Developing a Predictive Model for Hospital-Acquired Catheter-Associated Urinary Tract Infections ...

198 Pages·2016·2.13 MB·English

by  Jung In Park

Developing a Predictive Model for Hospital-Acquired Catheter-Associated Urinary Tract
Infections Using Electronic Health Records and Nurse Staffing Data

A Dissertation
SUBMITTED TO THE FACULTY OF
UNIVERSITY OF MINNESOTA
BY

Jung In Park

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

Bonnie L. Westra, Advisor

August, 2016
© Jung In Park, 2016
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Acknowledgements

The author acknowledges the support of the Beatrice L. Witt Endowment Fund,
Katharine Densford Dreves Scholarship, Lucille Casas Paradela International Fellowship
Fund, Connie W. Delaney Fellowship in Nursing Innovation, Violet A. Shea Nursing
Scholarship, Nursing Research Fellowship Fund, and the University of Minnesota,
School of Nursing for completion of academic work and writing of this dissertation.
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Dedication

I am grateful to the members of my dissertation committee for reviewing my
work and sharing their thoughts and suggestions:
Bonnie Westra
Connie Delaney
Donna Bliss
Chih-Lin Chi
Special gratitude and appreciation go to Drs. Westra and Delaney for their
devoted help in preparing this paper. I am thankful for their immeasurable time and
patience in route to publication. I have been fortunate to have learned from the great
pioneers of this era. Their passion, excellence, and wisdom as scholars have inspired me
greatly.
I would like to dedicate this dissertation to my dear parents, my brother, and my
beloved husband who supported and encouraged me along the way. Thanks for trusting
me, loving me, and being there for me. I am also thankful to my colleagues and friends. I
could not have done this without the help of my people.
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Abstract
There are a number of clinical guidelines and studies about hospital-acquired
catheter-associated urinary tract infections (CAUTIs), but the rate of CAUTI occurrence
is still rising. Hospitals are focusing on preventing hospital-acquired CAUTI, as the
Centers for Medicare and Medicaid Services (CMS) does not provide payment for
hospital-acquired infections anymore. There is a need to explore additional factors
associated with hospital-acquired CAUTI and develop a predictive model to detect
patients at high risk.
This study developed a predictive model for hospital-acquired CAUTIs using
electronic health records (EHRs) and nurse staffing data from multiple data sources.
Research using large amounts of data could provide additional knowledge about hospital-
acquired CAUTI. The first aim of the study was to create a quality, de-identified dataset
combining multiple data sources for machine learning tasks. To address the first aim of
the study, three datasets were combined into a single dataset. After integrating the
datasets, data were cleaned and prepared for analysis. The second aim of the study was to
develop and evaluate predictive models to find the best predictive model for hospital-
acquired CAUTI. For the second aim of the study, three predictive models were created
using the following data mining method: decision trees (DT), logistic regression (LR),
and support vector machine (SVM). The models were evaluated and DT model was
determined as the best predictive model for hospital-acquired CAUTI.
The findings from this study have presented factors associated with hospital-
acquired CAUTI. The study results demonstrated that female gender, old adult (≥56),
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Charlson comorbidity index score ≥ 3, longer length of stay, glucose lab result > 200
mg/dl, present of rationale for continued use of catheter, higher percent of direct care
RNs with associate’s degree in nursing, less total nursing hours per patient day, and lower
percent of direct care RNs with specialty nursing certification was related to CAUTI
occurrence.
Implications for future research include the use of different analytic software to
investigate detailed results for LR model, adding more factors associated with CAUTI in
modeling, using a larger sample with more patients with CAUTI, and patient outcomes
research using nursing-sensitive indicators. This study has important implications for
nursing practice. According to the study results, nurse specialty certification, nurse’s
education at the baccalaureate level or higher, and more nursing hours per patient day
were associated with better patient outcomes. Therefore, considerable efforts are needed
to promote possession of nurse specialty certification and higher level of nursing
education, as well as enough supply of nursing workforce.
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Table of Contents
ACKNOWLEDGEMENT ………………………………………………………….……. i
DEDICATION ………………………………………………………………….……….. ii
ABSTRACT …………………………………………………………………….…….... iii
TABLE OF CONTENTS ………………………………………………………….…….. v
LIST OF TABLES ………………………………………...……………………………. ix
LIST OF FIGURES ……………………………………..….…………………………… x

CHAPTER I INTRODUCTION…………………………..…………………………… 1
Significance of Catheter-Associated Urinary Tract Infection (CAUTI) ………… 3
Nursing-Sensitive Indicators and Nurse-Staffing Data …………………..……… 5
Big Data Research Using Electronic Health Records (EHRs)……….…….…..… 9
Scientific Importance of This Study………………………………..…………... 13
Purpose and Specific Aims of Study………………………………….………... 14
Purpose of Study……………………………………………………...… 14
Specific Aims of Study………………………………………..……...… 15
Conceptual Framework…………………………………………………………. 15
Developing the Conceptual Framework……………………………...… 15
Description of the Conceptual Framework…….……………………..… 16
Conclusion……………………………………………………………………… 17

CHAPTER II LITERATURE REVIEW…………………………………………… 19
Organization of the Literature Review…….…………………...…………….… 19
Methodology for Literature Search …………………………….……………… 19
Inclusion and Exclusion Selection Criteria…………….…….………………… 20
Selection and Abstraction of Studies ……………….....………………….…… 20
Definitions of Urinary Tract Infections (UTIs).. ……………………………… 21
Definition of UTI ……………………………………………………… 22
Definition of CAUTI ……………………………..…………………… 23
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Definition of Asymptomatic Bacteremic UTI ………….……………… 27
Factors Associated with Hospital-Acquired CAUTI ………...…………28
Patient Factors ……………………………….………………………… 30
Environmental Factors ………………………………………………… 32
Staff Factors …………………………………………………………… 35
Interventions …………………….……….…………………………….. 35
Overall Summary and Conclusion….……………………………………….… 36
Operational Definition for Hospital-Acquired CAUTI ………………… 39

CHAPTER III METHODOLOGY ………..……………………………………… 40
Research Design and Settings ……………………………………...…….….…. 40
Knowledge Discovery and Data Mining ………………..……….…..… 40
Setting …………………………………………………...….………..… 42
Description of Datasets ……………………………………….……..……..… 42
Sample for the Study…………………………………………………… 45
Variables ……………………………....….….….……………………….…..… 45
Outcome Variable…………….…………………………………..…….. 45
Factors Associated with Hospital-Acquired CAUTI …………..…….. ...49
Selected Variables from UMN AHC-IE, UMMC-ICU CAUTI, and
UMMC-NDNQI datasets ………….………………...………….……… 51
Selected Variables for Data Extraction and Mapping …………. 51
Selected Variables for Data Analysis ………….………….…… 52
Steps in the Analysis ………………………..…….…...…….…..……..….…. 67
Analytic Software …………………………….……………………………… 68
Data Analysis ………………………………………………………………… 68
Data Preprocessing ……………………………….…………………… 68
Data Transformation ………………………………..………………… 69
Data Mining Analysis ….………………………….………..………… 71
Linking Aims to the Analytic Plan ………………...………… 71
Data Mining Techniques for Data Analysis …………….…… 74
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Model Evaluation ……………………………………….…………… 77
Ethical Issues ………………………………………………………………… 79
Conclusion ……………………………………………….………….. 80

CHAPTER IV RESULTS ………………………………………………………….. 82
Introduction …………………………………………………………………… 82
Aim One: Create a Quality, De-identified Dataset Combining Multiple Data
Sources for Machine Learning Tasks ………….……………………………… 83
Creating an Integrated Dataset ………………………………………… 83
Data Preprocessing and Transformation …………………….………… 92
Characteristics of the Data ………………..…………………………… 93
Final Attributes for Data Analysis …………………………………… 102
Aim Two: Development and Evaluation of Predictive Models to Find the Best
Predictive Model for Hospital-Acquired CAUTI …...………………………103
Developing the Predictive Models …………………………………… 104
Cost-Sensitive Classifiers….…………………………………. 104
Decision Trees ..……………………………………………… 106
Logistic Regression ..………………………………………… 109
Support Vector Machines……………..……………………… 110
Model Evaluation …………………….……………………………… 112
Conclusion ………………………………………………………………….… 113

CHAPTER V DISCUSSION …………………………………………………….. 116
Significant Findings and Relationship to Existing Literature ……………….. 118
Creating an Integrated Dataset ………………………………….…… 118
Predictive Models ………………………………………………..….. 120
Factors Associated with Hospital-Acquired CAUTI ......……………. 121
Differences Between Patients with and without CAUTI ………………….… 124
Linking This Study to Theory ……………………………………………….. 126
Strengths and Limitations of This Study ………………………………….…. 127
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Implications for Future Study and Translation to the Clinical Setting ……..... 130
Implications for Future Research …………………….………….. 130
Implications for Nursing Practice ……………………………….. 132
Conclusion...……………………………………………………………………133

REFERENCES……………………………………………………………………… 136