Does stroke-associated pneumonia play an important role on risk of in-hospital mortality associated with severe stroke? A four-way decomposition analysis of a national cohort of stroke patients

Gittins, Matthew; Chaves, Marco Lobo; Vail, Andy; Smith, Craig J.

doi:10.1177/17474930231177881

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…The 4-way decomposition framework has the potential to disentangle causal effects of effective interventions, but has thus far only been applied in relatively few studies in clinical neurology. 18,19 To date, the 4-way decomposition method has also only rarely been applied to data from RCTs. 20 The assumption of control for mediator-outcome confounding is not needed to quantify the total effect of an intervention in a randomized trial, but it is needed for the decomposition of causal effects.…”

Section: Discussionmentioning

confidence: 99%

Guide to Decomposition of Causal Effects Into Mediation, Interaction, and Direct Effects

Hilkens,

Hammerton,

De Vries

et al. 2024

Neurology

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Mediation analysis can be applied in medical research with the aim of understanding the pathways that operate between an exposure and its effects on an outcome. This method can help to improve our understanding of pathophysiologic mechanisms and may guide the choice of potential treatment strategies. Traditional mediation analysis decomposes the total effect of an intervention on the outcome into 2 effects: (1) an indirect effect, from exposure using a mediator to the outcome, and (2) a direct effect, directly from exposure to outcome. A limitation of this method is that it assumes no interaction between the exposure and the mediator, which can either lead to an over-or underestimation of clinically relevant effects. The "4-way decomposition" method has the advantage of overcoming this limitation. Specifically, the total effect of an exposure on the outcome is decomposed into 4 elements: (1) reference interaction (interaction only), (2) mediated interaction (mediation and interaction), (3) the pure indirect effect (mediation but not interaction), and (4) the direct effect (no mediation and no interaction). We provide a guide to select the most appropriate method to investigate and decompose any causal effect given the research question at hand. We explain the application of the 4-way decomposition and illustrate this with a real-world example of how aerobic exercise may influence motor function in persons with Parkinson disease.

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Section: Discussionmentioning

confidence: 99%

Guide to Decomposition of Causal Effects Into Mediation, Interaction, and Direct Effects

Hilkens,

Hammerton,

De Vries

et al. 2024

Neurology

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show abstract

“…[42] Early identification and management of risk factors would reduce the occurrence of pneumonia and improve in-hospital survival. [43] Management of dysphagia, feeding, oral hygiene, and postural position may reduce the risk of pneumonia after stroke. [44] Elevated intracranial pressure is associated with brain swelling and could predict clinical deterioration and death in patients with large infarction.…”

Section: Definitionmentioning

confidence: 99%

Definition, prediction, prevention and management of patients with severe ischemic stroke and large infarction

Hua,

Liu,

2023

Chinese Medical Journal

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Severe ischemic stroke carries a high rate of disability and death. The severity of stroke is often assessed by the degree of neurological deficits or the extent of brain infarct, defined as severe stroke and large infarction, respectively. Critically severe stroke is a life-threatening condition that requires neurocritical care or neurosurgical intervention, which includes stroke with malignant brain edema, a leading cause of death during the acute phase, and stroke with severe complications of other vital systems. Early prediction of high-risk patients with critically severe stroke would inform early prevention and treatment to interrupt the malignant course to fatal status. Selected patients with severe stroke could benefit from intravenous thrombolysis and endovascular treatment in improving functional outcome. There is insufficient evidence to inform dual antiplatelet therapy and the timing of anticoagulation initiation after severe stroke. Decompressive hemicraniectomy (DHC) <48 h improves survival in patients aged <60 years with large hemispheric infarction. Studies are ongoing to provide evidence to inform more precise prediction of malignant brain edema, optimal indications for acute reperfusion therapies and neurosurgery, and the individualized management of complications and secondary prevention. We present an evidence-based review for severe ischemic stroke, with the aims of proposing operational definitions, emphasizing the importance of early prediction and prevention of the evolution to critically severe status, summarizing specialized treatment for severe stroke, and proposing directions for future research.

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A comparative study on the influencing factors and risk prediction models for stroke- associated pneumonia in patients with acute ischemic stroke and atrial fibrillation

Su,

Zhang,

Zhang

et al. 2024

Preprint

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Background To analyze the factors influencing stroke-associated pneumonia (SAP) in patients with acute ischemic stroke (AIS) and atrial fibrillation (AF), and to explore an optimal model for risk prediction. Methods Data were sourced from the Shandong Provincial Center for Disease Control and Prevention, encompassing all patients diagnosed with AIS and AF from 2020 to 2023. First, univariate analysis and LASSO (Least absolute shrinkage and selection operator) regression analysis methods were used to screen predictors. Secondly, the patients with AIS and AF were randomly divided into a training set, validation set, and test set in a ratio of 7:2:1, which were utilized for model training, model parameter adjustment, and model performance evaluation, respectively. The training set was balanced by synthetic minority oversampling technique (SMOTE), logistic regression, random forest (RF), and support vector machine (SVM),extreme gradient boosting (XGboost) models were constructed. Finally, we compared the models based on accuracy, sensitivity, specificity, AUC (area under the curve), and Youden index. We clarified the optimal prediction model and influencing factors ,the nomogram for risk prediction was constructed for SAP in patients with AIS and AF. Results Among the 4496 patients with AIS and AF, SAP was identified in 10.16% of cases. In the test set, the AUC for logistic regression, RF, SVM, and XGboost models were 0.866, 0.817, 0.816, and 0.838, respectively. The most predictive factors included coronary heart disease [OR = 1.05 (1.03, 1.07), p < 0.001], hypertension [OR = 1.05 (1.04, 1.07), p < 0.001], consciousness disorder [OR = 1.19 (1.16–1.23), p < 0.001], cognitive impairment [OR = 1.10 (1.08–1.13), p < 0.001], limb movement disorder [OR = 1.07 (1.04–1.09), p < 0.001], dysphagia [OR = 1.13 (1.08–1.19), p < 0.001], nasal feeding [OR = 0.95 (0.92–0.98), p = 0.003], and oxygen intake [OR = 0.65 (0.62–0.67), p < 0.001]. the nomogram average absolute error of calibration curve was 0.014. Conclusions Coronary artery disease, hypertension, consciousness disorder, cognitive impairment, limb movement disorder, and dysphagia were identified as independent risk factors for SAP in patients with AIS and AF. In contrast, nasal feeding and oxygen intake served as independent protective factors. The logistic regression model demonstrated the best predictive performance for SAP in patients with AIS and AF compared to RF, SVM, and XGboost models. The risk prediction model established by nomogram can better predict the risk of SAP.

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Does stroke-associated pneumonia play an important role on risk of in-hospital mortality associated with severe stroke? A four-way decomposition analysis of a national cohort of stroke patients

Cited by 3 publications

References 25 publications

Guide to Decomposition of Causal Effects Into Mediation, Interaction, and Direct Effects

Guide to Decomposition of Causal Effects Into Mediation, Interaction, and Direct Effects

Definition, prediction, prevention and management of patients with severe ischemic stroke and large infarction

A comparative study on the influencing factors and risk prediction models for stroke- associated pneumonia in patients with acute ischemic stroke and atrial fibrillation

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