Objective The management of mild traumatic brain injury (mTBI) with minor radiographic findings traditionally involves hospital admission for monitoring, although this practice is expensive with unclear benefit. We implemented a protocol to manage these patients in our emergency department observation unit (EDOU), hypothesizing that this pathway was cost effective and not associated with any difference in clinical outcome. Methods mTBI patients with minor radiographic findings were managed under the EDOU protocol over a 3‐year period from May 1, 2015 to April 30, 2018 (inclusions: ≥19 years old, isolated acute head trauma, normal neurological exam [except transient alteration in consciousness], and a computed tomography [CT] scan of the head with at least 1 of the following: cerebral contusions <1 cm in maximum extent, convexity subarachnoid hemorrhage, or closed, non‐displaced skull fractures). These patients were retrospectively analyzed; clinical outcomes and charges were compared to a control cohort of matched mTBI hospital admissions over the preceding 3 years. Results Sixty patients were observed in the EDOU over the 3‐year period, and 85 patients were identified for the control cohort. There were no differences in rate of radiographic progression, neurological exam change, or surgical intervention, and the overall incidence of hemorrhagic expansion was low in both groups. The EDOU group had a significantly faster time to interval CT scan (Mean Difference (MD) 3.92 hours, [95%CI 1.65, 6.19]), P = 0.001), shorter length of stay (MD 0.59 days [95% CI 0.29, 0.89], P = 0.001), and lower encounter charges (MD $3428.51 [95%CI 925.60, 5931.42], P = 0.008). There were no differences in 30‐day re‐admission, 30‐day mortality, or delayed chronic subdural formation, although there was a high rate of loss to follow‐up in both groups. Conclusions Compared to hospital admission, observing mTBI patients with minor radiographic findings in the EDOU was associated with significantly shorter time to interval scanning, shorter length of stay, and lower encounter charges, but no difference in observed clinical outcome. The overall risk of hemorrhagic progression in this subset of mTBI was very low. Using this approach can reduce unnecessary admissions while potentially yielding patient care and economic benefits. When designing a protocol, close attention should be given to clear inclusion criteria and a formal mechanism for patient follow‐up.
Compliance with the CMS SEP-1 sepsis bundle is a nationally reported metric and will impact hospital reimbursements. Performance overall is poor with some reporting compliance at 33%. While the translational pathway for prognostic models has been well characterized, few machine learning models have been externally validated. This study aims to be the first deep learning model that is integrated into an operational workflow to successfully detect sepsis in real-time. The primary outcome is SEP-1 bundle compliance and secondary outcomes include inpatient mortality, hospital and emergency department (ED) length of stay, as well as process measures such as time from ED arrival identification of "time zero" (as defined by SEP-1) and time to bundle completion.Methods: March, 2016, an interdisciplinary team consisting of clinicians, data scientists and machine learning experts at a large academic medical center embarked on an innovation pilot to develop a novel machine learning model to detect sepsis, named Sepsis Watch (SW). A computable sepsis definition and deep learning model were developed using a curated dataset capturing over 43,000 inpatient admissions between 10/1/2014 -12/31/2015. Ten sepsis definitions were compared and clinicians agreed on the following: >¼ 2 SIRS criteria, blood culture order, and end organ damage. A deep learning model was built to predict which patients would meet that phenotype of sepsis. The model incorporates 121 clinical variables that are continually collected and assessed in real time on every patient that presents to the ED and the first 6 hours of inpatient admission if applicable. Patient status is updated as being "low risk," "medium risk," "high risk," or "septic." If Sepsis Watch predicts a patient to be "High risk" or "septic" that information is communicated to the ED team. Data collected for operational feedback includes total volume of patients who are deemed "High Risk" or "septic", time of day when the model made these predictions, compliance with antibiotic administration, collection of repeat lactate and obtaining cultures all within 3 hours both individually and as a bundle.Results: Sepsis Watch operated in the pilot phase 11/06/18 -05/05/19 and analyzed over 39,000 ED patients for their risk of sepsis. It predicted that 1,318 patients were "high risk." Of those, 948 progressed to "septic" either in the ED or within 6 hours of admission. Bundle compliance within 3 hours was tracked. For those who were "High Risk" but never "septic," 77% had antibiotics administered, 75% had a repeat lactate collected and 72% had cultures collected in 3 hours. For those deemed "septic", 79% had antibiotics administered within 3 hours, 90% had a repeat lactate collected in 3 hours and 88% had cultures collected in 3 hours. Overall bundle compliance was 55.7% for "High Risk" patients and 67.1% for "Septic" patients.Conclusions: Sepsis Watch is the first deep learning model deployed to successfully detect sepsis in real time with operational workflows developed provide support directly to...
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