Monitoring surgical and medical outcomes: the Bernoulli cumulative SUM chart. A novel application to assess clinical interventions

• Published in: Postgraduate medical journal Vol. 81; no. 960; pp. 647 - 652
• Main Authors: Leandro, G, Rolando, N, Gallus, G, Rolles, K, Burroughs, A K
• Format: Journal Article
• Language: English
• Published: London The Fellowship of Postgraduate Medicine 01.10.2005; BMJ; Oxford University Press; BMJ Group
• Subjects: Adult; Bernoulli CUSUM; Biological and medical sciences; clinical outcomes; Cohort Studies; Control charts; cumulative sum; CUSUM; Datasets; ELTR; Endoscopy; European Liver Transplant Registry; False alarms; False Positive Reactions; Female; General aspects; Humans; liver transplantation; Liver Transplantation - mortality; Liver Transplantation - standards; Liver transplants; Male; Manufacturing; Medical sciences; Middle Aged; Models, Biological; Mortality; Original; Outcome and Process Assessment (Health Care) - standards; Outcome and Process Assessment (Health Care) - trends; Patients; Quality Assurance, Health Care - standards; Risk Assessment - methods; Risk factors; sequential probability ratio test; SPRT; Statistical process control; Success; Surveillance; Treatment Outcome; Medicine; Prognosis; Treatment; Surveillance; Surgery; Evolution; Medical application; Monitoring
• ISSN: 0032-5473; 1469-0756
• DOI: 10.1136/pgmj.2004.030619

Background: Monitoring clinical interventions is an increasing requirement in current clinical practice. The standard CUSUM (cumulative sum) charts are used for this purpose. However, they are difficult to use in terms of identifying the point at which outcomes begin to be outside recommended limits. Objective: To assess the Bernoulli CUSUM chart that permits not only a 100% inspection rate, but also the setting of average expected outcomes, maximum deviations from these, and false positive rates for the alarm signal to trigger. Methods: As a working example this study used 674 consecutive first liver transplant recipients. The expected one year mortality set at 24% from the European Liver Transplant Registry average. A standard CUSUM was compared with Bernoulli CUSUM: the control value mortality was therefore 24%, maximum accepted mortality 30%, and average number of observations to signal was 500—that is, likelihood of false positive alarm was 1:500. Results: The standard CUSUM showed an initial descending curve (nadir at patient 215) then progressively ascended indicating better performance. The Bernoulli CUSUM gave three alarm signals initially, with easily recognised breaks in the curve. There were no alarms signals after patient 143 indicating satisfactory performance within the criteria set. Conclusions: The Bernoulli CUSUM is more easily interpretable graphically and is more suitable for monitoring outcomes than the standard CUSUM chart. It only requires three parameters to be set to monitor any clinical intervention: the average expected outcome, the maximum deviation from this, and the rate of false positive alarm triggers.