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Volume 60, Issue 3, Pages 218-223 (March 2010)


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

Paulo C.F. MarottoaCorresponding Author Informationemail address, Albert I. Kobc, Cristiane Murta-Nascimentode, Antonio C. Segurof, Rogerio R. Pradog, Marcia C. Barbosaa, Sergio A. Cletoa, José Eluf-Netog

Accepted 7 December 2009. published online 18 January 2010.

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.

a Intensive Care Unit, Emílio Ribas Institute of Infectology, Sao Paulo, SP, Brazil

b Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil

c Division of Infectious Diseases, Department of Medicine, Weill Cornell Medical College, New York, NY, USA

d Evaluation and Clinical Epidemiology Unit, Hospital del Mar-IMAS, Barcelona, Spain

e CIBER in Epidemiology and Public Health (CIBERESP), Barcelona, Spain

f Department of Nephrology, Laboratory of Basic Science (LIM 12), University of Sao Paulo, Sao Paulo, SP, Brazil

g Department of Preventive Medicine, University of Sao Paulo, Sao Paulo, SP, Brazil

Corresponding Author InformationCorresponding author at: Unidade de Terapia Intensiva, Institudo de Infectologia Emílio Ribas, Av. Dr. Arnaldo 165, Divisao Cientifica, Sao Paulo, SP 01246-903; Brazil. Tel.: +55 11 3896 1207; fax: +55 11 3088 3602.

PII: S0163-4453(09)00389-2

doi:10.1016/j.jinf.2009.12.005


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