With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. ...Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated. In previous research, AutoML using high-dimensional data of varying types has been demonstrably robust, outperforming traditional approaches. However, considerations for application in clinical metabolic profiling remain to be evaluated. Particularly, regarding the robustness of AutoML to identify and adjust for common clinical confounders. In this study, we present a focused case study regarding AutoML considerations for using the Tree-Based Optimization Tool (TPOT) in metabolic profiling of exposure to metformin in a biobank cohort. First, we propose a tandem rank-accuracy measure to guide agnostic feature selection and corresponding threshold determination in clinical metabolic profiling endeavors. Second, while AutoML, using default parameters, demonstrated potential to lack sensitivity to low-effect confounding clinical covariates, we demonstrated residual training and adjustment of metabolite features as an easily applicable approach to ensure AutoML adjustment for potential confounding characteristics. Finally, we present increased homocysteine with long-term exposure to metformin as a potentially novel, non-replicated metabolite association suggested by TPOT; an association not identified in parallel clinical metabolic profiling endeavors. While warranting independent replication, our tandem rank-accuracy measure suggests homocysteine to be the metabolite feature with largest effect, and corresponding priority for further translational clinical research. Residual training and adjustment for a potential confounding effect by BMI only slightly modified the suggested association. Increased homocysteine is thought to be associated with vitamin B12 deficiency - evaluation for potential clinical relevance is suggested. While considerations for clinical metabolic profiling are recommended, including adjustment approaches for clinical confounders, AutoML presents an exciting tool to enhance clinical metabolic profiling and advance translational research endeavors.
► Research strategy for Accelerated Metallurgy project is outlined. ► Surprising symmetry among atomic, nanoscale and mechanical properties. ► Generalisation of Ashby diagrams via principal component ...analysis. ► Atomic-related properties can be described with linear regression. ► Mechanical properties modelled via Kocks–Mecking-type physical method.
Metals and alloys have been indispensable for technological progress, but only a fraction of the possible ternary systems (combinations of three elements) is known. Statistical inference methods combined with physical models are presented to discover new systems of enhanced properties. It is demonstrated that properties originating from atomic-level interactions can be described employing a linear regression analysis, but properties incorporating microstructural and thermal history effects require a balance between physical and statistical modelling. In spite of this, there is a remarkable degree of symmetry among all properties, and by employing a principal components analysis it is shown that ten properties essential to engineering can be described well in a three dimensional space. This will aid in the discovery of novel alloying systems.
Interactive alerts are used to enhance compliance with primary prevention and have been shown to improve quality metrics. However, the degree of impact of these alerts is controversial and there is ...concern with excessive alerting. Our objective is to develop reliable processes to assess the direct impact of interactive alerts on clinical performance. Here we present preliminary finding related to the evaluation of the performance gaps between alerts and clinical practice.
Organizations require their business processes goals and the underlying information technology (IT) to be in synchronization with each other, but the continual changes in business processes makes ...this difficult. To accomplish this synchronization, there needs to be an alignment between the business processes and the IT. Business processes are currently defined using such well-known notations as BPMN, and the IT is made available by different services. Hence, the alignment process can be defined as one between the organization's BPMNs and the services provided by its IT. In practice, however, this process is a complex task which is carried out by hand and hence is error prone. The present communication analyzes the conditions, relations, and incompatibilities between BPMNs and the service descriptions. The incompatibilities are formalized mathematically in order to facilitate their identification and resolution. Then, an alignment process is defined taking into account these incompatibilities and their solutions. The wrapper code needed to resolve each incompatibility identified during the alignment process is generated automatically. Finally, a case study is presented to validate and illustrate the use of the proposed alignment process. The results provided by the semiautomatic alignment process were similar to those obtained manually by a group of experts.
Clinical domain knowledge about diseases and their comorbidities, severity, treatment pathways, and outcomes can facilitate diagnosis, enhance preventive strategies, and help create smart ...evidence-based practice guidelines.
To introduce a new representation of patient data called disease severity hierarchy that leverages domain knowledge in a nested fashion to create subpopulations that share increasing amounts of clinical details suitable for risk prediction.
This retrospective cohort study included 51 969 patients aged 45 to 85 years, with 10 674 patients who received primary care at the Mayo Clinic between January 2004 and December 2015 in the training cohort and 41 295 patients who received primary care at Fairview Health Services from January 2010 to December 2017 in the validation cohort. Data were analyzed from May 2018 to December 2019.
Several binary classification measures, including the area under the receiver operating characteristic curve (AUC), Gini score, sensitivity, and positive predictive value, were used to evaluate models predicting all-cause mortality and major cardiovascular events at ages 60, 65, 75, and 80 years.
The mean (SD) age and proportions of women and white individuals were 59.4 (10.8) years, 6324 (59.3%) and 9804 (91.9%), respectively, in the training cohort and 57.4 (7.9) years, 21 975 (53.1%), and 37 653 (91.2%), respectively, in the validation cohort. During follow-up, 945 patients (8.9%) in the training cohort died, while 787 (7.4%) had major cardiovascular events. Models using the new representation achieved AUCs for predicting death in the training cohort at ages 60, 65, 75, and 80 years of 0.96 (95% CI, 0.94-0.97), 0.96 (95% CI, 0.95-0.98), 0.97 (95% CI, 0.96-0.98), and 0.98 (95% CI, 0.98-0.99), respectively, while standard methods achieved modest AUCs of 0.67 (95% CI, 0.55-0.80), 0.66 (95% CI, 0.56-0.79), 0.64 (95% CI, 0.57-0.71), and 0.63 (95% CI, 0.54-0.70), respectively.
In this study, the proposed patient data representation accurately predicted the age at which a patient was at risk of dying or developing major cardiovascular events substantially better than standard methods. The representation uses known relationships contained in electronic health records to capture disease severity in a natural and clinically meaningful way. Furthermore, it is expressive and interpretable. This novel patient representation can help to support critical decision-making, develop smart guidelines, and enhance health care and disease management by helping to identify patients with high risk.
The effectiveness of thyroid hormone suppressive therapy in reducing the volume of benign thyroid nodules is controversial. It is important to clarify this therapeutic effect of thyroid hormone, ...because its prolonged use needs to be carefully weighed against its potential deleterious effects in the skeletal and cardiovascular systems. To evaluate the best available evidence, we conducted a systematic review and meta-analysis of the randomized controlled trials that fulfill the following inclusion criteria: single thyroid nodules proven benign by fine needle aspiration, treatment, and follow-up of at least 6 months; documented suppression of TSH; measurement of thyroid nodule volume by ultrasound; and response to therapy defined as more than 50% volume reduction from baseline. Six randomized clinical trials published between 1987 and 1999, with 346 patients, were included in the meta-analysis. Ninety percent of the participants were female. Using a random effects model, the overall effect size showed a relative risk of 1.9 (95% confidence interval, 0.95–3.81) favoring a treatment effect. A sensitivity analysis showed significant changes in the results.
Suppressive thyroid hormone therapy for longer than 6 months is associated with a trend toward a reduction of more than 50% in volume of benign thyroid nodules, without achieving statistical significance. The results are highly sensitive to changes in the statistical analysis, especially if the method used ignores heterogeneity among the effect sizes. More studies are needed before this therapy can be widely recommended.
Physical and statistical models are combined to describe and design magnesium and high entropy alloys. A principal component analysis is applied to merge material datasets, and it is shown that ...limits in properties can be envisaged. Extrapolation techniques can be employed to devise properties of non-existing alloys, such as specific heat capacity, melting point and Young’s modulus. These in turn can be input to physical models to predict, for example, yield strength and modulus of toughness. The tools described herein can readily be used for materials discovery, and are being implemented in the Accelerated Metallurgy project.
Background
Prolongation of the QT on the surface electrocardiogram can be due to either genetic or acquired causes. Distinguishing congenital long QT syndrome (LQTS) from acquired QT prolongation has ...important prognostic and management implications. We aimed to investigate if quantitative T‐wave analysis could provide a tool for the physician to differentiate between congenital and acquired QT prolongation.
Methods
Patients were identified through an institution‐wide computer‐based QT screening system which alerts the physician if the QTc ≥ 500 ms. ECGs were retrospectively analyzed with an automated T‐wave analysis program. Congenital LQTS was compared in a 1:3 ratio to those with an identified acquired etiology for QT prolongation (electrolyte abnormality and/or prescription of known QT prolongation medications). Linear discriminant analysis was performed using 10‐fold cross‐validation to statistically test the selected features.
Results
The 12‐lead ECG of 38 patients with congenital LQTS and 114 patients with drug‐induced and/or electrolyte‐mediated QT prolongation were analyzed. In lead V5, patients with acquired QT prolongation had a shallower T wave right slope (−2,322 vs. −3,593 mV/s), greater T‐peak‐Tend interval (109 vs. 92 ms), and smaller T wave center of gravity on the x axis (290 ms vs. 310 ms; p < .001). These features could distinguish congenital from acquired causes in 77% of cases (sensitivity 90%, specificity 58%).
Conclusion
T‐wave morphological analysis on lead V5 of the surface ECG could successfully differentiate congenital from acquired causes of QT prolongation.
To inform the process of returning results in genome sequencing studies, we conducted a quantitative and qualitative assessment of challenges encountered during the Return of Actionable Variants ...Empiric (RAVE) study conducted at Mayo Clinic. Participants (
= 2535, mean age 63 ± 7, 57% female) were sequenced for 68 clinically actionable genes and 14 single nucleotide variants. Of 122 actionable results detected, 118 were returnable; results were returned by a genetic counselor-86 in-person and 12 by phone. Challenges in returning actionable results were encountered in a significant proportion (38%) of the cohort and were related to sequencing and participant contact. Sequencing related challenges (
= 14), affecting 13 participants, included reports revised based on clinical presentation (
= 3); reports requiring corrections (
= 2); mosaicism requiring alternative DNA samples for confirmation (
= 3); and variant re-interpretation due to updated informatics pipelines (
= 6). Participant contact related challenges (
= 44), affecting 38 participants, included nonresponders (
= 20), decedents (
= 1), and previously known results (
= 23). These results should be helpful to investigators preparing for return of results in large-scale genomic sequencing projects.
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