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1.
Mayo Clin Proc ; 96(9): 2332-2341, 2021 09.
Article in English | MEDLINE | ID: mdl-34481597

ABSTRACT

OBJECTIVES: To assess the impact of the COVID-19 pandemic on clinical research and the use of electronic approaches to mitigate this impact. METHODS: We compared the utilization of electronic consenting, remote visits, and remote monitoring by study monitors in all research studies conducted at Mayo Clinic sites (Arizona, Florida, and Minnesota) before and during the COVID-19 pandemic (ie, between May 1, 2019 and December 31, 2020). Participants are consented through a participant-tracking system linked to the electronic health record. RESULTS: Between May 2019, and December 2020, there were 130,800 new consents across every modality (electronic and paper) to participate in a non-trial (107,176 [82%]) or a clinical trial (23,624 [18%]). New consents declined from 5741 in February 2020 to 913 in April 2020 but increased to 11,864 in November 2020. The mean (standard deviation [SD]) proportion of electronic consent increased from 22 (2%) before to 45 (20%) during the pandemic (P=.001). Mean (SD) remote electronic consenting increased from 0.3 (0.5%) to 29 (21%) (P<.001). The mean (SD) number of patients with virtual visits increased from 3.5 (2.4%) to 172 (135%) (P=.003) per month between pre-COVID (July 2019 to February 2020) and post-COVID (March to December 2020) periods. Virtual visits used telemedicine (68%) or video (32%). Requests for remote monitor access to complete visits increased from 44 (17%) per month between May 2019 and February 2020 to 111 (74%) per month between March and December 2020 (P=.10). CONCLUSION: After a sharp early decline, the enrollment of new participants and ongoing study visits recovered during the COVID-19 pandemic. This recovery was accompanied by the increased use of electronic tools.


Subject(s)
Ambulatory Care/trends , COVID-19/epidemiology , Electronic Health Records/trends , SARS-CoV-2 , Telemedicine/trends , Humans , Pandemics , Retrospective Studies , United States/epidemiology
2.
Thromb Res ; 144: 40-5, 2016 Aug.
Article in English | MEDLINE | ID: mdl-27284980

ABSTRACT

BACKGROUND: Predictors of venous thromboembolism (VTE) after trauma are uncertain. OBJECTIVE: To identify independent predictors of VTE after acute trauma. METHODS: Using Rochester Epidemiology Project (REP) resources, we identified all Olmsted County, MN residents with objectively-diagnosed incident VTE within 92days after hospitalization for acute trauma over the 18-year period, 1988-2005. We also identified all Olmsted County residents hospitalized for acute trauma over this time period and chose one to two residents frequency-matched to VTE cases on sex, event year group and ICD-9-CM trauma code predictive of surgery. In a case-cohort study, demographic, baseline and time-dependent characteristics were tested as predictors of VTE after trauma using Cox proportional hazards modeling. RESULTS: Among 200 incident VTE cases, the median (interquartile range) time from trauma to VTE was 18 (6, 41) days. Of these, 62% cases developed VTE after hospital discharge. In a multiple variable model including 370 cohort members, patient age at injury, male sex, increasing injury severity as reflected by the Trauma Mortality Prediction Model (TMPM) Mortality Score, immobility prior to trauma, soft tissue leg injury, and prior superficial vein thrombosis were independent predictors of VTE (C-statistic=0.78). CONCLUSIONS: We have identified clinical characteristics which can identify patients at increased risk for VTE after acute trauma, independent of surgery. Almost two thirds of all incident VTE events occurred after initial hospital discharge (18day median time from trauma to VTE) which questions current practice of not extending VTE prophylaxis beyond hospital discharge.


Subject(s)
Venous Thromboembolism/etiology , Wounds and Injuries/complications , Acute Disease , Adult , Aged , Aged, 80 and over , Case-Control Studies , Cohort Studies , Female , Hospitalization , Humans , Male , Middle Aged , Patient Discharge , Proportional Hazards Models , Risk Factors , Trauma Severity Indices
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