Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery

SF-6D Normative Values Among Patients Undergoing Bariatric Surgery: Results Based on Real-World Evidence from the Scandinavian Obesity Surgery Registry (SOReg)

BACKGROUND

The SF-6D index can be used to calculate quality-adjusted life years in economic evaluations, which is required by reimbursement agencies and national advisory bodies, including the Swedish ones. However, despite that SF-36 has been largely applied among patients undergoing bariatric surgery, almost no study has accessed the short form six-dimensions (SF-6D) after bariatric surgery.

AIM

To establish normative values for the SF-6D index among patients undergoing bariatric surgery.

MATERIALS AND METHODS

All patients who received bariatric surgery in Sweden between 2011-01-01 and 2019-03-31 were obtained from the Scandinavian Obesity Surgery Registry (SOReg). Information includes patients' sociodemographic characteristics, details regarding the procedure, and postsurgical conditions. The SF-36 is applied at baseline and at follow-up years 1, 2, and 5. The multiple sequential imputation method was applied to handle missingness on SF-6D items. Based on the UK tariff, the SF-6D preference scores were calculated. The normative values for the mean (SD) SF-6D index were reported by timepoint and surgical complications for men and women, respectively. Multivariate analyses were applied to investigate how the SF-6D index is associated with timepoint, controlling for age, sex, BMI, and comorbidities in a stepwise manner.

RESULTS

The SF-6D index increased at 1 year relative to baseline and was roughly maintained at the same level at 2 years. The normative value of the SF-6D index can be used in economic evaluations for bariatric surgery.

Missing Data in Patient-Reported Outcomes Research: Utilizing Multiple Imputation to Address an Unavoidable Problem

BACKGROUND

Patient-reported outcomes (PROs) have become a focus in postoperative surgical care. Unfortunately, studies using PROs can be subject to missing data, which may lead to biases or inaccurate conclusions. Multiple imputation (MI) is a statistical method for addressing missing data in clinical research. The aim of this study was to explore MI as a way to address missing data in PRO research.

METHODS

A working example of MI using real-world data was performed using the BREAST-Q PRO measure in postmastectomy reconstruction. A retrospective review of immediate tissue expander breast reconstruction patients in 2019 was conducted to compare BREAST-Q physical well-being of the chest scores between prepectoral and subpectoral cohorts at 2 weeks postoperatively. The observed dataset and three hypothetical missingness situations were created to assess how increasing missingness affects MI results.

RESULTS

Overall, 916 patients were included in the analysis. When excluding patients with missing information and solely performing analysis on the completed cases, prepectoral patients had significantly higher physical well-being of the chest scores at 2 weeks postoperatively; however, this trend was reversed with increasing missingness scenarios, where subpectoral patients had higher scores. In comparison, all MI results showed that prepectoral patients had higher scores on average compared with subpectoral patients regardless of missingness scenario.

CONCLUSIONS

MI demonstrated consistent results with increasing missingness scenarios, whereas performing analysis in higher missingness scenarios without MI led to varying results. This working example emphasizes the need for missing data methodology to be considered in PRO research.

Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register

PURPOSE

To investigate whether the quality-adjusted life years (QALYs) of the patients who underwent bariatric surgery could be predicted using their baseline information.

MATERIALS AND METHODS

All patients who received bariatric surgery in Sweden between January 1, 2011 and March 31, 2019 were obtained from the Scandinavian Obesity Surgery Registry (SOReg). Baseline information included patients' sociodemographic characteristics, details regarding the procedure, and postsurgical conditions. QALYs were assessed by the SF-6D at follow-up years 1 and 2. The general and regularized linear regression models were used to predict postoperative QALYs.

RESULTS

All regression models demonstrated satisfactory and comparable performance in predicting QALYs at follow-up year 1, with R2 and relative root mean squared error (RRMSE) values of about 0.57 and 9.6%, respectively. The performance of the general linear regression model increased with the number of variables; however, the improvement was ignorable when the number of variables was more than 30 and 50 for follow-up years 1 and 2, respectively. Although minor L1 and L2 regularization provided better prediction ability, the improvement was negligible when the number of variables was more than 20. All the models showed poorer performance for predicting QALYs at follow-up year 2.

CONCLUSIONS

Patient characteristics before bariatric surgery including health related quality of life, age, sex, BMI, postoperative complications within six weeks, and smoking status, may be adequate in predicting their postoperative QALYs after one year. Understanding these factors can help identify individuals who require more personalized and intensive support before, during, and after surgery.