Fluctuations of consciousness after stroke: Associations with the confusion assessment method for the intensive care unit (CAM-ICU) and potential undetected delirium.

PURPOSE: To examine associations between fluctuating consciousness and Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) assessments in stroke patients compared to non-neurological patients. MATERIALS AND METHODS: We linked all recorded CAM-ICU assessments with corresponding Richmond Agitation Sedation Scale (RASS) measurements in patients with stroke or sepsis from a single-center ICU database. Fluctuating consciousness was defined by RASS variability using standard deviations (SD) over 24-h periods; regression analyses were performed to determine associations with RASS variability and CAM-ICU rating. RESULTS: We identified 16,509 paired daily summaries of CAM-ICU and RASS measurements in 546 stroke patients and 1586 sepsis patients. Stroke patients had higher odds of positive (OR 4.2, 95% CI 3.3-5.5) and "unable to assess" (UTA; OR 5.2, 95% CI 4.0-6.8) CAM-ICU ratings compared to sepsis patients, and CAM-ICU-positive and UTA assessment-days had higher RASS variability than CAM-ICU-negative assessment-days, especially in stroke patients. Based on model-implied associations of RASS variability (OR 2.0 per semi-IQR-difference in RASS-SD, 95% CI 1.7-2.2) and stroke diagnosis (OR 2.7, 95% CI 2.0-3.7) with CAM-ICU-positive assessments, over one-third of probable delirium cases among stroke patients were potentially missed by the CAM-ICU. CONCLUSIONS: Post-stroke delirium may frequently go undetected by the CAM-ICU, even in the setting of fluctuating consciousness.

Outcomes of protocolised analgesia and sedation in a neurocritical care unit.

OBJECTIVES: Providing analgesia and sedation while allowing for neurological assessment is important in the neurocritical care unit (NCCU), yet data are limited about the effects of protocolised analgesia and sedation. We developed an analgesia-based sedation protocol and evaluated its effect on medication utilisation and costs in the NCCU. METHODS: We conducted a retrospective cohort study of patients who are mechanically ventilated and admitted to a 12-bed NCCU over four years. To compare outcomes, we used gamma and negative binomial regression models, and interrupted time-series sensitivity analyses. RESULTS: The study cohort consisted of 1197 patients: 576 pre-protocol and 621 post-protocol. The protocol resulted in an increase in fentanyl use [incidence rate ratio (IRR) = 2.8, (95% confidence limits (CLs) 1.9, 4.2)] and a decrease in propofol use (IRR = 0.8, CLs 0.6, 1.0). There was a decrease in fentanyl (cost ratio = 0.8, CLs 0.5, 1.1) and propofol costs (cost ratio = 0.6, CLs 0.5, 0.8). The sensitivity analyses results were similar. There was no effect on healthcare utilisation, healthcare costs, and in-hospital mortality. CONCLUSION: Protocolised analgesia and sedation increased analgesia use, decreased sedative use, and reduced medication-associated costs in the NCCU. Our results suggest that similar NCCUs should consider use of population-specific protocols to manage analgesia and sedation.

Safety and Costs of Stroke Unit Admission for Select Acute Intracerebral Hemorrhage Patients.

BACKGROUND AND PURPOSE: There are limited data to guide intensive care unit (ICU) versus dedicated stroke unit (SU) admission for intracerebral hemorrhage (ICH) patients. We hypothesized select patients can be safely cared for in SU versus ICU at lower costs. METHODS: We conducted a retrospective cohort study of consecutive patients with predefined minor ICH (≤20 cm3, supratentorial, no coagulopathy) receiving care in either an ICU or an SU. Multiple linear regression and inverse probability weighting were used to adjust for differences in patient characteristics and nonrandom ICU versus SU assignment. The primary outcome was poor functional status at discharge (modified Rankin score [mRS] ≥3). Secondary outcomes included complications, discharge disposition, hospital length of stay, and direct inpatient costs. RESULTS: The study population included 104 patients (41 admitted to the ICU and 63 admitted to the SU). After controlling for differences in baseline characteristics, there were no differences in poor functional outcome at discharge (93% vs 85%, P = .26) or in mean mRS (2.9 vs 3.0, P = .73). Similarly, there were no differences in the rates of complications (6% vs 10%, P = .44), discharged dead or to a skilled nursing facility (8% vs 13%, P = .59), or direct patient costs (US$7100 vs US$6200, P = .33). Median length of stay was significantly longer in the ICU group (5 vs 4 days, P = .01). CONCLUSIONS: This study revealed a shorter length of stay but no large differences in functional outcome, safety, or cost among patients with minor ICH admitted to a dedicated SU compared to an ICU.

Post-Acute Care Data for Predicting Readmission After Ischemic Stroke: A Nationwide Cohort Analysis Using the Minimum Data Set.

BACKGROUND: Reducing hospital readmissions is a key component of reforms for stroke care. Current readmission prediction models lack accuracy and are limited by data being from only acute hospitalizations. We hypothesized that patient-level factors from a nationwide post-acute care database would improve prediction modeling. METHODS AND RESULTS: Medicare inpatient claims for the year 2008 that used International Classification of Diseases, Ninth Revision codes were used to identify ischemic stroke patients older than age 65. Unique individuals were linked to comprehensive post-acute care assessments through use of the Minimum Data Set (MDS). Logistic regression was used to construct risk-adjusted readmission models. Covariates were derived from MDS variables. Among 39 178 patients directly admitted to nursing homes after hospitalization due to acute stroke, there were 29 338 (75%) with complete MDS assessments. Crude rates of readmission and death at 30 days were 8448 (21%) and 2791 (7%), respectively. Risk-adjusted models identified multiple independent predictors of all-cause 30-day readmission. Model performance of the readmission model using MDS data had a c-statistic of 0.65 (95% CI 0.64 to 0.66). Higher levels of social engagement, a marker of nursing home quality, were associated with progressively lower odds of readmission (odds ratio 0.71, 95% CI 0.55 to 0.92). CONCLUSIONS: Individual clinical characteristics from the post-acute care setting resulted in only modest improvement in the c-statistic relative to previous models that used only Medicare Part A data. Individual-level characteristics do not sufficiently account for the risk of acute hospital readmission.