Smartphone pupillometry for detection of cerebral vasospasm after aneurysmal subarachnoid hemorrhage.

OBJECTIVES: Vasospasm is a complication of aneurysmal subarachnoid hemorrhage (aSAH) that can change the trajectory of recovery and is associated with morbidity and mortality. Earlier detection of vasospasm could improve patient outcomes. Our objective is to evaluate the accuracy of smartphone-based quantitative pupillometry in the detection of radiographic vasospasm and delayed cerebral ischemia (DCI) after aSAH. MATERIALS AND METHODS: We prospectively collected pupillary light reflex (PLR) parameters from patients with aSAH admitted to a neurocritical care unit at a single hospital twice daily using quantitative smartphone pupillometry recordings. PLR parameters included: Maximum pupil diameter, minimum pupil diameter, percent change in pupil diameter, latency in beginning of pupil constriction to light, mean constriction velocity, maximum constriction velocity, and mean dilation velocity. Two-tailed t-tests for independent samples were performed to determine changes in average concurrent PLR parameter values between the following comparisons: (1) patients with and without radiographic vasospasm (defined by angiography with the need for endovascular intervention) and (2) patients with and without DCI. RESULTS: 49 subjects with aSAH underwent 323 total PLR recordings. For PLR recordings taken with (n=35) and without (n=241) radiographic vasospasm, significant differences were observed in MIN (35.0 ± 7.5 pixels with vasospasm versus 31.6 ± 6.2 pixels without; p=0.002). For PLR recordings taken with (n=43) and without (n=241) DCI, significant differences were observed in MAX (48.9 ± 14.3 pixels with DCI versus 42.5 ± 9.2 pixels without; p<0.001). CONCLUSIONS: Quantitative smartphone pupillometry has the potential to be used to detect radiographic vasospasm and DCI after aSAH.

Sociodemographic Disparities in Tracheostomy Timing and Outcomes.

OBJECTIVE: Tracheostomies are commonly performed in critically ill patients requiring prolonged mechanical ventilation. Although early tracheostomy has been associated with improved outcomes, the reasons for delayed tracheostomy are complex. We examined the impact of sociodemographic factors on tracheostomy timing and outcomes. METHODS: Medical records were retrospectively reviewed of ventilator-dependent adult patients who underwent tracheostomy from 2021 to 2022. Tracheostomy timing was defined as routine (<21 days) versus late (21 days or more). Sociodemographic variables were compared between cohorts using univariate and multivariate models. Secondary outcomes included hospital length of stay (LOS), decannulation, tracheostomy-related complications, and inhospital mortality. RESULTS: One hundred forty-two patients underwent tracheostomy after initial intubation: 74.7% routine (n = 106) and 25.4% late (n = 36). In a multivariate model adjusted for age, race, surgical service, tracheostomy technique, and time between consultation and surgery, non-English speaking patients and women were more likely to receive a late tracheostomy compared with English speaking patients and men, respectively (odds ratio [OR] 3.18, 95% confidence interval [CI] 1.03, 9.81, p < 0.05), (OR 3.15, 95% CI 1.18, 8.41, p < 0.05). Late tracheostomy was associated with longer median hospital LOS (62 vs. 52 days, p < 0.05). Tracheostomy timing did not significantly impact mortality, decannulation or tracheostomy-related complications. CONCLUSION: Despite an association between earlier tracheostomy and shorter LOS, non-English speaking patients and female patients are more likely to receive a late tracheostomy. Standardized protocols for tracheostomy timing may address bias in the referral and execution of tracheostomy and reduce unnecessary hospital days. LEVEL OF EVIDENCE: 4 Laryngoscope, 134:582-587, 2024.

A smartphone pupillometry tool for detection of acute large vessel occlusion.

OBJECTIVES: Pupillary light reflex (PLR) parameters can be used as quantitative biomarkers of neurological function. Since digital infrared pupillometry is expensive, we sought to examine alterations in PLR parameters using a smartphone quantitative pupillometry platform in subjects with acute ischemic stroke (AIS). MATERIALS AND METHODS: Patients were enrolled if they presented to the emergency department as a stroke code activation and had evidence of a large vessel occlusion (LVO) on computed tomography angiography. Controls were enrolled from hospital staff. A smartphone pupillometer was used in AIS patients with LVO pre-mechanical thrombectomy, immediately post-thrombectomy, and at 24 h post-thrombectomy. Clinical and demographic data were collected, along with the proprietary Neurological Pupil index (NPi) score from the NPi-200 digital infrared pupillometer. PLR parameters were compared using mean differences. The absolute and non-absolute inter-eye difference in each parameter for each subject were also analyzed by measuring 1 - (R:L) to determine alteration in the equilibrium between subject pupils. The NPi was analyzed for mean differences between cohorts. RESULTS: Healthy controls (n = 132) and AIS patients (n = 31) were enrolled. Significant differences were observed in PLR parameters for healthy subjects when compared to pre-thrombectomy subjects in both mean and absolute inter-eye differences after post hoc Bonferroni correction. The proprietary NPi score was not significantly different for all groups and comparisons. CONCLUSIONS: Significant alterations in the PLR were observed in AIS patients with LVO before thrombectomy, indicating the potential use of smartphone pupillometry for detection of LVO.

Validation of a Smartphone Pupillometry Application in Diagnosing Severe Traumatic Brain Injury.

The pupillary light reflex (PLR) is an important biomarker for the detection and management of traumatic brain injury (TBI). We investigated the performance of PupilScreen, a smartphone-based pupillometry app, in classifying healthy control subjects and subjects with severe TBI in comparison to the current gold standard NeurOptics pupillometer (NPi-200 model with proprietary Neurological Pupil Index [NPi] TBI severity score). A total of 230 PLR video recordings taken using both the PupilScreen smartphone pupillometer and NeurOptics handheld device (NPi-200) pupillometer were collected from 33 subjects with severe TBI (sTBI) and 132 subjects who were healthy without self-reported neurological disease. Severe TBI status was determined by Glasgow Coma Scale (GCS) at the time of recording. The proprietary NPi score was collected from the NPi-200 pupillometer for each subject. Seven PLR curve morphological parameters were collected from the PupilScreen app for each subject. A comparison via t-test and via binary classification algorithm performance using NPi scores from the NPi-200 and PLR parameter data from the PupilScreen app was completed. This was used to determine how the frequently used NPi-200 proprietary NPi TBI severity score compares to the PupilScreen app in ability to distinguish between healthy and sTBI subjects. Binary classification models for this task were trained for the diagnosis of healthy or severe TBI using logistic regression, k-nearest neighbors, support vector machine, and random forest machine learning classification models. Overall classification accuracy, sensitivity, specificity, area under the curve, and F1 score values were calculated. Median GCS was 15 for the healthy cohort and 6 (interquartile range 2) for the severe TBI cohort. Smartphone app PLR parameters as well as NPi from the digital infrared pupillometer were significantly different between healthy and severe TBI cohorts; 33% of the study cohort had dark eye colors defined as brown eyes of varying shades. Across all classification models, the top performing PLR parameter combination for classifying subjects as healthy or sTBI for PupilScreen was maximum diameter, constriction velocity, maximum constriction velocity, and dilation velocity with accuracy, sensitivity, specificity, area under the curve (AUC), and F1 score of 87%, 85.9%, 88%, 0.869, and 0.85, respectively, in a random forest model. The proprietary NPi TBI severity score demonstrated greatest AUC value, F1 score, and sensitivity of 0.648, 0.567, and 50.9% respectively using a random forest classifier and greatest overall accuracy and specificity of 67.4% and 92.4% using a logistic regression model in the same classification task on the same dataset. The PupilScreen smartphone pupillometry app demonstrated binary healthy versus severe TBI classification ability greater than that of the NPi-200 proprietary NPi TBI severity score. These results may indicate the potential benefit of future study of this PupilScreen smartphone pupillometry application in comparison to the NPi-200 digital infrared pupillometer across the broader TBI spectrum, as well as in other neurological diseases.

Defining the Performance of Clinician's Ability to Screen for Laryngeal Mass From Voice.

OBJECTIVE: Defining a clinician's ability to perceptually identify mass from voice will inform the feasibility, design priorities, and performance standards for tools developed to screen for laryngeal mass from voice. This study defined clinician ability of and examined the impact of expertise on screening for laryngeal mass from voice. STUDY DESIGN: Task comparison study between experts and nonexperts rating voices for the probability of a laryngeal mass. SETTING: Online, remote. METHODS: Experts (voice-focused speech-language pathologists and otolaryngologists) and nonexperts (general medicine providers) rated 5-s/i/voice samples (with pathology defined by laryngoscopy) for the probability of laryngeal mass via an online survey. The intraclass correlation coefficient (ICC) estimated interrater and intrarater reliability. Diagnostic performance metrics were calculated. A linear mixed effects model examined the impact of expertise and pathology on ratings. RESULTS: Forty clinicians (21 experts and 19 nonexperts) evaluated 344 voice samples. Experts outperformed nonexperts, with a higher area under the curve (70% vs 61%), sensitivity (49% vs 36%), and specificity (83% vs 77%) (all comparisons p < .05). Interrater reliability was fair for experts and poor for nonexperts (ICC: 0.48 vs 0.34), while intrarater reliability was excellent and good, respectively (ICC: 0.9 and 0.6). The main effects of expertise and underlying pathology were significant in the linear model (p < .001). CONCLUSION: Clinicians demonstrate inadequate performance screening for laryngeal mass from voice to use auditory perception for dysphonia triage. Experts' superior performance indicates that there is acoustic information in a voice that may be utilized to detect laryngeal mass based on voice.

Mobile Smartphone-Based Digital Pupillometry Curves in the Diagnosis of Traumatic Brain Injury.

Objective: The pupillary light reflex (PLR) and the pupillary diameter over time (the PLR curve) is an important biomarker of neurological disease, especially in the diagnosis of traumatic brain injury (TBI). We investigated whether PLR curves generated by a novel smartphone pupillometer application could be easily and accurately interpreted to aid in the diagnosis of TBI. Methods: A total of 120 PLR curves from 42 healthy subjects and six patients with TBI were generated by PupilScreen. Eleven clinician raters, including one group of physicians and one group of neurocritical care nurses, classified 48 randomly selected normal and abnormal PLR curves without prior training or instruction. Rater accuracy, sensitivity, specificity, and interrater reliability were calculated. Results: Clinician raters demonstrated 93% accuracy, 94% sensitivity, 92% specificity, 92% positive predictive value, and 93% negative predictive value in identifying normal and abnormal PLR curves. There was high within-group reliability (k = 0.85) and high interrater reliability (K = 0.75). Conclusion: The PupilScreen smartphone application-based pupillometer produced PLR curves for clinical provider interpretation that led to accurate classification of normal and abnormal PLR data. Interrater reliability was greater than previous studies of manual pupillometry. This technology may be a good alternative to the use of subjective manual penlight pupillometry or digital pupillometry.

Oral manifestations as the first presenting sign of Crohn's disease in a pediatric patient.

Crohn's disease (CD) is a chronic inflammatory disorder affecting the gastrointestinal (GI) tract. Although the GI tract is the primary site of involvement, many patients, particularly in pediatric cases, first present with non-intestinal manifestations, including oral lesions. Oral manifestations of CD in children occur in around 50-80% of cases, and about 30% of CD cases in children occur first in the mouth. Recognizing such oral lesions in the pediatric population, and requesting a biopsy, may expedite the diagnosis of CD. We describe a 15 year old male who presented with oral findings of multiple aphthous ulcers and plaques of pink papules of the buccal vestibule. We highlight the initial pathology findings, including non-caseating granulomas, sialadenitis, and a notable plasmacytosis, from biopsy of the left retromolar pad area, which triggered further testing for CD. We provide discussion of how CD was eventually diagnosed and treated and highlight the significance of the pathological findings in this case as they relate to the pathogenesis of CD. Key words:Crohn's disease, Inflammatory bowel disease, Oral manifestations, Pediatric, Granulomatous inflammation, Monotypic plasma cells.