Mortality effects of timing alternatives for hip fracture surgery

Abstract

Background: The appropriate timing of hip fracture surgery remains a matter of debate. We sought to estimate the effect of changes in timing policy and the proportion of deaths attributable to surgical delay.

Methods: We obtained discharge abstracts from the Canadian Institute for Health Information for hip fracture surgery in Canada (excluding Quebec) between 2004 and 2012. We estimated the expected population-average risks of inpatient death within 30 days if patients were surgically treated on day of admission, inpatient day 2, day 3 or after day 3. We weighted observations with the inverse propensity score of surgical timing according to confounders selected from a causal diagram.

Results: Of 139 119 medically stable patients with hip fracture who were aged 65 years or older, 32 120 (23.1%) underwent surgery on admission day, 60 505 (43.5%) on inpatient day 2, 29 236 (21.0%) on day 3 and 17 258 (12.4%) after day 3. Cumulative 30-day in-hospital mortality was 4.9% among patients who were surgically treated on admission day, increasing to 6.9% for surgery done after day 3. We projected an additional 10.9 (95% confidence interval [CI] 6.8 to 15.1) deaths per 1000 surgeries if all surgeries were done after inpatient day 3 instead of admission day. The attributable proportion of deaths for delays beyond inpatient day 2 was 16.5% (95% CI 12.0% to 21.0%).

Interpretation: Surgery on admission day or the following day was estimated to reduce postoperative mortality among medically stable patients with hip fracture. Hospitals should expedite operating room access for patients whose surgery has already been delayed for nonmedical reasons.

Figure 1:

Dependencies among factors involved in producing the association between timing of surgery and in-hospital death after hip fracture. Orange nodes represent the following factors that influence both timing of surgery and occurrence of death through chains of dependencies (orange arrows): treatment era, hospital type, procedure type, age at admission, prefracture health status, and surgical readiness. Conditioning on these factors was sufficient to block all influences that might have produced the putative association between time to surgery and occurrence of death (green dashed arrow). The dependency graph was adapted from Sheehan and colleagues to reflect recent publications, adding new nodes (patient preference and prefracture health status44) and the following dependencies: between hospital type and socioeconomic status (SES), between prefracture health and SES, between resource availability and patient preference, and between complications and surgeon skills. Note: LOS = length of stay.

Figure 2:

Flow chart for the study population selection. Note: SCU = special care unit.

Figure 3:

Cumulative incidence of in-hospital death, by observed timing of surgery.

Figure 4:

Application of inverse propensity scores of surgical timing to the number observations in various strata. Shown are bars representing the weighted number of surgeries across the 64 multifactor strata. Within each bar, dots show the weighted number of deaths for each timing of surgery. Data are shown on a logarithmic scale to accommodate the range of values. The right panel shows the number of deaths in all strata combined, representing postoperative in-hospital mortality that would be expected if all patients in the study were to undergo surgery on the day of admission, on inpatient day 2, on inpatient day 3, or after inpatient day 3.