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      BOLD: Blood-gas and Oximetry Linked Dataset

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          Abstract

          Pulse oximeters measure peripheral arterial oxygen saturation (SpO 2) noninvasively, while the gold standard (SaO 2) involves arterial blood gas measurement. There are known racial and ethnic disparities in their performance. BOLD is a dataset that aims to underscore the importance of addressing biases in pulse oximetry accuracy, which disproportionately affect darker-skinned patients. The dataset was created by harmonizing three Electronic Health Record databases (MIMIC-III, MIMIC-IV, eICU-CRD) comprising Intensive Care Unit stays of US patients. Paired SpO 2 and SaO 2 measurements were time-aligned and combined with various other sociodemographic and parameters to provide a detailed representation of each patient. BOLD includes 49,099 paired measurements, within a 5-minute window and with oxygen saturation levels between 70–100%. Minority racial and ethnic groups account for ~25% of the data – a proportion seldom achieved in previous studies. The codebase is publicly available. Given the prevalent use of pulse oximeters in the hospital and at home, we hope that BOLD will be leveraged to develop debiasing algorithms that can result in more equitable healthcare solutions.

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          Most cited references14

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            MIMIC-III, a freely accessible critical care database

            MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.
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              The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine.

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                Author and article information

                Contributors
                jcmatos@mit.edu
                med@aiwong.com
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                24 May 2024
                24 May 2024
                2024
                : 11
                : 535
                Affiliations
                [1 ]Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, ( https://ror.org/042nb2s44) Cambridge, MA USA
                [2 ]Faculty of Engineering, University of Porto (FEUP), ( https://ror.org/043pwc612) Porto, Portugal
                [3 ]Institute for Systems and Computer Engineering, Technology and Science (INESCTEC), ( https://ror.org/05fa8ka61) Porto, Portugal
                [4 ]Duke University, Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, ( https://ror.org/00py81415) Durham, NC USA
                [5 ]Medical University Clinic, Kantonsspital Aarau, ( https://ror.org/056tb3809) Aarau, Switzerland
                [6 ]GRID grid.420545.2, ISNI 0000 0004 0489 3985, Department of Critical Care, , Guy’s and St Thomas’ NHS Trust, ; London, United Kingdom
                [7 ]Department of Ophthalmology, São Paulo Federal University, ( https://ror.org/02k5swt12) São Paulo, SP Brazil
                [8 ]Institute for Data Systems and Society, Massachusetts Institute of Technology, ( https://ror.org/042nb2s44) Cambridge, MA US
                [9 ]GRID grid.414252.4, ISNI 0000 0004 1761 8894, Center for Artificial Intelligence in Medicine, , The General Hospital of PLA, ; Beijing, China
                [10 ]Duke University, AI Health, ( https://ror.org/00py81415) Durham, NC USA
                [11 ]Duke University, Department of Medicine, Division of Geriatrics, ( https://ror.org/00py81415) Durham, NC USA
                [12 ]Duke University, Department of Biostatistics and Bioinformatics, Division of Translational Biomedical Informatics, ( https://ror.org/00py81415) Durham, NC, USA
                [13 ]Duke University, Department of Surgery, Division of Surgical Sciences, ( https://ror.org/00py81415) Durham, NC, USA
                [14 ]Emory University, Department of Radiology, ( https://ror.org/03czfpz43) Atlanta, GA USA
                [15 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biostatistics, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [16 ]Department of Medicine, Beth Israel Deaconess Medical Center, ( https://ror.org/04drvxt59) Boston, MA USA
                [17 ]Duke University, Department of Biostatistics and Biomedical Informatics, Division of Translational Biomedical Informatics, ( https://ror.org/00py81415) Durham, NC USA
                Author information
                http://orcid.org/0000-0003-1306-2334
                Article
                3225
                10.1038/s41597-024-03225-z
                11126612
                38789452
                18aaa3bc-b893-435b-9992-0f9a121cadbf
                © The Author(s) 2024

                This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 October 2023
                : 4 April 2024
                Funding
                Funded by: Fulbright / FLAD Grant, Portugal, AY 2022/2023
                Funded by: FundRef https://doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation);
                Award ID: P400PM_194497 / 1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000070, U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB);
                Award ID: R01 EB001659
                Award ID: R01 EB001659
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)
                Funded by: FundRef https://doi.org/10.13039/100006108, U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS);
                Award ID: UL1TR002553
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100006545, U.S. Department of Health & Human Services | NIH | National Institute on Minority Health and Health Disparities (NIMHD);
                Award ID: 5U54MD012530
                Award Recipient :
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                © Springer Nature Limited 2024

                physical examination,health policy
                physical examination, health policy

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