Predictors of social risk for post-ischemic stroke reintegration.

After stroke rehabilitation, patients need to reintegrate back into their daily life, workplace and society. Reintegration involves complex processes depending on age, sex, stroke severity, cognitive, physical, as well as socioeconomic factors that impact long-term outcomes post-stroke. Moreover, post-stroke quality of life can be impacted by social risks of inadequate family, social, economic, housing and other supports needed by the patients. Social risks and barriers to successful reintegration are poorly understood yet critical for informing clinical or social interventions. Therefore, the aim of this work is to predict social risk at rehabilitation discharge using sociodemographic and clinical variables at rehabilitation admission and identify factors that contribute to this risk. A Gradient Boosting modelling methodology based on decision trees was applied to a Catalan 217-patient cohort of mostly young (mean age 52.7), male (66.4%), ischemic stroke survivors. The modelling task was to predict an individual's social risk upon discharge from rehabilitation based on 16 different demographic, diagnostic and social risk variables (family support, social support, economic status, cohabitation and home accessibility at admission). To correct for imbalance in patient sample numbers with high and low-risk levels (prediction target), five different datasets were prepared by varying the data subsampling methodology. For each of the five datasets a prediction model was trained and the analysis involves a comparison across these models. The training and validation results indicated that the models corrected for prediction target imbalance have similarly good performance (AUC 0.831-0.843) and validation (AUC 0.881 - 0.909). Furthermore, predictor variable importance ranked social support and economic status as the most important variables with the greatest contribution to social risk prediction, however, sex and age had a lesser, but still important, contribution. Due to the complex and multifactorial nature of social risk, factors in combination, including social support and economic status, drive social risk for individuals.

Predictors of devastating functional outcome despite successful basilar thrombectomy.

BACKGROUND: Basilar thrombosis frequently leads to poor functional outcomes, even with good endovascular reperfusion. We studied factors associated with severe disability or death in basilar thrombectomy patients achieving revascularization. METHODS: We retrospectively analyzed records from a health system's code stroke registry, including successful basilar thrombectomy patients from January 2017 to May 2023 who were evaluated with pretreatment computed tomography perfusion. The primary outcome was devastating functional outcome (90-day modified Rankin Scale [mRS] score 5-6). A multivariable logistic regression model was constructed to determine independent predictors of the primary outcome. The area under the receiver operator characteristics curve (AUC) was calculated for the model distinguishing good from devastating outcome. RESULTS: Among 64 included subjects, with mean (standard deviation) age 65.6 (14.1) years and median (interquartile range) National Institutes of Health Stroke Scale (NIHSS) 18 (5.75-24.5), the primary outcome occurred in 28 of 64 (43.8%) subjects. Presenting NIHSS (odds ratio [OR] 1.08, 95% confidence interval [CI] 1.01-1.14, p = 0.02), initial glucose (OR 0.99, 95% CI 0.97-1.00, p < 0.05), and proximal occlusion site (OR 7.38, 95% CI 1.84-29.60, p < 0.01) were independently associated with 90-day mRS 5-6. The AUC for the multivariable model distinguishing outcomes was 0.81 (95% CI 0.70-0.92). CONCLUSION: We have identified presenting stroke severity, lower glucose, and proximal basilar occlusion as predictors of devastating neurological outcome in successful basilar thrombectomy patients. These factors may be used in medical decision making or for patient selection in future clinical trials.

Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy.

Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected during rehabilitation (72,002 entries collected between March 2007 and September 2019). The study population covered young-adult patients with a mean age of 49.51 years and only 4.47% above 65 years of age at the stroke event (no age filter applied). Twenty different classification algorithms (from Python's Scikit-learn library) are trained and evaluated, varying their hyper-parameters and the number of features received as input. Best-performing models reported Recall scores around 0.7 and F1 scores of 0.6, showing the model's ability to identify patients with poor cognitive improvement. The study includes a detailed feature importance report that helps interpret the model's inner decision workings and exposes the most influential factors in the cognitive improvement prediction. The study showed that certain therapy variables (e.g., the proportion of memory and orientation executed tasks) had an important influence on the final prediction of the cognitive improvement of patients at individual and population levels. This type of evidence can serve clinicians in adjusting the therapeutic settings (e.g., type and load of therapy activities) and selecting the one that maximizes cognitive improvement.

Visualization of the Anterior Temporal Artery as a Predictor of Outcome in Middle Cerebral Artery Occlusion Patients Achieving Successful Recanalization After Transfer.

Introduction Anterior temporal artery (ATA) visualization on computed tomography angiography (CTA) has been previously associated with good outcomes in middle cerebral artery (MCA) occlusions, but not in the setting of patients who initially present to non-thrombectomy centers. Methods We retrospectively identified acute MCA (M1) occlusion patients who underwent mechanical thrombectomy after transfer from non-thrombectomy-capable centers. Neuroradiologists confirmed the MCA (M1) as the most proximal site of occlusion on CTA and assessed for visualization of the ATA. Thrombolysis in Cerebral Infarction (TICI) 2b or greater revascularization scores were confirmed by neurointerventionalists blinded to patient outcomes. Ninety-day modified Rankin scale (mRS) scores were obtained via a structured telephone questionnaire. Results We identified 102 M1 occlusion patients over a three-and-a-half-year period presenting to a non-thrombectomy-capable center who underwent transfer and mechanical thrombectomy. There were no significant differences in age, gender, race, comorbidities, or median National Institute of Health Stroke Scale (NIHSS) scores between the ATA visualized (n = 47) versus non-visualized (n = 55) cohort, and no significant differences in baseline Alberta Stroke Program Early Computed Tomography (ASPECT) scores, post-intervention TICI scores, or interval from last known well to revascularization. There was a strong trend in functional independent outcome (mRS ≤ 2) for patients with ATA visualization (63.8% vs. 45.5%, p = 0.064). Conclusion For patients presenting to non-thrombectomy centers without CT perfusion capability, ATA visualization should be further investigated as an outcome predictor, given its association with functional independence after successful recanalization. This article was previously presented as a meeting abstract at the 2021 International Stroke Conference on March 17-19, 2021.

An audio processing pipeline for acquiring diagnostic quality heart sounds via mobile phone.

Recently, heart sound signals captured using mobile phones have been employed to develop data-driven heart disease detection systems. Such signals are generally captured in person by trained clinicians who can determine if the recorded heart sounds are of diagnosable quality. However, mobile phones have the potential to support heart health diagnostics, even where access to trained medical professionals is limited. To adopt mobile phones as self-diagnostic tools for the masses, we would need to have a mechanism to automatically establish that heart sounds recorded by non-expert users in uncontrolled conditions have the required quality for diagnostic purposes. This paper proposes a quality assessment and enhancement pipeline for heart sounds captured using mobile phones. The pipeline analyzes a heart sound and determines if it has the required quality for diagnostic tasks. Also, in cases where the quality of the captured signal is below the required threshold, the pipeline can improve the quality by applying quality enhancement algorithms. Using this pipeline, we can also provide feedback to users regarding the cause of low-quality signal capture and guide them towards a successful one. We conducted a survey of a group of thirteen clinicians with auscultation skills and experience. The results of this survey were used to inform and validate the proposed quality assessment and enhancement pipeline. We observed a high level of agreement between the survey results and fundamental design decisions within the proposed pipeline. Also, the results indicate that the proposed pipeline can reduce our dependency on trained clinicians for capture of diagnosable heart sounds.

Speech quality estimation with deep lattice networks.

Intrusive subjective speech quality estimation of mean opinion score (MOS) often involves mapping a raw similarity score extracted from differences between the clean and degraded utterance onto MOS with a fitted mapping function. More recent models such as support vector regression (SVR) or deep neural networks use multidimensional input, which allows for a more accurate prediction than one-dimensional (1-D) mappings but does not provide the monotonic property that is expected between similarity and quality. We investigate a multidimensional mapping function using deep lattice networks (DLNs) to provide monotonic constraints with input features provided by ViSQOL. The DLN improved the speech mapping to 0.24 mean-square error on a mixture of datasets that include voice over IP and codec degradations, outperforming the 1-D fitted functions and SVR as well as PESQ and POLQA. Additionally, we show that the DLN can be used to learn a quantile function that is well-calibrated and a useful measure of uncertainty. The quantile function provides an improved mapping of data driven similarity representations to human interpretable scales, such as quantile intervals for predictions instead of point estimates.

Listening Effort Informed Quality of Experience Evaluation.

Perceived quality of experience for speech listening is influenced by cognitive processing and can affect a listener's comprehension, engagement and responsiveness. Quality of Experience (QoE) is a paradigm used within the media technology community to assess media quality by linking quantifiable media parameters to perceived quality. The established QoE framework provides a general definition of QoE, categories of possible quality influencing factors, and an identified QoE formation pathway. These assist researchers to implement experiments and to evaluate perceived quality for any applications. The QoE formation pathways in the current framework do not attempt to capture cognitive effort effects and the standard experimental assessments of QoE minimize the influence from cognitive processes. The impact of cognitive processes and how they can be captured within the QoE framework have not been systematically studied by the QoE research community. This article reviews research from the fields of audiology and cognitive science regarding how cognitive processes influence the quality of listening experience. The cognitive listening mechanism theories are compared with the QoE formation mechanism in terms of the quality contributing factors, experience formation pathways, and measures for experience. The review prompts a proposal to integrate mechanisms from audiology and cognitive science into the existing QoE framework in order to properly account for cognitive load in speech listening. The article concludes with a discussion regarding how an extended framework could facilitate measurement of QoE in broader and more realistic application scenarios where cognitive effort is a material consideration.

ViSQOLAudio: An objective audio quality metric for low bitrate codecs.

Streaming services seek to optimise their use of bandwidth across audio and visual channels to maximise the quality of experience for users. This letter evaluates whether objective quality metrics can predict the audio quality for music encoded at low bitrates by comparing objective predictions with results from listener tests. Three objective metrics were benchmarked: PEAQ, POLQA, and VISQOLAudio. The results demonstrate objective metrics designed for speech quality assessment have a strong potential for quality assessment of low bitrate audio codecs.

Simulated performance intensity functions.

Measuring speech intelligibility for different hearing aid fitting methods in a simulated environment would allow rapid prototyping and early design assessment. A simulated performance intensity function (SPIF) test methodology has been developed to allow experimentation using an auditory nerve model to predict listeners' phoneme recognition. The test discriminates between normal hearing and progressively degrading levels of sensorineural hearing loss. Auditory nerve discharge patterns, presented as neurograms, can be subjectively ranked by visual inspection. Here, subjective inspection is substituted with an automated ranking using a new image similarity metric that can quantify neurogram degradation in a consistent manner. This work reproduces the test results of a real human listener with moderate hearing loss, in unaided and aided scenarios, using a simulation. The simulated results correlate within comparable error margins to the real listener test performance intensity functions.