Whole-Body PET Imaging in Humans Shows That 11C-PS13 Is Selective for Cyclooxygenase-1 and Can Measure the In Vivo Potency of Nonsteroidal Antiinflammatory Drugs.

Both cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2) convert arachidonic acid to prostaglandin H2, which has proinflammatory effects. The recently developed PET radioligand 11C-PS13 has excellent in vivo selectivity for COX-1 over COX-2 in nonhuman primates. This study sought to evaluate the selectivity of 11C-PS13 binding to COX-1 in humans and assess the utility of 11C-PS13 to measure the in vivo potency of nonsteroidal antiinflammatory drugs. Methods: Baseline 11C-PS13 whole-body PET scans were obtained for 26 healthy volunteers, followed by blocked scans with ketoprofen (n = 8), celecoxib (n = 8), or aspirin (n = 8). Ketoprofen is a highly potent and selective COX-1 inhibitor, celecoxib is a preferential COX-2 inhibitor, and aspirin is a selective COX-1 inhibitor with a distinct mechanism that irreversibly inhibits substrate binding. Because blood cells, including platelets and white blood cells, also contain COX-1, 11C-PS13 uptake inhibition from blood cells was measured in vitro and ex vivo (i.e., using blood obtained during PET scanning). Results: High 11C-PS13 uptake was observed in major organs with high COX-1 density, including the spleen, lungs, kidneys, and gastrointestinal tract. Ketoprofen (1-75 mg orally) blocked uptake in these organs far more effectively than did celecoxib (100-400 mg orally). On the basis of the plasma concentration to inhibit 50% of the maximum radioligand binding in the spleen (in vivo IC 50), ketoprofen (<0.24 µM) was more than 10-fold more potent than celecoxib (>2.5 µM) as a COX-1 inhibitor, consistent with the in vitro potencies of these drugs for inhibiting COX-1. Blockade of 11C-PS13 uptake from blood cells acquired during the PET scans mirrored that in organs of the body. Aspirin (972-1,950 mg orally) blocked such a small percentage of uptake that its in vivo IC 50 could not be determined. Conclusion: 11C-PS13 selectively binds to COX-1 in humans and can measure the in vivo potency of nonsteroidal antiinflammatory drugs that competitively inhibit arachidonic acid binding to COX-1. These in vivo studies, which reflect the net effect of drug absorption and metabolism in all organs of the body, demonstrated that ketoprofen had unexpectedly high potency, that celecoxib substantially inhibited COX-1, and that aspirin acetylation of COX-1 did not block binding of the representative nonsteroidal inhibitor 11C-PS13.

Effect of STN/GPi DBS on swallowing function in Parkinson's disease as assessed by Video fluoroscopy: A retrospective study.

INTRODUCTION: The goal of the study is to objectively assess changes in swallowing (using "gold standard" video fluoroscopy (VFS)) following Deep Brain Stimulation (DBS) surgery in Parkinson's disease (PD) patients. There are few studies on the effect of DBS on swallowing in PD. We use VFS to assess swallowing function pre- and post-DBS. METHODS: Our study participants underwent pre- and post-DBS VFS (6 months later) in the practically defined on state. We converted VFS reports into an objective numerical scale. Higher scores denote more severe dysphagia. We used non-parametric test (Wilcoxon signed rank test) to test if the difference between pre- and post-DBS swallow score is significantly different from 0. RESULTS: Fifty-four PD patients completed pre- and post-DBS evaluations. Twenty-five patients had bilateral GPi DBS (46.3%) and 29 had bilateral STN DBS (53.7%). The mean (SD) post-DBS swallow score is 1.9 (2.0) and pre-DBS swallow score is 1.6 (1.3). The difference is not significantly different from 0 (p = 0.16). In our study, swallow scores for majority of the patients (39 out of 54) did not change after DBS regardless of lead location. Six (11.1%) PD patients had post-DBS swallow score decrease on average by 1 (SD: 0) points. 9 (16.7%) patients had post-DBS swallow score increase on average by 2.7 (SD: 2.3) points. CONCLUSION: There was no statistically significant change in the swallow scores pre-and 6 months post-DBS with VFS when assessed in the practically defined on state, regardless of the site of bilateral lead implantation. Hence, we believe that DBS does not improve or reduce swallow function in a clinically meaningful way in PD.

High Long-Term Mortality Rate in Elderly Patients with Mild Traumatic Brain Injury and Subdural Hematoma due to Ground-Level Fall: Neurosurgery's Hip Fracture?

BACKGROUND: Moderate-to-severe traumatic brain injury (TBI) is a major source of morbidity and mortality in elderly patients. Little is known about long-term mortality in elderly patients following mild, nonfatal TBI and how the injury mechanism predicts survival. This study aimed to compare long-term mortality in elderly patients with mild TBI and traumatic subdural hematoma (tSDH) due to ground-level fall (GLF) versus those with TBI and tSDH due to another cause (i.e., non-ground-level fall [nGLF]). METHODS: This retrospective study comprised 288 patients ≥60 years old from a single Level I trauma center with tSDH and Glasgow Coma Scale scores 13-15. RESULTS: Median follow-up after initial TBI presentation was 2.9 years for the GLF group and 2.4 years for the nGLF group. During follow-up, 98 patients died, and median survival for all elderly patients with mild TBI and tSDH was 4.6 years. The GLF group had a higher mortality rate than the nGLF group, with 93 patients in GLF group dying during follow-up compared with 5 in nGLF group (P < 0.0001). The annual death rate for patients in the GLF group was 12.5% per year. For patients 60-69 years old, 39% in GLF group died compared with 4% in nGLF group during follow-up (P = 0.0002). Likewise, for patients 70-79 years old, 29% in GLF group died compared with 7% in nGLF group (P = 0.021). Finally, 56% of patients >80 years old in GLF group compared with 18% in nGLF group (P = 0.11). CONCLUSIONS: Elderly patients with mild TBI and tSDH due to GLF have significantly higher long-term mortality than patients with injuries due to nGLF.

Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis.

Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine learning-based early warning systems may reduce the time to recognition, but few systems have undergone clinical evaluation. In this prospective, multi-site cohort study, we examined the association between patient outcomes and provider interaction with a deployed sepsis alert system called the Targeted Real-time Early Warning System (TREWS). During the study, 590,736 patients were monitored by TREWS across five hospitals. We focused our analysis on 6,877 patients with sepsis who were identified by the alert before initiation of antibiotic therapy. Adjusting for patient presentation and severity, patients in this group whose alert was confirmed by a provider within 3 h of the alert had a reduced in-hospital mortality rate (3.3%, confidence interval (CI) 1.7, 5.1%, adjusted absolute reduction, and 18.7%, CI 9.4, 27.0%, adjusted relative reduction), organ failure and length of stay compared with patients whose alert was not confirmed by a provider within 3 h. Improvements in mortality rate (4.5%, CI 0.8, 8.3%, adjusted absolute reduction) and organ failure were larger among those patients who were additionally flagged as high risk. Our findings indicate that early warning systems have the potential to identify sepsis patients early and improve patient outcomes and that sepsis patients who would benefit the most from early treatment can be identified and prioritized at the time of the alert.

Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing.

Machine learning-based clinical decision support tools for sepsis create opportunities to identify at-risk patients and initiate treatments at early time points, which is critical for improving sepsis outcomes. In view of the increasing use of such systems, better understanding of how they are adopted and used by healthcare providers is needed. Here, we analyzed provider interactions with a sepsis early detection tool (Targeted Real-time Early Warning System), which was deployed at five hospitals over a 2-year period. Among 9,805 retrospectively identified sepsis cases, the early detection tool achieved high sensitivity (82% of sepsis cases were identified) and a high rate of adoption: 89% of all alerts by the system were evaluated by a physician or advanced practice provider and 38% of evaluated alerts were confirmed by a provider. Adjusting for patient presentation and severity, patients with sepsis whose alert was confirmed by a provider within 3 h had a 1.85-h (95% CI 1.66-2.00) reduction in median time to first antibiotic order compared to patients with sepsis whose alert was either dismissed, confirmed more than 3 h after the alert or never addressed in the system. Finally, we found that emergency department providers and providers who had previous interactions with an alert were more likely to interact with alerts, as well as to confirm alerts on retrospectively identified patients with sepsis. Beyond efforts to improve the performance of early warning systems, efforts to improve adoption are essential to their clinical impact and should focus on understanding providers' knowledge of, experience with and attitudes toward such systems.

Human-machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system.

While a growing number of machine learning (ML) systems have been deployed in clinical settings with the promise of improving patient care, many have struggled to gain adoption and realize this promise. Based on a qualitative analysis of coded interviews with clinicians who use an ML-based system for sepsis, we found that, rather than viewing the system as a surrogate for their clinical judgment, clinicians perceived themselves as partnering with the technology. Our findings suggest that, even without a deep understanding of machine learning, clinicians can build trust with an ML system through experience, expert endorsement and validation, and systems designed to accommodate clinicians' autonomy and support them across their entire workflow.

Timing of Restarting Anticoagulation and Antiplatelet Therapies After Traumatic Subdural Hematoma-A Single Institution Experience.

BACKGROUND: There is a paucity of information regarding the optimal timing of restarting antiplatelet therapy (APT) and anticoagulation therapy (ACT) after traumatic subdural hematoma (tSDH). Therefore, we sought to report our experience at a single level 1 trauma center with regard to restarting APT and/or ACT after tSDH. METHODS: A total of 456 consecutive records were reviewed for unplanned hematoma evacuation within 90 days of discharge and thrombotic/thromboembolic events before restarting APT and/or ACT. RESULTS: There was no difference in unplanned hematoma evacuation rate in patients not receiving APT or ACT (control) compared with those necessitating APT and/or ACT (6.4% control, 6.9% APT alone, 5.8% ACT alone, 5.4% APT and ACT). There was an increase in post-tSDH thrombosis/thromboembolism in patients needing to restart ACT (1.9% APT alone, P = 0.53 vs. control; 5.8% ACT alone, P = 0.04 vs. control; 16% APT and ACT; P < 0.001 vs. control). Subgroup analysis revealed that patients with coronary artery disease necessitating APT and patients with atrial fibrillation necessitating ACT had higher thrombosis/thromboembolism rates compared with controls (1.0% control vs. 6.1% coronary artery disease, P = 0.02; 1.0% control vs. 10.1% atrial fibrillation, P < 0.001). The median restart time of ACT was approximately 1 month after trauma; APT was restarted 2-4 weeks after trauma depending on clinical indication. CONCLUSIONS: Patients requiring reinitiation of APT and/or ACT after tSDH were at elevated risk of thrombotic/thromboembolic events but not unplanned hematoma evacuation. Therefore, patients should be followed closely until APT and/or ACT are restarted, and consideration for earlier reinitiation of blood thinners should be given on a case-by-case basis.

First-in-human evaluation of [11C]PS13, a novel PET radioligand, to quantify cyclooxygenase-1 in the brain.

PURPOSE: This study assessed whether the newly developed PET radioligand [11C]PS13, which has shown excellent in vivo selectivity in previous animal studies, could be used to quantify constitutive levels of cyclooxygenase-1 (COX-1) in healthy human brain. METHODS: Brain test-retest scans with concurrent arterial blood samples were obtained in 10 healthy individuals. The one- and unconstrained two-tissue compartment models, as well as the Logan graphical analysis were compared, and test-retest reliability and time-stability of total distribution volume (VT) were assessed. Correlation analyses were conducted between brain regional VT and COX-1 transcript levels provided in the Allen Human Brain Atlas. RESULTS: In the brain, [11C]PS13 showed highest uptake in the hippocampus and occipital cortex. The pericentral cortex also showed relatively higher uptake compared with adjacent neocortices. The two-tissue compartment model showed the best fit in all the brain regions, and the results from the Logan graphical analysis were consistent with those from the two-tissue compartment model. VT values showed excellent test-retest variability (range 6.0-8.5%) and good reliability (intraclass correlation coefficient range 0.74-0.87). VT values also showed excellent time-stability in all brain regions, confirming that there was no radiometabolite accumulation and that shorter scans were still able to reliably measure VT. Significant correlation was observed between VT and COX-1 transcript levels (r = 0.82, P = 0.007), indicating that [11C]PS13 binding reflects actual COX-1 density in the human brain. CONCLUSIONS: These results from the first-in-human evaluation of the ability of [11C]PS13 to image COX-1 in the brain justifies extending the study to disease populations with neuroinflammation. CLINICAL TRIAL REGISTRATION: NCT03324646 at https://clinicaltrials.gov/ . Registered October 30, 2017. Retrospectively registered.

Building a database for brain 18 kDa translocator protein imaged using [11C]PBR28 in healthy subjects.

Translocator protein 18 kDa (TSPO) has been widely imaged as a marker of neuroinflammation using several radioligands, including [11C]PBR28. In order to study the effects of age, sex, and obesity on TSPO binding and to determine whether this binding can be accurately assessed using fewer radio high-performance liquid chromatography (radio-HPLC) measurements of arterial blood samples, we created a database of 48 healthy subjects who had undergone [11C]PBR28 scans (23 high-affinity binders (HABs) and 25 mixed-affinity binders (MABs), 20 F/28 M, age: 40.6 ± 16.8 years). After analysis by Logan plot using 23 metabolite-corrected arterial samples, total distribution volume ( VT) was found to be 1.2-fold higher in HABs across all brain regions. Additionally, the polymorphism plot estimated nondisplaceable uptake ( VND) as 1.40 mL · cm-3, which generated a specific-to-nondisplaceable ratio ( BPND) of 1.6 ± 0.6 in HABs and 1.1 ± 0.6 in MABs. VT increased significantly with age in nearly all regions and was well estimated with radio-HPLC measurements from six arterial samples. However, VT did not correlate with body mass index and was not affected by sex. These results underscore which patient characteristics should be accounted for during [11C]PBR28 studies and suggest ways to perform such studies more easily and with fewer blood samples.

Comparison of Automated Sepsis Identification Methods and Electronic Health Record-based Sepsis Phenotyping: Improving Case Identification Accuracy by Accounting for Confounding Comorbid Conditions.

To develop and evaluate a novel strategy that automates the retrospective identification of sepsis using electronic health record data. DESIGN: Retrospective cohort study of emergency department and in-hospital patient encounters from 2014 to 2018. SETTING: One community and two academic hospitals in Maryland. PATIENTS: All patients 18 years old or older presenting to the emergency department or admitted to any acute inpatient medical or surgical unit including patients discharged from the emergency department. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: From the electronic health record, 233,252 emergency department and inpatient encounters were identified. Patient data were used to develop and validate electronic health record-based sepsis phenotyping, an adaptation of "the Centers for Disease Control Adult Sepsis Event toolkit" that accounts for comorbid conditions when identifying sepsis patients. The performance of this novel system was then compared with 1) physician case review and 2) three other commonly used strategies using metrics of sensitivity and precision relative to sepsis billing codes, termed "billing code sensitivity" and "billing code predictive value." Physician review of electronic health record-based sepsis phenotyping identified cases confirmed 79% as having sepsis; 88% were confirmed or had a billing code for sepsis; and 99% were confirmed, had a billing code, or received at least 4 days of antibiotics. At comparable billing code sensitivity (0.91; 95% CI, 0.88-0.93), electronic health record-based sepsis phenotyping had a higher billing code predictive value (0.32; 95% CI, 0.30-0.34) than either the Centers for Medicare and Medicaid Services Sepsis Core Measure (SEP-1) definition or the Sepsis-3 consensus definition (0.12; 95% CI, 0.11-0.13; and 0.07; 95% CI, 0.07-0.08, respectively). When compared with electronic health record-based sepsis phenotyping, Adult Sepsis Event had a lower billing code sensitivity (0.75; 95% CI, 0.72-0.78) and similar billing code predictive value (0.29; 95% CI, 0.26-0.31). Electronic health record-based sepsis phenotyping identified patients with higher in-hospital mortality and nearly one-half as many false-positive cases when compared with SEP-1 and Sepsis-3. CONCLUSIONS: By accounting for comorbid conditions, electronic health record-based sepsis phenotyping exhibited better performance when compared with other automated definitions of sepsis.

Evaluation of Two Potent and Selective PET Radioligands to Image COX-1 and COX-2 in Rhesus Monkeys.

This study assessed whether the newly developed PET radioligands 11C-PS13 and 11C-MC1 could image constitutive levels of cyclooxygenase (COX)-1 and COX-2, respectively, in rhesus monkeys. Methods: After intravenous injection of either radioligand, 24 whole-body PET scans were performed. To measure enzyme-specific uptake, scans of the 2 radioligands were also performed after administration of a nonradioactive drug preferential for either COX-1 or COX-2. Concurrent venous samples were obtained to measure parent radioligand concentrations. SUVs were calculated from 10 to 90 min. Results:11C-PS13 showed specific uptake in most organs, including spleen, gastrointestinal tract, kidneys, and brain, which was blocked by COX-1, but not COX-2, preferential inhibitors. Specific uptake of 11C-MC1 was not observed in any organ except the ovaries and possibly kidneys. Conclusion: The findings suggest that 11C-PS13 has adequate signal in monkeys to justify its extension to human subjects. In contrast, 11C-MC1 is unlikely to show significant signal in healthy humans, though it may be able to do so in inflammatory conditions.

Quantifying and Visualizing Medication Adherence in Patients Following Acute Myocardial Infarction.

Medication adherence is a critical component for recovery following acute myocardial infarction (AMI). Currently, numerous smartphone applications are capable of tracking medication adherence through patient-generated data (PGD), but few are integrated with the electronic health record (EHR). Integration of medication adherence PGD into the EHR can give both healthcare providers and patients increased insight into patterns of missed doses, effects on vital signs, and correlation with side effect symptomology to inform healthcare decisions. We propose the generation of a medication adherence "vital sign", a score calculated based upon the patient's reported doses taken collected through a smartphone application and streamed into the EHR on a daily basis. We also propose the creation of Patient Health Reports that incorporate relevant patient history, information from previous visits, and the medication adherence scores to give providers a comprehensive view of patients' health prior to clinic visits. These features are intended to incorporate PGD into clinical care to inform decision making in way that streamlines patient visits, reduces healthcare costs, and improves health outcomes.

A targeted real-time early warning score (TREWScore) for septic shock.

Sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic shock. Early aggressive treatment decreases morbidity and mortality. Although automated screening tools can detect patients currently experiencing severe sepsis and septic shock, none predict those at greatest risk of developing shock. We analyzed routinely available physiological and laboratory data from intensive care unit patients and developed "TREWScore," a targeted real-time early warning score that predicts which patients will develop septic shock. TREWScore identified patients before the onset of septic shock with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.83 [95% confidence interval (CI), 0.81 to 0.85]. At a specificity of 0.67, TREWScore achieved a sensitivity of 0.85 and identified patients a median of 28.2 [interquartile range (IQR), 10.6 to 94.2] hours before onset. Of those identified, two-thirds were identified before any sepsis-related organ dysfunction. In comparison, the Modified Early Warning Score, which has been used clinically for septic shock prediction, achieved a lower AUC of 0.73 (95% CI, 0.71 to 0.76). A routine screening protocol based on the presence of two of the systemic inflammatory response syndrome criteria, suspicion of infection, and either hypotension or hyperlactatemia achieved a lower sensitivity of 0.74 at a comparable specificity of 0.64. Continuous sampling of data from the electronic health records and calculation of TREWScore may allow clinicians to identify patients at risk for septic shock and provide earlier interventions that would prevent or mitigate the associated morbidity and mortality.