Obesity Paradox in Patients with Acute Coronary Syndrome: Is Malnutrition the Answer?

BACKGROUND: Obesity paradox has been reported in patients with cardiovascular disease, showing an inverse association between obesity as defined by BMI (in kg/m2) and prognosis. Nutritional status is associated with systemic inflammatory response and affects cardiovascular disease outcomes. OBJECTIVES: This study sought to examine the influence of obesity and malnutrition on the prognosis of patients with acute coronary syndrome (ACS). METHODS: This study included consecutive patients diagnosed with ACS and underwent coronary angiogram between January 2009 and February 2023. At baseline, patients were categorized according to their BMI as follows: underweight (<18), normal weight (18-24.9), overweight (25.0-29.9), and obese (>30.0). We assessed the nutritional status by Prognostic Nutritional Index (PNI). Malnutrition was defined as a PNI value of <38. RESULTS: Of the 21,651 patients with ACS, 582 (2.7%) deaths from any cause were observed over 28.7 months. Compared with the patient's state of normal weight, overweight, and obesity were associated with decreased risk of all-cause mortality. Malnutrition was independently associated with poor survival (hazards ratio: 2.64; 95% CI: 2.24, 3.12; P < 0.001). In malnourished patients, overweight and obesity showed a 39% and 72% reduction in the incidence of all-cause mortality, respectively. However, in nourished patients, no significant reduction in the incidence of all-cause mortality was observed (all P > 0.05). CONCLUSIONS: Obesity paradox appears to occur in patients with ACS. Malnutrition may be a significant independent risk factor for prognosis in patients with ACS. The obesity paradox is influenced by the status of malnutrition.

Cerebral small vessel disease combined with cerebral collaterals to predict the prognosis of patients with acute large artery atherosclerotic stroke.

Background and purpose: Besides cerebral collaterals, few studies have examined other additional factors affecting the prognosis of patients with large artery atherosclerotic (LAA) stroke. Our study aims to explore the effect of the cerebral small vessel disease (SVD) and the effects of its interaction with cerebral collaterals on the prognosis of patients with acute LAA stroke. Method: Patients aged 18 years or older with LAA stroke within 24 h after stroke onset were consecutively enrolled. The functional outcome was determined using the modified Rankin Scale (mRS) at 3 months after stroke onset. Logistic multivariate analyses were used to identify the risk factors for stroke prognosis. Receiver operating characteristic (ROC) curves were constructed to compare the effects of cerebral collaterals and SVD on predicting the prognosis. Results: Of the 274 enrolled patients, 174 (63.50%) were identified as having a favorable prognosis, and 100 (36.50%) were identified as having an unfavorable prognosis. After adjusting for covariates, the logistic regression analysis identified that unfavorable prognosis was related to the total SVD score (Model 1, adjusted odds ratio = 1.73, 95% CI: 1.15-2.61, P < 0.01; Model 2, adjusted odds ratio = 1.85, 95% CI: 1.23-2.79, P < 0.01) and Tan score (Model 1, adjusted odds ratio = 0.38, 95% CI: 0.23-0.64, P < 0.01; Model 2, adjusted odds ratio = 0.52, 95% CI: 0.33-0.82, P < 0.01). Compared with cerebral collaterals (AUC = 0.59; 95% CI: 0.52-0.67; P < 0.01) or SVD (AUC = 0.62; 95% CI: 0.56-0.69; P < 0.01) alone, the combination of collaterals and SVD (AUC = 0.66; 95% CI: 0.59-0.73; P < 0.01) had higher diagnostic value for an unfavorable prognosis, and the optimal sensitivity and specificity were 77.01 and 53.00%, respectively. Conclusions: The total SVD burden was related to the prognosis of patients with LAA stroke. Compared with cerebral collaterals or SVD alone, cerebral collaterals combined with total SVD burden are better at predicting the prognosis of patients with acute LAA stroke.

Effect of Serum Phosphate on the Prognosis of Septic Patients: A Retrospective Study Based on MIMIC-IV Database.

Objective: To assess the effect of serum inorganic phosphate (Pi) on the prognosis of patients with sepsis. Methods: A retrospective analysis of patients with sepsis selected from the Medical Information Mart for Intensive Care (MIMIC)-IV database was performed. Sepsis was diagnosed according to the Third International Consensus Definition for sepsis and septic shock (Sepsis-3). The time-weighted values of the serum Pi measurements within the first 24 h of sepsis were analyzed. The association between serum Pi and in-hospital mortality was evaluated with a generalized linear model (log-binomial model). Results: The analysis of 11,658 patients from six intensive care units (ICUs) showed a nearly linear correlation between serum Pi and in-hospital mortality in all patients with sepsis, especially in those with acute kidney injury (AKI). The increase of serum Pi was related to a higher risk of AKI, higher norepinephrine doses, ICU mortality, and in-hospital mortality. The generalized linear model showed that serum Pi was an independent predictor for in-hospital mortality in all patients with sepsis even within the normal range. The adjusted risk ratios (RRs) were also significant in subgroup analyses according to kidney function, gender, respiratory infection, vasopressor use, and Sequential Organ Failure Assessment (SOFA) score. Conclusion: Higher levels of serum Pi, even within the normal range, were significantly associated with a higher risk of in-hospital mortality in patients with sepsis regardless of kidney function, gender, respiratory infection, vasopressor use, and SOFA score.

MRI Radiomics Features From Infarction and Cerebrospinal Fluid for Prediction of Cerebral Edema After Acute Ischemic Stroke.

Neuroimaging biomarkers that predict the edema after acute stroke may help clinicians provide targeted therapies and minimize the risk of secondary injury. In this study, we applied pretherapy MRI radiomics features from infarction and cerebrospinal fluid (CSF) to predict edema after acute ischemic stroke. MRI data were obtained from a prospective, endovascular thrombectomy (EVT) cohort that included 389 patients with acute stroke from two centers (dataset 1, n = 292; dataset 2, n = 97), respectively. Patients were divided into edema group (brain swelling and midline shift) and non-edema group according to CT within 36 h after therapy. We extracted the imaging features of infarct area on diffusion weighted imaging (DWI) (abbreviated as DWI), CSF on fluid-attenuated inversion recovery (FLAIR) (CSFFLAIR) and CSF on DWI (CSFDWI), and selected the optimum features associated with edema for developing models in two forms of feature sets (DWI + CSFFLAIR and DWI + CSFDWI) respectively. We developed seven ML models based on dataset 1 and identified the most stable model. External validations (dataset 2) of the developed stable model were performed. Prediction model performance was assessed using the area under the receiver operating characteristic curve (AUC). The Bayes model based on DWI + CSFFLAIR and the RF model based on DWI + CSFDWI had the best performances (DWI + CSFFLAIR: AUC, 0.86; accuracy, 0.85; recall, 0.88; DWI + CSFDWI: AUC, 0.86; accuracy, 0.84; recall, 0.84) and the most stability (RSD% in DWI + CSFFLAIR AUC: 0.07, RSD% in DWI + CSFDWI AUC: 0.09), respectively. External validation showed that the AUC of the Bayes model based on DWI + CSFFLAIR was 0.84 with accuracy of 0.77 and area under precision-recall curve (auPRC) of 0.75, and the AUC of the RF model based on DWI + CSFDWI was 0.83 with accuracy of 0.81 and the auPRC of 0.76. The MRI radiomics features from infarction and CSF may offer an effective imaging biomarker for predicting edema.

Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI.

OBJECTIVES: To develop and externally validate a machine learning (ML) model based on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) to identify the onset time of wake-up stroke from MRI. METHODS: DWI and FLAIR images of stroke patients within 24 h of clear symptom onset in our hospital (dataset 1, n = 410) and another hospital (dataset 2, n = 177) were included. Seven ML models based on dataset 1 were developed to estimate the stroke onset time for binary classification (≤ 4.5 h or > 4.5 h): Random Forest (RF), support vector machine with kernel (svmLinear) or radial basis function kernel (svmRadial), Bayesian (Bayes), K-nearest neighbor (KNN), adaptive boosting (AdaBoost), and neural network (NNET). ROC analysis and RSD were performed to evaluate the performance and stability of the ML models, respectively, and dataset 2 was externally validated to evaluate the model generalization ability using ROC analysis. RESULTS: svmRadial achieved the best performance with the highest AUC and accuracy (AUC: 0.896, accuracy: 0.878), and was the most stable (RSD% of AUC: 0.08, RSD% of accuracy: 0.06). The svmRadial model was then selected as the final model, and the AUC of the svmRadial model for predicting the onset time external validation was 0.895, with 0.825 accuracy. CONCLUSIONS: The svmRadial model using DWI + FLAIR is the most stable and generalizable for identifying the onset time of wake-up stroke patients within 4.5 h of symptom onset. KEY POINTS: • Machining learning model helps clinicians to identify wake-up stroke patients within 4.5 h of symptom onset. • A prospective study showed that svmRadial model based on DWI + FLAIR was the most stable in predicting the stroke onset time. • External validation showed that svmRadial model has good generalization ability in predicting the stroke onset time.

Association of neuroimaging markers of cerebral small vessel disease with short-term outcomes in patients with minor cerebrovascular events.

BACKGROUND: Increasing evidences have showed that neuroimaging markers of SVD can predict the short-term outcome of acute ischemic stroke (AIS). It is unclear that whether neuroimaging markers of SVD are also associated with short-term outcomes of minor cerebrovascular events. In the present study, we investigate neuroimaging markers of SVD in order to explore their roles in prediction of short-term outcome in patients with minor cerebrovascular events. METHODS: Consecutive first-ever stroke patients (n = 546) from the Affiliated Jiangning Hospital of Nanjing Medical University were enrolled. A total of 388 patients were enrolled according to minor cerebrovascular events definition (National Institutes of Health Stroke Scale Score ≤ 3) and exclusion criteria. MRI scans were performed within 7 days of stroke onset, and then neuroimaging markers of SVD including WMH, lacunes, cerebral microbleeds (CMB), and perivascular spaces (PVS), SVD burden scores were assessed. We completed baseline characteristics and evaluated the relationships of short-term outcomes to SVD neuroimaging markers and SVD scores. The 90-day modified Rankin Scale (mRS) was thought as primary outcome and was dichotomized as good functional outcome (mRS 0-1) and poor outcome (mRS 2-6). Secondary outcomes were stroke progression and stroke recurrence. RESULTS: Higher age, National Institutes of Health Stroke Scale (NIHSS) upon admission, lipoprotein-associated phospholipase A2 (LP-PLA2) and lacunes, Fazekas score were correlated with poor functional outcome (P < 0.05), But after adjusting for confounding variables, among the neuroimaging markers of cerebral small vessel disease, only Fazekas score (OR, 1.343; 95% confidence interval, 1.020-1.770; P = 0.036) was found to be associated with poor outcome at 90 days. Higher Fazekas and SVD scores were not associated with stroke progression or stroke recurrence. CONCLUSION: WMH can predict the poor functional outcome of minor cerebrovascular events. Adding other neuroimaging markers of SVD and total SVD burden score, however, does not improve the prediction, which indicated WMH can as neuroimaging markers for guiding the treatment of minor cerebrovascular events.