Outcomes after pediatric open, laparoscopic, and robotic pyeloplasty at academic institutions

Abstract

Introduction:
Patient age and hospital volume have been shown to affect perioperative outcomes after pediatric pyeloplasty. However, there are few multicenter studies that focus on outcomes at teaching hospitals, where many of the operations are performed.

Objective:
The goal was to determine if surgical approach, age, case volume, or other factors influence perioperative outcomes in a large contemporary cohort.

Study design:
Using the clinical database/resource manager (CDB/RM) of the University Health-System Consortium (UHC), children who underwent open, laparoscopic, or robotic pyeloplasty from 2011 to 2014 were identified at 102 academic institutions. Surgery type, age, race, gender, insurance type, geographic region, comorbidities, surgeon volume, and hospital volume were measured. Multivariable mixed-effects logistic regression analysis was used to analyze independent variables associated with complication rates, length of stay (LOS), readmission rates, and ICU admission.

Results:
A total of 2219 patients were identified. Complication rates were 2.1%, 2.2%, and 3% after open, laparoscopic, and robotic pyeloplasty, respectively. Approximately 12% of patients had underlying comorbidities. Comorbidities were associated with 3.1 times increased odds for complication (p = 0.001) and a 35% longer length of stay (p < 0.001). Age, gender, insurance type, and hospital volume had no effect on complication rates. A trend was seen towards a lower rate of complications with higher surgeon volume (p = 0.08). The mean LOS was 2.0 days in the open pyeloplasty group, 2.4 days in the laparoscopic group and 1.8 days in the robotic group. Patients who underwent robotic surgery had an estimated LOS 11% shorter than those after open surgery (p = 0.03) (Table). Patients aged 5 years and under who had robotic surgery had an estimated LOS 14% shorter than those after open surgery (p = 0.06). ICU admission and hospital readmission were not associated with any variables.

Discussion:
The study is limited by the accuracy of the data submitted by the hospitals and is subject to coding error. Complication rates remain low in all three approaches, validating their safety. Patients, including younger patients, had shorter lengths of stay after robotic surgery. The statistically significant differences between approaches were small so clinically there may not be a difference.

Conclusions:
This large multicenter analysis demonstrates that patient comorbidity had the greatest impact upon complication rates and length of stay. Previous work showed that the benefits of laparoscopy were limited to older children. However, this large multicenter study suggests that these benefits now extend to young children with the application of robotics.

Full text

Outcomes after pediatric open, laparoscopic, and robotic
pyeloplasty at academic institutions
Yvonne Y. Chana, Blythe Durbin-Johnsonb, Renea M. Sturma, and Eric A. Kurzrocka,*
aDepartment of Urology, University of California Davis Children’s Hospital, Sacramento, CA, USA
bDivision of Biostatistics, University of California Davis, Davis, CA, USA
Summary
Introduction—Patient age and hospital volume have been shown to affect perioperative
outcomes after pediatric pyeloplasty. However, to date, there are few multicenter studies that focus
on outcomes at teaching hospitals, where many of the operations are performed.
Objective—The goal was to determine if surgical approach, age, case volume, or other factors
influence perioperative outcomes in a large contemporary cohort.
Study design—Using the clinical database/resource manager (CDB/RM) of the University
Health-System Consortium (UHC), children who underwent open, laparoscopic, or robotic
pyeloplasty from 2011 to 2014 were identified at 102 academic institutions. Surgery type, age,
race, gender, insurance type, geographic region, comorbidities, surgeon volume, and hospital
volume were measured. Multivariable mixed-effects logistic regression analysis was used to
analyze independent variables associated with complication rates, length of stay (LOS),
readmission rates, and ICU admission.
Results—A total of 2,219 patients were identified. Complication rates were 2.1%, 2.2%, and 3%
after open, laparoscopic, and robotic pyeloplasty, respectively. Approximately 12% of patients had
underlying comorbidities. Comorbidities were associated with 3.1 times increased odds for
complication (
p
= 0.001) and a 35% longer length of stay (
p
< 0.001). Age, gender, insurance type,
and hospital volume had no effect on complication rates. A trend was seen towards a lower rate of
complications with higher surgeon volume (
p
= 0.08). The mean LOS was 2.0 days in the open
pyeloplasty group, 2.4 days in the laparoscopic group and 1.8 days in the robotic group. Patients
who underwent robotic surgery had an estimated LOS 11% shorter than those after open surgery
(
p
= 0.03) (table). Patients aged 5 years and under who had robotic surgery had an estimated LOS
14% shorter than those after open surgery (
p
= 0.06). ICU admission and hospital readmission
were not associated with any variables.
*Corresponding author. UC Davis Children’s Hospital, 4860 Y Street, Suite 3500, Sacramento, CA 95817, USA. Tel.: +1 916 734
4561; fax: +1 916 734 8094. eakurzrock@ucdavis.edu (E.A. Kurzrock).
Conflict of interest
None.
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Discussion—The study is limited by the accuracy of the data submitted by the hospitals and is
subject to coding error. Complication rates remain low in all three approaches, validating their
safety. Patients, including younger patients, had shorter lengths of stay after robotic surgery. The
statistically significant differences between approaches were small so clinically there may not be a
difference.
Conclusions—This large multicenter analysis demonstrates that patient comorbidity had the
greatest impact upon complication rates and length of stay. Previous work showed that the benefits
of laparoscopy were limited to older children. However, this large multicenter study suggests that
these benefits now extend to young children with the application of robotics.
Keywords
Ureteral obstruction; Laparoscopy; Robotics; Treatment outcome
Introduction
Ureteropelvic junction (UPJ) obstruction is a common cause of pediatric hydronephrosis.
Since 1949, the Anderson–Hynes open dismembered pyeloplasty (OP) has been the gold
standard treatment [1]. Laparoscopic pyeloplasty (LP), first implemented in a 7-year-old
child in 1995 by Peters and colleagues [2], offered a safe and effective minimally invasive
approach. Adoption of laparoscopic pyeloplasty into common practice was limited by a
steep learning curve, with practitioners citing anatomical limitations in the pediatric
population, difficulty with instrument maneuverability, and challenges of intracorporeal
suturing as contributing factors [2–4]. The robotic interface has helped minimize this
learning curve and increased the utilization of laparoscopy [5,6]. Robot-assisted
laparoscopic pyeloplasty (RALP) has been shown to be feasible and safe in the pediatric
population [7].
LP and RALP offer a shorter length of stay (LOS) and decreased analgesia demands in older
patients [4,8]. A few studies have evaluated predictors of good postoperative recovery and
the incidences of intraoperative and postoperative complications after open, laparoscopic
and RALP. A previous study by Tanaka et al. [8] demonstrated that higher surgeon volume is
associated with shorter LOS after LP and that the LOS benefit of LP was limited to children
older than 10 years of age. Using the Nationwide Inpatient Sample (NIS) database, which
consists of information provided predominantly by community hospitals, Sukumar et al. [9]
revealed that postoperative complications were higher in low-volume centers (≤ 16 OP per
year). However, to date, there have been few multicenter studies that focus on outcomes at
teaching hospitals, where many of the surgeries are performed. The goal was to determine if
age, case volume, or other factors were associated with perioperative outcomes. Based on
Tanaka et al.’s [8] study that showed that the benefits of laparoscopy were limited to older
children, we hypothesized that a benefit of shorter LOS after robotic pyeloplasty would also
be limited to older children.
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Methods and materials
Data source
The University Health-System Consortium (UHC) is a non-profit, member-driven alliance of
more than 117 academic medical centers and over 333 of their affiliated hospitals. Its
clinical database/resource manager (CDB/RM) provides comparative data in clinical and
surgical outcomes. Compared to certain databases where data collection is not standardized
across states, CDB/RM is a more consistent database that has been used by many
investigators to study clinical and surgical outcomes [10–13].
In this study, pediatric admissions from 2011 to 2014 were obtained using the age criterion
less than 18 years as the query strategy. The International Classification of Diseases – ninth
revision (ICD-9) procedure code 55.87 was used to limit our cohort to pediatric patients
undergoing pyeloplasty. ICD-9 codes 17.42 and 54.21 were used to identify RALP and
conventional laparoscopic pyeloplasty, respectively. Patients undergoing concomitant
surgeries and those who were admitted prior to the date of pyeloplasty were excluded from
analysis.
Statistical analysis
For all patients, the variables analyzed included surgery type, age, race, gender, insurance
type (Medicaid, private, uninsured, or other), geographic region, comorbidities, surgeon
volume, and hospital volume. Comorbid conditions were defined using the criteria published
by Elixhauser et al. [14]. These conditions include diagnoses for congenital anomalies and
spina bifida (Supplementary Table 1). The outcomes examined were LOS, ICU admission,
hospital readmission and complications. Complication ICD-9 codes were adapted from
Sukumar et al. [9] with the addition of potential intraoperative complications during
pyeloplasty, including injury to the spleen, liver, and intestine (Supplementary 4 2). Any of
these codes reported during the admission and not present on admission were considered a
complication.
The associations between complications or ICU admission and patient/surgery
characteristics were analyzed using multivariable mixed-effects logistic regression models.
The associations between LOS and patient/surgery characteristics were analyzed using a
multivariable mixed-effects lognormal–Poisson mixture model, which is similar to a
negative binomial model.
All models included fixed effects for surgery type, age, sex, insurance type, comorbidities,
surgeon volume, and hospital volume, and random effects for surgeon and hospital. In
addition to analyzing age as a continuous variable, a model for LOS also included an
interaction effect between surgery type and age groups (ages 0–5, 5–10, 10–17 years) to
provide a stratification for possible clinical utility. We chose these age groups based upon
previous literature showing that age 10 years was of significance.
Patients with comorbidities may or may not be offered a particular surgical approach
because of their condition or perceived risk, which might add a bias. Since a small group of
patients with comorbidites can significantly affect the results, we performed a subset
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analysis of “healthy” patients, without comorbidity or complication, to determine if the
association between surgical approach and LOS persisted when only healthy patients
without perioperative events were included.
The analysis data set was constructed using SAS software for Windows, version 9.4 (SAS
Institute, Cary, NC). Statistical analyses were conducted using the statistical software
environment R, version 3.2.1. Mixed-effects modeling was conducted using the R package
lme4, version 1.1-8.
Results
Univariate analysis
A total of 2,219 patients from 102 academic centers met the inclusion criteria. Of these,
1,540 underwent OP, 46 underwent LP, and 633 underwent RALP. The median age was 36
months. Forty percent of the patients had Medicaid and 58% had private insurance.
Approximately 12% of the patients had underlying comorbidities (Table 1). The mean LOS
was 2.0 days in the OP group compared with 2.4 days in the LP group and 1.8 days in the
RALP group (Table 2 and Summary table). ICU admission rates were similar between the
OP and RALP groups, 3% and 3.6% respectively (Table 2). Only 9% of ICU admissions
were neonatal. In the “healthy” cohort, the mean LOS was less at 1.8 days in the OP group,
1.9 days in the LP group, and 1.6 days in the RALP group (Table 2).
Multivariate analysis
Complications—Adjusting for all other variables, subjects with comorbidities had 3.1
times higher odds for complication than patients without comorbidities (
p
= 0.001). The
most common complication was pulmonary collapse/atelectasis, which accounted for 19%
of the cases (Supplementary Table 3). A trend was seen towards a lower rate of
complications with higher surgeon volume (
p
= 0.08). Despite analyzing 300 surgeons, this
study was not powered to detect a significant effect of surgeon volume on complication
incidence because complications were rare (Table 3). There were no associations found
between patient age or surgical approach with type of complication (Supplementary Table
3).
Length of stay
Multivariable analysis of LOS demonstrated that age, gender, surgeon volume, and hospital
volume were not associated with LOS. Patients who had RALP had an estimated LOS 11%
shorter than those who had open surgery (
p
= 0.03) (Table 4 and Summary table). When
each age group was considered separately, patients aged 5 years and under who had RALP
tended to significance with an estimated LOS 14% shorter than those who had OP (
p
= 0.06,
CI 0.77–1.0). Likewise, no significant difference in LOS was seen between robotic and open
surgery in other age groups. Adjusting for all other variables, subjects with private or
military insurance were estimated to have a LOS 12% shorter than subjects with other
insurance types (
p
< 0.001), and subjects with comorbidities were estimated to have a LOS
34% longer than subjects without comorbidities (
p
< 0.001) (Table 4).
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Table 4 shows results of the mixed-effects lognormal–Poisson regression model of LOS in
healthy subjects (those without comorbidity or complication). Adjusting for all other
variables, healthy subjects who had RALP were estimated to have a LOS 17% shorter than
subjects who had OP (
p
= 0.005). When each age group was considered separately, subjects
aged 5 years and under who had RALP were estimated to have a LOS 22% shorter than
those who had OP (
p
= 0.008). No significant differences in LOS were seen by surgery type
in other age groups. Adjusting for all other variables, subjects with private or military
insurance were estimated to have a LOS 9% shorter than those with other types of insurance
(
p
= 0.017).
Readmission
Readmissions for all patients within 14 days of pyeloplasty were queried to capture
diagnoses of delayed postoperative complications requiring admission. There were 28
(1.26%) readmissions. Seven readmissions were associated with stent removal, 18 with stent
placement and three with percutaneous nephrostomy tube placement. This database only
includes inpatient encounters. Using a separate UHC billing database of a similar cohort,
which includes inpatient and outpatient encounters, we have previously shown that most
postoperative ureteral stent and nephrostomy tube placements are performed as an outpatient
basis. The associations between surgical approach, patient variables and need for
postoperative stents are detailed in that manuscript [15]. For this study, we found no other
same hospital readmissions other than for stent placement.
Discussion
There is an increasing trend towards robotic pyeloplasty. Liu et al. [6] demonstrated that LP
decreased from 12% to 3% from 2003 to 2009 (Kids’ Inpatient Databases [KID]) and RALP
increased to 12% of cases in 2009. Review of the NIS database by Sukumar et al. [16]
demonstrated that LP and RALP accounted for 17% of the cases between 2008 and 2010
[6]. Varda et al. [18] reviewed the Perspective database from 2003 to 2010 and also
demonstrated a steady increase in robotic pyeloplasty. Compared with these recent analyses
of KID (an extrapolation of 320 RALP) and NIS (an extrapolation of 206 LP+RALP cases),
this large and current analysis (633 RALP cases) revealed decreased use of LP (2%) and a
much higher application of robots (29%) at academic centers. We also found the mean age at
surgery for RALP has also substantially decreased. Thus, utilization of RALP continues to
rise and age to decrease, particularly at academic centers.
In a recent study, Koh et al. showed that shorter LOS correlated with less parental wage loss
and lower hospital expenses [17]. Length of stay after RALP is shorter than OP and LP, and
surprisingly, this effect is present in patients aged 5 and under in the healthy cohort.
However, there was no significant difference in LOS in older age groups between surgical
approaches. This result disproves our initial hypothesis that the benefits of RALP are limited
to older children. In the past, it was believed that the advantages of laparoscopy were limited
to the older pediatric population [4,8]. Varda et al. [18] demonstrated that patients aged 11–
18 years had 40 times the odds of undergoing robotic pyeloplasty compared with infants
(95% CI 8.6–191). However, there has been an increase in RALP in infants [6]. Avery et al.
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[19] recently demonstrated a 91% success rate after RALP in infants. Two small center
series have demonstrated the efficacy of RALP in infants with perioperative outcomes
similar to open surgery and lower analgesic requirements [20,21]. Collectively, these
findings suggest that urologic surgeons are slowly traversing the initial technical limitations
of robot-assisted laparoscopy in infants and the application of this technology in younger
children is increasing at academic centers.
Multivariate analysis indicated that surgical approach had minimal effect on the rates of
intraoperative and postoperative complications. ICU admissions were minimal in all surgical
groups. This is in accordance with other database and single center studies [18,22,23]. There
was a significant association between the presence of underlying medical comorbidities and
complication rates. Specifically, those with comorbidities had three times the odds of having
complications compared with those without underlying medical issues. We are limited by
the nature of a retrospective database study to evaluate association between comorbidities
and intraoperative anatomical and technical challenges. However, this finding nevertheless
highlights that patients with underlying medical issues are at higher risk for intra- and/or
perioperative complications.
An NIS study found that low-volume hospitals were associated with higher complications
[9]. Without the ability to identify surgeons in that study, it is quite possible the hidden
variable of low-volume surgeon was the critical factor affecting complication rates.
Although the results of our study did not indicate a significant effect of hospital or surgeon
volume on complication rates, a trend toward lower complications with higher surgeon
volume was noted (
p
= 0.08). This is in accordance with our previous studies of pediatric
ureteral reimplant and hypospadias surgery in which we found surgeon volume, not hospital
volume, was significantly associated with reduced perioperative morbidity [24,25].
The study is limited by the accuracy of the data submitted by the hospitals and is subject to
coding error. Owing to the limitations of a retrospective database study, we were unable to
determine the conversion rates to the open approach. To our knowledge, conversion rates for
pediatric RALP range from zero to 1.4% from multi-institution studies to zero in single
center studies [19,26,27]. Complications managed on an outpatient basis or possibly at
hospitals not within the UHC database were not captured. Despite the statistical significance
and very low
p
values, the confidence intervals approach 1.0 in both the standard and healthy
cohort analyses. Thus, the associated decrease in LOS after RALP is very small and may not
be clinically significant. Although no regional difference in LOS was found, many unknown
variables including traffic, availability of transport, and weather, which do not equally
distribute across populations, may also contribute to this finding. Nevertheless, this large,
retrospective analysis of 102 academic centers and 300 surgeons demonstrates that
complication rate and LOS are very similar to either the open or the robotic approach for all
ages. At the authors’ institution, RALP has been utilized for progressively younger patients
but not infants. The results of this large, multicenter analysis prompt consideration of a
further decrease in age selection for RALP at appropriate centers as the benefits of RALP
may not be limited to older children as previously believed.
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Conclusions
This large multicenter analysis demonstrates that patient comorbidity had the greatest impact
upon complication rates and length of stay. Previous work showed that the benefits of
laparoscopy were limited to older children. However, this large multicenter study suggests
that these benefits now extend to young children with the application of robotics.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
Data from the UHC Clinical Data Base/Resource Manager, Chicago, IL: UHC; 2012. https://www.uhc.edu.
Accessed July 8, 2014; used by permission of UHC. All rights reserved. The information contained in this article
was based in part on the Performance Package data maintained by the University HealthSystem Consortium (UHC).
Copyright 2015 UHC. All rights reserved.
Funding
This project was supported by the National Center for Advancing Translational Sciences, National Institutes of
Health, #UL1 TR000002.
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Table 1
Patient characteristics by surgery type (all patients).
Patient characteristic Open (n = 1,540) Laparoscopic (n = 46) Robotic (n = 633) All subjects (n = 2,219)
Age (months)
Mean (SD) 42.8 (57) 102.9 (68) 110.3 (65) 63.3 (66)
Median (range) 13 (0–215) 91.5 (4–214) 105 (2–215) 36 (0–215)
Age group
(n
, %)
5 years and under 1,191 (77%) 14 (30%) 210 (33%) 1,415 (64%)
6–10 years 192 (13%) 16 (35%) 182 (29%) 390 (18%)
11–17 years 157 (10%) 16 (35%) 241 (38%) 414 (19%)
Sex (
n
, %)
Male 1,120 (73%) 29 (63%) 388 (61%) 1,537 (69%)
Female 420 (27%) 17 (37%) 245 (39%) 682 (31%)
Race (
n
, %)
White 1,012 (66%) 38 (83%) 486 (77%) 1,536 (69%)
Black 170 (11%) 1 (2%) 42 (7%) 213 (10%)
Asian 65 (4%) 1 (2%) 15 (2%) 81 (4%)
Other 293 (19%) 6 (13%) 90 (14%) 389 (18%)
Insurance type (
n
, %)
Medicaid 652 (42%) 16 (35%) 230 (36%) 898 (41%)
Private 866 (56%) 30 (65%) 395 (62%) 1,291 (58%)
Uninsured/self-pay 9 (0.6%) 0 2 (0.3%) 11 (0.5%)
Other/unknown 13 (0.8%) 0 6 (0.9%) 19 (0.9%)
Region (
n
, %)
Midwest 488 (32%) 12 (26%) 186 (29%) 686 (31%)
Northeast 372 (24%) 14 (30%) 225 (36%) 611 (28%)
South 395 (26%) 15 (33%) 148 (23%) 558 (25%)
West 285 (19%) 5 (11%) 74 (12%) 364 (16%)
Comorbidities (
n
, %)
No 1,396 (91%) 32 (70%) 534 (84%) 1,962 (88%)
Yes 144 (9%) 14 (30%) 99 (16%) 257 (12%)
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Table 2
Patient outcomes by surgery type.
Patient outcome Open (n = 1,540) Laparoscopic (n = 46) Robotic (n = 633)
All patients
LOS
Mean (SD) 2.0 (1.2) 2.4 (1.7) 1.8 (1.3)
ICU admission (n, %) 56 (3.6%) 0 19 (3%)
ICU days
Mean (SD) 0.1 (0.3) 0 (0) 0.1 (0.4)
Any complications (
n
, %) 33 (2.1%) 1 (2.2%) 19 (3%)
Open (n = 1,278) Laparoscopic (n = 29) Robotic (n = 478)
Healthy patients
LOS
Mean (SD) 1.8 (1) 1.9 (1) 1.6 (0.8)
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Table 3
Multivariable mixed effects logistic regression analysis of any complications (all patients)
Covariate Odds ratio (95% CI) p
Surgery: laparoscopic vs. open 0.42 (0.05, 3.60) 0.43
Surgery: robotic vs. open 1.15 (0.54, 2.43) 0.72
Surgery: robotic vs. laparoscopic 2.73 (0.33, 22.8) 0.35
Age (years) 1.01 (0.95, 1.07) 0.74
Sex: female vs. male 0.95 (0.51, 1.77) 0.88
Insurance: private/military vs. other 0.83 (0.46, 1.50) 0.54
Comorbidities (yes vs. no) 3.00 (1.58, 5.71) 0.001
Hospital volume (cases/year) 0.97 (0.90, 1.04) 0.36
Surgeon volume (cases/year) 0.85 (0.71, 1.02) 0.08
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Table 4
Multivariable mixed effects lognormal–Poisson regression analysis of length of stay
Covariate Rate ratio (95% CI) p
All patients
Surgery: laparoscopic vs. open 1.06 (0.86, 1.31) 0.57
Surgery: robotic vs. open 0.90 (0.82, 0.99) 0.03
Surgery: robotic vs. laparoscopic 0.85 (0.68, 1.05) 0.12
Age (years) 1.00 (0.98, 1.02) 0.81
Sex: female vs. male 1.00 (0.94, 1.07) 0.99
Insurance: private/military vs. other 0.89 (0.83, 0.95) < 0.001
Comorbidities (yes vs. no) 1.34 (1.23, 1.46) < 0.001
Surgeon volume (cases/year) 0.99 (0.97, 1.01) 0.53
Hospital volume (cases/year) 1.00 (0.99, 1.01) 0.92
Surgery: laparoscopic vs. open (≤ 5 years) 1.11 (0.77, 1.59) 0.59
Surgery: robotic vs. open (≤ 5 years) 0.88 (0.77, 1.00) 0.06
Surgery: robotic vs. laparoscopic (≤ 5 years) 0.79 (0.54, 1.16) 0.23
Surgery: laparoscopic vs. open (6–10 years) 0.92 (0.64, 1.32) 0.64
Surgery: robotic vs. open (6–10 years) 0.91 (0.77, 1.06) 0.22
Surgery: robotic vs. laparoscopic (6–10 years) 0.99 (0.68, 1.42) 0.95
Surgery: laparoscopic vs. open (11–17 years) 1.19 (0.86, 1.65) 0.30
Surgery: robotic vs. open (11–17 years) 0.91 (0.79, 1.06) 0.25
Surgery: robotic vs. laparoscopic (11–17 years) 0.77 (0.56, 1.06) 0.11
Healthy patients
Surgery: laparoscopic vs. open 0.94 (0.70, 1.25) 0.66
Surgery: robotic vs. open 0.85 (0.77, 0.95) 0.005
Surgery: robotic vs. laparoscopic 0.91 (0.68, 1.22) 0.54
Age (years) 1.01 (0.99, 1.04) 0.22
Sex: female vs. male 1.01 (0.93, 1.09) 0.86
Insurance: private/military vs. other 0.91 (0.85, 0.98) 0.02
Surgeon volume (cases/year) 1.00 (0.98, 1.02) 0.83
Hospital volume (cases/year) 1.00 (0.99, 1.01) 0.70
Surgery: laparoscopic vs. open (≤ 5 years) 0.89 (0.55, 1.44) 0.64
Surgery: robotic vs. open (≤ 5 years) 0.82 (0.71, 0.96) 0.01
Surgery: robotic vs. laparoscopic (5 years) 0.92 (0.56, 1.51) 0.75
Surgery: laparoscopic vs. open (6–10 years) 0.93 (0.58, 1.50) 0.77
Surgery: robotic vs. open (6–10 years) 0.87 (0.72, 1.04) 0.13
Surgery: robotic vs. laparoscopic (6–10 years) 0.93 (0.58, 1.51) 0.78
Surgery: laparoscopic vs. open (11–17 years) 0.99 (0.60, 1.64) 0.97
Surgery: robotic vs. open (11–17 years) 0.88 (0.73, 1.05) 0.15
Surgery: robotic vs. laparoscopic (11–17 years) 0.88 (0.54, 1.45) 0.63
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Table
LOS by surgery type and multivariate analysis of LOS (all patients)
Open (n = 1,540) Laparoscopic (n = 46) Robotic (n = 633)
LOS (days) mean (SD) 2.0 (1.2) 2.4 (1.7) 1.8 (1.3)
Multivariable mixed-effects lognormal–Poisson regression analysis
Covariate Rate ratio (95% CI)
p
Surgery: laparoscopic vs. open 1.06 (0.86, 1.31) 0.57
Surgery: robotic vs. open 0.90 (0.82, 0.99) 0.03
Surgery: robotic vs. laparoscopic 0.85 (0.68, 1.05) 0.12
Age (years) 1.00 (0.98, 1.02) 0.81
Sex: female vs. male 1.00 (0.94, 1.07) 0.99
Insurance: private/military vs. other 0.89 (0.83, 0.95) < 0.001
Comorbidities (yes vs. no) 1.34 (1.23, 1.46) < 0.001
Surgeon volume (cases/year) 0.99 (0.97, 1.01) 0.53
Hospital volume (cases/year) 1.00 (0.99, 1.01) 0.92
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. Author manuscript; available in PMC 2018 February 01.