Dataset Information

Comparison of clinical scores for predicting stroke-associated pneumonia after intracerebral hemorrhage (ICH): potential tools for personalized care and clinical trials for ICH.

ABSTRACT:

Background

This study aimed to systematically compare the discrimination and calibration of 5 clinical scores for stroke-associated pneumonia (SAP) after intracerebral hemorrhage (ICH).

Methods

We derived a validation cohort from the Beijing Registration of Intracerebral Hemorrhage. SAP was then diagnosed according to the Center for Disease Control and Prevention's criteria for hospital-acquired pneumonia. The area under the receiver operating characteristic curve (AUROC) and Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration.

Results

A total of 1964 patients were enrolled in the study. The mean age was 56.8±14.4 years, and 67.6% were male. The median National Institutes of Health Stroke Scale (NIHSS) score at admission was 11 [interquartile range (IQR), 3-21], while the median length of stay (LOS) was 16 days (IQR, 8-22 days). A total of 575 (29.2%) patients were diagnosed with in-hospital SAP after ICH. The AUROC of the 5 clinical scores ranged from 0.732 to 0.800. In comparing these scores, we found that the ICH-associated pneumonia score-B (ICH-APS-B 0.800; 95% CI: 0.780-0.820; P<0.001) showed a statistically better discrimination than did the other risk models (all P<0.001). Furthermore, all clinical scores performed better in patients with an LOS >72 h. The ICH-APS-B (0.827; 95% CI: 0.806-0.848; P<0.001) still showed statistically better discrimination than did the other risk models in patients with an LOS longer than 72 hours. The Hosmer-Lemeshow test also revealed that the ICH-APS-B. had the largest Cox and Snell R2 result for in-hospital SAP after ICH.

Conclusions

Among the 5 models for predicting SAP after ICH, the ICH-APS-B showed the best predictive performance, suggests it may be a useful tool for implementing the personalized care of patients and conducting clinical trials of SAP after ICH.

SUBMITTER: Ji R

PROVIDER: S-EPMC9073778 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Comparison of clinical scores for predicting stroke-associated pneumonia after intracerebral hemorrhage (ICH): potential tools for personalized care and clinical trials for ICH.

Ji Ruijun R Liu Yanfang Y Liu Xinyu X Wang Linlin L Wang Dandan D Wang Wenjuan W Zhang Runhua R Jiang Ruixuan R Jia Jiaokun J Feng Hao H Ding Zeyu Z Liu Gaifen G Lu Jingjing J Ju Yi Y Zhao Xingquan X

Annals of translational medicine 20220401 7

<h4>Background</h4>This study aimed to systematically compare the discrimination and calibration of 5 clinical scores for stroke-associated pneumonia (SAP) after intracerebral hemorrhage (ICH).<h4>Methods</h4>We derived a validation cohort from the Beijing Registration of Intracerebral Hemorrhage. SAP was then diagnosed according to the Center for Disease Control and Prevention's criteria for hospital-acquired pneumonia. The area under the receiver operating characteristic curve (AUROC) and Hosm ...[more]

PMID: 35530955

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Project description:PurposeWe develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH).MethodWe applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA). During the training of ICH-LR2S2, patients were randomly divided into two groups - 80% for the training set and 20% for model validation. A prospective test set was developed using 12,523 patients recruited in 2019. To further verify its effectiveness, we tested ICH-LR2S2 on an external dataset of 24,860 patients from the China National Stroke Registration Management System II (CNSR II). The performance of ICH-LR2S2 was measured by the area under the receiver operating characteristic curve (AUROC).ResultsThe incidence of SAP in the dataset was 25.52%. A 24-point ICH-LR2S2 was developed from independent predictors, including age, modified Rankin Scale, fasting blood glucose, National Institutes of Health Stroke Scale admission score, Glasgow Coma Scale score, C-reactive protein, dysphagia, Chronic Obstructive Pulmonary Disease, and current smoking. The results showed that ICH-LR2S2 achieved an AUC = 0.749 [95% CI 0.739-0.759], which outperforms the best baseline ICH-APS (AUC = 0.704) [95% CI 0.694-0.714]. Compared with the previous ICH risk scores, ICH-LR2S2 incorporates fasting blood glucose and C-reactive protein, improving its discriminative ability. Machine learning methods such as XGboost (AUC = 0.772) [95% CI 0.762-0.782] can further improve our prediction performance. It also performed well when further validated by the external independent cohort of patients (n = 24,860), ICH-LR2S2 AUC = 0.784 [95% CI 0.774-0.794].ConclusionICH-LR2S2 accurately distinguishes SAP patients based on easily available clinical features. It can help identify high-risk patients in the early stages of diseases.

Project description:BackgroundStroke-associated pneumonia (SAP) contributes to high mortality rates in spontaneous intracerebral hemorrhage (sICH) populations. Accurate prediction and early intervention of SAP are associated with prognosis. None of the previously developed predictive scoring systems are widely accepted. We aimed to derive and validate novel supervised machine learning (ML) models to predict SAP events in supratentorial sICH populations.MethodsThe data of eligible supratentorial sICH individuals were extracted from the Risa-MIS-ICH database and split into training, internal validation, and external validation datasets. The primary outcome was SAP during hospitalization. Univariate and multivariate analyses were used for variable filtering, and logistic regression (LR), Gaussian naïve Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and ensemble soft voting model (ESVM) were adopted for ML model derivations. The accuracy, sensitivity, specificity, and area under the curve (AUC) were adopted to evaluate the predictive value of each model with internal/cross-/external validations.ResultsA total of 468 individuals with sICH were included in this work. Six independent variables [nasogastric feeding, airway support, unconscious onset, surgery for external ventricular drainage (EVD), larger sICH volume, and intensive care unit (ICU) stay] for SAP were identified and selected for ML prediction model derivations and validations. The internal and cross-validations revealed the superior and robust performance of the GNB model with the highest AUC value (0.861, 95% CI: 0.793-0.930), while the LR model had the highest AUC value (0.867, 95% CI: 0.812-0.923) in external validation. The ESVM method combining the other six methods had moderate but robust abilities in both cross-validation and external validation and achieved an AUC of 0.843 (95% CI: 0.784-0.902) in external validation.ConclusionThe ML models could effectively predict SAP in sICH populations, and our novel ensemble model demonstrated reliable robust performance outcomes despite the populational and algorithmic differences. This attempt indicated that ML application may benefit in the early identification of SAP.

Project description:Edaravone dexborneol (EDB) is widely recognized for its anti-inflammatory and antioxidant properties and is clinically applied in the treatment of acute cerebral infarction. Ferroptosis is a critical process in the pathophysiology of brain injury following intracerebral hemorrhage (ICH). However, it remains unclear whether EDB can ameliorate ICH through the modulation of ferroptosis. This study aimed to evaluate the function and mechanism of EDB in treatment of ICH. With a rat ICH model, animal behavior tests, histopathological staining, magnetic resonance imaging and evans blue staining were used to evaluate the neural protective function of EDB on ICH rats. The potential molecular mechanism was investigated using RNA sequencing. With the administration of Fer-1, a range of ferroptosis-related biomarkers, including Fe2+, 4-hydroxynonenal, malondialdehyde, etc., were analyzed to to ascertain whether EDB confers neuroprotective effects through the modulation of P53/GPX4 pathways to inhibit ferroptosis. Finally, the findings were further corroborated using an in vitro ICH model with a P53 inhibitor. EDB has the potential to markedly enhance nerve and motor function, mitigate pathological damage, facilitate hematoma clearance, and repair BBB injury in ICH rats. KEGG analysis revealed that the differentially expressed genes were associated with signaling pathways, including P53 and ferroptosis. Both EDB and Fer-1 substantially reduced the concentrations of Fe2+, 4-hydroxynonenal, malondialdehyde, increased the amount of anti-oxidants, decreased the expression of P53, and concurrently upregulated the expression of GPX4. Besides, the P53 inhibitor PFT-α was observed to significantly reduce the levels of 4-HNE and lipid peroxides, while concurrently increasing the expression of GPX4. This investigation has shed light on the crucial neuroprotective role of EDB by regulating ferroptosis in ICH disease, which provided a theoretical basis for the clinical application of EDB in the treatment of ICH.

Dataset Information

Comparison of clinical scores for predicting stroke-associated pneumonia after intracerebral hemorrhage (ICH): potential tools for personalized care and clinical trials for ICH.

Background

Methods

Results

Conclusions

Publications

Comparison of clinical scores for predicting stroke-associated pneumonia after intracerebral hemorrhage (ICH): potential tools for personalized care and clinical trials for ICH.

Similar Datasets

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