Accuracy of ICD-9 codes to identify nonunion and malunion and developing algorithms to improve case-finding of nonunion and malunion

• Published in: Bone (New York, N.Y.) Vol. 52; no. 2; pp. 596 - 601
• Main Authors: Boudreau, Denise M., Yu, Onchee, Spangler, Leslie, Do, Thy P., Fujii, Monica, Ott, Susan M., Critchlow, Cathy W., Scholes, Delia
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.02.2013; Elsevier
• Subjects: Adult; Algorithms; Biological and medical sciences; Diagnosis code; Female; Fracture; Fracture healing; Fractures, Malunited - classification; Fractures, Malunited - diagnosis; Fractures, Malunited - pathology; Fractures, Ununited - classification; Fractures, Ununited - diagnosis; Fractures, Ununited - pathology; Fundamental and applied biological sciences. Psychology; Humans; Injuries of the limb. Injuries of the spine; International Classification of Diseases; Male; Malunion; Medical sciences; Middle Aged; Nonunion; Orthopedics; Traumas. Diseases due to physical agents; Vertebrates: anatomy and physiology, studies on body, several organs or systems; Malunion; Nonunion; Fracture; Fracture healing; Diagnosis code; Mal union; Pseudoarthrosis; Diseases of the osteoarticular system; Trauma; Algorithm; Accuracy; Morphology; Diagnosis; Cicatrization
• ISSN: 8756-3282; 1873-2763; 1873-2763
• DOI: 10.1016/j.bone.2012.11.013

To evaluate the accuracy of using ICD-9 codes to identify nonunions (NU) and malunions (MU) among adults with a prior fracture code and to explore case-finding algorithms. Medical chart review of potential NU (N=300) and MU (N=288) cases. True NU cases had evidence of NU and no evidence of MU in the chart (and vice versa for MUs) or were confirmed by the study clinician. Positive predictive values (PPV) were calculated for ICD-9 codes. Case-finding algorithms were developed by a classification and regression tree analysis using additional automated data, and these algorithms were compared to true case status. Group Health Cooperative. Compared to true cases as determined from chart review, the PPV of ICD-9 codes for NU and MU were 89% (95% CI, 85–92%) and 47% (95% CI, 41–53%), respectively. A higher proportion of true cases (NU: 95%; 95% CI, 90–98%; MU: 56%; 95% CI, 47–66%) were found among subjects with 1+ additional codes occurring in the 12months following the initial code. There was no case-finding algorithm for NU developed given the high PPV of ICD-9 codes. For MU, the best case-finding algorithm classified people as an MU case if they had a fracture in the forearm, hand, or skull and had no visit with an NU diagnosis code in the 12-month post MU diagnosis. PPV for this MU case-finding algorithm increased to 84%. Identifying NUs with its ICD-9 code is reasonable. Identifying MUs with automated data can be improved by using a case-finding algorithm that uses additional information. Further validation of the MU algorithms in different populations is needed, as well as exploration of its performance in a larger sample. ► This is the first study of the accuracy of using health plan data to identify nonunion (NU) and malunion (MU). ► ICD-9 codes from health plan data yielded a positive predictive value (PPV) of 89% for NU and 47% for MU. ► PPV improved (95% NU and 56% MU) when 1+ additional ICD-9 code in the 12 months following the initial code was required. ► The PPV of the NU code was within the range observed for codes commonly used to identify other health outcomes. ► An algorithm incorporating additional automated data showed improvements in the PPV for MU (84%).