With Great Power Comes Great Responsibility: Big Data Research From the National Inpatient Sample.
With Great Power Comes Great Responsibility: Big Data Research From the National Inpatient Sample.
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DOI：
10.1161/circoutcomes.117.003846
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Krumholz HM
中科院分区：
文献类型：
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作者：
Khera R;Krumholz HM
2 Khera和Krumholz的研究来自全国住院患者样本的某些亚组。首先，美国各州并不是NIS抽样框架的一部分，因此，来自某个州的抽样排放并不代表该州的所有排放。4各州根据其医院和患者人群在国家景观中的代表性贡献住院人数。因此，除非一个州的医院特征（所有权、城市/农村位置、教学状况和床位大小）和患者特征（诊断相关组）（NIS抽样方法的组成部分）具有全国代表性，否则州一级的样本不代表该州的出院情况。因此，在一项评估该州公共报告法规变更前后针对急性心血管疾病进行特定手术的州级发生率的研究中，与国家独立体系中的其他州相比，可能会因抽样而存在偏差。各自的州。州与州之间的比较假设有代表性的样本，最好使用具有此属性的数据库进行。其次，分析供应商一级的交易量特别具有挑战性。一项评估个体医疗服务提供者所执行手术的量-结局相关性的研究也是不合适的，因为NIS中的医疗服务提供者代码字段与特定手术没有关联，并且在医院和州之间没有统一报告，涉及一些医院的个体医生和其他医院的医生组。5 4.行政代码：最后一个考虑因素是根据其描述性内涵识别疾病状况或程序，而无需正式验证。不直接影响报销的索赔代码可能在编码实践中容易发生变化。例如，使用NIS 1993年至2007年进行的一项研究发现，肺动脉高压住院率在研究期间突然下降。6然而，作者在其他数据集中适当地调查了这一趋势，并推断这并不代表真正的人口统计学趋势，但可能是因为建议限制使用肺动脉高血压特定索赔代码作为此期间所有肺动脉高血压相关住院的默认值。类似地，使用代码来识别特定的诊断亚组，例如所有急性心肌梗死中的ST段抬高型心肌梗死，所有心力衰竭中的射血分数保留的心力衰竭，以及院内心脏骤停，而没有亚组特定的报销值，也可能是不准确的，会引入噪音或偏倚。除主要诊断代码外，应谨慎解释次要诊断，特别是在识别住院期间可能发生的事件时。由于NIS没有伴随其次要诊断代码的入院时存在标志，也不允许对患者进行纵向评估，因此大多数次要代码在区分并发症和合并症方面可能不够可靠。在进行此类调查之前，必须对先前的验证研究进行严格的文献综述。鉴于其复杂性和不断发展的数据结构，医疗保健研究和质量机构建议仔细审查NIS的公开文件。7这包括关于具体年份的数据结构、7统计最佳做法、3和分析工具的细节。8此外，它还提供HCUPnet，9这是一个可公开访问的基于网络的门户网站，提供个人行政诊断/程序代码的国家估计数，这可以帮助...
2 Khera and Krumholz Research From the National Inpatient Sample certain subgroups. First, US states are not a part of the sampling framework of the NIS, and therefore, sampled discharges from a given state are not representative of all discharges from that state. 4 States contribute hospitalizations based on how representative their hospitals and patient population are to the national landscape. Hence, unless a state’s hospital characteristics (ownership, urban/rural location, teaching status, and bed size) and patient features (diagnosis-related groups), which are components of NIS sampling methodology, are nationally representative, state-level samples are not representative of the state’s discharges. Hence, in a study that assesses state-level rates of a specific procedure performed for an acute cardiovascular condition before and after changes in public-reporting regulations in that state, as compared with other states in the NIS, may be biased by the sampling in the respective states. State-to-state comparisons assume representative samples and are better conducted using databases that have this property. Second, analysis of provider-level volumes is particularly challenging. A study evaluating volume–outcomes associations for procedures performed by individual providers is also not appropriate because the provider code-field in NIS does not link to a specific procedure and is not reported uniformly across hospitals and states, referring to individual physicians at some hospitals, and physician groups at others. 5 4. Administrative codes: A final consideration is the identification of disease conditions or procedures based on their descriptive connotations without formal validation. The claim codes that do not affect reimbursement directly may be prone to variation in coding practices. As an example, a study conducted using NIS 1993 to 2007 found that rates of pulmonary artery hypertension hospitalizations declined abruptly during the study period. 6 The authors, however, appropriately investigated this trend in other data sets and inferred that this did not represent a true demographic trend but was likely because of a recommendation to limit the use of the pulmonary artery hypertension–specific claim code as a default for all pulmonary hypertension–related hospitalizations during this period. Similarly, using codes to identify specific diagnostic subgroups, such as the ST-segment–elevation myocardial infarction among all acute myocardial infarction, heart failure with preserved ejection fraction among all heart failure, and in-hospital cardiac arrest, without a subgroup-specific reimbursement value, may also be inaccurate, with noise or bias introduced. In addition to the primary diagnosis code, secondary diagnoses should be interpreted with caution, particularly for identifying events that may have occurred during a hospitalization. Because the NIS does not have present-on-admission flags accompanying its secondary diagnosis codes, or allow longitudinal assessment of patients, most secondary codes may not be sufficiently reliable in distinguishing complications from comorbid conditions. A rigorous literature review for prior validation studies before conducting such an investigation is warranted. Given its complexity and ever-evolving data structure, the Agency for Healthcare Research and Quality recommends a careful review of NIS’publicly available documentation. 7 This includes details on year-specific data structure, 7 statistical bestpractices, 3 and analytic tools. 8 In addition, it offers HCUPnet, 9 a publicly accessible, web-based portal that provides national estimates for individual administrative diagnosis/procedure codes, which can help with …