Shannon Manzi

Preprocedural fasting state and adverse events in children undergoing procedural sedation and analgesia in a pediatric emergency department

STUDY OBJECTIVE Assessment of preprocedural fasting is considered essential in minimizing the risks of procedural sedation and analgesia. Established fasting guidelines are difficult to follow in the emergency department (ED). We characterize the fasting status of patients receiving procedural sedation and analgesia in a pediatric ED and assess the relationship between fasting status and adverse events. METHODS A prospective case series was conducted in a childrens hospital ED during an 11-month period. All consecutive patients requiring procedural sedation and analgesia were included. Preprocedural fasting state and adverse events were recorded. The percentage of patients undergoing procedural sedation and analgesia who did not meet fasting guidelines was determined. Adverse events were analyzed in relation to fasting status. RESULTS One thousand fourteen patients underwent procedural sedation and analgesia, and data on fasting status were available for 905 (89%) patients. Of these 905 patients, 509 (56%; 95% confidence interval [CI] 53% to 60%) did not meet fasting guidelines. Seventy-seven adverse events occurred in 68 (6.7%; 95% CI 5.2% to 8.4%) of the 1,014 patients. All adverse events were minor and successfully treated. Adverse events occurred in 32 (8.1%; 95% CI 5.6% to 11.2%) of 396 patients who met and 35 (6.9%; 95% CI 4.8% to 9.4%) of 509 patients who did not meet fasting guidelines. There was no significant difference in median fasting duration between patients with and without adverse events and between patients with and without emesis. Emesis occurred in 15 (1.5%) patients. There were no episodes of aspiration (1-sided 97.5% CI 0% to 0.4%). CONCLUSION Fifty-six percent of children undergoing ED procedural sedation and analgesia were not fasted in accordance with established guidelines. There was no association between preprocedural fasting state and adverse events.

Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems.

We describe here the design and initial implementation of the eMERGE‐PGx project. eMERGE‐PGx, a partnership of the Electronic Medical Records and Genomics Network and the Pharmacogenomics Research Network, has three objectives: (i) to deploy PGRNseq, a next‐generation sequencing platform assessing sequence variation in 84 proposed pharmacogenes, in nearly 9,000 patients likely to be prescribed drugs of interest in a 1‐ to 3‐year time frame across several clinical sites; (ii) to integrate well‐established clinically validated pharmacogenetic genotypes into the electronic health record with associated clinical decision support and to assess process and clinical outcomes of implementation; and (iii) to develop a repository of pharmacogenetic variants of unknown significance linked to a repository of electronic health record–based clinical phenotype data for ongoing pharmacogenomics discovery. We describe site‐specific project implementation and anticipated products, including genetic variant and phenotype data repositories, novel variant association studies, clinical decision support modules, clinical and process outcomes, approaches to managing incidental findings, and patient and clinician education methods.

Predicting Adverse Drug Events Using Pharmacological Network Models

A network-based method that uses available pharmacosafety data can predict yet-to-be-discovered adverse drug events to help reduce drug-associated morbidity and mortality. The Power of Prediction We’ve all done it: googled a combination of medical terms to describe how we feel after taking a new medication. The result is a seemingly infinite list of Web sites telling us that the nausea is normal, or that the headaches warrant another visit to the doctor. Oftentimes, important adverse effects of drugs are discovered and added to the drug label only years after a drug goes on the market. But what if scientists could know about certain adverse drug effects before they are clinically discovered? Cami and colleagues develop a mathematical approach to predicting such adverse events associated with the drugs we take, in hopes of reducing drug-related morbidity—and mortality. After its release to the market, any given drug undergoes rigorous evaluation to determine associated ADEs (adverse drug effects). This post hoc analysis is usually unable to detect rare or delayed-onset ADEs until enough clinical evidence accumulates–a process that may take years. The method devised by Cami and coauthors does not need to wait for such evidence to accumulate. Instead, it can inform drug safety practitioners early on of likely ADEs that will be detected down the line. The authors first collected a “snapshot” of 809 drugs and their 852 related adverse events that had been documented in 2005. These drug-safety associations were combined with taxonomic and biological data to construct a network that is reminiscent of a web. Cami et al. then used this drug-ADE network to train a logistic regression predictive model—basically creating a formula that would indicate the likelihood of unknown side effects of any drug in the network. The predictive capabilities of the model were prospectively validated using drug-ADE associations newly reported between 2006 and 2010. Such prospective evaluation preserves the chronological order of drug adverse event reporting, making it a realistic method for predicting future ADEs. With their network, the authors were able to predict with high specificity seven of eight drug ADEs identified by pharmacological experts as having emerged after 2005, including the relationship between the anti-diabetes drug rosiglitazone (Avandia) and heart attack. The benefit for patients? With this powerful model in place, certain unknown adverse drug effects may be discovered earlier, helping to prevent drug-related morbidity and mortality through appropriate consumer label warnings. Early and accurate identification of adverse drug events (ADEs) is critically important for public health. We have developed a novel approach for predicting ADEs, called predictive pharmacosafety networks (PPNs). PPNs integrate the network structure formed by known drug-ADE relationships with information on specific drugs and adverse events to predict likely unknown ADEs. Rather than waiting for sufficient post-market evidence to accumulate for a given ADE, this predictive approach relies on leveraging existing, contextual drug safety information, thereby having the potential to identify certain ADEs earlier. We constructed a network representation of drug-ADE associations for 809 drugs and 852 ADEs on the basis of a snapshot of a widely used drug safety database from 2005 and supplemented these data with additional pharmacological information. We trained a logistic regression model to predict unknown drug-ADE associations that were not listed in the 2005 snapshot. We evaluated the model’s performance by comparing these predictions with the new drug-ADE associations that appeared in a 2010 snapshot of the same drug safety database. The proposed model achieved an AUROC (area under the receiver operating characteristic curve) statistic of 0.87, with a sensitivity of 0.42 given a specificity of 0.95. These findings suggest that predictive network methods can be useful for predicting unknown ADEs.

Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network

Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE‐PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE‐PGx data release includes sequence‐derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation‐Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.

Lacerations and Embedded Needles Caused by Epinephrine Autoinjector Use in Children

STUDY OBJECTIVE Epinephrine autoinjector use for anaphylaxis is increasing. There are reports of digit injections because of incorrect autoinjector use, but no previous reports of lacerations, to our knowledge. We report complications of epinephrine autoinjector use in children and discuss features of these devices, and their instructions for use, and how these may contribute to injuries. METHODS We queried emergency medicine e-mail discussion lists and social media allergy groups to identify epinephrine autoinjector injuries involving children. RESULTS Twenty-two cases of epinephrine autoinjector-related injuries are described. Twenty-one occurred during intentional use for the childs allergic reaction. Seventeen children experienced lacerations. In 4 cases, the needle stuck in the childs limb. In 1 case, the device lacerated a nurses finger. The device associated with the injury was operated by health care providers (6 cases), the patients parent (12 cases, including 2 nurses), educators (3 cases), and the patient (1 case). Of the 3 epinephrine autoinjectors currently available in North America, none include instructions to immobilize the childs leg. Only 1 has a needle that self-retracts; the others have needles that remain in the thigh during the 10 seconds that the user is instructed to hold the device against the leg. Instructions do not caution against reinjection if the needle is dislodged during these 10 seconds. CONCLUSION Epinephrine autoinjectors are lifesaving devices in the management of anaphylaxis. However, some have caused lacerations and other injuries in children. Minimizing needle injection time, improving device design, and providing instructions to immobilize the leg before use may decrease the risk of these injuries.

Pharmacointeraction Network Models Predict Unknown Drug-Drug Interactions

Drug-drug interactions (DDIs) can lead to serious and potentially lethal adverse events. In recent years, several drugs have been withdrawn from the market due to interaction-related adverse events (AEs). Current methods for detecting DDIs rely on the accumulation of sufficient clinical evidence in the post-market stage – a lengthy process that often takes years, during which time numerous patients may suffer from the adverse effects of the DDI. Detection methods are further hindered by the extremely large combinatoric space of possible drug-drug-AE combinations. There is therefore a practical need for predictive tools that can identify potential DDIs years in advance, enabling drug safety professionals to better prioritize their limited investigative resources and take appropriate regulatory action. To meet this need, we describe Predictive Pharmacointeraction Networks (PPINs) – a novel approach that predicts unknown DDIs by exploiting the network structure of all known DDIs, together with other intrinsic and taxonomic properties of drugs and AEs. We constructed an 856-drug DDI network from a 2009 snapshot of a widely-used drug safety database, and used it to develop PPIN models for predicting future DDIs. We compared the DDIs predicted based solely on these 2009 data, with newly reported DDIs that appeared in a 2012 snapshot of the same database. Using a standard multivariate approach to combine predictors, the PPIN model achieved an AUROC (area under the receiver operating characteristic curve) of 0.81 with a sensitivity of 48% given a specificity of 90%. An analysis of DDIs by severity level revealed that the model was most effective for predicting “contraindicated” DDIs (AUROC = 0.92) and less effective for “minor” DDIs (AUROC = 0.63). These results indicate that network based methods can be useful for predicting unknown drug-drug interactions.

Practical considerations in genomic decision support: The eMERGE experience

Background: Genomic medicine has the potential to improve care by tailoring treatments to the individual. There is consensus in the literature that pharmacogenomics (PGx) may be an ideal starting point for real-world implementation, due to the presence of well-characterized drug-gene interactions. Clinical Decision Support (CDS) is an ideal avenue by which to implement PGx at the bedside. Previous literature has established theoretical models for PGx CDS implementation and discussed a number of anticipated real-world challenges. However, work detailing actual PGx CDS implementation experiences has been limited. Anticipated challenges include data storage and management, system integration, physician acceptance, and more. Methods: In this study, we analyzed the experiences of ten members of the Electronic Medical Records and Genomics (eMERGE) Network, and one affiliate, in their attempts to implement PGx CDS. We examined the resulting PGx CDS system characteristics and conducted a survey to understand the unanticipated implementation challenges sites encountered. Results: Ten sites have successfully implemented at least one PGx CDS rule in the clinical setting. The majority of sites elected to create an Omic Ancillary System (OAS) to manage genetic and genomic data. All sites were able to adapt their existing CDS tools for PGx knowledge. The most common and impactful delays were not PGx-specific issues. Instead, they were general IT implementation problems, with top challenges including team coordination/communication and staffing. The challenges encountered caused a median total delay in system go-live of approximately two months. Conclusions: These results suggest that barriers to PGx CDS implementations are generally surmountable. Moreover, PGx CDS implementation may not be any more difficult than other healthcare IT projects of similar scope, as the most significant delays encountered were not unique to genomic medicine. These are encouraging results for any institution considering implementing a PGx CDS tool, and for the advancement of genomic medicine.

13-Year-Old Girl With Recurrent, Episodic, Persistent Vomiting: Out of the Pot and Into the Fire

Cyclic vomiting syndrome (CVS) is a well-established cause of recurrent vomiting in the pediatric population. Severe vomiting with chronic cannabis use, known as cannabinoid hyperemesis syndrome, has recently been more widely recognized as an etiology of persistent episodic vomiting. In turn, patients presenting with frequent episodes of CVS are now increasingly being screened for cannabinoid use. Because patients with persistent vomiting are also frequently prescribed a proton pump inhibitor (PPI) for their gastrointestinal symptoms, it is important to be aware of the potential for a PPI to cause an interaction that can lead to false-positive urine cannabinoid screening. We describe a case of a false-positive urine cannabinoid screen in a patient with CVS who received a dose of intravenous pantoprazole. The primary reference regarding drug screen interference from PPIs can be found in the pantoprazole package insert that refers to pre–Food and Drug Administration approval data. Although multiple sources on the Internet report the possibility of positive cannabinoid screens from pantoprazole, there are no known published reports of the phenomenon in the medical literature.

FEMA's Organized Response With a Pediatric Subspecialty Team: The National Disaster Medical System Response: A Pediatric Perspective

A hurricane in New Orleans, Louisiana, was no longer just a threat but a reality. At the New Orleans Louis Armstrong International Airport, a small group of Boston-based National Disaster Medical System (NDMS) physicians met with military officers, who informed us that 5 years ago they were charged with preparation for potential national disasters. A hurricane in New Orleans was 1 of 3 scenarios they predicted. As a nation, as individuals, and as a disaster team we were living that prediction. Although we took care of disaster victims of all ages and disaster-relief workers, for those of us in the field of pediatrics, our focus was on the youngest victims of the disaster. Preparation begins not days but years before a disaster. Teams deployed to areas affected by Hurricane Katrina are part of the NDMS, a federally coordinated system that augments the nations emergency medical response capacity. In 1984 the NDMS, by declaration of the President, became an agency of the US Public Health Service within the Department of Health and Human Services. It is a public/private partnership between government agencies, Disaster Medical Assistance Teams (DMATs), civilian hospitals, and emergency-response organizations. The role of the NDMS is to provide civilian medical support to the US government for victims of domestic disasters. A national network of teams provides a single integrated medical response to assist state and local authorities with medical care. The first NDMS team was formed in 1986. In March 2003, the NDMS was transferred from the US Public Health Service to the Response Division of the Federal Emergency Management Agency under the Department of Homeland Security. The NDMS, using government and private-sector resources, is mandated to provide medical response, patient evacuation, and definitive medical care. Specifically, the NDMS assesses health/medical needs; coordinates, mobilizes, and manages teams of medical … Address correspondence to Debra L. Weiner, MD, PhD, Emergency Medicine, Childrens Hospital Boston, 300 Longwood Ave, Boston, MA 02115. E-mail: debra.weiner{at}childrens.harvard.edu

Creating a scalable clinical pharmacogenomics service with automated interpretation and medical record result integration - experience from a pediatric tertiary care facility.

Objective: This paper outlines the implementation of a comprehensive clinical pharmacogenomics (PGx) service within a pediatric teaching hospital and the integration of clinical decision support in the electronic health record (EHR). Materials and Methods: An approach to clinical decision support for medication ordering and dispensing driven by documented PGx variant status in an EHR is described. A web-based platform was created to automatically generate a clinical report from either raw assay results or specified diplotypes, able to parse and combine haplotypes into an interpretation for each individual and compared to the reference lab call for accuracy. Results: Clinical decision support rules built within an EHR provided guidance to providers for 31 patients (100%) who had actionable PGx variants and were written for interacting medications. A breakdown of the PGx alerts by practitioner service, and alert response for the initial cohort of patients tested is described. In 90% (355/394) of the cases, thiopurine methyltranferase genotyping was ordered pre-emptively. Discussion: This paper outlines one approach to implementing a clinical PGx service in a pediatric teaching hospital that cares for a heterogeneous patient population. There is a focus on incorporation of PGx clinical decision support rules and a program to standardize report text within the electronic health record with subsequent exploration of clinician behavior in response to the alerts. Conclusion: The incorporation of PGx data at the time of prescribing and dispensing, if done correctly, has the potential to impact the incidence of adverse drug events, a significant cause of morbidity and mortality.