Mobile health in cardiology: a review of currently available medical apps and equipment for remote monitoring.

INTRODUCTION: Recent developments in implantable cardioverter-defibrillators (ICDs) and smartphone technology have increased the possibilities for remote monitoring. It is the purpose of this review to give an overview of these new possibilities. AREAS COVERED: Remote monitoring in ICD allows for early detection of lead fractures and remote follow-up of patients. Possible limitations are the lack of standardization and the possible unsafety of the data stored on the ICD. Secondly, remote monitoring of health parameters using smartphone compatible wearables and smartphone medical apps is addressed. Possible limitations include the fact that the majority of smartphone apps are unregulated by the regulatory authorities and privacy issues such as selling of app-generated data to third parties. Lastly, clinical studies with smartphone apps are discussed. Expert commentary: New technologies in ICDs and smartphones have the potential to be used for remote monitoring. However, unreliability of smartphone technology, inadequate legislation and lack of reimbursement impede implementation.

Development and validation of a cardiovascular risk assessment model in patients with established coronary artery disease.

Appropriate risk stratification of patients with established, stable coronary artery disease could contribute to the prevention of recurrent cardiovascular events. The purpose of the present study was to develop and validate risk prediction models for various cardiovascular end points in the EURopean trial On reduction of cardiac events with Perindopril in stable coronary Artery disease (EUROPA) database, consisting of 12,218 patients with established coronary artery disease, with a median follow-up of 4.1 years. Cox proportional hazards models were used for model development. The end points examined were cardiovascular mortality, noncardiovascular mortality, nonfatal myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, resuscitated cardiac arrest, and combinations of these end points. The performance measures included Nagelkerke's R², time-dependent area under the receiver operating characteristic curves, and calibration plots. Backward selection resulted in a prediction model for cardiovascular mortality (464 events) containing age, current smoking, diabetes mellitus, total cholesterol, body mass index, previous myocardial infarction, history of congestive heart failure, peripheral vessel disease, previous revascularization, and previous stroke. The model performance was adequate for this end point, with a Nagelkerke R² of 12%, and an area under the receiver operating characteristic curve of 0.73. However, the performance of models constructed for nonfatal and combined end points was considerably worse, with an area under the receiver operating characteristic curve of about 0.6. In conclusion, in patients with established coronary artery disease, the risk of cardiovascular mortality during longer term follow-up can be adequately predicted using the clinical characteristics available at baseline. However, the prediction of nonfatal outcomes, both separately and combined with fatal outcomes, poses major challenges for clinicians and model developers.

Microsimulation for clinical decision-making in individual patients with established coronary artery disease: a concept.

BACKGROUND: In cardiovascular disease, numerous evidence-based prognostic models have been created, usually based on regression analyses of isolated patient datasets. They tend to focus on one outcome event, based on just one baseline evaluation of the patient, and fail to take the disease process in its dynamic nature into account. We present so-called microsimulation as an attractive alternative for clinical decision-making in individual patients. We aim to further familiarize clinicians with the concept of microsimulation and to inform them about the modeling process. METHODS AND RESULTS: We describe the modeling process, advantages and disadvantages of microsimulation. We illustrate the concept using a hypothetical 60-year-old patient, with several cardiac risk factors, who is hospitalized for myocardial infarction. By using microsimulation, we calculate this patient's probability of death. In our example, this particular patient's estimated life expectancy turns out to be 8.9 years. While calculating this life expectancy, we were able to account for multiple outcome events and changing patient characteristics. CONCLUSIONS: Microsimulation takes into account the dynamic nature of coronary artery disease by estimating most likely outcomes regarding a broad range of clinical events. Moreover, microsimulation can be used to evaluate treatment effects by estimating the event-free life expectancy with and without treatment. Hence, microsimulation has several advantages compared to modeling techniques such as regression.

Impact of an alerting clinical decision support system for glucose control on protocol compliance and glycemic control in the intensive cardiac care unit.

BACKGROUND: Glycemic control in patients with acute cardiac conditions is a clinical challenge but may substantially improve patient outcome. The aim of the current study was to evaluate the effect of implementing an automated version of an existing insulin protocol for glucose regulation in the Intensive Cardiac Care Unit (ICCU) on compliance with the protocol and achievement of glycemic targets. METHODS: During an 11-month period, data of 667 patients with two or more glucose measurements were evaluated, 425 before and 242 after implementation of the clinical decision support system (CDSS) for glucose control at the Erasmus Medical Center ICCU (Rotterdam, The Netherlands). RESULTS: After implementation, compliance with the advised measurement time increased from 40% to 52% (P < 0.001), and compliance regarding insulin dosage increased from 49% to 61% (P < 0.001). Also, more patients had a mean glucose level within the target range of 81-126 mg/dL (31% vs. 43% [P = 0.01]). Monthly evaluation identified reasons for protocol noncompliance (e.g., nutritional status and time of day) and will be used to improve the existing CDSS. CONCLUSIONS: The CDSS implementation of an insulin protocol in an ICCU improved compliance, identified targets for further improvement of the protocol, and resulted in improved glucose regulation after implementation.

Evaluation of a clinical decision support system for glucose control: impact of protocol modifications on compliance and achievement of glycemic targets.

Treating hyperglycemia may improve patient outcome, but is a clinical challenge. Three variations of a computerized insulin protocol were compared with regard to protocol compliance and achievement of glucose target levels. In group 1, the existing protocol was applied, in group 2 the protocol was modified to account for decreasing glucose values; group 3 had a higher threshold for initiating insulin, wider glucose target ranges, and included instructions to regulate glucose around mealtimes. From July 28, 2008 until February 1, 2010, data from 1255 patients admitted to our Intensive Cardiac Care Unit with at least 2 glucose measurements were analyzed. Mean age was 64 +/- 15 years, 66% were male, 21% had diabetes. Groups 1 to 3 included 269, 814, and 142 patients, respectively. Protocol compliance in group 2 was lower with 44% of the glucose measurements performed on time versus 51% in group 1 (P < 0.001), and insulin was dosed correctly in 57% versus 67% (P < 0.001). In group 3, compliance increased, 52% of the measurements were done on time, and insulin was dosed correctly in 71%. Average glucose levels increased in group 3 due to a higher threshold for starting insulin and a wider target range: 70% (group 1), 66% (group 2), and 61% (group 3) had an average glucose of <8 mmol/L (P < 0.001). Also, we observed a decreasing trend in incidence of hypoglycemia and reporting of noncompliance. Further improvements in glucose measurement technology and protocols are needed to optimally treat hyperglycemia in the Intensive Cardiac Care Unit.