Effects of IKAP Health Education and Exercise Therapy on Clinical Outcomes in Hypertensive Intracerebral Hemorrhage

Hypertensive intracerebral hemorrhage (HICH) is a common and critical neurological emergency, accounting for approximately 10%-15% of all stroke cases. Although not the most frequent type of stroke, it is characterized by high incidence, substantial disability, and considerable mortality due to acute complications¹. Epidemiological data from China indicate approximately 1.6 million new HICH cases annually, with 30% experiencing deteriorating conditions or death due to complications such as acute-phase electrolyte imbalances and hepatic-renal dysfunction^2,3. Post-HICH, stress-induced pathological changes like sodium-potassium imbalance, azotemia, and coagulopathy not only directly impede neurological function recovery but may also trigger multiple organ dysfunction syndrome (MODS)⁴. Thus, effectively modulating homeostasis and protecting vital organ functions have become key to enhancing HICH prognosis.

Current clinical nursing interventions for HICH primarily focus on intracranial pressure control, blood pressure management, and early rehabilitation training, while systematic approaches to electrolyte balance and early detection of hepatic-renal injury remain inadequate⁵. Although one study in the past has attempted to correct metabolic disorders through nutritional support or pharmacological interventions, the effects are limited by individual variability and multi-system interactions⁶. Additionally, existing research mostly focuses on short-term changes in single indicators, lacking integrated analysis of electrolyte profiles, blood routine, hepatorenal function, and coagulation function, and failing to deeply explore the association between molecular-level pathological mechanisms and clinical outcomes⁷.

This study aims to explore the synergistic regulatory effect of Information-Knowledge-Attitude-Practice (IKAP)-based health education combined with exercise nursing on electrolyte homeostasis, hepatorenal function, and coagulation in HICH patients through multi-dimensional biomarker monitoring. It pioneers a multi-tiered evaluation system integrating molecular biomarkers with traditional biochemical indices. The research findings may provide new strategies for the early prevention and control of metabolic disorders after HICH and offer a reference paradigm for organ function support therapies in other critically ill patients. This interdisciplinary approach advances understanding of HICH pathophysiology and provides methodological insights for translational research.

This study involving human subjects was conducted in compliance with the Declaration of Helsinki and approved by the Ethical Committee of The People's Hospital of Yingshang (Approval No. 2025-18). Written informed consent was obtained from all participants prior to their enrollment in the study, with detailed explanations provided regarding the study's objectives, procedures, potential risks, and benefits.

Study design

This was a prospective non-randomized single-center study. The study population consisted of 102 patients diagnosed with HICH according to the HICH diagnostic criteria⁸ admitted to our hospital between October 2023 and June 2025 (inclusive). The sample size was estimated using sample size software (G-Power 3.1), with an effect size of 0.3, α=0.05, power (1-β) =0.9, and dropout rate of 10%, indicating a minimum sample size of 93. A total of 102 patients were enrolled to ensure sufficient statistical power. Inclusion criteria: Diagnosed with spontaneous intracerebral hemorrhage by cranial CT/MRI; history of hypertension or systolic blood pressure ≥140 mmHg at admission; modified Rankin Scale (mRS) score ≤2 points within 3 months prior to onset⁹; Glasgow Coma Scale (GCS) score ≥8 at admission¹⁰. Exclusion criteria: secondary intracerebral hemorrhage due to trauma, aneurysm/vascular malformation, or tumor; severe liver or kidney dysfunction; pregnant or lactating women; prior thrombolytic therapy or craniectomy before admission; inability to cooperate with nursing interventions or expected survival <7 days. In this study, a total of 27 patients developed MODS, and these patients were grouped into the MODS group, while the other 75 patients were grouped into the control group.

Intervention

All patients received IKAP health education upon admission, including information dissemination (through videos and manuals explaining the causes, triggers, and hazards of HICH and electrolyte disorders), knowledge reinforcement (daily bedside mini-classes with personalized interpretations based on patient test results), attitude change (through case sharing to enhance patient compliance), and practical guidance (guiding family members to participate in blood pressure monitoring and nasogastric feeding management). Physical rehabilitation training is also provided: Acute phase (≤7 days): Passive joint exercises for upper limbs - shoulder abduction/adduction, elbow flexion/extension; lower limbs - hip flexion/extension, knee flexion/extension; 2x daily, 15 min per session with movement amplitude: 30°-60° for joints at a speed of 5-10 movements per min. Subacute phase (8-14 days): Seated balance training with 3 sets per session for 5 min per set, with support initially, and then gradually reducing support; bedside standing at 2x daily at 10-15 min per session, with a weight-bearing ratio of 30%-50% initially, increasing to 50%-70%. Recovery phase (≥15 days): Progressive walking training for 30 min daily at a speed of 30-40 m per min initially, increasing by 5 m per min weekly, and an intensity of target heart rate = 60%-70% of maximum heart rate. The staged exercise rehabilitation training lasts for a minimum of 15 consecutive days in total, including 7 days for the acute phase, 7 days for the subacute phase, and no less than 15 days for the recovery phase. The overall adherence rate to IKAP health education was 82.3% (84/102), defined as attending ≥80% of bedside mini classes. The adherence rate to exercise training was 78.4% (80/102), with ≥80% of scheduled sessions completed.

Sample collection and testing

For analysis, 2 mL of venous blood was collected from each patient on an empty stomach in the morning for complete blood count (including WBC, RBC, HGB, and CRP) using EDTA-K2 anticoagulant (purple cap vacuum tube). After collection, the sample was gently inverted and mixed 8x. Samples were stored at room temperature and tested within 2 h; if testing was delayed, samples were stored at 2-8 °C for no more than 4 h. A fully automatic blood analyzer was used for testing. System-compatible control materials (low, normal, and high values) were run before testing each day, with a coefficient of variation (CV%) set below 2.0%.

For electrolyte testing (including Ca²⁺, Cl⁻, Na⁺, and K⁺), 3 mL of venous blood was collected using a gel-separated coagulation tube (yellow cap). The blood was allowed to stand for 30 min before being centrifuged at 3,000 x g for 10 min. Anticoagulants containing EDTA, citrate, or oxalate were avoided to prevent interference. The separated serum was transferred to microcentrifuge tubes and stored at -20 °C for no more than 1 week, with repeated freezing and thawing prohibited. Testing was performed using a fully automated biochemical analyzer, and high and low-range quality control was conducted daily before testing.

For liver and kidney function, 3 mL of venous blood was collected using a gel-coated tube (yellow cap) for the assessment of albumin (ALB), alkaline phosphatase (ALP), total bilirubin (TBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), blood urea nitrogen (BUN), serum creatinine (sCr), and urinalysis (UA). The blood was allowed to stand for 30 min followed by centrifugation at 3,000 x g for 10 min at room temperature. A fully automated biochemical analyzer was utilized for testing, with quality control implemented according to Westgard multiple rules (1₃s/2₂s/R₄s/4₁s/1₀x)¹¹. When quality control was out of range, alternative control materials, including third-party certified high and low concentration quality control sera (purchased from Randox Laboratories, UK), were activated. After switching to alternative controls, the calibration of the biochemical analyzer was repeated using the manufacturer's standard calibrator, and the validity of the alternative controls was verified by comparing the measured values with the target reference ranges before formal sample testing resumed.

Observation indicators

Changes in blood count, electrolytes, liver function, and kidney function before and after patient care were assessed. In addition, the incidence of MODS during patient care was statistically analyzed¹². MODS was diagnosed based on the Sequential Organ Failure Assessment (SOFA) score, with a score ≥2 points in at least one organ system indicating MODS. Based on the above indicators, relevant factors affecting MODS were analyzed, and a risk prediction model was established.

Statistical analysis

Statistical analysis was performed using SPSS 24.0 software. For categorical data [n (%)], comparisons were made using the chi-square test. For continuous data, after confirming normal distribution via the Shapiro-Wilk test, values were recorded as (x ± s). The Shapiro-Wilk test showed that all continuous variables had P>0.05 (range: 0.123-0.876), indicating normal distribution. Bonferroni correction was used for multiple comparisons to control Type I errors. Intra-group comparisons (before vs. after) were performed using paired t-tests; inter-group comparisons (MODS group vs. control group) were conducted using independent sample t-tests. Non-parametric tests (Mann-Whitney U test) were used if the data did not meet normal distribution. Related factors were analyzed using logistic regression analysis. The risk model was based on the results of the logistic regression analysis, selecting independent factors influencing MODS to establish a combined formula, which was validated using a ROC curve. P<0.05 indicated statistically significant differences.

Comparison of clinical baseline data

There was no statistically significant difference in age, gender, and APACHE II scores between the observation and the control groups (P>0.05), indicating comparability (Table 1).

Changes in blood count

First, the blood count results before and after nursing care were compared. It can be seen that there was no significant change in RBC and HGB in both groups after nursing care (P>0.05), but WBC and CRP were significantly lower than before nursing care (P<0.05, Table 2).

Changes in electrolytes

In terms of electrolytes, there was no significant difference between Ca2+ and Cl- levels before and after nursing care (P>0.05), but Na+ and K+ levels were significantly lower after nursing care than before (P<0.05, Table 3).

Changes in liver function and kidney function

The results of liver function and kidney function tests showed that there was no difference in ALB, ALP, and TBIL between before and after nursing care (P>0.05), but BUN, sCr, UA, ALT, AST, and GGT were all lower after nursing care than before (P<0.05, Table 4 and Table 5).

Univariate analysis of factors influencing MODS

Next, we conducted an in-depth analysis of the indicators that showed differences in the above analysis. To identify factors associated with MODS occurrence, we first conducted univariate analysis (independent sample t-tests) to compare pre-intervention indicators between the MODS group and control group, with the results presented in Table 6. Indicators showing statistical significance (P<0.05) were further included in multivariate logistic regression to screen independent risk factors. We compared the WBC, CRP, Na+, K+, BUN, Scr, UA, ALT, AST, and GGT levels before nursing intervention between the two groups. The results showed no significant differences in BUN, ALT, and AST (P>0.05), but the MODS group had significantly higher levels of WBC, CRP, Na+, K+, UA, sCr, and GGT compared to the control group (P<0.05), suggesting that these indicators are potential factors influencing MODS (Table 6).

Multivariate analysis of factors influencing MODS

Based on the univariate analysis results, we included WBC, CRP, Na+, K+, sCr, UA, and GGT as covariates in multivariate logistic regression analysis, with MODS occurrence as the dependent variable. Subsequently, we performed logistic regression analysis with MODS occurrence as the dependent variable and the above-mentioned potential factors as covariates. The results showed that WBC and Na+ were not independent factors influencing MODS (P>0.05), while CRP, K+, sCr, UA, and GGT were all independent risk factors for MODS (P<0.05, Table 7).

Development and validation of the MODS risk model

Based on the above analysis results, we developed a MODS risk model using the formula:

joint = -43.283 + 0.414 x CRP + 1.961 x K+ + 0.167 x sCr + 0.024 x UA + 0.199 x GGT

The ROC curve results showed that the model has a sensitivity of 74.07% and a specificity of 94.67% (P<0.001) for predicting the occurrence of MODS in HICH patients, with an AUC of 0.9195, demonstrating extremely high predictive value (Figure 1). Internal cross-validation (10-fold cross-validation) was performed to assess the model's stability, with a mean AUC of 0.896, confirming consistent predictive performance. Due to the single-center design, external validation with multi-center data is recommended in future studies to further verify the model's generalizability.

These representative results show the effectiveness of the standardized sampling protocols, rigorous quality control measures, and robust statistical methods described in this study. The consistent blood collection procedures and strict quality control (including Westgard rules and multi-level control materials) eliminated technical biases and led to significant post-intervention reductions in WBC, CRP, electrolytes, and hepatorenal function indicators. For the MODS risk model construction, multivariate logistic regression effectively identified independent risk factors by excluding confounding variables, while ROC curve analysis and 10-fold cross-validation comprehensively verified the model's predictive performance. Subsequent analyses are suggested to combine this model with clinical characteristics for stratified prediction and conduct dynamic monitoring of the five biomarkers to clarify their temporal correlation with MODS risk.

Data availability:

The dataset cannot be made available due to legal or ethical restrictions that prohibit public sharing of a dataset. The data used to support the findings of this study are available from the corresponding author upon request.

Figure 1: ROC curve analysis of the MODS risk prediction model in patients with HICH. The solid line represents the receiver operating characteristic (ROC) curve of the MODS risk prediction model constructed by combining five independent risk factors, including C-reactive protein (CRP), serum potassium (K+), serum creatinine (sCr), uric acid (UA), and gamma-glutamyl transferase (GGT). The area under the ROC curve (AUC) of the model is 0.9195, with a sensitivity of 74.07% and a specificity of 94.67% (P<0.001), indicating high predictive efficacy. The dotted line represents the reference line of the random prediction model (AUC=0.5). Please click here to view a larger version of this figure.

Table 1: Changes in blood count.

Table 2: Changes in electrolytes.

Table 3: Changes in liver and kidney function.

Table 4: Univariate analysis of factors influencing MODS.

Table 5: Multivariate analysis of factors influencing MODS.

Table 6: Univariate analysis of factors influencing MODS.

Table 7: Multivariate analysis of factors influencing MODS.

HICH patients often develop MODS due to electrolyte disturbances, hepatorenal function injury, and coagulation abnormalities, leading to adverse prognoses¹³. This study, as far as we are aware, is the first to apply IKAP-based health education plus exercise nursing to HICH patients. Through multi-dimensional biomarker monitoring, it was found that the patients' electrolyte, liver, and kidney functions were effectively improved. This demonstrates that our proposed framework improves organ protection in HICH patients via systemic homeostasis control, suggesting high translational relevance.

We believe that the modulation of IKAP-based health education plus exercise nursing on the internal homeostasis of HICH patients involves multi-level pathophysiological mechanisms, with the core being the remodeling of the body's metabolic network and inter-organ interactions through behavioral interventions. As an example, health education grounded in the IKAP model substantially improves patients' ability to regulate Na⁺ consumption through comprehensive education and action-oriented guidance. These findings align with He et al.'s cognitive-behavioral dual-path theory, which combines misconception correction (information layer) with measurable salt-reduction strategies (action layer) to modify high-salt dietary behaviors¹⁴. It is noteworthy that through attitudinal modification, the educational intervention also transforms patients' awareness of complication risks, which may secondarily impact electrolyte regulation-affirming the sustained behavioral influence of health education. Besides, exercise nursing may improve organ function through dual pathways of mechanical stress and metabolic stress. The exercise may improve renal function through the PI3K/Akt pathway, as hypothesized based on Wang et al.¹⁵, but this study did not detect relevant molecular markers (e.g., eNOS phosphorylation, Akt activation), so this mechanism remains a hypothesis to be verified in future studies. Experimental data from animal models revealed an 18% enhancement in renal cortical perfusion following 15 min daily joint movement sessions¹⁶, consistent with the decreasing trend of sCr levels observed in this study. Consistent with Díaz-Lara et al., exercise-induced skeletal muscle contraction activates the PGC-1α/TFAM axis, promoting mitochondrial biosynthesis and reducing reactive oxygen species (ROS) accumulation in renal tissue. This reduces renal tubular epithelial cell injury and improves glomerular filtration function, thereby decreasing sCr¹⁷. The study by Bae et al.¹⁸ shows that progressive exercise training can upregulate the LC3-II/I ratio and accelerate the clearance of damaged organelles. Golpasandi et al. also reported an increase in the expression of renal autophagy marker Beclin-1 in intracerebral hemorrhage rats following a 2-week rehabilitation training¹⁹, which is biologically consistent with the increased ALT levels found in our study. Finally, the combined intervention demonstrates synergistic immunomodulatory effects, notably through exercise-induced Th17/Treg rebalancing toward an anti-inflammatory phenotype-suppressing IL-17 while enhancing IL-10 production²⁰. This immunomodulatory effect synergizes with the anti-inflammatory consequences of education-induced psychological stress reduction, collectively tempering NF-κB overactivity²¹. Meanwhile, exercise-induced DNA demethylation can upregulate the activity of the eNOS promoter region, while educational intervention inhibits HDAC activity by reducing cortisol (Cor) levels, synergistically enhancing vascular endothelial repair capacity²². These effects are also reflected in the more significant decrease in WBC and CRP levels in patients. Our findings also echo the metabolic memory theory, that is, early electrolyte intervention reduces secondary neuronal damage caused by calcium overload by stabilizing the cell membrane potential. Through epigenetic remodeling, sustained exercise training establishes a protective barrier against subsequent pathological stimuli, providing novel experimental support for the early intensive intervention window period hypothesis²³.

The results of the analysis showed that the model achieved a sensitivity and specificity of 74.07% and 94.67%, respectively (AUC = 0.9195), demonstrating excellent clinical reference value. This model can be rapidly assessed within 1 h using automated analysis to identify at-risk patients for prompt treatment initiation. Unlike conventional scoring systems (e.g., SOFA)²⁴, this novel model combines molecular biomarkers with clinical parameters, demonstrating the superior potential of cross-disciplinary integration in advancing precision medicine.

Therefore, we recommend incorporating the combined IKAP-exercise nursing protocol into standardized HICH care pathways, complemented by the deployment of automated monitoring systems in medical institutions to enable high-frequency biochemical monitoring.

However, this study has certain limitations, such as a single-center design that may affect the generalizability of the results. Additionally, it did not evaluate long-term cognitive functional outcomes. Additionally, the study had no control group (e.g., a conventional nursing group) to compare the efficacy of the IKAP+exercise intervention, which limits the confirmation of its unique advantages. Moreover, we did not assess patient compliance with health education and exercise training, which may affect the interpretation of intervention effects. The follow-up ended at discharge or MODS onset, failing to assess the intervention's long-term effects on survival rate (e.g., 6-month/1-year survival) and neurological recovery (e.g., changes in mRS scores at 3 months post-discharge). This limits the evaluation of the intervention's comprehensive clinical value. Furthermore, the single-center design is geographically limited to Fuyang, Anhui, where local dietary habits (e.g., high sodium intake) may influence electrolyte homeostasis and MODS risk, potentially restricting the model's generalizability to regions with different dietary patterns. In future multi-center RCTs, populations from northern, southern, and eastern China should be included to verify the model's applicability, along with parallel investigations into potential mechanistic pathways, including gut microbiome dynamics and intestinal barrier integrity.

Conclusion

This prospective cohort study demonstrates that IKAP health education combined with staged exercise therapy effectively improves electrolyte homeostasis and protects liver function and kidney function in HICH patients. The MODS risk prediction model based on CRP, K+, Scr, UA, and GGT exhibits high predictive value (AUC=0.9195) and stability. Key innovations include the synergistic application of IKAP and staged exercise in HICH care, and the development of a practical, biomarker-based MODS risk model. Clinically, this model can be integrated into emergency/ICU pathways for rapid risk stratification, while the intervention protocol provides a standardized nursing strategy for organ function protection. Future research should conduct multi-center RCTs to verify the intervention's efficacy, explore underlying mechanisms (e.g., PGC-1α/TFAM axis), and extend follow-up to assess long-term outcomes.