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Background: In medical oncology settings, early specialist palliative care interventions have demonstrated improvements in patient quality of life and survival compared with usual oncologic care. However, the effect of early specialist palliative care interventions in surgical oncology settings is not well studied., Methods: The Surgery for Cancer with Option for Palliative Care Expert (SCOPE) Trial is a single-center, prospective, single-blind, randomized controlled trial of a specialist palliative care intervention for cancer patients undergoing non-palliative surgery. It will enroll 236 patients scheduled for major abdominal operations for malignancy, who will be randomized 1:1 at enrollment to receive usual care (control arm) or specialist palliative care consultation (intervention arm). Intervention arm patients will receive consultations from a palliative care specialist (physician or nurse practitioner) preoperatively and postoperatively. The primary outcome is physical and functional wellbeing at 90 days postoperatively. Secondary outcomes are quality of life at 90 days postoperatively, posttraumatic stress disorder symptoms at 180 days postoperatively, days alive at home without an emergency room visit in the first 90 postoperative days, and overall survival at 1 year postoperatively. Participants will be followed for 3 years after surgery for exploratory analyses of their ongoing quality of life, healthcare utilization, and mortality., Discussion: SCOPE is an ongoing randomized controlled trial evaluating specialist palliative care interventions for cancer patients undergoing non-palliative oncologic surgery. Findings from the study will inform ways to identify and improve care of surgical patients who will likely benefit from specialist palliative care services., Trial Registration: ClinicalTrials.gov Identifier: NCT03436290 First Registered: 16 February 2018 Enrollment Began: 1 March 2018 Last Update: 20 December 2018.

Background: The goal of this study was to develop and validate a dynamic risk model to predict daily changes in acute brain dysfunction (ie, delirium and coma), discharge, and mortality in ICU patients., Methods: Using data from a multicenter prospective ICU cohort, a daily acute brain dysfunction-prediction model (ABD-pm) was developed by using multinomial logistic regression that estimated 15 transition probabilities (from one of three brain function states [normal, delirious, or comatose] to one of five possible outcomes [normal, delirious, comatose, ICU discharge, or died]) using baseline and daily risk factors. Model discrimination was assessed by using predictive characteristics such as negative predictive value (NPV). Calibration was assessed by plotting empirical vs model-estimated probabilities. Internal validation was performed by using a bootstrap procedure., Results: Data were analyzed from 810 patients (6,711 daily transitions). The ABD-pm included individual risk factors: mental status, age, preexisting cognitive impairment, baseline and daily severity of illness, and daily administration of sedatives. The model yielded very high NPVs for "next day" delirium (NPV: 0.823), coma (NPV: 0.892), normal cognitive state (NPV: 0.875), ICU discharge (NPV: 0.905), and mortality (NPV: 0.981). The model demonstrated outstanding calibration when predicting the total number of patients expected to be in any given state across predicted risk., Conclusions: We developed and internally validated a dynamic risk model that predicts the daily risk for one of three cognitive states, ICU discharge, or mortality. The ABD-pm may be useful for predicting the proportion of patients for each outcome state across entire ICU populations to guide quality, safety, and care delivery activities., (Copyright © 2018 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

We examined population-based surveillance data from the Tennessee Emerging Infections Program to determine whether neighborhood socioeconomic status was associated with influenza hospitalization rates. Hospitalization data collected during October 2007-April 2014 were geocoded (N = 1,743) and linked to neighborhood socioeconomic data. We calculated age-standardized annual incidence rates, relative index of inequality, and concentration curves for socioeconomic variables. Influenza hospitalizations increased with increased percentages of persons who lived in poverty, had female-headed households, lived in crowded households, and lived in population-dense areas. Influenza hospitalizations decreased with increased percentages of persons who were college educated, were employed, and had health insurance. Higher incidence of influenza hospitalization was also associated with lower neighborhood socioeconomic status when data were stratified by race.

Purpose: To quantify the prevalence of prescribed opioid analgesics among pregnant women enrolled in Tennessee Medicaid from 1995 to 2009., Methods: Retrospective cohort study of 277,555 pregnancies identified from birth and fetal death certificates, and linked to previously validated, computerized pharmacy records. Poisson regression was used to estimate trends over time, rate ratios, and 95% confidence intervals (CI)., Results: During the study period, 29% of pregnant women filled a prescription for an opioid analgesic. From 1995 to 2009, any pregnancy-related use increased 1.90-fold (95% CI, 1.83-1.98), first trimester use increased 2.27-fold (95% CI, 2.14-2.41), and second or third trimester use increased 2.02-fold (95% CI, 1.93-2.12), after adjusting for maternal characteristics. Any pregnancy-related, first trimester, and second or third trimester use were each more likely among mothers who were at least 21 years old, white, non-Hispanic, prima gravid, resided in nonurban areas, enrolled in Medicaid owing to disability, and who had less than a high school education., Conclusions: Opioid analgesic use by Tennessee Medicaid-insured pregnant women increased nearly 2-fold from 1995 to 2009. Additional study is warranted to understand the implications of this increased use., (Copyright © 2013 Elsevier Inc. All rights reserved.)