ObjectiveTo analyze the associated risk factors of hidden blood loss in the internal fixation of intertrochanteric fracture. MethodsA retrospective analysis was made on the clinical data of 317 cases of intertrochanteric fractures which were treated by internal fixation between January 1993 and December 2008. There were 154 males and 163 females with an average disease duration of 4.58 days (range, 7 hours to 33 days); the age was (69.86±15.42) years; the average height was 1.64 m (range, 1.50-1.84 m);and the average weight was 62.26 kg (range, 39-85 kg). Of them, intramedullary fixation was used in 203 patients and extramedullary fixation in 114 patients. The operation time was (61.99±18.25) minutes. The red blood cell transfusion was given to 84 patients, and the transfusion amount was 200-1 000 mL. The drainage volume was 0-750 mL (mean, 61.85 mL). Hidden blood loss was calculated through change of hematocrit level before and after operation. The multiple linear regression was performed to analyse the risk factors of hidden blood loss. ResultsThe total blood loss was (918.60±204.44) mL, the hidden blood loss was (797.77±192.58) mL, and intraoperative visible blood loss was (257.32±271.24) mL. Single factor analysis showed hidden blood loss was significantly higher in variables as follows:gender, age, injury cause, fracture type, American anesthesiologists grading, anesthesia mode, hypertension, diabetes, disease duration, operation time, intraoperative transfusion of red blood cells, and fixation type. Multiple linear regression showed age, fracture type, anesthesia mode, and fixation type were significant risk factors. ConclusionThe risk factors of hidden blood loss are advanced age (>60 years), unstable fracture, general anesthesia, and imtramedullary fixation. Especially in elder patients with unstable fracture treated by intramedullary fixation under general anesthesia, hidden blood loss is more significant.
The SAS is considered as internationally-known standard software in the field of data processing and statistics, which is also excellent in conducting meta-analysis; however, it require users to have higher technical expertise due to its complex and difficult program coding. Assessing statistical power calculation of significance tests is one of important steps in meta-analysis. Guy Cafri et al., developed a macro (%metapower) for well implement this calculation in SAS. This macro is specifically designed to implement the statistical power calculation of overall results of meta-analysis, heterogenity, and subgroup analysis, which is easy to operate. This article introduces%metapower based on examples.