Abstract
Background
Recently published data support the use of a web-based risk calculator (www.anastomoticleak.com) for the prediction of anastomotic leak after colectomy. The aim of this study was to externally validate this calculator on a larger dataset.
Methods
Consecutive adult patients undergoing elective or emergency colectomy for colon cancer at a single institution over a 9-year period were identified using the Binational Colorectal Cancer Audit database. Patients with a rectosigmoid cancer, an R2 resection, or a diverting ostomy were excluded. The primary outcome was anastomotic leak within 90 days as defined by previously published criteria. Area under receiver operating characteristic curve (AUROC) was derived and compared with that of the American College of Surgeons National Surgical Quality Improvement Program® (ACS NSQIP) calculator and the colon leakage score (CLS) calculator for left colectomy. Commercially available artificial intelligence-based analytics software was used to further interrogate the prediction algorithm.
Results
A total of 626 patients were identified. Four hundred and fifty-six patients met the inclusion criteria, and 402 had complete data available for all the calculator variables (126 had a left colectomy). Laparoscopic surgery was performed in 39.6% and emergency surgery in 14.7%. The anastomotic leak rate was 7.2%, with 31.0% requiring reoperation. The anastomoticleak.com calculator was significantly predictive of leak and performed better than the ACS NSQIP calculator (AUROC 0.73 vs 0.58) and the CLS calculator (AUROC 0.96 vs 0.80) for left colectomy. Artificial intelligence-predictive analysis supported these findings and identified an improved prediction model.
Conclusions
The anastomotic leak risk calculator is significantly predictive of anastomotic leak after colon cancer resection. Wider investigation of artificial intelligence-based analytics for risk prediction is warranted.
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References
Midura EF, Hanseman D, Davis BR et al (2015) Risk factors and consequences of anastomotic leak after colectomy: a national analysis. Dis Colon Rectum 58(3):333–338. doi:10.1097/DCR.0000000000000249
Nachiappan S, Faiz O (2015) Anastomotic leak increases distant recurrence and long-term mortality after curative resection for colonic cancer. Ann Surg 262(6):e111. doi:10.1097/SLA.0000000000000741
Sammour T, Hayes IP, Jones IT, Steel MC, Faragher I, Gibbs P (2016) Impact of anastomotic leak on recurrence and survival after colorectal cancer surgery: a BioGrid Australia analysis. ANZ J Surg. doi:10.1111/ans.13648
Pinkney T, El-Hussuna A, Zmora O, European Society of Coloproctology Collaborating Group et al (2017) The relationship between method of anastomosis and anastomotic failure after right hemicolectomy and ileo-caecal resection: an international snapshot audit. Colorectal Dis. doi:10.1111/codi.13646
Smith EB (1978) The anastomotic leak syndrome. J Nat Med Assoc 70(1):49–50
Suding P, Jensen E, Abramson MA, Itani K, Wilson SE (2008) Definitive risk factors for anastomotic leaks in elective open colorectal resection. Arch Surg 143(9):907–911. doi:10.1001/archsurg.143.9.907 (discussion 911–902)
Alves A, Panis Y, Trancart D, Regimbeau JM, Pocard M, Valleur P (2002) Factors associated with clinically significant anastomotic leakage after large bowel resection: multivariate analysis of 707 patients. World J Surg 26(4):499–502. doi:10.1007/s00268-001-0256-4
McDermott FD, Heeney A, Kelly ME, Steele RJ, Carlson GL, Winter DC (2015) Systematic review of preoperative, intraoperative and postoperative risk factors for colorectal anastomotic leaks. Br J Surg 102(5):462–479. doi:10.1002/bjs.9697
Vallance A, Wexner S, Berho M et al (2017) A collaborative review of the current concepts and challenges of anastomotic leaks in colorectal surgery. Colorectal Dis 19(1):O1–O12. doi:10.1111/codi.13534
Daams F, Wu Z, Lahaye MJ, Jeekel J, Lange JF (2014) Prediction and diagnosis of colorectal anastomotic leakage: a systematic review of literature. World Gastrointest Surg 6(2):14–26. doi:10.4240/wjgs.v6.i2.14
Clavien PA, Dindo D (2007) Surgeon’s intuition: is it enough to assess patients’ surgical risk? World J Surg 31(10):1909–1911. doi:10.1007/s00268-007-9145-9
Sacks GD, Dawes AJ, Ettner SL et al (2016) Surgeon perception of risk and benefit in the decision to operate. Ann Surg 264(6):896–903. doi:10.1097/SLA.0000000000001784
Sammour T, Singh PP, Zargar-Shoshtari K, Su’a B, Hill AG (2016) Peritoneal cytokine levels can predict anastomotic leak on the first postoperative day. Dis Colon Rectum 59(6):551–556. doi:10.1097/DCR.0000000000000598
Rojas-Machado SA, Romero-Simo M, Arroyo A, Rojas-Machado A, Lopez J, Calpena R (2016) Prediction of anastomotic leak in colorectal cancer surgery based on a new prognostic index PROCOLE (prognostic colorectal leakage) developed from the meta-analysis of observational studies of risk factors. Int J Colorectal Dis 31(2):197–210. doi:10.1007/s00384-015-2422-4
Sammour T, Lewis M, Thomas ML, Lawrence MJ, Hunter A, Moore JW (2017) A simple web-based risk calculator (www.anastomoticleak.com) is superior to the surgeon’s estimate of anastomotic leak after colon cancer resection. Tech Coloproctol 21(1):35–41. doi:10.1007/s10151-016-1567-7
Frasson M, Flor-Lorente B, Rodriguez JL et al (2015) Risk factors for anastomotic leak after colon resection for cancer: multivariate analysis and nomogram from a multicentric, prospective, national study with 3193 patients. Ann Surg 262(2):321–330. doi:10.1097/SLA.0000000000000973
Bilimoria KY, Liu Y, Paruch JL et al (2013) Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg 217(5):833–842. doi:10.1016/j.jamcollsurg.2013.07.385 (e831–e833)
Liu Y, Cohen ME, Hall BL, Ko CY, Bilimoria KY (2016) Evaluation and enhancement of calibration in the American College of surgeons NSQIP surgical risk calculator. J Am Coll Surg 223(2):231–239. doi:10.1016/j.jamcollsurg.2016.03.040
Dekker JW, Liefers GJ, van Otterloo JCM, Putter H, Tollenaar RA (2011) Predicting the risk of anastomotic leakage in left-sided colorectal surgery using a colon leakage score. J Surg Res 166(1):e27–e34. doi:10.1016/j.jss.2010.11.004
Hoyt RE, Snider D, Thompson C, Mantravadi S (2016) IBM Watson analytics: automating visualization, descriptive, and predictive statistics. JMIR Publ Health Surveill 2(2):e157. doi:10.2196/publichealth.5810
IBM Big Data & Analytics Hub. http://www.ibmbigdatahub.com/blog/get-facts-ibm-watson-analytics. Accessed 26 Jan 2017
Chen Y, Elenee Argentinis JD, Weber G (2016) IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin Ther 38(4):688–701. doi:10.1016/j.clinthera.2015.12.001
Hunter RA, Moore J, Committee BO (2016) Evolution of the Bi-National Colorectal Cancer Audit: history, governance and future directions. ANZ J Surg 86(6):431–432. doi:10.1111/ans.13593
Teloken PE, Spilsbury K, Platell C, Committee BO (2016) Analysis of mortality in colorectal surgery in the Bi-National Colorectal Cancer Audit. ANZ J Surg 86(6):454–458. doi:10.1111/ans.13523
Clavien PA, Barkun J, de Oliveira ML et al (2009) The Clavien–Dindo classification of surgical complications: five-year experience. Ann Surg 250(2):187–196. doi:10.1097/SLA.0b013e3181b13ca2
Dindo D, Demartines N, Clavien PA (2004) Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg 240(2):205–213
Youden WJ (1950) Index for rating diagnostic tests. Cancer 3(1):32–35
Yu XQ, Zhao B, Zhou WP et al (2016) Utility of colon leakage score in left-sided colorectal surgery. J Surg Res 202(2):398–402. doi:10.1016/j.jss.2015.12.046
Kao LS, Millas SG (2012) Predicting the risk of anastomotic leakage in left-sided colorectal surgery using a Colon Leakage Score. J Surg Res 173(2):246–248. doi:10.1016/j.jss.2011.01.044
Scarborough JE, Mantyh CR, Sun Z, Migaly J (2015) Combined mechanical and oral antibiotic bowel preparation reduces incisional surgical site infection and anastomotic leak rates after elective colorectal resection: an analysis of colectomy-targeted ACS NSQIP. Ann Surg 262(2):331–337. doi:10.1097/SLA.0000000000001041
Domenech PE, Romero-Simo M, Rojas-Machado A, Arroyo A, Calpena R (2016) PROCOLE (prognostic colorectal leakage): a new prognostic index to predict the risk of anastomotic leak in colorectal cancer surgery. J Colitis Diverticulitis 1(2):1–5
Sacks GD, Dawes AJ, Ettner SL et al (2016) Impact of a risk calculator on risk perception and surgical decision making: a randomized trial. Ann Surg 264(6):889–895. doi:10.1097/SLA.0000000000001750
Ho B, Skaro A, Montag S, Zhao L (2017) Elementary, My dear Watson—the era of natural language processing in transplantation. Am J Transplant 17(3):595–596. doi:10.1111/ajt.14164
Srinivas TR, Taber DJ, Su Z et al (2017) Big data, predictive analytics, and quality improvement in kidney transplantation: a proof of concept. Am J Transplant 17(3):671–681. doi:10.1111/ajt.14099
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All authors made substantial contributions to the conception, design, acquisition, analysis, and interpretation of data; drafting the article; and revising it critically for important intellectual content and also gave final approval of the version to be published.
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Ethics approval was granted by the Royal Adelaide Hospital Human Research Ethics Committee (reference number: HREC/15/RAH/186, RAH Protocol No: 150524).
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Sammour, T., Cohen, L., Karunatillake, A.I. et al. Validation of an online risk calculator for the prediction of anastomotic leak after colon cancer surgery and preliminary exploration of artificial intelligence-based analytics. Tech Coloproctol 21, 869–877 (2017). https://doi.org/10.1007/s10151-017-1701-1
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DOI: https://doi.org/10.1007/s10151-017-1701-1