Clinical Prognostic Rules for Severe Acute Respiratory Syndrome in Low- and High-Resource Settings

Abstract

The model performs well and could be useful in assessing prognosis for patients who are infected with re-emergent SARS.

Effectiveness of Ribavirin and Corticosteroids for Severe Acute Respiratory Syndrome

“…We analyzed an integrated database containing clinical and epidemiologic details on all 1755 patients in Hong Kong reported to have "probable severe acute respiratory syndrome" according to the World Health Organization definition, 19 derived from the Hong Kong Hospital Authority Electronic Severe Acute Respiratory Syndrome system and the Hong Kong Department of Health's Master list. 19,20 For the Toronto data, the Canadian Severe Acute Respiratory Syndrome Research Network conducted a retrospective cohort study of all probable severe acute respiratory syndrome cases among adults (age Ն 16 years) admitted to a hospital during the outbreak. 20,21 Medical charts were reviewed by trained research staff, and clinical, radiographic, and laboratory data were double entered into a database.…”

“…19,20 For the Toronto data, the Canadian Severe Acute Respiratory Syndrome Research Network conducted a retrospective cohort study of all probable severe acute respiratory syndrome cases among adults (age Ն 16 years) admitted to a hospital during the outbreak. 20,21 Medical charts were reviewed by trained research staff, and clinical, radiographic, and laboratory data were double entered into a database.…”

“…the patients in Toronto, 20 we used multiple imputation to make best use of all available data in the propensity score model. [27][28][29][30] Ten datasets were constructed on the basis of all clinical, demographic, and outcome variables with imputed values based on a generalized additive model and predictive mean matching (function aregImpute in R library Hmisc).…”

“…For the primary analysis that compares all 4 treatment choices within the first 2 days after admission, we applied a multivariable multinomial regression model to estimate the propensity scores for each of the 4 treatment alternatives, including variables that were likely to be influential in early treatment decisions. The latter included the following known prognostic factors: age, gender, health care worker, presence of comorbid condition, hospital, delay in admission, onset date, lactate dehydrogenase level, chest radiograph, oxygen saturation on room air, neutrophil count, platelets, and lymphocyte count, 20 and 3 other factors that may have been potentially important in treatment assignment, including date of onset, onset-to-admission delay, and hospital. Because some prognostic factors were only available for a subset of up to approximately 30% of the patients in Hong Kong and 60% of the patients in Toronto, 20 we used multiple imputation to facilitate inclusion of these factors in the propensity score model.…”

“…The latter included the following known prognostic factors: age, gender, health care worker, presence of comorbid condition, hospital, delay in admission, onset date, lactate dehydrogenase level, chest radiograph, oxygen saturation on room air, neutrophil count, platelets, and lymphocyte count, 20 and 3 other factors that may have been potentially important in treatment assignment, including date of onset, onset-to-admission delay, and hospital. Because some prognostic factors were only available for a subset of up to approximately 30% of the patients in Hong Kong and 60% of the patients in Toronto, 20 we used multiple imputation to facilitate inclusion of these factors in the propensity score model. 27 The resulting propensity scores were then used to weigh the case fatality ratios in each treatment group, allowing estimation of the mean causal effect, 26 that is, for each treatment group we estimated the overall case fatality ratios had all patients been assigned to that particular treatment.…”