So You Have a Research Idea: A Survey of Databases Available for Plastic Surgery Research

Making the Most of Big Data in Plastic Surgery: Improving Outcomes, Protecting Patients, Informing Service Providers

ABSTRACT

In medicine, "big data" refers to the interdisciplinary analysis of high-volume, diverse clinical and lifestyle information on large patient populations. Recent advancements in data storage and electronic record keeping have enabled the expansion of research in this field. In the United Kingdom, Big data has been highlighted as one of the government's "8 Great Technologies," and the Medical Research Council has invested more than £100 million since 2012 in developing the Health Data Research UK infrastructure. The recent Royal College of Surgeons Commission of the Future of Surgery concluded that analysis of big data is one of the 4 most likely avenues to bring some of the most innovative changes to surgical practice in the 21st century.In this article, we provide an overview of the nascent field of big data analytics in plastic and highlight how it has the potential to improve outcomes, increase safety, and aid service planning.We outline the current resources available, the emerging role of big data within the subspecialties of burns, microsurgery, skin and breast cancer, and how these data can be used. We critically review the limitations and considerations raised with big data, offer suggestions regarding database optimization, and suggest future directions for research in this exciting field.

Swiss Validation of the Enhanced Recovery After Surgery (ERAS) Database

Background

Enhanced recovery after surgery (ERAS) pathways have considerably improved postoperative outcomes and are in use for various types of surgery. The prospective audit system (EIAS) could be a powerful tool for large-scale outcome research but its database has not been validated yet.

Methods

Swiss ERAS centers were invited to contribute to the validation of the Swiss chapter for colorectal surgery. A monitoring team performed on-site visits by the use of a standardized checklist. Validation criteria were (I) coverage (No. of operated patients within ERAS protocol; target threshold for validation: ≥ 80%), (II) missing data (8 predefined variables; target ≤ 10%), and (III) accuracy (2 predefined variables, target ≥ 80%). These criteria were assessed by comparing EIAS entries with the medical charts of a random sample of patients per center (range 15–20).

Results

Out of 18 Swiss ERAS centers, 15 agreed to have onsite monitoring but 13 granted access to the final dataset. ERAS coverage was available in only 7 centers and varied between 76 and 100%. Overall missing data rate was 5.7% and concerned mainly the variables “urinary catheter removal” (16.4%) and “mobilization on day 1” (16%). Accuracy for the length of hospital stay and complications was overall 84.6%. Overall, 5 over 13 centers failed in the validation process for one or several criteria.

Conclusion

EIAS was validated in most Swiss ERAS centers. Potential patient selection and missing data remain sources of bias in non-validated centers. Therefore, simplified validation of other centers appears to be mandatory before large-scale use of the EIAS dataset.

Supplementary Information

The online version contains supplementary material available at (10.1007/s00268-020-05926-z).

An Evaluation of the Utility of the Breast Reconstruction Risk Assessment Score Risk Model in Prepectoral Tissue Expander Breast Reconstruction

INTRODUCTION

Individualized postsurgical risk assessment models provide surgeons and patients with information that is vital to the surgical decision-making process. One such tool, the Breast Reconstruction Risk Assessment (BRA) score, uses a limited selection of patient-specific factors to predict 30-day postsurgical risk of surgical site infection, seroma, dehiscence, reoperation and explantation associated with immediate submuscular tissue expander breast reconstruction. This model's performance in prepectoral tissue expander reconstruction has not been previously reported. Here, we evaluate the performance of the BRA score model in a population of patients who underwent immediate prepectoral tissue expander breast reconstruction.

MATERIALS AND METHODS

A retrospective chart review was conducted of prepectoral breast reconstructions performed in 2 institutions between January 2017 and December 2018. Complications occurring within 30 days postoperatively were documented and compared with the BRA score predicted risk for each patient.

RESULTS

Overall 247 patients (average age, 49.2 years) were included in the study. The mean BRA score predicted 30-day risk of a complication was 13.0% (7.5-41.5%). The observed rate of 30-day postoperative complications was 31.2% (77 patients), though only 36 (14.6%) patients had complications included in the model. The remaining patients experienced skin necrosis or hematoma as their only early complication. The 30-day BRA score model demonstrated good fit for the overall occurrence of any of the BRA score predicted complications (Hosmer-Lemeshow 0.7167), though the model discrimination was poor (C statistic <0.60). Notably, half of the 30-day postsurgical complications observed in this study were due to skin necrosis, a complication not currently included in the 30-day BRA score model.

CONCLUSIONS

Our results indicate that the current 30-day BRA score model may have poor predictive value in prepectoral breast reconstruction. The most common early complication observed, skin necrosis, is not currently included in the model, suggesting that caution should be applied when using this risk predictive calculator as an adjunct to patient evaluation and counseling.

Clinical Research Informatics: Supporting the Research Study Lifecycle

Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

Prevalence of psychiatric comorbidities among women undergoing free tissue autologous breast reconstruction

BACKGROUND AND OBJECTIVES

Autologous breast reconstruction (BR) can be a stressful life event. Therefore, women undergoing mastectomy and autologous BR are required to have sufficient coping mechanisms. Although mental health problems are widespread, information regarding the prevalence of psychiatric diagnosis among these patients is scarce.

METHODS

Retrospective analysis was performed using data from a large tertiary teaching hospital and the Nationwide Inpatient Sample (NIS) database. Patients undergoing autologous BR after mastectomy were included and evaluated for psychiatric disorders. Prevalence of each disorder, timing of diagnosis (preoperative or postoperative), and data per age group were reviewed.

RESULTS

Between 2004 and 2014, 817 patients were included from the institutional database and 26 399 from the NIS database. Preoperatively, 15.3% of the patients were diagnosed with a psychiatric disorder within our institution and 17.6% nationwide (P < 0.001). Postoperatively, 20.5% of the institutional patients were diagnosed with a psychiatric disorder. No major differences in prevalence were seen between age groups.

CONCLUSIONS

Approximately, one in six patients were diagnosed with a psychiatric comorbidity preoperatively. Postoperatively, an additional 20.5% developed a psychiatric disorder. There was no difference in prevalence and timing of diagnosis between age groups.

External validation of the breast reconstruction risk assessment calculator

INTRODUCTION

The Breast reconstruction Risk Assessment (BRA) Score estimates patient-specific risk for postsurgical complications using an individual's unique combination of preoperative variables. In this report, we externally validate the BRA Score models for surgical site infection, seroma, and explantation in a large sample of intra-institutional patients who underwent prosthetic breast reconstruction.

METHODS

We reviewed all initiated tissue expander/implant reconstructions by the senior authors from January 2004 to December 2015. BRA Score risk estimates were computed for each patient and compared against observed rates of complications. Hosmer-Lemeshow goodness-of-fit test, concordance statistic, and Brier score were used to assess the calibration, discrimination, and accuracy of the models, respectively.

RESULTS

Of the 1152 patients (1743 breasts) reviewed, 855 patients (1333 breasts) had complete data for BRA-score calculations and were included for analysis. Hosmer-Lemeshow tests for calibration demonstrated a good agreement between observed and predicted outcomes for surgical site infection (SSI) and seroma models (P-values of 0.33 and 0.16, respectively). In contrast, predicted rates of explantation deviated from observed rates (Hosmer-Lemeshow P-value of 0.04). C statistics demonstrated good discrimination for SSI, seroma, and explantation (0.73, 0.69, and 0.78, respectively).

CONCLUSIONS

In this external validation study, the BRA Score tissue expander/implant reconstruction models performed with generally good calibration, discrimination, and accuracy. Some weaknesses in certain models were identified as targets for future improvement. Taken together, these analyses validate the clinical utility of the BRA score risk models in predicting 30-day outcomes.

Surgical research using national databases

Recent changes in healthcare and advances in technology have increased the use of large-volume national databases in surgical research. These databases have been used to develop perioperative risk stratification tools, assess postoperative complications, calculate costs, and investigate numerous other topics across multiple surgical specialties. The results of these studies contain variable information but are subject to unique limitations. The use of large-volume national databases is increasing in popularity, and thorough understanding of these databases will allow for a more sophisticated and better educated interpretation of studies that utilize such databases. This review will highlight the composition, strengths, and weaknesses of commonly used national databases in surgical research.