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.

Classification of neurologic outcomes from medical notes using natural language processing.

Neurologic disability level at hospital discharge is an important outcome in many clinical research studies. Outside of clinical trials, neurologic outcomes must typically be extracted by labor intensive manual review of clinical notes in the electronic health record (EHR). To overcome this challenge, we set out to develop a natural language processing (NLP) approach that automatically reads clinical notes to determine neurologic outcomes, to make it possible to conduct larger scale neurologic outcomes studies. We obtained 7314 notes from 3632 patients hospitalized at two large Boston hospitals between January 2012 and June 2020, including discharge summaries (3485), occupational therapy (1472) and physical therapy (2357) notes. Fourteen clinical experts reviewed notes to assign scores on the Glasgow Outcome Scale (GOS) with 4 classes, namely 'good recovery', 'moderate disability', 'severe disability', and 'death' and on the Modified Rankin Scale (mRS), with 7 classes, namely 'no symptoms', 'no significant disability', 'slight disability', 'moderate disability', 'moderately severe disability', 'severe disability', and 'death'. For 428 patients' notes, 2 experts scored the cases generating interrater reliability estimates for GOS and mRS. After preprocessing and extracting features from the notes, we trained a multiclass logistic regression model using LASSO regularization and 5-fold cross validation for hyperparameter tuning. The model performed well on the test set, achieving a micro average area under the receiver operating characteristic and F-score of 0.94 (95% CI 0.93-0.95) and 0.77 (0.75-0.80) for GOS, and 0.90 (0.89-0.91) and 0.59 (0.57-0.62) for mRS, respectively. Our work demonstrates that an NLP algorithm can accurately assign neurologic outcomes based on free text clinical notes. This algorithm increases the scale of research on neurological outcomes that is possible with EHR data.

Sleep staging in the ICU with heart rate variability and breathing signals. An exploratory cross-sectional study using deep neural networks.

Introduction: To measure sleep in the intensive care unit (ICU), full polysomnography is impractical, while activity monitoring and subjective assessments are severely confounded. However, sleep is an intensely networked state, and reflected in numerous signals. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods Methods: We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in age and sex-matched sleep laboratory patients Results: We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p < 0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients Conclusion: The cardiovascular and respiratory networks encode sleep state information, which, together with artificial intelligence methods, can be utilized to measure sleep state in the ICU.

Patterns of Care in Patients with Basilar Artery Occlusion (BAO): A Population-Based Study.

Basilar artery occlusion (BAO) is associated with high morbidity and mortality. Endovascular therapy (EVT) has been shown to be beneficial in acute BAO patients. This retrospective observational study used the National Inpatient Sample (NIS) database to identify BAO patients using the International Classification of Diseases (ICD). Multivariable models were used to evaluate the association of risk factors, comorbidities, length of stay (LOS) in hospital, total cost, disposition, and transfer status. A total of 1120 (447 females, 39.95%) patients were identified, with a higher proportion of White individuals (66.8% vs. 57.6%), atrial fibrillation (31.5% vs. 17.2%; p < 0.0001), and peripheral vascular disease (21.2% vs. 13.7%; p = 0.009). A lower proportion of individuals with diabetes mellitus (32.1% vs. 39.5%; p = 0.05) was found in the EVT group. Majority of the patients (924/1120, 82.5%) were treated at the urban teaching facility, which also performed most of the EVT procedures (164, 89.13%), followed by non-academic urban (166, 14.8%) and rural (30, 2.7%) hospitals. Most patients (19/30, 63%) admitted to rural hospitals were transferred to other facilities. Urban academic hospitals also had the highest median LOS (8.9 days), cost of hospitalization (USD 117,261), and disposition to home (32.6%). This study observed distinct patterns and geographical disparities in the acute treatment of BAO patients. There is a need for national- and state-level strategies to improve access to stroke care.

High prevalence of sleep-disordered breathing in the intensive care unit - a cross-sectional study.

PURPOSE: Sleep-disordered breathing may be induced by, exacerbate, or complicate recovery from critical illness. Disordered breathing during sleep, which itself is often fragmented, can go unrecognized in the intensive care unit (ICU). The objective of this study was to investigate the prevalence, severity, and risk factors of sleep-disordered breathing in ICU patients using a single respiratory belt and oxygen saturation signals. METHODS: Patients in three ICUs at Massachusetts General Hospital wore a thoracic respiratory effort belt as part of a clinical trial for up to 7 days and nights. Using a previously developed machine learning algorithm, we processed respiratory and oximetry signals to measure the 3% apnea-hypopnea index (AHI) and estimate AH-specific hypoxic burden and periodic breathing. We trained models to predict AHI categories for 12-h segments from risk factors, including admission variables and bio-signals data, available at the start of these segments. RESULTS: Of 129 patients, 68% had an AHI ≥ 5; 40% an AHI > 15, and 19% had an AHI > 30 while critically ill. Median [interquartile range] hypoxic burden was 2.8 [0.5, 9.8] at night and 4.2 [1.0, 13.7] %min/h during the day. Of patients with AHI ≥ 5, 26% had periodic breathing. Performance of predicting AHI-categories from risk factors was poor. CONCLUSIONS: Sleep-disordered breathing and sleep apnea events while in the ICU are common and are associated with substantial burden of hypoxia and periodic breathing. Detection is feasible using limited bio-signals, such as respiratory effort and SpO2 signals, while risk factors were insufficient to predict AHI severity.

Forecasting immune effector cell-associated neurotoxicity syndrome after chimeric antigen receptor t-cell therapy.

BACKGROUND: Immune effector cell-associated neurotoxicity syndrome (ICANS) is a clinical and neuropsychiatric syndrome that can occur days to weeks following administration chimeric antigen receptor (CAR) T-cell therapy. Manifestations of ICANS range from encephalopathy and aphasia to cerebral edema and death. Because the onset and time course of ICANS is currently unpredictable, prolonged hospitalization for close monitoring following CAR T-cell infusion is a frequent standard of care. METHODS: This study was conducted at Brigham and Women's Hospital from April 2015 to February 2020. A cohort of 199 hospitalized patients treated with CAR T-cell therapy was used to develop a combined hidden Markov model and lasso-penalized logistic regression model to forecast the course of ICANS. Model development was done using leave-one-patient-out cross validation. RESULTS: Among the 199 patients included in the analysis 133 were male (66.8%), and the mean (SD) age was 59.5 (11.8) years. 97 patients (48.7%) developed ICANS, of which 59 (29.6%) experienced severe grades 3-4 ICANS. Median time of ICANS onset was day 9. Selected clinical predictors included maximum daily temperature, C reactive protein, IL-6, and procalcitonin. The model correctly predicted which patients developed ICANS and severe ICANS, respectively, with area under the curve of 96.7% and 93.2% when predicting 5 days ahead, and area under the curve of 93.2% and 80.6% when predicting the entire future risk trajectory looking forward from day 5. Forecasting performance was also evaluated over time horizons ranging from 1 to 7 days, using metrics of forecast bias, mean absolute deviation, and weighted average percentage error. CONCLUSION: The forecasting model accurately predicts risk of ICANS following CAR T-cell infusion and the time course ICANS follows once it has begun.Cite Now.

VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations With Clinical Outcomes.

To develop a physiologic grading system for the severity of acute encephalopathy manifesting as delirium or coma, based on EEG, and to investigate its association with clinical outcomes. DESIGN: This prospective, single-center, observational cohort study was conducted from August 2015 to December 2016 and October 2018 to December 2019. SETTING: Academic medical center, all inpatient wards. PATIENTS/SUBJECTS: Adult inpatients undergoing a clinical EEG recording; excluded if deaf, severely aphasic, developmentally delayed, non-English speaking (if noncomatose), or if goals of care focused primarily on comfort measures. Four-hundred six subjects were assessed; two were excluded due to technical EEG difficulties. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A machine learning model, with visually coded EEG features as inputs, was developed to produce scores that correlate with behavioral assessments of delirium severity (Confusion Assessment Method-Severity [CAM-S] Long Form [LF] scores) or coma; evaluated using Spearman R correlation; area under the receiver operating characteristic curve (AUC); and calibration curves. Associations of Visual EEG Confusion Assessment Method Severity (VE-CAM-S) were measured for three outcomes: functional status at discharge (via Glasgow Outcome Score [GOS]), inhospital mortality, and 3-month mortality. Four-hundred four subjects were analyzed (mean [sd] age, 59.8 yr [17.6 yr]; 232 [57%] male; 320 [79%] White; 339 [84%] non-Hispanic); 132 (33%) without delirium or coma, 143 (35%) with delirium, and 129 (32%) with coma. VE-CAM-S scores correlated strongly with CAM-S scores (Spearman correlation 0.67 [0.62-0.73]; p < 0.001) and showed excellent discrimination between levels of delirium (CAM-S LF = 0 vs ≥ 4, AUC 0.85 [0.78-0.92], calibration slope of 1.04 [0.87-1.19] for CAM-S LF ≤ 4 vs ≥ 5). VE-CAM-S scores were strongly associated with important clinical outcomes including inhospital mortality (AUC 0.79 [0.72-0.84]), 3-month mortality (AUC 0.78 [0.71-0.83]), and GOS at discharge (0.76 [0.69-0.82]). CONCLUSIONS: VE-CAM-S is a physiologic grading scale for the severity of symptoms in the setting of delirium and coma, based on visually assessed electroencephalography features. VE-CAM-S scores are strongly associated with clinical outcomes.

Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).

OBJECTIVES: Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a tool that can help close this diagnostic gap: the Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S). DESIGN: Retrospective cohort study. SETTING: Single-center tertiary academic medical center. PATIENTS: Three-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We developed the E-CAM-S based on a learning-to-rank machine learning model of forehead electroencephalography signals. Clinical delirium severity was assessed using the Confusion Assessment Method Severity (CAM-S). We compared associations of E-CAM-S and CAM-S with hospital length of stay and inhospital mortality. E-CAM-S correlated with clinical CAM-S (R = 0.67; p < 0.0001). For the overall cohort, E-CAM-S and CAM-S were similar in their strength of association with hospital length of stay (correlation = 0.31 vs 0.41, respectively; p = 0.082) and inhospital mortality (area under the curve = 0.77 vs 0.81; p = 0.310). Even when restricted to noncomatose patients, E-CAM-S remained statistically similar to CAM-S in its association with length of stay (correlation = 0.37 vs 0.42, respectively; p = 0.188) and inhospital mortality (area under the curve = 0.83 vs 0.74; p = 0.112). In addition to previously appreciated spectral features, the machine learning framework identified variability in multiple measures over time as important features in electroencephalography-based prediction of delirium severity. CONCLUSIONS: The E-CAM-S is an automated, physiologic measure of delirium severity that predicts clinical outcomes with a level of performance comparable to conventional interview-based clinical assessment.

Cerebrovascular and cardiovascular disease burden in patients with hereditary hemorrhagic telangiectasia.

INTRODUCTION: Hereditary hemorrhagic telangiectasia (HHT) is a rare genetic disease with prevalence of approximately 1 in 5000-10,000. We evaluated the prevalence and association of cerebrovascular and cardiovascular comorbidities in HHT patients using national database. METHODS: Retrospective observational study was performed using National Inpatient Sampling (NIS) database for the year 2014. HHT patients and comorbidities were identified using ICD-9 codes. Univariate and multivariate analyses were performed using SAS. RESULTS: Prevalence of HHT was 0.0119% with predominance in White population. Mean age of HHT patients was 59 years. Increased proportion of HHT patients had hypertension (46.8% vs 42%), anemia (28.9% vs 15.1%), chronic pulmonary disease (24.8% vs 16.4%), congestive heart failure (15.7% vs 7.5%), liver disease (7.9% vs 2.8%), migraine (4.5% vs 1.5%), and cerebrovascular malformations (0.8% vs 0.03%), whereas chronic kidney disease (12.7% vs 12.2%), headaches (1.3% vs 1.1%), seizures (0.7% vs 0.9%), transient ischemic attacks (1.06% vs 1.03%), ischemic (1.2% vs 1.0%), and hemorrhagic (0.5% vs 0.3%) strokes were similar to those without HHT. Multivariable model shows increase in cerebrovascular malformations (OR 11.04, CI 2.49-22.26, p < 0.0001), migraine (OR 3.23, CI 2.30-4.52, p < 0.0001), chronic blood loss anemia (OR 6.83, CI 5.36-8.71, p < 0.0001), congestive heart failure (OR 1.55, CI 1.26-1.91, p < 0.0001), chronic pulmonary disease (OR 1.30, CI 1.09-1.56, p = 0.0038), and hepatic disease (OR 2.63, CI 2.01-3.45, p < 0.0001) in HHT patients as compared to non-HHT patients. CONCLUSION: There is a need for a large prospective registry of HHT patients that can corroborate these associations and burden of cerebrovascular and cardiovascular diseases.

Current updates on various treatment approaches in the early management of acute spinal cord injury.

Spinal cord injury (SCI) is a debilitating condition which often leads to a severe disability and ultimately impact patient's physical, psychological, and social well-being. The management of acute SCI has evolved over the couple of decades due to improved understanding of injury mechanisms and increasing knowledge of disease. Currently, the early management of acute SCI patient includes pharmacological agents, surgical intervention and newly experimental neuroprotective strategies. However, many controversial areas are still surrounding in the current treatment strategies for acute SCI, including the optimal timing of surgical intervention, early versus delayed decompression outcome benefits, the use of methylprednisolone. Due to the lack of consensus, the optimal standard of care has been varied across treatment centres. The authors have shed a light on the current updates on early treatment approaches and neuroprotective strategies in the initial management of acute SCI in order to protect the early neurologic injury and reduce the future disability.

The Emerging Role of Magnetic Resonance Imaging-Guided Focused Ultrasound in Functional Neurosurgery.

Functional disorders of the central nervous system (CNS) are diverse in terms of their etiology and symptoms, however, they can be quite debilitating. Many functional neurological disorders can progress to a level where pharmaceuticals and other early lines of treatment can no longer optimally treat the condition, therefore requiring surgical intervention. A variety of stereotactic and functional neurosurgical approaches exist, including deep brain stimulation, implantation, stereotaxic lesions, and radiosurgery, among others. Most techniques are invasive or minimally invasive forms of surgical intervention and require immense precision to effectively modulate CNS circuitry. Focused ultrasound (FUS) is a relatively new, safe, non-invasive neurosurgical approach that has demonstrated efficacy in treating a range of functional neurological diseases. It can function reversibly, through mechanical stimulation causing circuitry changes, or irreversibly, through thermal ablation at low and high frequencies respectively. In preliminary studies, magnetic resonance imaging-guided high-intensity focused ultrasound (MRgHIFU) has been shown to have long-lasting treatment effects in several disease types. The technology has been approved by the FDA and internationally for a number of treatment-resistant neurological disorders and currently clinical trials are underway for several other neurological conditions. In this review, the authors discuss the potential applications and emerging role of MRgHIFU in functional neurosurgery in the coming years.

Cytokine Registry In Stroke Patients (CRISP): Protocol of a prospective observational study.

Inflammation is an important pathophysiological process after an acute stroke (AS). Pro- and anti-inflammatory molecules (cytokines and interleukins) are the key players during this mechanism. Emerging evidence indicate that these molecules can serve as biomarkers of stroke progression and outcome and as novel therapeutics agents. The aim of this study is to explore the temporal changes in these molecules and validate them as biomarker of AS progression and neurological outcome.The "Cytokine Registry In Stroke Patients (CRISP)" is a prospective cohort study of 600 AS patients presenting to the tertiary hospital with-in 24 h of the onset of symptoms. Plasma cytokines and interleukins will be collected at admission and 24 h after and will be measured using enzyme-linked immunosorbent assay (ELISA) to evaluate the difference in their variation among different gender, race and ethnicity and their association with various neurological outcomes. The primary exposures are biological sex (male, female) and race/ethnicity. Confounding variables include age, vascular risk factors, infarct size, stroke onset to presentation time, and identified stroke etiologies. Matched controls will be used for the comparison and evaluation of the difference among gender and race/ethnicities.CRISP is a prospective observational study that investigates the role and relationship of molecular biomarkers identifying specific and relevant targets pertinent for monitoring the progression and outcome in AS patients.Trial Registration: The study is registered on ClinicalTrial.gov: https://clinicaltrials.gov/ (NCT03297827).

Current application of cannabidiol (CBD) in the management and treatment of neurological disorders.

Cannabidiol (CBD), which is nonintoxicating pharmacologically relevant constituents of Cannabis, demonstrates several beneficial effects. It has been found to have antioxidative, anti-inflammatory, and neuroprotective effects. As the medicinal use of CBD is gaining popularity for treatment of various disorders, the recent flare-up of largely unproven and unregulated cannabis-based preparations on medical therapeutics may have its greatest impact in the field of neurology. Currently, as lot of clinical trials are underway, CBD demonstrates remarkable potential to become a supplemental therapy in various neurological conditions. It has shown promise in the treatment of neurological disorders such as anxiety, chronic pain, trigeminal neuralgia, epilepsy, and essential tremors as well as psychiatric disorders. While recent FDA-approved prescription drugs have demonstrated safety, efficacy, and consistency enough for regulatory approval in spasticity in multiple sclerosis (MS) and in Dravet and Lennox-Gastaut Syndromes (LGS), many therapeutic challenges still remain. In the current review, the authors have shed light on the application of CBD in the management and treatment of various neurological disorders.

Cerebral vascular malformations and their imaging modalities.

Cerebrovascular malformations are uncommon diverse group of dysmorphic vascular communications that may occur sporadically or as part of genetic syndromes. These include non-neoplastic lesions such as arteriovenous malformations (AVM), cavernous malformations (CM), developmental venous anomalies (DVA), and telangiectasias as well as others like arteriovenous fistulas (AVF), vein of Galen malformations (VOGM), and mixed or unclassified angiomas. These lesions often carry a high degree of morbidity and mortality often requiring surgical or endovascular interventions. The field of cerebrovascular anomalies has seen considerable advancement in the last few years. Treatment and management options of various types of brain anomalies have evolved in neurological, neurosurgical, and neuro-interventional radiology arena. The use of radiological imaging studies is a critical element for treatment of such neurosurgical cases. As imaging modalities continue to evolve at a rapid pace, it is imperative for neurological surgeons to be familiar with current imaging modalities essential for a precise diagnosis. Better understanding of these cerebrovascular lesions along with their associated imaging findings assists in determining the appropriate treatment options. In the current review, authors highlight various cerebrovascular malformations and their current imaging modalities.

The Correlation of Burnout and Optimism among Medical Residents.

Introduction Burnout is common among clinical specialties and has implications on the residents' well-being and mental health. Evidence suggests that optimism and burnout are correlated, but research has not focused on the applicability to medical residents. The objective of the study was to define burnout in residents and correlate it with optimism. Methods The authors conducted a correlational, prospective cross-sectional study using self-reported single item burnout (1-10) and Life Oriented Test-Revised (LOT-R) (0-24) survey instruments among residents of neurosurgery, neurology, internal medicine, family medicine and emergency medicine at a community-based hospital. Residents were asked to fill out the survey once in the 2018 academic year and once again in the 2019 academic year. Burnout and optimism scores were correlated and compared across subgroups for each year. Results There was no statistical significance found amongst any subgroups in burnout in 2018 but significance was found for both internal medicine (t = 3.74, p = 0.001) and neurosurgery (t = -3.07, p = 0.01) in 2019. Mean burnout increased from 2018 to 2019 from 4.39 to 5.1. Optimism remained constant from 2018 to 2019 (16.7 and 16.76, respectively) and there was no difference across subgroups. There was a statistically significant negative correlation between burnout and optimism in both 2018 (r = -0.364, p = 0.006) and 2019 (r = -0.39, p = .001). Conclusion Burnout and optimism are negatively correlated. Although burnout increased over time, optimism remained constant indicating the stability of this trait and implication for future interventions.

Patient Perspectives on the Participation of Neurosurgery Resident Physicians in Their Care.

Introduction Surgical residents play vital roles in day-to-day patient care as well as in the operating room. However, there is a paucity of literature regarding patients' perspectives on neurosurgical residents and their participation in their care. This current study investigates the knowledge, attitudes, and beliefs of patients regarding neurosurgical residents and their involvement in their healthcare process. Methods Patients older than 18 years who had undergone brain or spine surgery were requested to complete a survey questionnaire. The 7-point Likert scale response ranging from "strongly agree", "agree", "more or less agree", "undecided", more or less disagree", "disagree" to "strongly disagree" was used. The primary objective was to assess patient understanding and attitudes towards resident participation in surgical and medical care within the specialty of neurosurgery. The duration of the study was eight months. Patients having prior exposure to an informed-consent procedure by a neurosurgery team within a year prior to this study were excluded. Data were analyzed using Student's t-test, one-way analysis of variance (ANOVA), and standard averaging of responses. Results Fifty-one patients who had undergone elective surgery participated in the study survey. The majority of these respondents were between the ages of 46 and 60 years. Most of the responses were similar across gender and different age groups for most of the questions on the Likert scale questionnaire. Overall, when asked to assess their comfort level in medical and surgical care participation by residents, patients responded positively (strongly agree: 80.4%; agree: 92.2%). Patients also either disagreed or strongly disagreed (76%) about residents lacking medical knowledge. Patients overwhelmingly disagreed (91.5%) when asked if residents were unprofessional. In addition, 72.5% of the patients were able to accurately define a resident's role. Conclusion Well-formatted surveys can offer a convenient route for patients to provide objective as well as subjective feedback. The results indicate that patients had an overall positive attitude toward having residents involved in their care. These trends also indicate that patients knew the role that residents played in their healthcare process and they were comfortable with them doing so. Further studies may expand the trial to include a larger number of patients, as well as other specialties, to expand the scope of the study. Patient survey questionnaires could be thought of as a useful tool by the Accreditation Council for Graduate Medical Education (ACGME) to incorporate as part of the evaluation process of resident physicians.

Recent update on basic mechanisms of spinal cord injury.

Spinal cord injury (SCI) is a life-shattering neurological condition that affects between 250,000 and 500,000 individuals each year with an estimated two to three million people worldwide living with an SCI-related disability. The incidence in the USA and Canada is more than that in other countries with motor vehicle accidents being the most common cause, while violence being most common in the developing nations. Its incidence is two- to fivefold higher in males, with a peak in younger adults. Apart from the economic burden associated with medical care costs, SCI predominantly affects a younger adult population. Therefore, the psychological impact of adaptation of an average healthy individual as a paraplegic or quadriplegic with bladder, bowel, or sexual dysfunction in their early life can be devastating. People with SCI are two to five times more likely to die prematurely, with worse survival rates in low- and middle-income countries. This devastating disorder has a complex and multifaceted mechanism. Recently, a lot of research has been published on the restoration of locomotor activity and the therapeutic strategies. Therefore, it is imperative for the treating physicians to understand the complex underlying pathophysiological mechanisms of SCI.

Sleep staging from electrocardiography and respiration with deep learning.

STUDY OBJECTIVES: Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. We hypothesized that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals. METHODS: Using a dataset including 8682 polysomnograms, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long- and short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals. RESULTS: ECG in combination with the abdominal respiratory effort achieved the best performance for staging all five sleep stages with a Cohen's kappa of 0.585 (95% confidence interval ±0.017); and 0.760 (±0.019) for discriminating awake vs. rapid eye movement vs. nonrapid eye movement sleep. Performance is better for younger ages, whereas it is robust for body mass index, apnea severity, and commonly used outpatient medications. CONCLUSIONS: Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large heterogeneous population. This opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible.

Impact of robot-assisted spine surgery on health care quality and neurosurgical economics: A systemic review.

Whenever any new technology is introduced into the healthcare system, it should satisfy all three pillars of the iron triangle of health care, which are quality, cost-effectiveness, and accessibility. There has been quite advancement in the field of spine surgery in the last two decades with introduction of new technological modalities such as CAN and surgical robotic devices. MAZOR SpineAssist/Renaissance was the first robotic system to be approved for the use in spine surgeries in the USA in 2004. In this review, the authors sought to determine if the current literature supports this technology to be cost-effective, accessible, and improve the quality of care for individuals and populations by increasing the likelihood of desired health outcomes. Robotic-assisted surgery seems to provide perfection in surgical ergonomics and surgical dexterity, consequently improving patient outcomes. A lot of data is present on the accuracy, effectiveness, and safety of the robotic-guided technology which reflects remarkable improvements in quality of care, making its utility convincingly undisputable. The technology has been claimed to be cost-effective but there seems to be lack of data in the literature on this topic to validate this claim. Apart from just the outcome parameters, there is an immense need of studies on real-time cost-efficacy, patient perspective, surgeon and resident learning curve, and their experience with this new technology. Furthermore, new studies looking into increased utilities of this technology, such as brain and spine tumor resection, deep brain stimulation procedures, and osteotomies in deformity surgery, might authenticate the cost of the equipment.

Postoperative Craniotomy Pain in Emergent versus Non-emergent Cases.

BACKGROUND: Postoperative pain control in craniotomies poses multiple challenges. Pain must be addressed, but the use of medications must be weighed against risks. Craniotomies risk neurologic injury and so postoperative examinations are critical. Medications used to address pain can alter the neurological examination or cause bleeding leading to misdiagnosis of complications. OBJECTIVE: Determine if there is a significant difference in postoperative pain from emergent craniotomies vs. non-emergent craniotomies Methods: A retrospective review included 102 cases performed from 2010-2016; pain scores were compared on post-operative days one, two, and three between emergent and non-emergent craniotomies. RESULTS: Pain scores for emergent cases on post-operative days one through three were 5.1 (standard deviation (SD)=2.9), 5.9 (SD=2.1), 4.7 (SD=3.0) respectively. Pain scores for non-emergent cases on post-operative days one through three were 5.7 (SD=2.6), 4.8 (SD=2.8), and 4.6 (SD=3.0) respectively. A one-way analysis of variance (ANOVA) was conducted to compare pain scores between groups for each post-operative day. On post-operative day, one there was no significant difference between the groups [F(1,100)=0.49, p=0.485]. On post-operative day two, there was no significant difference between the groups [F(1,100)=2.17, p=0.143]. On post-operative day three, there was no significant difference between the groups [F(1,98)=0.002, p=0.957]. CONCLUSION: There is no significant difference in the level of pain on postoperative days one through three between emergent and non-emergent craniotomy patients.