Comparison of Differences in Cohort (Forward) and Case Control (Backward) Methodologic Approaches for Validation of the Hypotension Prediction Index.

BACKGROUND: The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiologic changes that may lead to hypotension. The original validation used a case control (backward) analysis that has been suggested to be biased. This study therefore conducted a cohort (forward) analysis and compared this to the original validation technique. METHODS: A retrospective analysis of data from previously reported studies was conducted. All data were analyzed identically with two different methodologies, and receiver operating characteristic curves were constructed. Both backward and forward analyses were performed to examine differences in area under the receiver operating characteristic curves for the Hypotension Prediction Index and other hemodynamic variables to predict a mean arterial pressure (MAP) less than 65 mmHg for at least 1 min 5, 10, and 15 min in advance. RESULTS: The analysis included 2,022 patients, yielding 4,152,124 measurements taken at 20-s intervals. The area under the curve for the index predicting hypotension analyzed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947 to 0.964) versus 0.923 (95% CI, 0.912 to 0.933) 5 min in advance, 0.933 (95% CI, 0.924 to 0.942) versus 0.923 (95% CI, 0.911 to 0.933) 10 min in advance, and 0.929 (95% CI, 0.918 to 0.938) versus 0.926 (95% CI, 0.914 to 0.937) 15 min in advance. No variable other than MAP had an area under the curve greater than 0.7. The areas under the curve using forward analysis for MAP predicting hypotension 5, 10, and 15 min in advance were 0.932 (95% CI, 0.920 to 0.940), 0.929 (95% CI, 0.918 to 0.938), and 0.932 (95% CI, 0.921 to 0.940), respectively. The R2 for the variation in the index due to MAP was 0.77. CONCLUSIONS: Using an updated methodology, the study found that the utility of the Hypotension Prediction Index to predict future hypotensive events is high, with an area under the receiver operating characteristics curve similar to that of the original validation method.

Intraoperative Invasive Blood Pressure Monitoring and the Potential Pitfalls of Invasively Measured Systolic Blood Pressure.

Invasive intraarterial blood pressure measurement is currently the gold standard for intraoperative hemodynamic monitoring but accurate systolic blood pressure (SBP) measurement is difficult in everyday clinical practice, mostly because of problems with hyper-resonance or damping within the measurement system, which can lead to erroneous treatment decisions if these phenomena are not recognized. A hyper-resonant blood pressure trace significantly overestimates true systolic blood pressure while underestimating the diastolic pressure. Invasively measured systolic blood pressure is also significantly more affected than mean blood pressure by the site of measurement within the arterial system. Patients in the intraoperative period should be treated based on the invasively measured mean blood pressure rather than the systolic blood pressure. In this review, we discuss the pros/cons, mechanisms of invasive blood pressure measurements, and the interpretation of the invasively measured systolic blood pressure value.

Performance of the Hypotension Prediction Index with non-invasive arterial pressure waveforms in non-cardiac surgical patients.

An algorithm derived from machine learning uses the arterial waveform to predict intraoperative hypotension some minutes before episodes, possibly giving clinician's time to intervene and prevent hypotension. Whether the Hypotension Prediction Index works well with noninvasive arterial pressure waveforms remains unknown. We therefore evaluated sensitivity, specificity, and positive predictive value of the Index based on non-invasive arterial waveform estimates. We used continuous hemodynamic data measured from ClearSight (formerly Nexfin) noninvasive finger blood pressure monitors in surgical patients. We re-evaluated data from a trial that included 320 adults ≥ 45 years old designated ASA physical status 3 or 4 who had moderate-to-high-risk non-cardiac surgery with general anesthesia. We calculated sensitivity and specificity for predicting hypotension, defined as mean arterial pressure ≤ 65 mmHg for at least 1 min, and characterized the relationship with receiver operating characteristics curves. We also evaluated the number of hypotensive events at various ranges of the Hypotension Prediction Index. And finally, we calculated the positive predictive value for hypotension episodes when the Prediction Index threshold was 85. The algorithm predicted hypotension 5 min in advance, with a sensitivity of 0.86 [95% confidence interval 0.82, 0.89] and specificity 0.86 [0.82, 0.89]. At 10 min, the sensitivity was 0.83 [0.79, 0.86] and the specificity was 0.83 [0.79, 0.86]. And at 15 min, the sensitivity was 0.75 [0.71, 0.80] and the specificity was 0.75 [0.71, 0.80]. The positive predictive value of the algorithm prediction at an Index threshold of 85 was 0.83 [0.79, 0.87]. A Hypotension Prediction Index of 80-89 provided a median of 6.0 [95% confidence interval 5.3, 6.7] minutes warning before mean arterial pressure decreased to < 65 mmHg. The Hypotension Prediction Index, which was developed and validated with invasive arterial waveforms, predicts intraoperative hypotension reasonably well from non-invasive estimates of the arterial waveform. Hypotension prediction, along with appropriate management, can potentially reduce intraoperative hypotension. Being able to use the non-invasive pressure waveform will widen the range of patients who might benefit.Clinical Trial Number: ClinicalTrials.gov NCT02872896.

Dynamic Arterial Elastance as a Ventriculo-Arterial Coupling Index: An Experimental Animal Study.

Dynamic arterial elastance (Eadyn), the ratio between arterial pulse pressure and stroke volume changes during respiration, has been postulated as an index of the coupling between the left ventricle (LV) and the arterial system. We aimed to confirm this hypothesis using the gold-standard for defining LV contractility, afterload, and evaluating ventricular-arterial (VA) coupling and LV efficiency during different loading and contractile experimental conditions. Twelve Yorkshire healthy female pigs submitted to three consecutive stages with two opposite interventions each: changes in afterload (phenylephrine/nitroprusside), preload (bleeding/fluid bolus), and contractility (esmolol/dobutamine). LV pressure-volume data was obtained with a conductance catheter, and arterial pressures were measured via a fluid-filled catheter in the proximal aorta and the radial artery. End-systolic elastance (Ees), a load-independent index of myocardial contractility, was calculated during an inferior vena cava occlusion. Effective arterial elastance (Ea, an index of LV afterload) was calculated as LV end-systolic pressure/stroke volume. VA coupling was defined as the ratio Ea/Ees. LV efficiency (LVeff) was defined as the ratio between stroke work and the LV pressure-volume area. Eadyn was calculated as the ratio between the aortic pulse pressure variation (PPV) and conductance-derived stroke volume variation (SVV). A linear mixed model was used for evaluating the relationship between Ees, Ea, VA coupling, LVeff with Eadyn. Eadyn was inversely related to VA coupling and directly to LVeff. The higher the Eadyn, the higher the LVeff and the lower the VA coupling. Thus, Eadyn, an easily measured parameter at the bedside, may be of clinical relevance for hemodynamic assessment of the unstable patient.

Determinants of left ventricular ejection fraction and a novel method to improve its assessment of myocardial contractility.

BACKGROUND: The aim of this study was to quantify the impact of different cardiovascular factors on left ventricular ejection fraction (LVEF) and test a novel LVEF calculation considering these factors. RESULTS: 10 pigs were studied. The experimental protocol consisted of sequentially changing afterload, preload and contractility. LV pressure-volume (PV) loops and peripheral arterial pressure were obtained before and after each intervention. LVEF was calculated as stroke volume (SV)/end-diastolic volume (EDV). We studied global cardiac function variables: LV end-systolic elastance (Ees), effective arterial elastance (Ea), end-diastolic volume and heart rate. Diastolic function was evaluated by means of the ventricular relaxation time (τ) and ventricular stiffness constant (ß) obtained from the end-diastolic PV relationship. Ventriculo-arterial coupling (VAC), an index of cardiovascular performance, was calculated as Ea/Ees. LV mechanical efficiency (LVeff) was calculated as the ratio of stroke work to LV pressure-volume area. A linear mixed model was used to determine the impact of cardiac factors (Ees, Ea, EDV and heart rate), VAC and LVeff on LVEF during all experimental conditions. LVEF was mainly related to Ees and Ea. There was a strong relationship between LVEF and both VAC and LVeff (r2 = 0.69 and r2 = 0.94, respectively). The relationship between LVEF and Ees was good (r2 = 0.43). Adjusting LVEF to afterload ([Formula: see text]) performed better for estimating Ees (r2 = 0.75) and improved the tracking of LV contractility changes, even when a peripheral Ea was used as surrogate (Ea = radial MAP/SV; r2 = 0.73). CONCLUSIONS: LVEF was mainly affected by contractility and afterload changes and was strongly related to VAC and LVeff. An adjustment to LVEF that considers the impact of afterload provided a better assessment of LV contractility.

Reliability of effective arterial elastance using peripheral arterial pressure as surrogate for left ventricular end-systolic pressure.

To compare the effective arterial elastance (Ea) obtained from the arterial pressure with Ea calculated from left-ventricular (LV) pressure-volume analysis. Experimental study. LV pressure-volume data was obtained with a conductance catheter and arterial pressures were measured via a fluid-filled catheter placed in the proximal aorta, femoral and radial arteries. Ea was calculated as LV end-systolic pressure (ESP)/stroke volume (SV). Experimental protocol consisted sequentially changing afterload (phenylephrine/nitroprusside), preload (bleeding/fluid), and contractility (esmolol/dobutamine). 90% of systolic pressure (Eaao_SYS, Eafem_SYS, Earad_SYS), mean arterial pressure (Eaao_MAP, Eafem_MAP, Earad_MAP), and dicrotic notch pressure (Eaao_DIC, Eafem_DIC, Earad_DIC) were used as surrogates for LV ESP. SV was calculated from the LV pressure-volume data. When Ea was compared with estimations based on 90% SAP, the relationship was r2 = 0.95, 0.94 and 0.92; and the bias and limits of agreement (LOA): - 0.01 ± 0.12, - 0.09 ± 0.12, - 0.05 ± 0.15 mmHg ml-1, for Eaao_SYS, Eafem_SYS and Earad_SYS, respectively. For estimates using dicrotic notch, the relationship was r2 = 0.94, 0.95 and 0.94 for Eaao_DIC, Eafem_DIC and Earad_DIC, respectively; with a bias and LOA: 0.05 ± 0.11, 0.06 ± 0.12, 0.10 ± 0.12 mmHg ml-1, respectively. When Ea was compared with estimates using MAP, the relationship was r2 = 0.95, 0.96 and 0.95 for Eaao_MAP, Eafem_MAP and Earad_MAP, respectively; with a bias and LOA: 0.05 ± 0.11, 0.06 ± 0.11, 0.06 ± 0.11 mmHg ml-1, respectively. LV ESP can be estimated from the arterial pressure. Provided that the SV measurement is reliable, the ratio MAP/SV provides a robust Ea surrogate over a wide range of hemodynamic conditions and is interchangeably in any peripheral artery, so it should be recommended as an arterial estimate of Ea in further research.

Performance comparison of ventricular and arterial dP/dtmax for assessing left ventricular systolic function during different experimental loading and contractile conditions.

BACKGROUND: Maximal left ventricular (LV) pressure rise (LV dP/dtmax), a classical marker of LV systolic function, requires LV catheterization, thus surrogate arterial pressure waveform measures have been proposed. We compared LV and arterial (femoral and radial) dP/dtmax to the slope of the LV end-systolic pressure-volume relationship (Ees), a load-independent measure of LV contractility, to determine the interactions between dP/dtmax and Ees as loading and LV contractility varied. METHODS: We measured LV pressure-volume data using a conductance catheter and femoral and radial arterial pressures using a fluid-filled catheter in 10 anesthetized pigs. Ees was calculated as the slope of the end-systolic pressure-volume relationship during a transient inferior vena cava occlusion. Afterload was assessed by the effective arterial elastance. The experimental protocol consisted of sequentially changing afterload (phenylephrine/nitroprusside), preload (bleeding/fluid bolus), and contractility (esmolol/dobutamine). A linear-mixed analysis was used to assess the contribution of cardiac (Ees, end-diastolic volume, effective arterial elastance, heart rate, preload-dependency) and arterial factors (total vascular resistance and arterial compliance) to LV and arterial dP/dtmax. RESULTS: Both LV and arterial dP/dtmax allowed the tracking of Ees changes, especially during afterload and contractility changes, although arterial dP/dtmax was lower compared to LV dP/dtmax (bias 732 ± 539 mmHg⋅s- 1 for femoral dP/dtmax, and 625 ± 501 mmHg⋅s- 1 for radial dP/dtmax). Changes in cardiac contractility (Ees) were the main determinant of LV and arterial dP/dtmax changes. CONCLUSION: Although arterial dP/dtmax is a complex function of central and peripheral arterial factors, radial and particularly femoral dP/dtmax allowed reasonably good tracking of LV contractility changes as loading and inotropic conditions varied.

Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis.

WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors' goal was to apply machine learning to arterial pressure waveforms and create an algorithm to predict hypotension. The algorithm detects early alteration in waveforms that can herald the weakening of cardiovascular compensatory mechanisms affecting preload, afterload, and contractility. METHODS: The algorithm was developed with two different data sources: (1) a retrospective cohort, used for training, consisting of 1,334 patients' records with 545,959 min of arterial waveform recording and 25,461 episodes of hypotension; and (2) a prospective, local hospital cohort used for external validation, consisting of 204 patients' records with 33,236 min of arterial waveform recording and 1,923 episodes of hypotension. The algorithm relates a large set of features calculated from the high-fidelity arterial pressure waveform to the prediction of an upcoming hypotensive event (mean arterial pressure < 65 mmHg). Receiver-operating characteristic curve analysis evaluated the algorithm's success in predicting hypotension, defined as mean arterial pressure less than 65 mmHg. RESULTS: Using 3,022 individual features per cardiac cycle, the algorithm predicted arterial hypotension with a sensitivity and specificity of 88% (85 to 90%) and 87% (85 to 90%) 15 min before a hypotensive event (area under the curve, 0.95 [0.94 to 0.95]); 89% (87 to 91%) and 90% (87 to 92%) 10 min before (area under the curve, 0.95 [0.95 to 0.96]); 92% (90 to 94%) and 92% (90 to 94%) 5 min before (area under the curve, 0.97 [0.97 to 0.98]). CONCLUSIONS: The results demonstrate that a machine-learning algorithm can be trained, with large data sets of high-fidelity arterial waveforms, to predict hypotension in surgical patients' records.

Validity and variability of xBRS: instantaneous cardiac baroreflex sensitivity.

Spontaneous oscillations of blood pressure (BP) and interbeat interval (IBI) may reveal important information on the underlying baroreflex control and regulation of BP We evaluated the method of continuously measured instantaneous baroreflex sensitivity by cross correlation (xBRS) validating its mean value against the gold standard of phenylephrine (Phe) and nitroprusside (SNP) bolus injections, and focusing on its spontaneous changes quantified as variability around the mean. For this purpose, we analyzed data from an earlier study of eight healthy males (aged 25-46 years) who had received Phe and SNP in conditions of baseline and autonomic blocking agents: atropine, propranolol, and clonidine. Average xBRS corresponds well to Phe/SNP-BRS, with xBRS levels ranging from 1.2 (atropine) to 102 msec/mmHg (subject asleep under clonidine). Time shifts from BP- to IBI-signal increased from ≤1 sec (maximum correlations within the current heartbeat) to 3-5 sec (under atropine). Plotted on a logarithmic vertical scale, xBRS values show 40% variability (defined as SD/mean) over the whole range in the various conditions, except twice when the subjects had fallen asleep and it dropped to 20%. The xBRS oscillates at frequencies of 0.1 Hz and lower, dominant between 0.02-0.05 Hz. Although xBRS is the result of IBI/BP-changes, no linear coherence was found in the cross-spectra of the xBRS-signal and IBI or BP We speculate that the level of variability in the xBRS-signal may act as a probe into the central nervous condition, as evidenced in the two subjects who fell asleep with high xBRS and only 20% of relative variation.

Comparison of a non-invasive arterial pulse contour technique and echo Doppler aorta velocity-time integral on stroke volume changes in optimization of cardiac resynchronization therapy.

AIMS: We investigated the accuracy and feasibility of a non-invasive arterial pulse contour technique for continuous measurement of stroke volume (SV) in optimization of atrioventricular (AV) delay in cardiac resynchronization therapy (CRT), by comparing SV changes assessed by Nexfin CO-Trek® (Nexfin) and echo Doppler aortic velocity-time integral (VTIao). Furthermore, we investigated whether AV-delay optimization increases the effect of CRT when compared with a default AV delay (120 ms). METHODS AND RESULTS: In 23 CRT patients, biventricular pacing (BiVP) was applied at various AV delays, while recording 10 beats preceding BiVP (baseline) and the first 10 BiVP beats, for both methods in parallel. Agreement between Nexfin and VTIao measurements was evaluated (Bland-Altman) on beat-to-beat changes in SV, as well as on effects of BiVP (averaged over 8 beats) at various AV delays. Individual optimal AV delays, for Nexfin (AVopt-n) and VTIao (AVopt-ao), were derived from the second-order polynomial fitted to the effect measurements of 20 patients. In 252 episodes assessed, the difference between measurements (= Nexfin - VTIao) was -0.6 ± 8.1% for beat-to-beat SV changes and -1.3 ± 7.3% for effects of BiVP. Optimal AV delays for Nexfin were well related to AVopt-ao (R(2) = 0.69). The effect (%) of BiVP at the optimal AV delay was significantly larger than at the default AV delay: median difference (range) being +6.3% (0.1-14.4%; P < 0.001) for VTIao and +4.7% (0.0-14.0%; P < 0.001) for Nexfin. CONCLUSION: Individual AV optimization increases the effect of CRT. Nexfin is a promising tool in individual CRT optimization, as Nexfin agrees with VTIao on measuring beat-to-beat SV changes and on assessing relative effects of BiVP on SV at various AV delays.

The reliability of continuous noninvasive finger blood pressure measurement in critically ill children.

INTRODUCTION: Continuous noninvasive arterial blood pressure can be measured in finger arteries using an inflatable finger cuff (FINAP) with a special device and has proven to be feasible and reliable in adults. We studied prototype pediatric finger cuffs and pediatric software to compare this blood pressure measurement with intraarterially measured blood pressure (IAP) in critically ill children. METHODS: We included sedated and mechanically ventilated children admitted to our pediatric intensive care unit. We performed simultaneous arterial blood pressure measurements during a relatively stable hemodynamic period and compared FINAP, IAP, and the noninvasive blood pressure oscillometric technique. We also compared IAP to a reconstruction of brachial pressure from finger pressure. RESULTS: Thirty-five children between 2 and 22 kg body weight were included. In total, 152 attempts to record a FINAP pressure were performed of which 4.6% were unsuccessful. When comparing FINAP to IAP, bias was -16.2, -7.7, and -10.2 mm Hg for systolic arterial blood pressure, diastolic arterial blood pressure, and mean arterial blood pressure. Limits of agreement (LOA) were respectively 26.1%, 30.1%, and 22.6%. When reconstruction of brachial pressure from finger pressure was compared to IAP, these results were -11.8, 0.6, and -0.9 mm Hg for bias and 21.7%, 8.9%, and 8.9% for LOA. When noninvasive blood pressure oscillometric technique was compared to IAP, the results were: -6.8, -0.9, and -3.8 mm Hg for bias and 18.2%, 38.6%, and 22.1% for LOA. CONCLUSION: Beta type continuous noninvasive arterial blood pressure monitoring using a finger cuff with brachial arterial waveform reconstruction seems reliable in hemodynamically stable critically ill children.

Nexfin noninvasive continuous blood pressure validated against Riva-Rocci/Korotkoff.

BACKGROUND: The Finapres methodology offers continuous measurement of blood pressure (BP) in a noninvasive manner. The latest development using this methodology is the Nexfin monitor. The present study evaluated the accuracy of Nexfin noninvasive arterial pressure (NAP) compared with auscultatory BP measurements (Riva-Rocci/Korotkoff, RRK). METHODS: In supine subjects NAP was compared to RRK, performed by two observers using an electronic stethoscope with double earpieces. Per subject, three NAP-RRK differences were determined for systolic and diastolic BP, and bias and precision of differences were expressed as median (25th, 75th percentiles). Within-subject precision was defined as the (25th, 75th percentiles) after removing the average individual difference. RESULTS: A total of 312 data sets of NAP and RRK for systolic and diastolic BP from 104 subjects (aged 18-95 years, 54 males) were compared. RRK systolic BP was 129 (115, 150), and diastolic BP was 80 (72, 89), NAP-RRK differences were 5.4 (-1.7, 11.0) mm Hg and -2.5 (-7.6, 2.3) mm Hg for systolic and diastolic BP, respectively; within-subject precisions were (-2.2, 2.3) and (-1.6, 1.5) mm Hg, respectively. CONCLUSION: Nexfin provides accurate measurement of BP with good within-subject precision when compared to RRK.

Feasibility of noninvasive continuous finger arterial blood pressure measurements in very young children, aged 0-4 years.

Our goal was to study the feasibility of continuous noninvasive finger blood pressure (BP) monitoring in very young children, aged 0-4 y. To achieve this, we designed a set of small-sized finger cuffs based on the assessment of finger circumference. Finger arterial BP measured by a volume clamp device (Finapres technology) was compared with simultaneously measured intra-arterial BP in 15 very young children (median age, 5 mo; range, 0-48), admitted to the intensive care unit for vital monitoring. The finger cuff-derived BP waveforms showed good resemblance with the invasive arterial waveforms (mean root-mean-square error, 3 mm Hg). The correlation coefficient between both methods was 0.79 +/- 0.19 systolic and 0.74 +/- 0.24 diastolic. The correlation coefficient of beat-to-beat changes between both methods was 0.82 +/- 0.18 and 0.75 +/- 0.21, respectively. Three measurements were related to measurement errors (loose cuff application; wrong set-point). Excluding these erroneous measurements resulted in clinically acceptable measurement bias (-3.8 mm Hg) and 95% limits of agreement (-10.4 to + 2.8 mm Hg) of mean BP values. We conclude that continuous finger BP measurement is feasible in very young children. However, cuff application is critical, and the current set-point algorithm needs to be revised in very young children.