Traumatic brain injury (TBI) remains a major public health problem globally. Presently, there is no way to restore cognitive deficits caused by TBI. In this study, we seek to evaluate the effect of statins (simvastatin and atorvastatin) on the spatial learning and neurogenesis in rats subjected to controlled cortical impact. Rats were treated with atorvastatin and simvastatin 1 day after TBI and daily for 14 days. Morris water maze tests were performed during weeks 2 and 5 after TBI. Bromodeoxyuridine (BrdU; 50 mg/kg) was intraperitoneally injected 1 day after TBI and daily for 14 days. Brain tissue was processed for immunohistochemical staining to identify newly generated cells and vessels. Our data show that (1) treatment of TBI with statins improves spatial learning on days 31-35 after onset of TBI; (2) in the non-neurogenic region of the hippocampal CA3 region, statin treatment reduces the neuronal loss after TBI, demonstrating the neuroprotective effect of statins; (3) in the neurogenic region of the dentate gyrus, treatment of TBI with statins enhances neurogenesis; (4) statin treatment augments TBI-induced angiogenesis; and (5) treatment with simvastatin at the same dose provides a therapeutic effect superior to treatment with atorvastatin. These results suggest that statins may be candidates for treatment of TBI.

Key Points Question Can machine learning models predict patient risks of postoperative complications related to pneumonia, acute kidney injury, deep vein thrombosis, delirium, and pulmonary embolism? Findings In a cohort study of 111 888 operations at a large academic medical center, machine learning algorithms exhibited high areas under the receiver operating characteristic curve for predicting the risk of postoperative complications related to pneumonia, acute kidney injury, deep vein thrombosis, pulmonary embolism, and delirium. Meaning These findings suggest that machine learning models using preoperative and intraoperative data can predict postoperative complications and generate reliable and clinically meaningful interpretations for supporting clinical decisions along the perioperative care continuum.

BACKGROUND With limited numbers of intensive care unit (ICU) beds available, increasing patient acuity is expected to contribute to episodes of inpatient deterioration on general wards. OBJECTIVE To prospectively validate a predictive algorithm for clinical deterioration in general–medical ward patients, and to conduct a trial of real‐time alerts based on this algorithm. DESIGN Randomized, controlled crossover study. SETTING/PATIENTS Academic center with patients hospitalized on 8 general wards between July 2007 and December 2011. INTERVENTIONS Real‐time alerts were generated by an algorithm designed to predict the need for ICU transfer using electronically available data. The alerts were sent by text page to the nurse manager on intervention wards. MEASUREMENTS Intensive care unit transfer, hospital mortality, and hospital length of stay. RESULTS Patients meeting the alert threshold were at nearly 5.3‐fold greater risk of ICU transfer (95% confidence interval [CI]: 4.6‐6.0) than those not satisfying the alert threshold (358 of 2353 [15.2%] vs 512 of 17678 [2.9%]). Patients with alerts were at 8.9‐fold greater risk of death (95% CI: 7.4‐10.7) than those without alerts (244 of 2353 [10.4%] vs 206 of 17678 [1.2%]). Among patients identified by the early warning system, there were no differences in the proportion of patients who were transferred to the ICU or who died in the intervention group as compared with the control group. CONCLUSIONS Real‐time alerts were highly specific for clinical deterioration resulting in ICU transfer and death, and were associated with longer hospital length of stay. However, an intervention notifying a nurse of the risk did not result in improvement in these outcomes. Journal of Hospital Medicine 2013;8:236–242. © 2013 Society of Hospital Medicine

Background Episodes of patient deterioration on hospital units are expected to increasingly contribute to morbidity and healthcare costs. Objective To determine if real-time alerts sent to the rapid response team (RRT) improved patient care. Design Randomized, controlled trial. Setting Eight medicine units (Barnes-Jewish Hospital). Patients 571 patients. Intervention Real-time alerts generated by a validated deterioration algorithm were sent real-time to the RRT (intervention) or hidden (control). Measurements ICU transfer, hospital mortality, hospital duration. Results ICU transfer (17.8% versus 18.2%; odds ratio, 0.972; 95% CI, 0.635–1.490) and hospital mortality (7.3% versus 7.7%; odds ratio, 0.947; 95% CI, 0.509–1.764) were similar for the intervention and control groups. The number of patients requiring transfer to a nursing home or long-term acute care hospital was similar for patients in the intervention and control groups (26.9% versus 26.3%; odds ratio, 1.032; 95% CI, 0.712–1.495). Hospital duration [8.4 ± 9.5 days versus 9.4 ± 11.1 days; P = 0.038] was statistically shorter for the intervention group. The number of RRT calls initiated by the primary care team was similar for the intervention and control groups (19.9% versus 16.5%; odds ratio, 1.260; 95% CI, 0.823–1.931). Conclusions Real-time alerts sent to the RRT did not reduce ICU transfers, hospital mortality, or the need for subsequent long term care, however hospital length of stay was modestly reduced.

These data demonstrate that at the doses used, EPO and CEPO are equally effective in enhancing spatial learning and promoting neural plasticity after TBI.