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Objective:To construct a hypoglycemia random forest prediction model for older adults with type 2 diabetes, and assess the model′s prognostication performance through internal and external verification.Methods:From August 2022 to January 2023, 300 older adults with type 2 diabetes in Beijing Hospital were selected. The demographic characteristics, medical history, laboratory tests, and other data of the patients were collected, and the data set was randomly divided into the training set and verification set in a ratio of 7∶3. The hypoglycemia prediction model for older adults with type 2 diabetes was constructed and optimized based on the random forest algorithm. The calibration curve was used to evaluate the model′s calibration, and the ROC was used to evaluate the model′s discrimination. The clinical applicability of the model was assessed by the decision curve analysis. The risk factors for hypoglycemia in the older adults were explored by prioritizing the contributions of variables in prediction. The Bootstrap method was used for internal validation, and the validation set was used for external validation.Results:Among the 300 older adults with type 2 diabetes, 128 cases (42.67%) experienced hypoglycemia within one week. The predictive contributions of risk factors in the model were ranked as follows: the number of episodes of hypoglycemia in one month, HDL-C, heart disease, diabetes knowledge and education, combination therapy, age, duration of diabetes, staple food restriction, glycosylated hemoglobin, and gender. The internal and external calibration curves of the hypoglycemia random forest model for the older adults with type 2 diabetes fluctuated around the diagonal, indicating that the calibration degree of the predictive model is good. The AUROC of internal verification was 0.823 (95% CI 0.752-0.894), the sensitivity and specificity were 0.867 and 0.698, respectively. The external verification was 0.859 (95% CI 0.817 - 0.902), and sensitivity and specificity were 0.789 and 0.804, respectively, showing that the overall discrimination of the prediction model was good. The DCA curves were far from the all-positive line and all-negative line, which indicated that the prediction model had good clinical applicability. Conclusions:The predictive effect of this model is good, and it is suitable for predicting the risk of hypoglycemia in older adults with type 2 diabetes, and it provides a reference for early hypoglycemia screening and predictive intervention for this kind of patients.
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OBJECTIVE: To study the correlation of common cardiovascular risk factors with brain iron deposition. METHODS: Eighty-four elderly subjects without neurological diseases or brain trauma were included in the study. The cardiovascular risk factors were comprehensively assessed. MRI examination was performed to obtain high-resolution T1-weighted images and enhanced susceptibility weighted angiography (ESWAN) images, and R2* figure was obtained by post-processing the ESWAN sequence. High definition T1 images were segmented using computer segmentation technique. After registration to the ESWAN image, R2* values of each region of interest were extracted. Multiple linear regression analysis was used to analyze the relationship of R2* values in each area of interest with gender, age and vascular risk factors. RESULTS Smoking was associated with increased R2* values in the hippocampus, white matter and cortex (β=0.244, 0.317, 0.277, P<0.05 or P<0.01). Hypertension was correlated with the increase of R2* in the putamen (β=0.241, P=0.027). Hyperglycemia was associated with the increase of R2* in the thalamus (β=0.234, P<0.05). In the thalamus, the R2* value of males was higher than that of females (β=0.320, P<0.05). Age was correlated with the R2* values of thalamus, caudate nucleus, pallidus, white matter and cortex (β=-0.218、-0.254、0.216、-0.280 and -0.238, P<0.05 or P<0.01). CONCLUSIONS Common cardiovascular risk factors may lead to iron deposition in the brain, and the deposition patterns vary with the gender, age and different risk factors.
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<p><b>OBJECTIVE</b>To investigate the relationship between maximal infarct volume to benefit from intravenous thrombolysis (IVT) and onset to needle time (ONT).</p><p><b>METHODS</b>The clinical and image data of acute ischemic stroke patients who received IVT in the second Affiliated Hospital, Zhejiang University School of Medicine during May 2009 to June 2016 were retrospectively reviewed. Patients were classified into within-time-window group (ONT ≤ 4.5 h) and beyond-time-window group (ONT>4.5 h). Good and poor outcome were defined as modified Rankin scale (mRS) ≤ 2 or >2 at 3 months, respectively. The maximal infarct volume was analyzed by receiver operating characteristic (ROC) curve.</p><p><b>RESULTS</b>Among 587 patients (465 cases were within-time-window, 122 cases were beyond-time-window), baseline core volume was 15(2-46)mL,and 324 (55.2%) patients achieved good outcome. Compared with the good-outcome group, the baseline core volume was larger in the poor-outcome group (32 mL vs 5 mL,=-9.766,<0.01). After adjusting age, ONT, baseline National Institutes of Health Stroke Scale (NIHSS) and atrial fibrillation, baseline infarct core volume independently predicted poor outcome (=1.014, 95%:1.008-1.020,<0.01). The ROC curve analysis showed that the maximal infarct core volume for achieving good outcome in the within-time-window group and beyond-time-window group were 152 mL and 71mL, respectively. The maximal infarct volume to benefit from IVT diminished with the increasing delayed ONT of every 30 min (=-0.691,<0.05).</p><p><b>CONCLUSIONS</b>The maximal infarct volume to benefit from thrombolysis is larger in patients treated within time window than those beyond the time window, and that volume diminishes with ONT delay.</p>
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<p><b>OBJECTIVE</b>To investigate the association of serum folate level with the severity of white matter hyperintensity (WMH) and presence of cerebral microbleeds (CMB).</p><p><b>METHODS</b>Clinical data of WMH patients from the second affiliated Hospital, Zhejiang University school of Medicine during July 2011 and February 2016 were retrospectively reviewed. According to Fazekas score based on T2-Flair images, patients were classified into mild WMH (0-3) and severe WMH (4-6). The presence of CMB was assessed on susceptibility weighted imaging (SWI). Binary logistic analysis was conducted to identify the independent predictors for severe WMH and the presence of CMB.</p><p><b>RESULTS</b>Two hundred and twenty eight patients with WMH were included, among whom 149(65.35%)had severe WMH. In patients with high folate (≥ 15.68 nmol/L), low folate (6.8-15.67 nmol/L) and folate deficiency (<6.8 nmol/L), the proportions of severe WMH were 52.88%, 73.33% and 89.47%, respectively. Binary logistic regression analysis revealed that compared with high folate group, severe WMH was more common in groups with low folate (=2.109, 95%:1.112-4.001,<0.05) and folate deficiency (=6.383, 95%:1.168-34.866,<0.05). Eighty-eight(48.09%) of 183 patients receiving SWI scan presented with CMB. Although the subjects with CMB had lower serum folate level than those without CMB(13.42 vs 16.51 nmol/L,<0.01), binary logistic regression analysis did not reveal the independent association between serum folate level and the presence of CMB after adjusting for hyperhomocysteinemia (>0.05).</p><p><b>CONCLUSIONS</b>Lower serum folate level is independently associated with severe WMH, but not with the CMB concurrence.</p>