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Qiao S, Li X, Olatosi B, Young SD. Utilizing Big Data analytics and electronic health record data in HIV prevention, treatment, and care research: a literature review. AIDS Care 2024; 36:583-603. [PMID: 34260325 DOI: 10.1080/09540121.2021.1948499] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 06/22/2021] [Indexed: 01/07/2023]
Abstract
Propelled by the transformative power of modern information and communication technologies, digitalization of data, and the increasing affordability of high-performance computing, Big Data science has brought forth revolutionary advancement in many areas of business, industry, health, and medicine. The HIV research and care service community is no exception to the benefits from the availability and utilization of Big Data analytics. Electronic health record (EHR) data (e.g., administrative and billing data, electronic medical records, or other digital records of information pertinent to individual or population health) are an essential source of health and disease outcome data because of the large amount of real-world, comprehensive, and often longitudinal data, which provide a good opportunity for leveraging advanced Big Data analytics in addressing challenges in HIV prevention, treatment, and care. This review focuses on studies that apply Big Data analytics to EHR data with aims to synthesize the HIV-related issues that EHR data studies can tackle, identify challenges in the utilization of EHR data in HIV research and practice, and discuss future needs and directions that can realize the promising potential role of Big Data in ending the HIV epidemic.
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Affiliation(s)
- Shan Qiao
- South Carolina SmartState Center for Healthcare Quality (CHQ), Columbia, SC, USA
- University of South Carolina Big Data Health Science Center, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Xiaoming Li
- South Carolina SmartState Center for Healthcare Quality (CHQ), Columbia, SC, USA
- University of South Carolina Big Data Health Science Center, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Bankole Olatosi
- South Carolina SmartState Center for Healthcare Quality (CHQ), Columbia, SC, USA
- University of South Carolina Big Data Health Science Center, Columbia, SC, USA
- Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Sean D Young
- Department of Emergency Medicine, Department of Informatics, Institute for Prediction Technology, University of California, Irvine, CA, USA
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Musinguzi N, Ngure K, Bukusi EA, Mugo NR, Baeten JM, Anderson PL, Haberer JE. Performance of Multiple Adherence Measures for pre-exposure Prophylaxis (PrEP) Among Young Women in Kenya. AIDS Behav 2023; 27:3961-3969. [PMID: 37351684 DOI: 10.1007/s10461-023-04111-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2023] [Indexed: 06/24/2023]
Abstract
Understanding PrEP adherence is key in the formulation of HIV prevention strategies; however, measurement of adherence can be challenging. We compared multiple adherence measures in a two-year study of young Kenyan women at high risk of HIV acquisition. Among 289 participants, concordance between electronic adherence monitoring (EAM) and tenofovir diphosphate (TFV-DP) in dried blood spots ranged from 57 to 72% depending on selected thresholds. Using area under the receiver operating curve, discrimination of quantifiable TFV-DP was high at 0.85 with EAM and low at 0.49-0.54 for multiple self-reported measures. Correlation between EAM and self-reported measures was low (r < 0.11) although correlation within self-reported measures was moderate (r > 0.69). These findings indicate that both TFV-DP and EAM are useful PrEP adherence tools. Adherence would benefit from better availability of less expensive versions of both measurement tools. Additionally, further research on TFV-DP thresholds is needed to inform interpretation and use in understanding PrEP adherence in this population.
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Affiliation(s)
- Nicholas Musinguzi
- Global Health Collaborative, Mbarara University of Science and Technology, Mbarara, Uganda.
| | - Kenneth Ngure
- Center for Clinical Research (CCR), Kenya Medical Research Institute (KEMRI), Nairobi, Kenya
- Department of Community Health, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya
| | - Elizabeth A Bukusi
- Department of Obstetrics and Gynecology, University of Washington, Seattle, USA
- Department of Global Health, University of Washington, Seattle, USA
- Center for Microbiology Research (CMR), Kenya Medical Research Institute (KEMRI), Nairobi, Kenya
| | - Nelly R Mugo
- Center for Clinical Research (CCR), Kenya Medical Research Institute (KEMRI), Nairobi, Kenya
- Department of Global Health, University of Washington, Seattle, USA
| | - Jared M Baeten
- Department of Global Health, University of Washington, Seattle, USA
- Department of Epidemiology, University of Washington, Seattle, USA
- Department of Medicine, University of Washington, Seattle, USA
| | - Peter L Anderson
- Colorado Antiviral Pharmacology Laboratory, Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado-AMC, Aurora, CO, USA
| | - Jessica E Haberer
- Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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Esber AL, Dear NF, King D, Francisco LV, Sing'oei V, Owuoth J, Maswai J, Iroezindu M, Bahemana E, Kibuuka H, Shah N, Polyak CS, Ake JA, Crowell TA. Achieving the third 95 in sub-Saharan Africa: application of machine learning approaches to predict viral failure. AIDS 2023; 37:1861-1870. [PMID: 37418549 DOI: 10.1097/qad.0000000000003646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
OBJECTIVE Viral failure in people with HIV (PWH) may be influenced by multiple sociobehavioral, clinical, and context-specific factors, and supervised learning approaches may identify novel predictors. We compared the performance of two supervised learning algorithms to predict viral failure in four African countries. DESIGN Cohort study. METHODS The African Cohort Study is an ongoing, longitudinal cohort enrolling PWH at 12 sites in Uganda, Kenya, Tanzania, and Nigeria. Participants underwent physical examination, medical history-taking, medical record extraction, sociobehavioral interviews, and laboratory testing. In cross-sectional analyses of enrollment data, viral failure was defined as a viral load at least 1000 copies/ml among participants on antiretroviral therapy (ART) for at least 6 months. We compared the performance of lasso-type regularized regression and random forests by calculating area under the curve (AUC) and used each to identify factors associated with viral failure; 94 explanatory variables were considered. RESULTS Between January 2013 and December 2020, 2941 PWH were enrolled, 1602 had been on antiretroviral therapy (ART) for at least 6 months, and 1571 participants with complete case data were included. At enrollment, 190 (12.0%) had viral failure. The lasso regression model was slightly superior to the random forest in its ability to identify PWH with viral failure (AUC: 0.82 vs. 0.75). Both models identified CD4 + count, ART regimen, age, self-reported ART adherence and duration on ART as important factors associated with viral failure. CONCLUSION These findings corroborate existing literature primarily based on hypothesis-testing statistical approaches and help to generate questions for future investigations that may impact viral failure.
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Affiliation(s)
- Allahna L Esber
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Nicole F Dear
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - David King
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Leilani V Francisco
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Valentine Sing'oei
- U.S. Army Medical Research Directorate - Africa
- HJF Medical Research International, Kisumu
| | - John Owuoth
- U.S. Army Medical Research Directorate - Africa
- HJF Medical Research International, Kisumu
| | - Jonah Maswai
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
- U.S. Army Medical Research Directorate - Africa, Kericho, Kenya
| | - Michael Iroezindu
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
- HJF Medical Research International, Abuja, Nigeria
| | - Emmanuel Bahemana
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
- HJF Medical Research International, Mbeya, Tanzania
| | - Hannah Kibuuka
- Makerere University-Walter Reed Project, Kampala, Uganda
| | - Neha Shah
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
| | - Christina S Polyak
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Julie A Ake
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
| | - Trevor A Crowell
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
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Seboka BT, Yehualashet DE, Tesfa GA. Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals. Int J Gen Med 2023; 16:435-451. [PMID: 36760682 PMCID: PMC9904219 DOI: 10.2147/ijgm.s397031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 01/27/2023] [Indexed: 02/05/2023] Open
Abstract
Background Despite the success made in scaling up HIV treatment activities, there remains a tremendous unmet demand for the monitoring of the disease progression and treatment success, which threatens HIV/AIDS treatment and control. This research presented the assessments of viral load and CD4 classification of adults enrolled in ART care using machine learning algorithms. Methods We trained, validated, and tested eight machine learning (ML) classifier algorithms with historical data, including demographics, clinical, and laboratory data. Data were extracted from the ART registry database of Yirgacheffe Primary Hospital and Dilla University Referral Hospital. ML classifiers were trained to predict virological failure (viral load >1000 copies/mL) and poor CD4 (CD4 cell count <200 cells/mL). The model predictive performances were evaluated using accuracy, sensitivity, specificity, precision, f1-score, F-beta scores, and AUC. Results The mean age of the sample participants was 41.6 years (SD = 10.9). The experimental results showed that XGB classifier ranked as the best algorithm for viral load prediction in terms of sensitivity (97%), f1-score (96%), AUC (0.99), accuracy (96%), followed by RF. The GB classifier exhibited a better predictive capability in predicting participants with a CD4 cell count <200 cells/mL. Conclusion In this study, the XGB and RF models had the highest accuracy and outperformed on various evaluation metrics among the models examined for viral load classification. In the prediction of participants CD4, GB model had the highest accuracy.
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Affiliation(s)
- Binyam Tariku Seboka
- School of Public Health, Dilla University, Dilla, Ethiopia,Correspondence: Binyam Tariku Seboka, School of public health, Dilla University, P.O Box: 419, Dilla University, Dilla, Ethiopia, Tel +251 920612180, Fax +251 46-331-2568, Email
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Beydoun HA, Beydoun MA, Weiss J, Gautam RS, Hossain S, Alemu BT, Zonderman AB. Predictors of Covid-19 level of concern among older adults from the health and retirement study. Sci Rep 2022; 12:4396. [PMID: 35292672 PMCID: PMC8921703 DOI: 10.1038/s41598-022-08332-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 03/07/2022] [Indexed: 11/28/2022] Open
Abstract
The purpose of this longitudinal study is to construct a prediction model for Covid-19 level of concern using established Covid-19 socio-demographic, lifestyle and health risk characteristics and to examine specific contributions of obesity-related cardiometabolic health characteristics as predictors of Covid-19 level of concern among a representative sample of U.S. older adults. We performed secondary analyses of existing data on 2872 2006–2020 Health and Retirement Study participants and examined 19 characteristics in relation to the outcome of interest using logistic regression and machine learning algorithms. In mixed-effects ordinal logistic regression models, a history of diabetes, stroke as well as 1–2 cardiometabolic risk factors and/or chronic conditions were associated with greater Covid-19 level of concern, after controlling for confounders. Female sex, birth cohort, minority race, Hispanic ethnicity and total wealth as well as depressive symptoms were associated with higher level of Covid-19 concern, and education was associated with lower level of Covid-19 concern in fully adjusted mixed-effects ordinal logistic regression models. The selected socio-demographic, lifestyle and health characteristics accounted for < 70% of the variability in Covid-19 level of concern based on machine learning algorithms. Independent risk factors for Covid-19 level of concern among U.S. older adults include socio-demographic characteristics and depressive symptoms. Advanced research is needed to identify relevant predictors and elucidate underlying mechanisms of observed relationships.
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Affiliation(s)
- Hind A Beydoun
- Department of Research Programs, Fort Belvoir Community Hospital, 9300 DeWitt Loop, Fort Belvoir, VA, 22060, USA.
| | - May A Beydoun
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD, 21225, USA
| | - Jordan Weiss
- Department of Demography, UC Berkeley, Berkeley, CA, 94720, USA
| | - Rana S Gautam
- Department of Sociology and Human Services, University of North Georgia, Dahlonega, GA, 30597, USA
| | - Sharmin Hossain
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD, 21225, USA
| | - Brook T Alemu
- Health Sciences Program, School of Health Sciences, Western Carolina University, Cullowhee, NC, 28723, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD, 21225, USA
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Douthit BJ, Walden RL, Cato K, Coviak CP, Cruz C, D'Agostino F, Forbes T, Gao G, Kapetanovic TA, Lee MA, Pruinelli L, Schultz MA, Wieben A, Jeffery AD. Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature. Appl Clin Inform 2022; 13:161-179. [PMID: 35139564 PMCID: PMC8828453 DOI: 10.1055/s-0041-1742218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. OBJECTIVES This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. METHODS We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. RESULTS Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. CONCLUSION This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.
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Affiliation(s)
- Brian J. Douthit
- Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Rachel L. Walden
- Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States
| | - Kenrick Cato
- Department of Emergency Medicine, Columbia University School of Nursing, New York, New York, United States
| | - Cynthia P. Coviak
- Professor Emerita of Nursing, Grand Valley State University, Allendale, Michigan, United States
| | - Christopher Cruz
- Global Health Technology and Informatics, Chevron, San Ramon, California, United States
| | - Fabio D'Agostino
- Department of Medicine and Surgery, Saint Camillus International University of Health Sciences, Rome, Italy
| | - Thompson Forbes
- College of Nursing, East Carolina University, Greenville, North California, United States
| | - Grace Gao
- Department of Nursing, St Catherine University, Saint Paul, Minnesota, United States
| | - Theresa A. Kapetanovic
- College of Nursing, East Carolina University, Greenville, North California, United States
| | - Mikyoung A. Lee
- College of Nursing, Texas Woman's University, Denton, Texas, United States
| | - Lisiane Pruinelli
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States
| | - Mary A. Schultz
- Department of Nursing, California State University, San Bernardino, California, United States
| | - Ann Wieben
- School of Nursing, University of Wisconsin-Madison, Wisconsin, United States
| | - Alvin D. Jeffery
- School of Nursing, Vanderbilt University; Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, Tennessee, United States,Address for correspondence Alvin D. Jeffery, PhD, RN-BC, CCRN-K, FNP-BC 461 21st Avenue South, Nashville, TN 37240United States
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Zhang J, Olatosi B, Yang X, Weissman S, Li Z, Hu J, Li X. Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol. BMC Infect Dis 2022; 22:122. [PMID: 35120435 PMCID: PMC8817473 DOI: 10.1186/s12879-022-07047-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 01/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background Given the importance of viral suppression in ending the HIV epidemic in the US and elsewhere, an optimal predictive model of viral status can help clinicians identify those at risk of poor viral control and inform clinical improvements in HIV treatment and care. With an increasing availability of electronic health record (EHR) data and social environmental information, there is a unique opportunity to improve our understanding of the dynamic pattern of viral suppression. Using a statewide cohort of people living with HIV (PLWH) in South Carolina (SC), the overall goal of the proposed research is to examine the dynamic patterns of viral suppression, develop optimal predictive models of various viral suppression indicators, and translate the models to a beta version of service-ready tools for clinical decision support. Methods The PLWH cohort will be identified through the SC Enhanced HIV/AIDS Reporting System (eHARS). The SC Office of Revenue and Fiscal Affairs (RFA) will extract longitudinal EHR clinical data of all PLWH in SC from multiple health systems, obtain data from other state agencies, and link the patient-level data with county-level data from multiple publicly available data sources. Using the deidentified data, the proposed study will consist of three operational phases: Phase 1: “Pattern Analysis” to identify the longitudinal dynamics of viral suppression using multiple viral load indicators; Phase 2: “Model Development” to determine the critical predictors of multiple viral load indicators through artificial intelligence (AI)-based modeling accounting for multilevel factors; and Phase 3: “Translational Research” to develop a multifactorial clinical decision system based on a risk prediction model to assist with the identification of the risk of viral failure or viral rebound when patients present at clinical visits. Discussion With both extensive data integration and data analytics, the proposed research will: (1) improve the understanding of the complex inter-related effects of longitudinal trajectories of HIV viral suppressions and HIV treatment history while taking into consideration multilevel factors; and (2) develop empirical public health approaches to achieve ending the HIV epidemic through translating the risk prediction model to a multifactorial decision system that enables the feasibility of AI-assisted clinical decisions.
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Affiliation(s)
- Jiajia Zhang
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, 29208, USA
| | - Bankole Olatosi
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA. .,Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, 29208, USA. .,Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.
| | - Xueying Yang
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, 29208, USA.,Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Sharon Weissman
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Department of Internal Medicine, School of Medicine, University of South Carolina, Columbia, SC, 29208, USA
| | - Zhenlong Li
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, 29208, USA.,Geoinformation and Big Data Research Laboratory, University of South Carolina, Columbia, SC, 29208, USA
| | - Jianjun Hu
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, 29208, USA
| | - Xiaoming Li
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.,Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, 29208, USA.,Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
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Lind ML, Mooney SJ, Carone M, Althouse BM, Liu C, Evans LE, Patel K, Vo PT, Pergam SA, Phipps AI. Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant. JAMA Netw Open 2021; 4:e214514. [PMID: 33871619 PMCID: PMC8056279 DOI: 10.1001/jamanetworkopen.2021.4514] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
IMPORTANCE Sepsis disproportionately affects recipients of allogeneic hematopoietic cell transplant (allo-HCT), and timely detection is crucial. However, the atypical presentation of sepsis within this population makes detection challenging, and existing clinical sepsis tools have limited prognostic value among this high-risk population. OBJECTIVE To develop a full risk factor (demographic, transplant, clinical, and laboratory factors) and clinical factor-specific automated bacterial sepsis decision support tool for recipients of allo-HCT with potential bloodstream infections (PBIs). DESIGN, SETTING, AND PARTICIPANTS This prognostic study used data from adult recipients of allo-HCT transplanted at the Fred Hutchinson Cancer Research Center, Seattle, Washington, between June 2010 and June 2019 randomly divided into 70% modeling and 30% validation data sets. Tools were developed using the area under the curve (AUC) optimized SuperLearner, and their performance was compared with existing clinical sepsis tools: National Early Warning Score (NEWS), quick Sequential Organ Failure Assessment (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS), using the validation data set. Data were analyzed between January and October of 2020. MAIN OUTCOMES AND MEASURES The primary outcome was high-sepsis risk bacteremia (culture confirmed gram-negative species, Staphylococcus aureus, or Streptococcus spp bacteremia), and the secondary outcomes were 10- and 28-day mortality. Tool discrimination and calibration were examined using accuracy metrics and expected vs observed probabilities. RESULTS Between June 2010 and June 2019, 1943 recipients of allo-HCT received their first transplant, and 1594 recipients (median [interquartile range] age at transplant, 54 [43-63] years; 911 [57.2%] men; 1242 individuals [77.9%] identifying as White) experienced at least 1 PBI. Of 8131 observed PBIs, 238 (2.9%) were high-sepsis risk bacteremia. Compared with high-sepsis risk bacteremia, the full decision support tool had the highest AUC (0.85; 95% CI, 0.81-0.89), followed by the clinical factor-specific tool (0.72; 95% CI, 0.66-0.78). SIRS had the highest AUC of existing tools (0.64; 95% CI, 0.57-0.71). The full decision support tool had the highest AUCs for PBIs identified in inpatient (0.82; 95% CI, 0.76-0.89) and outpatient (0.82; 95% CI, 0.75-0.89) settings and for 10-day (0.85; 95% CI, 0.79-0.91) and 28-day (0.80; 95% CI, 0.75-0.84) mortality. CONCLUSIONS AND RELEVANCE These findings suggest that compared with existing tools and the clinical factor-specific tool, the full decision support tool had superior prognostic accuracy for the primary (high-sepsis risk bacteremia) and secondary (short-term mortality) outcomes in inpatient and outpatient settings. If used at the time of culture collection, the full decision support tool may inform more timely sepsis detection among recipients of allo-HCT.
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Affiliation(s)
- Margaret L. Lind
- Department of Epidemiology, University of Washington, Seattle
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stephen J. Mooney
- Department of Epidemiology, University of Washington, Seattle
- Harborview Injury Prevention and Research Center, Seattle, Washington
| | - Marco Carone
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Biostatistics, University of Washington, Seattle
- Department of Statistics, University of Washington, Seattle
| | - Benjamin M. Althouse
- Institute for Disease Modeling, Bellevue, Washington
- Information School, University of Washington, Seattle
- Department of Biology, New Mexico State University, Las Cruces
| | - Catherine Liu
- Division of Allergy and Infectious Disease, Department of Medicine, University of Washington, Seattle
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Antimicrobial and Outpatient Parenteral Antimicrobial Therapy Program, Seattle Cancer Care Alliance, Seattle, Washington
| | - Laura E. Evans
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle
| | - Kevin Patel
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle
- Oncology and Bone Marrow Transplant Intensive Care Unit, University of Washington, Seattle
- Medical Intensive Care Unit, University of Washington, Seattle
| | - Phuong T. Vo
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Division of Medical Oncology, University of Washington, Seattle
| | - Steven A. Pergam
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- School of Medicine, University of Washington, Seattle
| | - Amanda I. Phipps
- Department of Epidemiology, University of Washington, Seattle
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
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Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes. Curr HIV/AIDS Rep 2021; 18:229-236. [PMID: 33661445 DOI: 10.1007/s11904-021-00552-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2021] [Indexed: 10/22/2022]
Abstract
PURPOSE OF REVIEW This manuscript reviews the use of electronic medical record (EMR) data for HIV care and research along the HIV care continuum with a specific focus on machine learning methods and clinical informatics interventions. RECENT FINDINGS EMR-based clinical decision support tools and electronic alerts have been effectively utilized to improve HIV care continuum outcomes. Accurate EMR-based machine learning models have been developed to predict HIV diagnosis, retention in care, and viral suppression. Natural language processing (NLP) of clinical notes and data sharing between healthcare systems and public health agencies can enhance models for identifying people living with HIV who are undiagnosed or in need of relinkage to care. Challenges related to using these technologies include inconsistent EMR documentation, alert fatigue, and the potential for bias. Clinical informatics and machine learning models are promising tools for improving HIV care continuum outcomes. Future research should focus on methods for combining EMR data with additional data sources (e.g., social media, geospatial data) and studying how to effectively implement predictive models for HIV care into clinical practice.
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