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Development and validation of risk prediction models for COVID-19 positivity in a hospital setting
ObjectivesTo develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.MethodsPatients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).ResultsA total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV.ConclusionTwo simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation
Narrative assessment for cantonese-speaking children
Background: This study examined the narrative skills of Cantonese-speaking school-age children to fill a need for a normative language test for school-age children. Purpose: To provide a benchmark of the narrative skills of Cantonese-speaking children; to identify which of the microstructure components was the best predictor of age; and to determine the diagnostic accuracy of the test components. Method and Procedure: Data were collected from 1,120 Cantonese-speaking children between the ages of 4;10 (years;months) and 12;01, using a story-retell of a 24-frame picture series. Four narrative components (syntactic complexity, semantic score, referencing, and connective use) were measured. Outcomes and Results: Each measure reflected significant age-related differences in narrative ability. Regression analyses revealed that vocabulary and syntactic complexity were the best predictors of grade. All measures showed high sensitivity (86%–94%) but relatively low specificity (60%–90%) and modest likelihood ratio (LR) values: LR+ (2.15–9.42) and LR– (0.07–0.34).Conclusion and Implications: Narrative assessment can be standardized to be a reliable and valid instrument to assist in the identification of children with language impairment. Syntactic complexity is not only a strong predictor of grade but was also particularly vulnerable in Cantonese-speaking children with specific language impairment. Further diagnostic research using narrative analysis is warranted