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World Journal of Surgery

, Volume 40, Issue 11, pp 2591–2597 | Cite as

Perioperative Mortality Rates in Australian Public Hospitals: The Influence of Age, Gender and Urgency

  • David A. Watters
  • Wendy J. Babidge
  • Andreas Kiermeier
  • Glenn A. J. McCulloch
  • Guy J. Maddern
Original Scientific Report

Abstract

Introduction

A decline in surgical deaths has been observed in Australia since the introduction of the Australian and New Zealand Audit of Surgical Mortality (ANZASM). The current study was conducted to determine whether the perioperative mortality rate (POMR) has also declined.

Methods

This study is a retrospective review of the POMR for surgical procedures in Australian public hospitals between July 2009 and June 2013, using data obtained from the Australian Institute of Health and Welfare. Operative procedures contained in the Australian Refined Diagnosis Related Groups were selected and the POMR was modelled using urgency of admission, age and gender as explanatory covariates.

Results

The POMR in Australian public hospitals reduced by 15.4 % over the 4-year period. The emergency admissions POMR dropped from 1.40 to 1.12 %, and the elective admissions POMR from 0.09 to 0.08 %. The binary logistic regression model used to predict patient mortality showed emergency admissions to have a higher POMR than elective, being more evident at older ages. For emergency admissions, the difference in POMR between females and males increased with age, from about 55 years onwards, with females being lower. For elective surgeries, the difference between males and females was of little practical importance across ages.

Conclusions

The reduction in the POMR in Australia confirms the reduction in surgical deaths reported to ANZASM. Continuing to monitor POMR will be important to ensure the safest surgery in Australia. Further investigations into case-mix will allow better risk adjustment and comparison between regions and time-periods, to facilitate continuous quality improvement.

Keywords

Public Hospital Emergency Admission Binary Logistic Regression Model Surgical Mortality Elective Admission 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Claudia Retegan and Gordon Guy are thanked for their assistance in obtaining the data from AIHW.

Grant support

None to disclose.

Compliance with ethical standards

Conflict of Interest

None to disclose.

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Copyright information

© Société Internationale de Chirurgie 2016

Authors and Affiliations

  • David A. Watters
    • 1
    • 2
  • Wendy J. Babidge
    • 1
    • 3
  • Andreas Kiermeier
    • 1
    • 3
  • Glenn A. J. McCulloch
    • 1
  • Guy J. Maddern
    • 1
    • 3
  1. 1.Research, Audit and Academic Surgery DivisionRoyal Australasian College of SurgeonsMelbourneAustralia
  2. 2.Deakin UniversityBarwon HealthGeelongAustralia
  3. 3.Discipline of Surgery, The Queen Elizabeth HospitalUniversity of AdelaideAdelaideAustralia

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