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.