Preoperative predictors for mortality following hip fracture surgery: A systematic review and meta-analysis

Abstract Background Hip fractures are always associated with a high postoperative mortality, the preoperative predictors for mortality have neither been well identified or summarised. This systematic review and meta-analysis was performed to identify the preoperative non-interventional predictors for mortality in hip fracture patients, especially focused on 1 year mortality. Methods Non-interventional studies were searched in Pubmed, Embase, Cochrane central database (all to February 26th, 2011). Only prospective studies and retrospective studies with prospective collected data were included. Qualities of included studies were assessed by a standardised scale previous reported for observational studies. The effects of individual studies were combined with the study quality score using a previous reported model of best-evidence synthesis. The hazard ratios of strong evidence predictors were combined only by high quality studies. Results 75 included studies with 94 publications involving 64,316 patients were included and the available observations was a heterogeneous group. The overall inpatient or 1 month mortality was 13.3%, 3–6 months was 15.8%, 1 year 24.5% and 2 years 34.5%. There were strong evidence for 12 predictors, including advanced age, male gender, nursing home or facility residence, poor preoperative walking capacity, poor activities of daily living, higher ASA grading, poor mental state, multiple comorbidities, dementia or cognitive impairment, diabetes, cancer and cardiac disease. We also identified 7 moderate evidence and 12 limited evidence mortality predictors, and only the race was identified as the conflicting evidence predictor. Conclusion Whilst there is no conclusive evidence of the preoperative predictors for mortality following hip fractures, special attention should be paid to the above 12 strong evidence predictors. Future researches were still needed to evaluate the effects of these predictors.