Use of cumulative mortality data in patients with acute myocardial infarction for early detection of variation in clinical practice: observational study

Abstract

Objectives Use of cumulative mortality adjusted for case mix in patients with acute myocardial infarction for early detection of variation in clinical practice. Design Observational study. Setting 20 hospitals across the former Yorkshire region. Participants All 2153 consecutive patients with confirmed acute myocardial infarction identified during three months. Main outcome measures Variable life-adjusted displays showing cumulative differences between observed and expected mortality of patients; expected mortality calculated from risk model based on admission characteristics of age, heart rate, and systolic blood pressure. Results The performance of two individual hospitals over three months was examined as an example. One, the smallest district hospital in the region, had a series of 30 consecutive patients but had five more deaths than predicted. The variable life-adjusted display showed minimal variation from that predicted for the first 15 patients followed by a run of unexpectedly high mortality. The second example was the main tertiary referral centre for the region, which admitted 188 consecutive patients. The display showed a period of apparently poor performance followed by substantial improvement, where the plot rose steadily from a cumulative net lives saved of − 4 to 7. These variations in patient outcome are unlikely to have been revealed during conventional audit practice. Conclusions Variable life-adjusted display has been integrated into surgical care as a graphical display of risk-adjusted survival for individual surgeons or centres. In combination with a simple risk model, it may have a role in monitoring performance and outcome in patients with acute myocardial infarction.

On-line variable live-adjusted displays with internal and external risk-adjusted mortalities. A valuable method for benchmarking and early detection of unfavourable trends in cardiac surgery

“…The run length until the CUSUM signifies the deterioration in performance and the discriminative ability of the test depends on the choice of the control limits, which can be varied according to the medical context and the heterogeneity of the case mix. Other investigators do without prediction intervals [6,8]. They stress that the interpretation of VLADs requires the consideration of other qualitative trends and obvious changes in patient management is more important.…”

“…The method was refined by incorporating success and failure as well as previously estimated risk using the Parsonnet Score for each case [5,6] and further by adding prediction intervals to indicate increasing departures from expected mortalities [7]. There is one observational study that used variable live-adjusted display (VLAD) successfully to compare the performance of two individual hospitals in treating patients with myocardial infarction [8]. Beyond this pioneering work, there is only little experience with VLADs in medicine.…”

A Method for Detecting Runs of Good and Bad Clinical Outcomes on Variable Life-Adjusted Display (VLAD) Charts

“…In the United Kingdom it has gained particular prominence since events at Bristol Royal Infirmary, where a suspicion of poor performance prompted the UK Department of Health to instigate a major Public Inquiry [17]. Several graphical tools have been introduced to assist with such monitoring, and have been applied to a variety of disciplines within the hospital setting including cardiac surgery [3,10,16], intensive care [2], hospital acquired infections [11] and treatment of acute myocardial infarction [9]. More recently, another major Inquiry into mortality of patients under the care of the General Practitioner, Harold Shipman, has led to the suggestion of applying similar methods to Primary Care [1].…”

Using routine comparative data to assess the quality of health care: understanding and avoiding common pitfalls

“…[51][52][53] The success of each of these in turn rests upon high quality, locally derived and used, detailed clinical data sets. 47 Identifying the potential pitfalls, as we have done, highlights where most attention must be paid by quality researchers to ensure that the comparisons produced are robust and reliable indicators of real variations in quality practice.…”