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Figure. Age and Sex Distribution of the Annual Cohort of Patients Receiving Gastric Banding in 2005-2006 (N=6037) 35 Female 30

Male

Patients, %

25 20 15

Published Online: June 18, 2014. doi:10.1001/jamasurg.2014.93. Author Contributions: Mss Keating and Ananthapavan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: All authors. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: All authors. Critical revision of the manuscript for important intellectual content: Keating. Statistical analysis: Keating. Obtained funding: Keating. Conflict of Interest Disclosures: Ms Keating has previously received an independent research grant from Allergan Australia. No other disclosures are reported.

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Funding/Support: This project was funded by Deakin University.

5 0 0-24

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The source for the data is Medicare Australia. Identification was based on utilization of Medicare Benefits Schedule item 30511 (gastric reduction or gastroplasty for morbid obesity, by any method), which is primarily used for laparoscopic adjustable gastric banding in Australia.3

Results | The age and sex distribution of the LAGB population analyzed is provided in the Figure. During the 3 years after LAGB, the rate of revisional surgery was 18.9 events per 100 patients, comprising 11.4 intra-abdominal and 7.5 subcutaneous surgical procedures. The majority of revisional procedures were repeated or revisional LAGB procedures (8.3 events per 100 patients) and repairs or revisions of the LAGB reservoir (7.5 events per 100 patients). Conversions to another bariatric procedure (1.3 events per 100 patients) and LAGB reversals (1.9 events per 100 patients) were uncommon (Table). Discussion | The present study found that almost 1 in 5 patients undergoing LAGB require some revisional surgery within 3 years. These results from our national cohort study are similar, albeit slightly higher, than the results from previous singlecenter (15.3% of patients)1 and multicenter cohort studies (17.5% of patients).2 There are 2 key strengths of our study. First, the data analyzed are observed health care utilization data maintained by the Australian government; therefore, the level of reliability is high, and the data set is complete (no loss to follow-up). Second, the entire population of Australians who received Medicare-subsidized LAGB was analyzed, thus providing results reflective of LAGB as delivered in a “real-world” setting. Bariatric surgery is associated with dramatic weight loss and improvements in many clinical end points.2 The benefits of surgery must be compared with the risk of adverse events, the need for reoperations, and the associated costs for each patient. Catherine L. Keating, MPH Jaithri Ananthapavan, MPH Author Affiliations: Deakin Health Economics, Deakin University, Melbourne Burwood Campus, Burwood, Victoria, Australia. Corresponding Author: Catherine L. Keating, MPH, Deakin Health Economics, Deakin University, Melbourne Burwood Campus, 221 Burwood Hwy, Burwood, Victoria, Australia 3125 ([email protected]).

Role of the Sponsor: The funding agency had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Additional Contributions: We thank Medicare Australia for their assistance with the identification of the population analyzed in this study and for providing detailed medical data. We also thank Paul O’Brien, MD, FRACS, from the Centre for Obesity Research and Education, Monash University, Melbourne, Australia, for providing a critical review of the study from the perspective of a bariatric surgery expert. Dr O’Brien did not receive any compensation. Medicare Australia received an administrative fee to undertake the custom data extraction. 1. O’Brien PE, MacDonald L, Anderson M, Brennan L, Brown WA. Long-term outcomes after bariatric surgery: fifteen-year follow-up of adjustable gastric banding and a systematic review of the bariatric surgical literature. Ann Surg. 2013;257(1):87-94. 2. Courcoulas AP, Christian NJ, Belle SH, et al; Longitudinal Assessment of Bariatric Surgery (LABS) Consortium. Weight change and health outcomes at 3 years after bariatric surgery among individuals with severe obesity. JAMA. 2013; 310(22):2416-2425. 3. Australian Government Department of Health and Ageing. Draft report for reviewing existing MBS items. Review name: items for the surgical treatment of obesity. http://www.health.gov.au/internet/main/publishing.nsf/Content /A878E2C4992E9AE8CA257BF0001F3E82/$File/DRAFT%20obesity %20report.pdf. Published May 24, 2011. Accessed November 19, 2013. 4. Australian Government Department of Health. Medicare Benefits Schedule (MBS) Online. http://www.mbsonline.gov.au. Accessed November 19, 2013.

Avoiding Immortal Time Bias in the American College of Surgeons National Surgical Quality Improvement Program Readmission Measure Readmission has become a key quality metric because it is a frequent and costly adverse event for patients.1 Medicare penalizes hospitals if they have excess numbers of readmissions for certain diagnoses, including some within surgery. The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) began tracking readmission rates in 2011.2 While conducting research on readmission after surgery, we noted a problem w ith the NSQIP ’s definition of readmission.3 The NSQIP only counts readmissions during the 30 days following surgery, consistent with the interval they use for all postoperative outcomes. However, the standard period for readmission used by Medicare and others is 30 days after hospital discharge. This discrepancy creates an immortal time bias—patients cannot be readmitted before hospital discharge and are therefore “immortal” for this outcome until they leave the hospital.4 Including immortal time when calculat-

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The NSQIP began reporting the day of readmission in 2012. This information can be used to overcome the immortal time bias. Truncating our data at 30 days after surgery as if it were from the NSQIP, we estimated the 30-day postdischarge readmission rate using Kaplan-Meier methods. This resulted in an estimated readmission rate of 17.2% (95% CI, 14.5%-20.3%), which included the true value of 18.9%.

Figure. True vs NSQIP 30-Day Readmission Rates 40

Readmission, % of Patients

True NSQIP 30

20

10

0 0-4

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10-14

15-19

20-24

≥25

Length of Stay, d

The National Surgical Quality Improvement Program (NSQIP) method (30-day postoperative readmission) undercounts true 30-day postdischarge readmission. This effect worsens with longer lengths of stay and is statistically significant at 25 days or longer (P = .012, determined by use of the Fisher exact test).

ing readmission rates leads to a bias between the measured value and the true value for readmission rates. Logically, 30day postoperative readmission should systematically undercount 30-day postdischarge readmission. In addition, this bias should worsen for a longer length of stay. In our previous NSQIP analysis, we noted that readmission rates increased with longer lengths of stay, peaked at 13 days, and decreased thereafter. We hypothesized that readmission rates would increase continuously with longer lengths of stay if this immortal time bias were not present. Here we report data supporting this hypothesis and demonstrate how to avoid this bias.

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Conclusions | The NSQIP systematically undercounts 30-day postdischarge readmissions, and this bias worsens with longer lengths of stay. The Medicare Hospital Readmissions Reduction Program enforces larger and more widespread financial penalties than any other quality measure of any type, and it is expanding into surgery. The NSQIP and other programs like it, which are designed to help hospitals improve quality, should “teach to the test” and adopt the 30-day postdischarge readmission definition. Until this occurs, research on readmission using NSQIP data should use Kaplan-Meier methods to avoid underestimating readmission rates. Donald J. Lucas, MD, MPH Elliott R. Haut, MD Elizabeth M. Hechenbleikner, MD Elizabeth C. Wick, MD Timothy M. Pawlik, MD, MPH, PhD Author Affiliations: Department of Surgery, Walter Reed National Military Medical Center, Bethesda, Maryland (Lucas); Department of Surgery, Johns Hopkins Hospital, Baltimore, Maryland (Haut, Wick, Pawlik); Department of Surgery, Georgetown University Hospital, Washington, DC (Hechenbleikner). Corresponding Author: Timothy M. Pawlik, MD, MPH, PhD, Department of Surgery, Johns Hopkins Hospital, 600 N Wolfe St, Blalock 688, Baltimore, MD 21287 ([email protected]).

Methods | We examined patients who had a colorectal resection at Johns Hopkins Hospital in Baltimore, Maryland, between 2009 and 2011, survived to hospital discharge, and were captured by the NSQIP. Dates of admission, discharge, and readmission were obtained from the NSQIP, medical record review, and administrative data, as previously described.5 The NSQIP 30-day postoperative readmission rate was compared with the standard 30-day postdischarge readmission rates obtained from medical record review. A Kaplan-Meier method to estimate the true 30-day postdischarge readmission rate using only data collected by the NSQIP was then evaluated.6 Our study was approved by the Johns Hopkins Hospital institutional review board. Consent was waived owing to the retrospective nature of the analysis. Patients were not compensated.

Published Online: July 2, 2014. doi:10.1001/jamasurg.2014.115.

Results | We identified 708 patients, whose characteristics were described previously.6 Of these 708 patients, 134 (18.9%) were readmitted within 30 days after hospital discharge, but only 114 (16.1%) were readmitted within 30 days after surgery. Therefore, the NSQIP method had a sensitivity of 85% for 30-day postdischarge readmission in this population. This undercounting worsened with longer lengths of stay and was statistically significant at 25 days (P = .01, determined by use of the Fisher exact test) (Figure).

Disclaimer: The views expressed in this article are those of the authors and do not represent the official policy of the US Navy, the Department of Defense, or the US government.

Author Contributions: Drs Lucas and Pawlik had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Lucas, Haut, Pawlik. Acquisition, analysis, or interpretation of data: Lucas, Hechenbleikner, Wick, Pawlik. Drafting of the manuscript: Lucas, Hechenbleikner, Pawlik. Critical revision of the manuscript for important intellectual content: Lucas, Haut, Wick, Pawlik. Statistical analysis: Lucas, Pawlik. Administrative, technical, or material support: Hechenbleikner. Study supervision: Haut. Conflict of Interest Disclosures: Dr Haut receives royalties from Lippincott Williams & Wilkins for a book he coauthored (Avoiding Common ICU Errors). He has received honoraria for various speaking engagements regarding clinical and quality and safety topics and has given expert witness testimony in various medical malpractice cases. No other disclosures are reported.

Additional Information: Dr Haut is the primary investigator of the Mentored Clinician Scientist Development Award K08 1K08HS017952-01 from the Agency for Healthcare Research and Quality entitled “Does Screening Variability Make DVT an Unreliable Quality Measure of Trauma Care?” Dr Haut is the primary investigator of a contract with The Patient-Centered Outcomes Research Institute entitled “Preventing Venous Thromboembolism: Empowering Patients and Enabling Patient-Centered Care via Health Information Technology.”

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1. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):14181428. 2. American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). User guide for the 2011 Participant Use Data File. http: //site.acsnsqip.org/wp-content/uploads/2012/03/2011-User-Guide_Final.pdf. Published October 2012. Accessed May 28, 2013. 3. Lucas DJ, Haider A, Haut E, et al. Assessing readmission after general, vascular, and thoracic surgery using ACS-NSQIP. Ann Surg. 2013;258(3):430-439. 4. Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol. 2008; 167(4):492-499. 5. Hechenbleikner EM, Makary MA, Samarov DV, et al. Hospital readmission by method of data collection. J Am Coll Surg. 2013;216(6):1150-1158. 6. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958;53(282):457-481. doi:10.1080/01621459 .1958.10501452.

them to separate journals. In our Archives of Surgery article,1 we should have included a reference to the previously published article in the Journal of Pediatric Surgery,2 which was accepted and published first. In addition, we should have recognized that the descriptions in the Results and Comment sections of both articles needed to be clearly nonduplicative. We apologize for these important errors. Heather Rosen, MD, MPH Fady Saleh, MD, MPH Stuart R. Lipsitz, ScD Selwyn O. Rogers Jr, MD, MPH Atul A. Gawande, MD, MPH Author Affiliations: Vanderbilt University Medical Center, Nashville, Tennessee (Rosen); Department of Surgery, University of Toronto, Toronto, Ontario, Canada (Saleh); Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts (Lipsitz, Gawande); Department of Surgery, Temple University, Philadelphia, Pennsylvania (Rogers).

COMMENT & RESPONSE

Areas of Overlap To the Editor We write to alert readers that there are areas of overlap between 2 articles that we published, one in the Archives of Surgery1 and the other in the Journal of Pediatric Surgery.2 Both of these studies used the US National Trauma Data Bank and similar methods of analysis. However, our study in the Archives of Surgery focused on adult patients, whereas our study in the Journal of Pediatric Surgery focused on pediatric patients, and therefore different nonoverlapping subsets of the data bank were used for each study. Given the different analyses and target audiences, we decided to submit

Corresponding Author: Heather Rosen, MD, MPH, Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, TN 37232 (heather.rosen @vanderbilt.edu). Published Online: June 25, 2014. doi:10.1001/jamasurg.2014.782. Conflict of Interest Disclosures: None reported. 1. Rosen H, Saleh F, Lipsitz S, Rogers SO Jr, Gawande AA. Downwardly mobile: the accidental cost of being uninsured [published correction appears in Arch Surg. 2010;145(1):41]. Arch Surg. 2009;144(11):1006-1011. 2. Rosen H, Saleh F, Lipsitz SR, Meara JG, Rogers SO Jr. Lack of insurance negatively affects trauma mortality in US children. J Pediatr Surg. 2009;44(10): 1952-1957.

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Avoiding immortal time bias in the American College of Surgeons National Surgical Quality Improvement Program readmission measure.

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