Monitoring Oxygen Metabolism Using the Oxygen Reserve Index in Patients Undergoing Robotic Prostatectomy: A Prospective Observational Study

Abstract

Background

The oxygen reserve index (ORi), as an adjunct to standard intraoperative pulse oximetry (SpO₂), enables prediction of hypoxia and detection of hyperoxia. The aim of this study was to analyze intraoperative variations in oxygen metabolism using ORi and partial pressure of oxygen in arterial blood (PaO₂) under conditions of ventilation compromised by the Trendelenburg tilt and pneumoperitoneum during a robot-assisted prostatectomy.

Material/Methods

One hundred adult patients scheduled for an elective robotic prostatectomy participated in this study from January 2023 to March 2024. They were all anesthetized in the same way. ORi was monitored at 10 time points. Arterial blood gas analyses were performed to determine PaO₂: before the induction of anesthesia (G1), after the patient was placed in the Trendelenburg position (G2), and after returning the patient to initial position (G3). The obtained data were then analyzed.

Results

In multivariable analysis, statistically significant positive correlations were observed between ORi and PaO₂ at the following time points: (BEG) before the induction of anesthesia (Rho=0.410) and (PP+T) after the patient was placed in the Trendelenburg position (Rho=0.521). However, no relationship was observed between ORi and PaO₂ and the duration of the Trendelenburg position and pneumoperitoneum.

Conclusions

ORi strongly correlates with PaO₂ values and is sensitive in detecting hyperoxia. Therefore, it appears that ORi can serve as a non-invasive monitoring tool for better oxygen management during anesthesia. Further research is needed on its use in detecting impending hypoxia in conditions of steep Trendelenburg position and pneumoperitoneum.

Introduction

The basic method for intraoperative monitoring of oxygen metabolism is pulse oximetry (SpO₂) measurement. This technique, however, only detects changes within the partial pressure of oxygen in arterial blood (PaO₂) range of 0 to 100 mmHg [1]. SpO₂ cannot be used to diagnose hyperoxia or predict impending hypoxia. A parameter that makes such diagnosis possible is the oxygen reserve index (ORi). It is determined using a special sensor developed by Masimo Corporation (USA), which analyses changes in the absorption of incident light in arterial and venous blood. The ORi measures the oxygenation reserve status in the range of mild hyperoxia: PaO₂ of 100 to 200 mmHg. These values correspond to variations in the ORi from 0.00 (indicating PaO₂ ≤100 mmHg) to 1.00 (indicating PaO₂ ≥200 mmHg) [2]. During oxygen supplementation, when PaO₂ exceeds 100 mmHg, arterial oxygen saturation (SaO₂) peaks at 100%, but venous oxygen saturation (SvO₂) continues to increase until it reaches a plateau at PaO₂ of approximately 200 mmHg. It is the changes in SvO₂ in the PaO₂ range of 100–200 mmHg that provide a basis for determining the ORi [3].

The ORi also demonstrates high sensitivity in detecting low PaO₂ values, allowing prediction of impending hypoxia when SpO₂ values are still within the normal range [4]. This property can be used to predict the risk of hypoxia during endotracheal intubation and in patients with a difficult airway [5,6].

Minimally invasive surgery is becoming a dominant part of interventional medicine. This also applies to urology procedures. Robot-assisted radical prostatectomies involve less tissue trauma, less postoperative pain, and a shorter hospital stay. These procedures, however, require positioning the patient in a steep Trendelenburg position and establishing a pneumoperitoneum. These unfavourable conditions can significantly disrupt the body’s physiology, especially the respiratory and circulatory functions [7].

Changes occurring in the respiratory system are caused by a cranial displacement of abdominal contents and the diaphragm. These include increased airway resistance and peak airway pressure, decreased functional residual capacity, decreased lung compliance, and an altered ventilation-to-perfusion ratio, which can lead to atelectasis [8].

The aim of our study was to analyze intraoperative variations in oxygen metabolism using ORi and PaO₂ under conditions of ventilation compromised by the Trendelenburg tilt and pneumoperitoneum during a robot-assisted prostatectomy.

Material and Methods

Ethics Approval and Patient Consent

The study protocol was approved by the Bioethics Committee of the Medical University of Lublin (decision no. KE-0254/238/11) and complies with the tenets of the Declaration of Helsinki. Every patient received information about the study and gave written consent to participate.

Design and Patients

This was an observational prospective study. One hundred patients scheduled for an elective robotic prostatectomy participated in this study from 2 January 2023 to 22 March 2024. The inclusion criteria for the study were the patient’s informed consent, age 18 years or older, scheduled surgery using the Versius surgical robot (CMR Surgical, England), patients preoperatively classified in American Society of Anesthesiologist classification (ASA) as ASA I, II, or III. The exclusion criteria were as follows: baseline hemoglobin level below 10 g dl⁻¹, a history of brain injury, a history of neurosurgical procedure, a history of multiple diseases (ASA IV and V patients, particularly respiratory or circulatory diseases), a comorbid central nervous system pathology, or a confirmed cerebral circulation disorder. Each patient was anesthetised by the same anesthesia care team. Each surgery was performed by the same surgical team assisted by a Versius surgical robot.

Anesthesia

Immediately after admission to the operating room, standard monitoring of vital signs (heart rate [HR], blood pressure [BP], SpO₂) was implemented using the Infinity Delta XL monitor (Dräger, Germany). The radial artery was then cannulated under local anesthesia using a 20-gauge cannula (BD, Belgium). The FloTrac System (Edwards Lifesciences, Irvine, USA) was connected to the cannula to monitor hemodynamic parameters – cardiac output (CO), cardiac index (CI), stroke volume variation (SVV), stroke volume (SV), and mean arterial pressure (MAP) – and to obtain direct BP measurement. Hemodynamic parameters were measured using the Edwards HemoSphere Advanced Monitor. The arterial cannula was also used to collect blood samples for blood gas analysis. After pre-oxygenation with 100% oxygen, anesthesia was induced with fentanyl at a dose of 3 μg kg⁻¹ and propofol at a dose of 1 to 1.5 mg kg⁻¹. Muscle relaxation was achieved by administration of rocuronium (0.6 mg kg⁻¹), and then, when train of four was 0%, endotracheal intubation was performed. After intubation, mechanical ventilation was started in the pressure control ventilation mode with fraction of inspired oxygen (FiO₂) 0.45, maintaining tidal volume at 500 to 580 ml, and a respiratory rate of 12 to 18 min⁻¹, depending on the end-tidal carbon dioxide values (etCO₂). Desflurane inhalation (minimum alveolar concentration 0.8 to 1.2) and a continuous infusion of remifentanil (adjustable dose 0.01 to 0.2 μg kg⁻¹ min⁻¹) were used to maintain anesthesia. Rocuronium was also administered intraoperatively in a continuous infusion (0.3 to 0.4 mg kg⁻¹ h⁻¹).

After the induction of anesthesia, the patient was immobilised in a Vacuform 2.0 mattress (BuW Schmidt GmbH, Germany) to prevent them from sliding off the operating table when in a steep Trendelenburg position.

Pneumoperitoneum was induced by injecting carbon dioxide into the peritoneal cavity until a pressure of 15 mmHg was achieved. Patients were then placed in a steep Trendelenburg position at a 27° incline. During surgery, intra-abdominal pressure was maintained at 15 mmHg in all patients. At the end of the procedure, patients were returned to the horizontal position, and the insufflation gas was passively removed from the peritoneal cavity.

Monitoring

After arriving at the operating room, all patients had SedLine EEG Sensor electrodes (Masimo Corporation, USA) attached to their forehead to monitor the depth of anesthesia, and an ORi RD rainbow SET-2 Adt sensor (Masimo Corporation, USA) attached to the index finger to monitor the ORi. These parameters were observed using an O3 Regional Oximetry Root device (Masimo Corporation, USA).

ORi was monitored at the following time points: before induction of anesthesia (BEG), after pre-oxygenation with 100% oxygen (PREOX), after induction of anesthesia (INDUCT), after placing the patient in the Trendelenburg position and establishing pneumoperitoneum (PP+T), then every 20 minutes (AFT 10, AFT 30, AFT 50, and AFT 70), after returning the patient to the initial position (ZERO), after extubation (EXTUB), and before transferring the patient to the post-anesthesia care unit (PACU).

Arterial blood samples for blood gases analysis were determined: G1 – before the induction of anesthesia (time point BEG), G2 – after the patient was placed in the Trendelenburg position and pneumoperitoneum was established (time point PP+T) and G3 – after returning the patient to the initial position (time point ZERO). Arterial blood gas analysis was performed using the GEM Premier 5000 device. During each anesthesia, a member of the research team was present in the operating room, whose only task was to record the monitored parameters.

Assessment of Outcome Measurements

The main monitored parameter was ORi. The oxygen reserve index was analyzed at each time point of the study (BEG, PREOX, INDUCT, PP+T, AFT10, AFT30, AFT50, AFT70, ZERO, EXTUB, PACU). Along with the ORi measurements, we recorded MAP, SV, CO, CI, SpO₂, and Pi.

The existence of a relationship between the body’s oxygen management parameters (ORi, PaO₂, and SpO₂) and the patients’ BMI at different time points was also assessed. The relationship between ORi and hemodynamic parameters was monitored. To provide a more complete picture of changes in the body’s oxygen management, ORi was monitored in parallel with SpO_2, and ORi was compared with PaO₂ at selected study points (G1 – BEG, G2 – PP+T, G3 – ZERO).

Statistical Analysis

The data collected in the spreadsheet was analyzed statistically using MedCalc software (version 15.8 PL) and Statistica (version 13 PL). Since data on the correlation between ORi and PaO₂ at PP+T measurement point is limited in the literature, we calculated sample size post hoc based on our own data. Most medical studies consider a P value below 0.05 to reject the null hypothesis, thus a type I error (alpha) of 0.05 value was used. In the case of type II error (beta), we set a cut-off on 0.01 to achieve nearly 100% of statistical power. Considering Rho for correlation between ORi and PaO₂ at PP+T measurement point (primary endpoint) equal 0.521, the minimal study group was estimated as 58. Categorical variables were presented using absolute numbers and percentages. The normality of the distribution of continuous variables was tested using the D’Agostino-Pearson test. Because continuous variables turned out not to be normally distributed, the relationships between the variables were assessed using nonparametric tests. For the same reason, the median was used as a measure of data clustering, and the interquartile range and the minimum-maximum range were used to quantify dispersion. The Spearman rank correlation test was used to assess correlations between selected continuous variables including BMI and ORi, PaO₂, SpO₂ at different measurement points; ORi and oxygen metabolism parameters at different measurement points; Trendelenburg position time with ORi and PaO₂ at different measurement points. Statistically significant results of the correlation analysis were presented using scatter plots. Additionally, to assess independent correlations between BMI and selected oxygen metabolism parameters, a multivariable analysis (multiple regression) was performed including potential confounding factors. The co-variables for the multivariable analysis were selected based on clinical analysis of potential confounding factors, including ASA, age, presence of lung and heart disease, smoking, surgery and Trendelenburg position duration. After the backward elimination method, ASA and smoking were included as covariates in the final multivariable analysis of the correlations between BMI and the analyzed oxygen metabolism parameters. Similarly, to assess independent correlations between ORi and selected oxygen metabolism parameters in consecutive measurement points a multivariable analysis (multiple regression) was performed including potential confounding factors. The co-variables for the multivariable analysis were selected based on clinical analysis of potential confounding factors, including ASA, BMI, age, presence of lung and heart disease, smoking, surgery and Trendelenburg position duration. After the backward elimination method, the following covariates were included at specific ORi measurement points: BEG – ASA, Age, surgery, and Trendelenburg position duration; PP+T – BMI, surgery and Trendelenburg position duration, ZERO, EXTUB, INDUCT – BMI, Age, ASA; PACU - surgery and Trendelenburg position duration; PREOX – BMI; AFT10, AFT30, AFT50, AFT70 – BMI, surgery and Trendelenburg position duration. The distribution of continuous variables was compared between 2 independent groups using the Mann-Whitney U test. This test was used to compare ORi values between patients with and without lung disease. The distribution of continuous variables between individual time points was compared using the Friedman ANOVA (with post hoc analysis). This test was used to differentiate oxygen metabolism parameters, including ORi, PaO₂, and SpO₂ between subsequent measuring points. Statistically significant results of comparisons of continuous data between groups and between consecutive measurement points were visualised using box-and-whisker plots. The diagnostic usefulness of the ORi in detecting hyperoxia was assessed based on ROC curve analysis. For all statistical tests, results with an alpha error (P) less than 0.05 were considered statistically significant.

Results

Sample Description

Out of the total of 100 patients who underwent a robotic radical prostatectomy between January 2023 and March 2024, 96 were included in the study. Two patients were excluded due to serious comorbidities: 1 patient had dilated cardiomyopathy with heart ejection fraction 32% and the other had subarachnoid hemorrhage and had undergone carotid artery aneurysm surgery. Another patient was excluded from the study due to equipment failure, which required a change from a robotic to a laparoscopic approach. The fourth patient was disqualified because, due to cataract and glaucoma, he had to undergo the surgery at a less steep Trendelenburg position angle of 16 degrees.

The median age was 66 years. The median body mass index (BMI) was 27.7. In the study group, 67.7% of the patients were assessed as ASA II. Heart disease and lung disease were diagnosed in 65.6% and 14.6% of the patients, respectively. Regarding use of stimulants, 85.4% of the patients declared themselves as non-smokers. The median ORi value before the induction of anesthesia was 0. Detailed demographic and clinical data for the study group are shown in Table 1.

Table 1.

Demographic and clinical characteristics of the study group.

Variables	n (%) or median [IQR] (min–max)
Age (years)	66 [65–67.1] (46–79)
Weight (kg)	85 [82.9–88.1] (60–127)
Height (cm)	176 [175–176] (164–190)
BMI (kg m-2)	27.7 [27.1–28.1] (20.5–40.1)
ASA
I	16 (16.7%)
II	65 (67.7%)
III	15 (15.6%)
Heart disease
Yes	63 (65.6%)
No	33 (34.4%)
Lung disease
Yes	14 (14.6%)
No	82 (85.4%)
Smoking
Yes	17 (17.7%)
No	79 (82.3%)
ORi BEG	0 [0–0] (0–0.35)
Surgery time (min)	130 [122.9–137.2] (84–207)
Trendelenburg position time (min)	109.5 [104–118.1] (62–180)

The distribution of continuous variable was assessed using the D’Agostino-Pearson test. Due to the lack of normal distribution, quantitative variables are presented as medians, interquartile ranges [IQR], and min-max ranges. Qualitative variables are presented as n (%). BEG – before induction of anesthesia; BMI – body mass index; ASA – American Society of Anesthesiologist classification; ORi BEG – ORi in time point before induction of anesthesia.

Circulatory System and Oxygen Metabolism Parameters

The median SpO₂ was significantly lower at BEG compared with the other measurement points. The median ORi was significantly lower at BEG than at the remaining measurement points: PREOX, INDUCT, PP+T, AFT10, AFT30, AFT50, AFT70, ZERO, and EXTUB (Figure 1).

Figure 1.

Distribution of oxygen reserve index (ORi) values across subsequent measurement points. The figure shows box-and-whisker plots of ORi values at defined measurement points: BEG – before induction of anesthesia, PREOX – after pre-oxygenation with 100% oxygen, INDUCT – after induction of anesthesia, PP+T – after placing the patient in the Trendelenburg position and establishing pneumoperitoneum, AFT10 – 10 minutes after PP+T, AFT30 – after 30 minutes, AFT50 – after 50 minutes, AFT70 – after 70 minutes, ZERO – after returning the patient to the initial position, EXTUB – after extubation, PACU – before transferring the patient to the post-anesthesia care unit. Significant differences in ORi values were found between measurement points, with the lowest median recorded at the BEG point compared to the other measurement points (P<0.0001). The differences between the measurement points were assessed using Friedman ANOVA (with post hoc analysis) for multiple comparisons.

By contrast, the median perfusion index (Pi) value was significantly higher at BEG compared with the PREOX, EXTUB, and PACU measurement points and significantly lower at BEG than at PP+T, AFT10, AFT30, AFT50, and AFT70. Details of the parameter comparisons for the successive measurement points are shown in Table 2.

Table 2.

Comparison of tested circulatory system and oxygen metabolism parameters at consecutive measurement points.

	Measurement points											P
	BEG	PREOX	INDUCT	PP+T	AFT10	AFT30	AFT50	AFT70	ZERO	EXTUB	PACU
	Median [IQR]
MAP	97 [87 to 106]	90.5 [81 to 97]	83 [73 to 97]	82 [75 to 92]	77 [71 to 83]	76.5 [72 to 82]	76 [72 to 80]	74 [69 to 79]	73 [66 to 80]	91 [81 to 103]	90 [79 to 99]	<0.0001*
SV	81.5 [70 to 91]	80.5 [70 to 93]	58 [50 to 68]	67 [61 to 76]	70 [63 to 76]	69 [63 to 75]	68 [61 to 78]	71 [63 to 80]	77.5 [67 to 87]	75.5 [67 to 85]	77 [68 to 92]	<0.0001*
CO	5.3 [4.2 to 6.6]	5.3 [4.3 to 6.5]	4.7 [3.9 to 5.7]	4.4 [3.9 to 5]	4.4 [3.9 to 5]	4.3 [3.9 to 4.8]	4.3 [3.9 to 4.9]	4.6 [4.1 to 5.2]	4.9 [4.3 to 6.7]	5.9 [5.2 to 7.2]	5.9 [5.1 to 7.2]	<0.0001*
CI	2.6 [2.2 to 3.3]	2.6 [2.2 to 3.2]	2.3 [1.9 to 2.8]	2.2 [1.9 to 2.5]	2.2 [1.9 to 2.5]	2.1 [1.9 to 2.4]	2.1 [1.9 to 2.4]	2.2 [2 to 2.5]	2.4 [2.1 to 2.8]	3 [2.6 to 3.5]	2.9 [2.6 to 3.4]	<0.0001*
SpO2	96 [94 to 97]	99 [99 to 100]	99 [98 to 100]	98 [97 to 99]	98 [97 to 98]	98 [97 to 98]	98 [97 to 98]	98 [97 to 98]	98 [97 to 98.8]	99 [98 to 100]	97 [96 to 98]	<0.0001*
Pi	2.6 [1,4 to 4.2]	2.4 [1.3 to 3.8]	2.7 [1.7 to 3.9]	5.3 [4.3 to 6.5]	5.3 [4.3 to 6.4]	5.1 [4.1 to 6.2]	5 [3.9 to 6]	4.5 [3.4 to 5.4]	3.1 [2.2 to 4]	1.2 [0.8 to 1.8]	1.2 [0.8 to 2.3]	<0.0001*
ORi	0 [0 to 0]	0.5 [0.3 to 0.8]	0.4 [0.3 to 0.8]	0.2 [0 to 0.2]	0.2 [0 to 0.2]	0.2 [0 to 0.3]	0.2 [0 to 0.3]	0.2 [0 to 0.3]	0.3 [0.2 to 0.4]	0.4 [0 to 0.6]	0 [0 to 0]	<0.0001*

The data are presented as median and interquartile range (IQR). Comparisons between successive measurement points were performed using nonparametric analysis Friedman’s test with post hoc analysis. MAP – mean arterial pressure; SV – stroke volume; CO – cardiac output; CI – cardiac index; SpO₂ – pulse oximetry; Pi – perfusion index; ORi – oxygen reserve index. Time points: BEG – before induction of anesthesia; PREOX – after pre-oxygenation with 100% oxygen; INDUCT – after induction of anesthesia; PP+T – after placing the patient in the Trendelenburg position and establishing pneumoperitoneum; AFT10 – 10 minutes after PP+T; AFT30 – after 30 minutes; AFT50 – after 50 minutes; AFT70 – after 70 minutes; ZERO – after returning the patient to the initial position; EXTUB – after extubation; PACU – before transferring the patient to the post-anesthesia care unit.

In the study group, the median SVV was significantly higher at INDUCT compared with the PP+T, AFT10, AFT30, and ZERO measurement points. Additionally, etCO₂ had a significantly lower median at INDUCT than at AFT30, AFT50, AFT70, and ZERO. Detailed parameter comparisons for each successive measurement point are shown in Table 3.

Table 3.

Comparison of SVV and etCO₂ parameters for the individual measurement points.

	Measurement points							P
	INDUCT	PP+T	AFT10	AFT30	AFT50	AFT70	ZERO
	Median [IQR]
SVV	14 [11–20]	10 [8–14]	12 [10–15]	13 [11–16]	13 [12–16]	14 [12–17]	11 [8–13]	<0.0001*
etCO2	35 [34–36.5]	35 [34–37]	35 [34–36.5]	37 [35.5–39]	36.5 [35–38.5]	37 [36–39]	38 [36–40]	<0.0001*

Comparisons of SVV and etCO₂ values between subsequent measurement points were performed using nonparametric analysis of Friedman’s test with post hoc analysis. The data are presented as median and interquartile range [IQR]. SVV – stroke volume variation; etCO₂ – end-tidal carbon dioxide. Time points: INDUCT – after induction of anesthesia; PP+T – after placing the patient in the Trendelenburg position and establishing pneumoperitoneum; AFT10 – 10 minutes after PP+T; AFT30 – after 30 minutes; AFT50 – after 50 minutes; AFT70 – after 70 minutes; ZERO – after returning the patient to the initial position.

Correlation Between the BMI and Selected Variables

Univariable Analysis

The univariable analysis showed that BMI was significantly, negatively correlated with selected oxygen metabolism variables. Significant correlations were observed for PaO₂ at the BEG, PP+T, and ZERO measurement points. Additionally significant negative correlations were observed between BMI and ORi at PREOX, PP+T and subsequent intraoperative measurement points (AFT10, AFT30, AFT50, AFT70). For SpO₂, significant negative correlations were found at the BEG, INDUCT, PP+T, AFT10, AFT30, AFT50, ZERO, and PACU measurement points.

Multivariable Analysis

In multivariable analysis, BMI remained independently associated with selected oxygen metabolism variables. Significant negative correlations between BMI and PaO₂ persisted at BEG, PP+T, and ZERO points. Additionally, statistically significant negative correlations between ORi and BMI were observed at PREOX, PP+T and AFT10, AFT30, AFT50, and AFT70 measurement points. For SpO₂, significant negative correlations with BMI were also observed at BEG, INDUCT, PP+T, AFT10, AFT30, AFT50, AFT70, ZERO, EXTUB, and PACU measurement points.

Detailed data on correlations between the BMI and the selected variables are presented in Table 4.

Table 4.

Correlation between BMI and selected oxygen metabolism variables.

Variable	n	BMI (univariable)		BMI (multivariable)
		Rho	p	Rho	P
G1 PaO2 (BEG)	96	−0.213	0.0371*	−0.206	0.0469*
G2 PaO2 (PP+T)	96	−0.443	0.0000*	−0.409	<0.0001*
G3 PaO2 (ZERO)	96	−0.426	0.0000*	−0.425	<0.0001*
ORi BEG	82	−0.208	0.0610	−0.139	0.2175
ORi PREOX	82	−0.294	0.0073*	−0.265	0.0177*
ORi INDUCT	82	−0.019	0.8649	−0.080	0.4782
ORi PP+T	90	−0.368	0.0004*	−0.398	0.0001*
ORi AFT10	91	−0.333	0.0012*	−0.346	0.0009*
ORi AFT30	92	−0.352	0.0006*	−0.302	0.0037*
ORi AFT50	91	−0.313	0.0025*	−0.266	0.0117*
ORi AFT70	86	−0.322	0.0025*	−0.348	0.0012*
ORi ZERO	89	−0.113	0.2938	−0.066	0.5433
ORi EXTUB	69	0.054	0.6609	0.001	0.9918
ORi PACU	70	−0.214	0.0748	−0.135	0.2720
SpO2 BEG	96	−0.309	0.0022*	−0.232	0.0245*
SpO2 PREOX	95	−0.196	0.0566	−0.266	0.0100
SpO2 INDUCT	96	−0.204	0.0462*	−0.307	0.0026*
SpO2 PP+T	96	−0.308	0.0023*	−0.312	0.0022*
SpO2 AFT10	96	−0.272	0.0073*	−0.245	0.0172*
SpO2 AFT30	96	−0.272	0.0073*	−0.339	0.0008*
SpO2 AFT50	95	−0.303	0.0029*	−0.348	0.0006*
SpO2 AFT70	89	−0.205	0.0539	−0.268	0.0120*
SpO2 ZERO	96	−0.214	0.0366*	−0.316	0.0019*
SpO2 EXTUB	96	−0.189	0.0648	−0.2642	0.0101*
SpO2 PACU	96	−0.378	0.0001*	−0.375	0.0002*

The relationships between BMI and the analyzed parameters were assessed using Spearman’s rank correlation coefficient (univariable analysis). In addition, a multivariable analysis was performed to assess correlations between BMI and selected parameters after taking into account covariates. The table shows the correlation coefficients (Rho) for univariable analysis and partial correlation coefficients (r partial) with their corresponding levels of statistical significance. G1 PaO₂ – partial pressure of oxygen in arterial blood before the induction of anesthesia; G2 PaO₂ – partial pressure of oxygen in arterial blood after the patient was placed in the Trendelenburg position; G3 PaO₂ – partial pressure of oxygen in arterial blood after returning the patient to initial position; ORi – oxygen reserve index; SpO₂ – pulse oximetry. Time points: BEG – before induction of anesthesia; PREOX – after pre-oxygenation with 100% oxygen; INDUCT – after induction of anesthesia; PP+T – after placing the patient in the Trendelenburg position and establishing pneumoperitoneum; AFT10 – 10 minutes after PP+T; AFT30 – after 30 minutes; AFT50 – after 50 minutes; AFT70 – after 70 minutes; ZERO – after returning the patient to the initial position; EXTUB – after extubation; PACU – before transferring the patient to the post-anesthesia care unit.

Correlation Between the ORi and Pi

Univariable Analysis

Statistically significant negative correlations were observed between Pi and ORi values at the PREOX (Rho=−0.470), INDUCT (Rho=−0.279), ZERO (Rho=−0.290), EXTUB (Rho=−0.308), and PACU (Rho=−0.292) measurement points. A statistically significant negative correlation was also found between MAP and the ORi at EXTUB (Rho=−0.242), and a statistically significant positive correlation was noted between SVV and ORi at AFT30 (Rho=0.236).

Correlation Between the ORi and SpO₂

Univariable Analysis

In the study group, SpO₂ was significantly positively correlated with ORi at the following measurement points: BEG (Rho=0.281), PREOX (Rho=0.399), PP+T (Rho=0.703), AFT10 (Rho=0.713), AFT30 (Rho=0.556), AFT50 (Rho=0.630), AFT70 (Rho=0.535), ZERO (Rho=0.466); EXTUB (Rho=0.612), and PACU (Rho=0.612).

Correlation Between the ORi and PaO₂

Univariable Analysis

Statistically significant positive correlations were observed between G1 PaO₂ and ORi at BEG (Rho=0.303), between G2 PaO₂ and ORi at PP+T (Rho=0.789), and between G3 PaO₂ and ORi at ZERO (Rho=0.332) (Figure 2). No statistically significant correlation between ORi and PaO₂ values and the duration of the Trendelenburg position was found (Table 5). No statistically significant differences in ORi values were observed between the groups of patients with and without lung disease.

Figure 2.

Correlation between oxygen reserve index (ORi) and PaO₂ in BEG, PP+T, and ZERO time points. The figure shows scatter plots illustrating the correlation between ORi and PaO₂ at 3 selected measurement points: BEG – before induction of anesthesia, PP+T – after placing the patient in the Trendelenburg position and establishing pneumoperitoneum, and ZERO – after returning the patient to the initial position. (A) G1 PaO₂ – partial pressure of oxygen in arterial blood before induction of anesthesia, (B) G2 PaO₂ – partial pressure of oxygen in arterial blood after placing the patient in the Trendelenburg position and establishing pneumoperitoneum, (C) G3 PaO₂ – partial pressure of oxygen in arterial blood after returning the patient to the initial position. For each measurement point, individual observations are presented. The relationship between ORi and PaO₂ was assessed using Spearman’s rank correlation coefficient. Statistically significant positive correlations between ORi and PaO₂ were found at all measurement points analyzed (detailed data are presented in the text).

Table 5.

Correlation of Trendelenburg position time with selected oxygen metabolism variables.

	Trendelenburg position time
	n	Rho	P
ORi BEG	82	0.020	0.8614
ORi PREOX	82	−0.209	0.0596
ORi INDUCT	82	−0.155	0.1635
ORi PP+T	90	−0.058	0.5857
ORi AFT10	91	−0.049	0.6442
ORi AFT30	92	−0.010	0.9240
ORi AFT50	91	−0.004	0.9686
ORi AFT70	86	0.033	0.7605
ORi ZERO	89	0.117	0.2757
ORi EXTUB	69	0.054	0.6588
ORi PACU	70	−0.083	0.4955
G1 PaO2 (BEG)	96	0.147	0.1538
G2 PaO2 (PP+T)	96	−0.046	0.6530
G3 PaO2 (ZERO)	96	0.030	0.7689

Correlations between the duration of the Trendelenburg position and the analyzed oxygen metabolism parameters were assessed using Spearman’s rank correlation coefficient. G1 PaO₂ – partial pressure of oxygen in arterial blood before the induction of anesthesia; G2 PaO₂ – partial pressure of oxygen in arterial blood after the patient was placed in the Trendelenburg position; G3 PaO₂ – partial pressure of oxygen in arterial blood after returning the patient to initial position; ORi – oxygen reserve index. Time points: BEG – before induction of anesthesia; PREOX – after pre-oxygenation with 100% oxygen; INDUCT – after induction of anesthesia; PP+T – after placing the patient in the Trendelenburg position and establishing pneumoperitoneum; AFT10 – 10 minutes after PP+T; AFT30 – after 30 minutes; AFT50 – after 50 minutes; AFT70 – after 70 minutes; ZERO – after returning the patient to the initial position; EXTUB – after extubation; PACU – before transferring the patient to the post-anesthesia care unit.

Correlation Between the ORi and Selected Variables: Multivariable Analysis

In multivariable analysis, ORi remained independently associated with selected oxygen metabolism variables. Statistically significant positive correlations between ORi and PaO₂ persisted at BEG (Rho=0.412). Statistically significant positive correlations between ORi and PaO₂ as well as with SpO₂ persisted at PP+T (Rho=0.521 and Rho=528, respectively). Additionally, ORi was significantly negatively correlated with Pi at ZERO (Rho=−0.370), positively correlated with SpO₂ at EXTUB (Rho=0.486) and positively correlated with SpO₂ at AFT10 (Rho=0.580). Moreover, the statistically significant correlation between ORi and SpO₂ at PREOX was positive (Rho=0.371) and between ORi and Pi at PREOX it was negative (Rho=−0.385). There was statistically significant negative correlation between ORi and SVV at AFT30 (Rho=−0.334) and statistically significant positive correlations between ORi and SpO₂ at AFT30 (Rho=0.380), AFT50 (Rho=0.573), and AFT70 (Rho=0.434). Significant correlations were noted in the PACU: negative between ORi and Pi (Rho=−0.283) and positive between ORi and SpO₂ (Rho=0.532).

ORi and Hyperoxia

Figure 3 shows relationship between ORi and PaO₂ measurements at 3 different time points. The value of the ORi parameter was characterized by a statistically non-significant 17.65% sensitivity and 100% specificity in detecting hyperoxia at the BEG measurement point (cut-off point: >0; AUC=0.588, 95% CI: 0.474–0.696; P=0.2956). At the PP+T time point, the value of the ORi parameter was characterized by a statistically significant 81.93% sensitivity and 100% specificity in detecting hyperoxia (cut-off point: >0; AUC=0.910, 95% CI: 0.831–0.960; P<0.0001).The value of the ORi parameter was characterized by a statistically significant 91.95% sensitivity and 100% specificity in detecting hyperoxia at ZERO time point (cut-off point: >0; AUC=0.960, 95% CI: 0.895–0.990; P<0.0001).

Figure 3.

Usefulness of the oxygen reserve index (ORi) in detecting hyperoxia (ROC curve analysis). The figure shows ROC curves illustrating the diagnostic utility of ORi in detecting hyperoxia at 3 selected measurement points: (A) BEG – before induction of anesthesia, (B) PP+T – after placing the patient in the Trendelenburg position and establishing pneumoperitoneum, and (C) ZERO – after returning the patient to the initial position. For each point, the sensitivity and specificity were assessed at a cut-off point >0, and diagnostic utility was determined based on the AUC with a 95% confidence interval. Statistical significance was assessed based on ROC analysis. Detailed values of sensitivity, specificity, AUC, and significance levels are presented in the text.

Discussion

The aim of our study was to evaluate intraoperative ORi and its usefulness in monitoring patients undergoing robotic prostatectomy. The principal finding of this study was the strong and consistent association between ORi values and PaO₂ and SpO₂ at the various perioperative time points, including pre-induction, pre-oxygenation, and subsequent stages of anesthesia. This confirms that ORi reliably reflects changes in arterial oxygenation throughout the procedure and can provide clinically relevant information even before pre-oxygenation is initiated. This study therefore directly addresses a gap identified in previous reports, which largely focused on mixed surgical populations or non-robotic procedures.

While the usefulness of the ORi in monitoring oxygen metabolism during general anesthesia has been the subject of numerous scientific reports, only a few of those studies have evaluated the use of the ORi in robot-assisted procedures.

In the available literature, the authors of 2 reports have analyzed the relationship between ORi values and selected gasometric indices. Ryu et al examined over 878 patients, of whom only 329 were ultimately included in their study [9]. Those authors found a statistically significant relationship between low ORi values, increased BMI, and the risk of intraoperative hypoxia. Our findings extend these observations by demonstrating preserved ORi–PaO₂ coupling despite physiological stressors independent of BMI, such as pneumoperitoneum and Trendelenburg positioning. Importantly, that study did not focus exclusively on robotic procedures, which limits direct comparison with our homogeneous surgical cohort. It is also worth noting, that they did not use the same form of anesthesia or surgical technique across their study group, using different pneumoperitoneum pressures that did not have a significant statistical relationship with the ORi. In contrast, our standardized anesthetic and surgical protocol strengthens the internal validity of the observed ORi correlations. In another report, assessing the correlation of the ORi with PaO₂ in a group of only 24 patients who underwent robotic prostatectomy, the author focused on the PaO₂ cut-off value of 150 mmHg corresponding to an ORi of 0.22 as an indicator of hyperoxia [10].

Our results expand these observations by demonstrating a continuous and direct correlation between ORi, PaO₂, and SpO₂ across consecutive anesthetic stages, rather than focusing on a single cut-off value. Importantly, this relationship was evident both before and after pre-oxygenation, supporting the notion that ORi monitoring can be clinically useful throughout the entire perioperative period and that ORi reliably tracks dynamic oxygenation changes during robotic surgery, even under rapidly evolving ventilatory conditions [11,16]. This was clearly evident before the induction of anesthesia and pre-oxygenation, and throughout all standard stages of the procedure.

Changes in the patient’s position during a robotic prostatectomy, to and from the Trendelenburg position, were associated with variations in selected hemodynamic parameters. Such physiological alterations are known to coexist with significant respiratory compromise, including reduced lung compliance and increased shunt fraction [12]. Our observations are consistent with previous reports, whose authors noted a significant decrease in CO and an increase in HR [12–14].

Such changes are clinically relevant because they can influence fluid therapy or ventilation strategy. Kilinc et al found that changes in CI, stroke volume index (SVI), and MAP within 10 minutes after changing the patient’s position to the Trendelenburg tilt were not statistically significant [15]. Those authors noted that despite increased venous return after placement in the Trendelenburg position, cardiac contractility decreased without causing an increase in CI or MAP associated with increased preload. They pointed out that cardiac efficiency was impaired after the patient had been placed in the Trendelenburg position. Moreover, Kilinc et al showed that this position was associated solely with the occurrence of hypotension symptoms, without causing other negative cardiac consequences [15]. In our study, however, the observed hemodynamic changes were not accompanied by alterations in ORi values or an increased incidence of hypoxemia. This suggests that ORi is not confounded by transient cardiovascular instability and predominantly reflects pulmonary oxygen reserve rather than systemic hemodynamics.

Our study was extended to include an analysis of the correlation between surgery time, in particular the duration of the Trendelenburg position and pneumoperitoneum, and the ORi. We showed that pneumoperitoneum and the Trendelenburg position, which are both associated with impaired perfusion and ventilation, did not affect ORi values. This is a novel observation, as previous studies described marked deterioration in respiratory mechanics under these conditions, without evaluating ORi behavior [12].

To the best of our knowledge, this is one of the few studies to systematically evaluate ORi behavior under the combined conditions of pneumoperitoneum and steep Trendelenburg positioning, which are specific to robotic pelvic surgery. Importantly, our findings emphasize that ORi is correlated with the expected physiological responses associated with capnoperitoneum and the Trendelenburg position, as well as with their accompanying hemodynamic changes. This finding is particularly relevant, as capnoperitoneum and steep Trendelenburg positioning are a unique combination of respiratory and hemodynamic stressors that profoundly alter pulmonary mechanics, gas exchange, and venous return. Demonstrating preserved ORi behavior under these conditions provides important physiological validation of ORi monitoring in robotic surgery.

Our observations suggest that ORi monitoring remains stable even during significant physiological alterations associated with robotic surgery. This stability supports the potential extension of ORi monitoring to other procedures involving prolonged pneumoperitoneum or complex patient positioning.

Given that predicting impending hypoxia is one of the primary purposes of ORi monitoring, our results reinforce its potential role as an early warning tool that could allow clinicians to anticipate desaturation and intervene before clinically significant hypoxemia occurs.

The physiological effect of hypoxemia is largely mediated at the molecular level by hypoxia-inducible factors (HIF). Clinically, it is associated with a change in the chemoreceptor activity within the autonomic nervous system, leading to peripheral vasodilation and pulmonary vasoconstriction [17]. Practices aimed at avoiding hypoxemia and protecting patients against its harmful effects often lead to a more liberal oxygen supply. The potentially adverse effects of hyperoxemia are comparable to those of hypoxia. The literature shows that excess oxygen can lead to impaired ventilation/perfusion, hypercapnia, acute tracheobronchitis, diffuse alveolar damage, acute respiratory distress syndrome (ARDS), systemic vasoconstriction, decreased cardiac output, and even increased mortality in critically ill patients [18,19]. In the context of the role of hypoxemia in the development of cardiac complications ORi monitoring may be a clinically valuable marker by providing additional warning time before the onset of hypoxemia compared with SpO₂ alone [16]. In the setting of robotic surgery, where sudden ventilatory or perfusion changes can occur while SpO₂ remains preserved, this early warning capability of ORi may be of particular clinical importance. Therefore, ORi monitoring could contribute to a more balanced oxygen strategy by helping clinicians avoid both hypoxemia and hyperoxia.

In practical terms, ORi trends may support timely adjustments of FiO₂ and prompt earlier interventions when oxygen reserve begins to decline, while also helping to avoid unnecessary oxygen escalation during periods of stable oxygenation.

Our discussion intentionally highlights the often-underestimated problem of hyperoxia, emphasizing that ORi could also help prevent excessive oxygen administration. Such an approach could enhance perioperative patient safety and support individualized oxygen therapy protocols.

We also demonstrated a negative correlation of Pi with the ORi. A database search for the terms “peripheral perfusion index (Pi) and ORi” yielded a small number of study titles, mainly from the field of thoracic surgery. Sagiroglu et al showed that 5 minutes after lateral position with one-lung ventilation, the ORi was significantly negatively correlated with Pi, but it was not correlated with the pleth variability index [20]. Our study of patients undergoing robotic prostatectomy provides valuable data for investigating the correlation between the ORi and Pi. Pi and ORi levels in the context of lactate levels probably reflect responses to oxygen supply and changes in peripheral microcirculation.

Limitations

Several limitations of this study should be considered. First, it was performed at a single tertiary referral center and involved a relatively limited number of participants, which may restrict the external validity and generalizability of the findings. The sample size (n=96) was determined pragmatically rather than based on a formal power calculation; consequently, the study may have been insufficiently powered to detect smaller but potentially relevant differences. All patients were anesthetized by the same anesthesia team, which ensured procedural consistency and minimized inter-operator variability. However, this may limit the reproducibility of the results in other clinical settings where anesthetic management may differ.

Second, arterial blood gas analyses results were obtained at only 3 intraoperative time points, which were considered crucial. However, this may have constrained the assessment of continuous or transient fluctuations in oxygen metabolism during the procedure.

Third, our study did not include a control group, which may distort the interpretation of the obtained ORi results. In the future, a control group of patients should be included to enable monitoring of the oxygen reserve index without the influence of harmful factors such as pneumoperitoneum or Trendelenburg position.

Although ORi demonstrated high sensitivity and specificity at selected intraoperative time points, this study focused exclusively on intraoperative correlations between ORi and physiological parameters. It was not designed to assess associations between ORi values and postoperative clinical outcomes, including pulmonary or cardiovascular complications. Therefore, the clinical significance of intraoperative ORi changes in relation to broader perioperative outcomes remains uncertain, and future studies should aim to link ORi monitoring with patient-centered pulmonary and cardiovascular endpoints.

Finally, as the investigation included only male patients undergoing robot-assisted prostatectomy, extrapolation of the results to other surgical populations should be undertaken with caution.

Future multicenter investigations involving larger cohorts and incorporating clinically relevant outcome measures are needed to validate these observations and to further elucidate the clinical utility of ORi monitoring in perioperative practice.

Conclusions

The oxygen reserve index is a parameter that demonstrates a positive correlation with SpO₂ values. Furthermore, it strongly correlates with PaO₂ values obtained from blood gas analysis. It is a sensitive and highly specific tool for detecting hyperoxia. Crucially, ORi measurements are non-invasive and are continuously monitored, with results obtained in real time, which is a clear advantage over the method of obtaining PaO₂ values. It is important to remember that blood gas analysis is the most accurate method for determining blood oxygen tension. Nevertheless, ORi is a very good complementary tool, and in situations where a gas analyzer is unavailable or where there is concern about excessive blood loss, it can be a good indicator for better oxygen management during anesthesia. Further research is needed on the use of ORi in detecting impending hypoxia in conditions of steep Trendelenburg position and pneumoperitoneum.