Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed sepsis prediction model.
Sepsis remains a leading cause of morbidity and mortality among intensive care unit (ICU) patients. For each hour treatment initiation is delayed after diagnosis, sepsis-related mortality increases by approximately eight percent. Therefore, maximizing effective care requires early recognition and initiation of treatment protocols. Antecedent signs and symptoms of sepsis can be subtle and unrecognizable (e.g., loss of autonomic regulation of vital signs), causing treatment delays and harm to the patient. In this work we investigated the utility of high-resolution blood pressure (BP) and heart rate (HR) times series dynamics for the early prediction of sepsis in patients from an urban, academic hospital, meeting the third international consensus definition of sepsis (sepsis-III) during their ICU admission. Using a multivariate modeling approach we found that HR and BP dynamics at multiple time-scales are independent predictors of sepsis, even after adjusting for commonly measured clinical values and patient demographics and comorbidities. Earlier recognition and diagnosis of sepsis has the potential to decrease sepsis-related morbidity and mortality through earlier initiation of treatment protocols.
Medication dosing in a critical care environment is a complex task that involves close monitoring of relevant physiologic and laboratory biomarkers and corresponding sequential adjustment of the prescribed dose. Misdosing of medications with narrow therapeutic windows (such as intravenous [IV] heparin) can result in preventable adverse events, decrease quality of care and increase cost. Therefore, a robust recommendation system can help clinicians by providing individualized dosing suggestions or corrections to existing protocols. We present a clinician-in-the-loop framework for adjusting IV heparin dose using deep reinforcement learning (RL). Our main objectives were to learn a new IV heparin dosing policy based on the multi-dimensional features of patients, and evaluate the effectiveness of the learned policy in the presence of other confounding factors that may contribute to heparin-related side effects. The data used in the experiments included 2598 intensive care patients from the publicly available MIMIC database and 2310 patients from the Emory University clinical data warehouse. Experimental results suggested that the distance from RL policy had a statistically significant association with anticoagulant complications (p < 0.05), after adjusting for the effects of confounding factors.
Residents gain a large operative experience on ACS. An ACS model is viable in training, provides valuable operative experience, and should not be considered a drain on resident effort. Valuable ACS rotation experiences as a resident may encourage graduates to pursue ACS as a career.
Background Light and moderate drinkers respond differently to the effects of abused drugs, including stimulants such as amphetamine. The purpose of this study was to determine whether light and moderate drinkers differ in their sensitivity to the reinforcing and subjective effects of d-amphetamine. We hypothesized that moderate drinkers (i.e., participants that reported consuming at least seven alcohol-containing beverages per week) would be more sensitive to the reinforcing and positive subject-rated effects of d-amphetamine than light drinkers. Methods Data from four studies that employed similar d-amphetamine self-administration procedures and subject-rated drug-effect measures were included in the analysis. Light (N=17) and moderate (N=16) drinkers sampled placebo, low (8-10 mg) and high (16-20 mg) doses of oral d-amphetamine administered in eight (8) capsules. Following sampling sessions, participants worked for a maximum of eight capsules, each containing 12.5% of the previously sampled dose, on a modified progressive-ratio schedule of reinforcement. Results Both active doses of d-amphetamine functioned as a reinforcer in the moderate drinkers while only the high dose did so in the light drinkers. The moderate drinkers worked for significantly more capsules that contained the high dose of d-amphetamine than did the light drinkers. d-Amphetamine produced prototypical stimulant-like subjective effects (e.g. dose-dependent increases in ratings of Good Effects; Like Drug and Willing to Take Again). Moderate drinkers reported significantly greater subjective effects than the light drinkers. Conclusion These results are consistent with those from previous laboratory experiments and suggest that moderate alcohol consumption may increase vulnerability to the abuse-related effects of stimulants.
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