New algorithm to quantify cardiopulmonary interaction in patients with atrial fibrillation: a proof-of-concept study

Measurement error of pulse pressure variation

Dynamic preload parameters are used to guide perioperative fluid management. However, reported cut-off values vary and the presence of a gray zone complicates clinical decision making. Measurement error, intrinsic to the calculation of pulse pressure variation (PPV) has not been studied but could contribute to this level of uncertainty. The purpose of this study was to quantify and compare measurement errors associated with PPV calculations. Hemodynamic data of patients undergoing liver transplantation were extracted from the open-access VitalDatabase. Three algorithms were applied to calculate PPV based on 1 min observation periods. For each method, different durations of sampling periods were assessed. Best Linear Unbiased Prediction was determined as the reference PPV-value for each observation period. A Bayesian model was used to determine bias and precision of each method and to simulate the uncertainty of measured PPV-values. All methods were associated with measurement error. The range of differential and proportional bias were [- 0.04%, 1.64%] and [0.92%, 1.17%] respectively. Heteroscedasticity influenced by sampling period was detected in all methods. This resulted in a predicted range of reference PPV-values for a measured PPV of 12% of [10.2%, 13.9%] and [10.3%, 15.1%] for two selected methods. The predicted range in reference PPV-value changes for a measured absolute change of 1% was [- 1.3%, 3.3%] and [- 1.9%, 4%] for these two methods. We showed that all methods that calculate PPV come with varying degrees of uncertainty. Accounting for bias and precision may have important implications for the interpretation of measured PPV-values or PPV-changes.

The effects of respiratory rate and tidal volume on pulse pressure variation in healthy lungs-a generalized additive model approach may help overcome limitations

Pulse pressure variation (PPV) is a well-established method for predicting fluid responsiveness in mechanically ventilated patients. The predictive accuracy is, however, disputed for ventilation with low tidal volume (VT) or low heart-rate-to-respiratory-rate ratio (HR/RR). We investigated the effects of VT and RR on PPV and on PPV's ability to predict fluid responsiveness. We included patients scheduled for open abdominal surgery. Prior to a 250 ml fluid bolus, we ventilated patients with combinations of VT from 4 to 10 ml kg-1 and RR from 10 to 31 min-1. For each of 10 RR-VT combinations, PPV was derived using both a classic approach and a generalized additive model (GAM) approach. The stroke volume (SV) response to fluid was evaluated using uncalibrated pulse contour analysis. An SV increase > 10% defined fluid responsiveness. Fifty of 52 included patients received a fluid bolus. Ten were fluid responders. For all ventilator settings, fluid responsiveness prediction with PPV was inconclusive with point estimates for the area under the receiver operating characteristics curve between 0.62 and 0.82. Both PPV measures were nearly proportional to VT. Higher RR was associated with lower PPV. Classically derived PPV was affected more by RR than GAM-derived PPV. Correcting PPV for VT could improve PPV's predictive utility. Low HR/RR has limited effect on GAM-derived PPV, indicating that the low HR/RR limitation is related to how PPV is calculated. We did not demonstrate any benefit of GAM-derived PPV in predicting fluid responsiveness.Trial registration: ClinicalTrials.gov, reg. March 6, 2020, NCT04298931.

Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiology

Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant to disentangle the two effects. Generalized additive models (GAMs) allow estimating the effect of predictors as nonlinear, smooth functions. These smooth functions can represent the cardiac and respiratory cycles' effects on a physiological signal. We demonstrate how GAMs allow a decomposition of physiological signals from mechanically ventilated subjects into separate effects of the cardiac and respiratory cycles. Two examples are presented. The first is a model of the respiratory variation in pulse pressure. The second demonstrates how a central venous pressure waveform can be decomposed into a cardiac effect, a respiratory effect and the interaction between the two cycles. Generalized additive models provide an intuitive and flexible approach to modelling the repeating, smooth, patterns common in medical monitoring data.

Diagnostic values of different ECG durations in paroxysmal AF diagnosis

OBJECTIVE

To evaluate the application value of different-duration electrocardiograms (ECGs) in paroxysmal atrial fibrillation (AF) diagnosis.

METHODS

A total of 220 patients with paroxysmal atrial fibrillation diagnosed by 24-h dynamic electrocardiogram in our hospital from January 2019 to December 2020 were selected as the study subjects. The patients were monitored for 10 s, 5 min, and 24 hr, respectively. The results of the three different-duration ECGs were compared and analyzed in order to evaluate their paroxysmal AF diagnosis value.

RESULTS

Paroxysmal AF was detected in 18 patients (8.2%) with the 10-s ECG; in 89 patients (40.5%) with the 5-min ECG; and in 199 patients (90.5%) with the 24-h dynamic ECG.

CONCLUSION

In patients with paroxysmal AF, ECGs with longer detection times had higher detection rates.