S. Van Huffel*, J. Jacobs, D. De Smet*,J. Vanderhaegen**, G. Naulaers** , M. Wolf***,

1. The Partial Coherence method for assessment of impaired cerebral autoregulation using Near-Infrared Spectroscopy: potential and limitations S. Van Huffel*, J. Jacobs, D. De Smet*,J. Vanderhaegen**, G. Naulaers** , M. Wolf***, P. Lemmers**** and F. van Bel**** KATHOLIEKE UNIVERSITEIT LEUVEN, BELGIUM *DEPARTMENT OF ELECTRICAL ENGINEERING (ESAT-SCD) ** NEONATAL INTENSIVE CARE UNIT, UNIVERSITY HOSPITALS LEUVEN *** NEONATAL INTENSIVE CARE UNIT, UNIVERSITY HOSPITAL ZURICH ****DEPT. OF NEONATOLOGY, UMC, WILHELMINA CHILDREN’S HOSPITAL UTRECHT

2. Introduction • Introduction • Methods • Models • Datasets • Results • Conclusions • Problem :impaired cerebrovascular autoregulation • ΔCBF brain injuries • Preterm infants: high risk for development disorders • ΔMABP frequent • ΔMABP ΔCBF in some infants • To detect impaired autoregulation : • Δ MABP Δ CBF • Acronyms : • CBF : Cerebral Blood Flow • MABP : Mean Arterial blood Pressure

3. Introduction • Introduction • Methods • Models • Datasets • Results • Conclusions • As rSO2, TOI, HbD reflect CBF under certain assumptions (ISOTT 07 & 08), study instead impaired autoregulation • ΔMABP ΔHbD (TOI, rSO2) with ΔSaO2 = 0 • Acronyms : • HbD : cerebral intravascular oxygenation (=HbO2-HbR) • SaO2 : arterial oxygen saturation • rSO2: regional cerebral oxygen saturation • Classical Method to quantify concordance: COH (Tsuji 2000) • Aim : • PCOH is proposed which essentially measures COH between modified signals (influence of SaO2 removed) • Compare different physiological models of SaO2 influence • Compare results using three different datasets

4. Methods • Introduction • Methods • Models • Datasets • Results • Conclusions • PCOH • Start from x1(t)= SaO2, x2(t)= MABP, x3(t) =HbD (time domain) • Step 1 : Fourier transform X1(f), X2(f), X3(f) (freq. domain) • Step 2 : expression of the linear dependence between signals • (black-box model) • With: • P13 : CSD between x1 and x3 • P11 : PSD of x1 ( = ENERGY of x1 ) • X3(f) : dependent on X1 and X2 • X13(f) : dependent on X2 • + generalization to X12, X31, … and X123,X132, … • Step 3 : calculation of PCOH • + generalization

5. Models • Introduction • Methods • Models • Datasets • Results • Conclusions • The classical method COH assumes the following model: • MABP versus HbD (TOI, rSO2) • We compared the following four models for PCOH: • Model 1: MABP \ SaO2 versus HbD \ SaO2 • Model 2: MABP versus HbD \ SaO2 • Model 3: MABP \ i(SaO2) versus HbD \ i(SaO2) • Model 4: MABP versus HbD \ i(SaO2) • with i(SaO2) = SaO2 – f(MABP) • Remark: instead of HbD, TOI or rSO2 may be used

6. Datasets • Introduction • Methods • Models • Datasets • Results • Conclusions • 30 preterm infants with need for intensive care monitored during first day(s) of life: MABP monitored by indwelling arterial catheter, SaO2 by pulse oximetry on limb. • UZ Leuven (10): TOI (NIRO300, Hamamatsu), PMA=28.7±3.3 weeks, bodyweight=1125±504 g, recording time = 6h54min±3h33min • UMC Utrecht (10): rSO2 (INVOS4100, Somanetics), PMA= 29.3 ±1.3 weeks, bodyweight=1131±311g, recording time=49h36min± 11h48min • UZ Zurich (10) : HbD (Critikon2001, GE), PMA=28.1±2.1 weeks, bodyweight=1198±439g, recording time=2h11min± 27min, inspired O2 fraction changed SaO2↑ • All data downsampled to 0.333Hz (3s) and (pre)processed as follows: • If artifacts > 1.5 s, then exclude data else interpolate (Soul, 07) • Data only included if SaO2 satisfies: 80%< SaO2 < 100% • (P)COH restricted to 0.0033-0.04 Hz (25-300s cycles) (Soul,07) • Sliding windowing, half overlapping epochs of 12.5, 15, 10 min • Mean COH calibrated such that CSV=0.5 for impaired autoreg.

9. Results • Introduction • Methods • Models • Datasets • Results • Conclusions • Global Analysis • On average, PCOH > COH (mean scores, CPRT* and PPI**), STD of PCOH values slightly higher but not significantly. • Model 3 gives the highest values while model 2 the smallest ones for all datasets. • High Mean score values (> 0.5) are related to smaller PMA and less significantly to smaller birth weight and smaller survival rate • High PCOH values (mean>0.5, CPRT>0, PPI>0) better detect bad clinical outcome than COH (MDI<84, PDI<84, Apgar<7). • CPRT and PPI (0.1) better detect bad clinical outcome than mean score values. • Local Analysis: zoom in on epochs with higher var(SaO2) • All mean score values significantly higher, model 3 highest. • Using unpreprocessed data, score values are all significantly lower (factor 2) and don’t reflect concordances between signals • *CPRT = % of Recording Time with mean (P)COH >0.5 (ISOTT 2007) • **PPI = % of 10 min. epochs with mean (P)COH > 0.68 (Soul et al, 2007)

13. Conclusions • PCOH removes influence of SaO2 variations, thereby improving automated monitoring of (impaired) autoregulation . • (P)COH analysis performed using three different datasets (different center, different CBF reflector) • PCOH > COH: PCOH better detector of bad clinical outcome. CPRT and PPI (0.1) clearly better detectors than mean scores • 4 PCOH models compared  model 3 best • Further statistical analysis needed, as well as correlation of new scores with neurological outcomes on larger multicentric datasets • Introduction • Methods • Models • Datasets • Results • Conclusions