Emmanuele Salvati

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to post-acute sequelae of SARS-CoV-2 infection (PASC), or Long COVID, a chronic multisystemic condition with diverse symptoms and no objective diagnostic test. In this retrospective study, we developed a data-driven method to objectively detect persistent physiological changes using wearable device data in a large cohort of over 12,000 US military personnel. We analyzed physiological data from 663 symptomatic COVID-19 positive cases and 2,513 asymptomatic COVID-19 negative controls. Our method identified persistent physiological changes in 9.4% of COVID-19 positive individuals, most commonly manifesting as elevated nightly heart rate and reductions in some heart rate variability metrics. Our findings demonstrate that wearable technology can be used to objectively detect chronic physiological changes beyond the acute phase of COVID-19 illness. Although our method requires further clinical validation, it could potentially provide objective metrics to help standardize Long COVID diagnosis criteria.

IMPORTANCE: The opioid crisis is impacting people across the country and deserves attention to be able to curb the rise in opioid-related deaths. OBJECTIVES: To evaluate practice patterns in opioid infusion administration and dosing for patients with acute respiratory failure receiving invasive mechanical ventilation. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Patients from 21 hospitals in Kaiser Permanente Northern California and 96 hospitals in Philips electronic ICU Research Institute. MAIN OUTCOMES AND MEASURES: We assessed whether patients received opioid infusion and the dose of said opioid infusion. RESULTS: We identified patients with a diagnosis of acute respiratory failure who were initiated on invasive mechanical ventilation. From each patient, we determined if opioid infusions were administered and, among those who received an opioid infusion, the median daily dose of fentanyl infusion. We used hierarchical regression models to quantify variation in opioid infusion use and the median daily dose of fentanyl equivalents across hospitals. We included 13,140 patients in the KPNC cohort and 52,033 patients in the eRI cohort. A total of 7,023 (53.4%) and 16,311 (31.1%) patients received an opioid infusion in the first 21 days of mechanical ventilation in the KPNC and eRI cohorts, respectively. After accounting for patient- and hospital-level fixed effects, the hospital that a patient was admitted to explained 7% (95% CI, 3-11%) and 39% (95% CI, 28-49%) of the variation in opioid infusion use in the KPNC and eRI cohorts, respectively. Among patients who received an opioid infusion, the median daily fentanyl equivalent dose was 692 µg (interquartile range [IQR], 129-1341 µg) in the KPNC cohort and 200 µg (IQR, 0-1050 µg) in the eRI cohort. Hospital explained 4% (95% CI, 1-7%) and 20% (95% CI, 15-26%) of the variation in median daily fentanyl equivalent dose in the KPNC and eRI cohorts, respectively. CONCLUSIONS AND RELEVANCE: In the context of efforts to limit healthcare-associated opioid exposure, our findings highlight the considerable opioid exposure that accompanies mechanical ventilation and suggest potential under and over-treatment with analgesia. Our results facilitate benchmarking of hospitals' analgesia practices against risk-adjusted averages and can be used to inform usual care control arms of analgesia and sedation clinical trials.

The purpose of this thesis is the study of rare decays with the BABAR detector. In the first part we present the study of the leptonic decays B0 → e+e−, B0 → µ+µ− and B0 →e±µ∓, based on a dataset corresponding to (383.6± 4.2)× 106 B ¯B pairs. We do not find evidence of any of the three decay modes, and obtain upper limit on the branching fractions, at 90% confidence level, of BR(B0 → e+e−) < 11.3 × 10−8, BR(B0 →µ+µ−) <5.2×10−8, and BR(B0 → e±µ∓) < 9.2×10−8. In the second part we present the measurement of CP asymmetry in rare inclusive B → Xsγ decays on the recoil of fully-reconstructed hadronic B decays, using a dataset corresponding to (465.0±5.1)×106 B ¯ B pairs. We measure a value of ACP = −0.12 ±0.11±0.03.

The purpose of this thesis is the study of rare decays with the BABAR detector. In the first part we present the study of the leptonic decays B0 → e +e-, B0 → μ +μ- and B0 → e±μ∓, based on a dataset corresponding to (383.6 ± 4.2) × 106 BB¯ pairs. We do not find evidence of any of the three decay modes, and obtain upper limit on the branching fractions, at 90% confidence level, of [special characters omitted](B0 → e +e-) &lt; 11.3 × 10-8 , [special characters omitted](B0 → μ+μ -) &lt; 5.2 × 10-8, and [special characters omitted](B0 → e ±μ∓) &lt; 9.2 × 10-8. In the second part we present the measurement of CP asymmetry in rare inclusive B → Xsγ decays on the recoil of fully-reconstructed hadronic B decays, using a dataset corresponding to (465.0 ± 5.1) × 106 BB¯ pairs. We measure a value of ACP = -0.12 ± 0.11 ± 0.03.