Prediction of 90-day mortality after acute ischemic stroke using the National Institutes of Health Stroke Scale alone

Clinicians require a simple way to estimate prognosis after acute ischemic stroke (AIS). Existing models are complex and difficult to apply at bedside. We hypothesized that, after accounting for premorbid health, stroke severity predicts survival, and that the National Institutes of Health Stroke Scale (NIHSS) could serve as the basis for clinically intuitive mortality estimation.

We conducted a retrospective ecological cohort study using the TriNetX Research Network, a federated database of electronic health records from more than 100 healthcare organizations. Adults (≥18 years) with AIS and a documented admission NIHSS were included. Propensity score matching aligned each NIHSS level with NIHSS= 0 on demographics and comorbidities. The primary outcome was 90-day mortality. Linear regression models, trained on odd NIHSS values and tested on even values, evaluated performance. Functional outcomes were analyzed with competing-risk methods.

Among 147,391 eligible patients (median NIHSS=4), mortality increased linearly with severity: 90-day mortality (%) ≈ 1.7 × NIHSS. Model performance was strong (training R²=0.993; testing R²=0.986). Overall predicted and observed mortality were similar (13.3% vs 12.6%, P = .22), and discrimination was high (AUC=0.96). Functional outcomes displayed an inverted "u" pattern, driven by the competing risk of mortality at higher NIHSS levels.

In this large ecological analysis, stroke severity predicted mortality after accounting for pre-stroke health. A single equation based solely on NIHSS accurately approximated 90-day mortality after AIS. While subject to the limitations of population-level inference, this "1.7% equation" offers a transparent, scalable framework that may support bedside communication and guide future prospective validation.