This algorithm builds off prior phenotyping work from Pacheco & Thompson available in the PheKB phenotype "Diverticulosis and Diverticulitis" as well as the manuscripts from Joo et al (2023)(1) and De Roo et al (2023) (2) . The objective is to approximate diverticular disease severity from the electronic medical record into groups of Diverticulosis, Mild Diverticulitis, and Operative or Recurrent Inpatient Diverticulitis.
This combines CPT codes, ICD-9 codes, ICD-10 codes, setting of care, and temporal relationships between codes. The first part of the algorithm separates cases of diverticular disease from controls of no diverticular disease using an adaptation of the non-natural language processing algorithm described by Pacheco & Thompson. Then, the algorithm attempts to differentiate milder from more severe forms of the disease. Specifically, the Diverticulosis group contains cases only assigned diverticulosis codes. The Operative or Recurrent Inpatient Diverticulitis group includes patients with more than one inpatient admission for diverticulitis or a procedure (colectomy, percutaneous drain, intestinal fistula repair) performed for diverticulitis. The Mild Diverticulitis group looks at patients with only outpatient encounters or no greater than one inpatient encounter for diverticulitis.
Reference:
(1) Joo YY, Pacheco JA, Thompson WK, Rasmussen-Torvik LJ, Rasmussen LV, Lin FTJ, Andrade M, Borthwick KM, Bottinger E, Cagan A, Carrell DS, Denny JC, Ellis SB, Gottesman O, Linneman JG, Pathak J, Peissig PL, Shang N, Tromp G, Veerappan A, Smith ME, Chisholm RL, Gawron AJ, Hayes MG, Kho AN. Multi-ancestry genome- and phenome-wide association studies of diverticular disease in electronic health records with natural language processing enriched phenotyping algorithm. PLoS One. 2023 May 17;18(5):e0283553. doi: 10.1371/journal.pone.0283553. PMID: 37196047; PMCID: PMC10191288.
(2) De Roo AC, Chen Y, Du X, Handelman S, Byrnes M, Regenbogen SE, Speliotes EK, Maguire LH. Polygenic Risk Prediction in Diverticulitis. Ann Surg. 2023 Jun 1;277(6):e1262-e1268. doi: 10.1097/SLA.0000000000005623. Epub 2022 Jul 25. PMID: 35876359.
