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Quality of MBSAQIP data: bad luck, or lack of QA plan?

机译：MBSAQIP数据的质量：运气不好，或缺乏QA计划？

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Background National clinical registries are commonly used in clinical research, quality improvement, and health policy. However, little is known about methodological challenges associated with these registry analyses that could limit their impact and compromise patient safety. This study examined the quality of Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MSBASQIP) data to assess its usability potential and improve data collection methodologies. Methods We developed a single flat file (n = 168,093) using five subsets (Main, BMI, Readmission, Reoperation, and Intervention) of the 2015 MBSAQIP Participant User Data File (PUF). Logic and validity tests included (1) individual profiles of patient's body mass index (BMI) changes over time, (2) individual patient care pathways, and (3) correlation analysis between variable pairs associated with the same clinical encounters. Results 8888 (5.3%) patients did not have postoperative weight/BMI data; 20% of patients had different units for preoperative and postoperative weights. Postoperative weight measurements ranged between - 71 and 132% of preoperative weight. There were 325 (3.7%) hospital readmissions reported on the day of or day after MBS. The self-reporting of "emergency" vs. "planned" interventions did not correlate with the type of procedure and its indication. Up to 20% of data could potentially be unused for analysis due to data quality issues. Conclusions Our analysis revealed various data quality issues in the 2015 MBSAQIP PUF related to completeness, accuracy, and consistency. Since information on where the surgery was performed is lacking, it is not possible to conclude whether these issues represent data errors, patient outliers, or inappropriate care. Including automated data checks and biomedical informatics oversight, standardized coding for complications, additional de-identified facility and provider information, and training/mentorship opportunities in data informatics for all researchers who get access to the data have been shown to be effective in improving data quality and minimizing patient safety concerns.
机译：背景技术国家临床登记商通常用于临床研究，质量改进和健康政策。然而，关于与这些注册表分析相关的方法论挑战几乎是知之甚少，可能会限制其影响和损害患者安全性。本研究检测了代谢和肥胖症外科认证的质量和质量改进计划（MSBASQIP）数据，以评估其可用性潜力和改进数据收集方法。方法我们使用2015 MBSAQIP参与者用户数据文件（PUF）的五个子集（主要，BMI，读取，重新操作和干预）开发了单个平面文件（n = 168,093）。逻辑和有效性测试包括（1）患者体重指数（BMI）的个体轮廓随时间而变化，（2）个体患者护理途径，（3）与相同临床遭遇相关的可变对之间的相关分析。结果8888（5.3％）患者没有术后重量/ BMI数据; 20％的患者有不同的术前和术后重量的单位。术后重量测量范围间 - 71和132％的术前重量。在MBS之后的一天或一天​​报告，有325名（3.7％）医院入院。 “紧急”与“计划”干预措施的自我报告与程序类型及其迹象无关。由于数据质量问题，高达20％的数据可能会因分析而无法使用。结论我们的分析显示了2015 MBSAQIP PUF中的各种数据质量问题，与完整性，准确性和一致性相关。由于缺乏外科手术的信息，因此不可能得出这些问题是否代表数据错误，患者异常值，或不恰当的护理。包括自动化数据检查和生物医学信息学监督，标准化的复杂性编码，额外的除可设施和提供商信息，以及获取访问数据的所有研究人员的数据信息学中的培训/指导机会已被证明在提高数据质量方面有效并尽量减少患者安全问题。

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