in-hospital mortality and failure-to-rescue rates after radical cystectomy

In-hospital mortality and failure-to-rescue ratesafter radical cystectomyVincent Q. Trinh*†, Quoc-Dien Trinh‡, Zhe Tian†, Jim C. Hu§, Shahrokh F. Shariat¶,Paul Perrotte¶, Pierre I. Karakiewicz†** and Maxine Sun†

*Vattikuti Urology Institute, Henry Ford Health System, Detroit, MI, USA, †Cancer Prognostics and Health OutcomesUnit, University of Montreal Health Center, Montreal, Canada, ‡Department of Surgery, Brigham and Women'sHospital/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA, §Department of Urology, DavidGeffen School of Medicine, University of California, Los Angeles, CA, USA, ¶Department of Urology, Weill MedicalCollege, Cornell University, New York, NY, USA, and **Department of Urology, University of Montreal HealthCenter, Montreal, Canada

Objective• To show the underlying variability in peri-operative

mortality after radical cystectomy (RC) by analysingfailure-to-rescue (FTR) rates, i.e. deaths aftercomplications.

Materials and Methods• Patients undergoing RC for non-metastatic bladder

cancer (BCa) were identified from the NationwideInpatient Sample, 1999–2009, resulting in a weightedestimate of 79 972 patients.

• The FTR rates were assessed according to patient andhospital characteristics, as well as complication type.

• Generalized linear regression analyses were performed.

Results• Overall, 26 740 patients had a complication,

corresponding to a FTR rate of 5.5%. Septicaemia (oddsratio [OR]: 13.41, P < 0.001) and cardiac (OR: 3.97, P <0.001), wound-related (OR: 2.12, P < 0.001),genitourinary (OR: 1.62, P = 0.045) and haematological

(OR: 1.78, P = 0.008) complications were associatedwith FTR.

• Older age (OR: 1.05, P < 0.001), increasing comorbidities(OR: 1.33, P < 0.001), Medicare (OR: 1.52, P = 0.016), andMedicaid insurance status (OR: 2.10, P = 0.029) wereassociated with higher odds of FTR. Conversely,increasing hospital volume (OR: 0.992, P = 0.014)reduced the odds of FTR.

Conclusions• Whereas both patient and hospital characteristics were

associated with increased odds of FTR, the occurrence ofsepticaemia and cardiac complications were the moststrongly associated with a higher risk of in-hospitalmortality.

Keywordscystectomy, urinary bladder neoplasms, postoperativecomplications, failure to rescue, Nationwide InpatientSample

Introduction

Approximately 73 510 new cases of bladder cancer (BCa)were diagnosed in the USA in 2012, with 25% of thesecases being carcinoma invading the bladder muscle [1,2].The ‘gold standard’ treatment in these cases is radicalcystectomy (RC) with urinary diversion, a complexprocedure with high complication rates (21–57%) andperi-operative mortality ranging between 1 and 3% [2,3].

Important variations have consistently been recorded inseveral studies. For example, in a North Americaninstitutional report, Stein et al. [3] reported an overallcomplication rate of 28%, with a 2.5% peri-operativemortality rate. By comparison, within a European

institutional report, Novotny et al. [4] identified a 26%complication rate with a 0.6% percent mortality rate. Assuch, despite a similar prevalence of complication rates,peri-operative mortality rates differed by almost fivefold.

While the variability of peri-operative mortality maybe explained by several factors, including patientcharacteristics (age and baseline medical conditions)[5], disease characteristics (nodal involvement andlymphovascular invasion) [6] and surgical technique [7],the quality of postoperative care is equally important [8]. Inthis respect, recognition of adverse events and a promptmanagement of these complications influenceperi-operative mortality. The concept of failure to rescue(FTR) was coined in an original report [5], and

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subsequently confirmed in several other studies [8,9]. Sinceperi-operative mortality for RC remains variable incontemporary series, our goal was to determine whichpatient and hospital characteristics, as well as which typesof complications may merit an increased alertness for‘rescue’ after RC.

Materials and MethodsData Source, Sample Population andSurgical Procedures

Data ranging from 1999 to 2009 were extracted from theNationwide Inpatient Sample (NIS). The NIS includesinpatient discharge data collected via federal-statepartnerships, as part of the Agency for Healthcare Researchand Quality’s Healthcare Cost and Utilization Project.Patients with a primary diagnosis of BCa were identifiedusing the International Classification of Disease, 9th

Revision (ICD-9), Clinical Modification (CM) diagnosticcode: 188. Data from patients undergoing RC (57.71) aswell as total cystectomy (57.71) were obtained by using theICD-9 procedure codes.

Patient Characteristics

Available variables included age at hospitalization, gender,race, insurance status, comorbidities and median householdincome. While age was predominantly assessed as acontinuous variable, it was dichotomized as (�75 vs <75years) for the purpose of descriptives. Race was categorizedas White, Black, Hispanic, other (Asian or Pacific Islander,Native American) or unknown. Insurance status wasclassified based on the expected primary payer, andincluded Medicare, Medicaid, private insurance (BlueCross, commercial carriers, private health maintenanceorganizations and preferred provider organizations), andother payment forms, including those who were uninsured.Baseline comorbidities were determined using a CharlsonComorbidity Index (CCI)-derived score [10], adapted byDeyo et al. [11]. They were coded categorically in baselinecharacteristics (0, 1, 2 and �3), then as a continuousvariable in multivariable analyses. Median householdincome categories ($1–24 999, $25 000–34 999,$35 000–44 999, $45 000+ and unknown) were estimated byusing ZIP code as a surrogate.

Hospital Characteristics

Hospital volume was examined foremost as a continuousvariable. For descriptives, hospital volume was alsoclassified as high vs low/intermediate, using previouslydescribed methodology [12]. Briefly, hospital volume wasdetermined according to its mean annual RC volume,namely the number of RCs performed overall divided by

the number of years the hospital performed the operation,from 1999 to 2009. Subsequently, mean annual volume wasdivided into five equal quintiles of patients. High-volumehospitals consisted of institutions treating the highestquintile of mean annual volume.

Other hospital characteristics included hospital bed size,which was obtained from the American HospitalAssociation Annual Survey of Hospitals, and defined by theUS Census Bureau [13]. Hospital bed size was categorizedas small, medium and large, based on the urban/ruraldesignation of the hospital and its teaching status. Ahospital was considered a teaching hospital if it had anAmerican Medical Association-approved residencyprogramme, was a member of the Council of TeachingHospitals, or had a ratio of �0.25 of full-time interns andresidents to non-nursing home beds [14].

Postoperative Complications

Postoperative complications were defined using ICD-9diagnoses, according to previously established methodology[15], and updated using additional codes. For analyticalpurposes, we stratified postoperative complications into 11categories (cardiac, digestive, neurological, respiratory,genitourinary, vascular, iatrogenic, wound-related,haematological, infections and septicemia).

Primary Endpoint

The primary endpoint was FTR, defined as the number ofdeaths among patients who developed a complicationdivided by the number of patients who had a complication.In-hospital mortality information was coded fromdisposition of patient, and defined as 30-day mortality afterRC.

Statistical Analysis

Medians and interquartile ranges (IQRs) were generatedfor continuously coded variables, and frequencies andproportions were generated for categorical variables. Thechi-squared test was used to assess the statisticalsignificance of proportions.

First, we examined postoperative complications usinggeneralized linear regression models that adjusted forhospital clustering and the random effect of year ofadmission. Covariates comprised patient age, comorbidities,race, gender, insurance type, median household income,annual hospital volume and hospital bed size. Second,analyses were restricted to patients who had at least onerecorded event of postoperative complication. In this set,generalized linear regression models were used to predictin-hospital mortality (FTR). Covariates comprisedcomplication schemes as well as the aforementionedcovariates.

Failure to rescue after radical cystectomy

© 2013 BJU International E21

All tests were two-sided with a statistical significance set atP < 0.05. Analyses were conducted using the R statisticalpackage (the R foundation for Statistical Computing,version 2.15.2).

ResultsBaseline Descriptives

Patient and hospital characteristics are shown in Table 1.After sampling weights were applied, 79 972 patients withBCa undergoing RC were identified. The mean (median;IQR) patient age was 68.4 (70; 62–76) years. Most patientswere male (82%) and Caucasian (67%). Baselinecomorbidities distribution was 60, 29, 7.2 and 3.8% for aCCI score of 0, 1, 2 and �3, respectively. One third ofindividuals were privately insured, while the predominantinsurance status was Medicare (62%). The mean annualhospital volume was 19.2 (median 8, IQR: 4–22) cases.Most RCs (71%) were performed at hospitals with largebed size.

Postoperative Complications

Overall, 26 740 patients experienced at least one in-hospitalcomplication after RC. The most common complicationtypes were digestive (16.8%), iatrogenic (5.7%) andsepticaemia (3.9%). Multivariable adjustment results areshown in Table 2. Older age (odds ratio [OR]: 1.012, P <0.001) and increasing comorbidities (OR: 1.084, P < 0.001)were associated with higher odds of complications afterRC. Higher odds were also found in Black people (vsCaucasian OR: 1.222, P < 0.001), in other minoritiesincluding Asians, Pacific Islanders and Native Americans(OR: 1.241, P < 0.001) and in those insured by Medicaid orMedicare (vs private insurance OR: 1.229–1.265, both P <0.001). Conversely, female gender (OR: 0.915, P < 0.001),other insurance status, which included unknown anduninsured patients (OR: 0.914, P = 0.049), and larger bedsize (medium vs small OR: 0.860, P < 0.001; large vs smallOR: 0.933, P = 0.045) showed lower odds of havingcomplications after RC.

Table 1 Weighted estimates of 79 972 patients with BCa, from the NIS 1999–2009, who underwent RC.

Variables Overall No complication ≥1 complication P

Proportion of patients, % 100.0 66.6 33.4 –Mean (median; IQR) age, years 68.4 (70; 62–76) 67.8 (69; 61–76) 69.6 (71; 63–77) <0.001*Age groups, % <0.001

�75 years 31.5 29.2 36.1<75 years 68.5 70.8 63.9

CCI score, % <0.0010 59.5 60.9 56.71 29.4 28.5 31.32 7.2 7.2 7.2�3 3.8 3.4 4.8

Gender, % 0.011Male 81.6 81.4 82.1Female 18.4 18.6 17.9

Race, % <0.001Caucasian 66.5 67.2 65.1Black 3.5 3.3 3.7Hispanic 2.6 2.6 2.6Other 2.9 2.7 3.2Unknown 25.6 24.2 25.3

Insurance status, % <0.001Private 30.6 32.9 25.9Medicaid 3.9 3.9 3.8Medicare 61.6 58.8 67.1Other 4.0 4.4 3.1

Median income by household ZIP code, % <0.001�$24 999 14.6 14.6 14.6$25 000–34 999 24.7 24.5 24.9$35 000–44 999 26.9 26.5 27.6�$45 000 31.9 32.4 30.9Unknown 2.0 2.0 2.0

Hospital volume, % <0.001Low/Intermediate 78.1 78.1 82.0High 21.9 21.9 18.0

Hospital bed size, % 0.002Small 10.2 10.0 10.7Medium 18.5 18.7 18.2Large 71.2 71.3 71.1

*Mann–Whitney U-test.

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Failure-to-Rescue Rates According to Patient andHospital Characteristics

Overall, 1475 patients died amongst the 26 740 that had atleast one postoperative complication after RC, whichrepresents a 5.5% FTR rate (Table 3). FTR rates weresignificantly higher for patients aged �75 years (8.5 vs3.8%, P < 0.001), with CCI score �3 (9.9 vs 5.2%, P <0.001), females (6.5 vs 5.3%, P = 0.002), and those who wereMedicare beneficiaries (6.8 vs 2.7%, P < 0.001) relative totheir <75 years, CCI score of 0, male, and privately insuredcounterparts, respectively. FTR rates were also significantlyhigher amongst individuals who underwent RC at alow/intermediate-volume hospitals compared with thosetreated at high-volume hospitals (5.8 vs 4.1%, P < 0.001).Other significant differences were also recorded for FTRrates according to household income (�$45 000 vs<$25 000: 5.1 vs 5.9%, P = 0.02) and hospital bed size (largevs small: 5.2 vs 6.0%, P = 0.002).

Failure-to-Rescue Rates According toPostoperative Complication Types

Failure-to-rescue rates were highest amongst patients whohad an occurrence of septicaemia (23.7%, relative risk [RR]:

18.7, 95% CI: 16.9–20.7, P < 0.001), infections (11.4%, RR:5.8, 95% CI: 5.2–6.6, P < 0.001) and cardiac (10.5%, RR: 5.3,95% CI: 4.7–6.0, P < 0.001) complications (Table 4). FTRrates were also significantly higher with the presence ofother types of complications, ranging from 3.7% fordigestive-related complications to 8.5% for wound-relatedcomplications.

Multivariable Analyses

Failure-to-rescue rates were significantly higher amongstpatients who had an occurrence of septicaemia (odds ratio[OR]: 13.41, 95% CI: 10.14–17.72) and cardiac-relatedcomplications (OR: 3.97, 95% CI: 2.86–5.52 [Table 5]).Other significant associations were recorded for wound,genitourinary and haematological complications (OR:1.62–2.12, P � 0.045).

In terms of patient characteristics, increasing age (OR: 1.05,P < 0.001) and increasing comorbidities (OR: 1.33, P <0.001) were associated with a higher FTR rate. Moreover,patients with Medicaid insurance had a 2.1-fold higher FTRrate, while patients with Medicare insurance were 1.5-foldmore likely to die after a complication.

Finally, FTR rates also differed with respect to hospitalcharacteristics. Specifically, patients treated at hospitals witha higher RC volume (OR: 0.992, 95% CI: 0.985–0.998, P =0.014) were significantly less likely to experience FTR.

DiscussionRadical cystectomy is characterized as a morbid procedure,with 21–57% of patients experiencing complications andperi-operative mortality rates of 1–3% [2,3]. While severalfactors may contribute to peri-operative mortality, previousauthors suggest that a timely recognition of adverse eventsafter a high-risk procedure (here RC), as well as a promptand efficient management of such complications maybe just as important as other established factors ofperi-operative mortality (i.e. age, comorbidities, stage)[5,8,9,16].

Silber et al. [5] first characterized death after a complicationas FTR, and suggested FTR rates be used as a measureof quality of care. Subsequently, several other studiesconfirmed the importance of this concept [8,9]. Forexample, Ghaferi et al. [8] showed that, while the prevalenceof complications after high-risk surgery were stable acrosshospital volume categories, the occurrence of in-hospitaldeath varied significantly across hospital volume strata,despite similar complication rates.

In this regard, several studies have examined eitherpredictors associated with complications after RC, orpredictors associated with in-hospital mortality after RC.

Table 2 Generalized linear regression, adjusted for clustering bygeneralized estimating equation and for year of surgery randomeffects, predicting the occurrence of complication rates after RC forBCa.

Variables OR (95% CI) P

Age 1.012 (1.010–1.014)* <0.001Gender <0.001

Male Ref.Female 0.915 (0.881–0.950)

RaceCaucasian Ref.African American 1.222 (1.125–1.327) <0.001Hispanic 1.102 (0.986–1.231) 0.086Other 1.241 (1.130–1.363) <0.001Unknown 1.078 (1.025–1.135) 0.004

Comorbidities 1.084 (1.066–1.103)* <0.001Insurance status

Private Ref.Medicaid 1.265 (1.155–1.386) <0.001Medicare 1.229 (1.176–1.285) <0.001Other 0.914 (0.835–1.000) 0.049

Median household income by ZIP code$1–24 999 Ref.$25 000–34 999 1.017 (0.966–1.072) 0.517$35 000–44 999 1.044 (0.989–1.072) 0.119$45 000+ 0.965 (0.915–1.018) 0.190N/A 0.995 (0.889–1.114) 0.934

Annual hospital volume 0.997 (0.996–0.998)* <0.001Hospital bedsize

Small Ref.Medium 0.860 (0.796–0.928) <0.001Large 0.933 (0.872–0.998) 0.045

*, Continuous variable. Ref., referent category.



Table 3 Distribution of postoperative complications, in-hospital mortality, and failure-to-rescue (FTR) rates according to patient and hospitalcharacteristics amongst 79 972 patients who underwent RC for BCa, NIS, 1999–2009.

Variables ≥1 complication, n In-hospital Mortality, n FTR, %* P

No. of patients† 26 740 1475 5.5 –Age groups <0.001

�75 years 9 641 822 8.5<75 17 046 653 3.8

CCI score <0.0010 15 128 789 5.21 8 346 455 5.52 1 927 105 5.4�3 1 287 127 9.9

Sex 0.002Male 21 918 1168 5.3Female 4 765 308 6.5

Race 0.022White 17 353 937 5.4Black 993 53 5.3Hispanic 707 47 6.6Other 864 68 7.9Unknown 6 770 370 5.5

Insurance status <0.001Private 6 920 189 2.7Medicaid 1 025 45 4.4Medicare 17 909 1216 6.8Other 833 25 3.0

Median household income by ZIP code 0.023$1–24 999 3 896 228 5.9$25 000–34 999 6 654 406 6.1$35 000–44 999 7 373 385 5.2$45 000+ 8 231 419 5.1Unknown 533 37 6.9

Hospital volume <0.001Low/Intermediate 21 879 1277 5.8High 4 808 198 4.1

Hospital bedsize 0.002Small 2 853 172 6.0Medium 4 843 311 6.4Large 18 991 992 5.2

*Calculated as number of deaths/number of patients with �1 complication; †weighted estimate to approximate national representation, percentages are weighted and displayedin rows.

Table 4 Univariable analysis of FTR rates according to postoperative complication schemes.

Complication type n FTR, % RR (95% CI) P

Any postoperative*† 26 687 5.5 – –Cardiac 3 056 10.5 5.28 (4.65–5.98) <0.001Digestive 13 488 3.7 1.66 (1.50–1.84) <0.001Neurological 2 745 8.1 3.72 (3.22–4.31) <0.001Respiratory 2 490 7.8 3.61 (3.10–4.21) <0.001Genitourinary 2 353 8.1 3.69 (3.16–4.31) <0.001Vascular 486 7.2 3.07 (2.17–4.34) <0.001Iatrogenic 4 562 4.8 2.07 (1.79–2.39) <0.001Infections 3 012 11.4 5.84 (5.16–6.60) <0.001Haematological 2 629 5.9 2.55 (2.15–3.02) <0.001Wound 2 872 8.5 4.01 (3.49–4.61) <0.001Septicaemia 3 105 23.7 18.7 (16.94–20.67) <0.001

*Includes cardiac, digestive, neurological, respiratory, genitourinary, vascular, iatrogenic, haematological andwound-related complications, infections and septicemia. †RR was not computable because all patients who diedhad at least one postoperative complication.

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Until now, no study has studied FTR rates according topatient and hospital characteristics, as well as complicationschemes. Given the absence of data, we sought to examineFTR rates in a large population-based cohort.

Our study highlights several important points. First, 33% ofall patients who underwent RC during the period of thestudy had at least one complication. Of those, 1475in-hospital deaths occurred, corresponding to a FTR rate of5.5%. Despite accounting for other patient and hospitalcharacteristics, our analyses showed that certain types ofcomplications were inherently associated with higherpostoperative FTR rates. Specifically, patients whoexperienced septicaemia and/or cardiac complications hadthe highest risk of peri-operative mortality duringhospitalization after an adverse event. This finding suggeststhat the occurrence of any of these complication types cantrigger a series of events predisposing to death.

Nonetheless, the lack of chronological information withinthe NIS prevented us from determining the presence ofsuch associations [17].

Interestingly, patients who experienced digestivecomplications were not more likely to die duringhospitalization. This observation may seemcounterintuitive, given the known effect of major digestivecomplications such as anastomotic leak or small bowelobstruction on mortality after RC. That said, one mayhypothesize that the broad definition of digestivecomplications may also include minor events such aspostoperative ileus, for example, which would be ultimatelycaptured in the administrative coding. In the current series,it is also possible that patients who developed a digestivecomplication from which they died in hospital alsoexperienced other detrimental complicationssimultaneously (i.e. cardiac complications or septicaemia).

It is also noteworthy that an increase in septiccomplications occurred between 1999 and 2009. Theestimated annual percentage change for sepsis was +3.6%(95% CI 2.42–4.74; P < 0.001) in patients undergoing RC.Mortality in patients with sepsis also showed an upwardtrend, with a +4.4% estimated annual percentage change(95% CI 2.87–5.91%; P < 0.001). Explanations for theseobservations may include the increasing burden of elderlypatients with multiple morbidities, the increasingemergence of antibiotic resistance, as well as changes incoding practices [18].

Second, our analyses showed that specific hospitalcharacteristics were associated with FTR rates. Indeed,patients treated at institutions with higher hospitalvolume had lower rates of in-hospital mortality, given acomplication. This is consistent with previous studies[19–21], which suggests that some hospitals may be betterequipped to recognize important complications and ‘rescue’the patient in time. Whereas our analysis of FTR rates doesnot seek to direct criticism at any particular entity, ourfindings highlight the importance of hospital volume in thecontext of a high-risk procedure. As such, intra-hospitalpolicies and patient management routines stand out ascentral elements in FTR rates.

Third, patient characteristics followed similar trends to thosepreviously recorded, where older and sicker individuals, aswell as those without private insurance were more likely todie during hospitalization [22,23]. Our findings highlightthe need for specific interventions in at-risk populations,such as outreach teams. Nonetheless, the impact of suchinterventions on outcomes, such as mortality andre-admission rates, remains controversial [24].

From a practical perspective, our findings suggest thathospital characteristics such as higher RC volume are

Table 5 Generalized linear regression analysis, adjusted for hospitalclustering and year of admission, predicting FTR after RC for BCa inpatients in the NIS 1999–2009.

Variable OR (95% CI) P

Septicemia 13.41 (10.14–17.72) <0.001Cardiac 3.97 (2.86–5.52) <0.001Neurological 2.25 (0.76–6.65) 0.143Wound 2.12 (1.41–3.19) <0.001Genitourinary 1.62 (1.01–2.61) 0.045Vascular 1.63 (0.71–3.74) 0.253Haematological 1.78 (1.17–2.72) 0.008Infections 1.24 (0.83–1.86) 0.288Iatrogenic 1.47 (1.00–2.18) 0.053Digestive 1.04 (0.80–1.35) 0.751Respiratory 1.05 (0.35–3.18) 0.932Age 1.05 (1.03–1.07) <0.001Comorbidities 1.33 (1.19–1.48) <0.001Sex 0.214

Male Ref.Female 1.18 (0.91–1.54)

RaceWhite Ref.Black 1.37 (0.75–2.53) 0.308Hispanic 1.27 (0.67–2.43) 0.467Other 1.41 (0.79–2.52) 0.241Unknown 1.17 (0.91–1.50) 0.212

Insurance statusPrivate Ref.Medicaid 2.10 (1.08–4.08) 0.029Medicare 1.52 (1.08–2.14) 0.016Other 0.80 (0.33–1.93) 0.619

Median household income by ZIP code$1–24 999 Ref.$25 000–34 999 1.02 (0.73–1.44) 0.907$35 000–44 999 0.82 (0.59–1.14) 0.241>$45 000 0.76 (0.54–1.07) 0.120Unknown 1.34 (0.63–2.89) 0.449

Hospital bedsizeSmall Ref.Medium 0.90 (0.60–1.33) 0.582Large 0.83 (0.60–1.14) 0.246

Hospital volume 0.992 (0.985–0.998) 0.014

Ref., referent.



important determinants of the likelihood of survival inpatients undergoing RC. The issue of whether or not effortsshould be directed towards guiding patients towards ‘moreefficient’ settings, or improving postoperative care in ‘lessefficient’ settings, is a matter of great debate and certainlymerits future investigation.

Despite its novel approach, the present study has somelimitations. First, the severity of complications was notcoded in the ICD-9-CM system, and we lacked a gradingsystem such as the Clavien classification [25]. This wouldhave been particularly relevant since certain complicationsare more likely to be fatal. If such complications were tooccur more often, this would have biased the results for theless fatal complications. Second, the NIS does not containinformation on several important clinical and pathologicalcharacteristics. As such, there is certainly a case-mixscenario in which some hospitals may be dealing morefrequently with patients who have more advanced disease.We accounted for this limitation by adjusting for hospitalclustering. Nonetheless, we recognize that this is notsufficient, as previous studies have also noted [26,27].Third, the limitations that apply to retrospectiveobservational analyses also apply to our study. Ideally,future studies on FTR should be tested on a distinctdatabase to validate its application.

Overall, the current study identified several importantpredictors of FTR during hospital stay after RC. Ourfindings reinforce the importance of examining FTR ratesand complications separately. Certain factors, such ashospital volume, are important predictors of complicationsand FTR. Others, such as hospital bed size, aredeterminants of complications, but not of FTR. Suchindicators may explain the variability of in-hospital deathas a result of a complication. Timely recognition andprompt management of complications, especially invulnerable patients, is warranted and should be improvedupon in all hospital settings.

AcknowledgementsPierre I. Karakiewicz was partially supported by theUniversity of Montreal Health Centre Urology Specialists,Fonds de la Recherche en Santé du Québec, The Universityof Montreal Department of Surgery and the University ofMontreal Health Centre (CHUM) Foundation.

Conflict of InterestNone declared.

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Correspondence: Vincent Quoc-Huy Trinh, VattikutiUrology Institute, Henry Ford Health System, 2799 W,Grand Boulevard, Detroit, MI 48202, USA.

e-mail: [email protected]

Abbreviations: RC, radical cystectomy; FTR,failure-to-rescue; BCa, bladder cancer; OR, odds ratio; NIS,Nationwide Inpatient Sample; ICD-9, InternationalClassification of Disease, 9th Revision; CM, clinicalmodification; CCI, Charlson Comorbidity Index; IQR,interquartile range; RR, relative risk.



in-hospital mortality and failure-to-rescue rates after radical cystectomy

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