Parsimony of Hemodynamic Monitoring Data Sufficient for the Detection of Hemorrhage

Abstract

BACKGROUND: Individualized hemodynamic monitoring approaches are not well validated. Thus, we evaluated the discriminative performance improvement that might occur when moving from noninvasive monitoring (NIM) to invasive monitoring and with increasing levels of featurization associated with increasing sampling frequency and referencing to a stable baseline to identify bleeding during surgery in a porcine model. METHODS: We collected physiologic waveform (WF) data (250 Hz) from NIM, central venous (CVC), arterial (ART), and pulmonary arterial (PAC) catheters, plus mixed venous O2 saturation and cardiac output from 38 anesthetized Yorkshire pigs bled at 20 mL/min until a mean arterial pressure of 30 mm Hg following a 30-minute baseline period. Prebleed physiologic data defined a personal stable baseline for each subject independently. Nested models were evaluated using simple hemodynamic metrics (SM) averaged over 20-second windows and sampled every minute, beat to beat (B2B), and WF using Random Forest Classification models to identify bleeding with or without normalization to personal stable baseline, using a leave-one-pig-out cross-validation to minimize model overfitting. Model hyperparameters were tuned to detect stable or bleeding states. Bleeding models were compared use both each subject’s personal baseline and a grouped-average (universal) baseline. Timeliness of bleed onset detection was evaluated by comparing the tradeoff between a low false-positive rate (FPR) and shortest time to bleed detection. Predictive performance was evaluated using a variant of the receiver operating characteristic focusing on minimizing FPR and false-negative rates (FNR) for true-positive and true-negative rates, respectively. RESULTS: In general, referencing models to a personal baseline resulted in better bleed detection performance for all catheters than using universal baselined data. Increasing granularity from SM to B2B and WF progressively improved bleeding detection. All invasive monitoring outperformed NIM for both time to bleeding detection and low FPR and FNR. In that regard, when referenced to personal baseline with SM analysis, PAC and ART + PAC performed best; for B2B CVC, PAC and ART + PAC performed best; and for WF PAC, CVC, ART + CVC, and ART + PAC performed equally well and better than other monitoring approaches. Without personal baseline, NIM performed poorly at all levels, while all catheters performed similarly for SM, with B2B PAC and ART + PAC performing the best, and for WF PAC, ART, ART + CVC, and ART + PAC performed equally well and better than the other monitoring approaches. CONCLUSIONS: Increasing hemodynamic monitoring featurization by increasing sampling frequency and referencing to personal baseline markedly improves the ability of invasive monitoring to detect bleed.

Coronavirus Disease 2019 Calls for Predictive Analytics Monitoring—A New Kind of Illness Scoring System

“…Following lung function, like looking for signatures of illness, in our view requires continuous recording of organ function: the more highly resolved, the better. Pinsky et al recently demonstrated the additional information of noninvasive and invasive heart rate and waveform data in early detection of hemorrhage in pigs ( 68 , 69 ), affirming clinical studies ( 45 , 70 ). Heart rate analysis is directly applicable to clinical practice—each heartbeat sends an easily detected signal and allows for detailed analysis of long time-series of interbeat intervals using new and old mathematics ( 71 , 72 ).…”

Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs

“…Graphical features include the amplitude and time domain features extracted from PPG and ABP, and their first derivative waveform has been widely used for tracking hemodynamics [ 21 , 22 ]. Our previous studies also attempted to use features derived from vital signs and machine learning techniques to detect hemorrhage and reported the ability of PPG-waveform-derived features, heart rate, and BP-derived features in reflecting physiological condition changes in subjects suffering from blood loss [ 18 , 23 , 24 ]. Similar features were extracted as the feature metrics in this study.…”

“…Current early hemorrhage detection studies are based on machine learning approaches, where feature normalization by referencing their baseline value could mitigate large subject-to-subject variability [ 16 ]. Pinsky et al reported the efficiency of normalizing features derived by vital signs by referencing their baseline values [ 18 ]. It is thus hypothesized that normalized features derived by vital signs could be potential predictors for tracking hemorrhage and BLV.…”

Intelligent Clinical Decision Support

“…We have developed predictive models using controlled animal laboratory data and historical ICU data that demonstrate good performance in predicting clinically relevant tachycardia, hypotension, and bleeding. We also demonstrated the incremental benefit of high-frequency data in increasing the reliability of these prediction models [ 8 , 17 , 39 , 40 , 41 ]. For instance, advanced signal processing predicted clinically relevant tachycardia and hypotension in ICU patients [ 42 , 43 ].…”