A Novel Nomogram Based on Quantitative MRI and Clinical Features for the Prediction of Neonatal Intracranial Hypertension

Abstract

Intracranial hypertension (ICH) is a serious threat to the health of neonates. However, early and accurate diagnosis of neonatal intracranial hypertension remains a major challenge in clinical practice. In this study, a predictive model based on quantitative magnetic resonance imaging (MRI) data and clinical parameters was developed to identify neonates with a high risk of ICH. Newborns who were suspected of having intracranial lesions were included in our study. We utilized quantitative MRI to obtain the volumetric data of gray matter, white matter, and cerebrospinal fluid. After the MRI examination, a lumbar puncture was performed. The nomogram was constructed by incorporating the volumetric data and clinical features by multivariable logistic regression. The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Clinical parameters and volumetric quantitative MRI data, including postmenstrual age (p = 0.06), weight (p = 0.02), mode of delivery (p = 0.01), and gray matter volume (p = 0.003), were included in and significantly associated with neonatal intracranial hypertension risk. The nomogram showed satisfactory discrimination, with an area under the curve of 0.761. Our results demonstrated that decision curve analysis had promising clinical utility of the nomogram. The nomogram, incorporating clinical and quantitative MRI features, provided an individualized prediction of neonatal intracranial hypertension risk and facilitated decision making guidance for the early diagnosis and treatment for neonatal ICH. External validation from studies using a larger sample size before implementation in the clinical decision-making process is needed.

Keywords: intracranial hypertension; magnetic resonance imaging; neonate; prediction; volumetric segmentation.

Figure 1

Flowchart demonstrating the inclusion criteria, exclusion criteria, and the patient recruitment process in the present study. Note: ICH = intracranial hypertension.

Figure 2

Workflow of the image-based analysis in this study. Note: GM = gray matter, WM = white matter, CSF = cerebrospinal fluid.

Figure 3

Developed nomogram. The nomogram was developed using the gray matter (GM, liter), postmenstrual age (PA, week), birth weight (kilogram), and mode of delivery (MD,1 means cesarean section and 0 means vaginal delivery).

Figure 4

Receiver operating characteristic curve (A) and calibration curve (B) for the model. The area under the curve was 0.761. The red dot represents the specificity (0.649) and sensitivity (0.750) corresponding to cutoff value (0.670).

Figure 5

Decision curve analysis for the nomogram. The y-axis measures the net benefit. The green line represents the nomogram. The green line represents the nomogram. The red line represents the assumption that no patients have a risk for neonatal intracranial hypertension (NICH) and the black line represents all patients who will develop NICH.