Development and validation of a nomogram for predicting hematoma expansion in intracerebral hemorrhage

Development of a Nomogram Model Based on Lactate-To-Albumin Ratio for Prognostic Prediction in Hospitalized Patients with Intracerebral Hemorrhage

OBJECTIVE

This study aims to develop a nomogram model incorporating lactate-to-albumin ratio (LAR) to predict the prognosis of hospitalized patients with intracerebral hemorrhage (ICH) and demonstrate its excellent predictive performance.

METHODS

A total of 226 patients with ICH from the Medical information mart for intensive care III (MIMIC Ⅲ) database were randomly split into 8:2 ratio training and experimental groups, and 38 patients from the eICU-CRD for external validation. Univariate and multivariate Cox proportional hazards regression analysis was performed to identify independent factors associated with ICH, and multivariate Cox regression was used to construct nomograms for 7-day and 14-day overall survival (OS). The performance of nomogram was verified by the calibration curves, decision curves, and receiver operating characteristic (ROC) curves.

RESULTS

Our study identified LAR, glucose, mean blood pressure, sodium, and ethnicity as independent factors influencing in-hospital prognosis. The predictive performance of our nomogram model for predicting 7-day and 14 -day OS (AUCs: 0.845 and 0.830 respectively) are both superior to Oxford Acute Severity of Illness Score, Simplified acute physiology score II, and SIRS (AUCs: 0.617, 0.620 and 0.591 and AUCs: 0.709, 0.715 and 0.640, respectively) in internal validation, and also demonstrate favorable predictive performance in external validation (AUCs: 0.778 and 0.778 respectively).

CONCLUSIONS

LAR as a novel biomarker is closely associated with an increased risk of in-hospital mortality of patients with ICH. The nomogram model incorporating LAR along with glucose, mean blood pressure, sodium, and ethnicity demonstrate excellent predictive performance for predicting the prognosis of 7- and 14-day OS of hospitalized patients with ICH.

Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis

PURPOSE

Early identification of hematoma enlargement and persistent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinically effective tools, radiomics has been gradually introduced into the early identification of hematoma enlargement. Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to explore the value of radiomics in the early detection of HE in patients with cerebral hemorrhage.

METHODS

Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles are considered eligible. The radiomics quality scoring (RQS) tool was used to evaluate included studies.

RESULTS

A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical features. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical models (0.69 C-index in the training cohort and 0.70 C-index in the validation cohort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts.

CONCLUSIONS

Machine learning based on radiomic plus clinical features has the best predictive performance for HE, followed by machine learning based on radiomic features, and can be used as a potential tool to assist clinicians in early judgment.

The Attenuation Value Within the Non-hypodense Region on Non-contrast Computed Tomography of Spontaneous Cerebral Hemorrhage: A Long-Neglected Predictor of Hematoma Expansion

Background and Purpose

The ability of attenuation value of the non-hypodense region of hematoma in non-contrast computed tomography (NCCT) for predicting hematoma expansion (HE) remains unclear. Our purpose is to explore this relationship.

Methods

Two cohorts of patients were collected for analysis. The region where we measured hematoma attenuation values was limited to the non-hypodense region that was not adjacent to the normal brain tissue on NCCT. The critical attenuation value was derived via receiver operating characteristic (ROC) curve analysis in the derivation cohort and its predictive ability was validated in the validation cohort. Independent relationships between predictors, such as critical attenuation value of the non-hypodense region and HE were analyzed using the least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic analysis.

Results

The results showed that the attenuation value <64 Hounsfield units (HU) was independently associated with HE [odds ratio (OR), 4.118; 95% confidential interval (CI), 1.897–9.129, p < 0.001] and the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the curve (AUC) for predicting HE were 36.11%, 81.71%, 1.97, 0.78, 44.8%, 75.7%, and 0.589, respectively.

Conclusions

Our research explored and validated the relationship between the attenuation value of the non-hypodense region of hematoma and HE. The attenuation value < 64 HU was an appropriate indicator of early HE.