BACKGROUND: Hysterectomies are one of the most frequently performed surgical procedures in the United States. There are a wide variety of diagnoses that require a patient to obtain this procedure, but the majority of hysterectomies are performed for benign indications. Currently, gynecologists do not follow a standardized protocol surrounding postoperative laboratory ordering, and healthcare professionals can order a wide range of tests as often as they choose. Extraneous laboratory orders are disruptive to the patients’ well-being and risk their health following surgery. These orders are costly for hospital systems, take up precious time of hospital employees, and influence the course of patient treatment only in extremely rare circumstances.
There are few studies that develop exclusion criteria for patients who may not require a laboratory test following surgery. Though systems to predict postoperative hematocrit have been created, they are complicated and difficult to use. The few studies that were performed are yet to be accepted by the medical community, in part because of their limited scope. This study will be the first to incorporate the results of robotic surgery in the analysis.
OBJECTIVE: The purpose of this study is to determine concrete parameters to indicate that a patient is in need of postoperative laboratory work and at risk for anemia or transfusion. We aim to develop two comprehensive models that guide surgical practitioners to identify the cases which do not require laboratory data.
METHODS: A total of 1027 gynecologic surgeries were performed at Saint Francis Hospital and Medical Center between April 1, 2014 and May 31, 2016. This retrospective study extracted data from EPIC EMR according to 42 variables preconceived to be the leading indicators of postoperative hematocrit and overall healing. Five healthcare professionals were surveyed to identify the variables that influence their postsurgical patient assessments and their decisions to order blood testing. This information was developed into score sheets with differing levels of stringency. Correlation highlighted 14 of the initial 42 variables as contributors to postoperative hematocrit and an equation model was built. Stepwise linear regression was used for univariate and multivariate analyses, from which we created our equation to predict all patients’ postoperative hematocrit.
RESULTS: Out of the 1027 initial cases, a total of 602 cases were identified as hysterectomies for benign indications. Survey data gave the highest value to urine output and heart rate as key indicators of postoperative anemia. From the survey data, two clinical scoring sheets with differing stringency were created to guide practitioner laboratory ordering. These sheets gave parameters of heart rate and urine output the largest correlative weight in determining postoperative hematocrit. However, based on regression analysis, parameters of age (AGE), body mass index (BMI), preoperative platelet count (PPC), estimated blood loss during surgery (IO EBL), preoperative hematocrit (PHCT) and postoperative fluid bolus orders (POSTOP FB) proved to be the key variables impacting postoperative hematocrit (POSTOP HCT). These items were translated into the equation: POSTOP HCT = 22.51 – 0.40*POSTOP FB – 0.01*IO EBL + 0.25 PHCT + 0.09*BMI + 0.06*AGE – 0.01*PPC (R-squared = 0.310).
CONCLUSIONS: This study aims to decrease superfluous laboratory testing, as well as to contribute to a larger conversation considering the potential merits of clinical judgement in a data-driven healthcare system. We have created a number of comparable strategies in order to reduce the number of unnecessary blood draws: two clinical scoring sheets and an equation. The score sheets indicate when to order additional testing. These sheets are representative of a range of surgical practitioners’ conventional clinical judgement. The equation serves as an evidence-based guide for determining postoperative hematocrit following benign gynecologic surgery. These predictive mechanisms will be validated and a superior method determined as our research continues with prospective application. We eventually expect to use the most accurate mechanism to reduce postoperative blood testing following all surgeries.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/23817 |
| Date | 13 July 2017 |
| Creators | Mayer, Sarah A. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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