Effect of Intensive versus Standard BP Control on AKI and Subsequent Cardiovascular Outcomes and Mortality: Findings from the SPRINT EHR Study.

Background: Adjudication of inpatient AKI in the Systolic Blood Pressure Intervention Trial (SPRINT) was based on billing codes and admission and discharge notes. The purpose of this study was to evaluate the effect of intensive versus standard BP control on creatinine-based inpatient and outpatient AKI, and whether AKI was associated with cardiovascular disease (CVD) and mortality. Methods: We linked electronic health record (EHR) data from 47 clinic sites with trial data to enable creatinine-based adjudication of AKI. Cox regression was used to evaluate the effect of intensive BP control on the incidence of AKI, and the relationship between incident AKI and CVD and all-cause mortality. Results: A total of 3644 participants had linked EHR data. A greater number of inpatient AKI events were identified using EHR data (187 on intensive versus 155 on standard treatment) as compared with serious adverse event (SAE) adjudication in the trial (95 on intensive versus 61 on standard treatment). Intensive treatment increased risk for SPRINT-adjudicated inpatient AKI (HR, 1.51; 95% CI, 1.09 to 2.08) and for creatinine-based outpatient AKI (HR, 1.40; 95% CI, 1.15 to 1.70), but not for creatinine-based inpatient AKI (HR, 1.20; 95% CI, 0.97 to 1.48). Irrespective of the definition (SAE or creatinine based), AKI was associated with increased risk for all-cause mortality, but only creatinine-based inpatient AKI was associated with increased risk for CVD. Conclusions: Creatinine-based ascertainment of AKI, enabled by EHR data, may be more sensitive and less biased than traditional SAE adjudication. Identifying ways to prevent AKI may reduce mortality further in the setting of intensive BP control.

Use of Self-Collected Saliva Samples for the Detection of SARS-CoV-2.

OBJECTIVE: Using a US Food and Drug Administration (FDA) emergency use authorization (EUA) reverse transcription polymerase chain reaction (RT-PCR) method, we examined the analytic performance accuracy of saliva specimens as compared to nasopharyngeal (NP) specimens in symptomatic patients. Correlation between test results and symptoms was also evaluated. METHODS: Over a 5-week period in 2020, 89 matched saliva and nasopharyngeal swabs were collected from individuals exhibiting symptoms consistent with SARS-CoV-2. Specimens were tested with an FDA EUA-approved RT-PCR method, and performance characteristics were compared. RESULTS: The concordance rate between saliva and nasopharyngeal testing was 93.26%. The mean cycle threshold value of saliva when compared to the NP specimen was 3.56 cycles higher. As compared to NP swab, saliva testing demonstrates acceptable agreement but lower sensitivity. CONCLUSION: When compared to a reference method using NP swabs, the use of saliva testing proved to be a reliable method. Self-collected saliva testing for SARS-CoV-2 allows for a viable option when trained staff or collection materials are in short supply.

Utilization of an Electronic Health Record Integrated Risk Score to Predict Hospitalization Among COVID-19 Patients.

OBJECTIVE: To evaluate the performance of an Electronic Health Record (EHR) integrated risk score for COVID-19 positive outpatients to predict 30-day risk of hospitalization. PATIENTS AND METHODS: A retrospective observational study of 67 470 patients with COVID-19 confirmed by polymerase chain reaction (PCR) test between March 12, 2020 and February 8, 2021. Risk scores were calculated based on data in the chart at the time of the incident infection. RESULTS: The Mayo Clinic COVID-19 risk score consisted of 13 components included age, sex, chronic lung disease, congenital heart disease, congestive heart failure, coronary artery disease, diabetes mellitus, end stage liver disease, end stage renal disease, hypertension, immune compromised, nursing home resident, and pregnant. Univariate analysis showed all components, except pregnancy, have significant (P < .001) association with admission. The Mayo Clinic COVID-19 risk score showed a Receiver Operating Characteristic Area Under Curve (AUC) of 0.837 for the prediction of admission for this large cohort of COVID-19 positive patients. CONCLUSION: The Mayo Clinic COVID-19 risk score is a simple score that is easily integrated into the EHR with excellent predictive performance for severe COVID-19. It can be leveraged to stratify risk for severe COVID-19 at initial contact, when considering therapeutics or in the allocation of vaccine supply.

Performance Accuracy of a Laboratory-Developed Real-Time RT-PCR Method for Detection of SARS-CoV-2 in Self-Collected Saliva Specimens.

OBJECTIVE: The ongoing COVID-19 pandemic caused by SARS-CoV-2 has challenged diagnostic laboratories to re-examine traditional methods for collecting specimens and sample types used in molecular testing. Our goal was to demonstrate that saliva can be used for detecting SARS-CoV-2 and correlates well with established molecular methods using nasopharyngeal (NP) swabs. METHODS: We examined use of a saliva collection device in conjunction with a laboratory-developed real-time reverse transcription-polymerase chain reaction (LDPCR) method for detecting SARS-CoV-2 in a symptomatic population and compared results with 2 US Food and Drug Administration (FDA)-approved methods (emergency use authorization [EUA]) that use specimens from NP swabs. RESULTS: The sensitivity of LDPCR compared with the reference methods was 75.0% (21/28); specificity, 98.1% (104/106). When cycle threshold values were compared between paired specimens using the LDPCR and a EUA reverse transcription PCR method, both targeting the open-reading frame gene, the mean value for saliva was 4.66 cycles higher than for NP specimens. CONCLUSION: Use of self-collected saliva in conjunction with an LDPCR for SARS-CoV-2 compared favorably with 2 FDA EUA methods using NP swabs. The use of an alternative sample type and assay method will aid in expanding the availability of testing during the ongoing COVID-19 pandemic.

Addressing the Challenge of COVID-19: One Health Care Site's Leadership Response to the Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic created an extremely disruptive challenge for health care leaders that required a rapid, dynamic, and innovative response. The purpose of this manuscript is to share the leadership actions and decisions at Mayo Clinic in Florida during the first 6 months of the pandemic (February to July 2020). We note 4 strategies that contributed to an effective response: (1) leverage experience with disaster preparedness and mobilize regional and national networks; (2) use surge models to anticipate and to address supply chain issues as well as practical and financial effects of the pandemic; (3) adapt creatively to establish new safety and procedural protocols in various areas for various populations; and (4) communicate timely information effectively and be the common source of truth. Mayo Clinic in Florida was able to address the surges of patients with COVID-19, to provide ongoing tertiary care, and to restore function within the first 6 months with new, strengthened practices and protocols.

Risk of SARS-CoV-2 Transmission Among Coworkers in a Surgical Environment.

Health care workers are at high risk for contracting coronavirus disease 2019. However, little is known about the risk of transmission between coworkers. The objective of this study was to determine the risk of transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between coworkers in a surgical environment. This was an observational study of 394 health care workers in a surgical environment who were exposed to 2 known SARS-CoV-2-positive coworkers. Standard infection precautions were in place at the time of the exposure. All 394 exposed workers initially underwent nasopharyngeal swab testing for SARS-CoV-2 using the polymerase chain reaction technique. Of the original group, 387 were tested again with the same technique 1 week later. Of 394 SARS-CoV-2-exposed health care workers initially tested, 1 was positive. No new positive cases were found on repeated testing of 387 participants 1 week later. The risk of transmission of SARS-CoV-2 in a health care unit with universal masking and appropriate hand hygiene is low. This finding should provide some reassurance to surgical practices as they reopen.

Concordance Between Blood Pressure in the Systolic Blood Pressure Intervention Trial and in Routine Clinical Practice.

Importance: There are concerns with translating results from the Systolic Blood Pressure Intervention Trial (SPRINT) into clinical practice because the standardized protocol used to measure blood pressure (BP) may not be consistently applied in routine clinical practice. Objectives: To evaluate the concordance between BPs obtained in routine clinical practice and those obtained using the SPRINT protocol and whether concordance varied by target trial BP. Design, Setting, and Participants: This observational prognostic study linking outpatient vital sign information from electronic health records (EHRs) with data from 49 of the 102 SPRINT sites was conducted from November 8, 2010, to August 20, 2015, among 3074 adults 50 years or older with hypertension without diabetes or a history of stroke. Statistical analysis was performed from May 21, 2019, to March 20, 2020. Main Outcomes and Measures: Blood pressures measured in routine clinical practice and SPRINT. Results: Participant-level EHR data was obtained for 3074 participants (2482 men [80.7%]; mean [SD] age, 68.5 [9.1] years) with 3 or more outpatient and trial BP measurements. In the period from the 6-month study visit to the end of the study intervention, the mean systolic BP (SBP) in the intensive treatment group from outpatient BP recorded in the EHR was 7.3 mm Hg higher (95% CI, 7.0-7.6 mm Hg) than BP measured at trial visits; the mean difference between BP recorded in the outpatient EHR and trial SBP was smaller for participants in the standard treatment group (4.6 mm Hg [95% CI, 4.4-4.9 mm Hg]). Bland-Altman analyses demonstrated low agreement between outpatient BP recorded in the EHR and trial BP, with wide agreement intervals ranging from approximately -30 mm Hg to 45 mm Hg in both treatment groups. In addition, the difference between BP recorded in the EHR and trial BP varied widely by site. Conclusions and Relevance: Outpatient BPs measured in routine clinical practice were generally higher than BP measurements taken in SPRINT, with greater mean SBP differences apparent in the intensive treatment group. There was a consistent high degree of heterogeneity between the BPs recorded in the EHR and trial BPs, with significant variability over time, between and within the participants, and across clinic sites. These results highlight the importance of proper BP measurement technique and an inability to apply 1 common correction factor (ie, approximately 10 mm Hg) to approximate research-quality BP estimates when BP is not measured appropriately in routine clinical practice. Trial Registration: SPRINT ClinicalTrials.gov Identifier: NCT01206062.

Integrating pharmacogenomics into the electronic health record by implementing genomic indicators.

Pharmacogenomics (PGx) clinical decision support integrated into the electronic health record (EHR) has the potential to provide relevant knowledge to clinicians to enable individualized care. However, past experience implementing PGx clinical decision support into multiple EHR platforms has identified important clinical, procedural, and technical challenges. Commercial EHRs have been widely criticized for the lack of readiness to implement precision medicine. Herein, we share our experiences and lessons learned implementing new EHR functionality charting PGx phenotypes in a unique repository, genomic indicators, instead of using the problem or allergy list. The Gen-Ind has additional features including a brief description of the clinical impact, a hyperlink to the original laboratory report, and links to additional educational resources. The automatic generation of genomic indicators from interfaced PGx test results facilitates implementation and long-term maintenance of PGx data in the EHR and can be used as criteria for synchronous and asynchronous CDS.

Technical Challenges and Opportunities when Implementing Pharmacogenomics Decision Support Integrated in the Electronic Health Record.

Clinical use of pharmacogenomic (PGx) knowledge at the bedside is new and complex. Our program has implemented multiple PGx-CDS interventions in different clinical settings and in multiple commercial EHRs. Herein, we discuss lessons learned and propose general technical guidelines related to PGx implementation.

Multidisciplinary model to implement pharmacogenomics at the point of care.

PURPOSE: Despite potential clinical benefits, implementation of pharmacogenomics (PGx) faces many technical and clinical challenges. These challenges can be overcome with a comprehensive and systematic implementation model. METHODS: The development and implementation of PGx were organized into eight interdependent components addressing resources, governance, clinical practice, education, testing, knowledge translation, clinical decision support (CDS), and maintenance. Several aspects of implementation were assessed, including adherence to the model, production of PGx-CDS interventions, and access to educational resources. RESULTS: Between August 2012 and June 2015, 21 specific drug-gene interactions were reviewed and 18 of them were implemented in the electronic medical record as PGx-CDS interventions. There was complete adherence to the model with variable production time (98-392 days) and delay time (0-148 days). The implementation impacted approximately 1,247 unique providers and 3,788 unique patients. A total of 11 educational resources complementary to the drug-gene interactions and 5 modules specific for pharmacists were developed and implemented. CONCLUSION: A comprehensive operational model can support PGx implementation in routine prescribing. Institutions can use this model as a roadmap to support similar efforts. However, we also identified challenges that will require major multidisciplinary and multi-institutional efforts to make PGx a universal reality.Genet Med 19 4, 421-429.