Jeffrey K Lee1, Christopher D Jensen2, Alexander Lee1, Chyke A Doubeni3, Ann G Zauber4, Theodore R Levin2, Wei K Zhao2, Douglas A Corley2. 1. Department of Medicine, Division of Gastroenterology, University of California San Francisco, San Francisco, California, USA. 2. Division of Research, Kaiser Permanente Northern California, Oakland, California, USA. 3. Department of Family Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 4. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Abstract
BACKGROUND: Accurate determination of colonoscopy indication is required for managing clinical programs and performing research; however, existing algorithms that use available electronic databases (eg, diagnostic and procedure codes) have yielded limited accuracy. OBJECTIVE: To develop and validate an algorithm for classifying colonoscopy indication that uses comprehensive electronic medical data sources. DESIGN: We developed an algorithm for classifying colonoscopy indication by using commonly available electronic diagnostic, pathology, cancer, and laboratory test databases and validated its performance characteristics in comparison with a comprehensive review of patient medical records. We also evaluated the influence of each data source on the algorithm's performance characteristics. SETTING: Kaiser Permanente Northern California healthcare system. PATIENTS: A total of 300 patients who underwent colonoscopy between 2007 and 2010. INTERVENTIONS: Colonoscopy. MAIN OUTCOME MEASUREMENTS: Algorithm's sensitivity, specificity, and positive predictive value (PPV) for classifying screening, surveillance, and diagnostic colonoscopies. The reference standard was the indication assigned after comprehensive medical record review. RESULTS: For screening indications, the algorithm's sensitivity was 88.5% (95% confidence interval [CI], 80.4%-91.7%), specificity was 91.7% (95% CI, 87.0%-95.1%), and PPV was 83.3% (95% CI, 74.7%-90.0%). For surveillance indications, the algorithm's sensitivity was 93.4% (95% CI, 86.2%-97.5%), specificity was 92.8% (95% CI, 88.4%-95.9%), and PPV was 85.0% (95% CI, 76.5%-91.4%). The algorithm's sensitivity, specificity, and PPV for diagnostic indications were 81.4% (95% CI, 73.0%-88.1%), 96.8% (95% CI, 93.2%-98.8%), and 93.9% (95% CI, 87.2%-97.7%), respectively. LIMITATIONS: Validation was confined to a single healthcare system. CONCLUSION: An algorithm that uses commonly available modern electronic medical data sources yielded a high sensitivity, specificity, and PPV for classifying screening, surveillance, and diagnostic colonoscopy indications. This algorithm had greater accuracy than the indication listed on the colonoscopy report.
BACKGROUND: Accurate determination of colonoscopy indication is required for managing clinical programs and performing research; however, existing algorithms that use available electronic databases (eg, diagnostic and procedure codes) have yielded limited accuracy. OBJECTIVE: To develop and validate an algorithm for classifying colonoscopy indication that uses comprehensive electronic medical data sources. DESIGN: We developed an algorithm for classifying colonoscopy indication by using commonly available electronic diagnostic, pathology, cancer, and laboratory test databases and validated its performance characteristics in comparison with a comprehensive review of patient medical records. We also evaluated the influence of each data source on the algorithm's performance characteristics. SETTING: Kaiser Permanente Northern California healthcare system. PATIENTS: A total of 300 patients who underwent colonoscopy between 2007 and 2010. INTERVENTIONS: Colonoscopy. MAIN OUTCOME MEASUREMENTS: Algorithm's sensitivity, specificity, and positive predictive value (PPV) for classifying screening, surveillance, and diagnostic colonoscopies. The reference standard was the indication assigned after comprehensive medical record review. RESULTS: For screening indications, the algorithm's sensitivity was 88.5% (95% confidence interval [CI], 80.4%-91.7%), specificity was 91.7% (95% CI, 87.0%-95.1%), and PPV was 83.3% (95% CI, 74.7%-90.0%). For surveillance indications, the algorithm's sensitivity was 93.4% (95% CI, 86.2%-97.5%), specificity was 92.8% (95% CI, 88.4%-95.9%), and PPV was 85.0% (95% CI, 76.5%-91.4%). The algorithm's sensitivity, specificity, and PPV for diagnostic indications were 81.4% (95% CI, 73.0%-88.1%), 96.8% (95% CI, 93.2%-98.8%), and 93.9% (95% CI, 87.2%-97.7%), respectively. LIMITATIONS: Validation was confined to a single healthcare system. CONCLUSION: An algorithm that uses commonly available modern electronic medical data sources yielded a high sensitivity, specificity, and PPV for classifying screening, surveillance, and diagnostic colonoscopy indications. This algorithm had greater accuracy than the indication listed on the colonoscopy report.
Authors: Laura C Seeff; Thomas B Richards; Jean A Shapiro; Marion R Nadel; Diane L Manninen; Leslie S Given; Fred B Dong; Linda D Winges; Matthew T McKenna Journal: Gastroenterology Date: 2004-12 Impact factor: 22.682
Authors: Douglas K Rex; John L Petrini; Todd H Baron; Amitabh Chak; Jonathan Cohen; Stephen E Deal; Brenda Hoffman; Brian C Jacobson; Klaus Mergener; Bret T Petersen; Michael A Safdi; Douglas O Faigel; Irving M Pike Journal: Gastrointest Endosc Date: 2006-04 Impact factor: 9.427
Authors: Phuong L Mai; Anne O Garceau; Barry I Graubard; Marsha Dunn; Timothy S McNeel; Lou Gonsalves; Mitchell H Gail; Mark H Greene; Gordon B Willis; Louise Wideroff Journal: J Natl Cancer Inst Date: 2011-05-11 Impact factor: 13.506
Authors: David A Lieberman; Jennifer L Holub; Matthew D Moravec; Glenn M Eisen; Dawn Peters; Cynthia D Morris Journal: JAMA Date: 2008-09-24 Impact factor: 56.272
Authors: S J Winawer; A G Zauber; M N Ho; M J O'Brien; L S Gottlieb; S S Sternberg; J D Waye; M Schapiro; J H Bond; J F Panish Journal: N Engl J Med Date: 1993-12-30 Impact factor: 91.245
Authors: Reiko Nishihara; Kana Wu; Paul Lochhead; Teppei Morikawa; Xiaoyun Liao; Zhi Rong Qian; Kentaro Inamura; Sun A Kim; Aya Kuchiba; Mai Yamauchi; Yu Imamura; Walter C Willett; Bernard A Rosner; Charles S Fuchs; Edward Giovannucci; Shuji Ogino; Andrew T Chan Journal: N Engl J Med Date: 2013-09-19 Impact factor: 91.245
Authors: Ethan A Halm; Elisabeth F Beaber; Dale McLerran; Jessica Chubak; Douglas A Corley; Carolyn M Rutter; Chyke A Doubeni; Jennifer S Haas; Bijal A Balasubramanian Journal: J Gen Intern Med Date: 2016-06-08 Impact factor: 5.128
Authors: William E Barlow; Elisabeth F Beaber; Berta M Geller; Aruna Kamineni; Yingye Zheng; Jennifer S Haas; Chun R Chao; Carolyn M Rutter; Ann G Zauber; Brian L Sprague; Ethan A Halm; Donald L Weaver; Jessica Chubak; V Paul Doria-Rose; Sarah Kobrin; Tracy Onega; Virginia P Quinn; Marilyn M Schapira; Anna N A Tosteson; Douglas A Corley; Celette Sugg Skinner; Mitchell D Schnall; Katrina Armstrong; Cosette M Wheeler; Michael J Silverberg; Bijal A Balasubramanian; Chyke A Doubeni; Dale McLerran; Jasmin A Tiro Journal: J Natl Cancer Inst Date: 2020-03-01 Impact factor: 13.506
Authors: Alexander Lee; Christopher D Jensen; Amy R Marks; Wei K Zhao; Chyke A Doubeni; Ann G Zauber; Virginia P Quinn; Theodore R Levin; Douglas A Corley Journal: Gastrointest Endosc Date: 2016-10-01 Impact factor: 9.427
Authors: Amy R Marks; Ralph A Pietrofesa; Christopher D Jensen; Alexis Zebrowski; Douglas A Corley; Chyke A Doubeni Journal: Cancer Epidemiol Biomarkers Prev Date: 2015-09-16 Impact factor: 4.254
Authors: Jeffrey K Lee; Christopher D Jensen; Theodore R Levin; Chyke A Doubeni; Ann G Zauber; Jessica Chubak; Aruna S Kamineni; Joanne E Schottinger; Nirupa R Ghai; Natalia Udaltsova; Wei K Zhao; Bruce H Fireman; Charles P Quesenberry; E John Orav; Celette S Skinner; Ethan A Halm; Douglas A Corley Journal: Gastroenterology Date: 2019-10-04 Impact factor: 22.682
Authors: Theodore R Levin; Douglas A Corley; Christopher D Jensen; Joanne E Schottinger; Virginia P Quinn; Ann G Zauber; Jeffrey K Lee; Wei K Zhao; Natalia Udaltsova; Nirupa R Ghai; Alexander T Lee; Charles P Quesenberry; Bruce H Fireman; Chyke A Doubeni Journal: Gastroenterology Date: 2018-07-19 Impact factor: 22.682
Authors: Theodore A Tollivoro; Christopher D Jensen; Amy R Marks; Wei K Zhao; Joanne E Schottinger; Virginia P Quinn; Nirupa R Ghai; Ann G Zauber; Chyke A Doubeni; Theodore R Levin; Bruce Fireman; Charles P Quesenberry; Douglas A Corley Journal: Gastrointest Endosc Date: 2018-08-23 Impact factor: 9.427