Learning curves in surgery: variables, analysis and applications

Valsamis, Epaminondas Markos; Chouari, Tarak; O'Dowd-Booth, Christopher; Rogers, Benedict A.; Ricketts, David

doi:10.1136/postgradmedj-2018-135880

Cited by 63 publications

(65 citation statements)

References 34 publications

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“…In this study, we used an adaptation of a segmented linear regression technique previously published by one of the authors (EMV) in the context of learning curve modelling [9, 13].…”

Section: Methodsmentioning

confidence: 99%

Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare

Valsamis

Ricketts

Husband

et al. 2019

Computational and Mathematical Methods in Medicine

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Introduction In retrospective studies, the effect of a given intervention is usually evaluated by using statistical tests to compare data from before and after the intervention. A problem with this approach is that the presence of underlying trends can lead to incorrect conclusions. This study aimed to develop a rigorous mathematical method to analyse temporal variation and overcome these limitations. Methods We evaluated hip fracture outcomes (time to surgery, length of stay, and mortality) from a total of 2777 patients between April 2011 and September 2016, before and after the introduction of a dedicated hip fracture unit (HFU). We developed a novel modelling method that fits progressively more complex linear sections to the time series using least squares regression. The method was used to model the periods before implementation, after implementation, and of the whole study period, comparing goodness of fit using F-tests. Results The proposed method offered reliable descriptions of the temporal evolution of the time series and augmented conclusions that were reached by mere group comparisons. Reductions in time to surgery, length of stay, and mortality rates that group comparisons would have credited to the hip fracture unit appeared to be due to unrelated underlying trends. Conclusion Temporal analysis using segmented linear regression models can reveal secular trends and is a valuable tool to evaluate interventions in retrospective studies.

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“…In this study, we used an adaptation of a segmented linear regression technique previously published by one of the authors (EMV) in the context of learning curve modelling [9, 13].…”

Section: Methodsmentioning

confidence: 99%

Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare

Valsamis

Ricketts

Husband

et al. 2019

Computational and Mathematical Methods in Medicine

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“…Although other methods for modelling learning curves do exist ( e.g ., cumulative sum methods), the widespread use of mathematically valid regression techniques in orthopaedics remains sparse[16]. Researchers have recently developed mathematically rigorous segmented linear regression techniques that test multiple learning models and applied these to investigate the learning curves austerity across healthcare systems of total knee and total hip replacements when using imageless navigation[17,18] (Figures 1 and 2).…”

Section: Learningmentioning

confidence: 99%

Evaluating learning and change in orthopaedics: What is the evidence-base?

Valsamis¹,

Sukeik²

2019

WJO

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Learning and change are key elements of clinical governance and are responsible for the progression of our specialty. Although orthopaedics has been slow to embrace quality improvement, recent years have seen global developments in surgical education, quality improvement, and patient outcome research. This review covers recent advances in the evaluation of learning and change and identifies the most important research questions that remain unanswered. Research into proxies of learning is improving but more work is required to identify the best proxy for a given procedure. Learning curves are becoming commonplace but are poorly integrated into postgraduate training curricula and there is little agreement over the most appropriate method to analyse learning curve data. With various organisations promoting centralisation of care, learning curve analysis is more important than ever before. The use of simulation in orthopaedics is developing but is yet to be formally mapped to resident training worldwide. Patient outcome research is rapidly changing, with an increased focus on quality of life measures. These are key to patients and their care. Cost-utility analysis is increasingly seen in orthopaedic manuscripts and this needs to continue to improve evidence-based care. Large-scale international, multi-centre randomised trials are gaining popularity and updated guidance on sample size estimation needs to become widespread. A global lack of surgeon equipoise will need to be addressed. Quality improvement projects frequently employ interrupted time-series analysis to evaluate change. This technique’s limitations must be acknowledged, and more work is required to improve the evaluation of change in a dynamic healthcare environment where multiple interventions frequently occur. Advances in the evaluation of learning and change are needed to drive improved international surgical education and increase the reliability, validity, and importance of the conclusions drawn from orthopaedic research.

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“…Group comparison is generally considered unreliable as it can be influenced by secular trends which are often too subtle to detect by data inspection alone [3]. By accommodating for temporal trends, ITS enables more reliable conclusions but can suffer from bias because its application is focused on a designated point in time (usually the point of the intervention).…”

Section: Introductionmentioning

confidence: 99%

Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare

Valsamis

Husband

Chan

2019

Computational and Mathematical Methods in Medicine

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Introduction In healthcare, change is usually detected by statistical techniques comparing outcomes before and after an intervention. A common problem faced by researchers is distinguishing change due to secular trends from change due to an intervention. Interrupted time-series analysis has been shown to be effective in describing trends in retrospective time-series and in detecting change, but methods are often biased towards the point of the intervention. Binary outcomes are typically modelled by logistic regression where the log-odds of the binary event is expressed as a function of covariates such as time, making model parameters difficult to interpret. The aim of this study was to present a technique that directly models the probability of binary events to describe change patterns using linear sections. Methods We describe a modelling method that fits progressively more complex linear sections to the time-series of binary variables. Model fitting uses maximum likelihood optimisation and models are compared for goodness of fit using Akaike's Information Criterion. The best model describes the most likely change scenario. We applied this modelling technique to evaluate hip fracture patient mortality rate for a total of 2777 patients over a 6-year period, before and after the introduction of a dedicated hip fracture unit (HFU) at a Level 1, Major Trauma Centre. Results The proposed modelling technique revealed time-dependent trends that explained how the implementation of the HFU influenced mortality rate in patients sustaining proximal femoral fragility fractures. The technique allowed modelling of the entire time-series without bias to the point of intervention. Modelling the binary variable of interest directly, as opposed to a transformed variable, improved the interpretability of the results. Conclusion The proposed segmented linear regression modelling technique using maximum likelihood estimation can be employed to effectively detect trends in time-series of binary variables in retrospective studies.

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Learning curves in surgery: variables, analysis and applications

Cited by 63 publications

References 34 publications

Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare

Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare

Evaluating learning and change in orthopaedics: What is the evidence-base?

Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare

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