Andrew Swartz

Are the frail destined to fail? Frailty index as predictor of surgical morbidity and mortality in the elderly.

BACKGROUND America’s aging population has led to an increase in the number of elderly patients necessitating emergency general surgery. Previous studies have demonstrated that increased frailty is a predictor of outcomes in medicine and surgical patients. We hypothesized that use of a modification of the Canadian Study of Health and Aging Frailty Index would be a predictor of morbidity and mortality in patients older than 60 years undergoing emergency general surgery. METHODS Data were obtained from the National Surgical Quality Improvement Program Participant Use Files database in compliance with the National Surgical Quality Improvement Program Data Use Agreement. We selected all emergency cases in patients older than 60 years performed by general surgeons from 2005 to 2009. The effect of increasing frailty on multiple outcomes including wound infection, wound occurrence, any infection, any occurrence, and mortality was then evaluated. RESULTS Total sample size was 35,334 patients. As the modified frailty index increased, associated increases occurred in wound infection, wound occurrence, any infection, any occurrence, and mortality. Logistic regression of multiple variables demonstrated that the frailty index was associated with increased mortality with an odds ratio of 11.70 (p < 0.001). CONCLUSION Frailty index is an important predictive variable in emergency general surgery patients older than 60 years. The modified frailty index can be used to evaluate risk of both morbidity and mortality in these patients. Frailty index will be a valuable preoperative risk assessment tool for the acute care surgeon. (J Trauma Acute Care Surg. 2012;72: 1526–1531. Copyright

Limitations of Patient-Associated Co-Morbidity Model in Predicting Postoperative Morbidity and Mortality in Pancreatic Operations

BackgroundPatient-associated co-morbidities are a potential cause of postoperative complications. The National Surgical Quality Improvement Project (NSQIP) collects data on patient outcomes to provide risk-adjusted outcomes data to participating hospitals. However, operations which may have a high distribution of technically-related complications, such as pancreatic operations, may not be adequately assessed using such predictive models.MethodsA combined data set of NSQIP Public Use files (PUF) from 2005 to 2008 was created. Using this database, multiple logistic regression analyses were used to generate a predictive model of 30-day postoperative morbidity and mortality for pancreatic operations and all other operations recorded in NSQIP. Receiver-operator characteristic curves were generated and the area under those curves (AUROC) used to generate a c-statistic to assess the model’s discriminatory ability. Observed-to-expected (O/E) ratios of for mortality and morbidity using not only patient-associated co-morbidities, but operation-associated information, such as work relative-value units and Current Procedural Terminology codes, were generated. Data were analyzed in SPSS.ResultsIn the 4-year period analyzed, there were 7,097 complex pancreatic procedures done which were compared to 568,371 procedures that were not. For postoperative mortality, the AUROC was less for pancreatic operations (0.741) compared to all other operation (0.947) and all other inpatient operations (0.927). Similarly for postoperative morbidity, the AUROC was less for pancreatic operations (0.598) compared to all other operations (0.764) and all other inpatient operations (0.817). However, the O/E ratios were similar in both groups for mortality (all other operations, 0.94 vs. pancreatic operations, 0.92) and morbidity (0.98 for both).ConclusionsThese data imply that the factors used to assess postoperative mortality and morbidity may not completely explain postoperative outcomes in pancreatic operations. These procedures are technically demanding and can have morbidities not related to pre-existing co-morbid conditions; therefore, preoperative prediction based on pre-existing co-morbidities may have limitations in these types of operations.

Developing an experimental model for surgical drainage investigations: An initial report

BACKGROUND We sought to pilot and initiate validation of a surgical drainage model. METHODS We designed a laboratory model to compare Jackson-Pratt surgical drains using 3 soups to emulate body fluids of serous, purulent, and necrotic debris. Each drain was trialed with each of the 3 fluids. Time and completeness of drainage were recorded. A survey of surgical residents and faculty was performed for convenience sampling. RESULTS Under serous conditions, the round Jackson-Pratt drained the cavity quicker, but left a larger residual volume of fluid. Under purulent conditions, the round Jackson-Pratt was slower and drained less fluid. With debris fluid, the round Jackson-Pratt was quicker with less residual fluid whereas the flat type clogged each time. Survey results showed adequate concordance with surgeons in agreement on soup choice. CONCLUSIONS The Jackson-Pratt drains perform differently depending on the drainage situation. The surgical community requires improved drain data to drive practice patterns.

e-Portfolio Competency Metadata: Pilot Study for a Call to Action

The six competency domains required by the Accreditation Council for Graduate Medical Education (ACGME) have led to a proliferation of measurement tools, assessment methods, and all forms of data from paper to electronic. The need exists to develop a standardized electronic (e)-portfolio to provide the aggregate data to improve education and patient care. This process requires a sound methodology using XML metadata to allow portability of e-portfolio data. We surveyed publicly available metadata and developed an e-portfolio system for the Henry Ford Hospital General Surgery Residency Program. Based on our implementation of e-portfolios for 70 physicians, we call upon the ACGME, the Residency Review Committees, and the American Board of Medical Specialties to establish a method to formalize and develop a standard for residency competency metadata. Using an approach similar to that of our study can streamline data and lead to improved medical education and ultimately better patient care.

Accuracy of National Surgery Quality Improvement Program Models in Predicting Postoperative Morbidity in Patients Undergoing Colectomy

BACKGROUND The National Surgery Quality Improvement Program (NSQIP) is the standard for assessment of acuity-adjusted outcomes in surgery. The validity of NSQIP has not been well established in colorectal surgery. Technical and process variables, which NSQIP may not consider, affect morbidity rate. OBJECTIVE A retrospective observational study was undertaken to determine the accuracy of NSQIP models in predicting morbidity for patients undergoing laparoscopic or open colectomy. METHODS NSQIP participant use files for 2005 to 2008 were obtained. Data were selected using Current Procedural Terminology coding for open or laparoscopic colectomy. NSQIP-generated predicted morbidities were used to create area under the receiver operator curves (AUROCs). RESULTS AUROCs demonstrated an accurate predictive model if the value was above 0.8 and indicated a marginal predictor mode if below 0.7. The AUROC for the general NSQIP model was 0.817 (confidence interval [CI] = 0.815-0.819, p < 0.001). AUROC for the combined laparoscopic and open colectomy group was 0.703 (CI = 0.698-0.709, p value < 0.001). AUROCs for the individual laparoscopic and open colectomy groups were 0.627 (CI = 0.615-0.640, p < 0.001) and 0.701 (CI = 0.695-0.707, p < 0.001). CONCLUSION This study demonstrates that although NSQIP-generated morbidities used to create AUROCs are accurate for patients in an overall surgical model, predictive models for morbidity are marginal for laparoscopic and open abdominal colectomies. NSQIP risk models tend to emphasize comorbidities rather than intraoperative details or technical aspects of colonic resections.