A cost-sensitive multiclass machine learning framework for postoperative neurosurgical triage (Neuro-TACTIC)

TL;DR

This paper introduces Neuro-TACTIC, a machine learning framework that helps decide postoperative care levels for neurosurgery patients by balancing safety and resource use.

Contribution

The novel contribution is a cost-sensitive, three-tier triage model that allows tuning based on local resource constraints and risk thresholds.

Findings

Neuro-TACTIC achieved AUCμ of 0.67 in the development cohort and 0.60 in the independent evaluation cohort.

Operative duration, tumor volume, and patient age were identified as key predictors for triage decisions.

The framework showed stable performance across different cost settings in cross-validation and bootstrap analyses.

Abstract

Postoperative placement of patients into a regular ward, an intermediate-care unit (IMC), or an intensive care unit (ICU) is critical for balancing patient safety against resource constraints. Most existing models collapse this decision into a binary ICU versus non-ICU choice and lack a mechanism to tune risk thresholds to local staffing ratios or definitions of ICU‐level events. We developed Neuro-TACTIC, a cost-sensitive machine learning framework that stratifies postoperative neurosurgical patients into three monitoring levels: regular ward, intermediate care unit, and intensive care unit. An XGBoost-based classifier was trained on 27 demographic, intraoperative, and imaging-derived features from a retrospective cohort of 1072 patients undergoing elective craniotomy. A tunable parameter ζ integrates resource-related and harm-related costs to adjust the balance between over- and under-triage. Generalization was assessed in an independent cohort. Across repeated cross-validation and bootstrap analyses, the framework demonstrated stable behavior across cost settings. At the operating point ζ = 0.975, performance was AUCμ = 0.67 ± 0.03 and weighted F1 = 0.49 ± 0.03 in the development cohort, and AUCμ = 0.60 ± 0.04 and weighted F1 = 0.44 ± 0.06 in the independent evaluation cohort (n = 81). Feature importance analyses identified operative duration, tumor volume, surgical position, body mass index, and patient age as the most influential predictors. This study demonstrates the feasibility of cost-sensitive, three-tier postoperative triage modeling in neurosurgical patients. Neuro-TACTIC is a methodological proof-of-concept; prospective validation and multicenter evaluation are required before clinical deployment.

The online version contains supplementary material available at 10.1038/s41598-026-45092-1.

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