A “datathon” model combines complementary knowledge and skills to formulate inquiries and drive research that addresses information gaps faced by clinicians.

Cardiovascular variables such as heart rate (HR) and blood pressure (BP) are regulated by an underlying control system, and therefore, the time series of these vital signs exhibit rich dynamical patterns of interaction in response to external perturbations (e.g., drug administration), as well as pathological states (e.g., onset of sepsis and hypotension). A question of interest is whether “similar” dynamical patterns can be identified across a heterogeneous patient cohort, and be used for prognosis of patients’ health and progress. In this paper, we used a switching vector autoregressive framework to systematically learn and identify a collection of vital sign time series dynamics, which are possibly recurrent within the same patient and may be shared across the entire cohort. We show that these dynamical behaviors can be used to characterize the physiological “state” of a patient. We validate our technique using simulated time series of the cardiovascular system, and human recordings of HR and BP time series from an orthostatic stress study with known postural states. Using the HR and BP dynamics of an intensive care unit (ICU) cohort of over 450 patients from the MIMIC II database, we demonstrate that the discovered cardiovascular dynamics are significantly associated with hospital mortality (dynamic modes 3 and 9, $p=0.001$ , $p=0.006$ from logistic regression after adjusting for the APACHE scores). Combining the dynamics of BP time series and SAPS-I or APACHE-III provided a more accurate assessment of patient survival/mortality in the hospital than using SAPS-I and APACHE-III alone ( $p=0.005$ and $p=0.045$ ). Our results suggest that the discovered dynamics of vital sign time series may contain additional prognostic value beyond that of the baseline acuity measures, and can potentially be used as an independent predictor of outcomes in the ICU.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Meta-analyses have been extensively used to evaluate the efficacy of neurofeedback (NFB) treatment for Attention Deficit/Hyperactivity Disorder (ADHD) in children and adolescents. However, each meta-analysis published in the past decade has contradicted the methods and results from the previous one, thus making it difficult to determine a consensus of opinion on the effectiveness of NFB. This works brings continuity to the field by extending and discussing the last and much controversial meta-analysis by Cortese et al. (1). The extension comprises an update of that work including the latest control trials, which have since been published and, most importantly, offers a novel methodology. Specifically, NFB literature is characterized by a high technical and methodological heterogeneity, which partly explains the current lack of consensus on the efficacy of NFB. This work takes advantage of this by performing a Systematic Analysis of Biases (SAOB) in studies included in the previous meta-analysis. Our extended meta-analysis (k = 16 studies) confirmed the previously obtained results of effect sizes in favor of NFB efficacy as being significant when clinical scales of ADHD are rated by parents (non-blind, p-value = 0.0014), but not when they are rated by teachers (probably blind, p-value = 0.27). The effect size is significant according to both raters for the subset of studies meeting the definition of “standard NFB protocols” (parents' p-value = 0.0054; teachers' p-value = 0.043, k = 4). Following this, the SAOB performed on k = 33 trials identified three main factors that have an impact on NFB efficacy: first, a more intensive treatment, but not treatment duration, is associated with higher efficacy; second, teachers report a lower improvement compared to parents; third, using high-quality EEG equipment improves the effectiveness of the NFB treatment. The identification of biases relating to an appropriate technical implementation of NFB certainly supports the efficacy of NFB as an intervention. The data presented also suggest that the probably blind assessment of teachers may not be considered a good proxy for blind assessments, therefore stressing the need for studies with placebo-controlled intervention as well as carefully reported neuromarker changes in relation to clinical response.

To determine if a prediction rule for hospital mortality using dynamic variables in response to treatment of hypotension in patients with sepsis performs better than current models. Retrospective cohort study. All ICUs at a tertiary care hospital. Adult patients admitted to ICUs between 2001 and 2007 of whom 2,113 met inclusion criteria and had sufficient data. None. We developed a prediction algorithm for hospital mortality in patients with sepsis and hypotension requiring medical intervention using data from the Multiparameter Intelligent Monitoring in Intensive Care II. We extracted 189 candidate variables, including treatments, physiologic variables and laboratory values collected before, during, and after a hypotensive episode. Thirty predictors were identified using a genetic algorithm on a training set (n = 1500) and validated with a logistic regression model on an independent validation set (n = 613). The final prediction algorithm used included dynamic information and had good discrimination (area under the receiver operating curve = 82.0%) and calibration (Hosmer–Lemeshow C statistic = 10.43, p = 0.06). This model was compared with Acute Physiology and Chronic Health Evaluation IV using reclassification indices and was found to be superior with an Net Reclassification Improvement of 0.19 (p < 0.001) and an Integrated Discrimination Improvement of 0.09 (p < 0.001). Hospital mortality predictions based on dynamic variables surrounding a hypotensive event is a new approach to predicting prognosis. A model using these variables has good discrimination and calibration and offers additional predictive prognostic information beyond established ones.