Development and validation of radiology-clinical statistical and machine learning model for stroke-associated pneumonia after first intracerebral haemorrhage.

BACKGROUND: Society is burdened with stroke-associated pneumonia (SAP) after intracerebral haemorrhage (ICH). Cerebral small vessel disease (CSVD) complicates clinical manifestations of stroke. In this study, we redefined the CSVD burden score and incorporated it into a novel radiological-clinical prediction model for SAP. MATERIALS AND METHODS: A total of 1278 patients admitted to a tertiary hospital between 1 January 2010 and 31 December 2019 were included. The participants were divided into training and testing groups using fivefold cross-validation method. Four models, two traditional statistical models (logistic regression and ISAN) and two machine learning models (random forest and support vector machine), were established and evaluated. The outcomes and baseline characteristics were compared between the SAP and non-SAP groups. RESULTS: Among the of 1278 patients, 281(22.0%) developed SAP after their first ICH. Multivariate analysis revealed that the logistic regression (LR) model was superior in predicting SAP in both the training and testing groups. Independent predictors of SAP after ICH included total CSVD burden score (OR, 1.29; 95% CI, 1.03-1.54), haematoma extension into ventricle (OR, 2.28; 95% CI, 1.87-3.31), haematoma with multilobar involvement (OR, 2.14; 95% CI, 1.44-3.18), transpharyngeal intubation operation (OR, 3.89; 95% CI, 2.7-5.62), admission NIHSS score ≥ 10 (OR, 2.06; 95% CI, 1.42-3.01), male sex (OR, 1.69; 95% CI, 1.16-2.52), and age ≥ 67 (OR, 2.24; 95% CI, 1.56-3.22). The patients in the SAP group had worse outcomes than those in the non-SAP group. CONCLUSION: This study established a clinically combined imaging model for predicting stroke-associated pneumonia and demonstrated superior performance compared with the existing ISAN model. Given the poor outcomes observed in patients with SAP, the use of individualised predictive nomograms is vital in clinical practice.

Enhanced Prediction of Malignant Cerebral Edema in Large Vessel Occlusion with Successful Recanalization Through Automated Weighted Net Water Uptake.

BACKGROUND: Malignant cerebral edema (MCE) is associated with both net water uptake (NWU) and infarct volume. We hypothesized that NWU weighted by the affected Alberta Stroke Program Early Computed Tomography Score (ASPECTS) regions could serve as a quantitative imaging biomarker of aggravated edema development in acute ischemic stroke with large vessel occlusion (LVO). The aim of this study was to evaluate the performance of weighted NWU (wNWU) to predict MCE in patients with mechanical thrombectomy (MT). METHODS: We retrospectively analyzed consecutive patients who underwent MT due to LVO. NWU was computed from nonenhanced computed tomography scans upon admission using automated ASPECTS software. wNWU was derived by multiplying NWU with the number of affected ASPECTS regions in the ischemic hemisphere. Predictors of MCE were assessed through multivariate logistic regression analysis and receiver operating characteristic curves. RESULTS: NWU and wNWU were significantly higher in MCE patients than in non-MCE patients. Vessel recanalization status influenced the performance of wNWU in predicting MCE. In patients with successful recanalization, wNWU was an independent predictor of MCE (adjusted odds ratio 1.61; 95% confidence interval [CI] 1.24-2.09; P < 0.001). The model integrating wNWU, National Institutes of Health Stroke Scale, and collateral score exhibited an excellent performance in predicting MCE (area under the curve 0.80; 95% CI 0.75-0.84). Among patients with unsuccessful recanalization, wNWU did not influence the development of MCE (adjusted odds ratio 0.99; 95% CI 0.60-1.62; P = 0.953). CONCLUSIONS: This study revealed that wNWU at admission can serve as a quantitative predictor of MCE in LVO with successful recanalization after MT and may contribute to the decision for early intervention.

Low blood flow ratio is associated with hemorrhagic transformation secondary to mechanical thrombectomy in patients with acute ischemic stroke.

BACKGROUND AND PURPOSE: A significant decrease of cerebral blood flow (CBF) is a risk factor for hemorrhagic transformation (HT) in acute ischemic stroke (AIS). This study aimed to ascertain whether the ratio of different CBF thresholds derived from computed tomography perfusion (CTP) is an independent risk factor for HT after mechanical thrombectomy (MT). METHODS: A retrospective single center cohort study was conducted on patients with AIS undergoing MT at the First Affiliated Hospital of Wenzhou Medical University from August 2018 to December 2023. The perfusion parameters before thrombectomy were obtained according to CTP automatic processing software. The low blood flow ratio (LFR) was defined as the ratio of brain volume with relative CBF <20 % over volume with relative CBF <30 %. HT was evaluated on the follow-up CT images. Binary logistic regression was used to analyze the correlation between parameters that differ between the two groups with regards to HT occurrence. The predictive efficacy was assessed utilizing the receiver operating characteristic curve. RESULTS: In total, 243 patients met the inclusion criteria. During the follow-up, 46.5 % of the patients (113/243) developed HT. Compared with the Non-HT group, the HT group had a higher LFR (0.47 (0.34-0.65) vs. 0.32 (0.07-0.56); P < 0.001). According to the binary logistic regression analysis, the LFR (aOR: 6.737; 95 % CI: 1.994-22.758; P = 0.002), Hypertension history (aOR: 2.231; 95 % CI: 1.201-4.142; P = 0.011), plasma FIB levels before MT (aOR: 0.641; 95 % CI: 0.456-0.902; P = 0.011), and the mismatch ratio (aOR: 0.990; 95 % CI: 0.980-0.999; P = 0.030) were independently associated with HT secondary to MT. The area under the curve of the regression model for predicting HT was 0.741. CONCLUSION: LFR, a ratio quantified via CTP, demonstrates potential as an independent risk factor of HT secondary to MT.