Challenging the supremacy of evidence-based medicine through artificial intelligence: the time has come for a change of paradigms

Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review

Background and objectives: cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD). Standard cardiovascular disease risk prediction models used in the general population are not validated in patients with CKD. We aim to systematically review the up-to-date literature on reported outcomes of computational methods such as artificial intelligence (AI) or regression-based models to predict CVC in CKD patients. Materials and methods: the electronic databases of MEDLINE/PubMed, EMBASE, and ScienceDirect were systematically searched. The risk of bias and reporting quality for each study were assessed against transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and the prediction model risk of bias assessment tool (PROBAST). Results: sixteen papers were included in the present systematic review: 15 non-randomized studies and 1 ongoing clinical trial. Twelve studies were found to perform AI or regression-based predictions of CVC in CKD, either through single or composite endpoints. Four studies have come up with computational solutions for other CV-related predictions in the CKD population. Conclusions: the identified studies represent palpable trends in areas of clinical promise with an encouraging present-day performance. However, there is a clear need for more extensive application of rigorous methodologies. Following the future prospective, randomized clinical trials, and thorough external validations, computational solutions will fill the gap in cardiovascular predictive tools for chronic kidney disease.

Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension

BACKGROUND AND OBJECTIVES

Intradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we applied a deep learning model (recurrent neural network) to predict the risk of intradialytic hypotension using a timestamp-bearing dataset.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We obtained 261,647 hemodialysis sessions with 1,600,531 independent timestamps (i.e., time-varying vital signs) and randomly divided them into training (70%), validation (5%), calibration (5%), and testing (20%) sets. Intradialytic hypotension was defined when nadir systolic BP was <90 mm Hg (termed intradialytic hypotension 1) or when a decrease in systolic BP ≥20 mm Hg and/or a decrease in mean arterial pressure ≥10 mm Hg on the basis of the initial BPs (termed intradialytic hypotension 2) or prediction time BPs (termed intradialytic hypotension 3) occurred within 1 hour. The area under the receiver operating characteristic curves, the area under the precision-recall curves, and F1 scores obtained using the recurrent neural network model were compared with those obtained using multilayer perceptron, Light Gradient Boosting Machine, and logistic regression models.

RESULTS

The recurrent neural network model for predicting intradialytic hypotension 1 achieved an area under the receiver operating characteristic curve of 0.94 (95% confidence intervals, 0.94 to 0.94), which was higher than those obtained using the other models (P<0.001). The recurrent neural network model for predicting intradialytic hypotension 2 and intradialytic hypotension 3 achieved area under the receiver operating characteristic curves of 0.87 (interquartile range, 0.87-0.87) and 0.79 (interquartile range, 0.79-0.79), respectively, which were also higher than those obtained using the other models (P≤0.001). The area under the precision-recall curve and F1 score were higher using the recurrent neural network model than they were using the other models. The recurrent neural network models for intradialytic hypotension were highly calibrated.

CONCLUSIONS

Our deep learning model can be used to predict the real-time risk of intradialytic hypotension.

Longer or shorter dual antiplatelet therapy in dialysis patients receiving a coronary drug-eluting stent? A rope game still ongoing

In this issue of Clinical Kidney Journal, Park et al. presents the results of a nationwide population-based trial that included >5000 dialysis patients receiving a drug-eluting stent (DES). The main objective was to evaluate the effectiveness and the safety of prolonged dual antiplatelet therapy (DAPT). The primary outcome was a composite of mortality, non-fatal myocardial infarction, coronary revascularization and stroke, significantly lowered by a longer DAPT regimen at 12, 15 and 18 months, respectively. Longer DAPT tended to be correlated with higher bleeding events at all landmarks, with no statistical significance. An important element was that almost 75% of the index events were acute coronary syndromes. This study presents the first solid evidence for a significant benefit of prolonged DAPT in dialysis patients receiving a DES. We believe that end-stage renal disease is still in the middle of a rope game, being pulled to one side or another by other features, inclining towards a higher bleeding risk or towards higher ischaemic risk. The acute versus elective presentation seems to weigh in choosing the antiplatelet regimen. The 'one-size-fits-all strategy' is not suitable for this particular group. Probably in the future, practitioners will be provided with decision pathways generated by artificial intelligence algorithms yielding 'truly individualized' DAPT protocols for every single patient.