Hierarchical clustering of prolonged post-concussive symptoms after 12 months: symptom-centric analysis and association with functional impairments.

BACKGROUND: Following a concussion, approximately 15% of individuals experience persistent symptoms that can lead to functional deficits. However, underlying symptom-clusters that persist beyond 12 months have not been adequately characterized, and their relevance to functional deficits are unclear. The aim of this study was to characterize the underlying clusters of prolonged post-concussive symptoms lasting more than 12 months, and to investigate their association with functional impairments. METHODS: Although hierarchical clustering is ideally suited in evaluating subjective symptom severities, it has not been applied to the Rivermead Post-Concussion Questionnaire (RPQ). The RPQ and functional impairments questions were administered via a smartphone application to 445 individuals who self-reported prolonged post-concussive symptoms. Symptom-clusters were obtained using agglomerative hierarchical clustering, and their association with functional deficits were investigated with sensitivity analyses, and corrected for multiple comparisons. RESULTS: Five symptom-clusters were identified: headache-related, sensitivity to light and sound, cognitive, mood-related, and sleep-fatigue. Individuals with more severe RPQ symptoms were more likely to report functional deficits (p < 0.0001). Whereas the headache and sensitivity clusters were associated with at most one impairment, at-least-mild sleeping difficulties and fatigue were associated with four, and moderate-to-severe cognitive difficulties with five (all p < 0.01). CONCLUSIONS: Symptom-clusters may be clinically useful for functional outcome stratification for targeted rehabilitation therapies. Further studies are required to replicate these findings in other cohorts and questionnaires, and to ascertain the effects of symptomatic intervention on functional outcomes.

Optimizing Existing Mental Health Screening Methods in a Dementia Screening and Risk Factor App: Observational Machine Learning Study.

BACKGROUND: Mindstep is an app that aims to improve dementia screening by assessing cognition and risk factors. It considers important clinical risk factors, including prodromal symptoms, mental health disorders, and differential diagnoses of dementia. The 9-item Patient Health Questionnaire for depression (PHQ-9) and the 7-item Generalized Anxiety Disorder Scale (GAD-7) are widely validated and commonly used scales used in screening for depression and anxiety disorders, respectively. Shortened versions of both (PHQ-2/GAD-2) have been produced. OBJECTIVE: We sought to develop a method that maintained the brevity of these shorter questionnaires while maintaining the better precision of the original questionnaires. METHODS: Single questions were designed to encompass symptoms covered in the original questionnaires. Answers to these questions were combined with PHQ-2/GAD-2, and anonymized risk factors were collected by Mindset4Dementia from 2235 users. Machine learning models were trained to use these single questions in combination with data already collected by the app: age, response to a joke, and reporting of functional impairment to predict binary and continuous outcomes as measured using PHQ-9/GAD-7. Our model was developed with a training data set by using 10-fold cross-validation and a holdout testing data set and compared to results from using the shorter questionnaires (PHQ-2/GAD-2) alone to benchmark performance. RESULTS: We were able to achieve superior performance in predicting PHQ-9/GAD-7 screening cutoffs compared to PHQ-2 (difference in area under the curve 0.04, 95% CI 0.00-0.08, P=.02) but not GAD-2 (difference in area under the curve 0.00, 95% CI -0.02 to 0.03, P=.42). Regression models were able to accurately predict total questionnaire scores in PHQ-9 (R2=0.655, mean absolute error=2.267) and GAD-7 (R2=0.837, mean absolute error=1.780). CONCLUSIONS: We app-adapted PHQ-4 by adding brief summary questions about factors normally covered in the longer questionnaires. We additionally trained machine learning models that used the wide range of additional information already collected in Mindstep to make a short app-based screening tool for affective disorders, which appears to have superior or equivalent performance to well-established methods.

Optimizing atrio-ventricular delay in pacemakers using potentially implantable physiological biomarkers.

BACKGROUND: Hemodynamically optimal atrioventricular (AV) delay can be derived by echocardiography or beat-by-beat blood pressure (BP) measurements, but analysis is labor intensive. Laser Doppler perfusion monitoring measures blood flow and can be incorporated into future implantable cardiac devices. We assess whether laser Doppler can be used instead of BP to optimize AV delay. METHODS: Fifty eight patients underwent 94 AV delay optimizations with biventricular or His-bundle pacing using laser Doppler and simultaneous noninvasive beat-by-beat BP. Optimal AV delay was defined using a curve of hemodynamic response to switching from AAI (reference state) to DDD (test state) at several AV delays (40-320 ms), with automatic quality control checking precision of the optimum. Five subsequent patients underwent an extended protocol to test the impact of greater numbers of alternations on optimization quality. RESULTS: 55/94 optimizations passed quality control resulting in an optimal AV delay on laser Doppler similar to that derived by BP (median absolute deviation 12 ms). An extended protocol with increasing number of replicates consistently improved quality and reduced disagreement between laser Doppler and BP optima. With only five replicates, no optimization passed quality control, and the median absolute deviation would be 29 ms. These improved progressively until at 50 replicates, all optimizations passed quality control and the median absolute deviation was only 13 ms. CONCLUSIONS: Laser Doppler perfusion produces hemodynamic optima equivalent to BP. Quality control can be automatic. Adding more replicates, consistently improves quality. Future implantable devices could use such methods to dynamically and reliably optimize AV delays.

Evolving Diagnostic and Therapeutic Options for Visceral Artery Aneurysms.

BACKGROUND: Visceral artery aneurysms (VAAs) are associated with a very high mortality rate when ruptured and may present as a surgical emergency. Due to their rarity and varying pathophysiology, literature concerning the optimal management of VAAs is limited. This review evaluates the evolving management options for VAAs with a focus on open and endovascular repair. METHODS: A combination of databases including OVID, PubMed and Medline were used to perform a literature search. Search terms employed include 'visceral artery aneurysms', 'angiography', '3D-volumetric rendering', 'management', 'open repair' and 'endovascular repair', amongst others. RESULTS: 3D modelling in conjunction with existing diagnostic techniques, such as computed tomography and angiography, may improve diagnostic sensitivity. The literature surrounding operative management of VAAs highlights the effectiveness of endovascular repair for anatomically suitable aneurysms. Advances in endovascular technologies may expand the type and number of aneurysms amenable to catheter-based treatment approaches. For aneurysms not amenable to endovascular treatment, or those with an emergency indication, open repair remains an appropriate management choice. CONCLUSION: Although rare, VAAs pose a high mortality risk, especially when ruptured. Practical limitations that restrict current operative approaches may be overcome by recent developments including novel neurointerventional techniques that have been applied in VAA management.