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Learning curves for cardiothoracic and vascular surgical procedures – a systematic review a

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Karan Singh Arora , Nuzhath Khan , Hamid Abboudi , Mohammed S. Khan , Prokar Dasgupta

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& Kamran Ahmed a

Department of Urology, King’s Health Partners, MRC Centre for Transplantation, King’s College London, Guy’s Hospital, St Thomas Street, London, UK Published online: 28 May 2015.

Click for updates To cite this article: Karan Singh Arora, Nuzhath Khan, Hamid Abboudi, Mohammed S. Khan, Prokar Dasgupta & Kamran Ahmed (2015) Learning curves for cardiothoracic and vascular surgical procedures – a systematic review, Postgraduate Medicine, 127:2, 202-214 To link to this article: http://dx.doi.org/10.1080/00325481.2014.996113

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http://informahealthcare.com/pgm ISSN: 0032-5481 (print), 1941-9260 (electronic) Postgrad Med, 2015; 127(2): 202–214 DOI: 10.1080/00325481.2014.996113

ORIGINAL RESEARCH

Learning curves for cardiothoracic and vascular surgical procedures – a systematic review Karan Singh Arora , Nuzhath Khan, Hamid Abboudi, Mohammed S. Khan, Prokar Dasgupta & Kamran Ahmed

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Department of Urology, King’s Health Partners, MRC Centre for Transplantation, King’s College London, Guy’s Hospital, St Thomas Street, London, UK

Abstract

Keywords

Objectives. The aim of this systematic review is to evaluate the learning curve (LC) literature and identify the LC of cardiothoracic and vascular surgical procedures. Summary and background. The LC describes an observation that a learner’s performance improves over time during acquisition of new motor skills. Measuring the LC of surgical procedures has important implications for surgical innovation, education, and patient safety. Numerous studies have investigated LCs of isolated operations in cardiothoracic and vascular surgeries, but a lack of uniformity in the methods and variables used to measure LCs has led to a lack of systematic reviews. Methods. The MEDLINE, EMBASE, and PsycINFO databases were systematically searched until July 2013. Articles describing LCs for cardiothoracic and vascular procedures were included. The type of procedure, statistical analysis, number of participants, procedure setting, level of participants, outcomes, and LCs were reviewed. Results. A total of 48 studies investigated LCs in cardiothoracic and vascular surgeries. Based on operating time, the LC for coronary artery bypass surgery ranged between 15 and 100 cases; for endoscopic vessel harvesting and other cardiac vessel surgery between 7 and 35 cases; for valvular surgery, which included repair and replacement, between 20 and 135 cases; for video-assisted thoracoscopic surgery, between 15 and 35 cases; for vascular neurosurgical procedures between 100 and 500 cases, based on complications; for endovascular vessel repairs between 5 and 40 cases; and for ablation procedures between 25 and 60 cases. However there was a distinct lack of standardization in the variables/outcome measures used, case selection, prior experience, and supervision of participating surgeons and a range of statistical analyses to compute LCs was noted. Conclusion. LCs in cardiothoracic and vascular procedures are hugely variable depending on the procedure type, outcome measures, level of prior experience, and methods/statistics used. Uniformity in methods, variables, and statistical analysis is needed to derive meaningful comparisons of LCs. Acknowledgment and application of learning processes other than those reliant on volume–outcomes relationship will benefit LC research and training of surgeons.

Assessment, learning curve, medical education, surgery, training

Introduction The learning curve (LC) describes an observation that a learner’s performance improves over time during acquisition of new motor skills. This improvement is more rapid in the initial stages and then plateaus with subsequent small improvements. Three main parameters typically describe a LC, namely: the ‘starting point’ which is the level at which the performance begins, the ‘asymptote’ or ‘learning plateau’, where the performance stabilizes, and the ‘rate of learning’, which is the speed at which a defined level of performance is reached [1]. Measuring LC pertains to choosing the right variables, and the two main types identified are measures of surgical process and measures of patient outcomes. Whereas the former includes variables such as time for procedure completion, procedure success rate, rate of conversion to open surgery for minimally invasive procedures, resection and margin

History Received 17 October 2014 Accepted 4 December 2014 Published online 22 December 2014

involvement for cancer surgery, the latter concerns with the amount of blood loss, intraoperative/postoperative complications and patient mortality [2]. Inferring from LCs of an individual surgeon, however, is problematic as surgical performance, like all learning, is complex and is influenced by a number of factors. Identifying LC can contribute to all spheres of surgery. In surgical research, statistical assessment of LCs during analysis of randomized controlled trials can obliterate the oftsighted distortion when comparing standard operations with novel and technically difficult ones [1]. Since LCs can apply to individuals and groups of individuals working toward a common outcome, a characteristic of surgery, institutional LCs can be computed and quoted for whole teams and centers [1,2]. In surgical education, training resources can be steered toward overcoming the initial steep curve during introduction of new techniques when trainees are more likely

Correspondence: Kamran Ahmed, Urology Registrar/Hon Clinical Lecturer, MRC Centre for Transplantation, King’s College London, Guy’s Hospital, London SE1 9RT, UK. Tel: +44 02071886795. E-mail: [email protected]  2015 Informa UK Ltd.

DOI: 10.1080/00325481.2014.996113

A systematic review of LCs in cardiothoracic and vascular surgeries

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to commit errors. In surgical practice, LC data can help set quantifiable thresholds on failure rates and negative outcomes, while incorporating new surgical techniques and devices. Knowledge about the number of cases or procedures required before a surgeon can operate safely without supervision can prove important in ensuring minimal risk to the patients [1,2]. Surgeons are also increasingly finding their performance data and patient-related outcomes being published for public consumption, which makes it imperative that all modifiable limiting factors in training and education such as LCs are understood and better dealt with. With improved information sources for patients and patient support groups, LCs of surgeons, irrespective of their experience and familiarity with operative procedures, are tolerated less than ever [2,3]. Last, with all learning of surgeons coming at a cost to the healthcare providing trusts and to the national service, improved research to seek surgical approaches with shorter LCs is the need of the hour. Cardiothoracic and vascular surgery’s LCs This article aims to systemically review the LC literature on cardiovascular, thoracic and vascular surgical procedures. We aim to present a detailed analysis of the LCs in terms of the number of cases needed to reach optimum performance on the variable being measured. There is also a discussion and evaluation of the choice of variables, methods, and statistical analysis employed by the studies.

Materials and methods This study was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (www.prisma-statement.org/). Study eligibility criteria Empirical studies describing the LCs (in the operating theater and on simulators) in cardiovascular surgery were included. Review articles, studies describing models, letters, bulletins, comments, and studies describing non-technical skills were excluded from analysis. Information sources and search A broad search of the English language literature was performed in August 2013 using MEDLINE (1950 to July 2013), EMBASE (1980 to July 2013), and PsychINFO (1966 to June 2013) databases. The following key words were used during the search: ‘cardiac surgery’ ‘off pump’, ‘beating heart’, ‘mitral valve’, ‘aortic valve’, ‘aorta’, ‘septal’, ‘PCI’, ‘angioplasty’, ‘valve repair’ ‘valve surgery’, ‘vascular surgery’ combined with the text words ‘CUSUM’, ‘control charts’, ‘learning curve’, and training. The Cochrane database and the Database of Abstracts of Reviews of Effectiveness were reviewed. Study selection The full text of each article was obtained and further screened for addition, if it had information pertaining to LCs of

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cardiovascular surgical procedures. We included conference abstracts as well as full-published studies. Studies assessing LCs within a virtual reality setting were also included. We excluded editorials, letters, bulletins, and studies not related to LCs. Data collection Three reviewers (KSA, NK, and HA) independently identified potentially relevant articles. Conflicts between reviewers were subsequently discussed, such that agreement was >0.85 (Cohen coefficient). Data items An electronic data collection form (Microsoft Excel 2007, Redmond, WA, USA) was used to extract data including name of the procedure, statistical analysis, number of surgeons who contributed to development of the LC, prior experience of the surgeons, the procedure setting, the variables or outcome measures used to measure the LC, and the LC itself. Disagreement in the assessment and data extraction were resolved by consensus.

Results Study selection A total of 2103 potentially relevant publications were identified by the search, of which 1924 were excluded from analysis after the abstract review. Of the remaining 179 studies, we excluded a further 128 after reviewing the full text because of repetition, metrics employed for LC, and relevance to this study. Thus, 48 studies were finally included in the systematic review (Figure 1). Table 1 summarizes the LCs of cardiovascular surgeries. Forty-eight studies investigated the LCs in cardiac vessel, thoracic and vascular surgeries. The subspecialties were cardiac vessel surgery with coronary artery bypass graft (CABG) surgery (11 studies), endoscopic vessel harvesting (2 studies); valvular surgery with aortic and mitral valve surgery (11 studies); thoracic surgery with video-assisted thoracoscopic surgery (VATS) (5 studies); vascular surgery with vascular neurosurgery (4 studies) and endovascular surgery (8 studies); and other cardiovascular interventions (7 studies) Cardiac vessel surgery Eleven studies investigated the LC of CABG and barring one, they all included surgery in the real-world with 2666 patients in total. Five studies explored the single surgeon experience for variants of CABG. The earliest of these, Novick et al. [4], aimed to use the cumulative sum (CUSUM) analysis method to compare patient outcomes in an established CABG technique (on-pump) with a novel one (off-pump) employing a single experienced surgeon. With predefined alert and alarm lines, it found the former technique reaching the desired

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Postgrad Med, 2015; 127(2):202–214

2103 potentially relevant articles identified from MEDLINE, EMBASE & PSYCHINFO

1924 articles excluded after abstract review

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179 articles reviewed for more detailed information

128 articles exclued after full text review and removal of duplicates

48 articles identified in final analysis

Figure 1. Flow diagram showing study selection.

level of complication-free reassurance boundary only after 85 cases. Une et al. [5] examined the effects of LC on clinical outcomes and operating time (OT) in minimally invasive CABG for a single experienced surgeon. They found a correlation between experience and OT in off-pump procedures (mean durations of 124 ± 31 and 241 ± 80 min for single vessel and multivessel thoracotomies [MVSTs], respectively). The CUSUM analysis found the learning period to stabilize at 45 cases for off-pump MVST. Another single surgeon experience of minimally invasive CABG was examined by Halkos et al. [6] who aimed to find a relation between surgical experience and rate of conversion to sternotomy. No significant difference in conversion rates was found on comparison of the consecutive quartiles. To define the LC for robot-assisted left internal mammary artery (LIMA) harvest, Hemli et al. [7] analyzed the results of 77 robot-assisted minimally invasive CABG and noticed a decline in all time variables (instrument positioning time, mean LIMA harvest time, total robot time) with increasing surgical experience. This improvement was most visible within the first 15 cases. Logarithmic LCs computed for LIMA harvest time and total robot time revealed a 10% performance improvement with each instance of doubling of cases. Song et al. [8] observed the LC of a lone practicing cardiac surgeon to enumerate the number of cases required before safe surgery. A CUSUM curve was plotted for 50 CABG procedures completed under supervision. The curve approximated the alert line (0.80 confidence) until the 23rd case and after 73 cases, LIMA harvesting remained below it. Measuring performance of residents in training was the theme of three studies. Donias et al. [9] with an aim to create an appropriate surgical training tool for surgeons to learn robot-assisted

coronary artery surgery investigated LC of arterial anastomosis using a porcine beating heart model and found that both anastomotic time (19.3 min vs 15.0 min; not statistically significant) and number of sutures placed per minute (0.77 vs 0.56, p < 0.001) decreased in the second half of the study. Caputo et al. [10] compared surgical performance in offpump CABG of four residents with one consultant and although the trainees managed to trump their senior counterpart in conversion and failure rates (8.2% as opposed to 9.2% for consultant), performance was seen to stabilize after 100 cases. Chen and Wan [11] aimed to analyze the training process of two CABG-novice surgeons. After being trained in Offpump coronary artery bypass (OPCAB) for 24–28 months they were assigned to do 100 cases each, with the results and outcomes compared with that of a proficient surgeon. LCs for both trainees showed steep elevation in the first 30 cases but total mortality and incidence of complications was the same as the consultant surgeon. Three studies analyzed the LC of beating heart totally endoscopic coronary artery bypass (BH TECAB) with the aid of the Da Vinci Surgical System. Fleck et al. [12] found the mean OT of BH TECAB to steadily decline throughout the study period (342 ± 61 min vs 290 ± 53 min, comparing the first five and last four procedures). Length of hospital stay was 8.4 ± 2.8 days and total conversion rate was 35%. Bonatti et al. [13] conducted a large single-center series for arrested heart totally endoscopic CABG, where LIMA harvests were completed using the da Vinci Surgical System. They found that the conversion rate to large thoracic incisions fell from 28% for patients in the earliest phase to 4% for patients in the last. The LC did not optimize for operative times and clinical outcomes even after 100 procedures. Desai et al. [14] found the LC for robotic CABG to be 40 cases (CUSUM) based on the following complications: operative mortality, stroke, deep sternal infection, renal failure, reoperation for bleeding, and prolonged ventilation. Endoscopic vessel harvesting and other cardiac vessel surgery Two studies investigated the LC of endoscopic vein or artery harvesting. Shapira et al. [15] found that the LC of endoscopic radial artery harvest for CABG to be 20 cases based on harvest time (75 min for the first 20 cases and 63 min for the last 50 cases). Vaidyanathan et al. [16] found the LC for endoscopic vein harvesting for CABG to be 20–35 cases based on procedure time. Valve implantation/valve surgery Eleven studies analyzed the LC of valve implantation/surgery with a total of 7015 patients. Six of these concerned the aortic valve and five concerned the mitral and other valves. Four studies investigated transcatheter aortic valve implantation (TAVI). Gurvitch et al. [17] explored the LC of transfemoral TAVI and transapical TAVI (TA-TAVI) approaches and found that procedural success rate (92.6% vs 97.8%, p = 0.05) and 30-day mortality (13.3% vs 9.6%, p = 0.04)

2

Chen an d Wan 2009 [11]

Patients (n = 213)

2

1

1

Halkos et al. 2012 [6]

Hemli et al. 2013 [7]

Une et al. 2013 [5]

Board certified, supervised by three OPCABG inexperienced, supervised

Previously trained in robotic surgery Consultant and 4 residents

Experienced OPCAB surgeon

Prior experience

Endoscopic vessel harvesting Shapira et al. 2004 [15] Patients (n = 75) Vaidyanathan et al. 2008 [16] Patients (n = 161) Valvular surgery AV surgery: includes transcatheter implantation and Ross procedure Ruiz Ortiz et al. 2010 [21] Patients (n = 83) Ross procedure Gurvitch et al. 2011 [17] Patients (n = 270) TAVI Wendler et al. 2011 [19] Patients (n = 1394) TAVI Alli et al. 2012 [18] Patients (n = 44) TAVI Hayashida et al. 2012 [20] Patients (n = 264) TAVI Murzi et al. 2012 [22] Patients (n = 100) Previous RAMT experience MIS-AV + RAMT MV surgery: includes minimally invasive, endoscopic, telemanipulation, and repair using Mitraclip Cerillo et al. 2011 [23] 4 Patients (n = 400) Trainee surgeons MIS-MV Schillinger et al. 2011 [27] Patients (n = 75) MV repair

Patients (n = 200)

Patients (n = 77)

Patients (n = 100) Patients (n = 192)

Bonatti et al. 2009 [13] Desai et al. 2010 [14]

Patients (n = 200)

Patients (n = 50)

Patients (n = 1372)

1

5

Caputo et al. 2004 [10]

Animal models

Song et al. 2005 [8]

3

Donias et al. 2003 [9]

Patients (n = 248)

Patients (n = 14)

1

Cardiac vessel surgery CABG Novick et al. 2001 [4]

Procedure subjects (no. of subjects) Procedure type

Fleck et al. 2005 [12]

No. of participants

Author, arranged by study year

Table 1. Learning curves of cardiothoracic and vascular surgeries.

CUSUM, propensity score analysis CUSUM, propensity score analysis

M, HS, C M, C, FR Durability + completeness, OT, C

SR, C

Spearman’s test, Wilcoxon test Univariate analysis

OT, radiation exposure

M, C, Cv

SR, M, C

Undetermined (later cohort = lower OT, C, and better durability)

20 cases (FR)

Undetermined (late cohort = lower C & failure) No LC for RAMT Undetermined for MIS-AV

Undetermined (late cohort = lower Cv and C) 30–44 cases

Undetermined period (LC exists for C) 135 cases (SR, M)

C, autograft failure

Undetermined (OT decrease in certain surgery types)

Undetermined (Cv due to patient factors, not learning) 15 cases

100 cases (M) 40 cases (M, C)

Gradual decline in OT 8.4 ± 2.8 days (HS) 34% Cv 23 cases for CABG 73 cases for LIMA 30 cases

Undetermined (28 cases in study) 100 cases for OPCAB

85 cases for ONCABG (C) OPCAB: unidentified

LC (demonstrating variable)

20 cases (OT) 20–35 cases (OT) Multivariate analysis

Logistic regression, CUSUM

Logarithmic LC analysis

CUSUM

CUSUM

Probability ratio test charts

Fisher’s exact test, Wilcoxon test, Turkey test, CUSUM Unpaired Student’s t-test

Statistical analysis

OT, C OT

OT, LIMA harvest time, robot time OT, M

OT, Cv, M, anastomoses number, ICU stay periods OT, HS, C, Cv, M M, C (6 study-defined adverse outcomes) Cv, M

OT, M, cross-clamp time

Anastomosis time, no. of sutures M, C (10 study-defined adverse events) OT, HS, Cv

C

Variables investigated

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DOI: 10.1080/00325481.2014.996113

A systematic review of LCs in cardiothoracic and vascular surgeries 205

17

2

1

Holzhey et al. 2013 [24]

Thoracic surgery VATS lobectomy Khan and Amer 2009 [31]

Amer 2010 [30]

Patients (n = 185)

Patients (n = 150) VMPR Patients (n = 90)

Patients (n = 60)

Patients (n = 3895) MIS-MV

Patients (n = 458) MV repair: robotic Patients (n = 32) TERMR

Procedure subjects (no. of subjects) Procedure type

Experienced in VATS

Trainee resident, varying supervision

Trained team of 2 surgeons, 1 anesthesiologist, 1 perfusionist + 2 nurses

Prior experience

2

Fourneau et al. 2008 [35]

Lupattelli et al. 2013 [44]

Bechara et al. 2013 [42]

Patients (n = 67) Aortic repairs Patients (n = 50) Laparoscopic aortobifemoral bypass Patients (n = 40) Robot-assisted aortofemoral bypass Patients (n = 99) PEVAR Patients (n = 1202) EV tx for CCSVI

Patients (n = 627) CAS Patients (n = 1004) CAS Patients (n = 188) IAS Experienced vascular surgeon

CUSUM

OT, anastomosis time, clamping time

SR (azygos dilatation), C

Logistic and Poisson regression

Student’s t-test, Fisher’s exact test

OT, C, BL, clamping time

FR, OT

CUSUM

Multivariable logistic analysis, CUSUM

Chi-square, Fisher’s exact test

Regression analysis

SR

C

C (peri- and post-operative)

C, intraoperative shunting, strokes, carotid occlusion, stenosis recurrence M, stroke incidence

Cv, M

OT, C, HS, BL, Cv, dissected LNs OT, C, Cv, M, m

OT,

OT, Cv

CUSUM

Multiple regression analysis

OT, HS, C OT, C

Regression model

Statistical analysis

Robot time

Variables investigated

100 cases for stenosis recurrence

18 ± 3 cases (OT, M, and surgeon comfort) 15–20 cases

30–60 cases (BL and OT)

Undetermined (later cohort = lower Cv) 35 cases (OT)

75–125 cases

Learning percentage of 95% Undetermined but 18.7% reduction in OT noted

LC (demonstrating variable)

K. S. Arora et al.

Novotny et al. 2011 [36]

3

1

EV and other vascular surgery Forbes et al. 2007 [41]

Cai et al. 2013 [40]

Bijuklic et al. 2011 [37]

Verzini et al. 2006 [38]

Wright 2012 [32] Patients (n = 343) Vascular surgery Vascular neurosurgery (includes carotid artery stenting, angioplasty, and endarterectomy) Brothers 2005 [39] 1 Patients (n = 100) Carotid endarterectomy

Meyer et al. 2012 [28]

Zhao et al. 2010 [29]

6

No. of participants

Yaffee et al. 2013 [25]

Charland et al. 2011 [26]

Author, arranged by study year

Table 1. (Continued)

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10

18

Riga et al. 2011 [33]

Boyle et al. 2011 [34] Patients (n = 107,721) StarClose vascular closure device use

VR EV simulator RAS task

Simulated EV suite

Procedure subjects (no. of subjects) Procedure type

7

2

Patients (n = 73) Alcohol septal ablation for HOCM Patients (n = 62) Ablation for AVNRT Patients (n = 1728) Robotic ablation for AF Patients (n = 190) PVAC ablation Simulator Patient-based procedures Inexperienced electrophysiology fellows

Experienced operator (> 100 cases)

Novices

Novices

Prior experience

PV isolation rate, OT, ablation time Catheter placement, supervision, fluoroscopy time, positioning time

SR, C

OT, C, recurrence

OT, M, C, residual shunt volume, bypass time, aortic occlusion time Cannulation time

SR, fluoroscopy time

OT, catheter tip movements, vessel wall hits OT, contrast volume, balloon placement accuracy, handling errors C, clinical device deployment success

Variables investigated

Regression analysis

Estimating equation model, risk-adjusted clinical risk prediction

Friedman test, Wilcoxon signed-rank test Simulator-measured performance metrics

Statistical analysis

LC present after simulator training

50 cases (less in high volume centers) 60 cases

27 cases

LC present for cannulation time

Steep LC for OT, bypass, and occlusion times

135 cases, this cohort was also noticed to have less complications such as vascular injury rate (5.2% vs 8.1%, p = 0.33), coronary vessel occlusion, device embolization or failure, and the need for a new pacemaker. Alli et al. [18], exploring LC for TAVI by transfemoral approach found the LC to be 30 cases based on procedural time, volume of contrast used, and radiation exposure. Wendler et al. [19] analyzed the LC of TA-AVI approach and found that amid all the variables, the conversion rate (3.7% vs 1.5%, p = 0.0315) and aortic regurgitation (4.52% vs 2.1%, p = 0.011) decreased by a statistically significant amount in the second consecutive patient cohort after experience with 575 TA-AVIs. Hayashida et al. [20] attempted to evaluate device failure predictors and incidence of complications for Prostar, a percutaneous vascular surgical system used in TAVI. They found that increasing experience was related to more successful closures (95.7% vs 85.7, p = 0.047) and fewer vascular complications (11.6% vs 28.6%, p = 0.012) and that early experience was a predictor for device failure (hazard ratio [HR]: 3.66, 95% confidence interval [CI]: 1.04–13.89, p = 0.047). Two studies explored LC in aortic valve repair. Ruiz Ortiz et al. [21], investigating the incidence and predictive factors for aortic autograft failure during follow up of 102 consecutive patients who had undergone the Ross procedure, conducted a multivariate analysis and showed that surgery during the first 6 months of the surgeon’s LC was an independent predictor of autograft failure (HR: 9.1, 95% CI: 1.4–59.4, p = 0.021). Murzi et al. [22] used CUSUM analysis to assess a single surgeon’s LC conducting a novel surgical approach of right anterior minithoracotomy for aortic valve replacement (AVR) and compared mortality, failure rates, and adverse events with standard AVR. No LC effect was observed. Five studies explored LCs in mitral valve surgery. Two of these involved minimally invasive procedures. Cerillo et al. [23] employed CUSUM analysis to compare LCs and perioperative outcomes of four trainees and a procedure-proficient consultant. They found that after 20 procedures, the trainees performed same as the consultant with similar failure risks. Holzhey et al. [24] analyzed data from a large, single-center of 17 surgeons performing minimally invasive mitral, tricuspid, and ablation procedures. They used CUSUM analysis for OT and complication rates and found the number to overcome the LC to be 75–100 operations. Two studies explored LCs in robotic mitral valve surgery. Yaffee et al. [25] studied the effect of training a surgical team with multiple professionals to reduce the risk of learning totally endoscopic mitral valve repair (TERMR). Post-training, when this team performed TERMR procedures, a decrease in total OT was observed, computed as 18.7% learning percentage (p = 0.002). LCs did not influence cross-clamp times, postoperative complications, and mortality. Charland et al. [26] investigated LCs for mitral valve repairs using telemanipulation technology and found a statistically significant LC (p < 0.01) and learning percentage of 95% for safe operation using the technology.

Postgrad Med, 2015; 127(2):202–214

Schillinger et al. [27] looked at the LC of using a surgical device, MitraClip, by residents for mitral valve repair on various outcomes. They found a distinct decrease in device and procedure time from first through third period (180, 105 min in first period to 95, 55 min in third period, p < 0.005), increased procedural success (92% vs 80% in first two periods, p = 0.46), and more durable valve repair (89.4% vs 65.0% in first period, p = 0.46). VATS lobectomy Five studies investigated the LC in VATS lobectomy, comprising a total of 828 procedures. Meyer et al. [28], found the LC for robotic VATS lobectomy to be 18 ± 3 cases, based on OTs, surgeon comfort, and overall mortality. A decrease in morbidity and hospital stay was also found with increasing experience, but conversion rate showed no correlation. Zhao et al. [29], studying the outcome of 90 consecutive VATS lobectomies for lung cancer, found the LC to be 30–60 cases based on blood loss and OT. No correlation between experience and other variables such as conversion rate, hospital stay duration, postoperative complications, and number of lymph nodes harvested was found. Amer [30] investigating a single surgeon’s LC during 150 consecutive VATS lobectomies found that, comparing the first and last 50 cases, OT and complications were lower: 3 h 20 min versus 2 h 45 min and 4 versus 1, respectively. They surmised the LC to be 35 cases. Khan and Amer [31] compared LCs for VATS lobectomies of a surgeon and an assisting senior resident. It was found that the eventual LC for the resident, as he moved from assisting to completing the procedure independently was significantly shorter than the consultant’s initial curve. No difference in patient-related outcomes was found in consecutive patient groups. Wright [32], attempting to capture a snapshot of 5-year center experience with VATS in a single Australian hospital, estimated the LC to be in the range of 15–20 cases. Vascular surgery Twelve studies analyzed the LC of various types of vascular surgery. Barring two, a total of 111,098 patients’ data were analyzed by these studies. Two studies involved the use of a simulator. Riga et al. [33] investigated the LC of conventional, manually steerable, and robotic endovascular catheters (CC, MSC, and RC respectively) in a simulated environment and found the LC to plateau after 9 sessions for MSC and RC and after 12 sessions for CC based on OT, vessel wall hits, and catheter movements. Boyle et al. [34] enrolled 18 endovascular novices to perform renal artery stenting on a virtual reality simulator and randomized them into three groups of differing performance feedback. The resultant six trials showed clear LCs with groups receiving expert feedback from consultant vascular surgeon making fewer errors (p = 0.009) and performing better on video-based error assessment (p = 0.002) than those without it. Two studies explored LCs in laparoscopic aortofemoral bypass (LAFB) using modern surgical techniques. Fourneau et al. [35] used sliding averages to measure LCs in

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DOI: 10.1080/00325481.2014.996113

A systematic review of LCs in cardiothoracic and vascular surgeries

50 patients undergoing LAFB and observed a clear turning point after 20–30 cases for various operative variables. However, mortality and morbidity were unaffected by LC. Novotny et al. [36] aimed to find the LC for robot-assisted LAFB in consecutive cohort-grouped 40 patients with aortoiliac occlusive disease using CUSUM analysis of conversion rate and operative time. Whereas the operative time LC was flat, all other time variables showed a reduction (anastomosis time was 28 vs 21.5 min, p = 0.01, and clamping time was 62.5 vs 50 min, p = 0.01) and a short LC was observed for robotic anastomosis creation. An acceptable conversion rate of 5% was achieved in the second cohort. Four studies investigated the LCs in carotid artery and intracranial vascular procedures. Bijuklic et al. [37] achieved this by analyzing surgical performance data at a high carotid artery stenting (CAS) volume center and found that the inhospital complication rates fell to 3% and 1% after 100 and 500 procedures, respectively. Verzini et al. [38] analyzed the LC for CAS and found that the 30-day mortality and stroke rate was reduced when comparing the first 195 to the following 432 procedures (8.2% vs 2.7%, p = 0. 005). Brothers [39] investigated LC of a single surgeon performing eversion carotid endarterectomy using retrospective analysis of 200 consecutively grouped patients. CUSUM analysis showed the LC not plateauing within the first 100 patients. Cai et al. [40] explored LCs for intracranial angioplasty and stenting using retrospective analysis of 188 patients. The LC to overcome procedure’s complications was 21 cases and operator experience was found to be an independent predictor for complications. Two studies computed LCs in endovascular aortic lesion repairs. Forbes et al. [41] found the LC of thoracic endovascular thoracic aortic repairs to be 5–10 cases for a target success rate of 95% based on CUSUM analysis. Bechara et al. [42], investigating LCs for the percutaneous technique, found a strong correlation between experience and failure event incidence (p < 0.007). Predicted vascular device closure failure risk also decreased from 45% to 5% for every patient in the later patient cohort. Resnic et al. [43], in an attempt to quantify the LC for safe and efficient deployment of a novel vascular closure device, performed a retrospective analysis of 107,710 procedures performed involving the device at 468 centers of varying case volume. They found that 75–130 cases per operator were required to achieve a success rate of 98%. A triphasic LC in higher volume institutions was observed. An initial phase of rapid learning for first 21 implants was followed by declining success rate for the next 28 cases (decline of 17%, p < 0.001) tailed eventually by a steady-state rate of higher device success rate after 50 cases. Lupattelli et al. [44], aiming to evaluate the feasibility and safety of endovascular treatment for chronic cerebrospinal venous insufficiency, analyzed 1202 cases and found a LC of 400 cases based on complications. Other cardiovascular surgery and interventions Four studies investigated LC for ablation procedures conducted to correct arrhythmias. The earliest study, by

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Bubolz et al. [45], examined LCs in pediatric cardiac interventions and found the success rates to improve significantly, to as high as 93% with experience of 50 cases, p = 0.044), whereas in higher-volume centers, LC was non-existent with a stable complication rate. Buergler et al. [49], examining LCs for magnetically assisted intervention to complete alcohol septal ablation in patients with hypertrophic obstructive cardiomyopathy, found an improvement in all patient-related outcomes such as symptoms, exercise time, and surgical-performance related variables such as guidewire cannulation time (third tertile vs first tertile (3 vs 10.5 min, p = 0.004). This improvement was also seen in the control group where intervention was completed using conventional guidewire techniques. De Ponti et al. [50] evaluated multiple parameters on a high fidelity stimulator to study the impact of simulator training on placing electrophysiology catheter during early years of LC. The fluoroscopy times and positioning times per catheter placement were found to be significantly reduced after the training (1.71 ± 1.24 to 0.42 ± 0.68, p < 0.001) Bonaros et al. [51] investigated LC for endoscopic atrial septal defect repair and noticed significant LCs for all time variables of the procedure during the course of 17 cases: total operative time reduced by >175 min (p = 0.002); cardiopulmonary bypass time reduced by >150 min (p = 0.003); and aortic occlusion time reduced by >100 min (p = 0.04).

Discussion Summary of findings This systematic review analyzed contemporary LC literature in the domains of cardiothoracic and vascular surgeries. Based on OT, which was the most common variable measured, the LC for CABG which included on-pump and offpump approaches ranged between 15 and 100 cases [4-14]; for endoscopic vessel harvesting, LCs lied between 20 and 35 cases [15,16]; for valvular surgery, which included valvular repair and valve replacement surgeries, LC were found to be between 20 and 135 cases [17-27]; for VATS, LCs were between 15 and 35 cases [28-32]; for vascular neurosurgical procedures, LCs were computed to be between 100 and 500 cases based on complications [37-40]; for endovascular vessel repairs, LCs were between 5 and 40 cases

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[33-36,41-44]; and for ablation procedures, LCs were between 25 and 60 cases [45-51].

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Methods and statistical analysis The review demonstrated both the variety and variation in the use of statistical methods in LC literature to monitor surgical performance. The methods varied from simple graphical or descriptive data presentations without any statistical analysis to linear regression and curve fitting methods (e.g., least squares regression, logarithmic or negative exponential curves, control charts, etc.), all employed to derive a graphical representation of the ‘learning curve’ aided by the group splitting method of dividing consecutive cases into cohorts. The curve fitting method was one of the most widely used methods to quantify LCs but it has its limitations. Although it assumes that the LC for a procedure is overcome when the curve reaches a plateau, the point when a surgeon’s performance stabilizes after a certain number of attempts, it offers no assurance that learning occurs to the highest level of competency. Besides, LCs are totally different between individuals which makes it difficult to compare performance. Some LCs do not plateau if the case number is not large enough – a fact illustrated by research in vascular surgery [36,39]. Some studies used expert-derived proficiency levels to compare performance of learners. This proved useful when used as a threshold for trainees’ LC plateaus [10,23,31]. However, there remained issues with defining expertise and standardizing the measurement of expert performance. The group splitting method, seen extensively in the evidence of this review [4-54], also has some limitations. Group sizes are arbitrary, with no rationale for splitting points. Although, it is good for comparing large number of cases, with big groups it can be difficult to pinpoint the exact number of cases required to overcome the LC. Also, the rate of learning cannot be identified using this method as evident in many studies included in this review [5,6,9,19-22,25,27,31,34,51]. Another statistical method very prevalent in the LC literature reviewed here was CUSUM method. This is a sequential ‘change point’ analysis method that detects subtle changes in the individual surgeon’s performance and allows one to judge if a given variation is within acceptable limits of random variation. Its use started in pediatric cardiac surgery of 1990s with de Leval et al.’s case-by-case outcome monitoring [52]. They formulated the basic cumulative failure chart that plotted the cumulative sum of outcomes such as deaths/morbidity/failures on Y-axis with a sequence of cases on X-axis. The number of failures increased the slope’s gradient that alerted the surgeon to drop in performance. To ascertain that these performance changes remained within acceptable limits, control boundaries were plotted and remain a routine practice in the more advanced CUSUM and other control charts. In this review, we witnessed a more intuitive-looking variation of the original CUSUM failure chart, the cumulative observed minus expected failure chart, formulated by Novick et al. [4] to investigate LCs in OPCAB. Another variation, which overcame the failure of Novick et al.’s chart to compensate for the assorted cases was the risk-adjusted CUSUM curve that enlisted patient-specific risk of failure rather than common

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expected failure rate and was used in various vascular and valvular surgery LC studies [22,40]. Incorporation of CUSUM method is a sensitive and practical way to expose the subtlest of suboptimal performance changes, as shown by Novick et al. [4], where a lone surgeon’s experience of two different surgical techniques, onpump and off-pump CABG, would otherwise appear more similar in patient outcomes if standard statistical analyses were employed. Experimenting surgeons are constantly making decisions on adopting or denouncing new surgical devices and techniques, most noticeable in this review in the various studies exploring LCs for OPCAB [4,10]. Researchers also used the CUSUM analysis at an institutional level in their retrospective analyses and investigated LCs of a group of surgeons to determine whether the examined treatment center was achieving the desirable patient outcomes [36,39,40]. Outcome variables Another fact highlighted by this review was the sheer number of variables used for measuring LCs. This variety made it difficult to compare LCs in studies investigating the same surgical procedure. A standardized classification system for variables such as complications might offer a solution to make future comparisons easier. Many authors have defined classification systems of complications based on grading, therapeutic consequences, hospital stay, etc. For example, Clavien et al. [53] described a classification system of postoperative complications for cholecystectomies based on four grades. Operative or procedural times, although quoted by >95% of the studies in the review, were understood to have little correlation with patient outcomes. Acceptability of using certain variables as markers of learning varied between researchers. For example, although mortality post-CABG was quoted as an outcome variable in eight different LC studies [5-8,11,13,14], Caputo et al. [10] deemed it unsuitable as a monitor of performance due to its rare incidence in the institution. Instead, they included perioperative death and 10 other operative complications to define a new variable, surgical failure. Prior experience, supervision, and mentorship The review also highlighted the variation in recording and accounting for surgeons’ prior experience in the LC literature. Many studies did not fully quantify prior experience and some studies failed to even define it. This had implications as research has shown that although trainees’ cumulative experience is not related to patient outcomes such as mortality and morbidity, their contemporaneous case volume is [54,55]. Combined with the non-standardization of senior supervision available and case selection in this review, this led to erroneous conclusions from the trends, especially when performance metrics such as operative times were quoted. The role of surgical experience in enhancing time variables for procedures is unequivocal. Elbardissi et al. [56], in their retrospective analysis of patients undergoing CABG, demonstrated the additive effect of the operative experience of two members of the surgical team consisting of a cardiothoracic fellow and an

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DOI: 10.1080/00325481.2014.996113

A systematic review of LCs in cardiothoracic and vascular surgeries

attending cardiac surgeon on cardiopulmonary bypass and cross-clamp times. Although the influence of mentorship and expert feedback was resoundingly concluded by studies on both simulators and real patients [8,10], the supervision available or given to a trainee in the studies above was variable. Presence of supervision was found to correlate positively to eventual patient mortality and complication profile [3,55,57], yet it was defined properly only when tied to the design of the study. Caputo et al. [10] underlined the importance of a known consultant acting as first assistant. Even in well-defined supervision protocols, the inherent complexity of supervision from seniors when operating on real patients remained unmeasured. Trainees have confessed on being directed or prompted by a consultant about an anatomical structure or a procedural nuance in varying capacities, and researchers everywhere seldom managed to capture these nuances of supervisor–novice interaction in research metrics [10]. Beyond research, supervision and mentorship remain integral to skill acquisition in cardiac surgery. According to Cohen et al. [3], cardiothoracic surgeons face considerable challenges in undertaking reoperative cardiothoracic surgery, especially on patients who have previously undergone an operation that they have never observed or performed. Nonetheless, surgical and medical management of aging patients who have undergone historical or rare and eponymous surgical operations such as the Ross procedure, the Vineberg procedure, the Waterston and Cabrol shunt, plastic ball plombage, etc., in the past is a necessity and demand a surgeon equipped with complex set of skills to complete the re-do procedure [3]. There is little alternative in such scenarios to expert mentorship. In addition to guiding a trainee in mastering the technical skill set in cardiac surgery, it has been found that perceptive mentees can assist in identifying the interpersonal skills of trainees and help them with applying themselves and their relationship with others in complex operative situations that are rife with uncertainty and stress for better operative outcomes [3]. Case selection and case volume Although all the studies used in the review used consecutive cohorts to stratify the patients into earlier and later groups to track surgical performance in a number of cases spread over time, the selection and assignment of patients to a participating trainee with appropriate level of expertise was less methodical. Case selection is known to have a huge impact on surgical outcomes, sometimes completely explaining improved patient mortality that was earlier assigned toward LC of surgeons [55]. As the true risk was difficult to reflect in predicted risk scores used during patient-surgeon assignment, the eventual trend of learning was skewed, making the participating surgeons’ outcomes appear more favorable on graphs [10]. As a general rule, trainees and junior residents operated on lower-risk patients and inadvertently had better apparent surgical performance [10,23,30]. Also noteworthy were the LCs computed by decade-long retrospective series, which with injection of new treatment modalities and novel surgical

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approaches, were likely to have a different case mix of patients at the end of the study period than at the start [35,41]. Not controlling for these factors weakened the eventual analyses that compared proficiency among different grades of surgeons and their respective LCs. Since the number of operations done by a surgeon was one of the defining factors that determined the speed with which a LC is surmounted, the review reinforced the fact that LC calculated on trainees operating in a low-volume center will be different from those in the high-volume [48]. Acknowledging the complexity of learning A common strand that tied the LC literature reviewed here together was the quantification of LCs. There were not many convincing attempts at analyzing or applying metrics to variables other than those directly implicated in volume–outcome relationships, which has its own implications. Huesch and Sakakibara [55], from their review of LC studies in CABG conceptualized marginal mortality impact (MMI), which is a numerical representation of expected improvement in patient mortality rate for one additional procedure per year. They found that the majority of LC studies resulted in a clinically insignificant level of MMI, thus demonstrating the fact that competing explanations for learning and outcomes exist and must be explored by future research. The authors proposed a schematic framework for volumeoutcomes where volume measures of a surgeon were divided into contemporaneous, lagged recent and cumulative historical volumes. In addition, learning processes that are often overlooked due to their irreducibility to econometric obligations such as ‘social learning’ that relates to constant knowledge spillovers in everyday reality of surgical practice and ‘static scale effects’ that includes institution-wide positive effects manifest by increased volume but totally unrelated to learning should be embraced. Another incentive to pursue competing explanations for learning in cardiac surgery is that at an institutional level, having a high case volume might not be the most practical solution. Pettinari et al. [58] explored the implications of having specific computed number of completed cases as the only yardstick for a trainee to surmount the steep slope of his/her individual LC. When faced with adopting OPCAB as the standard choice of cardiac revascularization at a center they attempted a multidisciplinary initiative that integrated the conceptual (the ‘know-why’ or cause–effect relationship of the adverse events in OPCAB) and operational learning processes (the ‘know-how’ or validation of action-outcome links by designing and testing appropriate responses to identified adverse events) to better manage learning rates. By methodically analyzing perioperative data and conducting outcome analyses, they identified novel preventative operative practices, codified the tacit procedural knowledge and demonstrated a statistically significant improvement in the postoperative survival [58]. Simulation and LCs Surgical simulation has proved to be immensely successful in the fields of laparoscopic and endovascular surgery and holds

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immense value to all levels of surgeons. In this review, several studies investigated LCs of trainees on different simulators, from animal models to virtual reality simulator trainers, all buoyed with the proven transferability of expertise to the live operating room [4,33,34,50]. Novel techniques such as robot-assisted surgery using telemanipulation had shorter LCs than traditional surgical approaches [4,34], and use of highfidelity simulators in training new surgeons helped flatten out the LCs and acquire complex operative skills [33]. Persistent practice of basic surgical skills like knot-tying and anastomosis creation has been proven to accelerate the LC of junior surgeons without exposing them to the pressures and repercussions of operating on live patients [59]. At an intermediate skill level, centralized workshops that allow senior trainees to practice skills such as dissection and saphenofemoral junction disconnection have proven to correlate with better surgical performance in the operating theater. Simulation’s value as an assessment tool can be gauged by the incorporation of simulated endovascular and anastomosis creation skills into the Fellowship of the European Board of Vascular Surgery after significant differences were observed in dissection (p < 0.001), anastomotic skill (p = 0.002), and dexterity (p = 0.005) between trainees and examiners who operated on models [59]. Limitation of studies Besides the lack of standardization of key outcome and confounding variables that made summation difficult for the review, a major limitation of the LC literature perused for this review and the LC research at large is the volume–outcome relationship as the sole end point of the comprehensive tracking and analysis of outcomes. It is placed on an implicit and somewhat intuitive presumption that practicing on an increased number of cases leads to better outcomes [3-51]. Although a negative correlation between volume and mortality is well proven, extrapolating from LC research’s almost singular conclusion of higher case volume equating to better surgical outcomes and learning is not without its fallacies and should be implemented in the real world with caution [58]. A prime example is the designation of the Bristol hospital in 1980s as the specialized center for infant cardiac surgery to concentrate operative volumes. The increased case volume was never matched with an appropriate nonsurgical multidisciplinary expertise, and training framework resulting in disastrous perioperative outcomes and the LC of practicing surgeons, as mentioned above, held liable for these [55,60]. Although incredibly sensitive to small variations, CUSUM analysis, the most often used method of statistical analysis in this review, is not helpful in comparing interoperator differences. The researcher-defined reassurance boundaries, alert lines, and other control levels used in this method are known to be contentious [4].

Conclusion Measuring LCs of surgeons is indispensable if innovation, education, and patient safety are to be pursued earnestly in surgery. The advent of control chart methods like CUSUM

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analysis have helped flag suboptimal outcomes and have given a spectacular acuity in monitoring surgical performance. This systematic review of cardiothoracic and vascular surgical procedures highlighted a remarkable amount of heterogeneity in the reporting of outcome variables and computation of LCs. To assist future trainees overcome their LCs while maneuvering through their ever-shortening specialist training tenures and increasingly litigious workplace climate, it is imperative that educators and researchers recognize the need to recruit parallel training methods in research and postgraduate education such as simulation in order to accelerate development of specific skills. It is also important for all involved to look beyond the simplistic volume–outcome metrics and incorporate a multidimensional approach to conceptualize new research and training curriculums.

Declaration of interest The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents, received or pending, or royalties.

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Learning curves for cardiothoracic and vascular surgical procedures--a systematic review.

The aim of this systematic review is to evaluate the learning curve (LC) literature and identify the LC of cardiothoracic and vascular surgical proced...
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