ObjectiveHearing loss (HL) is highly prevalent, yet underrecognized and underdiagnosed. Lack of standardized screening, awareness, cost, and access to hearing testing present barriers to HL identification. To facilitate prescreening and selection of patients who warrant audiometric evaluation, we developed a machine learning (ML) model to predict speech‐frequency pure‐tone average (PTA).Study DesignCross‐sectional study.SettingNational Health and Nutrition Examination Survey (NHANES).MethodsThe cohort included 8918 adults (≥20 years) who completed audiometric testing with NHANES (2012‐2018). The primary outcome measure was the prediction of better hearing ear speech‐frequency PTA. Relevant predictors included demographics, medical conditions, and subjective assessment of hearing. Supervised ML with a tree‐based architecture was used. Regression performance was determined by the mean absolute error (MAE) with binary classification assessed with area under the receiver operating characteristic curve (AUC).ResultsUsing the full set of predictors, the test set MAE between the ML‐predicted and actual PTA was 5.29 dB HL (95% confidence interval [CI]: 4.97‐5.61). The 5 most influential predictors of higher PTA were increased age, worse subjective hearing, male gender, increased body mass index, and history of smoking. The 5‐factor abbreviated model performed comparably to the extended feature set with MAE 5.36 (95% CI: 5.03‐5.69) and AUC for PTA > 25 dB HL of 0.92 (95% CI: 0.90‐0.94).ConclusionThe ML model was able to predict PTA with patient demographics, clinical factors, and subjective hearing status. ML‐based prediction may be used to identify individuals who could benefit most from audiometric evaluation.
Aliphatic polyesters are potential sustainable alternatives to PVC for use in medical devices, such as IV bags. Our candidate replacement of PVC-based IV bags use P4MCL, a sustainable polymer with demonstrated uses in mechanically robust materials. The goal of our project was to compare the mechanical and biocompatibility characteristics of P4MCL/PLLA star block copolymer TPEs with commercial PVC-based IV bags. P4MCL/PLLA TPEs were synthesized according to previously reported methods. Uniaxial tensile testing was conducted pre- and post-autoclave. Impact and tear resistance testing was performed on non-autoclaved specimens according to ASTM standards. Cytotoxicity was examined using NIH 3T3 Fibroblasts with an AlamarBlue assay. A student’s t-test was used to compare results with statistical significance of P < 0.05. PVC tended to be stiffer but P4MCL/PLLA was more extensible. The tensile properties for the P4MCL-based material did not change after autoclaving. When compared to PVC-based IV bags, the P4MCL/PLLA TPE demonstrated a lower peak force and average force but a greater elongation at break and total absorbed energy (P<0.05). P4MCL/PLLA, unlike PVC-based materials with DEHP plasticizer, was non-cytotoxic. In summary, P4MCL/PLLA has desirable mechanical and biocompatibility advantages compared to PVC making the material a potential sustainable alternative for medical grade plastics.
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