Urinary di-(2-ethylhexyl) phthalate metabolites for detecting transfusion of autologous blood stored in plasticizer-free bags.

BACKGROUND: Autologous blood transfusion (ABT) efficiently increases sport performance and is the most challenging doping method to detect. Current methods for detecting this practice center on the plasticizer di(2-ethlyhexyl) phthalate (DEHP), which enters the stored blood from blood bags. Quantification of this plasticizer and its metabolites in urine can detect the transfusion of autologous blood stored in these bags. However, DEHP-free blood bags are available on the market, including n-butyryl-tri-(n-hexyl)-citrate (BTHC) blood bags. Athletes may shift to using such bags to avoid the detection of urinary DEHP metabolites. STUDY DESIGN AND METHODS: A clinical randomized double-blinded two-phase study was conducted of healthy male volunteers who underwent ABT using DEHP-containing or BTHC blood bags. All subjects received a saline injection for the control phase and a blood donation followed by ABT 36 days later. Kinetic excretion of five urinary DEHP metabolites was quantified with liquid chromatography coupled with tandem mass spectrometry. RESULTS: Surprisingly, considerable levels of urinary DEHP metabolites were observed up to 1 day after blood transfusion with BTHC blood bags. The long-term metabolites mono-(2-ethyl-5-carboxypentyl) phthalate and mono-(2-carboxymethylhexyl) phthalate were the most sensitive biomarkers to detect ABT with BTHC blood bags. Levels of DEHP were high in BTHC bags (6.6%), the tubing in the transfusion kit (25.2%), and the white blood cell filter (22.3%). CONCLUSIONS: The BTHC bag contained DEHP, despite being labeled DEHP-free. Urinary DEHP metabolite measurement is a cost-effective way to detect ABT in the antidoping field even when BTHC bags are used for blood storage.

Hepcidin as a new biomarker for detecting autologous blood transfusion.

Autologous blood transfusion (ABT) is an efficient way to increase sport performance. It is also the most challenging doping method to detect. At present, individual follow-up of haematological variables via the athlete biological passport (ABP) is used to detect it. Quantification of a novel hepatic peptide called hepcidin may be a new alternative to detect ABT. In this prospective clinical trial, healthy subjects received a saline injection for the control phase, after which they donated blood that was stored and then transfused 36 days later. The impact of ABT on hepcidin as well as haematological parameters, iron metabolism, and inflammation markers was investigated. Blood transfusion had a particularly marked effect on hepcidin concentrations compared to the other biomarkers, which included haematological variables. Hepcidin concentrations increased significantly: 12 hr and 1 day after blood reinfusion, these concentrations rose by seven- and fourfold, respectively. No significant change was observed in the control phase. Hepcidin quantification is a cost-effective strategy that could be used in an "ironomics" strategy to improve the detection of ABT.

Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration.

Purpose: Neovascular age-related macular degeneration (nAMD) shows variable treatment response to intravitreal anti-VEGF. This analysis compared the potential of different artificial intelligence (AI)-based machine learning models using OCT and clinical variables to accurately predict at baseline the best-corrected visual acuity (BCVA) at 9 months in response to ranibizumab in patients with nAMD. Design: Retrospective analysis. Participants: Baseline and imaging data from patients with subfoveal choroidal neovascularization secondary to age-related macular dengeration. Methods: Baseline data from 502 study eyes from the HARBOR (NCT00891735) prospective clinical trial (monthly ranibizumab 0.5 and 2.0 mg arms) were pooled; 432 baseline OCT volume scans were included in the analysis. Seven models, based on baseline quantitative OCT features (Least absolute shrinkage and selection operator [Lasso] OCT minimum [min], Lasso OCT 1 standard error [SE]); on quantitative OCT features and clinical variables at baseline (Lasso min, Lasso 1SE, CatBoost, RF [random forest]); or on baseline OCT images only (deep learning [DL] model), were systematically compared with a benchmark linear model of baseline age and BCVA. Quantitative OCT features were derived by a DL segmentation model on the volume images, including retinal layer volumes and thicknesses, and retinal fluid biomarkers, including statistics on fluid volume and distribution. Main Outcome Measures: Prognostic ability of the models was evaluated using coefficient of determination (R2) and median absolute error (MAE; letters). Results: In the first cross-validation split, mean R2 (MAE) of the Lasso min, Lasso 1SE, CatBoost, and RF models was 0.46 (7.87), 0.42 (8.43), 0.45 (7.75), and 0.43 (7.60), respectively. These models ranked higher than or similar to the benchmark model (mean R2, 0.41; mean MAE, 8.20 letters) and better than OCT-only models (mean R2: Lasso OCT min, 0.20; Lasso OCT 1SE, 0.16; DL, 0.34). The Lasso min model was selected for detailed analysis; mean R2 (MAE) of the Lasso min and benchmark models for 1000 repeated cross-validation splits were 0.46 (7.7) and 0.42 (8.0), respectively. Conclusions: Machine learning models based on AI-segmented OCT features and clinical variables at baseline may predict future response to ranibizumab treatment in patients with nAMD. However, further developments will be needed to realize the clinical utility of such AI-based tools. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.