Laparoscopic surgical box model training for surgical trainees with no prior laparoscopic experience

  • Protocol
  • Intervention

Authors


Abstract

This is the protocol for a review and there is no abstract. The objectives are as follows:

To compare the benefits and harms of box model training versus no training, animal models, or cadaveric model training for surgical trainees with no prior laparoscopic experience.

Background

Description of the condition

Surgical training has traditionally been one of apprenticeship, where the surgical trainee learns to perform surgery under the supervision of a trained surgeon. Different procedures have different learning curves (Herrell 2005; Tekkis 2005a; Tekkis 2005b). Surgeons experienced in one procedure may not be experienced in another, and results improve with experience in an individual procedure (Herrell 2005; Tekkis 2005a; Tekkis 2005b).

An increasing number of surgical procedures are being done laparoscopically (abdominal key hole surgery). This includes laparoscopic cholecystectomy (removal of gallbladder), laparoscopic anti-reflux procedures (surgery for heart-burn), laparoscopic hysterectomy (removal of uterus), and laparoscopic nephrectomy (removal of kidney) (Ghezzi 2006; Keus 2006; Salminen 2007; Venkatesh 2007). The different methods of laparoscopic surgical training include live animal training, human and animal cadaver training, training using a box trainer (also called video trainer), and virtual reality training (training using computer simulation) (Munz 2004).

The price of the simulators can vary depending upon the learning outcome. Traditional training is not without costs. The operating time increases significantly for junior surgeons compared to senior surgeons (Farnworth 2001; Babineau 2004; Wilkiemeyer 2005; Kauvar 2006; Harrington 2007). Bridges and Diamond reported the average costs of this increased operating time to be about USD 12,000 per year per resident during the period 1993 to 1997 (Bridges 1999). The complication rate is also higher for junior surgeons compared to senior surgeons (Wilkiemeyer 2005; Kauvar 2006). Bridges and Diamond did not include the cost of the complications in their cost analysis. Thus, the cost of the simulators has to be balanced against the cost of increased operating time and complication rates during traditional surgical training and the costs of traditional training.

Description of the intervention

Training using a box model involves performance of tasks that are encountered in laparoscopic surgery using animal tissues, plastic models, foam, cloth, or other materials. The images can be obtained using a laparoscope (camera) and viewed on monitors. This is called video-box trainer. Another type of box trainer is the mirrored-box trainer, in which mirrors are used to show the working field and direct vision of the working field is prevented (Keyser 2000).

How the intervention might work

Laparoscopic surgery is different from open surgery because of the increased need for hand-eye co-ordination to perform tasks when looking at a screen and to compensate for not being able to operate under direct vision; increased need for manual dexterity to compensate for the use of long instruments (the fine motor skills required for performing laparoscopic surgery are greater than in open surgery since small movements are more amplified in laparoscopic surgery than open surgery because of the longer instruments used in laparoscopic surgery), which can amplify any error in movement; the fulcrum effect of the body wall, that is, when the surgeon moves his hand to the patient's right the operating end of the instrument moves to the patient's left on the monitor (Gallagher 1999); the need for handling tissues carefully (to compensate for the lack of sensation of touch using hands); and the lack of three-dimensional images.Training by box-trainer may work by repeated practice and improve the hand-eye-co-ordination and manual dexterity.

Why it is important to do this review

In a previous systematic review, we have shown that virtual reality training can supplement standard laparoscopic training (Gurusamy 2008; Gurusamy 2009a). Sutherland et al concluded in a systematic review that there was no evidence that a box trainer was effective in laparoscopic training (Sutherland 2006). There have been no other systematic reviews and there are no other Cochrane reviews on this topic. The proposed review will provide evidence as to whether laparoscopic surgical box-model training is beneficial for surgical trainees with no prior laparoscopic experience.

Objectives

To compare the benefits and harms of box model training versus no training, animal models, or cadaveric model training for surgical trainees with no prior laparoscopic experience.

Methods

Criteria for considering studies for this review

Types of studies

We will include randomised clinical trials irrespective of blinding, language, publication status, or sample size. We will exclude quasi-randomised studies (for example, allocation by date of birth, day of the week, etc) and observational studies for reported benefit, but we will include them for the report on harm.

Types of participants

We will include surgical trainees with no prior laparoscopic experience in the proposed review. We will consider the effectiveness of box model training for surgical trainees with limited prior laparoscopic experience in another review (Gurusamy 2013).

Types of interventions

We will include the following comparisons.

  • Box-model training alone or supplementing standard surgical training versus standard surgical training.

  • Box-model training versus animal model training.

  • Box-model training versus cadaveric model training.

  • Video-box trainer versus mirrored-box trainer.

  • One type of video-box trainer versus another type of video-box trainer.

  • One type of mirrored-box trainer versus another type of mirrored-box trainer.

As mentioned in the background, the box-model trainer has been compared with a virtual model trainer in another Cochrane review (Gurusamy 2008; Gurusamy 2009a).

We will allow co-interventions if used equally in all intervention groups and the control group of the trial.

Types of outcome measures

Primary outcomes
  1. Time taken to complete the task.

  2. Error score (however defined by authors).

  3. Accuracy (however defined by authors).

  4. Composite score of the above.

Secondary outcomes
  1. Mortality and morbidity (when animal model is used to assess the trainees).

  2. Movements:

    1. distance;

    2. error.

  3. Trainee satisfaction (however defined by authors).

We will present all the outcomes in the 'Summary of findings table' created using GRADEpro 3.6 (http://ims.cochrane.org/revman/other-resources/gradepro).

Search methods for identification of studies

Electronic searches

We will search the Cochrane Hepato-Biliary Group Controlled Trials Register (Gluud 2013), the Cochrane Central Register of Controlled Trials (CENTRAL) in The Cochrane Library, MEDLINE, EMBASE, and Science Citation Index Expanded (Royle 2003). We have given the preliminary search strategies with the expected time spans of the searches in Appendix 1. As the review progresses, and if needed, we will improve the search strategies.

Searching other resources

We will search the references of the identified trials to identify further relevant trials. We will search the metaRegister of Controlled Trials (mRCT) (http://www.controlled-trials.com/mrct/) and the WHO clinical trials platform. The meta-register includes the ISRCTN Register and NIH ClinicalTrials.gov Register among other registers.

Data collection and analysis

We will perform the systematic review following the instructions given in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011) and the Cochrane Hepato-Biliary Group Module (Gluud 2013).

Selection of studies

Two authors (KG and BRD) will identify the trials for inclusion independently of each other. We will list the excluded studies with the reasons for the exclusion. Any differences will be resolved through discussion.

Data extraction and management

Both authors will independently extract the following data.

  1. Year and language of publication.

  2. Country.

  3. Year of conduct of the trial.

  4. Inclusion and exclusion criteria.

  5. Sample size.

  6. Details of the previous experience of surgical trainees.

  7. Details of the box trainer used.

  8. Details of the training regimen used.

  9. Outcomes (described above).

  10. Risk of bias (described below).

Any unclear or missing information will be sought by contacting the authors of the individual trials. If there is any doubt whether the trials share the same patients, completely or partially (by identifying common authors and centres), we will contact the authors of the trials to clarify whether the trial report has been duplicated. We will resolve any differences in opinion through discussion.

Assessment of risk of bias in included studies

We will follow the instructions given in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011) and the Cochrane Hepato-Biliary Group Module (Gluud 2013). According to empirical evidence (Schulz 1995; Moher 1998; Kjaergard 2001; Wood 2008; Lundh 2012; Savovic 2012; Savovic 2012a), the risk of bias of the trials will be assessed based on the following bias risk domains.

Allocation sequence generation
  • Low risk of bias: sequence generation was achieved using computer random number generation or a random number table. Drawing lots, tossing a coin, shuffling cards, and throwing dice are adequate if performed by an independent person not otherwise involved in the trial.

  • Uncertain risk of bias: the method of sequence generation was not specified.

  • High risk of bias: the sequence generation method was not random.

Allocation concealment
  • Low risk of bias: the participant allocations could not have been foreseen in advance of, or during, enrolment. Allocation was controlled by a central and independent randomisation unit. The allocation sequence was unknown to the investigators (for example, if the allocation sequence was hidden in sequentially numbered, opaque, and sealed envelopes).

  • Uncertain risk of bias: the method used to conceal the allocation was not described so that intervention allocations may have been foreseen in advance of, or during, enrolment.

  • High risk of bias: the allocation sequence was likely to be known to the investigators who assigned the participants.

Blinding of participants and personnel*
  • Low risk of bias: blinding was performed adequately, or the assessment of outcomes was not likely to be influenced by lack of blinding.

  • Uncertain risk of bias: there was insufficient information to assess whether blinding was likely to introduce bias on the results.

  • High risk of bias: no blinding or incomplete blinding, and the assessment of outcomes were likely to be influenced by lack of blinding. 

*It is impossible to blind the surgical trainees and any assisting personnel. Provided that the outcome assessors are blinded, we will consider that there is low risk of bias due to lack of blinding of participants and any assisting personnel for all outcomes except for surgical trainee satisfaction.

Blinding of outcome assessors
  • Low risk of bias: blinding was performed adequately, or the assessment of outcomes was not likely to be influenced by lack of blinding.

  • Uncertain risk of bias: there was insufficient information to assess whether blinding was likely to induce bias on the results.

  • High risk of bias: no blinding or incomplete blinding, and the assessment of outcomes were likely to be influenced by lack of blinding. 

Incomplete outcome data
  • Low risk of bias: missing data were unlikely to make treatment effects depart from plausible values. Sufficient methods, such as multiple imputation, has been employed to handle missing data.

  • Uncertain risk of bias: there was insufficient information to assess whether missing data in combination with the method used to handle missing data were likely to induce bias on the results.

  • High risk of bias: the results were likely to be biased due to missing data.

Selective outcome reporting
  • Low risk of bias: all outcomes were pre-defined and reported, or all clinically relevant and reasonably expected outcomes were reported. For this purpose, the trial should have been registered either on the www.clinicaltrials.gov web site or a similar register, or there should be a protocol,  eg, published in a paper journal. In the case when the trial was run and published in the years when trial registration was not required, we will carefully scrutinize all publications reporting on the trial to identify the trial objectives and outcomes and determine whether usable data are provided in the publications results section on all outcomes specified in the trial objectives.

  • Uncertain risk of bias: it is unclear whether all pre-defined and clinically relevant and reasonably expected outcomes were reported.

  • High risk of bias: one or more clinically relevant and reasonably expected outcomes were not reported, and data on these outcomes were likely to have been recorded.

For-profit bias
  • Low risk of bias: the trial appears to be free of industry sponsorship or other kind of for-profit support that may manipulate the trial design, conductance, or results of the trial.

  • Uncertain risk of bias: the trial may or may not be free of for-profit bias as no information on clinical trial support or sponsorship is provided.

  • High risk of bias: the trial is sponsored by the industry or has received other kind of for-profit support.

We will consider trials which are classified as low risk of bias in all the above domains as trials with low risk of bias and the remaining as trials with high risk of bias.

Measures of treatment effect

For dichotomous variables, we will calculate the risk ratio (RR) with 95% confidence interval (CI). Risk ratio calculations do not include trials in which no events occurred in either group, whereas risk difference calculations do. We will report the risk difference if the results using this association measure were different from risk ratio. For continuous variables, we will calculate the mean difference (MD) with 95% CI for outcomes such as hospital stay, and standardised mean difference (SMD) with 95% CI for quality of life (where different scales might be used).

Unit of analysis issues

The unit of analysis will be the aggregate data on surgical trainees who underwent training according to the randomised group.

Dealing with missing data

We will perform an intention-to-treat analysis (Newell 1992) whenever possible. We will impute data for binary outcomes using various scenarios such as good outcome analysis, bad outcome analysis, best-case scenario, and worst-case scenario (Gurusamy 2009b; Gluud 2013).

For continuous outcomes, we will use available-case analysis. We will impute the standard deviation from P values according to the instructions given in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011), and we will use the median for the meta-analysis when the mean is not available. If it is not possible to calculate the standard deviation from the P value or the CI, we will impute the standard deviation as the highest standard deviation in the other trials included under that outcome, fully recognising that this form of imputation will decrease the weight of the study for calculation of mean differences and bias the effect estimate to no effect in the case of standardised mean difference (Higgins 2011).

Assessment of heterogeneity

We will explore heterogeneity by the Chi2 test with significance set at a P value of 0.10, and measure the quantity of heterogeneity by the I2 statistic (Higgins 2002). We will also use overlapping of CIs on the forest plot to determine heterogeneity.

Assessment of reporting biases

We will use visual asymmetry on a funnel plot to explore reporting bias (Egger 1997; Macaskill 2001). We will perform the linear regression approach described by Egger 1997 to determine the funnel plot asymmetry. Selective reporting will also be considered as evidence for reporting bias.

Data synthesis

We will perform the meta-analyses using the software package Review Manager 5 (RevMan 2012), following the recommendations of The Cochrane Collaboration (Higgins 2011) and the Cochrane Hepato-Biliary Group Module (Gluud 2013). We will use both random-effects model (DerSimonian 1986) and fixed-effect model (DeMets 1987) meta-analyses. In the case of discrepancy between the two models we will report both results; otherwise we will report the results of the fixed-effect model. We will use the generic inverse method to combine the hazard ratios for time-to-event outcomes.

Trial sequential analysis

Cumulative meta-analyses are at risk of random errors due to sparse data and repetitive testing of accumulating data. Trial sequential analysis can be used to assess the risks of random errors. Trial sequential analysis combines the calculation of a required information size (calculating a meta-analytic sample size to detect or reject a certain intervention effect) with alpha-spending and beta-spending monitoring boundaries for statistical significance or lack thereof (Wetterslev 2008; Brok 2008; Thorlund 2011). We will perform trial sequential analysis (CTU 2011; Thorlund 2011) to guard against random errors due to sparse data or repetitive analyses of the cumulative meta-analyses and examine if futility has been reached. The calculation of the required information size will be based on the proportion in the control group with the outcome in question; the relative risk reduction estimated in the trials preferably with low risk of bias and of 20%; an alpha of 5%; a beta of 20% (power equal to 80%); and a diversity adjustment of the required information size (Brok 2008; Wetterslev 2008; Brok 2009; Thorlund 2009, Wetterslev 2009; Thorlund 2010).

Subgroup analysis and investigation of heterogeneity

We will perform the following subgroup analyses.

  • Trials with low risk of bias compared to trials with high risk of bias.

  • Different types of box trainers (video trainer or mirror trainer).

  • Different levels of prior surgical experience.

We will use the 'test for interaction' to identify the differences between subgroups (Altman 1995).

Sensitivity analysis

We will perform a sensitivity analysis by imputing data for binary outcomes using various scenarios such as good outcome analysis, bad outcome analysis, best-case scenario, and worst-case scenario (Gurusamy 2009b; Gluud 2013). We will perform a sensitivity analysis by excluding the trials in which the mean and the standard deviation were imputed.

Acknowledgements

To the Cochrane Hepato-Biliary Group.

Peer Reviewers: Antonia Stergiopoulou, Greece; M Mulla, UK; Marco La Torre, Italy.
Contact Editor: Saboor A'Khan, UK; Christian Gluud, DK.

This project was funded by the National Institute for Health Research.
Disclaimer of the Department of Health: 'The views and opinions expressed in the review are those of the authors and do not necessarily reflect those of the National Institute for Health Research (NIHR), National Health Services (NHS), or the Department of Health'.

Appendices

Appendix 1. Search strategies for identification of studies

DatabasePeriod of SearchSearch Strategy
Cochrane Hepato-Biliary Group Controlled Trials RegisterDate will be given at review stage.(laparoscop* OR coelioscop* OR celioscop* OR peritoneoscop*) AND (video OR mirror OR box OR simulat*) AND train*
Cochrane Central Register of Controlled Trials (CENTRAL) in The Cochrane Library (Wiley)Latest issue.

#1 laparoscop* OR coelioscop* OR celioscop* OR peritoneoscop*

#2 MeSH descriptor Laparoscopy explode all trees

#3 #1 OR #2
#4 video OR mirror OR box OR simulat*

#5 train*

#6 #3 AND #4 AND #5

MEDLINE (PubMed)1987 to the date of search.(laparoscop* OR coelioscop* OR celioscop* OR peritoneoscop* OR "Laparoscopy"[Mesh]) AND (video OR mirror OR box OR simulat*) AND train* AND ((randomised controlled trial [pt] OR controlled clinical trial [pt] OR randomised [tiab] OR placebo [tiab] OR drug therapy [sh] OR randomly [tiab] OR trial [tiab] OR groups [tiab]) NOT (animals [mh] NOT humans [mh]))
EMBASE (OvidSP)1987 to the date of search.

1. exp crossover-procedure/ or exp double-blind procedure/ or exp randomised controlled trial/ or single-blind procedure/  

2. (random* OR factorial* OR crossover* OR placebo*).af.

3. 1 OR 2

4. (laparoscop$ or coelioscop$ or celioscop$ or peritoneoscop$).af.
5. exp Laparoscopic surgery/
6. 4 or 5
7. (video OR mirror OR box OR simulat*).af.
8. simulator/
9. 7 OR 8
10. train*.af.
11. surgical training/
12. 10 OR 11
13. 3 AND 6 AND 9 AND 12

Science Citation Index Expanded (ISI Web of Knowledge)(http://apps.isiknowledge.com/)1987 to the date of search.

#1 TS=(laparoscop* OR coelioscop* OR celioscop* OR peritoneoscop*)

#2 TS=(video OR mirror OR box OR simulat*)

#3 TS=(train*)
#4 TS=(random* OR rct* OR crossover OR masked OR blind* OR placebo* OR meta-analysis OR systematic review* OR meta-analys*)
#5 #4 AND #3 AND #2 AND #1

metaRegister of Controlled Trials (http://www.controlled-trials.com/mrct/)Date will be given at review stage.(laparoscop* OR coelioscop* OR celioscop* OR peritoneoscop*) AND (video OR mirror OR box OR simulat*) AND (train*)

Contributions of authors

KS Gurusamy wrote the protocol. The protocol was developed after discussion with BR Davidson.

Declarations of interest

None

Sources of support

Internal sources

  • None, Not specified.

External sources

  • National Insitute for Health Research (NIHR), UK.

    NIHR is the health research wing of the UK Government. It part-funds K Gurusamy's salary and funds all the materials needed for the preparation of this review.

Ancillary