How to interpret clinical outcome data in plastic surgery research and clinical practice

Use of Minimal Important Difference for Patient-Reported Outcome Measures in Plastic Surgery: A Systematic Review

BACKGROUND

The minimal important difference (MID) is vital to consider when interpreting the clinical importance of observed changes from surgical interventions assessed by patient-reported outcome measures (PROMs). There is no accepted standard for how to calculate MIDs, and uptake in the plastic surgery literature is unknown, leading to methodologic and interpretation issues.

METHODS

Medline and Embase were searched to identify all plastic surgery randomized controlled trials (RCTs) using PROMs as outcomes and MID estimation studies for PROMs used by RCTs. Included studies were assessed for uptake and application of MIDs, and MID estimation methodology and values were categorized.

RESULTS

A total of 554 RCTs using PROMs as outcomes were identified. Of these, 419 RCTs had the possibility of incorporating a previously published MID. The uptake rate of MIDs was 11.5% ( n = 48 of 419). The most common ways MIDs were applied were to calculate sample size (37.5%) or to determine whether results were clinically important (35.4%). A total of 99 studies estimating MID values for the most common PROMs in plastic surgery, based on our review, were analyzed. The most common estimation methodologies were receiver operating characteristic curve analysis (49%), change difference (31%), and SD (25%).

CONCLUSIONS

This review highlights limited uptake and application of MIDs in plastic surgery. The authors propose 4 major barriers: (1) no repository of published MIDs for PROMs used in plastic surgery exists; (2) available MIDs are not specific to plastic surgery populations; (3) high heterogeneity in MID estimation methodology was present; and (4) there are wide ranges in MID values, with no superior choice identified.

Reporting of patient-reported outcomes amongst randomized clinical trials in plastic surgery: a systematic review using CONSORT-PRO

BACKGROUND

Patient-reported outcomes (PROs) are key to investigating patient perspectives in randomized controlled trials (RCTs). Standardization of PRO reporting is critical for trial generalizability and the application of findings to clinical practice. This systematic review aimed to evaluate the reporting quality of RCTs published in the top plastic surgery journals according to the consolidated standards of reporting trials (CONSORT)-PRO extension.

METHODS

We completed a comprehensive search of MEDLINE, Embase, and Cochrane Central Register of Controlled Trials. All RCTs with a validated PRO endpoint published in the top 10 plastic surgery journals (based on the 2021 Web of Science Impact Factor) from 2014 to 2023 were included. Two reviewers independently extracted data and scored the included studies using the CONSORT-PRO checklist. Univariate regression was applied to assess factors associated with reporting adherence. Studies were assessed for their risk of bias using the Cochrane Risk of Bias 2.0 tool.

RESULTS

A total of 88 RCTs were included. PROs were the primary endpoint in 50 (57%) and the secondary endpoint of 38 (43%) studies. Mean overall reporting adherence was poor (39% (±12) and 36% (±13) in studies with PRO as primary and secondary endpoints, respectively). The presence of industry support was significantly associated with greater adherence.

CONCLUSIONS

There is low adherence to the CONSORT-PRO extension among plastic surgery RCTs published in the top 10 plastic surgery journals. We encourage journals and authors to endorse and apply the CONSORT-PRO extension. This may optimize the dissemination of clinical findings from RCTs and assist patient-centered care.

Developing Machine Learning Algorithms to Support Patient-centered, Value-based Carpal Tunnel Decompression Surgery

Background

Carpal tunnel syndrome (CTS) is extremely common and typically treated with carpal tunnel decompression (CTD). Although generally an effective treatment, up to 25% of patients do not experience meaningful benefit. Given the prevalence, this amounts to considerable morbidity and cost without return. Being able to reliably predict which patients would benefit from CTD preoperatively would support more patient-centered and value-based care.

Methods

We used registry data from 1916 consecutive patients undergoing CTD for CTS at a regional hand center between 2010 and 2019. Improvement was defined as change exceeding the respective QuickDASH subscale's minimal important change estimate. Predictors included a range of clinical, demographic and patient-reported variables. Data were split into training (75%) and test (25%) sets. A range of machine learning algorithms was developed using the training data and evaluated with the test data. We also used a machine learning technique called chi-squared automatic interaction detection to develop flowcharts that could help clinicians and patients to understand the chances of a patient improving with surgery.

Results

The top performing models predicted functional and symptomatic improvement with accuracies of 0.718 (95% confidence interval 0.660, 0.771) and 0.759 (95% confidence interval 0.708, 0.810), respectively. The chi-squared automatic interaction detection flowcharts could provide valuable clinical insights from as little as two preoperative questions.

Conclusions

Patient-reported outcome measures and machine learning can support patient-centered and value-based healthcare. Our algorithms can be used for expectation management and to rationalize treatment risks and costs associated with CTD.