Sound and light levels in intensive care units in a large urban hospital in the United States

Intensive care units (ICUs) may disrupt sleep. Quantitative ICU studies of concurrent and continuous sound and light levels and timings remain sparse in part due to the lack of ICU equipment that monitors sound and light. Here, we describe sound and light levels across three adult ICUs in a large urban United States tertiary care hospital using a novel sensor. The novel sound and light sensor is composed of a Gravity Sound Level Meter for sound level measurements and an Adafruit TSL2561 digital luminosity sensor for light levels. Sound and light levels were continuously monitored in the room of 136 patients (mean age = 67.0 (8.7) years, 44.9% female) enrolled in the Investigation of Sleep in the Intensive Care Unit study (ICU-SLEEP; Clinicaltrials.gov: #NCT03355053), at the Massachusetts General Hospital. The hours of available sound and light data ranged from 24.0 to 72.2 hours. Average sound and light levels oscillated throughout the day and night. On average, the loudest hour was 17:00 and the quietest hour was 02:00. Average light levels were brightest at 09:00 and dimmest at 04:00. For all participants, average nightly sound levels exceeded the WHO guideline of < 35 decibels. Similarly, mean nightly light levels varied across participants (minimum: 1.00 lux, maximum: 577.05 lux). Sound and light events were more frequent between 08:00 and 20:00 than between 20:00 and 08:00 and were largely similar on weekdays and weekend days. Peaks in distinct alarm frequencies (Alarm 1) occurred at 01:00, 06:00, and at 20:00. Alarms at other frequencies (Alarm 2) were relatively consistent throughout the day and night, with a small peak at 20:00. In conclusion, we present a sound and light data collection method and results from a cohort of critically ill patients, demonstrating excess sound and light levels across multiple ICUs in a large tertiary care hospital in the United States. ClinicalTrials.gov, #NCT03355053. Registered 28 November 2017, https://clinicaltrials.gov/ct2/show/NCT03355053.

Abstract WMP58: Scaling Behaviors Of Deep Learning And Linear Algorithms For The Prediction Of Stroke Severity

Introduction: Deep learning (DL) has allowed for substantial progress in many, often machine vision-focused medical scenarios. Since DL models typically require 10 5 -10 7 examples, it is currently unknown whether DL can also enhance predictions of outcomes post-stroke in real-world samples of stroke patients that are several magnitudes smaller. We here compared the capacities of linear and DL algorithms regarding their prediction of stroke severity.

Methods: Our analyses relied on a total of 1435 patients assembled from the MRI-GENIE study and a Massachusetts General Hospital-based study. The primary outcome was acute NIHSS-based stroke severity, which we predicted by means of lesion location. We automatically derived lesion segmentations from DWI scans, performed spatial normalization and principal component analysis (PCA), retaining 95% of the variance of the original data. We then separated a train, validation, and test set, of sizes 918, 230, and 287 respectively. We downsampled the train set to 100, 300, and 900 and learned to predict the NIHSS score for each sample size with regularized linear regression and an 8-layered neural network. We selected hyperparameters on the validation set and conducted 500 dataset splits. We here report explained variance (R-squared) in the test set.

Results: While linear regression performed favorably for a sample size of 100 patients (R-squared=0.28), DL started to significantly outperform linear regression when trained on 900 patients (R-squared=0.34, Figure 1 ). Average prediction performance improved by ~20% when increasing the sample size 9x.

Conclusions: For sample sizes of ~1000 patients, DL showed a more convincing prediction performance for stroke severity than typically employed linear methods. Our findings suggest the existence of non-linear relationships between lesion location and outcome post-stroke that can be utilized for an improved prediction performance once samples are reasonably large.

Hospital complications among older adults: Better processes could reduce the risk of delirium

Using observational data and variation in hospital admissions across days of the week, we examined the association between ED boarding time and development of delirium within 72 hours of admission among patients aged 65+ years admitted to an inpatient neurology ward. We exploited a natural experiment created by potentially exogenous variation in boarding time across days of the week because of competition for the neurology floor beds. Using proportional hazard models adjusting for socio-demographic and clinical characteristics in a propensity score, we examined the time to delirium onset among 858 patients: 2/3 were admitted for stroke, with the remaining admitted for another acute neurologic event. Among all patients, 81.2% had at least one delirium risk factor in addition to age. All eligible patients received delirium prevention protocols upon admission to the floor and received at least one delirium screening event. While the clinical and social-demographic characteristics of admitted patients were comparable across days of the week, patients with ED arrival on Sunday or Tuesday were more likely to have had delayed floor admission (waiting time greater than 13 hours) and delirium (adjusted HR = 1.54, 95%CI:1.37-1.75). Delayed initiation of delirium prevention protocol appeared to be associated with greater risk of delirium within the initial 72 hours of a hospital admission.

Multiphoton microscopy and amyloid angiopathy

The underlying pathophysiological processes of amyloid angiopathy have been difficult to study because the vessel size affected is too smallfor imaging in the human condition, and animal models have not yet been adequately developed or characterized. Herein we present characterization of animal models of overexpression of the human AbetaPP gene, initially developed as models of Alzheimer's disease, but which fortuitously also develop marked cerebral amyloid angiopathy. We also present a novelform of microscopy that allows in vivo imaging of vessels affected by amyloid in the anesthetized, but intact, animal.