Abstract
Background
Inflammatory and coagulation pathways contribute to the pathophysiology of acute ischemic stroke. High sensitivity C reactive protein and D Dimer are routinely measured biomarkers; however, their clinical relevance for early stroke severity and functional outcome, particularly across age groups, remains incompletely defined.
Methods
This retrospective cohort study included 622 patients with imaging confirmed acute ischemic stroke admitted between January 2023 and June 2025. Admission National Institutes of Health Stroke Scale scores, vascular risk factors, and laboratory biomarkers including hs CRP and D Dimer were collected within twenty four hours of admission. Functional outcome at discharge was assessed using the modified Rankin Scale and dichotomized as good (≤ 2) or poor (> 2). Associations between biomarkers, stroke severity, and outcome were evaluated using correlation analyses and multivariable logistic regression. Age stratified analyses were performed as a secondary objective.
Results
The median admission NIHSS score was 2 (interquartile range 1–5). hs CRP levels were positively correlated with NIHSS scores (Spearman rho 0.21, p < 0.001), as were D Dimer levels (Spearman rho 0.16, p < 0.001). Patients with poor functional outcome had higher admission hs CRP levels (p = 0.004) and higher NIHSS scores (p < 0.001). In multivariable analysis, higher NIHSS score (odds ratio 1.32, 95% confidence interval 1.18–1.48) and hypertension (odds ratio 2.11, 95% confidence interval 1.19–3.72) were independently associated with poor outcome, while hs CRP showed a borderline association and D Dimer was not independently associated after adjustment. Associations between biomarkers and stroke severity were consistent across age groups.
Conclusions
Admission hs CRP and D Dimer levels are modestly associated with initial stroke severity in acute ischemic stroke, while elevated hs CRP is associated with poor early functional outcome at hospital discharge. These associations appear largely mediated by stroke severity. Stroke severity and hypertension remain the strongest predictors of early recovery. Inflammatory biomarkers may provide adjunctive information for early risk stratification when interpreted alongside established clinical assessments.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12883-026-04797-6.
Keywords: Acute ischemic stroke, High sensitivity C reactive protein, D Dimer, Stroke severity, Functional outcome, Inflammation, Biomarkers
Introduction
Acute ischemic stroke (AIS) results from an abrupt interruption of cerebral blood flow that initiates inflammatory activation, endothelial dysfunction, and coagulation disturbances. These interconnected processes contribute to infarct expansion, secondary injury, and early neurological deterioration [1, 2]. Circulating biomarkers that reflect systemic inflammation and thrombosis have therefore attracted interest as potential adjuncts for clinical risk assessment in AIS [3]. High sensitivity C reactive protein (hs CRP) is a marker of systemic inflammatory response that increases following vascular injury, while D Dimer reflects fibrin formation and degradation and is commonly elevated in active thrombotic states. Previous studies have reported associations between these biomarkers and stroke severity or outcomes; however, their clinical relevance during the acute hospitalization period remains incompletely defined, particularly after accounting for established measures of neurological severity [4–7].
Most existing studies have focused on medium or long term outcomes, such as ninety day disability or mortality, whereas comparatively fewer investigations have examined the relationship between inflammatory and coagulation biomarkers and early functional status at hospital discharge [8–10]. Early outcome assessment is clinically meaningful, as discharge status directly influences rehabilitation planning, care transitions, and short term healthcare utilization, as well as communication with patients and families [11, 12]. Despite this relevance, the extent to which hs CRP and D Dimer reflect early neurological severity and short term functional outcome in routine clinical practice has not been fully clarified.
Age related heterogeneity further complicates interpretation of biomarker associations in AIS. Younger and older adults differ in vascular risk factor burden, comorbidities, and stroke mechanisms, which may influence inflammatory and coagulation responses to ischemic injury [13–15]. Although age is a well established determinant of stroke outcome, relatively few studies have directly compared biomarker severity relationships across age groups within the same clinical cohort [16]. Evidence addressing whether the associations between hs CRP, D Dimer, neurological severity, and early functional outcome differ meaningfully by age therefore remains limited [17, 18].
Against this background, the present study examines the associations of admission hs CRP and D Dimer levels with initial neurological severity and early functional outcome at hospital discharge in patients with AIS. Age group comparisons are included as a secondary objective to explore potential differences in biomarker severity relationships between younger and older adults. By focusing on routinely available laboratory markers obtained within the first twenty four hours of admission, this study aims to clarify the adjunctive clinical relevance of hs CRP and D Dimer in the acute phase of AIS and to contextualize their role alongside established clinical assessments.
Materials and methods
Study design and patient selection
This retrospective cohort study was conducted at Lianyungang Municipal Oriental Hospital, a tertiary care center that manages patients with acute neurological emergencies. Consecutive adult patients admitted with suspected acute ischemic stroke between January 2023 and June 2025 were screened for eligibility. The study protocol was approved by the hospital ethics committee, and the requirement for informed consent was waived due to the retrospective design and the use of anonymized data.
Stroke diagnosis and eligibility criteria
Acute ischemic stroke was diagnosed through clinical evaluation supported by neuroimaging. All included patients had stroke confirmation based on either diffusion restricted magnetic resonance imaging or non contrast computed tomography performed according to standard institutional protocols.
Patients were eligible if they met the following criteria:
Age 18 years or older.
Confirmed diagnosis of acute ischemic stroke.
Availability of hs CRP and D Dimer results within twenty four hours of admission.
Documented NIHSS score at presentation.
Documented modified Rankin Scale score at hospital discharge.
Exclusion criteria included transient ischemic attack, intracerebral or subarachnoid hemorrhage, missing laboratory or outcome data, and incomplete medical records [19].
Age stratification into young adults (18 to 50 years) and older adults (older than 50 years) was used only for secondary analyses and was not part of the primary study aim.
Clinical and demographic variables
Electronic medical records were reviewed to obtain demographic characteristics and baseline clinical information. These included:
Age and sex
Vascular risk factors such as hypertension, diabetes mellitus, smoking history, and alcohol use
Stroke severity at admission was assessed using the National Institutes of Health Stroke Scale (NIHSS), a widely validated clinical tool for quantifying neurological impairment in acute stroke [20].
Stroke etiology classified by TOAST criteria was recorded but was not included in outcome analyses due to incomplete availability across the cohort and is therefore not reported in detail.
Functional outcome at discharge was evaluated using the modified Rankin Scale (mRS), which measures the degree of disability or dependence in daily activities [21].
The modified Rankin Scale was dichotomized as follows:
Good outcome: mRS of 0 to 2.
Poor outcome: mRS greater than 2.
Laboratory measurements
Peripheral venous blood samples were collected within twenty four hours of hospital admission as part of routine clinical care. The following biomarkers were extracted from laboratory records:
High sensitivity C reactive protein measured by immunoturbidimetric assay.
D Dimer measured using an enzyme linked immunosorbent assay.
Low density lipoprotein cholesterol.
Glycated hemoglobin.
The timing of blood collection was standardized to minimize variability. All assays were performed in the hospital diagnostic laboratory using validated clinical instruments.
Outcomes
The primary outcomes were:
Initial stroke severity quantified using the NIHSS.
Early functional outcome at discharge measured using the modified Rankin Scale.
The secondary outcome was the comparison of biomarker severity relationships across age groups.
Statistical analysis
Continuous variables were assessed for normality using the Shapiro-Wilk test [22] in conjunction with visual inspection of data distributions. Normally distributed variables were summarized as mean with standard deviation, whereas non normally distributed variables were reported as median with interquartile range. Group comparisons were performed using the independent samples t test for normally distributed variables and the Mann-Whitney U test for non normally distributed variables, as appropriate [23]. Categorical variables were compared using the chi square test [24].
Associations between admission biomarker levels and National Institutes of Health Stroke Scale scores were evaluated using Spearman correlation coefficients [25]. To identify predictors of poor functional outcome at discharge, a multivariable logistic regression model was constructed [26]. Variables were selected based on clinical relevance and significance in univariable analyses and included NIHSS score, hs CRP, D Dimer, hypertension, diabetes mellitus, age group, and sex. Model assumptions, including independence of observations and multicollinearity, were assessed prior to analysis.
Statistical significance was defined as a two sided p value less than 0.05 [27]. All statistical analyses were performed using IBM SPSS Statistics for Windows, version 26.0 [28].
Results
Study population and baseline characteristics
A total of 622 patients with acute ischemic stroke were included in the final analysis after application of eligibility criteria. The median age of the cohort was 66 years (interquartile range 55–78), and 164 patients (26.4%) were aged 50 years or younger. Male patients accounted for 57.1% of the study population.
The median National Institutes of Health Stroke Scale score at admission was 2 (interquartile range 1–5), indicating predominantly mild neurological deficits at presentation. Hypertension and diabetes mellitus were the most common vascular risk factors, with a higher prevalence observed among older adults. Admission levels of hs CRP and D Dimer showed wide interindividual variability.
Baseline demographic characteristics, vascular risk factors, stroke severity, and laboratory parameters for the overall cohort and age-stratified subgroups are presented in Table 1.
Table 1.
Baseline characteristics of patients with acute ischemic stroke stratified by age group
| Variable | Overall (n = 622) | Young ≤ 50 years (n = 164) | Older > 50 years (n = 458) | p value |
|---|---|---|---|---|
| Age, years, median (IQR) | 66 (55–78) | 43 (38–47) | 72 (63–81) | < 0.001 |
| Male sex, n (%) | 355 (57.1) | 102 (62.2) | 253 (55.2) | 0.12 |
| Hypertension, n (%) | 401 (64.4) | 73 (44.5) | 328 (71.6) | < 0.001 |
| Diabetes mellitus, n (%) | 156 (25.1) | 28 (17.1) | 128 (28.0) | 0.004 |
| Smoking history, n (%) | 212 (34.1) | 58 (35.4) | 154 (33.6) | 0.67 |
| NIHSS at admission, median (IQR) | 2 (1–5) | 3 (1–5) | 2 (1–5) | 0.09 |
| hs-CRP, mg/L, median (IQR) | 4.8 (1.2–12.3) | 4.5 (1.1–12.0) | 5.0 (1.3–12.5) | 0.41 |
| D-Dimer, ng/mL, median (IQR) | 135 (80–290) | 128 (70–260) | 140 (85–310) | 0.28 |
Association between biomarkers and initial stroke severity
Admission hs CRP levels demonstrated a statistically significant positive correlation with NIHSS scores (Spearman rho = 0.21, p < 0.001), indicating that higher inflammatory marker levels were associated with greater neurological severity at presentation. This association is illustrated in Fig. 1, which shows an upward trend between hs CRP levels and NIHSS scores.
Fig. 1.
Relationship between hs-CRP and stroke severity. Scatter plot illustrating the association between admission hs-CRP levels and National Institutes of Health Stroke Scale scores
Similarly, D Dimer levels were positively correlated with NIHSS scores (Spearman rho = 0.16, p < 0.001). As shown in Fig. 2, higher D Dimer levels tended to occur in patients with greater stroke severity, although substantial variability was observed across the cohort.
Fig. 2.
Relationship between D-Dimer and stroke severity. Scatter plot illustrating the association between admission D-Dimer levels and National Institutes of Health Stroke Scale scores
Biomarkers and early functional outcome
Functional outcome was assessed at hospital discharge using the modified Rankin Scale. A total of 170 patients (27.3%) experienced a poor functional outcome, defined as a modified Rankin Scale score greater than 2.
Patients with poor outcomes had significantly higher admission NIHSS scores compared with those with good outcomes (median 6 versus 2, p < 0.001). Admission hs CRP levels were also significantly higher in patients with poor outcomes (median 7.9 mg/L versus 4.2 mg/L, p = 0.004). This difference is illustrated in Fig. 3.
Fig. 3.
hs-CRP levels by early functional outcome. Boxplot showing admission hs-CRP levels stratified by functional outcome at hospital discharge
D Dimer levels were higher among patients with poor outcomes; however, this difference did not reach statistical significance. Comparisons of NIHSS scores and biomarker levels between outcome groups are summarized in Table 2.
Table 2.
Biomarkers and Functional Outcome at Discharge
| Variable | Good outcome (mRS ≤ 2) | Poor outcome (mRS > 2) | p value |
|---|---|---|---|
| Number of patients | 452 | 170 | |
| NIHSS, median (IQR) | 2 (1–4) | 6 (4–11) | < 0.001 |
| hs-CRP, mg/L, median (IQR) | 4.2 (1.1–10.8) | 7.9 (2.4–18.5) | 0.004 |
| D-Dimer, ng/mL, median (IQR) | 130 (75–270) | 150 (90–370) | 0.07 |
Multivariable predictors of poor functional outcome
A multivariable logistic regression analysis was performed to identify independent predictors of poor functional outcome at discharge. After adjustment for demographic variables, vascular risk factors, stroke severity, and laboratory markers, higher admission NIHSS score remained the strongest independent predictor of poor outcome (odds ratio 1.32, 95% confidence interval 1.18–1.48, p < 0.001).
Hypertension was also independently associated with poor functional outcome (odds ratio 2.11, 95% confidence interval 1.19–3.72, p = 0.010). hs CRP demonstrated a borderline association with outcome after adjustment, whereas D Dimer and age group were not independently associated with poor outcome in the final model.
The complete multivariable regression results are presented in Table 3.
Table 3.
Multivariable logistic regression analysis for predictors of poor functional outcome
| Predictor | Odds ratio | 95% confidence interval | p value |
|---|---|---|---|
| NIHSS at admission | 1.32 | 1.18–1.48 | < 0.001 |
| Hypertension | 2.11 | 1.19–3.72 | 0.010 |
| hs-CRP | 1.02 | 0.99–1.05 | 0.058 |
| D-Dimer | 1.00 | 0.99–1.00 | 0.61 |
| Age group | 1.14 | 0.72–1.80 | 0.58 |
| Diabetes mellitus | 1.29 | 0.81–2.08 | 0.28 |
Secondary age stratified analysis
Secondary analyses were conducted to explore whether associations between biomarkers and stroke severity differed between younger and older adults. Positive correlations between NIHSS scores and both hs CRP and D Dimer were observed in both age groups, with comparable effect sizes.
Although younger patients tended to have slightly higher NIHSS scores at presentation, this difference was modest and not clinically meaningful. No significant interaction between age group and biomarker levels was identified. Distributions of hs CRP, D Dimer, and NIHSS scores by age group are shown in Supplementary Figures S1–S3.
Discussion
In this retrospective cohort study of patients with acute ischemic stroke, we examined the associations between admission inflammatory and coagulation biomarkers and early clinical outcomes, with age stratification included as a secondary analysis. The principal findings were that admission hs-CRP and D-Dimer levels were modestly associated with initial stroke severity, and that elevated hs-CRP was associated with poor early functional outcome at hospital discharge in unadjusted analyses. Importantly, admission NIHSS score and hypertension emerged as the strongest independent predictors of early outcome, while hs-CRP demonstrated only a borderline association after multivariable adjustment and D-Dimer was not independently associated with functional outcome. Age group was not independently associated with early functional status.
Higher admission hs-CRP levels were associated with greater neurological severity at presentation, supporting previous observations that systemic inflammatory activation accompanies more severe ischemic injury. Similarly, D-Dimer levels were positively associated with NIHSS scores, indicating that increased coagulation activity is related to acute stroke severity. These findings are consistent with prior studies reporting associations between inflammatory and coagulation biomarkers and stroke severity; however, the modest effect sizes observed in our cohort underscore the multifactorial nature of neurological injury in ischemic stroke, which is influenced by infarct location, collateral circulation, and timing of reperfusion in addition to systemic biomarker levels.
Early functional outcome at hospital discharge was strongly determined by admission neurological severity, reinforcing the established role of the NIHSS as the most robust early clinical indicator of prognosis. Although hs-CRP levels were higher among patients with poor early outcomes, this association was attenuated after adjustment for stroke severity and vascular risk factors, suggesting that inflammatory burden may primarily reflect the extent of acute neurological injury rather than act as an independent determinant of early recovery. In contrast, D-Dimer levels were not independently associated with functional outcome, indicating that coagulation activity may be more closely linked to initial stroke severity than to short-term functional status once established clinical predictors are considered.
In the multivariable analysis, hypertension remained independently associated with poor early outcome alongside NIHSS score. This finding likely reflects the cumulative effects of chronic hypertensive arteriopathy, including impaired cerebrovascular autoregulation and reduced collateral capacity, which may limit recovery following ischemic injury. The lack of an independent association between age group and early outcome highlights that short-term recovery after stroke is more closely related to acute neurological damage and vascular health than to chronological age alone.
Secondary age-stratified analyses demonstrated that the associations between hs-CRP, D-Dimer, and stroke severity were comparable in younger and older adults. Although younger patients tended to present with slightly higher NIHSS scores, this difference was modest and not clinically meaningful. The consistency of biomarker–severity relationships across age groups suggests that thrombo-inflammatory responses to ischemic injury operate through broadly similar biological pathways in adult patients, supporting the interpretation that hs-CRP and D-Dimer function as general markers of acute stroke pathophysiology rather than age-specific indicators.
From a clinical perspective, hs-CRP and D-Dimer are inexpensive and widely available laboratory tests that are routinely measured in hospitalized patients. While these biomarkers should not be viewed as substitutes for established clinical assessments, hs-CRP in particular may provide adjunctive information for early risk stratification when interpreted alongside NIHSS scores and vascular risk factors. Their greatest potential value lies in complementing, rather than replacing, neurological examination in the acute phase of ischemic stroke.
Several limitations of this study should be acknowledged. The retrospective single-center design limits generalizability and introduces potential selection bias. Functional outcome was assessed only at hospital discharge, and medium- or long-term outcomes such as ninety-day modified Rankin Scale scores were not available, limiting conclusions regarding sustained recovery. Important clinical variables, including infarct volume, stroke subtype based on TOAST classification, and acute reperfusion therapies such as intravenous thrombolysis or endovascular thrombectomy, were not included in the outcome models. The absence of these factors may confound the observed associations between biomarkers, stroke severity, and outcome and limits causal interpretation. In addition, biomarker measurements were obtained at a single time point, precluding assessment of temporal trends.
In conclusion, admission hs-CRP and D-Dimer levels are modestly associated with initial stroke severity in patients with acute ischemic stroke, while hs-CRP shows an association with poor early functional outcome that is largely mediated by stroke severity. Admission NIHSS score and hypertension remain the strongest independent predictors of early recovery. These findings support the role of inflammatory biomarkers as adjunct markers of acute stroke severity rather than independent prognostic tools and highlight their potential utility when interpreted in conjunction with established clinical assessments.
Conclusions
In patients with acute ischemic stroke, admission hs-CRP and D-Dimer levels are modestly associated with initial neurological severity, while elevated hs-CRP is associated with poor early functional outcome at hospital discharge. However, admission NIHSS score and hypertension remain the strongest independent predictors of early outcome, and the associations observed for inflammatory biomarkers appear largely mediated by stroke severity rather than reflecting independent prognostic effects. Age group was not independently associated with early recovery.
These findings support the role of inflammatory biomarkers, particularly hs-CRP, as adjunct markers that may complement established clinical assessments during the acute phase of ischemic stroke, rather than serving as standalone prognostic tools. Further prospective studies incorporating longitudinal biomarker measurements, detailed imaging data, reperfusion variables, and longer-term functional outcomes are needed to better define the clinical utility of these markers in stroke risk stratification and prognostication.
Supplementary Information
Acknowledgements
The authors thank the medical records department of Lianyungang Municipal Oriental Hospital for facilitating data retrieval and the neurology ward staff for their support during data collection. No GenAI tools were used in data analysis; any text editing was limited to standard grammar and formatting improvements.
Institutional review board statement
The study entitled “Associations of hs-CRP and D-Dimer with Stroke Severity and Early Functional Outcomes in Ischemic Stroke: A Retrospective Age-Stratified Cohort Study” was re-viewed and approved by the Institutional Review Board/Ethics Committee of The Affiliated Lianyungang Municipal Oriental Hospital of Xuzhou Medical University (Reference No. 202500901). Informed consent was waived due to the non-interventional, retrospective analysis of anonymized patient data conducted in accordance with the Declaration of Helsinki.
Informed consent statement
Patient consent was waived due to the retrospective, anonymized nature of the data analysis (Ethics Committee exemption granted).
Abbreviations
- AIS
Acute ischemic stroke
- hs CRP
High sensitivity C reactive protein
- NIHSS
National Institutes of Health Stroke Scale
- mRS
Modified Rankin Scale
- LDL
Low density lipoprotein
- HbA1c
Glycated hemoglobin
- CT
Computed tomography
- MRI
Magnetic resonance imaging
- OR
Odds ratio
- CI
Confidence interval
Authors’ contributions
Conceptualization, S.P.B. and C.H.X.; Methodology, S.P.B, P.K.J.; Soft-ware, P.K.J.; Validation, S.P.B., P.K.J. and M.S.R.; Formal analysis, S.P.B. and M.S.R.; Investigation, S.P.B. and M.S.R.; Resources, C.H.X.; Data curation, M.S.R.; Writing – original draft preparation, S.P.B.; Writing – review & editing, C.H.X., P.K.J. and M.S.R.; Visualization, P.K.J.; Super-vision, C.H.X.; Project administration, C.H.X.; Funding acquisition, C.H.X. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Jiangsu Provincial Health Commission Research Project (Grant No. M2021110).
Data availability
The datasets generated and analyzed during the current study are not publicly available due to patient confidentiality constraints but are available from the corresponding author on reasonable request. SPSS syntax used for all statistical analyses is likewise available upon request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Xu A, Zhang H, Zhang Y, Wu J, Huang Z. Ischemic stroke and intervention strategies based on the timeline of stroke progression: review and prospects. Acta Pharm Sin B. 2025. [DOI] [PMC free article] [PubMed]
- 2.Pensato U, Demchuk AM, Menon BK, Nguyen TN, Broocks G, Campbell BC, et al. Cerebral infarct growth: pathophysiology, pragmatic assessment, and clinical implications. Stroke. 2025;56(1):219–29. [DOI] [PubMed] [Google Scholar]
- 3.Makris K, Haliassos A, Chondrogianni M, Tsivgoulis G. Blood biomarkers in ischemic stroke: potential role and challenges in clinical practice and research. Crit Rev Clin Lab Sci. 2018;55(5):294–328. [DOI] [PubMed] [Google Scholar]
- 4.Kamath DY, Xavier D, Sigamani A, Pais P. High sensitivity C-reactive protein and cardiovascular disease: an Indian perspective. Indian J Med Res. 2015;142(3):261–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Peng X, Zhao J, Liu J, Li S. Advances in biomarkers of cerebral small vessel disease. J Neurorestoratology. 2019;7(4):171–83. [Google Scholar]
- 6.Liu LB, Li M, Zhuo WY, Zhang YS, Xu AD. The role of hs-CRP, D-dimer and fibrinogen in differentiating etiological subtypes of ischemic stroke. PLoS ONE. 2015;10(2):e0118301. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Mouliou DS. C-reactive protein: pathophysiology, diagnosis, false test results and a novel diagnostic algorithm for clinicians. Diseases. 2023;11(4):132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Demissei BG, Postmus D, Cleland JG, O’Connor CM, Metra M, Ponikowski P, et al. Plasma biomarkers to predict or rule out early post-discharge events after hospitalization for acute heart failure. Eur J Heart Fail. 2017;19(6):728–38. [DOI] [PubMed] [Google Scholar]
- 9.Hatab I, Kneihsl M, Bisping E, et al. The value of clinical routine blood biomarkers in predicting long-term mortality after stroke. Eur Stroke J. 2023;8(2):532–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kopp K, Lichtenauer M, Paar V, Hoppe UC, Rakhimova RF, Badykova EA, et al. Diagnostic biomarkers for risk estimation of in-hospital and post-discharge cardiovascular mortality in ST-segment elevation myocardial infarction patients. J Clin Med. 2025;14(18):6632. 10.3390/jcm14186632. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ohta B, Mola A, Rosenfeld P, Ford S. Early discharge planning and improved care transitions: pre-admission assessment for readmission risk in an elective orthopedic and cardiovascular surgical population. Int J Integr Care. 2016;16(2):10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Prick JCM, Engelhardt EG, Rotgerink FL, Deijle IA, van Schaik SM, Garvelink MM, et al. Implementation of a patient decision aid for discharge planning of hospitalized patients with stroke: a process evaluation using a mixed-methods approach. Patient Educ Couns. 2025;136:108716. [DOI] [PubMed] [Google Scholar]
- 13.Di Lazzaro G, Paolini Paoletti F, Bellomo G, et al. Effect of aging on biomarkers and clinical profile in Parkinson’s disease. J Neurol. 2025;272:651. 10.1007/s00415-025-13384-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Wei YC, Kung YC, Lin C, Yeh CH, Chen PY, Huang WY, et al. Differential neuropsychiatric associations of plasma biomarkers in older adults with major depression and subjective cognitive decline. Transl Psychiatry. 2024;14(1):333. 10.1038/s41398-024-03049-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Huang X, Han X, Chang H, et al. Associations between trajectories of plasma biomarkers for Alzheimer’s disease, brain structures, and cognitive function: a prospective cohort study in the UK Biobank. Mol Psychiatry. 2025. 10.1038/s41380-025-03166-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Aging Biomarker Consortium, Zhang L, Guo J, Liu Y, Sun S, Liu B, et al. A framework of biomarkers for vascular aging: a consensus statement by the Aging Biomarker Consortium. Life Med. 2023;2(4):lnad033. 10.1093/lifemedi/lnad033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Zazzara MB, Triolo F, Biscetti L, Paparazzo E, Fiorillo M, Vetrano DL et al. Biomarkers of multimorbidity: a systematic review. Ageing Res Rev. 2025;102870. [DOI] [PubMed]
- 18.Moqri M, Herzog C, Poganik JR, Justice J, Belsky DW, Higgins-Chen A, et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell. 2023;186(18):3758–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Qureshi AI, Zahoor K, Bhatti IA, Merchant R, Beall J, Cassarly CN, et al. Exclusion criteria in randomized clinical trials of subarachnoid hemorrhage. Neurosurg Rev. 2025;48(1):394. 10.1007/s10143-025-03516-y. [DOI] [PubMed] [Google Scholar]
- 20.Brott T, Adams HP Jr, Olinger CP, Marler JR, Barsan WG, Biller J, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. 1989;20(7):864–70. [DOI] [PubMed] [Google Scholar]
- 21.van Swieten JC, Koudstaal PJ, Visser MC, Schouten HJ, van Gijn J. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19(5):604–7. [DOI] [PubMed] [Google Scholar]
- 22.Butul M, Usharani P, Subbalaxmi M. Statistical analysis using Shapiro–Wilk test, Mann–Whitney test, and Friedman test for non normally distributed variables.
- 23.Eltas Ö. Comparison of the Mann–Whitney U test and independent samples t test in terms of power in small sample biostatistical studies. 2021.
- 24.Vierra A, Razzaq A, Andreadis A. Categorical variable analyses: chi-square, Fisher exact, and Mantel–Haenszel. Translational Surgery. Academic; 2023. pp. 171–5.
- 25.Zheng Z. D-dimer levels and NIHSS as prognostic predictors in elderly patients with cerebral infarction. Clin Interv Aging. 2025:505–11. [DOI] [PMC free article] [PubMed]
- 26.Holliday E, Lillicrap T, Kleinig T, Choi PMC, Maguire J, Bivard A, et al. Developing a multivariable prediction model for functional outcome after reperfusion therapy for acute ischaemic stroke: study protocol for the TOTO multicentre cohort study. BMJ Open. 2020;10(4):e038180. 10.1136/bmjopen-2020-038180. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Kwak S. Are only p-values less than 0.05 significant? A p-value greater than 0.05 is also significant. J Lipid Atheroscler. 2023;12(2):89–95. 10.12997/jla.2023.12.2.89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.IBM Corp. IBM SPSS Statistics for Windows. Version 26.0. Armonk. (NY): IBM Corp; 2019. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and analyzed during the current study are not publicly available due to patient confidentiality constraints but are available from the corresponding author on reasonable request. SPSS syntax used for all statistical analyses is likewise available upon request.