Parkulo, Mark A.

Key Points Identifying ways to prevent AKI may reduce mortality further in the setting of intensive BP control. Creatinine-based ascertainment of AKI, enabled by electronic health record data, may be more sensitive and less biased than traditional serious adverse event adjudication. 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.

Abstract 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.

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.

Abstract 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.

AbstractBackgroundMedication discrepancies are common between transitions in healthcare and are associated with higher healthcare utilisation and hospital readmission following discharge. Medication reconciliation is a key intervention to reduce medication list discrepancy. The transition between discharge and first outpatient appointment is critical for patient recovery. The proportion of medication discrepancies during this interval is not well characterised.AimThe aim of this study was to evaluate the accuracy of patients’ medication list between discharge from hospital or emergency department to first outpatient follow‐up with their primary care physician.MethodsA cross‐sectional observational study was performed between November 2015 and May 2016. A 10‐question survey was designed to identify potential areas of inaccuracies in the medication lists.ResultsIn all, 106 patients completed the survey, of whom 67.9% (n = 72) had complete data. Eighty per cent of patients (n = 85) had a provider review their medication list prior to discharge. At the time of follow‐up, 22.6% (n = 28) were not taking the listed medications; of this cohort, 35% (n = 8) had a physician instruct them to stop taking medication. Six per cent of patients (n = 6) reported incorrect medication doses.ConclusionMedication discrepancies occur in the transition from hospital discharge to first follow‐up. This poses a threat to patient safety. A standardised process of medication review is critical to prevent such errors.