Increasing Cardiovascular Data Sampling Frequency and Referencing It to Baseline Improve Hemorrhage Detection

Abstract

We hypothesize that knowledge of a stable personalized baseline state and increased data sampling frequency would markedly improve the ability to detect progressive hypovolemia during hemorrhage earlier and with a lower false positive rate than when using less granular data.

Design: Prospective temporal challenge.

Setting: Large animal research laboratory, University Medical Center.

Subjects: Fifty-one anesthetized Yorkshire pigs.

Interventions: Pigs were instrumented with arterial, pulmonary arterial, and central venous catheters and allowed to stabilize for 30 minutes then bled at a constant rate of either 5 mL·min^-1 (n = 13) or 20 (n = 38) until mean arterial pressure decreased to 40 or 30 mm Hg in the 5 and 20 mL·min^-1 pigs, respectively.

Measurements and main results: Data during the stabilization period served as baseline. Hemodynamic variables collected at 250 Hz were used to create predictive models of "bleeding" using featurized beat-to-beat and waveform data and compared with models using mean unfeaturized hemodynamic variables averaged over 1-minute as simple hemodynamic metrics using random forest classifiers to identify bleeding with or without baseline data. The robustness of the prediction was evaluated in a leave-one-pig-out cross-validation. Predictive performance of models was compared by their activity monitoring operating characteristic and receiver operating characteristic profiles. Primary hemodynamic threshold data poorly identified bleed onset unless very stable initial baseline reference data were available. When referenced to baseline, bleed detection at a false positive rates of 10^-2 with time to detect 80% of pigs bleeding was similar for simple hemodynamic metrics, beat-to-beat, and waveform at about 3-4 minutes. Whereas when universally baselined, increasing sampling frequency reduced latency of bleed detection from 10 to 8 to 6 minutes, for simple hemodynamic metrics, beat-to-beat, and waveform, respectively. Some informative features differed between simple hemodynamic metrics, beat-to-beat, and waveform models.

Conclusions: Knowledge of personal stable baseline data allows for early detection of new-onset bleeding, whereas if no personal baseline exists increasing sampling frequency of hemodynamic monitoring data improves bleeding detection earlier and with lower false positive rate.

Keywords: animal model; hemodynamic monitoring; hemorrhage; machine learning; predictive models.

Figure 1.

Activity monitoring operating characteristic curves showing the average time to detection needed to detect the onset of 80% of animals for the 5 mL·min^–1 cohort for simple metrics (SM), beat-to-beat (B2B), and waveform (WF) using a universal baseline (A), or personalized baseline (B). FPR = false positive rate.

Figure 2.

Activity monitoring operating characteristic curves showing the average (time to detection needed to detect the onset of 80% of animals for the 20 mL·min^–1 cohort for simple metrics (SM), beat-to-beat (B2B), and waveform (WF) using a universal baseline (A), or personalized baseline (B). FPR = false positive rate.

Figure 3.

Receiver operating characteristic curves for the 5 mL·min^–1 cohort for simple metrics (SM), beat-to-beat (B2B), and waveform (WF) models using a universal baseline (A), or personalized baseline (B). Plots show the tradeoff between detection performance (true positive rate [TPR] and true negative rate [TNR]) and error rate (false positive rate [FPR] and false negative rate [FNR]).

Figure 4.

Receiver operating characteristics for the 20 mL·min^–1 cohort for simple metrics (SM), beat-to-beat (B2B), and waveform (WF) models using a universal baseline (A), or personalized baseline (B) using the same format as Figure 5. FNR = false negative rate, FPR = false positive rate, TNR = true negative rate, TPR = true positive rate.

Figure 5.

Activity monitoring operating characteristic curves comparison of the detection performance in terms of volume of blood lost before detection between the 5 and 20 mL·min^–1 cohorts for the simple metrics (SM) (top), beat-to-beat (B2B) (middle), and waveform (WF) (bottom) models using either a universal baseline (left) or personalized baseline (right). FPR = false positive rate.