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.
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.
Funding Acknowledgements Type of funding sources: Other. Main funding source(s): BRAVO trial: BHF SP/10/002/28189, FS/10/038, FS/11/92/29122, FS/13/44/30291) National Institute for Health Research Imperial Biomedical Research Centre. HOPE-HF trial: British Heart Foundation (CS/15/3/31405, FS/13/44/30291, FS/15/53/31615, FS/14/27/30752, FS/10/038). Introduction The optimal atrioventricular (AV) delay for implantable cardiac devices can be derived by echocardiography or beat-by-beat blood pressure measurements. However, both of these approaches are labour intensive and neither could be incorporated into an implantable cardiac device for frequent repeated optimisations. Laser Doppler perfusion monitoring (LDPM) measures blood flow through tissue. LDPM has been miniaturised ready to be incorporated into future implantable cardiac devices. Purpose We studied if LDPM is a clinically reliable alternative method to blood-pressure measurements to determine optimal AV delay. Methods Data from 58 patients undergoing 94 clinical AVD optimisations using LDPM and simultaneous non-invasive beat-by-beat blood pressure was obtained. The optimal AV delay for each method and for each optimisation was determined using a curve of haemodynamic response to switching from AAI (reference state) to DDD (test state) at a series of AV delays (40, 80, 120, 160, 200, 240 ms). We then compared the derived optimal AV delays between the two measurement approaches. We also assessed the impact of the paced heart-rate on agreement between laser Doppler and Blood-Pressure derived optimal AV delays. Results The AV delay derived using LDPM was not clinically significant different from that derived by blood pressure changes. The median difference was -9ms (IQR -26 to 7, p = 0.05). Variability between the two methods was low (median absolute deviation 17ms). Optimisations performed at higher heart-rates resulted in a non-significant smaller difference between the LDPM and blood-pressure derived AV delays (median absolute deviation 12 vs 22 ms, p = 0.11). Conclusions Optimal AVDs derived from non-invasive blood-pressure or laser Doppler perfusion methods are clinically equivalent. The addition of laser Doppler to future implantable cardiac devices may enable devices to dynamically and reliably optimise AV delays. Abstract Figure 1
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