The pan-immune-inflammation value predicts stroke-associated pneumonia and poor outcome in spontaneous intracerebral hemorrhage: a machine learning approach

 You're that incompetent you don't know predictions DO NOTHING TOWARDS RECOVERY! Solve the correct problem!  You've known of the need to prevent pneumonia for years! I'd have you all fired for incompetence!

• 11% Stroke-associated pneumonia (21 posts to October 2020)

The pan-immune-inflammation value predicts stroke-associated pneumonia and poor outcome in spontaneous intracerebral hemorrhage: a machine learning approach

• 

  Anni Chen †

• J

  Jiahui Wang †

• Q

  Qiaoying Huang †

• 

  Chaoxiong Shen

• 

  Zhizhou Hu

• 

  Xiaohong Hu *

• Department of Neurology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China

Abstract

Objective: 

Stroke-associated pneumonia (SAP) constitutes a major complication following spontaneous intracerebral hemorrhage (sICH), posing a significant clinical challenge for accurate prediction. This study aimed to evaluate whether integrating the pan-immune-inflammation value (PIV) enhances the predictive performance of machine learning (ML) models for SAP and poor functional outcome.

Methods: 

A retrospective cohort of 371 sICH patients was analyzed. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO). Predictive models for SAP and poor outcome [modified Rankin Scale score (mRS) > 2 at 90 days] were developed and compared across nine ML algorithms. Model performance was assessed by discrimination (area under the receiver operating characteristic curve, AUC), calibration, and decision curve analysis (DCA). Interpretability was achieved via SHapley Additive exPlanations (SHAP).

Results: 

Elevated PIV was independently associated with SAP [odds ratio (OR) 13.55, 95% confidence interval (CI) 4.14–44.39; p < 0.0001] and poor 90-day functional outcome (OR 25.27, 95% CI 7.75–82.34; p < 0.0001). Among the algorithms, extreme Gradient Boosting (XGBoost) and logistic regression (LR) demonstrated the highest predictive performance for poor outcome (AUC 0.912) and SAP (AUC 0.856), respectively. Incorporating PIV significantly improved discriminatory ability (XGBoost: 0.926–0.942; LR: 0.905–0.931; both p < 0.05). The models demonstrated good calibration, provided net clinical benefit on DCA, and were interpretable through SHAP analysis, which consistently identified PIV as a critical predictor.

Interpretation: 

The integration of PIV into interpretable ML models significantly improves the accuracy of predicting SAP and functional outcome after sICH. This strategy, combining a systemic inflammatory biomarker with explainable ML, holds promise for advancing personalized risk stratification in neurocritical care.

Development and validation of a machine learning model based on multi-source clinical data for predicting the risk of early neurological deterioration in patients with ischemic stroke

 

Totally wrong objective; Survivors actually want early neurological deterioration prevented! Predictions DO NOTHING TOWARDS RECOVERY! 

What prevents early neurological deterioration is the needed research, not this crapola! You've known of the need for almost a decade but INCOMPETENTLY did this instead! You're fired!

• Early neurological deterioration (36 posts to January 2017)

Development and validation of a machine learning model based on multi-source clinical data for predicting the risk of early neurological deterioration in patients with ischemic stroke

• 

  Yue Li

• W

  Wei Wang

• Y

  Yilan Wei

• J

  Jing Han

• Y

  Yuan Shi

• Q

  Quping Ouyang *

• Neurological Disease Center, Beijing Shunyi District Hospital, Beijing, China

Abstract

Background and objective: 

Early neurological deterioration (END) is a critical clinical event associated with poor patient outcomes after acute ischemic stroke. Early identification of high-risk patients is crucial for timely clinical management. This study aimed to develop and validate a model for predicting END risk for acute ischemic stroke patients using machine learning algorithms.

Methods: 

This study retrospectively and consecutively enrolled 1,151 patients with acute ischemic stroke from the Stroke Center of Beijing Shunyi District Hospital between January 2021 and December 2024. END was defined as progressive worsening of neurological deficit symptoms after onset. Predictive variables were screened using univariate analysis and multiple feature selection methods (Treebag, Boruta, Bayesian). Nine machine learning algorithms (Decision Tree, Efficient Neural Network, K-Nearest Neighbors, Light Gradient Boosting Machine, Logistic Regression, Multilayer Perceptron, Random Forest, Simplified Support Vector Machine, Extreme Gradient Boosting) were employed to construct prediction models. Hyperparameters were optimized via 10-fold cross-validation, and model performance was evaluated in an internal validation cohort (30% of the sample). Primary evaluation metrics included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, F1 score, and net benefit from decision curve analysis (DCA). The SHAP method was used to interpret the optimal model.

Results: 

A total of 161 patients (14.0%) developed END. Feature selection ultimately identified five key predictors: ischemic stroke etiological subtype, Oxford Community Stroke Project (OCSP) classification, age, atrial fibrillation history, and prior stroke history. In both the development and internal validation cohorts, the logistic regression model demonstrated favorable and stable performance (development cohort AUC: 0.787, 95% CI: 0.735–0.839; internal validation cohort AUC: 0.751, 95% CI: 0.668–0.834), with a low log-loss value. DCA suggested potential clinical utility of the logistic regression model. SHAP analysis revealed that the etiological subtype of ischemic stroke and age were the features contributing most to the model’s predictions.

Conclusion: 

This study successfully developed and validated a logistic regression model for predicting END risk. The model incorporates five routinely available clinical variables and demonstrates satisfactory predictive performance and interpretability. The developed online tool may assist clinicians in early risk stratification, providing a reference for personalized intervention.

Association between lactate-to-albumin ratio and in-hospital mortality in ICU patients with acute ischemic stroke: a retrospective cohort study

 And you think predicting mortality is more important than research to prevent mortality? YOU'RE FIRED!

Association between lactate-to-albumin ratio and in-hospital mortality in ICU patients with acute ischemic stroke: a retrospective cohort study

• 

  Xiaocui Yang

• Y

  Yue Zhang

• M

  Min Ou

• 

  Yiwen Liu *

• West China Hospital, Sichuan University, Chengdu, Sichuan, China

Abstract

Background: 

Early risk stratification is critical for critically ill patients with ischemic stroke. The prognostic value of the lactate-to-albumin ratio (LAR) remains unclear in this population.

Methods: 

This single-center retrospective cohort study enrolled adult ICU patients with ischemic stroke from West China Hospital. LAR was calculated using the first lactate and albumin values on ICU admission. Log-transformed LAR was adopted as the main exposure, and the primary outcome was in-hospital mortality. Multivariable logistic regression, ROC analysis, quartile stratification, subgroup, and sensitivity analyses were performed among 749 patients with complete exposure and outcome data, of whom 576 had complete covariate data and comprised the primary multivariable regression cohort.

Results: 

A total of 749 patients were included, with an in-hospital mortality rate of 30.7%. In complete-case multivariable analysis (N = 576; deaths = 172), log-transformed LAR was independently associated with in-hospital mortality (OR = 1.44, 95%CI: 1.09–1.91, p = 0.010) after full adjustment. Each two-fold increase in LAR was associated with a 29% increase in mortality odds. The highest LAR quartile had a significantly higher mortality risk than the lowest quartile (OR = 2.16, 95%CI: 1.21–3.92). LAR showed modest predictive ability (AUC = 0.628). When modeled as a raw continuous LAR, the direction of association remained consistent but did not reach conventional statistical significance (OR 1.23; 95% CI 0.97–1.56; p = 0.083). Moreover, no significant interaction or non-linearity was found.

Conclusion: 

Elevated LAR is independently linked to higher in-hospital mortality among critically ill ischemic stroke patients. It may serve as a simple adjunctive marker for early risk stratification.

Serial immune-inflammatory recovery trajectories after acute ischemic stroke: associations with early neurological deterioration and 90-day functional outcome in a retrospective cohort

 

'Associations' don't get you recovered, or are you too fucking stupid to see that? What prevents early neurological deterioration is the needed research, not this crapola! You've known of the need for almost a decade but INCOMPETENTLY did this instead! You're fired!

• Early neurological deterioration (36 posts to January 2017)

Serial immune-inflammatory recovery trajectories after acute ischemic stroke: associations with early neurological deterioration and 90-day functional outcome in a retrospective cohort

• 

  Ruihan Ni 1

• 

  Lan Chu 1,2*

• 1. Suzhou Medical College, Soochow University, Suzhou, Jiangsu, China

• 2. Department of Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China

Abstract

Background: 

Systemic inflammation after acute ischemic stroke is usually assessed using admission biomarkers, which may not distinguish transient stress from sustained immune-inflammatory activation. We examined whether serial immune-inflammatory recovery trajectories were associated with early neurological deterioration (END) and 90-day functional outcome.

Methods: 

This single-center retrospective cohort included 760 adults with acute ischemic stroke identified from a hospital stroke database. NLR, SII, SIRI, and hs-CRP measured at admission, 24 h, 72 h, and day 7 were log-transformed and standardized, and equally weighted values were averaged at each observed time point. Latent class mixed models with 2–5 classes were compared without using outcome information, and a five-class solution was selected before association and prediction analyses. The primary outcome was 90-day mRS 3–6. END was defined as an NIHSS increase of ≥2 points within 72 h; the landmark END analysis excluded END within 24 h and tested 0–24 h immune change for END from 24 to 72 h.

Results: 

The five LCMM classes comprised 182 low-stable, 223 transient moderate, 161 delayed recovery, 122 persistent high, and 72 extreme persistent high patients. Among 751 patients with 90-day outcome data, the observed rate of mRS 3–6 increased from 21.5 to 30.6%, 44.2, 71.9, and 98.6% across these classes. After adjustment, persistent high (OR 2.46, 95% CI 1.31–4.61; p = 0.005) and extreme persistent high inflammation (OR 20.24, 95% CI 2.48–165.26; p = 0.005) were associated with mRS 3–6; a bias-reduced sensitivity estimate for the extreme class remained large (OR 13.61, 95% CI 2.34–79.26). Ordinal analysis showed progressively worse mRS for delayed recovery (common OR 2.73), persistent high (4.18), and extreme persistent high (6.72). The 0–24 h composite immune-change metric was not associated with landmark END (OR 1.12, 95% CI 0.92–1.36; p = 0.265). Adding LCMM-5 to the clinical model increased AUC from 0.828 to 0.841, but did not clearly outperform adding admission NLR/SII.

Conclusion: 

Longitudinal classification identified five immune-inflammatory recovery trajectories with a marked gradient in 90-day disability. Persistent-high and extreme persistent-high classes retained independent associations with poor functional outcome.

Multimodal deep learning outperforms clinical and brain region models in predicting stroke-associated pneumonia: an explainable AI study

 

Predictions are useless!  Solve the correct problem!  You've known of the need to prevent pneumonia for years! I'd have you all fired for incompetence!

• 11% Stroke-associated pneumonia (21 posts to October 2020)

Multimodal deep learning outperforms clinical and brain region models in predicting stroke-associated pneumonia: an explainable AI study

• 

  Haoyang He 1†

• 

  Xu Zhang 1†

• 

  Lijuan Gu 2*

• 

  Zhihong Jian 1*

• 

  Xiaoxing Xiong 1*

• 1. Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China

• 2. Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China

Abstract

Background: 

Stroke-associated pneumonia (SAP) is a frequent complication after acute ischemic stroke (AIS) and is associated with poor outcomes. This study aimed to develop an interpretable multimodal deep learning model integrating MRI, lesion-related brain regions, and clinical variables for early SAP prediction.

Methods: 

A total of 426 AIS patients were retrospectively enrolled, including 71 patients with SAP. Multimodal MRI data (DWI, T1WI, and T2-FLAIR) were processed using standardized registration and lesion segmentation. A 3D convolutional neural network was used to extract imaging representations, which were fused with clinical variables and AAL3-based brain-region features. Model performance was assessed using stratified five-fold cross-validation and nested cross-validation when applicable, with further evaluation based on receiver operating characteristic (ROC) analysis, calibration analysis, and decision curve analysis. Grad-CAM was applied for model interpretation.

Results: 

The multimodal fusion model achieved the best performance for SAP prediction, with an AUC of 0.782 (95% CI: 0.712–0.839), compared with the clinical model based on conventional clinical variables (AUC = 0.756), the imaging model based on 3D CNN representations (AUC = 0.693), and the brain-region model based on AAL3-derived lesion location features (AUC = 0.501). The fusion model showed superior clinical utility and favorable calibration. Grad-CAM visualization demonstrated that model predictions were mainly driven by lesion-related cortical and subcortical regions.

Conclusion: 

A multimodal deep learning framework integrating MRI, brain-region information, and clinical characteristics improved SAP prediction after AIS and provided an interpretable approach for individualized risk stratification.

Construction and multi-center validation of a model based on blending ensemble learning for predicting stroke-associated pneumonia following acute ischemic stroke

 

Predictions are useless!  Solve the correct problem!  You've known of the need to prevent pneumonia for years! I'd have you all fired for incompetence!

• 11% Stroke-associated pneumonia (21 posts to October 2020)

Construction and multi-center validation of a model based on blending ensemble learning for predicting stroke-associated pneumonia following acute ischemic stroke

• 

  Xinying Liu 1,2†

• Q

  Qiang Fu 1,2†

• S

  Shuo Yang 1,2†

• 

  Lin Zhang 1,2

• 

  Boxuan Feng 1,2

• 

  Ying Gao 1,2

• 

  Chengboya Zhao 1,2

• D

  Di Gao 1,2

• G

• 

• X

  Xiaokun Yang 1,2*

• 1. First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China

• 2. National Clinical Research Center for Chinese Medicine, Tianjin, China

Abstract

Background: 

Stroke-associated pneumonia (SAP) is a common and serious infectious complication following acute ischemic stroke (AIS), leading to worsened clinical outcomes. We aimed to develop and validate a novel predictive model based on Blending Ensemble Learning, to enable early, precise screening and individualized risk stratification for SAP.

Methods: 

In this multi-center study, a derivation cohort was initially established using 1,123 patients admitted between 2022 and 2024. Subsequently, 199 patients were prospectively enrolled in early 2025 as a temporal validation set, and 287 patients were extracted from a regional health big data platform (covering 12 hospitals) to serve as an external validation set. High-value clinical and biomarker features were identified via least absolute shrinkage and selection operator (LASSO) regression. To overcome the limitations of single classifiers, a Blending Ensemble Learning strategy was adopted to construct the final predictive model. The model underwent comprehensive evaluation for discrimination, calibration, and clinical utility, and was benchmarked against single random forest (RF) and extreme gradient boosting (XGBoost) models.

Results: 

Seventeen predictors were identified, including systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), the National Institutes of Health Stroke Scale (NIHSS), and D-dimer. The Blending ensemble model demonstrated superior discrimination in the validation cohort, achieving an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.898 (95% CI: 0.845–0.951), which significantly outperformed the optimal single model. Furthermore, SHapley Additive exPlanations (SHAP) analysis highlighted hyperinflammation, immunothrombosis, and nutritional depletion as pivotal mechanisms driving SAP pathogenesis.

Conclusion: 

The proposed Blending ensemble model exhibits excellent generalizability and high sensitivity. The accompanying web-based tool provides robust decision support for identifying high-risk patients and implementing early precision interventions.