Pupillary light reflex in children with autism spectrum disorders

Pupillary light reflex (PLR) refers to the phenomenon of pupil size changing with respect to retinal illumination. It's a noninvasive, functional test which can reveal a rich set of information about nervous system. Abnormal PLR in children with autism spectrum disorders (ASD) was previously reported in a small population. In this research, a series of systematic studies were carried out to investigate the association of atypical PLR with ASD in a large population. An experimental protocol was developed to measure PLR simultaneously with heart rate variability (HRV), a commonly used autonomic nervous system (ANS) measure. Our results indicate that variations of PLR and HRV are not associated in typically developing children. However, significant age effects on both PLR and HRV were observed in this population. In typically developing children, the resting pupil diameter increased with age significantly up to age 12. PLR constriction increased with age in children younger than 8 years old and reached a plateau thereafter. PLR latency decreased significantly from 6 to 9 years and stabilized thereafter. The average heart rate (AHR) decreased with age in typically developing children. Standard deviation of normal-to-normal intervals (SDNN) showed little change before 12 years of age but was increased in older children. High frequency normalized power (HFN) decreased with age in typically developing (TD) group. PLR and HRV were also measured in 152 children with ASD and 36 children with non-ASD neurodevelopmental disorders (NDDs). The results showed atypical PLR in the ASD group including longer PLR latency, reduced relative constriction amplitude, and shorter constriction/redilation time. Similar atypical PLR parameters were observed in the NDD group. The ASD and NDD groups had faster AHR than the TD group. The NDD group also showed a significantly faster AHR than the ASD group. The age effect on PLR latency which was observed in typically developing children of 6-9 years old was not observed in the ASD and NDD gro

Assessing effect of beat detector on detection dependent signal quality indices

Patient monitoring algorithms which use multimodal physiological waveforms are needed to reduce alarm fatigue by alarming only for physiologic events and not signal artifacts. When combining information from multiple ECG signals, computational approaches that automatically identify artifacts in ECG signals play an important role. Signal quality indices (SQIs) have been derived which can differentiate between ECG signal artifacts and normal QRS morphology. Some of these SQIs are derived using beat detections and might be affected by the beat detector used. Using ECG signals from the PhysioNet/Computing in Cardiology Challenge 2015 training set, we studied the effect of beat detector on previously reported ECG SQIs derived using beat detections. We found that, while being affected by the beat detector, some of these SQIs can predict beat detector failure. Using beat detector specific SQIs can improve the designs of robust monitoring algorithms

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous non-linearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems

Development of an algorithm for finding pertussis episodes in a population-based electronic health record database

While tetanus-diphtheria-acellular pertussis (Tdap) vaccines for adolescents and adults were licensed in 2005 and immunization strategies proposed, the burden of pertussis in this population remains under-recognized mainly due to atypical disease presentation, undermining efforts to optimize protection through vaccination. We developed a machine learning algorithm to identify undiagnosed/misdiagnosed pertussis episodes in patients diagnosed with acute respiratory disease (ARD) using signs, diseases and symptoms from clinician notes and demographic information within electronic health-care records (Optum Humedica repository [2007–2019]). We used two patient cohorts aged ≥11 years to develop the model: a positive pertussis cohort (4,515 episodes in 4,316 patients) and a negative pertussis (ARD) cohort (4,573,445 episodes and patients), defined using ICD 9/10 codes. To improve contrast between positive pertussis and negative pertussis (ARD) episodes, only episodes with ≥7 symptoms were selected. LightGBM was used as the machine learning model for pertussis episode identification. Model validity was determined using laboratory-confirmed pertussis positive and negative cohorts. Model explainability was obtained using the Shapley additive explanations method. The predictive performance was as follows: area under the precision–recall curve, 0.24 (SD, 7 × 10−3); recall, 0.72 (SD, 4 × 10−3); precision, 0.012 (SD, 1 × 10−3); and specificity, 0.94 (SD, 7 × 10−3). The model applied to laboratory-confirmed positive and negative pertussis episodes had a specificity of 0.846. Predictive probability for pertussis increased with presence of whooping cough, whoop, and post-tussive vomiting in clinician notes, but decreased with gastrointestinal bleeding, sepsis, pulmonary symptoms, and fever. In conclusion, machine learning can help identify pertussis episodes among those diagnosed with ARD

Estimating the pertussis burden in adolescents and adults in the United States between 2007 and 2019

We developed a machine learning algorithm to identify undiagnosed pertussis episodes in adolescent and adult patients with reported acute respiratory disease (ARD) using clinician notes in an electronic healthcare record (EHR) database. Here, we utilized the algorithm to better estimate the overall pertussis incidence within the Optum Humedica clinical repository from 1 January 2007 through 31 December 2019. The incidence of diagnosed pertussis episodes was 1–5 per 100,000 annually, consistent with data registered by the US Centers for Disease Control and Prevention (CDC) over the same time period. Among 18,573,496 ARD episodes assessed, 1,053,946 were identified (i.e. algorithm-identified) as likely undiagnosed pertussis episodes. Accounting for these undiagnosed pertussis episodes increased the estimated pertussis incidence by 110-fold on average (34–474 per 100,000 annually). Risk factors for pertussis episodes (diagnosed and algorithm-identified) included asthma (Odds ratio [OR] 2.14; 2.12–2.16), immunodeficiency (OR 1.85; 1.78–1.91), chronic obstructive pulmonary disease (OR 1.63; 1.61–1.65), obesity (OR 1.44; 1.43–1.45), Crohn’s disease (OR 1.39; 1.33–1.45), diabetes type 1 (OR 1.21; 1.17–1.24) and type 2 (OR 1.12; 1.1–1.13). Of note, all these risk factors, except Crohn’s disease, increased the likelihood of severe pertussis. In conclusion, the incidence of pertussis in the adolescent and adult population in the USA is likely substantial, but considerably under-recognized, highlighting the need for improved clinical awareness of the disease and for improved control strategies in this population. These results will help better inform public health vaccination and booster programs, particularly in those with underlying comorbidities

Multivariate physiological recordings in an experimental hemorrhage model

In this paper we describe a data set of multivariate physiological measurements recorded from conscious sheep (N = 8; 37.4 ± 1.1 kg) during hemorrhage. Hemorrhage was experimentally induced in each animal by withdrawing blood from a femoral artery at two different rates (fast: 1.25 mL/kg/min; and slow: 0.25 mL/kg/min). Data, including physiological waveforms and continuous/intermittent measurements, were transformed to digital file formats (European Data Format [EDF] for waveforms and Comma-Separated Values [CSV] for continuous and intermittent measurements) as a comprehensive data set and stored and publicly shared here (Appendix A). The data set comprises experimental information (e.g., hemorrhage rate, animal weight, event times), physiological waveforms (arterial and central venous blood pressure, electrocardiogram), time-series records of non-invasive physiological measurements (SpO2, tissue oximetry), intermittent arterial and venous blood gas analyses (e.g., hemoglobin, lactate, SaO2, SvO2) and intermittent thermodilution cardiac output measurements. A detailed explanation of the hemodynamic and pulmonary changes during hemorrhage is available in a previous publication (Scully et al., 2016) [1]