Deep Learning for Dynamic Prognostic Prediction in Minimally Invasive Surgery for Intracerebral Hemorrhage: Model Development and Validation Study

TL;DR

A new deep learning model called MultiStep Transformer improves dynamic prediction of survival and recovery outcomes for patients undergoing minimally invasive surgery for brain hemorrhage.

Contribution

The MultiStep Transformer model introduces a novel approach to dynamic prognostic prediction using multi-time point data and handles imbalanced datasets in ICH patients.

Findings

The MultiStep Transformer model outperformed traditional models in predicting survival and functional outcomes with AUROCs of 0.87, 0.85, and 0.75.

The model demonstrated clinical utility with decision curve analysis showing net benefit across various probability thresholds.

The model effectively handles imbalanced data and provides individualized prognosis assessment for ICH patients.

Abstract

The pathological and physiological state of patients with intracerebral hemorrhage (ICH) after minimally invasive surgery (MIS) is a dynamic evolution, and the traditional models cannot dynamically predict prognosis. Clinical data at multiple time points often show the characteristics of different categories, different numbers, and missing data. The existing models lack methods to deal with imbalanced data.

This study aims to develop and validate a dynamic prognostic model using multi–time point data from patients with ICH undergoing MIS to predict survival and functional outcomes.

In this study, 287 patients who underwent MIS for ICH were retrospectively collected on the day of surgery, days 1, 3, 7, and 14 after surgery, and the day of drainage tube removal. Their general information, vital signs, laboratory test findings, neurological function scores, head hematoma volume, and MIS-related indicators were collected. In addition, this study proposes a multistep attention model, namely the MultiStep Transformer. The model can simultaneously output 3 types of prediction probabilities for 30-day survival probability, 180-day survival probability, and 180-day favorable functional outcome (modified Rankin Scale [mRS] 0-3) probability. Five-fold cross-validation was used to evaluate the performance of the model and compare it with mainstream models and traditional scores. The main evaluation indexes included accuracy, precision, recall, and F1-score. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves; its calibration was assessed via calibration curves; and its clinical utility was examined using decision curve analysis (DCA). Attributable value analysis was conducted to assess the key predictive features.

The 30‑day survival rate, 180‑day survival rate, and 180‑day favorable functional outcome rate among 287 patients were 92.3%, 88.8%, and 52.3%, respectively. In terms of predictive efficacy for survival and functional outcomes, the MultiStep Transformer model showed a remarkable superiority over traditional scoring systems and other deep learning models. For these three outcomes, the model achieved areas under the receiver operating characteristic curves (AUROCs) of 0.87 (95% CI 0.82-0.92), 0.85 (95% CI 0.77-0.93), and 0.75 (95% CI 0.72-0.78), with corresponding Brier scores of 0.1041, 0.1115, and 0.231. DCA confirmed that the model provided a definite clinical net benefit when threshold probabilities ranged within 0.06-0.26, 0.04-0.5, and 0.21-0.71.

The MultiStep Transformer model proposed in this study can effectively use imbalanced data to construct a model. It possesses good dynamic prediction ability for short-term and long-term survival and functional outcome of patients with ICH undergoing MIS, providing a novel tool for individualized assessment of prognosis among patients with ICH undergoing MIS.

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