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Effect of a Disposable Automated Suturing Device on Cost and Operating Room Time in Benign Total Laparoscopic Hysterectomy Procedures

Identifieur interne : 002C08 ( Ncbi/Merge ); précédent : 002C07; suivant : 002C09

Effect of a Disposable Automated Suturing Device on Cost and Operating Room Time in Benign Total Laparoscopic Hysterectomy Procedures

Auteurs : Stuart Hart ; Lobat Hashemi ; Craig J. Sobolewski

Source :

RBID : PMC:3866052

Abstract

Use of disposable automated laparoscopic suturing device was more cost-effective than the da Vinci surgical system or traditional laparoscopic suturing techniques for performance of total laparoscopic hysterectomy for benign disease.


Url:
DOI: 10.4293/108680813X13693422522231
PubMed: 24398190
PubMed Central: 3866052

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PMC:3866052

Le document en format XML

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<p>Use of disposable automated laparoscopic suturing device was more cost-effective than the da Vinci surgical system or traditional laparoscopic suturing techniques for performance of total laparoscopic hysterectomy for benign disease.</p>
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<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">JSLS</journal-id>
<journal-id journal-id-type="iso-abbrev">JSLS</journal-id>
<journal-id journal-id-type="hwp">jsls</journal-id>
<journal-id journal-id-type="pmc">jsls</journal-id>
<journal-id journal-id-type="publisher-id">JSLS</journal-id>
<journal-title-group>
<journal-title>JSLS : Journal of the Society of Laparoendoscopic Surgeons</journal-title>
</journal-title-group>
<issn pub-type="ppub">1086-8089</issn>
<issn pub-type="epub">1938-3797</issn>
<publisher>
<publisher-name>Society of Laparoendoscopic Surgeons</publisher-name>
<publisher-loc>Miami, FL</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">24398190</article-id>
<article-id pub-id-type="pmc">3866052</article-id>
<article-id pub-id-type="publisher-id">JSLS-D-13-00002</article-id>
<article-id pub-id-type="doi">10.4293/108680813X13693422522231</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Scientific Papers</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Effect of a Disposable Automated Suturing Device on Cost and Operating Room Time in Benign Total Laparoscopic Hysterectomy Procedures</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Hart</surname>
<given-names>Stuart</given-names>
</name>
<degrees>MD</degrees>
<aff>Department of Obstetrics and Gynecology, USF Health Center for Advanced Medical Learning and Simulation, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.</aff>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Hashemi</surname>
<given-names>Lobat</given-names>
</name>
<degrees>MS</degrees>
<aff>Healthcare Economics and Outcomes Research, Covidien, Mansfield, MA, USA.</aff>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Sobolewski</surname>
<given-names>Craig J.</given-names>
</name>
<degrees>MD</degrees>
<aff>Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics and Gynecology, Duke University, Durham, NC, USA.</aff>
</contrib>
</contrib-group>
<author-notes>
<fn fn-type="conflict">
<p>Disclosure of conflicts of interest and sources of financial support: S.H., speaker and consultant for Covidien, Stryker, and Boston Scientific; L.H., employee of Covidien; and C.J.S., speaker and consultant for Covidien, consultant and stock for Transenterix, and advisory board for CareFusion.</p>
</fn>
<corresp>Address correspondence to: Stuart Hart, MD,
<addr-line>Department of Obstetrics and Gynecology, USF Health Center for Advanced Medical Learning and Simulation, Morsani College of Medicine, University of South Florida, Tampa, FL, USA. 124 South Franklin Street, Tampa, FL 33602</addr-line>
, Telephone:
<phone>(813) 259-8671</phone>
, Fax:
<fax>(813) 259-8671</fax>
, E-mail:
<email>shart1@health.usf.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="ppub">
<season>Oct-Dec</season>
<year>2013</year>
</pub-date>
<volume>17</volume>
<issue>4</issue>
<fpage>508</fpage>
<lpage>516</lpage>
<permissions>
<copyright-statement>© 2013 by JSLS, Journal of the Society of Laparoendoscopic Surgeons.</copyright-statement>
<copyright-year>2013</copyright-year>
<copyright-holder>Society of Laparoendoscopic Surgeons, Inc.</copyright-holder>
<license license-type="open-access">
<license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc-nd/3.0/us/">http://creativecommons.org/licenses/by-nc-nd/3.0/us/</ext-link>
), which permits for noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited and is not altered in any way.</license-p>
</license>
</permissions>
<self-uri xlink:title="pdf" xlink:type="simple" xlink:href="jls00413000508.pdf"></self-uri>
<abstract abstract-type="precis">
<p>Use of disposable automated laparoscopic suturing device was more cost-effective than the da Vinci surgical system or traditional laparoscopic suturing techniques for performance of total laparoscopic hysterectomy for benign disease.</p>
</abstract>
<abstract>
<sec>
<title>Background and Objectives:</title>
<p>To determine the effect of a disposable automated laparoscopic suturing device, the Endo Stitch (ES) (Covidien, Mansfield, MA, USA), on hospital cost and surgical time in patients undergoing a benign total laparoscopic hysterectomy procedure compared with the use of the da Vinci surgical system (Intuitive Surgical, Sunnyvale, CA, USA) or traditional laparoscopic suturing technique.</p>
</sec>
<sec>
<title>Methods:</title>
<p>The Premier Perspective Database (Premier, Charlotte, NC, USA) was used to identify all inpatient hospital discharges with the primary procedure of a total laparoscopic hysterectomy (
<italic>International Classification of Diseases, Ninth Revision, Clinical Modification</italic>
code 68.41) for benign conditions between January 1, 2009, and June 30, 2011. Patients were further categorized into 3 groups: (1) those for whom the ES was used during the laparoscopic hysterectomy procedure, (2) those for whom robotic assistance (RA) was used, and (3) those for whom neither ES nor RA (NER) was used. Multivariate analysis was performed to examine the association among the ES, RA, and NER groups with respect to hospital cost, length of stay, and surgery time. The multivariate analysis controlled for the patient's age, race, severity of illness, and comorbid conditions, as well as hospital characteristics, such as bed size, region, and teaching status.</p>
</sec>
<sec>
<title>Results:</title>
<p>A total of 9308 patients undergoing an inpatient total laparoscopic hysterectomy procedure between January 1, 2009, and June 30, 2011, were eligible for the study. The ES was used in 974 of the patients (10%), RA was used in 3971 (43%), and neither technique was used in 4363 (47%). After adjusting for confounding variables, the mean hospital cost was $1769 (
<italic>P</italic>
= .0332) lower, with a 42-minute (
<italic>P</italic>
< .001) surgery time savings, for the ES group compared with the RA group. The mean hospitalization cost for the ES group was also $634 (
<italic>P</italic>
< .0879) less expensive, with a 21-minute (
<italic>P</italic>
= .0131) surgery time savings, compared with the NER group.</p>
</sec>
<sec>
<title>Conclusion:</title>
<p>Use of a disposable automated laparoscopic suturing device, the ES, is significantly more cost-effective than the use of the da Vinci surgical system or traditional laparoscopic suturing techniques for the performance of a total laparoscopic hysterectomy procedure for benign conditions.</p>
</sec>
</abstract>
<kwd-group>
<kwd>Total laparoscopic hysterectomy</kwd>
<kwd>Robotic-assisted total laparoscopic hysterectomy</kwd>
<kwd>Automated suturing device</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec>
<title>INTRODUCTION</title>
<p>Hysterectomy is the most common major gynecologic surgical procedure performed in the United States. Although there are more than 600000 hysterectomies performed annually, most of these procedures (>65%) are performed through open laparotomy incisions and for benign indications. Overall, total laparoscopic hysterectomy represents only a small percentage of all hysterectomy procedures performed, despite the many benefits of this approach, including decreased postoperative pain and blood loss, shorter hospitalization and return to normal activity, fewer wound infections, and smaller reduction in hemoglobin levels.
<sup>
<xref ref-type="bibr" rid="B1">1</xref>
–
<xref ref-type="bibr" rid="B5">5</xref>
</sup>
</p>
<p>The poor adoption of the total laparoscopic hysterectomy procedure for benign conditions probably relates to several factors, including the steep learning curve and longer operative times of the procedure, as well as the development of the necessary laparoscopic suturing skills.
<sup>
<xref ref-type="bibr" rid="B4">4</xref>
–
<xref ref-type="bibr" rid="B10">10</xref>
</sup>
Gynecologic surgeons have more recently adopted robotic-assisted hysterectomy as a way to mitigate some of the challenges associated with the total laparoscopic hysterectomy procedure while maintaining many benefits of the laparoscopic surgical approach. The da Vinci surgical system (Intuitive Surgical, Sunnyvale, CA, USA), the only Food and Drug Administration–approved robotic surgical platform for gynecologic surgery, offers an articulating wristed motion with 7
<italic>df</italic>
that mimics many of the movements of the surgeon's hand, 3-dimensional stereoscopic visualization, and improved ergonomics. This may enable the surgeon to perform more natural laparoscopic suturing during vaginal cuff closure, which is often considered a rate-limiting step in developing the appropriate skills needed to perform the laparoscopic hysterectomy procedure.
<sup>
<xref ref-type="bibr" rid="B9">9</xref>
–
<xref ref-type="bibr" rid="B17">17</xref>
</sup>
</p>
<p>Despite these benefits, the cost of performing robotic-assisted hysterectomy procedures remains high, and such procedures may not result in any operating room time savings or overall improved outcomes.
<sup>
<xref ref-type="bibr" rid="B14">14</xref>
–
<xref ref-type="bibr" rid="B17">17</xref>
</sup>
The Endo Stitch (ES) (Covidien, Mansfield, MA, USA) is a disposable automated laparoscopic suturing device that allows the surgeon to perform efficient laparoscopic suturing using traditional laparoscopic surgical technique, without the associated steep learning curve of using needle drivers for laparoscopic suturing or the need for robotic surgical assistance. This device has been shown to reduce laparoscopic suturing and knot-tying time.
<sup>
<xref ref-type="bibr" rid="B18">18</xref>
–
<xref ref-type="bibr" rid="B20">20</xref>
</sup>
The following question is then raised: Can a disposable automated laparoscopic suturing device, by facilitating more efficient vaginal cuff closure, enable surgeons to perform more cost-effective total laparoscopic hysterectomy procedures for benign conditions? The goal of this study is to evaluate hospital cost and operating room time differences in the performance of an inpatient total laparoscopic hysterectomy procedure, by use of a large standardized multihospital database, to compare an ES-facilitated total laparoscopic hysterectomy for benign conditions and a total laparoscopic hysterectomy without the use of the device using traditional laparoscopic technique, as well as a robotic-assisted laparoscopic hysterectomy.</p>
</sec>
<sec sec-type="methods">
<title>METHODS</title>
<sec>
<title>Data Source</title>
<p>The University of South Florida Institutional Review Board reviewed the study and determined that Institutional Review Board approval was not necessary to conduct this study. A retrospective study was conducted using data from the database maintained by Premier (Premier Perspective Database [PPD]; Premier, Charlotte, NC, USA) (2010). The PPD is one of the largest hospital-based, service-level comparative databases in the United States, providing detailed resource utilization data along with patients' principal and secondary diagnoses and procedure codes. The PPD contains drug utilization data on >150 million hospital-outpatient visits and >45 million inpatient visits over the past 7-year period from >400 hospitals. This equates to approximately 1 of every 5 inpatient discharges in the United States. The PPD provides a broad perspective of patient discharge data from a variety of health systems, including not-for-profit, nongovernmental, community, and teaching hospitals.</p>
<p>The Uniform Billing–92 discharge forms provide data on demographic characteristics, discharge diagnoses, and discharge status (including death, but not its cause). Patient records in the PPD used for this study were deidentified in compliance with the Health Insurance Portability and Accountability Act of 1996. Each record in the PPD documents a single hospital discharge episode. The discharge record includes medical and surgical procedure codes (
<italic>International Classification of Diseases, Ninth Revision, Clinical Modification</italic>
[ICD-9-CM] procedure codes), hospital length of stay, and hospitalization cost. In the PPD costs are defined as the actual cost to treat the patient, which includes all supplies, labor, and depreciation of equipment. These costs include both fixed costs, which do not vary based on the volume of procedures performed, and variable costs, which are the direct costs and may vary based on the volume of procedures performed, hospital census, and average wholesale price. Hospitalization costs include, but are not limited to, costs associated with room and board (including the intensive care unit), surgery (including operating room costs), central supplies (including all medical devices and disposables such as staplers, sutures, and ES refills), anesthesia, laboratory, pharmacy, emergency department, pathology, blood bank, and ultrasonography. The cost of a device, such as an ES and its reloads, are included in central supplies. All direct hospital costs are reported, with the exception of the acquisition or maintenance costs of the da Vinci surgical system.</p>
</sec>
<sec>
<title>Study Population</title>
<p>All inpatient discharges with a primary procedure for a total laparoscopic hysterectomy (ICD-9-CM procedure code 68.41) between January 1, 2009, and June 30, 2011, were selected. Detailed billing data were used to search for the use of ES and robotic procedures. Patients were further categorized into 3 groups: (1) those for whom ES was used during the procedure, (2) those for whom robotic assistance (RA) was used, and (3) those for whom neither ES nor RA (NER) was used. In addition to billing data, ICD-9-CM procedure code 17.42 (laparoscopic robotic-assisted procedures) was used to identify robotic-assisted surgery discharges. All patients with a primary diagnosis of cervical, uterine, ovarian, or tubal cancers were excluded because the performance of a laparoscopic hysterectomy in these patients is typically more complicated and time-consuming, these procedures are generally performed by subspecialist gynecologic oncologists, and these procedures are not reflective of most benign laparoscopic hysterectomy procedures performed in the United States.</p>
</sec>
<sec>
<title>Adjustment for Comorbid Conditions and Severity of Illness</title>
<p>The Charlson Comorbidity Index was used to identify comorbid conditions.
<sup>
<xref ref-type="bibr" rid="B21">21</xref>
</sup>
The Charlson Comorbidity Index was developed in 1987 and was based on 1-year mortality data from a New York hospital. Nineteen comorbidities were identified with this method (myocardial infarction, diabetes, etc.).</p>
<p>All Patient–Refined Diagnosis-Related Groups are a joint development of 3M Health Information Systems (3M™ Health Information Systems, Salt Lake City, UT) and the National Association of Children's Hospitals and Related Institutions. The All Patient–Refined Diagnosis-Related Groups expand the basic diagnosis-related group structure by adding 4 subclasses to each diagnosis-related group. The addition of 4 subclasses addresses patient differences related to severity of illness and risk of death. The severity of illness and risk of death relate to distinct patient attributes. The 4 severity-of-illness subclasses and the 4 risk-of-death subclasses are numbered sequentially from 1 to 4, indicating minor, moderate, major, and extreme severity of illness, respectively.
<sup>
<xref ref-type="bibr" rid="B22">22</xref>
</sup>
</p>
</sec>
<sec>
<title>Statistical Analysis</title>
<p>Descriptive statistics were calculated for patient demographic and hospital characteristics. Comparison of patient demographic and hospital characteristics was performed with 2-sided χ
<sup>2</sup>
tests for categorical variables and
<italic>t</italic>
tests for continuous variables. Multivariate analysis was performed to examine the association among the ES, RA, and NER groups with respect to hospital cost, length of stay, and surgery time. Hospital cost data were transformed by taking the natural log of the cost values to reduce the effects of skewed distribution. Generalized estimating equation models with gamma distribution and log-link function were used to measure the impact of the technique used on the outcomes. Generalized estimating equation models represent an alternative generalization of generalized linear models that is often used to analyze correlated data. For patients treated in the same hospital, the overall cost and clinical outcomes are often correlated. The multivariate analysis controlled for the patient's age, race, severity of illness, and comorbid conditions, as well as hospital characteristics, such as bed size, region, and teaching status. Comorbid conditions included chronic obstructive pulmonary disease, diabetes, hypertension, and obesity. The analysis included clustering of patients receiving care from the same hospital. Statistical significance was evaluated at α = .05. All analyses were performed with SAS software, version 9.1 (SAS Institute, Cary, NC, USA).</p>
</sec>
</sec>
<sec sec-type="results">
<title>RESULTS</title>
<p>A total of 11133 patients with a primary procedure of an inpatient total laparoscopic hysterectomy between January 1, 2009, and June 30, 2011, were identified. All patients with a primary diagnosis of cervical, uterine, ovarian, or tubal cancers were excluded (n = 1825, 16.4%). Of the remaining 9308 patients, the ES was used in 974 (10%), RA was used in 3971 (43%), and neither technique was used in 4363 (47%) (
<bold>
<xref ref-type="fig" rid="F1">Figure 1</xref>
</bold>
). Patients in the ES group were approximately 1.5 years younger than those in the RA and NER groups (
<bold>
<xref ref-type="table" rid="T1">Table 1</xref>
</bold>
). There were significantly more Hispanics in the ES group compared with the other 2 groups (
<italic>P</italic>
< .001). There was no difference in severity of illness among the 3 groups. Similarly, there were no differences in the number of patients with chronic pulmonary disease, diabetes, or non-obstetric/gynecologic cancers among the 3 groups. However, patients in the RA and NER groups had a higher rate of hypertension and obesity compared with the ES group. Teaching hospitals were more likely to use RA compared with the other 2 groups (
<italic>P</italic>
< .001).</p>
<fig id="F1" position="float">
<label>Figure 1.</label>
<caption>
<p>Patient selection flowchart.</p>
</caption>
<graphic xlink:href="jls0031330330001"></graphic>
</fig>
<table-wrap id="T1" position="float">
<label>Table 1.</label>
<caption>
<p>Demographic Characteristics, Comorbid Conditions, and Hospital Characteristics for Patients With Use of ES, Use of RA, and Use of Neither Technique</p>
</caption>
<table frame="hsides" rules="groups">
<thead valign="bottom">
<tr>
<th rowspan="1" colspan="1"></th>
<th align="left" rowspan="1" colspan="1">ES (n = 974), n (%)</th>
<th align="left" rowspan="1" colspan="1">RA (n = 3971), n (%)</th>
<th align="left" rowspan="1" colspan="1">
<italic>P</italic>
Value
<xref ref-type="table-fn" rid="TF1-1">*</xref>
</th>
<th align="left" rowspan="1" colspan="1">NER, n (%)</th>
<th align="left" rowspan="1" colspan="1">
<italic>P</italic>
Value
<xref ref-type="table-fn" rid="TF1-2">†</xref>
</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left" rowspan="1" colspan="1">Demographic characteristics</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Age, mean ± SD, y</td>
<td align="left" rowspan="1" colspan="1">44.3 ± 10</td>
<td align="left" rowspan="1" colspan="1">45.8 ± 11</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
<td align="left" rowspan="1" colspan="1">45.7 ± 10</td>
<td align="left" rowspan="1" colspan="1">.0004</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Race</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        White</td>
<td align="left" rowspan="1" colspan="1">619 (63.6)</td>
<td align="left" rowspan="1" colspan="1">2909 (73.3)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
<td align="left" rowspan="1" colspan="1">2979 (68.3)</td>
<td align="left" rowspan="1" colspan="1">.0044</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        African American</td>
<td align="left" rowspan="1" colspan="1">127 (13.0)</td>
<td align="left" rowspan="1" colspan="1">469 (11.8)</td>
<td align="left" rowspan="1" colspan="1">.2913</td>
<td align="left" rowspan="1" colspan="1">568 (13.0)</td>
<td align="left" rowspan="1" colspan="1">.9863</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Hispanic</td>
<td align="left" rowspan="1" colspan="1">91 (9.3)</td>
<td align="left" rowspan="1" colspan="1">201 (5.1)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
<td align="left" rowspan="1" colspan="1">192 (4.4)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Other</td>
<td align="left" rowspan="1" colspan="1">137 (14.1)</td>
<td align="left" rowspan="1" colspan="1">392 (9.9)</td>
<td align="left" rowspan="1" colspan="1">.0001</td>
<td align="left" rowspan="1" colspan="1">624 (14.3)</td>
<td align="left" rowspan="1" colspan="1">.8487</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    APR-DRG severity of illness</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Mild</td>
<td align="left" rowspan="1" colspan="1">715 (73.4)</td>
<td align="left" rowspan="1" colspan="1">2827 (71.2)</td>
<td align="left" rowspan="1" colspan="1">.1689</td>
<td align="left" rowspan="1" colspan="1">3136 (71.9)</td>
<td align="left" rowspan="1" colspan="1">.335</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Moderate</td>
<td align="left" rowspan="1" colspan="1">232 (23.8)</td>
<td align="left" rowspan="1" colspan="1">1051 (26.5)</td>
<td align="left" rowspan="1" colspan="1">.0912</td>
<td align="left" rowspan="1" colspan="1">1107 (25.4)</td>
<td align="left" rowspan="1" colspan="1">.312</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Severe</td>
<td align="left" rowspan="1" colspan="1">23 (2.4)</td>
<td align="left" rowspan="1" colspan="1">83 (2.1)</td>
<td align="left" rowspan="1" colspan="1">.6004</td>
<td align="left" rowspan="1" colspan="1">107 (2.5)</td>
<td align="left" rowspan="1" colspan="1">.8676</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Extreme</td>
<td align="left" rowspan="1" colspan="1">4 (0.4)</td>
<td align="left" rowspan="1" colspan="1">8 (0.2)</td>
<td align="left" rowspan="1" colspan="1">.2344</td>
<td align="left" rowspan="1" colspan="1">13 (0.3)</td>
<td align="left" rowspan="1" colspan="1">.5724</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Comorbid conditions</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Chronic pulmonary disease</td>
<td align="left" rowspan="1" colspan="1">107 (11)</td>
<td align="left" rowspan="1" colspan="1">425 (10.7)</td>
<td align="left" rowspan="1" colspan="1">.7984</td>
<td align="left" rowspan="1" colspan="1">435 (10.0)</td>
<td align="left" rowspan="1" colspan="1">.3428</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Diabetes</td>
<td align="left" rowspan="1" colspan="1">52 (5.3)</td>
<td align="left" rowspan="1" colspan="1">296 (7.5)</td>
<td align="left" rowspan="1" colspan="1">.0207</td>
<td align="left" rowspan="1" colspan="1">306 (7.0)</td>
<td align="left" rowspan="1" colspan="1">.0589</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Non-gynecologic primary cancer</td>
<td align="left" rowspan="1" colspan="1">24 (2.5)</td>
<td align="left" rowspan="1" colspan="1">88 (2.2)</td>
<td align="left" rowspan="1" colspan="1">.6411</td>
<td align="left" rowspan="1" colspan="1">73 (1.7)</td>
<td align="left" rowspan="1" colspan="1">.0948</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Hypertension</td>
<td align="left" rowspan="1" colspan="1">198 (20.3)</td>
<td align="left" rowspan="1" colspan="1">987 (24.9)</td>
<td align="left" rowspan="1" colspan="1">.003</td>
<td align="left" rowspan="1" colspan="1">1062 (24.3)</td>
<td align="left" rowspan="1" colspan="1">.0077</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Obesity</td>
<td align="left" rowspan="1" colspan="1">103 (10.6)</td>
<td align="left" rowspan="1" colspan="1">582 (14.7)</td>
<td align="left" rowspan="1" colspan="1">.001</td>
<td align="left" rowspan="1" colspan="1">556 (12.7)</td>
<td align="left" rowspan="1" colspan="1">.0629</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Provider characteristics</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Region</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        South</td>
<td align="left" rowspan="1" colspan="1">476 (48.9)</td>
<td align="left" rowspan="1" colspan="1">1308 (32.9)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
<td align="left" rowspan="1" colspan="1">1723 (39.5)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Northeast</td>
<td align="left" rowspan="1" colspan="1">179 (18.4)</td>
<td align="left" rowspan="1" colspan="1">664 (16.7)</td>
<td align="left" rowspan="1" colspan="1">.2179</td>
<td align="left" rowspan="1" colspan="1">885 (20.3)</td>
<td align="left" rowspan="1" colspan="1">.1782</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        Midwest</td>
<td align="left" rowspan="1" colspan="1">166 (17.0)</td>
<td align="left" rowspan="1" colspan="1">1301 (32.8)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
<td align="left" rowspan="1" colspan="1">1071 (24.6)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        West</td>
<td align="left" rowspan="1" colspan="1">153 (15.7)</td>
<td align="left" rowspan="1" colspan="1">698 (17.6)</td>
<td align="left" rowspan="1" colspan="1">.1661</td>
<td align="left" rowspan="1" colspan="1">684 (15.7)</td>
<td align="left" rowspan="1" colspan="1">.9807</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Hospital characteristics</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Teaching hospital</td>
<td align="left" rowspan="1" colspan="1">268 (27.5)</td>
<td align="left" rowspan="1" colspan="1">1423 (35.8)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
<td align="left" rowspan="1" colspan="1">1642 (37.6)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Urban</td>
<td align="left" rowspan="1" colspan="1">956 (98.2)</td>
<td align="left" rowspan="1" colspan="1">3104 (78.2)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
<td align="left" rowspan="1" colspan="1">3970 (91.0)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Hospital bed size</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        <250</td>
<td align="left" rowspan="1" colspan="1">176 (18.1)</td>
<td align="left" rowspan="1" colspan="1">626 (15.8)</td>
<td align="left" rowspan="1" colspan="1">.0803</td>
<td align="left" rowspan="1" colspan="1">930 (21.3)</td>
<td align="left" rowspan="1" colspan="1">.038</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        250–500</td>
<td align="left" rowspan="1" colspan="1">366 (37.6)</td>
<td align="left" rowspan="1" colspan="1">2008 (50.6)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
<td align="left" rowspan="1" colspan="1">2369 (54.3)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">        >500</td>
<td align="left" rowspan="1" colspan="1">432 (44.4)</td>
<td align="left" rowspan="1" colspan="1">1337 (33.7)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
<td align="left" rowspan="1" colspan="1">1064 (24.4)</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn fn-type="other" id="TF1-1">
<label>*</label>
<p>For the ES and RA groups, the
<italic>P</italic>
value is based on χ
<sup>2</sup>
and
<italic>t</italic>
tests for categorical and continuous variables, respectively.</p>
</fn>
<fn fn-type="other" id="TF1-2">
<label>†</label>
<p>For the ES and NER groups, the
<italic>P</italic>
value is based on χ
<sup>2</sup>
and
<italic>t</italic>
tests for categorical and continuous variables, respectively.</p>
</fn>
<fn fn-type="abbr">
<p>APR-DRG = All Patient–Refined Diagnosis-Related Group.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>
<bold>
<xref ref-type="table" rid="T2">Tables 2</xref>
</bold>
and
<bold>
<xref ref-type="table" rid="T3">3</xref>
</bold>
show the unadjusted differences in total hospital costs, departmental costs, length of stay, and surgery time for the ES group versus RA group and the ES group versus NER group, respectively. The use of RA for the laparoscopic hysterectomy procedure was associated with a higher mean hospitalization cost compared with the use of the ES alone, a difference of $1287 (
<italic>P</italic>
< .001). This higher cost was mostly because of a higher surgery cost ($2760 vs $3585,
<italic>P</italic>
< .001). The mean surgery time was significantly shorter for the ES group compared with the RA group, a difference of 41 minutes (
<italic>P</italic>
< .001). The use of the ES was also associated with a lower mean hospitalization cost compared with the NER group, a difference of $655 (
<italic>P</italic>
< .001). This lower cost was mostly attributed to lower surgery cost ($2760 vs $3435,
<italic>P</italic>
< .001). The mean surgery time was significantly shorter for patients in whom the ES was used compared with the NER group, a difference of 29 minutes (
<italic>P</italic>
< .001).</p>
<table-wrap id="T2" position="float">
<label>Table 2.</label>
<caption>
<p>Unadjusted Hospital Cost, Departmental Cost, Length of Stay, Surgery Time, and Cost Between ES and RA Groups</p>
</caption>
<table frame="hsides" rules="groups">
<thead valign="bottom">
<tr>
<th rowspan="2" colspan="1"></th>
<th align="left" colspan="2" rowspan="1">ES (n = 974)
<hr></hr>
</th>
<th align="left" colspan="2" rowspan="1">RA (n = 3971)
<hr></hr>
</th>
<th align="left" rowspan="2" colspan="1">Difference</th>
<th align="left" rowspan="2" colspan="1">
<italic>P</italic>
Value</th>
</tr>
<tr>
<th align="left" rowspan="1" colspan="1">Mean</th>
<th align="left" rowspan="1" colspan="1">95% Confidence Interval</th>
<th align="left" rowspan="1" colspan="1">Mean</th>
<th align="left" rowspan="1" colspan="1">95% Confidence Interval</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left" rowspan="1" colspan="1">Hospitalization cost, $</td>
<td align="left" rowspan="1" colspan="1">8073</td>
<td align="left" rowspan="1" colspan="1">7852, 8295</td>
<td align="left" rowspan="1" colspan="1">9360</td>
<td align="left" rowspan="1" colspan="1">9216, 9504</td>
<td align="left" rowspan="1" colspan="1">−1287</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Room and board</td>
<td align="left" rowspan="1" colspan="1">1266</td>
<td align="left" rowspan="1" colspan="1">1184, 1348</td>
<td align="left" rowspan="1" colspan="1">1106</td>
<td align="left" rowspan="1" colspan="1">1069, 1142</td>
<td align="left" rowspan="1" colspan="1">160</td>
<td align="left" rowspan="1" colspan="1">.0002</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Central supply</td>
<td align="left" rowspan="1" colspan="1">2249</td>
<td align="left" rowspan="1" colspan="1">2161, 2338</td>
<td align="left" rowspan="1" colspan="1">2718</td>
<td align="left" rowspan="1" colspan="1">2649, 2786</td>
<td align="left" rowspan="1" colspan="1">−469</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Surgery</td>
<td align="left" rowspan="1" colspan="1">2760</td>
<td align="left" rowspan="1" colspan="1">2656, 2864</td>
<td align="left" rowspan="1" colspan="1">3583</td>
<td align="left" rowspan="1" colspan="1">3502, 3664</td>
<td align="left" rowspan="1" colspan="1">−823</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Anesthesia</td>
<td align="left" rowspan="1" colspan="1">574</td>
<td align="left" rowspan="1" colspan="1">537, 612</td>
<td align="left" rowspan="1" colspan="1">446</td>
<td align="left" rowspan="1" colspan="1">432, 460</td>
<td align="left" rowspan="1" colspan="1">129</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Pharmacy</td>
<td align="left" rowspan="1" colspan="1">379</td>
<td align="left" rowspan="1" colspan="1">356, 402</td>
<td align="left" rowspan="1" colspan="1">396</td>
<td align="left" rowspan="1" colspan="1">374, 419</td>
<td align="left" rowspan="1" colspan="1">−18</td>
<td align="left" rowspan="1" colspan="1">.2893</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Laboratory</td>
<td align="left" rowspan="1" colspan="1">90</td>
<td align="left" rowspan="1" colspan="1">82, 98</td>
<td align="left" rowspan="1" colspan="1">102</td>
<td align="left" rowspan="1" colspan="1">97, 108</td>
<td align="left" rowspan="1" colspan="1">−12</td>
<td align="left" rowspan="1" colspan="1">.0437</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Other</td>
<td align="left" rowspan="1" colspan="1">896</td>
<td align="left" rowspan="1" colspan="1">837, 954</td>
<td align="left" rowspan="1" colspan="1">1231</td>
<td align="left" rowspan="1" colspan="1">1188, 1273</td>
<td align="left" rowspan="1" colspan="1">−335</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Hospital length of stay, d</td>
<td align="left" rowspan="1" colspan="1">1.53</td>
<td align="left" rowspan="1" colspan="1">1.46, 1.60</td>
<td align="left" rowspan="1" colspan="1">1.40</td>
<td align="left" rowspan="1" colspan="1">1.36, 1.43</td>
<td align="left" rowspan="1" colspan="1">0.14</td>
<td align="left" rowspan="1" colspan="1">.0008</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Surgery time, min</td>
<td align="left" rowspan="1" colspan="1">163</td>
<td align="left" rowspan="1" colspan="1">157.90, 167.10</td>
<td align="left" rowspan="1" colspan="1">203.80</td>
<td align="left" rowspan="1" colspan="1">200.50, 207.00</td>
<td align="left" rowspan="1" colspan="1">−41.30</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Surgery cost/min, $</td>
<td align="left" rowspan="1" colspan="1">17</td>
<td align="left" rowspan="1" colspan="1">17, 18</td>
<td align="left" rowspan="1" colspan="1">17</td>
<td align="left" rowspan="1" colspan="1">16, 17</td>
<td align="left" rowspan="1" colspan="1">0.81</td>
<td align="left" rowspan="1" colspan="1">.0539</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T3" position="float">
<label>Table 3.</label>
<caption>
<p>Unadjusted Hospital Cost, Departmental Cost, Length of Stay, Surgery Time, and Cost Between ES and NER Groups</p>
</caption>
<table frame="hsides" rules="groups">
<thead valign="bottom">
<tr>
<th rowspan="2" colspan="1"></th>
<th align="left" colspan="2" rowspan="1">ES (n = 974)
<hr></hr>
</th>
<th align="left" colspan="2" rowspan="1">NER (n = 4363)
<hr></hr>
</th>
<th align="left" rowspan="2" colspan="1">Difference</th>
<th align="left" rowspan="2" colspan="1">
<italic>P</italic>
Value</th>
</tr>
<tr>
<th align="left" rowspan="1" colspan="1">Mean</th>
<th align="left" rowspan="1" colspan="1">95% Confidence Interval</th>
<th align="left" rowspan="1" colspan="1">Mean</th>
<th align="left" rowspan="1" colspan="1">95% Confidence Interval</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left" rowspan="1" colspan="1">Hospitalization cost, $</td>
<td align="left" rowspan="1" colspan="1">8073</td>
<td align="left" rowspan="1" colspan="1">7852, 8295</td>
<td align="left" rowspan="1" colspan="1">8728</td>
<td align="left" rowspan="1" colspan="1">8556, 8900</td>
<td align="left" rowspan="1" colspan="1">−655</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Room and board</td>
<td align="left" rowspan="1" colspan="1">1266</td>
<td align="left" rowspan="1" colspan="1">1184, 1348</td>
<td align="left" rowspan="1" colspan="1">1294</td>
<td align="left" rowspan="1" colspan="1">1223, 1365</td>
<td align="left" rowspan="1" colspan="1">−28</td>
<td align="left" rowspan="1" colspan="1">.6195</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Central supply</td>
<td align="left" rowspan="1" colspan="1">2249</td>
<td align="left" rowspan="1" colspan="1">2161, 2338</td>
<td align="left" rowspan="1" colspan="1">2133</td>
<td align="left" rowspan="1" colspan="1">2083, 2183</td>
<td align="left" rowspan="1" colspan="1">117</td>
<td align="left" rowspan="1" colspan="1">.0249</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Surgery</td>
<td align="left" rowspan="1" colspan="1">2760</td>
<td align="left" rowspan="1" colspan="1">2656, 2864</td>
<td align="left" rowspan="1" colspan="1">3435</td>
<td align="left" rowspan="1" colspan="1">3360, 3511</td>
<td align="left" rowspan="1" colspan="1">−675</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Anesthesia</td>
<td align="left" rowspan="1" colspan="1">574</td>
<td align="left" rowspan="1" colspan="1">537, 612</td>
<td align="left" rowspan="1" colspan="1">572</td>
<td align="left" rowspan="1" colspan="1">553, 592</td>
<td align="left" rowspan="1" colspan="1">2</td>
<td align="left" rowspan="1" colspan="1">.9138</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Pharmacy</td>
<td align="left" rowspan="1" colspan="1">379</td>
<td align="left" rowspan="1" colspan="1">356, 402</td>
<td align="left" rowspan="1" colspan="1">398</td>
<td align="left" rowspan="1" colspan="1">371, 425</td>
<td align="left" rowspan="1" colspan="1">−19</td>
<td align="left" rowspan="1" colspan="1">.2966</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Laboratory</td>
<td align="left" rowspan="1" colspan="1">90</td>
<td align="left" rowspan="1" colspan="1">82, 98</td>
<td align="left" rowspan="1" colspan="1">113</td>
<td align="left" rowspan="1" colspan="1">106, 120</td>
<td align="left" rowspan="1" colspan="1">−23</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Other</td>
<td align="left" rowspan="1" colspan="1">896</td>
<td align="left" rowspan="1" colspan="1">837, 954</td>
<td align="left" rowspan="1" colspan="1">948</td>
<td align="left" rowspan="1" colspan="1">916, 981</td>
<td align="left" rowspan="1" colspan="1">−53</td>
<td align="left" rowspan="1" colspan="1">.1229</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Hospital length of stay, d</td>
<td align="left" rowspan="1" colspan="1">1.53</td>
<td align="left" rowspan="1" colspan="1">1.46, 1.60</td>
<td align="left" rowspan="1" colspan="1">1.54</td>
<td align="left" rowspan="1" colspan="1">1.49, 1.59</td>
<td align="left" rowspan="1" colspan="1">−0.01</td>
<td align="left" rowspan="1" colspan="1">.8798</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Surgery time, min</td>
<td align="left" rowspan="1" colspan="1">163</td>
<td align="left" rowspan="1" colspan="1">158, 167</td>
<td align="left" rowspan="1" colspan="1">191</td>
<td align="left" rowspan="1" colspan="1">189, 193</td>
<td align="left" rowspan="1" colspan="1">−29</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Surgery cost/min, $</td>
<td align="left" rowspan="1" colspan="1">17</td>
<td align="left" rowspan="1" colspan="1">17, 18</td>
<td align="left" rowspan="1" colspan="1">17</td>
<td align="left" rowspan="1" colspan="1">17, 18</td>
<td align="left" rowspan="1" colspan="1">0.27</td>
<td align="left" rowspan="1" colspan="1">.487</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The results of the multivariate analysis, adjusted for confounders (including patient's age, race, severity of illness, and comorbid conditions and hospital teaching status, region, and bed size) are shown in
<bold>
<xref ref-type="table" rid="T4">Tables 4</xref>
</bold>
and
<bold>
<xref ref-type="table" rid="T5">5</xref>
</bold>
. The mean hospital cost remained significantly lower for the ES group compared with the RA group after we adjusted for confounding variables, with the difference increasing to $1769 (
<italic>P</italic>
= .0332). Similar to the unadjusted results, most of the cost difference observed was because of a lower mean surgery cost in the ES group compared with the RA group: $2572 compared with $4120 (
<italic>P</italic>
= .0065). The mean surgery time remained significantly shorter for patients in the ES group compared with the RA group, a difference of 42 minutes (
<italic>P</italic>
< .001).</p>
<table-wrap id="T4" position="float">
<label>Table 4.</label>
<caption>
<p>Adjusted Hospital Cost, Departmental Cost, Length of Stay, Surgery Time, and Cost Between ES and RA Groups</p>
</caption>
<table frame="hsides" rules="groups">
<thead valign="bottom">
<tr>
<th rowspan="2" colspan="1"></th>
<th align="left" colspan="2" rowspan="1">ES (n = 974)
<hr></hr>
</th>
<th align="left" colspan="2" rowspan="1">RA (n = 3971)
<hr></hr>
</th>
<th align="left" rowspan="2" colspan="1">Difference</th>
<th align="left" rowspan="2" colspan="1">
<italic>P</italic>
Value</th>
</tr>
<tr>
<th align="left" rowspan="1" colspan="1">Mean</th>
<th align="left" rowspan="1" colspan="1">95% Confidence Interval</th>
<th align="left" rowspan="1" colspan="1">Mean</th>
<th align="left" rowspan="1" colspan="1">95% Confidence Interval</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left" rowspan="1" colspan="1">Hospitalization cost, $</td>
<td align="left" rowspan="1" colspan="1">7859</td>
<td align="left" rowspan="1" colspan="1">6730, 9176</td>
<td align="left" rowspan="1" colspan="1">9628</td>
<td align="left" rowspan="1" colspan="1">9216, 9504</td>
<td align="left" rowspan="1" colspan="1">−1769</td>
<td align="left" rowspan="1" colspan="1">.0332</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Room and board</td>
<td align="left" rowspan="1" colspan="1">1271</td>
<td align="left" rowspan="1" colspan="1">1078, 1500</td>
<td align="left" rowspan="1" colspan="1">1091</td>
<td align="left" rowspan="1" colspan="1">1069, 1142</td>
<td align="left" rowspan="1" colspan="1">180</td>
<td align="left" rowspan="1" colspan="1">.0523</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Central supply</td>
<td align="left" rowspan="1" colspan="1">2333</td>
<td align="left" rowspan="1" colspan="1">1894, 2874</td>
<td align="left" rowspan="1" colspan="1">2686</td>
<td align="left" rowspan="1" colspan="1">2649, 2786</td>
<td align="left" rowspan="1" colspan="1">−352</td>
<td align="left" rowspan="1" colspan="1">.1934</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Surgery</td>
<td align="left" rowspan="1" colspan="1">2572</td>
<td align="left" rowspan="1" colspan="1">1960, 3374</td>
<td align="left" rowspan="1" colspan="1">4120</td>
<td align="left" rowspan="1" colspan="1">3502, 3664</td>
<td align="left" rowspan="1" colspan="1">−1548</td>
<td align="left" rowspan="1" colspan="1">.0065</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Anesthesia</td>
<td align="left" rowspan="1" colspan="1">474</td>
<td align="left" rowspan="1" colspan="1">373, 603</td>
<td align="left" rowspan="1" colspan="1">481</td>
<td align="left" rowspan="1" colspan="1">432, 460</td>
<td align="left" rowspan="1" colspan="1">−7</td>
<td align="left" rowspan="1" colspan="1">.8299</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Pharmacy</td>
<td align="left" rowspan="1" colspan="1">346</td>
<td align="left" rowspan="1" colspan="1">301, 398</td>
<td align="left" rowspan="1" colspan="1">354</td>
<td align="left" rowspan="1" colspan="1">374, 419</td>
<td align="left" rowspan="1" colspan="1">−8</td>
<td align="left" rowspan="1" colspan="1">.7129</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Laboratory</td>
<td align="left" rowspan="1" colspan="1">101</td>
<td align="left" rowspan="1" colspan="1">81, 126</td>
<td align="left" rowspan="1" colspan="1">110</td>
<td align="left" rowspan="1" colspan="1">173, 3</td>
<td align="left" rowspan="1" colspan="1">−10</td>
<td align="left" rowspan="1" colspan="1">.3363</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Other</td>
<td align="left" rowspan="1" colspan="1">1126</td>
<td align="left" rowspan="1" colspan="1">912, 1392</td>
<td align="left" rowspan="1" colspan="1">1073</td>
<td align="left" rowspan="1" colspan="1">1188, 1273</td>
<td align="left" rowspan="1" colspan="1">53</td>
<td align="left" rowspan="1" colspan="1">.5237</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Hospital length of stay, d</td>
<td align="left" rowspan="1" colspan="1">1.48</td>
<td align="left" rowspan="1" colspan="1">1.37, 1.60</td>
<td align="left" rowspan="1" colspan="1">1.39</td>
<td align="left" rowspan="1" colspan="1">1.30, 1.43</td>
<td align="left" rowspan="1" colspan="1">0.10</td>
<td align="left" rowspan="1" colspan="1">.062</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Surgery time, min</td>
<td align="left" rowspan="1" colspan="1">176</td>
<td align="left" rowspan="1" colspan="1">154, 200</td>
<td align="left" rowspan="1" colspan="1">218</td>
<td align="left" rowspan="1" colspan="1">203, 234</td>
<td align="left" rowspan="1" colspan="1">−42.38</td>
<td align="left" rowspan="1" colspan="1"><.001</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Surgery cost/min, $</td>
<td align="left" rowspan="1" colspan="1">14</td>
<td align="left" rowspan="1" colspan="1">11, 16</td>
<td align="left" rowspan="1" colspan="1">17</td>
<td align="left" rowspan="1" colspan="1">15, 21</td>
<td align="left" rowspan="1" colspan="1">−3.91</td>
<td align="left" rowspan="1" colspan="1">.0015</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T5" position="float">
<label>Table 5.</label>
<caption>
<p>Adjusted Hospital Cost, Departmental Cost, Length of Stay, Surgery Time, and Cost Between ES and NER Groups</p>
</caption>
<table frame="hsides" rules="groups">
<thead valign="bottom">
<tr>
<th rowspan="2" colspan="1"></th>
<th align="left" colspan="2" rowspan="1">ES (n = 974)
<hr></hr>
</th>
<th align="left" colspan="2" rowspan="1">NER (n = 4363)
<hr></hr>
</th>
<th align="left" rowspan="2" colspan="1">Difference</th>
<th align="left" rowspan="2" colspan="1">
<italic>P</italic>
Value</th>
</tr>
<tr>
<th align="left" rowspan="1" colspan="1">Mean</th>
<th align="left" rowspan="1" colspan="1">95% Confidence Interval</th>
<th align="left" rowspan="1" colspan="1">Mean</th>
<th align="left" rowspan="1" colspan="1">95% Confidence Interval</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left" rowspan="1" colspan="1">Hospitalization cost, $</td>
<td align="left" rowspan="1" colspan="1">8186</td>
<td align="left" rowspan="1" colspan="1">7475, 8966</td>
<td align="left" rowspan="1" colspan="1">8820</td>
<td align="left" rowspan="1" colspan="1">8282, 9394</td>
<td align="left" rowspan="1" colspan="1">−634</td>
<td align="left" rowspan="1" colspan="1">.0879</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Room and board</td>
<td align="left" rowspan="1" colspan="1">1256</td>
<td align="left" rowspan="1" colspan="1">1052, 1500</td>
<td align="left" rowspan="1" colspan="1">1270</td>
<td align="left" rowspan="1" colspan="1">1129, 1428</td>
<td align="left" rowspan="1" colspan="1">−13</td>
<td align="left" rowspan="1" colspan="1">.6195</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Central supply</td>
<td align="left" rowspan="1" colspan="1">2192</td>
<td align="left" rowspan="1" colspan="1">1928, 2493</td>
<td align="left" rowspan="1" colspan="1">1988</td>
<td align="left" rowspan="1" colspan="1">1795, 2202</td>
<td align="left" rowspan="1" colspan="1">205</td>
<td align="left" rowspan="1" colspan="1">.0511</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Surgery</td>
<td align="left" rowspan="1" colspan="1">3142</td>
<td align="left" rowspan="1" colspan="1">2733, 3611</td>
<td align="left" rowspan="1" colspan="1">3745</td>
<td align="left" rowspan="1" colspan="1">3375, 4156</td>
<td align="left" rowspan="1" colspan="1">−603</td>
<td align="left" rowspan="1" colspan="1">.0225</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Anesthesia</td>
<td align="left" rowspan="1" colspan="1">522</td>
<td align="left" rowspan="1" colspan="1">448, 609</td>
<td align="left" rowspan="1" colspan="1">554</td>
<td align="left" rowspan="1" colspan="1">478, 643</td>
<td align="left" rowspan="1" colspan="1">−32</td>
<td align="left" rowspan="1" colspan="1">.1114</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Pharmacy</td>
<td align="left" rowspan="1" colspan="1">328</td>
<td align="left" rowspan="1" colspan="1">282, 381</td>
<td align="left" rowspan="1" colspan="1">354</td>
<td align="left" rowspan="1" colspan="1">308, 407</td>
<td align="left" rowspan="1" colspan="1">−26</td>
<td align="left" rowspan="1" colspan="1">.2544</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Laboratory</td>
<td align="left" rowspan="1" colspan="1">92</td>
<td align="left" rowspan="1" colspan="1">75, 114</td>
<td align="left" rowspan="1" colspan="1">109</td>
<td align="left" rowspan="1" colspan="1">92, 128</td>
<td align="left" rowspan="1" colspan="1">−17</td>
<td align="left" rowspan="1" colspan="1">.0281</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">    Other</td>
<td align="left" rowspan="1" colspan="1">917</td>
<td align="left" rowspan="1" colspan="1">778, 1081</td>
<td align="left" rowspan="1" colspan="1">939</td>
<td align="left" rowspan="1" colspan="1">820, 1076</td>
<td align="left" rowspan="1" colspan="1">−22</td>
<td align="left" rowspan="1" colspan="1">.6851</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Hospital length of stay, d</td>
<td align="left" rowspan="1" colspan="1">1.44</td>
<td align="left" rowspan="1" colspan="1">1.32, 1.57</td>
<td align="left" rowspan="1" colspan="1">1.48</td>
<td align="left" rowspan="1" colspan="1">1.38, 1.58</td>
<td align="left" rowspan="1" colspan="1">−0.04</td>
<td align="left" rowspan="1" colspan="1">.3611</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Surgery time, min</td>
<td align="left" rowspan="1" colspan="1">180</td>
<td align="left" rowspan="1" colspan="1">163, 199</td>
<td align="left" rowspan="1" colspan="1">201</td>
<td align="left" rowspan="1" colspan="1">192, 211</td>
<td align="left" rowspan="1" colspan="1">−21</td>
<td align="left" rowspan="1" colspan="1">.0103</td>
</tr>
<tr>
<td align="left" rowspan="1" colspan="1">Surgery cost/min, $</td>
<td align="left" rowspan="1" colspan="1">17</td>
<td align="left" rowspan="1" colspan="1">16, 19</td>
<td align="left" rowspan="1" colspan="1">19</td>
<td align="left" rowspan="1" colspan="1">17, 20</td>
<td align="left" rowspan="1" colspan="1">−1.45</td>
<td align="left" rowspan="1" colspan="1">.0131</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The mean hospitalization cost for the ES group also remained lower in the adjusted data (though not significantly) compared with the NER group, a difference of $634 (
<italic>P</italic>
< .0879). The mean surgery cost for the ES group was significantly lower than that for the NER group, a difference of $603 (
<italic>P</italic>
= .0225). The mean surgery time was also significantly shorter for the ES group compared with the NER group, a difference of 21 minutes (
<italic>P</italic>
= .0103).</p>
</sec>
<sec sec-type="discussion">
<title>DISCUSSION</title>
<p>One of the significant limiting factors preventing more surgeons from shifting from an open abdominal hysterectomy to a total laparoscopic hysterectomy procedure is the ability to suture laparoscopically. Although both RA and the ES automated suturing device facilitate more efficient development of this skill set, this study suggests that a benign total laparoscopic hysterectomy performed with a disposable automated laparoscopic suturing device is significantly more cost-effective than when performed with the da Vinci surgical system or with traditional laparoscopic suturing technique. This is also consistent with the limited data available in the medical literature, which have shown that overall costs are reduced by use of traditional laparoscopic techniques, compared with RA, for the performance of a laparoscopic hysterectomy procedure.
<sup>
<xref ref-type="bibr" rid="B14">14</xref>
–
<xref ref-type="bibr" rid="B16">16</xref>
</sup>
As cost containment takes on even greater importance in the US health care system, lower-cost alternatives to more expensive technologies will increasingly become necessary, and economic outcomes data will be used more often in appropriate surgical instrumentation use decision making.</p>
<p>Despite the many advantages of performing a total laparoscopic hysterectomy, as well as the increasing popularity of this procedure, most hysterectomies in the United States are still performed by the open abdominal route. This appears to be partially related to the steep learning curve of the total laparoscopic hysterectomy procedure, as well as the limited exposure of residents to this procedure during training.
<sup>
<xref ref-type="bibr" rid="B1">1</xref>
–
<xref ref-type="bibr" rid="B10">10</xref>
</sup>
The da Vinci surgical system has several advantages over traditional laparoscopic instrumentation, including 3-dimensional visualization, 7-
<italic>df</italic>
wristed motion, and improved ergonomics. These advantages have been promoted as a way for gynecologic surgeons to reduce the steep learning curve of the total laparoscopic hysterectomy procedure, especially related to the development of laparoscopic suturing skills, which is essential for laparoscopic vaginal cuff closure and represents one of the most challenging aspects of this procedure.
<sup>
<xref ref-type="bibr" rid="B4">4</xref>
,
<xref ref-type="bibr" rid="B9">9</xref>
–
<xref ref-type="bibr" rid="B17">17</xref>
</sup>
</p>
<p>These advantages of the da Vinci surgical system have led to a more rapid adoption of robotic-assisted hysterectomy procedures,
<sup>
<xref ref-type="bibr" rid="B23">23</xref>
</sup>
although widespread use is still limited by cost, lack of haptic feedback, and insufficient clinical comparative data showing improved outcome over traditional laparoscopic hysterectomy procedures. A Cochrane Database Systematic Review in 2012 concluded that the limited evidence in the medical literature did not show the benefit of robotic surgery for women with benign gynecologic diseases, including hysterectomy, in terms of effectiveness or safety.
<sup>
<xref ref-type="bibr" rid="B16">16</xref>
</sup>
Although this review only evaluated 2 randomized clinical trials consisting of 158 women, the authors concluded that the robotic-assisted approach to benign gynecologic procedures may increase postoperative complications and cost, may lengthen the operating time, and failed to show any superiority compared with laparoscopic surgery.</p>
<p>In 2010 Sarlos et al.
<sup>
<xref ref-type="bibr" rid="B15">15</xref>
</sup>
performed a prospective matched case-control study of their first 40 consecutive robotic-assisted hysterectomies for benign conditions and matched these cases 1:1 with their laparoscopic hysterectomy cases. They found that the robotic hysterectomy procedure was feasible and safe with a quick learning curve and that surgical outcomes (complications, conversion to laparotomy, intraoperative bleeding, and hospital stay) were comparable. They also found that robotic-assisted hysterectomy procedures were associated with longer operating room times and higher costs compared with the matched laparoscopic hysterectomy cases. The mean operating room time was 109 minutes versus 82.9 minutes and the mean cost was €4067 versus €2151 in the robotic-assisted hysterectomy group versus the laparoscopic hysterectomy group. Most of the cost difference, €2217 versus €822, was in the material costs of the procedure.</p>
<p>Also in 2010, Pasic et al.
<sup>
<xref ref-type="bibr" rid="B14">14</xref>
</sup>
examined data in the Premier hospital database on 36188 patients treated at 358 hospitals undergoing a laparoscopic hysterectomy in 2007 and 2008. In this study 1661 robotic-assisted hysterectomy procedures were compared with 34527 traditional laparoscopic hysterectomy procedures. The authors found that both inpatient and outpatient robotic-assisted hysterectomy procedural costs and surgical times were significantly higher than those of laparoscopic hysterectomy procedures, without any difference in adverse events. Inpatient robotic-assisted hysterectomy procedures were approximately $2600 more expensive and 0.4 hours longer and outpatient robotic-assisted hysterectomy procedures were approximately $1900 more expensive and 0.5 hours longer than a laparoscopic hysterectomy procedure.</p>
<p>In our study the cost of performing a robotic-assisted hysterectomy was significantly greater than the cost of performing an ES-assisted total laparoscopic hysterectomy after we adjusted for confounders (including patients' age, race, severity of illness, and comorbid conditions, as well as hospital teaching status, region, and bed size). The hospital cost of a robotic-assisted laparoscopic hysterectomy ($9628), as compared with the ES-assisted total laparoscopic hysterectomy ($7859), showed a cost difference of $1769 per surgical case. Most of the additional hospital cost differences between these 2 techniques were related to the cost of surgery ($1548 difference) and longer operating times (42-minute difference), making the robotic-assisted hysterectomy procedure substantially more expensive and time-consuming. Even with the additional cost of the ES device to facilitate laparoscopic suturing during a total laparoscopic hysterectomy procedure, it was still more cost-effective compared with performing a traditional laparoscopic hysterectomy procedure without the device, resulting in a $634 cost savings and 21-minute time savings.</p>
<p>The data in this study only examined costs directly related to hospitalization and did not take into account the acquisition and maintenance costs of the da Vinci surgical system. The da Vinci surgical system costs between $1.0 and $2.3 million, with an annual service agreement of $100,000 to $170,000. Instruments and accessories cost approximately $1300 to $2200 per surgical procedure.
<sup>
<xref ref-type="bibr" rid="B23">23</xref>
</sup>
Although these costs are typically amortized over several years, this would still add significant additional costs to the robotic-assisted hysterectomy procedure and thus further increase the cost difference compared with an ES-assisted total laparoscopic hysterectomy procedure.</p>
<sec>
<title>Strengths and Limitations</title>
<p>Strengths of our study include evaluation of a large database of patients and hospitals with a wide geographic representation of actual surgical costs throughout US hospitals. Additional strengths include the ability to compare cofounders including severity of illness, comorbid conditions, hospital teaching status, hospital region, and hospital bed size so that accurate cost comparisons are performed. Weaknesses of our study include dependence on billing data, which can have inherent inaccuracies, as well as having to infer the actual use of the ES during the total laparoscopic hysterectomy procedure. The indication for the total laparoscopic hysterectomy was also not available, which could potentially skew the type of hysterectomy performed toward robotic-assisted procedures for the more difficult cases. Information was also not available as to the surgeon's experience with the total laparoscopic hysterectomy procedure or the technique of laparoscopic suturing used in the NER group, which could impact surgical time. Another limitation of our study is the non-experimental study design. Although we have controlled for confounding variables, by using multivariate analysis, there still may exist variables that we were unable to control for.</p>
</sec>
</sec>
<sec sec-type="conclusions">
<title>CONCLUSION</title>
<p>Our study is consistent with the limited cost data that are available in the medical literature on laparoscopic versus robotic-assisted hysterectomy procedures. In this study we found that the use of a disposable automated laparoscopic suturing device, the ES, is significantly more cost-effective than the use of the da Vinci surgical system for the performance of an inpatient total laparoscopic hysterectomy procedure for benign conditions. The ES also does not add cost compared with traditional total laparoscopic hysterectomy procedures because of the time savings that it enables in laparoscopic suturing. Therefore a disposable automated laparoscopic suturing device is an efficient and cost-effective method for laparoscopic suturing to close the vaginal cuff during a total laparoscopic hysterectomy procedure for benign conditions.</p>
</sec>
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