No clinical benefit in mortality associated with hydroxychloroquine treatment in patients with COVID-19.

BACKGROUND: The use of hydroxychloroquine (HCQ), with or without concurrent administration of azithromycin (AZM), for treatment of COVID-19 has received considerable attention. The purpose of this study was to determine whether HCQ administration is associated with improved mortality in COVID-19 patients. METHODS: We conducted a retrospective analysis of data collected during the care process for COVID-19 positive patients discharged from facilities affiliated with a large healthcare system in the United States as of April 27, 2020. Patients were categorized by treatment with HCQ (in addition to standard supportive therapy) or receipt of supportive therapy with no HCQ. Patient outcomes were evaluated for in-hospital mortality. Patient demographics and clinical characteristics were accounted for through a multivariable regression analysis. RESULTS: A total of 1669 patients were evaluated (no HCQ, n = 696; HCQ, n = 973). When adjusting for patient characteristics, receipt of AZM, and severity of disease at admission, there was no beneficial effect of receipt of HCQ on the risk of death. In this population, there was an 81% increase in the risk of mortality among patients who received HCQ at any time during their hospital stay versus no HCQ exposure (OR: 1.81, 95% CI: 1.20-2.77, p = 0.01). CONCLUSIONS: In this retrospective analysis, we found that there was no benefit of administration of HCQ on mortality in COVID-19 patients. These results support recent changes to clinical trials that discourage the use of HCQ in COVID-19 patients.

Patient characteristics and admitting vital signs associated with coronavirus disease 2019 (COVID-19)-related mortality among patients admitted with noncritical illness.

OBJECTIVE: To determine risk factors for mortality among COVID-19 patients admitted to a system of community hospitals in the United States. DESIGN: Retrospective analysis of patient data collected from the routine care of COVID-19 patients. SETTING: System of >180 acute-care facilities in the United States. PARTICIPANTS: All admitted patients with positive identification of COVID-19 and a documented discharge as of May 12, 2020. METHODS: Determination of demographic characteristics, vital signs at admission, patient comorbidities and recorded discharge disposition in this population to construct a logistic regression estimating the odds of mortality, particular for those patients characterized as not being critically ill at admission. RESULTS: In total, 6,180 COVID-19+ patients were identified as of May 12, 2020. Most COVID-19+ patients (4,808, 77.8%) were admitted directly to a medical-surgical unit with no documented critical care or mechanical ventilation within 8 hours of admission. After adjusting for demographic characteristics, comorbidities, and vital signs at admission in this subgroup, the largest driver of the odds of mortality was patient age (OR, 1.07; 95% CI, 1.06-1.08; P < .001). Decreased oxygen saturation at admission was associated with increased odds of mortality (OR, 1.09; 95% CI, 1.06-1.12; P < .001) as was diabetes (OR, 1.57; 95% CI, 1.21-2.03; P < .001). CONCLUSIONS: The identification of factors observable at admission that are associated with mortality in COVID-19 patients who are initially admitted to non-critical care units may help care providers, hospital epidemiologists, and hospital safety experts better plan for the care of these patients.

Changes in hospitalized coronavirus disease 2019 (COVID-19) patient characteristics and resource use in a system of community hospitals in the United States.

Coronavirus disease 2019 (COVID-19) has migrated to regions that were initially spared, and it is likely that different populations are currently at risk for illness. Herein, we present our observations of the change in characteristics and resource use of COVID-19 patients over time in a national system of community hospitals to help inform those managing surge planning, operational management, and future policy decisions.

2019 John M. Eisenberg Patient Safety and Quality Awards: SPOTting Sepsis to Save Lives: A Nationwide Computer Algorithm for Early Detection of Sepsis: Innovation in Patient Safety and Quality at the National Level (Eisenberg Award).

BACKGROUND: In recognition of the potential of data to drive care and the need for early identification of patients with sepsis, HCA Healthcare developed an automated sepsis detection algorithm-SPOT (Sepsis Prediction and Optimization of Therapy). The algorithm was deployed at scale and served as a mechanism to reduce the time to detection and improve sepsis mortality in 173 hospitals across the United States. METHODS: The SPOT algorithm was designed as a rules-based detection of defined criteria that would interpret available electronic and basic laboratory data in near real time. Working from an organizational recognition of the need to construct a national clinical data warehouse to allow for the aggregation and analysis of data streams, HCA Healthcare designed and deployed SPOT and delivered the alert from the algorithm to the bedside to initiate existing clinical workflows for patients with sepsis. RESULTS: SPOT improved the timeliness of sepsis detection by providing alerts when signals of sepsis become available, triggering initiation of sepsis screens. This gave an advantage of about six hours over the legacy practice of sepsis screening at shift change. When deployed alongside existing sepsis improvement initiatives, SPOT was associated with an acceleration of improvement in mortality-particularly in the not-present-on-admission (NPOA) septic shock population, the patients at greatest risk for mortality. This population had seen little improvement with prior initiatives, but mortality improved 3.9 percentage points from 2018 to 2019. When accounting for seasonal variation, there was a decline in mortality rate following the deployment of SPOT, as compared to the year prior, of 9.9% for NPOA severe sepsis and 5.1% for NPOA septic shock. CONCLUSION: Development of the SPOT algorithm for the detection of sepsis from data available in the electronic health record resulted in more timely recognition, faster initiation of treatment, and improved survival for patients.

Subthalamic Nucleus Deep Brain Stimulation May Reduce Medication Costs in Early Stage Parkinson's Disease.

BACKGROUND: Subthalamic nucleus deep brain stimulation (STN-DBS) is well-known to reduce medication burden in advanced stage Parkinson's disease (PD). Preliminary data from a prospective, single blind, controlled pilot trial demonstrated that early stage PD subjects treated with STN-DBS also required less medication than those treated with optimal drug therapy (ODT). OBJECTIVE: The purpose of this study was to analyze medication cost and utilization from the pilot trial of DBS in early stage PD and to project 10 year medication costs. METHODS: Medication data collected at each visit were used to calculate medication costs. Medications were converted to levodopa equivalent daily dose, categorized by medication class, and compared. Medication costs were projected to advanced stage PD, the time when a typical patient may be offered DBS. RESULTS: Medication costs increased 72% in the ODT group and decreased 16% in the DBS+ODT group from baseline to 24 months. This cost difference translates into a cumulative savings for the DBS+ODT group of $7,150 over the study period. Projected medication cost savings over 10 years reach $64,590. Additionally, DBS+ODT subjects were 80% less likely to require polypharmacy compared with ODT subjects at 24 months (p <  0.05; OR = 0.2; 95% CI: 0.04-0.97). CONCLUSIONS: STN-DBS in early PD reduced medication cost over the two-year study period. DBS may offer substantial long-term reduction in medication cost by maintaining a simplified, low dose medication regimen. Further study is needed to confirm these findings, and the FDA has approved a pivotal, multicenter clinical trial evaluating STN-DBS in early PD.