Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2017
Development of a scoring system using a statistical model to predict cure status in patients with cutaneous leishmaniasis.
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Khoshhali Mehri, Hosseini Sayed Mohsen, Nilforoushzadeh Mohammad Ali, Jaffary Fariba, Baghbaderani Azadeh Zolfaghari
Аннотация
BACKGROUND: The present study was performed to develop a scoring system for predicting cure status in patients with cutaneous leishmaniasis (CL).
MATERIALS AND METHODS: This study included 199 patients with CL from Skin Diseases and Leishmaniasis Research Center (Isfahan, Iran). Data were collected as longitudinal in each visit of patients. We applied ordinal logistic generalized estimating equation regression to predict score on this correlated data. To evaluate the fitted model, split sample validation method was applied. SPSS software was used for data analysis.
RESULTS: The regression coefficients of the fitted model were used to calculate score for cure status. Based on split-sample validation method, overall correct classification rate was 82%.
CONCLUSION: This study suggested a scoring system predict cure status in CL patients based on clinical characteristics. Using this method, score for a CL patient is easily obtained by physicians or health workers.
MATERIALS AND METHODS: This study included 199 patients with CL from Skin Diseases and Leishmaniasis Research Center (Isfahan, Iran). Data were collected as longitudinal in each visit of patients. We applied ordinal logistic generalized estimating equation regression to predict score on this correlated data. To evaluate the fitted model, split sample validation method was applied. SPSS software was used for data analysis.
RESULTS: The regression coefficients of the fitted model were used to calculate score for cure status. Based on split-sample validation method, overall correct classification rate was 82%.
CONCLUSION: This study suggested a scoring system predict cure status in CL patients based on clinical characteristics. Using this method, score for a CL patient is easily obtained by physicians or health workers.