Abstract
Background
Post-induction hypotension (PIH) is a common complication during general anesthesia. Perfusion index (PI), a non-invasive indicator reflecting peripheral tissue perfusion and hemodynamic status, has shown potential predictive value for PIH. We hypothesized that the change of PI (
PI) between supine and 45° semi-recumbent positions can predict PIH in patients undergoing elective general anesthesia, and a cut-off value of 
PI for identifying a higher risk of PIH can be determined.
Methods
This prospective observational study enrolled eligible adults with American Society of Anesthesiologists (ASA) physical status I–III undergoing elective surgery under general anesthesia. Before anesthesia induction, the PI of each patient was recorded after a period of supine rest to achieve hemodynamic stability; subsequently, PI was measured again after achieving stability in the 45° semi-recumbent position. 
PI between the two positions was calculated for analysis. PIH was defined as systolic blood pressure (SBP) 
mmHg, mean arterial pressure (MAP) 
mmHg, or a reduction in MAP by 
from baseline within 15 minutes after anesthesia induction. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of 
PI for PIH, including calculation of the area under the curve (AUC), optimal cut-off value, sensitivity, and specificity.
Results
Data from 96 patients were analyzed. After anesthesia induction, 51 patients (53.1%) developed PIH. The AUC of 
PI for predicting PIH was 0.824 (95% CI: 0.741–0.908). The optimal cut-off value of 
PI was 22.0%, which yielded a sensitivity of 72.5% and a specificity of 86.7%.
Trial registration
This study was registered in the Clinical Trial Registry of China on 17/12/2024 (ChiCTR2400094135).
Conclusion
The 
PI between supine and 45° semi-recumbent positions was a reliable predictor of post-induction hypotension after general anesthesia induction, wherein 
PI greater than 22.0% was the threshold.
Keywords: Post-induction hypotension, Perfusion index, Risk factors, Predictive model
Introduction
Post-induction hypotension (PIH) is a common clinical complication during general anesthesia, with reported incidence rates varying from 18.1% to 64.9% [1–3]. The pathophysiology of PIH is multifactorial, and may involve hypovolemia secondary to preoperative fasting, vasodilation and myocardial depression caused by anesthetic agents, excessive anesthetic depth, and patient-specific factors such as impaired autonomic nervous function [4–6]. This hemodynamic instability is linked to severe postoperative complications, including acute kidney injury and myocardial injury, both of which are important predictors of adverse long-term patient outcomes [2, 7]. Therefore, accurate prediction and effective prevention of PIH are crucial for enhancing perioperative safety and optimizing surgical prognosis.
Current research has extensively investigated reliable predictors of PIH. Consistent studies confirm key risk factors: lower baseline blood pressure, age 
50 years, emergency surgery, chronic use of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs), and propofol-based anesthetic induction [5, 8, 9]. These factors only provide a basis for clinical risk stratification but have limited predictive accuracy. In contrast, a growing body of evidence supports the superior predictive value of various dynamic variables. For example, the preoperative inferior vena cava collapsibility index (IVCCI) has been validated as a reliable indicator for predicting PIH [3, 10]. In addition, preanesthetic heart rate variability (HRV) analysis and stroke volume variation (SVV) monitoring can also predict PIH [11, 12]. Nevertheless, the clinical utility of these dynamic metrics is frequently constrained by their reliance on specialized equipment or invasive techniques, which hinders their widespread adoption in routine clinical practice.
The perfusion index (PI), derived from pulse oximetry, is a non-invasive indicator reflecting peripheral tissue perfusion and hemodynamic status. It has been proposed as a potential predictor of PIH [13–17]. Studies suggest that PI serves as a useful marker for assessing blood volume status and autonomic nervous system (ANS) regulation [18–20]. However, conflicting evidence has emerged: a prospective study by Højlund et al. reported that baseline PI levels were ineffective in predicting hemodynamic instability during anesthesia induction [17].
Postural change test is a commonly used method for evaluating autonomic nervous function and hemodynamic regulation. When the subject moves from the supine position to a 45° semi-recumbent position, PI decreases significantly [21]. A head-up tilt (HUT) test study found that with increasing age, the dynamic capacity of cardiac autonomic regulation declines, whereas vascular responses become more important [22]. This provides a new perspective for assessing patients’ hemodynamic regulation using PI changes induced by postural variation.
Based on the potential of postural changes to alter peripheral perfusion, we hypothesized that the 
PI between supine and 45° semi-recumbent positions can predict PIH in patients undergoing elective general anesthesia, and that a specific cut-off value of 
PI can be determined to identify patients at higher risk.
Materials and methods
Study population
This study received approval from the Medical Ethics Committee of the First Affiliated Hospital of Guangxi Medical University and was registered in the Chinese Clinical Trial Registry. This study was conducted between November 2024 and March 2025, and enrolled patients undergoing elective endotracheal intubation under general anesthesia for sinus endoscopic surgery at the First Affiliated Hospital of Guangxi Medical University. The inclusion criteria encompassed patients aged 
18 years, without discrimination of sex, scheduled for elective sinus endoscopic surgery under general anesthesia with endotracheal intubation, meeting ASA grades ranging I–III, with preoperative IVCCI <50% to exclude hypovolemic status, and who provided written informed consent.The exclusion criteria were as follows: (1) Patients with a history of allergy or contraindication to anesthetic drugs used in the study; (2) Patients with severe dysfunction of the heart, liver, lung, kidney, or other major organs; (3) Patients with preoperatively assessed difficult airway; (4) Patients unable to cooperate with the study due to cognitive impairment or other reasons; (5) Patients with preoperative hypovolemia (IVCCI 
50%); (6) Patients who refused to participate in the study.Additionally, patients who met any of the following conditions were excluded during the study implementation: (1) Patients with sustained blood pressure exceeding 180/110 mmHg after entering the operating room; (2) Patients who experienced severe adverse events such as anaphylaxis or cardiac arrest during anesthesia induction; (3) Patients with unanticipated difficult airway during intubation; (4) Patients who did not use drugs in accordance with the study protocol; (5) Patients who withdrew from the study for other reasons.All participants provided informed consent, either personally or through their family members, by signing an informed consent form.
Collection of preoperative data
Data collection was performed prospectively and standardized. First, general data including sex, age, height, weight, body mass index (BMI), history of hypertension, diabetes mellitus, coronary heart disease, and cerebral infarction, New York Heart Association (NYHA) cardiac function classification, smoking history, drinking history, hemoglobin (Hb), and American Society of Anesthesiologists (ASA) classification were recorded.
All patients followed the routine preoperative protocol of 8-hour fasting and 4-hour fluid restriction. On entering the anesthesia preparation room, they were placed in the supine position, and the IVCCI was measured via a Sonosite SII ultrasound system with a 2.0–5.0 MHz convex array probe. Patients with an IVCCI < 50% who met all other inclusion criteria and did not meet any exclusion criteria were enrolled in the study. Enrolled patients remained in the supine position to be connected to a Mindray BeneView T5 monitor for measuring heart rate (HR), non-invasive blood pressure (NIBP), and PI. Concurrently, a CNAP Monitor 500 system was used to monitor stroke volume (SV), cardiac output (CO), and systemic vascular resistance (SVR). After 5 minutes of quiet rest, all aforementioned hemodynamic parameters were recorded: PI was calculated by averaging six continuous samples taken every 10 seconds, while CNAP-derived SV, CO, and SVR values were determined by averaging three consecutive beat-to-beat readings. The patient’s posture was then adjusted to a 45° semi-recumbent position, and the same parameters were re-recorded using the identical method after another 5 minutes of rest, with supine and semi-recumbent values denoted as 
/
/
/
/
/
and 
/
/
/
/
/
, respectively. The percentage change rates of each parameter between the supine and 45° semi-recumbent positions (
PI, 
CO, 
SV, 
SVR, 
MAP, 
HR) were calculated as [(supine value − semi-recumbent value)/supine value] 
100%.
Induction of general anesthesia procedure
After preoperative hemodynamic data collection, enrolled patients were transferred to the operating room. Peripheral venous access was established in a lower limb, with lactated Ringer’s solution infused at 10 mL/kg/h; electrocardiogram (ECG), NIBP (measured using the same oscillometric cuff on the same upper arm as in the preparation room), and peripheral oxygen saturation (SpO
) were monitored continuously. Anesthesia was induced with intravenous sufentanil (0.2–0.3 
g/kg), followed by synchronous target-controlled infusion (TCI) of propofol (effect-site concentration: 2.0–4.0 
g/mL) and remifentanil (effect-site concentration: 2.0–4.0 ng/mL) via a TCI pump; subsequently, intravenous cisatracurium besilate (0.2–0.3 mg/kg) was administered after the patient lost consciousness. Endotracheal intubation was then performed, and mechanical ventilation was initiated with the following target parameters: tidal volume of 6–8 mL/kg (based on ideal body weight), respiratory rate of 12–16 breaths/min, positive end-expiratory pressure of 5–8 cmH
O, and inspiratory-to-expiratory ratio of 1:2, with end-tidal carbon dioxide maintained at 35–45 mmHg. Before anesthesia induction (
, defined as baseline) and at 1 to 15 minutes after induction (
–
), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), HR were recorded. Additionally, the minimum values of MAP and HR at 
–
were recorded. Hypotension was defined as SBP < 90 mmHg, or MAP < 65 mmHg or a reduction of more than 30% from the baseline (
) value; upon occurrence, it was managed with an intravenous bolus of 5–10 mg ephedrine hydrochloride, with blood pressure monitored continuously until stabilized. No surgical manipulations or procedures were performed during the data collection period.
Statistical analysis
Sample size
A preliminary pilot study was conducted, enrolling 30 patients scheduled for elective sinus endoscopic surgery under general anesthesia with tracheal intubation. The results showed that the incidence of PIH was 47%, and the area under the ROC curve (AUC) of the 
PI for predicting PIH was 0.75. Sample size estimation was statistically performed using ROC curve analysis as the primary efficacy parameter. A two-sided significance level (
) was set at 0.05, and the statistical power (1–
) was set at 0.9. The clinically meaningful threshold for the AUC of the intended prediction model was predefined to be 
0.70. After calculation, and considering an anticipated 10% dropout rate, the minimum required sample size was finally determined to be 92 patients.
Data analysis
Statistical analyses were performed using SPSS 26.0 software. The normality of data distribution was assessed using the Shapiro-Wilk test. Continuous variables with a normal distribution were presented as mean ± SD and compared using the t-test, while those with a skewed distribution were expressed as median (IQR) and analyzed by the Wilcoxon rank-sum test. Categorical variables were described as frequencies and percentages, and comparisons were conducted using the chi-square test or Fisher’s exact test, as appropriate. Univariate analysis was first performed to screen for factors associated with the occurrence of PIH. Subsequently, multivariate logistic regression analysis was used to identify independent factors related to hypotension after anesthesia induction. ROC curves were plotted based on the screened relevant factors to evaluate their predictive efficacy for PIH, and the optimal threshold values were determined. A two-tailed P-value 
was considered statistically significant.
Results
Patient data
A total of 96 patients were included in the analysis: 51 in the hypotension group (Group H) and 45 in the non-hypotension group (Group N) [Fig. 1]. Compared with Group N, patients in Group H were significantly older, had a higher proportion of ASA physical status Class II/III, lower hemoglobin levels, and a higher proportion of females (all 
; Table 1).
Fig. 1.
Patient flow diagram
Table 1.
Patient baseline characteristics
| Parameter | All(n=96) | H(n=51) | N(n=45) | P Value |
|---|---|---|---|---|
| Age (years) |  |
 |
 |
0.001 |
| Sex (male/female) | 50/46 | 18/33 | 32/13 |  |
BMI (kg/m ) |
 |
 |
 |
0.530 |
| ASA status (I/II/III) | 17/75/4 | 6/41/4 | 11/34/0 | 0.026 |
| Diabetes (with/without) | 4/92 | 2/48 | 2/43 | 1.000 |
| Hypertension (with/without) | 10/86 | 4/47 | 6/39 | 0.508 |
| Cerebral infarction (with/without) | 3/93 | 2/49 | 1/44 | 1.000 |
| Hemoglobin (g/l) |  |
 |
 |
 |
| Smoking history (with/without) | 24/72 | 9/42 | 15/30 | 0.077 |
| Alcohol history (with/without) | 16/80 | 5/46 | 11/34 | 0.055 |
| NYHA (I/II) | 52/44 | 24/27 | 28/17 | 0.137 |
ASA American Society of Anesthesiology, BMI Body Mass Index, NYHA New York Heart Association
Incidence and characteristics of PIH by diagnostic criteria
The incidence of PIH varied across the three diagnostic criteria (Table 2). The SBP 
mmHg criterion identified 36 patients (70.6%), followed by MAP 
mmHg (30 patients, 58.8%) and MAP reduction 
(24 patients, 47.1%). The median time to onset was consistent across definitions, occurring at 4 minutes (IQR: 3–4 min) for SBP 
mmHg and 4 minutes (IQR: 3–5 min) for both MAP-based criteria. Of the 51 patients who developed PIH, 21 patients (41.2%) met a single criterion, 21 patients (41.2%) met two criteria, and 9 patients (17.6%) met all three criteria.
Table 2.
Distribution of PIH Diagnostic Criteria
| Category | Specific Details | Number (n=51) | Percentage (%) | Median Time (min), IQR |
|---|---|---|---|---|
| Individual | SBP  mmHg |
36 | 70.6 | 4 (3–4) |
| Criterion | MAP  mmHg |
30 | 58.8 | 4 (3–5) |
| Incidence | MAP reduction  from baseline |
24 | 47.1 | 4 (3–5) |
| Number of PIH criteria met | One criterion | 21 | 41.2 | — |
| Two criteria | 21 | 41.2 | — | |
| Three criteria | 9 | 17.6 | — |
PIH Post-induction hypotension, SBP Systolic Blood Pressure, MAP Mean Arterial Pressure, IQR Interquartile Range
Comparison of hemodynamic parameters and their changes between the two groups across different body positions
Compared with Group N, Group H showed lower values of PI
, SV
, and 
SVR, but higher values of 
PI, 
CO, and 
SV (
). No statistically significant differences were observed between the two groups in other indicators, including HR and MAP. Detailed data are presented in Table 3.
Table 3.
Hemodynamic parameters and their changes across different body positions
| Parameter | H (n=51) | N (n=45) | P Value |
|---|---|---|---|
PI (%) |
 |
 |
0.029 |
 PI (%) |
 |
 |
 |
CO (L/min) |
 |
 |
0.071 |
 CO (%) |
 |
 |
0.003 |
SV (mL/beat) |
 |
 |
0.046 |
 SV (%) |
 |
 |
 |
SVR (dyn s/cm ) |
 |
 |
0.202 |
 SVR (%) |
-24 (-32 to -18) | -14 (-19 to -6) |  |
HR (beats/min) |
75 (67 to 81) | 71 (68 to 81) | 0.542 |
 HR (%) |
0 (-5 to 2) | 0 (-3 to 2) | 0.612 |
MAP (mmHg) |
 |
 |
0.402 |
 MAP (%) |
-5.7 (-6.8 to 6.9) | 3.3 (-6.2 to 6.4) | 0.708 |
PI
Perfusion index in supine, CO
Cardiac output in supine, SV
Stroke volume in supine, SVR
Systemic vascular resistance in supine, HR
Heart rate in supine, MAP
Mean arterial pressure in supine, 
PI/
CO/
SV/
SVR/
HR/
MAP calculated as [(supine value − 45° semi-recumbent value)/supine value] 
100%
Changes in MAP and HR after anesthesia induction between the two groups
No statistically significant difference in baseline MAP (T
) was observed between the two groups, and Group H had significantly lower MAP at all time points from 1 to 15 minutes after induction (T
–T
) (
) (Fig. 2). For HR, there was no significant difference in baseline HR (T
) between the two groups, with no statistically significant differences found at time points from T
to T
after induction; however, Group H exhibited significantly higher HR at T
and T
compared with Group N (
) (Fig. 3). Furthermore, the minimum post-induction MAP in Group H (59.52 ± 4.89 mmHg) was significantly lower than that in Group N (73.18 ± 7.14 mmHg) (
), while the minimum post-induction HR showed no significant difference between the two groups (60.04 ± 8.63 beats/min vs. 57.98 ± 7.09 beats/min, 
).
Fig. 2.
Comparison of MAP between the two groups of patients at various time points
Fig. 3.
Comparison of HR between the two groups of patients at various time points
Regression analysis
Univariate logistic regression analysis identified significant associations (
) between the two groups and the following variables: age, sex, Hb, PI
, PI
, CO
, SV
, SVR
, 
SVR, 
PI, 
CO, and 
SV. No statistically significant differences were observed for the remaining indicators. Detailed data are presented in Table 4.
Table 4.
Univariate logistic regression
| Parameter | Odds Ratio | 95%Confidence Interval of Odds Ratio | P Value |
|---|---|---|---|
| Age | 1.059 | 1.021-1.100 | 0.002 |
| Sex | 4.513 | 1.903-10.700 | 0.001 |
| BMI | 0.969 | 0.861-1.089 | 0.594 |
| ASA status | 2.211 | 0.76-7.001 | 0.155 |
| Diabetes | 0.878 | 0.118-6.499 | 0.898 |
| Hypertension | 0.533 | 0.146-2.101 | 0.553 |
| Cerebral infarction | 1.796 | 0.157-20.496 | 0.637 |
| Hemoglobin | 0.949 | 0.921-0.977 | 0.001 |
| Smoking history | 0.429 | 0.166-1.108 | 0.080 |
| Alcohol history | 0.336 | 0.107-1.057 | 0.062 |
| NYHA | 1.853 | 0.820-4.189 | 0.138 |
 |
0.851 | 0.729-0.982 | 0.032 |
 |
0.734 | 0.598-0.88 | 0.002 |
 |
0.622 | 0.359-1.033 | 0.076 |
 |
0.457 | 0.251-0.774 | 0.006 |
 |
0.964 | 0.927-0.999 | 0.051 |
 |
0.936 | 0.894-0.974 | 0.002 |
 |
1.001 | 0.999-1.003 | 0.201 |
 |
1.002 | 1.001-1.003 | 0.004 |
 |
1.168 | 1.1-1.257 |  |
 |
1.107 | 1.035-1.194 | 0.005 |
 |
1.146 | 1.061-1.255 | 0.001 |
 |
1.063 | 1.029-1.104 | 0.001 |
 |
1.003 | 0.965-1.043 | 0.872 |
 |
1.000 | 0.969-1.032 | 0.998 |
 |
1.005 | 0.972-1.040 | 0.756 |
 |
0.981 | 0.939-1.025 | 0.399 |
 |
0.993 | 0.949-1.040 | 0.796 |
 |
0.965 | 0.907-1.027 | 0.264 |
Due to strong collinearity between PI
and PI
, and since PI
exhibited a smaller P-value in the univariate analysis, PI
was retained while PI
was excluded. Consequently, a multivariate logistic regression model was constructed incorporating the following 11 variables: age, sex, hemoglobin, PI
, CO
, SV
, SVR
, 
SVR, 
PI, 
CO, and 
SV. Multivariate logistic regression analysis revealed that 
PI was an independent predictor of PIH. This indicates that 
PI has an independent association with the occurrence of PIH, even after adjusting for age, sex, hemoglobin levels, and other hemodynamic parameters (Table 5, 
).
Table 5.
Multivariate logistic regression
| Parameter | Regression Coefficient | Wald | Odds Ratio | 95%Confidence Interval of Odds Ratio | P Value |
|---|---|---|---|---|---|
| Age | 0.050 | 2.630 | 1.051 | 0.990-1.117 | 0.105 |
| Sex | -1.078 | 2.050 | 2.938 | 0.672-12.849 | 0.152 |
| Hemoglobin | -0.031 | 2.346 | 0.969 | 0.932-1.009 | 0.126 |
 |
-0.045 | 0.101 | 0.956 | 0.722-1.264 | 0.750 |
 |
-0.018 | 0.001 | 0.983 | 0.318-3.036 | 0.976 |
 |
-0.026 | 0.582 | 0.974 | 0.910-1.042 | 0.446 |
 |
0.000 | 0.011 | 1.000 | 0.997-1.003 | 0.917 |
 |
0.119 | 7.835 | 1.126 | 1.036-1.224 | 0.005 |
 |
0.043 | 0.523 | 1.044 | 0.929-1.174 | 0.470 |
 |
0.115 | 2.762 | 1.122 | 0.980-1.286 | 0.097 |
 |
0.018 | 0.375 | 1.018 | 0.962-1.077 | 0.540 |
ROC curve analysis for all patients
The ROC curve analysis for predicting post-induction hypotension under general anesthesia showed that when using 
PI as a predictive indicator, the diagnostic accuracy was good, with an AUC of 0.824 (95% confidence interval: 0.741–0.908; 
). The optimal cut-off value for 
PI was 22.0%, with a sensitivity of 72.5% and a specificity of 86.7%. In comparison, when PI
and PI
were used as predictive indicators, the diagnostic accuracy was inferior, with AUC values of 0.611 and 0.685, respectively (Table 6, Fig. 4).
Table 6.
Diagnostic performance of PI
, PI
and 
PI to predict post-induction hypotension
| AUC (95%CI) | P Value | Optimal cut-off value | Sensitivity | Specificity | |
|---|---|---|---|---|---|
PI
|
0.611 | 0.062 | 4.35 | 62.2% | 68.6% |
PI
|
0.685 | 0.002 | 7.04 | 46.7% | 80.4% |
 PI |
0.824 | < 0.001 | 22.0% | 72.5% | 86.7% |
Fig. 4.
ROC curve of PIsup, PIsemi and 
PI predicting post-induction hypotension with general anesthesia
Discussion
The core finding of this study is that the 
PI induced by pre-operative postural change is a strong and independent predictor of PIH. Its predictive performance is significantly superior to static baseline PI measurements (AUC: 0.824 vs. 0.611), with an optimal cutoff value of 22.0% (sensitivity: 72.5%, specificity: 86.7%). This advantage reflects a critical limitation of static measurements: baseline PI varies considerably with body temperature, peripheral vascular disease, monitoring site, and individual physiological states [23]. Our innovative approach—using postural change to elicit a hemodynamic response—effectively addresses this limitation by assessing functional reserve rather than relying on a single static measurement.
At present, there is no unified diagnostic criterion for PIH. To ensure the comprehensiveness and clinical relevance of the assessment, this study adopted three complementary diagnostic criteria: SBP < 90 mmHg, MAP < 65 mmHg, and a MAP reduction of > 30% from the baseline. Among these, the absolute thresholds of SBP and MAP represent classic indicators of systemic hypoperfusion and impaired perfusion of vital organs, respectively [24, 25]; whereas the relative decrease criterion can capture hemodynamically unstable states missed by absolute thresholds due to individual differences in baseline blood pressure. Our data reveal a notable dissociation between the timing of hypotensive events and the specific criteria used to define them. While the onset time was consistent across all definitions (median: 4 minutes), the patient populations identified varied substantially. Notably, 41.2% of patients met only a single diagnostic criterion. This finding implies that relying on a single definition risks missing a significant portion of at-risk patients.
The mechanism underlying 
PI’s predictive ability likely involves the integration of volume status and ANS function. When transitioning from supine to 45° semi-recumbent position, venous return decreases, reducing cardiac preload. According to the Frank-Starling mechanism, this preload reduction triggers compensatory adjustments in myocardial contractility and peripheral vascular tone [26]. Research by Smith et al. [27] demonstrated that during head-up tilt, healthy individuals exhibit a stable phase response characterized by 30 – 40% increase in total vascular resistance and 15 – 30% decrease in cardiac output.
PI is mainly determined by SVR and SV [28]. Changes in PI are thus influenced by the opposing effects of SV (which tends to increase PI) and SVR (which tends to decrease PI), ultimately maintaining a balance. In the awake state, PI is primarily influenced by SVR [14, 16]. During general anesthesia, due to sympathetic inhibition, changes in SV become the main determinant of PI [15, 29].
We speculate that the predictive value of 
PI primarily reflects subclinical hypovolemia compromising hemodynamic reserve. Although we excluded patients with overt hypovolemia (IVCCI 
50%), subclinical volume deficits may still compromise the hemodynamic response to postural changes — especially in those with more severe subclinical hypovolemia, whose compensatory mechanisms may be already operating at maximal capacity. In euvolemic individuals, the transition to semi-recumbent position elicits moderate preload reduction that is readily compensated by sympathetic activation [26, 30]. Conversely, even mild hypovolemia may impair this compensatory capacity, resulting in more pronounced PI reduction (i.e., higher 
PI) due to inadequate peripheral perfusion. This hypothesis is supported by our observation that the hypotension group exhibited significantly larger fluctuations in 
SV and 
SVR, suggesting a reliance on maximal compensatory efforts to maintain blood pressure in the setting of limited preload reserve.
Beyond volume status, individual variation in ANS function may also contribute to 
PI differences. Previous studies have confirmed associations between preoperative autonomic function and PIH occurrence [11, 31, 32]. However, direct ANS assessment was not performed, and this mechanistic link remains speculative pending further investigation.
Consistent with prior literature [3, 8, 33, 34], we identified several demographic and clinical factors associated with PIH in univariate analysis, including older age, female sex, higher ASA physical status, and lower hemoglobin levels. However, in the multivariate logistic regression model, 
PI emerged as the sole independent predictor. This suggests that the physiological frailty reflected by 
PI is a more proximate and powerful determinant of PIH risk than chronological age or other factors in our cohort. Thus, while factors like age cannot be altered, an abnormal 
PI provides an actionable target for immediate optimization to mitigate PIH risk.
Limitations
Our study has several limitations. First, it is a single-center study with a limited sample size, which may restrict the generalizability of our findings; larger, multi-center studies are needed to validate these results. Second, the potential link between 
PI and ANS function proposed in this study is only based on indirect hemodynamic changes, without direct assessment of ANS activity. This association remains speculative and warrants further dedicated investigation in future studies with specific ANS evaluation tools. Third, the study was a prospective observational study without interventional design; we only identified 
PI as an independent predictor of PIH but did not verify whether prophylactic volume administration in patients with elevated 
PI (
22.0%) could reduce the incidence of PIH.
Conclusions
Despite these limitations, the 
PI test is non-invasive and quick, making it easy to fit into preoperative workflows. Future research should validate the thresholds we propose, explore its predictive value in different patient populations, and examine whether prophylactic interventions guided by 
PI can effectively reduce PIH incidence and adverse outcomes—converting a predictive tool into a treatment strategy.
Acknowledgements
We would like to thank all the doctors, nurses, technicians, and patients involved in this study for their cooperation.
Authors’ contributions
Study design: Jing Chen, Qingguo Li. Writing the first draft: Maolin Su,Qingguo Li. Data interpretation, discussion and preparation of the final manuscript: Qingguo Li , Maolin Su, Liying Qin, Jicai Deng, Jing Chen. All authors have read and approved the manuscript to be published.
Funding
This work was supported by the Clinical Research Climbing Plan of the First Affiliated Hospital of Guangxi Medical University (No. YYZS2021004).
Data availability
Data is provided within the manuscript or supplementary information files.
Materials availability
Data is provided within the manuscript or supplementary information files.
Code availability
Data is provided within the manuscript or supplementary information files.
Declarations
Ethics approval and consent to participate
The study protocol was approved by the Ethics Committee of The First Affiliated Hospital of Guangxi Medical University (Grant No: 2024-K462-01). The informed consent has been obtained from all patients as well as from legal guardians of all subjects. The study was conducted in accordance with Good Clinical Practice guidelines and principles of the Declaration of Helsinki and abided by STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Qingguo Li and Maolin Su contributed equally to this work.
Contributor Information
Jicai Deng, Email: 247545782@qq.com.
Jing Chen, Email: 352880721@qq.com.
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Data is provided within the manuscript or supplementary information files.