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Discussion
For the first time, we show that MMT-associated biomarkers are relevant for prediction of PD drop-out: MMP-2, TN-C, IL-11, PAI-1, PSTN, VEGF-A, COL-13, CDH-13, TSP-1, BMP-7, IL-6, FAP and IL-33. Interestingly, immunoassays have different detection ranges, and robust predictions are tied to those.8 All biomarkers had proper NMIFS and SHAP values, indicating that MMT processes may have a causal relationship with the development of CVD-UFF in PD. We confirmed that small longitudinal PD datasets, attribute shrinkage and gold-standard algorithms (ET, LDA) with overfitting testing and class imbalances predict PD endurance and technique failure.27
We acknowledge that our study has several constraints. Our train and validation datasets possessed a preponderance of white males with class imbalances of PD technique failure. Most patients with PD dropoff due to CVD were only registered with missing samples and/or reports. We note that this study comprised merely a small cohort, limiting the prediction power.
Nevertheless, we expanded common knowledge that MTC, UFR4H, RRF and electrolyte sieving, among others, are indeed predictors of PD endurance and can even be cardioprotective.4 External validations verified predictions only for MMT-UFF-CVD patients, preventing vague risk scores with a time resolution of 5–7 years.6 20 MAUXI predictions, requiring only effluents, avoid patient invasiveness and time burden.4
We divided PD outcomes into four categories (cohort selection) based on the observed historical registry of the entire dataset of patients since the first registry in the training dataset (1979). There were no other outcomes in the hospitals with whom we collaborated in the European Union. In our study, only real-world patients were integrated. We included as well patients who left PD for a transplant, failing shortly afterwards and returning to PD until failure due to MMT or CVD. Further, we did not include any outcome related to final transplant, or any failure not related to MMT (catheter, abdominal perforations, surgery, etc.).
ET—as an ensemble method building multiple DTs with random splits—effectively handles repeated measures (time series data) by modelling complex relationships and robustness against noise. By correctly formatting temporal data (natural logarithm, identification of longitudinal samples), ET captured patterns across time points.
LDA was adapted to include temporal features, indirectly accounting for repeated measures, using feature engineering. Despite LDA’s limitations compared with ET, it was effective for this dataset due to the prevalence of linear dependencies. The study highlights the importance of aligning models with specific data structures to enhance medical predictions.
MAUXI software intends to provide more predictability of the PD technique failure, avoiding unprecedented neurological, cardiovascular and UFF consequences. Further studies should unravel molecular mechanisms of these MMT biomarkers in PD.
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