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Building prognostic models for adverse outcomes in a prospective cohort of hospitalised patients with acute leptospirosis infection in the Philippines
Author(s) -
Nathaniel Lee,
Emi Kitashoji,
Nobuo Koizumi,
Talitha Lea Lacuesta,
Maricel R. Ribo,
Efren M. Dimaano,
Nobuo Saito,
Motoi Suzuki,
Koya Ariyoshi,
Christopher M. Parry
Publication year - 2017
Publication title -
transactions of the royal society of tropical medicine and hygiene
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.725
H-Index - 105
eISSN - 1878-3503
pISSN - 0035-9203
DOI - 10.1093/trstmh/try015
Subject(s) - leptospirosis , medicine , prospective cohort study , cohort , intensive care medicine , cohort study , emergency medicine , pediatrics , virology
Leptospirosis is endemic to the Philippines. Ten per cent of cases will develop severe or fatal disease. Predicting progression to severity is difficult. Risk factors have been suggested, but few attempts have been made to create predictive models to guide clinical decisions. We present two models to predict the risk of mortality and progression to severe disease. Data was used from a prospective cohort study conducted between 2011 and 2013 in San Lazaro Hospital, Manila. Predictive factors were identified from a literature review. A strategy utilizing backwards stepwise-elimination and multivariate fractional polynomials identified key predictive factors. A total of 203 patients met the inclusion criteria. The overall mortality rate was 6.84%. Multivariable logistic regression revealed that neutrophil counts [OR 1.38, 95% CI 1.15-1.67] and platelet counts [OR 0.99, 95% CI 0.97-0.99] were predictive for risk of mortality. Multivariable logistic regression revealed that male sex (OR 3.29, 95% CI 1.22-12.57) and number of days between symptom onset and antibiotic use (OR 1.28, 95% CI 1.08-1.53) were predictive for risk of progression to severe disease. The multivariable prognostic models for the risks of mortality and progression to severe disease developed could be useful in guiding clinical management by the early identification of patients at risk of adverse outcomes.

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