetics during a lateral false-step maneuver.

CONTEXT: Cutting maneuvers have been implicated as a mechanism of noncontact anterior cruciate ligament (ACL) injuries in collegiate female basketball players. OBJECTIVE: To investigate knee kinematics and kinetics during running when the width of a single step, relative to the path of travel, was manipulated, a lateral false-step maneuver. DESIGN: Crossover design. SETTING: University biomechanics laboratory. PATIENTS OR OTHER PARTICIPANTS: Thirteen female collegiate basketball athletes (age = 19.7 +/- 1.1 years, height = 172.3 +/- 8.3 cm, mass = 71.8 +/- 8.7 kg). INTERVENTION(S): Three conditions: normal straight-ahead running, lateral false step of width 20% of body height, and lateral false step of width 35% of body height. MAIN OUTCOME MEASURE(S): Peak angles and internal moments for knee flexion, extension, abduction, adduction, internal rotation, and external rotation. RESULTS: Differences were noted among conditions in peak knee angles (flexion [P < .01], extension [P = .02], abduction [P < .01], and internal rotation [P < .01]) and peak internal knee moments (abduction [P < .01], adduction [P < .01], and internal rotation [P = .03]). The lateral false step of width 35% of body height was associated with larger peak flexion, abduction, and internal rotation angles and larger peak abduction, adduction, and internal rotation moments than normal running. Peak flexion and internal rotation angles were also larger for the lateral false step of width 20% of body height than for normal running, whereas peak extension angle was smaller. Peak internal rotation angle increased progressively with increasing step width. CONCLUSIONS: Performing a lateral false-step maneuver resulted in changes in knee kinematics and kinetics compared with normal running. The differences observed for lateral false steps were consistent with proposed mechanisms of ACL loading, suggesting that lateral false steps represent a hitherto neglected mechanism of noncontact ACL injury.