Peter Hicks

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

BACKGROUND With improvements in short-term mortality after cardiac surgery, the sensitivity of the standardized mortality ratio (SMR) as a performance-monitoring tool has declined. We assessed acute risk change (ARC) as a new and potentially more sensitive metric to differentiate overall cardiac surgical unit performance. METHODS Retrospective analysis of the Australian and New Zealand Society of Cardiac and Thoracic Surgeons database and Australian and New Zealand Intensive Care Society Adult Patient Database was performed. The 16 656 patients who underwent coronary artery bypass grafting or cardiac valve procedures during a 4 yr period were included. The ARC was generated using the change between preoperative and postoperative probability of death. Outlier institutions were those with higher (outside 99.8% confidence intervals) ARC or SMR on annual and 4 yr funnel plots. Outliers were grouped and compared with non-outliers for baseline characteristics, intraoperative events, and postoperative morbidity. RESULTS No outliers were identified using SMR. Two outliers were identified using ARC. Outliers had higher rates of new renal failure (5.7 vs 4.5%, P=0.017), stroke (1.6 vs 0.9%, P=0.001), reoperation (9 vs 6.0%, P<0.001), and prolonged ventilation (15.3 vs 9.5%, P<0.001). Outliers transfused more blood products (P<0.001) and had longer cardiopulmonary bypass times (P<0.001) and less senior surgeons operating (P<0.001). CONCLUSIONS Acute risk change was able to discriminate between units where SMR could not. Outliers had more adverse events. Acute risk change can be calculated before mortality outcome and identifies outliers with lower patient numbers. This may allow early recognition and investigation of outlier units.

Taking Intensive Care Patients Home to Die

Between 1999 and 2002, 10 intensive care patients were taken home and had their supportive treatment withdrawn. Two other patients could not go home because community medical or nursing support was unavailable. Clinical diagnoses included coma from cardiac arrest, trauma or cerebral infarction, renal failure from sepsis or cardiac surgery, pancreatic cancer, and chronic respiratory failure. Median time from treatment withdrawal until death was 6 hours (range .5 – 30 hours). All patients had either inotropic support or mechanical ventilation at time of transfer. Minor problems occurred with house access, availability of suction, narcotic prescription and death certification. No problems with patient pain or distress were reported. Families appreciated the facilities and comfort of home, being able to provide care themselves, better patient access and social support. Medical and nursing staff valued the sense of completeness, and increased dignity afforded to patients.

Designing and implementing an Australian and New Zealand intensive care data audit study

PURPOSE The aim of this pilot audit study is to develop and test a model to examine existing adult patient database (APD) data quality. DESIGN/METHODOLOGY/APPROACH A database was created to audit 50 records per site to determine accuracy. The audited records were randomly selected from the calendar year 2004 and four sites participated in the pilot audit study. A total of 41 data elements were assessed for data quality--those elements required for APACHE II scoring system. FINDINGS Results showed that the audit was feasible; missing audit data were an unplanned problem; analysis was complicated owing to the way the APACHE calculations are performed and 50 records per site was too time-consuming. ORIGINALITY/VALUE This is the first audit study of intensive care data within the ANZICS APD and demonstrates how to determine data quality in a large database containing individual patient records.