Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma.

OBJECTIVE: To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT). METHODS: Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram. RESULTS: The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P = 0.029 OR = 4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR = 1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model. CONCLUSION: The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.

The Effect of a Three-Level Remote Alliance on Critical Care in Grassroot Areas: A Multi-Center, Retrospective Study.

Purpose: To explore an effective model to promote the homogeneous development of intensive care units (ICUs) in grassroot, impoverished and remote areas. Methods: A three-level remote alliance model (in-place and online assistance) was adopted to guide the cross-talk of ICUs between counties and cities. The observed indicators included the mortality of ICU patients and those with APACHE II scores ≥15 points, deep vein thrombosis, ventilator-associated pneumonia, the completion rate of septic shock goals in 3-hour and 6-hour bundles, and the rates of patient transfers. Results: After the implementation of the remote alliance, there was significant reduction in the mortality of ICU patients in the county and city-level tertiary hospitals (7.6% vs 4.5%, P = 0.004; OR = 1.734, 95% CI 1.189-2.532) and the mortality rates of patients with APACHE II scores ≥15 points (11.9% vs 7.1%, P = 0.004; OR = 1.763, 95% CI 1.189-2.614). There was a significant reduction in the incidence of ventilator-associated pneumonia (0.9% vs 5.0%, P < 0.001) and deep vein thrombosis (52.4% vs 13.6%, P < 0.001). The completion rate of 3-hour bundle therapies for septic shock was significantly improved (95.7% vs 68.4%, P < 0.001), as well as 6-hour bundle therapies for septic shock (97.9% vs 81.6%, P < 0.001). The hospital transfer rate decreased significantly in the grassroots and impoverished areas (2.6% vs 4.7%, P < 0.001). Conclusion: A three-level remote alliance might be helpful in improving the quality of critical care in remote areas and promoting the homogeneous development of disciplines.

Nomogram based on clinical and brain computed tomography characteristics for predicting more than 5 cerebral microbleeds in the hypertensive population.

Background: Cerebral microbleeds (CMBs) are common in the hypertensive population and can only be detected with magnetic resonance imaging (MRI). The anticoagulation and thrombolytic regimens for patients with >5 CMBs are different from those for patients with ≤ 5 CMBs. However, MRI is not suitable for evaluating CMBs in patients with MRI contraindications or acute ischemic stroke urgently requiring thrombolysis. We aimed to develop and validate a nomogram combining clinical and brain computed tomography (CT) characteristics for predicting >5 CMBs in a hypertensive population. Materials and methods: In total, 160 hypertensive patients from 2016 to 2020 who were confirmed by MRI to have >5 (77 patients) and ≤ 5 CMBs (83) were retrospectively analyzed as the training cohort. Sixty-four hypertensive patients from January 2021 to February 2022 were included in the validation cohort. Multivariate logistic regression was used to evaluate >5 CMBs. A combined nomogram was constructed based on the results, while clinical and CT models were established according to the corresponding characteristics. Receiver operating characteristic (ROC) and calibration curves and decision curve analysis (DCA) were used to verify the models. Results: In the multivariable analysis, the duration of hypertension, level of homocysteine, the number of lacunar infarcts (LIs), and leukoaraiosis (LA) score were included as factors associated with >5 CMBs. The clinical model consisted of the duration of hypertension and level of homocysteine, while the CT model consisted of the number of LIs and LA. The combined model consisted of the duration of hypertension, level of homocysteine, LI, and LA. The combined model achieved an area under the curve (AUC) of 0.915 (95% confidence interval [CI]: 0.860-0.953) with the training cohort and 0.887 (95% CI: 0.783-0.953) with the validation cohort, which were higher than those of the clinical model [training cohort: AUC, 0.797 (95% CI: 0.726, 0.857); validation cohort: AUC, 0.812 (95% CI: 0.695, 0.899)] and CT model [training cohort: AUC, 0.884 (95% CI: 0.824, 0.929); validation cohort: AUC, 0.868 (95% CI: 0.760, 0.940)]. DCA showed that the clinical value of the combined model was superior to that of the clinical model and CT model. Conclusion: A combined model based on clinical and CT characteristics showed good diagnostic performance for predicting >5 CMBs in hypertensive patients.

Genetic correlations and causal inferences in ischemic stroke.

BACKGROUND AND PURPOSE: Considerable studies have reported inconsistent relationships between ischemic stroke and a large number of factors. These uncertainties may reflect the susceptibility to confounding in observational studies. We aimed to assess genetic correlations and causal relationships between ischemic stroke and diverse phenotypes. METHODS: Summary-level data for ischemic stroke (34,217 cases and 406,111 controls) from the MEGASTROKE consortium were used as the outcome. Exposures were derived from two GWAS statistics curated databases. We explored the genetic correlations and causalities between hundreds of traits and ischemic stroke, using linkage disequilibrium score regression and Mendelian randomization (MR), respectively. Multiple sensitivity analyses were also performed. RESULTS: Genetic correlation analyses reflected genetic overlaps between ischemic stroke and physical activity, cardiometabolic factors, smoking, and lung function. Applying MR, we found suggestive evidence that genetic predisposition to higher concentration of low-density lipoprotein particles (LDL.P) and cholesterol carried in different sizes of LDL.P (LDL.C) were associated with higher risk of ischemic stroke, particular large artery stroke. The strongest effect was observed for small LDL.P in large artery stroke (OR 1.31, 95% CI 1.09-1.56, p = 0.003). The results were overall robust for sensitivity analyses. We further observed significant positive associations of genetically predicted LDL.P and LDL.C with coronary artery disease and myocardial infarction. CONCLUSIONS: Shared genetic overlaps might exist between ischemic stroke and physical activity, cardiometabolic factors, smoking, and lung function. We provided suggestive evidence for a potential causal role of LDL.P and LDL.C in ischemic stroke, particularly in large artery stroke. Future researches are required to confirm these findings.