Data of two prospective, single-centre studies was combined, comprising a population of patients with and without AoS. The first study included patients with AoS who underwent elective TAVR, recruited from March 2017 until February 2019, registered at ClinicalTrials.gov (NCT03088787). The second study consisted of patients who underwent elective cardiac surgery, including SAVR. These patients were recruited from October 2019 until May 2022, and registered with the Netherland Trial Register (NL7810).

We excluded patients with a body weight below 40 kg, age younger than 65 years, a congenital unicuspid or bicuspid valve, mechanical aortic valve prosthesis, atrial fibrillation or flutter, a mitral valve insufficiency categorized as higher than mild, no TTE available to the procedure, or inability to perform non-invasive blood pressure measurements.

Severity of AoS was derived from TTE reports extracted from the electronic patient records, executed at least within 6 months before either TAVR or surgery, or within 12 months in case moderate or severe AoS was detected. AoS was graded according to the EAE/ASE guideline [19]. This always included, but was not limited to, assessment of: AoS jet velocity, the trans-aortic gradient and the valve area by continuity equation. The final grading severity was at the discretion of accredited echocardiographers. Patients with mild or no AoS were classified as no AoS patients, and patients with a moderate or severe AoS were considered AoS patients.

Non-invasive blood pressure data was obtained in all patients using a finger cuff with built-in light emitting- and receiving plethysmography diodes (ccNexfin, Edwards Lifesciences, Irvine, CA, USA), applied to the middle or index finger. Within the device, the finger blood pressure curve was automatically transformed to the brachial blood pressure waveform with a sample frequency of 200 Hz [20]. Non-invasive measurement of blood pressure has shown to be accurate in patients with severe aortic stenosis [21, 22].

Blood pressure data was collected shortly before procedure until either induction of general anaesthesia (in case of surgery) or local anaesthesia (in case of TAVR) was administered. Two researchers (EK and JS) manually selected a segment of ten minutes of consecutive data. In case of artefacts in the data, a shorter data segment was selected, with a minimum length of 3 min.

The ccNexfin automatically calculates several parameters, such as the systolic (SAP), mean (MAP), and diastolic (DAP) arterial blood pressure [20]. Furthermore, the interbeat interval (IBI), heart rate (HR), left ventricular ejection time (LVET), stroke volume (SV), stroke volume index (SVI) cardiac output (CO), cardiac index (CI), systemic vascular resistance (SVR), systemic vascular resistance index (SVRI), and an estimated index of left ventricular contractility (dP/dt, the maximum value of the first time-derivative of pressure), are automatically calculated.

From these derived parameters, several extra features were calculated offline. First, the pulse pressure (PP) was calculated subtracting DAP from SAP; stroke work (SW) was calculated multiplying SV with MAP [23]. The instantaneous baroreflex sensitivity (xBRS), a measure of autonomic function, was computed and expressed as millisecond (ms) change in IBI per mmHg change in SAP [24]. Here, the regression line with the highest correlation between the two changes, while shifting in time, was calculated. The slope of this line was defined as the gain, and the corresponding shift in time was described as the delay [24].

From the raw blood pressure data, several extra features were calculated for individual beats. After applying the smoothing Savitzky-Golay filter, the timing of SAP, dicrotic notch and corresponding time, area under the curve (AUC) of SAP/DAP, based on the AUC of the beat until/from the dicrotic notch, were calculated. The dicrotic notch was calculated by averaging the time of the second maximum of the first and second derivative of the raw blood pressure beat [25]. Furthermore, for each beat, the area under the curve, but above the dicrotic notch, maximum slope of the up- and down-stroke of the systolic part of the beat, based on the maximum and minimum of the first derivative, were calculated.

In total, 27 features based on non-invasive blood pressure measurement were derived and used to calculate the final features used for the model. From these features, the median, interquartile range (IQR), variance, the 1st and 9th decile of the change were derived. Next, the features were divided by the patients’ age, to adjust for age-dependent differences, and then split into a training and a test set. The training set was used to derive the most optimal model, whereas the test set was used to test this model. The training set was based on 75% of the data, and consisted of an imbalanced set (75 patient with AoS, and 36 with no AoS). To create a more balanced dataset, the set of 36 patients without AoS was oversampled with the Synthetic Minority Over-sampling Technique (SMOTE), to match the 75 patient with AoS [26]. Next, the training dataset was normalized with MinMaxScaler (Scikit-Learn 1.1.3) and used as the input to several classifiers; logistic regression, K-nearest neighbours, decision tree, support vector machine, and random forest. Additionally, the hyperparameters of all classifiers were optimized through a grid search with four-fold cross validation. Training was performed towards the highest possible area under the receiver operating curve (AUROC).

Difference between patients with and without AoS was tested statistically with the unpaired t-test or Wilcoxon rank sum test in case of non-parametric data, or with the Fisher’s exact test when it concerned discrete data. For descriptive purposes, significant differences of the features between the two populations were calculated based on the value of the features before correcting for age. Descriptive data are presented as mean with (SD) or median with (1st–3rd quartile), when applicable. A p-value < 0.05 was considered statistically significant. All data and statistical analyses were performed with Matlab (Version 2020b, the Mathworks Inc., Nattick, MA, USA) or Python (Version 3.9, package: Scikit-learn 1.1.3).