Dionisio Acosta

Relative clinical and economic impact of exercise echocardiography vs. exercise electrocardiography, as first line investigation in patients without known coronary artery disease and new stable angina: a randomized prospective study

Aims Exercise electrocardiography (ExECG) is widely used in suspected stable angina (SA) as the initial test for the evaluation of coronary artery disease (CAD). We hypothesized that exercise stress echo (ESE) would be efficacious with cost advantage over ExECG when utilized as the initial test. Methods and results Consecutive patients with suspected SA, without known CAD were randomized into ExECG or ESE. Patients with positive tests were offered coronary angiography (CA) and with inconclusive tests were referred for further investigations. All patients were followed-up for cardiac events (death, myocardial infarction, and unplanned revascularization). Cost to diagnosis of CAD was calculated by adding the cost of all investigations, up to and including CA. In the 194 and 191 patients in the ExECG vs. ESE groups, respectively, pre-test probability of CAD was similar (34 ± 23 vs. 35 ± 25%, P = 0.6). Results of ExECG were: 108 (55.7%) negative, 14 (7.2%) positive, 72 (37.1%) inconclusive and of ESE were 181 (94.8%) negative, 9 (4.7%) positive, 1 (0.5%) inconclusive, respectively. Patients with obstructive CAD following positive ESE vs. Ex ECG were 9/9 vs. 9/14, respectively (P = 0.04). Cost to diagnosis of CAD was £266 for ESE vs. £327 for ExECG (P = 0.005). Over a mean follow-up period of 21 ± 5 months, event rates were similar between the two groups. Conclusion In this first randomized study, ESE was more efficacious and demonstrated superior cost-saving, compared with ExECG when used as the initial investigation for the evaluation of CAD in patients with new-onset suspected SA without known CAD.

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision‐support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo‐tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended.

Relative diagnostic, prognostic and economic value of stress echocardiography versus exercise electrocardiography as initial investigation for the detection of coronary artery disease in patients with new onset suspected angina

Objectives We hypothesised that stress echocardiography (SE), may be superior to exercise ECG (ExECG), for predicting CAD and outcome, and cost-beneficial, when performed as initial investigation in newly suspected angina. Methods All patients seen in 2011, with suspected angina, no history of CAD, pre-test likelihood of CAD of > 10% and who underwent SE or ExECG as first line were identified retrospectively. Cost to diagnosis was calculated by adding the cost of all tests, up to and including coronary angiography (CA), on an intention-to-treat basis. Follow-up data on cardiac death and myocardial infarction (MI) were collected, 26 months after the presentation of the last study patient. Results A total of 456 patients underwent ExECG (224 (49%) negative, 93 (20%) positive, 139 (31%) inconclusive) and 241 underwent SE (200 (83%) negative, 35 (15%) positive, 6 (2%) inconclusive) as first line. In patients subsequently undergoing CA, CAD was present in 46% (37/80) of patients with positive ExECG vs. 72% (23/32) patients with positive SE (p = 0.01). Mean cost to diagnosis was £456 for the ExECG vs. £360 for the SE group (p = 0.002). Over a mean follow-up period of 31 ± 5 months, cardiac events were 2% each in negative SE vs. negative ExECG (p = 0.9). Conclusions SE is superior to ExECG for prediction of CAD and is cost-beneficial when used as initial test in patients with no history of CAD presenting with suspected angina.

Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting

BackgroundData capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems.MethodsCase report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project.ResultsThe analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records.ConclusionsCommon data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.

Challenges in delivering decision support systems: the MATE experience

Cancer Multidisciplinary Meeting (MDM) is a widely endorsed mechanism for ensuring high quality evidence-based health care. However, there are shortcomings that could ultimately result in unintended patient harm. On the other hand, clinical guidelines and clinical decision support systems (DSS) have been shown to improve decision-making in various measures. Nevertheless, their clinical use requires seamlessly interoperation with the existing electronic health record (EHR) platform to avoid the detrimental effects that duplication of data and work has in the quality of care. The aim of this work is to propose a computational framework to provide a clinical guideline-based DSS for breast cancer MDM. We discuss a range of design and implementation issues related to knowledge representation and clinical service delivery of the system, and propose a service oriented architecture based on the HL7 EHR functional model. The main result is the DSS named MATE (Multidisciplinary Assistant and Treatment sElector), which demonstrates that decision support can be effectively deployed in a real clinical setting and suggest that the technology could be generalised to other cancer MDMs.

Using computerized decision support to improve compliance of cancer multidisciplinary meetings with evidence-based guidance.

79 Background: The cancer multidisciplinary team (MDT) meeting is regarded as the best platform to reduce unwarranted variation in cancer care through evidence-compliant management. However, MDT meetings are often overburdened with many different agendas, and hence struggle to achieve their full potential. METHODS We have developed an interactive computer system called MATE to facilitate explicit, evidence-based decision-making in MDT meetings for breast cancer care. MATE provides prognostication and risk assessment and also flags up patients eligible for recruiting into ongoing research trials. We describe the system; share our experience of implementing MATE and report initial audit and survey results. MATE was used to record the proceedings of breast MDT meetings between 2008-2009 to gather 1,295 cases discussed in the MDMs during this period and to audit the MDT decisions and MATE recommendations against NICE, NHSBSP, and NCCN guidelines. RESULTS MATE identified 61% more patients who were eligible for recruitment into clinical trials than the MDT and its recommendations demonstrated high concordance with MDT decisions (93.2 %). MATE is in routine use in breast MDT meetings at Royal Free Hospital, London, and deployment of the system in other NHS trusts is being explored. CONCLUSIONS Sophisticated decision support systems can enhance the conduct of MDT meetings in a way that is acceptable to and valued by the clinical team. Further rigorous evaluations are required to examine cost-effectiveness, measure the impact on patient outcomes and test the generalizability of the system in different hospital setups and in different cancers.

A decision-theoretic planning approach for clinical practice guideline modelling

Current formalisms for modelling a Clinical Practice Guideline (CPG) as a Computer-Interpretable Guideline (CIG) do not support the explicit representation of uncertainty due to the unpredictability of treatment outcomes and the incomplete knowledge of the actual patient condition. Given this limitation, the existing CIG approaches support only the computation of patient-specific diagnostic and treatment recommendations for the immediate next step in the patient care-pathway from the clinical perspective. Some existing approaches allow the computation of patient-specific care plans accounting for resource and time constraints, however they do not explicitly model outcomes and clinical data uncertainty. In this paper we propose a decision-theoretic planning approach to modelling and automatic computation of patient-specific care plans that addresses these limitations, whereby adherence to CPGs is maximised given specific, but uncertain, patient data and clinical outcomes. The premise of this work is that both patient and clinician decision making are better modelled and supported by computing care plans instead of single point-of-care recommendations. In this paper we introduce and motivate the clinical problem, present a formal mathematical specification of the approach and we compare our approach with existing ones from the point of view of CIG modelling. Finally we outline further research and discuss aspects that would prove crucial for the implementation of the approach.

An advanced computerized decision support technology to support breast multidisciplinary meetings

658 Background: Cancer multidisciplinary meetings (MDM) or tumour boards have become standard of care in management of breast cancer. As the number of cases continue to increase, better tools are required to support these heavily loaded meetings which play a pivotal role in patient management. We investigate the role of an advanced computerized decision support (CDS) technology in further enhancing the outputs of breast MDM. Methods: We have built a novel decision-support system called MATE to assist evidence-based decision making in breast MDM. MATE implements recommendations from 17 high quality breast screening, diagnosis and management guidelines. MATE provides prognostication and risk assessment and also flags up patients eligible for trials. In our pilot study, the data of 1,000 consecutive breast cases presented at the Royal Free Hospital breast MDM along with their documented MDM recommendations were entered into MATE. MDM recommendations and MATE suggestions were analyzed and compared. Results: T...

A Novel Evidence-Adaptive Computerised Decision Support System for Breast Cancer Multidisciplinary Meetings: Results of an Evaluation Study.

Background: Multidisciplinary meetings (MDM) also known as multidisciplinary cancer conferences are a forum for providing evidence-based care and are widely accepted as a part of standard cancer care worldwide. In United Kingdom, it is a mandatory requirement that the care of all breast cancer patients is managed through breast MDMs. However the lack of required support for conducting MDM is documented in many recent reviews. Advanced Computerised Decision Support (CDS) technology can play an important role in supporting MDMs and improving its functioning. We present a novel breast MDM support tool which integrates a CDS system into an electronic patient record to assist breast Multi Disciplinary Team (MDT) in making an evidence based, transparent treatment decisions during MDM. To the best of our knowledge there are no published studies of implementing computerised decision support systems for breast MDM. Methods: The Multi-disciplinary meeting Assistant and Treatment sElector (MATE) is a tool designed to assist breast clinicians in making management decisions for their patients in MDM. MATE is developed using a PRO forma CDS technology which is funded by the Cancer Research UK. The tool is implemented in the breast unit of Royal Free hospital, London for its pilot testing. MATE evaluates patient9s clinical facts and suggests optimal management options according to incorporated national and international clinical guidelines. The evidence base used in MATE can be updated as and when new evidence is published. MATE recommendations are not binding and the final decision is taken by breast MDT. MATE facilitates the flexible conduct of MDM. Additionally, it highlights if the patient is eligible to take part in any local, national or international clinical trials. In the evaluation study, the data of 300 consecutive breast patients presented at the Royal Free breast MDM along with their documented MDM recommendations are entered in MATE. MDM recommendations and MATE suggestions are analysed. Results: MATE system is able to suggest the treatment recommendations in concordance with breast MDT in most of the cases (91 %). MATE suggested more management options per patient than the documented MDT recommendations (3.4% vs. 1.2 %). MATE also identified 65% more patients suitable for ongoing clinical trials. Deviations that occur specially in unaided MDMs can be minimised using electronic data capture and decision support system like MATE. Conclusion: This evaluation study has shown the feasibility of implementing MATE into MDM and its potential to improve certain aspects of MDM by helping overburdened clinicians. The evidence adaptive decision support component of MATE can improve the guideline-compliance and transparency in the decision-making and also identify more patients to be considered for recruitment in clinical trials. Another important benefit could be completeness of documentation. Further evaluations of MATE in a randomised controlled trial are under way. If found beneficial, the system could easily be adapted for other cancers. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 5118.