Predicting Candidacy for Neurosurgical Intervention in Stroke: A Nomogram and Decision Tree in a Prospective Cohort of 250 Patients at Bouaké Teaching Hospital, C？te d’Ivoire

Background and Objectives: In resource-limited settings, the decision to operate on a stroke rests on individual experience, with no formal tool. We developed and internally validated a model estimating the probability that a patient meets the criteria for a neurosurgical indication at admission. Methods: Prospective cohort of 250 consecutive patients admitted for imaging-confirmed stroke between January 2022 and December 2023. The target outcome was the operative indication recorded in the chart. A logistic regression with backward selection was translated into a nomogram and an integer score, with a classification and regression tree built in parallel. Internal validation replicated the whole process across 1000 bootstrap resamples. Results: An indication was made in 107 patients (42.8%). Four variables were independently associated with it: hydrocephalus (adjusted odds ratio 4.15; 95% CI 2.03 - 8.46), mass effect (2.42), intraventricular extension (2.31) and impaired consciousness (2.23). The apparent area under the curve was 0.77 (0.71 - 0.83), the optimism-corrected area 0.73 and the corrected calibration slope 0.84. The model outperformed existing scores (ICH score 0.66; ≤0.60 for the others). The integer score (0 - 5) reproduced a gradient from 15.7% to 95.0%. Of 107 indications, only 45 were operated on, and none of the 15 aneurysmal indications. The decision tree generalised less well (cross-validated area 0.69). Conclusion: A four-variable bedside tool estimates the probability of a neurosurgical indication in stroke and outperforms prognostic scores repurposed for that end. It documents a 58% access gap. External multicentre validation remains necessary.