• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

验证美国索赔数据中与体重指数(BMI)相关的 ICD-9-CM 和 ICD-10-CM 行政诊断代码。

Validation of body mass index (BMI)-related ICD-9-CM and ICD-10-CM administrative diagnosis codes recorded in US claims data.

机构信息

Epidemiology, Medical Devices, Johnson & Johnson, New Brunswick, NJ, USA.

出版信息

Pharmacoepidemiol Drug Saf. 2018 Oct;27(10):1092-1100. doi: 10.1002/pds.4617. Epub 2018 Jul 12.

DOI:10.1002/pds.4617
PMID:30003617
Abstract

PURPOSE

To quantify the sensitivity and positive predictive value (PPV) of body mass index (BMI)-related ICD-9-CM and ICD-10-CM diagnosis codes in claims data.

METHODS

De-identified electronic health record (EHR) and claims data were obtained from the Optum Integrated Claims-Clinical Database for cross-sections of commercial and Medicare Advantage health plan members age ≥ 20 years in 2013, 2014, and 2016. In each calendar year, health plan members' BMI as coded in the insurance claims data (error-prone measure) was compared with their BMI as recorded in the EHR (gold standard) to estimate the sensitivity and PPV of BMI-related ICD-9-CM and ICD-10-CM diagnosis codes. The unit of analysis was the person-year.

RESULTS

The study sample included 746 763 distinct health plan members who contributed 1 116 283 eligible person-years (median age 56 years; 57% female; 65% commercially insured and 35% with Medicare Advantage). BMI-related diagnoses were coded for 14.6%. The sensitivity of BMI-related diagnoses codes for the detection of underweight, normal weight, overweight, and obesity was 10.1%, 3.7%, 6.0%, and 25.2%, and the PPV was 49.0% for underweight, 89.6% for normal weight, 73.4% for overweight, and 92.4% for obesity, respectively. The sensitivity of BMI-related diagnosis codes was higher in the ICD-10-CM era relative to the ICD-9-CM era.

CONCLUSIONS

The PPV of BMI-related diagnosis codes for normal weight, overweight, and obesity was high (>70%) but the sensitivity was low (<30%). BMI-related diagnoses were more likely to be coded in patients with class II or III obesity (BMI ≥35 kg/m ), and in 2016 relative to 2013 or 2014.

摘要

目的

定量分析医疗保险索赔数据中与身体质量指数(BMI)相关的国际疾病分类第 9 版(ICD-9-CM)和国际疾病分类第 10 版(ICD-10-CM)诊断代码的灵敏度和阳性预测值(PPV)。

方法

从 Optum 综合索赔-临床数据库中获取 2013 年、2014 年和 2016 年各年龄段(≥20 岁)商业保险和医疗保险优势计划成员的去标识电子健康记录(EHR)和索赔数据。在每个日历年中,将保险索赔数据中编码的 BMI(易出错的指标)与 EHR 中记录的 BMI(金标准)进行比较,以估计 BMI 相关 ICD-9-CM 和 ICD-10-CM 诊断代码的灵敏度和 PPV。分析单位为人-年。

结果

研究样本包括 746763 名不同的健康计划成员,他们提供了 1116283 人-年的合格数据(中位年龄 56 岁;57%为女性;65%商业保险,35%有医疗保险优势)。BMI 相关诊断编码占 14.6%。BMI 相关诊断代码检测体重不足、正常体重、超重和肥胖的灵敏度分别为 10.1%、3.7%、6.0%和 25.2%,PPV 分别为体重不足 49.0%、正常体重 89.6%、超重 73.4%和肥胖 92.4%。与 ICD-9-CM 时代相比,ICD-10-CM 时代 BMI 相关诊断代码的灵敏度更高。

结论

BMI 相关诊断代码检测正常体重、超重和肥胖的 PPV 较高(>70%),但灵敏度较低(<30%)。BMI 相关诊断在 BMI≥35kg/m2 的 II 类或 III 类肥胖患者中更易编码,并且在 2016 年比 2013 年或 2014 年更易编码。

相似文献

1
Validation of body mass index (BMI)-related ICD-9-CM and ICD-10-CM administrative diagnosis codes recorded in US claims data.验证美国索赔数据中与体重指数(BMI)相关的 ICD-9-CM 和 ICD-10-CM 行政诊断代码。
Pharmacoepidemiol Drug Saf. 2018 Oct;27(10):1092-1100. doi: 10.1002/pds.4617. Epub 2018 Jul 12.
2
Validation of obesity-related diagnosis codes in claims data.验证索赔数据中与肥胖相关的诊断代码。
Diabetes Obes Metab. 2021 Dec;23(12):2623-2631. doi: 10.1111/dom.14512. Epub 2021 Aug 18.
3
A systematic review of validated methods for identifying orthopedic implant removal and revision using administrative data.使用行政数据系统评价识别骨科植入物取出和翻修的验证方法。
Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:265-73. doi: 10.1002/pds.2309.
4
Challenges of Using ICD-9-CM and ICD-10-CM Codes for Soft-Tissue Sarcoma in Databases for Health Services Research.在卫生服务研究数据库中使用ICD - 9 - CM和ICD - 10 - CM编码对软组织肉瘤进行编码的挑战。
Perspect Health Inf Manag. 2019 Apr 1;16(Spring):1a. eCollection 2019 Spring.
5
Improving discharge data fidelity for use in large administrative databases.提高用于大型管理数据库的出院数据保真度。
Neurosurg Focus. 2014 Jun;36(6):E2. doi: 10.3171/2014.3.FOCUS1459.
6
Positive predictive value between medical-chart body-mass-index category and obesity versus codes in a claims-data warehouse.在一个理赔数据仓库中,病历身体质量指数类别与肥胖症之间的阳性预测值与编码的比较。
Curr Med Res Opin. 2018 Jan;34(1):117-121. doi: 10.1080/03007995.2017.1366302. Epub 2017 Sep 5.
7
Use of International Classification of Diseases, Ninth Revision Codes for Obesity: Trends in the United States from an Electronic Health Record-Derived Database.使用国际疾病分类第九版代码对肥胖进行分类:来自电子健康记录衍生数据库的美国趋势
Popul Health Manag. 2018 Jun;21(3):222-230. doi: 10.1089/pop.2017.0092. Epub 2017 Sep 26.
8
Applying machine learning approaches for predicting obesity risk using US health administrative claims database.应用机器学习方法,利用美国健康管理数据库预测肥胖风险。
BMJ Open Diabetes Res Care. 2024 Sep 26;12(5):e004193. doi: 10.1136/bmjdrc-2024-004193.
9
Accuracy of ICD-10-CM claims-based definitions for epilepsy and seizure type.基于 ICD-10-CM 索赔的癫痫和发作类型定义的准确性。
Epilepsy Res. 2020 Oct;166:106414. doi: 10.1016/j.eplepsyres.2020.106414. Epub 2020 Jul 11.
10
The Nationwide Inpatient Sample database does not accurately reflect surgical indications for fusion.全国住院患者样本数据库不能准确反映融合手术的适应症。
J Neurosurg Spine. 2014 Dec;21(6):984-93. doi: 10.3171/2014.8.SPINE131113. Epub 2014 Oct 17.

引用本文的文献

1
Semaglutide and Tirzepatide in Patients With Heart Failure With Preserved Ejection Fraction.司美格鲁肽和替尔泊肽用于射血分数保留的心力衰竭患者
JAMA. 2025 Aug 31. doi: 10.1001/jama.2025.14092.
2
Epidemiology Landscape and Impact of Overweight and Obesity in Adults: Multi-country Results from the IMPACT-O Study.成人超重和肥胖的流行病学概况及影响:IMPACT-O研究的多国结果
Adv Ther. 2025 Aug 19. doi: 10.1007/s12325-025-03333-1.
3
Costs of obesity, obesity-related complications, and weight loss in the United States: A systematic literature review.
美国肥胖、肥胖相关并发症及减肥的成本:一项系统的文献综述。
J Manag Care Spec Pharm. 2025 Sep;31(9):851-861. doi: 10.18553/jmcp.2025.25051. Epub 2025 Jul 17.
4
Applying machine learning approaches for predicting obesity risk using US health administrative claims database.应用机器学习方法,利用美国健康管理数据库预测肥胖风险。
BMJ Open Diabetes Res Care. 2024 Sep 26;12(5):e004193. doi: 10.1136/bmjdrc-2024-004193.
5
Patient Factors Associated With Gastroesophageal Reflux Disease Diagnostic Evaluation Strategies: A Retrospective Cohort Study Using Real-World Evidence From a Large U.S. Medical Claims Database.与胃食管反流病诊断评估策略相关的患者因素:一项使用美国大型医疗索赔数据库的真实世界证据的回顾性队列研究。
Gastro Hep Adv. 2022 May 6;1(4):563-572. doi: 10.1016/j.gastha.2022.03.001. eCollection 2022.
6
Use of sensitivity analyses to assess uncontrolled confounding from unmeasured variables in observational, active comparator pharmacoepidemiologic studies: a systematic review.在观察性、活性对照药物流行病学研究中,使用敏感性分析评估未测量变量导致的未控制混杂因素:一项系统评价
Am J Epidemiol. 2025 Feb 5;194(2):524-535. doi: 10.1093/aje/kwae234.
7
Association of Atrial Fibrillation Burden and Mortality Among Patients With Cardiac Implantable Electronic Devices.心房颤动负荷与植入心脏电子设备患者的死亡率之间的关系。
Circulation. 2024 Jul 30;150(5):350-361. doi: 10.1161/CIRCULATIONAHA.124.069757. Epub 2024 Jun 28.
8
Association of severe obesity with risk of conversion to open in laparoscopic cholecystectomy for acute cholecystitis.严重肥胖与急性胆囊炎腹腔镜胆囊切除术中转开腹风险的相关性
Surg Open Sci. 2024 May 18;20:1-6. doi: 10.1016/j.sopen.2024.05.005. eCollection 2024 Aug.
9
Using Claims Data to Predict Pre-Operative BMI Among Bariatric Surgery Patients: Development of the BMI Before Bariatric Surgery Scoring System (B3S3).利用索赔数据预测减肥手术患者的术前体重指数:减肥手术前体重指数评分系统(B3S3)的开发。
Pragmat Obs Res. 2024 Mar 27;15:65-78. doi: 10.2147/POR.S450229. eCollection 2024.
10
Proposing a framework to quantify the potential impact of pharmacokinetic drug-drug interactions caused by a new drug candidate by using real world data about the target patient population.提出一个框架,通过目标患者人群的真实世界数据来量化新候选药物引起的药物代谢动力学药物相互作用的潜在影响。
Clin Transl Sci. 2024 Mar;17(3):e13741. doi: 10.1111/cts.13741.