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The American Society of Anesthesiologist recommends peripheral physiological monitoring during general anesthesia, which offers no information regarding the effects of anesthetics on the brain. Since no "gold standard" method exists for this evaluation, such a technique is needed to ensure patient comfort, procedure quality and safety. In this study we investigated functional near infrared spectroscopy (fNIRS) as possible monitor of anesthetic effects on the prefrontal cortex. Anesthetic drugs, such as sevoflurane, suppress the cerebral metabolism and alter the cerebral blood flow. We hypothesize that fNIRS derived features carry information on the effects of anesthetics on the prefrontal cortex (PFC) that can be used for the classification of the anesthetized state. In this study, patients were continuously monitored using fNIRS, BIS and standard monitoring during surgical procedures under sevoflurane general anesthesia. Maintenance and emergence states were identified and fNIRS features were identified and compared between states. Linear and non-linear machine learning algorithms were investigated as methods for the classification of maintenance/emergence. The results show that changes in oxygenated (HbO2) and deoxygenated hemoglobin (HHb) concentration and blood volume measured by fNIRS were associated with the transition between maintenance and emergence that occurs as a result of sevoflurane washout. We observed that during maintenance the signal is relatively more stable than during emergence. Maintenance and emergence states were classified with 94.7% accuracy with a non-linear model using the locally derived mean total hemoglobin, standard deviation of HbO2, minimum and range of HbO2 and HHb as features. These features were found to be correlated with the effects of sevoflurane and to carry information that allows real time and automatic classification of the anesthetized state with high accuracy. [ABSTRACT FROM AUTHOR]

With 15 drugs currently approved for the treatment of metastatic renal cell carcinoma (mRCC) and even more combination regimens with immunotherapy on the horizon, there remains a distinct lack of molecular biomarkers for therapeutic efficacy. Our study reports on real-world clinical outcomes of mRCC patients from a tertiary academic medical center treated with empirically selected standard-of-care therapy. We utilized the Stanford Renal Cell Carcinoma Database (RCCD) to report on various outcome measures, including overall survival (OS) and the median number of lines of targeted therapies received from the time of metastatic diagnosis. We found that most metastatic patients did not survive long enough to attempt even half of the available targeted therapies. We also noted that patients who failed to receive a clinical benefit within the first two lines of therapy could still go on to experience clinical benefit in later lines of therapy. The term, "clinical benefit" was assigned to a line of therapy if a patient remained on drug treatment for three months or longer. Moreover, patients with clinical benefit in at least one line of therapy experienced significantly longer OS compared to those who did not have clinical benefit in at least one line of therapy. Developing biomarkers that identify patients who will receive clinical benefit in individual lines of therapy is one potential strategy for achieving rational drug sequencing in mRCC. [ABSTRACT FROM AUTHOR]

Importance: Thirty-day readmission rates have been suggested as a marker for quality of care. By investigating the factors associated with readmissions in all otolaryngology subspecialties we provide data relevant for the development of risk stratification systems to improve outcomes., Objective: To establish the association of surgical and hospital volume and patient characteristics with 30-day readmission rates to guide the development of otolaryngology-specific risk stratification models., Design, Setting, and Participants: A retrospective cohort study including adult patients who underwent inpatient otolaryngology surgery in New York State between 1995 and 2015 was conducted using the Statewide Planning and Research Cooperative System (SPARCS). Regression techniques were used to describe relationships of patient-level factors, hospital, and surgeon volume to 30-day readmission rates in New York State., Main Outcomes and Measures: The main outcome measures were patient-, surgeon-, and hospital-level risk factors for readmission. Secondary outcome measures were rate of readmissions by subspecialty procedure and by diagnosis on readmission., Results: We identified 254 257 cases of otolaryngology surgery (147 065 women [58%], mean [SD] age 50 [17] years). The 30-day readmission rate was 6%. In a multivariable model, odds ratios (ORs) identified Medicaid insurance (OR, 1.46; 99% CI, 1.36-1.57), Medicare insurance (OR, 1.32; 99% CI, 1.24-1.42), bottom quartile income (OR, 1.08; 99% CI, 1.01-1.15), patient comorbidities measured by the Charlson Comorbidity Index (CCI) (CCI >1; OR, 2.31; 99% CI, 2.16-2.47), length of stay (LOS) (LOS >10 days; OR, 2.29; 99% CI, 2.00-2.45), rhinology (OR, 1.37; 99% CI, 1.24-1.51), laryngology (OR, 1.98; 99% CI, 1.62-2.43), and head and neck cancer (OR, 1.27; 99% CI, 1.17-1.37) procedures as readmission predictors. High-volume surgeons were protective of 30-day readmission (OR, 0.67; 99% CI, 0.635-0.708) relative to low volume. Hospital volume was not significantly associated to readmissions. The most common causes of readmission included wound- (2682 patients, 18%), respiratory- (1776 patients, 12%), cardiovascular- (1210 patients, 8%), and volume- (1089 patients, 7%) related disorders., Conclusions and Relevance: This study evaluated the combined effects of patient-, surgeon-, and hospital-level factors on 30-day readmission after otolaryngology surgery. Socioeconomic factors, patient comorbidities, surgeon volumes, and procedure were significantly associated with 30-day readmission. Though the cause of 30-day readmission is multifactorial, a large portion is driven by socioeconomic factors. Addressing these disparities at the system level is necessary to address the described readmission disparities. The development of risk-stratification models based on patient-, procedure-, and surgeon-level factors may help facilitate resource distribution.