Advances in genotyping technology have, over the past decade, enabled the focused search for common genetic variation associated with human diseases and traits. With the recently increased availability of detailed phenotypic data from electronic health records and epidemiological studies, the impact of one or more genetic variants on the phenome is starting to be characterized both in clinical and population-based settings using phenome-wide association studies (PheWAS). These studies reveal a number of challenges that will need to be overcome to unlock the full potential of PheWAS for the characterization of the complex human genome-phenome relationship.

Unravelling the human genome-phenome relationship using phenome-wide association studies

Stroke is the leading cause of adult disability in North America and is the fifth most common cause of death.1 ,2 The natural history of patients with acute ischemic stroke and occlusion of a major intracranial vessel such as the internal carotid artery (ICA), middle cerebral artery (MCA), or basilar artery is dismal, with high rates of mortality and low rates of disability-free survival.3 ,4 We introduce the term ‘Emergent Large Vessel Occlusion (ELVO)’ to describe this clinical scenario.

Among acute ischemic stroke, ELVO accounts for the greatest proportion of patients with long-term disability. For the past two decades the use of endovascular therapy has been performed in many centers across the world. The therapies have spanned from infusion of thrombolytic agents5 ,6 to mechanical embolectomy with the introduction of first-generation devices,7 ,8 aspiration-based embolectomy techniques,9 ,10 and the use of stent-retriever based procedures.11 ,12 However, these embolectomy trials were single-arm trials demonstrating safety of the procedure and technique or superiority over another, without direct comparison with standard medical therapy alone.

In the past 3 years, several major trials have been published comparing endovascular therapy with standard medical therapy alone. The purpose of this document is to summarize the results of these trials and synthesize the level of evidence supporting the use of embolectomy in patients with ELVO.

This document was prepared by the Standards and Guidelines Committee of the Society of NeuroInterventional Surgery, a multidisciplinary society representing the leaders in the field of endovascular therapy for neurovascular disease. The strength of the evidence supporting each recommendation was summarized using a scale previously described by the American Heart Association.

### Role of intravenous thrombolysis

In 1996 the FDA approved the use of recombinant tissue plasminogen activator (tPA) for the treatment of acute ischemic stroke …

https://jnis.bmj.com/content/neurintsurg/early/2015/03/12/neurintsurg-2015-011717.full.pdf

Embolectomy for stroke with emergent large vessel occlusion (ELVO): report of the Standards and Guidelines Committee of the Society of NeuroInterventional Surgery

Background: The national mandate for health systems to transition from ICD-9-CM to ICD-10-CM in October 2015 has an impact on research activities. Clinical phenotypes defined by ICD-9-CM codes need to be converted to ICD-10-CM, which has nearly four times more codes and a very different structure than ICD-9-CM. Methods:  We used the Centers for Medicare & Medicaid Services (CMS) General Equivalent Maps (GEMs) to translate, using four different methods, condition-specific ICD-9-CM code sets used for pragmatic trials (n=32) into ICD-10-CM. We calculated the recall, precision, and F‑score of each method. We also used the ICD-9-CM and ICD-10-CM value sets defined for electronic quality measure as an additional evaluation of the mapping methods. Results:  The forward-backward mapping (FBM) method had higher precision, recall and F‑score metrics than simple forward mapping (SFM). The more aggressive secondary (SM) and tertiary mapping (TM) methods resulted in higher recall but lower precision. For clinical phenotype definition, FBM was the best (F=0.67), but was close to SM (F=0.62) and TM (F=0.60), judging on the F‑scores alone. The overall difference between the four methods was statistically significant (one-way ANOVA, F=5.749, p=0.001). However, pairwise comparisons between FBM, SM, and TM did not reach statistical significance. A similar trend was found for the quality measure value sets. Discussion:  The optimal method for using the GEMs depends on the relative importance of recall versus precision for a given use case. It appears that for clinically distinct and homogenous conditions, the recall of FBM is sufficient. The performance of all mapping methods was lower for heterogeneous conditions. Since code sets used for phenotype definition and quality measurement can be very similar, there is a possibility of cross-fertilization between the two activities. Conclusion:  Different mapping approaches yield different collections of ICD-10-CM codes. All methods require some level of human validation.

Preparing for the ICD-10-CM Transition: Automated Methods for Translating ICD Codes in Clinical Phenotype Definitions

Objectives: The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics. Results: Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data. Conclusions: The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI's key role in the infrastructure of a learning healthcare system.

/pdf/clinical-research-informatics-and-electronic-health-record-39dfxal0ky.pdf

Clinical Research Informatics and Electronic Health Record Data

Management of patients with chronic conditions relies on accurate measurement. It is unknown how transition to the ICD-10 coding system affected reporting of chronic condition rates over time. We measured chronic condition rates 2 years before and 1 year after the transition to ICD-10 to examine changes in prevalence rates and potential measurement issues in the Veterans Affairs (VA) health care system. We developed definitions for 34 chronic conditions using ICD-9 and ICD-10 codes and compared the prevalence rates of these conditions from FY2014 to 2016 in a 20% random sample (1.0 million) of all VA patients. In each year we estimated the total number of patients diagnosed with the conditions. We regressed each condition on an indicator of ICD-10 (versus ICD-9) measurement to obtain the odds ratio associated with ICD-10. Condition prevalence estimates were similar for most conditions before and after ICD-10 transition. We found significant changes in a few exceptions. Alzheimer’s disease and spinal cord injury had more than twice the odds of being measured with ICD-10 compared to ICD-9. HIV/AIDS had one-third the odds, and arthritis had half the odds of being measured with ICD-10. Alcohol dependence and tobacco/nicotine dependence had half the odds of being measured in ICD-10. Many chronic condition rates were consistent from FY14–16, and there did not appear to be widespread undercoding of conditions after ICD-10 transition. It is unknown whether increased sensitivity or undercoding led to decreases in mental health conditions.

/pdf/comparing-chronic-condition-rates-using-icd-9-and-icd-10-in-2zq4cmxzar.pdf

Ilir Zenku

Papers

The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics, case study, and mitigating tools.

Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM