Dataset Information

A Growth-Accommodating Implant for Paediatric Applications.

ABSTRACT: Medical implants of fixed size cannot accommodate normal tissue growth in children, and often require eventual replacement or in some cases removal, leading to repeated interventions, increased complication rates and worse outcomes. Implants that can correct anatomic deformities and accommodate tissue growth remain an unmet need. Here, we report the design and use of a growth-accommodating device for paediatric applications that consists of a biodegradable core and a tubular braided sleeve, with inversely related sleeve length and diameter. The biodegradable core constrains the diameter of the sleeve, and gradual core degradation following implantation enables sleeve and overall device elongation in order to accommodate tissue growth. By using mathematical modeling and ex vivo experiments using harvested swine hearts, we demonstrate the predictability and tunability of the behavior of the device for disease- and patient-specific needs. We also used the rat tibia and the piglet heart valve as two models of tissue growth to demonstrate that polymer degradation enables device expansion and growth accommodation in vivo.

SUBMITTER: Feins EN

PROVIDER: S-EPMC5995571 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A Growth-Accommodating Implant for Paediatric Applications.

Feins Eric N EN Lee Yuhan Y O'Cearbhaill Eoin D ED Vasilyev Nikolay V NV Shimada Shogo S Friehs Ingeborg I Perrin Douglas D Hammer Peter E PE Yamauchi Haruo H Marx Gerald G Gosline Andrew A Arabagi Veaceslav V Karp Jeffrey M JM Del Nido Pedro J PJ

Nature biomedical engineering 20171010

Medical implants of fixed size cannot accommodate normal tissue growth in children, and often require eventual replacement or in some cases removal, leading to repeated interventions, increased complication rates and worse outcomes. Implants that can correct anatomic deformities and accommodate tissue growth remain an unmet need. Here, we report the design and use of a growth-accommodating device for paediatric applications that consists of a biodegradable core and a tubular braided sleeve, with ...[more]

PMID: 29900036

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Project description:ObjectiveAssess the real-world performance of popular imputation algorithms on cochlear implant (CI) candidate audiometric data.Methods7,451 audiograms from patients undergoing CI candidacy evaluation were pooled from 32 institutions with complete case analysis yielding 1,304 audiograms. Imputation model performance was assessed with nested cross-validation on randomly generated sparse datasets with various amounts of missing data, distributions of sparsity, and dataset sizes. A threshold for safe imputation was defined as root mean square error (RMSE) <10dB. Models included univariate imputation, interpolation, multiple imputation by chained equations (MICE), k-nearest neighbors, gradient boosted trees, and neural networks.ResultsGreater quantities of missing data were associated with worse performance. Sparsity in audiometric data is not uniformly distributed, as inter-octave frequencies are less commonly tested. With 3-8 missing features per instance, a real-world sparsity distribution was associated with significantly better performance compared to other sparsity distributions (Δ RMSE 0.3 dB- 5.8 dB, non-overlapping 99% confidence intervals). With a real-world sparsity distribution, models were able to safely impute up to 6 missing datapoints in an 11-frequency audiogram. MICE consistently outperformed other models across all metrics and sparsity distributions (p < 0.01, Wilcoxon rank sum test). With sparsity capped at 6 missing features per audiogram but otherwise equivalent to the raw dataset, MICE imputed with RMSE of 7.83 dB [95% CI 7.81-7.86]. Imputing up to 6 missing features captures 99.3% of the audiograms in our dataset, allowing for a 5.7-fold increase in dataset size (1,304 to 7,399 audiograms) as compared with complete case analysis.ConclusionPrecision medicine will inevitably play an integral role in the future of hearing healthcare. These methods are data dependent, and rigorously validated imputation models are a key tool for maximizing datasets. Using the largest CI audiogram dataset to-date, we demonstrate that in a real-world scenario MICE can safely impute missing data for the vast majority (>99%) of audiograms with RMSE well below a clinically significant threshold of 10dB. Evaluation across a range of dataset sizes and sparsity distributions suggests a high degree of generalizability to future applications.

Dataset Information

A Growth-Accommodating Implant for Paediatric Applications.

Publications

A Growth-Accommodating Implant for Paediatric Applications.

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