Caraballo, Pedro J.

Abstract Introduction Heparin induced thrombocytopenia (HIT) is often not considered as a potential etiology of thrombocytopenia. The gradual 5 to 14 day decline in platelet count (PC) may not be easily recognized by busy clinicians. The life-threatening complications of HIT are potentially preventable with prompt recognition and management. Implementation of computer-based clinical decision support systems (CDSS) may aid in addressing these issues. Such systems have shown promise in increasing provider recognition, appropriate testing, and management of a variety of conditions. Methods We developed, implemented and evaluated a CDSS designed to identify patients at risk for HIT. The CDSS was integrated into the electronic medical record (Centricity Enterprise, GE) at Mayo Clinic Rochester. The CDSS identified inpatients with recent or active use of heparin medications, who experienced a 50% PC drop. For such patients, the system generated an electronic message alerting their care team of the 50% PC drop and asked them to consider the possibility of HIT. A link to a website with recommendations for the diagnosis and management of HIT was included with the alert. Providers were encouraged to assess for HIT using the 4Ts Score and, if indicated, the anti-heparin/platelet factor 4 (PF4) antibody assay. We performed a retrospective chart review of consecutive patients identified by the system and equal number of randomly generated controls that did not trigger alerts. The accuracy of the algorithm was assessed and errors were analyzed for clinical significance. Patient risk of HIT was evaluated by using the 4Ts Score and the HIT Expert Probability (HEP) Score. Alert effect was classified as: Results Over the study period, 2,785 patients had a total of 37,060 PC (13.31 PC/patient) and the CDSS triggered a total of 124 alerts. About 10% of alerted clinicians reviewed the website with recommendations on the management of HIT. A total of 117 patients generating alerts were compared to 124 controls. Demographics between the two groups were similar. The CDSS had a 95% sensitivity for accurately detecting a 50% platelet drop for inpatients on heparin. The specificity was 74%. The Objective alert effect was found in 21% of the cases (16% of providers changed their management and 5% documented that HIT was felt to be unlikely). The most common actions taken after receiving the alert were obtaining a HIT antibody assay, withholding heparin, consulting a subspecialty service, and/or changing anticoagulants. The Probable alert effect was 14% of the cases. HIT had been considered before the alert was sent in 11% of patients. Alert effect was classified as None for the remaining 54% of patients. Anti-heparin/PF4 testing for HIT was ordered for 25% of the intervention patients compared to 3% of controls. For the intervention group, PF4 was negative for 86% of tested patients, equivocal for 10%, and positive in 3%. All PF4 tests were negative in the control group. 4Ts score was greater than or equal to 4 in 38% of patients and averaged 3.1 (range 7 to 0). HEP score averaged 4 (range 14 to -4). Risk calculations showed a trend to higher risk with therapeutic heparin compared to prophylactic heparin preparations. HIT risk scores were comparable by admitting service and diagnosis. Conclusions Our CDSS successfully identified upwards of 95% of inpatients at risk for HIT. However, approximately 26% of the alerts were false positives. Only a small proportion of clinicians accessed the website with recommendations for the diagnosis and management of HIT but patients with a suspected HIT were managed appropriately. Of the patients triggering a HIT alert, 38% had an intermediate or high clinical probability of HIT based on the 4Ts Score that would be an indication for further testing. This result highlights the success of the CDSS in identifying patients needing further evaluation but also the difficulties of a computer-based CDSS in representing all the important clinical criteria for the diagnosis of HIT. Disclosures: No relevant conflicts of interest to declare.

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.

Abstract To better understand the real-world effects of pharmacogenomic (PGx) alerts, this study aimed to characterize alert design within the eMERGE Network, and to establish a method for sharing PGx alert response data for aggregate analysis. Seven eMERGE sites submitted design details and established an alert logging data dictionary. Six sites participated in a pilot study, sharing alert response data from their electronic health record systems. PGx alert design varied, with some consensus around the use of active, post-test alerts to convey Clinical Pharmacogenetics Implementation Consortium recommendations. Sites successfully shared response data, with wide variation in acceptance and follow rates. Results reflect the lack of standardization in PGx alert design. Standards and/or larger studies will be necessary to fully understand PGx impact. This study demonstrated a method for sharing PGx alert response data and established that variation in system design is a significant barrier for multi-site analyses.

AbstractThe eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.

BackgroundProlongation of the QT on the surface electrocardiogram can be due to either genetic or acquired causes. Distinguishing congenital long QT syndrome (LQTS) from acquired QT prolongation has important prognostic and management implications. We aimed to investigate if quantitative T‐wave analysis could provide a tool for the physician to differentiate between congenital and acquired QT prolongation.MethodsPatients were identified through an institution‐wide computer‐based QT screening system which alerts the physician if the QTc ≥ 500 ms. ECGs were retrospectively analyzed with an automated T‐wave analysis program. Congenital LQTS was compared in a 1:3 ratio to those with an identified acquired etiology for QT prolongation (electrolyte abnormality and/or prescription of known QT prolongation medications). Linear discriminant analysis was performed using 10‐fold cross‐validation to statistically test the selected features.ResultsThe 12‐lead ECG of 38 patients with congenital LQTS and 114 patients with drug‐induced and/or electrolyte‐mediated QT prolongation were analyzed. In lead V5, patients with acquired QT prolongation had a shallower T wave right slope (−2,322 vs. −3,593 mV/s), greater T‐peak‐Tend interval (109 vs. 92 ms), and smaller T wave center of gravity on the x axis (290 ms vs. 310 ms; p < .001). These features could distinguish congenital from acquired causes in 77% of cases (sensitivity 90%, specificity 58%).ConclusionT‐wave morphological analysis on lead V5 of the surface ECG could successfully differentiate congenital from acquired causes of QT prolongation.

Advances in pharmacogenomics (PGx) have the potential to transform healthcare by allowing precision medicine to become a reality. However, PGx knowledge is new, complex, and evolving, and relying on the cognition of clinicians alone is insufficient for clinical implementation. Integrating clinical decision support (CDS) tools in the electronic health record (EHR) is critical for translating PGx into clinical practice. Herein, we review current strategies to implement PGx using EHR‐CDS functionalities.