A nomogram for predicting the in-hospital risk of recurrence among patients with minor non-cardiac stroke

Abstract

Background

Patients with minor stroke suffer a substantial risk of further recurrences, especially in the first two weeks. We aimed to develop and validate a prognostic nomogram to predict in-hospital stroke recurrence among patients with acute minor stroke.

Methods

A total of 1326 patients with minor non-cardiac stroke (NIHSS) ≤5) from three centers were divided into development cohort (1016 patients from two centers) and validation cohort (310 patients from another center). Recurrent stroke was defined as a new ischemic stroke. A logistic regression model was employed to develop the nomogram to predict in-hospital stroke recurrence in patients with minor stroke using demographic, medical and imaging information. We then validated the nomogram externally. The predictive discrimination and calibration of the nomogram were assessed in the development and validation cohorts by area under the curve (AUC) and calibration plots.

Results

During a median length of stay of 12 days, stroke recurrence occurred in 34 patients (3.3%). Predictors of in-hospital recurrence included prior history of transient ischemic attack, baseline NIHSS score, multiple infarctions, and carotid stenosis. The clinical and imaging-based nomogram B demonstrated adequate calibration and discrimination (AUC = 0.777), which was validated among 273 patients in a separate validation cohort (AUC = 0.753). Our clinical-imaging based nomogram was determined to be superior to the clinical-based nomogram and the RRE90 score in terms of discrimination.

Conclusion

A prognostic nomogram that integrates clinical and imaging information to predict the in-hospital risk of stroke recurrence among patients after acute minor stroke was constructed and validated externally. The nomogram demonstrated adequate calibration and discrimination in both the development and validation cohort.

Keywords:

• Minor stroke
• in-hospital stroke recurrence
• nomogram
• prediction model

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Correction

Transparency

Declaration of funding

This paper was not funded.

Declaration of financial/other relationships

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. CMRO peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

K.Z. had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: X.N. Data collection: K.Z., H.F., J.R., J.L., Y.W. and Y.L. Drafting of the manuscript: K.Z. and Y.F. Critical revision of the manuscript for important intellectual content: X.N., C.L. and T.L. Statistical analysis: K.Z., Y.W. L.Q. and J.M. served as consultants for imaging information. Study supervision: X.N., X.L. and X.W.

Ethics statement

This study was approved by the Ethics Review Board of the First Hospital of Shanxi Medical University.

Correction Statement

This article was originally published with errors, which have now been corrected in the online version. Please see Correction (http://dx.doi.org/10.1080/03007995.2023.2246730)