Quality control in medicine is generating more and more interest. Industrial concepts of quality control have been refined and transformed to be useful in healthcare monitoring. Whereas medical practitioners first reaction to this new concept of quality control was negative 'we're treating patients, we're not a part of an industrial process', some dramatic cases of inferior medical performance urged the need to adequately monitor healthcare outcomes. To date, several methods have been described, and more and more reports deal with the subject. Most of us, however, are overwhelmed by the new and different tools in use such as Shewhart control charts, cumulative sum charts and funnel plots. This paper will review the methodology of statistical process control and its application in medical practice.

No abstract

Improved quality of life is a major goal for cardiac surgery. This review concerns 29 articles published between January 2004 and December 2010. Only nine studies present preoperative and postoperative registered quality of life data. These studies have a short follow-up and a limited number of patients included. Most other studies starts at a certain point in the follow-up and compare different patient groups or techniques, but do not evaluate postoperative vs. preoperative quality of life. In an era of evidence-based medicine, there is a lack of major and well-organized clinical studies dealing with quality of life after cardiac surgery. Based on this review, five requirements for 'good' studies on this subject can be formulated: information about the total number of patients that could be included; the number of patients actually included; information about preoperative quality of life; information on what was done about patients with missing data; and at least minimum information about demographics, co-morbidity and the cardiac risk of patients who were not included or who dropped out. These points seem to us to be essential for validation of the results presented.

ObjectiveWe hypothesised that frailty assessment is of additional value to predict delirium and mortality after transcatheter aortic valve implantation (TAVI).MethodsObservational study in 89 consecutive patients who underwent TAVI. Inclusion from November 2012 to February 2014, follow-up until April 2014. Measurement of the association of variables from frailty assessment and cardiological assessment with delirium and mortality after TAVI, respectively.ResultsIncidence of delirium after TAVI: 25/89 (28%). Variables from frailty assessment protectively associated with delirium were: Mini Mental State Examination, (OR 0.79; 95% CI 0.65 to 0.96; p=0.02), Instrumental Activities of Daily Living (OR 0.79; 95% CI 0.63 to 0.99; p=0.04) and gait speed (OR 0.05; 95% CI 0.01 to 0.50; p=0.01). Timed Up and Go was predictively associated with delirium (OR 1.14; 95% CI 1.03 to 1.26; p=0.01). From cardiological assessment, pulmonary hypertension was protectively associated with delirium (OR 0.34; 95% CI 0.12 to 0.98; p=0.05). Multivariate logistic analysis: Nagelkerke R2=0.359, Mini Mental State Examination was independently associated with delirium. Incidence of mortality: 11/89 (12%). Variables predictively associated with mortality were: the summary score Frailty Index (HR 1.66, 95% CI 1.06 to 2.60; p=0.03), European System for Cardiac Operative Risk Evaluation (EuroSCORE) II (HR 1.14, 95% CI 1.06 to 1.22; p<0.001) and complications (HR 4.81, 95% CI 1.03 to 22.38; p=0.05). Multivariate Cox proportional hazards analysis: Nagelkerke R2=0.271, Frailty Index and EuroSCORE II were independently associated with mortality.ConclusionsDelirium frequently occurs after TAVI. Variables from frailty assessment are associated with delirium and mortality, independent of cardiological assessment. Thus, frailty assessment may have additional value in the prediction of delirium and mortality after TAVI.

No abstract