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Research Article

Admission-Parameter Nomogram for Early Neurological Deterioration after Minor Small Subcortical Infarction: A Retrospective Cohort Study

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DOI:

10.3791/71645

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August 28th, 2026

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In This Article

Summary

This study developed an admission-variable nomogram for early neurological deterioration after minor small subcortical infarction and reported bootstrap internal validation, imaging and treatment sensitivity analyses, and a 24-hlandmark analysis of blood pressure variability.

Abstract

Early neurological deterioration (END) was common after minor subcortical infarction, but prediction was complicated by heterogeneous mechanisms, including intrinsic small-vessel disease and branch atheromatous disease (BAD). A retrospective analysis included 240 patients admitted within 24 h of symptom onset with a National Institutes of Health Stroke Scale (NIHSS) score of 5 or less. END was defined as an increase of at least 2 NIHSS points within 72 h after admission. The primary logistic model retained admission systolic blood pressure (SBP) as a continuous variable and included baseline NIHSS, neutrophil-to-lymphocyte ratio (NLR), and vascular stenosis of at least 50%. A three-knot restricted cubic spline was used to assess SBP nonlinearity. Internal validation used 1,000 bootstrap resamples, and calibration used bootstrap out-of-bag predictions. END occurred in 54 of 240 patients (22.5%). For each 10 mmHg increase in SBP, the adjusted odds ratio (OR) was 1.19 (95% confidence interval [CI], 1.04–1.36); the corresponding ORs were 1.36 (1.04–1.78) per NIHSS point, 1.34 (1.10–1.62) per NLR unit, and 2.21 (1.13–4.31) for stenosis. Evidence of SBP nonlinearity was absent (P = 0.595). The primary model had an apparent area under the receiver operating characteristic curve (AUC) of 0.762 (95% bootstrap CI, 0.686–0.826), an optimism-corrected AUC of 0.741, and an out-of-bag Brier score of 0.158. MRI-only, parent-artery-stenosis exclusion, and ischemic-END analyses were directionally consistent. Suspected BAD was present in 83.3% of END cases and materially increased performance in an expanded exploratory model, emphasizing etiologic heterogeneity. In a 24-h landmark analysis, the addition of SBP variability improved discrimination for deterioration after 24 h, but these post-admission metrics were not included in the admission-only model. The nomogram provided an internally validated prognostic estimate for enhanced monitoring and reassessment; external validation was required before treatment selection or bedside implementation.

Introduction

Acute ischemic stroke remained a major cause of death and long-term disability worldwide1. A low baseline National Institutes of Health Stroke Scale (NIHSS) score was often described as a minor stroke, but “minor” reflected the measured deficit at presentation rather than biological stability or the absence of disability. Initially, mild weakness, gait impairment, dysarthria, or loss of hand function could worsen during the first h or days. This early neurological deterioration (END) could alter monitoring needs, diagnostic reassessment, rehabilitation planning, and prognosis. In lacunar or small subcortical stroke, pooled data indicated that END occurred in approximately one-fifth to one-quarter of patients; an increase of at least 2 NIHSS points was the most frequently used threshold and produced comparatively consistent estimates2. Patients who deteriorated had worse functional outcomes than those who remained stable3.

The definition of the underlying phenotype also required care. Small subcortical infarction described an anatomic pattern, small-artery occlusion was an etiologic classification, and a lacunar syndrome was a clinical presentation. These concepts overlapped but were not interchangeable. A compatible syndrome could arise from an intrinsic perforator lesion, plaque at a penetrating-artery origin, or parent-artery atherosclerosis, and routine classification could not always distinguish these mechanisms4. Lipohyalinosis, microatheroma, thrombus propagation, impaired collateral supply, and parent-artery disease could coexist5,6. Branch atheromatous disease (BAD), often inferred from a lesion at least 15 mm in diameter, involvement of at least three axial slices, or extension to the ventral pontine surface, was particularly associated with progression7. Magnetic resonance diffusion-weighted imaging improved lesion confirmation, whereas computed tomography could exclude hemorrhage without directly visualizing a small acute subcortical infarct. Accordingly, the broader term “minor small subcortical infarction” was used, and imaging-related and BAD-related uncertainty was explicitly examined.

Deep perforating arteries had limited collateral connections, and a small increase in lesion volume could interrupt densely packed strategic pathways. END could therefore reflect extension of ischemia, thrombus propagation along a perforator, edema, recurrent ischemia, or fluctuating perfusion. Ischemia-related inflammation and endothelial injury could further amplify tissue damage8. Although these mechanisms were not directly measured using a bedside model, routinely available clinical, laboratory, and vascular variables could serve as practical markers of tissue vulnerability.

Admission blood pressure (BP) was one such marker, but its interpretation was complex. Higher systolic BP (SBP) could represent chronic hypertension, acute stress, or compensation to preserve perfusion; excessive pressure could also contribute to endothelial and blood–brain barrier stress when autoregulation was impaired9,10. The association was therefore prognostic rather than necessarily causal. Baseline NIHSS, when complemented by SBP, summarized initial neurological severity and could indirectly reflect lesion burden or strategic pathway involvement. Modeling SBP continuously avoided assuming that a single data-derived cutoff separated low- and high-risk patients, whereas an assessment of nonlinearity could determine whether a simple linear trend was adequate.

Inflammatory and vascular measurements could add further information. The neutrophil-to-lymphocyte ratio (NLR) was inexpensive, rapidly available, and summarized the balance between circulating neutrophils and lymphocytes. Higher NLR has been associated with END, although it remains a nonspecific marker influenced by infection, physiological stress, and comorbidity11. Recorded arterial stenosis could indicate atherosclerotic burden, compromised inflow, or a parent-artery mechanism in an apparent lacunar presentation. Combining SBP, baseline NIHSS, NLR, and stenosis could therefore represent hemodynamic, clinical, inflammatory, and vascular dimensions of risk without specialized biomarkers.

BP also changed after admission. Short-term BP variability had been associated with END12, potentially through unstable perfusion when autoregulation was impaired. However, variability indices required repeated measurements, were unavailable at admission, and could be affected by deterioration or treatment. Directly adding these indices to a baseline model would have created a temporal mismatch. A 24-h landmark analysis among patients who remained free of END could instead assess whether accumulated BP information improved the prediction of later deterioration.

Published END scores were derived from different populations and cannot be assumed to transfer directly to the cohort. A progressive lacunar stroke score used diabetes, diastolic BP, pure motor syndrome, and asymptomatic intracranial atheromatosis13, whereas an END-of-ischemic-origin score for thrombolysed minor stroke with large-vessel occlusion used occlusion site and thrombus length14. These predictors reflected different mechanisms, treatments, and eligibility criteria. Furthermore, an apparent development-sample advantage of a multivariable model could diminish after correction for overfitting. Internal validation, calibration assessment, and sensitivity analyses were therefore needed before bedside application or comparison with existing tools.

The retrospective cohort was reanalyzed to develop an admission-variable nomogram for END after minor small subcortical infarction. The analysis was designed to report imaging, acute treatment, missing-data, and outcome-ascertainment procedures transparently; retain continuous SBP, assess nonlinearity, and quantify internal optimism; and test robustness in MRI-only, parent-artery-stenosis, BAD, ischemic-END, and treatment-adjusted analyses. A separate 24-hlandmark analysis distinguished dynamic prediction using BP variability from baseline prediction. Admission SBP, baseline NIHSS, NLR, and recorded vascular stenosis were hypothesized to provide complementary prognostic information. The model was intended to support enhanced observation and timely reassessment, not to establish causality, mandate treatment, or imply improved outcomes without prospective external validation.

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Protocol

The study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional ethics committee of Jinhua Municipal Central Hospital (No. 2022129). The requirement for written informed consent was waived because only retrospective, de-identified data were analyzed. The research tools used in this protocol are listed in the Table of Materials.

1. Study design and source population
A retrospective observational cohort study was conducted at the comprehensive stroke center of Jinhua Municipal Central Hospital, Zhejiang, China. Consecutive admissions from June 2024 through September 2025 were screened. Patients were included if they had been admitted within 24 h of symptom onset, had a baseline NIHSS score of 5 or less, and had received a treating-team diagnosis of minor ischemic stroke compatible with a small subcortical or perforator-territory process. Patients were excluded for other documented etiologic subtypes, a baseline NIHSS score greater than 5, an onset-to-admission time greater than 24 h, severe pre-existing neurological dysfunction or severe systemic disease, or incomplete core clinical or imaging data. Every exclusion was recorded in the flow diagram.

2. Etiologic classification and brain imaging
The etiologic classification assigned during routine care by the treating stroke team was abstracted. Because this was a retrospective electronic-record study, the assignment was not considered prospective, centralized, blinded, or independently re-adjudicated. For MRI-confirmed cases, an acute subcortical lesion compatible with the neurological syndrome on diffusion-weighted imaging, with a corresponding apparent diffusion coefficient abnormality and no cortical infarction, was required. For CT-only cases, exclusion of hemorrhage and a cortical territorial infarct, together with a compatible lacunar clinical syndrome, was required. CT alone was acknowledged as potentially unable to directly confirm a small acute subcortical infarct.

Imaging modality, onset-to-imaging time, maximum lesion diameter, slice thickness, number of involved axial slices, ventral pontine surface involvement, parent-artery stenosis, and nonculprit stenosis were recorded. Suspected BAD was operationalized as any of the following: a maximum lesion diameter of at least 15 mm, involvement of at least three axial slices, or ventral pontine surface involvement. Detailed lesion side, anatomic location, responsible artery segment, and a traceable, independent, blinded reread were unavailable and were not imputed.

3. Angiographic assessment of stenosis
CTA or MRA stenosis measurements were abstracted from the clinical imaging reports. The percentage of intracranial stenosis was calculated using the WASID formula:

[1  -  (diameter at the narrowest lumen
    /diameter of the adjacent normal reference artery)]  x  10015

Parent-artery stenosis was defined as narrowing of at least 50% in the artery supplying the infarct territory. Nonculprit stenosis was defined as narrowing of at least 50% outside that territory. The primary model variable, any vascular stenosis of at least 50%, was considered positive if either component was positive. Because the responsible artery segment was not retained, segment-specific measurements could not be independently verified.

4. Baseline clinical, laboratory, and treatment variables
Age, sex, vascular history, smoking, alcohol use, pre-stroke antiplatelet use, pre-stroke modified Rankin Scale score, onset-to-admission time, admission SBP and diastolic BP (DBP), baseline NIHSS, platelet count, blood glucose, neutrophil count, lymphocyte count, and blood-draw time were extracted. NLR was calculated as the absolute neutrophil count divided by the absolute lymphocyte count.

Intravenous thrombolysis, single versus dual antiplatelet strategy, treatment-start time after admission, statin intensity, in-hospital BP-lowering treatment, glucose management, anticoagulation, and the recorded timing of antithrombotic exposure relative to blood collection were reported. No participant received in-hospital anticoagulation. Intravenous fluid volume was not included because it was not reliably recoverable. These interventions were treated as potential confounders or consequences of the clinical state rather than randomized exposures.

5. Neurological assessments and END
NIHSS scores documented at admission, approximately 24 and 72 h were used, and additional assessments triggered by clinical worsening were included. The scores were recorded by the routine stroke team. Predictor blinding and centralized duplicate scoring were not documented. Primary END was defined as an increase of at least 2 total NIHSS points within 72 h after admission relative to baseline, consistent with contemporary lacunar-stroke literature2,3. The time and documented cause of deterioration were recorded. All-cause qualifying deterioration was included in the primary outcome, and the model was repeated using ischemic-cause END only.

6. Missing-data handling
Missingness was audited before analysis. For three records with a missing raw NLR value but available neutrophil and lymphocyte counts, NLR was calculated deterministically. For four records with a missing raw 24-h SBP SD but available mean SBP and coefficient of variation, SD was calculated as coefficient of variation × mean SBP. After these deterministic derivations, the four primary predictors and the outcome were complete, and all 240 patients were included in the primary model without multiple imputation. The single record with a missing pre-blood-draw antithrombotic status was excluded only from the treatment-adjusted exploratory model.

7. Statistical analysis and model development
Approximately symmetric continuous variables were summarized as mean ± SD, and skewed or ordinal variables were summarized as median (interquartile range). Welch t tests or Mann–Whitney U tests were used as appropriate. Categorical variables were summarized as n (%) and compared using chi-square or Fisher's exact tests.

The primary logistic regression model included continuous admission SBP scaled per 10 mmHg, baseline NIHSS per point, NLR per unit, and any vascular stenosis of at least 50%. The intercept, coefficients, standard errors, ORs, 95% CIs, and two-sided P values were reported. SBP nonlinearity was examined using a restricted cubic spline with three knots at the 10th, 50th, and 90th percentiles (130.9, 157.0, and 194.0 mmHg), adjusted for NIHSS, NLR, and stenosis. A likelihood-ratio test was used for the nonlinear term. The linear SBP term was used when nonlinearity was not supported.

An SBP threshold of 169 mmHg was retained only as a secondary simplified analysis. The threshold was selected using the maximum Youden index in the development data, and cutoff uncertainty was quantified using 1,000 bootstrap resamples. The unvalidated 0–12 score and low-, intermediate-, and high-risk categories were not reinstated.

Discrimination was estimated using the AUC and 95% bootstrap CI. The combined model was compared with each component using paired bootstrap differences, and superiority was not claimed when the difference CI included zero. Optimism was estimated using 1,000 bootstrap resamples. Out-of-bag bootstrap predictions were generated for calibration and calculation of the Brier score. Decision-curve analysis was conducted only across threshold probabilities of 5%–30%, for which the modeled action was enhanced neurological observation and clinician reassessment rather than automatic treatment.

Sensitivity analyses were performed in the MRI-only cohort, after exclusion of parent-artery stenosis of at least 50%, after exclusion of suspected BAD, and using ischemic-cause END only. Exploratory ridge-penalized expanded models containing imaging, onset-time, and treatment variables were fitted to reduce instability. In a 24-hlandmark analysis, patients who deteriorated before 24 hwere removed, and a landmark model was compared with models that added SBP SD, coefficient of variation (CV), or average real variability (ARV). These models were interpreted as dynamic post-admission predictions. Transparent prediction-model reporting principles were followed16. The reproducible revision analysis was performed using Python 3.13.5, pandas 2.3.3, NumPy 2.3.3, and SciPy 1.16.2.

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Results

Cohort and baseline characteristics
Of 965 screened patients, 240 were included (Figure 1). Among the included patients, 194 (80.8%) underwent MRI, and 46 (19.2%) were classified using CT-only imaging. END occurred in 54 patients (22.5%). Baseline NIHSS was 3 (IQR, 2–4) in the END group and 2 (IQR, 1–3) in the no-END group (P < 0.001). NLR was 3.2 (IQR, 2.7–4.3) and 2.3 (IQR, 1.8–3.2), respectively (P < 0.001). Admission SBP was 169.1 ± 25.8 mmHg in the END group a...

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Discussion

In this development cohort, END occurred in 22.5%, closely matching contemporary pooled estimates for lacunar ischemic stroke2. Continuous admission SBP, baseline NIHSS, NLR, and any recorded stenosis were associated with END in the four-variable model. However, the optimism-corrected AUC of 0.741 and the out-of-bag Brier score of 0.158 supported moderate, rather than definitive, prognostic performance.

The revised SBP analysis materially changed the interpretation. The...

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Disclosures

The authors disclosed no conflicts of interest.

Acknowledgements

The medical staff in the Department of Neurology and the Advanced Stroke Center of Jinhua Municipal Central Hospital were thanked for their assistance with patient care and data collection.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Automated hematology analyzerSysmex CorporationXN-1000Routine blood counts used to derive the neutrophil-to-lymphocyte ratio
Computed tomography scannerSiemens HealthineersSOMATOM DriveNoncontrast computed tomography and computed tomography angiography acquisition during routine clinical care
CTA/MRA image acquisition and stenosis assessmentDepartment of Radiology, Jinhua Municipal Central HospitalInstitutional clinical CTA/MRA protocols; WASID measurement convention for intracranial stenosisRetrospective abstraction from signed routine-care radiology reports; no separate research imaging platform. No protocol identifier was assigned
Electronic medical record and clinical data sourceJinhua Municipal Central HospitalInstitutional hospital information, laboratory, medication, and radiology-report systemsRetrospective extraction of de-identified routine-care data; no standalone research electronic medical record product. Commercial catalog number, version, and RRID not applicable.
Magnetic resonance imaging scannerGE HealthCareSIGNA Pioneer 3.0TDiffusion-weighted imaging, apparent diffusion coefficient imaging, and magnetic resonance angiography acquisition during routine clinical care
Microsoft ExcelMicrosoft CorporationMicrosoft 365Table review and submission packaging
Microsoft WordMicrosoft CorporationMicrosoft 365Manuscript preparation
National Institutes of Health Stroke ScaleNational Institute of Neurological Disorders and StrokeNIHSS assessment formRoutine neurological assessments
NumPyOpen-source Python package2.3.3Numerical computation; no project-specific RRID was reported.
pandasOpen-source Python package2.3.3Data management; no project-specific RRID was reported.
PythonPython Software Foundation3.13.5Reproducible statistical analysis; RRID: SCR_008394.
SciPyOpen-source Python package1.16.2Statistical functions; no project-specific RRID was reported.

References

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