Objectives. To develop a simple scoring system to predict dengue infection severity based on patient characteristics and routine clinical profiles. Methods. Retrospective data of children with dengue infection from 3 general hospitals in Thailand were reviewed. Dengue infection was categorized into 3 severity levels: dengue infection (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS). Coefficients of significant predictors of disease severity under ordinal regression analysis were transformed into item scores. Total scores were used to classify patients into 3 severity levels. Results. Significant clinical predictors of dengue infection severity were age >6 years, hepatomegaly, hematocrit ≥40%, systolic pressure <90 mmHg, white cell count >5000 /μL, and platelet ≤50000 /μL. The derived total scores, which ranged from 0 to 18, classified patients into 3 severity levels: DF (scores <2.5, n = 451, 58.1%), DHF (scores 2.5–11.5, n = 276, 35.5%), and DSS (scores >11.5, n = 50, 6.4%). The derived score correctly classified patients into their original severity levels in 60.7%. An under-estimation of 25.7% and an over-estimation of 13.5% were clinically acceptable. Conclusions. The derived dengue infection severity score classified patients into DF, DHF, or DSS, correctly into their original severity levels. Validation of the score should be reconfirmed before application of routine practice.
ObjectiveTo validate a simple scoring system to classify dengue viral infection severity to patients in different settings.MethodsThe developed scoring system derived from 777 patients from three tertiary-care hospitals was applied to 400 patients in the validation data obtained from another three tertiary-care hospitals. Percentage of correct classification, underestimation, and overestimation was compared. The score discriminative performance in the two datasets was compared by analysis of areas under the receiver operating characteristic curves.ResultsPatients in the validation data were different from those in the development data in some aspects. In the validation data, classifying patients into three severity levels (dengue fever, dengue hemorrhagic fever, and dengue shock syndrome) yielded 50.8% correct prediction (versus 60.7% in the development data), with clinically acceptable underestimation (18.6% versus 25.7%) and overestimation (30.8% versus 13.5%). Despite the difference in predictive performances between the validation and the development data, the overall prediction of the scoring system is considered high.ConclusionThe developed severity score may be applied to classify patients with dengue viral infection into three severity levels with clinically acceptable under- or overestimation. Its impact when used in routine clinical practice should be a topic for further study.
Correct emission factors are necessary for evaluating vehicle emissions and making proper decisions to manage air pollution in the transportation sector. In this study, using a chassis dynamometer at the Automotive Emission Laboratory, CO2 and CH4 emission factors of light-duty vehicles (LDVs) were developed by fuel types and driving speeds. The Bangkok driving cycle was used for the vehicle’s running and controlling under the standard procedure. Results present that the highest average CO2 and CH4 emission factors were emitted from LDG vehicles, at 232.25 g/km and 9.50 mg/km, respectively. The average CO2 emission factor of the LDD vehicles was higher than that of the LDG vehicles, at 182.53 g/km and 171.01 g/km, respectively. Nevertheless, the average CH4 emission factors of the LDD vehicles were lower than those of the LDG vehicles, at 2.21 mg/km and 3.02 mg/km, respectively. The result reveals that the lower driving speed emitted higher CO2 emission factors for LDVs. It reflects the higher fuel consumption rate (L/100 km) and the lower fuel economy rate (km/L). Moreover, the portion of CO2 emissions emitted from LDVs was 99.96% of total GHG emissions. The CO2 and CH4 emission factors developed through this study will be used to support the greenhouse gas reduction policies, especially concerning the CO2 and CH4 emitted from vehicles. Furthermore, it can be used as a database that encourages Thailand’s green transportation management system.
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