| 1. |
- Mishra, A., et al.
(author)
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Stroke genetics informs drug discovery and risk prediction across ancestries
- 2022
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 611, s. 115-123
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Journal article (peer-reviewed)abstract
- Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.
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| 2. |
- Reps, J. M., et al.
(author)
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Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study
- 2021
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In: JMIR Medical Informatics. - : JMIR Publications Inc.. - 2291-9694. ; 9:4
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Journal article (peer-reviewed)abstract
- Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated. Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.
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| 3. |
- Williams, R. D., et al.
(author)
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Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network
- 2022
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In: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 22:1
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Journal article (peer-reviewed)abstract
- Background: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69–0.81, COVER-I: 0.73–0.91, and COVER-F: 0.72–0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. Conclusions: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use. © 2022, The Author(s).
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| 5. |
- Cornel, Jan H., et al.
(author)
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Anticoagulant therapy and outcomes in patients with prior or acute heart failure and acute coronary syndromes : Insights from the APixaban for PRevention of Acute ISchemic Events 2 trial
- 2015
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In: American Heart Journal. - : Elsevier BV. - 0002-8703 .- 1097-6744. ; 169:4, s. 531-538
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Journal article (peer-reviewed)abstract
- Background Clinical outcomes and the effects of oral anticoagulants among patients with acute coronary syndrome (ACS) and either a history of or acute heart failure (HF) are largely unknown. We aimed to assess the relationship between prior HF or acute HF complicating an index ACS event and subsequent clinical outcomes and the efficacy and safety of apixaban compared with placebo in these populations. Methods High-risk patients were randomly assigned post-ACS to apixaban 5.0 mg or placebo twice daily. Median follow-up was 8 (4-12) months. The primary outcome was cardiovascular death, myocardial infarction, or stroke. The main safety outcome was thrombolysis in myocardial infarction major bleeding. Results Heart failure was reported in 2,995 patients (41%), either as prior HF (2,076 [28%]) or acute HF (2,028 [27%]). Patients with HF had a very high baseline risk and were more often managed medically. Heart failure was associated with a higher rate of the primary outcome (prior HF: adjusted hazard ratio [HR] 1.73, 95% CI 1.42-2.10, P < .0001, acute HF: adjusted HR 1.65, 95% CI 1.35-2.01, P < .0001) and cardiovascular death (prior HF: HR 2.54, 95% CI 1.82-3.54, acute HF: adjusted HR 2.52, 95% CI 1.82-3.50). Patients with acute HF also had significantly higher rates of thrombolysis in myocardial infarction major bleeding (prior HF: adjusted HR 1.22, 95% CI 0.65-2.27, P = .54, acute HF: adjusted HR 1.78, 95% CI 1.03-3.08, P = .04). There was no statistical evidence of a differential effect of apixaban on clinical events or bleeding in patients with or without prior HF; however, among patients with acute HF, there were numerically fewer events with apixaban than placebo (14.8 vs 19.3, HR 0.76, 95% CI 0.57-1.01, interaction P = .13), a trend that was not seen in patients with prior HF or no HF. Conclusions In high-risk patients post-ACS, both prior and acute HFs are associated with an increased risk of subsequent clinical events. Apixaban did not significantly reduce clinical events and increased bleeding in patients with and without HF; however, there was a tendency toward fewer clinical events with apixaban in patients with acute HF.
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| 6. |
- Khan, Razi, et al.
(author)
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Characterising and predicting bleeding in high-risk patients with an acute coronary syndrome
- 2015
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In: Heart. - : BMJ. - 1355-6037 .- 1468-201X. ; 101:18, s. 1475-1484
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Journal article (peer-reviewed)abstract
- Objective In the Apixaban for Prevention of Acute Ischemic Events (APPRAISE-2) trial, the use of apixaban, when compared with placebo, in high-risk patients with a recent acute coronary syndrome (ACS) resulted in a significant increase in bleeding without a reduction in ischaemic events. The aim of this analysis was to provide further description of these bleeding events and to determine the baseline characteristics associated with bleeding in high-risk post-ACS patients. Methods APPRAISE-2 was a multinational clinical trial including 7392 high-risk patients with a recent ACS randomised to apixaban (5 mg twice daily) or placebo. Bleeding including Thrombolysis in Myocardial Infarction (TIMI) major or minor bleeding, International Society on Thrombosis and Haemostasis (ISTH) major or clinically relevant non-major (CRNM) bleeding, and any bleeding were analysed using an on-treatment analysis. Kaplan-Meier curves were plotted to describe the timing of bleeding, and a Cox proportional hazards model was used to identify predictors of ISTH major or CRNM bleeding and any bleeding. Median follow-up was 241 days. Results The proportion of patients who experienced TIMI major or minor, ISTH major or CRNM, and any bleeding was 1.5%, 2.2% and 13.3%, respectively. The incidence of bleeding was highest in the immediate post-ACS period (0.11 in the first 30 days vs 0.03 after 30 days events per 1 patient-year); however, > 60% of major bleeding events occurred > 30 days after the end of the index hospitalisation. Gastrointestinal bleeding was the most common cause of major bleeding, accounting for 45.9% of TIMI major or minor and 39.5% of ISTH major or CRNM bleeding events. Independent predictors of ISTH major or CRNM bleeding events included older age, renal dysfunction, dual oral antiplatelet therapy, smoking history, increased white cell count and coronary revascularisation. Conclusions When compared with placebo, the use of apixaban is associated with an important short-term and long-term risk of bleeding in high-risk post-ACS patients, with gastrointestinal bleeding being the most common source of major bleeding. The baseline predictors of major bleeding appear to be consistent with those identified in lower-risk ACS populations with shorter-term follow-up.
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| 7. |
- Krishnan, S., et al.
(author)
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SWIRL : A SequentialWindowed Inverse Reinforcement Learning Algorithm for Robot Tasks With Delayed Rewards
- 2020
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In: Springer Proceedings in Advanced Robotics. - Cham : Springer Nature. - 2511-1256. ; 13, s. 672-687
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Journal article (peer-reviewed)abstract
- Inverse Reinforcement Learning (IRL) allows a robot to generalize from demonstrations to previously unseen scenarios by learning the demonstrator’s reward function. However, in multi-step tasks, the learned rewards might be delayed and hard to directly optimize. We present Sequential Windowed Inverse Reinforcement Learning (SWIRL), a three-phase algorithm that partitions a complex task into shorter-horizon subtasks based on linear dynamics transitions that occur consistently across demonstrations. SWIRL then learns a sequence of local reward functions that describe the motion between transitions. Once these reward functions are learned, SWIRL applies Q-learning to compute a policy that maximizes the rewards. We compare SWIRL (demonstrations to segments to rewards) with Supervised Policy Learning (SPL - demonstrations to policies) and Maximum Entropy IRL (MaxEnt-IRL demonstrations to rewards) on standard Reinforcement Learning benchmarks: Parallel Parking with noisy dynamics, Two-Link acrobot, and a 2D GridWorld. We find that SWIRL converges to a policy with similar success rates (60%) in 3x fewer time-steps than MaxEnt-IRL, and requires 5x fewer demonstrations than SPL. In physical experiments using the da Vinci surgical robot, we evaluate the extent to which SWIRL generalizes from linear cutting demonstrations to cutting sequences of curved paths.
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| 9. |
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| 10. |
- Zetterström, P., et al.
(author)
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Nanoscale defect clusters in metallic glasses
- 2007
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In: Journal of Physics. - : IOP Publishing. - 0953-8984 .- 1361-648X. ; 19:37, s. 376217-
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Journal article (peer-reviewed)abstract
- The reverse Monte Carlo method was used to obtain three-dimensional discrete distributions of constitutional atoms in melt-spun CuZr and CuZrTi metallic glasses from neutron and x-ray diffraction data. It was found that the icosahedral short-range order is less stable in the CuZr binary alloy than in the Ti-doped ternary alloy. The present investigation also provides evidence on the medium-range order, characterized by some nanoscale clusters of defects, in the metallic-glassy state.
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