ABSTRACT
BACKGROUND: Leptospirosis is an important health problem in Thailand. People infected with leptospirosis may not have any mild symptoms, whereas some people have acute and severe illnesses. It is crucial to strengthen critical patients' diagnosis and treatment to prevent severe complications and reduce mortality. This study was performed to explore a set of parameters for the prediction of severe leptospirosis illness under routine clinical practice. METHODS: A case-control study was conducted in eight general hospitals in Thailand. Retrospective collection data were used, and key information was retrieved from inpatient medical files. Patients were grouped into two severity categories, severe and non-severe infection. Backward elimination was used to reach the final multivariate model. RESULTS: The six significant predictors identified in the study were hemoptysis (OR = 25.80, 95% CI 5.69, 116.92), hypotension (blood pressure < 90/60 mmHg) (OR = 17.33, 95% CI 6.89, 43.58), platelet count < 100,000/µL (OR = 8.37, 95% CI 4.65, 15.09), white blood cell count (WBC) > 14,000/µL (OR = 5.12, 95% CI 2.75, 9.51), hematocrit ≤ 30% (OR = 3.49, 95% CI 1.61, 7.57), and jaundice (OR = 3.11, 95% CI 1.71, 5.65). These predictors could correctly predict the severity of leptospirosis infection in 91.31% of the area under the receiver operation curve (AuROC). CONCLUSIONS: The results of this study showed that severe leptospirosis infections have identifiable predictors. The predictors may be used to develop a scoring system for predicting the level of severity.
ABSTRACT
OBJECTIVE: To validate a simple scoring system to classify dengue viral infection severity to patients in different settings. METHODS: The 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. RESULTS: Patients 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. CONCLUSION: The 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.
ABSTRACT
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 / µ L, and platelet ≤50000 / µ L. 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.
