An external validation of a novel predictive algorithm for male nipple areolar positioning: an improvement to current practice through a multicenter endeavor

238 Background: Numerous novel therapies for castration relapsed prostate cancer (CRPC) have led to a rapidly evolving approach to its management. Rationalisation of treatment combinations and sequencing of therapy requires identification of men who are more likely to benefit from a particular treatment. An unmet clinical need exists in this domain. We present the findings of a review of patients treated with abiraterone at our centre, and describe a novel predictive algorithm in this setting employing previously undefined pre-determinants of response. Methods: Patients with CRCP treated with abiraterone post-docetaxel at Queen Elizabeth Hospital between August 2010 and February 2012 were identified. Electronic patient records were utilised to extract variables including patient demographics, tumor characteristics, treatment history and other potential predictors of response such as prostate-specific antigen (PSA), hemoglobin (Hb), and alkaline phosphatase (ALP). Outcome measures included overall survival (OS), adverse events and PSA response rate. OS was estimated using the Kaplan-Meier method, and univariate and multivariate analyses were performed on potential prognosticators. Multivariate Beta coefficients were used to generate a predictive algorithm to identify two distinct risk groups. Results: Sixty one patients met the inclusion criteria. From starting abiraterone, the median OS was 12.6m, and median duration of follow-up was 11.5m. In univariate analysis seven factors impacted OS: age, response duration to androgen deprivation therapy (ADT), Hb, time from diagnosis to starting abiraterone, and ALP. Subsequent multivariate analysis identified three independent predictors of OS: duration of response to ADT (HR: 0.95, p=0.006), performance status (HR: 0.71, p=0.013), and baseline Hb (HR: 0.47, p=<0.001). A predictive algorithm dividing the cohort into high- and low-risk groups derived a diverging Kaplan-Meier curve without overlapping 95% CI’s. The low risk group did not reach median survival. Conclusions: This retrospective review has identified a predictive scoring algorithm for response to abiraterone in CRPC. We suggest that further analysis in the form of external validation is needed in order to justify its use in individualisation of patient management and stratification of patients for prospective clinical trials.

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Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data

Translational Psychiatry ◽

10.1038/s41398-021-01488-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Dekel Taliaz ◽

Amit Spinrad ◽

Ran Barzilay ◽

Zohar Barnett-Itzhaki ◽

Dana Averbuch ◽

...

Keyword(s):

Machine Learning ◽

Demographic Data ◽

External Validation ◽

Antidepressant Medication ◽

Research Network ◽

Test Set ◽

Antidepressant Medications ◽

Predictive Algorithm ◽

Data Driven Approach ◽

Antidepressant Use

AbstractMajor depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42–53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm’s capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm’s citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p’s < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.

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Translating Principles of Speech Science to Clinical Practice: Current and Future Trends in Craniofacial Disorders

Perspectives on Speech Science and Orofacial Disorders ◽

10.1044/ssod18.1.31 ◽

2008 ◽

Vol 18 (1) ◽

pp. 31-40 ◽

Cited By ~ 2

Author(s):

David J. Zajac

Keyword(s):

Clinical Practice ◽

Speech Intelligibility ◽

Current Practice ◽

Computer Assisted ◽

Behavioral Management ◽

Single Word ◽

Future Directions ◽

Speech Science ◽

The Impact ◽

Opinion Article

Abstract The purpose of this opinion article is to review the impact of the principles and technology of speech science on clinical practice in the area of craniofacial disorders. Current practice relative to (a) speech aerodynamic assessment, (b) computer-assisted single-word speech intelligibility testing, and (c) behavioral management of hypernasal resonance are reviewed. Future directions and/or refinement of each area are also identified. It is suggested that both challenging and rewarding times are in store for clinical researchers in craniofacial disorders.

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Principles of Solution Focused Therapy and Their Application to Audiology

Perspectives on Public Health Issues Related to Hearing and Balance ◽

10.1044/phi15.1.27 ◽

2014 ◽

Vol 15 (1) ◽

pp. 27-33

Author(s):

James C. Blair

Keyword(s):

Basic Assumption ◽

Current Practice ◽

Helping Professions ◽

Patient Centered ◽

Therapy Approach ◽

Agents Of Change ◽

Centered Care ◽

Client Centered Therapy ◽

Solution Focused ◽

Solution Focused Therapy

The concept of client-centered therapy (Rogers, 1951) has influenced many professions to refocus their treatment of clients from assessment outcomes to the person who uses the information from this assessment. The term adopted for use in the professions of Communication Sciences and Disorders and encouraged by The American Speech-Language-Hearing Association (ASHA) is patient-centered care, with the goal of helping professions, like audiology, focus more centrally on the patient. The purpose of this paper is to examine some of the principles used in a patient-centered therapy approach first described by de Shazer (1985) named Solution-Focused Therapy and how these principles might apply to the practice of audiology. The basic assumption behind this model is that people are the agents of change and the professional is there to help guide and enable clients to make the change the client wants to make. This model then is focused on solutions, not on the problems. It is postulated that by using the assumptions in this model audiologists will be more effective in a shorter time than current practice may allow.

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