Machine learning for predicting cognitive deficits using auditory and demographic factors.

IMPORTANCE: Predicting neurocognitive deficits using complex auditory assessments could change how cognitive dysfunction is identified, and monitored over time. Detecting cognitive impairment in people living with HIV (PLWH) is important for early intervention, especially in low- to middle-income countries where most cases exist. Auditory tests relate to neurocognitive test results, but the incremental predictive capability beyond demographic factors is unknown. OBJECTIVE: Use machine learning to predict neurocognitive deficits, using auditory tests and demographic factors. SETTING: The Infectious Disease Center in Dar es Salaam, Tanzania. PARTICIPANTS: Participants were 939 Tanzanian individuals from Dar es Salaam living with and without HIV who were part of a longitudinal study. Patients who had only one visit, a positive history of ear drainage, concussion, significant noise or chemical exposure, neurological disease, mental illness, or exposure to ototoxic antibiotics (e.g., gentamycin), or chemotherapy were excluded. This provided 478 participants (349 PLWH, 129 HIV-negative). Participant data were randomized to training and test sets for machine learning. MAIN OUTCOME(S) AND MEASURE(S): The main outcome was whether auditory variables combined with relevant demographic variables could predict neurocognitive dysfunction (defined as a score of <26 on the Kiswahili Montreal Cognitive Assessment) better than demographic factors alone. The performance of predictive machine learning algorithms was primarily evaluated using the area under the receiver operational characteristic curve. Secondary metrics for evaluation included F1 scores, accuracies, and the Youden's indices for the algorithms. RESULTS: The percentage of individuals with cognitive deficits was 36.2% (139 PLWH and 34 HIV-negative). The Gaussian and kernel naïve Bayes classifiers were the most predictive algorithms for neurocognitive impairment. Algorithms trained with auditory variables had average area under the curve values of 0.91 and 0.87, F1 scores (metric for precision and recall) of 0.81 and 0.76, and average accuracies of 86.3% and 81.9% respectively. Algorithms trained without auditory variables as features were statistically worse (p < .001) in both the primary measure of area under the curve (0.82/0.78) and the secondary measure of accuracy (72.3%/74.5%) for the Gaussian and kernel algorithms respectively. CONCLUSIONS AND RELEVANCE: Auditory variables improved the prediction of cognitive function. Since auditory tests are easy-to-administer and often naturalistic tasks, they may offer objective measures or predictors of neurocognitive performance suitable for many global settings. Further research and development into using machine learning algorithms for predicting cognitive outcomes should be pursued.

Engaging the Poetic Perspective on Care of the Surgical Patient: Lessons from W.H. Auden's "Surgical Ward".

Attention has been brought to the importance of cultivating empathy, communication skills, reflective writing, and self-care in surgeons-in-training. Classical literature and poetry pertaining to themes of surgery, specifically sonnets, can be exemplary methods for cultivating such skills. "Surgical Ward" by W.H. Auden is such a sonnet. Here we suggest that working poems such as "Surgical Ward" can cultivate transferable skills for analysis of text, context and subtext, as well as providing a substrate for discussion of multiple perspectives. These skills can aid in the development of surgical decision-making to produce positive outcomes, yet also benefit self-reflection when mistakes are inevitably made.

Managing Fall Prevention through Exercise in Older Adults Afflicted by Cognitive and Strength Impairment.

INTRODUCTION: A growing trend in medicine is older adults and increased need for geriatric services. Falls contribute heavily to hospitalizations and worsening of overall health in this frail demographic. There are numerous biological and physical culprits which, if targeted, can prevent falls. The objective was to review benefits of different types of exercises for fall prevention for older persons who are community-dwelling or living in long-term care facilities. METHODS: A systematic review was conducted to determine the different types of exercises for fall prevention. Data extraction via a standardized protocol was performed to assess study design, outcomes, limitations, and author's conclusions. Corroborative themes were identified and the authors responsible for the contributing research were cited as they came up. Nineteen randomized controlled trials were identified, between 1990 and 2018, using MEDLINE, PubMed, Cochrane, CINAHL, and Web of Science databases. Studies involving adults greater than age 60 in high-risk community or nursing home populations in the English language with a duration longer than or equal to 6 weeks with focuses on either low-risk balance, strength, or combination of both and whole-body vibration. RESULTS: Balance exercise training increased balance at 6 and 12-month intervals involving balance, strength, and cognitive training. DISCUSSION: Insignificant results were seen in whole-body vibration and differing results existed for Tai Chi. It is important to recognize that although exercises help reduce the risk of falling and play a significant role in improving mobility safely, there will always be a risk of falls.