Causality and the volume outcome relationship in surgery

Measuring the Volume-Outcome Relation for Complex Hospital Surgery

causality and the volume outcome relationship in surgery

Evidence on the relationship between volume and outcome of surgery is .. The relation between surgical volume and mortality: an exploration of causal factors. Hospital Volume, Structure and Process Indicators, and Surgical Outcomes, has been . Previous Studies of the Volume-Outcome Relationship The volume-outcome relationship: practice-makes-perfect or selective-referral . The relation between surgical volume and mortality: an exploration of causal.

This is most clear-cut in colorectal cancer, bariatric surgery, and breast cancer where reviews of high quality show large effects. Conclusions When taking into account its limitations, this overview can serve as an informational basis for decision makers. However, forthcoming reviews should pay more attention to methodology specific to volume-outcome relationship.

Due to the lack of information, any numerical recommendations for minimum volume thresholds are not possible. Further research is needed for this issue. Electronic supplementary material The online version of this article doi: Systematic review of systematic reviews, Volume-outcome, Surgeon volume, Clinical outcome, Quality assurance, Patient safety Background In particular, in surgical disciplines, lots of studies have been published on the volume-outcome relationship since Luft et al.

Mortality and survival have been explored most in this debate. Many different primary studies as well as systematic reviews indicate a positive relationship between hospital as well as surgeon volume and clinical outcomes for different surgical procedures [ 3 — 5 ].

It has been suggested that surgeon volume is more important than hospital volume for procedures with a shorter length of stay and specific intraoperative processes and skills e. The existence or nonexistence of surgeon volume-outcome relationship is important for different issues. It can be of importance for the methodological refinement of clinical studies on surgical innovations.

The evaluation of innovations vs. These trials might overestimate effects for established procedures in comparison to innovations as surgeons are more familiar in performing these surgeries.

causality and the volume outcome relationship in surgery

Therefore, such trials might lead to better outcomes for established procedures only due to its longer existence and not due to the procedure itself [ 6 ]. Additionally, only few multicenter trials report about provider effects due to variation in expertise. Low-volume and high-volume providers are often included in the same trials which might cause misleading conclusions [ 7 ].

Moreover, it is also important to know whether high-volume surgeons HVS perform better in order to provide patients with a good medical treatment. A sound knowledge about surgeon volume-outcome relationship might have important implications for designing training for surgeons. Furthermore, minimum volume thresholds for surgeons might come into force.

  • The volume-outcome relationship: practice-makes-perfect or selective-referral patterns?

There already exist recommendations by the Expert Panel on Weight Loss Surgery [ 8 ] for bariatric surgery, and an international expert panel defined appropriate and inappropriate surgeon volumes for a variety of gastric procedures [ 9 ].

Many systematic reviews have been published on this topic, so that it becomes more and more difficult to deal with the huge amount of literature.

Therefore, the specific scope of this paper is to provide an overview of all the systematic reviews and to perform a synthesis of the evidence on the surgeon volume-outcome relationship. We analyze if the clinical outcomes of patients undergoing any kind of surgery will be favorable if they are operated by HVS in comparison to low-volume surgeons.

Methods This systematic review of systematic reviews was undertaken in particular according to the methods prescribed in the chapter on overviews in the Cochrane Handbook for Systematic Reviews of Interventions [ 10 ] and is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses PRISMA [ 11 ] see Additional file 1. There was no formal protocol for our work. However, being part of a master thesis, a short project proposal was prepared.

Therein, it was specified a priori to follow basically the same methods as in the previous analysis of our research group on hospital volume [ 12 ]. Literature search strategy We performed a systematic literature search to identify all published systematic reviews on the association between surgeon volume and clinical outcomes. Medline via PubmedEmbase via Embaseand Cochrane database of systematic reviews via Wiley Online Library were searched all search strategies can be found in Additional file 2.

Reference lists of relevant articles were hand-searched to identify additional articles not retrieved by our search strategy. All searches were done without time restriction in October Study selection In consideration for this review, the following inclusion criteria were applied to each systematic review: Articles dealing solely with the relationship between specialization or hospital volume and clinical outcomes were excluded. Systematic reviews investigating the relationship between both hospital volume and surgeon volume were included, if results for surgeon volume were reported separately or could be derived from text.

All titles and abstracts were screened independently by two members of the research team. The full texts of potentially eligible articles were obtained. Two reviewers assessed the eligibility of the full texts against the review inclusion criteria.

Any disagreements were resolved by discussion. Data collection Data were extracted by one reviewer into structured summary tables and checked for accuracy by a second reviewer. Any disagreements were discussed until consensus was reached.

Relationship between surgeon volume and outcomes: a systematic review of systematic reviews

However, Lapar et al. This study addresses three major questions. First, does a volume effect exist in any of six major cancer resection procedures?

causality and the volume outcome relationship in surgery

Second, how does the regression framework used affect this answer? Third, for a given data set, which regression framework is most appropriate? To address these questions, we analyze a year panel data set of hospital-discharge data of patients who underwent one of six cancer procedures.

We fit the data for each of these procedures to basic logistic, fixed-effects logistic, and random-effects logistic regressions. We could have taken a different approach, using Monte Carlo simulation to generate samples with and without volume-outcome effects and testing whether each of the three estimation approaches correctly identify the presence or absence of a relationship between procedure volume and outcomes.

We chose instead to focus on an application involving actual clinical data. This approach is more relevant to clinicians and policy makers, who are most likely to shape future decisions on whether or not to centralize complex care.

Measuring the Volume-Outcome Relation for Complex Hospital Surgery

Previous Literature The vast majority of studies dealing with binary patient outcomes such as mortality employ a simple logistic regression framework. A few studies have used the random-effects model [ 312 — 16 ]. However, most studies using the random-effects model do not check whether their data satisfy the assumptions of the random-effects model, nor do they test alternative model specifications.

Moreover, most do not discuss omitted-variable bias as justification for their model choice. The fixed-effects regression framework uses the variation within a group to exclude omitted-variable bias from time-invariant factors [ 17 ].

With a few exceptions [ 18 — 20 ], the fixed-effects model is rarely used in the volume-outcome literature. However, previous research confirms that controlling for unobserved heterogeneity by using a fixed-effect model can yield drastically different results. In a study of child immunization in China, Xie et al. In a study of hip-fracture patients, Hamilton et al. It is crucial for researchers to correctly measure volume-outcome effects, because there are potential unintended consequences of centralization.

Centralization typically reduces the competitiveness of healthcare markets. It is not clear a priori whether the benefit if any from a volume effect would outweigh the welfare loss associated with reduced competition.

In his seminal paper identifying an empirical relation between surgical volume and mortality for 12 different operations, Luft et al.

Relationship between surgeon volume and outcomes: a systematic review of systematic reviews

More recently, economists have applied instrumental variables analysis to distinguish between volume driving patient outcomes a learning by doing effectversus better outcomes leading to higher volume selective referral.

In these studies, distance to providers or the number of patients and other hospitals within close vicinity of a particular hospital are used as instruments for hospital volume that are unlikely to be confounded by selective referral [ 2526 ].

These studies find that the proposed instruments are valid predictors of hospital volume. Hypothesis tests also reveal no evidence for selective referral. Another paper conducts hypothesis tests for the exogeneity of hospital volume in explaining patient mortality and finds no evidence that the volume-outcome relation is the result of selective referral [ 27 ].

Given that past studies that test for patient selection in the volume-outcome relation find no evidence of selective referral, we chose not to apply instrumental variables analysis in this paper. Instead, we focus on comparing random- and fixed-effects models. The majority of clinicians consult clinical journals when they seek to learn whether a volume-outcome relation exists for a particular operation [ 128 ].

causality and the volume outcome relationship in surgery

And the overwhelming majority of clinical studies apply a simple logistic regression to test for a volume-outcome relation, while most of the rest apply random-effects analysis. Estimation with fixed effects can be readily applied to the same data sets that have been analyzed in these published studies. It is critical for clinicians and policy makers to know whether failing to control for potentially systematic but unobservable differences between high- and low-volume hospitals can yield misleading conclusions regarding the presence of a volume-outcome effect.

We apply three additional inclusion criteria: However, analysis suggests that this problem, if present, is negligible. Variables Our outcome measure is in-hospital mortality.

To define hospital volume, we compute the total number of patients treated by each hospital for each procedure within each year.

He didn't understand the potential outcome of his actions.

Volumes are computed before applying inclusion criteria to avoid endogeneity. Patient characteristics include admission status, age, cancer stage, Elixhauser co-morbidities, race, and sex.

Statistical Analysis For each procedure, we fit three models with cluster-robust standard errors: For each model, the log odds of mortality is regressed on patient characteristics, hospital characteristics, and indicator variables for each subprocedure if any and year. In fitting the fixed-effects model, estimating an indicator variable for each hospital would lead to inconsistent estimates, known as the incidental-parameters problem.

To avoid this, we use the conditional logistic distribution suggested by Chamberlain [ 30 ]. By conditioning the likelihood function on the sum of the dependent variables, a sufficient statistic, we obtain a conditional likelihood function that does not depend on the hospital indicator variables. In Stata, this approach is effected by the -clogit- command. Two aspects of our analysis merit brief discussion. First, we do not include surgeon fixed effects.

The cancer procedures analyzed here typically have very low surgeon volume: As one might expect with such low volumes, the percentage of surgeons having nontrivial variation in outcome i. This specification risks misattributing a surgeon-level volume effect to the hospital level.