Shi Junming, Hubbard Alan E, Fong Nicholas, Pirracchio Romain
Division of Biostatistics, University of California Berkeley, Berkeley, CA, USA.
Department of Anesthesia and Perioperative Care, Zuckerberg San Francisco General Hospital and Trauma Center, 1001 Potrero Avenue, CA94110, San Francisco, CA, USA.
BMC Med Inform Decis Mak. 2025 Jul 1;25(1):241. doi: 10.1186/s12911-025-03058-9.
Systematic disparities in data collection within electronic health records (EHRs), defined as non-random patterns in the measurement and recording of clinical variables across demographic groups, can be reflective of underlying implicit bias and may affect patient outcome. Identifying and mitigating these biases is critical for ensuring equitable healthcare. This study aims to develop an analytical framework for measurement patterns, defined as the combination of measurement frequency (how often variables are collected) and missing data rates (the frequency of missing recordings), evaluate the association between them and demographic factors, and assess their impact on in-hospital mortality prediction.
We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care III (MIMIC-III) database, which includes data on over 40,000 ICU patients from Beth Israel Deaconess Medical Center (2001-2012). Adult patients with ICU stays longer than 24 h were included. Measurement patterns, including missing data rates and measurement frequencies, were derived from EHR data and analyzed. Targeted Machine Learning (TML) methods were used to assess potential systematic disparities in measurement patterns across demographic factors (age, gender, race/ethnicity) while controlling for confounders such as other demographics and disease severity. The predictive power of measurement patterns on in-hospital mortality was evaluated.
Among 23,426 patients, significant demographic systematic disparities were observed in the first 24 h of ICU stays. Elderly patients (≥ 65 years) had more frequent temperature measurements compared to younger patients, while males had slightly fewer missing temperature measurements than females. Racial disparities were notable: White patients had more frequent blood pressure and oxygen saturation (SpO2) measurements compared to Black and Hispanic patients. Measurement patterns were associated with ICU mortality, with models based solely on these patterns achieving an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI: 0.74-0.77).
This study underscores the significance of measurement patterns in ICU EHR data, which are associated with patient demographics and ICU mortality. Analyzing patterns of missing data and measurement frequencies provides valuable insights into patient monitoring practices and potential systemic disparities in healthcare delivery. Understanding these disparities is critical for improving the fairness of healthcare delivery and developing more accurate predictive models in critical care settings.
Not applicable.
电子健康记录（EHR）中数据收集的系统性差异，定义为不同人口群体临床变量测量和记录中的非随机模式，可能反映潜在的隐性偏见，并可能影响患者预后。识别和减轻这些偏见对于确保公平的医疗保健至关重要。本研究旨在开发一个针对测量模式的分析框架，测量模式定义为测量频率（变量收集的频率）和缺失数据率（缺失记录的频率）的组合，评估它们与人口统计学因素之间的关联，并评估它们对院内死亡率预测的影响。
我们使用重症监护医学信息集市III（MIMIC-III）数据库进行了一项回顾性队列研究，该数据库包含贝斯以色列女执事医疗中心40000多名ICU患者的数据（2001 - 2012年）。纳入ICU住院时间超过24小时的成年患者。从EHR数据中得出测量模式，包括缺失数据率和测量频率，并进行分析。使用靶向机器学习（TML）方法评估不同人口统计学因素（年龄、性别、种族/民族）测量模式中潜在的系统性差异，同时控制其他人口统计学和疾病严重程度等混杂因素。评估测量模式对院内死亡率的预测能力。
在23426名患者中，在ICU住院的前24小时观察到显著的人口统计学系统性差异。老年患者（≥65岁）与年轻患者相比，体温测量更频繁，而男性体温测量缺失次数略少于女性。种族差异显著：与黑人和西班牙裔患者相比，白人患者血压和血氧饱和度（SpO2）测量更频繁。测量模式与ICU死亡率相关，仅基于这些模式的模型在受试者工作特征曲线下面积（AUC）为0.76（95%CI：0.74 - 0.77）。
本研究强调了ICU EHR数据中测量模式的重要性，这些模式与患者人口统计学和ICU死亡率相关。分析缺失数据和测量频率模式可为患者监测实践和医疗服务中的潜在系统性差异提供有价值的见解。了解这些差异对于提高医疗服务的公平性以及在重症监护环境中开发更准确的预测模型至关重要。
不适用。
