Graduation Year

2011

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

José L. Zayas-Castro, Ph.D.

Co-Major Professor

Peter J. Fabri, M.D., Ph.D.

Committee Member

Tapas Das, Ph.D.

Committee Member

Hui Yang, Ph.D.

Committee Member

Phillip Foulis, Ph.D.

Keywords

Time series, clustering, patient monitoring, platelet count, plasma calcium

Abstract

In this dissertation is proposed a methodology to identify patient's recovery patterns after cardiovascular surgery based on laboratory tests results. The main purpose is to enhance the understanding of the manifestations of postsurgical complications in patients who underwent cardiovascular surgery. The analysis of patients' recovery process is based on the relationship between plasma calcium, ionized calcium and platelet count over time.

Laboratory results from the James A. Haley Veterans' Hospital databases, related to patients admitted to the Surgical Intensive Care Unit (SICU) after cardiac surgery (coronary artery bypass, aortic value replacement and mitral valve replacement), are used. These databases contain information regarding commonly ordered tests such as Complete Blood Count tests (CBC) and Basic Metabolic Panel (BMP) for a large group of patients over time. Physicians usually order these tests as a component of screening, routine evaluation, or serial assessment. These test results, contain a large amount of information used by most physicians during the diagnosis process and patient monitoring.

This study creates time series of some components of the aforementioned tests to analyze their behavior during the perioperative and postoperative period. Time series based clusters are developed to determine the similarities among tests results from four different types of patients: patients who had a satisfactory recovery process without any manifestation of complications, patients who experienced complications but survived, patients who experienced complications and then died during their recovery and patients who died during the perioperative period. As a conclusion, the time series based clustering techniques were able to identify whether a patient is likely to fully recover from the surgery, but it does not have the power to detect effectively results corresponding to a patient experiencing complications.

The development of this methodology provides statistical evidence of the differences among different patterns on patient recovery. It is clear that patients experiencing complications have a steeper drop of test results after surgery, and also a non-stable trend towards normal levels. The appropriate use of the proposed methodology could help to timely anticipate complications in patient condition, improve the comprehensiveness of the assessment of patient condition based on laboratory test results and enhance the utilization of laboratory results databases.

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