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Abstract

Introduction

While survival following liver transplantation has improved over the past 3 decades, few studies have examined the changes over time in hospital length of stay (LOS), a surrogate for healthcare expenditure and an important short-term outcome measure.

Research question

The purpose of this study was to compare post-transplantation LOS over the last 3 decades.

Design

A cross-sectional analysis of 150 603 adult liver transplant recipients between September 1987 and July 2021 from the UNOS database was conducted. The patients were placed into 3 eras (1987-1989, 1990-1999, and 2000-2021) based on a Join point regression analysis of significant time points of change in LOS trends. Risk factors that were significant in univariate analysis (P < .05) were included in the multivariable Cox regression analysis, which controlled for 29 donor/recipient characteristics.

Results

Among adult patients, the average LOS following liver transplantation changed from 51.5 days in 1987 to 16.3 days in 2021, with a relatively steeper decline prior to 2000. On multivariable Cox regression, patients in both the 1987-1989 cohort (hazard ratio [HR]: 0.54, 95% confidence interval [CI]: 0.52-0.57) and the 1990-1999 cohort (HR: 0.77, CI: 0.76-0.78) had significantly prolonged lengths of stay (HR < 1 associated with later hospital discharge) compared to the 2000-2021 cohort.

Conclusions

This analysis found that mean LOS decreased over time in adult liver transplant recipients, even after controlling for donor and recipient-level factors. Future studies are needed to elucidate root cause factors for this decline in LOS over time.

Keywords

research quantitative methods regression statistics descriptive systems health liver transplant hospital outcomes

Introduction

Over the past several decades, mortality following liver transplantation has improved considerably, due in part to advances in surgical techniques as well as improvements in the medical and critical care of transplant recipients.¹ In addition to this increase in post-transplant survival, metrics beyond mortality are critical to improving patient outcomes and gauging the improvements in liver transplantation. For example, while the model for end-stage liver disease (MELD) and pediatric end-stage liver disease (PELD) scoring systems have led to improvements in waitlist mortality, their implementation has also been linked to increased medical costs, demonstrating that the sole consideration of survival metrics in clinical decision making may provide a limited perspective in evaluating overall transplantation outcomes.²

Recently, several studies have indicated that hospital length of stay (LOS) is both an important short-term outcome measure and a surrogate for healthcare expenditures.³ Several studies have established LOS as a measure of operational success, directly associating prolonged LOS with adverse post-transplantation outcomes and increased complications.^4,5 Likewise, some studies have linked LOS to center-level characteristics, such as program experience and administrative support.^6,7 Furthermore, LOS has been used as a measure of healthcare resource utilization, since prolonged LOS required more critical care and financial resources.⁸ As healthcare facilities face increased economic and resource constraints, this facet of LOS has become increasingly relevant to the delivery of care.

Most research in the field has focused on associations between LOS and pre/posttransplant patient-level predictors.^9–12 For example, the LOS index used 22 donor and recipient factors to predict hospital LOS following liver transplantation.¹³ Similarly, several studies have used LOS in predictive models to forecast clinical outcomes following surgery.^14,15 Although there is existing literature on LOS as an outcome measure, there is limited research on long-term changes in LOS over time. Examining longitudinal trends in LOS can provide additional insight into the trajectory of liver transplantation practices in the United States while offering actionable avenues for improvements in clinical and financial domains.

The primary objective of this observational study was to identify general trends in hospital LOS following liver transplantation from 1987 to 2021. A second objective was to identify patterns in LOS over time for high- and low-acuity patients and for specific Organ Procurement and Transplantation Network (OPTN) regions.

Design/Methods

Design

This study was a retrospective, cross-sectional analysis of liver transplant patients in the United States. Institutional Review Board (IRB) approval was obtained prior to the analysis of data and drafting of the manuscript.

Population

This analysis used the liver registry with de-identified patient-level data collected by the Organ Procurement and Transplantation Network (OPTN). The database included all patients listed for liver transplantation in the United States.

Sampling

A retrospective analysis was performed of all adult recipients who underwent liver transplantation between September 30, 1987, and June 30, 2021. All transplant recipients older than 18 years were included in the analysis. Donor and recipient characteristics were reported at the time of transplant. Follow-up information was collected at 6 months and then yearly after transplantation. Recipients undergoing multivisceral transplants (n = 11 248) were excluded from the study. Recipients with a missing discharge date were also excluded (n = 6767). Traditional measures of patient health such as the MELD score lacked sufficient data for the earlier years in the study; therefore, the LOS index, which accounts for a comprehensive list of patient risk factors with relatively complete entry completion through time, was used to define relatively low (<10th percentile LOS index) and high (>90th percentile LOS index) acuity patients for potential hospital LOS.

Analysis

Data were analyzed using a standard statistical software package, Stata^® 17.0 (Stata Corp, College Station, TX, USA). Continuous variables were reported as a mean (standard deviation) and compared using the Student's t-test. Contingency table analysis was used to compare categorical variables. Results were considered significant at a P-value of <.05. All reported P-values were 2-sided. The primary outcome measure was discharge from the hospital. Time to discharge was assessed as time from the date of transplantation to the date of discharge. A Poisson regression model and Monte Carlo Permutation Test for model selection were used to determine 3 statistically significant eras for the study using the Joinpoint Regression Program, Version 4.9.1.0. Excel was used to generate mean LOS and transplant volume graphs with trendlines. Kaplan-Meier analysis was used for time-to-event analysis. The LOS was the dependent variable in the Cox regression analysis. The 3 eras (1987-1989, 1990-1999, 2000-2021) and recipient and donor risk factors were the independent variables.

Twenty-nine recipient, donor, and center-level factors found to be significant in prior analyses (P < .05) were used in the univariate analyses.¹³ Continuous variables were categorized using clinically relevant groupings. Normally distributed continuous variables were analyzed using ANOVA, while non-normally distributed continuous variables were analyzed using the Kruskal-Wallis test. Pearson's chi-square test was used to analyze binary and categorical variables. Recipient risk factors were collected at the time of transplantation. ICU and hospital admission refers to the type of admission at the time of transplantation.

Missing variables were imputed using predictive mean matching imputation method for incomplete predictors in the OPTN database. The following donor variables were imputed: functional status at transplant (0.44% missing entry completion), donor age (0.02%), recipient's bilirubin concentration at transplant (0.01%), distance from donor hospital to transplant center (1.35%), recipient BMI (1.67%), recipient weight (0.79%), donor weight (1.8%), recipient's albumin levels at transplant (1.86%), recipient height (0.66%), donor height (4.99%), cold ischemia time (5.33%). A sensitivity analysis coding missing entries as an absence of the risk factor was performed. Among the factors with the highest missing entry completion, such as cold ischemia time and donor-recipient height difference, none were significant in multivariable analysis.

Results

Study Characteristics

The sample included 150 603 transplant recipients. Recipients were placed into 3 time periods: 1987-1989 (2389 patients), 1990-1999 (28 083 patients), and 2000-2021 (120 131 patients). Mean hospital LOS was 17.9 days. The overall average LOS index of recipients decreased significantly over the 3 eras. In contrast, the average donor and recipient age increased significantly over time. The proportion of patients admitted to the ICU and on a ventilator decreased significantly across the 3 eras, while the proportion of patients on dialysis increased ( Table 1 ).

Table 1.

Demographic Characteristics of Adult Liver Transplant Recipients from 1987 to 2021.

Demographic factors		TotalN = 150 603	1987-1989N = 2389	1990-1999N = 28 083	2000-2021N = 120 131	P-value
Demographic factors		Median [IRQ]	Median [IRQ]	Median [IRQ]	Median [IRQ]	P-value
LOS index		4 [−1-12]	7 [2-13]	5 [1-12]	4 [−1 to 11]	<.001
Recipient factors
		Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)
Recipient age		52.7 (11.1)	44.6 (11.7)	48.7 (11.0)	53.8 (10.8)	<.001
Recipient BMI		34.7 (1332.7)	25.1 (5.4)	27.0 (6.0)	36.6 (1488.7)	.55
		N (%)	N (%)	N (%)	N (%)
Female recipient		53 163 (35.3)	1132 (47.4)	11 486 (40.9)	40 484 (33.7)	<.001
Recipient race	Black	12 199 (8.1)	134 (5.6)	1853 (6.6)	10 331 (8.6)	<.001
	White	113 856 (75.6)	2057 (86.1)	22 438 (79.9)	89 257 (74.3)	<.001
	Hispanic	16 868 (11.2)	117 (4.9)	2612 (9.3)	14 055 (11.7)	<.001
	Asian	5723 (3.80)	60 (2.5)	871 (3.1)	4805 (4.0)	<.001
	Other Ethnicity	1958 (1.3)	22 (0.9)	309 (1.1)	1682 (1.4)	<.001
Donor factors
Donor age		39.7 (16.7)	26.4 (11.2)	34.8 (16.6)	41.2 (16.5)	<.001
Donor BMI		26.9 (6.1)	23.3 (4.2)	24.5 (4.9)	27.3 (6.2)	<.001
Female donor		60 241 (40.0)	769 (32.2)	10 643 (37.9)	48 893 (40.7)	<.001
Donor race	Black	22 440 (14.9)	172 (7.2)	2893 (10.3)	19 341 (16.1)	<.001
	White	105 573 (70.1)	2035 (85.2)	22 101 (78.7)	81 449 (67.8)	<.001
	Hispanic	17 319 (11.5)	136 (5.7)	2303 (8.2)	14 896 (12.4)	<.001
	Asian	3163 (2.1)	19 (0.8)	393 (1.4)	2763 (2.3)	<.001
	Other Ethnicity	N = 2259 (1.5)	N = 29 (1.2)	N = 365 (1.3)	1802 (1.5)	.012

Abbreviations: LOS, length of stay; BMI, body mass index.

Data Entry Rate

Data entry completion for variables is listed in Table 2 . Missing variables were imputed using predictive mean matching imputation method. Variables that were less than 90% populated were not used in the analysis.

Table 2.

Data Entry Completion and Multivariable Regression for Dependent Variable Length of Stay.

Reference group	Study group	Entry completion	Percent of patients	Hazard ratio	P-value	95% CI
2000-2021	1987-1989	100.00%	1.59%	0.53	<.001	(0.50-0.55)
2000-2021	1990-1999	100.00%	18.65%	0.77	<.001	(0.76-0.79)
ABO identical	ABO incompatible	99.98%	1.01%	0.96	.129	(0.91-1.01)
ABO identical	ABO compatible	99.98%	7.22%	0.95	<.001	(0.93-0.97)
Recipient age 30-60	Recipient age 18-30	100.00%	4.73%	1.09	<.001	(1.06-1.11)
	Recipient age 60-65	100.00%	16.48%	0.90	<.001	(0.89-0.91)
	Recipient age >65	100.00%	13.55%	0.90	<.001	(0.89-0.92)
Donor age 30-45	Donor age 15-20	99.98%	12.11%	1.03	.003	(1.01-1.05)
	Donor age 21-30	99.98%	19.28%	1.01	.345	(0.99-1.02)
	Donor age 45-55	99.98%	19.40%	0.97	<.001	(0.96-0.99)
	Donor age 55-60	99.98%	8.20%	0.96	<.001	(0.94-0.98)
	Donor age 60-70	99.98%	9.89%	0.99	.213	(0.97-1.01)
	Donor age >70	99.98%	3.69%	1.02	.192	(0.99-1.05)
Recipient albumin >2.5	Recipient albumin 2.0-2.5	98.14%	18.04%	0.93	<.001	(0.92-0.95)
	Recipient albumin 1.5-2.0	98.14%	6.38%	0.90	<.001	(0.88-0.92)
	Recipient albumin <1.5	98.14%	1.11%	0.87	<.001	(0.83-0.92)
No spontaneous bacterial peritonitis	Spontaneous bacterial peritonitis	92.21%	6.74%	0.98	.031	(0.96-1.00)
No hepatitis C	Hepatitis C	100.00%	24.06%	0.98	.003	(0.97-0.99)
Not dialysis dependent	Dialysis dependent	99.79%	7.51%	0.76	<.001	(0.74-0.78)
BMI <30	BMI 30-34	98.33%	20.82%	0.99	.351	(0.98-1.01)
BMI <30	BMI >40	98.33%	3.79%	0.92	<.001	(0.89-0.95)
Cold ischemia time 6-10h	Cold ischemia time <4h	94.67%	11.69%	1.10	<.001	(1.08-1.12)
	Cold ischemia time 4-6h	94.67%	29.93%	1.11	<.001	(1.09-1.12)
	Cold ischemia time 10-12h	94.67%	9.28%	0.91	<.001	(0.89-0.93)
	Cold ischemia time >12h	94.67%	6.66%	0.87	<.001	(0.85-0.89)
Not donated after cardiac death	Donation after cardiac death	4.40%	4.33%	0.99	.347	(0.96-1.01)
Distance from hospital less than 500 miles	Distance from hospital 500-1000 miles	98.65%	4.88%	0.97	.048	(0.95-1.00)
Distance from hospital less than 500 miles	Distance from hospital over 1000 miles	98.65%	2.00%	0.99	.495	(0.95-1.03)
Did not graduate high school	High school degree	91.90%	34.19%	1.13	<.001	(1.12-1.15)
	Technical degree	91.90%	19.47%	1.15	<.001	(1.13-1.17)
	Bachelor's degree	91.90%	13.94%	1.15	<.001	(1.13-1.17)
	Doctorate degree	91.90%	5.74%	1.14	<.001	(1.11-1.17)
	Hepatocellular carcinoma	99.68%	15.36%	1.23	<.001	(1.21-1.25)
White recipient	Hispanic recipient	100.00%	12.67%	0.99	.281	(0.97-1.01)
White recipient	Asian recipient	100.00%	4.04%	1.01	.696	(0.98-1.03)
Asian donor	White donor	100.00%	69.77%	1.03	.095	(0.99-1.07)
	African American donor	100.00%	14.86%	1.03	.13	(0.99-1.07)
	Hispanic donor	100.00%	11.76%	0.99	.682	(0.95-1.03)
	Other ethnicity donor	100.00%	1.48%	1.09	.004	(1.03-1.15)
Karnofsky score 30 and 40	Karnofsky score 20	99.56%	12.01%	0.92	<.001	(0.90-0.94)
	Karnofsky score 50	99.56%	7.86%	1.05	<.001	(1.03-1.08)
	Karnofsky score 60	99.56%	8.35%	1.19	<.001	(1.17-1.22)
	Karnofsky score 70	99.56%	9.93%	1.24	<.001	(1.22-1.27)
	Karnofsky score 80	99.56%	9.21%	1.28	<.001	(1.25-1.31)
	Karnofsky score 90	99.56%	3.33%	1.33	<.001	(1.29-1.37)
	Karnofsky score 100	99.56%	1.19%	1.37	<.001	(1.30-1.44)
Donor-recipient height difference −30 cm to 30 cm	Height difference −60 cm to −30 cm	95.01%	0.89%	0.93	.014	(0.88-0.99)
No life support	Life support	99.00%	4.58%	0.91	<.001	(0.88-0.94)
Admit from home	ICU admission	99.98%	15.25%	0.76	<.001	(0.74-0.77)
Admit from home	Hospital admission	99.98%	18.56%	0.82	<.001	(0.81-0.83)
Primary transplant	Previous transplant	99.51%	6.62%	1.05	.001	(1.02-1.08)
No ventilator	Ventilator	100.00%	6.29%	0.79	<.001	(0.77-0.81)
No portal vein thrombosis	Portal vein thrombosis	99.99%	8.03%	0.91	<.001	(0.89-0.93)
Other types of insurance	Private insurance	93.02%	54.74%	1.17	<.001	(1.15-1.18)
Other types of insurance	Medicaid insurance	93.02%	13.20%	1.06	<.001	(1.04-1.08)
No previous surgery	Previous surgery	99.94%	40.13%	0.97	<.001	(0.96-0.99)
Regions 7 and 8	Region 1	100.00%	3.82%	0.86	<.001	(0.83-0.88)
	Region 2	100.00%	12.82%	0.84	<.001	(0.83-0.86)
	Region 3	100.00%	15.31%	1.08	<.001	(1.06-1.10)
	Region 4	100.00%	9.10%	1.11	<.001	(1.08-1.13)
	Region 5	100.00%	14.80%	0.99	.458	(0.98-1.01)
	Region 6	100.00%	3.06%	1.07	<.001	(1.04-1.11)
	Region 8	100.00%	6.95%	0.99	.527	(0.97-1.02)
	Region 9	100.00%	6.77%	0.82	<.001	(0.80-0.84)
Donor-recipient weight difference −45 to 45 kg	Weight difference 45-70kg	98.20%	4.34%	0.96	.002	(0.93-0.98)
	Weight difference >70 kg	98.20%	0.70%	0.97	.297	(0.90-1.03)
	Weight difference −45 to −70 kg	98.20%	1.75%	0.95	.02	(0.92-0.99)
Bilirubin 2-8	Bilirubin <2	99.99%	29.42%	1.09	<.001	(1.08-1.10)
	Bilirubin 8-16	99.99%	12.39%	0.93	<.001	(0.92-0.95)
	Bilirubin 16-32	99.99%	11.14%	0.93	<.001	(0.91-0.95)
	Bilirubin >32	99.99%	6.25%	0.88	<.001	(0.86-0.90)
Local sharing	Regional sharing	100.00%	24.55%	1.01	.121	(1.00-1.02)
	National sharing	100.00%	7.99%	1.01	.23	(0.99-1.04)
	Foreign sharing	100.00%	0.08%	0.96	.659	(0.80-1.16)

Computation of Length of Stay Groups

Eras were delineated by joinpoints, which were generated by the Joinpoint Regression Program, Version 4.9.1.0. These time ranges are not equal in duration or sample size; they were selected based on a Poisson regression model, which identified statistically significant points at which the trends in LOS changed. The optimal number of joinpoints was determined using a Monte Carlo Permutation method for significance. The modeled annual percent change in mean LOS over time was found to be: −14.19 (CI: −18.3 to −9.9, P < .001) from 1987 to 1989, −6.01 (CI: −7.1-4.9, P < .001) from 1990 to 1999, and −0.74 (CI: −1.1 to 0.3, P = .001) from 2000 to 2021.

Mean Length of Stay

Among adult recipients, the average LOS following liver transplantation changed from 51.5 days in 1987 to 16.3 days in 2021, with a relatively steeper decline prior to 2000 ( Figure 1 ). From 2000 to 2021, LOS stayed relatively constant over time, with no drastic changes. The volume of transplants steadily increased overall, with a slight decline from 2006 to 2012 followed by an increase in transplant volume post-2012. Among high-acuity adults with a LOS index in the 90th percentile, mean LOS decreased from 74.38 days in 1987 to 21.35 days in 2021. Following a spike in mean LOS to 111.27 days in 1988, the mean LOS for high-acuity adults decreased steadily before plateauing after the year 2000. In contrast, the number of transplants performed on high-acuity adult patients has increased steadily over the last 4 decades. Among low-acuity adults with a LOS index in the 10th percentile, mean LOS decreased from 28.21 days in 1988 to 8.91 days in 2021, with the steepest decline occurring before 1998. Other than an unusually high mean LOS of 50.76 days in 1996, the mean LOS decreased gradually over time for low-acuity adults, while the volume of low-acuity adult transplants increased over time. These findings were supported by the Kaplan-Meier curves in Figure 2 , which demonstrated significant decreases in LOS over time for both high- and low-acuity patients.

Figure 1.

Mean length of stay and number of transplants in adults, 1987-2021.

Figure 2.

Differences in length of stay among high- and low-acuity patients. (A) LOS trends from 1987 to 2021 in high-acuity adults (>90th percentile in LOS index), (B) LOS trends from 1987 to 2021 in low-acuity adults (<10th percentile in LOS index). Abbreviation: LOS, length of stay.

In OPTN Region 9, a high transplant demand region, mean LOS decreased irregularly over time from 44.7 days in 1988 to 17.0 days in 2021. Transplant volume also fluctuated significantly, peaking in 2006 and 2020. In contrast, OPTN Region 3, a low transplant demand region, had a much steeper and smoother decline in mean LOS over time, from 79.7 days in 1987 to 11.8 days in 2021. In contrast to Region 9, Region 3 had a steady increase in the number of transplants performed over the last 4 decades. The abnormalities in transplant volume and mean LOS from 2020 to 2021 can be explained by incomplete data collection for 2021.

LOS Comparison Between Eras

Twenty-nine recipient, donor, and center-level factors were considered. Risk factors that were significant in univariate analysis (P < .05) were included in the multivariable Cox regression analysis, which controlled for 29 donor- and recipient-level characteristics. The risk factors that were included in multivariable analysis are presented in Table 3 . In this model, the outcome of interest was defined as hospital discharge, with a hazard ratio > 1 signifying a shorter LOS.

Table 3.

Recipient, Donor, and Center-Level Risk Factors of Adult Liver Transplant Recipients from 1987 to 2021.

Risk factors		TotalN = 150 603	1987-1989N = 2389	1990-1999N = 28 083	2000-2021N = 120 131	P-value
Risk factors		Median [IQR]	Median [IQR]	Median [IQR]	Median [IQR]	P-value
Donor-recipient height difference (cm)		0.0[−8.0 to 9.8]	−2.6[−9.9 to 6.7]	−0.8[−10.0 to 8.0]	0.0[−7.8 to 10.0]	<.001
Donor-recipient weight difference (kg)		5.07[−10.0 to 20.9]	3.38[−5.1 to 16.1]	5.88[−7.3 to 20.0]	4.99[−10.8 to 21.0]	<.001
Cold ischemia time (hrs)		6.5[5.0-8.7]	10[6.0-14.0]	9[6.9-11.9]	6[4.7-8.0]	<.001
Distance from hospital (miles)		67[8.0-194.0]	233[69.0-529.0]	73[9.0-201.0]	64[7.0-187.0]	<.001
		N (%)	N (%)	N (%)	N (%)
Donation after cardiac death		6476(4.3%)	0(0.0%)	56(0.2%)	6487(5.4%)	<.001
ABO incompatible		1506(1.0%)	29(1.2%)	365(1.3%)	1081(0.9%)	<.001
ABO compatible		10 994(7.3%)	217(9.1%)	2219(7.9%)	8529(7.1%)	<.001
OPTNregion	Region 1	5723(3.8%)	136(5.7%)	1011(3.6%)	4565(3.8%)	<.001
	Region 2	19 578(13.0%)	753(31.5%)	4212(15.0%)	14 656(12.2%)	<.001
	Region 3	23 193(15.4%)	86(3.6%)	3117(11.1%)	19 942(16.6%)	<.001
	Region 4	13 554(9.0%)	205(8.6%)	2303(8.2%)	10 932(9.1%)	<.001
	Region 5	21 536(14.3%)	323(13.5%)	4578(16.3%)	16 698(13.9%)	<.001
	Region 6	4669(3.1%)	17(0.7%)	983(3.5%)	3604(3.0%)	<.001
	Region 7	13 705(9.1%)	294(12.3%)	2864(10.2%)	10 572(8.8%)	<.001
	Region 8	10 693(7.1%)	241(10.1%)	2303(8.2%)	8049(6.7%)	<.001
	Region 9	9940(6.6%)	43(1.8%)	2050(7.3%)	7929(6.6%)	<.001
	Region 10	13 554(9.0%)	220(9.2%)	2359(8.4%)	11 052(9.2%)	<.001
	Region 11	14 458(9.6%)	72(3.0%)	2359(8.4%)	12 133(10.1%)	<.001
Regional sharing		36 898(24.5%)	614(25.7%)	7077(25.2%)	29 192(24.3%)	.002
National sharing		12 199(8.1%)	970(40.6%)	2836(10.1%)	8289(6.9%)	<.001
Foreign sharing		151(0.1%)	41(1.7%)	84(0.3%)	0(0.0%)	<.001
Previous transplant		10 241(6.8%)	616(25.8%)	2471(8.8%)	7088(5.9%)	<.001
Private insurance		83 886(55.7%)	74(3.1%)	13 115(46.7%)	70 757(58.9%)	<.001
Medicaid insurance		18 675(12.4%)	5(0.2%)	2640(9.4%)	16 098(13.4%)	<.001

Abbreviations: OPTN, organ procurement and transplantation network.

In the multivariable Cox regression, recipients in both the 1987-1989 cohort (hazard ratio [HR]: 0.53, 95% confidence interval [CI]: 0.50-0.55) and the 1990-1999 cohort (HR: 0.77, CI: 0.76-0.79) had significantly prolonged lengths of stay (HR < 1 associated with later hospital discharge) compared to the 2000-2021 cohort. The most significant risk factors for prolonged hospital LOS were ICU admission (HR: 0.76, CI: 0.74-0.77), dialysis in the week prior to transplant (HR: 0.76, CI: 0.74-0.78), and being on ventilator (HR: 0.79, CI: 0.77-0.81). The most significant risk factor for shorter hospital LOS was increased functional status at transplant. Overall, recipients from Region 9, an area with high organ demand and relatively low supply, had significantly prolonged LOS (HR: 0.82, CI: 0.80-0.84), while recipients from Region 3, an area with low demand and relatively high supply, had a slightly shorter LOS (HR: 1.08, CI: 1.06-1.1).

Discussion

In providing a longitudinal view of LOS over the last 4 decades, this analysis demonstrated that the changes in LOS over time happened in the context of changes in the overall field of liver transplantation. There has been a great improvement in LOS from 1987 to 2021, with the greatest declines occurring mostly prior to 2000. Since the late 20th century, the field of liver transplantation has witnessed a huge expansion in the number of procedures performed, as well as an evolution in surgical and allocation techniques. For example, the method of preserving the inferior vena cava was popularized in the late 80s, while the Child-Turcotte-Pugh and MELD scores altered liver allocation methods.^1,16 Thus, this pattern of decreasing LOS was supported by the transformative historical advances in the field prior to 2000 and furthermore reflects a maturation period of the field. Likewise, both low- and high-acuity recipients witnessed a decline in LOS over the last 4 decades along similar patterns, which was also expected given the advances in the field.

These declines in LOS among liver transplant recipients have been mirrored by similar reductions in LOS across various surgical fields and countries. For example, one study conducted in Denmark found that LOS following a cesarean section declined significantly from 4.0 days in 2004 to 2.4 days in 2016.¹⁷ Likewise, another study conducted in the United States found that hospital LOS for total hip arthroplasty procedures decreased significantly from 3.8 days in 2006-2009 to 2.7 days in 2014-2016.¹⁸ Within this context, the analysis illustrated the similarities in LOS trends over time between liver transplantation and other surgical procedures. The decreases in LOS over time have been much more dramatic in the field of liver transplantation.

In recent years, LOS has become an important outcome measure in clinical settings.¹⁴ As a measure of the number of days that a patient stays in an inpatient healthcare facility, LOS can be used as a representative metric of healthcare resource utilization and costs.^8,19 From an operational perspective, LOS also serves as a measure of patient flow through a healthcare system.¹⁴ From a medical perspective, LOS has been used as a surrogate for a number of clinical outcomes, from hospital mortality to the health status of a patient.^20,21 Because LOS is an important factor in both administrative efficiency and patient experience, it has become an increasingly significant outcome measure in clinical evaluations.

In the field of transplant surgery, limited resources and the significant cost of transplant procedures can place constraints on transplant centers, making LOS a particularly relevant outcome marker.³ Because of the complex nature of the transplantation process, there were many factors that contributed to postoperative outcomes such as patient and graft survival.^22,23 Recently, LOS has been used to predict healthcare expenditures and recipient survival following transplantation.^3,8 Several studies have even identified certain patient and transplant factors that predicted a longer hospital LOS.^13,24 Understanding the factors that contribute to a longer LOS can help healthcare providers identify best practices and factors to improve transplantation outcomes.

Region 9, a traditionally high-demand region, was associated with a longer LOS, while Region 3, a low-demand region, was associated with a shorter LOS. These findings align with current literature, which has found that high-demand regions have recipients with a higher average MELD score at transplant than low-demand regions with a less limited organ supply.¹⁹ Likewise, several factors identified in this analysis have been previously shown in other literature to be significant for prolonged stay following liver transplant. Specifically, multiple studies have linked longer cold ischemia times with a longer LOS.⁴ Previous literature found that patients admitted to the ICU were more likely to have longer hospital LOS, higher mortality, and increased complications. Recipients with an educational degree (high school, bachelor's, technical, or doctoral) had a shorter LOS compared to recipients who did not finish high school. This finding aligns with existing literature, since increased health literacy and education levels have been shown to have a positive impact on transplantation outcomes such as survival.^4,25,26 Recipients with private insurance had shorter LOS compared to recipients with other forms of insurance. These findings are supported by previous evidence that private insurance is associated with better transplantation outcomes than public insurance.²⁷ Although existing literature has found differences in transplantation outcomes by race, this analysis found no significant relationship between donor or recipient race and LOS.¹³

Prior to this study, there was no analysis that examined the long-term trends in LOS. This analysis aims to both illustrate trends in LOS over the last 4 decades and identify relevant recipient and donor characteristics associated with changes in LOS. These findings could be used in clinical practice to identify actionable factors to improve patient outcomes and hospital resource utilization. Future potential directions include identifying center-level characteristics associated with prolonged LOS and using a mixed methods qualitative study or a prospective study to further explore avenues for improving clinical practice in transplantation.

Although data entry is mandatory for all US transplant centers, all patient registries have variability in data entry, such as data entry errors or missing data, that may lead to inaccurate findings. Because this analysis was based on a large sample of patients, it is unlikely that small amounts of missing or erroneous data would have significantly impacted the findings. Imputation analysis was performed to correct for missing data. Another limitation of the study was that center-level characteristics could not be analyzed due to the nature of the database. Finally, because the current database only contained data up to July 2021, the values for 2021 may not be as robust compared to other years.

Conclusions

This analysis showed that there has been a statistically significant decrease in the mean LOS over time in adult recipients following liver transplantation, even after controlling for donor and recipient level factors. This study demonstrated the potential to identify actionable root cause factors to avoid prolonged LOS in clinical transplantation settings.

Footnotes

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iDs

Spoorthi Kamepalli

Ashley Montgomery

Liam Ferreira

Anna Lang

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A Three Decade Analysis of Trends in Length of Stay After Liver Transplantation

Abstract

Introduction

Research question

Design

Results

Conclusions

Keywords

Introduction

Design/Methods

Design

Population

Sampling

Analysis

Results

Study Characteristics

Data Entry Rate

Computation of Length of Stay Groups

Mean Length of Stay

LOS Comparison Between Eras

Discussion

Conclusions

Footnotes

Funding

Declaration of Conflicting Interests

ORCID iDs

References