Background Neutralizing monoclonal antibody (NmAb) treatments have received emergency use authorization to treat patients with mild or moderate COVID-19 infection. To date, no real- world data about the efficacy of NmAb has been reported from clinical practice. We assessed the impact of NmAb treatment given in the outpatient clinical practice setting on hospital utilization. Methods Electronic medical records were used to identify adult COVID-19 patients who received NmAbs [bamlanivimab (BAM) or casirivimab and imdevimab (REGN-COV2)] and historic COVID-19 controls. Post-index hospitalization rates were compared. Results 707 confirmed COVID-19 patients received NmAb and 1709 historic COVID-19 controls were included; 553 (78%) received BAM, 154 (22%) received REGN-COV2. Patients receiving NmAb infusion had significantly lower hospitalization rate (5.8% vs. 11.4%, p<0.0001); a shorter length of stay if hospitalized (mean 5.2 days vs. 7.4 days, p=0.02), and fewer ED visits within 30 days post-index (8.1% vs 12.3%, p=0.003) than controls. Hospitalization-free survival was significantly longer in NmAb patients compared to controls (p<0.0001). There was a trend towards a lower hospitalization rate among patients who received NmAb within 2-4 days after symptom onset. In multivariate analysis, having received a NmAb transfusion was independently associated with a lower risk of hospitalization after adjustment for age, sex, race, BMI and referral source: adjusted hazard ratio (95% CI) = 0.54 (0.38 – 0.79), p=0.0012. Overall mortality was not different between the two groups. Conclusions and Relevance NmAb treatment reduced hospital utilization especially when received within a few days of symptom onset. Further study is needed to validate these findings.
is the guarantor of this work and had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final version of the manuscript.
The ever-increasing volume of scientific discoveries, clinical knowledge, novel diagnostic tools, and treatment options juxtaposed with rising costs in health care challenge physicians to identify, prioritize, and use new information rapidly to deliver efficient and high-quality care to a growing and aging patient population. CancerLinQ, a rapid learning health care system in oncology, is an initiative of the American Society of Clinical Oncology and its Institute for Quality that addresses these challenges by collecting information from the electronic health records of large numbers of patients with cancer. CancerLinQ is first and foremost a quality measurement and reporting system through which oncologists can harness the depth and power of their patients' clinical records and other data to assess, monitor, and improve the care they deliver. However, in light of privacy and security concerns with regard to collection, use, and disclosure of patient information, this article addresses the need to collect protected health information as defined under the Health Insurance Portability and Accountability Act of 1996 to drive rapid learning through CancerLinQ.
PURPOSE ASCO, through its wholly owned subsidiary, CancerLinQ LLC, developed CancerLinQ, a learning health system for oncology. A learning health system is important for oncology patients because less than 5% of patients with cancer enroll in clinical trials, leaving evidence gaps for patient populations not enrolled in trials. In addition, clinical trial populations often differ from the overall cancer population with respect to age, race, performance status, and other clinical parameters. MATERIALS AND METHODS Working with subscribing practices, CancerLinQ accepts data from electronic health records and transforms the local representation of a patient’s care into a standardized representation on the basis of the Quality Data Model from the National Quality Forum. CancerLinQ provides this information back to the subscribing practice through a series of tools that support quality improvement. CancerLinQ also creates de-identified data sets for secondary research use. RESULTS As of March 2020, CancerLinQ includes data from 63 organizations across the United States that use nine different electronic health records. The database includes 1,426,015 patients with a primary cancer diagnosis, of which 238,680 have had additional information abstracted from unstructured content. CONCLUSION As CancerLinQ continues to onboard subscribing practices, the breadth of potential applications for a learning health care system widen. Future practice-facing tools could include real-world data visualization, recommendations for treatment of patients with actionable genetic variations, and identification of patients who may be eligible for clinical trials. Feeding these insights back into oncology practice ensures that we learn how to treat patients with cancer not just on the basis of the selective experience of the 5% that enroll in clinical trials, but from the real-world experience of the entire spectrum of patients with cancer in the United States.
Background Given the rapid spread of COVID-19 and its associated morbidity and mortality, healthcare providers throughout the world have been forced to constantly update and change their care delivery models. Objective To assess the outcomes of COVID-19 hospitalized patients during the course of the pandemic in a well-integrated health system. Methods The study used data from the electronic health medical records to assess trends in clinical profile and outcomes of hospitalized adult COVID-19 patients hospitalized in our 5-hospital health system from March 2020-May 2021 (n = 6865). Integration of the health system began in February 2020 and was fully actualized by March 30, 2020. Results Mortality decreased from 15% during first peak (March-May 2020; the rate includes 19% in March-April and 10% in May 2020) to 6% in summer-fall 2020, increased to 13% during the second peak (November 2020-January 2021), and dropped to 7% during the decline period (February-May 2021) (p<0.01). Resource utilization followed a similar pattern including a decrease in ICU use from 35% (first peak) to 16% (decline period), mechanical ventilation from 16% (first peak, including 45% in March 2020) to 9–11% in subsequent periods (p<0.01). Independent predictors of inpatient mortality across multiple study periods included older age, male sex, higher multi-morbidity scores, morbid obesity, and indicators of severe illness on admission such as oxygen saturation ≤90% and high qSOFA score (all p<0.05). However, admission during the first peak remained independently associated with increased mortality even after adjustment for patient-related factors: odds ratio = 1.8 (1.4–2.4) (p<0.0001). Conclusions The creation of a fully integrated health system allowed us to dynamically respond to the everchanging COVID-19 landscape. In this context, despite the increasing patient acuity, our mortality and resource utilization rates have improved during the pandemic.
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