Inflammatory burden index is associated with poor functional outcome and stroke-associated pneumonia in patients with primary brainstem hemorrhage: a retrospective cohort study

 Predicting failure to recover is grounds for firing! PURE INCOMPETENCE DISPLAYED!

Competent researchers would create pneumonia prevention protocols!

You've known of the problem for years and DONE NOTHING; YOU'RE FIRED!

• 11% Stroke-associated pneumonia (18 posts to October 2020)

Stroke research should deliver recovery protocols. You'll be screaming for them when you are the 1 in 4 per WHO that has a stroke!  

Inflammatory burden index is associated with poor functional outcome and stroke-associated pneumonia in patients with primary brainstem hemorrhage: a retrospective cohort study

• 

  Fangjie Luo 1† *

• J

  Jialiang Luo 2†

• X

  Xiaoyun Li 1

• 1. Department of Neurosurgery, The Affiliated Dongguan Songshanhu Central Hospital of Guangdong Medical University, Dongguan, Guangdong, China

• 2. Sun Yat-sen University, Dongguan, Guangdong, China

Abstract

Background: 

Primary brainstem hemorrhage (PBH) is a severe, poorly-prognostic stroke subtype. Since single biomarkers cannot reflect global inflammatory status, we constructed a composite Inflammatory Burden Index (IBI) incorporating neutrophil-to-lymphocyte ratio (NLR), C-reactive protein-to-albumin ratio (CAR), and systemic immune-inflammation index (SII), and assessed its links with 90-day poor functional outcome and stroke-associated pneumonia (SAP) in PBH patients.

Methods: 

This retrospective cohort included 347 PBH patients (2015–2023). The IBI was calculated as 0.50 × NLRz + 0.30 × CARz + 0.20 × SIIz, where the weights were assigned a priori from a qualitative synthesis of prior literature and expert judgment rather than derived from the study data. Patients were stratified into tertiles. Primary outcome was poor 90-day functional outcome (modified Rankin Scale 3–6). Secondary outcomes were 90-day mortality and SAP. Multivariable logistic regression, restricted cubic spline analysis, and decision curve analysis were performed. Incremental value over a reference clinical model (age, Glasgow Coma Scale, hematoma volume, intraventricular hemorrhage) was assessed via DeLong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results: 

The overall rates of poor outcome, mortality, and SAP were 30.3, 12.1, and 16.7%, respectively. Higher IBI tertiles were associated with progressively higher rates of poor functional outcome (18.1, 27.8, and 44.8%; p < 0.001), with graded but non-significant trends for SAP and mortality. In multivariable analysis, IBI independently predicted poor outcome (OR 2.31, 95% CI 1.64–3.26, p < 0.001) and SAP (OR 1.79, 95% CI 1.23–2.59, p = 0.002). RCS confirmed a monotonic dose–response relationship for poor outcome. IBI had the best AUC for poor outcome (0.663), outperforming SII and CAR, though not significantly better than NLR. Adding IBI to the clinical model improved AUC from 0.623 to 0.701 (DeLong p = 0.008; NRI 0.31, IDI 0.045). For SAP, NLR alone was marginally superior (AUC 0.615 vs. 0.614). Decision curve analysis showed net clinical benefit for the IBI model.

Conclusion: 

IBI independently predicts poor functional outcome and adds incremental value over clinical factors and most individual markers. However, its utility for SAP does not exceed that of NLR alone; hence IBI should be used selectively for functional prognostication, while NLR remains the preferred biomarker for SAP risk assessment, pending further external validation.

Graphical Abstract