Stress hyperglycemia ratio as a predictor of hydrocephalus in critically ill patients with intracerebral hemorrhage: a retrospective study and machine learning-based model

Stress hyperglycemia ratio as a predictor of hydrocephalus in critically ill patients with intracerebral hemorrhage: a retrospective study and machine learning-based model | 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 Article Stress hyperglycemia ratio as a predictor of hydrocephalus in critically ill patients with intracerebral hemorrhage: a retrospective study and machine learning-based model Shuang Zhao, Lin Zhang, Yue Xiao, Yifan Miao, Yun Lu, Yang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7608266/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 Hydrocephalus is a severe complication of intracerebral hemorrhage (ICH), and its early prediction remains challenging. The stress hyperglycemia ratio (SHR), reflecting acute glucose elevation relative to baseline, has been associated with poor outcomes in ICH, but its relationship with hydrocephalus is unclear. We retrospectively analyzed 1,383 critically ill ICH patients from the MIMIC-IV database and evaluated associations between SHR and hydrocephalus using multivariable logistic regression, restricted cubic spline analysis, and subgroup analyses. Hydrocephalus occurred in 8.97% of patients, with higher incidence in those with elevated SHR (11.91% vs. 5.59%, P < 0.001). SHR was independently associated with hydrocephalus as both a continuous (OR = 1.72, 95% CI: 1.20–2.48, P = 0.003) and categorical variable (OR = 1.75, 95% CI: 1.15–2.69, P = 0.01), with linear risk increase above 1.05. A logistic regression model combining SHR with six clinical variables (mean SpO₂, intraventricular hemorrhage, mannitol use, mechanical ventilation, sepsis, and age) achieved best predictive performance (AUC = 0.81) and maintained accuracy in an external cohort (AUC = 0.76, n = 513). These findings indicate that SHR is a valuable predictor of hydrocephalus after ICH and may facilitate early risk stratification and individualized clinical management. Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Health sciences/Risk factors Stress hyperglycemia ratio Stress-induced hyperglycemia Intracerebral hemorrhage Hydrocephalus Prediction model External validation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Intracerebral hemorrhage (ICH) is a life-threatening neurological emergency characterized by high mortality and long-term disability 1 . Approximately 30% of ICH patients require intensive care unit (ICU) admission, reflecting the clinical severity of this condition 2 , 3 . In addition to the primary mass effect of the hematoma, secondary pathological processes such as neuroinflammation can exacerbate brain injury and precipitate serious complications 4 . Among these, hydrocephalus is the second most frequent complication of ICH, affecting approximately 11% of inpatients, and two-thirds of cases manifest within 72 hours of hemorrhage onset 5 , 6 . Furthermore, hydrocephalus is associated with longer hospital stays, increased healthcare costs, and worse functional recovery 7 . These findings underscore the prognostic significance of hydrocephalus and highlight the importance of early recognition and risk stratification. In this context, identifying reliable early predictors of hydrocephalus is essential to improve outcomes in critically ill ICH patients. Stress-induced hyperglycemia (SIH) is an early metabolic disturbance and occurs in nearly 60% of patients with ICH irrespective of diabetes status 8 . It is typically reflected by elevated admission blood glucose (ABG) levels 9 . However, ABG alone may not accurately capture SIH severity, as it is shaped by both acute stress responses and pre-existing glycemic status. To address this limitation, the stress hyperglycemia ratio (SHR), which incorporates both ABG and glycated hemoglobin A1c (HbA1c), has been proposed as a more reliable indicator of SIH 10 . In patients with ICH, elevated SHR has been associated with hematoma expansion and neuroinflammation 11 – 12 . Since both factors may contribute to hydrocephalus pathogenesis 13 , we hypothesized that elevated SHR may play a role in the development of hydrocephalus. Therefore, we conducted a retrospective study to investigate the association between SHR and the development of hydrocephalus following ICH in critically ill patients. Additionally, we developed machine learning-based models incorporating multiple routinely available clinical variables to predict the risk of hydrocephalus and support early risk stratification in neurocritical care. Materials and methods Data source and ethical approval We conducted a retrospective study using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and developed predictive models with machine learning algorithms based on this dataset. Data from the Neurointensive Care Unit of the Third Hospital of Mianyang (2020–2024) were used for external validation of the optimal model. An overview of the study design is illustrated in Fig. 1 . The MIMIC-IV database (version 3.1), developed by the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center, contains de-identified patient records from 2008 to 2019. As a publicly available dataset, it is exempt from institutional review board approval and informed consent. Access to the database requires completion of the National Institutes of Health online course. The first author completed this course and obtained certification (ID: 13356665). The external validation cohort from the Third Hospital of Mianyang was approved by the institutional ethics committee, with informed consent waived due to de-identification. Study population Patients with ICH were identified using ICD-9 codes (431, 4329) and ICD-10 codes (I610–I619, I629). To ensure data completeness and clinical consistency, we applied the following exclusion criteria: (1) missing ABG or HbA1c measurements; (2) ICU stay shorter than 24 hours; (3) traumatic brain injury, subarachnoid hemorrhage, brain tumors, or hemorrhagic transformation secondary to other conditions; and (4) age younger than 18 years. For patients with multiple ICU admissions, only the first admission was included. The primary outcome was the development of hydrocephalus. Hydrocephalus diagnosis relied on ICD codes (ICD-9: 3313, 3314; ICD-10: G91, G910-911, G914, G919). Variable extraction All variables were retrieved from the MIMIC-IV database using structured query language scripts run in pgAdmin, with data restricted to the first 24 hours after ICU admission. Demographic information included age, sex, and weight. Laboratory tests encompassed red blood cell (RBC) count, white blood cell (WBC) count, neutrophil count, platelet count (PLT), C-reactive protein (CRP), blood urea nitrogen (BUN), serum creatinine, pH, lactate, anion gap, serum electrolytes (sodium, potassium, calcium), carbon dioxide pressure, arterial oxygen pressure, international normalized ratio (INR), D-dimer, prothrombin time (PT), partial thromboplastin time (PTT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), ABG, and HbA1c. Severity was evaluated using the Glasgow Coma Scale (GCS), Oxford Acute Severity of Illness Score (OASIS), Systemic Inflammatory Response Syndrome (SIRS) score, Sequential Organ Failure Assessment (SOFA) score, and Simplified Acute Physiology Score II (SAPS II). Vital signs comprised averaged measurements of body temperature, heart rate (HR), and respiratory rate (RR), blood pressure (including arterial pressure, systolic and diastolic pressures), as well as peripheral oxygen saturation (SpO 2 ). Comorbidities included intraventricular hemorrhage (IVH), diabetes, hypertension, myocardial infarction (MI), heart failure (HF), chronic obstructive pulmonary disease (COPD), sepsis, and chronic kidney disease (CKD). Treatment methods included mannitol use, oxygen delivery, surgery, vasoactive drugs, mechanical ventilation, and statin use. SHR calculation Although SHR is widely recognized as an objective indicator of SIH, there is currently no standardized method for its calculation. Two primary approaches have been reported: one based on fasting glucose and HbA1c 14 , and the other utilizing ABG and HbA1c 2 . In this study, we adopted the latter method, as ABG is readily available upon hospital arrival and does not require prior fasting, thus offering greater practicality in acute care settings. Accordingly, SHR was calculated using the formula 15 : $$\:\text{SHR}\text{=}\text{ABG}\text{(}\text{mg}\text{/}\text{dL}\text{)/[28.7×}\text{HbA}\text{1}\text{c}\text{(\%)−46.7]}$$ Statistical analysis Retrospective study As a retrospective study using an existing database, no a priori sample size calculation was performed. Variables with > 20% missing data were excluded, while those with ≤ 20% missingness were imputed using the random forest-based MissForest algorithm. We evaluated multicollinearity among the independent variables via the variance inflation factor (VIF). Variables with VIF values above 5 were sequentially excluded, guided by clinical relevance, until all remaining features showed acceptable levels of multicollinearity. Patients were stratified into two groups according to the optimal cutoff value of SHR, which was determined by maximizing the Youden Index derived from the receiver operating characteristic curve to achieve the best balance of sensitivity and specificity. The normality of continuous variables was evaluated using the Shapiro-Wilk test. Data with a normal distribution were summarized as means and standard deviations (SD) and compared using independent t-tests. Variables not following a normal distribution were expressed as medians with interquartile ranges (IQR) and analyzed using the Mann-Whitney U test. Categorical variables were reported as frequencies and proportions, and group differences were assessed using either the chi-square test or Fisher’s exact test, as appropriate. Logistic regression (LR) analysis was employed to explore the relationship between SHR and the development of hydrocephalus. The analysis began with a crude (unadjusted) model, followed by Model I, which adjusted for age, sex, and weight. Model II extended the adjustments to include clinical comorbidities including IVH, diabetes, hypertension, and sepsis, along with severity scores (GCS, OASIS, SIRS, SOFA, and SAPS II). A restricted cubic spline (RCS) analysis with four knots was applied to investigate potential non-linear relationships between SHR and hydrocephalus. To assess the consistency of the association across different populations, stratified subgroup analyses were performed based on age, sex, and the presence of diabetes, sepsis, and IVH. Establishment and validation of the prediction models Prior to model development, missing data and outliers in the MIMIC-IV dataset were addressed through preprocessing procedures conducted in the retrospective study. The externally collected dataset was manually curated and contained no missing or aberrant values. The MIMIC-IV cohort was randomly divided into a development set (70%) and an internal validation set (30%). External validation data included only the final model predictors, outcome (hydrocephalus), and key demographics (age, sex, comorbidities). Variable definitions and measurements were consistent across datasets. Due to class imbalance in the dependent variables, oversampling was used to balance the data. Feature selection was conducted in two steps. First, the Boruta algorithm, a wrapper method based on random forest that compares variable importance with randomized shadow features, was used to identify all relevant predictors 16 . Second, least absolute shrinkage and selection operator (LASSO) regression was applied to improve model interpretability and reduce overfitting risk. This method uses L1 regularization to shrink irrelevant coefficients to zero 17 . Four algorithms were used for model development: LR, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Hyperparameters were tuned via five-fold cross-validation. Model performance was assessed by discrimination using the AUC. Calibration curves were used to evaluate agreement between predicted and observed outcomes. Decision curve analysis (DCA) was applied to assess clinical utility. Statistical analyses were conducted using Stata (version 18.0) for the retrospective study and R (version 4.1.2) for model development and validation. A two-sided P value < 0.05 was considered statistically significant. Results Retrospective study Baseline characteristics A total of 1,383 critically ill ICH patients were included in this study. The missing data proportions and VIFs are detailed in Figure S1 and Table S1 . Baseline characteristics are summarized in Table 1 . Of the included patients, 636 (45.99%) were female, with a mean age of 69.53 ± 14.59 years and a mean weight of 80.08 ± 21.91 kg. Comorbidities included IVH (12.94%), hypertension (62.83%), diabetes (30.73%), HF (17.86%), COPD (6.07%), MI (6.15%), CKD (12.94%), and sepsis (37.53%). Table 1 Baseline characteristics according to SHR Overall, N = 1383 Group1, SHR < 1.03, N = 644 Group2, SHR ≥ 1.03, N = 739 P value Age, (years) 69.53 ± 14.59 70.37 ± 14.46 68.80 ± 14.67 0.046 Sex, female, n(%) 636 (45.99) 293 (45.50) 343 (46.41) 0.733 Weight, (kg) 80.08 ± 21.91 79.07 ± 21.66 80.94 ± 22.09 0.113 Laboratory indicators RBC, (m/uL) 4.19 ± 0.70 4.24 ± 0.70 4.15 ± 0.70 0.018 WBC, (K/uL) 10.63 ± 4.20 9.59 ± 3.37 11.54 ± 4.63 < 0.001 INR 1.22 ± 0.28 1.21 ± 0.29 1.22 ± 0.28 0.742 PTT 28.60 (26.00,31.10) 28.80 (26.40,31.20) 28.30 (25.60,31.10) 0.014 PLT, (K/uL) 220.32 ± 80.27 225.68 ± 79.79 215.64 ± 80.44 0.020 Creatinie, (mg/dL) 0.90 (0.70,1.10) 0.90 (0.80,1.10) 0.90 (0.70,1.20) 0.954 Potassium, (mEq/L) 4.06 ± 0.66 4.06 ± 0.63 4.05 ± 0.68 0.654 BUN, (mg/dL) 16.00 (13.00,22.00) 16.00 (12.00,22.00) 17.00 (13.00,23.00) 0.057 Disease severity score GCS 15.00 (14.00,15.00) 15.00 (14.00,15.00) 15.00 (13.00,15.00) 0.066 OASIS 31.00 (26.00,36.00) 30.00 (25.00,36.00) 31.00 (26.00,37.00) 0.003 SIRS 2.00 (2.00,3.00) 2.00 (1.00,3.00) 2.00 (2.00,3.00) < 0.001 SOFA 3.00 (1.00,4.00) 2.00 (1.00,4.00) 3.00 (2.00,5.00) < 0.001 SAPS II 32.78 ± 11.56 32.26 ± 11.25 33.23 ± 11.80 0.118 Vital signs Mean HR, (beats/min) 80.56 ± 13.73 78.02 ± 13.01 82.78 ± 13.96 < 0.001 Mean SBP, (mmHg) 131.25 ± 13.94 131.17 ± 13.72 131.31 ± 14.14 0.845 Mean BP, (mmHg) 87.05 ± 10.49 87.85 ± 10.54 86.35 ± 10.41 0.008 Mean RR, (times/min) 18.89 ± 3.08 18.49 ± 2.87 19.23 ± 3.21 < 0.001 Mean temperature, (℃) 37.01 ± 0.41 36.99 ± 0.42 37.03 ± 0.40 0.058 Mean SpO 2 , (%) 96.94 ± 1.78 96.78 ± 1.80 97.07 ± 1.76 0.003 Comorbidities IVH, n(%) 179 (12.94) 56 (8.70) 123 (16.64) < 0.001 COPD, n(%) 84 (6.07) 42 (6.52) 42 (5.68 ) 0.515 Diabetes, n(%) 425 (30.73) 180 (27.95) 245 (33.15) 0.036 Hypertension, n(%) 869 (62.83) 394 (61.18) 475 (64.28) 0.235 MI, n(%) 85 (6.15) 43 (6.68) 42 (5.68) 0.443 HF, n(%) 247 (17.86) 119 (18.48) 128 (17.32) 0.575 CKD, n(%) 179 (12.94) 92 (14.29) 87 (11.77) 0.165 Sepsis, n(%) 519 (37.53) 215 (33.39) 304 (41.14) 0.003 Treatments Mannitol use, n(%) 139 (10.05) 51 (7.92) 88 (11.91) 0.014 Oxygen Delivery, n(%) 904 (65.37) 394 (61.18) 510 (69.01) 0.002 Surgery, n(%) 23 (1.66) 9 (11.40) 14 (1.89) 0.471 Vasoactive drug, n(%) 168 (12.15) 71 (11.02) 97 (13.13) 0.233 Mechanical ventilation, n(%) 973 (70.35) 402 (62.42) 571 (77.27) < 0.001 Statin use, n(%) 703 (50.83) 347 (53.88) 356 (48.17) 0.034 Outcomes Hydrocephalus, n(%) 124 (8.97) 36 (5.59) 88 (11.91) < 0.001 Continuous variables were expressed as mean ± standard deviation (SD) for normally distributed variables, median (IQR) for non-normally distributed continuous variables, and numbers (%) for categorical variables. RBC Red Blood Cell; WBC White Blood Cell; INR International Normalized Ratio; PTT Partial Thromboplastin Time; PLT Platelet; BUN Blood Urea Nitrogen; GCS Glasgow cComa Scale; OASIS Oxford Acute Severity of Illness Score; SIRS Systemic Inflammatory Response Syndrome; SOFA Sequential Organ Failure Assessment; SAPSII Simplified Acute Physiology Score II; HR Heart Rate; SBP Systolic Blood Pressure; BP Blood Pressure; RR Respiratory Rate; SpO 2 Peripheral Oxygen Saturation; IVH Intraventricular Hemorrhage; COPD Chronic Obstructive Pulmonary Disease; MI Myocardial Infarction; HF Heart Failure; CKD Chronic Kidney Disease. Patients were stratified into two groups based on the optimal SHR cutoff of 1.03 (Group 1: SHR < 1.03; Group 2: SHR ≥ 1.03). Compared to Group 1, Group 2 patients were generally younger and had higher HR, RR, SpO₂, OASIS, SIRS, and SOFA scores, along with elevated WBC count. They also had increased rates of IVH, diabetes, and sepsis, and were more likely to receive mannitol, oxygen therapy, mechanical ventilation, and statins. Conversely, Group 2 showed lower mean blood pressure, RBC count, PTT, and PLT levels. Baseline characteristics stratified by hydrocephalus status are presented in Table S2. Patients with hydrocephalus were younger and had higher OASIS, SIRS, and SOFA scores. They also had higher rates of IVH and sepsis and were more frequently treated with mechanical ventilation and mannitol. Creatinine, WBC count, and ABG levels were elevated in this group. Notably, SHR was significantly higher in patients with hydrocephalus (1.26 ± 0.42) than in those without (1.10 ± 0.39) (P < 0.001). Clinical outcomes In the overall cohort, the incidence of hydrocephalus was 8.97%, with a significantly higher rate observed in Group 2 than Group 1 (11.91% vs. 5.59%, P < 0.001) (Table 1 ). Multivariable LR analysis demonstrated that SHR was independently associated with the risk of hydrocephalus, whether assessed as a continuous variable (odds ratio [OR] = 1.72, 95% confidence interval [CI]: 1.20–2.48, P = 0.003) or dichotomized (OR = 1.75, 95% CI: 1.15–2.69, P = 0.01) (Table 2 ). RCS analysis indicated a positive linear relationship between SHR and hydrocephalus risk (P for non-linearity = 0.17; overall P < 0.001), with the probability of hydrocephalus rising steadily when SHR exceeded approximately 1.05 (Fig. 2 ). Subgroup analyses consistently supported this association across all predefined strata, with no significant interaction detected (all P for interaction > 0.05) (Fig. 3 ). Table 2 Association between SHR and hydrocephalus: logistic regression analysis Crude model Model I Model II OR(95% CI) P value OR(95% CI) P value OR(95% CI) P value SHR as Continuous 2.18 (1.36–3.49) 0.001 2.15 (1.34–3.44) 0.001 1.72 (1.20–2.48) 0.003 SHR < 1.03 Reference Reference Reference SHR ≥ 1.03 2.28 (1.53–3.42) < 0.001 2.22 (1.48–3.33) < 0.001 1.75 (1.15–2.69) 0.01 Establishment and validation of the prediction models Feature selection was first performed using the Boruta algorithm (Fig. 4 A), which identified important (green-labeled) and unimportant (red-labeled) variables. Subsequently, LASSO regression with the parsimonious λ.1se penalty (Fig. 4 B, Fig. 4 C) retained seven predictors with non-zero coefficients: SHR, age, mean SpO₂, IVH, mannitol use, mechanical ventilation, and sepsis. Model performance metrics are presented in Fig. 5 . The LR model achieved the highest discriminative ability (AUC = 0.81), outperforming XGBoost (0.75), LightGBM (0.73), and RF (0.70). Calibration curves (Figure S2) indicated good agreement between predicted and observed probabilities, while DCA (Figure S3) demonstrated favorable net clinical benefit across all models. Given its superior performance and interpretability, the LR model was further visualized with coefficient plots and 95% CIs (Figure S4). A nomogram was subsequently developed (Fig. 6 ) to enable individualized risk prediction of hydrocephalus in critically ill ICH patients. A total of 513 patients were included for external validation. Their baseline clinical characteristics are summarized in Table S3. Although there were differences in baseline characteristics between the two cohorts, the model maintained good generalizability (AUC = 0.76, Figure S5). Discussion This study demonstrated that elevated SHR was independently associated with an increased risk of hydrocephalus in critically ill patients with ICH, showing a linear association beyond an SHR threshold of 1.05. Notably, this threshold is close to the cutoff value used in our study, supporting the rationale of employing a dichotomized grouping method based on this cutoff. Across multiple predictive models, SHR consistently emerged as a key predictor. The LR model showed the best overall performance in terms of discrimination, calibration, and clinical utility. It also retained robust generalizability in external validation. To our knowledge, this is the first study to comprehensively assess the association between SHR and hydrocephalus after ICH. The association between elevated SHR and hydrocephalus may be partly mediated by inflammation. In our study, patients with higher SHR showed increased WBC counts, higher SIRS scores, and a greater incidence of sepsis, reflecting systemic inflammatory activation. SIH is known to trigger inflammatory pathways through glucocorticoid release and sympathetic nervous system stimulation 18 , 19 . Inflammation, in turn, is a key driver of hydrocephalus after ICH 20 . Several mechanisms may underlie this link. First, SIH can induce systemic and central inflammatory responses that stimulate cerebrospinal fluid (CSF) hypersecretion by activating epithelial cells of the choroid plexus. Elevated glucose levels promote the release of proinflammatory cytokines such as interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) 21,22 . These cytokines upregulate sodium-potassium-chloride cotransporter 1 (NKCC1) and aquaporin-1 (AQP1) expression on choroid plexus epithelial cells, thereby enhancing fluid production across the blood–CSF barrier 23 , 13 . Second, SIH can promote neutrophil priming and facilitates the formation of the neutrophil extracellular traps (NETs) 24 . These extracellular networks can accumulate near the ventricular system, obstructing CSF flow, and simultaneously act as damage-associated molecular patterns (DAMPs) that activate microglial Toll-like receptor signaling, perpetuating neuroinflammation 8 , 23 . Third, SIH drives microglial polarization toward the proinflammatory M1 phenotype, promoting interleukin-6, reactive oxygen species, and nitric oxide release. These mediators can injure periventricular ependymal cells and hinder CSF absorption through arachnoid granulations 25 , 4 . Together, these inflammatory cascades may provide a mechanistic basis for how elevated SHR contributes to the development of hydrocephalus after ICH. In addition to inflammation, IVH is a major risk factor for hydrocephalus after ICH. In our study, higher SHR was associated with a greater incidence of IVH. This suggests that acute glucose dysregulation may facilitate intraventricular extension of hemorrhage. Previous studies also reported that SIH increases the risk of hematoma expansion, thereby raising the chance of ventricular rupture. SHR has also been identified as a reliable predictor of early hematoma growth 26 , 27 . Mechanistically, SIH promotes leukocyte-endothelial adhesion and upregulates matrix metalloproteinases such as Matrix Metalloproteinase-9 (MMP-9). These enzymes degrade extracellular matrix and disrupt tight junctions, weakening the integrity of cerebral microvessels 28 , 29 , 30 . This vascular injury predisposes to hematoma propagation and ventricular entry. In addition, preclinical ICH models show that hyperglycemia activates plasma kallikrein. This inhibits platelet aggregation and impairs clot stability, further predisposing to hematoma expansion 31 . Such hemorrhagic progression into the ventricular system provides a plausible structural link between elevated SHR and the development of hydrocephalus after ICH. In this study, we also developed a simple and clinically applicable model to predict hydrocephalus in ICH patients. The model incorporated seven readily available variables: SHR, IVH, mean SpO₂, mannitol use, mechanical ventilation, sepsis, and age. All variables differed significantly between hydrocephalus and non-hydrocephalus groups, underscoring their clinical relevance. As previously discussed, SHR and IVH confirmed their prognostic value. Mechanical ventilation reflected severe neurological impairment and reduced intracranial compliance, predisposing to CSF circulation disturbances 32 . By contrast, higher mean SpO₂ in hydrocephalus patients likely reflected ventilatory support rather than preserved physiological reserve. Mannitol use indicated intracranial hypertension and was associated with more severe ICH and secondary complications 33 . Sepsis denoted systemic inflammation that exacerbate brain injury and impair CSF dynamics 34 . Notably, both cohort data and model contributions revealed a negative correlation between age and hydrocephalus risk, with younger patients at higher risk. This pattern may partly reflect case-mix differences. Younger patients are more likely to have structural etiologies such as arteriovenous malformations. These conditions often present with IVH and are associated with higher rates of CSF diversion 35 . Conversely, older patients frequently face early decisions to limit life-sustaining treatment, introducing competing risks that may obscure the detection of hydrocephalus before death 36 . Collectively, these predictors capture complementary aspects of disease severity and pathophysiology, forming a mechanistic foundation for the model’s interpretability. Building on this framework, the LR model demonstrated robust performance, translating these pathophysiological insights into clinically actionable tools. It enables clear risk stratification and guides targeted monitoring adjustments. The derived nomogram translates these predictors into a point-based system. Patients with multiple high-risk factors accumulate higher scores, warranting urgent neuroimaging. Compared to models that rely on detailed neuroimaging 31 or invasive CSF analysis 37 , our model offers greater timeliness and clinical utility. It integrates metabolic, structural, and physiological parameters. This approach aligns with the need for rapid decision-making in ICH management. Although our findings are promising, this study has several limitations. First, it used a retrospective design, which may introduce selection bias and prevents causal inference. Some confounders were missing or unadjusted. Second, imaging parameters such as hematoma volume and location were unavailable. Their absence may reduce model accuracy. Finally, the timing of hydrocephalus onset was not recorded, limiting temporal analysis. Future studies should include prospective data, detailed imaging parameters, and time-to-event analyses to strengthen the robustness and clinical applicability of the findings. Conclusion In conclusion, elevated SHR was independently associated with hydrocephalus in critically ill patients with ICH. A prediction model incorporating SHR and routine clinical variables showed good discrimination and generalizability, offering a practical means for early risk stratification. These findings highlight SHR as both a marker of disease severity and a useful component of prediction tools. Prospective studies are needed to confirm these results. Abbreviations ICH Intracerebral hemorrhage SHR Stress hyperglycemia ratio ICU Intensive care unit SIH Stress-induced hyperglycemia ABG Admission blood glucose HbA1 C Glycated Hemoglobin A1c MIMIC-IV Medical Information Mart for Intensive Care IV IVH Intraventricular hemorrhage VIF Variance inflation factor GCS Glasgow coma scale OASIS Oxford acute severity of illness score SIRS Systemic inflammatory response syndrome SOFA Sequential organ failure assessment SAPS II Simplified acute physiology score II LR Logistic regression RF Random forest XGBoost Extreme gradient boosting LightGBM Light gradient boosting machine AUC Area under the receiver operating characteristic curve DCA Decision curve analysis CSF Cerebrospinal fluid Declarations Acknowledgements We sincerely thank the MIMIC-IV program team for their efforts in developing and maintaining the MIMIC-IV database, and the Information Center of the Third Hospital of Mianyang for their support in data extraction. Authors' contributions Shuang Zhao was responsible for data collection, statistical analysis and paper writing. Yang Liu and Yun Lu were responsible for the analysis of the study, and provided multiple reviews. Lin Zhang, Yue Xiao and Yifan Miao were responsible for technical guidance. Availability of data and materials The MIMIC-IV database is publicly accessible and can be downloaded from the official website. Data from the external validation cohort are included in the manuscript and supplementary materials. Further inquiries can be directed to the corresponding author. Ethics statement The MIMIC-IV database is a publicly available, de-identified dataset that is exempt from institutional review board approval. The external validation cohort was approved by the Ethics Committee of Mianyang Third People’s Hospital (approval number: 2025-030-3). Competing interests The authors declare no competing interests. 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Exploration and comparison of stress hyperglycemia-related indicators to predict clinical outcomes in patients with spontaneous intracerebral hemorrhage. NEUROSURG. REV. 47 , 887 (2024). Chen, Q. et al. Intracerebral Hematoma Contributes to Hydrocephalus After Intraventricular Hemorrhage via Aggravating Iron Accumulation. STROKE 46 , 2902–2908 (2015). Currò, C. T. et al. Stress hyperglycemia indexes and early neurological deterioration in spontaneous intracerebral hemorrhage. NEUROL SCI (2025). Zhang, F. et al. Association between neutrophil to lymphocyte ratio and blood glucose level at admission in patients with spontaneous intracerebral hemorrhage. SCI. REP-UK . 9 , 15623 (2019). Chen, A. et al. Development and validation of a nomogram for predicting early acute hydrocephalus after spontaneous intracerebral hemorrhage: a single-center retrospective study. SCI. REP-UK . 14 , 28185 (2024). Taran, S., McCredie, V. A. & Goligher, E. C. Noninvasive and invasive mechanical ventilation for neurologic disorders. Handb. Clin. Neurol. 189 , 361–386 (2022). Dastur, C. K. & Yu, W. Current management of spontaneous intracerebral haemorrhage. STROKE VASC NEUROL. 2 , 21–29 (2017). Sekino, N., Selim, M. & Shehadah, A. Sepsis-associated brain injury: underlying mechanisms and potential therapeutic strategies for acute and long-term cognitive impairments. J. NEUROINFLAMM . 19 , 101 (2022). Ye, Z., Ai, X., Hu, X., Fang, F. & You, C. Clinical features and prognostic factors in patients with intraventricular hemorrhage caused by ruptured arteriovenous malformations. MEDICINE 96 , e8544 (2017). Lee, S., Ju, Y., Kang, D. H. & Lee, J. E. Characteristics and outcomes of patients with do-not-resuscitate and physician orders for life-sustaining treatment in a medical intensive care unit: a retrospective cohort study. BMC PALLIAT. CARE . 23 , 42 (2024). Wang, Z. et al. Prediction of adult post-hemorrhagic hydrocephalus: a risk score based on clinical data. SCI. REP-UK . 12 , 12213 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.zip 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. 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1","display":"","copyAsset":false,"role":"figure","size":388183,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection and study design.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eICH\u003c/strong\u003e\u003c/em\u003e Intracerebral Hemorrhage; \u003cem\u003e\u003cstrong\u003eICU\u003c/strong\u003e\u003c/em\u003eIntensive Care Unit, \u003cem\u003e\u003cstrong\u003eNICU\u003c/strong\u003e\u003c/em\u003e Neurointensive Care Unit, \u003cem\u003e\u003cstrong\u003eMIMIC-IV\u003c/strong\u003e\u003c/em\u003eMedical Information Mart for Intensive Care IV, \u003cem\u003e\u003cstrong\u003eABG \u003c/strong\u003e\u003c/em\u003eAdmission Blood Glucose, \u003cem\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003eGlycated Hemoglobin A1c, \u003cem\u003e\u003cstrong\u003eTBI \u003c/strong\u003e\u003c/em\u003eTraumatic Brain Injury, \u003cem\u003e\u003cstrong\u003eSAH \u003c/strong\u003e\u003c/em\u003eSubarachnoid 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\u003cem\u003e\u003cstrong\u003eINR\u003c/strong\u003e\u003c/em\u003e International Normalized Ratio; \u003cem\u003e\u003cstrong\u003eSBP\u003c/strong\u003e\u003c/em\u003e Systolic Blood Pressure; \u003cem\u003e\u003cstrong\u003eBP\u003c/strong\u003e\u003c/em\u003e Blood pressure; \u003cem\u003e\u003cstrong\u003eRR\u003c/strong\u003e\u003c/em\u003e Respiratory Rate; \u003cem\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/em\u003e Heart Rate; \u003cem\u003e\u003cstrong\u003ePTT\u003c/strong\u003e\u003c/em\u003e Partial Thromboplastin Time; \u003cem\u003e\u003cstrong\u003eBUN\u003c/strong\u003e\u003c/em\u003e Blood Urea Nitrogen; \u003cem\u003e\u003cstrong\u003eWBC\u003c/strong\u003e\u003c/em\u003e White Blood Cell; \u003cem\u003e\u003cstrong\u003eRBC\u003c/strong\u003e\u003c/em\u003e Red Blood Cell; \u003cem\u003e\u003cstrong\u003eGCS\u003c/strong\u003e\u003c/em\u003e Glasgow Coma Scale; \u003cem\u003e\u003cstrong\u003eSIRS\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003eSystemic Inflammatory Response Syndrome; \u003cem\u003e\u003cstrong\u003eSpO\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e Peripheral Oxygen Saturation; \u003cem\u003e\u003cstrong\u003eOASIS\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003eOxford Acute Severity of Illness Score; \u003cem\u003e\u003cstrong\u003eSHR\u003c/strong\u003e\u003c/em\u003e Stress Hyperglycemia Ratio; \u003cem\u003e\u003cstrong\u003eSAPSII\u003c/strong\u003e\u003c/em\u003e Simplified Acute Physiology Score II; \u003cem\u003e\u003cstrong\u003eSOFA\u003c/strong\u003e\u003c/em\u003e Sequential Organ Failure Assessment; \u003cem\u003e\u003cstrong\u003eIVH \u003c/strong\u003e\u003c/em\u003eIntraventricular Hemorrhage; \u003cem\u003e\u003cstrong\u003eLASSO\u003c/strong\u003e\u003c/em\u003e Least absolute shrinkage and selection operator.\u003c/p\u003e","description":"","filename":"F4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7608266/v1/75a4166cd1ebf95a3b0796fa.jpg"},{"id":94480399,"identity":"2117eb95-0f9b-459f-b22c-b564e60e2a02","added_by":"auto","created_at":"2025-10-27 16:10:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":143177,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curves for Predictive Models.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eROC\u003c/strong\u003e\u003c/em\u003e Receiver Operating Characteristic; \u003cem\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/em\u003eArea Under The Receiver Operating Characteristic Curve; \u003cem\u003e\u003cstrong\u003eXGBoost\u003c/strong\u003e\u003c/em\u003e Extreme Gradient Boosting; \u003cem\u003e\u003cstrong\u003eLightGBM\u003c/strong\u003e\u003c/em\u003eLight Gradient Boosting Machine.\u003c/p\u003e","description":"","filename":"F5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7608266/v1/b63eacbb30965ca6fbc5971d.jpg"},{"id":94480136,"identity":"8d3b6a25-d66e-42b4-94f0-a547b61da28a","added_by":"auto","created_at":"2025-10-27 16:09:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":121706,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for the prediction of hydrocephalus.\u003c/p\u003e\n\u003cp\u003eLines 2-8 indicate the seven predictive variables included in the model: SHR, Mean SpO\u003csub\u003e2\u003c/sub\u003e, IVH, Mannitol use, Mechanical ventilation, Sepsis, Age. 0 indicates no, 1 indicates yes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSHR\u003c/strong\u003e\u003c/em\u003e Stress Hyperglycemia Ratio; \u003cem\u003e\u003cstrong\u003eSpO\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e Peripheral Oxygen Saturation; \u003cem\u003e\u003cstrong\u003eIVH\u003c/strong\u003e\u003c/em\u003e Intraventricular Hemorrhage.\u003c/p\u003e","description":"","filename":"F6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7608266/v1/b8b9b47ce4d23b81fa84132b.jpg"},{"id":100041187,"identity":"8a8ad583-6927-47d1-8451-d72c8c37a183","added_by":"auto","created_at":"2026-01-12 11:09:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2570594,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7608266/v1/09eb856b-e13b-4857-98f3-3ed85bcda7b0.pdf"},{"id":94480401,"identity":"608a3cb8-c721-4da9-a138-5ada793a7081","added_by":"auto","created_at":"2025-10-27 16:10:58","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":420243,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.zip","url":"https://assets-eu.researchsquare.com/files/rs-7608266/v1/30d16f908d7ea45058f3efb0.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stress hyperglycemia ratio as a predictor of hydrocephalus in critically ill patients with intracerebral hemorrhage: a retrospective study and machine learning-based model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIntracerebral hemorrhage (ICH) is a life-threatening neurological emergency characterized by high mortality and long-term disability\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Approximately 30% of ICH patients require intensive care unit (ICU) admission, reflecting the clinical severity of this condition\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In addition to the primary mass effect of the hematoma, secondary pathological processes such as neuroinflammation can exacerbate brain injury and precipitate serious complications\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Among these, hydrocephalus is the second most frequent complication of ICH, affecting approximately 11% of inpatients, and two-thirds of cases manifest within 72 hours of hemorrhage onset\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Furthermore, hydrocephalus is associated with longer hospital stays, increased healthcare costs, and worse functional recovery\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These findings underscore the prognostic significance of hydrocephalus and highlight the importance of early recognition and risk stratification.\u003c/p\u003e\u003cp\u003eIn this context, identifying reliable early predictors of hydrocephalus is essential to improve outcomes in critically ill ICH patients. Stress-induced hyperglycemia (SIH) is an early metabolic disturbance and occurs in nearly 60% of patients with ICH irrespective of diabetes status\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It is typically reflected by elevated admission blood glucose (ABG) levels\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, ABG alone may not accurately capture SIH severity, as it is shaped by both acute stress responses and pre-existing glycemic status. To address this limitation, the stress hyperglycemia ratio (SHR), which incorporates both ABG and glycated hemoglobin A1c (HbA1c), has been proposed as a more reliable indicator of SIH\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In patients with ICH, elevated SHR has been associated with hematoma expansion and neuroinflammation\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Since both factors may contribute to hydrocephalus pathogenesis\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, we hypothesized that elevated SHR may play a role in the development of hydrocephalus.\u003c/p\u003e\u003cp\u003eTherefore, we conducted a retrospective study to investigate the association between SHR and the development of hydrocephalus following ICH in critically ill patients. Additionally, we developed machine learning-based models incorporating multiple routinely available clinical variables to predict the risk of hydrocephalus and support early risk stratification in neurocritical care.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source and ethical approval\u003c/h2\u003e\u003cp\u003e We conducted a retrospective study using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and developed predictive models with machine learning algorithms based on this dataset. Data from the Neurointensive Care Unit of the Third Hospital of Mianyang (2020\u0026ndash;2024) were used for external validation of the optimal model. An overview of the study design is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe MIMIC-IV database (version 3.1), developed by the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center, contains de-identified patient records from 2008 to 2019. As a publicly available dataset, it is exempt from institutional review board approval and informed consent. Access to the database requires completion of the National Institutes of Health online course. The first author completed this course and obtained certification (ID: 13356665). The external validation cohort from the Third Hospital of Mianyang was approved by the institutional ethics committee, with informed consent waived due to de-identification.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003ePatients with ICH were identified using ICD-9 codes (431, 4329) and ICD-10 codes (I610\u0026ndash;I619, I629). To ensure data completeness and clinical consistency, we applied the following exclusion criteria: (1) missing ABG or HbA1c measurements; (2) ICU stay shorter than 24 hours; (3) traumatic brain injury, subarachnoid hemorrhage, brain tumors, or hemorrhagic transformation secondary to other conditions; and (4) age younger than 18 years. For patients with multiple ICU admissions, only the first admission was included. The primary outcome was the development of hydrocephalus. Hydrocephalus diagnosis relied on ICD codes (ICD-9: 3313, 3314; ICD-10: G91, G910-911, G914, G919).\u003c/p\u003e\n\u003ch3\u003eVariable extraction\u003c/h3\u003e\n\u003cp\u003eAll variables were retrieved from the MIMIC-IV database using structured query language scripts run in pgAdmin, with data restricted to the first 24 hours after ICU admission. Demographic information included age, sex, and weight. Laboratory tests encompassed red blood cell (RBC) count, white blood cell (WBC) count, neutrophil count, platelet count (PLT), C-reactive protein (CRP), blood urea nitrogen (BUN), serum creatinine, pH, lactate, anion gap, serum electrolytes (sodium, potassium, calcium), carbon dioxide pressure, arterial oxygen pressure, international normalized ratio (INR), D-dimer, prothrombin time (PT), partial thromboplastin time (PTT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), ABG, and HbA1c. Severity was evaluated using the Glasgow Coma Scale (GCS), Oxford Acute Severity of Illness Score (OASIS), Systemic Inflammatory Response Syndrome (SIRS) score, Sequential Organ Failure Assessment (SOFA) score, and Simplified Acute Physiology Score II (SAPS II). Vital signs comprised averaged measurements of body temperature, heart rate (HR), and respiratory rate (RR), blood pressure (including arterial pressure, systolic and diastolic pressures), as well as peripheral oxygen saturation (SpO\u003csub\u003e2\u003c/sub\u003e). Comorbidities included intraventricular hemorrhage (IVH), diabetes, hypertension, myocardial infarction (MI), heart failure (HF), chronic obstructive pulmonary disease (COPD), sepsis, and chronic kidney disease (CKD). Treatment methods included mannitol use, oxygen delivery, surgery, vasoactive drugs, mechanical ventilation, and statin use.\u003c/p\u003e\n\u003ch3\u003eSHR calculation\u003c/h3\u003e\n\u003cp\u003eAlthough SHR is widely recognized as an objective indicator of SIH, there is currently no standardized method for its calculation. Two primary approaches have been reported: one based on fasting glucose and HbA1c\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and the other utilizing ABG and HbA1c\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In this study, we adopted the latter method, as ABG is readily available upon hospital arrival and does not require prior fasting, thus offering greater practicality in acute care settings. Accordingly, SHR was calculated using the formula \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{SHR}\\text{=}\\text{ABG}\\text{(}\\text{mg}\\text{/}\\text{dL}\\text{)/[28.7\u0026times;}\\text{HbA}\\text{1}\\text{c}\\text{(\\%)\u0026minus;46.7]}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003eRetrospective study\u003c/h2\u003e\u003cp\u003eAs a retrospective study using an existing database, no a priori sample size calculation was performed. Variables with \u0026gt;\u0026thinsp;20% missing data were excluded, while those with \u0026le;\u0026thinsp;20% missingness were imputed using the random forest-based MissForest algorithm. We evaluated multicollinearity among the independent variables via the variance inflation factor (VIF). Variables with VIF values above 5 were sequentially excluded, guided by clinical relevance, until all remaining features showed acceptable levels of multicollinearity.\u003c/p\u003e\u003cp\u003ePatients were stratified into two groups according to the optimal cutoff value of SHR, which was determined by maximizing the Youden Index derived from the receiver operating characteristic curve to achieve the best balance of sensitivity and specificity. The normality of continuous variables was evaluated using the Shapiro-Wilk test. Data with a normal distribution were summarized as means and standard deviations (SD) and compared using independent t-tests. Variables not following a normal distribution were expressed as medians with interquartile ranges (IQR) and analyzed using the Mann-Whitney U test. Categorical variables were reported as frequencies and proportions, and group differences were assessed using either the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate.\u003c/p\u003e\u003cp\u003eLogistic regression (LR) analysis was employed to explore the relationship between SHR and the development of hydrocephalus. The analysis began with a crude (unadjusted) model, followed by Model I, which adjusted for age, sex, and weight. Model II extended the adjustments to include clinical comorbidities including IVH, diabetes, hypertension, and sepsis, along with severity scores (GCS, OASIS, SIRS, SOFA, and SAPS II). A restricted cubic spline (RCS) analysis with four knots was applied to investigate potential non-linear relationships between SHR and hydrocephalus. To assess the consistency of the association across different populations, stratified subgroup analyses were performed based on age, sex, and the presence of diabetes, sepsis, and IVH.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eEstablishment and validation of the prediction models\u003c/h3\u003e\n\u003cp\u003ePrior to model development, missing data and outliers in the MIMIC-IV dataset were addressed through preprocessing procedures conducted in the retrospective study. The externally collected dataset was manually curated and contained no missing or aberrant values.\u003c/p\u003e\u003cp\u003eThe MIMIC-IV cohort was randomly divided into a development set (70%) and an internal validation set (30%). External validation data included only the final model predictors, outcome (hydrocephalus), and key demographics (age, sex, comorbidities). Variable definitions and measurements were consistent across datasets.\u003c/p\u003e\u003cp\u003eDue to class imbalance in the dependent variables, oversampling was used to balance the data. Feature selection was conducted in two steps. First, the Boruta algorithm, a wrapper method based on random forest that compares variable importance with randomized shadow features, was used to identify all relevant predictors\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Second, least absolute shrinkage and selection operator (LASSO) regression was applied to improve model interpretability and reduce overfitting risk. This method uses L1 regularization to shrink irrelevant coefficients to zero\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Four algorithms were used for model development: LR, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Hyperparameters were tuned via five-fold cross-validation. Model performance was assessed by discrimination using the AUC. Calibration curves were used to evaluate agreement between predicted and observed outcomes. Decision curve analysis (DCA) was applied to assess clinical utility.\u003c/p\u003e\u003cp\u003eStatistical analyses were conducted using Stata (version 18.0) for the retrospective study and R (version 4.1.2) for model development and validation. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eRetrospective study\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eBaseline characteristics\u003c/h2\u003e\u003cp\u003eA total of 1,383 critically ill ICH patients were included in this study. The missing data proportions and VIFs are detailed in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the included patients, 636 (45.99%) were female, with a mean age of 69.53\u0026thinsp;\u0026plusmn;\u0026thinsp;14.59 years and a mean weight of 80.08\u0026thinsp;\u0026plusmn;\u0026thinsp;21.91 kg. Comorbidities included IVH (12.94%), hypertension (62.83%), diabetes (30.73%), HF (17.86%), COPD (6.07%), MI (6.15%), CKD (12.94%), and sepsis (37.53%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics according to SHR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall,\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1383\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup1,\u003c/p\u003e\u003cp\u003eSHR\u0026thinsp;\u0026lt;\u0026thinsp;1.03, N\u0026thinsp;=\u0026thinsp;644\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGroup2, SHR\u0026thinsp;\u0026ge;\u0026thinsp;1.03, N\u0026thinsp;=\u0026thinsp;739\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.53\u0026thinsp;\u0026plusmn;\u0026thinsp;14.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.37\u0026thinsp;\u0026plusmn;\u0026thinsp;14.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.80\u0026thinsp;\u0026plusmn;\u0026thinsp;14.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, female, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e636 (45.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e293 (45.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e343 (46.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.08\u0026thinsp;\u0026plusmn;\u0026thinsp;21.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.07\u0026thinsp;\u0026plusmn;\u0026thinsp;21.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.94\u0026thinsp;\u0026plusmn;\u0026thinsp;22.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory indicators\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC, (m/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, (K/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.60 (26.00,31.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.80 (26.40,31.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.30 (25.60,31.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT, (K/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220.32\u0026thinsp;\u0026plusmn;\u0026thinsp;80.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e225.68\u0026thinsp;\u0026plusmn;\u0026thinsp;79.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e215.64\u0026thinsp;\u0026plusmn;\u0026thinsp;80.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinie, (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.90 (0.70,1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90 (0.80,1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.70,1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium, (mEq/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN, (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.00 (13.00,22.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.00 (12.00,22.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.00 (13.00,23.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDisease severity score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 (14.00,15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00 (14.00,15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.00 (13.00,15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOASIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.00 (26.00,36.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.00 (25.00,36.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.00 (26.00,37.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00 (2.00,3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00 (1.00,3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00 (2.00,3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00 (1.00,4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00 (1.00,4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00 (2.00,5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAPS II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.78\u0026thinsp;\u0026plusmn;\u0026thinsp;11.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.26\u0026thinsp;\u0026plusmn;\u0026thinsp;11.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.23\u0026thinsp;\u0026plusmn;\u0026thinsp;11.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVital signs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean HR, (beats/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.56\u0026thinsp;\u0026plusmn;\u0026thinsp;13.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.02\u0026thinsp;\u0026plusmn;\u0026thinsp;13.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.78\u0026thinsp;\u0026plusmn;\u0026thinsp;13.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean SBP, (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131.25\u0026thinsp;\u0026plusmn;\u0026thinsp;13.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.17\u0026thinsp;\u0026plusmn;\u0026thinsp;13.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131.31\u0026thinsp;\u0026plusmn;\u0026thinsp;14.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean BP, (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.05\u0026thinsp;\u0026plusmn;\u0026thinsp;10.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.85\u0026thinsp;\u0026plusmn;\u0026thinsp;10.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.35\u0026thinsp;\u0026plusmn;\u0026thinsp;10.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean RR, (times/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean temperature, (℃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean SpO\u003csub\u003e2\u003c/sub\u003e, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVH, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e179 (12.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (8.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123 (16.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (6.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (6.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (5.68 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e425 (30.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180 (27.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e245 (33.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e869 (62.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e394 (61.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e475 (64.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (6.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (6.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (5.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247 (17.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (18.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128 (17.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e179 (12.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (14.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (11.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e519 (37.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e215 (33.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e304 (41.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatments\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMannitol use, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 (10.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (7.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88 (11.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxygen Delivery, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e904 (65.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e394 (61.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e510 (69.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (11.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVasoactive drug, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168 (12.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (11.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97 (13.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMechanical ventilation, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e973 (70.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402 (62.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e571 (77.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatin use, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e703 (50.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e347 (53.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e356 (48.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHydrocephalus, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124 (8.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (5.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88 (11.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed variables, median (IQR) for non-normally distributed continuous variables, and numbers (%) for categorical variables.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eRBC\u003c/b\u003e Red Blood Cell; \u003cb\u003eWBC\u003c/b\u003e White Blood Cell; \u003cb\u003eINR\u003c/b\u003e International Normalized Ratio; \u003cb\u003ePTT\u003c/b\u003e Partial Thromboplastin Time; \u003cb\u003ePLT\u003c/b\u003e Platelet; \u003cb\u003eBUN\u003c/b\u003e Blood Urea Nitrogen; \u003cb\u003eGCS\u003c/b\u003e Glasgow cComa Scale; \u003cb\u003eOASIS\u003c/b\u003e Oxford Acute Severity of Illness Score; \u003cb\u003eSIRS\u003c/b\u003e Systemic Inflammatory Response Syndrome; \u003cb\u003eSOFA\u003c/b\u003e Sequential Organ Failure Assessment; \u003cb\u003eSAPSII\u003c/b\u003e Simplified Acute Physiology Score II; \u003cb\u003eHR\u003c/b\u003e Heart Rate; \u003cb\u003eSBP\u003c/b\u003e Systolic Blood Pressure; \u003cb\u003eBP\u003c/b\u003e Blood Pressure; \u003cb\u003eRR\u003c/b\u003e Respiratory Rate; \u003cb\u003eSpO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e Peripheral Oxygen Saturation; \u003cb\u003eIVH\u003c/b\u003e Intraventricular Hemorrhage; \u003cb\u003eCOPD\u003c/b\u003e Chronic Obstructive Pulmonary Disease; \u003cb\u003eMI\u003c/b\u003e Myocardial Infarction; \u003cb\u003eHF\u003c/b\u003e Heart Failure; \u003cb\u003eCKD\u003c/b\u003e Chronic Kidney Disease.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePatients were stratified into two groups based on the optimal SHR cutoff of 1.03 (Group 1: SHR\u0026thinsp;\u0026lt;\u0026thinsp;1.03; Group 2: SHR\u0026thinsp;\u0026ge;\u0026thinsp;1.03). Compared to Group 1, Group 2 patients were generally younger and had higher HR, RR, SpO₂, OASIS, SIRS, and SOFA scores, along with elevated WBC count. They also had increased rates of IVH, diabetes, and sepsis, and were more likely to receive mannitol, oxygen therapy, mechanical ventilation, and statins. Conversely, Group 2 showed lower mean blood pressure, RBC count, PTT, and PLT levels.\u003c/p\u003e\u003cp\u003eBaseline characteristics stratified by hydrocephalus status are presented in Table S2. Patients with hydrocephalus were younger and had higher OASIS, SIRS, and SOFA scores. They also had higher rates of IVH and sepsis and were more frequently treated with mechanical ventilation and mannitol. Creatinine, WBC count, and ABG levels were elevated in this group. Notably, SHR was significantly higher in patients with hydrocephalus (1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42) than in those without (1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eClinical outcomes\u003c/h2\u003e\u003cp\u003eIn the overall cohort, the incidence of hydrocephalus was 8.97%, with a significantly higher rate observed in Group 2 than Group 1 (11.91% vs. 5.59%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Multivariable LR analysis demonstrated that SHR was independently associated with the risk of hydrocephalus, whether assessed as a continuous variable (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.72, 95% confidence interval [CI]: 1.20\u0026ndash;2.48, P\u0026thinsp;=\u0026thinsp;0.003) or dichotomized (OR\u0026thinsp;=\u0026thinsp;1.75, 95% CI: 1.15\u0026ndash;2.69, P\u0026thinsp;=\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). RCS analysis indicated a positive linear relationship between SHR and hydrocephalus risk (P for non-linearity\u0026thinsp;=\u0026thinsp;0.17; overall P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the probability of hydrocephalus rising steadily when SHR exceeded approximately 1.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subgroup analyses consistently supported this association across all predefined strata, with no significant interaction detected (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between SHR and hydrocephalus: logistic regression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCrude model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel II\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR as Continuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.18 (1.36\u0026ndash;3.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.15 (1.34\u0026ndash;3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.72 (1.20\u0026ndash;2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u0026thinsp;\u0026lt;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u0026thinsp;\u0026ge;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.28 (1.53\u0026ndash;3.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.22 (1.48\u0026ndash;3.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.75 (1.15\u0026ndash;2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEstablishment and validation of the prediction models\u003c/h2\u003e\u003cp\u003eFeature selection was first performed using the Boruta algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), which identified important (green-labeled) and unimportant (red-labeled) variables. Subsequently, LASSO regression with the parsimonious λ.1se penalty (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) retained seven predictors with non-zero coefficients: SHR, age, mean SpO₂, IVH, mannitol use, mechanical ventilation, and sepsis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eModel performance metrics are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The LR model achieved the highest discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.81), outperforming XGBoost (0.75), LightGBM (0.73), and RF (0.70). Calibration curves (Figure S2) indicated good agreement between predicted and observed probabilities, while DCA (Figure S3) demonstrated favorable net clinical benefit across all models. Given its superior performance and interpretability, the LR model was further visualized with coefficient plots and 95% CIs (Figure S4). A nomogram was subsequently developed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) to enable individualized risk prediction of hydrocephalus in critically ill ICH patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA total of 513 patients were included for external validation. Their baseline clinical characteristics are summarized in Table S3. Although there were differences in baseline characteristics between the two cohorts, the model maintained good generalizability (AUC\u0026thinsp;=\u0026thinsp;0.76, Figure S5).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated that elevated SHR was independently associated with an increased risk of hydrocephalus in critically ill patients with ICH, showing a linear association beyond an SHR threshold of 1.05. Notably, this threshold is close to the cutoff value used in our study, supporting the rationale of employing a dichotomized grouping method based on this cutoff. Across multiple predictive models, SHR consistently emerged as a key predictor. The LR model showed the best overall performance in terms of discrimination, calibration, and clinical utility. It also retained robust generalizability in external validation. To our knowledge, this is the first study to comprehensively assess the association between SHR and hydrocephalus after ICH.\u003c/p\u003e\u003cp\u003eThe association between elevated SHR and hydrocephalus may be partly mediated by inflammation. In our study, patients with higher SHR showed increased WBC counts, higher SIRS scores, and a greater incidence of sepsis, reflecting systemic inflammatory activation. SIH is known to trigger inflammatory pathways through glucocorticoid release and sympathetic nervous system stimulation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Inflammation, in turn, is a key driver of hydrocephalus after ICH\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Several mechanisms may underlie this link. First, SIH can induce systemic and central inflammatory responses that stimulate cerebrospinal fluid (CSF) hypersecretion by activating epithelial cells of the choroid plexus. Elevated glucose levels promote the release of proinflammatory cytokines such as interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α)\u003csup\u003e21,22\u003c/sup\u003e. These cytokines upregulate sodium-potassium-chloride cotransporter 1 (NKCC1) and aquaporin-1 (AQP1) expression on choroid plexus epithelial cells, thereby enhancing fluid production across the blood\u0026ndash;CSF barrier\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Second, SIH can promote neutrophil priming and facilitates the formation of the neutrophil extracellular traps (NETs)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These extracellular networks can accumulate near the ventricular system, obstructing CSF flow, and simultaneously act as damage-associated molecular patterns (DAMPs) that activate microglial Toll-like receptor signaling, perpetuating neuroinflammation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Third, SIH drives microglial polarization toward the proinflammatory M1 phenotype, promoting interleukin-6, reactive oxygen species, and nitric oxide release. These mediators can injure periventricular ependymal cells and hinder CSF absorption through arachnoid granulations\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Together, these inflammatory cascades may provide a mechanistic basis for how elevated SHR contributes to the development of hydrocephalus after ICH.\u003c/p\u003e\u003cp\u003eIn addition to inflammation, IVH is a major risk factor for hydrocephalus after ICH. In our study, higher SHR was associated with a greater incidence of IVH. This suggests that acute glucose dysregulation may facilitate intraventricular extension of hemorrhage. Previous studies also reported that SIH increases the risk of hematoma expansion, thereby raising the chance of ventricular rupture. SHR has also been identified as a reliable predictor of early hematoma growth\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Mechanistically, SIH promotes leukocyte-endothelial adhesion and upregulates matrix metalloproteinases such as Matrix Metalloproteinase-9 (MMP-9). These enzymes degrade extracellular matrix and disrupt tight junctions, weakening the integrity of cerebral microvessels\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This vascular injury predisposes to hematoma propagation and ventricular entry. In addition, preclinical ICH models show that hyperglycemia activates plasma kallikrein. This inhibits platelet aggregation and impairs clot stability, further predisposing to hematoma expansion\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Such hemorrhagic progression into the ventricular system provides a plausible structural link between elevated SHR and the development of hydrocephalus after ICH.\u003c/p\u003e\u003cp\u003eIn this study, we also developed a simple and clinically applicable model to predict hydrocephalus in ICH patients. The model incorporated seven readily available variables: SHR, IVH, mean SpO₂, mannitol use, mechanical ventilation, sepsis, and age. All variables differed significantly between hydrocephalus and non-hydrocephalus groups, underscoring their clinical relevance. As previously discussed, SHR and IVH confirmed their prognostic value. Mechanical ventilation reflected severe neurological impairment and reduced intracranial compliance, predisposing to CSF circulation disturbances\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. By contrast, higher mean SpO₂ in hydrocephalus patients likely reflected ventilatory support rather than preserved physiological reserve. Mannitol use indicated intracranial hypertension and was associated with more severe ICH and secondary complications\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Sepsis denoted systemic inflammation that exacerbate brain injury and impair CSF dynamics\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Notably, both cohort data and model contributions revealed a negative correlation between age and hydrocephalus risk, with younger patients at higher risk. This pattern may partly reflect case-mix differences. Younger patients are more likely to have structural etiologies such as arteriovenous malformations. These conditions often present with IVH and are associated with higher rates of CSF diversion\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Conversely, older patients frequently face early decisions to limit life-sustaining treatment, introducing competing risks that may obscure the detection of hydrocephalus before death\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Collectively, these predictors capture complementary aspects of disease severity and pathophysiology, forming a mechanistic foundation for the model\u0026rsquo;s interpretability.\u003c/p\u003e\u003cp\u003eBuilding on this framework, the LR model demonstrated robust performance, translating these pathophysiological insights into clinically actionable tools. It enables clear risk stratification and guides targeted monitoring adjustments. The derived nomogram translates these predictors into a point-based system. Patients with multiple high-risk factors accumulate higher scores, warranting urgent neuroimaging. Compared to models that rely on detailed neuroimaging\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e or invasive CSF analysis\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, our model offers greater timeliness and clinical utility. It integrates metabolic, structural, and physiological parameters. This approach aligns with the need for rapid decision-making in ICH management.\u003c/p\u003e\u003cp\u003eAlthough our findings are promising, this study has several limitations. First, it used a retrospective design, which may introduce selection bias and prevents causal inference. Some confounders were missing or unadjusted. Second, imaging parameters such as hematoma volume and location were unavailable. Their absence may reduce model accuracy. Finally, the timing of hydrocephalus onset was not recorded, limiting temporal analysis. Future studies should include prospective data, detailed imaging parameters, and time-to-event analyses to strengthen the robustness and clinical applicability of the findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, elevated SHR was independently associated with hydrocephalus in critically ill patients with ICH. A prediction model incorporating SHR and routine clinical variables showed good discrimination and generalizability, offering a practical means for early risk stratification. These findings highlight SHR as both a marker of disease severity and a useful component of prediction tools. Prospective studies are needed to confirm these results.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eICH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intracerebral hemorrhage\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Stress hyperglycemia ratio\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intensive care unit\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSIH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Stress-induced hyperglycemia\u003c/p\u003e\n\u003cp\u003eABG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Admission blood glucose\u003c/p\u003e\n\u003cp\u003eHbA1\u003csub\u003eC\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Glycated Hemoglobin A1c\u003c/p\u003e\n\u003cp\u003eMIMIC-IV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Medical Information Mart for Intensive Care IV\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIVH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Intraventricular hemorrhage\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVIF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Variance inflation factor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGCS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Glasgow coma scale\u003c/p\u003e\n\u003cp\u003eOASIS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Oxford acute severity of illness score\u003c/p\u003e\n\u003cp\u003eSIRS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Systemic inflammatory response syndrome\u003c/p\u003e\n\u003cp\u003eSOFA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sequential organ failure assessment\u003c/p\u003e\n\u003cp\u003eSAPS II \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Simplified acute physiology score II\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Logistic regression\u003c/p\u003e\n\u003cp\u003eRF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Random forest\u003c/p\u003e\n\u003cp\u003eXGBoost \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Extreme gradient boosting\u003c/p\u003e\n\u003cp\u003eLightGBM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Light gradient boosting machine\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area under the receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003eDCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Decision curve analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCSF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cerebrospinal fluid\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the MIMIC-IV program team for their efforts in developing and maintaining the MIMIC-IV database, and the Information Center of the Third Hospital of Mianyang for their support in data extraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuang Zhao was responsible for data collection, statistical analysis and paper writing. Yang Liu and Yun Lu were responsible for the analysis of the study, and provided multiple reviews. Lin Zhang, Yue Xiao and Yifan Miao were responsible for technical guidance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MIMIC-IV database is publicly accessible and can be downloaded from the official website. Data from the external validation cohort are included in the manuscript and supplementary materials. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MIMIC-IV database is a publicly available, de-identified dataset that is exempt from institutional review board approval. The external validation cohort was approved by the Ethics Committee of Mianyang Third People’s Hospital (approval number: 2025-030-3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial scientific and technological research topic of Sichuan Provincial Administration of Traditional Chinese Medicine (2023zd003).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi, S. et al. Stress hyperglycemia is predictive of clinical outcomes in patients with spontaneous intracerebral hemorrhage. \u003cem\u003eBMC NEUROL.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 236 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, Y. et al. 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Clinical features and prognostic factors in patients with intraventricular hemorrhage caused by ruptured arteriovenous malformations. \u003cem\u003eMEDICINE\u003c/em\u003e \u003cb\u003e96\u003c/b\u003e, e8544 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee, S., Ju, Y., Kang, D. H. \u0026amp; Lee, J. E. Characteristics and outcomes of patients with do-not-resuscitate and physician orders for life-sustaining treatment in a medical intensive care unit: a retrospective cohort study. \u003cem\u003eBMC PALLIAT. CARE\u003c/em\u003e. \u003cb\u003e23\u003c/b\u003e, 42 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, Z. et al. Prediction of adult post-hemorrhagic hydrocephalus: a risk score based on clinical data. \u003cem\u003eSCI. REP-UK\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 12213 (2022).\u003c/span\u003e\u003c/li\u003e\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":"Stress hyperglycemia ratio, Stress-induced hyperglycemia, Intracerebral hemorrhage, Hydrocephalus, Prediction model, External validation","lastPublishedDoi":"10.21203/rs.3.rs-7608266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7608266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHydrocephalus is a severe complication of intracerebral hemorrhage (ICH), and its early prediction remains challenging. The stress hyperglycemia ratio (SHR), reflecting acute glucose elevation relative to baseline, has been associated with poor outcomes in ICH, but its relationship with hydrocephalus is unclear. We retrospectively analyzed 1,383 critically ill ICH patients from the MIMIC-IV database and evaluated associations between SHR and hydrocephalus using multivariable logistic regression, restricted cubic spline analysis, and subgroup analyses. Hydrocephalus occurred in 8.97% of patients, with higher incidence in those with elevated SHR (11.91% vs. 5.59%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). SHR was independently associated with hydrocephalus as both a continuous (OR\u0026thinsp;=\u0026thinsp;1.72, 95% CI: 1.20\u0026ndash;2.48, P\u0026thinsp;=\u0026thinsp;0.003) and categorical variable (OR\u0026thinsp;=\u0026thinsp;1.75, 95% CI: 1.15\u0026ndash;2.69, P\u0026thinsp;=\u0026thinsp;0.01), with linear risk increase above 1.05. A logistic regression model combining SHR with six clinical variables (mean SpO₂, intraventricular hemorrhage, mannitol use, mechanical ventilation, sepsis, and age) achieved best predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.81) and maintained accuracy in an external cohort (AUC\u0026thinsp;=\u0026thinsp;0.76, n\u0026thinsp;=\u0026thinsp;513). These findings indicate that SHR is a valuable predictor of hydrocephalus after ICH and may facilitate early risk stratification and individualized clinical management.\u003c/p\u003e","manuscriptTitle":"Stress hyperglycemia ratio as a predictor of hydrocephalus in critically ill patients with intracerebral hemorrhage: a retrospective study and machine learning-based model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 15:21:11","doi":"10.21203/rs.3.rs-7608266/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":"bd2326b7-069e-4051-bd5d-ea9ba24af4e5","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56741661,"name":"Health sciences/Diseases"},{"id":56741662,"name":"Health sciences/Medical research"},{"id":56741663,"name":"Health sciences/Neurology"},{"id":56741664,"name":"Biological sciences/Neuroscience"},{"id":56741665,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-12T11:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 15:21:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7608266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7608266","identity":"rs-7608266","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}