Heart rate and blood-pressure variability stratify hemorrhagic shock severity and reveal vagal-dependent autonomic–vascular coupling: a controlled rat study

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Lewis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9013916/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Hemorrhagic shock (HS) is a leading cause of preventable trauma mortality, with most deaths occurring within hours of injury. Early identification of the transition from compensatory to decompensatory shock remains challenging because conventional vital signs often remain within normal ranges until late stages. Beat-to-beat heart rate variability (HRV) and blood pressure variability (BPV), obtainable from continuous arterial waveforms, reflect dynamic autonomic regulation and may provide earlier indicators of physiological decompensation. We investigated whether peripheral autonomic and hemodynamic markers can non-invasively stratify HS severity and whether vagal integrity modulates these predictive signatures. Methods Male Sprague–Dawley rats were subjected to graded hemorrhagic shock using a delayed fluid resuscitation (DFR) paradigm and classified as moderate (20% DFR) or severe (50% DFR) hemorrhagic shock, defined by delayed fluid resuscitation volume. Animals were assigned to non-vagotomized (HS) and subdiaphragmatic vagotomized (Vag+HS) groups. Heart rate variability (HRV), blood pressure variability (BPV), heart period (HP), and vagal efficiency (VE) were derived from arterial pressure recordings obtained during steady state (pre-hemorrhage) and the early compensatory nadir phase. Stepwise linear discriminant function analysis was used to develop predictive models of shock severity. Model performance was assessed using classification accuracy, cross-validation, and receiver operating characteristic (ROC) analysis. Results In non-vagotomized animals, a model incorporating HRV and BPV measures classified moderate versus severe HS with 93.3% accuracy (cross-validated accuracy 86.7%). High-frequency diastolic BP variability during nadir and systolic pressure dynamics were the strongest discriminators. In vagotomized animals, the model showed apparent complete separation in this dataset (AUC=1.00), which should be interpreted cautiously given the sample size. In vagotomized animals, a broader combination of autonomic and hemodynamic features, including low-frequency variability during steady state, vagal efficiency, and heart period dynamics, complete separation between shock severity groups on ROC analysis. Regression analysis demonstrated a significant association between pre-shock sympathetic modulation and impaired post-resuscitation baroreflex sensitivity in intact animals, a relationship that was absent after vagotomy, indicating disruption of autonomic–vascular coupling. Conclusions Continuous analysis of HRV and BPV enables accurate stratification of hemorrhagic shock severity during early compensatory phases. Vagal integrity modifies the structure of predictive autonomic signatures during hemorrhage. These findings support further evaluation of variability-based analytics as an adjunct to continuous ICU waveform monitoring to identify patients at risk of hemodynamic deterioration before overt hypotension develops. Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research Biological sciences/Physiology Early shock detection Heart rate variability Blood pressure variability Figures Figure 1 Background Hemorrhagic shock (HS) is a life-threatening form of hypovolemic shock and a leading cause of preventable mortality in trauma and critical illness, accounting for approximately 1.5 million deaths worldwide each year [ 1 – 3 ]. Mortality is highest during the early hours following injury, underscoring the importance of timely recognition and intervention [ 3 , 5 ]. Physiologically, HS progresses through distinct stages, beginning with a compensatory phase in which autonomic reflexes preserve arterial pressure and tissue perfusion, followed by decompensation characterized by cardiovascular instability and, if untreated, irreversible organ failure [ 4 , 28 ]. Accurate identification of the transition from compensation to decompensation is therefore critical for guiding resuscitative management. In clinical practice, conventional vital signs, including heart rate, systolic blood pressure, and respiratory rate, often remain within acceptable ranges during early compensatory phases, despite substantial reductions in circulating blood volume [ 8 , 11 – 13 ]. Marked inter-individual variability in tolerance to hemorrhage further limits the sensitivity of static vital signs for early detection of physiological deterioration [ 10 , 12 ]. Composite indices derived from these measures, such as the Shock Index (heart rate/systolic blood pressure), have demonstrated association with transfusion requirements and mortality in large trauma registries [ 9 ]. However, the Shock Index remains a static metric derived from single time-point measurements and does not capture beat-to-beat autonomic dynamics that may precede overt hemodynamic deterioration. As a result, reliance on these measures alone may delay escalation of care and contribute to adverse outcomes. Heart rate variability (HRV) and blood pressure variability (BPV) reflect beat-to-beat autonomic and hemodynamic regulation and provide dynamic physiological information not captured by traditional vital signs. In modern ICUs, continuous arterial waveform data are routinely available, yet underutilized for real-time physiological risk stratification. Prior studies have demonstrated associations between altered HRV and BPV and injury severity, impaired organ perfusion, and mortality in trauma and critical illness, suggesting potential utility as adjunctive markers of shock progression [ 14 – 19 , 21 – 23 ]. However, translation into routine clinical monitoring has been limited by heterogeneous findings, lack of individualized baseline reference, and the absence of validated real-time classification frameworks. Autonomic regulation during hemorrhage is highly dynamic. Early blood loss is associated with increased sympathetic activity to preserve arterial pressure, while parasympathetic (vagal) influences modulate cardiac chronotropy and may shape the structure of cardiovascular variability signals during stress [ 19 , 20 ]. Experimental studies in animal models suggest that disruption of vagal pathways alters cardiovascular compensation, supporting the possibility that vagal integrity influences the predictive value of variability metrics during hemorrhage [ 24 – 26 ]. In the present study, we investigated whether combined HRV and BPV metrics derived from peripheral arterial waveforms can stratify hemorrhagic shock severity during early, compensatory phases. Using a controlled rat model with intact and disrupted vagal pathways, we developed and evaluated classification algorithms to distinguish moderate from severe HS. By focusing on physiological signals routinely available in critical care monitoring, this work aims to inform the development of adjunctive tools for early identification of patients at risk for hemodynamic decompensation in the intensive care unit. Methods Experimental design and animal model All experimental procedures were approved the Ethics Committee of Shiraz University of Medical Sciences, Shiraz, Iran (approval codes: IR.SUMS.MED.REC.1396.S203) and were performed in accordance with the relevant guidelines and regulations for the care and use of laboratory animals.Adult male Sprague–Dawley rats were obtained from the Centre of Experimental Animals of Shiraz University of Medical Sciences. The experimental protocol and surgical procedures have been described in detail previously [ 27 ]. Briefly, male Sprague–Dawley rats were subjected to controlled hemorrhagic shock (HS) using a delayed fluid resuscitation (DFR) paradigm and assigned to either a non-vagotomized group (HS) or a subdiaphragmatic vagotomized group (Vag + HS). Within each group, shock severity was defined by the proportion of shed blood volume returned at the end of the compensatory phase [ 6 , 7 ], with animals classified as moderate HS (20% DFR) or severe HS (50% DFR). Hemodynamic and autonomic data were collected during three predefined experimental phases: steady state (pre-hemorrhage baseline), Nadir-1 (early compensatory phase following hemorrhage), and post-resuscitation. The present analysis focused on discriminating of shock severity (20% vs. 50% DFR) using data obtained during the steady-state and Nadir-1 phases, corresponding to clinically relevant periods preceding overt cardiovascular decompensation. All variability metrics were derived from signals routinely available from standard arterial monitoring and calculated in 30-second epochs, supporting potential feasibility for real-time implementation. Heart rate variability analysis Heart rate variability (HRV) was derived from arterial blood pressure waveforms as described previously [ 27 ]. Beat-to-beat inter-beat intervals (IBIs) were extracted and analyzed in 30-s epochs. Signals underwent preprocessing, including smoothing and finite impulse response (FIR) filtering, to isolate low- and high-frequency components reflecting autonomic modulation. HRV metrics were computed in both time and frequency domains according to established methods. Blood pressure variability analysis Blood pressure variability (BPV) was assessed using systolic and diastolic arterial pressure derived on a beat-to-beat basis from continuously recorded arterial waveforms (PowerLab, ADInstruments). Systolic and diastolic pressure time series were uniformly resampled at 20 Hz to permit spectral analysis. To separate slow aperiodic trends from rhythmic variability, signals were smoothed using a Savitzky–Golay low-pass filter (cubic polynomial). Window sizes of 401 and 21 points were applied to isolate frequency components below 0.05 Hz and 1 Hz, respectively. Edge effects introduced by filtering were minimized by trimming signal segments at the beginning and end of each epoch. High- and low-frequency BPV components were then extracted using FIR band-pass filtering (0.05–1 Hz and 1–5 Hz). The magnitude of each component was quantified as the logarithmic variance within 30-s epochs. For each experimental phase, mean values of high- and low-frequency systolic and diastolic BPV were calculated and used for subsequent analyses. Heart period Heart period (HP) was calculated as the mean IBI within each 30-s epoch. HP was analyzed in preference to heart rate due to its linear relationship with autonomic control and improved sensitivity to autonomic reactivity [ 27 ]. Vagal efficiency Vagal efficiency (VE) was quantified as the slope of the linear regression between respiratory sinus arrhythmia (RSA; independent variable) and heart period (HP; dependent variable). VE reflects the effectiveness of vagal modulation of cardiac chronotropy across experimental phases and was calculated separately for steady-state and Nadir-1 periods. Slopes of autonomic and hemodynamic variables To characterize dynamic changes during early shock, temporal slopes of HRV, BPV, and HP were calculated across the steady-state and Nadir-1 phases. The Nadir-1 phase was defined as the compensatory period during which mean arterial pressure was maintained following hemorrhage, ending at the compensation breakpoint. The steady-state phase corresponded to baseline measurements obtained before hemorrhage induction. Results A total of 31 male Sprague–Dawley rats were assigned to vagotomized and non-vagotomized groups and subjected to graded hemorrhagic shock using a delayed fluid resuscitation (DFR) paradigm. Hemorrhagic shock severity was operationally defined by the proportion of shed blood volume returned at the end of the compensatory phase, with animals classified as moderate HS (20% DFR) or severe HS (50% DFR). Animals receiving no fluid return (0% DFR) were excluded from the present classification analyses. Hemodynamic and autonomic data obtained during steady state (pre-hemorrhage baseline) and Nadir-1 (early compensatory phase) were used for all classification and regression analyses. Descriptive hemodynamic and autonomic variability metrics during steady state and the early compensatory (Nadir-1) phase are presented in Table 1. Transition from steady state to Nadir-1 was associated with reductions in systolic and diastolic arterial pressure and concurrent modulation of frequency-domain variability indices across both intact and vagotomized animals. Differences between moderate (20% DFR) and severe (50% DFR) groups were observable at the feature level within this early compensatory period. These descriptive findings provide a physiological context for the subsequent discriminant analyses. Table 1: Hemodynamic and autonomic variability metrics during steady state and early compensatory (Nadir-1) phase Variable HS 20% DFR (n=6) Steady state HS 20% DFR Nadir-1 HS 50% DFR (n=9) Steady state HS 50% DFR Nadir-1 Vag+HS 20% DFR (n=10) Steady state Vag+HS 20% DFR Nadir-1 Vag+HS 50% DFR (n=6) Steady state Vag+HS 50% DFR Nadir-1 SBP (mmHg) 129.6 ± 15.6 38.6 ± 4.8 135.9 ± 9.9 40.7 ± 2.9 128.0 ± 11.5 35.2 ± 1.8 129.8 ± 10.6 37.3 ± 4.3 DBP (mmHg) 97.7 ± 6.8 31.2 ± 3.0 102.3 ± 9.8 33.7 ± 2.9 97.3 ± 6.9 28.5 ± 1.9 96.9 ± 8.5 31.6 ± 4.1 Heart period (ms) 156.8 ± 17.2 224.3 ± 74.4 158.7 ± 12.1 212.0 ± 55.3 160.5 ± 10.2 187.8 ± 27.3 152.4 ± 5.7 174.1 ± 16.2 HF-DBPV (log variance) −1.07 ± 0.70 −3.41 ± 0.48 −0.77 ± 0.69 −3.96 ± 0.45 −1.71 ± 0.80 −3.48 ± 0.39 −0.71 ± 1.30 −3.44 ± 0.77 HF-SBPV (log variance) −2.10 ± 0.61 −3.93 ± 0.82 −2.00 ± 0.63 −4.57 ± 1.03 −2.14 ± 0.67 −3.72 ± 0.87 −1.14 ± 0.71 −4.02 ± 1.05 LF mean (HRV, ln(ms) 2 ) −1.83 ± 1.73 0.33 ± 0.88 −1.47 ± 0.94 0.83 ± 1.45 −2.48 ± 1.59 −0.15 ± 1.22 −2.02 ± 0.59 −0.90 ± 1.22 RSA (HRV, ln(ms) 2 ) 0.45 ± 1.01 2.56 ± 0.52 0.68 ± 0.73 2.57 ± 0.59 0.08 ± 1.02 2.28 ± 0.72 0.12 ± 0.82 2.82 ± 1.13 Abbreviations: DFR, delayed fluid resuscitation; SBP, systolic blood pressure; DBP, diastolic blood pressure; HF, high frequency; LF, low frequency; DBPV, diastolic blood pressure variability; SBPV, systolic blood pressure variability; HRV, heart rate variability; RSA, Respiratory Sinus Arrhythmia Classification of shock severity in non-vagotomized animals In the non-vagotomized HS group, stepwise linear discriminant function analysis identified heart rate variability (HRV) and blood pressure variability (BPV) metrics as significant discriminators of shock severity. The resulting model classified moderate (20% DFR) versus severe (50% DFR) hemorrhagic shock with an overall accuracy of 93.3% in the original dataset and 86.7% under leave-one-out cross-validation (LOOCV) (Table 2). Sensitivity for moderate HS was 83.3%, while sensitivity for severe HS was 100%. Receiver operating characteristic (ROC) analysis demonstrated optimal classification performance at a discriminant score threshold of 0.2599 (Youden’s Index = 0.833), corresponding to 83.3% sensitivity and 100% specificity. ROC analysis demonstrated strong discriminatory performance (area under the curve [AUC] > 0.85), consistent with cross-validated classification accuracy. Stepwise discriminant function analysis identified high-frequency diastolic blood pressure variability during the nadir phase (HF-DBPV nadir ; Wilks’ Lambda = 0.722, p = 0.044) and the slope of systolic blood pressure during nadir (SBP 30,nadir ); Wilks’ Lambda = 0.534, p = 0.023) as the strongest contributors to group separation. Table 2: Classification accuracy of linear stepwise discriminant analysis for stratifying hemorrhagic shock severity in non-vagotomized rats. Values represent the number of animals correctly and incorrectly classified in original and cross-validated analyses. Validation Actual HS severity Predicted Moderate HS (20% DFR) Predicted Severe HS (50% DFR) Correct classification (%) Original Moderate HS (20% DFR) 5 1 83.3 Severe HS (50% DFR) 0 9 100 Overall accuracy 93.3 Cross-validated Moderate HS (20% DFR) 4 2 66.7 Severe HS (50% DFR) 0 9 100 Overall accuracy 86.7 Abbreviations: DFR, delayed fluid resuscitation; HS, hemorrhagic shock. Classification of shock severity in vagotomized animals In the vagotomized (Vag+HS) group, the discriminant model achieved complete separation in this experimental dataset for both moderate and severe hemorrhagic shock in both original and LOOCV analyses (Table 3). Group-size–weighted prior probabilities were applied to account for unequal class sizes (10 vs. 6 animals). ROC analysis demonstrated complete separation between moderate and severe HS, with an optimal cutoff score of 0.851 yielding 100% sensitivity and specificity (AUC = 1.00). Table 3 : Classification accuracy of stepwise discriminant analysis for stratifying hemorrhagic shock severity in vagotomized rats Validation Actual HS severity Predicted Moderate HS (20% DFR) Predicted Severe HS (50% DFR) Correct classification (%) Original Moderate HS (20% DFR) 10 0 100 Severe HS (50% DFR) 0 6 100 Overall accuracy 100 Leave-one-out cross-validation Moderate HS (20% DFR) 10 0 100 Severe HS (50% DFR) 0 6 100 Overall accuracy 100 Abbreviations: DFR, delayed fluid resuscitation; HS, hemorrhagic shock. Variables were entered sequentially based on their contribution to group discrimination as measured by Wilks’ Lambda. The final model incorporated five variables: mean high-frequency systolic blood pressure variability (SBP HF,mean ),systolic blood pressure variability slope during nadir (SBP HF,slope,nadir ), low-frequency variability during steady state (LF mean,SS ), vagal efficiency during nadir (VE nadir ), and heart period slope during nadir (HP slope,nadir ). The final model demonstrated strong separation between groups (Wilks’ Lambda = 0.123). Among these predictors, LF mean,SS (standardized coefficient = 1.87), SBP HF,slope,nadir (1.45), and VE nadir (1.30) contributed most strongly and positively to the discriminant function, whereas HP slope,nadir contributed negatively (–0.94). Autonomic–baroreflex relationships following hemorrhagic shock In non-vagotomized animals, linear regression analysis demonstrated a significant inverse relationship between low-frequency spectral power during steady state (LF mean,SS ) and diastolic blood pressure baroreflex gain after resuscitation (DBP LF,mean,AR ). LF mean,SS explained 46.2% of the variance in DBP LF,mean,AR (R² = 0.462; Figure 1A). In contrast, no significant linear relationship was observed between LF mean,SS and DBP LF,mean,AR in vagotomized animals (Figure 1B). Discriminant scores derived from HRV and BPV variables stratified moderate versus severe hemorrhagic shock with optimal cutoffs of 0.2599 in non-vagotomized animals and 0.851 in vagotomized animals, as determined by ROC analysis under leave-one-out cross-validation (LOOCV). Stepwise variable entry statistics, canonical correlations, and standardized discriminant coefficients for the non-vagotomized and vagotomized model are provided in Supplementary Table S1 and S2. Discussion Hemorrhagic shock remains a time-sensitive cause of preventable mortality in trauma and critical illness[1-4], and outcomes are closely linked to recognition of physiological decompensation before conventional vital signs deteriorate. In this controlled model of graded hemorrhage, variability metrics derived from heart period and arterial pressure waveforms stratified shock severity during the early compensatory phase, preceding overt hypotension. In non-vagotomized animals, a parsimonious model incorporating HRV and BPV features demonstrated high classification performance under cross-validation, with high-frequency diastolic pressure variability at nadir and systolic pressure dynamics contributing most strongly. In vagotomized animals, classification required a broader set of autonomic and hemodynamic features and showed apparent complete separation in this dataset; given sample size, these results should be interpreted cautiously and require external validation. These findings support the concept that beat-to-beat variability captures aspects of physiological reserve that are not reflected in static measures of heart rate or arterial pressure[14-19]. Continuous arterial waveform data are routinely available in modern intensive care units but are typically underutilized for dynamic physiological risk stratification. The present findings suggest that variability-based metrics derived from these signals may serve as an adjunct to conventional hemodynamic monitoring and augment existing early warning systems[9]. By leveraging beat-to-beat variability rather than static pressure values, such approaches may improve detection of impaired physiological reserve during the compensatory phase of hemorrhage, enabling earlier risk enrichment for patients vulnerable to hemodynamic deterioration. The computational simplicity of the present models further supports the feasibility of integration into real-time bedside monitoring platforms, although prospective validation in human populations is required. The differences observed between intact and vagotomized animals suggest that vagal integrity modifies the structure of predictive variability signatures during hemorrhagic stress[24-26]. In intact animals, the association between steady-state low-frequency variability and post-resuscitation diastolic baroreflex gain was consistent with coordinated autonomic–vascular regulation [19], whereas this relationship was not observed after vagotomy. Although these observations do not establish causality, they are consistent with the interpretation that vagal pathways contribute to coupling between cardiac-autonomic dynamics and vascular control during hemorrhage and recovery [19,24-26]. Implications for trauma and critical care monitoring The ability to identify impending decompensation during the compensatory phase has important implications across the continuum of trauma care[1-3]. In prehospital and combat settings, real-time analysis of HRV and BPV may enable earlier recognition of occult hemorrhage when standard vital signs remain deceptively reassuring. Within the intensive care unit, continuous autonomic monitoring could serve as an early warning system for cardiovascular collapse, prompting timely resuscitative interventions before irreversible injury occurs[14,16,18,22]. Importantly, the computational simplicity of the present algorithms supports potential integration into wearable or bedside monitoring platforms, extending advanced physiological assessment to resource-limited and austere environments. While translation to human populations will require further validation, these findings provide a mechanistic foundation for incorporating autonomic variability into next-generation hemorrhage monitoring strategies. More broadly, these results may also inform future development of field-deployable or prehospital monitoring strategies, although such applications remain speculative and require substantial validation in human populations. Limitations and future directions This study was conducted in an animal model, and extrapolation to heterogeneous human trauma populations should be undertaken cautiously. The sample size, particularly within subgroup analyses, was modest. Although cross-validation was performed, the potential for model overfitting, particularly in the vagotomized cohort, cannot be excluded. External validation in larger cohorts and evaluation in clinically relevant hemorrhage phenotypes will be necessary before clinical translation. Future studies should determine whether incorporation of variability metrics into multimodal monitoring frameworks improves early detection of hemodynamic instability and alters patient-centered outcomes. CONCLUSION In this experimental model of graded hemorrhagic shock, autonomic variability metrics derived from heart rate and blood pressure signals accurately stratified shock severity during the early compensatory phase, before overt hemodynamic collapse. Vagal integrity fundamentally influenced these predictive signatures, highlighting the role of parasympathetic pathways in coordinating cardiovascular compensation and recovery. Together, these findings support the feasibility of variability-based monitoring as a non-invasive approach for early detection of hemorrhagic decompensation. Future studies should focus on validation in human trauma populations and on integrating autonomic biomarkers into real-time monitoring platforms to enhance early decision-making in trauma and critical care settings. Abbreviations AUC Area under the curve BP Blood pressure BPV Blood pressure variability DBP Diastolic blood pressure DBPV Diastolic blood pressure variability DFR Delayed fluid resuscitation FIR Finite impulse response HF High frequency HP Heart period HR Heart rate HRV Heart rate variability HS Hemorrhagic shock IBI Inter-beat interval ICU Intensive care unit LDA Linear discriminant analysis LF Low frequency LOOCV Leave-one-out cross-validation MAP Mean arterial pressure ROC Receiver operating characteristics RSA Respiratory sinus arrhythmia SBP Systolic blood pressure SBPV Systolic blood pressure variability VE Vagal efficiency Vag + HS Vagotomy with hemorrhagic shock Declarations Acknowledgements This work was supported by the Research Council of Shiraz University of Medical Sciences, grant No 96-01-01-14378, the Research Center for Thoracic and Vascular Surgery, and the Research Council of University of Shiraz, as a part of work of acquiring a Ph.D degree in physiology by F. Khodadadi. The authors confirm that none of these organizations had a role in the design of the study, data collection, data analysis, interpretation of data, or in writing the manuscript. Author’ contributions F.K-M performed experiments; S.P and G.L analyzed data; S.P, F.K-M and G.L interpreted results of the experiments; S.P prepared figures; S.P, F.K-M and G.L drafted the manuscript; S.P, F.K-M,and G. L edited and revised manuscript; all authors have read and approved final version of the manuscript. Data availability Data will be made available at reasonable request. Ethics approval and consent to participate All the experimental procedures were carried out based on the international standards and national legislation on animal care and the Animal Research Reporting In Vivo Experiments (ARRIVE) guidelines. All methods are reported in accordance with ARRIVE guidelines. All procedures in this study were approved by the Center for Comparative and Experimental Medicine and the Ethical Committee of Animal Care at Shiraz University of Medical Sciences, Shiraz, Iran (approval code no: IR.SUMS.MED.REC.1396.s203, Date: 03, 21, 2017). Funding Declaration The authors received no specific funding for this work. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Socioneural Physiology Lab, The Traumatic Stress Research Consortium at the Kinsey Institute, Indiana University, Bloomington, IN, United States 2 Dalton Cardiovascular Research Center, Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, United States 3 Intelligent Systems Engineering, Indiana University, The Traumatic Stress Research Consortium at the Kinsey Institute, Indiana University, Bloomington, IN, United States References Sauaia A, Moore FA, Moore EE, Moser KS, Brennan R, Read RA, Pons PT. 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Blood pressure variability and early neurological outcomes in acute and subacute stroke in Southwestern Uganda. eNeurologicalSci. 2023;33:100482. Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.docx Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviews received at journal 25 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Editor invited by journal 16 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9013916","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":608127057,"identity":"10112291-7b62-4e0a-ae7e-b9bfbd2d0e76","order_by":0,"name":"Sujata Punait","email":"","orcid":"","institution":"Indiana University Bloomington","correspondingAuthor":false,"prefix":"","firstName":"Sujata","middleName":"","lastName":"Punait","suffix":""},{"id":608127058,"identity":"4b4cd830-3d56-48f5-82fb-6b953d1fb5a2","order_by":1,"name":"Fateme Khodadadi-Mericle","email":"","orcid":"","institution":"University of Missouri","correspondingAuthor":false,"prefix":"","firstName":"Fateme","middleName":"","lastName":"Khodadadi-Mericle","suffix":""},{"id":608127059,"identity":"5ce58de0-8df6-4dc3-8d30-76bffaf20c4b","order_by":2,"name":"Gregory F. Lewis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYBACPiA+wMBgwwPiSBClhQ2iJY1ELUBwmIEELew9hge/7jkvY3CA+eBtHqK08JwxOCzz7DaPwQG2ZGvitEjkbjgscQCkhcdMmjgt8m9BWs4BtfB/I1KLBO+Ggx8OHADZwkakFp78D4cZDiTzSB5mM7acQ4wWfvZjyR9/HLCz5zve/PDGG2K0gAAz2D3MxCoHAcYfpKgeBaNgFIyCkQcAW7Mu4Hgn9qwAAAAASUVORK5CYII=","orcid":"","institution":"Indiana University Bloomington","correspondingAuthor":true,"prefix":"","firstName":"Gregory","middleName":"F.","lastName":"Lewis","suffix":""}],"badges":[],"createdAt":"2026-03-02 21:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9013916/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9013916/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105057447,"identity":"c6f17d1b-4d62-41c8-8353-f35e9aaaec77","added_by":"auto","created_at":"2026-03-20 12:05:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53931,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between low-frequency spectral power during steady state (LF\u003csub\u003emean,SS\u003c/sub\u003e) and diastolic blood pressure baroreflex gain after resuscitation (DBP\u003csub\u003eLF,mean,AR\u003c/sub\u003e\u003cbr\u003e\n) in (A) non-vagotomized(n=15) and (B) vagotomized(n=16) animals. A significant inverse association was observed in non-vagotomized animals, whereas no such relationship was present following vagotomy.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9013916/v1/2adbf6219050a50254c617f0.png"},{"id":106092958,"identity":"6601f4e2-f5b0-41d1-9cf2-1136259d447e","added_by":"auto","created_at":"2026-04-03 11:31:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":992093,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9013916/v1/aa07443f-7468-44d0-ab44-270e06be5dad.pdf"},{"id":105057450,"identity":"f50f70f3-a7dc-41ea-b22f-98d89c4acc97","added_by":"auto","created_at":"2026-03-20 12:05:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":173799,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-9013916/v1/a9ede1605bebb87c3acf102e.docx"},{"id":105562885,"identity":"fb807284-e4c3-4f4f-96cf-819700dcecc3","added_by":"auto","created_at":"2026-03-27 12:45:07","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15642,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9013916/v1/d9756f99634ad62a3a7065b4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heart rate and blood-pressure variability stratify hemorrhagic shock severity and reveal vagal-dependent autonomic–vascular coupling: a controlled rat study","fulltext":[{"header":"Background","content":"\u003cp\u003eHemorrhagic shock (HS) is a life-threatening form of hypovolemic shock and a leading cause of preventable mortality in trauma and critical illness, accounting for approximately 1.5\u0026nbsp;million deaths worldwide each year [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Mortality is highest during the early hours following injury, underscoring the importance of timely recognition and intervention [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Physiologically, HS progresses through distinct stages, beginning with a compensatory phase in which autonomic reflexes preserve arterial pressure and tissue perfusion, followed by decompensation characterized by cardiovascular instability and, if untreated, irreversible organ failure [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Accurate identification of the transition from compensation to decompensation is therefore critical for guiding resuscitative management.\u003c/p\u003e \u003cp\u003eIn clinical practice, conventional vital signs, including heart rate, systolic blood pressure, and respiratory rate, often remain within acceptable ranges during early compensatory phases, despite substantial reductions in circulating blood volume [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Marked inter-individual variability in tolerance to hemorrhage further limits the sensitivity of static vital signs for early detection of physiological deterioration [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Composite indices derived from these measures, such as the Shock Index (heart rate/systolic blood pressure), have demonstrated association with transfusion requirements and mortality in large trauma registries [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the Shock Index remains a static metric derived from single time-point measurements and does not capture beat-to-beat autonomic dynamics that may precede overt hemodynamic deterioration. As a result, reliance on these measures alone may delay escalation of care and contribute to adverse outcomes.\u003c/p\u003e \u003cp\u003eHeart rate variability (HRV) and blood pressure variability (BPV) reflect beat-to-beat autonomic and hemodynamic regulation and provide dynamic physiological information not captured by traditional vital signs. In modern ICUs, continuous arterial waveform data are routinely available, yet underutilized for real-time physiological risk stratification. Prior studies have demonstrated associations between altered HRV and BPV and injury severity, impaired organ perfusion, and mortality in trauma and critical illness, suggesting potential utility as adjunctive markers of shock progression [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, translation into routine clinical monitoring has been limited by heterogeneous findings, lack of individualized baseline reference, and the absence of validated real-time classification frameworks.\u003c/p\u003e \u003cp\u003eAutonomic regulation during hemorrhage is highly dynamic. Early blood loss is associated with increased sympathetic activity to preserve arterial pressure, while parasympathetic (vagal) influences modulate cardiac chronotropy and may shape the structure of cardiovascular variability signals during stress [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Experimental studies in animal models suggest that disruption of vagal pathways alters cardiovascular compensation, supporting the possibility that vagal integrity influences the predictive value of variability metrics during hemorrhage [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, we investigated whether combined HRV and BPV metrics derived from peripheral arterial waveforms can stratify hemorrhagic shock severity during early, compensatory phases. Using a controlled rat model with intact and disrupted vagal pathways, we developed and evaluated classification algorithms to distinguish moderate from severe HS. By focusing on physiological signals routinely available in critical care monitoring, this work aims to inform the development of adjunctive tools for early identification of patients at risk for hemodynamic decompensation in the intensive care unit.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design and animal model\u003c/h2\u003e \u003cp\u003e All experimental procedures were approved the Ethics Committee of Shiraz University of Medical Sciences, Shiraz, Iran (approval codes: IR.SUMS.MED.REC.1396.S203) and were performed in accordance with the relevant guidelines and regulations for the care and use of laboratory animals.Adult male Sprague\u0026ndash;Dawley rats were obtained from the Centre of Experimental Animals of Shiraz University of Medical Sciences. The experimental protocol and surgical procedures have been described in detail previously [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBriefly, male Sprague\u0026ndash;Dawley rats were subjected to controlled hemorrhagic shock (HS) using a delayed fluid resuscitation (DFR) paradigm and assigned to either a non-vagotomized group (HS) or a subdiaphragmatic vagotomized group (Vag\u0026thinsp;+\u0026thinsp;HS). Within each group, shock severity was defined by the proportion of shed blood volume returned at the end of the compensatory phase [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with animals classified as moderate HS (20% DFR) or severe HS (50% DFR).\u003c/p\u003e \u003cp\u003eHemodynamic and autonomic data were collected during three predefined experimental phases: steady state (pre-hemorrhage baseline), Nadir-1 (early compensatory phase following hemorrhage), and post-resuscitation. The present analysis focused on discriminating of shock severity (20% vs. 50% DFR) using data obtained during the steady-state and Nadir-1 phases, corresponding to clinically relevant periods preceding overt cardiovascular decompensation.\u003c/p\u003e \u003cp\u003eAll variability metrics were derived from signals routinely available from standard arterial monitoring and calculated in 30-second epochs, supporting potential feasibility for real-time implementation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHeart rate variability analysis\u003c/h3\u003e\n\u003cp\u003eHeart rate variability (HRV) was derived from arterial blood pressure waveforms as described previously [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Beat-to-beat inter-beat intervals (IBIs) were extracted and analyzed in 30-s epochs. Signals underwent preprocessing, including smoothing and finite impulse response (FIR) filtering, to isolate low- and high-frequency components reflecting autonomic modulation. HRV metrics were computed in both time and frequency domains according to established methods.\u003c/p\u003e\n\u003ch3\u003eBlood pressure variability analysis\u003c/h3\u003e\n\u003cp\u003eBlood pressure variability (BPV) was assessed using systolic and diastolic arterial pressure derived on a beat-to-beat basis from continuously recorded arterial waveforms (PowerLab, ADInstruments). Systolic and diastolic pressure time series were uniformly resampled at 20 Hz to permit spectral analysis.\u003c/p\u003e \u003cp\u003eTo separate slow aperiodic trends from rhythmic variability, signals were smoothed using a Savitzky\u0026ndash;Golay low-pass filter (cubic polynomial). Window sizes of 401 and 21 points were applied to isolate frequency components below 0.05 Hz and 1 Hz, respectively. Edge effects introduced by filtering were minimized by trimming signal segments at the beginning and end of each epoch.\u003c/p\u003e \u003cp\u003eHigh- and low-frequency BPV components were then extracted using FIR band-pass filtering (0.05\u0026ndash;1 Hz and 1\u0026ndash;5 Hz). The magnitude of each component was quantified as the logarithmic variance within 30-s epochs. For each experimental phase, mean values of high- and low-frequency systolic and diastolic BPV were calculated and used for subsequent analyses.\u003c/p\u003e\n\u003ch3\u003eHeart period\u003c/h3\u003e\n\u003cp\u003eHeart period (HP) was calculated as the mean IBI within each 30-s epoch. HP was analyzed in preference to heart rate due to its linear relationship with autonomic control and improved sensitivity to autonomic reactivity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eVagal efficiency\u003c/h3\u003e\n\u003cp\u003eVagal efficiency (VE) was quantified as the slope of the linear regression between respiratory sinus arrhythmia (RSA; independent variable) and heart period (HP; dependent variable). VE reflects the effectiveness of vagal modulation of cardiac chronotropy across experimental phases and was calculated separately for steady-state and Nadir-1 periods.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSlopes of autonomic and hemodynamic variables\u003c/h2\u003e \u003cp\u003eTo characterize dynamic changes during early shock, temporal slopes of HRV, BPV, and HP were calculated across the steady-state and Nadir-1 phases. The Nadir-1 phase was defined as the compensatory period during which mean arterial pressure was maintained following hemorrhage, ending at the compensation breakpoint. The steady-state phase corresponded to baseline measurements obtained before hemorrhage induction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 31 male Sprague\u0026ndash;Dawley rats were assigned to vagotomized and non-vagotomized groups and subjected to graded hemorrhagic shock using a delayed fluid resuscitation (DFR) paradigm. Hemorrhagic shock severity was operationally defined by the proportion of shed blood volume returned at the end of the compensatory phase, with animals classified as moderate HS (20% DFR) or severe HS (50% DFR). Animals receiving no fluid return (0% DFR) were excluded from the present classification analyses.\u003c/p\u003e\n\u003cp\u003eHemodynamic and autonomic data obtained during steady state (pre-hemorrhage baseline) and Nadir-1 (early compensatory phase) were used for all classification and regression analyses.\u003c/p\u003e\n\u003cp\u003eDescriptive hemodynamic and autonomic variability metrics during steady state and the early compensatory (Nadir-1) phase are presented in Table 1. Transition from steady state to Nadir-1 was associated with reductions in systolic and diastolic arterial pressure and concurrent modulation of frequency-domain variability indices across both intact and vagotomized animals. Differences between moderate (20% DFR) and severe (50% DFR) groups were observable at the feature level within this early compensatory period. These descriptive findings provide a physiological context for the subsequent discriminant analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eHemodynamic and autonomic variability metrics during steady state and early compensatory (Nadir-1) phase\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS 20% DFR (n=6)\u003cbr\u003e\u0026nbsp;Steady state\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS 20% DFR\u003cbr\u003e\u0026nbsp;Nadir-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS 50% DFR (n=9)\u003cbr\u003e\u0026nbsp;Steady state\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS 50% DFR\u003cbr\u003e\u0026nbsp;Nadir-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVag+HS 20% DFR (n=10)\u003cbr\u003e\u0026nbsp;Steady state\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVag+HS 20% DFR\u003cbr\u003e\u0026nbsp;Nadir-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVag+HS 50% DFR (n=6)\u003cbr\u003e\u0026nbsp;Steady state\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVag+HS 50% DFR\u003cbr\u003e\u0026nbsp;Nadir-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e129.6 \u0026plusmn; 15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e38.6 \u0026plusmn; 4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e135.9 \u0026plusmn; 9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e40.7 \u0026plusmn; 2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e128.0 \u0026plusmn; 11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e35.2 \u0026plusmn; 1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e129.8 \u0026plusmn; 10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e37.3 \u0026plusmn; 4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e97.7 \u0026plusmn; 6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31.2 \u0026plusmn; 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e102.3 \u0026plusmn; 9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e33.7 \u0026plusmn; 2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e97.3 \u0026plusmn; 6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e28.5 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e96.9 \u0026plusmn; 8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e31.6 \u0026plusmn; 4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHeart period (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e156.8 \u0026plusmn; 17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e224.3 \u0026plusmn; 74.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e158.7 \u0026plusmn; 12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e212.0 \u0026plusmn; 55.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e160.5 \u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e187.8 \u0026plusmn; 27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e152.4 \u0026plusmn; 5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e174.1 \u0026plusmn; 16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHF-DBPV (log variance)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026minus;1.07 \u0026plusmn; 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026minus;3.41 \u0026plusmn; 0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026minus;0.77 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026minus;3.96 \u0026plusmn; 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;1.71 \u0026plusmn; 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;3.48 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;0.71 \u0026plusmn; 1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;3.44 \u0026plusmn; 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHF-SBPV (log variance)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026minus;2.10 \u0026plusmn; 0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026minus;3.93 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026minus;2.00 \u0026plusmn; 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026minus;4.57 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;2.14 \u0026plusmn; 0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;3.72 \u0026plusmn; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;1.14 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;4.02 \u0026plusmn; 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLF\u003csub\u003emean\u003c/sub\u003e (HRV, ln(ms)\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026minus;1.83 \u0026plusmn; 1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.33 \u0026plusmn; 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026minus;1.47 \u0026plusmn; 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.83 \u0026plusmn; 1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;2.48 \u0026plusmn; 1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;0.15 \u0026plusmn; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;2.02 \u0026plusmn; 0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus;0.90 \u0026plusmn; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRSA (HRV, ln(ms)\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.45 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2.56 \u0026plusmn; 0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.68 \u0026plusmn; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2.57 \u0026plusmn; 0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.08 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.28 \u0026plusmn; 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.12 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.82 \u0026plusmn; 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: DFR, delayed fluid resuscitation; SBP, systolic blood pressure; DBP, diastolic blood pressure; HF, high frequency; LF, low frequency; DBPV, diastolic blood pressure variability; SBPV, systolic blood pressure variability; HRV, heart rate variability; RSA, Respiratory Sinus Arrhythmia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification of shock severity in non-vagotomized animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the non-vagotomized HS group, stepwise linear discriminant function analysis identified heart rate variability (HRV) and blood pressure variability (BPV) metrics as significant discriminators of shock severity. The resulting model classified moderate (20% DFR) versus severe (50% DFR) hemorrhagic shock with an overall accuracy of 93.3% in the original dataset and 86.7% under leave-one-out cross-validation (LOOCV) (Table 2).\u003c/p\u003e\n\u003cp\u003eSensitivity for moderate HS was 83.3%, while sensitivity for severe HS was 100%. Receiver operating characteristic (ROC) analysis demonstrated optimal classification performance at a discriminant score threshold of 0.2599 (Youden\u0026rsquo;s Index = 0.833), corresponding to 83.3% sensitivity and 100% specificity. ROC analysis demonstrated strong discriminatory performance (area under the curve [AUC] \u0026gt; 0.85), consistent with cross-validated classification accuracy.\u003c/p\u003e\n\u003cp\u003eStepwise discriminant function analysis identified high-frequency diastolic blood pressure variability during the nadir phase (HF-DBPV\u003csub\u003enadir\u003c/sub\u003e; Wilks\u0026rsquo; Lambda = 0.722, \u003cem\u003ep\u003c/em\u003e = 0.044) and the slope of systolic blood pressure during nadir (SBP\u003csub\u003e30,nadir\u003c/sub\u003e); Wilks\u0026rsquo; Lambda = 0.534, \u003cem\u003ep\u003c/em\u003e = 0.023) as the strongest contributors to group separation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eClassification accuracy of linear stepwise discriminant analysis for stratifying hemorrhagic shock severity in non-vagotomized rats. Values represent the number of animals correctly and incorrectly classified in original and cross-validated analyses.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActual HS severity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted Moderate HS (20% DFR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted Severe HS (50% DFR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrect classification (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eOriginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eModerate HS (20% DFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSevere HS (50% DFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eOverall accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e93.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eCross-validated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eModerate HS (20% DFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSevere HS (50% DFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eOverall accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e86.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Abbreviations: DFR, delayed fluid resuscitation; HS, hemorrhagic shock.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification of shock severity in vagotomized animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the vagotomized (Vag+HS) group, the discriminant model achieved complete separation in this experimental dataset for both moderate and severe hemorrhagic shock in both original and LOOCV analyses (Table 3). Group-size\u0026ndash;weighted prior probabilities were applied to account for unequal class sizes (10 vs. 6 animals).\u003c/p\u003e\n\u003cp\u003eROC analysis demonstrated complete separation between moderate and severe HS, with an optimal cutoff score of 0.851 yielding 100% sensitivity and specificity (AUC = 1.00).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Classification accuracy of stepwise discriminant analysis for stratifying hemorrhagic shock severity in vagotomized rats\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActual HS severity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted Moderate HS (20% DFR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted Severe HS (50% DFR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrect classification (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eOriginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eModerate HS (20% DFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSevere HS (50% DFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eOverall accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLeave-one-out cross-validation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eModerate HS (20% DFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSevere HS (50% DFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eOverall accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;Abbreviations: DFR, delayed fluid resuscitation; HS, hemorrhagic shock.\u003c/p\u003e\n\u003cp\u003eVariables were entered sequentially based on their contribution to group discrimination as measured by Wilks\u0026rsquo; Lambda. The final model incorporated five variables: mean high-frequency systolic blood pressure variability (SBP\u003csub\u003eHF,mean\u003c/sub\u003e),systolic blood pressure variability slope during nadir (SBP\u003csub\u003eHF,slope,nadir\u003c/sub\u003e), low-frequency variability during steady state (LF\u003csub\u003emean,SS\u003c/sub\u003e), vagal efficiency during nadir (VE\u003csub\u003enadir\u003c/sub\u003e), and heart period slope during nadir (HP\u003csub\u003eslope,nadir\u003c/sub\u003e). The final model demonstrated strong separation between groups (Wilks\u0026rsquo; Lambda = 0.123).\u003c/p\u003e\n\u003cp\u003eAmong these predictors, LF\u003csub\u003emean,SS\u003c/sub\u003e (standardized coefficient = 1.87), SBP\u003csub\u003eHF,slope,nadir\u003c/sub\u003e(1.45), and VE\u003csub\u003enadir\u003c/sub\u003e (1.30) contributed most strongly and positively to the discriminant function, whereas \u0026nbsp;HP\u003csub\u003eslope,nadir\u003c/sub\u003e contributed negatively (\u0026ndash;0.94).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutonomic\u0026ndash;baroreflex relationships following hemorrhagic shock\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn non-vagotomized animals, linear regression analysis demonstrated a significant inverse relationship between low-frequency spectral power during steady state (LF\u003csub\u003emean,SS\u003c/sub\u003e) and diastolic blood pressure baroreflex gain after resuscitation (DBP\u003csub\u003eLF,mean,AR\u003c/sub\u003e). LF\u003csub\u003emean,SS\u003c/sub\u003e explained 46.2% of the variance in DBP\u003csub\u003eLF,mean,AR\u003c/sub\u003e (R\u0026sup2; = 0.462; Figure 1A).\u003c/p\u003e\n\u003cp\u003eIn contrast, no significant linear relationship was observed between LF\u003csub\u003emean,SS\u003c/sub\u003e\u0026nbsp; and DBP\u003csub\u003eLF,mean,AR\u003c/sub\u003e\u0026nbsp; in vagotomized animals (Figure 1B).\u003c/p\u003e\n\u003cp\u003eDiscriminant scores derived from HRV and BPV variables stratified moderate versus severe hemorrhagic shock with optimal cutoffs of 0.2599 in non-vagotomized animals and 0.851 in vagotomized animals, as determined by ROC analysis under leave-one-out cross-validation (LOOCV).\u003c/p\u003e\n\u003cp\u003eStepwise variable entry statistics, canonical correlations, and standardized discriminant coefficients for the non-vagotomized and vagotomized model are provided in Supplementary Table S1 and S2.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHemorrhagic shock remains a time-sensitive cause of preventable mortality in trauma and critical illness[1-4], and outcomes are closely linked to recognition of physiological decompensation before conventional vital signs deteriorate. In this controlled model of graded hemorrhage, variability metrics derived from heart period and arterial pressure waveforms stratified shock severity during the early compensatory phase, preceding overt hypotension. In non-vagotomized animals, a parsimonious model incorporating HRV and BPV features demonstrated high classification performance under cross-validation, with high-frequency diastolic pressure variability at nadir and systolic pressure dynamics contributing most strongly. In vagotomized animals, classification required a broader set of autonomic and hemodynamic features and showed apparent complete separation in this dataset; given sample size, these results should be interpreted cautiously and require external validation.\u003c/p\u003e\n\u003cp\u003eThese findings support the concept that beat-to-beat variability captures aspects of physiological reserve that are not reflected in static measures of heart rate or arterial pressure[14-19]. Continuous arterial waveform data are routinely available in modern intensive care units but are typically underutilized for dynamic physiological risk stratification. The present findings suggest that variability-based metrics derived from these signals may serve as an adjunct to conventional hemodynamic monitoring and augment existing early warning systems[9]. By leveraging beat-to-beat variability rather than static pressure values, such approaches may improve detection of impaired physiological reserve during the compensatory phase of hemorrhage, enabling earlier risk enrichment for patients vulnerable to hemodynamic deterioration. The computational simplicity of the present models further supports the feasibility of integration into real-time bedside monitoring platforms, although prospective validation in human populations is required.\u003c/p\u003e\n\u003cp\u003eThe differences observed between intact and vagotomized animals suggest that vagal integrity modifies the structure of predictive variability signatures during hemorrhagic stress[24-26]. In intact animals, the association between steady-state low-frequency variability and post-resuscitation diastolic baroreflex gain was consistent with coordinated autonomic–vascular regulation [19], whereas this relationship was not observed after vagotomy. Although these observations do not establish causality, they are consistent with the interpretation that vagal pathways contribute to coupling between cardiac-autonomic dynamics and vascular control during hemorrhage and recovery [19,24-26].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for trauma and critical care monitoring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ability to identify impending decompensation during the compensatory phase has important implications across the continuum of trauma care[1-3]. In prehospital and combat settings, real-time analysis of HRV and BPV may enable earlier recognition of occult hemorrhage when standard vital signs remain deceptively reassuring. Within the intensive care unit, continuous autonomic monitoring could serve as an early warning system for cardiovascular collapse, prompting timely resuscitative interventions before irreversible injury occurs[14,16,18,22].\u003c/p\u003e\n\u003cp\u003eImportantly, the computational simplicity of the present algorithms supports potential integration into wearable or bedside monitoring platforms, extending advanced physiological assessment to resource-limited and austere environments. While translation to human populations will require further validation, these findings provide a mechanistic foundation for incorporating autonomic variability into next-generation hemorrhage monitoring strategies.\u003c/p\u003e\n\u003cp\u003eMore broadly, these results may also inform future development of field-deployable or prehospital monitoring strategies, although such applications remain speculative and require substantial validation in human populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and future directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in an animal model, and extrapolation to heterogeneous human trauma populations should be undertaken cautiously. The sample size, particularly within subgroup analyses, was modest. Although cross-validation was performed, the potential for model overfitting, particularly in the vagotomized cohort, cannot be excluded. External validation in larger cohorts and evaluation in clinically relevant hemorrhage phenotypes will be necessary before clinical translation. Future studies should determine whether incorporation of variability metrics into multimodal monitoring frameworks improves early detection of hemodynamic instability and alters patient-centered outcomes.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this experimental model of graded hemorrhagic shock, autonomic variability metrics derived from heart rate and blood pressure signals accurately stratified shock severity during the early compensatory phase, before overt hemodynamic collapse. Vagal integrity fundamentally influenced these predictive signatures, highlighting the role of parasympathetic pathways in coordinating cardiovascular compensation and recovery. Together, these findings support the feasibility of variability-based monitoring as a non-invasive approach for early detection of hemorrhagic decompensation. Future studies should focus on validation in human trauma populations and on integrating autonomic biomarkers into real-time monitoring platforms to enhance early decision-making in trauma and critical care settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood pressure variability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic blood pressure variability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDelayed fluid resuscitation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFinite impulse response\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart period\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart rate variability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemorrhagic shock\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInter-beat interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinear discriminant analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOOCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeave-one-out cross-validation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean arterial pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRespiratory sinus arrhythmia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic blood pressure variability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVagal efficiency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVag\u0026thinsp;+\u0026thinsp;HS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVagotomy with hemorrhagic shock\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Research Council of Shiraz University of Medical Sciences, grant No 96-01-01-14378, the Research Center for Thoracic and Vascular Surgery, and the Research Council of University of Shiraz, as a part of work of acquiring a Ph.D degree in physiology by F. Khodadadi. The authors confirm that none of these organizations had a role in the design of the study, data collection, data analysis, interpretation of data, or in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;F.K-M performed experiments; S.P and G.L analyzed data; S.P, F.K-M and G.L interpreted results of the experiments; S.P prepared figures; S.P, F.K-M and G.L drafted the manuscript; S.P, F.K-M,and G. L edited and revised manuscript; all authors have read and approved final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available at reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the experimental procedures were carried out based on the international standards and national legislation on animal care and the Animal Research Reporting In Vivo Experiments (ARRIVE) guidelines. All methods are reported in accordance with ARRIVE guidelines. All procedures in this study were approved by the Center for Comparative and Experimental Medicine and the Ethical Committee of Animal Care at Shiraz University of Medical Sciences, Shiraz, Iran (approval code no: IR.SUMS.MED.REC.1396.s203, Date: 03, 21, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor details\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Socioneural Physiology Lab, The Traumatic Stress Research Consortium at the Kinsey Institute, Indiana University, Bloomington, IN, United States\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eDalton Cardiovascular Research Center, Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, United States\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eIntelligent Systems Engineering, Indiana University, The Traumatic Stress Research Consortium at the Kinsey Institute, Indiana University, Bloomington, IN, United States\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSauaia A, Moore FA, Moore EE, Moser KS, Brennan R, Read RA, Pons PT. Epidemiology of trauma deaths: a reassessment. J Trauma. 1995;38:185\u0026ndash;193.\u003c/li\u003e\n \u003cli\u003eKauvar DS, Lefering R, Wade CE. Impact of hemorrhage on trauma outcome: an overview of epidemiology, clinical presentations, and therapeutic considerations. J Trauma. 2006;60:S3\u0026ndash;S11.\u003c/li\u003e\n \u003cli\u003eTisherman SA, Schmicker RH, Brasel KJ, Bulger EM, Kerby JD, Minei JP, Powell JL, Reiff DA, Rizoli SB, Schreiber MA. Detailed description of all deaths in both the shock and traumatic brain injury hypertonic saline trials of the Resuscitation Outcomes Consortium. Ann Surg. 2015;261:586\u0026ndash;590. doi:10.1097/SLA.0000000000000837.\u003c/li\u003e\n \u003cli\u003eGutierrez G, Reines HD, Wulf-Gutierrez ME. Clinical review: hemorrhagic shock. Crit Care. 2004;8:373\u0026ndash;381.\u003c/li\u003e\n \u003cli\u003eHinojosa-Laborde C, Hudson IL, Ross E, Xiang L, Ryan KL. Pathophysiology of hemorrhage as it relates to the warfighter. Physiology (Bethesda). 2022;37:141\u0026ndash;153. doi:10.1152/physiol.00028.2021.\u003c/li\u003e\n \u003cli\u003eWiggers CJ. Experimental hemorrhagic shock. \u003cem\u003ePhysiol Rev.\u003c/em\u003e 1950;30(4):477\u0026ndash;528.\u003c/li\u003e\n \u003cli\u003ePhysiological mechanisms associated with the development of hypotension during progressive central hypovolemia. \u003cem\u003eJ Physiol.\u003c/em\u003e 2008;586(8):2245\u0026ndash;2256. doi:10.1113/jphysiol.2007.149575.\u003c/li\u003e\n \u003cli\u003eTinawi M. New trends in the utilization of intravenous fluids. Cureus. 2021;13:e14619. doi:10.7759/cureus.14619.\u003c/li\u003e\n \u003cli\u003eMutschler M, Nienaber U, M\u0026uuml;nzberg M, W\u0026ouml;lfl C, Schoechl H, Paffrath T, Bouillon B, Maegele M; TraumaRegister DGU. The Shock Index revisited \u0026ndash; a fast guide to transfusion requirement? A retrospective analysis on 21,853 patients derived from the TraumaRegister DGU. \u003cem\u003eCrit Care.\u003c/em\u003e 2013;17(4):R172. doi:10.1186/cc12851.\u003c/li\u003e\n \u003cli\u003eHooper N, Armstrong TJ. Hemorrhagic shock. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2024.\u003c/li\u003e\n \u003cli\u003eJ\u0026aacute;vor P, Han\u0026aacute;k L, Hegyi P, et al. Predictive value of tachycardia for mortality in trauma-related haemorrhagic shock: a systematic review and meta-regression. BMJ Open. 2022;12:e059271. doi:10.1136/bmjopen-2021-059271.\u003c/li\u003e\n \u003cli\u003eBrasel KJ, Guse C, Gentilello LM. Heart rate: is it truly a vital sign? J Trauma. 2007;62:812\u0026ndash;817.\u003c/li\u003e\n \u003cli\u003eGuly HR, Bouamra O, Spiers M, et al. Vital signs and estimated blood loss in patients with major trauma: testing the validity of the ATLS classification of hypovolaemic shock. Resuscitation. 2011;82:556\u0026ndash;559.\u003c/li\u003e\n \u003cli\u003eVictorino GP, Battistella FD, Wisner DH. Does tachycardia correlate with hypotension after trauma? J Am Coll Surg. 2003;196:679\u0026ndash;684.\u003c/li\u003e\n \u003cli\u003eCooke WH, Salinas J, McManus JM, Ryan KL, Rickards CA, Holcomb JB, Convertino VA. Heart period variability in trauma patients may predict mortality and allow remote triage. Aviat Space Environ Med. 2006;77:1107\u0026ndash;1112.\u003c/li\u003e\n \u003cli\u003eBatchinsky AI, Skinner JE, Necsoiu C, Jordan BS, Weiss D, Cancio LC. New measures of heart-rate complexity: effect of chest trauma and hemorrhage. J Trauma. 2010;68:1178\u0026ndash;1185.\u003c/li\u003e\n \u003cli\u003eCancio LC, Batchinsky AI, Salinas J, Kuusela TA, Convertino VA, Wade CE, Holcomb JB. Heart-rate complexity for prediction of prehospital lifesaving interventions in trauma patients. J Trauma. 2008;65:813\u0026ndash;819.\u003c/li\u003e\n \u003cli\u003eOng MEH, Padmanabhan P, Chan YH, Lin Z, Overton J, Ward KR, Fei DY. Use of heart rate variability as a predictor of clinical outcomes in prehospital ambulance patients. Resuscitation. 2008;78:289\u0026ndash;297.\u003c/li\u003e\n \u003cli\u003eKing DR, Ogilvie MP, Pereira BM, Chang Y, Manning RJ, Conner JA, Schulman CI, McKenney MG, Proctor KG. Heart rate variability as a triage tool in patients with trauma during prehospital helicopter transport. J Trauma. 2009;67:436\u0026ndash;440.\u003c/li\u003e\n \u003cli\u003eHinojosa-Laborde C, Rickards CA, Ryan KL, Convertino VA. Heart rate variability during simulated hemorrhage with lower body negative pressure in high- and low-tolerant subjects. Front Physiol. 2011;2:85. doi:10.3389/fphys.2011.00085.\u003c/li\u003e\n \u003cli\u003ede la Sierra A. Blood pressure variability as a risk factor for cardiovascular disease. J Clin Med. 2023;12:6167. doi:10.3390/jcm12196167.\u003c/li\u003e\n \u003cli\u003eTang Y, Sorenson J, Lanspa M, Grissom CK, Mathews VJ, Brown SM. Systolic blood pressure variability in early severe sepsis or septic shock. BMC Anesthesiol. 2017;17:82.\u003c/li\u003e\n \u003cli\u003eMurphy EK, Bertsch SR, Klein SB, et al. Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability. Sci Rep. 2024;14:8719.\u003c/li\u003e\n \u003cli\u003eCzura CJ, Schultz A, Kaipel M, et al. Vagus nerve stimulation regulates hemostasis in swine. Shock. 2010;33:608\u0026ndash;613.\u003c/li\u003e\n \u003cli\u003eRezende-Neto JB, Alves RL, Carvalho M Jr, et al. Vagus nerve stimulation improves coagulopathy in hemorrhagic shock. J Trauma Manag Outcomes. 2014;8:15.\u003c/li\u003e\n \u003cli\u003eKhodadadi F, Ketabchi F, Khodabandeh Z, et al. Subdiaphragmatic vagotomy alters heart rate variability and lung inflammation in hemorrhagic shock. BMC Cardiovasc Disord. 2022;22:181.\u003c/li\u003e\n \u003cli\u003eKhodadadi F, Punait S, Ketabchi F, et al. Comparison of heart rate variability, hemodynamic, metabolic and inflammatory parameters in decompensatory hemorrhagic shock. BMC Cardiovasc Disord. 2024;24:661.\u003c/li\u003e\n \u003cli\u003eKulaba N, et al. Blood pressure variability and early neurological outcomes in acute and subacute stroke in Southwestern Uganda. eNeurologicalSci. 2023;33:100482.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Early shock detection, Heart rate variability, Blood pressure variability","lastPublishedDoi":"10.21203/rs.3.rs-9013916/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9013916/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\nHemorrhagic shock (HS) is a leading cause of preventable trauma mortality, with most deaths occurring within hours of injury. Early identification of the transition from compensatory to decompensatory shock remains challenging because conventional vital signs often remain within normal ranges until late stages. Beat-to-beat heart rate variability (HRV) and blood pressure variability (BPV), obtainable from continuous arterial waveforms, reflect dynamic autonomic regulation and may provide earlier indicators of physiological decompensation. We investigated whether peripheral autonomic and hemodynamic markers can non-invasively stratify HS severity and whether vagal integrity modulates these predictive signatures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale Sprague–Dawley rats were subjected to graded hemorrhagic shock using a delayed fluid resuscitation (DFR) paradigm and classified as moderate (20% DFR) or severe (50% DFR) hemorrhagic shock, defined by delayed fluid resuscitation volume. Animals were assigned to non-vagotomized (HS) and subdiaphragmatic vagotomized (Vag+HS) groups. Heart rate variability (HRV), blood pressure variability (BPV), heart period (HP), and vagal efficiency (VE) were derived from arterial pressure recordings obtained during steady state (pre-hemorrhage) and the early compensatory nadir phase. Stepwise linear discriminant function analysis was used to develop predictive models of shock severity. Model performance was assessed using classification accuracy, cross-validation, and receiver operating characteristic (ROC) analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nIn non-vagotomized animals, a model incorporating HRV and BPV measures classified moderate versus severe HS with 93.3% accuracy (cross-validated accuracy 86.7%). High-frequency diastolic BP variability during nadir and systolic pressure dynamics were the strongest discriminators. In vagotomized animals, the model showed apparent complete separation in this dataset (AUC=1.00), which should be interpreted cautiously given the sample size.\u003c/p\u003e\n\u003cp\u003eIn vagotomized animals, a broader combination of autonomic and hemodynamic features, including low-frequency variability during steady state, vagal efficiency, and heart period dynamics, complete separation between shock severity groups on ROC analysis. Regression analysis demonstrated a significant association between pre-shock sympathetic modulation and impaired post-resuscitation baroreflex sensitivity in intact animals, a relationship that was absent after vagotomy, indicating disruption of autonomic–vascular coupling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous analysis of HRV and BPV enables accurate stratification of hemorrhagic shock severity during early compensatory phases. Vagal integrity modifies the structure of predictive autonomic signatures during hemorrhage. These findings support further evaluation of variability-based analytics as an adjunct to continuous ICU waveform monitoring to identify patients at risk of hemodynamic deterioration before overt hypotension develops.\u003c/p\u003e","manuscriptTitle":"Heart rate and blood-pressure variability stratify hemorrhagic shock severity and reveal vagal-dependent autonomic–vascular coupling: a controlled rat study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 12:05:00","doi":"10.21203/rs.3.rs-9013916/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T10:19:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T15:48:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T09:35:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285081850214199041357967412438357314530","date":"2026-03-20T08:15:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131951535930477938561565211398733383385","date":"2026-03-18T09:04:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T02:59:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T16:42:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-17T03:33:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T00:50:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-11T16:56:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f251af7-cedb-43a8-8fc7-e6c15a30736c","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64709469,"name":"Health sciences/Cardiology"},{"id":64709470,"name":"Health sciences/Diseases"},{"id":64709471,"name":"Health sciences/Medical research"},{"id":64709472,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-05-19T02:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 12:05:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9013916","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9013916","identity":"rs-9013916","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}