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Abstract

Background:

The model for end-stage liver disease (MELD) has been widely used to predict the mortality and morbidity of various surgical procedures.

Objectives:

We aimed to correlate a high preoperative MELD score with adverse 30-day postoperative complications following radical cystectomy.

Design and Methods:

Patients who underwent elective, non-emergency radical cystectomy were identified from the American College of Surgeons–National Surgical Quality Improvement Program (ACS-NSQIP) database from 2005 to 2017. Patients were categorized according to a calculated MELD score. The primary outcomes of this study were 30-day postoperative mortality, morbidity, and length of hospital stay following radical cystectomy. For further sensitivity analysis, propensity score matching was used to yield a total of 1387 matched pairs and primary outcomes were also assessed in the matched cohort.

Results:

Compared with patients with a MELD < 10, those with MELD ⩾ 10 had significantly higher rates of mortality [odds ratio (OR) = 1.71, p = 0.004], major complications (OR = 1.42, p < 0.001), and prolonged hospital stay (OR = 1.29, p < 0.001) on multivariate analysis. Following risk-adjustment for race, propensity-matched groups revealed that patients with MELD score ⩾ 10 were significantly associated with higher mortality (OR = 1.85, p = 0.008), major complications (OR = 1.34, p < 0.001), yet similar length of hospital stay (OR = 1.17, p = 0.072).

Conclusion:

MELD score ⩾ 10 is associated with higher mortality and morbidity in patients undergoing radical cystectomy compared with lower MELD scores. Risk-stratification using MELD score may assist clinicians in identifying high-risk patients to provide adequate preoperative counseling, optimize perioperative conditions, and even consider nonsurgical alternatives.

Keywords

cystectomy liver disease risk assessment urinary bladder neoplasms urology

Introduction

Bladder cancer (BC) is the second-most common cause of death among all genitourinary tumors and ranks 10th among the most common malignancies in the world.¹ The predominant form of BC manifests as urothelial carcinoma, with a 5-year overall survival rate for localized, regional, and distant disease reported to be 91%, 48%, and 8%, respectively.² Radical cystectomy (RC) with extended lymphadenectomy is the gold standard treatment modality for muscle invasive BC (MIBC), as well as non-muscle invasive BC (NMIBC) that do not respond to transurethral resection of bladder tumor (TURBT) and intravesical therapy.³ It is generally known that RC entails a highly comorbid procedure associated with several complications and significant rates of postoperative morbidities and mortalities.^4,5 The ability to identify high-risk patients enables clinicians to optimize perioperative care and even consider alternative approaches to RC, such as bladder-sparing trimodal therapy.⁶ As such, patient selection strategies that are based on objective measures of pre-operative risks of surgery are highly warranted. Model for end-stage liver disease (MELD) was originally introduced in 1999 as a 3-month survival predictor model in patients undergoing transjugular intrahepatic portosystemic shunting (TIPS)^7,8 and was calculated from routinely derived serum chemistries; it was further adjusted to be used in liver transplant recipient selection.^9,10 MELD was also shown to predict adverse surgical outcomes in patients regardless of their liver state, where it was found to be an independent predictor of morbidity and mortality in patients undergoing emergent or elective colorectal surgery,^11–13 pancreatoduodenectomy,¹⁴ gastrectomy,¹⁵ and lower extremity bypass.¹⁶ Few studies have explored MELD score as an independent predictor of morbidity and mortality in RC. One case-series demonstrated that three patients with liver cirrhosis undergoing RC demonstrated an increased likelihood of complications with higher MELD scores.¹⁷ Others have recommended that patients with localized BC and MELD > 10 differ from RC and pursue alternatives.¹⁸

Using the American College of Surgeons–National Surgical Quality Improvement Program (ACS-NSQIP) database, we sought to explore the MELD score’s ability to accurately risk-stratify patients undergoing RC and be an independent predictor of 30-day postoperative outcomes following RC.

Materials and methods

Study design and patient population

Our cohort was retrospectively derived from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) Participant Use Data Files (PUFs) between the years 2005 and 2017. We selected patients who underwent elective, non-emergent RC using the following Current Procedural Terminology (CPT) codes: 51570, 51575, 51580, 51585, 51590, 51595, and 51596. Urinary diversion (UD) methods were coded according to their corresponding CPT codes: 51585 [ureterosigmoidostomy (USD) or ureterocutaneous diversion (UCD) with lymph node dissection (LND)], 51590 [ileal conduit (IC) or sigmoid bladder (SB)], 51595 (IC or SB with LND), and 51596 [neobladder (NB)]. We excluded patients who had missing laboratory values included in the MELD score, and those who underwent other concomitant procedures such as chemotherapy or radiotherapy. The ACS-NSQIP database is a nationally validated, risk-adjusted, outcome-based program that encompasses 963 centers and more than 65 collaboratives both inside and outside the United States. The ACS-NSQIP database is managed by a board of surgical clinical experts who are responsible for collection and maintenance of the data. Interrater reliability (IRR) audits occur at participant centers to periodically review data files ensuring the highest quality and standards possible.

Ethics approval

Patient consent and Institutional Review Board (IRB) approval were not required as the data are de-identified.

Patient demographics and variables

Patient demographics, pre-operative laboratory values, medical comorbidities, and preoperative events and UD were reported. Patient demographics included age, body mass index (BMI), sex, race, history of smoking within 1-year status, and American Society of Anesthesiologists’ (ASA) classification. Patient comorbidities included diabetes, history of chronic obstructive pulmonary disease (COPD), congestive heart failure, hypertension requiring medication, acute renal failure, steroid use, weight loss defined as > 10% weight loss in the last 6 months, and bleeding disorders. Pre-operative events recorded include blood transfusion of packed red blood cells 72 h before surgery, pre-operative dialysis, and systemic sepsis. UD included IC or SB with or without LND, NB, and USD or UCD with LND. Missing values were imputed using mean or median for continuous variables and mode for categorical variables. Values were imputed only if they amounted to < 10% from the total amount of values in the variable. If a variable had > 10% missing values, it was excluded from the analysis.

MELD score calculation

MELD score was calculated using preoperative serum creatinine, bilirubin, and international normalized ratio (INR). MELD score was calculated using the standard United Network for Organ Sharing (UNOS)-based formula: MELD = 3.78 × ln [bilirubin (mg/dL)] + 11.2 × ln [INR] + 9.57 × ln [creatinine (mg/dL)] + 6.43. The result was rounded to the nearest integer. All values less than 1.0 were rounded up to 1.0 to avoid a negative score and creatinine values > 4 mg/dL were trimmed down to 4.0 mg/dL in a previously validated manner.¹⁴ Patients were then categorized into two groups depending on the MELD score: < 10 and ⩾ 10. The numerical cutoff of 10 was chosen based on previous literature that clearly correlated that cut-off with worse postoperative mortality.^19,20

Outcomes

We sought to compare 30-day outcomes that included mortality, major morbidity, and prolonged length of hospital stay defined as greater than 11 days (75th percentile). Major morbidity included deep surgical site infection (SSI), organ SSI, wound disturbance, pneumonia, unplanned intubation, pulmonary embolism, failure to wean off ventilator > 48 h, acute renal failure, renal insufficiency, cardiac arrest requiring cardiopulmonary resuscitation (CPR), myocardial infarction, bleeding requiring transfusion, sepsis, septic shock, or deep vein thrombosis (DVT).

Statistical analysis and model construction

Patient demographics, comorbidities, pre-operative incidences, diversion type, and 30-day outcomes were compared between the two MELD groups. Categorical variables were compared using chi-square test and presented as count and percentages while continuous variables were analyzed using independent t-test and presented as mean and standard error of the mean. Next, to assess for confounding variables and effect modifiers on our outcomes of interest, we performed multivariate logistic regression models controlling for differences in demographics, comorbidities, pre-operative events, and UD type. Multivariate logistic regression models were adjusted for age, race, sex, ASA, smoking status, diabetes, congestive heart failure, hypertension, acute renal failure, dialysis, weight loss, bleeding disorder, blood transfusion, and diversion type.

Propensity score matching

As a sensitivity analysis, we performed propensity score matching by 1:1 match between the two MELD groups. The variables we included for the propensity matching were age, race, sex, ASA, diabetes, smoking status, congestive heart failure, hypertension, acute renal failure, dialysis, weight loss, bleeding disorder, blood transfusion, and diversion type. For variables that did not successfully match, we performed a multivariate logistic regression for those variables in the propensity-matched cohorts. Significance level was set at < 0.05 for all analyses. Statistical analyses were performed using the IBM SPSS statistical package (version 28, IBM Corp., Armonk N.Y., USA).

Data availability

The ACS-NSQIP data are subject to a data use agreement. To access the dataset, a request to the ACS-NSQIP participant use form should be placed at the following link (https://www.facs.org/quality-programs/acs-nsqip/participant-use). The American University of Beirut Medical Center (AUBMC) is enrolled in ACS-NSQIP as a participating center. As such, the data were made available by the ACS-NSQIP center and the AUBMC Department of Surgery after signing the data use agreement.

Results

Patients demographics and general characteristics stratified by MELD score

Between 2005 and 2017, out of 7120 cystectomy cases, 5038 elective cystectomy cases had quantifiable MELD scores. MELD groups included 3486 patients with MELD < 10 and 1552 patients with MELD ⩾ 10. In our study, all categorical variables had no missing values and continuous variables not related to the MELD score calculations were imputed such as no variable consisted of > 10% missing values. Overall, individuals with MELD scores ⩾ 10 were older, males, ASA class > 2, diabetic, history of congestive heart failure, hypertensive, with acute renal failure, > 10% weight loss, with bleeding disorders, pre-operative bleeding transfusion, and dialysis. Patients with MELD scores ⩾ 10 were also more likely to undergo IC or SB with or without LND and less likely to undergo NB (p < 0.007). After propensity score matching, the matched cohort consisted of 2764 patients where two MELD groups matched on all factors except for race (Table 1).

Table 1.

Characteristics of patients who underwent cystectomy between the years 2005 and 2017 stratified by MELD score.

N = 5038 UnmatchedN = 2764 Matched		Unmatched			Propensity-matched
		MELD score		p-value	MELD score		p value
		MELD < 10N = 3486	MELD ⩾ 10N = 1552		MELD < 10N = 1382	MELD ⩾ 10N = 1382
		N (%) or Mean (±SD)	N (%) or Mean (±SD)		N (%) or Mean (±SD)	N (%) or Mean (±SD)
Patient demographics
Age		66.46 ± 11.17	69.89 ± 10.44	< 0.001*	69.16 ± 10.3	69.54 ± 10.3	0.330
BMI		28.26 ± 5.90	28.60 ± 5.84	0.064	28.72 ± 5.86	28.55 ± 5.66	0.441
Sex	Male	2687 (77.1)	1370 (88.3)	< 0.001*	1219 (88.2)	1211 (87.6)	0.641
	Female	799 (22.9)	182 (11.7)		163 (11.8)	171 (12.4)
Race	White	2791 (80.1)	1246 (80.3)	0.001*	1182 (85.5)	1111 (80.4)	0.002*
	Black	172 (4.9)	111 (7.2)		69 (5)	94 (6.8)
	Others	523 (15)	195 (12.6)		131 (9.5)	177 (12.8)
Smoker		892 (25.6)	300 (19.3)	< 0.001*	269 (19.5)	269 (19.5)	0.99
ASA Class > 2		207 (5.9)	177 (11.4)	< 0.001*	111 (8.0)	116 (8.4)	0.729
Medical comorbidities
Diabetes		623 (17.9)	404 (26)	< 0.001*	351 (25.4)	339 (24.5)	0.598
COPD		241 (6.9)	113 (7.3)	0.637	106 (7.1)	99 (7.2)	0.611
Congestive heart failure		18 (0.5)	31 (2)	< 0.001*	7 (0.5)	7 (0.5)	0.99
Hypertension requiring medication		1915 (54.9)	1129 (72.7)	< 0.001*	995 (72)	981 (71)	0.555
Acute renal failure		5 (0.1)	25 (1.6)	< 0.001*	3 (0.2)	4 (0.3)	0.99
Steroid use for chronic condition		135 (3.9)	72 (4.6)	0.206	51 (3.7)	64 (4.6)	0.216
Weight loss		113 (3.2)	75 (4.8)	0.006*	54 (3.9)	52 (3.8)	0.843
Bleeding disorders		130 (3.7)	96 (6.2)	< 0.001*	74 (5.4)	68 (4.9)	0.605
Pre-operative event and urinary diversion
Diversion type	IC or SB + LND	2122 (60.9)	1007 (64.9)	0.007*	882 (63.8)	900 (65.1)	0.474
	IC or SB	570 (16.4)	309 (19.9)	0.002*	264 (19.1)	268 (19.4)	0.847
	Neobladder	699 (20.1)	182 (11.7)	< 0.001*	196 (14.2)	174 (12.6)	0.219
	USD or UCD + LND	33 (0.9)	15 (1)	0.947	16 (1.2)	15 (1.1)	0.857
Bleeding transfusion		64 (1.8)	55 (3.5)	< 0.001*	33 (2.4)	32 (2.3)	0.90
Dialysis		2 (0.1)	34 (2.2)	< 0.001*	2 (0.1)	2 (0.1)	0.99
Systemic sepsis	No	3419 (98.1)	1508 (97.2)	0.114	1351 (97.8)	1351 (97.8)	0.899
	SIRS	51 (1.5)	32 (2.1)		19 (1.4)	21 (1.5)
	Sepsis	16 (0.5)	12 (0.7)		12 (0.9)	10 (0.7)
	Septic shock	0 (0)	0 (0)		0 (0)	0 (0)

ASA, American Society of Anesthesiologists; BMI, body mass index (kg/m²); COPD, chronic obstructive pulmonary disease; IC, ileal conduit; LND, lymph node dissection; MELD, model for end-stage liver disease; SB, sigmoid bladder; SIRS, systemic inflammatory response syndrome; UCD, ureterocutaneous diversion; USD, ureterosigmoid diversion. Weight loss indicates > 10% loss body weight in last 6 months; bleeding transfusion indicates transfusion > 4 units PRBCs 72 h before surgery.*p < 0.05.

Thirty-day postoperative outcomes stratified by MELD score

In the unmatched cohorts, patients with MELD ⩾ 10 had significantly higher rates of mortality (4.4%, p < 0.001), major morbidity (61.0%, p < 0.001), and prolonged length of stay (31.9 days, p < 0.001) as compared with those with MELD < 10 (Table 2). After propensity score matching, we were left with a total of 2764 patients (1382 per group). In this cohort, a similar trend was seen where patients with MELD ⩾ 10 still had significantly higher rates of mortality (3%, p = 0.006) and major morbidity (59.3%, p < 0.001) as compared with those with MELD < 10. As for length of hospital stay, patients with MELD ⩾ 10 had higher rates of prolonged hospitals stays (> 11 days; 30.5%, p = 0.026) when compared with MELD < 10.

Table 2.

Description of the main postoperative outcomes stratified by MELD score.

N = 5,038 UnmatchedN = 2,764 Matched	Unmatched			Propensity-matched
	MELD score		p-value	MELD score		p-value
	MELD < 10N = 3486	MELD ⩾ 10N = 1552		MELD < 10N = 1382	MELD ⩾ 10N = 1382
	N (%)	N (%)		N (%)	N (%)
Return to operating room	188 (5.4)	91 (5.9)	0.500	73 (5.3)	77 (5.6)	0.737
Re-admission	703 (20.2)	314 (20.2)	0.957	263 (19)	268 (19.4)	0.809
Prolonged length of stay^a	850 (24.4)	495 (31.9)	< 0.001*	369 (26.7)	422 (30.5)	0.026*
Mortality	69 (2.0)	68 (4.4)	< 0.001*	30 (2.2)	55 (4)	0.006*
Major morbidity^b	1780 (51.1)	946 (61.0)	< 0.001*	716 (51.8)	819 (59.3)	< 0.001*
Deep incisional SSI	46 (1.3)	33 (2.1)	0.033*	22 (1.6)	29 (2.1)	0.322
Organ space SSI	243 (7.0)	93 (6.0)	0.199	81 (5.9)	82 (5.9)	0.936
Wound disrupt	92 (2.6)	46 (3.0)	0.514	27 (2)	41 (3)	0.086
Pneumonia	102 (2.9)	57 (3.7)	0.162	44 (3.2)	46 (3.3)	0.830
Unplanned intubation	88 (2.5)	63 (4.1)	0.003*	39 (2.8)	22 (1.6)	0.138
Pulmonary embolism	82 (2.4)	27 (1.7)	0.168	39 (2.8)	53 (3.8)	0.028*
Failure to wean^c	72 (2.1)	43 (2.8)	0.122	40 (2.9)	35 (2.5)	0.558
Renal insufficiency	57 (1.6)	43 (2.8)	0.008*	30 (2.2)	37 (2.7)	0.387
Renal failure	28 (0.8)	45 (2.9)	< 0.001*	12 (0.9)	37 (2.7)	< 0.001*
Myocardial infarction	50 (1.4)	21 (1.4)	0.821	28 (2)	16 (1.2)	0.068
Cardiac arrest requiring CPR	35 (1)	23 (1.5)	0.142	9 (0.7)	19 (1.4)	0.058
DVT	104 (3)	60 (3.9)	0.086	42 (3.0)	52 (3.8)	0.294
Sepsis	356 (10.2)	153 (9.9)	0.700	138 (10)	131 (9.5)	0.653
Septic shock	88 (2.5)	67 (4.3)	< 0.001*	42 (3.0)	56 (4.1)	0.150
Bleeding transfusion	1298 (37.2)	761 (49.0)	< 0.001*	534 (38.6)	656 (47.5)	< 0.001*

CPR, cardiopulmonary resuscitation; DVT, deep vein thrombosis; MELD, model for end-stage liver disease; SSI, surgical site infection.

Prolonged length of stay is defined as length of stay greater than 11 days.

Major morbidity is the composite outcome of postoperative incidence of major complications include deep surgical site infection, organ surgical infection, wound disturbance, pneumonia, unplanned intubation, pulmonary embolism, failure to wean off ventilator > 48 h, renal insufficiency, acute renal failure, cardiac arrest requiring CPR, myocardial infarction, bleeding requiring transfusion, sepsis, septic shock, or deep vein thrombosis.

Failure to wean indicates failure to wean off ventilator > 48 h.*p < 0.05.

Multivariate analysis of outcomes in unmatched and propensity-matched cohorts

In the unmatched cohort, a multivariate logistic regression was performed to control for age, race, sex, diabetes, smoker, congestive heart failure, hypertension, acute renal failure, dialysis, weight loss, bleeding disorder, blood transfusion, ASA, and diversion type (Table 3). The model showed that patients with MELD ⩾ 10 had higher odds of mortality (OR = 1.71, p = 0.004), major morbidity (OR = 1.42, p < 0.001), and prolonged length of stay (OR = 1.29, p < 0.001) as compared with those with MELD < 10. For the propensity-matched cohorts, a multivariate logistic regression was performed to control for race. The model showed that patients with MELD ⩾ 10 had higher odds of mortality (OR = 1.85, p = 0.008) and major morbidity (OR = 1.34, p < 0.001); whereas differences in length of hospital did not reach statistical significance (OR = 1.17, p = 0.072).

Table 3.

Univariable and multivariable analysis of characteristics and risk factors of patients who have undergone cystectomy between 2005 and 2017 stratified by MELD score.

Variable	MELD < 10	Univariable regression		Multivariate regression		Propensity matched
Variable		MELD ⩾ 10^a	p-value	MELD ⩾ 10^b	p-value	MELD ⩾ 10^c	p-value
Mortality	Ref	2.27 [1.614, 3.189]	< 0.001*	1.71 [1.189, 2.468]	0.004*	1.85 [1.174, 2.902]	0.008*
Major morbidity^d	Ref	1.50 [1.325, 1.690]	< 0.001*	1.42 [1.249, 1.619]	< 0.001*	1.34 [1.150, 1.555]	< 0.001*
Prolonged length of stay	Ref	1.45 [1.273, 1.657]	< 0.001*	1.29 [1.116, 1.483]	< 0.001*	1.17 [0.986, 1.377]	0.072

MELD, model for end-stage liver disease; Ref: reference.

Univariate logistic regression model.

Multivariate logistic model adjusted for age, race, sex, diabetes, smoker, congestive heart failure, hypertension, acute renal failure, dialysis, weight loss, bleeding disorder, blood transfusion, ASA (American Society of Anesthesiologists’ classification), urinary diversion type.

Multivariate logistic regression model adjusted for race.

Major morbidity is the composite outcome of postoperative incidence of major complications include deep surgical site infection, organ surgical infection, wound disturbance, pneumonia, unplanned intubation, pulmonary embolism, failure to wean off ventilator > 48 h, renal insufficiency, acute renal failure, cardiac arrest requiring CPR (cardiopulmonary resuscitation), myocardial infarction, bleeding requiring transfusion, sepsis, septic shock, or deep vein thrombosis.*p < 0.05.

Discussion

There exists an increasing interest for pre-operative risk stratification for which various attempts were made to develop and validate scores as independent predictors of overall complications. These include frailty and comorbidity indices such as the five-item frailty score, Charlson comorbidity index, and Johns Hopkins Adjusted Clinical Groups frailty index.^21–23

Baseline liver disease is associated with a higher risk of perioperative complications and long-term postoperative adverse outcomes in non-hepatic surgeries.^19,24–27 And so, the idea of quantifying a patient’s preoperative risk by using an already available liver disease score arose. The UNOS-stratified MELD score (⩽ 10, 11–18, 19–24, 25–35, and > 36), with a higher score associated with an increase in estimated 3-month mortality rates.^28,29 MELD is already known to accurately risk-stratify patients with liver cirrhosis undergoing abdominal, musculoskeletal, cardiovascular, and urologic surgeries;^20,30–34 in fact, irrespective of underlying liver disease, it was shown to be an independent predictor of morbidity and mortality in patients undergoing several emergency or elective procedures.^11–16

Higher MELD scores are independently associated with an increased 30-day mortality and morbidity following gastrectomy and colorectal surgery,^11,13 whereas a MELD score > 11 was associated with a twofold increase in mortality for patients undergoing pancreatectomy.¹⁴ In addition, Krafcik et al.¹⁶ correlated a higher MELD score with prolonged hospital stay in patients undergoing lower extremity bypass.

Similarly, the results presented in this study highlight the role of MELD scores in predicting outcomes following elective RC where a MELD score ⩾ 10 revealed to be an independent predictor of overall mortality, major morbidity, and prolonged hospital stay following RC. Our findings revealed that irrespective of underlying liver disease, patients undergoing RC with MELD scores ⩾ 10 performed poorly in the postoperative setting as compared with those with MELD < 10. Furthermore, patients with MELD ⩾ 10 had prolonged length of stay and were at a higher risk of mortality and major morbidity such as deep incisional surgical site infections, unplanned intubation, renal insufficiency, renal failure, septic shock, and bleeding requiring transfusion.

In addition, perioperative mortality after RC within 30 days of surgery or prior to hospital discharge has decreased from 20% in 1970 to nearly 1–2% in recent studies, whereas perioperative mortality within 60- and 90-days post-surgery remains higher at 2.4% and 3.9%, respectively.^35,36 As such, several attempts were made to determine variables that can predict mortality following RC. Many of those variables were difficult to quantify such as the surgeon’s experience and the institution’s surgical volume.^36,37 Similarly, attempts at finding variables to predict prolonged hospital length of stay following RC were equally difficult to quantify.³⁸ On the contrary, MELD score provides a noninvasive objective tool to help identify high-risk patients for perioperative complications and prolonged hospital length of stay.

Furthermore, other factors that could influence outcomes post-RC include surgical approach. Robotic-assisted RC (RARC) has been found to be comparable with open RC (ORC) in terms of peri-operative outcomes, progression free-survival, and health-related quality of life indicators.^39–41 One randomized clinical trial demonstrated that RARC with intracorporeal UD displayed lower peri-operative transfusion rates (22% versus 41%) as compared with ORC.⁴² In addition, an interim analysis of the same trial showed that RARC and ORC displayed similar patient-reported quality of life domains.⁴³ Nevertheless, patients undergoing ORC displayed a decrease in role functioning and higher symptom scales after 1 year; however, RARC candidates reported an increase in urinary symptoms and problems.⁴³ In our study, the type of surgical approach could have influenced peri-operative outcomes between the different MELD categories. Unfortunately, the NSQIP dataset lacks CPT codes that help indicate the surgical approach utilized (open, robotic, or laparoscopic) and this could be a subject of future research when comparing surgical techniques in RC.

The type of UD during RC is an essential predictor of peri-operative complications as it adds great complexity to an already complicated procedure. Different types of UDs have been compared in regard to patient selection, peri-operative complications and quality of life parameters. Studies have shown that IC patients are generally older with greater comorbidities and complications and report poorer quality of life when compared with NB candidates.⁴⁴ One study showed that the complication rate of NB was around 58% with most common complications being infectious, genitourinary, gastrointestinal, and wound related ones.⁴⁵ Furthermore, NB have been shown to be more technically demanding procedures with longer operative times and hospital stays when compared with IC.^44,46 Regarding overall morbidity and mortality, NB have displayed similar rates when compared with IC with greater infectious complications in the NB group as compared with wound complications in the IC group.^46–48 Whereas other studies have indicated superiority of incontinent diversions to continent diversions.⁴⁹ In our study, patients with lower MELD scores were in fact younger and generally less comorbid before propensity score matching and were more likely to undergo NBs and less likely to undergo IC as compared with individuals with higher MELD scores. However, after matching for diversion types, the two groups displayed consistent results. Further randomized studies are required with homogenized patient populations whereby morbidity and mortality can be further compared.

Pre-operative MELD scores can be used as a prognostic marker of peri-operative complications regardless of a pre-existing liver disease. Given the aforementioned results, clinicians may consider using the MELD score as a convenient and available model to risk-stratify patients, provide proper patient counseling, and consider less invasive alternatives such as trimodal therapy.⁶

Limitations

Several limitations exist in our study and results must be interpreted within the context of the study design. First, our study is based on a retrospective analysis using the ACS-NSQIP database and might be subject to selection bias. Second, the ACS-NSQIP database misses out on important factors such as tumor stage and complexity, which may alter outcomes significantly and impact morbidity and mortality after surgery. Third, MELD score was originally optimized to select cirrhotic patients for TIPS and later for liver transplantation. It’s recent extrapolation to be used as a risk assessor for non-transplant surgeries may be influenced by many factors including a recent illness or dehydration.¹⁴ Furthermore, the NSQIP dataset lacks CPT codes indicating the surgical approach during RC (open, robotic, or laparoscopic). Hence, a comparison between different surgical approaches was not possible. In addition, alcohol intake was not reported in NSQIP datasets after 2012, which is considered an important exclusion criterion from MELD scores. The final limitation is the follow-up interval where the ACS NSQIP user file registers a 30-day outcomes follow-up rather than the usual 90-day outcomes follow-up.

Conclusion

The findings presented in this study highlight the role of applying an established scoring system to pre-operatively identify high-risk patients for RC. The ability of MELD score to accurately risk-stratify 30-days surgical outcomes in a large patient cohort may assist clinicians in identifying high-risk patients to provide adequate preoperative counseling, optimize perioperative conditions, and even consider nonsurgical alternatives. It also sheds light on the implications of subclinical liver disease on 30-day postoperative complications. Future studies are needed to develop a complex model that integrate several scores as a prognostic modality for RC patients.

Footnotes

Acknowledgements

None.

Declarations

ORCID iD

Christian Habib Ayoub

References

Saginala

Barsouk

Aluru

, et al. Epidemiology of bladder cancer. Med Sci 2020; 8: 15.

Abdollah

Gandaglia

Thuret

, et al. Incidence, survival and mortality rates of stage-specific bladder cancer in United States: a trend analysis. Cancer Epidemiol 2013; 37: 219–225.

Witjes

Bruins

Cathomas

, et al. European Association of Urology guidelines on muscle-invasive and metastatic bladder cancer: summary of the 2020 guidelines. Eur Urol 2021; 79: 82–104.

Alfred Witjes

Lebret

Compérat

, et al. Updated 2016 EAU guidelines on muscle-invasive and metastatic bladder cancer. Eur Urol 2017; 71: 462–475.

Gakis

Efstathiou

Lerner

, et al. ICUD-EAU International Consultation on Bladder Cancer 2012: radical cystectomy and bladder preservation for muscle-invasive urothelial carcinoma of the bladder. Eur Urol 2013; 63: 45–57.

Ploussard

Daneshmand

Efstathiou

, et al. Critical analysis of bladder sparing with trimodal therapy in muscle-invasive bladder cancer: a systematic review. Eur Urol 2014; 66: 120–137.

Malinchoc

Kamath

Gordon

, et al. A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. Hepatology 2000; 31: 864–871.

Kamath

Wiesner

Malinchoc

, et al. A model to predict survival in patients with end-stage liver disease. Hepatology 2001; 33: 464–470.

Said

Williams

Holden

, et al. Model for end stage liver disease score predicts mortality across a broad spectrum of liver disease. J Hepatol 2004; 40: 897–903.

10.

Wiesner

Edwards

Freeman

, et al. Model for end-stage liver disease (MELD) and allocation of donor livers. Gastroenterology 2003; 124: 91–96.

11.

Hedrick

Swenson

Friel

CM.

Model for End-stage Liver Disease (MELD) in predicting postoperative mortality of patients undergoing colorectal surgery. Am Surg 2013; 79: 347–352.

12.

Causey

Nelson

Johnson

, et al. The impact of Model for End-Stage Liver Disease-Na in predicting morbidity and mortality following elective colon cancer surgery irrespective of underlying liver disease. Am J Surg 2014; 207: 520–526.

13.

Lange

Jensen

Melton

, et al. Relationship between model for end-stage liver disease score and 30-day outcomes for patients undergoing elective colorectal resections: an American college of surgeons-national surgical quality improvement program study. Dis Colon Rectum 2015; 58: 494–501.

14.

Al Abbas

Borrebach

Bellon

, et al. Does preoperative MELD score predict adverse outcomes following pancreatic resection: an ACS NSQIP analysis. J Gastrointest Surg 2020; 24: 2259–2268.

15.

Khachfe

Araji

Nassereldine

, et al. Preoperative MELD score predicts adverse outcomes following gastrectomy: an ACS NSQIP analysis. Am J Surg 2022; 224: 501–505.

16.

Krafcik

Farber

Eslami

, et al. The role of Model for End-Stage Liver Disease (MELD) score in predicting outcomes for lower extremity bypass. J Vasc Surg 2016; 64: 124–130.

17.

Zachos

Zachou

Dalekos

, et al. Management of patients with liver cirrhosis and invasive bladder cancer: a case-series. J Transl Int Med 2019; 7: 29–33.

18.

Kim

Randhawa

Sands

, et al. Muscle-invasive bladder cancer in patients with liver cirrhosis: a review of pertinent considerations. Bladder Cancer 2021; 7: 261–278.

19.

Newman

Johnson

Cornia

, et al. Perioperative evaluation and management of patients with cirrhosis: risk assessment, surgical outcomes, and future directions. Clin Gastroenterol Hepatol 2020; 18: 2398–2414.

20.

Northup

Wanamaker

Lee

, et al. Model for End-Stage Liver Disease (MELD) predicts nontransplant surgical mortality in patients with cirrhosis. Ann Surg 2005; 242: 244–251.

21.

Guzzo

Dluzniewski

Orosco

, et al. Prediction of mortality after radical prostatectomy by Charlson comorbidity index. Urology 2010; 76: 553–557.

22.

Rosiello

Palumbo

Knipper

, et al. Preoperative frailty predicts adverse short-term postoperative outcomes in patients treated with radical prostatectomy. Prostate Cancer Prostatic Dis 2020; 23: 573–580.

23.

Shahait

Labban

Dobbs

, et al. A 5-Item Frailty Index for predicting morbidity and mortality after radical prostatectomy: an analysis of the American College of Surgeons National Surgical Quality Improvement Program Database. J Endourol 2021; 35: 483–489.

24.

del Olmo

Flor-Lorente

Flor-Civera

, et al. Risk factors for nonhepatic surgery in patients with cirrhosis. World J Surg 2003; 27: 647–652.

25.

Johnson

Newman

Green

, et al. Incidence and risk factors of postoperative mortality and morbidity after elective versus emergent abdominal surgery in a national sample of 8193 patients with cirrhosis. Annals of Surgery 2021; 274: e345–e354.

26.

Mansour

Watson

Shayani

, et al. Abdominal operations in patients with cirrhosis: still a major surgical challenge. Surgery 1997; 122: 730–735; discussion 735.

27.

Neeff

Mariaskin

Spangenberg

, et al. Perioperative mortality after non-hepatic general surgery in patients with liver cirrhosis: an analysis of 138 operations in the 2000s using child and MELD scores. J Gastrointest Surg 2011; 15: 1–11.

28.

Casadaban

Gabra

Parvinian

, et al. Impact of transjugular intrahepatic portosystemic shunt creation on intermediate-term model for end-stage liver disease score progression. Transplant Proc 2014; 46: 1384–1388.

29.

Jacob

Copley

Lewsey

, et al. Pretransplant MELD score and post liver transplantation survival in the UK and Ireland. Liver Transpl 2004; 10: 903–907.

30.

Morimoto

Okada

Okita

The model for end-stage liver disease (MELD) predicts early and late outcomes of cardiovascular operations in patients with liver cirrhosis. Ann Thorac Surg 2013; 96: 1672–1678.

31.

Minhem

Sarkis

Safadi

, et al. Comparison of early morbidity and mortality between sleeve gastrectomy and gastric bypass in high-risk patients for liver disease: analysis of American College of Surgeons National Surgical Quality Improvement Program. Obes Surg 2018; 28: 2844–2851.

32.

Millwala

Nguyen

Thuluvath

PJ.

Outcomes of patients with cirrhosis undergoing non-hepatic surgery: risk assessment and management. World J Gastroenterol 2007; 13: 4056–4063.

33.

Lopez-Delgado

Esteve

Javierre

, et al. Influence of cirrhosis in cardiac surgery outcomes. World J Hepatol 2015; 7: 753–760.

34.

Nicoll

Surgical risk in patients with cirrhosis. J Gastroenterol Hepatol 2012; 27: 1569–1575.

35.

Quek

Stein

Daneshmand

, et al. A critical analysis of perioperative mortality from radical cystectomy. J Urol 2006; 175: 886–889; discussion 889.

36.

Isbarn

Jeldres

Zini

, et al. A population based assessment of perioperative mortality after cystectomy for bladder cancer. J Urol 2009; 182: 70–77.

37.

Elting

Pettaway

Bekele

, et al. Correlation between annual volume of cystectomy, professional staffing, and outcomes. Cancer 2005; 104: 975–984.

38.

Ray-Zack

Shan

Mehta

, et al. Hospital length of stay following radical cystectomy for muscle-invasive bladder cancer: development and validation of a population-based prediction model. Urol Oncol 2019; 37: 837–843.

39.

Nix

Smith

Kurpad

, et al. Prospective randomized controlled trial of robotic versus open radical cystectomy for bladder cancer: perioperative and pathologic results. Eur Urol 2010; 57: 196–201.

40.

Parekh

Reis

Castle

, et al. Robot-assisted radical cystectomy versus open radical cystectomy in patients with bladder cancer (RAZOR): an open-label, randomised, phase 3, non-inferiority trial. Lancet 2018; 391: 2525–2536.

41.

Bochner

Dalbagni

Sjoberg

, et al. Comparing open radical cystectomy and robot-assisted laparoscopic radical cystectomy: a randomized clinical trial. Eur Urol 2015; 67: 1042–1050.

42.

Mastroianni

Ferriero

Tuderti

, et al. Open radical cystectomy versus robot-assisted radical cystectomy with intracorporeal urinary diversion: early outcomes of a single-center randomized controlled trial. J Urol 2022; 207: 982–992.

43.

Mastroianni

Tuderti

Anceschi

, et al. Comparison of patient-reported health-related quality of life between open radical cystectomy and robot-assisted radical cystectomy with intracorporeal urinary diversion: interim analysis of a randomised controlled trial. Eur Urol Focus 2022; 8: 465–471.

44.

Crozier

Hennessey

Sengupta

, et al. A Systematic review of ileal conduit and neobladder outcomes in primary bladder cancer. Urology 2016; 96: 74–79.

45.

Hautmann

de Petriconi

Volkmer

BG.

Lessons learned from 1,000 neobladders: the 90-day complication rate. J Urol 2010; 184: 990–994; quiz 1235.

46.

Parekh

Gilbert

Koch

, et al. Continent urinary reconstruction versus ileal conduit: a contemporary single-institution comparison of perioperative morbidity and mortality. Urology 2000; 55: 852–855.

47.

Abe

Takada

Shinohara

, et al. Comparison of 90-day complications between ileal conduit and neobladder reconstruction after radical cystectomy: a retrospective multi-institutional study in Japan. Int J Urol 2014; 21: 554–559.

48.

Monn

Kaimakliotis

Cary

, et al. Short-term morbidity and mortality of Indiana pouch, ileal conduit, and neobladder urinary diversion following radical cystectomy. Urol Oncol 2014; 32: 1151–1157.

49.

Rezaee

Atwater

Bihrle

, et al. Ileal conduit versus continent urinary diversion in radical cystectomy: a retrospective cohort study of 30-day complications, readmissions, and mortality. Urology. Epub ahead of print 22 August 2022. DOI: 10.1016/j.urology.2022.08.020.

Preoperative MELD score predicts mortality and adverse outcomes following radical cystectomy: analysis of American College of Surgeons National Surgical Quality Improvement Program

Abstract

Background:

Objectives:

Design and Methods:

Results:

Conclusion:

Keywords

Introduction

Materials and methods

Study design and patient population

Ethics approval

Patient demographics and variables

MELD score calculation

Outcomes

Statistical analysis and model construction

Propensity score matching

Data availability

Results

Patients demographics and general characteristics stratified by MELD score

Thirty-day postoperative outcomes stratified by MELD score

Multivariate analysis of outcomes in unmatched and propensity-matched cohorts

Discussion

Limitations

Conclusion

Footnotes

Acknowledgements

Declarations

ORCID iD

References