New equations for predicting postoperative risk in patients with hip fracture

Abstract

Predicting the postoperative course of patients with hip fractures would be helpful for surgical planning and risk management. We therefore established equations to predict the morbidity and mortality rates in candidates for hip fracture surgery using the Estimation of Physiologic Ability and Surgical Stress (E-PASS) risk-scoring system. First we evaluated the correlation between the E-PASS scores and postoperative morbidity and mortality rates in all 722 patients surgically treated for hip fractures during the study period (Group A). Next we established equations to predict morbidity and mortality rates. We then applied these equations to all 633 patients with hip fractures treated at seven other hospitals (Group B) and compared the predicted and actual morbidity and mortality rates to assess the predictive ability of the E-PASS and Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) systems. The ratio of actual to predicted morbidity and mortality rates was closer to 1.0 with the E-PASS than the POSSUM system. Our data suggest the E-PASS scoring system is useful for defining postoperative risk and its underlying algorithm accurately predicts morbidity and mortality rates in patients with hip fractures before surgery. This information then can be used to manage their condition and potentially improve treatment outcomes.

Level of evidence: Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.

Fig. 1A–B

(A) A bar chart shows the relationship between the PRS and postoperative morbidity and mortality rates in 722 patients in Group A. The PRS correlated with the morbidity (ρ = 0.17, p < 0.0001) and mortality rates (ρ = 0.16, p < 0.0001). (B) A bar chart shows the relationship between the CRS and postoperative morbidity and mortality rates. The CRS correlated with the morbidity (ρ = 0.17, p < 0.0001) and mortality rates (ρ = 0.18, p < 0.0001).

Fig. 2

Postoperative morbidity and mortality curves were defined by the PRS. The relationship between the PRS and mortality rates was analyzed by polynomial regression analysis. Each coordinate indicates the average PRS and morbidity rate at each PRS range. The equations used to obtain the estimated rates for morbidity and mortality were Y = −16.15(PRS)² + 48.189(PRS) − 9.535 (R = 0.991, n = 6, p = 0.002) and Y = 13.362(PRS)² − 11.277(PRS) + 1.969 (R = 0.988, n = 6, p = 0.004), respectively.