A Chinese version of the Language Screening Test (CLAST) for early-stage stroke patients

<div><p>There is a severe lack of aphasia screening tools for bedside use in Chinese. A number of aphasia assessment tools have recently been developed abroad, but some of these scales were not suitable for patients with acute stroke. The Language Screening Test (which includes two parallel versions [a/b]) in French has been proven to be an effective and time-saving aphasia screening scale for early-stage stroke patients. Therefore, we worked out a Chinese version of the LAST taking into consideration Chinese language and culture. Two preliminary parallel versions (a/b) were tested on 154 patients with stroke at acute phase and 107 patients with stroke at non-acute phase, with the Western Aphasia Battery serving as a gold standard. The equivalence between the two parallel versions and the reliability/validity of each version were assessed. The median time to complete one preliminary Chinese version (each had some item redundancy) was 98 seconds. Two final parallel versions were established after adjustment/elimination of the redundant items and were found to be equivalent (intra-class correlation coefficient: 0.991). Internal consistency is(Cronbach α for each version [a/b] was 0.956 and 0.965, respectively) good. Internal validity was fine: (a) no floor or ceiling effect/item redundancy; (b) construct validity revealed a 1-dimension structure, just like the French version. The higher educated subjects scored higher than their lower educated counterparts (<i>p<0</i>.<i>01</i>). The external validity: at the optimum cut-off point where the score of version a/b <14 in higher educated group(<13 in lower): the specificity of each version was 0.878/0.902(1/1 in lower) and sensitivity was 0.972/0.944(0.944/0.944 in lower). Inter-rater equivalence (intra-class correlation coefficient) was 1. The Chinese version of the Language Screening Test was proved to be an efficient and time-saving bedside aphasia screening tool for stroke patients at acute phase and can be used by an average medical physician.</p></div

Baseline data of all patients, including the educational level of all patients(n = 261) and lesion sites of the non-acute patients(n = 107).

<p>Baseline data of all patients, including the educational level of all patients(n = 261) and lesion sites of the non-acute patients(n = 107).</p

Scatter diagram between Aphasia quotient (AQ) of the Western Aphasia Battery (WAB) and total scores of the Chinese version of the Language Screening Test (CLAST-a/CLAST-b).

<p>The correlation between AQ of WAB and total score of CLAST-a/CLAST-b is represented by blue circles and green circles, respectively.</p

Histographic representation of the correlation between the Chinese version of the (CLAST) subtests and corresponding Western Aphasia Battery (WAB) items.

<p>The CLAST-a and CLAST-b are indicated by white and black bars, respectively. The coefficient of correlation between the CLAST and WAB ranged from 0.68 to 0.885 (p<0.01).</p

Schematic representation of validation process of Chinese version of the Language Screening Test (CLAST).

<p>Please note that the floor and ceiling effects were assessed in aphasia group, and discrimination validity was assessed in the non-acute group.</p

The flow chart to administer aphasia scales in acute and non-acute stroke patients.

<p>The flow chart to administer aphasia scales in acute and non-acute stroke patients.</p

Factor loading details of the 14 items with each version of CLAST.

<p>Rotated component matrix using the Generalized Least Squares analysis, limited the number of extracted factor to 1 in each version and Oblique rotations (n = 261).</p

Box plot representing the total scores of the Chinese version of the Language Screening Test (CLAST-a/CLAST-b) in terms of educational levels.

<p>The maximum and the lower quartile of CLAST score were found to be identical.</p

Table_1_Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China.docx

ObjectiveInsulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the “common soil” of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings.MethodsWe analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models.ResultsThe LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc.ConclusionThe ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.</p