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1

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Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45 000 donors

Di Angelantonio, Emanuele ; Thompson, Simon G ; Kaptoge, Stephen ; [et al.]

Elsevier BV ; 2017

In: The Lancet Vol. 390, No. 10110 ( 2017-11), p. 2360-2371

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In: The Lancet, Elsevier BV, Vol. 390, No. 10110 ( 2017-11), p. 2360-2371

Type of Medium: Online Resource

ISSN: 0140-6736

URL: Article

DOI: 10.1016/S0140-6736(17)31928-1

RVK:

XA 10000

Language: English

Publisher: Elsevier BV

Publication Date: 2017

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detail.hit.zdb_id: 3306-6

detail.hit.zdb_id: 1476593-7

SSG: 5,21

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2

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Prospective observational cohort study on grading the severity of postoperative complications in global surgery research

International Surgical Outcomes Study (ISOS) group ; Abbott, T E F ; Greaves, K E ; [et al.]

Oxford University Press (OUP) ; 2019

In: British Journal of Surgery Vol. 106, No. 2 ( 2019-01-08), p. e73-e80

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In: British Journal of Surgery, Oxford University Press (OUP), Vol. 106, No. 2 ( 2019-01-08), p. e73-e80

Abstract: The Clavien–Dindo classification is perhaps the most widely used approach for reporting postoperative complications in clinical trials. This system classifies complication severity by the treatment provided. However, it is unclear whether the Clavien–Dindo system can be used internationally in studies across differing healthcare systems in high- (HICs) and low- and middle-income countries (LMICs). Methods This was a secondary analysis of the International Surgical Outcomes Study (ISOS), a prospective observational cohort study of elective surgery in adults. Data collection occurred over a 7-day period. Severity of complications was graded using Clavien–Dindo and the simpler ISOS grading (mild, moderate or severe, based on guided investigator judgement). Severity grading was compared using the intraclass correlation coefficient (ICC). Data are presented as frequencies and ICC values (with 95 per cent c.i.). The analysis was stratified by income status of the country, comparing HICs with LMICs. Results A total of 44 814 patients were recruited from 474 hospitals in 27 countries (19 HICs and 8 LMICs). Some 7508 patients (16·8 per cent) experienced at least one postoperative complication, equivalent to 11 664 complications in total. Using the ISOS classification, 5504 of 11 664 complications (47·2 per cent) were graded as mild, 4244 (36·4 per cent) as moderate and 1916 (16·4 per cent) as severe. Using Clavien–Dindo, 6781 of 11 664 complications (58·1 per cent) were graded as I or II, 1740 (14·9 per cent) as III, 2408 (20·6 per cent) as IV and 735 (6·3 per cent) as V. Agreement between classification systems was poor overall (ICC 0·41, 95 per cent c.i. 0·20 to 0·55), and in LMICs (ICC 0·23, 0·05 to 0·38) and HICs (ICC 0·46, 0·25 to 0·59). Conclusion Caution is recommended when using a treatment approach to grade complications in global surgery studies, as this may introduce bias unintentionally.

Type of Medium: Online Resource

ISSN: 0007-1323 , 1365-2168

URL: Article

DOI: 10.1002/bjs.11025

Language: English

Publisher: Oxford University Press (OUP)

Publication Date: 2019

detail.hit.zdb_id: 2006309-X

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3

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The surgical safety checklist and patient outcomes after surgery: a prospective observational cohort study, systematic review and meta-analysis

Abbott, T.E.F. ; Ahmad, T. ; Phull, M.K. ; [et al.]

Elsevier BV ; 2018

In: British Journal of Anaesthesia Vol. 120, No. 1 ( 2018-01), p. 146-155

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In: British Journal of Anaesthesia, Elsevier BV, Vol. 120, No. 1 ( 2018-01), p. 146-155

Type of Medium: Online Resource

ISSN: 0007-0912

URL: Article

DOI: 10.1016/j.bja.2017.08.002

Language: English

Publisher: Elsevier BV

Publication Date: 2018

detail.hit.zdb_id: 2011968-9

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4

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Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics

Varatharajah, Yogatheesan ; Ramanan, Vijay K. ; Iyer, Ravishankar ; [et al.]

Springer Science and Business Media LLC ; 2019

In: Scientific Reports Vol. 9, No. 1 ( 2019-02-19)

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In: Scientific Reports, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2019-02-19)

Abstract: In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression of CR1 (complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.

Type of Medium: Online Resource

ISSN: 2045-2322

URL: Article

DOI: 10.1038/s41598-019-38793-3

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2019

detail.hit.zdb_id: 2615211-3

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5

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Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non-convex Penalty

Du, Lei ; Liu, Kefei ; Yao, Xiaohui ; [et al.]

Springer Science and Business Media LLC ; 2017

In: Scientific Reports Vol. 7, No. 1 ( 2017-10-25)

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In: Scientific Reports, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-10-25)

Abstract: Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose $${\ell }_{{\bf{1}}}$$ ℓ 1 -norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the $${\ell }_{{\bf{1}}}$$ ℓ 1 -norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce the estimation bias in regression tasks. But using them in SCCA remains largely unexplored. In this paper, we design a unified non-convex SCCA model, based on seven non-convex functions, for unbiased estimation and stable feature selection simultaneously. We also propose an efficient optimization algorithm. The proposed method obtains both higher correlation coefficients and better canonical loading patterns. Specifically, these SCCA methods with non-convex penalties discover a strong association between the APOE e4 rs429358 SNP and the hippocampus region of the brain. They both are Alzheimer’s disease related biomarkers, indicating the potential and power of the non-convex methods in brain imaging genetics.

Type of Medium: Online Resource

ISSN: 2045-2322

URL: Article

DOI: 10.1038/s41598-017-13930-y

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2017

detail.hit.zdb_id: 2615211-3

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6

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Morphometricity as a measure of the neuroanatomical signature of a trait

Sabuncu, Mert R. ; Ge, Tian ; Holmes, Avram J. ; [et al.]

Proceedings of the National Academy of Sciences ; 2016

In: Proceedings of the National Academy of Sciences Vol. 113, No. 39 ( 2016-09-27)

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In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 113, No. 39 ( 2016-09-27)

Abstract: Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.

Type of Medium: Online Resource

ISSN: 0027-8424 , 1091-6490

URL: Article

DOI: 10.1073/pnas.1604378113

RVK:

TA 1000

RVK:

WA 15000

Language: English

Publisher: Proceedings of the National Academy of Sciences

Publication Date: 2016

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SSG: 11

SSG: 12

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7

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Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

Zhang, Xiuming ; Mormino, Elizabeth C. ; Sun, Nanbo ; [et al.]

Proceedings of the National Academy of Sciences ; 2016

In: Proceedings of the National Academy of Sciences Vol. 113, No. 42 ( 2016-10-18)

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In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 113, No. 42 ( 2016-10-18)

Abstract: We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

Type of Medium: Online Resource

ISSN: 0027-8424 , 1091-6490

URL: Article

DOI: 10.1073/pnas.1611073113

RVK:

TA 1000

RVK:

WA 15000

Language: English

Publisher: Proceedings of the National Academy of Sciences

Publication Date: 2016

detail.hit.zdb_id: 209104-5

detail.hit.zdb_id: 1461794-8

SSG: 11

SSG: 12

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8

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Identical and Nonidentical Twins: Risk and Factors Involved in Development of Islet Autoimmunity and Type 1 Diabetes

Triolo, Taylor M. ; Fouts, Alexandra ; Pyle, Laura ; [et al.]

American Diabetes Association ; 2019

In: Diabetes Care Vol. 42, No. 2 ( 2019-02-01), p. 192-199

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In: Diabetes Care, American Diabetes Association, Vol. 42, No. 2 ( 2019-02-01), p. 192-199

Abstract: There are variable reports of risk of concordance for progression to islet autoantibodies and type 1 diabetes in identical twins after one twin is diagnosed. We examined development of positive autoantibodies and type 1 diabetes and the effects of genetic factors and common environment on autoantibody positivity in identical twins, nonidentical twins, and full siblings. RESEARCH DESIGN AND METHODS Subjects from the TrialNet Pathway to Prevention Study (N = 48,026) were screened from 2004 to 2015 for islet autoantibodies (GAD antibody [GADA], insulinoma-associated antigen 2 [IA-2A] , and autoantibodies against insulin [IAA]). Of these subjects, 17,226 (157 identical twins, 283 nonidentical twins, and 16,786 full siblings) were followed for autoantibody positivity or type 1 diabetes for a median of 2.1 years. RESULTS At screening, identical twins were more likely to have positive GADA, IA-2A, and IAA than nonidentical twins or full siblings (all P & lt; 0.0001). Younger age, male sex, and genetic factors were significant factors for expression of IA-2A, IAA, one or more positive autoantibodies, and two or more positive autoantibodies (all P ≤ 0.03). Initially autoantibody-positive identical twins had a 69% risk of diabetes by 3 years compared with 1.5% for initially autoantibody-negative identical twins. In nonidentical twins, type 1 diabetes risk by 3 years was 72% for initially multiple autoantibody–positive, 13% for single autoantibody–positive, and 0% for initially autoantibody-negative nonidentical twins. Full siblings had a 3-year type 1 diabetes risk of 47% for multiple autoantibody–positive, 12% for single autoantibody–positive, and 0.5% for initially autoantibody-negative subjects. CONCLUSIONS Risk of type 1 diabetes at 3 years is high for initially multiple and single autoantibody–positive identical twins and multiple autoantibody–positive nonidentical twins. Genetic predisposition, age, and male sex are significant risk factors for development of positive autoantibodies in twins.

Type of Medium: Online Resource

ISSN: 0149-5992 , 1935-5548

URL: Article

DOI: 10.2337/dc18-0288

Language: English

Publisher: American Diabetes Association

Publication Date: 2019

detail.hit.zdb_id: 1490520-6

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A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk

Redondo, Maria J. ; Geyer, Susan ; Steck, Andrea K. ; [et al.]

American Diabetes Association ; 2018

In: Diabetes Care Vol. 41, No. 9 ( 2018-09-01), p. 1887-1894

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In: Diabetes Care, American Diabetes Association, Vol. 41, No. 9 ( 2018-09-01), p. 1887-1894

Abstract: We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals. RESEARCH DESIGN AND METHODS We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients’ relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2–51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial–Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables. RESULTS Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06–1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS & gt;0.295, 95% CI 1.47–3.51; P = 0.0002). CONCLUSIONS The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.

Type of Medium: Online Resource

ISSN: 0149-5992 , 1935-5548

URL: Article

DOI: 10.2337/dc18-0087

Language: English

Publisher: American Diabetes Association

Publication Date: 2018

detail.hit.zdb_id: 1490520-6

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10

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Longer-term efficiency and safety of increasing the frequency of whole blood donation (INTERVAL): extension study of a randomised trial of 20 757 blood donors

Kaptoge, Stephen ; Di Angelantonio, Emanuele ; Moore, Carmel ; [et al.]

Elsevier BV ; 2019

In: The Lancet Haematology Vol. 6, No. 10 ( 2019-10), p. e510-e520

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In: The Lancet Haematology, Elsevier BV, Vol. 6, No. 10 ( 2019-10), p. e510-e520

Type of Medium: Online Resource

ISSN: 2352-3026

URL: Article

DOI: 10.1016/S2352-3026(19)30106-1

Language: English

Publisher: Elsevier BV

Publication Date: 2019

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