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. 2010 Mar;60(3):218-23.
doi: 10.1016/j.jinf.2009.12.005. Epub 2009 Dec 21.

Early identification of leptospirosis-associated pulmonary hemorrhage syndrome by use of a validated prediction model

Paulo C F Marotto  1 , Albert I Ko, Cristiane Murta-Nascimento, Antonio C Seguro, Rogerio R Prado, Marcia C Barbosa, Sergio A Cleto, José Eluf-Neto
Affiliations

Early identification of leptospirosis-associated pulmonary hemorrhage syndrome by use of a validated prediction model

Paulo C F Marotto et al. J Infect. 2010 Mar.

Abstract

Objective: To identify prediction factors for the development of leptospirosis-associated pulmonary hemorrhage syndrome (LPHS).

Methods: We conducted a prospective cohort study. The study comprised of 203 patients, aged > or =14 years, admitted with complications of the severe form of leptospirosis at the Emílio Ribas Institute of Infectology (Sao Paulo, Brazil) between 1998 and 2004. Laboratory and demographic data were obtained and the severity of illness score and involvement of the lungs and others organs were determined. Logistic regression was performed to identify independent predictors of LPHS. A prospective validation cohort of 97 subjects with severe form of leptospirosis admitted at the same hospital between 2004 and 2006 was used to independently evaluate the predictive value of the model.

Results: The overall mortality rate was 7.9%. Multivariate logistic regression revealed that five factors were independently associated with the development of LPHS: serum potassium (mmol/L) (OR = 2.6; 95% CI = 1.1-5.9); serum creatinine (micromol/L) (OR = 1.2; 95% CI = 1.1-1.4); respiratory rate (breaths/min) (OR = 1.1; 95% CI = 1.1-1.2); presenting shock (OR = 69.9; 95% CI = 20.1-236.4), and Glasgow Coma Scale Score (GCS) < 15 (OR = 7.7; 95% CI = 1.3-23.0). We used these findings to calculate the risk of LPHS by the use of a spreadsheet. In the validation cohort, the equation classified correctly 92% of patients (Kappa statistic = 0.80).

Conclusions: We developed and validated a multivariate model for predicting LPHS. This tool should prove useful in identifying LPHS patients, allowing earlier management and thereby reducing mortality.

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Figures

Fig. 1
Fig. 1
ROC curve assessing the performance of the logistic regression model in predicting pulmonary hemorrhage
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