# 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.

## Key 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.