Association between Cerebral Autoregulation-Derived Mean Arterial Pressure Deviation and Neurological Prognosis in NICU Patients: A Prospective, Exploratory, Observational Study

Association between Cerebral Autoregulation-Derived Mean Arterial Pressure Deviation and Neurological Prognosis in NICU Patients: A Prospective, Exploratory, Observational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Cerebral Autoregulation-Derived Mean Arterial Pressure Deviation and Neurological Prognosis in NICU Patients: A Prospective, Exploratory, Observational Study Xichi Jiang, Ling Li, Sibo Wen, Tengzhu Ren, Yuzhe Wang, Xianglian Liao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8684150/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Purpose Cerebral autoregulation (CA) is a core physiological mechanism that maintains adequate cerebral blood flow (CBF) despite fluctuations in cerebral perfusion pressure. Patients in the neurological intensive care unit (NICU) often exhibit varying degrees of CA impairment, rendering the brain vulnerable to hypoperfusion and insufficient oxygen delivery even at blood pressure levels recommended by population-based guidelines. CA-derived indices enable the assessment of individualized mean arterial pressure (MAP) within specific time windows. This study aimed to investigate whether personalized CA-derived MAP deviation is associated with neurological prognosis in NICU patients. Methods We conducted a retrospective analysis of prospective data from adult patients (≥ 18 years) admitted to the NICU of a neurocenter between June 2025 and October 2025. Near-infrared spectroscopy (NIRS) was used to synchronously monitor the dynamic linear correlation between cerebral tissue oxygen saturation (rSO₂) and MAP, and the cerebral oximetry index (COx) reflecting CA function was calculated. A multi-window weighting algorithm was applied to determine the time-varying optimal MAP (MAPₒₚₜ) for each patient, and the median of the NIRS-MAP reference interval (NMM) within the target time window was identified. The NIRS-MAP Median Deviation (NMMD) was defined as the absolute difference between NMM and the mean MAP (MM) within 24h, 48h, and 72h respectively. Unfavorable neurological outcomes were defined as a higher modified Rankin Scale (mRS) score at discharge compared with admission. Multivariate binary logistic regression models adjusted for age, gender, and underlying diseases were used to examine the association between NMMD (in different time windows and under the condition of MM < NMM, i.e., toward the lower limit of autoregulation [LLA]) and unfavorable neurological outcomes. Results Among the 34 enrolled patients, 23 (67.6%) had favorable outcomes and 11 (32.4%) had unfavorable outcomes. NMMD at 24h and 48h was significantly associated with unfavorable neurological outcomes, with odds ratios (OR) ranging from 0.729 to 0.852 (p ≤ 0.033). Notably, the association remained significant for 24h NMMD when MM < NMM (OR: 0.776, 95% confidence interval [CI]: 0.612–0.985, p = 0.037), and the corresponding time burden parameter (tNMMD) also showed statistical significance (OR: 0.766, 95% CI: 0.592–0.989, p = 0.041). Receiver operating characteristic (ROC) analysis demonstrated that 24h NMMD had the strongest discriminative ability (area under the curve [AUC] = 0.84). Conclusion In NICU patients, a larger NMMD during hospitalization, especially within the first 24h, is independently associated with a reduced risk of unfavorable neurological outcomes, and this association persists even when MAP deviates toward the LLA. This finding suggests that a moderate degree of MAP deviation may be beneficial for preserving CA function and exerting its compensatory effects, highlighting the independent importance of early CA assessment and individualized blood pressure control within 24h for neurological prognosis. Future studies with larger sample sizes are needed to further validate its clinical significance. Figures Figure 1 Figure 2 Figure 3 Introduction Cerebral autoregulation (CA) is the core physiological mechanism maintaining CBF homeostasis by dynamically regulating cerebrovascular resistance (CVR) to offset the impact of cerebral perfusion pressure (CPP) fluctuations on cerebral oxygen delivery [ 1 – 3 ] . Patients in the neurological intensive care unit (NICU) often experience varying degrees of CA impairment due to primary diseases, systemic inflammatory responses, or secondary injuries [ 3 – 5 ] . This impairment makes population-based standardized blood pressure guidelines inadequate for meeting individual needs, as inter-individual differences in CA thresholds and the relative changes of mean arterial pressure (MAP) against these thresholds lead to heterogeneous effects on prognosis [ 6 – 10 ] . The "fixed blood pressure threshold" approach thus fails to cover the actual effective range of individual CA, limiting the ability of CA to compensate for perfusion fluctuations and prevent the progression of cerebral tissue damage. Near-infrared spectroscopy (NIRS) provides robust support for personalized CA assessment. It has been validated to effectively reflect the dynamic association between cerebral oxygenation and blood pressure, serving as a reliable tool for non-invasive CA monitoring [ 11 ] . Previous studies have shown that early CA impairment assessed by NIRS is significantly associated with death or severe neuroimaging abnormalities in preterm infants [ 12 ] . However, the clinical consistency and superiority of different CA monitoring indices, as well as their synergistic effects with MAP, remain unclear [ 13 – 16 ] . Additionally, most prior studies on CA in NICU patients have focused on pediatric populations, with relatively limited evidence for CA-guided blood pressure management in adult NICU patients. Notably, the complexity of CA function necessitates individualized assessment: the lower limit of autoregulation (LLA) is not a fixed value but a transitional region where cerebrovascular resistance undergoes gradual adjustment [ 17 ] , further highlighting the limitations of the "fixed blood pressure threshold" management model. Moreover, adult NICU patients often present with more complex underlying diseases and fluctuating physiological states, leading to distinct characteristics of CA impairment compared with children [ 18 ] . There is an urgent need for targeted studies to clarify the association between CA-derived blood pressure parameters and prognosis in this population. Based on these research gaps, this study aimed to explore the association between NIRS-based CA-derived MAP Median Deviation (NMMD) and neurological prognosis in adult NICU patients during hospitalization, analyzing the effects of different time windows. The findings are expected to provide evidence for optimizing personalized blood pressure management strategies in adult NICU patients. Methods Study Design and Setting This single-center prospective cohort study was conducted in strict accordance with the STROBE guidelines. We performed a retrospective analysis of consecutive patients admitted to the NICU of a neurocenter between June 2025 and October 2025. The study was approved by the Institutional Review Board of the participating center of the Affiliated Guangdong Second Provincial General Hospital of Jinan University (ethics approval number: 2025-KY-KZ-285-03). Due to its observational nature, the requirement for informed consent was waived. Clinical trial number: not applicable. Human Ethics and Consent to Participate declarations: not applicable. Participants All patients admitted to the NICU were managed by experienced neurologists and nursing teams with standardized monitoring. Eligibility criteria were applied to select participants. Inclusion and Exclusion Criteria Inclusion Criteria: - Age ≥ 18 years; - Direct admission to the NICU immediately after hospital admission; - At least 6 blood pressure measurements with an interval of 4 hours; - Continuous NIRS monitoring for at least 1 hour. Exclusion Criteria: - Baseline modified Rankin Scale (mRS) score ≥ 2 before onset; - End-stage systemic diseases (NYHA class IV heart failure, stage 5 chronic kidney disease [eGFR < 15 mL/min/1.73 m²], Child-Pugh class C cirrhosis); - Incomplete medical records or loss to follow-up before discharge; - Inability to complete NIRS monitoring or poor signal quality. Data Collection All patients underwent non-invasive brachial artery blood pressure monitoring and continuous NIRS monitoring. The first blood pressure measurement was recorded within 2 hours after admission to the NICU, and systolic blood pressure (SBP) values were extracted from electronic health records at 4-hour intervals until 72 hours post-admission or if no new values were recorded within 12 hours. Obviously erroneous values (e.g., SBP 250 mmHg) were excluded by two independent researchers after confirming they were technical artifacts rather than true hemodynamic events. During NIRS monitoring, unnecessary interventions (e.g., routine care that might interfere with data collection) were avoided as much as possible, and relevant indicators from the monitor were collected at 1-second intervals. Blood Pressure Indicators: Systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and pulse index (PI) were recorded. NIRS Indicators: Cerebral tissue oxygen saturation (rSO₂), tissue oxygen index (TOI), and total hemoglobin concentration (HbT) were recorded. Clinical Variables Demographic characteristics (age, gender); underlying diseases (hypertension, diabetes); other comorbidities (cardiac, hepatic, or renal dysfunction, pulmonary infection); coagulation function (platelet count, D-dimer); stroke type (ischemic or hemorrhagic); and use of antihypertensive drugs, vasopressors, and previous antiplatelet/anticoagulant medications during hospitalization were documented. Outcome Measure The primary outcome was neurological deterioration, defined as an increase in mRS score at discharge compared with admission, assessed by professional neurologists. Cerebral Autoregulation Indices Based on synchronously recorded NIRS data and MAP, three types of CA-derived indices were constructed: - COx: Dynamic Pearson correlation coefficient between MAP and rSO₂; - TOIx: Dynamic Pearson correlation coefficient between MAP and TOI; - THx: Dynamic Pearson correlation coefficient between MAP and HbT. A sliding window method was used to calculate dynamic correlation coefficients: - window size was set to 30 seconds (1 Hz sampling frequency, containing 30 data points) with a step size of 1 second; - correlation coefficients (COR) between MAP and target NIRS indicators were calculated for each window. Invalid data with no variation or missing values in the window were excluded; COR, mean MAP within the window, and start time of valid windows were retained for subsequent analysis. A quadratic polynomial fitting was used to construct the U-shaped relationship model between COR and MAP, determining personalized CA parameters: - MAP means were binned at 5 mmHg intervals, and the mean COR and standard error (SE_COR) for each bin were calculated. A quadratic fitting model \(y = ax² + bx + c\) was constructed with the midpoint of each bin as the independent variable and the mean COR as the dependent variable; - A COR value of 0.3 was defined as the threshold for CA impairment. Two real roots of the equation \(ax² + bx + (c - 0.3) = 0\) were solved, which were designated as the lower limit of autoregulation (LLA) and upper limit of autoregulation (ULA), respectively. Only data with ≥4 bins were used for fitting; LLA/ULA was marked as missing if no real roots existed. Calculation of Core Derived Indices and Time Burden Parameters: - NMM (NIRS MAP Median): Median of the MAP reference interval derived from NIRS monitoring; - MM (mean MAP): Mean MAP within 24h, 48h, or 72h; - NMMD (NIRS MAP Median Deviation): Absolute difference between NMM and MM, used as the indicator of MAP deviation; - tNMMD (time-weighted NIRS MAP Median Deviation): Product of the time proportion of MAP measurements below NMM in each time window (24h/48h/72h) and NMMD. Statistical Analysis Continuous variables were expressed as mean ± standard deviation or median (interquartile range) based on normality test results. Categorical variables were presented as frequencies and percentages. Intergroup comparisons were performed using Student's t-test or Mann-Whitney U test for continuous variables, and chi-square test or Fisher's exact test for categorical variables. Considering variations in the number of blood pressure measurements and length of hospital stay among patients, three time windows (24h, 48h, 72h) were set to ensure statistical robustness. Multivariate binary logistic regression models (backward LR) were constructed for each window, and final adjustments were made for variables with p<0.20 (or considered to be important factors influencing the outcomes) in univariate analysis to ensure potential confounders were included. Additionally, box plots, density plots, and ROC curves were generated for the three time windows to visually demonstrate statistical differences in various blood pressure variability indicators between the unfavorable and favorable outcome groups. Statistical significance was set at p < 0.05. All statistical analyses were performed using IBM SPSS Statistics 27.0.1 and R software version 4.5.1. Results Patient Characteristics and Outcomes Among the 34 enrolled patients, 23 (67.6%) achieved favorable outcomes, and 11 (32.4%) experienced unfavorable outcomes. Baseline characteristics were balanced between the two groups, with no significant differences in gender, underlying diseases, comorbidities, or stroke type. Significant differences were only observed in age and coagulation function (platelet count and D-dimer) in the unfavorable outcome group (Table 1). Only 4 patients did not use short-acting antihypertensive drugs, and 5 patients used vasopressors, indicating minimal impact of medication use on statistical analysis. Association between Derived Indices and Outcomes Multivariate logistic regression analysis showed that after adjusting for demographic characteristics, comorbidities, sedation status, and stroke type, NMMD at 24h and 48h was significantly associated with unfavorable neurological outcomes, with OR ranging from 0.729 to 0.852 (p ≤ 0.033, Table 2). Given the high proportion of MAP measurements below NMM in all time windows (43%-100%, mean 87%), additional analyses were performed to explore the clinical value of NMMD under the condition of MM < NMM. Among the three time windows, only 24h NMMD reached statistical significance (p < 0.05, Table 3). Similarly, for the time burden parameter tNMMD under MM < NMM, only the 24h window showed significant association with unfavorable outcomes (p < 0.05, Table 4). Collectively, these results indicate that a larger NMMD during hospitalization, especially within the first 24h, is stably associated with a reduced risk of unfavorable neurological outcomes in NICU patients, even when MAP deviates toward the LLA. Table 1. Baseline Characteristics of Patients by Outcome Group Variable Favorable Outcome (n=23) Unfavorable Outcome (n=11) P value Male sex, n (%) 12 (52.2) 4 (36.4) 0.110 Female sex, n (%) 11 (47.8) 7 (63.6) - Age, years (mean ± SD) 62.48 ± 14.53 70.36 ± 12.58 0.018 Underlying diseases, n (%) 12 (52.2) 9 (81.8) 0.096 Platelet count, 10 9 /L (mean ± SD) 256.87 ± 47.32 231.00 ± 114.78 0.039 D-dimer, mg/L (mean ± SD) 4.89 ± 9.85 5.14 ± 4.57 0.042 Cardiac/hepatic/renal dysfunction, n (%) 10 (43.5) 9 (81.8) 0.064 Complicated pulmonary infection, n (%) 12 (52.2) 10 (90.9) 0.149 Stroke, n (%) 14 (60.9) 6 (54.5) 0.273 Vasopressor_use, n (%) 3 (13.0) 2 (18.2) 0.152 Antihypertensive_short, n (%) 19 (82.6) 11 (100.0) 0.141 Abbreviations: SD, standard deviation. P values by chi-square, Fisher’s exact, Mann-Whitney U test, or Student’s t-test as appropriate. Table 2. Comparison of NMMD Median Deviation Between Patients Grouped by Outcomes Time Window OR (All records, n=34) 95% CI P value 24h NMMD 0.729 0.557-0.954 0.021 48h NMMD 0.852 0.736-0.987 0.033 72h NMMD 0.922 0.839-1.014 0.093 Abbreviations: NMMD = NIRS MAP Median Deviation; OR = odds ratio; CI = confidence interval. Odds ratios are from multivariate binary logistic regression models adjusted for demographic characteristics, underlying diseases, comorbidities, coagulation function and stroke. Table 3. Comparison of NMMD Median Deviation Among Patients with NMMD Below the NMM Threshold Grouped by Outcomes Time Window OR (All records, n=34) 95% CI P value 24h NMMD 0.776 0.612-0.985 0.037 48h NMMD 0.892 0.780-1.020 0.096 72h NMMD 0.920 0.829-1.021 0.115 Abbreviations: NMMD = NIRS MAP Median Deviation; NMM = NIRS MAP Median; OR = odds ratio; CI = confidence interval. Odds ratios are from multivariate binary logistic regression models adjusted for demographic characteristics, underlying diseases, comorbidities, coagulation function and stroke. Table 4. Comparison of tNMMD Median Deviation Between Patients Grouped by Outcomes Time Window OR (All records, n=34) 95% CI P value 24h tNMMD 0.766 0.592-0.989 0.041 48h tNMMD 0.887 0.773-1.019 0.090 72h tNMMD 0.928 0.828-1.039 0.195 Abbreviations: tNMMD = time-weighted NIRS MAP Median Deviation; OR = odds ratio; CI = confidence interval. Odds ratios are from multivariate binary logistic regression models adjusted for demographic characteristics, underlying diseases, comorbidities, coagulation function and stroke. Discriminative Performance Box plots and density plots confirmed that NMMD was significantly higher in patients with favorable outcomes compared with those with unfavorable outcomes (all p < 0.05; Figures 1 and 2). ROC analysis demonstrated that 24h NMMD had the optimal predictive performance (AUC=0.84, Figure 3), with a cut-off value of 10.48, sensitivity of 0.91, and specificity of 0.70. The AUC values for 48h and 72h NMMD were 0.80 and 0.73, respectively. Discussion This study, using NIRS to assess CA function, is the first to confirm an independent association between personalized CA-derived MAP Median Deviation (NMMD) and neurological prognosis in adult NICU patients with mixed etiologies. Notably, NMMD within the 24h window showed the best predictive value, and this association remained stable even when MAP deviated toward the lower limit of autoregulation (LLA). These core findings fill the evidence gap in CA-guided blood pressure management for adult NICU patients, providing key support for transitioning from traditional "fixed blood pressure thresholds" to personalized hemodynamic management. Clinical Context and Challenges of CA-Guided Blood Pressure Management CA impairment is a common pathological feature of NICU patients, meaning that traditional fixed blood pressure guidelines based on population data cannot adapt to individual differences in CA thresholds [3-5] . Previous studies have mostly focused on children or preterm infants [4,9,12] , confirming that CA dysfunction is closely associated with adverse outcomes such as intracranial hemorrhage and death [19,20] . However, evidence for personalized blood pressure management in adult NICU patients is relatively scarce, leading to clinical dilemmas of "overcorrection" or "hypoperfusion" [5] . Fixed blood pressure targets fail to address such heterogeneous needs. This study demonstrated that moderate MAP deviation from fixed thresholds is not absolutely harmful; instead, it may exert a protective effect by aligning with individual CA reserve [10] . This is consistent with the core conclusion in adult critical care that "CA-guided blood pressure management is superior to fixed thresholds" [5,15] . A study on frail older adults showed that intensive antihypertensive treatment based on personalized assessment and monitoring did not reduce cerebral blood flow or cause orthostatic hypotension, and CA function remained normal [21] , further confirming the safety and effectiveness of blood pressure control in CA-guided management of adult critically ill patients. Personalized CA Assessment via NIRS Monitoring NIRS technology provided solid technical support for the study results: Klop et al. confirmed its ability to monitor the dynamic association between blood pressure and cerebral oxygenation during postural changes [11] ; although Thudium et al. found differences among various CA assessment indices, all were effective in identifying CA failure [14] ; applications in cardiac surgery and other scenarios have also verified that NIRS-based CA assessment can guide personalized blood pressure management and reduce the risk of postoperative organ damage [22] . The significant correlation of NMMD in this study further validates the reliability of personalized CA assessment-derived indices under NIRS monitoring, indicating that NIRS can capture the dynamic association between cerebral oxygenation and blood pressure in adult critically ill patients, laying the foundation for CA assessment in adult NICU patients. Personalized NMM is determined based on the patient's real-time CA status. Moderate MAP deviation can avoid cerebral perfusion "overshoot" or "deficit" caused by traditional fixed targets, maintaining basic perfusion through residual CA function (e.g., arteriolar dilation) [4,10] . Moreover, LLA is not a fixed point where cerebrovascular dilation capacity is exhausted but a transitional region where resistance can still be adjusted slowly [17] , which may explain why moderate MAP deviation toward LLA can still maintain stable cerebral perfusion through residual CA function, consistent with the study's finding that "24h NMMD toward LLA is associated with reduced risk of adverse outcomes". Based on the CA-derived NMMD index assessed via NIRS, this study addresses a critical research gap in adult NICU patients with heterogeneous etiologies. Furthermore, it confirms that the 24-hour period represents the most vulnerable yet highly plastic stage of CA function in this patient cohort, with NMMD during this window demonstrating the highest predictive utility. This highlights that early assessment of CA function—especially within the initial 24 hours—is of paramount importance for adult NICU patients. Optimization of cerebral perfusion during this critical timeframe effectively mitigates secondary brain injury, thereby expanding the scope of CA-guided management to encompass not only static threshold-based targets but also the cumulative impact of dynamic hemodynamic fluctuations [23] . Clinical Implications and Paradigm Shift In summary, this study contributes to the paradigm shift in NICU blood pressure management from "fixed threshold achievement" to "CA-guided personalized regulation", with key clinical implications as follows: - 24h Critical Window: Early acquisition of NMMD via NIRS monitoring can quickly identify CA function status, providing a basis for targeted blood pressure adjustment. This avoids overemphasis on fixed blood pressure values, highlighting the universal importance of early CA assessment in adult neurocritical patients. - Routine Hemodynamic Monitoring: Continuous hemodynamic monitoring is expected to become a routine part of NICU care, particularly for patients with fragile CA function. Multimodal neuromonitoring (including NIRS) is the future direction of personalized NICU care [24] , and the study results provide direct evidence for its application in adult NICU settings. - Clinical Value of "Moderate Deviation": Blood pressure management in adult NICU patients should adopt a dynamic adjustment concept, with maintaining CA function compensation as the core goal. This is consistent with previous safe practice experiences of personalized antihypertensive treatment in older adults [21] . Limitations Several limitations should be considered when interpreting the study results. First, the prospective design limits the ability to establish causal relationships and may introduce selection bias. Second, the relatively small sample size due to single-center recruitment may restrict the generalizability of results and reduce the statistical power of subgroup analyses. Third, individual differences in blood pressure measurement frequency and NIRS monitoring duration may affect the accuracy and reliability of NMMD calculations, potentially introducing measurement bias in longitudinal assessments. Fourth, the analysis focused solely on MAP deviation, without further distinguishing the differential effects of systolic and diastolic blood pressure. Other limitations include the lack of a standardized blood pressure management protocol among patients and the failure to consider potential confounding factors such as medication adherence, comorbidity severity, and procedural differences. Finally, the single-center design may limit external validity across different healthcare systems and patient populations. Future studies with larger sample sizes, standardized protocols, and multicenter randomized controlled designs are needed to validate these findings, clarify the optimal MAP deviation range and blood pressure control targets for different disease subtypes, and develop standardized management protocols to optimize prognosis. Conclusion Our prospective cohort study demonstrates that a larger NMMD during hospitalization, especially within the first 24h, is independently associated with a reduced risk of unfavorable neurological outcomes in NICU patients. This association remains stable even when MAP deviates toward the LLA, suggesting that a moderate degree of MAP deviation is beneficial for preserving CA function and exerting its compensatory effects. These findings highlight the important value of early CA assessment and personalized blood pressure control for patient prognosis. This study supports the paradigm shift in NICU blood pressure management from traditional threshold achievement to early personalized hemodynamic management combined with NIRS monitoring. Future validation in multicenter large-cohort studies is required to clarify the optimal MAP deviation range for different disease subtypes and develop standardized management protocols to improve neurological prognosis in NICU patients. Abbreviations CA: Cerebral autoregulation; CBF: Cerebral blood flow; NICU: Neurological intensive care unit; MAP: Mean arterial pressure; NIRS: Near-infrared spectroscopy; rSO₂: Cerebral tissue oxygen saturation; COx: Cerebral oximetry index; MAPₒₚₜ: Optimal mean arterial pressure; NMM: NIRS-MAP reference interval median; NMMD: NIRS-MAP Median Deviation; MM: Mean MAP; LLA: Lower limit of autoregulation; ULA: Upper limit of autoregulation; mRS: Modified Rankin Scale; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; PI: Pulse index; TOI: Tissue oxygen index; HbT: Total hemoglobin concentration; CPP: Cerebral perfusion pressure; CVR: Cerebrovascular resistance; eGFR: Estimated glomerular filtration rate; NYHA: New York Heart Association; OR: Odds ratio; CI: Confidence interval; AUC: Area under the curve; ROC: Receiver operating characteristic; SE_COR: Standard error of correlation coefficient; tNMMD: Time-weighted NIRS MAP Median Deviation. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of the Affiliated Guangdong Second Provincial General Hospital of Jinan University (ethics approval number: 2025-KY-KZ-285-03). Due to the observational nature of the study, the requirement for informed consent was waived. Consent for publication All study data have been de-identified to protect patient privacy, and the ethical approval for this study includes permission for the publication of de-identified study results. No individual patient identifiers are presented in the manuscript, so explicit separate consent for publication is not required. Availability of data and materials The data supporting the findings of this study are available from the corresponding authors (Aiwu Zhang and Tao Jiang) upon reasonable request. Competing Interests None declared. Funding This research was supported by the In-hospital Clinical Research Program of Guangdong Second Provincial General Hospital (Grant No. LCYJ-2025006) and the Guangdong Provincial Administration of Traditional Chinese Medicine Research Project (Grant No. 202510110). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions X.J. (Xichi Jiang) drafted the manuscript, designed the study, and led data analysis. L.L. (Ling Li) and S.W. (Sibo Wen) collected clinical data and verified patient eligibility. T.R. (Tengzhu Ren) and Y.W. (Yuzhe Wang) assisted with NIRS data processing and statistical analysis. X.L. (Xianglian Liao) and Z.J. (Zhikun Jia) prepared Figures 1-3 and Tables 1-4. T.J. (Tao Jiang) and A.Z. (Aiwu Zhang) (corresponding authors) supervised the study, provided critical revisions, and approved the final version. All authors reviewed the manuscript. Acknowledgements Not applicable. 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Physiologic Determinants of Near-Infrared Spectroscopy-Derived Cerebral and Tissue Oxygen Saturation Measurements in Critically Ill Patients. Crit Care Explor. 2024 May 10;6(5):e1094. Hoffman SB, Cheng YJ, Magder LS, Shet N, Viscardi RM. Cerebral autoregulation in premature infants during the first 96 hours of life and relationship to adverse outcomes. Arch Dis Child Fetal Neonatal Ed. 2019 Sep;104(5):F473-F479. da Costa CS, Czosnyka M, Smielewski P, Austin T. Optimal Mean Arterial Blood Pressure in Extremely Preterm Infants within the First 24 Hours of Life. J Pediatr. 2018 Dec;203:242-248. Weijs RWJ, de Roos BM, Thijssen DHJ, Claassen JAHR. Intensive antihypertensive treatment does not lower cerebral blood flow or cause orthostatic hypotension in frail older adults. Geroscience. 2024 Oct;46(5):4635-4646. Hori D, Hogue C, Adachi H, Max L, Price J, Sciortino C, Zehr K, Conte J, Cameron D, Mandal K. Perioperative optimal blood pressure as determined by ultrasound tagged near infrared spectroscopy and its association with postoperative acute kidney injury in cardiac surgery patients. Interact Cardiovasc Thorac Surg. 2016 Apr;22(4):445-51. Shah VA, Humayun M, Radzik B, Healy R, Palmisano C, Anderson-White M, Calvillo E, Geocadin R, Brown C 4th, Hogue C, Ziai W, Cho SM, Suarez JI, Rivera-Lara L. Early Relative Hypotension Below Noninvasive Cerebral Oximetry-Derived Optimal Blood Pressure Thresholds in Aneurysmal Subarachnoid Hemorrhage: A Pilot Study. Crit Care Med. 2025 Nov 1;53(11):e2323-e2330. Schettler KF. Neuromonitoring in neonatal intensive care units-an important need towards individualized neuroprotective care. Eur J Pediatr. 2024 Sep;183(9):3647-3653. Additional Declarations No competing interests reported. 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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-8684150","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587791829,"identity":"b2fb447b-0e3f-402a-9f13-585b51cd3f33","order_by":0,"name":"Xichi Jiang","email":"","orcid":"","institution":"Third Affiliated Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xichi","middleName":"","lastName":"Jiang","suffix":""},{"id":587791831,"identity":"691387d7-cdb3-4452-b723-0c8b6b0f90df","order_by":1,"name":"Ling Li","email":"","orcid":"","institution":"The Affiliated Guangdong Second Provincial General Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Li","suffix":""},{"id":587791832,"identity":"11287e25-8b53-4c4a-a852-63e5651c29d2","order_by":2,"name":"Sibo Wen","email":"","orcid":"","institution":"Third Affiliated Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sibo","middleName":"","lastName":"Wen","suffix":""},{"id":587791833,"identity":"9f363c6b-d4ac-458c-b54c-1852f4b8a7b9","order_by":3,"name":"Tengzhu Ren","email":"","orcid":"","institution":"The Affiliated Guangdong Second Provincial General Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Tengzhu","middleName":"","lastName":"Ren","suffix":""},{"id":587791834,"identity":"5ddc2d1b-0e9e-46f5-b54d-8964af60fe82","order_by":4,"name":"Yuzhe Wang","email":"","orcid":"","institution":"Third Affiliated Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuzhe","middleName":"","lastName":"Wang","suffix":""},{"id":587791835,"identity":"16d150ca-417b-41cc-b956-c158a46a2c03","order_by":5,"name":"Xianglian Liao","email":"","orcid":"","institution":"The Affiliated Guangdong Second Provincial General Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Xianglian","middleName":"","lastName":"Liao","suffix":""},{"id":587791836,"identity":"d92bc807-c8c5-42f9-acaa-264e7d9ae1df","order_by":6,"name":"Zhikun Jia","email":"","orcid":"","institution":"The Affiliated Guangdong Second Provincial General Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Zhikun","middleName":"","lastName":"Jia","suffix":""},{"id":587791837,"identity":"9393578c-8931-4c29-a9ee-ae951de56586","order_by":7,"name":"Tao Jiang","email":"","orcid":"","institution":"Third Affiliated Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Jiang","suffix":""},{"id":587791838,"identity":"2dfbd08a-c36e-46a3-a21e-ea89403dbb79","order_by":8,"name":"Aiwu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBACNvbmww8kDCTk+InWwsdzLM3AosLGWLKBWC1yEjkGEhVn0hI3HCDaYTzHEgxuth02Nj6evIHhR8U2IrSwNx94OLPtsJzZmWcFjD1nbhNni7Ek0BazGzkGzIxtxGgB+kX6b9vhxM0zSNEiIQHyvgTRWkCBLAEMZAmgXw4S5Rf5dlhUtidvfPCjgggtSCDB4ABJ6sFaSNUxCkbBKBgFIwQAALC9Pri27+ZPAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Guangdong Second Provincial General Hospital of Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Aiwu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-01-24 06:08:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8684150/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8684150/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102381801,"identity":"36def65e-105e-4581-be36-be87573fe325","added_by":"auto","created_at":"2026-02-11 06:56:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox plots of NMMD Metrics Comparison Between Patients Grouped by Composite Outcomes.\u003c/strong\u003eEach panel shows the distribution of a single metric—24-hour NMMD, 48-hour NMMD, and 72-hour NMMD—for patients with good (green) and poor (orange) outcomes.Data points represent individual patients; boxes span the interquartile range with median lines, and whiskers extend to the 10th and 90th percentiles. P values from Mann–Whitney U tests (with Benjamini-Hochberg correction) are annotated above each panel. All three metrics are lower in the poor‐outcome group (24h NMMD: p=0.003; 48h NMMD: p=0.006; 72h NMMD: p=0.034), indicating reduced NMMD levels among patients who experienced adverse outcomes.\u003c/p\u003e\n\u003cp\u003eAbbreviations: NMMD, NIRS MAP Median Deviation.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8684150/v1/53f57d5c5ea89ed391b804fe.png"},{"id":102381819,"identity":"9f329966-38cf-4525-8681-847708524598","added_by":"auto","created_at":"2026-02-11 06:56:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDensity plots of Three NMMD Metrics Distribution Between Patients Grouped by Composite Outcomes.\u003c/strong\u003eEach panel shows the distribution of a single metric—24-hour NMMD, 48-hour NMMD, and 72-hour NMMD—for patients with good (green) and poor (red) outcomes. For each metric, median and interquartile range (IQR) are as follows: 24h NMMD: Poor group (median: 4.43, IQR: 1.62–7.19, n=11); Good group (median: 15.48, IQR: 7.95–26.81, n=23); 48h NMMD: Poor group (median: 6.03, IQR: 0.91–9.08, n=11); Good group (median: 14.95, IQR: 8.31–24.71, n=23); 72h NMMD: Poor group (median: 7.67, IQR: 2.64–9.54, n=11); Good group (median: 14.18, IQR: 7.76–24.83, n=23). P values from Mann–Whitney U tests are annotated above each panel (24h NMMD: p \u0026lt; 0.001; 48h NMMD: p=0.004; 72h NMMD: p=0.034), indicating the poor outcomes group has significantly lower NMMD levels across all three metrics compared to the good outcomes group.\u003c/p\u003e\n\u003cp\u003eAbbreviations: NMMD, NIRS MAP Median Deviation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8684150/v1/fee4ce15aa7e488d24914bf7.png"},{"id":102381902,"identity":"700fc65f-e4ac-42e0-9773-9e9da428d3ed","added_by":"auto","created_at":"2026-02-11 06:56:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":229091,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves and predictive performance of NMMD metrics for predicting adverse composite outcomes.\u003c/strong\u003eLeft panel: ROC curves for 24h NMMD (red), 48h NMMD (blue), and 72h NMMD (green). Triangles mark optimal cut-off points identified via the Youden index. Right panel: Bar chart showing the area under the ROC curve (AUC) for each metric, with corresponding optimal cut-off values, sensitivity (Sens), and specificity (Spec): 24h NMMD (AUC: 0.84, Cut-off: 10.48, Sens: 0.91, Spec: 0.70); 48h NMMD (AUC: 0.80, Cut-off: 9.49, Sens: 0.82, Spec: 0.74); 72h NMMD (AUC: 0.73, Cut-off: 10.01, Sens: 0.82, Spec: 0.70).\u003c/p\u003e\n\u003cp\u003eAbbreviations: AUC = area under the curve; NMMD, NIRS MAP Median Deviation; Sens = Sensitivity; Spec = Specificity\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8684150/v1/efa45f886e17c323b483e158.png"},{"id":104781365,"identity":"656e5eae-dc9a-46c6-9a12-5b1fdd976f80","added_by":"auto","created_at":"2026-03-17 07:55:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1342298,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8684150/v1/1352bb4a-0d3f-40dc-9084-f5d59eea9d18.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Cerebral Autoregulation-Derived Mean Arterial Pressure Deviation and Neurological Prognosis in NICU Patients: A Prospective, Exploratory, Observational Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCerebral autoregulation (CA) is the core physiological mechanism maintaining CBF homeostasis by dynamically regulating cerebrovascular resistance (CVR) to offset the impact of cerebral perfusion pressure (CPP) fluctuations on cerebral oxygen delivery \u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Patients in the neurological intensive care unit (NICU) often experience varying degrees of CA impairment due to primary diseases, systemic inflammatory responses, or secondary injuries \u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. This impairment makes population-based standardized blood pressure guidelines inadequate for meeting individual needs, as inter-individual differences in CA thresholds and the relative changes of mean arterial pressure (MAP) against these thresholds lead to heterogeneous effects on prognosis \u003csup\u003e[\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The \"fixed blood pressure threshold\" approach thus fails to cover the actual effective range of individual CA, limiting the ability of CA to compensate for perfusion fluctuations and prevent the progression of cerebral tissue damage.\u003c/p\u003e \u003cp\u003eNear-infrared spectroscopy (NIRS) provides robust support for personalized CA assessment. It has been validated to effectively reflect the dynamic association between cerebral oxygenation and blood pressure, serving as a reliable tool for non-invasive CA monitoring \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown that early CA impairment assessed by NIRS is significantly associated with death or severe neuroimaging abnormalities in preterm infants \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. However, the clinical consistency and superiority of different CA monitoring indices, as well as their synergistic effects with MAP, remain unclear \u003csup\u003e[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Additionally, most prior studies on CA in NICU patients have focused on pediatric populations, with relatively limited evidence for CA-guided blood pressure management in adult NICU patients.\u003c/p\u003e \u003cp\u003eNotably, the complexity of CA function necessitates individualized assessment: the lower limit of autoregulation (LLA) is not a fixed value but a transitional region where cerebrovascular resistance undergoes gradual adjustment \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, further highlighting the limitations of the \"fixed blood pressure threshold\" management model. Moreover, adult NICU patients often present with more complex underlying diseases and fluctuating physiological states, leading to distinct characteristics of CA impairment compared with children \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. There is an urgent need for targeted studies to clarify the association between CA-derived blood pressure parameters and prognosis in this population.\u003c/p\u003e \u003cp\u003eBased on these research gaps, this study aimed to explore the association between NIRS-based CA-derived MAP Median Deviation (NMMD) and neurological prognosis in adult NICU patients during hospitalization, analyzing the effects of different time windows. The findings are expected to provide evidence for optimizing personalized blood pressure management strategies in adult NICU patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center prospective cohort study was conducted in strict accordance with the STROBE guidelines. We performed a retrospective analysis of consecutive patients admitted to the NICU of a neurocenter between June 2025 and October 2025. The study was approved by the Institutional Review Board of the participating center of the Affiliated Guangdong Second Provincial General Hospital of Jinan University (ethics approval number: 2025-KY-KZ-285-03). Due to its observational nature, the requirement for informed consent was waived. Clinical trial number: not applicable. Human Ethics and Consent to Participate declarations: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients admitted to the NICU were managed by experienced neurologists and nursing teams with standardized monitoring. Eligibility criteria were applied to select participants.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Inclusion and Exclusion Criteria\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Inclusion Criteria:\u003c/p\u003e\n\u003cp\u003e- Age \u0026ge; 18 years;\u003c/p\u003e\n\u003cp\u003e- Direct admission to the NICU immediately after hospital admission;\u003c/p\u003e\n\u003cp\u003e- At least 6 blood pressure measurements with an interval of 4 hours;\u003c/p\u003e\n\u003cp\u003e- Continuous NIRS monitoring for at least 1 hour.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Exclusion Criteria:\u003c/p\u003e\n\u003cp\u003e- Baseline modified Rankin Scale (mRS) score \u0026ge; 2 before onset;\u003c/p\u003e\n\u003cp\u003e- End-stage systemic diseases (NYHA class IV heart failure, stage 5 chronic kidney disease [eGFR \u0026lt; 15 mL/min/1.73 m\u0026sup2;], Child-Pugh class C cirrhosis);\u003c/p\u003e\n\u003cp\u003e- Incomplete medical records or loss to follow-up before discharge;\u003c/p\u003e\n\u003cp\u003e- Inability to complete NIRS monitoring or poor signal quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients underwent non-invasive brachial artery blood pressure monitoring and continuous NIRS monitoring. The first blood pressure measurement was recorded within 2 hours after admission to the NICU, and systolic blood pressure (SBP) values were extracted from electronic health records at 4-hour intervals until 72 hours post-admission or if no new values were recorded within 12 hours. Obviously erroneous values (e.g., SBP \u0026lt; 50 mmHg or \u0026gt; 250 mmHg) were excluded by two independent researchers after confirming they were technical artifacts rather than true hemodynamic events. During NIRS monitoring, unnecessary interventions (e.g., routine care that might interfere with data collection) were avoided as much as possible, and relevant indicators from the monitor were collected at 1-second intervals.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Blood Pressure Indicators: Systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and pulse index (PI) were recorded.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;NIRS Indicators: Cerebral tissue oxygen saturation (rSO₂), tissue oxygen index (TOI), and total hemoglobin concentration (HbT) were recorded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Demographic characteristics (age, gender); underlying diseases (hypertension, diabetes); other comorbidities (cardiac, hepatic, or renal dysfunction, pulmonary infection); coagulation function (platelet count, D-dimer); stroke type (ischemic or hemorrhagic); and use of antihypertensive drugs, vasopressors, and previous antiplatelet/anticoagulant medications during hospitalization were documented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Measure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was neurological deterioration, defined as an increase in mRS score at discharge compared with admission, assessed by professional neurologists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCerebral Autoregulation Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on synchronously recorded NIRS data and MAP, three types of CA-derived indices were constructed:\u003c/p\u003e\n\u003cp\u003e- COx: Dynamic Pearson correlation coefficient between MAP and rSO₂;\u003c/p\u003e\n\u003cp\u003e- TOIx: Dynamic Pearson correlation coefficient between MAP and TOI;\u003c/p\u003e\n\u003cp\u003e- THx: Dynamic Pearson correlation coefficient between MAP and HbT.\u003c/p\u003e\n\u003cp\u003eA sliding window method was used to calculate dynamic correlation coefficients:\u003c/p\u003e\n\u003cp\u003e- window size was set to 30 seconds (1 Hz sampling frequency, containing 30 data points) with a step size of 1 second;\u003c/p\u003e\n\u003cp\u003e- correlation coefficients (COR) between MAP and target NIRS indicators were calculated for each window. Invalid data with no variation or missing values in the window were excluded; COR, mean MAP within the window, and start time of valid windows were retained for subsequent analysis.\u003c/p\u003e\n\u003cp\u003eA quadratic polynomial fitting was used to construct the U-shaped relationship model between COR and MAP, determining personalized CA parameters:\u003c/p\u003e\n\u003cp\u003e- MAP means were binned at 5 mmHg intervals, and the mean COR and standard error (SE_COR) for each bin were calculated. A quadratic fitting model \\(y = ax\u0026sup2; + bx + c\\) was constructed with the midpoint of each bin as the independent variable and the mean COR as the dependent variable;\u003c/p\u003e\n\u003cp\u003e- A COR value of 0.3 was defined as the threshold for CA impairment. Two real roots of the equation \\(ax\u0026sup2; + bx + (c - 0.3) = 0\\) were solved, which were designated as the lower limit of autoregulation (LLA) and upper limit of autoregulation (ULA), respectively. Only data with \u0026ge;4 bins were used for fitting; LLA/ULA was marked as missing if no real roots existed.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Calculation of Core Derived Indices and Time Burden Parameters:\u003c/p\u003e\n\u003cp\u003e- NMM (NIRS MAP Median): Median of the MAP reference interval derived from NIRS monitoring;\u003c/p\u003e\n\u003cp\u003e- MM (mean MAP): Mean MAP within 24h, 48h, or 72h;\u003c/p\u003e\n\u003cp\u003e- NMMD (NIRS MAP Median Deviation): Absolute difference between NMM and MM, used as the indicator of MAP deviation;\u003c/p\u003e\n\u003cp\u003e- tNMMD (time-weighted NIRS MAP Median Deviation): Product of the time proportion of MAP measurements below NMM in each time window (24h/48h/72h) and NMMD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were expressed as mean \u0026plusmn; standard deviation or median (interquartile range) based on normality test results. Categorical variables were presented as frequencies and percentages. Intergroup comparisons were performed using Student\u0026apos;s t-test or Mann-Whitney U test for continuous variables, and chi-square test or Fisher\u0026apos;s exact test for categorical variables.\u003c/p\u003e\n\u003cp\u003eConsidering variations in the number of blood pressure measurements and length of hospital stay among patients, three time windows (24h, 48h, 72h) were set to ensure statistical robustness. Multivariate binary logistic regression models (backward LR) were constructed for each window, and final adjustments were made for variables with p\u0026lt;0.20 (or considered to be important factors influencing the outcomes) in univariate analysis to ensure potential confounders were included.\u003c/p\u003e\n\u003cp\u003eAdditionally, box plots, density plots, and ROC curves were generated for the three time windows to visually demonstrate statistical differences in various blood pressure variability indicators between the unfavorable and favorable outcome groups.\u003c/p\u003e\n\u003cp\u003eStatistical significance was set at p \u0026lt; 0.05. All statistical analyses were performed using IBM SPSS Statistics 27.0.1 and R software version 4.5.1.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient Characteristics and Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 34 enrolled patients, 23 (67.6%) achieved favorable outcomes, and 11 (32.4%) experienced unfavorable outcomes. Baseline characteristics were balanced between the two groups, with no significant differences in gender, underlying diseases, comorbidities, or stroke type. Significant differences were only observed in age and coagulation function (platelet count and D-dimer) in the unfavorable outcome group (Table 1). Only 4 patients did not use short-acting antihypertensive drugs, and 5 patients used vasopressors, indicating minimal impact of medication use on statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between Derived Indices and Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression analysis showed that after adjusting for demographic characteristics, comorbidities, sedation status, and stroke type, NMMD at 24h and 48h was significantly associated with unfavorable neurological outcomes, with OR ranging from 0.729 to 0.852 (p \u0026le; 0.033, Table 2).\u003c/p\u003e\n\u003cp\u003eGiven the high proportion of MAP measurements below NMM in all time windows (43%-100%, mean 87%), additional analyses were performed to explore the clinical value of NMMD under the condition of MM \u0026lt; NMM. Among the three time windows, only 24h NMMD reached statistical significance (p \u0026lt; 0.05, Table 3). Similarly, for the time burden parameter tNMMD under MM \u0026lt; NMM, only the 24h window showed significant association with unfavorable outcomes (p \u0026lt; 0.05, Table 4).\u003c/p\u003e\n\u003cp\u003eCollectively, these results indicate that a larger NMMD during hospitalization, especially within the first 24h, is stably associated with a reduced risk of unfavorable neurological outcomes in NICU patients, even when MAP deviates toward the LLA.\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline Characteristics of Patients by Outcome Group\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFavorable Outcome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnfavorable Outcome (n=11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eMale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e12 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e4 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eFemale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e11 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e7 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eAge, years (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e62.48 \u0026plusmn; 14.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e70.36 \u0026plusmn; 12.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eUnderlying diseases, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e12 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e9 (81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003ePlatelet count, 10\u003csup\u003e9\u003c/sup\u003e/L (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e256.87 \u0026plusmn; 47.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e231.00 \u0026plusmn; 114.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eD-dimer, mg/L (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e4.89 \u0026plusmn; 9.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e5.14 \u0026plusmn; 4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eCardiac/hepatic/renal dysfunction, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e10 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e9 (81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eComplicated pulmonary infection, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e12 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e10 (90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eStroke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e14 (60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e6 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eVasopressor_use, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e3 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e2 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eAntihypertensive_short, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e19 (82.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e11 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: SD, standard deviation. P values by chi-square, Fisher\u0026rsquo;s exact, Mann-Whitney U test, or Student\u0026rsquo;s t-test as appropriate.\u003c/p\u003e\n\u003cp\u003eTable 2. Comparison of NMMD Median Deviation Between Patients Grouped by \u0026nbsp;Outcomes\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime Window\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (All records, n=34)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e24h NMMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.557-0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e48h NMMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.736-0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e72h NMMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.839-1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: NMMD = NIRS MAP Median Deviation; OR = odds ratio; CI = confidence interval. Odds ratios are from multivariate binary logistic regression models adjusted for demographic characteristics, underlying diseases, comorbidities, coagulation function and stroke.\u003c/p\u003e\n\u003cp\u003eTable 3. Comparison of NMMD Median Deviation Among Patients with NMMD Below the NMM Threshold Grouped by Outcomes\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime Window\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (All records, n=34)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e24h NMMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.612-0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e48h NMMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.780-1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e72h NMMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.829-1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: NMMD = NIRS MAP Median Deviation; NMM = NIRS MAP Median; OR = odds ratio; CI = confidence interval. Odds ratios are from multivariate binary logistic regression models adjusted for demographic characteristics, underlying diseases, comorbidities, coagulation function and stroke.\u003c/p\u003e\n\u003cp\u003eTable 4. Comparison of tNMMD Median Deviation Between Patients Grouped by \u0026nbsp;Outcomes\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime Window\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (All records, n=34)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e24h tNMMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.592-0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e48h tNMMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.773-1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e72h tNMMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.828-1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: tNMMD = time-weighted NIRS MAP Median Deviation; OR = odds ratio; CI = confidence interval. Odds ratios are from multivariate binary logistic regression models adjusted for demographic characteristics, underlying diseases, comorbidities, coagulation function and stroke.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscriminative Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBox plots and density plots confirmed that NMMD was significantly higher in patients with favorable outcomes compared with those with unfavorable outcomes (all p \u0026lt; 0.05; Figures 1 and 2). ROC analysis demonstrated that 24h NMMD had the optimal predictive performance (AUC=0.84, Figure 3), with a cut-off value of 10.48, sensitivity of 0.91, and specificity of 0.70. The AUC values for 48h and 72h NMMD were 0.80 and 0.73, respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study, using NIRS to assess CA function, is the first to confirm an independent association between personalized CA-derived MAP Median Deviation (NMMD) and neurological prognosis in adult NICU patients with mixed etiologies. Notably, NMMD within the 24h window showed the best predictive value, and this association remained stable even when MAP deviated toward the lower limit of autoregulation (LLA). These core findings fill the evidence gap in CA-guided blood pressure management for adult NICU patients, providing key support for transitioning from traditional \u0026quot;fixed blood pressure thresholds\u0026quot; to personalized hemodynamic management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Context and Challenges of CA-Guided Blood Pressure Management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCA impairment is a common pathological feature of NICU patients, meaning that traditional fixed blood pressure guidelines based on population data cannot adapt to individual differences in CA thresholds \u003csup\u003e[3-5]\u003c/sup\u003e. Previous studies have mostly focused on children or preterm infants\u003csup\u003e\u0026nbsp;[4,9,12]\u003c/sup\u003e, confirming that CA dysfunction is closely associated with adverse outcomes such as intracranial hemorrhage and death\u003csup\u003e\u0026nbsp;[19,20]\u003c/sup\u003e. However, evidence for personalized blood pressure management in adult NICU patients is relatively scarce, leading to clinical dilemmas of \u0026quot;overcorrection\u0026quot; or \u0026quot;hypoperfusion\u0026quot; \u003csup\u003e[5]\u003c/sup\u003e. Fixed blood pressure targets fail to address such heterogeneous needs.\u003c/p\u003e\n\u003cp\u003eThis study demonstrated that moderate MAP deviation from fixed thresholds is not absolutely harmful; instead, it may exert a protective effect by aligning with individual CA reserve\u003csup\u003e\u0026nbsp;[10]\u003c/sup\u003e. This is consistent with the core conclusion in adult critical care that \u0026quot;CA-guided blood pressure management is superior to fixed thresholds\u0026quot; \u003csup\u003e[5,15]\u003c/sup\u003e. A study on frail older adults showed that intensive antihypertensive treatment based on personalized assessment and monitoring did not reduce cerebral blood flow or cause orthostatic hypotension, and CA function remained normal\u003csup\u003e\u0026nbsp;[21]\u003c/sup\u003e, further confirming the safety and effectiveness of blood pressure control in CA-guided management of adult critically ill patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePersonalized CA Assessment via NIRS Monitoring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNIRS technology provided solid technical support for the study results: Klop et al. confirmed its ability to monitor the dynamic association between blood pressure and cerebral oxygenation during postural changes\u003csup\u003e\u0026nbsp;[11]\u003c/sup\u003e; although Thudium et al. found differences among various CA assessment indices, all were effective in identifying CA failure\u003csup\u003e\u0026nbsp;[14]\u003c/sup\u003e; applications in cardiac surgery and other scenarios have also verified that NIRS-based CA assessment can guide personalized blood pressure management and reduce the risk of postoperative organ damage\u003csup\u003e\u0026nbsp;[22]\u003c/sup\u003e. The significant correlation of NMMD in this study further validates the reliability of personalized CA assessment-derived indices under NIRS monitoring, indicating that NIRS can capture the dynamic association between cerebral oxygenation and blood pressure in adult critically ill patients, laying the foundation for CA assessment in adult NICU patients.\u003c/p\u003e\n\u003cp\u003ePersonalized NMM is determined based on the patient\u0026apos;s real-time CA status. Moderate MAP deviation can avoid cerebral perfusion \u0026quot;overshoot\u0026quot; or \u0026quot;deficit\u0026quot; caused by traditional fixed targets, maintaining basic perfusion through residual CA function (e.g., arteriolar dilation)\u003csup\u003e\u0026nbsp;[4,10]\u003c/sup\u003e. Moreover, LLA is not a fixed point where cerebrovascular dilation capacity is exhausted but a transitional region where resistance can still be adjusted slowly\u003csup\u003e\u0026nbsp;[17]\u003c/sup\u003e, which may explain why moderate MAP deviation toward LLA can still maintain stable cerebral perfusion through residual CA function, consistent with the study\u0026apos;s finding that \u0026quot;24h NMMD toward LLA is associated with reduced risk of adverse outcomes\u0026quot;.\u003c/p\u003e\n\u003cp\u003eBased on the CA-derived NMMD index assessed via NIRS, this study addresses a critical research gap in adult NICU patients with heterogeneous etiologies. Furthermore, it confirms that the 24-hour period represents the most vulnerable yet highly plastic stage of CA function in this patient cohort, with NMMD during this window demonstrating the highest predictive utility. This highlights that early assessment of CA function\u0026mdash;especially within the initial 24 hours\u0026mdash;is of paramount importance for adult NICU patients. Optimization of cerebral perfusion during this critical timeframe effectively mitigates secondary brain injury, thereby expanding the scope of CA-guided management to encompass not only static threshold-based targets but also the cumulative impact of dynamic hemodynamic fluctuations\u003csup\u003e\u0026nbsp;[23]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Implications and Paradigm Shift\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn summary, this study contributes to the paradigm shift in NICU blood pressure management from \u0026quot;fixed threshold achievement\u0026quot; to \u0026quot;CA-guided personalized regulation\u0026quot;, with key clinical implications as follows:\u003c/p\u003e\n\u003cp\u003e- 24h Critical Window: Early acquisition of NMMD via NIRS monitoring can quickly identify CA function status, providing a basis for targeted blood pressure adjustment. This avoids overemphasis on fixed blood pressure values, highlighting the universal importance of early CA assessment in adult neurocritical patients.\u003c/p\u003e\n\u003cp\u003e- Routine Hemodynamic Monitoring: Continuous hemodynamic monitoring is expected to become a routine part of NICU care, particularly for patients with fragile CA function. Multimodal neuromonitoring (including NIRS) is the future direction of personalized NICU care\u003csup\u003e\u0026nbsp;[24]\u003c/sup\u003e, and the study results provide direct evidence for its application in adult NICU settings.\u003c/p\u003e\n\u003cp\u003e- Clinical Value of \u0026quot;Moderate Deviation\u0026quot;: Blood pressure management in adult NICU patients should adopt a dynamic adjustment concept, with maintaining CA function compensation as the core goal. This is consistent with previous safe practice experiences of personalized antihypertensive treatment in older adults\u003csup\u003e\u0026nbsp;[21]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered when interpreting the study results. First, the prospective design limits the ability to establish causal relationships and may introduce selection bias. Second, the relatively small sample size due to single-center recruitment may restrict the generalizability of results and reduce the statistical power of subgroup analyses. Third, individual differences in blood pressure measurement frequency and NIRS monitoring duration may affect the accuracy and reliability of NMMD calculations, potentially introducing measurement bias in longitudinal assessments. Fourth, the analysis focused solely on MAP deviation, without further distinguishing the differential effects of systolic and diastolic blood pressure.\u003c/p\u003e\n\u003cp\u003eOther limitations include the lack of a standardized blood pressure management protocol among patients and the failure to consider potential confounding factors such as medication adherence, comorbidity severity, and procedural differences. Finally, the single-center design may limit external validity across different healthcare systems and patient populations. Future studies with larger sample sizes, standardized protocols, and multicenter randomized controlled designs are needed to validate these findings, clarify the optimal MAP deviation range and blood pressure control targets for different disease subtypes, and develop standardized management protocols to optimize prognosis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur prospective cohort study demonstrates that a larger NMMD during hospitalization, especially within the first 24h, is independently associated with a reduced risk of unfavorable neurological outcomes in NICU patients. This association remains stable even when MAP deviates toward the LLA, suggesting that a moderate degree of MAP deviation is beneficial for preserving CA function and exerting its compensatory effects. These findings highlight the important value of early CA assessment and personalized blood pressure control for patient prognosis.\u003c/p\u003e\n\u003cp\u003eThis study supports the paradigm shift in NICU blood pressure management from traditional threshold achievement to early personalized hemodynamic management combined with NIRS monitoring. Future validation in multicenter large-cohort studies is required to clarify the optimal MAP deviation range for different disease subtypes and develop standardized management protocols to improve neurological prognosis in NICU patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCA: Cerebral autoregulation; CBF: Cerebral blood flow; NICU: Neurological intensive care unit; MAP: Mean arterial pressure; NIRS: Near-infrared spectroscopy; rSO₂: Cerebral tissue oxygen saturation; COx: Cerebral oximetry index; MAPₒₚₜ: Optimal mean arterial pressure; NMM: NIRS-MAP reference interval median; NMMD: NIRS-MAP Median Deviation; MM: Mean MAP; LLA: Lower limit of autoregulation; ULA: Upper limit of autoregulation; mRS: Modified Rankin Scale; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; PI: Pulse index; TOI: Tissue oxygen index; HbT: Total hemoglobin concentration; CPP: Cerebral perfusion pressure; CVR: Cerebrovascular resistance; eGFR: Estimated glomerular filtration rate; NYHA: New York Heart Association; OR: Odds ratio; CI: Confidence interval; AUC: Area under the curve; ROC: Receiver operating characteristic; SE_COR: Standard error of correlation coefficient; tNMMD: Time-weighted NIRS MAP Median Deviation.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of the Affiliated Guangdong Second Provincial General Hospital of Jinan University (ethics approval number: 2025-KY-KZ-285-03). Due to the observational nature of the study, the requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll study data have been de-identified to protect patient privacy, and the ethical approval for this study includes permission for the publication of de-identified study results. No individual patient identifiers are presented in the manuscript, so explicit separate consent for publication is not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding authors (Aiwu Zhang and Tao Jiang) upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the In-hospital Clinical Research Program of Guangdong Second Provincial General Hospital (Grant No. LCYJ-2025006) and the Guangdong Provincial Administration of Traditional Chinese Medicine Research Project (Grant No. 202510110). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.J. (Xichi Jiang) drafted the manuscript, designed the study, and led data analysis. L.L. (Ling Li) and S.W. (Sibo Wen) collected clinical data and verified patient eligibility. T.R. (Tengzhu Ren) and Y.W. (Yuzhe Wang) assisted with NIRS data processing and statistical analysis. X.L. (Xianglian Liao) and Z.J. (Zhikun Jia) prepared Figures 1-3 and Tables 1-4. T.J. (Tao Jiang) and A.Z. (Aiwu Zhang) (corresponding authors) supervised the study, provided critical revisions, and approved the final version. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKlein SP, De Sloovere V, Meyfroidt G, Depreitere B. Differential Hemodynamic Response of Pial Arterioles Contributes to a Quadriphasic Cerebral Autoregulation Physiology. J Am Heart Assoc. 2022 Jan 4;11(1):e022943.\u003c/li\u003e\n \u003cli\u003eAries MJ, Elting JW, De Keyser J, Kremer BP, Vroomen PC. Cerebral autoregulation in stroke: a review of transcranial Doppler studies. Stroke. 2010 Nov;41(11):2697-704.\u003c/li\u003e\n \u003cli\u003eBudohoski KP, Czosnyka M, Kirkpatrick PJ, Smielewski P, Steiner LA, Pickard JD. Clinical relevance of cerebral autoregulation following subarachnoid haemorrhage. Nat Rev Neurol. 2013 Mar;9(3):152-63.\u003c/li\u003e\n \u003cli\u003eThewissen L, Naulaers G, Hendrikx D, Caicedo A, Barrington K, Boylan G, Cheung PY, Corcoran D, El-Khuffash A, Garvey A, Macko J, Marlow N, Miletin J, O\u0026apos;Donnell CPF, O\u0026apos;Toole JM, Straň\u0026aacute;k Z, Van Laere D, Wiedermannova H, Dempsey E. Cerebral oxygen saturation and autoregulation during hypotension in extremely preterm infants. Pediatr Res. 2021 Aug;90(2):373-380.\u003c/li\u003e\n \u003cli\u003eHofmann BB, Donaldson DM, Fischer I, Karadag C, Neyazi M, Piedade GS, Abusabha Y, Muhammad S, Rubbert C, H\u0026auml;nggi D, Beseoglu K. Blood Pressure Affects the Early CT Perfusion Imaging in Patients with aSAH Reflecting Early Disturbed Autoregulation. Neurocrit Care. 2023 Aug;39(1):125-134.\u003c/li\u003e\n \u003cli\u003eO\u0026apos;Leary H, Gregas MC, Limperopoulos C, Zaretskaya I, Bassan H, Soul JS, Di Salvo DN, du Plessis AJ. Elevated cerebral pressure passivity is associated with prematurity-related intracranial hemorrhage. Pediatrics. 2009 Jul;124(1):302-9.\u003c/li\u003e\n \u003cli\u003eKirschen MP, Schneider ALC, Majmudar T, Hsu JY, Burnett R, Douglas R, Sawhney S, Graham K, Agarwal K, Whelan C, Ko T, Morgan RW, Nadkarni VM, Diaz-Arrastia R, Berg RA, Topjian A. Association Between Deviations From Cerebral Autoregulation-Derived Optimal Blood Pressure and Outcome After Pediatric Cardiac Arrest. Neurology. 2025 Sep 9;105(5):e214019.\u003c/li\u003e\n \u003cli\u003eMenyh\u0026aacute;rt \u0026Aacute;, Varga DP, M T\u0026oacute;th O, Makra P, Bari F, Farkas E. Transient Hypoperfusion to Ischemic/Anoxic Spreading Depolarization is Related to Autoregulatory Failure in the Rat Cerebral Cortex. Neurocrit Care. 2022 Jun;37(Suppl 1):112-122.\u003c/li\u003e\n \u003cli\u003eCohen E, Baerts W, Caicedo Dorado A, Naulaers G, van Bel F, Lemmers PMA. Cerebrovascular autoregulation in preterm fetal growth restricted neonates. Arch Dis Child Fetal Neonatal Ed. 2019 Sep;104(5):F467-F472.\u003c/li\u003e\n \u003cli\u003eBusch DR, Baker WB, Mavroudis CD, Ko TS, Lynch JM, McCarthy AL, DuPont-Thibodeau G, Buckley EM, Jacobwitz M, Boorady TW, Mensah-Brown K, Connelly JT, Yodh AG, Kilbaugh TJ, Licht DJ. Noninvasive optical measurement of microvascular cerebral hemodynamics and autoregulation in the neonatal ECMO patient. Pediatr Res. 2020 Dec;88(6):925-933.\u003c/li\u003e\n \u003cli\u003eShali RK, Setarehdan SK, Seifi B. Functional near-infrared spectroscopy based blood pressure variations and hemodynamic activity of brain monitoring following postural changes: A systematic review. Physiol Behav. 2024 Jul 1;281:114574.\u003c/li\u003e\n \u003cli\u003eChock VY, Kwon SH, Ambalavanan N, Batton B, Nelin LD, Chalak LF, Tian L, Van Meurs KP. Cerebral Oxygenation and Autoregulation in Preterm Infants (Early NIRS Study). J Pediatr. 2020 Dec;227:94-100.e1.\u003c/li\u003e\n \u003cli\u003eTiba MH, McCracken BM, Leander DC, Colmenero Mahmood CI, Greer NL, Picton P, Williamson CA, Ward KR. Trans-Ocular Brain Impedance Indices Predict Pressure Reactivity Index Changes in a Porcine Model of Hypotension and Cerebral Autoregulation Perturbation. Neurocrit Care. 2022 Feb;36(1):139-147.\u003c/li\u003e\n \u003cli\u003eThudium M, Moestl S, Hoffmann F, Hoff A, Kornilov E, Heusser K, Tank J, Soehle M. Cerebral blood flow autoregulation assessment by correlation analysis between mean arterial blood pressure and transcranial doppler sonography or near infrared spectroscopy is different: A pilot study. PLoS One. 2023 Jun 22;18(6):e0287578.\u003c/li\u003e\n \u003cli\u003eWongtangman K, Wachtendorf LJ, Blank M, Grabitz SD, Linhardt FC, Azimaraghi O, Raub D, Pham S, Kendale SM, Low YH, Houle TT, Eikermann M, Pollard RJ. Effect of Intraoperative Arterial Hypotension on the Risk of Perioperative Stroke After Noncardiac Surgery: A Retrospective Multicenter Cohort Study. Anesth Analg. 2021 Oct 1;133(4):1000-1008.\u003c/li\u003e\n \u003cli\u003eThamjamrassri T, Watanitanon A, Moore A, Chesnut RM, Vavilala MS, Lele AV. A Pilot Prospective Observational Study of Cerebral Autoregulation and 12-Month Outcomes in Children With Complex Mild Traumatic Brain Injury: The Argument for Sufficiency Conditions Affecting TBI Outcomes. J Neurosurg Anesthesiol. 2022 Oct 1;34(4):384-391.\u003c/li\u003e\n \u003cli\u003eKho E, van den Dool REC, Mahes SS, Corsmit OT, Vlaar APJ, Koolbergen DR, Veelo DP, Sperna Weiland NH, Immink RV. Regulation of cerebrovascular resistance below the lower limit of cerebral autoregulation during induced hypotension: an observational study. Br J Anaesth. 2025 Apr;134(4):1009-1017.\u003c/li\u003e\n \u003cli\u003eCody N, Bradbury I, McMullan RR, Quinn G, O\u0026apos;Neill A, Ward K, McCann J, McAuley DF, Silversides JA. Physiologic Determinants of Near-Infrared Spectroscopy-Derived Cerebral and Tissue Oxygen Saturation Measurements in Critically Ill Patients. Crit Care Explor. 2024 May 10;6(5):e1094.\u003c/li\u003e\n \u003cli\u003eHoffman SB, Cheng YJ, Magder LS, Shet N, Viscardi RM. Cerebral autoregulation in premature infants during the first 96 hours of life and relationship to adverse outcomes. Arch Dis Child Fetal Neonatal Ed. 2019 Sep;104(5):F473-F479.\u003c/li\u003e\n \u003cli\u003eda Costa CS, Czosnyka M, Smielewski P, Austin T. Optimal Mean Arterial Blood Pressure in Extremely Preterm Infants within the First 24 Hours of Life. J Pediatr. 2018 Dec;203:242-248.\u003c/li\u003e\n \u003cli\u003eWeijs RWJ, de Roos BM, Thijssen DHJ, Claassen JAHR. Intensive antihypertensive treatment does not lower cerebral blood flow or cause orthostatic hypotension in frail older adults. Geroscience. 2024 Oct;46(5):4635-4646.\u003c/li\u003e\n \u003cli\u003eHori D, Hogue C, Adachi H, Max L, Price J, Sciortino C, Zehr K, Conte J, Cameron D, Mandal K. Perioperative optimal blood pressure as determined by ultrasound tagged near infrared spectroscopy and its association with postoperative acute kidney injury in cardiac surgery patients. Interact Cardiovasc Thorac Surg. 2016 Apr;22(4):445-51.\u003c/li\u003e\n \u003cli\u003eShah VA, Humayun M, Radzik B, Healy R, Palmisano C, Anderson-White M, Calvillo E, Geocadin R, Brown C 4th, Hogue C, Ziai W, Cho SM, Suarez JI, Rivera-Lara L. Early Relative Hypotension Below Noninvasive Cerebral Oximetry-Derived Optimal Blood Pressure Thresholds in Aneurysmal Subarachnoid Hemorrhage: A Pilot Study. Crit Care Med. 2025 Nov 1;53(11):e2323-e2330.\u003c/li\u003e\n \u003cli\u003eSchettler KF. Neuromonitoring in neonatal intensive care units-an important need towards individualized neuroprotective care. Eur J Pediatr. 2024 Sep;183(9):3647-3653.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8684150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8684150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Purpose\u003c/h2\u003e \u003cp\u003eCerebral autoregulation (CA) is a core physiological mechanism that maintains adequate cerebral blood flow (CBF) despite fluctuations in cerebral perfusion pressure. Patients in the neurological intensive care unit (NICU) often exhibit varying degrees of CA impairment, rendering the brain vulnerable to hypoperfusion and insufficient oxygen delivery even at blood pressure levels recommended by population-based guidelines. CA-derived indices enable the assessment of individualized mean arterial pressure (MAP) within specific time windows. This study aimed to investigate whether personalized CA-derived MAP deviation is associated with neurological prognosis in NICU patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective analysis of prospective data from adult patients (\u0026ge;\u0026thinsp;18 years) admitted to the NICU of a neurocenter between June 2025 and October 2025. Near-infrared spectroscopy (NIRS) was used to synchronously monitor the dynamic linear correlation between cerebral tissue oxygen saturation (rSO₂) and MAP, and the cerebral oximetry index (COx) reflecting CA function was calculated. A multi-window weighting algorithm was applied to determine the time-varying optimal MAP (MAPₒₚₜ) for each patient, and the median of the NIRS-MAP reference interval (NMM) within the target time window was identified. The NIRS-MAP Median Deviation (NMMD) was defined as the absolute difference between NMM and the mean MAP (MM) within 24h, 48h, and 72h respectively. Unfavorable neurological outcomes were defined as a higher modified Rankin Scale (mRS) score at discharge compared with admission. Multivariate binary logistic regression models adjusted for age, gender, and underlying diseases were used to examine the association between NMMD (in different time windows and under the condition of MM\u0026thinsp;\u0026lt;\u0026thinsp;NMM, i.e., toward the lower limit of autoregulation [LLA]) and unfavorable neurological outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 34 enrolled patients, 23 (67.6%) had favorable outcomes and 11 (32.4%) had unfavorable outcomes. NMMD at 24h and 48h was significantly associated with unfavorable neurological outcomes, with odds ratios (OR) ranging from 0.729 to 0.852 (p\u0026thinsp;\u0026le;\u0026thinsp;0.033). Notably, the association remained significant for 24h NMMD when MM\u0026thinsp;\u0026lt;\u0026thinsp;NMM (OR: 0.776, 95% confidence interval [CI]: 0.612\u0026ndash;0.985, p\u0026thinsp;=\u0026thinsp;0.037), and the corresponding time burden parameter (tNMMD) also showed statistical significance (OR: 0.766, 95% CI: 0.592\u0026ndash;0.989, p\u0026thinsp;=\u0026thinsp;0.041). Receiver operating characteristic (ROC) analysis demonstrated that 24h NMMD had the strongest discriminative ability (area under the curve [AUC]\u0026thinsp;=\u0026thinsp;0.84).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn NICU patients, a larger NMMD during hospitalization, especially within the first 24h, is independently associated with a reduced risk of unfavorable neurological outcomes, and this association persists even when MAP deviates toward the LLA. This finding suggests that a moderate degree of MAP deviation may be beneficial for preserving CA function and exerting its compensatory effects, highlighting the independent importance of early CA assessment and individualized blood pressure control within 24h for neurological prognosis. Future studies with larger sample sizes are needed to further validate its clinical significance.\u003c/p\u003e","manuscriptTitle":"Association between Cerebral Autoregulation-Derived Mean Arterial Pressure Deviation and Neurological Prognosis in NICU Patients: A Prospective, Exploratory, Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 06:54:07","doi":"10.21203/rs.3.rs-8684150/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b0ffa45c-48ce-4285-a66d-d51f89e9ea09","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T04:10:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 06:54:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8684150","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8684150","identity":"rs-8684150","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}