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

Summary 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 ≥14 years, admitted with complications of the severe form of leptospirosis...

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Published inThe Journal of infection Vol. 60; no. 3; pp. 218 - 223
Main Authors Marotto, Paulo C.F, Ko, Albert I, Murta-Nascimento, Cristiane, Seguro, Antonio C, Prado, Rogerio R, Barbosa, Marcia C, Cleto, Sergio A, Eluf-Neto, José
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier Ltd 01.03.2010
Elsevier
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Summary:Summary 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 ≥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 (μmol/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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ISSN:0163-4453
1532-2742
DOI:10.1016/j.jinf.2009.12.005