Exploring the Optimal Range of Blood Pressure in Elderly Sepsis Patients: A Retrospective Study Based on MIMIC-IV Data

Exploring the Optimal Range of Blood Pressure in Elderly Sepsis Patients: A Retrospective Study Based on MIMIC-IV Data | 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 Exploring the Optimal Range of Blood Pressure in Elderly Sepsis Patients: A Retrospective Study Based on MIMIC-IV Data Ye Yuanwen, Li Feifei, Lin Liangen, Chen Linglong, XIE Yuequn, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6011063/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To explore the optimal range of blood pressure (BP) in elderly sepsis patients. Methods A retrospective case-control study was conducted. Demographic information, coexisting illnesses, vital signs, laboratory parameters, critical illness scores, and clinical treatment information for elderly sepsis patients were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV). Restricted cubic spline (RCS) analysis was employed to examine and visualize the nonlinear relationship between blood pressure and the incidences of in-hospital mortality and atrial fibrillation. Optimal systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) ranges were identified, and their association with 28-day mortality was validated using Cox regression analysis, propensity score matching (PSM), inverse probability weighting (IPTW), doubly robust model estimation (DR), and Kaplan–Meier survival curves (K-M). Results A total of 2,253 patients met the inclusion criteria, of whom 516 (22.9%) died during hospitalization and 1,087 (48.2%) experienced atrial fibrillation during hospitalization. Restricted cubic spline analysis revealed a nonlinear, L-shaped relationship between blood pressure and in-hospital mortality among elderly sepsis patients. When atrial fibrillation was used as the endpoint, the upper limit of blood pressure was constrained. The optimal SBP, DBP and MAP ranges for elderly sepsis patients were 108–118, 51–57, and 69–74 mmHg, respectively. Further statistical models confirmed that patients within the optimal blood pressure range exhibited decreased 28-day mortality compared to those outside this range [optimal blood pressure group: SBP (108–118 mmHg): Cox regression analysis: hazard ratio (HR) = 0.76, 95% confidence interval (CI) 0.64–0.91, P = 0.002; PSM: HR = 0.78, 95% CI 0.64–0.95, P = 0.015; IPTW: HR = 0.79, 95% CI 0.65–0.95, P = 0.015; DR: HR = 0.78, 95% CI 0.64–0.96, P = 0.018; DBP (51–57 mmHg): Cox regression analysis: HR = 0.79, 95% CI 0.67–0.95, P = 0.010; PSM: HR = 0.72, 95% CI 0.64–0.88, P = 0.001; IPTW: HR = 0.80, 95% CI 0.66–0.96, P = 0.015; DR: HR = 0.81, 95% CI 0.67–0.98, P = 0.032; MAP (69–74 mmHg): Cox regression analysis: HR = 0.83, 95% CI 0.69–0.99, P = 0.044; PSM: HR = 0.78, 95% CI 0.64–0.95, P = 0.016; IPTW: HR = 0.82, 95% CI 0.67–0.99, P = 0.040; DR: HR = 0.85, 95% CI 0.67–1.07, P = 0.172]. K–M survival analysis demonstrated that patients within the optimal blood pressure range had a higher probability of survival than those outside the range (SBP: Log-Rank test: χ² = 4.9, P = 0.0268; DBP: Log-Rank test: χ² = 5.06, P = 0.0244; MAP: Log-Rank test: χ² = 7.76, P = 0.00533). Conclusions During hospitalization, both elevated and reduced blood pressure levels in elderly sepsis patients are associated with an increased risk of mortality. The optimal ranges for SBP, DBP, and MAP in elderly sepsis patients are 108–118, 51–57, and 69–74 mmHg, respectively. Sepsis elderly blood pressure 28-day mortality optimal range Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Sepsis poses a significant threat to life, marked by elevated morbidity and mortality rates, as well as substantial treatment costs, thereby exerting considerable pressure on healthcare systems worldwide and adversely affecting patient well-being [ 1 ] . The optimization of sepsis management, particularly within critical care environments, remains a formidable challenge [ 2 ] . Blood pressure is a crucial physiological parameter in the management of sepsis, as maintaining it within appropriate ranges ensures adequate organ perfusion and improves patient outcomes [ 3 ] . According to the 2021 guidelines from the Surviving Sepsis Campaign, adult patients experiencing septic shock who are on vasopressors should target an initial mean arterial pressure (MAP) of 65 mmHg [ 4 ] . Nonetheless, individualized blood pressure management may be warranted for different patient populations. For example, research by Martin Dünser et al. suggests that a MAP exceeding 75 mmHg may be necessary to achieve adequate renal perfusion in septic patients [ 5 ] , while Lina Zhao et al. advocate for higher blood pressure targets in hypertensive patients compared to normotensive individuals, recommending an optimal MAP of 70–80 mmHg (65–73 mmHg for those without hypertension) and a diastolic blood pressure (DBP) of 54–62 mmHg (50–60 mmHg for non-hypertensive patients) [ 6 ] . Despite the well-documented association between blood pressure (BP) control and sepsis prognosis, the majority of research has concentrated on general septic populations, with limited investigation into optimal BP targets for elderly patients [ 7 ] . Given the global increase in the aging population, the incidence of sepsis among the elderly is rising, accounting for up to 60% of cases in certain studies [ 8 ] . These patients often exhibit reduced physiological reserves, multiple comorbidities, and impaired vascular regulation, rendering them more vulnerable to BP fluctuations during septic shock. Failure to address these specific needs may elevate the risk of organ failure, a significant contributor to the high mortality rate in this demographic [ 9 – 11 ] . Consequently, the development of individualized BP management strategies tailored to the unique physiological and pathological characteristics of elderly patients is imperative. This retrospective study utilizes the MIMIC-IV database to examine clinical outcomes in elderly septic patients across varying blood pressure levels, with the objective of identifying an optimal blood pressure "golden zone" that enhances prognosis. Through rigorous data analysis, the study aims to establish evidence-based blood pressure targets for elderly patients undergoing hemodynamic support, thereby providing a theoretical basis for personalized treatment strategies. By addressing this research gap, the findings are expected to offer significant insights for future prospective studies and clinical practice, ultimately contributing to the reduction of morbidity and mortality in elderly septic patients and advancing global public health initiatives. Materials and Methods Study Settings: This retrospective analytic study was based exclusively on data obtained from the MIMIC-IV database (MIMIC-IV 2.0 version). Researchers completed the "Protecting Human Research Participants" course and obtained ethical approval from BIDMC and the Massachusetts Institute of Technology (MIT) (Record ID: 43546933). Patients Inclusion Criteria: Sepsis-3.0. Exclusion Criteria: Repeated admissions. Intensive Care Unit stay less than 48 hours. Admission age less than 75 years. Patients not receiving vasopressor drugs. History of atrial fibrillation. Data Collection Data extraction was performed using PostgreSQL. Variables extracted included demographic information, coexisting illnesses, vital signs, laboratory parameters, critical illness scoring systems, and clinical treatment information. Demographic Information: Demographic information included age and gender. Coexisting Illnesses: Coexisting illnesses encompassed myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, mild liver disease, diabetes without complications, diabetes with complications, paraplegia, renal disease, malignant cancer, severe liver disease, metastatic solid tumor, and acquired immune deficiency syndrome. Additionally, the Charlson Comorbidity Index (CCI) was calculated to assess the impact of comorbidities. Vital Signs: Vital signs included temperature (T), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure (MBP), heart rate (HR), respiratory rate (RR), and SpO₂. For analysis, the median values of vital signs measured during hospitalization were used as the measure of central tendency for each patient. Laboratory Parameters : Laboratory parameters included white blood cell count (WBC), hemoglobin (Hb), platelet count (PLT), creatinine, blood urea nitrogen (BUN), potassium, sodium, chloride, calcium, glucose, bicarbonate, and lactate. For analysis, the median values of laboratory parameters during hospitalization were used as the measure of central tendency for each patient. Severity Scoring Systems: Severity scoring systems included the Glasgow Coma Scale (GCS) and Sequential Organ Failure Assessment (SOFA). For analysis, the median values of these scores during hospitalization were used as the measure of central tendency for each patient. Clinical Treatment Information: Clinical treatment information included the use of continuous renal replacement therapy (CRRT), mechanical ventilation (MV), occurrence of acute kidney injury (KDIGO Stage 3), length of hospital stay, length of ICU stay, atrial fibrillation, in-hospital mortality, and 28-day mortality. The primary study variables were SBP, DBP and MAP during hospitalization, with the median values used to represent each patient’s blood pressure levels during hospitalization. When using restricted cubic spline (RCS) analysis, the primary endpoints were atrial fibrillation and in-hospital mortality; when using statistical modeling, the primary endpoint was 28-day mortality. Atrial fibrillation was defined as any history of atrial fibrillation occurring during hospitalization. In-hospital mortality was defined based on survival status at discharge, and 28-day mortality was defined as the survival status 28 days after admission. To mitigate potential bias due to missing data, variables with more than 30% missing values were excluded from subsequent analyses. For variables with less than 30% missing values, multiple imputation (MI) methods were employed. Statistical Analysis The Shapiro–Wilk test was used to assess the distribution of the data. Continuous variables with non-normal distributions were analyzed and are presented as the median and interquartile range (IQR), while categorical variables are expressed as counts and percentages. Continuous variables between the two groups were compared using nonparametric tests, and categorical variables were compared using Fisher’s exact test. Restricted cubic spline analysis was employed to examine and visualize the nonlinear relationship between blood pressure levels during hospitalization and the incidences of in-hospital mortality and atrial fibrillation. Based on these results, optimal ranges for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were identified, and the study cohort was divided into two subgroups with different blood pressure levels for subsequent analyses. Various statistical models were employed to confirm the association between blood pressure levels and 28-day mortality. Lasso Regression + Cox Regression Analysis: To reduce multicollinearity among variables, a Lasso regression model was used to select covariates. Subsequently, the relationship between optimal blood pressure range and 28-day mortality was tested using a Cox regression model. Propensity Score Matching: Propensity scores were calculated using a gradient boosted model (GBM), which minimized differences in covariates between the two groups. A matched cohort was then obtained through 1:1 matching based on these propensity scores, and Cox regression analysis was performed on the matched cohort. Propensity Score IPTW: An inverse probability weighting (IPTW) model was used to generate a weighted cohort using estimated propensity scores as weights, and Cox regression analysis was performed on the weighted cohort. Doubly Robust Model Estimation: The relationship between optimal blood pressure range and 28-day mortality was further explored in the matched cohort using a doubly robust analysis that combined multifactorial regression with propensity score adjustment. The Kaplan–Meier (KM) method was employed to plot survival curves, and the log-rank test was used to compare differences between groups. A series of sensitivity analyses were conducted to further validate the robustness of the study results. All statistical analyses were performed using R 3.5.0 software, with a significance level set at P < 0.05. Results Patient selection A total of 32,971 patients diagnosed with sepsis were identified from the MIMIC-IV database. Following the application of exclusion criteria, 30,718 patients were omitted, resulting in a final study cohort comprising 2,253 patients. This cohort was stratified into a survival group (n = 1,737) and a mortality group (n = 516) based on the occurrence of in-hospital mortality events. Furthermore, the cohort was categorized into an atrial fibrillation group (n = 1,087) and a non-atrial fibrillation group (n = 1,166) according to the presence or absence of in-hospital atrial fibrillation. The patient screening process is depicted in FIGURE 1. Baseline characteristics Appendix Table 1 provides a detailed account of the baseline characteristics of the patients. Compared to the sepsis-survival group, patients in the mortality group were older, had a higher prevalence of comorbidities, exhibited increased heart rates and respiratory rates, elevated pulse oximetry readings, higher Sequential Organ Failure Assessment (SOFA) scores, and greater utilization of renal replacement therapy (RRT) and mechanical ventilation (MV). Additionally, they experienced a higher incidence of acute kidney injury (AKI) at KDIGO Stage 3 during hospitalization. Notably, the mortality group had shorter durations of hospital and intensive care unit (ICU) stays.Laboratory analyses demonstrated significant differences between the two cohorts, with the exception of blood sodium levels. In comparison to the sepsis-survival group, patients in the mortality group exhibited lower blood pressure during hospitalization (SBP: 116 [109–124] vs. 110 [104–119] mmHg, p < 0.001; DBP: 54.5 [50.0–59.0] vs. 53.0 [48.0–58.0] mmHg, p < 0.001; MAP: 72.0 [68.0–77.0] vs. 70.0 [66.0–75.0] mmHg, p < 0.001), indicating that hypotension during hospitalization may contribute to increased in-hospital mortality. In a comparative analysis between patients with atrial fibrillation (AF) and those without, individuals in the AF cohort demonstrated a significantly elevated utilization of renal replacement therapy (RRT) and mechanical ventilation (MV), a higher incidence of acute kidney injury (AKI) at KDIGO Stage 3, prolonged durations of hospital and intensive care unit (ICU) stays, and an increased rate of in-hospital mortality. These findings indicate a substantial association between in-hospital atrial fibrillation and adverse clinical outcomes. Although the difference in systolic blood pressure (SBP) was not statistically significant (SBP: 114 [107–123] vs. 115 [107–124], p = 0.196), the AF group exhibited significantly higher diastolic blood pressure (DBP: 53.0 [49.0–58.0] vs. 55.0 [51.0–60.0], p < 0.001) and mean arterial pressure (MAP: 71.0 [67.0–75.0] vs. 73.0 [68.0–77.5], p < 0.001) compared to the non-AF group. This suggests that elevated blood pressure during hospitalization may be linked to a higher incidence of atrial fibrillation, potentially contributing to more adverse outcomes. Restricted Cubic Splines analysis to estimate the blood pressure targets for elderly sepsis patients In this study, we investigated the nonlinear associations between systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and two clinical outcomes: in-hospital mortality and atrial fibrillation, utilizing restricted cubic spline (RCS) analysis. The findings revealed an L-shaped relationship between SBP, DBP, MAP, and in-hospital mortality, suggesting that lower blood pressure levels are correlated with increased in-hospital mortality among elderly patients with sepsis. Mortality rates decreased progressively with rising blood pressure. Specifically, in-hospital mortality was significantly reduced when SBP was ≥ 108 mmHg (see Fig. 2 A), DBP was ≥ 51 mmHg (see Fig. 2 B), and MAP was ≥ 69 mmHg (see Fig. 2 C). Conversely, when atrial fibrillation was considered as the endpoint, the upper thresholds for SBP, DBP, and MAP were identified. The incidence of atrial fibrillation markedly increased when SBP exceeded 118 mmHg (see Fig. 2 A), DBP exceeded 57 mmHg (see Fig. 2 B), and MAP exceeded 74 mmHg (see Fig. 2 C). Based on the RCS analysis, the optimal blood pressure ranges for elderly sepsis patients were determined to be: SBP 108–118 mmHg, DBP 51–57 mmHg, and MAP 69–74 mmHg. Restricted Cubic Splines analysis to estimate the relationship between SBP, DBP, MAP and incidence of In-hospital mortality and incidence of atrial fibrillation in elderly sepsis patients. A . The relationship between SBP and incidence of In-hospital mortality and incidence of atrial fibrillation. B . The relationship between DBP and incidence of In-hospital mortality and incidence of atrial fibrillation. C . The relationship between MAP and incidence of In-hospital mortality and incidence of atrial fibrillation. Prognostic analysis of blood pressure targets and elderly sepsis patients To further evaluate the impact of target values for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) on patient prognosis, specifically 28-day mortality, as informed by the results of the restricted cubic spline (RCS) analysis, we utilized four distinct statistical methodologies: conventional statistical analysis, propensity score matching (PSM), inverse probability weighting (IPTW), and doubly robust modeling. Consistently, across all four statistical approaches, SBP (108–118 mmHg), DBP (51–57 mmHg), and MAP (69–74 mmHg) emerged as protective factors against 28-day mortality in elderly patients with sepsis (see FIGURE 3 ). Application of multiple statistical models confirmed the relationship between optimal range of blood pressure and 28-day mortality of elderly sepsis patients. In the conventional statistical analysis model, to mitigate multicollinearity among variables, we employed the Lasso regression model to screen all variables, excluding SBP, DBP, and MAP, to identify potential risk factors influencing 28-day mortality. The process of variable selection is depicted in Appendix FIGURE 1A. Appendix Fig. 1B illustrates that, following the exclusion of certain covariates, two models were derived: the dashed line on the left represents the minimal model, which includes 37 variables, while the dashed line on the right represents the streamlined model, comprising 14 variables. Subsequently, systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were incorporated as independent variables into the Cox regression model, with 28-day mortality serving as the dependent variable and the 14 variables identified through Lasso regression as covariates. In the propensity score matching model, we matched 38 covariates (excluding SBP, DBP, and MAP) in a 1:1 ratio to address or reduce imbalances between the blood pressure target group (SBP 108–118 mmHg, DBP 51–57 mmHg, and MAP 69–74 mmHg) and the non-target group. Appendix Fig. 2 demonstrates the effective balance achieved between groups post-propensity score matching. To further enhance group balance, we combined propensity score matching with inverse probability of treatment weighting (IPTW) to adjust for intergroup differences. The balance of variables following IPTW is depicted in Fig. 4 . Kaplan–Meier survival curves were employed to illustrate the prognostic disparities between elderly sepsis patients within the blood pressure target group (systolic blood pressure [SBP] 108–118 mmHg, diastolic blood pressure [DBP] 51–57 mmHg, and mean arterial pressure [MAP] 69–74 mmHg) and those outside this target group, as depicted in FIGURE 5 . Furthermore, subgroup analyses indicated that maintaining blood pressure within the specified target range (SBP 108–118 mmHg, DBP 51–57 mmHg, and MAP 69–74 mmHg) served as a protective factor against 28-day mortality in elderly sepsis patients across most subgroups, as presented in Appendix TABLE 2. Discussion The findings of this study indicate that the optimal blood pressure ranges for elderly patients with sepsis are 108–118 mmHg for systolic blood pressure (SBP), 51–57 mmHg for diastolic blood pressure (DBP), and 69–74 mmHg for mean arterial pressure (MAP). Multiple statistical models have corroborated the protective effect of maintaining blood pressure within these specified ranges, revealing a significant association with decreased 28-day mortality rates. These results imply that this range may constitute the optimal hemodynamic target for elderly sepsis patients. Blood pressure management is a fundamental aspect of sepsis treatment, as it directly affects both systemic hemodynamics and microcirculatory perfusion [ 12 – 13 ] . Given the pivotal role of adequate organ perfusion in determining sepsis outcomes, identifying the appropriate blood pressure range has been a primary focus of research [ 14 – 15 ] . The 2021 Surviving Sepsis Campaign (SSC) guidelines recommend an initial MAP target of 65 mmHg for adult patients with septic shock receiving vasopressors [ 4 ] . Emerging evidence indicates that a uniform approach may be inadequate for managing sepsis patients. Dünser et al. recommend maintaining a mean arterial pressure (MAP) above 75 mmHg to ensure sufficient renal perfusion in individuals with sepsis [ 5 ] . Conversely, Zhao et al. suggest that patients with hypertension might require higher blood pressure targets compared to normotensive individuals, advocating for a MAP range of 70–80 mmHg, as opposed to 65–73 mmHg for normotensive controls, and a diastolic blood pressure (DBP) range of 54–62 mmHg, compared to 50–60 mmHg for controls [ 6 ] . Furthermore, sepsis patients with cerebrovascular disease may not achieve adequate cerebral perfusion with the standard MAP target of 65 mmHg, potentially elevating the risk of neurological complications. For these high-risk patients, increasing the MAP target to optimize cerebral blood flow and preserve neurological function may improve patient outcomes. The Sepsis-BRAIN trial, a prospective, non-randomized, single-center study, is designed to evaluate optimal blood pressure targets under cerebral hemodynamic modulation in patients with septic shock [ 16 ] . In summary, personalized blood pressure management, tailored to the unique characteristics of individual patients, is vital for optimizing clinical outcomes. Elderly patients are intrinsically more susceptible to hemodynamic fluctuations compared to younger individuals due to a combination of physiological decline, diminished physiological reserve, immunosenescence, and a higher prevalence of comorbidities [ 9 – 11 ] . These factors collectively heighten their vulnerability to hemodynamic instability, underscoring the importance of individualized blood pressure management in the context of sepsis care. Adapting treatment strategies to accommodate the specific physiological attributes of elderly patients is crucial for enhancing their prognosis and quality of life. Despite its clinical significance, the optimal blood pressure target for elderly patients with sepsis remains contentious. The Surviving Sepsis Campaign (SSC) guidelines advocate for maintaining a mean arterial pressure (MAP) of ≥ 65 mmHg in patients with septic shock; however, there is a lack of consensus regarding individualized targets for elderly patients [ 4 ] . A meta-analysis conducted by Lamontagne et al. (2020) indicates that a standardized mean arterial pressure (MAP) target of 65 mmHg is generally applicable to the majority of sepsis patients. However, its appropriateness for elderly individuals remains a subject of debate due to their increased sensitivity to hypotension, which elevates the risk of cerebral hypoperfusion and renal dysfunction, potentially necessitating a higher MAP target [ 17 ] . The 65 Trial (Lamontagne et al., 2020) investigated the tolerability of a MAP below 65 mmHg in sepsis patients aged 65 and older [ 18 ] . The findings revealed that mild hypotension did not significantly elevate mortality rates; nevertheless, specific subpopulations, such as those with coronary artery disease or chronic kidney disease, exhibited heightened vulnerability to its adverse effects. Furthermore, elderly patients with pre-existing conditions such as hypertension, renal insufficiency, or cerebrovascular disease may derive benefit from a slightly elevated MAP target. In contrast, a retrospective study by Maheshwari et al. (2018) underscores the potential risks associated with excessive vasopressor use [ 19 ] . Elevated doses of norepinephrine can result in excessive vasoconstriction, thereby increasing cardiac workload, precipitating tissue ischemia, and causing disturbances in microcirculation. Furthermore, maintaining a mean arterial pressure (MAP) target above 75 mmHg may heighten the risk of cerebrovascular incidents and elevate myocardial oxygen consumption, potentially leading to increased in-hospital mortality, especially in patients requiring high-dose vasopressors. These findings emphasize the critical balance necessary in managing blood pressure among elderly patients with sepsis. While hypotension can result in organ dysfunction, the aggressive use of vasopressors may impose a substantial cardiac burden and elevate the risk of complications. This underscores the necessity for precise, individualized blood pressure management within a narrow safety margin. However, a universally accepted target for blood pressure in elderly sepsis patients remains undefined, highlighting the need for further research to refine hemodynamic goals and optimize patient outcomes. In this retrospective study involving 2,253 elderly sepsis patients from the MIMIC-IV database, we identified an optimal blood pressure range correlated with reduced in-hospital mortality and a decreased risk of atrial fibrillation (AF). Our analysis demonstrated that non-survivors exhibited significantly lower blood pressure during hospitalization compared to survivors, with median systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) values consistently lower in the mortality group (SBP: 110 vs. 116 mmHg; DBP: 53.0 vs. 54.5 mmHg; MAP: 70.0 vs. 72.0 mmHg; all p < 0.001). These findings suggest that hypotension is a critical factor contributing to adverse outcomes in elderly sepsis patients. Conversely, although AF was associated with poorer clinical outcomes—including increased rates of renal replacement therapy (RRT) and mechanical ventilation (MV) use, higher incidence of acute kidney injury (AKI), and prolonged ICU/hospital stays—patients with AF presented slightly higher DBP and MAP compared to those without AF. This indicates a potential association between elevated blood pressure and the onset of AF. Restricted cubic spline (RCS) analysis further delineated a nonlinear relationship between blood pressure and clinical outcomes. We identified an L-shaped relationship between systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and in-hospital mortality, where the risk decreased as blood pressure increased beyond SBP ≥ 108 mmHg, DBP ≥ 51 mmHg, and MAP ≥ 69 mmHg. Conversely, the incidence of atrial fibrillation (AF) significantly increased when blood pressure exceeded SBP 118 mmHg, DBP 57 mmHg, and MAP 74 mmHg. These observations delineate an optimal blood pressure range (SBP: 108–118 mmHg, DBP: 51–57 mmHg, MAP: 69–74 mmHg) that effectively balances the risks associated with hypotension-related mortality and hypertension-induced AF. The protective effect of this target range on 28-day mortality was corroborated through multiple statistical models, including propensity score matching (PSM) and inverse probability weighting (IPW). Our findings are consistent with and expand upon existing research on blood pressure management in sepsis patients, particularly among the elderly. Hypotension has been consistently recognized as a critical factor in poor prognosis, with low MAP increasing mortality risk due to inadequate organ perfusion and subsequent multiorgan failure. Our study indicates that a marginally elevated mean arterial pressure (MAP) threshold (≥ 69 mmHg) may be more suitable for elderly patients with sepsis, corroborating recent evidence that suggests elderly individuals require higher perfusion pressures to ensure adequate tissue oxygenation [ 20 – 21 ] . Moreover, our findings contribute to the ongoing discourse on sepsis-related atrial fibrillation (AF) [ 22 ] . While previous research has identified sepsis as a risk factor for new-onset AF, our results refine this association by demonstrating that elevated systolic blood pressure (SBP), diastolic blood pressure (DBP), and MAP (exceeding 118 mmHg, 57 mmHg, and 74 mmHg, respectively) are correlated with an increased incidence of AF. This observation is consistent with evidence indicating that excessive sympathetic activation and fluid resuscitation in sepsis may facilitate the onset of AF, particularly in patients with preexisting cardiovascular dysfunction [ 22 ] . Furthermore, our data support the hypothesis that AF exacerbates sepsis outcomes by contributing to hemodynamic instability and increasing thromboembolic risk [ 23 ] . This study presents several significant strengths and contributions to the existing body of research on blood pressure management in elderly patients with sepsis. Firstly, it leverages the high-quality MIMIC-IV database, which comprises a large and diverse cohort of real-world critically ill patients, thereby ensuring a robust dataset for analysis. Secondly, the study employs advanced statistical methodologies, including restricted cubic splines (RCS), propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and doubly robust analysis. RCS facilitates a precise evaluation of nonlinear associations between blood pressure levels and key outcomes such as atrial fibrillation and in-hospital mortality. The application of multiple statistical models enhances the robustness of the results and mitigates confounding biases. Thirdly, the study offers clinically relevant insights by proposing an optimal blood pressure target for elderly sepsis patients receiving vasopressors. Notably, it suggests a higher mean arterial pressure (MAP) threshold (≥ 69 mmHg), challenging the conventional guideline recommendation of 65 mmHg and potentially influencing individualized treatment strategies.Moreover, this study highlights the intricate relationship between blood pressure regulation and the risk of atrial fibrillation, thereby contributing to the broader academic discourse on hemodynamic optimization in sepsis management. Despite its contributions, the study is not without limitations. As a retrospective analysis utilizing the MIMIC-IV database, it is susceptible to selection bias and residual confounding. Although comprehensive statistical adjustments were employed, unmeasured variables—such as fluid balance, medication dosages, and specific etiologies of sepsis—may still affect the findings. Prospective trials are necessary to confirm whether the optimal blood pressure targets identified in this study lead to improved clinical outcomes. Furthermore, the study's reliance on median blood pressure values during hospitalization may not adequately capture hemodynamic variability. Given the significant fluctuations in blood pressure observed in sepsis, a single median value may not accurately represent exposure to hypotensive or hypertensive episodes, potentially resulting in misclassification bias. Future research should investigate continuous or time-dependent blood pressure modeling to provide a more precise evaluation of its impact on patient outcomes. This study highlights that maintaining blood pressure within optimal ranges (SBP 108–118 mmHg, DBP 51–57 mmHg, MAP 69–74 mmHg) may improve outcomes in elderly sepsis patients, suggesting a potential reevaluation of current blood pressure management guidelines in this population. Declarations Ethical Approval This study utilized data from the Medical Information Mart for Intensive Care (MIMIC) database (version 2.0), which is a publicly available de-identified database containing retrospective healthcare data from critically ill patients. Individual patient consent was waived due to the retrospective and anonymized nature of the data. Funding Wenzhou Basic Public Welfare Research Project Y2023575. Author Contribution AUTHOR CONTRIBUTIONSYuanwen YE: Data curation, writing – original draft; writing – review and editing. LinglongCHEN: Writing – original draft. Baohua YANG: Data curation; supervision. Yuequn XIE: Data curation; supervision; picture and table editing. Wang LV: Data curation. Feifei LI: Writing – original draft; writing – review and editing.Liangen LIN: Data curation Acknowledgement We would like to acknowledge all the study investigators for conducting this study. Data Availability All data in this article can be obtained from MIMIC-IV datebase(https://mimic.mit.edu/) References Fleischmann C, Scherag A, Adhikari NK, Hartog CS, Tsaganos T, Schlattmann P, et al. Assessment of global incidence and mortality of hospital-treated sepsis. Current estimates and limitations. Am J Respir Crit Care Med. (2016) 193:259–72. 10.1164/rccm.201504-0781OC Rudd KE, Kissoon N, Limmathurotsakul D, Bory S, Mutahunga B, Seymour CW, et al. The global burden of sepsis: barriers and potential solutions. Crit Care. (2018) 22:232. 10.1186/s13054-018-2157-z Corrêa TD, Jakob SM, Takala J. Arterial blood pressure targets in septic shock: is it time to move to an individualized approach? Crit Care. 2015 Jun 18;19(1):264. doi: 10.1186/s13054-015-0958-x. 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PMID: 36185579; PMCID: PMC9507666. Pan P, Xie LX. [Surviving Sepsis Campaign guideline update:problems and progress]. Zhonghua Jie He He Hu Xi Za Zhi. 2024 Oct 12;47(10):901-905. Chinese. doi: 10.3760/cma.j.cn112147-20240422-00215. PMID: 39406534. Sheats MK. A Comparative Review of Equine SIRS, Sepsis, and Neutrophils. Front Vet Sci. 2019 Mar 12;6:69. doi: 10.3389/fvets.2019.00069. PMID: 30931316; PMCID: PMC6424004. Giustozzi M, Ehrlinder H, Bongiovanni D, Borovac JA, Guerreiro RA, Gąsecka A, Papakonstantinou PE, Parker WAE. Coagulopathy and sepsis: Pathophysiology, clinical manifestations and treatment. Blood Rev. 2021 Nov;50:100864. doi: 10.1016/j.blre.2021.100864. Epub 2021 Jun 25. PMID: 34217531. Cury P, Passos RDH, Alves F, Brasil S, Frigieri G, Taccone FS, Panerai RB, Caldas J. Impact of different blood pressure targets on cerebral hemodynamics in septic shock: A prospective pilot study protocol-SEPSIS-BRAIN. PLoS One. 2024 Oct 14;19(10):e0304412. doi: 10.1371/journal.pone.0304412. PMID: 39401208; PMCID: PMC11472940. Lamontagne F, Day AG, Meade MO, Cook DJ, Guyatt GH, Hylands M, Radermacher P, Chrétien JM, Beaudoin N, Hébert P, D'Aragon F, Meziani F, Asfar P. Pooled analysis of higher versus lower blood pressure targets for vasopressor therapy septic and vasodilatory shock. Intensive Care Med. 2018 Jan;44(1):12-21. doi: 10.1007/s00134-017-5016-5. Epub 2017 Dec 19. PMID: 29260272. Lamontagne F, Richards-Belle A, Thomas K, Harrison DA, Sadique MZ, Grieve RD, Camsooksai J, Darnell R, Gordon AC, Henry D, Hudson N, Mason AJ, Saull M, Whitman C, Young JD, Rowan KM, Mouncey PR; 65 trial investigators. Effect of Reduced Exposure to Vasopressors on 90-Day Mortality in Older Critically Ill Patients With Vasodilatory Hypotension: A Randomized Clinical Trial. JAMA. 2020 Mar 10;323(10):938-949. doi: 10.1001/jama.2020.0930. PMID: 32049269; PMCID: PMC7064880. Maheshwari K, Nathanson BH, Munson SH, Khangulov V, Stevens M, Badani H, Khanna AK, Sessler DI. The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients. Intensive Care Med. 2018 Jun;44(6):857-867. doi: 10.1007/s00134-018-5218-5. Epub 2018 Jun 5. PMID: 29872882; PMCID: PMC6013508. Alhamyani AH, Alamri MS, Aljuaid NW, Aloubthani AH, Alzahrani S, Alghamdi AA, Lajdam AS, Alamoudi H, Alamoudi AA, Albulushi AM, AlQarni SN. Sepsis in Aging Populations: A Review of Risk Factors, Diagnosis, and Management. Cureus. 2024 Dec 2;16(12):e74973. doi: 10.7759/cureus.74973. PMID: 39744263; PMCID: PMC11691596. Ibarz M, Haas LEM, Ceccato A, Artigas A. The critically ill older patient with sepsis: a narrative review. Ann Intensive Care. 2024 Jan 10;14(1):6. doi: 10.1186/s13613-023-01233-7. PMID: 38200360; PMCID: PMC10781658.] Bosch NA, Cohen DM, Walkey AJ. Risk Factors for New-Onset Atrial Fibrillation in Patients With Sepsis: A Systematic Review and Meta-Analysis. Crit Care Med. 2019 Feb;47(2):280-287. doi: 10.1097/CCM.0000000000003560. PMID: 30653473; PMCID: PMC9872909. Boos CJ. Infection and atrial fibrillation: inflammation begets AF. Eur Heart J. 2020 Mar 7;41(10):1120-1122. doi: 10.1093/eurheartj/ehz953. PMID: 31971996. Corica B, Romiti GF, Basili S, Proietti M. Prevalence of New-Onset Atrial Fibrillation and Associated Outcomes in Patients with Sepsis: A Systematic Review and Meta-Analysis. J Pers Med. 2022 Mar 30;12(4):547. doi: 10.3390/jpm12040547. PMID: 35455662; PMCID: PMC9026551. Additional Declarations No competing interests reported. Supplementary Files Stable1.docx Stable2sbp.docx Stable2map.docx Stable2dbp.docx Sfigure1.docx Sfigure2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6011063","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415215657,"identity":"940fcef1-2bcc-4549-8a32-c3dab8cc05f1","order_by":0,"name":"Ye Yuanwen","email":"","orcid":"","institution":"Wenzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Yuanwen","suffix":""},{"id":415215658,"identity":"1eb40436-a05f-4196-bbd4-b2d476ec4e47","order_by":1,"name":"Li Feifei","email":"","orcid":"","institution":"Wenzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Feifei","suffix":""},{"id":415215659,"identity":"26451141-8203-4dd5-bf00-9ba04730ed24","order_by":2,"name":"Lin Liangen","email":"","orcid":"","institution":"Wenzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Liangen","suffix":""},{"id":415215660,"identity":"7a9dc4b4-43da-4e2a-9026-eb878b1ff529","order_by":3,"name":"Chen Linglong","email":"","orcid":"","institution":"Wenzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Linglong","suffix":""},{"id":415215661,"identity":"52e2c60c-5d8c-471a-a6cd-660a2940c441","order_by":4,"name":"XIE Yuequn","email":"","orcid":"","institution":"Wenzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"XIE","middleName":"","lastName":"Yuequn","suffix":""},{"id":415215663,"identity":"0fc62651-d6f1-46d9-bfdb-9faf5d228864","order_by":5,"name":"Lv Wang","email":"","orcid":"","institution":"Wenzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lv","middleName":"","lastName":"Wang","suffix":""},{"id":415215665,"identity":"155d232f-48e6-40cc-834d-b2d7a1260601","order_by":6,"name":"Yang Baohua","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYLCCDxUJcuwNIJaBBXE6GGecSTDmOQDWIkGcFmbetoTEHrAWBiK0GNzIMWDmOZOW3sPeY7rhR4EEA397dwJBLYxzKnJye3iOpd3sATpM4szZDQS05G5geHOmIne/RPKxGzxALQYSuURo4W2rSOeRSGy7+YdYLYy8bTkJPEBbbhNli+SZ9x+AgZxmCPLLbRkDCR6CfuE7npYAjMpkeR72HrObb/7YyPG39+LXonAhgf0HsgAPXuUgIN9/gKCaUTAKRsEoGOkAAD4HSnDrPPITAAAAAElFTkSuQmCC","orcid":"","institution":"Wenzhou People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Baohua","suffix":""}],"badges":[],"createdAt":"2025-02-12 02:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6011063/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6011063/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76573325,"identity":"2849d9d5-d4e6-4cae-9762-c036541643c6","added_by":"auto","created_at":"2025-02-18 13:54:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24152,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/9191fbae086a8d560b6a3fd7.png"},{"id":76572003,"identity":"c4feac55-33eb-4353-98b4-331b00c0b64a","added_by":"auto","created_at":"2025-02-18 13:46:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109909,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted Cubic Splines analysis to estimate the relationship between SBP, DBP, MAP and incidence of In-hospital mortality and incidence of atrial fibrillation in elderly sepsis patients. \u003cstrong\u003eA\u003c/strong\u003e. The relationship between SBP and incidence of In-hospital mortality and incidence of atrial fibrillation. \u003cstrong\u003eB\u003c/strong\u003e. The relationship between DBP and incidence of In-hospital mortality and incidence of atrial fibrillation. \u003cstrong\u003eC\u003c/strong\u003e. The relationship between MAP and incidence of In-hospital mortality and incidence of atrial fibrillation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/e828cb0fe10666252921213c.png"},{"id":76572005,"identity":"d735260d-ffc8-4385-a0c7-27818a3ed96b","added_by":"auto","created_at":"2025-02-18 13:46:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71022,"visible":true,"origin":"","legend":"\u003cp\u003eApplication of multiple statistical models confirmed the relationship between optimal range of blood pressure and 28-day mortality of elderly sepsis patients.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/9f8067d5748a75ff97308065.png"},{"id":76573326,"identity":"b8e50189-4c9a-48d4-a954-ec3c5eeee6ee","added_by":"auto","created_at":"2025-02-18 13:54:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113370,"visible":true,"origin":"","legend":"\u003cp\u003eThe standardized mean difference (SMD) of the original cohort was compared with the IPTW cohorts, which showed that covariates were well balanced between classes after IPTW (\u0026lt;0.1). SMD stands for standardized mean difference, and IPTW refers to inverse probability of treatment weighting.(A). SBP; (B). DBP; (C). MAP.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/538516f160e2dba6f46a4e5b.png"},{"id":76572022,"identity":"bb9a26f5-769e-4871-8666-b2625321fa93","added_by":"auto","created_at":"2025-02-18 13:46:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82237,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan–Meier survival curves of the blood pressure target group and the no blood pressure target group, based on Restricted Cubic Splines analysis. A. The survival curves of systolic blood pressure (SBP). B. The survival curves of diastolic blood pressure (DBP). C. The survival curves of mean arterial pressure (MAP).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/894b240d910efc872b2bd3f6.png"},{"id":81281745,"identity":"1936eb8f-bb9e-403b-8834-58ed735cafe7","added_by":"auto","created_at":"2025-04-24 10:17:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1117280,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/00a04298-8132-4601-9f00-a0936de1fcd9.pdf"},{"id":76573324,"identity":"56742267-4656-4cf2-bf21-3bda0f7dead7","added_by":"auto","created_at":"2025-02-18 13:54:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57556,"visible":true,"origin":"","legend":"","description":"","filename":"Stable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/db41e0dfd15b0b2aa60f78d8.docx"},{"id":76572010,"identity":"0cd50f20-ac2b-4434-a033-0065a3a7ad4a","added_by":"auto","created_at":"2025-02-18 13:46:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":60970,"visible":true,"origin":"","legend":"","description":"","filename":"Stable2sbp.docx","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/5de2a00e4625ae18e1139db3.docx"},{"id":76572012,"identity":"55283466-b77d-45af-8125-bc340774be68","added_by":"auto","created_at":"2025-02-18 13:46:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":61612,"visible":true,"origin":"","legend":"","description":"","filename":"Stable2map.docx","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/a9fb70d5e271d1990ad8e26c.docx"},{"id":76572016,"identity":"46dc3ab8-ed84-4547-ab59-3d8dc90f7d8f","added_by":"auto","created_at":"2025-02-18 13:46:48","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":61224,"visible":true,"origin":"","legend":"","description":"","filename":"Stable2dbp.docx","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/7f05befbbfc31dd75511c57d.docx"},{"id":76572027,"identity":"b7ce524a-4a68-4c0f-a38e-8da834e82ff5","added_by":"auto","created_at":"2025-02-18 13:46:48","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":287153,"visible":true,"origin":"","legend":"","description":"","filename":"Sfigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/d56f1f5f716c070531f41825.docx"},{"id":76572035,"identity":"bfd06b86-6335-47ec-b86a-d93e7565279c","added_by":"auto","created_at":"2025-02-18 13:46:49","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":559291,"visible":true,"origin":"","legend":"","description":"","filename":"Sfigure2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6011063/v1/30379ef317b4a84070bbda2e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Optimal Range of Blood Pressure in Elderly Sepsis Patients: A Retrospective Study Based on MIMIC-IV Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis poses a significant threat to life, marked by elevated morbidity and mortality rates, as well as substantial treatment costs, thereby exerting considerable pressure on healthcare systems worldwide and adversely affecting patient well-being \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The optimization of sepsis management, particularly within critical care environments, remains a formidable challenge \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBlood pressure is a crucial physiological parameter in the management of sepsis, as maintaining it within appropriate ranges ensures adequate organ perfusion and improves patient outcomes \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. According to the 2021 guidelines from the Surviving Sepsis Campaign, adult patients experiencing septic shock who are on vasopressors should target an initial mean arterial pressure (MAP) of 65 mmHg \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Nonetheless, individualized blood pressure management may be warranted for different patient populations. For example, research by Martin D\u0026uuml;nser et al. suggests that a MAP exceeding 75 mmHg may be necessary to achieve adequate renal perfusion in septic patients \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, while Lina Zhao et al. advocate for higher blood pressure targets in hypertensive patients compared to normotensive individuals, recommending an optimal MAP of 70\u0026ndash;80 mmHg (65\u0026ndash;73 mmHg for those without hypertension) and a diastolic blood pressure (DBP) of 54\u0026ndash;62 mmHg (50\u0026ndash;60 mmHg for non-hypertensive patients) \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Despite the well-documented association between blood pressure (BP) control and sepsis prognosis, the majority of research has concentrated on general septic populations, with limited investigation into optimal BP targets for elderly patients \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the global increase in the aging population, the incidence of sepsis among the elderly is rising, accounting for up to 60% of cases in certain studies \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. These patients often exhibit reduced physiological reserves, multiple comorbidities, and impaired vascular regulation, rendering them more vulnerable to BP fluctuations during septic shock. Failure to address these specific needs may elevate the risk of organ failure, a significant contributor to the high mortality rate in this demographic \u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Consequently, the development of individualized BP management strategies tailored to the unique physiological and pathological characteristics of elderly patients is imperative.\u003c/p\u003e \u003cp\u003eThis retrospective study utilizes the MIMIC-IV database to examine clinical outcomes in elderly septic patients across varying blood pressure levels, with the objective of identifying an optimal blood pressure \"golden zone\" that enhances prognosis. Through rigorous data analysis, the study aims to establish evidence-based blood pressure targets for elderly patients undergoing hemodynamic support, thereby providing a theoretical basis for personalized treatment strategies. By addressing this research gap, the findings are expected to offer significant insights for future prospective studies and clinical practice, ultimately contributing to the reduction of morbidity and mortality in elderly septic patients and advancing global public health initiatives.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Settings:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective analytic study was based exclusively on data obtained from the MIMIC-IV database (MIMIC-IV 2.0 version). Researchers completed the \u0026quot;Protecting Human Research Participants\u0026quot; course and obtained ethical approval from BIDMC and the Massachusetts Institute of Technology (MIT) (Record ID: 43546933).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria:\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eSepsis-3.0.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion Criteria:\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eRepeated admissions.\u003c/li\u003e\n \u003cli\u003eIntensive Care Unit stay less than 48 hours.\u003c/li\u003e\n \u003cli\u003eAdmission age less than 75 years.\u003c/li\u003e\n \u003cli\u003ePatients not receiving vasopressor drugs.\u003c/li\u003e\n \u003cli\u003eHistory of atrial fibrillation.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData extraction was performed using PostgreSQL. Variables extracted included demographic information, coexisting illnesses, vital signs, laboratory parameters, critical illness scoring systems, and clinical treatment information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic information included age and gender.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoexisting Illnesses:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCoexisting illnesses encompassed myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, mild liver disease, diabetes without complications, diabetes with complications, paraplegia, renal disease, malignant cancer, severe liver disease, metastatic solid tumor, and acquired immune deficiency syndrome. Additionally, the Charlson Comorbidity Index (CCI) was calculated to assess the impact of comorbidities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVital Signs:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVital signs included temperature (T), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure (MBP), heart rate (HR), respiratory rate (RR), and SpO₂. For analysis, the median values of vital signs measured during hospitalization were used as the measure of central tendency for each patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory Parameters\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLaboratory parameters included white blood cell count (WBC), hemoglobin (Hb), platelet count (PLT), creatinine, blood urea nitrogen (BUN), potassium, sodium, chloride, calcium, glucose, bicarbonate, and lactate. For analysis, the median values of laboratory parameters during hospitalization were used as the measure of central tendency for each patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSeverity Scoring Systems:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeverity scoring systems included the Glasgow Coma Scale (GCS) and Sequential Organ Failure Assessment (SOFA). For analysis, the median values of these scores during hospitalization were used as the measure of central tendency for each patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Treatment Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical treatment information included the use of continuous renal replacement therapy (CRRT), mechanical ventilation (MV), occurrence of acute kidney injury (KDIGO Stage 3), length of hospital stay, length of ICU stay, atrial fibrillation, in-hospital mortality, and 28-day mortality.\u003c/p\u003e\n\u003cp\u003eThe primary study variables were SBP, DBP and MAP during hospitalization, with the median values used to represent each patient\u0026rsquo;s blood pressure levels during hospitalization. When using restricted cubic spline (RCS) analysis, the primary endpoints were atrial fibrillation and in-hospital mortality; when using statistical modeling, the primary endpoint was 28-day mortality. Atrial fibrillation was defined as any history of atrial fibrillation occurring during hospitalization. In-hospital mortality was defined based on survival status at discharge, and 28-day mortality was defined as the survival status 28 days after admission.\u003c/p\u003e\n\u003cp\u003eTo mitigate potential bias due to missing data, variables with more than 30% missing values were excluded from subsequent analyses. For variables with less than 30% missing values, multiple imputation (MI) methods were employed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Shapiro\u0026ndash;Wilk test was used to assess the distribution of the data. Continuous variables with non-normal distributions were analyzed and are presented as the median and interquartile range (IQR), while categorical variables are expressed as counts and percentages. Continuous variables between the two groups were compared using nonparametric tests, and categorical variables were compared using Fisher\u0026rsquo;s exact test.\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline analysis was employed to examine and visualize the nonlinear relationship between blood pressure levels during hospitalization and the incidences of in-hospital mortality and atrial fibrillation. Based on these results, optimal ranges for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were identified, and the study cohort was divided into two subgroups with different blood pressure levels for subsequent analyses. Various statistical models were employed to confirm the association between blood pressure levels and 28-day mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLasso Regression + Cox Regression Analysis:\u003c/strong\u003e To reduce multicollinearity among variables, a Lasso regression model was used to select covariates. Subsequently, the relationship between optimal blood pressure range and 28-day mortality was tested using a Cox regression model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePropensity Score Matching:\u003c/strong\u003e Propensity scores were calculated using a gradient boosted model (GBM), which minimized differences in covariates between the two groups. A matched cohort was then obtained through 1:1 matching based on these propensity scores, and Cox regression analysis was performed on the matched cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePropensity Score IPTW:\u003c/strong\u003e An inverse probability weighting (IPTW) model was used to generate a weighted cohort using estimated propensity scores as weights, and Cox regression analysis was performed on the weighted cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoubly Robust Model Estimation:\u003c/strong\u003e The relationship between optimal blood pressure range and 28-day mortality was further explored in the matched cohort using a doubly robust analysis that combined multifactorial regression with propensity score adjustment.\u003c/p\u003e\n\u003cp\u003eThe Kaplan\u0026ndash;Meier (KM) method was employed to plot survival curves, and the log-rank test was used to compare differences between groups. A series of sensitivity analyses were conducted to further validate the robustness of the study results.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R 3.5.0 software, with a significance level set at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003eA total of 32,971 patients diagnosed with sepsis were identified from the MIMIC-IV database. Following the application of exclusion criteria, 30,718 patients were omitted, resulting in a final study cohort comprising 2,253 patients. This cohort was stratified into a survival group (n\u0026thinsp;=\u0026thinsp;1,737) and a mortality group (n\u0026thinsp;=\u0026thinsp;516) based on the occurrence of in-hospital mortality events. Furthermore, the cohort was categorized into an atrial fibrillation group (n\u0026thinsp;=\u0026thinsp;1,087) and a non-atrial fibrillation group (n\u0026thinsp;=\u0026thinsp;1,166) according to the presence or absence of in-hospital atrial fibrillation. The patient screening process is depicted in FIGURE 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eAppendix Table\u0026nbsp;1 provides a detailed account of the baseline characteristics of the patients. Compared to the sepsis-survival group, patients in the mortality group were older, had a higher prevalence of comorbidities, exhibited increased heart rates and respiratory rates, elevated pulse oximetry readings, higher Sequential Organ Failure Assessment (SOFA) scores, and greater utilization of renal replacement therapy (RRT) and mechanical ventilation (MV). Additionally, they experienced a higher incidence of acute kidney injury (AKI) at KDIGO Stage 3 during hospitalization. Notably, the mortality group had shorter durations of hospital and intensive care unit (ICU) stays.Laboratory analyses demonstrated significant differences between the two cohorts, with the exception of blood sodium levels. In comparison to the sepsis-survival group, patients in the mortality group exhibited lower blood pressure during hospitalization (SBP: 116 [109\u0026ndash;124] vs. 110 [104\u0026ndash;119] mmHg, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DBP: 54.5 [50.0\u0026ndash;59.0] vs. 53.0 [48.0\u0026ndash;58.0] mmHg, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; MAP: 72.0 [68.0\u0026ndash;77.0] vs. 70.0 [66.0\u0026ndash;75.0] mmHg, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that hypotension during hospitalization may contribute to increased in-hospital mortality.\u003c/p\u003e \u003cp\u003eIn a comparative analysis between patients with atrial fibrillation (AF) and those without, individuals in the AF cohort demonstrated a significantly elevated utilization of renal replacement therapy (RRT) and mechanical ventilation (MV), a higher incidence of acute kidney injury (AKI) at KDIGO Stage 3, prolonged durations of hospital and intensive care unit (ICU) stays, and an increased rate of in-hospital mortality. These findings indicate a substantial association between in-hospital atrial fibrillation and adverse clinical outcomes. Although the difference in systolic blood pressure (SBP) was not statistically significant (SBP: 114 [107\u0026ndash;123] vs. 115 [107\u0026ndash;124], p\u0026thinsp;=\u0026thinsp;0.196), the AF group exhibited significantly higher diastolic blood pressure (DBP: 53.0 [49.0\u0026ndash;58.0] vs. 55.0 [51.0\u0026ndash;60.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and mean arterial pressure (MAP: 71.0 [67.0\u0026ndash;75.0] vs. 73.0 [68.0\u0026ndash;77.5], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the non-AF group. This suggests that elevated blood pressure during hospitalization may be linked to a higher incidence of atrial fibrillation, potentially contributing to more adverse outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRestricted Cubic Splines analysis to estimate the blood pressure targets for elderly sepsis patients\u003c/h2\u003e \u003cp\u003eIn this study, we investigated the nonlinear associations between systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and two clinical outcomes: in-hospital mortality and atrial fibrillation, utilizing restricted cubic spline (RCS) analysis. The findings revealed an L-shaped relationship between SBP, DBP, MAP, and in-hospital mortality, suggesting that lower blood pressure levels are correlated with increased in-hospital mortality among elderly patients with sepsis. Mortality rates decreased progressively with rising blood pressure. Specifically, in-hospital mortality was significantly reduced when SBP was \u0026ge;\u0026thinsp;108 mmHg (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), DBP was \u0026ge;\u0026thinsp;51 mmHg (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and MAP was \u0026ge;\u0026thinsp;69 mmHg (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eConversely, when atrial fibrillation was considered as the endpoint, the upper thresholds for SBP, DBP, and MAP were identified. The incidence of atrial fibrillation markedly increased when SBP exceeded 118 mmHg (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), DBP exceeded 57 mmHg (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and MAP exceeded 74 mmHg (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eBased on the RCS analysis, the optimal blood pressure ranges for elderly sepsis patients were determined to be: SBP 108\u0026ndash;118 mmHg, DBP 51\u0026ndash;57 mmHg, and MAP 69\u0026ndash;74 mmHg.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRestricted Cubic Splines analysis to estimate the relationship between SBP, DBP, MAP and incidence of In-hospital mortality and incidence of atrial fibrillation in elderly sepsis patients. \u003cb\u003eA\u003c/b\u003e. The relationship between SBP and incidence of In-hospital mortality and incidence of atrial fibrillation. \u003cb\u003eB\u003c/b\u003e. The relationship between DBP and incidence of In-hospital mortality and incidence of atrial fibrillation. \u003cb\u003eC\u003c/b\u003e. The relationship between MAP and incidence of In-hospital mortality and incidence of atrial fibrillation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic analysis of blood pressure targets and elderly sepsis patients\u003c/h2\u003e \u003cp\u003eTo further evaluate the impact of target values for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) on patient prognosis, specifically 28-day mortality, as informed by the results of the restricted cubic spline (RCS) analysis, we utilized four distinct statistical methodologies: conventional statistical analysis, propensity score matching (PSM), inverse probability weighting (IPTW), and doubly robust modeling. Consistently, across all four statistical approaches, SBP (108\u0026ndash;118 mmHg), DBP (51\u0026ndash;57 mmHg), and MAP (69\u0026ndash;74 mmHg) emerged as protective factors against 28-day mortality in elderly patients with sepsis (see FIGURE \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eApplication of multiple statistical models confirmed the relationship between optimal range of blood pressure and 28-day mortality of elderly sepsis patients.\u003c/p\u003e \u003cp\u003eIn the conventional statistical analysis model, to mitigate multicollinearity among variables, we employed the Lasso regression model to screen all variables, excluding SBP, DBP, and MAP, to identify potential risk factors influencing 28-day mortality. The process of variable selection is depicted in Appendix FIGURE 1A. Appendix Fig.\u0026nbsp;1B illustrates that, following the exclusion of certain covariates, two models were derived: the dashed line on the left represents the minimal model, which includes 37 variables, while the dashed line on the right represents the streamlined model, comprising 14 variables. Subsequently, systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were incorporated as independent variables into the Cox regression model, with 28-day mortality serving as the dependent variable and the 14 variables identified through Lasso regression as covariates.\u003c/p\u003e \u003cp\u003eIn the propensity score matching model, we matched 38 covariates (excluding SBP, DBP, and MAP) in a 1:1 ratio to address or reduce imbalances between the blood pressure target group (SBP 108\u0026ndash;118 mmHg, DBP 51\u0026ndash;57 mmHg, and MAP 69\u0026ndash;74 mmHg) and the non-target group. Appendix Fig.\u0026nbsp;2 demonstrates the effective balance achieved between groups post-propensity score matching. To further enhance group balance, we combined propensity score matching with inverse probability of treatment weighting (IPTW) to adjust for intergroup differences. The balance of variables following IPTW is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival curves were employed to illustrate the prognostic disparities between elderly sepsis patients within the blood pressure target group (systolic blood pressure [SBP] 108\u0026ndash;118 mmHg, diastolic blood pressure [DBP] 51\u0026ndash;57 mmHg, and mean arterial pressure [MAP] 69\u0026ndash;74 mmHg) and those outside this target group, as depicted in FIGURE \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Furthermore, subgroup analyses indicated that maintaining blood pressure within the specified target range (SBP 108\u0026ndash;118 mmHg, DBP 51\u0026ndash;57 mmHg, and MAP 69\u0026ndash;74 mmHg) served as a protective factor against 28-day mortality in elderly sepsis patients across most subgroups, as presented in Appendix TABLE 2.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study indicate that the optimal blood pressure ranges for elderly patients with sepsis are 108\u0026ndash;118 mmHg for systolic blood pressure (SBP), 51\u0026ndash;57 mmHg for diastolic blood pressure (DBP), and 69\u0026ndash;74 mmHg for mean arterial pressure (MAP). Multiple statistical models have corroborated the protective effect of maintaining blood pressure within these specified ranges, revealing a significant association with decreased 28-day mortality rates. These results imply that this range may constitute the optimal hemodynamic target for elderly sepsis patients.\u003c/p\u003e \u003cp\u003eBlood pressure management is a fundamental aspect of sepsis treatment, as it directly affects both systemic hemodynamics and microcirculatory perfusion \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Given the pivotal role of adequate organ perfusion in determining sepsis outcomes, identifying the appropriate blood pressure range has been a primary focus of research \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The 2021 Surviving Sepsis Campaign (SSC) guidelines recommend an initial MAP target of 65 mmHg for adult patients with septic shock receiving vasopressors \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Emerging evidence indicates that a uniform approach may be inadequate for managing sepsis patients. D\u0026uuml;nser et al. recommend maintaining a mean arterial pressure (MAP) above 75 mmHg to ensure sufficient renal perfusion in individuals with sepsis \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Conversely, Zhao et al. suggest that patients with hypertension might require higher blood pressure targets compared to normotensive individuals, advocating for a MAP range of 70\u0026ndash;80 mmHg, as opposed to 65\u0026ndash;73 mmHg for normotensive controls, and a diastolic blood pressure (DBP) range of 54\u0026ndash;62 mmHg, compared to 50\u0026ndash;60 mmHg for controls \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Furthermore, sepsis patients with cerebrovascular disease may not achieve adequate cerebral perfusion with the standard MAP target of 65 mmHg, potentially elevating the risk of neurological complications. For these high-risk patients, increasing the MAP target to optimize cerebral blood flow and preserve neurological function may improve patient outcomes. The Sepsis-BRAIN trial, a prospective, non-randomized, single-center study, is designed to evaluate optimal blood pressure targets under cerebral hemodynamic modulation in patients with septic shock \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In summary, personalized blood pressure management, tailored to the unique characteristics of individual patients, is vital for optimizing clinical outcomes.\u003c/p\u003e \u003cp\u003eElderly patients are intrinsically more susceptible to hemodynamic fluctuations compared to younger individuals due to a combination of physiological decline, diminished physiological reserve, immunosenescence, and a higher prevalence of comorbidities \u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. These factors collectively heighten their vulnerability to hemodynamic instability, underscoring the importance of individualized blood pressure management in the context of sepsis care. Adapting treatment strategies to accommodate the specific physiological attributes of elderly patients is crucial for enhancing their prognosis and quality of life. Despite its clinical significance, the optimal blood pressure target for elderly patients with sepsis remains contentious. The Surviving Sepsis Campaign (SSC) guidelines advocate for maintaining a mean arterial pressure (MAP) of \u0026ge;\u0026thinsp;65 mmHg in patients with septic shock; however, there is a lack of consensus regarding individualized targets for elderly patients \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. A meta-analysis conducted by Lamontagne et al. (2020) indicates that a standardized mean arterial pressure (MAP) target of 65 mmHg is generally applicable to the majority of sepsis patients. However, its appropriateness for elderly individuals remains a subject of debate due to their increased sensitivity to hypotension, which elevates the risk of cerebral hypoperfusion and renal dysfunction, potentially necessitating a higher MAP target \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The 65 Trial (Lamontagne et al., 2020) investigated the tolerability of a MAP below 65 mmHg in sepsis patients aged 65 and older \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The findings revealed that mild hypotension did not significantly elevate mortality rates; nevertheless, specific subpopulations, such as those with coronary artery disease or chronic kidney disease, exhibited heightened vulnerability to its adverse effects. Furthermore, elderly patients with pre-existing conditions such as hypertension, renal insufficiency, or cerebrovascular disease may derive benefit from a slightly elevated MAP target. In contrast, a retrospective study by Maheshwari et al. (2018) underscores the potential risks associated with excessive vasopressor use \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Elevated doses of norepinephrine can result in excessive vasoconstriction, thereby increasing cardiac workload, precipitating tissue ischemia, and causing disturbances in microcirculation. Furthermore, maintaining a mean arterial pressure (MAP) target above 75 mmHg may heighten the risk of cerebrovascular incidents and elevate myocardial oxygen consumption, potentially leading to increased in-hospital mortality, especially in patients requiring high-dose vasopressors. These findings emphasize the critical balance necessary in managing blood pressure among elderly patients with sepsis. While hypotension can result in organ dysfunction, the aggressive use of vasopressors may impose a substantial cardiac burden and elevate the risk of complications. This underscores the necessity for precise, individualized blood pressure management within a narrow safety margin. However, a universally accepted target for blood pressure in elderly sepsis patients remains undefined, highlighting the need for further research to refine hemodynamic goals and optimize patient outcomes.\u003c/p\u003e \u003cp\u003eIn this retrospective study involving 2,253 elderly sepsis patients from the MIMIC-IV database, we identified an optimal blood pressure range correlated with reduced in-hospital mortality and a decreased risk of atrial fibrillation (AF). Our analysis demonstrated that non-survivors exhibited significantly lower blood pressure during hospitalization compared to survivors, with median systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) values consistently lower in the mortality group (SBP: 110 vs. 116 mmHg; DBP: 53.0 vs. 54.5 mmHg; MAP: 70.0 vs. 72.0 mmHg; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that hypotension is a critical factor contributing to adverse outcomes in elderly sepsis patients. Conversely, although AF was associated with poorer clinical outcomes\u0026mdash;including increased rates of renal replacement therapy (RRT) and mechanical ventilation (MV) use, higher incidence of acute kidney injury (AKI), and prolonged ICU/hospital stays\u0026mdash;patients with AF presented slightly higher DBP and MAP compared to those without AF. This indicates a potential association between elevated blood pressure and the onset of AF. Restricted cubic spline (RCS) analysis further delineated a nonlinear relationship between blood pressure and clinical outcomes. We identified an L-shaped relationship between systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and in-hospital mortality, where the risk decreased as blood pressure increased beyond SBP\u0026thinsp;\u0026ge;\u0026thinsp;108 mmHg, DBP\u0026thinsp;\u0026ge;\u0026thinsp;51 mmHg, and MAP\u0026thinsp;\u0026ge;\u0026thinsp;69 mmHg. Conversely, the incidence of atrial fibrillation (AF) significantly increased when blood pressure exceeded SBP 118 mmHg, DBP 57 mmHg, and MAP 74 mmHg. These observations delineate an optimal blood pressure range (SBP: 108\u0026ndash;118 mmHg, DBP: 51\u0026ndash;57 mmHg, MAP: 69\u0026ndash;74 mmHg) that effectively balances the risks associated with hypotension-related mortality and hypertension-induced AF. The protective effect of this target range on 28-day mortality was corroborated through multiple statistical models, including propensity score matching (PSM) and inverse probability weighting (IPW).\u003c/p\u003e \u003cp\u003eOur findings are consistent with and expand upon existing research on blood pressure management in sepsis patients, particularly among the elderly. Hypotension has been consistently recognized as a critical factor in poor prognosis, with low MAP increasing mortality risk due to inadequate organ perfusion and subsequent multiorgan failure. Our study indicates that a marginally elevated mean arterial pressure (MAP) threshold (\u0026ge;\u0026thinsp;69 mmHg) may be more suitable for elderly patients with sepsis, corroborating recent evidence that suggests elderly individuals require higher perfusion pressures to ensure adequate tissue oxygenation \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Moreover, our findings contribute to the ongoing discourse on sepsis-related atrial fibrillation (AF) \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. While previous research has identified sepsis as a risk factor for new-onset AF, our results refine this association by demonstrating that elevated systolic blood pressure (SBP), diastolic blood pressure (DBP), and MAP (exceeding 118 mmHg, 57 mmHg, and 74 mmHg, respectively) are correlated with an increased incidence of AF. This observation is consistent with evidence indicating that excessive sympathetic activation and fluid resuscitation in sepsis may facilitate the onset of AF, particularly in patients with preexisting cardiovascular dysfunction \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Furthermore, our data support the hypothesis that AF exacerbates sepsis outcomes by contributing to hemodynamic instability and increasing thromboembolic risk \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study presents several significant strengths and contributions to the existing body of research on blood pressure management in elderly patients with sepsis. Firstly, it leverages the high-quality MIMIC-IV database, which comprises a large and diverse cohort of real-world critically ill patients, thereby ensuring a robust dataset for analysis. Secondly, the study employs advanced statistical methodologies, including restricted cubic splines (RCS), propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and doubly robust analysis. RCS facilitates a precise evaluation of nonlinear associations between blood pressure levels and key outcomes such as atrial fibrillation and in-hospital mortality. The application of multiple statistical models enhances the robustness of the results and mitigates confounding biases. Thirdly, the study offers clinically relevant insights by proposing an optimal blood pressure target for elderly sepsis patients receiving vasopressors. Notably, it suggests a higher mean arterial pressure (MAP) threshold (\u0026ge;\u0026thinsp;69 mmHg), challenging the conventional guideline recommendation of 65 mmHg and potentially influencing individualized treatment strategies.Moreover, this study highlights the intricate relationship between blood pressure regulation and the risk of atrial fibrillation, thereby contributing to the broader academic discourse on hemodynamic optimization in sepsis management.\u003c/p\u003e \u003cp\u003eDespite its contributions, the study is not without limitations. As a retrospective analysis utilizing the MIMIC-IV database, it is susceptible to selection bias and residual confounding. Although comprehensive statistical adjustments were employed, unmeasured variables\u0026mdash;such as fluid balance, medication dosages, and specific etiologies of sepsis\u0026mdash;may still affect the findings. Prospective trials are necessary to confirm whether the optimal blood pressure targets identified in this study lead to improved clinical outcomes. Furthermore, the study's reliance on median blood pressure values during hospitalization may not adequately capture hemodynamic variability. Given the significant fluctuations in blood pressure observed in sepsis, a single median value may not accurately represent exposure to hypotensive or hypertensive episodes, potentially resulting in misclassification bias. Future research should investigate continuous or time-dependent blood pressure modeling to provide a more precise evaluation of its impact on patient outcomes.\u003c/p\u003e \u003cp\u003e This study highlights that maintaining blood pressure within optimal ranges (SBP 108\u0026ndash;118 mmHg, DBP 51\u0026ndash;57 mmHg, MAP 69\u0026ndash;74 mmHg) may improve outcomes in elderly sepsis patients, suggesting a potential reevaluation of current blood pressure management guidelines in this population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThis study utilized data from the Medical Information Mart for Intensive Care (MIMIC) database (version 2.0), which is a publicly available de-identified database containing retrospective healthcare data from critically ill patients. Individual patient consent was waived due to the retrospective and anonymized nature of the data.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eWenzhou Basic Public Welfare Research Project Y2023575.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAUTHOR CONTRIBUTIONSYuanwen YE: Data curation, writing \u0026ndash; original draft; writing \u0026ndash; review and editing. LinglongCHEN: Writing \u0026ndash; original draft. Baohua YANG: Data curation; supervision. Yuequn XIE: Data curation; supervision; picture and table editing. Wang LV: Data curation. Feifei LI: Writing \u0026ndash; original draft; writing \u0026ndash; review and editing.Liangen LIN: Data curation\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would like to acknowledge all the study investigators for conducting this study.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll data in this article can be obtained from MIMIC-IV datebase(https://mimic.mit.edu/)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFleischmann C, Scherag A, Adhikari NK, Hartog CS, Tsaganos T, Schlattmann P, et al. Assessment of global incidence and mortality of hospital-treated sepsis. Current estimates and limitations. Am J Respir Crit Care Med. (2016) 193:259\u0026ndash;72. 10.1164/rccm.201504-0781OC\u003c/li\u003e\n\u003cli\u003eRudd KE, Kissoon N, Limmathurotsakul D, Bory S, Mutahunga B, Seymour CW, et al. The global burden of sepsis: barriers and potential solutions. Crit Care. (2018) 22:232. 10.1186/s13054-018-2157-z\u003c/li\u003e\n\u003cli\u003eCorr\u0026ecirc;a TD, Jakob SM, Takala J. Arterial blood pressure targets in septic shock: is it time to move to an individualized approach? Crit Care. 2015 Jun 18;19(1):264. doi: 10.1186/s13054-015-0958-x. PMID: 26084781; PMCID: PMC4472269.\u003c/li\u003e\n\u003cli\u003eEvans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. \u003cem\u003eIntensive Care Med\u003c/em\u003e. 2021;47(11):1181-1247. doi:10.1007/s00134-021-06506-y\u003c/li\u003e\n\u003cli\u003eDunser MW, Takala J, Ulmer H, Mayr VD, Luckner G, Jochberger S, et al. Arterial blood pressure during early sepsis and outcome. Intensive Care Med (2009) 35(7):1225\u0026ndash;33. doi: 10.1007/s00134-009-1427-2\u003c/li\u003e\n\u003cli\u003eZhao L, Fan Y, Wang Z, et al. The blood pressure targets in sepsis patients with acute kidney injury: An observational cohort study of multiple ICUs. \u003cem\u003eFront Immunol\u003c/em\u003e. 2022;13:1060612. Published 2022 Dec 15. doi:10.3389/fimmu.2022.1060612\u003c/li\u003e\n\u003cli\u003eDeng J, Li L, Feng Y, Yang J. Comprehensive Management of Blood Pressure in Patients with Septic AKI. \u003cem\u003eJ Clin Med\u003c/em\u003e. 2023;12(3):1018. Published 2023 Jan 28. doi:10.3390/jcm12031018\u003c/li\u003e\n\u003cli\u003eLee SH, Hsu TC, Lee MG, et al. Nationwide Trend of Sepsis: A Comparison Among Octogenarians, Elderly, and Young Adults. \u003cem\u003eCrit Care Med\u003c/em\u003e. 2018;46(6):926-934. doi:10.1097/CCM.0000000000003085\u003c/li\u003e\n\u003cli\u003eHe W, Xiao K, Fang M, Xie L. Immune Cell Number, Phenotype, and Function in the Elderly with Sepsis. \u003cem\u003eAging Dis\u003c/em\u003e. 2021;12(1):277-296. Published 2021 Feb 1. doi:10.14336/AD.2020.0627\u003c/li\u003e\n\u003cli\u003eChen XC, Yang YF, Wang R, Gou HF, Chen XZ. Epidemiology and microbiology of sepsis in mainland China in the first decade of the 21st century. Int J Infect Dis. 2015;31:9\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eTiruvoipati, Ravindranath et al. \u0026ldquo;Hypothermia predicts mortality in critically ill elderly patients with sepsis.\u0026rdquo; \u003cem\u003eBMC geriatrics\u003c/em\u003e vol. 10 70. 27 Sep. 2010, doi:10.1186/1471-2318-10-70\u003c/li\u003e\n\u003cli\u003eJiang M, Wu W, Wang X, Zhao C. Analysis of the Predictive Effect of Lactic Acid Combined with Cardiac Troponin T and 5-Hydroxytryptophan on the Severity of Sepsis in ICU Patients and Its Correlation with Prognosis. Contrast Media Mol Imaging. 2022 Sep 16;2022:6215282. doi: 10.1155/2022/6215282. Retraction in: Contrast Media Mol Imaging. 2023 Jul 26;2023:9863270. doi: 10.1155/2023/9863270. PMID: 36185579; PMCID: PMC9507666.\u003c/li\u003e\n\u003cli\u003ePan P, Xie LX. [Surviving Sepsis Campaign guideline update:problems and progress]. Zhonghua Jie He He Hu Xi Za Zhi. 2024 Oct 12;47(10):901-905. Chinese. doi: 10.3760/cma.j.cn112147-20240422-00215. PMID: 39406534.\u003c/li\u003e\n\u003cli\u003eSheats MK. A Comparative Review of Equine SIRS, Sepsis, and Neutrophils. Front Vet Sci. 2019 Mar 12;6:69. doi: 10.3389/fvets.2019.00069. PMID: 30931316; PMCID: PMC6424004.\u003c/li\u003e\n\u003cli\u003eGiustozzi M, Ehrlinder H, Bongiovanni D, Borovac JA, Guerreiro RA, Gąsecka A, Papakonstantinou PE, Parker WAE. Coagulopathy and sepsis: Pathophysiology, clinical manifestations and treatment. Blood Rev. 2021 Nov;50:100864. doi: 10.1016/j.blre.2021.100864. Epub 2021 Jun 25. PMID: 34217531.\u003c/li\u003e\n\u003cli\u003eCury P, Passos RDH, Alves F, Brasil S, Frigieri G, Taccone FS, Panerai RB, Caldas J. Impact of different blood pressure targets on cerebral hemodynamics in septic shock: A prospective pilot study protocol-SEPSIS-BRAIN. PLoS One. 2024 Oct 14;19(10):e0304412. doi: 10.1371/journal.pone.0304412. PMID: 39401208; PMCID: PMC11472940.\u003c/li\u003e\n\u003cli\u003eLamontagne F, Day AG, Meade MO, Cook DJ, Guyatt GH, Hylands M, Radermacher P, Chr\u0026eacute;tien JM, Beaudoin N, H\u0026eacute;bert P, D\u0026apos;Aragon F, Meziani F, Asfar P. Pooled analysis of higher versus lower blood pressure targets for vasopressor therapy septic and vasodilatory shock. Intensive Care Med. 2018 Jan;44(1):12-21. doi: 10.1007/s00134-017-5016-5. Epub 2017 Dec 19. PMID: 29260272.\u003c/li\u003e\n\u003cli\u003eLamontagne F, Richards-Belle A, Thomas K, Harrison DA, Sadique MZ, Grieve RD, Camsooksai J, Darnell R, Gordon AC, Henry D, Hudson N, Mason AJ, Saull M, Whitman C, Young JD, Rowan KM, Mouncey PR; 65 trial investigators. Effect of Reduced Exposure to Vasopressors on 90-Day Mortality in Older Critically Ill Patients With Vasodilatory Hypotension: A Randomized Clinical Trial. JAMA. 2020 Mar 10;323(10):938-949. doi: 10.1001/jama.2020.0930. PMID: 32049269; PMCID: PMC7064880.\u003c/li\u003e\n\u003cli\u003eMaheshwari K, Nathanson BH, Munson SH, Khangulov V, Stevens M, Badani H, Khanna AK, Sessler DI. The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients. Intensive Care Med. 2018 Jun;44(6):857-867. doi: 10.1007/s00134-018-5218-5. Epub 2018 Jun 5. PMID: 29872882; PMCID: PMC6013508.\u003c/li\u003e\n\u003cli\u003eAlhamyani AH, Alamri MS, Aljuaid NW, Aloubthani AH, Alzahrani S, Alghamdi AA, Lajdam AS, Alamoudi H, Alamoudi AA, Albulushi AM, AlQarni SN. Sepsis in Aging Populations: A Review of Risk Factors, Diagnosis, and Management. Cureus. 2024 Dec 2;16(12):e74973. doi: 10.7759/cureus.74973. PMID: 39744263; PMCID: PMC11691596.\u003c/li\u003e\n\u003cli\u003eIbarz M, Haas LEM, Ceccato A, Artigas A. The critically ill older patient with sepsis: a narrative review. Ann Intensive Care. 2024 Jan 10;14(1):6. doi: 10.1186/s13613-023-01233-7. PMID: 38200360; PMCID: PMC10781658.]\u003c/li\u003e\n\u003cli\u003eBosch NA, Cohen DM, Walkey AJ. Risk Factors for New-Onset Atrial Fibrillation in Patients With Sepsis: A Systematic Review and Meta-Analysis. Crit Care Med. 2019 Feb;47(2):280-287. doi: 10.1097/CCM.0000000000003560. PMID: 30653473; PMCID: PMC9872909.\u003c/li\u003e\n\u003cli\u003eBoos CJ. Infection and atrial fibrillation: inflammation begets AF. Eur Heart J. 2020 Mar 7;41(10):1120-1122. doi: 10.1093/eurheartj/ehz953. PMID: 31971996.\u003c/li\u003e\n\u003cli\u003eCorica B, Romiti GF, Basili S, Proietti M. Prevalence of New-Onset Atrial Fibrillation and Associated Outcomes in Patients with Sepsis: A Systematic Review and Meta-Analysis. J Pers Med. 2022 Mar 30;12(4):547. doi: 10.3390/jpm12040547. PMID: 35455662; PMCID: PMC9026551.\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":"Sepsis, elderly, blood pressure, 28-day mortality, optimal range","lastPublishedDoi":"10.21203/rs.3.rs-6011063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6011063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo explore the optimal range of blood pressure (BP) in elderly sepsis patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective case-control study was conducted. Demographic information, coexisting illnesses, vital signs, laboratory parameters, critical illness scores, and clinical treatment information for elderly sepsis patients were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV). Restricted cubic spline (RCS) analysis was employed to examine and visualize the nonlinear relationship between blood pressure and the incidences of in-hospital mortality and atrial fibrillation. Optimal systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) ranges were identified, and their association with 28-day mortality was validated using Cox regression analysis, propensity score matching (PSM), inverse probability weighting (IPTW), doubly robust model estimation (DR), and Kaplan\u0026ndash;Meier survival curves (K-M).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 2,253 patients met the inclusion criteria, of whom 516 (22.9%) died during hospitalization and 1,087 (48.2%) experienced atrial fibrillation during hospitalization. Restricted cubic spline analysis revealed a nonlinear, L-shaped relationship between blood pressure and in-hospital mortality among elderly sepsis patients. When atrial fibrillation was used as the endpoint, the upper limit of blood pressure was constrained. The optimal SBP, DBP and MAP ranges for elderly sepsis patients were 108\u0026ndash;118, 51\u0026ndash;57, and 69\u0026ndash;74 mmHg, respectively. Further statistical models confirmed that patients within the optimal blood pressure range exhibited decreased 28-day mortality compared to those outside this range [optimal blood pressure group: SBP (108\u0026ndash;118 mmHg): Cox regression analysis: hazard ratio (HR)\u0026thinsp;=\u0026thinsp;0.76, 95% confidence interval (CI) 0.64\u0026ndash;0.91, P\u0026thinsp;=\u0026thinsp;0.002; PSM: HR\u0026thinsp;=\u0026thinsp;0.78, 95% CI 0.64\u0026ndash;0.95, P\u0026thinsp;=\u0026thinsp;0.015; IPTW: HR\u0026thinsp;=\u0026thinsp;0.79, 95% CI 0.65\u0026ndash;0.95, P\u0026thinsp;=\u0026thinsp;0.015; DR: HR\u0026thinsp;=\u0026thinsp;0.78, 95% CI 0.64\u0026ndash;0.96, P\u0026thinsp;=\u0026thinsp;0.018; DBP (51\u0026ndash;57 mmHg): Cox regression analysis: HR\u0026thinsp;=\u0026thinsp;0.79, 95% CI 0.67\u0026ndash;0.95, P\u0026thinsp;=\u0026thinsp;0.010; PSM: HR\u0026thinsp;=\u0026thinsp;0.72, 95% CI 0.64\u0026ndash;0.88, P\u0026thinsp;=\u0026thinsp;0.001; IPTW: HR\u0026thinsp;=\u0026thinsp;0.80, 95% CI 0.66\u0026ndash;0.96, P\u0026thinsp;=\u0026thinsp;0.015; DR: HR\u0026thinsp;=\u0026thinsp;0.81, 95% CI 0.67\u0026ndash;0.98, P\u0026thinsp;=\u0026thinsp;0.032; MAP (69\u0026ndash;74 mmHg): Cox regression analysis: HR\u0026thinsp;=\u0026thinsp;0.83, 95% CI 0.69\u0026ndash;0.99, P\u0026thinsp;=\u0026thinsp;0.044; PSM: HR\u0026thinsp;=\u0026thinsp;0.78, 95% CI 0.64\u0026ndash;0.95, P\u0026thinsp;=\u0026thinsp;0.016; IPTW: HR\u0026thinsp;=\u0026thinsp;0.82, 95% CI 0.67\u0026ndash;0.99, P\u0026thinsp;=\u0026thinsp;0.040; DR: HR\u0026thinsp;=\u0026thinsp;0.85, 95% CI 0.67\u0026ndash;1.07, P\u0026thinsp;=\u0026thinsp;0.172]. K\u0026ndash;M survival analysis demonstrated that patients within the optimal blood pressure range had a higher probability of survival than those outside the range (SBP: Log-Rank test: χ\u0026sup2; = 4.9, P\u0026thinsp;=\u0026thinsp;0.0268; DBP: Log-Rank test: χ\u0026sup2; = 5.06, P\u0026thinsp;=\u0026thinsp;0.0244; MAP: Log-Rank test: χ\u0026sup2; = 7.76, P\u0026thinsp;=\u0026thinsp;0.00533).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDuring hospitalization, both elevated and reduced blood pressure levels in elderly sepsis patients are associated with an increased risk of mortality. The optimal ranges for SBP, DBP, and MAP in elderly sepsis patients are 108\u0026ndash;118, 51\u0026ndash;57, and 69\u0026ndash;74 mmHg, respectively.\u003c/p\u003e","manuscriptTitle":"Exploring the Optimal Range of Blood Pressure in Elderly Sepsis Patients: A Retrospective Study Based on MIMIC-IV Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-18 13:46:43","doi":"10.21203/rs.3.rs-6011063/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":"3dee7944-d4fb-4f6c-80d8-150675333dfb","owner":[],"postedDate":"February 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-24T10:09:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-18 13:46:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6011063","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6011063","identity":"rs-6011063","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}