The purpose of this study is to determine the value of a detailed medical history, acquired by computer-directed, patient self-entry of data, for efficient disposition of patients presenting to the emergency department (ED) with chest pain and non-diagnostic first ECG and/or serum markers for acute coronary syndrome (ACS). The research questions posed are: will the added data and systems-based analysis of the data provided by computerization of these processes, as compared with routine physician-acquired histories and analysis; (1) safely risk stratify patients with chest pain, and (2) save time and resources?
Computerized, patient-entered histories will be collected with the software program CLEOS running on tablets (iPad®, Apple Inc, Cupertino, California, USA). The histories to be collected include demographic data, present illness, systems review, past medical history, prescription drugs, previous adverse drug reactions, social history, life-style risks, and family history. Histories will be collected during wait times in the ED, e.g., before patients are seen by a physician and while patients wait for reports of laboratory data collected by routine care.
Data acquired by CLEOS will be related to clinical outcomes in the acute setting and at 30 days and 1 year post-presentation for patients without documented ACS in the acute setting. Outcomes for all patients will be extracted from hospital records and national registries.
For the validation and future development of CLEOS, interviews with patients for the evaluation of patient experience regarding feasibility, acceptance, comprehensiveness and technical aspects of answering the CLEOS interview will take place within one to three months after the ED visit.
This is an exploratory study in which the calculation of the number of participating patients is based on the desired precision of sensitivity and specificity. Assuming that the prevalence is 0.5 (50 %), a power calculation with nQuery® version 7.0 (Statistical Solutions Ltd, Boston, Massachusetts, USA) shows that with 1000 patients the estimated precision of sensitivity and specificity is ±0.03 (3 %). The more the extreme the result, i.e. sensitivity or specificity approaching 0 or 1 (100 %), the higher the precision. The models will be developed in the first 50% of the data acquired (training data set) and validated in the last 50% of the data acquired (validation data set). We also intend to make estimates in subgroups. To ascertain that such estimates can be done, an even larger number of patients must be recruited. Thus, the study intends to recruit data from approximately 2000 patients.
Taken together, this study:
1. will evaluate established clinical guidelines risk scores, as populated with CLEOS data, and compare these results with data obtained during the concurrent ED visit and made available in the standard hospital medical records;
2. will assess how data collected with CLEOS in combination with established risk scores can rule-in and rule-out a diagnosis of an ACS, we will calculate sensitivity, specificity and negative and positive predictive value;
3. aim to develop new risk prediction scores for patients with chest pain, based on data collected with CLEOS; and
4. will evaluate economic aspects of routine care, as compared with management by CLEOS by using standard health economy procedures and analyses to determine the resource utilization and cost differences between management according to guidelines, based on data collected with CLEOS, and current clinical practice.
