We thank Drs. Hwang and Jeon and Drs. Kehlet and Jørgensen for their letters and welcome the opportunity to discuss the strengths and limitations of our study.1 

As stated in the letter from Drs. Hwang and Jeon and acknowledged in our article,1  we were unable to identify whether each nerve block studied was actually clinically effective. When considered from the perspective of an explanatory research question, this is clearly a limitation. However, because the aim of our study was comparative effectiveness, our specific objective was in the realm of pragmatic research, that is, how effective and generalizable might the intervention be in real-world practice.2  From this perspective, we hope that our measures of association provide useful insights into the impact that the peripheral nerve blocks have on system outcomes across a generalizable large sample of patients across an entire healthcare system.

With respect to the assertion by Drs. Hwang and Jeon that our lack of control for intraoperative and postoperative variables and complications is a limitation, we would argue the contrary. In observational comparative effectiveness research, efforts must be made to adjust for indication bias and confounding bias (among other sources). When selecting variables that may be confounders, one must ensure that they meet the definition of a confounder, specifically that they differentially impact exposure (i.e., receipt of a block), differentially impact outcome, and are not on the causal pathway.3  Therefore, although complications may contribute to differences in length of stay (LOS), they are not true confounders because they occur after exposure and are likely on the causal pathway to prolonged LOS. Furthermore, it has been shown that control for variables such as these that are not true confounders can lead to spurious associations.4 

Finally, we agree with Drs. Hwang and Jeon that the choice of analytic approach when performing propensity score–based analyses impacts interpretation of study results.5  Specifically, matched analyses such as ours estimate the average treatment effect in the treated (ATT), because some individuals are excluded if they received treatment but no adequate match was available or if they were untreated and again went unmatched to a treated subject. Although this may decrease overall generalizability, it may also decrease bias. In contrast, methods such as inverse probability of treatment weighting (IPTW) or regression analysis provide an average treatment effect (ATE), that is, what might happen if the entire population was shifted from untreated to treated.6  Although the ATT and ATE are typically similar in direction and magnitude, this is not always the case. In fact, in the case of IPTW, including individuals who were treated despite a very low propensity for treatment can greatly over-weight their contribution to the analysis, especially if extreme tails of the distribution are not trimmed.5  Furthermore, matched analyses can provide an estimate of the absolute risk difference, as opposed to IPTW and regression-based approaches that are typically limited to estimating relative outcome differences. Lastly, in our sensitivity analysis we used a regression-based multilevel multivariable regression analysis, which estimated an ATE for single shot blocks that was identical in direction and magnitude to the ATT estimated from the propensity score–matched analysis.

We would also like to thank Drs. Kehlet and Jørgensen for their commentary regarding our publication1  and in particular their interest in promoting improvements in reporting, analysis, and overall research efforts related to LOS. First, we agree that different patterns of care between jurisdictions or individual hospitals can skew LOS findings. As Hart et al.7  outlined in an analysis of Canadian versus American total joint arthroplasty outcomes, LOS in Canadian hospitals tends to be approximately 1.3 to 1.4 days longer, a finding that may be attributable to a 21 to 27% increase in rates of discharge to short-term rehabilitation from American hospitals. Data from Hart et al.7  also suggest a mean LOS after joint replacement in Canadian hospitals of slightly more than four days, a figure consistent with mean LOS reported in our study, which included a larger cross-section of hospitals than would have been included in the National Surgical Quality Improvement Program data file.

Regarding differences in practice between hospitals, we fully acknowledge that our data sources do not allow us to measure whether specific fast-track processes of care were used at certain hospitals and for specific patients in our study; this is a limitation. For this reason, we ensured that all of our analyses accounted for clustering of patients within hospitals to allow us to account for unmeasured variation between hospitals, both in the use of perioperative processes of care as well as discharge patterns. In our propensity score–matched analysis this involved direct matching within hospitals along with a propensity score, a method that has been shown to decrease both bias and error in estimating causal effects relative to simply matching on the propensity score.8  In our sensitivity analysis, which used regression analysis, we accounted for clustering of patients in hospitals using a multivariable-adjusted generalized linear model and generalized estimating equation methods. We certainly encourage the use of analytic strategies that account for hierarchal data in all comparative effectiveness research where between-center variation is a consideration.

In summary, across a universal healthcare system we report the population-based association between peripheral nerve block exposure and LOS using best-practice methods for comparative effectiveness research and report a LOS consistent with other reports from our jurisdiction. We agree that our data, like any observational data set, have limitations that must be considered when appraising our findings. We also agree that understanding why patients remain in the hospital after surgery is a high-priority area of research and that minimizing variation and instituting best practices should lead to improved patient and system outcomes.

Competing Interests

The authors declare no competing interests.

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