Gamble, Dale M.

Abstract Background In the United States, rare disease (RD) is defined as a condition that affects fewer than 200,000 individuals. Collectively, RD affects an estimated 30 million Americans. A significant portion of RD has an underlying genetic cause; however, this may go undiagnosed. To better serve these patients, the Mayo Clinic Program for Rare and Undiagnosed Diseases (PRaUD) was created under the auspices of the Center for Individualized Medicine (CIM) aiming to integrate genomics into subspecialty practice including targeted genetic testing, research, and education. Methods Patients were identified by subspecialty healthcare providers from 11 clinical divisions/departments. Targeted multi-gene panels or custom exome/genome-based panels were utilized. To support the goals of PRaUD, a new clinical service model, the Genetic Testing and Counseling (GTAC) unit, was established to improve access and increase efficiency for genetic test facilitation. The GTAC unit includes genetic counselors, genetic counseling assistants, genetic nurses, and a medical geneticist. Patients receive abbreviated point-of-care genetic counseling and testing through a partnership with subspecialty providers. Results Implementation of PRaUD began in 2018 and GTAC unit launched in 2020 to support program expansion. Currently, 29 RD clinical indications are included in 11 specialty divisions/departments with over 142 referring providers. To date, 1,152 patients have been evaluated with an overall solve rate of 14.1% and as high as 66.7% depending on the phenotype. Noteworthy, 42.7% of the solved or likely solved patients underwent changes in medical management and outcome based on genetic test results. Conclusion Implementation of PRaUD and GTAC have enabled subspecialty practices advance expertise in RD where genetic counselors have not historically been embedded in practice. Democratizing access to genetic testing and counseling can broaden the reach of patients with RD and increase the diagnostic yield of such indications leading to better medical management as well as expanding research opportunities.

Background: Causative Classification of Stroke (CCS) system is a novel, web-based, and fully-computerized algorithm that integrates clinical, diagnostic, and etiologic stroke characteristics in an evidence-based manner (available at http://ccs.mgh.harvard.edu ) and provides in a given patient both phenotypic subtypes where abnormal evaluation findings are simply organized in major etiologic categories and causative subtypes where abnormal evaluation findings are filtered through a decision making process to identify the most likely cause of stroke. In this study, we sought to identify the concordance between phenotypic and causative CCS subtypes in the NINDS Stroke Genetics Network (SiGN). Methods: A total of 24 adjudicators from 15 US and European sites certified in CCS retrospectively determined CCS subtypes through chart review in 7134 patients. The CCS software provided causative subtypes in 3 confidence levels as “evident”, “probable” or “possible” based on the weight of causal evidence. We estimated how often CCS classified a given major abnormal evaluation finding as the causative stroke mechanism. Results: CCS assigned 55% of patients with the phenotype of moderate-to-severe atherosclerotic stenosis into the causative category of “evident large artery atherosclerosis”. Likewise, 50% of patients with a major cardiac source (such as atrial fibrillation) were classified into “evident cardio-aortic embolism”, and 74% of those with imaging evidence of a typical lacunar infarct into “evident small artery occlusion”. In 20% to 30% of patients, phenotypic and causative categories were the same but the causative subtype was assigned with a lower level of confidence. Six to 20% of patients with a given phenotypic feature were classified into a different causative category. Failure to investigate for other subtypes in the presence of a positive test finding and the presence of multiple competing etiologies were key contributors to the observed disconcordance between phenotypic and causative subtypes. Conclusions: Our findings show that the presence of a major abnormality in stroke evaluation does not necessarily mean that it is always the cause. The ability of CCS system to discriminate between phenotypic and causative characteristics of stroke etiology provides the opportunity to refine stroke phenotypes for use in research studies.

Background: Recruitment and phenotyping constitute major costs in stroke genetics research. Clinical trial datasets that include biological samples provide an opportunity to leverage richly phenotyped cases for genomic research. However, the heterogeneity of stroke and methodological differences across studies present challenges. We developed and validated a systematic method of mapping data abstracted from a multicenter randomized clinical trial to the inputs of the CCS stroke subtyping system for the SiGN study. Methods: The SiGN Phenotype Committee and the Secondary Prevention of Small Subcortical Strokes (SPS3) research team systematically reviewed SPS3 case report forms (CRF) to identify key elements required to generate CCS subtyping, and drafted and refined mapping rules using a derivation set of 30 charts. The resultant algorithm was compared to manual entry of clinical information into the CCS in a test set of 30 randomly selected charts. This identified problems due to multiple versions of CRFs (up to 7 versions) and prompted revision of the mapping rules. We assessed the revised algorithm in an independent test set. Results: None of the subtype classifications agreed in the initial testing prompting revision to allow capture information from different versions of the CRFs. The revised mapping algorithm applied to the same test set performed well; 29/30 agreed - 20 small artery occlusion (SAO) evident, 4 SAO possible, 2 SAO probable and 3 supra-aortic large artery atherosclerosis evident). The one chart that disagreed was classified as SAO evident by manual entry but as undetermined by the data mapping rules. In the 2nd independent test set, 30/30 agreed: 22 SAO evident, 4 SAO possible, 3 supra-aortic large artery atherosclerosis evident and 1 undetermined unknown - incomplete evaluation. Conclusions: We created mapping rules using CRFs from a clinical trial to allow reliable subtype classification using the CCS. Issues related to multiple iterations of CRFs presented challenges. Highly phenotyped stroke cases from clinical trials represent a cost-effective opportunity for genomic research.

Background: The Stroke Genetics Network (SiGN) funded by the NINDS aims to identify genetic risk factors in ischemic stroke using whole-genome association studies (GWAS). High quality phenotyping is crucial to successful application of GWAS. As a heterogenous disorder, stroke poses specific challenges. The Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification is a broadly used, but its validity is challenged especially when performed by multiple investigators with differing interpretations of the system. The Causative Classification System for Ischemic Stroke (CCS) system is a new, web-based, and computerized algorithm that integrates clinical, diagnostic, and etiologic stroke characteristics in an evidence-based manner ( ccs.mgh.harvard.edu ) to generate subtypes. Methods: In planning the SiGN proposal, a sample of 20 coded charts were collected from a subset of participating studies to assess feasibility of central adjudication and comparability to study-specific TOAST. Two central adjudicators reviewed all records and generated TOAST and CCS subtypes. These were compared to study-specific TOAST subtype and the CCS phenotype generated for SiGN by local trained adjudicators. CCS data is now available for 7134 included cases using both a 5 and a 7 category system as defined in the table . Results: All 4 phenotypes were available for 115 ischemic stroke cases from 6 studies in SiGN. Basic demographics were 54% women, 63% white, and median age between 65-74. Table 1 provides the agreement between the various subtypes. Table 2 describes the types of disagreement. Conclusions: Central adjudication with only two adjudicators and curated medical records yielded more consistent subtyping independent of phenotyping system. The agreement for TOAST was higher than published rates by independent groups (∼0.50). In contrast, the agreement for CCS was lower than previously published (0.85-0.95). Site adjudicators' familiarity with TOAST and inexperience with CCS may contribute. Although CCS is an automated algorithm and has a number of user friendly features, our findings suggest that formal training and certification process before starting to use CCS may be worthwhile to achieve optimal benefit from the system.

Stroke subtype classification, applying standard criteria to clinical data, can reduce the heterogeneity of ischemic stroke for genetics studies. To date, most of the replicated genetic loci for ischemic stroke appear subtype-specific. Increasing homogeneity of phenotype comes with a trade-off in sample size, which influences the potential for successful identification of new loci. We estimated genetic associations using the union and intersection of two widely used stroke subtyping systems to assess the influence of sample size and homogeneity on test statistics. Methods: The Stroke Genetics Network (SiGN) study used both the Causative Classification of Stroke (CCS) and the Trial of ORG10172 Acute Stroke Trial (TOAST) classification systems. The CCS generates both a causal and a phenotypic subtype. Using all available data from case-control strata from the a previously performed GWAS, we focused on three main stroke subtypes: cardioembolic (CE), large artery atherosclerosis (LAA), and small artery occlusion (SAO). We estimated genetic associations with the union of the two CCS outputs and TOAST ( eg called CE by at least one method) and the intersection ( eg called CE by all three methods). Our modelling approach included a fixed-effects meta-analysis, i.e. pooling stratum specific estimates from logistic regression models using 10 principle components to account for genetic ancestry responsible for population stratification. Results: The majority of confirmatory findings from SiGN ( PITX2 and ZFHX3 for CE and HDAC9 for LAA) were evident in both models and in both the union and intersection of the subtyping systems. Some findings ( TSPAN2 in LAA) showed up best in the intersection with stronger corresponding p-values and odds ratios whereas other findings (12q24 for SAO) appear stronger in all of the union analyses, but were also evident in intersection analyses. We identify 16 potential novel loci, 6 of which appear in numerous analyses. Conclusion: Both expanding and refining stroke subtypes may help in the identification of additional stroke genetic risk variants and should be considered as complementary to a single classification system. We are pursuing replication of the novel findings.

Introduction: Outcomes from endovascular procedures are highly dependent on the experience and skill of the operating physician. The multi-disciplinary CREST-2 Interventional Management Committee (IMC) was charged with credentialing a cohort of skilled interventionists with adequate contemporary case volumes. Methods: Applicants were required to submit 25 consecutive cases completed within 5 years as primary operator out of a required total experience of ≥ 50 cases (≥ 20 for operators completing training). Interventionists not approved on initial review were asked to submit additional cases (with procedural angiograms), the number depending on quality and recent-quantity of the cases. Results: The IMC has had 102 meetings, and 283 interventionists have been evaluated: 104 (37%) interventionists were cardiologists, 64 (23%) vascular surgeons, 42 (15%) neurosurgeons, 32 (11%) neuroradiologists, 26 (9%) neurologists, 9 (3%) interventional radiologists, and 6 (2%) other. The mean total experience among the 226 interventionists with available information was 220±263 carotid stent cases (median 135; range 10-2500). A total of 7037 cases have been reviewed by the IMC, dating from August 2001 to April 2016, with 3366 symptomatic, 3541 asymptomatic and 130 undetermined. The range of cases reviewed per interventionist was 5 to 50. Of the 251 interventionists with sufficient periprocedural follow-up data, no stroke events were reported by 152 (60.5%), and at least one or more stroke events were reported by 99 (39.5%). The IMC has approved 115 interventionists, 29 at the first review and 86 subsequently, based upon submission and review of 631 additional contemporary cases (mean=7 cases per interventionist); 122 have approval pending submission of additional cases; 33 have been denied; 8 have been deferred; 4 have been approved for the CREST-2 Companion Registry only; and 1 is pending decision. Discussion: Rigorous evaluation and credentialing of carotid stenters in CREST-2 has been demanding, for the candidates and for the evaluators. Yet the cohort of interventionists so selected should be able to provide the high-quality stenting outcomes necessary for acceptance of the trial results.

Introduction We assessed patients’ perceptions of physician empathy during telemedicine consultations as compared to in-person consultations during clinical encounters for acute stroke. Methods This prospective cohort study was undertaken at a comprehensive stroke centre hub in collaboration with a distant community hospital spoke site. Eligible participants presented to hub or spoke emergency departments with suspected acute stroke within three hours of symptom onset. Participants were evaluated at the hub site in person or at the remote site via telemedicine by the same group of neurologists. Following acute care decisions, single-visit data including participant-reported assessments of physician empathy were collected within 24 h. The primary outcome was the Consultation and Relational Empathy score. The secondary outcome for the telemedicine cohort was the Telemedicine Patient Satisfaction Measure score. Results Between 31 May 2013–13 March 2019, 70 patients completed the study. Fifty patients were seen by telemedicine and 20 patients were seen in person. Median Consultation and Relational Empathy scores (with a possible score of 10–50) were 49 (range 27–50) for telemedicine and 45 (range 26–50) for in-person consultations (Wilcoxon rank sum p = 0.18). Each item of the Consultation and Relational Empathy questionnaire was rated very good or excellent by at least 87% of participants in the telemedicine group. The median Telemedicine Patient Satisfaction Measure score was 54 (range 12–60), with each item rated agree or strongly agree by at least 84% of participants. Discussion We found no difference between telemedicine and in-person visits in patient perception of physician empathy in acute stroke care. Therefore, we conclude that empathy can be conveyed by facial expression, voice and attentiveness in a telemedicine encounter and, in the setting of acute stroke care, does not require physical touch or proximity.

Background and Purpose— Meta-analyses of extant genome-wide data illustrate the need to focus on subtypes of ischemic stroke for gene discovery. The National Institute of Neurological Disorders and Stroke SiGN (Stroke Genetics Network) contributes substantially to meta-analyses that focus on specific subtypes of stroke. Methods— The National Institute of Neurological Disorders and Stroke SiGN includes ischemic stroke cases from 24 genetic research centers: 13 from the United States and 11 from Europe. Investigators harmonize ischemic stroke phenotyping using the Web-based causative classification of stroke system, with data entered by trained and certified adjudicators at participating genetic research centers. Through the Center for Inherited Diseases Research, the Network plans to genotype 10 296 carefully phenotyped stroke cases using genome-wide single nucleotide polymorphism arrays and adds to these another 4253 previously genotyped cases, for a total of 14 549 cases. To maximize power for subtype analyses, the study allocates genotyping resources almost exclusively to cases. Publicly available studies provide most of the control genotypes. Center for Inherited Diseases Research–generated genotypes and corresponding phenotypes will be shared with the scientific community through the US National Center for Biotechnology Information database of Genotypes and Phenotypes, and brain MRI studies will be centrally archived. Conclusions— The Stroke Genetics Network, with its emphasis on careful and standardized phenotyping of ischemic stroke and stroke subtypes, provides an unprecedented opportunity to uncover genetic determinants of ischemic stroke.

Background and Purpose— Ischemic stroke (IS) is a multifactorial disorder with strong evidence from twin, family, and animal model studies suggesting a genetic influence on risk and prognosis. Several candidate genes for IS have been proposed, but few have been replicated. We investigated the contribution of 67 candidate genes (369 single nucleotide polymorphisms [SNPs]) on the risk of IS in a North American population of European descent. Methods— Two independent studies were performed. In the first, 342 SNPs from 52 candidate genes were genotyped in 307 IS cases and 324 control subjects. The SNPs significantly associated with IS were tested for replication in another cohort of 583 IS cases and 270 control subjects. In the second study, 212 SNPs from 62 candidate genes were analyzed in 710 IS cases with subtyping available and 3751 control subjects. Results— None of the candidate genes (SNPs) were significantly associated with IS risk independent of known stroke risk factors after correction for multiple hypotheses testing. Conclusion— These results are consistent with previous meta-analyses that demonstrate an absence of genetic association of variants in plausible candidate genes with IS risk. Our study suggests that the effect of the investigated SNPs may be weak or restricted to specific populations or IS subtypes.