A problem-snapshot view of patient history data for cardiac preoperative evaluation

Preoperative risk evaluation is a very important work before a cardiac surgery. Clinicians always have to spend much time reviewing the information about operative risk factors. In this paper, we have made a few tentative attempts to help clinicians improve the efficiency of risk evaluation. First a surgery-problem network for cardiac surgeries is built to extract the associated medical problems from patient history problems. Then a set of data filter rules are defined for each medical problem to retrieve the required data, which is clustered around the problems and forms a set of snapshots. After that, we propose a multi-level-timeline method to visualize the problems and snapshots extracted. Finally, a special problem-snapshot visualization tool for cardiac preoperative evaluation is designed and developed as a result. The tool provides a quick way of reviewing the information which is helpful for cardiac preoperative risk evaluation through information visualization technology. It can save a lot of tedious work.

[1]  T Kameda,et al.  Stroke after coronary artery bypass grafting in patients with cerebrovascular disease. , 2000, The Annals of thoracic surgery.

[2]  Rossella Fattori,et al.  Simple risk models to predict surgical mortality in acute type A aortic dissection: the International Registry of Acute Aortic Dissection score. , 2007, The Annals of thoracic surgery.

[3]  J. Ornato,et al.  [ACC/AHA 2007 Guidelines on Perioperative Cardiovascular Evaluation and Care for Noncardiac Surgery]. , 2009, Masui. The Japanese journal of anesthesiology.

[4]  Huilong Duan,et al.  Integrated Visualization of Multi-Modal Electronic Health Record Data , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[5]  K Caidahl,et al.  Long term prognosis after CABG in relation to preoperative left ventricular ejection fraction. , 2000, International journal of cardiology.

[6]  Huilong Duan,et al.  An Act Indexing Information Model for Clinical Data Integration , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[7]  Lawrence J Saidman,et al.  Prevalence of risk factors, and not gender per se, determines short- and long-term survival after coronary artery bypass surgery. , 2003, Journal of cardiothoracic and vascular anesthesia.

[8]  S. Lemeshow,et al.  European system for cardiac operative risk evaluation (EuroSCORE). , 1999, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[9]  Denise R. Aberle,et al.  TimeLine: Visualizing Integrated Patient Records , 2007, IEEE Transactions on Information Technology in Biomedicine.

[10]  Jeffrey L Carson,et al.  Diabetes mellitus increases short-term mortality and morbidity in patients undergoing coronary artery bypass graft surgery. , 2002, Journal of the American College of Cardiology.

[11]  M. Dalby,et al.  Description of modern practices of percutaneous coronary intervention and identification of risk factors for adverse outcome in the French nationwide OPEN registry , 2004, Heart.

[12]  R Pifarre,et al.  Cardiac surgery for chronic renal dialysis patients. , 1989, Chest.

[13]  M. Kuduvalli,et al.  Risk of morbidity and in-hospital mortality in obese patients undergoing coronary artery bypass surgery. , 2002, European Journal of Cardio-Thoracic Surgery.

[14]  Zeeshan Syed,et al.  Predicting Surgical Risk: How Much Data is Enough? , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[15]  Zhe Wu,et al.  Level of detail navigation and visualization of electronic health records , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[16]  Herko Grubitzsch,et al.  Emergency Coronary Artery Bypass Grafting: Does Excessive Preoperative Anticoagulation Increase Bleeding Complications and Transfusion Requirements? , 2001 .