Frédéric Capuano

Quality indicators for acute myocardial infarction: A position paper of the Acute Cardiovascular Care Association

Evaluation of quality of care is an integral part of modern healthcare, and has become an indispensable tool for health authorities, the public, the press and patients. However, measuring quality of care is difficult, because it is a multifactorial and multidimensional concept that cannot be estimated solely on the basis of patients’ clinical outcomes. Thus, measuring the process of care through quality indicators (QIs) has become a widely used practice in this context. Other professional societies have published QIs for the evaluation of quality of care in the context of acute myocardial infarction (AMI), but no such indicators exist in Europe. In this context, the European Society of Cardiology (ESC) Acute Cardiovascular Care Association (ACCA) has reflected on the measurement of quality of care in the context of AMI (ST segment elevation myocardial infarction (STEMI) and non-ST segment elevation myocardial infarction (NSTEMI)) and created a set of QIs, with a view to developing programmes to improve quality of care for the management of AMI across Europe. We present here the list of QIs defined by the ACCA, with explanations of the methodology used, scientific justification and reasons for the choice for each measure.

Method for developing national quality indicators based on manual data extraction from medical records

Developing quality indicators (QI) for national purposes (eg, public disclosure, paying-for-performance) highlights the need to find accessible and reliable data sources for collecting standardised data. The most accurate and reliable data source for collecting clinical and organisational information still remains the medical record. Data collection from electronic medical records (EMR) would be far less burdensome than from paper medical records (PMR). However, the development of EMRs is costly and has suffered from low rates of adoption and barriers of usability even in developed countries. Currently, methods for producing national QIs based on the medical record rely on manual extraction from PMRs. We propose and illustrate such a method. These QIs display feasibility, reliability and discriminative power, and can be used to compare hospitals. They have been implemented nationwide in France since 2006. The method used to develop these QIs could be adapted for use in large-scale programmes of hospital regulation in other, including developing, countries.

Factors associated with the length of stay of patients discharged from emergency department in France.

Objectives The length of stay in the emergency department (ED) has been proposed as an indicator of performance in many countries. We conducted a survey of length of stay in two large areas in France and tested the hypothesis that patient and ED-related variables may influence it. Patients and methods During 2007, we examined lengths of stay in ambulatory patients, that is, excluding admitted patients. The following variables were considered: (a) at the patient level, age, sex, the day and month of the visit, and the French clinical classification of emergency patients (CCEP) class; (b) at the ED level, annual ED total number of visits, mean age, the proportions of patients less than 15 and more than 75 years, and the proportions of admitted and clinically stable patients with CCEP class 1 and 2. A multilevel hierarchical analysis was carried out. Results We analyzed 988 591 visits in 53 EDs. The ED-specific median length of stay was 98 (IQR: 62–137) min and the ED-specific median proportion of patients with length of stay of more than 4 h was 15 (5–24) %. There was a strong correlation between the ED-specific median length of stay and the ED-specific proportion of patients with a length of stay of more than 4 h (R=0.96, P<0.001). Using a multilevel analysis, only three variables were associated significantly with the length of stay: the age and the CCEP class of the patient, and the ED census. Conclusion We observed that the length of stay in the ED needs to be stratified by case mix and the total number of visits of the ED.

Suitability of three indicators measuring the quality of coordination within hospitals

BackgroundCoordination within hospitals is a major attribute of medical care and influences quality of care. This study tested the validity of 3 indicators covering two key aspects of coordination: the transfer of written information between professionals (medical record content, radiology exam order) and the holding of multidisciplinary team meetings during treatment planning.MethodsThe study was supervised by the French health authorities (COMPAQH project). Data for the three indicators were collected in a panel of 30 to 60 volunteer hospitals by 6 Clinical Research Assistants. The metrological qualities of the indicators were assessed: (i) Feasibility was assessed using a grid of 19 potential problems, (ii) Inter-observer reliability was given by the kappa coefficient () and internal consistency by Cronbachs alpha test, (iii) Discriminatory power was given by an analysis of inter-hospital variability using the Gini coefficient as a measure of dispersion.ResultsOverall, 19281 data items were collected and analyzed. All three indicators presented acceptable feasibility and reliability (, 0.59 to 0.97) and showed wide differences among hospitals (Gini, 0.08 to 0.11), indicating that they are suitable for making comparisons among hospitals.ConclusionThis set of 3 indicators provides a proxy measurement of coordination. Further research on the indicators is needed to find out how they can generate a learning process. The medical record indicator has been included in the French national accreditation procedure for healthcare organisations. The two other indicators are currently being assessed for inclusion.

Hospital Case Volume and Appropriate Prescriptions at Hospital Discharge After Acute Myocardial Infarction A Nationwide Assessment

Background— In acute myocardial infarction, the relationship between volume and quality indicators (QIs) is poorly documented. Through a nationwide assessment of QIs at discharge repeated for 3 years, we aimed to quantify the relationship between volume and QIs in survivors after acute myocardial infarction. Methods and Results— Almost all healthcare centers in France participated. Medical records were randomly selected. Data collection was performed by an independent group. QIs for acute myocardial infarction were defined by an expert consensus group as appropriate prescription at discharge of aspirin, clopidogrel, &bgr;-blocker, statin, and an angiotensin-converting enzyme inhibitor in patients with left ventricular ejection fraction <0.40. A composite QI was calculated through the use of the all-or-none method. Volume was classified into 7 categories based on the number of admissions for acute myocardial infarctions in 2008 (centers with <10 acute myocardial infarctions were excluded). Odds ratios adjusted for age and sex with 95% confidence interval for volume categories were calculated by use of logistic regression for each QI. Temporal changes were tested in centers that participated in all 3 campaigns. A total of 46 390 records were examined: 18 159 in 2008, 12 837 in 2009, and 15 394 in 2010. Two hundred ninety-one centers were eligible for the temporal analysis. There was a significant increase between 2008 and 2009 in appropriate prescription of antiplatelet agents, &bgr;-blockers, angiotensin-converting enzyme inhibitor, statins at discharge, and the composite indicator. Similarly, a significant increase was observed between 2009 and 2010 in appropriate prescription of angiotensin-converting enzyme inhibitor and &bgr;-blockers and in the composite QI. Compared with a volume of >300, a significantly lower rate of all QIs was observed in centers with the lowest volume. Odds ratios progressively decreased with increasing volume. Despite a significant increase in the composite QI over the 3 years, a significant relationship persisted between volume and quality of care. Conclusions— Analysis of QIs at discharge demonstrates the existence of a relationship between volume and appropriate prescriptions at discharge. Centers with the highest volume perform better on quality measures than centers with lower volumes. Temporal analysis over 3 consecutive years confirms this relationship and shows that it persists despite improvement in QIs between 2008 and 2010.

A new national quality indicator reflecting pain relief in the PACU has been launched and initial results show positive performance of French teams

Postoperative pain remains largely under treated. This has been shown repeatedly by surveys performed in various countries [1,2]. In a recent meta-analysis, for example, it was shown that less than 15% of patients have their pain adequately relieved during mobilisation for the first 24 hours after surgery [3]. Unfortunately, this has not changed very much over the last 20 years [4] despite many efforts, including improvement in the quality of research, guideline production [5] and audits [6]. Audits indeed show a great variability in the use of multimodal analgesia [7] or in prescription of postoperative opioids [8]. These deficits in practice patterns and in patients’ outcomes are not specific to postoperative pain management but are observed in various aspects of medical practice [9] and changes are needed. Meissner et al. suggest several ways to improve postoperative management [10]. First, they call for development of procedurespecific protocols as it is likely that they would be more adapted and better applied [11]. It is also likely that development of Acute Pain Services would be helpful by providing advices from local experts and feedback from audit results [12]. A special emphasis should be put on enhanced recovery after surgery programmes (ERAS) as they would be likely to fail if patients remaining in pain since they would not be able to mobilise as quickly as expected. An additional tool that might have a leverage effect would be the reinforcement of training in both undergraduate and postgraduate training. In the US, a recent survey showed that many topics included in the International Association for the Study of Pain core curriculum received little or no coverage and pain education is limited, variable, and often fragmentary [13]. Patients’ involvement in their own care is more and more thought to be of value, not only to highlight the concept of autonomy but also to increase their role in the decision process as end-results of care might be better with increased empowerment. For example, when patients’ A R T I C L E I N F O

Hospital performance based on treatment delays: comparison of ranking methods

Background Reducing time-to-care is crucial in many acute and chronic diseases. Quality indicators based on target delays derived from guidelines are used to compare hospital performance but there is no accepted methodology for comparing performance when no target delay has been established. Aim To explore by different statistical methods the uncertainty in hospital comparisons that are based on delay indicators, when no target delay is available. Methods Data for hospital door-to-needle time were extracted from a 2010 study of 1699 patients in 57 hospitals with ST-elevated myocardial infarction. We determined whether the times in each hospital were statistically different from the overall mean time or the median time for all hospitals by (i) one-way analysis of variance (ANOVA), (ii) non-parametric ANOVA with Nelson–Hsu adjustment (ANOVA R) and (iii) the proportional hazard model (PHM). We also tested for the assumptions underlying the methods: normal distribution for ANOVA, homogeneity of variances (homoscedasticity) for ANOVA and ANOVA R, and proportionality for PHM. Results Door-to-needle times were available for 889 patients in 44 hospitals. Data distribution was not Gaussian. Test assumptions were verified for ANOVA R (homoscedasticity) for one data subset (>48-h times (48H) excluded) and for PHM (proportionality) for two data subsets (48H or >95th percentile (P95) times excluded). The same five significantly better performers were identified in each case (although ANOVA R missed one). ANOVA R (48H) identified two significantly poorer performers, PHM (48H) identified three and PHM (P95) just one. Poorer performers differed according to method. Conclusions The tested statistical methods yielded broadly similar results but no method was truly satisfactory. A transparency statement should therefore always specify the ranking method used to compare hospital performance.