Dynamic nomogram for predicting early tracheotomy in patients diagnosed with supratentorial deep seated intracranial hemorrhage

TL;DR

This paper introduces a dynamic nomogram to predict early tracheotomy needs in patients with deep brain hemorrhage.

Contribution

A novel dynamic nomogram model for predicting early tracheotomy in supratentorial deep-seated intracranial hemorrhage patients.

Findings

A dynamic nomogram model was developed using GCS, WBC, PLT, and HR to predict early TT requirements.

The model showed good performance with an AUC of 0.817 in the training set and 0.768 in the validation set.

Calibration and decision curve analyses confirmed the model's strong predictive and clinical utility.

Abstract

Tracheotomy (TT) is frequently performed in patients diagnosed with supratentorial deep-seated intracranial hemorrhage (SDICH). However, predicting whether early TT is necessary remains a challenge for neurosurgeons. As such, the present study constructed a dynamic nomogram prediction algorithm to determine whether patients with SDICH immediately required early TT on arrival to hospital.

Clinical and baseline data from patients diagnosed with SDICH at The Second Affiliated Hospital of Fujian Medical University (Fujian, China) and The Second Hospital & Clinical Medical School of Lanzhou University (Gansu, China) between January 1, 2019 and January 1, 2023 were retrospectively collected and analyzed. A dynamic nomogram prediction model was constructed and used to examine the impact on early TT endpoints.

Data from 1,046 patients with SDICH fulfilled the inclusion and exclusion criteria. Of these, 379 patients from Lanzhou University Second Hospital comprised the external validation set and 667 from The Second Affiliated Hospital of Fujian Medical University comprised the training set. A total of 199 (19.02%) patients underwent early TT. White blood cell (WBC), platelet (PLT), heart rate (HR), and Glasgow Coma Score (GCS) were used to build a dynamic nomogram prediction model. An area under the curve (AUC) of receiver operating characteristic (ROC) was 0.817, and 95% confidence interval (CI) of 0.785–0.845 were obtained from ROC curve analysis of data from the training set, cut-off value of training set was >0.139. The AUC was 0.768 in the validation set (95% CI 0.722–0.809), and cut-off value was >0.182. A strong association was found between observation and prediction of early TT according to dynamic nomogram calibration curves and clinical decision curve analysis.

A dynamic nomogram prediction model for early TT in patients diagnosed with SDICH was developed and validated. GCS, WBC, PLT, and HR were valid markers for early requirement of TT.

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