Jamie E. Anderson

The first national examination of outcomes and trends in robotic surgery in the United States.

BACKGROUND There are few population-based data describing outcomes of robotic-assisted surgery. We compared outcomes of robotic-assisted, laparoscopic, and open surgery in a nationally representative population database. STUDY DESIGN A retrospective analysis of the Nationwide Inpatient Sample database from October 2008 to December 2009 was performed. We identified the most common robotic procedures by ICD-9 procedure codes and grouped them into categories by procedure type. Multivariate analyses examined mortality, length of stay (LOS), and total hospital charges, adjusting for age, race, sex, Charlson comorbidity index, and teaching hospital status. RESULTS A total of 368,239 patients were identified. On adjusted analysis, compared with open, robotic-assisted laparoscopic surgery was associated with decreased odds of mortality (odds ratio = 0.1; 95% CI, 0.0-0.2; p < 0.001), decreased mean LOS (-2.4 days; 95% CI, -2.5 to 2.3; p < 0.001), and increased mean total charges in all procedures (range

Comparing the National Surgical Quality Improvement Program With the Nationwide Inpatient Sample Database

3,852 to

Using Electronic Health Records for Surgical Quality Improvement in the Era of Big Data

15,329) except coronary artery bypass grafting (-

Brief Tool to Measure Risk-Adjusted Surgical Outcomes in Resource-Limited Hospitals

17,318; 95% CI, -34,492 to -143; p = 0.048) and valvuloplasty (not statistically significant). Compared with laparoscopic, robotic-assisted laparoscopic surgery was associated with decreased odds of mortality (odds ratio = 0.1; 95% CI, 0.0-0.6; p = 0.008), decreased LOS overall (-0.6 days; 95% CI, -0.7 to -0.5; p < 0.001), but increased LOS in prostatectomy and other kidney/bladder procedures (0.3 days; 95% CI, 0.1-0.4; p = 0.006; 0.8 days; 95% CI, 0.0-1.6; p = 0.049), and increased total charges (

Pediatric surgical readmissions: Are they truly preventable?☆

1,309; 95% CI, 519-2,099; p = 0.001). CONCLUSIONS Data suggest that, compared with open surgery, robotic-assisted surgery results in decreased LOS and diminished likelihood of death. However, these benefits are not as apparent when comparing robotic-assisted laparoscopic with nonrobotic laparoscopic procedures.

Surgical patients travel longer distances than non-surgical patients to receive care at a rural hospital in Mozambique

PACIFIC COAST SURGICAL ASSOCIATION Comparing the National Surgical Quality Improvement Program With the Nationwide Inpatient Sample Database Bothrawandrisk-adjustedoutcomesareincreasinglybeingmade publicly available.1-3 The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) is heralded as the most robust database to examine surgical outcomes. However, enrollment in the NSQIP is expensive, and the use of administrative databases may be more cost-effective.2-4 In our study, we compare the receiver operating characteristic curves of the Nationwide Inpatient Sample (NIS) with those of the NSQIP to determine which is superior at performing analyses of risk-adjusted outcomes for several operations.

Does the Effect of Surgical Volume on Outcomes Diminish Over Time

IMPORTANCE Risk adjustment is an important component of quality assessment in surgical health care. However, data collection places an additional burden on physicians. There is also concern that outcomes can be gamed depending on the information recorded for each patient. OBJECTIVE To determine whether a number of machine-collected data elements could perform as well as a traditional full-risk adjustment model that includes other physician-assessed and physician-recorded data elements. DESIGN, SETTINGS, AND PARTICIPANTS All general surgery patients from the National Surgical Quality Improvement Program database from January 1, 2005, to December 31, 2010, were included. Separate multivariate logistic regressions were performed using either all 66 preoperative risk variables or only 25 objective variables. The area under the receiver operating characteristic curve (AUC) of each regression using objective preoperative risk variables was compared with its corresponding regression with all preoperative variables. Subset analyses were performed among patients who received certain operations. MAIN OUTCOMES AND MEASURES Mortality or any surgical complication captured by the National Surgical Quality Improvement Program, both inpatient and within 30 days postoperatively. RESULTS Data from a total of 745 053 patients were included. More than 15.8% of patients had at least 1 complication and the mortality rate was 2.8%. When examining inpatient mortality, the AUC was 0.9104 with all 66 variables vs 0.8918 with all 25 objective variables. The difference in AUC comparing models with all variables with objective variables ranged from -0.0073 to 0.1944 for mortality and 0.0198 to 0.0687 for complications. In models predicting mortality, the difference in AUC was less than 0.05 among all patients and subsets of patients with abdominal aortic aneurysm repair, pancreatic resection, colectomy, and appendectomy. In models predicting complications, the difference in AUC was less than 0.05 among all patients and subsets of patients with pancreatic resection, laparoscopic cholecystectomy, colectomy, and appendectomy. CONCLUSIONS AND RELEVANCE Rigorous risk-adjusted surgical quality assessment can be performed solely with objective variables. By leveraging data already routinely collected for patient care, this approach allows for wider adoption of quality assessment systems in health care. Identifying data elements that can be automatically collected can make future improvements to surgical outcomes and quality analyses.

ECD kidney transplantation outcomes are improved when matching donors to recipients using a novel creatinine clearance match ratio (CCMR)

OBJECTIVES To develop and validate a risk-adjusted tool with fewer than 10 variables to measure surgical outcomes in resource-limited hospitals. DESIGN All National Surgical Quality Improvement Program (NSQIP) preoperative variables were used to develop models to predict inpatient mortality. The models were built by sequential addition of variables selected based on their area under the receiver operator characteristic curve (AUROC) and externally validated using data based on medical record reviews at 1 hospital outside the data set. SETTING Model development was based on data from the NSQIP from 2005 to 2009. Validation was based on data from 1 nonurban hospital in the United States from 2009 to 2010. PATIENTS A total of 631 449 patients in NSQIP and 239 patients from the validation hospital. MAIN OUTCOME MEASURES The AUROC value for each model. RESULTS The AUROC values reached higher than 90% after only 3 variables (American Society of Anesthesiologists class, functional status at time of surgery, and age). The AUROC values increased to 91% with 4 variables but did not increase significantly with additional variables. On validation, the model with the highest AUROC was the same 3-variable model (0.9398). CONCLUSIONS Fewer than 6 variables may be necessary to develop a risk-adjusted tool to predict inpatient mortality, reducing the cost of collecting variables by 95%. These variables should be easily collectable in resource-poor settings, including low- and middle-income countries, thus creating the first standardized tool to measure surgical outcomes globally. Research is needed to determine which of these limited-variable models is most appropriate in a variety of clinical settings.

Access to Obstetric Care in the United States from the National Health Interview Survey

BACKGROUND/PURPOSE Reimbursement penalties for excess hospital readmissions have begun for the pediatric population. Therefore, research determining incidence and predictors is critical. METHODS A retrospective review of University HealthSystem Consortium database (N=258 hospitals; 2,723,621 patients) for pediatric patients (age 0-17years) hospitalized from 9/2011 to 3/2015 was performed. Outcome measures were 7-, 14-, and 30-day readmission rates. Hospital and patient characteristics were evaluated to identify predictors of readmission. RESULTS Readmission rates at 7, 14, and 30days were 2.1%, 3.1%, and 4.4%. For pediatric surgery patients (N=260,042), neither index hospitalization length of stay (LOS) nor presence of a complication predicted higher readmissions. Appendectomy was the most common procedure leading to readmission. Evaluating institutional data (N=5785), patients admitted for spine surgery, neurosurgery, transplant, or surgical oncology had higher readmission rates. Readmission diagnoses were most commonly infectious (37.2%) or for nausea/vomiting/dehydration (51.1%). Patients with chronic medical conditions comprised 55.8% of patients readmitted within 7days. 92.0% of patients requiring multiple rehospitalizations had comorbidities. CONCLUSIONS Readmission rates for pediatric patients are significantly lower than adults. Risk factors for adult readmissions do not predict pediatric readmissions. Readmission may be a misnomer for the pediatric surgical population, as most are related to chronic medical conditions and other nonmodifiable risk factors. LEVEL OF EVIDENCE Level IV.

Projected Lifetime Risks and Hospital Care Expenditure for Traumatic Injury

BACKGROUND Surgical care is increasingly recognised as an important component of global health delivery. However, there are still major gaps in knowledge related to access to surgical care in low-income countries. In this study, we compare distances travelled by surgical patients with patients seeking other medical services at a first-level hospital in rural Mozambique. METHODS Data were collected on all inpatients at Hospital Rural de Chókwè in rural Mozambique between 20 June 2012 and 3 August 2012. Euclidean distances travelled by surgical versus non-surgical patients using coordinates of each patients city of residence were compared. Data were analysed using ArcGIS 10 and STATA. RESULTS In total, 500 patients were included. Almost one-half (47.6%) lived in the city where the hospital is based. By hospital ward, the majority (62.0%) of maternity patients came from within the hospitals city compared with only 35.2% of surgical patients. The average distance travelled was longest for surgical patients (42 km) compared with an average of 17 km for patients on all other wards. CONCLUSIONS Patients seeking surgical care at this first-level hospital travel farther than patients seeking other services. While other patients may have access to at community clinics, surgical patients depend more heavily on the services available at first-level hospitals.