Nomogram Models for Predicting Poor Prognosis in Lobar Intracerebral Hemorrhage: A Multicenter Study

Area of Science:

• Neurology
• Clinical Medicine
• Medical Research

Background:

• Lobar intracerebral hemorrhage (ICH) poses significant risks for patient outcomes.
• Predicting functional prognosis and mortality in ICH is crucial for effective clinical management.
• Existing prognostic tools may require refinement for lobar ICH specifically.

Purpose of the Study:

• To identify prognostic factors for unfavorable functional outcomes in lobar ICH.
• To develop and validate predictive models for 3-month unfavorable outcomes or all-cause death in lobar ICH patients.
• To provide clinicians with tools for informed decision-making in lobar ICH management.

Main Methods:

• A derivation cohort of 322 patients with spontaneous lobar ICH was analyzed.
• Least Absolute Shrinkage and Selection Operator (LASSO) analysis and multivariable logistic regression were used for variable selection.
• Nomogram models were constructed and validated using Area Under The Receiver Operating Characteristic Curve (AUROC), calibration curves, and decision curve analysis (DCA).

Main Results:

• Key predictors for unfavorable outcomes included age, dyslipidemia, ICH volume, NIHSS score, Stroke-Associated Pneumonia (SAP), and lipid-lowering therapy.
• Predictors for all-cause mortality comprised age, GCS score, NIHSS score, antihypertensive therapy, in-hospital rehabilitation, and ICH volume.
• The models demonstrated strong discriminative ability (AUC 0.897 for outcomes, 0.894 for death) and excellent calibration and clinical utility.

Conclusions:

• Nomogram models for predicting 3-month unfavorable outcomes or death in lobar ICH were successfully developed and validated.
• These models provide valuable prognostic information to aid clinical decision-making.
• The study offers robust tools for assessing patient prognosis after lobar ICH.