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«A Thesis Presented to the faculty of the School of Engineering and Applied Science University of Virginia In Partial Fulfillment of the requirements ...»

-- [ Page 1 ] --

Are Patients at Higher Risk if Having Operations

at Nights, on Weekends or Holidays?

A Thesis Presented to

the faculty of the School of Engineering and Applied Science

University of Virginia

In Partial Fulfillment of the requirements for the Degree of

Master of Science in Systems and Information Engineering

By

Hui Guo

July 2003

Approval Sheet

This thesis is submitted in partial fulfillment of the requirements for the degree of Master

of Science in Systems and Information Engineering.

Hui Guo, Author

This thesis has been read and approved by the Examining Committee:

Dr. Donald E. Brown, Committee Chair Dr. Stephanie Guerlain, Advisor Dr. Frank E. Harrell Jr.

Dr. Florence E. Turrentine Accepted for the School of Engineering and Applied Science Dean, School of Engineering and Applied Science July, 2000 i Abstract To improve patient safety is one of the ultimate goals for nearly all medical centers. However, the influence of intrinsic variability associated with different medical situations on patient outcomes makes it difficult to identify key factors to improve. One question related to patient safety is whether the patients having surgery at nights, on weekends or holidays are at higher risk than those having surgery during the regular operating room hours, e.g., due to reduced staffing. Previous studies have suggested that this is true, but did not take into account many of the other factors that might cause such a difference.

The records from 1996 to 2000 for all patients who underwent surgery at the University of Virginia Health System were gathered. Two risk indexes were employed to evaluate surgical outcomes: 30-days mortality and the patient’s length of stay in the hospital. In addition to the service time (Night, Weekend and Holiday), patient age, gender, ASA score, co-morbidity, case type, diagnosis and surgeon’s experience were used in the model.

Logistic regression analysis for patient mortality did show a significantly higher death rate for patients having surgeries at night; however, the surgeries performed on weekends or holidays did not show such difference from those performed during the regular operating hours. Patient age, co-morbidity, ASA score and case type were all significant factors affecting surgery outcome. Further analysis within each case type showed the night factor was only significantly associated with emergent cases, but not with elective and late posted cases. The weekend and holiday stayed insignificant for

–  –  –

to patient’s sickness, health status and case type rather than patient’s age or surgeon’s experience.

The linear regression analysis for patient length of stay found both night and weekend were statistically significant. Holiday was not significant for length of stay.

A strong interaction was found between night with ASA score and case type, which would even strengthen the night effectiveness on length of stay. Again, patient age, comorbidity, ASA score, and case type were significantly associated with length of stay.

Besides patients’ sickness and health status, surgeon’s experience contributed to the negative outcome on weekends, which was another significant factor associated with length of stay.

However, Cox proportional hazards model for length of stay after surgery censored by discharge status failed to find the significance of Night, neither to Holiday and Weekend. Age, ASA, case type, co-morbidities, the attending’s experience and diagnosis are significant predictors.

In summary, night is one significant factor affecting surgical outcomes. Surgery at night is associated with a higher mortality and longer length of stay; weekend is only significantly associated with length of stay; holiday is an insignificant factor for surgical outcomes. Patients’ sickness and health status are major reasons of the negative surgery outcome at night or on weekends. Another reason affecting length of stay can be attributed to surgeon’s experience. According to these results, suggestions to improve patient safety for surgeries at night and on weekends are discussed. For future research, surgical outcome can be analyzed within surgical procedure and disease which can

–  –  –

Writing a thesis for me is a process of self-identification with the profession;

without many people’s support and assistance, I would not have come this far. For many reasons, the first one I would like to thank is my advisor: Dr. Stephanie Guerlain. She is knowledgeable and considerate. As an advisor, she is always there to support her students.

What I have learned from her is not only the knowledge, but the way to carry on a research project and how to put an idea into reality. Thanks to Dr. Turrentine, without her help, I could not have finished this project. She provided a lot of help, especially when I met difficulties in the medical area. I could always feel her encouragement during the whole procedure. I also should give thanks to Dr. Harrell. He was always so patient to answer my questions and teach me how to go further on my research. Thanks for Dr.

Brown, who led me into this area from his class and gave me a great deal of important advice for this project.

I would also like to thank the following people: Debbie Lamb, Ken Scully, Paige Williams, Jin Jun, Jeff Young, David Bogdonoff and Stuart Lowson who assisted me in accomplishing this research.





Finally, I dedicate this dissertation to Min Zheng, my wife. I genuinely appreciate her love and support.

–  –  –

Abstract

Acknowledgements

Chapter 1. Introduction

1.1 Patient Safety in the Operating Room

1.2 Thesis purpose and research question

1.3 Thesis Outline

Chapter 2 Literature Review

2.1 Patient Safety Evaluation

2.2 Mortality Variation in Time

2.3 Previous Research for Mortality during the Nights and Holidays/Weekends

Chapter 3 Methodology

3.1 Data Source

3.2 Data pre-processing

3.2.1 Data cleaning

3.2.2 Variable definition

3.2.3 Data Error

3.3 Modeling Strategy

3.3.1 Variable selection

3.3.2 Missing Values

3.3.3 Model Validation

3.3.4 Predictor Complexity

3.3.5 Modeling strategy for hypothesis testing

3.4 Software

Chapter 4 Statistical Analysis and Result

4.1 Pre-Analysis

4.1.1 Descriptive statistics

4.1.2 Predictor Pre-analysis

4.2 Mortality analysis

4.2.1 Full model

4.2.2 Model within CaseType

4.2.3 Model Within Diagnosis

4.2.4 Conclusion for Mortality analysis:

4.3 LOS (Length of Staying) Analysis

4.3.1 Response Transformation

4.3.2 Full Model Analysis

4.3.3 Model within diagnostic category

4.3.4 Conclusion for LOS analysis

4.4 LOS Analysis by Cox Proportional Hazards Model

4.4.1 Model without Stratification

4.4.2 Model with Stratification

4.4.3 Assumption Test

4.4.4 Conclusion for Cox PH model

4.5 Other Analysis

v

4.6 Summary

Chapter 5 Conclusion and Future Work

5.1 Conclusion

5.2 Future work

5.3 Summary

Appendix A: ICD-9 Disease Categories

Appendix B: Co-morbidity

Appendix: C. Procedure deleted

Appendix D. Statistical Methodology

D.1 Multiple linear regression

D.1.1 Model and assumption

D.1.2 Significance test

D.1.3 Goodness-of-Fit

D.1.4 Predictive accuracy

D.2 Logistic Regression

D.2.1 Model

D.2.2 Parameter interpretation

D.2.3 Significance test

D.2.4 Goodness-of-Fit

D.2.5 Predictive Accuracy

D.3 Cox Proportional Hazards Model

D.3.1 Model

D.3.2 Parameter Interpretation and Assumption

D.3.3 Test Statistics

Appendix E: Descriptive Statistics

Appendix F: Mortality Analysis

Appendix G: LOS Analysis

Appendix H: Cox Proportional Hazards Model

Appendix I: Splus Code

vi

List of Table

Table 3- 1 ASA code description

Table 3- 2 Death Summary

Table 3- 3 Case type error summary

Table 3- 4 ASA score summary

Table 3- 5 Audit result of ASA 6

Table 3- 6 Audit result of missing ASA

Table 3- 7 Audit results for selected sample

Table 3- 8 IPOP code and discharge date summary

Table 4- 1 Descriptive statistics of continuous variable in SOR dataset

Table 4- 2 Descriptive statistics of categorical and ordinal variable

Table 4- 3 Test Result for Status and LOS grouped by Gender, IPOP

Table 4- 4 Test for variables of interest grouped by Night, Weekend and Holiday......... 38 Table 4- 5 Frequencies of surgeries by time period and case type

Table 4- 6 Wald Statistics for Holiday, Night and Weekend

Table 4- 7 Test Statistics- Full Model without interaction

Table 4- 8 Validation Result for the Full model

Table 4- 9 Odds Ratio of the important variables

Table 4- 10 Test Statistics- Full model within EL CaseType

Table 4- 11 Odds Ratio for variables of interest (EL cases only)

Table 4- 12 Test Statistics- Full model within EM CaseType

Table 4- 13 Odds Ratio for variables of interest (EM Cases only)

Table 4- 14 Test Statistics- Full model within LP CaseType

Table 4- 15 Odds Ratio for variables of interest (LP cases only)

Table 4- 16 Death summary for combined diagnostic groups

Table 4- 17 Test Statistics in diagnosis 2

Table 4- 18 Odds Ratio for variables of interest (diagnosis 2)

Table 4- 19 Test Statistics in diagnosis 7

Table 4- 20 Odds Ratio for variables of interest (diagnosis 7)

Table 4- 21 Test Statistics in diagnosis 9

Table 4- 22 Odds Ratio for some important variables (diagnosis 9)

Table 4- 23 Test Statistics in diagnosis 17

Table 4- 24 Odds Ratio for some important variables (diagnosis 17)

Table 4- 25 Test Statistics for Missing Diagnosis code

Table 4- 26 Odds Ratio for some important variables (missing code group)

Table 4- 27 Test Statistics for other groups

Table 4- 28 Odds Ratio for some important variables (other groups)

Table 4- 29 Test statistics for Holiday, Weekend and Night

Table 4- 30 Test Statistics for each variable (except co-morbidities)

Table 4- 31 Predictor effects on TLOS

Table 4- 32 Result of model validation

vii Table 4- 33 Insignificant Variable and relation within each diagnostic regression.......... 73 Table 4- 34 Test statistics Cox PH model stratified by Gender

Table 4- 35 Hazard Ratio for Variables of interest

Table 4- 36 Summary for the number of surgery for each hospitalization

Table 4- 37 Model performance for two datasets

viii

List of Figure

Figure 4- 1 Variable clustering (a)

Figure 4- 2 Variable clustering (b)

Figure 4- 3 Spearman’s ρ 2 rank correlation (response: Status)

Figure 4- 4 Spearman’s ρ 2 rank correlation (response: LOS)

Figure 4- 5 Nonparametric regression estimation for continuous variable against Status 43 Figure 4- 6 Nonparametric regression estimation for continuous variable against LOS.. 43 Figure 4- 7 Log odds of the continuous predictors to the response Status

Figure 4- 8 Power Transformation using Box-Cox Method

Figure 4- 9 Histogram of the LOS distribution

Figure 4- 10 Variable Effects on TLOS

Figure 4- 11 Residual Analysis (with 95% confidence level)

Figure 4- 12 Q-Q plot of residual

Figure 4- 13 Log relative prob. Of discharge alive vs. continuous variable

1 Chapter 1. Introduction

1.1 Patient Safety in the Operating Room The U.S Institute of Medicine estimates that there are about 44,000~98,000 Americans who die each year from medical mistakes, which costs the nation about $17~29 billion each year. However, those numbers do not include the much larger number of medical mistakes that lead to sickness or injury but do not result in death, about 770,000 per year (Kohn, et al., 2000) The operating room (OR) is a high-risk environment and one of the largest consumers of hospital resources. Patients undergoing surgeries usually have a higher risk of dying than those not undergoing surgery. Surgeons, anesthesiologists and nurses may experience under stress and fatigue. Intrinsic variability is associated with different surgical situations and complex modern operative technology. All these make Adverse Events (AEs) more likely to happen in the OR.

Research has found that more than two-thirds of AEs are preventable, where AEs are defined as unintended injuries that are caused by medical management and result in measurable disability (Leape, et al., 1993). (If an AE could not be prevented based on the current state of knowledge, then it is classified as unpreventable; on the other hand, AE is preventable if no error is identified, but a high incidence of this type of complication results from low standards of care or technical service.) It was also noticed that surgical AEs are more likely to be prevented than those resulting from non-surgical management (Leape, et al. 1993). Unfortunately, the complexity of the surgical environment makes

–  –  –

1.2 Thesis purpose and research question The first step for patient safety improvement would be identifying the system failures underlying the errors that occurred. In other words, it is necessary to recognize each contributor to the AEs. Recognition of these factors will allow a better management of them, which in turn will minimize the probability of AE’s happening.



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