The Differential Role of Anesthetic Technique by Etiology of Postpartum Hemorrhage: A Dual-Cohort Analysis of Emergency Cesarean Delivery and Placenta Accreta Spectrum

The Differential Role of Anesthetic Technique by Etiology of Postpartum Hemorrhage: A Dual-Cohort Analysis of Emergency Cesarean Delivery and Placenta Accreta Spectrum | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Differential Role of Anesthetic Technique by Etiology of Postpartum Hemorrhage: A Dual-Cohort Analysis of Emergency Cesarean Delivery and Placenta Accreta Spectrum Yusuf Ziya KIZILDEMİR¹, Yavuz SAYGILI² This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7381007/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Based on the hypothesis that the effect of general anesthesia (GA) versus neuraxial anesthesia (NA) on postpartum hemorrhage (PPH) varies according to its underlying etiology, this study aimed to investigate the impact of the anesthetic technique on the risk of severe PPH in two distinct clinical scenarios: (1) emergency cesarean deliveries at risk for uterine atony and (2) cases of placenta accreta spectrum (PAS) at risk for massive surgical hemorrhage. Methods In this retrospective dual-cohort study, patients receiving general anesthesia (GA) in Cohort 1 (atony risk) were matched 1:3 to patients receiving neuraxial anesthesia (NA) using propensity score matching (PSM) to control for baseline confounders. Cohort 2 comprised patients with placenta accreta spectrum (PAS) who underwent scheduled cesarean hysterectomy. The primary endpoint was the incidence of severe postpartum hemorrhage (PPH), defined as a composite result of quantitative blood loss > 1500 mL, transfusion of ≥ 2 units of packed red blood cells, or the need for an invasive hemostatic procedure. Results In the matched Cohort 1 (n = 600), the incidence of severe PPH was significantly higher in the GA group compared to the NA group (21.3% vs. 9.8%). After adjusting for operative duration and tranexamic acid use, GA was independently associated with an almost threefold increased risk of severe PPH (Adjusted Odds Ratio [aOR]: 2.91; 95% Confidence Interval [CI]: 1.80–4.69; p 0.05). However, a post-hoc Bayesian analysis indicated a high probability (91%) that GA is associated with increased blood loss. Conclusion In our matched cohort, general anesthesia was associated with an almost threefold increase in the risk of severe PPH in emergency cesarean deliveries susceptible to uterine atony (aOR 2.91). In cases such as the placenta accreta spectrum, the primary determinant of hemorrhage is the underlying surgical pathology, and the role of anesthetic management appears to be secondary. However, these findings should be considered exploratory due to the limited statistical power of the cohort. In general, these results strongly support the personalization of anesthetic strategies based on the expected etiology of hemorrhage to reduce maternal morbidity and mortality. General Anesthesia Neuraxial Anesthesia Postpartum Hemorrhage Placenta Accreta Spectrum Uterine Atony Dual-Cohort Study Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Despite significant technological and pharmacological advances in modern obstetrics, postpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality worldwide [1]. Central to this paradox are the increasing rates of cesarean delivery and the inherent risk of hemorrhage associated with the procedure. Anesthetic management, which plays a critical role in ensuring maternal safety during and after cesarean delivery, has been the focus of a longstanding debate [2]: Is the anesthetic technique merely a passive bystander in the hemorrhagic process or does it actively contribute to initiating or exacerbating bleeding? The answer to this fundamental question lies in the pharmacodynamic differences between general anesthesia (GA) and neuraxial anesthesia (NA). Although NA provides targeted analgesia to the lower half of the body, GA uses potent agents with systemic effects [3]. There is a strong biological rationale suggesting that volatile anesthetics used during GA can induce uterine atony—the most common cause of PPH—by suppressing uterine contractions. This effect is not limited to the direct inhibition of calcium channels, but is also supported by molecular evidence demonstrating that these agents can blunt the response to uterotonic drugs by disrupting oxytocin receptor signaling pathways [4]. However, this pharmacological "accusation" represents only one aspect of obstetric hemorrhage. In conditions such as the Placenta Accreta Spectrum (PAS), where the primary cause of bleeding is anatomical anomaly, the role of anesthetic management changes from a primary factor to a supporting element in managing massive surgical trauma. The existing literature is stuck in a methodological loop due to its inability to differentiate between these two distinct hemorrhage scenarios. Most studies on atony-related hemorrhage are affected by a fundamental bias known as "confounding by indication," which occurs because GA is often selected for the most urgent and critically ill patients[5]. This bias has made it impossible to determine whether the increased bleeding observed with GA is due to the anesthetic itself or to the patient's pre-existing high-risk condition. However, on the contrary, the PAS literature has focused mainly on surgical techniques, largely neglecting the impact of anesthetic choice on results in this unique population of patients. To overcome this methodological impasse and clarify the role of anesthetic techniques in both pathophysiological contexts, this study employs an innovative dual-cohort design. Our primary objective was to investigate the association between anesthetic technique and severe PPH related to atonies in a matched non-PAS cohort using propensity score matching (PSM). Our secondary objective was to descriptively evaluate these outcomes in a dedicated cohort of patients with PAS who underwent planned cesarean hysterectomy. 2. MATERIALS AND METHODS 2.1. Study Design and Ethical Framework This study was conducted with a retrospective dual-cohort design. The study reporting was carried out according to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational studies. The approval for the study was obtained from the Harran University Clinical Research Ethics Committee (Decision Date: January 15, 2025, Decision No: HRÜ/2025/01–18), and due to the retrospective nature of the study, the requirement for informed consent of patients was waived. Patient data confidentiality was ensured by removing all identification information and assigning a unique code to each patient prior to analysis. 2.2. Population of patients and Selection Criteria Study data were obtained from our institution's Hospital Information Management System (HIMS) and the digital anesthesia recording system, covering all deliveries between January 1, 2020 and December 31, 2024. Patients included in the analysis were divided into two main cohorts based on the underlying pathophysiology of hemorrhage. Cohort 1 (Non-PAS Emergency Cesarean Deliveries) : This cohort consists of patients without a diagnosis of Placenta Accreta Spectrum (PAS) who underwent emergency cesarean delivery for an American College of Obstetricians and Gynecologists (ACOG) Category I indication (conditions that immediately threaten maternal or fetal life and generally require a decision-to-delivery time of less than 30 minutes)[6]. Cohort 2 (Placenta Accreta Spectrum Patients) : This cohort includes patients who were diagnosed with PAS antenatally by ultrasonography and/or pelvic magnetic resonance imaging (MRI) according to the criteria of FIGO (International Federation of Gynecology and Obstetrics) and were therefore scheduled for a planned cesarean-hysterectomy operation[7]. The specific inclusion and exclusion criteria for each cohort are detailed in Table 1 . Table 1 Detailed Inclusion and Exclusion Criteria for Cohorts Criterion Cohort 1: Non-PAS Emergency C-Section Cohort 2: Placenta Accreta Spectrum Inclusion Criteria 1. Singleton pregnancy, ≥ 34 gestational weeks 1. Singleton pregnancy, ≥ 34 gestational weeks 2. Emergency cesarean section for ACOG Category I indication 2. Antenatal diagnosis of PAS (with USG/MRI) 3. Having received General or Neuraxial Anesthesia 3. Planned Cesarean-Hysterectomy Exclusion Criteria 1. Known or intraoperatively detected PAS 1. Absence of PAS diagnosis 2. Pre-existing coagulopathy 2. Pre-existing coagulopathy 3. Use of therapeutic anticoagulants 3. Use of therapeutic anticoagulants 4. Missing key data (e.g., blood loss) 4. Missing key data 2.3. Matching Procedure (Cohort 1 Only) To control for baseline confounders, each patient receiving GA was matched to three patients receiving NA. This 1:3 ratio was chosen to maximize statistical power while adjusting for the lower prevalence of GA use for emergency cesareans in our institution. This ratio was chosen to maximize statistical power while effectively reducing bias, considering the prevalence of GA:NA use at our institution. The matching was performed using the MatchIt package in R software with a greedy nearest neighbor matching algorithm and a caliper width set at 0.2 times the standard deviation of the propensity score. The matching was based on the following four variables: 1) the specific indication for emergency cesarean delivery, 2) preoperative hemoglobin level, 3) gestational age and 4) maternal Body Mass Index (BMI). Operative duration was not included in the PSM model as it is considered a post-treatment variable whose value is determined after the anesthetic choice is made. However, it was included as a key covariate in the final regression analysis to control for its potential confounding effects. Other potential confounders, such as dynamically changing uterotonic protocols, were not included in the model because of difficulties in their retrospective measurement; this is further discussed in the study's limitations section. The quality of the match was assessed by the standardized mean differences (SMD) presented in Table 2 and the Love Plot shown in Fig. 1 . Table 2 Baseline Demographic and Clinical Characteristics of Study Cohorts Characteristic GA (n = 150) - Cohort 1 NA (n = 450) - Cohort 1 SMD* - Cohort 1 GA (n = 45) - Cohort 2 NA (n = 30) - Cohort 2 Age (years, mean ± SD) 29.8 ± 5.1 30.1 ± 4.9 0.06 34.5 ± 4.2 35.1 ± 3.9 Prior Cesarean Deliveries (n, mean) 1.1 1.0 0.08 3.2 3.4 BMI (kg/m², mean ± SD) 31.2 ± 4.5 31.0 ± 4.7 0.04 32.1 ± 5.0 31.8 ± 4.8 Preoperative Hb (g/dL, mean ± SD) 11.1 ± 1.2 11.2 ± 1.3 0.08 10.8 ± 1.1 10.9 ± 1.2 Nulliparity, n (%) 78 (52.0) 241 (53.6) 0.03 0 (0) 0 (0) Gestational Age (weeks, mean ± SD) 38.1 ± 1.5 38.2 ± 1.4 0.07 36.2 ± 1.1 36.5 ± 1.3 Duration of Surgery (min, mean ± SD) 52.4 ± 14.1 48.1 ± 12.2 0.35 185 ± 45 170 ± 51 * *SMD (Standardized Mean Difference) is reported for the matched Cohort 1 only. An SMD value < 0.1 indicates a good balance between the groups after matching. The significant remaining imbalance observed in the duration of surgery (SMD = 0.35) likely reflects the preferential use of GA in cases with greater urgency (e.g., severe fetal distress) or unexpected surgical difficulties. This indicates an underlying case complexity that is not fully captured by the matching variables. To control for this important potential confounder, the duration of surgery was included as a covariate in the final multivariate regression analysis. Although our matching model aims to control for the baseline confounders present, due to the retrospective nature of our data set, we acknowledge the presence of unmeasured potential confounders that can influence the selection of GA. These include factors such as the degree of urgency within ACOG Category I (e.g., decision-to deliver time), maternal hemodynamic stability before anesthesia, or unforeseen intraoperative surgical difficulties. The presence of these unmeasured variables creates the potential for residual confounding, which is discussed in more detail in the limitations section of our study. Data availability for all key variables was> 95% (e.g., preoperative Hb data was available for 580 of the 600 patients in Cohort 1). 2.4. Data Collection Process and Variable Definitions Study data were retrospectively obtained from the institution's Hospital Information Management System (HIMS) and digital anesthesia recording system. Baseline Demographic and Clinical Variables The baseline characteristics and clinical histories of the patients included the following variables: · Demographic Data: Maternal age (years), preoperative Body Mass Index (BMI, kg/m²). · Obstetric History: Gravida, parity, number of previous cesarean deliveries, and gestational week. · Preoperative Laboratory Values: Preoperative hemoglobin was defined as the most recent hemoglobin value (g/dL) measured within the 24 hours preceding delivery. Variables of the anesthesthetic and Surgical process · Anesthetic Technique: The primary exposure of the study, the anesthetic technique, was classified as "General Anesthesia (GA)" or "Neuraxial Anesthesia (NA)" based on anesthesia records. · Operative Duration: Calculated as the total time (in minutes) from the initial skin incision to the placement of the final skin suture. · Volatile Agent Exposure: For patients in the GA group, this was quantified as MAC-hours by multiplying the average end-tidal MAC (Minimum Alveolar Concentration) value, electronically recorded during anesthesia maintenance, by the duration of exposure (in hours). This variable was used as a measure of the administered volatile anesthetic dose. Outcome Variables The primary and secondary endpoints of the study are described below. · Primary Outcome: Severe Postpartum Hemorrhage (PPH) · Severe PPH was defined as a composite endpoint, characterized by the presence of at least one of the following three criteria: o Massive Blood Loss: Quantitative blood loss (QBL) exceeding 1500 mL. o Blood Transfusion: Transfusion of two or more units of packed red blood cells (PRBCs). o Invasive Hemostatic Intervention: Application of one of the following to control bleeding not responsive to standard uterotonic therapy: a Bakri balloon, compression sutures (e.g. B-Lynch) or emergency peripartum hysterectomy. · Secondary Outcomes: o Total quantitative blood loss (QBL, mL). o Total number of packed red blood cell (PRBC) units transfused. o Hemoglobin change (Delta Hb): The difference between preoperative and postoperative hemoglobin values (measured within 24-48 hours) (g/dL). o Need for second-line uterotonics: The requirement for at least one agent such as methylergonovine, carboprost, or misoprostol in addition to the standard oxytocin infusion. o Use and total dose of tranexamic acid (TXA). · Blood Loss Measurement (QBL): The QBL was prospectively recorded for each case by operating room nurses who receive regular in-service training on this standardized institutional protocol. This protocol includes visual and practical training materials for all personnel and aims to ensure measurement consistency. The measurement process includes the following steps: 1) Measure the difference between the dry weight prior to surgery and the wet weight after surgery of all surgical sponges and pads (assuming 1 gram = 1 mL of blood); 2) Subtracting the volume of irrigation fluid used from the total volume of aspirated blood and fluids, the accuracy of which can vary depending on the surgical team's diligence; and 3) Estimating the amount of blood on surgical drapes and the floor using standardized visual guides according to our protocol. Although a formal inter-rater reliability analysis was not performed due to our retrospective design, the standardized protocol and regular training aimed to enhance measurement consistency. The potential variability in this measurement method is acknowledged as a limitation of our study. 2.5. Anesthesia Protocols The typical anesthesia practices at our institution are summarized in Table 3. In the GA group, the primary goal was to maintain hemodynamic stability (defined as keeping the mean arterial pressure within 20% of the baseline value), whereas in the NA group, the aim was to achieve and maintain a sensory block level between T4 and T6. The final decision regarding the anesthetic technique was made by the attending anesthesiologist based on the patient's clinical condition and the degree of urgency. Table 3. Details of Typical Anesthesia Protocols by Group Annesthetic Agent / Technique General Anesthesia (GA) Group (Typical Application) Neuraxial Anesthesia (NA) Group (Typical Application) Anesthetic Induction Propofol (2-2.5 mg/kg) + Rocuronium (0.6-1 mg/kg) Not Applicable Anesthetic Maintenance Sevoflurane or Desflurane (MAC target 0.8-1.2) Not Applicable Primary Analgesic Agent Fentanyl (intermittent bolus, 1-2 mcg/kg total) Hyperbaric Bupivacaine (10-12.5 mg, intrathecal) Adjuvant Agent Not Applicable Fentanyl (15-25 mcg, intrathecal) or Morphine (100-150 mcg, intrathecal) Airway Management Endotracheal Intubation Spontaneous Breathing (with oxygen support if needed) *The protocols outlined in this table represent typical applications in our institution. Specific agents, dosages, and target depth of anesthesia or sensory block level could vary depending on the patient's immediate clinical condition, the degree of urgency, and the discretion of the attending anesthesiologist. MAC: Minimum Alveolar Concentration. 2.6. Statistical Analysis A multitude of software packages were utilized for the statistical analysis of the data. Basic descriptive statistics, t-tests, chi-square tests, and multivariate logistic regression analyses were performed using IBM SPSS Statistics. To mitigate the potential for "indication-based confounding bias," more specialized analyses were performed, including propensity score matching (PSM) and the creation of a Love Plot graph (see Figure 1) to assess the quality of the matching process. These analyses were executed using the 'MatchIt' package within the R software environment. In a similar vein, the post-hoc Bayesian analysis for Cohort 2 was also executed in R. In all analyses, the statistical significance threshold was set at p<0.05. In accordance with the two-cohort design, analyses were conducted separately for each cohort. Given that the rate of data loss for key variables was below 5%, it was determined that all analyses would be completed on a complete-case basis. Analysis of Cohort 1 (Non-PAS Emergency Cesarean Deliveries) In this cohort, Propensity Score Matching (PSM) was used to reduce "confounding by indication" bias. Propensity scores, which reflect the probability of receiving general anesthesia (GA), were calculated using a logistic regression model that included the following variables: specific cesarean indication, preoperative hemoglobin, gestational age, and BMI. Subsequently, each patient in the GA group (n=150) was matched in a 1:3 ratio to three patients in the NA group (n=450) using a nearest-neighbor algorithm with a caliper of 0.2. The balance of post-match between groups was assessed using the Standardized Mean Difference (SMD < 0.1). To determine the independent effect of the anesthetic technique on severe PPH, a multivariable logistic regression analysis was used on the matched data set, adjusting for operative duration and the use of tranexamic acid (TXA). Given that operative duration differed significantly between the matched groups and is a well-established risk factor for PPH, it was included as a primary adjustment covariate. However, we acknowledge that the duration of the operation could potentially function as both a confounder and a mediator (i.e., a consequence of the anesthetic technique itself, influencing the outcome). To explore this complex relationship and test the robustness of our findings, a sensitivity analysis was also performed in which the model was run without adjusting for operative duration. Secondary outcomes were compared using t-tests and Chi-square tests. Analysis of Cohort 2 (PAS Patients) Due to the small sample size of this cohort, non-parametric tests were preferred for intergroup comparisons. The Mann-Whitney U test was used for continuous variables, and Fisher's Exact test was used for categorical variables. A post-hoc power analysis showed that with the current sample, the study had 80% power to detect a blood loss difference of ~750 mL, but only 23% power to detect the observed difference of 350 mL. Therefore, the findings in this cohort should be considered exploratory. 3. RESULTS 3.1. Baseline Characteristics of the Groups The final study population consisted of 600 patients in Cohort 1 (150 GA, 450 NA) and 75 patients in Cohort 2 (45 GA, 30 NA). The baseline characteristics for both cohorts are presented in Table 2 . The mean age of the patients in Cohort 1 was approximately 30 years, while patients with PAS in Cohort 2 were, as expected, older (mean age ~ 35 years). Similarly, the mean number of previous cesarean deliveries was significantly higher among PAS patients (3.2–3.4) compared to the non-PAS cohort (1.0–1.1). Following matching of the propensity score, an excellent balance was achieved between the GA and NA groups in Cohort 1 across all baseline covariates, including age, BMI, and preoperative hemoglobin (all SMD < 0.1), a finding visually confirmed by the Love Plot in Fig. 1 . Only the duration of surgery remained significantly longer in the GA group (52.4 vs. 48.1 minutes; p = 0.008), and this variable was statistically adjusted for in subsequent analyzes. 3.2. Cohort 1: Anesthetic Technique and Postpartum Hemorrhage Outcomes In Cohort 1, which consisted of non-PAS emergency cesarean patients, the incidence of the primary outcome, severe PPH, was significantly higher in the GA group than in the NA group (21.3% vs. 9.8%, respectively; p < 0.001) (Table 4 ). In the final multivariable analysis adjusted for operative duration and tranexamic acid (TXA) use, GA was independently associated with a nearly threefold increased risk of severe PPH (Adjusted Odds Ratio [aOR]: 2.91, 95% Confidence Interval [CI]: 1.80–4.69, p < 0.001). This corresponds to an absolute risk increase of 11.5% attributable to GA (21.3% vs. 9.8%). The distribution of the individual components comprising the primary outcome is detailed by group in Fig. 2 . Table 4 Primary and Secondary Bleeding Outcomes by Anesthesia Type According to Cohort Outcome Variable GA (n = 150) - Cohort 1 NA (n = 450) - Cohort 1 p-value GA (n = 45) - Cohort 2 NA (n = 30) - Cohort 2 Primary Outcome Severe PPH*, n (%) 32 (21.3) 44 (9.8) < 0.001 41 (91.1) 26 (86.7) aOR (95% CI) 2.91 (1.80–4.69) Reference < 0.001 - - Secondary Outcomes Quantitative Blood Loss (mL, mean ± SD) 1280 ± 550 890 ± 410 < 0.001 3850 ± 1200 3500 ± 1100 Hemoglobin Drop (g/dL, mean ± SD) 2.4 ± 1.1 1.7 ± 0.9 < 0.001 4.1 ± 1.5 3.8 ± 1.3 ≥ 2 Units PRBC Transfusion, n (%) 24 (16.0) 31 (6.9) 1500 mL, transfusion of ≥ 2 units of packed red blood cells (PRBCs) or an invasive hemostatic procedure. aOR: Adjusted Odds Ratio, adjusted for the duration of surgery. Secondary outcomes corroborated this primary finding. The mean quantitative blood loss (QBL) was approximately 400 mL higher in the GA group compared to the NA group (1280 mL vs. 890 mL). Similarly, the mean hemoglobin drop (2.4 g/dL vs. 1.7 g/dL) and the proportion of patients requiring transfusion of ≥ 2 units of packed red blood cells (16.0% vs. 6.9%) were significantly higher in the GA group (Table 4 ). This marked difference in the distribution of blood loss between the groups is further illustrated in Fig. 3 . Regarding hemorrhage management, 45.3% of patients in the GA group required additional uterotonics, compared to 28.9% in the NA group. The use of tranexamic acid was also more frequent in the GA group (36.7% vs. 21.1%) (Table 5 ). Further analyses exploring the effect of GA revealed a positive dose-response relationship between volatile agent exposure and blood loss; each 0.1 MAC-hour increment was associated with an estimated 120 mL increase in blood loss (95% CI: 35–205 mL, p = 0.006). Table 5 Use of ureterotonics and Tranexamic Acid According to Cohort Use of medication GA (n = 150) - Cohort 1 NA (n = 450) - Cohort 1 GA (n = 45) - Cohort 2 NA (n = 30) - Cohort 2 Oxytocin (Total Units, mean ± SD) 35 ± 15 28 ± 12 75 ± 25 70 ± 22 Need for Additional Uterotonics*, n (%) 68 (45.3) 130 (28.9) 43 (95.6) 28 (93.3) Tranexamic Acid Use, n (%) 55 (36.7) 95 (21.1) 44 (97.8) 29 (96.7) 3.3. Cohort 1: Supplementary and Sensitivity Analyses The mean concentration of volatile agents administered during maintenance in the GA group was 1.1 ± 0.2 MAC (Minimum Alveolar Concentration). In the sensitivity analysis conducted to evaluate the potential mediator role of the duration of surgery, when the multivariable model was repeated without the duration of surgery, the relationship between GA and severe PPH remained significant (aOR: 2.75, 95% CI: 1.71–4.42, p < 0.001), supporting the robustness of our findings. The elevating effect of GA on the risk of PPH was consistent in different subgroups of patients. As summarized in the Forest Plot in Fig. 3 , the risk-elevating effect of GA did not differ significantly by patient parity (nulliparous vs. multiparous; p for interaction = 0.82) or by the indication for cesarean delivery. Sensitivity analyzes conducted to test the robustness of the findings also supported the primary result. When the subgroup of patients (3.2%) who were converted from NA to GA due to neuraxial failure was excluded from the analysis, the association between GA and PPH remained significant (aOR 2.52, 95% CI 1.58–4.02). 3.4. Cohort 2: Placenta Accreta Spectrum (PAS) Findings In PAS patients who underwent planned cesarean-hysterectomy (Cohort 2), the incidence of severe PPH was, as expected, extremely high in both anesthesia groups, with no statistically significant differences observed between them (91.1% for GA vs. 86.7% for NA; p > 0.05). Quantitatively, the mean blood loss was 3850 mL in the GA group versus 3500 mL in the NA group; this mean difference of 350 mL while not statistically significant, is clinically relevant as it approximates the volume of one unit of packed red blood cells, warranting further investigation in larger cohorts. (95% CI: -250 to + 950 mL). However, it is noteworthy that this difference may be clinically relevant, corresponding to approximately one unit of packed red blood cells (Table 4 ). Similarly, nearly all patients in both groups required additional uterotonics (95.6% vs. 93.3%) and tranexamic acid (97.8% vs. 96.7%), illustrating the severity of the hemorrhage and the aggressive nature of its management (Table 5 ). 4. DISCUSSION The innovative dual-cohort design of our study addresses a significant literature gap by suggesting that the association between anesthetic technique and postpartum hemorrhage (PPH) is not a single absolute truth, but rather varies fundamentally depending on the underlying pathophysiology of the hemorrhage. Rather than treating PPH as a monolithic entity, we separated it into two distinct scenarios—uterine atony and invasive placentation—and evaluated the role of anesthetic management within its respective clinical context. Our findings clearly establish that general anesthesia (GA) is an independent and significant risk factor for PPH in atony-prone deliveries, but its effect becomes secondary in cases of massive surgical hemorrhage, such as Placenta Accreta Spectrum (PAS). The approach of this study, which examines PPH by distinguishing it according to its underlying pathophysiology (atony and PAS), is consistent with the current trend in the literature. A recent systematic review underscored the notion that each etiology of PPH likely possesses its own unique set of risk factors. The review suggested that a comprehensive evaluation of these etiologies may enhance the ability to determine patient risk[8]. A similar conclusion was reached in a study by Butwick et al., which used layered analyzes based on subtypes of cesarean section (CD) (before labor and intrapartum). This study concluded that the risk factor profiles for severe postpartum hemorrhage (PPH) differed between these two cohorts[9]. These findings support the validity and importance of the methodology of our study in addressing PPH not as a single entity, but as separate conditions with distinct pathophysiological mechanisms and evaluating risk factors in this context. The findings of Cohort 1 (non-PAS emergency cesarean deliveries) provide compelling evidence for the active role of GA in the development of PPH. In our multivariable analysis, adjusted for significant confounders such as operative duration and tranexamic acid (TXA) use, we found that GA independently increased the risk of severe PPH by nearly 2.9 times (aOR: 2.91). The tangible clinical implication of this statistical finding is even clearer: the calculated Number of injuries to be repaired (NNH) of 14 indicates that for every 14 emergency cesarean patients who receive GA instead of NA, an additional case of severe PPH occurs due to the anesthetic technique. This causal relationship is further strengthened by two key pieces of evidence: first, the positive dose-response relationship we identified between increasing exposure to volatile agents (per 0.1 MAC-hour) and blood loss; and second, the biological plausibility of our finding, which is consistent with in-vitro studies showing that volatile anesthetics inhibit myometrial contractions by inhibiting L-type calcium channels and disrupting oxytocin receptor signaling. Our finding of a nearly threefold increase in the risk of PPH associated with GA (aOR 2.91) is consistent with, although slightly higher than, the findings of previous large-scale studies such as the work by Bateman et al., which also reported a significantly elevated risk of hemorrhage with general anesthesia. Moreover, the fact that the adjusted odds ratio (aOR) increased after adjusting for TXA use suggests that the severity of bleeding in the GA group may have been somewhat masked by TXA, implying the true effect of GA could be even stronger. The approximately threefold increased risk of severe PPH associated with GA (aOR 2.91) found in this study is consistent with the effect direction reported in previous large-scale observational studies. However, the magnitude of this effect varies considerably in the literature. For instance, a stratified case-control study by Butwick et al. found that the risk associated with GA was remarkably high (aOR 22.3) in planned, pre-labor cesarean sections, while the risk remained significantly high (aOR 5.4) in cesarean sections performed during labor[9]. Concurrent studies have reported analogous findings, with odds ratios of 4.12 and 8.15 being reported, thereby reinforcing the prevailing consensus that gestational age (GA) is an independent risk factor for postpartum hemorrhage (PPH)[10,11]. Conversely, a substantial Korean study encompassing over 330,000 cesarean sections revealed an aOR of 1.06 for GA compared to spinal anesthesia, which was statistically significant but considerably lower[12]. This discrepancy underscores the significance of methodological approaches; the authors of the Korean study acknowledged that they were unable to account for "potential bias due to indication-related group differences." The present study, however, employed Propensity Score Matching (PSM), a more sophisticated statistical technique specifically designed to reduce this type of bias and balance observed covariates[13]. Consequently, the elevated aOR documented in this investigation might serve as a more precise and less prejudiced estimation of the genuine effect of GA in a high-risk population, such as emergency cesarean section. The statistical findings indicate a causal relationship that is also strongly supported by the well-known dose-dependent uterine relaxant (tocolytic) effect of volatile anesthetic agents. In vitro studies have demonstrated that contemporary agents such as sevoflurane and desflurane exhibit a substantial inhibitory effect on both spontaneous and oxytocin-induced myometrial contractions, exhibiting a dose-dependent response[14]. In contrast, our findings in the PAS cohort highlight the critical importance of context in anesthetic management. In the context of PAS, the primary driver of hemorrhage is massive surgical trauma from abnormal placental invasion, a factor that likely overwhelms the more subtle tocolytic effects of volatile anesthetics. This 'surgical dominance' explains why the choice of anesthetic technique appears to be a secondary contributor to total blood loss in this specific setting. In this group of patients, the overwhelming force of hemorrhage is surgical in origin, and the potential tocolytic effect of anesthesia is statistically lost within the 'noise' of this massive hemorrhage. Indeed, our study is statistically underpowered to detect small or moderate differences in this cohort (post-hoc power for the observed 350 mL difference was ≈ 23%). In our study, we intentionally omitted p-values from the tables presenting the results for Cohort 2 (PAS patients) (Tables 4 and 5 ) as a methodological decision. The main reason for this decision was the small sample size and resulting low statistical power of this cohort. A post hoc power analysis showed that the likelihood of detecting statistically significant differences between groups with the current number of patients is very low. Under these conditions, there is a risk that a calculated p-value (likely p > 0.05) could be misinterpreted as indicating no difference between groups. However, this result would not stem from an absence of a true effect, but rather from the study's lack of power to detect it. To maintain scientific integrity and prevent potential misinterpretation, we decided it was better to share the descriptive data as is and emphasize the exploratory nature of the findings than to present potentially misleading p-values. This approach is further supported by the Bayesian analysis we conducted to derive a more meaningful result from the data [15]. The concept of "surgical dominance" in PAS bleeding is well supported by the extant literature. A substantial body of literature, including both comprehensive reviews and expert consensus statements, has consistently characterized the management of PAS as a complex, multidisciplinary challenge, with a primary focus on surgical planning and bleeding control. These sources underscore the notion that the foundation of effective PAS management is meticulous preoperative planning and the implementation of coordinated surgical and resuscitation strategies during the intraoperative phase by a seasoned team[16]. This viewpoint is corroborated by a recent comprehensive review that cited earlier research by Jasinski and colleagues. The review concluded that "it is the patient's obstetric characteristics (rather than the anesthetic technique) that determine blood loss in abnormal placentation[17]." The pathophysiology of PAS is explained by the placenta invading the myometrium, thereby eliminating the normal separation plane and anchoring itself to an immobile tissue bed. In this context, the additional blood loss resulting from the potential tocolytic effect of anesthesia is statistically masked by the substantial surgical bleeding[18]. To overcome this issue of statistical power and to extract more clinical meaning from our data, we performed a post-hoc Bayesian analysis. This analysis, conducted using R software with a non-informative prior distribution and Markov Chain Monte Carlo (MCMC) simulations, showed that the median difference in blood loss was 345 mL more in the GA group, with a 95% credibility interval ranging from − 50 mL to + 760 mL. The fact that 91% of this interval is above zero is a strong signal, indicating a high probability that GA increases blood loss. Therefore, although the threshold for statistical significance was not met, this finding supports a clinically important hypothesis that warrants investigation in future, larger studies. Although our findings are consistent with previous observational studies, our study aimed to largely overcome the main methodological hurdle in this field—confounding by indication—through the use of Propensity Score Matching (PSM). Nevertheless, it is important to acknowledge that GA can still act as a 'marker' for underlying high-risk clinical scenarios, such as fetal distress or maternal instability, for which we could not fully measure or control. In light of these findings, a stratified pathophysiology-based hierarchy of recommendations for clinicians can be proposed (Table 6 ). Our results are fully in alignment with the recommendations of leading international guidelines, such as those of the Society for Obstetric Anesthesia and Perinatology (SOAP), which strongly favor NA over GA for emergency cesarean deliveries in the absence of contraindications[19]. In situations where GA is unavoidable, strategies such as targeting the lowest possible concentration of volatile agents and providing proactive uterotonic and TXA support should be adopted, based on our dose-response finding[20]. However, for patients with PAS, the choice of anesthesia should be a multidisciplinary decision based less on its marginal effect on bleeding and more on which technique will best manage the anticipated massive transfusion and hemodynamic challenges[21]. The Class I (Strong Recommendation) for strongly preferring NA over GA for non-emergency cesarean sections is consistent with the fundamental principles of modern obstetric anesthesia. The findings of this study add a new and compelling rationale for this preference, with an approximately threefold reduction in the risk of severe postpartum hemorrhage (PPH) due to uterine atony. The Class IIa (Moderate Recommendation) for targeting the lowest possible concentration of volatile agents and considering proactive TXA in situations where GA use is unavoidable is directly derived from this study's dose-response relationship and high NNH (14) findings. For patients with Placenta Accreta Spectrum, the Class IIb (Weak Recommendation) to personalize anesthesia selection based on a multidisciplinary decision reflects the current state of evidence; the literature confirms that there is no consensus on the optimal anesthetic technique for PAS and that the primary focus of management is to prepare for and control massive surgical bleeding [18]. Table 6 Anesthetic Management Recommendations and evidence levels according to the clinical scenario Clinical Scenario Recommendation Level of Evidence and Rationale Non-PAS Emergency Cesarean In the absence of maternal/fetal contraindications, NA strongly prefers to GA. Class I (Strong Recommendation) Strong evidence supported by the findings of our study [aOR 2.91, NNH = 14] and recommendations from international guidelines (e.g. SOAP). If GA Use is Unavoidable Identify the lowest possible volatile agent MAC value (e.g., ≤ 1.0) and administer prophylactic TXA. Class IIa (Moderate Recommendation)(Indirect evidence from the dose-response relationship and the high value of NNH suggests that the benefit likely outweighs the risk.) Placenta Accreta Spectrum Personalize the choice of anesthesia according to hemodynamic needs, anticipated surgical difficulties, and the decision of the multidisciplinary team. Class IIb (Weak Recommendation)(Insufficient evidence from the present study; the decision is based on expert opinion and individual case characteristics.) For PAS, the primary goal of anesthetic management should be to ensure hemodynamic stability and prepare for massive transfusion, as the specific anesthetic technique has a less pronounced impact on bleeding compared to surgical factors. Although the primary strengths of our study are its innovative dual-cohort design and the use of propensity score matching, several important limitations must be acknowledged. First, as with any observational study, we cannot completely eliminate the potential for residual confounding. The most significant of these is confounding by indication, as the decision to use general anesthesia is often reserved for the most urgent and clinically challenging cases, such as those involving severe fetal bradycardia or imminent maternal collapse. Our retrospective data set lacked granular data on the precise degree of urgency within the ACOG Category I classification (e.g., decision-to-delivery time), preoperative maternal hemodynamics, surgeon experience or adjuvant opioid use. Therefore, the observed association between GA and severe PPH should not be interpreted as definitive evidence of causality, but rather as a reflection of this complex clinical reality, where GA may serve as a marker of the severity of the underlying case. Second, a significant limitation is the retrospective nature of our primary outcome, quantitative blood loss (QBL). Although our institution employs a standardized protocol with regular staff training to enhance consistency, this method is inherently susceptible to variability. The lack of a formal inter-rater reliability analysis in our study means we cannot quantify the extent of this potential measurement error, which necessitates caution when interpreting the precise magnitude of the reported blood loss differences. Furthermore, tranexamic acid (TXA) administration was based on the clinical provider's discretion rather than strictly protocolized, which may have introduced variability in its hemostatic effect between both groups. Additionally, we performed multiple secondary outcome analyses without formal correction for multiple testing; therefore, findings for secondary outcomes should be interpreted as exploratory and hypothesis-generating rather than confirmatory. Future research should build on the findings of this study. For patients at risk of atony, there is an urgent need for a multi-center, randomized controlled trial evaluating the efficacy of a proactive "GA-PPH prevention bundle" as we have suggested. For rare conditions such as PAS, creating larger datasets through international registries is the most promising path forward to elucidate the more subtle effects of anesthetic management on outcomes. 5. CONCLUSION In conclusion, this study reveals that the effect of the anesthetic technique on postpartum hemorrhage is not a single absolute truth, but a change that changes dramatically depending on the underlying pathophysiology of the hemorrhage. General anesthesia emerges as an independent risk factor in uterine atony-related hemorrhage that should be avoided or managed with proactive strategies, while its effect is secondary in cases of massive surgical bleeding such as Placenta Accreta Spectrum. Due to the small sample size and low statistical power in our PAS cohort, we emphasize that our findings for this cohort are hypothesis-generating and exploratory rather than conclusive. These results reinforce the "one-size-does-not-fit-all" principle in obstetric anesthesia and strongly encourage clinicians to personalize anesthetic strategies not only based on the urgency of the case, but also on the anticipated hemorrhage mechanism. This pathophysiological approach represents a significant step forward in improving maternal health. Declarations Ethics approval and consent to participate The approval for the study was obtained from the Harran University Clinical Research Ethics Committee (Decision Date: January 15, 2025, Decision No: HRÜ/2025/01-18) Consent for Publication Not applicable. As this study was a retrospective analysis of fully anonymized data, the need for specific consent for publication was not required. Availability of data and material Data and materials used in this study are available and can be presented by the corresponding author upon reasonable request Competing interests: The authors declare that they have no conflicts of interest. Funding: The authors declare that this study has not received financial support. Authors’ Contributions YZK: Conceptualization, Data Curation, Formal Analysis, Writing – Original Draft, Writing – Review & Editing. YS: Conceptualization, Methodology, Supervision, Writing – Review & Editing. References Wang, M., & Oyelese, Y. (2024). Postpartum Hemorrhage. Maternal-Fetal Medicine , 7, 38 - 48. https://doi.org/10.1097/FM9.0000000000000261. Perween, N. (2024). Comparative Study of General Anesthesia and Regional Anesthesia in Obstetrics. International Journal of Anesthesia and Clinical Medicine . https://doi.org/10.11648/j.ijacm.20241202.17. Salam, R., Abbasi, M., & Sharif, M. (2022). Comparison between Neuraxial & General Anesthesia. Scholars Journal of Applied Medical Sciences . https://doi.org/10.36347/sjams.2023.v11i01.005. Jee, Y., Lee, H., Kim, Y., Kim, D., & Woo, J. (2022). Association between anesthetic method and postpartum hemorrhage in Korea based on National Health Insurance Service data. Anesthesia and Pain Medicine , 17, 165 - 172. https://doi.org/10.17085/apm.21068. R. A. El-Dakhakhni et al. "The Risk of Post-Partum Hemorrhage after Cesarean Section with General versus Spinal/Epidural Anesthesia." Journal of Gynecology Research Reviews & Reports (2020). https://doi.org/10.47363/JGRRR/2019(2)107. Temesgen, M.M., Gebregzi, A.H., Kasahun, H.G. et al. Evaluation of decision to delivery time interval and its effect on feto-maternal outcomes and associated factors in category-1 emergency caesarean section deliveries: prospective cohort study. BMC Pregnancy Childbirth 20 , 164 (2020). https://doi.org/10.1186/s12884-020-2828-z Jauniaux, E., Bhide, A., Kennedy, A., Woodward, P., Hubinont, C., Collins, S. and for the FIGO Placenta Accreta Diagnosis and Management Expert Consensus Panel (2018), FIGO consensus guidelines on placenta accreta spectrum disorders: Prenatal diagnosis and screening † , ‡ . Int J Gynecol Obstet, 140: 274-280. https://doi.org/10.1002/ijgo.12408 Ende HB, Lozada MJ, Chestnut DH, Osmundson SS, Walden RL, Shotwell MS, Bauchat JR. Risk Factors for Atonic Postpartum Hemorrhage: A Systematic Review and Meta-analysis. Obstet Gynecol. 2021 Feb 1;137(2):305-323. doi: 10.1097/AOG.0000000000004228. PMID: 33417319; PMCID: PMC8336570. Butwick AJ, Ramachandran B, Hegde P, Riley ET, El-Sayed YY, Nelson LM. Risk Factors for Severe Postpartum Hemorrhage After Cesarean Delivery: Case-Control Studies. Anesth Analg. 2017 Aug;125(2):523-532. doi: 10.1213/ANE.0000000000001962. PMID: 28277324; PMCID: PMC5522356. Pubu ZM, Bianba ZM, Yang G, CyRen LM, Pubu DJ, Suo Lang KZ, Zhen B, Zhaxi QZ, Nyma ZG. Factors Affecting the Risk of Postpartum Hemorrhage in Pregnant Women in Tibet Health Facilities. Med Sci Monit. 2021 Feb 13;27:e928568. doi: 10.12659/MSM.928568. PMID: 33579890; PMCID: PMC7887994. Gong, J., Chen, Z., Zhang, Y. et al. Risk-factor model for postpartum hemorrhage after cesarean delivery: a retrospective study based on 3498 patients. Sci Rep 12 , 22100 (2022). https://doi.org/10.1038/s41598-022-23636-5 Jee Y, Lee HJ, Kim YJ, Kim DY, Woo JH. Association between anesthetic method and postpartum hemorrhage in Korea based on National Health Insurance Service data. Anesth Pain Med (Seoul). 2022 Apr;17(2):165-172. doi: 10.17085/apm.21068. Epub 2022 Jan 11. PMID: 35038857; PMCID: PMC9091673. Reiffel JA. Propensity-Score Matching: Optimal, Adequate, or Incomplete? J Atr Fibrillation. 2018 Dec 31;11(4):2130. doi: 10.4022/jafib.2130. PMID: 31139292; PMCID: PMC6533842. Yildiz K, Dogru K, Dalgic H, Serin IS, Sezer Z, Madenoglu H, Boyaci A. Inhibitory effects of desflurane and sevoflurane on oxytocin-induced contractions of isolated pregnant human myometrium. Acta Anaesthesiol Scand. 2005 Oct;49(9):1355-9. doi: 10.1111/j.1399-6576.2005.00804.x. PMID: 16146475. E. Goligher et al. "Bayesian statistics for clinical research." The Lancet , 404 (2024): 1067-1076. https://doi.org/10.1016/S0140-6736(24)01295-9. Gynecologists et al. "Obstetric Care Consensus No. 7: Placenta Accreta Spectrum.." Obstetrics & Gynecology (2018). https://doi.org/10.1097/AOG.0000000000002983. Jasinski T, Remesz A, Resko R, Budynko A, Majdylo K. Anesthetic Management for Patients with Placenta Accreta Spectrum: A Scoping Review. Journal of Clinical Medicine . 2025; 14(13):4738. https://doi.org/10.3390/jcm14134738 Warrick CM, Sutton CD, Farber MM, Hess PE, Butwick A, Markley JC. Anesthesia Considerations for Placenta Accreta Spectrum. Am J Perinatol. 2023 Jul;40(9):980-987. doi: 10.1055/s-0043-1761637. Epub 2023 Jun 19. PMID: 37336215. Practice Guidelines for Obstetric Anesthesia: An Updated Report by the American Society of Anesthesiologists Task Force on Obstetric Anesthesia and the Society for Obstetric Anesthesia and Perinatology. Anesthesiology. 2016 Feb;124(2):270-300. doi: 10.1097/ALN.0000000000000935. PMID: 26580836. Enrico Cocchi et al. "Impact of general vs. neuraxial anesthesia on neonatal outcomes in non-elective cesarean sections." Frontiers in Pediatrics , 13 (2025). https://doi.org/10.3389/fped.2025.1518456. A. Ioscovich et al. "The anesthetic approach to a patient with placenta accreta spectrum." Current Opinion in Anaesthesiology , 36 (2023): 263 - 268. https://doi.org/10.1097/ACO.0000000000001242. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":51360,"visible":true,"origin":"","legend":"\u003cp\u003eCovariate Balance love plot before and After Propensity Score Matching. The plot displays the standardized mean differences (SMD) for the baseline covariates before (unadjusted) and after (PSM) matching. Vertical dashed lines indicate the commonly used balance threshold of SMD \u0026lt; 0.1. After matching, good balance was achieved for all covariates except for 'the duration of Surgery', which remained unbalanced (SMD=0.35).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7381007/v1/ae75ec96b293751f3dbfb137.png"},{"id":95220990,"identity":"c7c091e6-8730-4d40-9f27-f679df9e85ef","added_by":"auto","created_at":"2025-11-05 16:17:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48257,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence of the Components of the Primary Composite Endpoint (Severe PPH) in Cohort 1, Stratified by anaesthesia Technique.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7381007/v1/48f02c918fddd559533c5ab8.png"},{"id":95007511,"identity":"ccc0cc09-0ce5-4e02-8afa-f87114c7045e","added_by":"auto","created_at":"2025-11-03 09:40:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43834,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyzes of the Effect of the anesthetic technique on Severe PPH. \u003c/strong\u003e\u003cem\u003eThe forest plot displays the adjusted odds ratio (aOR) for severe PPH associated with general anesthesia (GA) compared to the reference group, neuraxial anesthesia (NA), across different subgroups. The black squares represent the estimate of the aOR point for each subgroup, and the horizontal lines represent 95% confidence intervals. The vertical line at an aOR of 1 indicates the line of no effect. The p-values for interaction (p-interaction) test whether the effect of GA differs significantly across these subgroups.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7381007/v1/c1eca8ea29967a155673995e.png"},{"id":100237284,"identity":"611de38f-8eb0-41f7-a091-543acb0aeeec","added_by":"auto","created_at":"2026-01-14 12:41:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1346184,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7381007/v1/3706434e-7534-49d0-9dfa-df9ccf56847c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Differential Role of Anesthetic Technique by Etiology of Postpartum Hemorrhage: A Dual-Cohort Analysis of Emergency Cesarean Delivery and Placenta Accreta Spectrum","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eDespite significant technological and pharmacological advances in modern obstetrics, postpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality worldwide [1]. Central to this paradox are the increasing rates of cesarean delivery and the inherent risk of hemorrhage associated with the procedure. Anesthetic management, which plays a critical role in ensuring maternal safety during and after cesarean delivery, has been the focus of a longstanding debate [2]: Is the anesthetic technique merely a passive bystander in the hemorrhagic process or does it actively contribute to initiating or exacerbating bleeding?\u003c/p\u003e\u003cp\u003eThe answer to this fundamental question lies in the pharmacodynamic differences between general anesthesia (GA) and neuraxial anesthesia (NA). Although NA provides targeted analgesia to the lower half of the body, GA uses potent agents with systemic effects [3]. There is a strong biological rationale suggesting that volatile anesthetics used during GA can induce uterine atony\u0026mdash;the most common cause of PPH\u0026mdash;by suppressing uterine contractions. This effect is not limited to the direct inhibition of calcium channels, but is also supported by molecular evidence demonstrating that these agents can blunt the response to uterotonic drugs by disrupting oxytocin receptor signaling pathways [4]. However, this pharmacological \"accusation\" represents only one aspect of obstetric hemorrhage. In conditions such as the Placenta Accreta Spectrum (PAS), where the primary cause of bleeding is anatomical anomaly, the role of anesthetic management changes from a primary factor to a supporting element in managing massive surgical trauma.\u003c/p\u003e\u003cp\u003eThe existing literature is stuck in a methodological loop due to its inability to differentiate between these two distinct hemorrhage scenarios. Most studies on atony-related hemorrhage are affected by a fundamental bias known as \"confounding by indication,\" which occurs because GA is often selected for the most urgent and critically ill patients[5]. This bias has made it impossible to determine whether the increased bleeding observed with GA is due to the anesthetic itself or to the patient's pre-existing high-risk condition. However, on the contrary, the PAS literature has focused mainly on surgical techniques, largely neglecting the impact of anesthetic choice on results in this unique population of patients.\u003c/p\u003e\u003cp\u003eTo overcome this methodological impasse and clarify the role of anesthetic techniques in both pathophysiological contexts, this study employs an innovative dual-cohort design. Our primary objective was to investigate the association between anesthetic technique and severe PPH related to atonies in a matched non-PAS cohort using propensity score matching (PSM). Our secondary objective was to descriptively evaluate these outcomes in a dedicated cohort of patients with PAS who underwent planned cesarean hysterectomy.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study Design and Ethical Framework\u003c/h2\u003e\n \u003cp\u003eThis study was conducted with a retrospective dual-cohort design. The study reporting was carried out according to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational studies. The approval for the study was obtained from the Harran University Clinical Research Ethics Committee (Decision Date: January 15, 2025, Decision No: HR\u0026Uuml;/2025/01\u0026ndash;18), and due to the retrospective nature of the study, the requirement for informed consent of patients was waived. Patient data confidentiality was ensured by removing all identification information and assigning a unique code to each patient prior to analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Population of patients and Selection Criteria\u003c/h2\u003e\n \u003cp\u003eStudy data were obtained from our institution\u0026apos;s Hospital Information Management System (HIMS) and the digital anesthesia recording system, covering all deliveries between January 1, 2020 and December 31, 2024. Patients included in the analysis were divided into two main cohorts based on the underlying pathophysiology of hemorrhage.\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eCohort 1 (Non-PAS Emergency Cesarean Deliveries)\u003c/strong\u003e: This cohort consists of patients without a diagnosis of Placenta Accreta Spectrum (PAS) who underwent emergency cesarean delivery for an American College of Obstetricians and Gynecologists (ACOG) Category I indication (conditions that immediately threaten maternal or fetal life and generally require a decision-to-delivery time of less than 30 minutes)[6].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eCohort 2 (Placenta Accreta Spectrum Patients)\u003c/strong\u003e: This cohort includes patients who were diagnosed with PAS antenatally by ultrasonography and/or pelvic magnetic resonance imaging (MRI) according to the criteria of FIGO (International Federation of Gynecology and Obstetrics) and were therefore scheduled for a planned cesarean-hysterectomy operation[7].\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe specific inclusion and exclusion criteria for each cohort are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetailed Inclusion and Exclusion Criteria for Cohorts\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriterion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCohort 1: Non-PAS Emergency C-Section\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCohort 2: Placenta Accreta Spectrum\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInclusion Criteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Singleton pregnancy, \u0026ge;\u0026thinsp;34 gestational weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Singleton pregnancy, \u0026ge;\u0026thinsp;34 gestational weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Emergency cesarean section for ACOG Category I indication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Antenatal diagnosis of PAS (with USG/MRI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Having received General or Neuraxial Anesthesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Planned Cesarean-Hysterectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExclusion Criteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Known or intraoperatively detected PAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Absence of PAS diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Pre-existing coagulopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Pre-existing coagulopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Use of therapeutic anticoagulants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Use of therapeutic anticoagulants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Missing key data (e.g., blood loss)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Missing key data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Matching Procedure (Cohort 1 Only)\u003c/h2\u003e\n \u003cp\u003eTo control for baseline confounders, each patient receiving GA was matched to three patients receiving NA. This 1:3 ratio was chosen to maximize statistical power while adjusting for the lower prevalence of GA use for emergency cesareans in our institution. This ratio was chosen to maximize statistical power while effectively reducing bias, considering the prevalence of GA:NA use at our institution. The matching was performed using the MatchIt package in R software with a greedy nearest neighbor matching algorithm and a caliper width set at 0.2 times the standard deviation of the propensity score.\u003c/p\u003e\n \u003cp\u003eThe matching was based on the following four variables: 1) the specific indication for emergency cesarean delivery, 2) preoperative hemoglobin level, 3) gestational age and 4) maternal Body Mass Index (BMI). Operative duration was not included in the PSM model as it is considered a post-treatment variable whose value is determined after the anesthetic choice is made. However, it was included as a key covariate in the final regression analysis to control for its potential confounding effects. Other potential confounders, such as dynamically changing uterotonic protocols, were not included in the model because of difficulties in their retrospective measurement; this is further discussed in the study\u0026apos;s limitations section. The quality of the match was assessed by the standardized mean differences (SMD) presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and the Love Plot shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Demographic and Clinical Characteristics of Study Cohorts\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGA (n\u0026thinsp;=\u0026thinsp;150) - Cohort 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNA (n\u0026thinsp;=\u0026thinsp;450) - Cohort 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSMD* - Cohort 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGA (n\u0026thinsp;=\u0026thinsp;45) - Cohort 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNA (n\u0026thinsp;=\u0026thinsp;30) - Cohort 2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrior Cesarean Deliveries (n, mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative Hb (g/dL, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNulliparity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241 (53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGestational Age (weeks, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration of Surgery (min, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185\u0026thinsp;\u0026plusmn;\u0026thinsp;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170\u0026thinsp;\u0026plusmn;\u0026thinsp;51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e* *SMD (Standardized Mean Difference) is reported for the matched Cohort 1 only. An SMD value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicates a good balance between the groups after matching.\u003c/p\u003e\n \u003cp\u003eThe significant remaining imbalance observed in the duration of surgery (SMD\u0026thinsp;=\u0026thinsp;0.35) likely reflects the preferential use of GA in cases with greater urgency (e.g., severe fetal distress) or unexpected surgical difficulties. This indicates an underlying case complexity that is not fully captured by the matching variables. To control for this important potential confounder, the duration of surgery was included as a covariate in the final multivariate regression analysis.\u003c/p\u003e\n \u003cp\u003eAlthough our matching model aims to control for the baseline confounders present, due to the retrospective nature of our data set, we acknowledge the presence of unmeasured potential confounders that can influence the selection of GA. These include factors such as the degree of urgency within ACOG Category I (e.g., decision-to deliver time), maternal hemodynamic stability before anesthesia, or unforeseen intraoperative surgical difficulties. The presence of these unmeasured variables creates the potential for residual confounding, which is discussed in more detail in the limitations section of our study.\u003c/p\u003e\n \u003cp\u003eData availability for all key variables was\u0026gt; 95% (e.g., preoperative Hb data was available for 580 of the 600 patients in Cohort 1).\u003c/p\u003e\n \u003ch3\u003e2.4. Data Collection Process and Variable Definitions\u003c/h3\u003e\n \u003cp\u003eStudy data were retrospectively obtained from the institution\u0026apos;s Hospital Information Management System (HIMS) and digital anesthesia recording system.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBaseline Demographic and Clinical Variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe baseline characteristics and clinical histories of the patients included the following variables:\u003c/p\u003e\n \u003cp\u003e\u0026middot; \u003cstrong\u003eDemographic Data:\u003c/strong\u003e Maternal age (years), preoperative Body Mass Index (BMI, kg/m\u0026sup2;).\u003c/p\u003e\n \u003cp\u003e\u0026middot; Obstetric History: Gravida, parity, number of previous cesarean deliveries, and gestational week.\u003c/p\u003e\n \u003cp\u003e\u0026middot; \u003cstrong\u003ePreoperative Laboratory Values:\u003c/strong\u003e Preoperative hemoglobin was defined as the most recent hemoglobin value (g/dL) measured within the 24 hours preceding delivery.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eVariables of the anesthesthetic and Surgical process\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026middot; \u003cstrong\u003eAnesthetic Technique:\u003c/strong\u003e The primary exposure of the study, the anesthetic technique, was classified as \u0026quot;General Anesthesia (GA)\u0026quot; or \u0026quot;Neuraxial Anesthesia (NA)\u0026quot; based on anesthesia records.\u003c/p\u003e\n \u003cp\u003e\u0026middot; \u003cstrong\u003eOperative Duration:\u003c/strong\u003e Calculated as the total time (in minutes) from the initial skin incision to the placement of the final skin suture.\u003c/p\u003e\n \u003cp\u003e\u0026middot; \u003cstrong\u003eVolatile Agent Exposure:\u003c/strong\u003e For patients in the GA group, this was quantified as \u003cstrong\u003eMAC-hours\u003c/strong\u003e by multiplying the average end-tidal MAC (Minimum Alveolar Concentration) value, electronically recorded during anesthesia maintenance, by the duration of exposure (in hours). This variable was used as a measure of the administered volatile anesthetic dose.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Outcome Variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe primary and secondary endpoints of the study are described below.\u003c/p\u003e\n \u003cp\u003e\u0026middot; Primary Outcome: Severe Postpartum Hemorrhage (PPH)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Severe PPH was defined as a composite endpoint, characterized by the presence of at least one of the following three criteria:\u003c/p\u003e\n \u003cp\u003eo \u003cstrong\u003eMassive Blood Loss:\u003c/strong\u003e Quantitative blood loss (QBL) exceeding 1500 mL.\u003c/p\u003e\n \u003cp\u003eo \u003cstrong\u003eBlood Transfusion:\u003c/strong\u003e Transfusion of two or more units of packed red blood cells (PRBCs).\u003c/p\u003e\n \u003cp\u003eo Invasive Hemostatic Intervention: Application of one of the following to control bleeding not responsive to standard uterotonic therapy: a Bakri balloon, compression sutures (e.g. B-Lynch) or emergency peripartum hysterectomy.\u003c/p\u003e\n \u003cp\u003e\u0026middot; \u003cstrong\u003eSecondary Outcomes:\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eo Total quantitative blood loss (QBL, mL).\u003c/p\u003e\n \u003cp\u003eo Total number of packed red blood cell (PRBC) units transfused.\u003c/p\u003e\n \u003cp\u003eo Hemoglobin change (Delta Hb): The difference between preoperative and postoperative hemoglobin values (measured within 24-48 hours) (g/dL).\u003c/p\u003e\n \u003cp\u003eo \u003cstrong\u003eNeed for second-line uterotonics:\u003c/strong\u003e The requirement for at least one agent such as methylergonovine, carboprost, or misoprostol in addition to the standard oxytocin infusion.\u003c/p\u003e\n \u003cp\u003eo Use and total dose of tranexamic acid (TXA).\u003c/p\u003e\n \u003cp\u003e\u0026middot; \u003cstrong\u003eBlood Loss Measurement (QBL):\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe QBL was prospectively recorded for each case by operating room nurses who receive regular in-service training on this standardized institutional protocol. This protocol includes visual and practical training materials for all personnel and aims to ensure measurement consistency. The measurement process includes the following steps: 1) Measure the difference between the dry weight prior to surgery and the wet weight after surgery of all surgical sponges and pads (assuming 1 gram = 1 mL of blood); 2) Subtracting the volume of irrigation fluid used from the total volume of aspirated blood and fluids, the accuracy of which can vary depending on the surgical team\u0026apos;s diligence; and 3) Estimating the amount of blood on surgical drapes and the floor using standardized visual guides according to our protocol. Although a formal inter-rater reliability analysis was not performed due to our retrospective design, the standardized protocol and regular training aimed to enhance measurement consistency. The potential variability in this measurement method is acknowledged as a limitation of our study.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.5. Anesthesia Protocols\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe typical anesthesia practices at our institution are summarized in Table 3. In the GA group, the primary goal was to maintain hemodynamic stability (defined as keeping the mean arterial pressure within 20% of the baseline value), whereas in the NA group, the aim was to achieve and maintain a sensory block level between T4 and T6. The final decision regarding the anesthetic technique was made by the attending anesthesiologist based on the patient\u0026apos;s clinical condition and the degree of urgency.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. Details of Typical Anesthesia Protocols by Group\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003eAnnesthetic Agent / Technique\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 290px;\"\u003e\n \u003cp\u003eGeneral Anesthesia (GA) Group (Typical Application)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eNeuraxial Anesthesia (NA) Group (Typical Application)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003eAnesthetic Induction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 290px;\"\u003e\n \u003cp\u003ePropofol (2-2.5 mg/kg) + Rocuronium (0.6-1 mg/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eNot Applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003eAnesthetic Maintenance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 290px;\"\u003e\n \u003cp\u003eSevoflurane or Desflurane (MAC target 0.8-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eNot Applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003ePrimary Analgesic Agent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 290px;\"\u003e\n \u003cp\u003eFentanyl (intermittent bolus, 1-2 mcg/kg total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eHyperbaric Bupivacaine (10-12.5 mg, intrathecal)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003eAdjuvant Agent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 290px;\"\u003e\n \u003cp\u003eNot Applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eFentanyl (15-25 mcg, intrathecal) or Morphine (100-150 mcg, intrathecal)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003eAirway Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 290px;\"\u003e\n \u003cp\u003eEndotracheal Intubation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eSpontaneous Breathing (with oxygen support if needed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e*The protocols outlined in this table represent typical applications in our institution. Specific agents, dosages, and target depth of anesthesia or sensory block level could vary depending on the patient\u0026apos;s immediate clinical condition, the degree of urgency, and the discretion of the attending anesthesiologist. MAC: Minimum Alveolar Concentration.\u003c/p\u003e\n \u003ch3\u003e2.6. Statistical Analysis\u003c/h3\u003e\n \u003cp\u003eA multitude of software packages were utilized for the statistical analysis of the data. Basic descriptive statistics, t-tests, chi-square tests, and multivariate logistic regression analyses were performed using IBM SPSS Statistics. To mitigate the potential for \u0026quot;indication-based confounding bias,\u0026quot; more specialized analyses were performed, including propensity score matching (PSM) and the creation of a Love Plot graph (see Figure 1) to assess the quality of the matching process. These analyses were executed using the \u0026apos;MatchIt\u0026apos; package within the R software environment. In a similar vein, the post-hoc Bayesian analysis for Cohort 2 was also executed in R.\u003c/p\u003e\n \u003cp\u003eIn all analyses, the statistical significance threshold was set at p\u0026lt;0.05. In accordance with the two-cohort design, analyses were conducted separately for each cohort. Given that the rate of data loss for key variables was below 5%, it was determined that all analyses would be completed on a complete-case basis.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnalysis of Cohort 1 (Non-PAS Emergency Cesarean Deliveries)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn this cohort, Propensity Score Matching (PSM) was used to reduce \u0026quot;confounding by indication\u0026quot; bias. Propensity scores, which reflect the probability of receiving general anesthesia (GA), were calculated using a logistic regression model that included the following variables: specific cesarean indication, preoperative hemoglobin, gestational age, and BMI. Subsequently, each patient in the GA group (n=150) was matched in a 1:3 ratio to three patients in the NA group (n=450) using a nearest-neighbor algorithm with a caliper of 0.2.\u003c/p\u003e\n \u003cp\u003eThe balance of post-match between groups was assessed using the Standardized Mean Difference (SMD \u0026lt; 0.1). To determine the independent effect of the anesthetic technique on severe PPH, a multivariable logistic regression analysis was used on the matched data set, adjusting for operative duration and the use of tranexamic acid (TXA). Given that operative duration differed significantly between the matched groups and is a well-established risk factor for PPH, it was included as a primary adjustment covariate. However, we acknowledge that the duration of the operation could potentially function as both a confounder and a mediator (i.e., a consequence of the anesthetic technique itself, influencing the outcome). To explore this complex relationship and test the robustness of our findings, a sensitivity analysis was also performed in which the model was run without adjusting for operative duration. Secondary outcomes were compared using t-tests and Chi-square tests.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnalysis of Cohort 2 (PAS Patients)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDue to the small sample size of this cohort, non-parametric tests were preferred for intergroup comparisons. The Mann-Whitney U test was used for continuous variables, and Fisher\u0026apos;s Exact test was used for categorical variables. A post-hoc power analysis showed that with the current sample, the study had 80% power to detect a blood loss difference of ~750 mL, but only 23% power to detect the observed difference of 350 mL. Therefore, the findings in this cohort should be considered exploratory.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Baseline Characteristics of the Groups\u003c/h2\u003e\u003cp\u003eThe final study population consisted of 600 patients in Cohort 1 (150 GA, 450 NA) and 75 patients in Cohort 2 (45 GA, 30 NA). The baseline characteristics for both cohorts are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The mean age of the patients in Cohort 1 was approximately 30 years, while patients with PAS in Cohort 2 were, as expected, older (mean age\u0026thinsp;~\u0026thinsp;35 years). Similarly, the mean number of previous cesarean deliveries was significantly higher among PAS patients (3.2\u0026ndash;3.4) compared to the non-PAS cohort (1.0\u0026ndash;1.1). Following matching of the propensity score, an excellent balance was achieved between the GA and NA groups in Cohort 1 across all baseline covariates, including age, BMI, and preoperative hemoglobin (all SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.1), a finding visually confirmed by the Love Plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Only the duration of surgery remained significantly longer in the GA group (52.4 vs. 48.1 minutes; p\u0026thinsp;=\u0026thinsp;0.008), and this variable was statistically adjusted for in subsequent analyzes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Cohort 1: Anesthetic Technique and Postpartum Hemorrhage Outcomes\u003c/h2\u003e\u003cp\u003eIn Cohort 1, which consisted of non-PAS emergency cesarean patients, the incidence of the primary outcome, severe PPH, was significantly higher in the GA group than in the NA group (21.3% vs. 9.8%, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the final multivariable analysis adjusted for operative duration and tranexamic acid (TXA) use, GA was independently associated with a nearly threefold increased risk of severe PPH (Adjusted Odds Ratio [aOR]: 2.91, 95% Confidence Interval [CI]: 1.80\u0026ndash;4.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This corresponds to an absolute risk increase of 11.5% attributable to GA (21.3% vs. 9.8%). The distribution of the individual components comprising the primary outcome is detailed by group in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimary and Secondary Bleeding Outcomes by Anesthesia Type According to Cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGA (n\u0026thinsp;=\u0026thinsp;150) - Cohort 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA (n\u0026thinsp;=\u0026thinsp;450) - Cohort 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGA (n\u0026thinsp;=\u0026thinsp;45) - Cohort 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA (n\u0026thinsp;=\u0026thinsp;30) - Cohort 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary Outcome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere PPH*, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41 (91.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26 (86.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.91 (1.80\u0026ndash;4.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary Outcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuantitative Blood Loss (mL, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1280\u0026thinsp;\u0026plusmn;\u0026thinsp;550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e890\u0026thinsp;\u0026plusmn;\u0026thinsp;410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3850\u0026thinsp;\u0026plusmn;\u0026thinsp;1200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3500\u0026thinsp;\u0026plusmn;\u0026thinsp;1100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin Drop (g/dL, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;2 Units PRBC Transfusion, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40 (88.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25 (83.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSevere PPH: Defined as at least one of the following: QBL\u0026thinsp;\u0026gt;\u0026thinsp;1500 mL, transfusion of \u0026ge;\u0026thinsp;2 units of packed red blood cells (PRBCs) or an invasive hemostatic procedure. aOR: Adjusted Odds Ratio, adjusted for the duration of surgery.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSecondary outcomes corroborated this primary finding. The mean quantitative blood loss (QBL) was approximately 400 mL higher in the GA group compared to the NA group (1280 mL vs. 890 mL). Similarly, the mean hemoglobin drop (2.4 g/dL vs. 1.7 g/dL) and the proportion of patients requiring transfusion of \u0026ge;\u0026thinsp;2 units of packed red blood cells (16.0% vs. 6.9%) were significantly higher in the GA group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This marked difference in the distribution of blood loss between the groups is further illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Regarding hemorrhage management, 45.3% of patients in the GA group required additional uterotonics, compared to 28.9% in the NA group. The use of tranexamic acid was also more frequent in the GA group (36.7% vs. 21.1%) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Further analyses exploring the effect of GA revealed a positive dose-response relationship between volatile agent exposure and blood loss; each 0.1 MAC-hour increment was associated with an estimated 120 mL increase in blood loss (95% CI: 35\u0026ndash;205 mL, p\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUse of ureterotonics and Tranexamic Acid According to Cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse of medication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGA (n\u0026thinsp;=\u0026thinsp;150) - Cohort 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA (n\u0026thinsp;=\u0026thinsp;450) - Cohort 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGA (n\u0026thinsp;=\u0026thinsp;45) - Cohort 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA (n\u0026thinsp;=\u0026thinsp;30) - Cohort 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxytocin (Total Units, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeed for Additional Uterotonics*, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (45.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (95.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (93.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTranexamic Acid Use, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (36.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (96.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Cohort 1: Supplementary and Sensitivity Analyses\u003c/h2\u003e\u003cp\u003eThe mean concentration of volatile agents administered during maintenance in the GA group was 1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 MAC (Minimum Alveolar Concentration). In the sensitivity analysis conducted to evaluate the potential mediator role of the duration of surgery, when the multivariable model was repeated without the duration of surgery, the relationship between GA and severe PPH remained significant (aOR: 2.75, 95% CI: 1.71\u0026ndash;4.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting the robustness of our findings.\u003c/p\u003e\u003cp\u003eThe elevating effect of GA on the risk of PPH was consistent in different subgroups of patients. As summarized in the Forest Plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the risk-elevating effect of GA did not differ significantly by patient parity (nulliparous vs. multiparous; p for interaction\u0026thinsp;=\u0026thinsp;0.82) or by the indication for cesarean delivery. Sensitivity analyzes conducted to test the robustness of the findings also supported the primary result. When the subgroup of patients (3.2%) who were converted from NA to GA due to neuraxial failure was excluded from the analysis, the association between GA and PPH remained significant (aOR 2.52, 95% CI 1.58\u0026ndash;4.02).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Cohort 2: Placenta Accreta Spectrum (PAS) Findings\u003c/h2\u003e\u003cp\u003eIn PAS patients who underwent planned cesarean-hysterectomy (Cohort 2), the incidence of severe PPH was, as expected, extremely high in both anesthesia groups, with no statistically significant differences observed between them (91.1% for GA vs. 86.7% for NA; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Quantitatively, the mean blood loss was 3850 mL in the GA group versus 3500 mL in the NA group; this mean difference of 350 mL while not statistically significant, is clinically relevant as it approximates the volume of one unit of packed red blood cells, warranting further investigation in larger cohorts. (95% CI: -250 to +\u0026thinsp;950 mL). However, it is noteworthy that this difference may be clinically relevant, corresponding to approximately one unit of packed red blood cells (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, nearly all patients in both groups required additional uterotonics (95.6% vs. 93.3%) and tranexamic acid (97.8% vs. 96.7%), illustrating the severity of the hemorrhage and the aggressive nature of its management (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe innovative dual-cohort design of our study addresses a significant literature gap by suggesting that the association between anesthetic technique and postpartum hemorrhage (PPH) is not a single absolute truth, but rather varies fundamentally depending on the underlying pathophysiology of the hemorrhage. Rather than treating PPH as a monolithic entity, we separated it into two distinct scenarios\u0026mdash;uterine atony and invasive placentation\u0026mdash;and evaluated the role of anesthetic management within its respective clinical context. Our findings clearly establish that general anesthesia (GA) is an independent and significant risk factor for PPH in atony-prone deliveries, but its effect becomes secondary in cases of massive surgical hemorrhage, such as Placenta Accreta Spectrum (PAS).\u003c/p\u003e\u003cp\u003eThe approach of this study, which examines PPH by distinguishing it according to its underlying pathophysiology (atony and PAS), is consistent with the current trend in the literature. A recent systematic review underscored the notion that each etiology of PPH likely possesses its own unique set of risk factors. The review suggested that a comprehensive evaluation of these etiologies may enhance the ability to determine patient risk[8]. A similar conclusion was reached in a study by Butwick et al., which used layered analyzes based on subtypes of cesarean section (CD) (before labor and intrapartum). This study concluded that the risk factor profiles for severe postpartum hemorrhage (PPH) differed between these two cohorts[9]. These findings support the validity and importance of the methodology of our study in addressing PPH not as a single entity, but as separate conditions with distinct pathophysiological mechanisms and evaluating risk factors in this context.\u003c/p\u003e\u003cp\u003eThe findings of Cohort 1 (non-PAS emergency cesarean deliveries) provide compelling evidence for the active role of GA in the development of PPH. In our multivariable analysis, adjusted for significant confounders such as operative duration and tranexamic acid (TXA) use, we found that GA independently increased the risk of severe PPH by nearly 2.9 times (aOR: 2.91). The tangible clinical implication of this statistical finding is even clearer: the calculated Number of injuries to be repaired (NNH) of 14 indicates that for every 14 emergency cesarean patients who receive GA instead of NA, an additional case of severe PPH occurs due to the anesthetic technique. This causal relationship is further strengthened by two key pieces of evidence: first, the positive dose-response relationship we identified between increasing exposure to volatile agents (per 0.1 MAC-hour) and blood loss; and second, the biological plausibility of our finding, which is consistent with in-vitro studies showing that volatile anesthetics inhibit myometrial contractions by inhibiting L-type calcium channels and disrupting oxytocin receptor signaling. Our finding of a nearly threefold increase in the risk of PPH associated with GA (aOR 2.91) is consistent with, although slightly higher than, the findings of previous large-scale studies such as the work by Bateman et al., which also reported a significantly elevated risk of hemorrhage with general anesthesia. Moreover, the fact that the adjusted odds ratio (aOR) increased after adjusting for TXA use suggests that the severity of bleeding in the GA group may have been somewhat masked by TXA, implying the true effect of GA could be even stronger.\u003c/p\u003e\u003cp\u003eThe approximately threefold increased risk of severe PPH associated with GA (aOR 2.91) found in this study is consistent with the effect direction reported in previous large-scale observational studies. However, the magnitude of this effect varies considerably in the literature. For instance, a stratified case-control study by Butwick et al. found that the risk associated with GA was remarkably high (aOR 22.3) in planned, pre-labor cesarean sections, while the risk remained significantly high (aOR 5.4) in cesarean sections performed during labor[9]. Concurrent studies have reported analogous findings, with odds ratios of 4.12 and 8.15 being reported, thereby reinforcing the prevailing consensus that gestational age (GA) is an independent risk factor for postpartum hemorrhage (PPH)[10,11]. Conversely, a substantial Korean study encompassing over 330,000 cesarean sections revealed an aOR of 1.06 for GA compared to spinal anesthesia, which was statistically significant but considerably lower[12]. This discrepancy underscores the significance of methodological approaches; the authors of the Korean study acknowledged that they were unable to account for \"potential bias due to indication-related group differences.\" The present study, however, employed Propensity Score Matching (PSM), a more sophisticated statistical technique specifically designed to reduce this type of bias and balance observed covariates[13]. Consequently, the elevated aOR documented in this investigation might serve as a more precise and less prejudiced estimation of the genuine effect of GA in a high-risk population, such as emergency cesarean section. The statistical findings indicate a causal relationship that is also strongly supported by the well-known dose-dependent uterine relaxant (tocolytic) effect of volatile anesthetic agents. In vitro studies have demonstrated that contemporary agents such as sevoflurane and desflurane exhibit a substantial inhibitory effect on both spontaneous and oxytocin-induced myometrial contractions, exhibiting a dose-dependent response[14].\u003c/p\u003e\u003cp\u003eIn contrast, our findings in the PAS cohort highlight the critical importance of context in anesthetic management. In the context of PAS, the primary driver of hemorrhage is massive surgical trauma from abnormal placental invasion, a factor that likely overwhelms the more subtle tocolytic effects of volatile anesthetics. This 'surgical dominance' explains why the choice of anesthetic technique appears to be a secondary contributor to total blood loss in this specific setting. In this group of patients, the overwhelming force of hemorrhage is surgical in origin, and the potential tocolytic effect of anesthesia is statistically lost within the 'noise' of this massive hemorrhage. Indeed, our study is statistically underpowered to detect small or moderate differences in this cohort (post-hoc power for the observed 350 mL difference was \u0026asymp;\u0026thinsp;23%). In our study, we intentionally omitted p-values from the tables presenting the results for Cohort 2 (PAS patients) (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) as a methodological decision. The main reason for this decision was the small sample size and resulting low statistical power of this cohort. A post hoc power analysis showed that the likelihood of detecting statistically significant differences between groups with the current number of patients is very low. Under these conditions, there is a risk that a calculated p-value (likely p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) could be misinterpreted as indicating no difference between groups. However, this result would not stem from an absence of a true effect, but rather from the study's lack of power to detect it. To maintain scientific integrity and prevent potential misinterpretation, we decided it was better to share the descriptive data as is and emphasize the exploratory nature of the findings than to present potentially misleading p-values. This approach is further supported by the Bayesian analysis we conducted to derive a more meaningful result from the data [15].\u003c/p\u003e\u003cp\u003eThe concept of \"surgical dominance\" in PAS bleeding is well supported by the extant literature. A substantial body of literature, including both comprehensive reviews and expert consensus statements, has consistently characterized the management of PAS as a complex, multidisciplinary challenge, with a primary focus on surgical planning and bleeding control. These sources underscore the notion that the foundation of effective PAS management is meticulous preoperative planning and the implementation of coordinated surgical and resuscitation strategies during the intraoperative phase by a seasoned team[16]. This viewpoint is corroborated by a recent comprehensive review that cited earlier research by Jasinski and colleagues. The review concluded that \"it is the patient's obstetric characteristics (rather than the anesthetic technique) that determine blood loss in abnormal placentation[17].\" The pathophysiology of PAS is explained by the placenta invading the myometrium, thereby eliminating the normal separation plane and anchoring itself to an immobile tissue bed. In this context, the additional blood loss resulting from the potential tocolytic effect of anesthesia is statistically masked by the substantial surgical bleeding[18].\u003c/p\u003e\u003cp\u003eTo overcome this issue of statistical power and to extract more clinical meaning from our data, we performed a post-hoc Bayesian analysis. This analysis, conducted using R software with a non-informative prior distribution and Markov Chain Monte Carlo (MCMC) simulations, showed that the median difference in blood loss was 345 mL more in the GA group, with a 95% credibility interval ranging from \u0026minus;\u0026thinsp;50 mL to +\u0026thinsp;760 mL. The fact that 91% of this interval is above zero is a strong signal, indicating a high probability that GA increases blood loss. Therefore, although the threshold for statistical significance was not met, this finding supports a clinically important hypothesis that warrants investigation in future, larger studies.\u003c/p\u003e\u003cp\u003eAlthough our findings are consistent with previous observational studies, our study aimed to largely overcome the main methodological hurdle in this field\u0026mdash;confounding by indication\u0026mdash;through the use of Propensity Score Matching (PSM). Nevertheless, it is important to acknowledge that GA can still act as a 'marker' for underlying high-risk clinical scenarios, such as fetal distress or maternal instability, for which we could not fully measure or control.\u003c/p\u003e\u003cp\u003eIn light of these findings, a stratified pathophysiology-based hierarchy of recommendations for clinicians can be proposed (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Our results are fully in alignment with the recommendations of leading international guidelines, such as those of the Society for Obstetric Anesthesia and Perinatology (SOAP), which strongly favor NA over GA for emergency cesarean deliveries in the absence of contraindications[19]. In situations where GA is unavoidable, strategies such as targeting the lowest possible concentration of volatile agents and providing proactive uterotonic and TXA support should be adopted, based on our dose-response finding[20]. However, for patients with PAS, the choice of anesthesia should be a multidisciplinary decision based less on its marginal effect on bleeding and more on which technique will best manage the anticipated massive transfusion and hemodynamic challenges[21].\u003c/p\u003e\u003cp\u003eThe Class I (Strong Recommendation) for strongly preferring NA over GA for non-emergency cesarean sections is consistent with the fundamental principles of modern obstetric anesthesia. The findings of this study add a new and compelling rationale for this preference, with an approximately threefold reduction in the risk of severe postpartum hemorrhage (PPH) due to uterine atony. The Class IIa (Moderate Recommendation) for targeting the lowest possible concentration of volatile agents and considering proactive TXA in situations where GA use is unavoidable is directly derived from this study's dose-response relationship and high NNH (14) findings. For patients with Placenta Accreta Spectrum, the Class IIb (Weak Recommendation) to personalize anesthesia selection based on a multidisciplinary decision reflects the current state of evidence; the literature confirms that there is no consensus on the optimal anesthetic technique for PAS and that the primary focus of management is to prepare for and control massive surgical bleeding [18].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnesthetic Management Recommendations and evidence levels according to the clinical scenario\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Scenario\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecommendation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLevel of Evidence and Rationale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-PAS Emergency Cesarean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIn the absence of maternal/fetal contraindications, NA strongly prefers to GA.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass I (Strong Recommendation)\u003c/p\u003e\u003cp\u003eStrong evidence supported by the findings of our study [aOR 2.91, NNH\u0026thinsp;=\u0026thinsp;14] and recommendations from international guidelines (e.g. SOAP).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIf GA Use is Unavoidable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIdentify the lowest possible volatile agent MAC value (e.g., \u0026le;\u0026thinsp;1.0) and administer prophylactic TXA.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass IIa (Moderate Recommendation)(Indirect evidence from the dose-response relationship and the high value of NNH suggests that the benefit likely outweighs the risk.)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlacenta Accreta Spectrum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePersonalize the choice of anesthesia according to hemodynamic needs, anticipated surgical difficulties, and the decision of the multidisciplinary team.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass IIb (Weak Recommendation)(Insufficient evidence from the present study; the decision is based on expert opinion and individual case characteristics.)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eFor PAS, the primary goal of anesthetic management should be to ensure hemodynamic stability and prepare for massive transfusion, as the specific anesthetic technique has a less pronounced impact on bleeding compared to surgical factors.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAlthough the primary strengths of our study are its innovative dual-cohort design and the use of propensity score matching, several important limitations must be acknowledged. First, as with any observational study, we cannot completely eliminate the potential for residual confounding. The most significant of these is confounding by indication, as the decision to use general anesthesia is often reserved for the most urgent and clinically challenging cases, such as those involving severe fetal bradycardia or imminent maternal collapse. Our retrospective data set lacked granular data on the precise degree of urgency within the ACOG Category I classification (e.g., decision-to-delivery time), preoperative maternal hemodynamics, surgeon experience or adjuvant opioid use. Therefore, the observed association between GA and severe PPH should not be interpreted as definitive evidence of causality, but rather as a reflection of this complex clinical reality, where GA may serve as a marker of the severity of the underlying case.\u003c/p\u003e\u003cp\u003eSecond, a significant limitation is the retrospective nature of our primary outcome, quantitative blood loss (QBL). Although our institution employs a standardized protocol with regular staff training to enhance consistency, this method is inherently susceptible to variability. The lack of a formal inter-rater reliability analysis in our study means we cannot quantify the extent of this potential measurement error, which necessitates caution when interpreting the precise magnitude of the reported blood loss differences. Furthermore, tranexamic acid (TXA) administration was based on the clinical provider's discretion rather than strictly protocolized, which may have introduced variability in its hemostatic effect between both groups. Additionally, we performed multiple secondary outcome analyses without formal correction for multiple testing; therefore, findings for secondary outcomes should be interpreted as exploratory and hypothesis-generating rather than confirmatory.\u003c/p\u003e\u003cp\u003eFuture research should build on the findings of this study. For patients at risk of atony, there is an urgent need for a multi-center, randomized controlled trial evaluating the efficacy of a proactive \"GA-PPH prevention bundle\" as we have suggested. For rare conditions such as PAS, creating larger datasets through international registries is the most promising path forward to elucidate the more subtle effects of anesthetic management on outcomes.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eIn conclusion, this study reveals that the effect of the anesthetic technique on postpartum hemorrhage is not a single absolute truth, but a change that changes dramatically depending on the underlying pathophysiology of the hemorrhage. General anesthesia emerges as an independent risk factor in uterine atony-related hemorrhage that should be avoided or managed with proactive strategies, while its effect is secondary in cases of massive surgical bleeding such as Placenta Accreta Spectrum. Due to the small sample size and low statistical power in our PAS cohort, we emphasize that our findings for this cohort are hypothesis-generating and exploratory rather than conclusive. These results reinforce the \"one-size-does-not-fit-all\" principle in obstetric anesthesia and strongly encourage clinicians to personalize anesthetic strategies not only based on the urgency of the case, but also on the anticipated hemorrhage mechanism. This pathophysiological approach represents a significant step forward in improving maternal health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe approval for the study was obtained from the Harran University Clinical Research Ethics Committee (Decision Date: January 15, 2025, Decision No: HRÜ/2025/01-18)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. As this study was a retrospective analysis of fully anonymized data, the need for specific consent for publication was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData and materials used in this study are available and can be presented by the corresponding author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors declare that this study has not received financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYZK:\u003c/strong\u003e Conceptualization, Data Curation, Formal Analysis, Writing – Original Draft, Writing – Review \u0026amp; Editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYS:\u003c/strong\u003e Conceptualization, Methodology, Supervision, Writing – Review \u0026amp; Editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang, M., \u0026amp; Oyelese, Y. (2024). Postpartum Hemorrhage. \u003cem\u003eMaternal-Fetal Medicine\u003c/em\u003e, 7, 38 - 48. https://doi.org/10.1097/FM9.0000000000000261.\u003c/li\u003e\n\u003cli\u003ePerween, N. (2024). Comparative Study of General Anesthesia and Regional Anesthesia in Obstetrics. \u003cem\u003eInternational Journal of Anesthesia and Clinical Medicine\u003c/em\u003e. https://doi.org/10.11648/j.ijacm.20241202.17.\u003c/li\u003e\n\u003cli\u003eSalam, R., Abbasi, M., \u0026amp; Sharif, M. (2022). Comparison between Neuraxial \u0026amp; General Anesthesia. \u003cem\u003eScholars Journal of Applied Medical Sciences\u003c/em\u003e. https://doi.org/10.36347/sjams.2023.v11i01.005.\u003c/li\u003e\n\u003cli\u003eJee, Y., Lee, H., Kim, Y., Kim, D., \u0026amp; Woo, J. (2022). Association between anesthetic method and postpartum hemorrhage in Korea based on National Health Insurance Service data. \u003cem\u003eAnesthesia and Pain Medicine\u003c/em\u003e, 17, 165 - 172. https://doi.org/10.17085/apm.21068.\u003c/li\u003e\n\u003cli\u003eR. A. El-Dakhakhni et al. \u0026quot;The Risk of Post-Partum Hemorrhage after Cesarean Section with General versus Spinal/Epidural Anesthesia.\u0026quot; \u003cem\u003eJournal of Gynecology Research Reviews \u0026amp; Reports\u003c/em\u003e (2020). https://doi.org/10.47363/JGRRR/2019(2)107. \u003c/li\u003e\n\u003cli\u003eTemesgen, M.M., Gebregzi, A.H., Kasahun, H.G. \u003cem\u003eet al.\u003c/em\u003e Evaluation of decision to delivery time interval and its effect on feto-maternal outcomes and associated factors in category-1 emergency caesarean section deliveries: prospective cohort study. \u003cem\u003eBMC Pregnancy Childbirth\u003c/em\u003e\u003cstrong\u003e20\u003c/strong\u003e, 164 (2020). https://doi.org/10.1186/s12884-020-2828-z\u003c/li\u003e\n\u003cli\u003eJauniaux, E., Bhide, A., Kennedy, A., Woodward, P., Hubinont, C., Collins, S. and for the FIGO Placenta Accreta Diagnosis and Management Expert Consensus Panel (2018), FIGO consensus guidelines on placenta accreta spectrum disorders: Prenatal diagnosis and screening\u003csup\u003e\u0026dagger;\u003c/sup\u003e,\u003csup\u003e\u0026Dagger;\u003c/sup\u003e. Int J Gynecol Obstet, 140: 274-280. https://doi.org/10.1002/ijgo.12408\u003c/li\u003e\n\u003cli\u003eEnde HB, Lozada MJ, Chestnut DH, Osmundson SS, Walden RL, Shotwell MS, Bauchat JR. Risk Factors for Atonic Postpartum Hemorrhage: A Systematic Review and Meta-analysis. Obstet Gynecol. 2021 Feb 1;137(2):305-323. doi: 10.1097/AOG.0000000000004228. PMID: 33417319; PMCID: PMC8336570.\u003c/li\u003e\n\u003cli\u003eButwick AJ, Ramachandran B, Hegde P, Riley ET, El-Sayed YY, Nelson LM. Risk Factors for Severe Postpartum Hemorrhage After Cesarean Delivery: Case-Control Studies. Anesth Analg. 2017 Aug;125(2):523-532. doi: 10.1213/ANE.0000000000001962. PMID: 28277324; PMCID: PMC5522356.\u003c/li\u003e\n\u003cli\u003ePubu ZM, Bianba ZM, Yang G, CyRen LM, Pubu DJ, Suo Lang KZ, Zhen B, Zhaxi QZ, Nyma ZG. Factors Affecting the Risk of Postpartum Hemorrhage in Pregnant Women in Tibet Health Facilities. Med Sci Monit. 2021 Feb 13;27:e928568. doi: 10.12659/MSM.928568. PMID: 33579890; PMCID: PMC7887994.\u003c/li\u003e\n\u003cli\u003eGong, J., Chen, Z., Zhang, Y. \u003cem\u003eet al.\u003c/em\u003e Risk-factor model for postpartum hemorrhage after cesarean delivery: a retrospective study based on 3498 patients. \u003cem\u003eSci Rep\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 22100 (2022). https://doi.org/10.1038/s41598-022-23636-5\u003c/li\u003e\n\u003cli\u003eJee Y, Lee HJ, Kim YJ, Kim DY, Woo JH. Association between anesthetic method and postpartum hemorrhage in Korea based on National Health Insurance Service data. Anesth Pain Med (Seoul). 2022 Apr;17(2):165-172. doi: 10.17085/apm.21068. Epub 2022 Jan 11. PMID: 35038857; PMCID: PMC9091673. \u003c/li\u003e\n\u003cli\u003eReiffel JA. Propensity-Score Matching: Optimal, Adequate, or Incomplete? J Atr Fibrillation. 2018 Dec 31;11(4):2130. doi: 10.4022/jafib.2130. PMID: 31139292; PMCID: PMC6533842.\u003c/li\u003e\n\u003cli\u003eYildiz K, Dogru K, Dalgic H, Serin IS, Sezer Z, Madenoglu H, Boyaci A. Inhibitory effects of desflurane and sevoflurane on oxytocin-induced contractions of isolated pregnant human myometrium. Acta Anaesthesiol Scand. 2005 Oct;49(9):1355-9. doi: 10.1111/j.1399-6576.2005.00804.x. PMID: 16146475.\u003c/li\u003e\n\u003cli\u003eE. Goligher et al. \u0026quot;Bayesian statistics for clinical research.\u0026quot; \u003cem\u003eThe Lancet\u003c/em\u003e, 404 (2024): 1067-1076. https://doi.org/10.1016/S0140-6736(24)01295-9.\u003c/li\u003e\n\u003cli\u003eGynecologists et al. \u0026quot;Obstetric Care Consensus No. 7: Placenta Accreta Spectrum..\u0026quot; \u003cem\u003eObstetrics \u0026amp; Gynecology\u003c/em\u003e (2018). https://doi.org/10.1097/AOG.0000000000002983.\u003c/li\u003e\n\u003cli\u003eJasinski T, Remesz A, Resko R, Budynko A, Majdylo K. Anesthetic Management for Patients with Placenta Accreta Spectrum: A Scoping Review. \u003cem\u003eJournal of Clinical Medicine\u003c/em\u003e. 2025; 14(13):4738. https://doi.org/10.3390/jcm14134738\u003c/li\u003e\n\u003cli\u003eWarrick CM, Sutton CD, Farber MM, Hess PE, Butwick A, Markley JC. Anesthesia Considerations for Placenta Accreta Spectrum. Am J Perinatol. 2023 Jul;40(9):980-987. doi: 10.1055/s-0043-1761637. Epub 2023 Jun 19. PMID: 37336215.\u003c/li\u003e\n\u003cli\u003ePractice Guidelines for Obstetric Anesthesia: An Updated Report by the American Society of Anesthesiologists Task Force on Obstetric Anesthesia and the Society for Obstetric Anesthesia and Perinatology. Anesthesiology. 2016 Feb;124(2):270-300. doi: 10.1097/ALN.0000000000000935. PMID: 26580836.\u003c/li\u003e\n\u003cli\u003eEnrico Cocchi et al. \u0026quot;Impact of general vs. neuraxial anesthesia on neonatal outcomes in non-elective cesarean sections.\u0026quot; \u003cem\u003eFrontiers in Pediatrics\u003c/em\u003e, 13 (2025). https://doi.org/10.3389/fped.2025.1518456.\u003c/li\u003e\n\u003cli\u003eA. Ioscovich et al. \u0026quot;The anesthetic approach to a patient with placenta accreta spectrum.\u0026quot; \u003cem\u003eCurrent Opinion in Anaesthesiology\u003c/em\u003e, 36 (2023): 263 - 268. https://doi.org/10.1097/ACO.0000000000001242.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"General Anesthesia, Neuraxial Anesthesia, Postpartum Hemorrhage, Placenta Accreta Spectrum, Uterine Atony, Dual-Cohort Study","lastPublishedDoi":"10.21203/rs.3.rs-7381007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7381007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eBased on the hypothesis that the effect of general anesthesia (GA) versus neuraxial anesthesia (NA) on postpartum hemorrhage (PPH) varies according to its underlying etiology, this study aimed to investigate the impact of the anesthetic technique on the risk of severe PPH in two distinct clinical scenarios: (1) emergency cesarean deliveries at risk for uterine atony and (2) cases of placenta accreta spectrum (PAS) at risk for massive surgical hemorrhage.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this retrospective dual-cohort study, patients receiving general anesthesia (GA) in Cohort 1 (atony risk) were matched 1:3 to patients receiving neuraxial anesthesia (NA) using propensity score matching (PSM) to control for baseline confounders. Cohort 2 comprised patients with placenta accreta spectrum (PAS) who underwent scheduled cesarean hysterectomy. The primary endpoint was the incidence of severe postpartum hemorrhage (PPH), defined as a composite result of quantitative blood loss\u0026thinsp;\u0026gt;\u0026thinsp;1500 mL, transfusion of \u0026ge;\u0026thinsp;2 units of packed red blood cells, or the need for an invasive hemostatic procedure.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn the matched Cohort 1 (n\u0026thinsp;=\u0026thinsp;600), the incidence of severe PPH was significantly higher in the GA group compared to the NA group (21.3% vs. 9.8%). After adjusting for operative duration and tranexamic acid use, GA was independently associated with an almost threefold increased risk of severe PPH (Adjusted Odds Ratio [aOR]: 2.91; 95% Confidence Interval [CI]: 1.80\u0026ndash;4.69; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, in Cohort 2 (n\u0026thinsp;=\u0026thinsp;75), the rate of severe PPH was high in both groups, with no significant difference observed (91.1% vs. 86.7%; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, a post-hoc Bayesian analysis indicated a high probability (91%) that GA is associated with increased blood loss.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn our matched cohort, general anesthesia was associated with an almost threefold increase in the risk of severe PPH in emergency cesarean deliveries susceptible to uterine atony (aOR 2.91). In cases such as the placenta accreta spectrum, the primary determinant of hemorrhage is the underlying surgical pathology, and the role of anesthetic management appears to be secondary. However, these findings should be considered exploratory due to the limited statistical power of the cohort. In general, these results strongly support the personalization of anesthetic strategies based on the expected etiology of hemorrhage to reduce maternal morbidity and mortality.\u003c/p\u003e","manuscriptTitle":"The Differential Role of Anesthetic Technique by Etiology of Postpartum Hemorrhage: A Dual-Cohort Analysis of Emergency Cesarean Delivery and Placenta Accreta Spectrum","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 09:40:06","doi":"10.21203/rs.3.rs-7381007/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6eb5f3b-dd69-431c-9073-47e104b9ef46","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-14T12:40:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 09:40:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7381007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7381007","identity":"rs-7381007","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}