Acute risk change (ARC) identifies outlier institutions in perioperative cardiac surgical care when the standardized mortality ratio cannot

Coulson, Tim G; Bailey, Michael; Reid, Christopher M.; Tran, Lavinia; Mullany, Daniel; Parker, Jane; Hicks, Peter; Pilcher, David

doi:10.1093/bja/aew180

Cited by 13 publications

(7 citation statements)

References 31 publications

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“…Although physiological data are important for predicting mortality with certain diagnoses, these effects are likely not consistent across all diagnostic groups. The findings of the present study may suggest a possible influence of physiological data on mortality in cardiac surgery patients and is consistent with previous studies 13,[37][38][39][40] conducted elsewhere. Moreover, the results also suggest the need for further research to assess performance of ANZROD 0 in sepsis and trauma patients before it can be applied to these specific patient subgroups.…”

Section: Discussionsupporting

confidence: 92%

Assessing Contemporary Intensive Care Unit Outcome: Development and Validation of the Australian and New Zealand Risk of Death Admission Model

Paul¹,

Bailey²,

Kasza

et al. 2017

Anaesth Intensive Care

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The Australian and New Zealand Risk of Death (ANZROD) model currently used for benchmarking intensive care units (ICUs) in Australia and New Zealand utilises physiological data collected up to 24 hours after ICU admission to estimate the risk of hospital mortality. This study aimed to develop the Australian and New Zealand Risk of Death admission (ANZROD 0 ) model to predict hospital mortality using data available at presentation to ICU and compare its performance with the ANZROD in Australian and New Zealand hospitals. Data pertaining to all ICU admissions between 1 January 2006 and 31 December 2015 were extracted from the Australian and New Zealand Intensive Care Society Adult Patient Database. Hospital mortality was modelled using logistic regression with development (two-thirds) and validation (one-third) datasets. All predictor variables available at ICU admission were considered for inclusion in the ANZROD 0 model. Model performance was assessed using Brier score, standardised mortality ratio and area under the receiver operating characteristic curve. The relationship between ANZROD 0 and ANZROD predicted risk of death was assessed using linear regression. After standard exclusions, 1,097,416 patients were available for model development and validation. Observed mortality was 9.5%. Model performance measures (Brier score, standardised mortality ratio and area under the receiver operating characteristic curve) for the ANZROD 0 and ANZROD in the validation dataset were 0.069, 1.0 and 0.853; 0.057, 1.0 and 0.909, respectively. There was a strong positive correlation between the mortality predictions with an overall R 2 of 0.73. We found that the ANZROD 0 model had acceptable calibration and discrimination. Predictions from the models had high correlations in all major diagnostic groups, with the exception of cardiac surgery and possibly trauma and sepsis.

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Section: Discussionsupporting

confidence: 92%

Assessing Contemporary Intensive Care Unit Outcome: Development and Validation of the Australian and New Zealand Risk of Death Admission Model

Paul¹,

Bailey²,

Kasza

et al. 2017

Anaesth Intensive Care

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“…A rising or falling ARC therefore may represent a change in patient status during peri-operative care and may, thus, be associated with 'negative' or 'positive' occurrences during the peri-operative period, respectively. In previous studies, ARC was shown to be associated with intra-operative events and morbidity at an individual and unit level [12,13]. Although further validation is required, it is plausible that ARC may be useful to identify peri-operative team misadventure in the surgical period.…”

Section: Author Manuscriptmentioning

confidence: 96%

The association between peri‐operative acute risk change (ARC) and long‐term survival after cardiac surgery

et al. 2017

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Acute risk change has been described as the difference in calculated mortality risk between the pre-operative and postoperative periods of cardiac surgery. We aimed to assess whether this was associated with long-term survival after cardiac surgery. We retrospectively analysed 22,570 cardiac surgical patients, with minimum and maximum follow-up of 1.0 and 6.7 years. Acute risk change was calculated as the arithmetic difference between pre- and postoperative mortality risk. 'Rising risk' represented an increase in risk from pre- to postoperative phase. The primary outcome was one-year mortality. Secondary outcomes included mortality at 3 and 5 years and time to death. Univariable and multivariable analyses were undertaken to examine the relationship between acute risk change and outcomes. Rising risk was associated with higher mortality (5.6% vs. 3.5%, p < 0.001). After adjusting for baseline risk, rising risk was independently associated with increased 1-year mortality (OR 2.6, 95%CI 2.2-3.0, p < 0.001). The association of rising risk with long-term survival was greatest in patients with highest baseline risk. Cox regression confirmed rising risk was associated with shorter time to death (HR 1.86, 1.68-2.05, p < 0.001). Acute risk change may represent peri-operative clinical events in combination with unmeasured patient risk and noise. Measuring risk change could potentially identify patterns of events that may be amenable to investigation and intervention. Further work with case review, and risk scoring with shared variables, may identify mechanisms, including the interaction between miscalibration of risk and true differences in peri-operative care.

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“…The importance of this has been recognised and benchmarking systems have been explored. [17][18][19] Risk-adjusted mortality in adult cardiac surgery in the UK has reduced by 25% in the last 10 years. 6,7,10,20 The contemporary in-hospital and operative mortality are similarly low in the United States, ranging from 1.0 to 5.1%.…”

Section: Possible Explanations and Implications For Clinicians And Policymakersmentioning

confidence: 99%

Variation between hospitals in outcomes following cardiac surgery in the UK

Soppa

Theodoropoulos

Bilkhu

et al. 2019

annals

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Introduction We examine the influence of variations in provision of cardiac surgery in the UK at hospital level on patient outcomes and also to assess whether there is an inequality of access and delivery of healthcare. Cardiothoracic surgery has pioneered the reporting of surgeon-specific outcomes, which other specialties have followed. We set out to identify factors other than the individual surgeon, which can affect outcomes and enable other surgical specialties to adopt a similar model. Materials and methods A retrospective analysis of prospectively collected data of patient and hospital level factors between 2013 and 2016 from 16 cardiac surgical units in the UK were analysed through the Society for Cardiothoracic Surgery of Great Britain and Ireland and the Royal College of Surgeons Research Collaborative. Patient demographic data, risks factors, postoperative complications and in-hospital mortality, as well as hospital-level factors such as number of beds and operating theatres, were collected. Correlation between outcome measures was assessed using Pearson’s correlation coefficient. Associations between hospital-level factors and outcomes were assessed using univariable and multivariable regression models. Results Of 50,871 patients (60.5% of UK caseload), 25% were older than 75 years and 29% were female. There was considerable variation between units in patient comorbidities, bed distribution and staffing. All hospitals had dedicated cardiothoracic intensive care beds and consultants. Median survival was 97.9% (range 96.3–98.6%). Postoperative complications included re-sternotomy for bleeding (median 4.8%; range 3.5–6.9%) and mediastinitis (0.4%; 0.1–1.0%), transient ischaemic attack/cerebrovascular accident (1.7%; range 0.3–3.0%), haemofiltration (3.7%; range 0.8–6.8%), intra-aortic balloon pump use (3.3%; range 0.4–7.4%), tracheostomy (1.6%; range 1.3–2.6%) and laparotomy (0.3%; range 0.2–0.6%). There was variation in outcomes between hospitals. Univariable analysis showed a small number of positive associations between hospital-level factors and outcomes but none remained significant in multivariable models. Conclusions Variations among hospital level factors exists in both delivery of, and outcomes, following cardiac surgery in the UK. However, there was no clear association between these factors and patient outcomes. This negative finding could be explained by differences in outcome definition, differences in risk factors between centres that are not captured by standard risk stratification scores or individual surgeon/team performance.

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Acute risk change (ARC) identifies outlier institutions in perioperative cardiac surgical care when the standardized mortality ratio cannot

Cited by 13 publications

References 31 publications

Assessing Contemporary Intensive Care Unit Outcome: Development and Validation of the Australian and New Zealand Risk of Death Admission Model

Assessing Contemporary Intensive Care Unit Outcome: Development and Validation of the Australian and New Zealand Risk of Death Admission Model

The association between peri‐operative acute risk change (ARC) and long‐term survival after cardiac surgery

Variation between hospitals in outcomes following cardiac surgery in the UK

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