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  • Springer Science and Business Media LLC  (2)
  • 1
    Online Resource
    Online Resource
    A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults
    Springer Science and Business Media LLC ; 2019
    In:  Scientific Data Vol. 6, No. 1 ( 2019-02-12)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 6, No. 1 ( 2019-02-12)
    Abstract: We present a publicly available dataset of 227 healthy participants comprising a young (N=153, 25.1±3.1 years, range 20–35 years, 45 female) and an elderly group (N=74, 67.6±4.7 years, range 59–77 years, 37 female) acquired cross-sectionally in Leipzig, Germany, between 2013 and 2015 to study mind-body-emotion interactions. During a two-day assessment, participants completed MRI at 3 Tesla (resting-state fMRI, quantitative T1 (MP2RAGE), T2-weighted, FLAIR, SWI/QSM, DWI) and a 62-channel EEG experiment at rest. During task-free resting-state fMRI, cardiovascular measures (blood pressure, heart rate, pulse, respiration) were continuously acquired. Anthropometrics, blood samples, and urine drug tests were obtained. Psychiatric symptoms were identified with Standardized Clinical Interview for DSM IV (SCID-I), Hamilton Depression Scale, and Borderline Symptoms List. Psychological assessment comprised 6 cognitive tests as well as 21 questionnaires related to emotional behavior, personality traits and tendencies, eating behavior, and addictive behavior. We provide information on study design, methods, and details of the data. This dataset is part of the larger MPI Leipzig Mind-Brain-Body database.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/sdata.2018.308
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2775191-0
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  • 2
    Online Resource
    Online Resource
    Predicting radiocephalic arteriovenous fistula success with machine learning
    Springer Science and Business Media LLC ; 2022
    In:  npj Digital Medicine Vol. 5, No. 1 ( 2022-10-25)
    Details
    In: npj Digital Medicine, Springer Science and Business Media LLC, Vol. 5, No. 1 ( 2022-10-25)
    Abstract: After creation of a new arteriovenous fistula (AVF), assessment of readiness for use is an important clinical task. Accurate prediction of successful use is challenging, and augmentation of the physical exam with ultrasound has become routine. Herein, we propose a point-of-care tool based on machine learning to enhance prediction of successful unassisted radiocephalic arteriovenous fistula (AVF) use. Our analysis includes pooled patient-level data from 704 patients undergoing new radiocephalic AVF creation, eligible for hemodialysis, and enrolled in the 2014–2019 international multicenter PATENCY-1 or PATENCY-2 randomized controlled trials. The primary outcome being predicted is successful unassisted AVF use within 1-year, defined as 2-needle cannulation for hemodialysis for ≥90 days without preceding intervention. Logistic, penalized logistic (lasso and elastic net), decision tree, random forest, and boosted tree classification models were built with a training, tuning, and testing paradigm using a combination of baseline clinical characteristics and 4–6 week ultrasound parameters. Performance assessment includes receiver operating characteristic curves, precision-recall curves, calibration plots, and decision curves. All modeling approaches except the decision tree have similar discrimination performance and comparable net-benefit (area under the ROC curve 0.78–0.81, accuracy 69.1–73.6%). Model performance is superior to Kidney Disease Outcome Quality Initiative and University of Alabama at Birmingham ultrasound threshold criteria. The lasso model is presented as the final model due to its parsimony, retaining only 3 covariates: larger outflow vein diameter, higher flow volume, and absence of 〉 50% luminal stenosis. A point-of-care online calculator is deployed to facilitate AVF assessment in the clinic.
    Type of Medium: Online Resource
    ISSN: 2398-6352
    URL: Article
    DOI: 10.1038/s41746-022-00710-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2925182-5
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