An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database

• Published in: JAMIA open 2021-07, Vol.4 (3), p.ooab057-ooab057
• Main Authors: Afshar, Ali S, Li, Yijun, Chen, Zixu, Chen, Yuxuan, Lee, Jae Hun, Irani, Darius, Crank, Aidan, Singh, Digvijay, Kanter, Michael, Faraday, Nauder, Kharrazi, Hadi
• Format: Article
• Language: English
• Subjects: Analysis; Brief Communications; Electronic records; Heart beat; Hospital patients; Information management; Medical care; Medical records; Prognosis; Quality management
• ISSN: 2574-2531; 2574-2531
• DOI: 10.1093/jamiaopen/ooab057

Abstract Physiological data, such as heart rate and blood pressure, are critical to clinical decision-making in the intensive care unit (ICU). Vital signs data, which are available from electronic health records, can be used to diagnose and predict important clinical outcomes; While there have been some reports on the data quality of nurse-verified vital sign data, little has been reported on the data quality of higher frequency time-series vital signs acquired in ICUs, that would enable such predictive modeling. In this study, we assessed the data quality issues, defined as the completeness, accuracy, and timeliness, of minute-by-minute time series vital signs data within the MIMIC-III data set, captured from 16009 patient-ICU stays and corresponding to 9410 unique adult patients. We measured data quality of four time-series vital signs data streams in the MIMIC-III data set: heart rate (HR), respiratory rate (RR), blood oxygen saturation (SpO2), and arterial blood pressure (ABP). Approximately, 30% of patient-ICU stays did not have at least 1 min of data during the time-frame of the ICU stay for HR, RR, and SpO2. The percentage of patient-ICU stays that did not have at least 1 min of ABP data was ∼56%. We observed ∼80% coverage of the total duration of the ICU stay for HR, RR, and SpO2. Finally, only 12.5%%, 9.9%, 7.5%, and 4.4% of ICU lengths of stay had ≥ 99% data available for HR, RR, SpO2, and ABP, respectively, that would meet the three data quality requirements we looked into in this study. Our findings on data completeness, accuracy, and timeliness have important implications for data scientists and informatics researchers who use time series vital signs data to develop predictive models of ICU outcomes.