Validity of Major Osteoporotic Fracture Diagnoses in the Danish National Patient Registry

IF 3.4 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Anne Clausen, Sören Möller, Michael Kriegbaum Skjødt, Rasmus Bank Lynggaard, Pernille Just Vinholt, Martin Lindberg-Larsen, Jens Søndergaard, Bo Abrahamsen, Katrine Hass Rubin
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引用次数: 0

Abstract

Objective: To evaluate the validity of diagnosis codes for Major Osteoporotic Fracture (MOF) in the Danish National Patient Registry (NPR) and secondly to evaluate whether the fracture was incident/acute using register-based definitions including date criteria and procedural codes.
Methods: We identified a random sample of 2400 records with a diagnosis code for a MOF in the NPR with dates in the year of 2018. Diagnoses were coded with the 10th revision of the International Classification of Diseases (ICD-10). The sample included 2375 unique fracture patients from the Region of Southern Denmark. Medical records were retrieved for the study population and reviewed by an algorithmic search function and medical doctors to verify the MOF diagnoses. Register-based definitions of incident/acute MOF was evaluated in NPR data by applying date criteria and procedural codes.
Results: The PPV for MOF diagnoses overall was 0.99 (95% CI: 0.98;0.99) and PPV=0.99 for the four individual fracture sites, respectively. Further, analyses of incident/acute fractures applying date criteria, procedural codes and using patients’ first contact in the NPR resulted in PPV=0.88 (95% CI: 0.84;0.91) for hip fractures, PPV=0.78 (95% CI: 0.74;0.83) for humerus fractures, PPV=0.78 (95% CI: 0.73;0.83) for clinical vertebral fractures and PPV=0.87 (95% CI: 0.83;0.90) for wrist fractures.
Conclusion: ICD-10 coded MOF diagnoses are valid in the NPR. Furthermore, a set of register-based criteria can be applied to qualify if the MOF fracture was incident/acute. Thus, the NPR is a valuable and reliable data source for epidemiological research on osteoporotic fractures.

Keywords: major osteoporotic fractures, validity, positive predictive value, the Danish National Patient Register, algorithmic search function, epidemiology
丹麦全国患者登记处重大骨质疏松性骨折诊断的有效性
目的评估丹麦国家患者登记处(NPR)中重大骨质疏松性骨折(MOF)诊断代码的有效性,其次使用基于登记处的定义(包括日期标准和程序代码)评估骨折是否为偶发/急性骨折:我们随机抽取了 2400 份记录,这些记录在 NPR 中带有 MOF 诊断代码,日期为 2018 年。诊断采用《国际疾病分类》第 10 版(ICD-10)编码。样本包括来自南丹麦大区的 2375 名独特的骨折患者。对研究人群的医疗记录进行了检索,并通过算法搜索功能和医生对医疗记录进行了审查,以核实 MOF 诊断。通过应用日期标准和程序代码,在NPR数据中对基于登记册的事故/急性MOF定义进行了评估:MOF诊断的总体PPV为0.99(95% CI:0.98;0.99),四个骨折部位的PPV分别为0.99。此外,应用日期标准、程序代码和使用患者在 NPR 中的首次接触对事故/急性骨折进行分析,结果显示髋部骨折的 PPV=0.88 (95% CI: 0.84;0.91),髋部骨折的 PPV=0.78(95% CI:0.74;0.83),临床椎体骨折的PPV=0.78(95% CI:0.73;0.83),腕部骨折的PPV=0.87(95% CI:0.83;0.90):结论:ICD-10编码的MOF诊断在全国人口普查中是有效的。结论:ICD-10 编码的 MOF 诊断在 NPR 中是有效的。此外,一套基于登记的标准可用于判定 MOF 骨折是否为偶发/急性骨折。因此,国家患者登记册是骨质疏松性骨折流行病学研究的一个宝贵而可靠的数据来源。
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来源期刊
Clinical Epidemiology
Clinical Epidemiology Medicine-Epidemiology
CiteScore
6.30
自引率
5.10%
发文量
169
审稿时长
16 weeks
期刊介绍: Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.
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