Association between dehydration trajectory, delayed cerebral ischemia, and functional outcome in patients with aneurysmal subarachnoid hemorrhage: assessment of interaction and mediation

Abstract

Background

Blood urea/creatinine (U/Cr) ratio is considered to be an ideal biomarker of dehydration. We investigated the association between the U/Cr ratio trajectory and delayed cerebral ischemia (DCI) as well as functional outcome in aneurysmal subarachnoid hemorrhage (aSAH). Additionally, we explored the role of DCI as a mediator and its interaction with dehydration.

Methods

Consecutive aSAH patients were reviewed. A latent class growth mixture model (LCGMM) was applied to classify the dehydration trajectory over 7 days. Multivariate logistic regression was conducted to examine associations between dehydration trajectories, DCI, and poor outcome. Furthermore, causal mediation analysis combined with a four-way decomposition approach was employed to quantify the extent to which DCI mediates or interacts with dehydration in influencing poor outcomes.

Results

A total of 519 aSAH patients were included. By applying the LCGMM method, we categorized participants into three dehydration trajectory groups: low group (n=353), decreasing group (n=97), and high group (n=69). Multivariate analysis demonstrated that dehydration trajectory was independently associated with both DCI and poor outcome. The effect of dehydration trajectory on poor outcome was partially mediated by DCI, involving both pure mediation and mediated interaction. Specifically, the excess relative risk of DCI was decomposed into four components: controlled direct effect (66.42%), mediation only (16.35%), interaction only (6.09%), and mediated interaction (11.16%).

Conclusion

Among aSAH patients, dehydration trajectory was significantly associated with poor functional outcome, with DCI serving as a partial mediator through both direct and interaction effects.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• Blood urea/creatinine (U/Cr) ratio is considered to be an ideal biomarker of dehydration. However, this association between the dehydration trajectory, delayed cerebral ischemia (DCI), and poor outcome has not yet been investigated in patients with aSAH. Besides, the mediation and interaction of DCI within the chain of events remain unclear.

WHAT THIS STUDY ADDS

• We identified three latent trajectory classes of U/Cr ratio, characterized by low dehydration, decreasing dehydration, and high dehydration. The dehydration trajectory is independently associated with DCI and poor outcome in patients with aSAH. In addition, the effect of dehydration on the risk of poor outcome may partly be explained by the DCI, where there is both pure mediation and mediated interaction.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• The U/Cr ratio-based dehydration trajectory is a feasible and clinically applicable tool for dehydration surveillance and can help inform clinicians’ management decisions such as early DCI prevention or optimal fluid management, based on the potential causal chain of dehydration–DCI–poor outcome. However, evidence from further prospective outcome studies is needed.

Introduction

Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating disease that leads to severe morbidity and mortality in populations. Besides the initial bleeding, the primary event contributing to worse outcome is the development of secondary brain injury, primarily characterized by delayed cerebral ischemia (DCI) occurring in up to 40% of patients.^{1 2} There is growing evidence that hydration status is the modifiable factor affecting cerebral oxygenation and cerebral blood flow, and it appears to be independently associated with the occurrence of DCI and poor neurological outcome.^{3 4} Furthermore, fluid augmentation, coupled with vasopressors, may additionally be necessary to achieve induced hypertension, which remains an integral part of the treatment strategy to prevent DCI in patients with aSAH.^{3 5 6}

Dehydration-related biomarkers can be used to reflect the hydration status, most commonly using the urea/creatinine (U/Cr) ratio, blood urea nitrogen/Cr ratio, urine specific gravity, hematocrit, and plasma osmolality. As in previous reports, the U/Cr ratio is a routinely available indicator of hydration, and dehydration has been defined as a U/Cr ratio>80, with good sensitivity and specificity.⁷,⁹ Furthermore, recent studies have indicated that an increased U/Cr ratio is associated with a higher risk of hypercoagulability status, such as cerebral venous thrombosis and ischemic stroke, and its associated adverse outcomes following hospitalization. However, relying on the assessment of hydration status and patient prognosis solely based on the admission laboratory results, without considering the longitudinal characteristics of U/Cr ratio changes, is an oversimplification, as it overlooks the complexities of this heterogeneous syndrome. A multi-timepoint evaluation would provide a more accurate and detailed understanding of how dehydration evolves throughout the course of the disease and how it relates to patient outcomes. In other words, longitudinal trajectories and dynamic changes in dehydration status could better predict the individual risk of aSAH patients. Furthermore, the relationship between dehydration trajectories and functional outcome in patients with aSAH has not yet been reported. Theoretically, dehydration is related to the occurrence of DCI, because it can reduce regional blood flow, increase blood viscosity, and induce thrombus formation. Accordingly, it is plausible to hypothesize that DCI plays a mediating role in the relationship between dehydration and poor functional outcome in patients with aSAH. Alternatively, a synergistic interaction between dehydration and DCI may contribute to poor outcome when they coexist.

Therefore, this study aimed to examine the association between dehydration trajectories and poor outcome as well as DCI as a mediator and its potential interaction effect, which can provide new insights for optimal fluid management in patients with aSAH and recommend a potential hydration management program based on fluid consumption and dehydration parameters.

Methods

Patient population

We conducted a retrospective analysis of patients diagnosed with aSAH who were hospitalized between June 2019 and December 2022. The inclusion criteria were as follows: (1) age>18 years; (2) admitted within 72 hours of initial symptom onset; (3) intracranial aneurysms diagnosed through the use of CT angiography or digital subtraction angiography (DSA), and SAH confirmed by CT scan; and (4) patients treated with endovascular therapy (EVT). The exclusion criteria were as follows: (1) aneurysms associated with blood bubble, dissection, fusiform, infectious, cerebrovascular, or moyamoya malformation; (2) preoperative unexpected events that could affect the outcome; (3) functional or neurological deficit before disease onset; (4) other critical complications which seriously affect the prognosis of patients, such as neoplasm, severe heart, liver, or kidney dysfunction; (5) missing data, including medical, radiological, and laboratory information; and (6) incomplete 6-month follow-up. The study protocol received approval from the Ethics Committee in Clinical Research of First Affiliated Hospital of Wenzhou Medical University (KY2024-R162).

Patient management and treatment

All patients were treated homogeneously according to (inter)national guidelines.^{1 10} Specifically, after presentation at the emergency department, patients were either admitted to the brain care unit or the intensive care unit. For aSAH patients, the treatment strategies (clipping or EVT) were joint discussions and development by neurosurgeons and endovascular neurosurgeons. Ruptured intracranial aneurysms were preferably treated with coiling without stent assistance, if EVT was the selected treatment. Patients received antiplatelet treatment both intraoperatively and postoperatively in those undergoing the stent technique. If patients had raised intracranial pressure or (suspected) hydrocephalus, an external ventricular drain or an external lumbar drain was placed. Patients suspected of DCI were treated with noradrenaline-induced normovolemic hypertension induction. All patients were monitored in the intensive care unit until the overall stabilization of their condition.

Data collection and definitions

The files of aSAH patients were reviewed. Then, the following information was collected: patient’s age, sex, smoking, drinking, and comorbidities (hypertension, diabetes mellitus, heart disease, previous stroke, or hyperlipidemia). Additionally, clinical and radiological status on admission was assessed using the Glasgow Coma Scale (GCS) score, World Federation of Neurosurgical Societies (WFNS) grade, Hunt and Hess (HH) grade, modified Fisher Scale (mFS) grade, as well as the presence of acute hydrocephalus. Furthermore, aneurysm morphology was analyzed, including aneurysm size, location, neck width, maximum height, maximum width, aneurysm maximum height to neck diameter (AR) ratio, and shape. In cases when multiple aneurysms were treated with a single endovascular coiling, the characteristics of the largest-sized aneurysm were analyzed. Additionally, the timing of treatment, procedural record, and angiographic results were also documented. The immediate angiographic outcomes were independently evaluated by two endovascular neurosurgeons using the Raymond–Roy classification. Moreover, relevant laboratory data were obtained, including the glucose level, routine blood tests, and biochemical tests.

Identification of dehydration trajectories

In this study, we adopted a definition including laboratory parameters and determined the dehydration status by means of the U/Cr ratio.⁷,⁹¹¹ We used the latent class growth mixture model (LCGMM), implemented in the R package lcmm, which employs maximum likelihood parameter estimation to delineate the group-based longitudinal trajectories of the U/Cr ratio.¹² Starting with the highest polynomial, models with different functional forms are compared by the level of significance of the cubic, quadratic, and linear terms. To avoid a low proportion of individuals in each latent class, models with trajectories grouped in groups 1–5 were fitted separately in each functional form. The optimal trajectory class model was screened according to the following principles: (1) using Akaike information criterion (AIC) and (2) Bayesian information criterion (BIC), where smaller values indicate better model fit; (3) ensuring that the number of participants assigned to each trajectory is no less than 10% of the total participants; (4) requiring a mean posterior probability>0.7 for each trajectory class; and (5) the proportion of individuals with high posterior probabilities (>0.7) in each class was >75%.¹²,¹⁴ Ultimately, each participant was assigned to the latent class with the highest membership probability.

Outcome ascertainment

The primary outcome was evaluated using the modified Rankin Scale (mRS) score at 6 months post-discharge, with an mRS score of ≥3 defined as a poor functional outcome. The secondary outcome was the occurrence of DCI during hospitalization, defined in accordance with the criteria used by Vergouwen et al.¹⁵ The detailed definition of DCI is described in Appendix E1. Meanwhile, the day of DCI onset after ictus was noted in patients with DCI.

Statistical analysis

Data are presented as mean±SD, median (IQR) or number (percentage) as appropriate. The basic characteristics among participants with different dehydration trajectories were compared using the t-test, Mann–Whitney U test, χ² test, or Fisher’s exact test. To minimize selection bias and mitigate baseline imbalances, we performed stabilized inverse probability of treatment weighting (sIPTW) to adjust for imbalances of covariates among dehydration trajectory groups.^{16 17} Multivariate logistic regression analysis was conducted to identify independent predictors of poor outcome and DCI, with adjustments for confounding variables based on univariate analysis results (p≤0.10). Collinearity among variables was evaluated using Pearson’s correlation coefficient (PCC) and variance inflation factor (VIF). A VIF of >5 and PCC>0.7 indicate high multicollinearity of the variables.^{18 19} WFNS grade and GCS score, and HH grade, and mFS grade had multicollinearity and hence they were removed (online supplemental figure S1).

Subgroup analyses were conducted to assess the effects of dehydration on the incidence of poor outcome, including age, hypertension, WFNS grade, aneurysm location, white blood cells (WBC), and DCI, with added interaction terms to test for heterogeneity among subgroups.^{20 21} To assess the mediation effect of DCI, causal mediation analysis (CMA) and sensitivity testing were performed under a counterfactual framework providing a general framework that offers clear definitions of causal mediation and related effects.^{22 23} We utilized the med4way command to obtain appropriate estimates of the four components, facilitating the estimation of the proportion of interaction or mediation.²⁴ The details of constructing a causal mediation model and four‐way decomposition analysis are described in online supplemental appendix E2-3.

Four-way decomposition analysis was performed using Stata 16.0, and other statistical analyses were conducted with SPSS 25.0 and R 4.2.0 software. All tests were two-sided, with statistical significance defined as p<0.05.

Results

Patients’ characteristics

In total, 519 consecutive patients with aSAH were included in the study (figure 1). The median patient age was 59 years, with men accounting for 38.2%. On admission, the median GCS score was 14 (IQR 13–15), WFNS grade was 3 (IQR 1–3), HH grade was 2 (IQR 1–3), and mFS grade was 2 (IQR 2–3). DCI was observed in 21.6% of patients, with onset occurring at a median of 8 days (online supplemental figure S2). At the 6- month follow-up, 154 patients (29.7%) had an unfavorable outcome. Baseline patient characteristics are presented in table 1.

Figure 1. Flow diagram of study patients. aSAH, aneurysmal subarachnoid hemorrhage; CT, computed tomography; CTA, computed tomographic angiography; DSA, digital subtraction angiography; SAH, subarachnoid hemorrhage.

Table 1. Baseline characteristics and outcomes of 519 patients with aneurysmal subarachnoid hemorrhage.

Characteristic	Overall
Patients (n)	519
Age (years)	59.0 (51.0, 68.0)
Age>55 years	308 (59.3)
Female	321 (61.8)
Medical history
Smoking	189 (36.4)
Alcohol	174 (33.5)
Hypertension	97 (18.7)
Diabetes	121 (23.3)
Heart disease	32 (6.2)
Previous stroke	51 (9.8)
Hyperlipidemia	41 (7.9)
Admission clinical grade
GCS score	14.0 (13.0, 15.0)
13–15 points	413 (79.6)
9–12 points	65 (12.5)
<9 points	41 (7.9)
WFNS grade	3.0 (1.0, 3.0)
IV–V	106 (20.4)
HH grade	2.0 (1.0, 3.0)
IV–V	52 (10.0)
mFS grade	2.0 (2.0, 3.0)
III–IV	200 (38.5)
Acute hydrocephalus	60 (11.6)
Aneurysm characteristics
Maximum size (mm)	6.5 (5.5, 7.5)
<3 mm	15 (2.9)
3–10 mm	454 (87.5)
>10 mm	50 (9.6)
Maximum height (mm)	5.3 (4.4, 6.8)
Neck width (mm)	2.9 (1.4, 4.0)
≥4 mm	134 (25.8)
AR ratio	2.0 (1.7, 2.8)
≤2	260 (50.1)
Wide neck	281 (54.1)
Posterior location	98 (18.9)
Irregular shape	187 (36.0)
Timing of treatment
≤24 hours	291 (56.1)
24–72 hours	148 (28.5)
>72 hours	80 (15.4)
Stent-assisted coiling	230 (44.3)
Raymond–Roy Class II–III	81 (15.6)
Blood glucose (mmol/L)	7.3 (6.2, 9.0)
>9.2 mmol/L	116 (22.4)
WBC (×109/L)	10.3 (9.0, 11.8)
>12 (×109/L)	116 (22.4)
Trajectory
Class 1	353 (68.0)
Class 2	97 (18.7)
Class 3	69 (13.3)
DCI	112 (21.6)
Poor outcome	154 (29.7)

Data are expressed as n (%), mean±SD, or median (IQR) as appropriate.

AR, aneurysm maximum height to neck diameter ratio; DCI, delayed cerebral ischemia; GCS, Glasgow Coma Scale; HH, Hunt and Hess; IQR, interquartile range; mFS, modified Fisher Scale; SD, standard deviation; WBC, white blood cells; WFNS, World Federation of Neurosurgical Societies.

Dehydration trajectories and baseline characteristics

Based on LCGMM parameters, the model with three trajectories and a quadratic function demonstrated the best fit to the data (online supplemental table S1-2). Following the U/Cr ratio-related time-varying pattern over 7 days, we identified three distinct trajectory classes (figure 2): class 1 (low group) accounted for 68.02% of the cases, class 2 (decreasing group) accounted for 18.69% of the cases, and class 3 (high group) accounted for 13.29% of the cases. table 2 outlines the basic characteristics of these participants based on dehydration trajectories. Compared with the low group, the decreasing group and high group had greater prevalence of diabetes, heart disease, and previous stroke; higher levels of aneurysm size, neck width, blood glucose, and WBC; higher frequencies of posterior aneurysms; as well as greater percentages of delayed surgical treatment (p<0.05). To account for confounding bias among differing dehydration trajectory groups sIPTW were performed, after which two evenly balanced cohorts were available for the analysis of outcomes (online supplemental figure S3). Interestingly, the proportion of patients with DCI and poor functional outcome was significantly increased in the high dehydration trajectory group as well (online supplemental tables S3).

Figure 2. Trajectories of dehydration during the first 7 days after admission.

Table 2. Comparisons of baseline characteristics and outcomes between different dehydration trajectories in patients with aneurysmal subarachnoid hemorrhage.

Characteristic	Dehydration			P value
	Class 1	Class 2	Class 3
Patients (n)	353	97	69
Age (years)	59.0 (51.0, 68.0)	58.0 (51.0, 67.0)	59.0 (51.0, 68.0)	0.941
Age>55 years	206 (58.4)	61 (62.9)	41 (59.4)	0.724
Female	217 (61.5)	60 (61.9)	44 (63.8)	0.938
Medical history
Smoking	131 (37.1)	31 (32.0)	27 (39.1)	0.570
Alcohol	120 (34.0)	31 (32.0)	23 (33.3)	0.931
Hypertension	64 (18.1)	17 (17.5)	16 (23.2)	0.583
Diabetes	65 (18.4)	39 (40.2)	17 (24.6)	<0.001
Heart disease	16 (4.5)	6 (6.2)	10 (14.5)	0.007
Previous stroke	36 (10.2)	4 (4.1)	11 (15.9)	0.038
Hyperlipidemia	24 (6.8)	8 (8.2)	9 (13.0)	0.211
Admission clinical grade
GCS score	14.0 (13.0, 15.0)	14.00 (13.0, 15.0)	13.0 (13.0, 14.0)	0.584
13–15 points	285 (80.7)	76 (78.4)	52 (75.4)	0.796
9–12 points	40 (11.3)	14 (14.4)	11 (15.9)
<9 points	28 (7.9)	7 (7.2)	6 (8.7)
WFNS grade	3.0 (1.0, 3.0)	3.0 (1.0, 3.0)	3.0 (2.0, 3.0)	0.388
IV–V	68 (19.3)	21 (21.6)	17 (24.6)	0.567
HH grade	2.0 (1.0, 3.0)	2.0 (1.0, 3.0)	2.0 (2.0, 3.0)	0.511
IV–V	34 (9.6)	9 (9.3)	9 (13.0)	0.664
mFS grade	2.0 (2.0, 3.0)	2.0 (2.0, 3.0)	2.0 (2.0, 3.0)	0.809
III–IV	140 (39.7)	35 (36.1)	25 (36.2)	0.745
Acute hydrocephalus	39 (11.0)	10 (10.3)	11 (15.9)	0.464
Aneurysm characteristics
Maximum size (mm)	6.4 (5.6, 7.3)	6.6 (4.6, 8.4)	6.9 (5.5, 8.5)	0.524
<3 mm	4 (1.1)	8 (8.2)	3 (4.3)	<0.001
3–10 mm	326 (92.4)	72 (74.2)	56 (81.2)
>10 mm	23 (6.5)	17 (17.5)	10 (14.5)
Maximum height (mm)	5.2 (4.5, 6.4)	5.4 (3.6, 7.7)	5.5 (4.5, 7.8)	0.303
Neck width (mm)	2.7 (1.4, 3.8)	3.6 (1.3, 4.8)	3.4 (1.7, 4.4)	0.016
≥4 mm	68 (19.3)	41 (42.3)	25 (36.2)	<0.001
AR ratio	2.0 (1.7, 2.9)	1.9 (1.6, 2.7)	1.9 (1.7, 2.6)	0.236
≤2	171 (48.4)	51 (52.6)	38 (55.1)	0.520
Wide neck	184 (52.1)	55 (56.7)	42 (60.9)	0.351
Posterior location	57 (16.1)	19 (19.6)	22 (31.9)	0.009
Irregular shape	128 (36.3)	32 (33.0)	27 (39.1)	0.710
Timing of treatment
≤24 hours	192 (54.4)	69 (71.1)	30 (43.5)	0.005
24–72 hours	107 (30.3)	15 (15.5)	26 (37.7)
>72 hours	54 (15.3)	13 (13.4)	13 (18.8)
Stent-assisted coiling	148 (41.9)	49 (50.5)	33 (47.8)	0.263
Raymond–Roy II–III	50 (14.2)	15 (15.5)	16 (23.2)	0.168
Blood glucose (mmol/L)	7.1 (6.0, 8.6)	7.50 (6.2, 9.3)	8.2 (6.8, 9.6)	0.001
>9.2 mmol/L	64 (18.1)	26 (26.8)	26 (37.7)	0.001
WBC (×109/L)	10.6 (9.4, 11.9)	10.7 (8.7, 11.9)	8.4 (7.4, 9.8)	<0.001
>12 (×109/L)	82 (23.2)	24 (24.7)	10 (14.5)	0.231
DCI	54 (15.3)	28 (28.9)	30 (43.5)	<0.001
Poor outcome	78 (22.1)	36 (37.1)	40 (58.0)	<0.001

Data are expressed as n (%), mean±SD, or median (IQR) as appropriate. Bold type indicates p-values that attained statistical significance.

AR, aneurysm maximum height to neck diameter ratio; DCI, delayed cerebral ischemia; GCS, Glasgow Coma Scale; HH, Hunt and Hess; IQR, interquartile range; mFS, modified Fisher Scale; SD, standard deviation; WBC, white blood cells; WFNS, World Federation of Neurosurgical Societies.

Association of dehydration trajectories with DCI and poor outcome

After we divided the subjects into three groups according to the dehydration trajectories, the incidence of DCI was 15.3%, 28.9%, and 43.5%, respectively. Patients with low, decreasing, and high trajectories had unfavorable 6-month outcomes at 22.1%, 37.1%, and 58.0% respectively (online supplemental figure S4). The differences in the DCI and poor outcome incidence between groups were statistically significant by the log rank test (p<0.05). Furthermore, we found that there were significantly higher U/Cr ratios in patients with DCI and poor outcome compared with those without (online supplemental table S4 and figure S5-6). To examine the association between dehydration and DCI as well as poor outcome, the trajectory groups were included as independent variables in a logistic regression model, and the low stable group was considered as the reference group. In univariate analysis, the risk of DCI occurrence was increased in other trajectory groups, and the unadjusted HRs (95% CI) were 2.25 (1.33 to 3.80) and 4.26 (2.44 to 7.44) for the decreasing group and high group, respectively. In addition, the decreasing group and high group remained significant after adjusting for potential confounders, with HRs of 2.44 (1.36 to 4.38) and 6.56 (3.44 to 12.51). Similar associations were found between dehydration trajectories and poor outcome. After multivariate adjustment, participants with a decreasing (OR 1.89, 95% CI 1.09 to 3.26) and a high pattern (OR 4.49, 95% CI 2.4 to 8.37) had an increased risk of developing poor functional outcome compared with those with a low pattern (table 3online supplemental table S5-6).

Table 3. Multivariable logistic regression models evaluating the association of dehydration with delayed cerebral ischemia and poor outcome in patients with aneurysmal subarachnoid hemorrhage.

Variable	Multivariate analysis for DCI		Multivariate analysis for poor outcome
	Adjusted OR*	P value	Adjusted OR†	P value
Dehydration
Class 1	Reference		Reference
Class 2	2.44 (1.36 to 4.38)	0.003	1.89 (1.09 to 3.26)	0.023
Class 3	6.56 (3.44 to 12.51)	<0.001	4.49 (2.4 to 8.37)	<0.001

Bold type indicates p-values that attained statistical significance.

^*Multivariable regression model adjusted for the confounders (WFNS grade and WBC), which were statistically significant in univariate analysis (p≤0.10).

^†Multivariable regression model adjusted for the confounders (age, hypertension, WFNS grade, aneurysm location, WBC, and DCI), which were statistically significant in univariate analysis (p≤0.10).

DCI, delayed cerebral ischemia; OR, odds ratio; WBC, white blood cells; WFNS, World Federation of Neurosurgical Societies.

Subgroup analysis of the risk of poor outcome

Online supplemental table S7 shows the association between dehydration trajectories and the risk of poor outcome in the stratified and interaction analyses. Each stratification was adjusted for all the factors (i.e., age, hypertension, WFNS grade, aneurysm location, WBC, and DCI), except for the stratification factor itself. The subgroup analysis results suggested a consistent relationship between dehydration trajectories and poor outcome. Interaction tests revealed that the relationship between dehydration trajectories and poor outcome was not statistically different across strata, indicating that age, hypertension, WFNS grade, aneurysm location, and WBC did not significantly impact this positive correlation (p for interaction>0.05). Notably, there was a significant interaction between DCI on the association between dehydration trajectories and poor outcome (p for interaction<0.05; Figure 3).

Figure 3. Multivariable-adjusted hazard ratios for the association between dehydration trajectory and poor outcome according to the delayed cerebral ischemia subgroups in patients with aneurysmal subarachnoid hemorrhage. CI, confidence interval; DCI, delayed cerebral ischemia; HR, hazard ratio; Ref, reference.

Mediation effect of dehydration trajectories on poor outcome via DCI

To investigate the direct and indirect effects of dehydration on poor outcome we performed the CMA. In the unadjusted model, DCI, as a mediator, accounted for 4.9% (95% CI 2.4% to 8.0%; p<0.001) of the relationship between dehydration and poor outcome without considering other covariates. Following adjustment, the mediation effect decreased to 2.9% but remained statistically significant (95% CI 1.1% to 5.0%, p<0.001; figure 4). Sensitivity testing was performed on the mediator-outcome model, and the results indicated a rho (ρ) at which mediation equals zero of 0.13 (online supplemental figure S7A). Moreover, coefficients of determination (R²) for the mediator-outcome model were used to create a graph of the amount of variance that an unobserved confounder would have to explain to totally eliminate the mediation effect of DCI on the DCI–poor outcome relationship (online supplemental figure S7B).

Figure 4. Causal mediation analysis was performed under a counterfactual framework to elucidate the mediating effect of delayed cerebral ischemia (DCI) in the association between dehydration trajectory and poor outcome for aneurysmal subarachnoid hemorrhage patients. DE, direct effect; IE, indirect effect; TE, total effect.

Four-way decomposition of the association between dehydration trajectories and DCI as well as poor outcome

Online supplemental table S8 displays the findings from a four-way decomposition analysis. The total effect was 2.46 (95% CI 0.82 to 4.11), demonstrating a significant association between dehydration and an increased risk of poor outcome. The controlled direct effect (CDE), which excludes mediation and interaction, comprised 66.42% (excess relative risk = 1.63, 95% CI 0.37 to 2.89), indicating a positive correlation between dehydration and poor functional outcomes independent of DCI. INTref (excess relative risk = 0.15, 95% CI –0.31 to 0.60) accounted for 6.09% of the total effect, highlighting that the relationship between dehydration and poor functional outcome is particularly pronounced in patients with DCI. The proportion of mediated interaction (INTmed, excess relative risk = 0.27, 95% CI –0.57 to 1.11), reflecting both mediation and interaction, accounted for 11.16% of the total effect, indicating that DCI partially mediates the effect of dehydration. Furthermore, the combined mediating effects of dehydration and DCI (INTmed + PIE) represented 27.51% of the total effect, while the interaction effect (INTref + INTmed) contributed 17.25%. Overall, the proportion eliminated (INTref + INTmed + PIE) was 33.60% (figure 5).

Figure 5. Four-way decomposition of the association between dehydration trajectory and delayed cerebral ischemia (DCI) as well as poor outcome in aneurysmal subarachnoid hemorrhage patients.

Discussion

In the current study, we identified three latent trajectory classes of U/Cr ratio, characterized by low dehydration, decreasing dehydration, and high dehydration. As expected, dehydration trajectory is independently associated with both DCI and poor outcome in patients with aSAH. Additionally, a potential positive interaction effect exists between dehydration and DCI on poor outcome. DCI also serves as a mediator, with its effect decomposed into 66.42% CDE, 16.35% PIE, 6.09% INT_ref, and 11.16% INT_med.

Reduced intravascular volume, commonly referred to as dehydration, is common for both ischemic and hemorrhagic stroke patients at the time of hospital presentation. As in previous reports, dehydration can be detected with biomarkers of reduced blood water, and a U/Cr ratio>80 was considered the ideal biomarker of dehydration.⁷,⁹ Lehmann et al⁹ reported dehydration at the time of admission in 31% of patients admitted with spontaneous intracerebral hemorrhage, and so dehydration status might serve as a significant and independent predictor of short-term mortality. Chen et al²⁵ included 961 patients with non-traumatic subarachnoid hemorrhage and performed univariable regression analysis, multivariable regression analysis, and propensity score matching to reduce the interference of underlying confounders. They found that a higher level of the U/Cr ratio was evidently associated with an increased risk of in-hospital mortality. In addition, an increased negative influence on the clinical course of dehydration has also been described repeatedly in patients suffering from other neurological diseases.⁸²⁶,²⁸ Therefore, we aimed to measure the frequency, risk factors, and associations of dehydration based on the U/Cr ratio (as a simple and convenient indicator of hydration that is routinely available) in aSAH population. Our findings similarly showed that patients with a higher U/Cr ratio experienced DCI and poor functional outcome more frequently, consistent with the abovementioned findings.

The literature suggests that dehydration increases hemoconcentration and blood viscosity and decreases blood pressure, factors that may worsen the effects of brain ischemia, resulting in greater brain damage and more unfavorable outcomes.^{29 30} Conversely, it is important to note that patients with high dehydration more frequently had the underlying medical history (eg, diabetes, heart disease, and previous stroke), according to the comparison among the three trajectory groups. Indeed, dehydration is significantly linked to age-related pathophysiological changes, sometimes not attentive enough in controlling these pathophysiological aspects to maintain a good hydration state. Hence, all the abovementioned factors must be taken into account before generalizing about the dehydration status. In brief, it seems reasonable to infer that dehydration after aSAH shows a strongly positive association with DCI and results in a poorer vital and functional prognosis. Although the U/Cr ratio is widely accepted as a reference standard for hydration status in patients, with standardized cutoffs to diagnose current and impending dehydration, there is however substantial variation in hydration status definition and diagnostic approach to dehydration compared with already established clinical variables.⁷

Currently, no research has investigated the correlation between an early alteration in the U/Cr ratio and the functional outcome of patients with aSAH. This longitudinal parameter may serve as a valuable biomarker, potentially reflecting both the dynamic pathophysiological changes and disease progression trajectory in this patient population. Accordingly, this study does not focus on the precise evaluation of dehydration status at a certain point in time but rather attempts to identify the longitudinal trajectories of dehydration. LCGMM is an unsupervised clustering analysis, and its clustering effect depends on the differences between samples and has been widely employed in psychiatry, nephrology, and neurology.³¹,³³ We employed an unsupervised model LCMM to identify three distinct trajectories of U/Cr ratio in aSAH patients within the first 7 days after admission and showed that persistent high and decreasing dehydration trajectories were associated with an increased risk of DCI and poor outcome. In addition, after adjustments for confounding factors, our multivariable logistic regression analysis revealed that the dehydration trajectory was significantly associated with DCI and poor functional outcome. Notably, it would be particularly valuable to further apply LCMM analysis to repeated measurement data to identify individuals at risk among aSAH patients. The trajectories we identified in this study extend our understanding of the early changes in U/Cr ratio and provide a reference for dehydration surveillance and prevention in clinical practice.

Furthermore, the current study represents a pioneering investigation into the complex interplay between hydration status and DCI in influencing poor functional outcome, while simultaneously elucidating the mediating role of DCI within this pathophysiological cascade. In the early stages following aSAH, consciousness disorder or dysphagia are the main causes of dehydration, which was a common finding in patients.^{25 34} According to the literature, it is likely that dehydration can cause contraction of the total plasma volume, increase blood viscosity, reduce cardiac output, and retard cerebral collateral circulation.^{34 35} In theory, dehydration promotes microthrombosis formation not only in the arteries but also in the veins in the brain. Data also indicate that dehydration affects the tone and regulation of cerebral blood vessels, taking into consideration the presence of sympathetic and neurohormonal overactivity.³⁶ DCI has been associated with numerous pathophysiological sequelae, including large artery vasospasm and microvascular thrombosis, and dehydration can lead to microthrombosis and microcirculatory constriction. Therefore, given the links between dehydration, DCI, and poor outcome found in previous studies,^{8 11 25 37} it seems reasonable to hypothesize that DCI mediates the prognostic association in aSAH patients. Despite the clinical significance of DCI, few studies have explored its mechanistic role within the event chain. Our findings provide novel evidence of a strong synergistic interaction between dehydration and DCI in increasing the risk of poor functional outcome, with DCI serving as an important mediator. This underscores the importance of implementing more intensive DCI prevention strategies for aSAH patients with dehydration, based on the causal mediation chain of dehydration leading to DCI and subsequent poor outcome. In other words, treating DCI could potentially reduce the risk due to its mediating role.

Unfortunately, the recently developed four-way effect decomposition did not yield statistically significant results for INT_ref and INT_med. We postulate that this outcome may be attributed to the limited sample size, which likely lacked the statistical power required for complex modeling. If the sample size were larger, it might enhance the likelihood of detecting significant effects. Nevertheless, integrating this analytical approach highlights the potential relevance of these interactions. Mediation analysis within a counterfactual framework was employed to elucidate the interrelationships among dehydration, DCI, and clinical outcome. This methodology has been demonstrated to offer deeper insights into causal mechanisms in similar studies.^{22 24 38} Overall, our findings suggest that interaction and mediation analyses contribute to understanding the pathophysiology underlying poor outcomes and inform management decisions. Specifically, these results underscore the importance of prioritizing DCI prevention and optimizing fluid management in aSAH patients, based on the potential causal chain of dehydration–DCI–poor outcome.

Strengths and limitations

This study possesses several strengths. First, itmployed repeated U/Cr ratio measurements within the first week after admission, instead of a single measurement, as a key biomarker of dehydration. Moreover, we first used the LCGMM model to uncover trajectories of dehydration in aSAH patients. Alternatively, this investigation used four-way decomposition analysis to innovatively assess the complex interrelationships between dehydration, DCI, and poor outcome in patients with aSAH. Nevertheless, several limitations should also be noted. First, we use a single marker for the diagnosis of ‘dehydration’. Plasma osmolality and other markers, such as hematocrit, blood urea nitrogen, and urine specific gravity, were not assessed in the enrolled participants. However, given the robust results of the U/Cr ratio, it seems unlikely that the analysis of additional indicators would have yielded diverging results because the dehydration-related biomarkers are usually a good correlation. Indeed, more comprehensive evaluation of renal, urinary, and peripheral biomarkers through a multiomics approach might enhance the accurate assessment of dehydration status and inform targeted therapeutic strategies warranting further investigation. Second, our study only focused on the mediator–outcome model (dehydration–DCI–poor outcome) and there may be other potential mediating pathways related to dehydration. Third, the present study is merely observational; we cannot determine the precise nature and quantity of the optimal fluid management for aSAH patients, limiting the practical implications of the comprehensive assessment of intravascular/extravascular volume status. Finally, this was only a retrospective single-center study and, as such, there was the possibility of selection bias. Currently, we plan to conduct a multicenter, prospective clinical trial with other institutions. The goal is to evaluate the effect of a hydration management program for aSAH patients. In the future, we hope to have opportunities to share the research results with the Journal's readers.

Conclusions

In this study we have identified three distinct dehydration trajectories and demonstrated that such trajectories are associated with both DCI and poor outcome in patients with aSAH. Furthermore, the effect of dehydration trajectory on poor outcome was partially mediated by DCI, involving both pure mediation and mediated interaction. However, the interplay between these factors is complex. Further research is needed to validate our findings and clarify their precise role in the pathophysiology of aSAH.

Data availability statement

Data are available upon reasonable request.