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- Broberg, C. S., et al.
(författare)
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Long-Term Outcomes After Atrial Switch Operation for Transposition of the Great Arteries
- 2022
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Ingår i: Journal of the American College of Cardiology. - : Elsevier BV. - 0735-1097. ; 80:10, s. 951-963
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Tidskriftsartikel (refereegranskat)abstract
- Background: For patients with d-loop transposition of the great arteries (d-TGA) with a systemic right ventricle after an atrial switch operation, there is a need to identify risks for end-stage heart failure outcomes. Objectives: The authors aimed to determine factors associated with survival in a large cohort of such individuals. Methods: This multicenter, retrospective cohort study included adults with d-TGA and prior atrial switch surgery seen at a congenital heart center. Clinical data from initial and most recent visits were obtained. The composite primary outcome was death, transplantation, or mechanical circulatory support (MCS). Results: From 1,168 patients (38% female, age at first visit 29 ± 7.2 years) during a median 9.2 years of follow-up, 91 (8.8% per 10 person-years) met the outcome (66 deaths, 19 transplantations, 6 MCS). Patients experiencing sudden/arrhythmic death were younger than those dying of other causes (32.6 ± 6.4 years vs 42.4 ± 6.8 years; P < 0.001). There was a long duration between sentinel clinical events and end-stage heart failure. Age, atrial arrhythmia, pacemaker, biventricular enlargement, systolic dysfunction, and tricuspid regurgitation were all associated with the primary outcome. Independent 5-year predictors of primary outcome were prior ventricular arrhythmia, heart failure admission, complex anatomy, QRS duration >120 ms, and severe right ventricle dysfunction based on echocardiography. Conclusions: For most adults with d-TGA after atrial switch, progress to end-stage heart failure or death is slow. A simplified prediction score for 5-year adverse outcome is derived to help identify those at greatest risk.
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| 2. |
- Schiller, D, et al.
(författare)
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The Human Affectome
- 2024
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Ingår i: Neuroscience and biobehavioral reviews. - 1873-7528. ; 158, s. 105450-
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Tidskriftsartikel (refereegranskat)
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| 3. |
- Chen, C. Y., et al.
(författare)
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Genetic validation of bipolar disorder identified by automated phenotyping using electronic health records
- 2018
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Ingår i: Translational Psychiatry. - : Springer Science and Business Media LLC. - 2158-3188. ; 8:1, s. 1-8
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Tidskriftsartikel (refereegranskat)abstract
- Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363-372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h 2 g) and genetic correlation (r g) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency-"coded-strict", "coded-broad", and "coded-broad based on a single clinical encounter" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h 2 g were 0.24 (p = 0.015), 0.09 (p = 0.064), 0.13 (p = 0.003), 0.00 (p = 0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h 2 g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h 2 g) was 0.12 (p = 0.004). These h 2 g were lower or similar to the h 2 g observed by the ICCBD + PGCBD (0.23, p = 3.17E-80, total N = 33,181). However, the r g between ICCBD + PGCBD and the EHR-based cases were high for 95-NLP (0.66, p = 3.69 × 10-5), coded-strict (1.00, p = 2.40 × 10-4), and coded-broad (0.74, p = 8.11 × 10-7). The r g between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.
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