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Published in: Trials 1/2016

Open Access 01-12-2016 | Methodology

Assessment of learning curves in complex surgical interventions: a consecutive case-series study

Authors: Olympia Papachristofi, David Jenkins, Linda D. Sharples

Published in: Trials | Issue 1/2016

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Abstract

Background

Surgical interventions are complex, which complicates their rigorous assessment through randomised clinical trials. An important component of complexity relates to surgeon experience and the rate at which the required level of skill is achieved, known as the learning curve. There is considerable evidence that operator performance for surgical innovations will change with increasing experience. Such learning effects complicate evaluations; the start of the trial might be delayed, resulting in loss of surgeon equipoise or, if an assessment is undertaken before performance has stabilised, the true impact of the intervention may be distorted.

Methods

Formal estimation of learning parameters is necessary to characterise the learning curve, model its evolution and adjust for its presence during assessment. Current methods are either descriptive or model the learning curve through three main features: the initial skill level, the learning rate and the final skill level achieved. We introduce a fourth characterising feature, the duration of the learning period, which provides an estimate of the point at which learning has stabilised. We propose a two-phase model to estimate formally all four learning curve features.

Results

We demonstrate that the two-phase model can be used to estimate the end of the learning period by incorporating a parameter for estimating the duration of learning. This is achieved by breaking down the model into a phase describing the learning period and one describing cases after the final skill level is reached, with the break point representing the length of learning. We illustrate the method using cardiac surgery data.

Conclusions

This modelling extension is useful as it provides a measure of the potential cost of learning an intervention and enables statisticians to accommodate cases undertaken during the learning phase and assess the intervention after the optimal skill level is reached. The limitations of the method and implications for the optimal timing of a definitive randomised controlled trial are also discussed.
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Literature
1.
go back to reference Campbell M, Fitzpatrick R, Haines A, Kinmonth AL, Sandercock P, Spiegelhalter D, et al. Framework for design and evaluation of complex interventions to improve health. Br Med J. 2000; 321:694–6.CrossRef Campbell M, Fitzpatrick R, Haines A, Kinmonth AL, Sandercock P, Spiegelhalter D, et al. Framework for design and evaluation of complex interventions to improve health. Br Med J. 2000; 321:694–6.CrossRef
4.
go back to reference Mowatt G, Bower DJ, Brebner JA, Cairns JA, Grant AM, McKee L. When is the ‘right’ time to initiate an assessment of a health technology?Int J Technol Assess Health Care. 1998; 14:372–86.CrossRefPubMed Mowatt G, Bower DJ, Brebner JA, Cairns JA, Grant AM, McKee L. When is the ‘right’ time to initiate an assessment of a health technology?Int J Technol Assess Health Care. 1998; 14:372–86.CrossRefPubMed
5.
go back to reference Ergina PL, Cook JA, Blazeby JM, Boutron I, Clavien PA, Reeves BC, Seiler CM, the Balliol collaboration. Challenges in evaluating surgical innovation. Lancet. 2009; 374:1097–104.CrossRefPubMedPubMedCentral Ergina PL, Cook JA, Blazeby JM, Boutron I, Clavien PA, Reeves BC, Seiler CM, the Balliol collaboration. Challenges in evaluating surgical innovation. Lancet. 2009; 374:1097–104.CrossRefPubMedPubMedCentral
6.
7.
go back to reference Ramsay CR, Grant AM, Wallace SA, Garthwaite PH, Monk AF, Russel IT. Statistical assessment of the learning curves of health technologies. Health Technol Assess. 2001; 5:1–79.CrossRefPubMed Ramsay CR, Grant AM, Wallace SA, Garthwaite PH, Monk AF, Russel IT. Statistical assessment of the learning curves of health technologies. Health Technol Assess. 2001; 5:1–79.CrossRefPubMed
8.
go back to reference Russell I. Evaluating new surgical procedures. Br Med J. 1995; 311:1243–4.CrossRef Russell I. Evaluating new surgical procedures. Br Med J. 1995; 311:1243–4.CrossRef
9.
go back to reference Farrokhyar F, Karanicolas PJ, Thoma A, Simunovic M, Bhandari M, Deveraux PJ, Anvari M, Adili A, Guyatt G. Randomised controlled trials of surgical interventions. Ann Surg. 2010; 251:409–16.CrossRefPubMed Farrokhyar F, Karanicolas PJ, Thoma A, Simunovic M, Bhandari M, Deveraux PJ, Anvari M, Adili A, Guyatt G. Randomised controlled trials of surgical interventions. Ann Surg. 2010; 251:409–16.CrossRefPubMed
10.
go back to reference McCulloch P, Altman DG, Campbell B, Flum DR, Glasziou P, Marshall J, Nicholl JC, the Balliol Collaboration. No surgical innovation without evaluation: the IDEAL recommendations. Lancet. 2009; 374:1105–12.CrossRefPubMed McCulloch P, Altman DG, Campbell B, Flum DR, Glasziou P, Marshall J, Nicholl JC, the Balliol Collaboration. No surgical innovation without evaluation: the IDEAL recommendations. Lancet. 2009; 374:1105–12.CrossRefPubMed
11.
go back to reference Harrysson IJ, Cook J, Sirimanna P, Feldman L, Darzi A, Aggarwal R. Systematic review of learning curves for minimally invasive abdominal surgery: a review of the methodology of data collection, depiction of outcomes and statistical analysis. Ann Surg. 2014; 260:37–45.CrossRefPubMed Harrysson IJ, Cook J, Sirimanna P, Feldman L, Darzi A, Aggarwal R. Systematic review of learning curves for minimally invasive abdominal surgery: a review of the methodology of data collection, depiction of outcomes and statistical analysis. Ann Surg. 2014; 260:37–45.CrossRefPubMed
12.
go back to reference Ramsay CR, Wallace SA, Garthwaite PH, Monk AF, Russel IT, Grant AM. Assessing the learning curve effect in health technologies: lessons from the nonclinical literature. Int J Technol Assess Health Care. 2002; 18:1–10.PubMed Ramsay CR, Wallace SA, Garthwaite PH, Monk AF, Russel IT, Grant AM. Assessing the learning curve effect in health technologies: lessons from the nonclinical literature. Int J Technol Assess Health Care. 2002; 18:1–10.PubMed
13.
go back to reference Khan N, Abboudi H, Khan MS, Dasgupta P, Ahmed K. Measuring the surgical ‘learning curve’: methods, variables and competency. BJU Int. 2013; 113:504–8.CrossRefPubMed Khan N, Abboudi H, Khan MS, Dasgupta P, Ahmed K. Measuring the surgical ‘learning curve’: methods, variables and competency. BJU Int. 2013; 113:504–8.CrossRefPubMed
14.
go back to reference Moser KM, Auger WR, Fedullo PF, Jamieson SW. Chronic thromboembolic pulmonary hypertension: clinical picture and surgical treatment. Eur Respir J. 1992; 5:334–42.PubMed Moser KM, Auger WR, Fedullo PF, Jamieson SW. Chronic thromboembolic pulmonary hypertension: clinical picture and surgical treatment. Eur Respir J. 1992; 5:334–42.PubMed
15.
go back to reference Thistlethwaite PA, Kaneko K, Madani MM, Jamieson SJ. Technique and outcomes of pulmonary endarterectomy surgery. Ann Thorac Cardiovasc Surg. 2008; 14:274–82.PubMed Thistlethwaite PA, Kaneko K, Madani MM, Jamieson SJ. Technique and outcomes of pulmonary endarterectomy surgery. Ann Thorac Cardiovasc Surg. 2008; 14:274–82.PubMed
16.
go back to reference Delaney PF, Reder LM, Staszewski JJ, Ritter FE. The strategy-specific nature of improvement: the power law applies by strategy within task. Psychol Sci. 1998; 9:1–7.CrossRef Delaney PF, Reder LM, Staszewski JJ, Ritter FE. The strategy-specific nature of improvement: the power law applies by strategy within task. Psychol Sci. 1998; 9:1–7.CrossRef
17.
go back to reference Heathcote A, Brown S, Mewhort DJK. The power law repealed: the case for an exponential law of practice. Psychonomic Bull Rev. 2000; 7:185–207.CrossRef Heathcote A, Brown S, Mewhort DJK. The power law repealed: the case for an exponential law of practice. Psychonomic Bull Rev. 2000; 7:185–207.CrossRef
18.
go back to reference Sammon J, Perry A, Beaule L, Kinkead T, Clark D, Hansen M. Robot-assisted radical prostatectomy: learning rate analysis as an objective measure of the acquisition of surgical skill. BJU Int. 2010; 106:855–60.CrossRefPubMed Sammon J, Perry A, Beaule L, Kinkead T, Clark D, Hansen M. Robot-assisted radical prostatectomy: learning rate analysis as an objective measure of the acquisition of surgical skill. BJU Int. 2010; 106:855–60.CrossRefPubMed
19.
go back to reference Pinheiro JC, Bates DM. Mixed-effects models in S and S-PLUS. New York: Springer; 2000.CrossRef Pinheiro JC, Bates DM. Mixed-effects models in S and S-PLUS. New York: Springer; 2000.CrossRef
Metadata
Title
Assessment of learning curves in complex surgical interventions: a consecutive case-series study
Authors
Olympia Papachristofi
David Jenkins
Linda D. Sharples
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Trials / Issue 1/2016
Electronic ISSN: 1745-6215
DOI
https://doi.org/10.1186/s13063-016-1383-4

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