Association Between Wait Time and 30-Day Mortality in Adults Undergoing Hip Fracture Surgery

Importance Although wait times for hip fracture surgery have been linked to mortality and are being used as quality-of-care indicators worldwide, controversy exists about the duration of the wait that leads to complications. Objective To use population-based wait-time data to identify the optimal time window in which to conduct hip fracture surgery before the risk of complications increases. Design, Setting, and Participants Population-based, retrospective cohort study of adults undergoing hip fracture surgery between April 1, 2009, and March 31, 2014, at 72 hospitals in Ontario, Canada. Risk-adjusted restricted cubic splines modeled the probability of each complication according to wait time. The inflection point (in hours) when complications began to increase was used to define early and delayed surgery. To evaluate the robustness of this definition, outcomes among propensity-score matched early and delayed surgical patients were compared using percent absolute risk differences (RDs, with 95% CIs). Exposure Time elapsed from hospital arrival to surgery (in hours). Main Outcomes and Measures Mortality within 30 days. Secondary outcomes included a composite of mortality or other medical complications (myocardial infarction, deep vein thrombosis, pulmonary embolism, and pneumonia). Results Among 42 230 patients with hip fracture (mean [SD] age, 80.1 years [10.7], 70.5% women) who met study entry criteria, overall mortality at 30 days was 7.0%. The risk of complications increased when wait times were greater than 24 hours, irrespective of the complication considered. Compared with 13 731 propensity-score matched patients who received surgery earlier, 13 731 patients who received surgery after 24 hours had a significantly higher risk of 30-day mortality (898 [6.5%] vs 790 [5.8%]; % absolute RD, 0.79; 95% CI, 0.23-1.35) and the composite outcome (1680 [12.2%]) vs 1383 [10.1%]; % absolute RD, 2.16; 95% CI, 1.43-2.89). Conclusions and Relevance Among adults undergoing hip fracture surgery, increased wait time was associated with a greater risk of 30-day mortality and other complications. A wait time of 24 hours may represent a threshold defining higher risk.

[1]  M. Fu,et al.  Surgery for a fracture of the hip within 24 hours of admission is independently associated with reduced short‐term post‐operative complications , 2017, The bone & joint journal.

[2]  Alan J. Forster,et al.  Association of delay of urgent or emergency surgery with mortality and use of health care resources: a propensity score–matched observational cohort study , 2017, Canadian Medical Association Journal.

[3]  H. Kreder,et al.  Outcomes of After-Hours Hip Fracture Surgery , 2017, The Journal of bone and joint surgery. American volume.

[4]  M. Whitehouse,et al.  The association between the day of the week of milestones in the care pathway of patients with hip fracture and 30-day mortality: findings from a prospective national registry – The National Hip Fracture Database of England and Wales , 2017, BMC Medicine.

[5]  I. Harris,et al.  Patient preferences for emergency or planned hip fracture surgery: a cross-sectional study , 2016, Journal of Orthopaedic Surgery and Research.

[6]  L. Beaupre,et al.  In-hospital mortality after hip fracture by treatment setting , 2016, Canadian Medical Association Journal.

[7]  P. Guy,et al.  Patient and system factors of mortality after hip fracture: a scoping review , 2016, BMC Musculoskeletal Disorders.

[8]  S. Jaglal,et al.  Constructing an episode of care from acute hospitalization records for studying effects of timing of hip fracture surgery , 2016, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[9]  P. Schilling,et al.  Development and Validation of Perioperative Risk-Adjustment Models for Hip Fracture Repair, Total Hip Arthroplasty, and Total Knee Arthroplasty. , 2016, The Journal of bone and joint surgery. American volume.

[10]  K. Gromov,et al.  Time to Surgery Is Associated with Thirty-Day and Ninety-Day Mortality After Proximal Femoral Fracture: A Retrospective Observational Study on Prospectively Collected Data from the Danish Fracture Database Collaborators. , 2015, The Journal of bone and joint surgery. American volume.

[11]  J. Zuckerman,et al.  Delay in Hip Fracture Surgery: An Analysis of Patient-Specific and Hospital-Specific Risk Factors , 2015, Journal of orthopaedic trauma.

[12]  P. Austin,et al.  Relation between surgeon volume and risk of complications after total hip arthroplasty: propensity score matched cohort study , 2014, BMJ : British Medical Journal.

[13]  G. Guyatt,et al.  Accelerated care versus standard care among patients with hip fracture: the HIP ATTACK pilot trial , 2013, Canadian Medical Association Journal.

[14]  R. Middleton,et al.  Early and ultra-early surgery in hip fracture patients improves survival. , 2013, Injury.

[15]  V. Prasad,et al.  Prespecified falsification end points: can they validate true observational associations? , 2013, JAMA.

[16]  T. Gomes,et al.  A Population‐Based Assessment of the Drug Interaction Between Levothyroxine and Warfarin , 2012, Clinical pharmacology and therapeutics.

[17]  A. Liberati,et al.  Timing Matters in Hip Fracture Surgery: Patients Operated within 48 Hours Have Better Outcomes. A Meta-Analysis and Meta-Regression of over 190,000 Patients , 2012, PloS one.

[18]  R. Weiss,et al.  Dichotomizing Continuous Variables in Statistical Analysis , 2012, Medical decision making : an international journal of the Society for Medical Decision Making.

[19]  D. Lubarsky,et al.  An economic evaluation of a systems-based strategy to expedite surgical treatment of hip fractures. , 2011, The Journal of bone and joint surgery. American volume.

[20]  P. Austin Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples , 2011, Statistics in medicine.

[21]  G. Guyatt,et al.  Effect of early surgery after hip fracture on mortality and complications: systematic review and meta-analysis , 2010, Canadian Medical Association Journal.

[22]  J. Frood,et al.  Improving measures of hip fracture wait times: a focus on ontario. , 2010, Healthcare quarterly.

[23]  W. Leslie,et al.  Population-based Canadian hip fracture rates with international comparisons , 2010, Osteoporosis International.

[24]  P. Austin,et al.  Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies , 2010, Pharmaceutical statistics.

[25]  P. Austin Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples , 2009, Statistics in medicine.

[26]  Ruth Ann Marrie,et al.  Quantile regression and restricted cubic splines are useful for exploring relationships between continuous variables. , 2009, Journal of clinical epidemiology.

[27]  T. To,et al.  Identifying Individuals with Physcian Diagnosed COPD in Health Administrative Databases , 2009, COPD.

[28]  Karen Tu,et al.  Accuracy of administrative databases in identifying patients with hypertension , 2007, Open medicine : a peer-reviewed, independent, open-access journal.

[29]  L. Bisanti,et al.  The influence of socioeconomic status on utilization and outcomes of elective total hip replacement: a multicity population-based longitudinal study. , 2007, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[30]  J. Avorn,et al.  Variable selection for propensity score models. , 2006, American journal of epidemiology.

[31]  M. Mamdani,et al.  Lipid-lowering therapy with statins in high-risk elderly patients: the treatment-risk paradox. , 2004, JAMA.

[32]  Peter C Austin,et al.  A multicenter study of the coding accuracy of hospital discharge administrative data for patients admitted to cardiac care units in Ontario. , 2002, American heart journal.

[33]  Janet E Hux,et al.  Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm. , 2002, Diabetes care.

[34]  B. Stricker,et al.  Confounding by indication: an example of variation in the use of epidemiologic terminology. , 1999, American journal of epidemiology.

[35]  R. Deyo,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.

[36]  N. Herrmann,et al.  Identification of Physician-Diagnosed Alzheimer's Disease and Related Dementias in Population-Based Administrative Data: A Validation Study Using Family Physicians' Electronic Medical Records. , 2016, Journal of Alzheimer's disease : JAD.

[37]  M. Parker,et al.  Early surgery for patients with a fracture of the hip decreases 30-day mortality. , 2015, The bone & joint journal.

[38]  D. Juurlink,et al.  Enhancing the effectiveness of health care for Ontarians through research Canadian Institute for Health Information Discharge Abstract Database : A Validation Study , 2006 .