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Platinum Priority – EducationEditorial by XXX on pp. x–y of this issue

Intraoperative Adverse Incident Classification (EAUiaiC) bythe European Association of Urology ad hoc ComplicationsGuidelines Panel

Chandra Shekhar Biyani a,y,*, Jakub Pecanka b,y, Morgan Roupret c, Jørgen Bjerggaard Jensen d,Dionysios Mitropoulos e

aDepartment of Urology, St. James’s University Hospital, Leeds, UK; bMedical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Centre,

Leiden, The Netherlands; c Sorbonne Université, GRC ndeg, ONCOTYPE-URO, Urology, AP-HP, Hôpital Pitié-Salpêtrière, F-75013, Paris, France; dDepartment of

Urology, Aarhus University Hospital, Aarhus, Denmark; e1st Department of Urology, Medical School, National and Kapodistrian University of Anthens, Athens, Greece

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ava i lable at www.sc iencedirect .com

journa l homepage: www.europea nurology.com

Article info

Article history:

Accepted November 19, 2019

Associate Editor: Andrew Vickers

Statistical Editor:

Andrew Vickers

Keywords:

ComplicationsIntraoperativeStandardisationUrologySurgery

Abstract

Background: A surgical adverse incident (AI) is defined as any deviation from thenormal operative course. Current complication-grading systems mostly focus on post-operative events.Objective: To propose an intraoperative AI classification (EAUiaiC) to facilitate reporting.Design, setting, and participants: The classification was developed using a modifiedDelphi process in which experts answered two rounds of survey questionnaires orga-nised by the European Association of Urology ad hoc Complications Guidelines Panel.Experts evaluated AI terminology using a 5-point Likert scale for clarity, exhaustiveness,hierarchical order, mutual exclusivity, clinical utility, and quality improvement.Outcome measures and statistical analysis: We considered �70% agreement for inclu-sion or exclusion. The resultant EAUiaiC was evaluated using ten sample clinicalscenarios. Numerical and graphical statistical techniques were used to report the results.Results and limitations: In total, 343 respondents participated. The proposed EAUiaiCsystem comprises eight AI grades ranging from grade 0 (no deviation and no consequenceto the patient) to grade 5B (wrong surgery site or intraoperative death). In the validationstage, EAUiaiC was rated highly favourably in terms of relevance and reliability (consis-tency) by 126 experts. Ratings for self-reported ease of use were at acceptable levels.Conclusions: We propose a novel intraoperative AI classification (EAUiaiC) for use forurological procedures. Both the initial assessment of feasibility and the subsequentassessment of reliability suggest that it is a simple and effective tool for classifyingintraoperative complications.Patient summary: Complications in surgery are common. It is helpful to classifycomplications in a uniform and objective manner so that surgeons can easily compareoutcomes and learn from complications. Here we propose a new classification system forcomplications that occur during urological surgical procedures.

An abstract of this work was presented at the 2018 congress of the EuropeanAssociation of Urology.

soc

ute. De113biy

© 2019 European As

y These authors contrib* Corresponding author113 2066017, Fax: +44

E-mail address: shekhar

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https://doi.org/10.1016/j.eururo.2019.11.0150302-2838/© 2019 European Association of Urology. Published by Elsevier B

iation of Urology. Published by Elsevier B.V. All rights reserved.

d equally to this work.partment of Urology, St. Jamess University Hospital, Leeds, UK. Tel. +44 2064920.ani@hotmail.com (C.S. Biyani).

e Adverse Incident Classification (EAUiaiC) by the Europeanur Urol (2019), https://doi.org/10.1016/j.eururo.2019.11.015

.V. All rights reserved.

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1. Introduction

An intraoperative adverse incident (AI) is defined as anundesired event due to the surgical intervention occurringbetween skin incision and skin closure. There is lack of astandardised reporting system for intraoperative AIs.Despite a venerable tradition of morbidity meetings,nonuniform systems are used to report surgical complica-tions [1–4]. Clavien and Dindo proposed a system based onthe type of intervention required to resolve the postopera-tive complications which has been internationally widelyaccepted [5]. The success of this system is linked to itslogical design and reproducibility. The Clavien-Dindosystem (CDS) has previously been applied in urologicalsurgery [6–12]. Various authors have reported limitationswhen using the CDS to grade intraoperative AIs [7,11–13] asit was originally designed to report postoperative compli-cations. In addition, it is generally acknowledged that theremay still be some subjectivity [14] in the recording ofcomplications by individual surgeon and in the interpreta-tion of grading of complications [6–12].

As the CDS was not designed to include reporting ofintraoperative complications and there is currently anemphasis on improving quality and transparency in healthcare, there is a need to develop a simple, systematic,objective, and reproducible grading system for intraoper-ative AIs. This new classification should ideally increase ourability to (1) identify quality-of-care measures for bench-marking, (2) make comparisons between individual sur-geons or institutions, (3) improve the characterisation ofsurgical morbidity, (4) compare different surgical techni-ques, and (5) portray risks accurately to patients.

2. Materials and methods

2.1. Analysis of the literature

A member of the European Association of Urology (EAU) adhoc Complications Guidelines Panel conducted a literaturereview by searching the PubMed database using thefollowing keywords: intraoperative complications; intra-operative events; intraoperative incidents; classification;grading; categories. The goal was to identify intraoperativeAI grading systems available in the literature. Subsequently,the systems identified were tested by the panel using fourhypothetical intraoperative scenarios. The panel alsoevaluated the global applicability of the systems selectedusing a method described by Kaafarani et al [15]. Amultistage approach was used by the panel to create anew grading system for intraoperative AIs called theintraoperative AI classification (EAUiaiC). The developmentstages included two rounds of expert consensus followed byvalidation in hypothetical clinical scenarios to test perfor-mance in the clinical setting.

2.2. Questionnaire design through consensus

A two-round modified Delphi process [16] was used toassess expert agreement on items for the new grading

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system. Multiple iterations and controlled feedback wereused to achieve consensus (�70% agreement) [17], followedby a discussion of the results within the panel. It wasdecided that intraoperative AIs should be categorisedaccording to their potential impact on patient outcomes.The grading system was piloted with 17 experts (Supple-mentary material). The experts were asked to evaluatevarious categories of the grading system for intraoperativeAIs using a similar response scale [18] and globalapplicability scales. All responses and free text commentswere analysed. Following this exercise, grading categorieswere revised and a further column “Not sure” was added tothe global applicability Likert scale for participants toimprove the accuracy of the responses. The final cycleincluded four demographic questions and the revisedclassification with the global applicability scale. Survey-Monkey (www.surveymonkey.com) was used to create anddistribute a questionnaire (Supplementary material) viaMailChimp between May 2017 and June 2018. In addition,the panel actively encouraged participation using Twitter.Data collection concluded 12 wk after the launch.

Following the standard set in many existing Delphistudies [19,20], the panel used a threshold of �70%agreement on relevance (AOR; ie, the fraction of “Veryrelevant and succinct” and “Relevant but needs minoralterations” responses among all responses) for inclusion orexclusion in the final round of development.

2.3. Validation for reproducibility and usefulness

In round 1, 30 scenarios involving intraoperative complica-tions were selected from the literature and personalcommunications [21]. A digital questionnaire was designedto obtain classification of each scenario using the EAUiaiCsystem (Supplementary material). The questionnaire wasdistributed to several hundred urology experts via the EAUportal. For each scenario, the questionnaire asked therespondent to (1) classify the scenario using the EAUiaiCsystem and (2) provide a score for ease of use (Likert scaleranging from 1 [Very difficult] to 5 [Very easy]) in classifyingeach scenario according to the EAUiaiC. The respondentswere encouraged to provide comments about their experi-ence in using EAUiaiC, which provided further useful inputabout the system.

For round 2, in light of the relatively low response rate forthe first round, a decision was taken to reduce the numberof scenarios and redistribute the questionnaire among thenonresponsive experts. Out of the 30 scenarios (originallyassembled in no particular order), the first ten wereredistributed.

2.4. Statistical analysis

In the design stage, responses to the agreement/disagree-ment scale were analysed using simple descriptive statisticsand graphical methods. The free text comments obtainedwere assessed by the panel. In the validation stage, theinter-rater reliability (IRR) and ease of use of EAUiaiC wereassessed using graphical (Cleveland dot plots) and numeri-

e Adverse Incident Classification (EAUiaiC) by the Europeanur Urol (2019), https://doi.org/10.1016/j.eururo.2019.11.015

Table 1 – Distribution of responses to the questionnaire on grading of intraoperative adverse incidentsa.

Grade Response AOR Total Revisions

Notrelevant

Unable toassess

relevance

Relevant butneeds minoralteration

Very relevantand succinct

(%) responses suggested

Grade 0: event requiring nointervention or change in operativeapproach, no deviation from plannedintraoperative steps

26% (88) 7% (23) 14% (47) 53% (178) 67 336 1

Grade 1: event requiring change inplanned intraoperative steps, notlife-threatening, no tissue or organremoval. Event addressed in acontrolled manner with no long-term side effects

5% (18) 6% (21) 33% (109) 56% (187) 89 335 3

Grade 2: event requiring change inoperative approach but NOT life-threatening. Event addressed in acontrolled manner, but may haveshort/long-term side effects

2% (6) 3% (10) 29% (98) 66% (219) 95 333 2

Grade 3: event requiring deviationfrom planned intraoperative steps,becoming life-threatening but NOTrequiring tissue or organ removal

3% (9) 2% (7) 20% (66) 75% (252) 95 334 1

Grade 4: event requiring deviationfrom planned intraoperative stepsand with short/long-termconsequences to patientGrade 4A: requiring tissue or organremoval

2% (7) 4% (13) 16% (52) 78% (256) 94 328

Grade 4B: unable to completeprocedure as planned owing to asurgical event or technical issue orunplanned stoma

3% (10) 5% (18) 20% (68) 71% (237) 91 333

Grade 5A: wrong site or side for opensurgery or wrong patient or noconsent

10% (31) 6% (18) 18% (57) 67% (218) 85 324 1

Grade 5B: death 5% (18) 1% (4) 5% (16) 89% (296) 94 334 0

AOR = agreement on relevance.a Percentages are rounded and mode values are shown in bold font.

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cal (Fleiss k) [22] statistical methods. Summary tables of thedata from the design and validation stages are reported. Thedistributions observed for the classification categories andease of use ratings in the form of bar graphs and a two-waycontingency table are also reported.

3. Results

The initial literature review identified three relevantclassification systems [15,23,24]. Applying these systemsto four hypothetical intraoperative complication scenariosrevealed significant variations in grading and globalapplicability among the systems. This demonstrated theneed to develop a new system to grade intraoperativecomplications.

3.1. Intraoperative AI classification

A total of 343 responders participated in the design stagesurvey. Of these, 77% were faculty/consultants and 12% weretrainees; the remainder fell in the “other” category. Theresponders had on average 17 yr of experience in urology

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and reported subspecialties that included oncology (41%),endourology (25%), andrology (4%), general urology (12%),functional urology (9%), and other (9%).

The relevance scores reported for the categories aresummarised in Table 1. There were eight categories(including subgroups) in total. Of these, seven wereconsidered either “Relevant but needs minor alterations”or “Very relevant and succinct” by between 88% and 96%of the respondents. Grade 0 had the lowest AOR (66%),with 26% of the respondents considering it not relevant.Conversely, five of the eight categories (grades 2, 3, 4A, 4B,and 5B.) were considered relevant by more than 90% ofrespondents (Fig. 1). The distribution of the responses forthe global applicability of EAUiaiC is shown in Table 2 andFig. 2.

3.2. Expert comments

In total there were 279 free text comments for the eightcategories (including subgroups). We applied a threshold offive respondents making comparable suggestions forincorporation in the new grading system. For grade 0, the

e Adverse Incident Classification (EAUiaiC) by the Europeanur Urol (2019), https://doi.org/10.1016/j.eururo.2019.11.015

Fig. 1 – Distribution of responses from experts to the intraoperative adverse incidents grading questionnaire.

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main themes of the comments by respondents were: “Doesit need reporting?”, “Start grading from 1 instead of 0”,“Provide examples”, and “Add no consequence for patient”.Three suggestions for grade 1 were added to our originalstatement. A total of 47 comments were received for grade2. Of these, six respondents suggested stent removal for atight ureter as an example for this category and foursuggested greater differentiation and clarity betweengrades 1 and 2. Grade 3 had the highest AOR rate of95.2%. A number of AI examples were suggested byrespondents for grades 4A and 4B. Three respondentssuggested combining grades 4A and 4B into a single grade4 category. An additional three respondents questioned theterm unplanned stoma in the text and suggested rewording.There were 56 comments for grade 5A, with a majorityarguing that this category represented “malpractice” or“never events” that should not be classed as complications.A suggestion to include minimally invasive surgery in thetext was made by five respondents. For grade 5B, sixrespondents suggested classifying death as intraoperative.

3.3. Reliability: agreement on classification among experts

In round 1 of the validation survey a total of 18 responseswere obtained for 29 of the 30 scenarios. The low responserate might be attributable to the large number of scenarios,resulting in response fatigue. An additional 110 responseswere recorded for the ten scenarios selected for round 2. Intotal, we obtained 126 responses from urology experts.Table 3 shows the breakdown of the classifications observedfor the first ten scenarios. The mode frequencies observedwere between 40% and 80%, indicating reasonably highagreement. The IRR measure was k = 0.243, indicating fair

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agreement. The Cleveland dot plot in Fig. 3 shows the degreeof clustering of the classifications for each of the tenscenarios. The spread of the points suggests non-negligibledisagreement among the experts regarding classification ofthe intraoperative complications described in the scenarios,which is consistent with k = 0.243.

3.4. Self-reported ease of use of the classification

Fig. 4A shows the overall distribution of the categoryclassifications selected by the experts and the self-reportedease-of-use ratings for each category classification. Fig. 4Bshows the distribution of self-reported ease-of-use ratingsand the category classifications for each ease-of-use rating.The joint distribution of classification categories and ease-of-use ratings is shown in Table 4. The most prevalentclassification categories for the scenarios provided were 1,2, and 3, and the majority of the ease-of-use ratingsreported (>80%) were either very easy, easy, or intermedi-ate, both in the three most prevalent categories and overall(Fig. 4).

4. Discussion

Whereas postoperative complication grading systems arewidely used, intraoperative reporting of complications ismuch more sparsely evaluated. There is a lack of consensusin the literature on methods for reporting intraoperative AIs[1–4] and therefore there is a strong need for a newclassification system to record the severity of intraoperativeAIs based on integrated patient-related factors (eg, qualityof life, pain, and morbidity due to an AI). Here we propose aframework for classifying intraoperative AIs in the hope of

e Adverse Incident Classification (EAUiaiC) by the Europeanur Urol (2019), https://doi.org/10.1016/j.eururo.2019.11.015

Table 2 – Distribution of responses for the 5-point Likert scale for the global applicability of the intraoperative adverse incidents grading(mode values are shown in bold font).

Domain Likert score Totalresponses

Medianscore

1 (low) 2 3 4 5 (high) Not sure

Clarity: how clearly defined are the different levels ofclassification of intraoperative adverse events?

3.8%(13)

6.4%(22)

17%(58)

36%(123)

35%(120)

2%(7)

343 4

Exhaustiveness: how well does this system cover theentire range of intraoperative complications?

2.9%(10)

7%(24)

16%(55)

45%(153)

25%(87)

4.1%(14)

343 4

Mutual exclusivity: how mutually exclusive are adjacentlevels of intraoperative adverse events in this system?

3.2%(11)

9.9%(34)

23%(78)

40%(138)

18%(62)

5.8%(20)

343 4

Hierarchy: in terms of order of severity, how wellorganised is this classification system?

2%(7)

5.5%(19)

17%(58)

34%(118)

38%(131)

2.9%(10)

343 4

Clinical utility: how clinically useful is this classificationsystem?

2.9%(10)

4.1%(14)

18%(62)

32%(110)

38%(129)

5.2%(18)

343 4

Quality assessment and improvement: how useful doyou think such a system is from a quality assessment andimprovement perspective?

2.6%(9)

3.5%(12)

15%(52)

37%(126)

36%(122)

6.4%(22)

343 4

Fig. 2 – Distributions of Likert scale grades observed for assessment of the global applicability of EAUiaiC by the experts surveyed.

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providing a suitable tool or, at the very least, initiating acrucial consensus-building process to develop such aninstrument.

Three alternative grading systems have been describedin the literature [15,23,24]. We feel that none of thesesystems fully accomplishes the task at hand. The first model[23] classifies intraoperative complications into threegrades. Unfortunately, grade 3 includes an unrecognisederror during the intraoperative period that does notmanifest until the postoperative period, which can leadto confusion when grading an incident. In addition,misinterpretation of a grade is possible because of a lackof hierarchy in this three-tiered system. The second system[15] excludes any conversions from laparoscopic procedures

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in the classification, which is undesirable since anyconversion from a laparoscopic or robotic procedure toan open procedure may affect postoperative outcomes andshould be included in any grading system for complications.One study showed that anticipatory conversion is associat-ed with better outcomes than reactive conversion [25]. Thethird system [24] mainly focuses on surgical events and isthus of limited utility. An internal evaluation by the panel ofthe three grading systems using four hypothetical scenarioswith intraoperative AIs demonstrated inconsistency and ahigh level of inter-rater variability (personal communica-tions). Given these limitations, the panel sought to proposea new grading system that would provide a standardisedsystem for more unified patient stratification.

e Adverse Incident Classification (EAUiaiC) by the Europeanur Urol (2019), https://doi.org/10.1016/j.eururo.2019.11.015

Table 3 – Distribution of responses to intraoperative complication scenarios 1–10 using the EAUiaiC.

# Complication and procedurea Responses MF

n %

1 Ascending colon perforation during adhesiolysis, faecalcontents observed and identification of a relatively largeperforation during laparoscopic radical prostatectomy

41%

Grade 0 2 1.6Grade 1 1 0.79Grade 2 29 23Grade 3 9 7.1Grade 4A 52 41Grade 4B 33 26

2 Perforation of the left external iliac artery duringlaparoscopic salvage pelvic lymphadenectomy for prostatecancer

66%

Grade 0 1 0.79Grade 1 1 0.79Grade 2 12 9.5Grade 3 83 66Grade 4A 12 9.5Grade 4B 16 13Grade 5B 1 0.79

3 Perforation of the proximal ureter (detected via retrogradepyelography) during right primary semi-rigid ureteroscopyand laser lithotripsy

50%

Grade 0 1 0.79Grade 1 63 50Grade 2 51 40Grade 3 4 3.2Grade 4B 7 5.6

4 Bowel injury during open right radical nephrectomy withan IVC thrombus

44%

Grade 1 9 7.1Grade 2 55 44Grade 3 14 11Grade 4A 46 37Grade 4B 2 1.6

5 Urethral injury during dissection when inserting anartificial urinary sphincter

39%

Grade 0 1 0.79Grade 1 15 12Grade 2 44 35Grade 3 12 9.5Grade 4A 3 2.4Grade 4B 49 39Grade 5A 2 1.6

6 False passage with extravasation and failure to accessbladder during repeat optical internal urethrotomy

49%

Grade 0 1 0.79Grade 1 39 31Grade 2 62 49Grade 3 7 5.6Grade 4A 1 0.79Grade 4B 16 13

7 Rectal injury >50% circumference during roboticcystectomy

41%

Grade 0 2 1.6Grade 1 23 18Grade 2 52 41Grade 3 37 29Grade 4A 8 6.3Grade 4B 4 3.2

8 Right corpus cavernosum perforation towards the root ofthe penis during insertion of a penile prosthesis

57%

Grade 0 5 4Grade 1 72 57Grade 2 41 33Grade 3 6 4.8Grade 4B 2 1.6

9 Cystoscopy and stent inserted on the left side when regularstent replacement had been planned for the right side

61%

Grade 0 4 3.2

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Table 3 (Continued )

# Complication and procedurea Responses MF

n %

Grade 1 23 18Grade 2 18 14Grade 3 1 0.79Grade 4A 1 0.79Grade 4B 2 1.6Grade 5A 77 61

10 Renal vein avulsion with blood loss of 3 l during dissectionfor an open right nephrectomy for a 12-cm renal tumour

79%

Grade 0 3 2.4Grade 1 3 2.4Grade 2 6 4.8Grade 3 100 79Grade 4A 11 8.7Grade 4B 2 1.6Grade 5B 1 0.79

MF = mode frequency; IVC = inferior vena cava.a Further details regarding patient history and post-complication management for each scenario are listed in the Supplementary material.

Fig. 4 – Bar graphs showing (A) the distributions of classification categories observed and (B) ease-of-use ratings. For each bar, colours indicate therelative frequencies observed for the ease-of-use ratings within each category classification and for category classifications within each ease-of-userating. The graphs are based on responses for scenarios 1–10.

Table 4 – Two-way contingency table of classifications and ease-of-use ratings observed for validation scenarios 1–10.

Classification Ease of use

Very difficult Difficult Intermediate Easy Very easy Sum

Grade 0 0 6 6 4 4 20Grade 1 2 34 87 116 10 250Grade 2 11 70 173 111 5 372Grade 3 3 41 116 102 11 276Grade 4A 0 25 60 42 7 134Grade 4B 6 34 42 46 5 133Grade 5A 1 4 14 31 29 79Grade 5B 0 0 0 2 0 2Sum 23 214 498 454 71 1260

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Fig. 3 – Cleveland dot plot of the validation scenario classifications observed for scenarios 1–10.

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We believe that given the high ratings by expertsregarding its formulation, feasibility, usefulness, andreliability, EAUiaiC is an excellent candidate system thatallows for greater reporting detail than the existing systems

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[15,23,24]. During its development, we modified theoriginal grading system (Table 5) by incorporating sugges-tions provided by the experts. Technological advances inhealth care have led to a decrease in mortality following

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Table 5 – European Association of Urology intraoperative adverse incidents grading: alterations suggested from surveys are in bold italicfont.

Grade Description

0 Event requiring no intervention or change in operative approach, no deviation from planned intraoperative steps, no consequence for the patient1 Event requiring additional/alternative procedure in planned intraoperative steps, not life-threatening or involving part or full organ removal. The

event was addressed in a controlled manner with no long-term side effects2 Event requiring major additional/alternative procedure in operative approach but NOT immediately life-threatening. The event was addressed in a

controlled manner, however may have short- or long-term side effects3 Event requiring major additional/alternative procedure in addition to planned intraoperative steps and incident becoming immediately life-

threatening but NOT requiring part or full organ removal; may have short- or long-term side effects4 Event requiring major additional/alternative procedure in addition to planned intraoperative steps becoming immediately life-threatening and

with short- or long-term consequences to patientA. Requiring part or full organ removalB. Unable to complete planned procedure as planned due to a technical issue or surgical event and/or required unplanned stoma (change in bodyimage, eg stoma, major skin flap)

5 A. Wrong site or side for ablative surgery or removal of an organ or wrong patient or no consentB. Intraoperative death

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major surgery, so health care providers are looking towardsdifferent endpoints such as morbidity, return to work, andcost [26,27].

Unsurprisingly, the numerous inconsistent definitionsand various complication reporting types have resulted inmultiple undesirable phenomena such as the “tower of Babelsyndrome” and under-reporting of surgical AIs [5]. Therefore,surgeons should be required to report these incidents. Thelevel of agreement among the experts was >90% for grades 2,3, 4, and 5B, and >80% for grades 1 and 5A. Three suggestionswere made for grade 1 and two each for grades 2 and 4. Themedian score of 4 for global applicability (based on valuesranging between 1 and 5) for all domains indicates that thissystem has high overall usefulness. The complexity and thenumber of grades of a complication reporting system shouldalways be balanced against the expected completeness of thedata collected. The benefits of a complex and detailed systemmay be lost if the number of correctly reported cases is low.This stands in contrast to a simple system with full coveragebut with less detailed information. With regard to reliability,the inter-rater agreement analysis demonstrated a fairdegree of agreement. The dot plot (Fig. 3) clearly showsclustering of classifications over neighbouring categories,suggesting that a proportion of the disagreement could bedue to the novelty of EAUiaiC and the lack of experience withthe system among the experts. In addition, applicability wasreported as easy to intermediate, which we judge asacceptable to low. Interestingly, significant differences wereobserved in the interpretation of grading using the CDS[14]. Implementation in clinical practice should improveclarity and acceptance. All of the categories demonstratedwhat we judged to be a satisfactory level of agreement amongthe responders. An important aspect was our use of a mixedmethod, particularly in terms of considering a participatorydesign approach involving both generalists and experts.

The process of how and by whom AIs should beregistered is an unresolved matter. The common-sensenotion is that every health care professional (surgeon,anaesthetist, theatre staff) should record any AI during asurgical procedure for every patient with a view topreventing similar events in the future. There are manychallenges associated with reporting of AIs in surgery as

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shown by Phillips et al [28]. Dindo et al [29] suggested thatdedicated personnel should collect data, but data collectionby a person not present during an operation can besuboptimal and unreliable. A simple option would be toadd an extra point (Any intraoperative AI: yes/no) to thesign-out section of the World Health Organisation SurgicalSafety Checklist. If yes, then the EAUiaiC grading systemshould be applied. All stakeholders are present during sign-out, which should allow more reliable data collection. Toensure that a useful and comprehensive picture of the safetyprofile is provided to all relevant parties, clear reporting ofAIs should be considered.

As with the development of any reporting scale, certaincategories may not be clear and might require someclarification. Subjectivity is a potential limitation in the useof such a classification. To address the potential limitation ofsubjectivity and to determine the reproducibility of thisclassification system, scenarios for each category could bedeveloped with wider implementation. This process wouldallow global validation of the system. The major limitation inour study was the use of scenarios from the literature andpersonal experiences. However, the appropriateness andreproducibility of the CDS were based on 11 clinical casesgraded by experts from seven centres [5]. The proposed newgrading system is based on a Delphi process and may requirea further updated version with wider clinical application.

5. Conclusions

The results indicate that the newly proposed EAUiaiCintraoperative AI grading system is simple, useful, andreliable and has wide applicability.

Author contributions: Chandra Shekhar Biyani had full access to all thedata in the study and takes responsibility for the integrity of the data andthe accuracy of the data analysis.Study concept and design: Biyani, Rouprêt, Jensen, Mitropoulos.Acquisition of data: Biyani.Analysis and interpretation of data: Biyani, Pecanka, Rouprêt, Jensen.Drafting of the manuscript: Biyani, Pecanka, Jensen, Rouprêt.Critical revision of the manuscript for important intellectual content:Rouprêt, Pecanka, Mitropoulos.

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Statistical analysis: Pecanka.Obtaining funding: None.Administrative, technical, or material support: Karin Plass, EAU Guide-lines ManagerSupervision: Mitropoulos.Other: None.

Financial disclosures: Chandra Shekhar Biyani certifies that all conflictsof interest, including specific financial interests and relationships andaffiliations relevant to the subject matter or materials discussed in themanuscript (eg, employment/affiliation, grants or funding, consultan-cies, honoraria, stock ownership or options, expert testimony, royalties,or patents filed, received, or pending), are the following: None.

Funding/Support and role of the sponsor: None.

Acknowledgments: We would like to express our gratitude to Dr. WalterArtibani, Prof. Michael Truss, and Prof. Mesut Remzi for their initialsupport. Coordination and facilitation of the survey on the EAU site wasonly possible with the assistance of many individuals from the EAUOffice. We would like to acknowledge the excellent support we receivedfrom Karin Plass, EAU Guidelines Manager, in expediting the survey. Wealso thank Dr. Theodoros Tokas (Department of Urology and Andrology,Tirol Kliniken, General Hospital Hall, Hall in Tirol, Austria) and Dr. BenVan Cleynenbreugel (University Hospitals Leuven, Leuven, Belgium) forproviding clinical scenarios with intraoperative complications.

Appendix A. Supplementary data

Supplementary material related to this article can befound, in the online version, at doi:https://doi.org/10.1016/j.eururo.2019.11.015.

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e Adverse Incident Classification (EAUiaiC) by the Europeanur Urol (2019), https://doi.org/10.1016/j.eururo.2019.11.015