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Goldberg E, Kao D, Kwan B, Patel H, Hassell A, Zane R. UCHealth's virtual health center: How Colorado's largest health system creates and integrates technology into patient care. NPJ Digit Med 2024; 7:187. [PMID: 38992097 PMCID: PMC11239912 DOI: 10.1038/s41746-024-01184-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 07/01/2024] [Indexed: 07/13/2024] Open
Affiliation(s)
- Elizabeth Goldberg
- University of Colorado Anschutz Medical Campus, Department of Emergency Medicine, Aurora, CO, USA.
| | - Dave Kao
- University of Colorado Anschutz Medical Campus, Department of Medicine - Cardiology, Aurora, CO, USA
| | - Bethany Kwan
- University of Colorado Anschutz Medical Campus, Department of Emergency Medicine, Aurora, CO, USA
| | - Hemali Patel
- University of Colorado Anschutz Medical Campus, Department of Medicine - Hospital Medicine, Aurora, CO, USA
| | - Amy Hassell
- UCHealth Nursing Administration, Aurora, CO, USA
| | - Richard Zane
- University of Colorado Anschutz Medical Campus, Department of Emergency Medicine, Aurora, CO, USA
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2
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Vagg T, Deasy KF, Chapman WW, Ranganathan SC, Plant BJ, Shanthikumar S. Virtual monitoring in CF - the importance of continuous monitoring in a multi-organ chronic condition. Front Digit Health 2023; 5:1196442. [PMID: 37214343 PMCID: PMC10192704 DOI: 10.3389/fdgth.2023.1196442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Abstract
Cystic Fibrosis (CF) is a chronic life-limiting condition that affects multiple organs within the body. Patients must adhere to strict medication regimens, physiotherapy, diet, and attend regular clinic appointments to manage their condition effectively. This necessary but burdensome requirement has prompted investigations into how different digital health technologies can enhance current care by providing the opportunity to virtually monitor patients. This review explores how virtual monitoring has been harnessed for assessment or performance of physiotherapy/exercise, diet/nutrition, symptom monitoring, medication adherence, and wellbeing/mental-health in people with CF. This review will also briefly discuss the potential future of CF virtual monitoring and some common barriers to its current adoption and implementation within CF. Due to the multifaceted nature of CF, it is anticipated that this review will be relevant to not only the CF community, but also those investigating and developing digital health solutions for the management of other chronic diseases.
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Affiliation(s)
- Tamara Vagg
- Cork Centre for Cystic Fibrosis (3CF), Cork University Hospital, Cork, Ireland
- HRB Clinical Research Facility Cork, University College Cork, Cork, Ireland
- Department of Medicine, University College Cork, Cork, Ireland
| | - Kevin F. Deasy
- Cork Centre for Cystic Fibrosis (3CF), Cork University Hospital, Cork, Ireland
- HRB Clinical Research Facility Cork, University College Cork, Cork, Ireland
- Department of Medicine, University College Cork, Cork, Ireland
| | - Wendy W. Chapman
- The Centre for Digital Transformation of Health, University of Melbourne, Melbourne, VIC, Australia
| | - Sarath C. Ranganathan
- Respiratoryand Sleep Medicine Department, Royal Children’s Hospital, Melbourne, VIC, Australia
- Respiratory Diseases Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia
| | - Barry J. Plant
- Cork Centre for Cystic Fibrosis (3CF), Cork University Hospital, Cork, Ireland
- HRB Clinical Research Facility Cork, University College Cork, Cork, Ireland
- Department of Medicine, University College Cork, Cork, Ireland
| | - Shivanthan Shanthikumar
- Respiratoryand Sleep Medicine Department, Royal Children’s Hospital, Melbourne, VIC, Australia
- Respiratory Diseases Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia
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3
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Gecili E, Brokamp C, Rasnick E, Afonso PM, Andrinopoulou ER, Dexheimer JW, Clancy JP, Keogh RH, Ni Y, Palipana A, Pestian T, Vancil A, Zhou GC, Su W, Siracusa C, Ryan P, Szczesniak RD. Built environment factors predictive of early rapid lung function decline in cystic fibrosis. Pediatr Pulmonol 2023; 58:1501-1513. [PMID: 36775890 PMCID: PMC10121820 DOI: 10.1002/ppul.26352] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 01/13/2023] [Accepted: 02/05/2023] [Indexed: 02/14/2023]
Abstract
BACKGROUND The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined. OBJECTIVE To identify built environment characteristics predictive of rapid CF lung function decline. METHODS We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6-20 years, 2012-2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1 ) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. MEASUREMENTS AND MAIN RESULTS The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 μg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103% predicted/year [-0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. CONCLUSION Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions.
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Affiliation(s)
- Emrah Gecili
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Cole Brokamp
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Erika Rasnick
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Pedro M. Afonso
- Department of Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Eleni-Rosalina Andrinopoulou
- Department of Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Judith W. Dexheimer
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- Division of Emergency Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - John P. Clancy
- Department of Pediatrics, University of Cincinnati, 3333 Burnet Ave, Cincinnati, OH, USA
- Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Cystic Fibrosis Foundation, 4550 Montgomery Ave, Bethesda, MD, USA
| | - Ruth H. Keogh
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - Yizhao Ni
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Anushka Palipana
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Teresa Pestian
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Andrew Vancil
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Grace Chen Zhou
- Division of Statistics and Data Science, Department of Mathematics, University of Cincinnati, 155B McMicken Hall, Cincinnati, OH, USA
- St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Weiji Su
- Eli Lilly and Company, Indianapolis, IN, USA
| | - Christopher Siracusa
- Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Patrick Ryan
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Rhonda D. Szczesniak
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, 3333 Burnet Ave, Cincinnati, OH, USA
- Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
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4
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An Animated Functional Data Analysis Interface to Cluster Rapid Lung Function Decline and Enhance Center-Level Care in Cystic Fibrosis. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6671833. [PMID: 34094041 PMCID: PMC8140832 DOI: 10.1155/2021/6671833] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 04/27/2021] [Indexed: 11/17/2022]
Abstract
Identifying disease progression through enhanced decision support tools is key to chronic management in cystic fibrosis at both the patient and care center level. Rapid decline in lung function relative to patient level and center norms is an important predictor of outcomes. Our objectives were to construct and utilize center-level classification of rapid decliners to develop an animated dashboard for comparisons within patients over time, multiple patients within centers, or between centers. A functional data analysis technique known as functional principal components analysis was applied to lung function trajectories from 18,387 patients across 247 accredited centers followed through the United States Cystic Fibrosis Foundation Patient Registry, in order to cluster patients into rapid decline phenotypes. Smaller centers (<30 patients) had older patients with lower baseline lung function and less severe rates of decline and had maximal decline later, compared to medium (30-150 patients) or large (>150 patients) centers. Small centers also had the lowest prevalence of early rapid decliners (17.7%, versus 24% and 25.7% for medium and large centers, resp.). The animated functional data analysis dashboard illustrated clustering and center-specific summaries of the rapid decline phenotypes. Clinical scenarios and utility of the center-level functional principal components analysis (FPCA) approach are considered and discussed.
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Pearson TA, Califf RM, Roper R, Engelgau MM, Khoury MJ, Alcantara C, Blakely C, Boyce CA, Brown M, Croxton TL, Fenton K, Green Parker MC, Hamilton A, Helmchen L, Hsu LL, Kent DM, Kind A, Kravitz J, Papanicolaou GJ, Prosperi M, Quinn M, Price LN, Shireman PK, Smith SM, Szczesniak R, Goff DC, Mensah GA. Precision Health Analytics With Predictive Analytics and Implementation Research: JACC State-of-the-Art Review. J Am Coll Cardiol 2021; 76:306-320. [PMID: 32674794 DOI: 10.1016/j.jacc.2020.05.043] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 05/04/2020] [Indexed: 12/14/2022]
Abstract
Emerging data science techniques of predictive analytics expand the quality and quantity of complex data relevant to human health and provide opportunities for understanding and control of conditions such as heart, lung, blood, and sleep disorders. To realize these opportunities, the information sources, the data science tools that use the information, and the application of resulting analytics to health and health care issues will require implementation research methods to define benefits, harms, reach, and sustainability; and to understand related resource utilization implications to inform policymakers. This JACC State-of-the-Art Review is based on a workshop convened by the National Heart, Lung, and Blood Institute to explore predictive analytics in the context of implementation science. It highlights precision medicine and precision public health as complementary and compelling applications of predictive analytics, and addresses future research and training endeavors that might further foster the application of predictive analytics in clinical medicine and public health.
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Affiliation(s)
- Thomas A Pearson
- College of Medicine and College of Public Health and Health Professions, University of Florida Health Science Center, Gainesville, Florida.
| | - Robert M Califf
- School of Medicine and Duke Clinical Research Institute, Duke University, Durham, North Carolina
| | - Rebecca Roper
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Michael M Engelgau
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Muin J Khoury
- Office of Genomics and Precision Public Health, Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | - Craig Blakely
- School of Public Health and Information Science, University of Louisville, Louisville, Kentucky
| | - Cheryl Anne Boyce
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Marishka Brown
- Division of Lung Diseases, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Thomas L Croxton
- Division of Lung Diseases, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Kathleen Fenton
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Melissa C Green Parker
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | | | - Lorens Helmchen
- Health Policy and Management, Milken Institute School of Public Health, George Washington University, Washington, DC
| | - Lucy L Hsu
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Sackler School of Graduate Biomedical Sciences, Tufts University, Tufts Medical Center, Boston, Massachusetts
| | - Amy Kind
- Department of Medicine Health Services and Care Research Program, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | | | - George John Papanicolaou
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Mattia Prosperi
- College of Medicine and College of Public Health and Health Professions, University of Florida Health Science Center, Gainesville, Florida
| | - Matt Quinn
- Health Technology, Telemedicine and Advanced Technology Research Center, Frederick, Maryland
| | - LeShawndra N Price
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Paula K Shireman
- School of Medicine, University of Texas Health Science Center at San Antonio and South Texas Veterans Health Care System, San Antonio, Texas
| | - Sharon M Smith
- Division of Blood Diseases and Resources, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Rhonda Szczesniak
- Division of Biostatistics & Epidemiology, Division of Pulmonary Medicine, Cincinnati Children's Hospital, Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio
| | - David Calvin Goff
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - George A Mensah
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
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6
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Caley L, Smith L, White H, Peckham D. Average rate of lung function decline in adults with cystic fibrosis in the United Kingdom: Data from the UK CF registry. J Cyst Fibros 2021; 20:86-90. [DOI: 10.1016/j.jcf.2020.04.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/20/2020] [Accepted: 04/21/2020] [Indexed: 10/24/2022]
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7
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Wolfe C, Pestian T, Gecili E, Su W, Keogh RH, Pestian JP, Seid M, Diggle PJ, Ziady A, Clancy JP, Grossoehme DH, Szczesniak RD, Brokamp C. Cystic Fibrosis Point of Personalized Detection (CFPOPD): An Interactive Web Application. JMIR Med Inform 2020; 8:e23530. [PMID: 33325834 PMCID: PMC7773511 DOI: 10.2196/23530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/02/2020] [Accepted: 10/30/2020] [Indexed: 01/01/2023] Open
Abstract
Background Despite steady gains in life expectancy, individuals with cystic fibrosis (CF) lung disease still experience rapid pulmonary decline throughout their clinical course, which can ultimately end in respiratory failure. Point-of-care tools for accurate and timely information regarding the risk of rapid decline is essential for clinical decision support. Objective This study aims to translate a novel algorithm for earlier, more accurate prediction of rapid lung function decline in patients with CF into an interactive web-based application that can be integrated within electronic health record systems, via collaborative development with clinicians. Methods Longitudinal clinical history, lung function measurements, and time-invariant characteristics were obtained for 30,879 patients with CF who were followed in the US Cystic Fibrosis Foundation Patient Registry (2003-2015). We iteratively developed the application using the R Shiny framework and by conducting a qualitative study with care provider focus groups (N=17). Results A clinical conceptual model and 4 themes were identified through coded feedback from application users: (1) ambiguity in rapid decline, (2) clinical utility, (3) clinical significance, and (4) specific suggested revisions. These themes were used to revise our application to the currently released version, available online for exploration. This study has advanced the application’s potential prognostic utility for monitoring individuals with CF lung disease. Further application development will incorporate additional clinical characteristics requested by the users and also a more modular layout that can be useful for care provider and family interactions. Conclusions Our framework for creating an interactive and visual analytics platform enables generalized development of applications to synthesize, model, and translate electronic health data, thereby enhancing clinical decision support and improving care and health outcomes for chronic diseases and disorders. A prospective implementation study is necessary to evaluate this tool’s effectiveness regarding increased communication, enhanced shared decision-making, and improved clinical outcomes for patients with CF.
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Affiliation(s)
- Christopher Wolfe
- Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Teresa Pestian
- Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Emrah Gecili
- Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Weiji Su
- Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, United States
| | - Ruth H Keogh
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - John P Pestian
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Michael Seid
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States.,Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,James M Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Peter J Diggle
- Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom.,Health Data Research UK, London, United Kingdom
| | - Assem Ziady
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States.,Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - John Paul Clancy
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States.,Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Cystic Fibrosis Foundation, Bethesda, MD, United States
| | - Daniel H Grossoehme
- Haslinger Family Pediatric Palliative Care Center, Akron Children's Hospital, Akron, OH, United States.,Rebecca D Considine Research Institute, Akron Children's Hospital, Akron, OH, United States.,Division of Family & Community Medicine, Akron Children's Hospital, Akron, OH, United States
| | - Rhonda D Szczesniak
- Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States.,Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Cole Brokamp
- Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States
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8
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Su W, Wang X, Szczesniak RD. Flexible link functions in a joint hierarchical Gaussian process model. Biometrics 2020; 77:754-764. [PMID: 32413169 DOI: 10.1111/biom.13291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 04/28/2020] [Accepted: 05/01/2020] [Indexed: 11/30/2022]
Abstract
Many longitudinal studies often require jointly modeling a biomarker and an event outcome, in order to provide more accurate inference and dynamic prediction of disease progression. Cystic fibrosis (CF) studies have illustrated the benefits of these models, primarily examining the joint evolution of lung-function decline and survival. We propose a novel joint model within the shared-parameter framework that accommodates nonlinear lung-function trajectories, in order to provide more accurate inference on lung-function decline over time and to examine the association between evolution of lung function and risk of a pulmonary exacerbation (PE) event recurrence. Specifically, a two-level Gaussian process (GP) is used to estimate the nonlinear longitudinal trajectories and a flexible link function is introduced for a more accurate depiction of the binary process on the event outcome. Bayesian model assessment is used to evaluate each component of the joint model in simulation studies and an application to longitudinal data on patients receiving care from a CF center. A nonlinear structure is suggested by both longitudinal continuous and binary evaluations. Including a flexible link function improves model fit to these data. The proposed hierarchical GP model with a flexible power link function where Laplace distribution is the baseline (spep) has the best fit of all joint models considered, characterizing how accelerated lung-function decline corresponds to increased odds of experiencing another PE.
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Affiliation(s)
- Weiji Su
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Ohio.,Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Xia Wang
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Ohio
| | - Rhonda D Szczesniak
- Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio
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9
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Szczesniak RD, Su W, Brokamp C, Keogh RH, Pestian JP, Seid M, Diggle PJ, Clancy JP. Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression. Stat Med 2020; 39:740-756. [PMID: 31816119 PMCID: PMC7028099 DOI: 10.1002/sim.8443] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 09/23/2019] [Accepted: 11/16/2019] [Indexed: 11/29/2022]
Abstract
Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung-function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for "nowcasting" rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between-patient heterogeneity through random effects. Corresponding lung-function decline at time t is defined as the rate of change, S'(t). We predict S'(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single-center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real-time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1-Q3) were 0.817 (0.814-0.822) and 0.745 (0.741-0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical-monitoring approach.
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Affiliation(s)
- Rhonda D. Szczesniak
- Division of Biostatistics & EpidemiologyCincinnati Children's Hospital Medical Center and Department of Pediatrics, University of CincinnatiCincinnatiOhio
| | - Weiji Su
- Department of Mathematical SciencesUniversity of CincinnatiCincinnatiOhio
| | - Cole Brokamp
- Division of Biostatistics & EpidemiologyCincinnati Children's Hospital Medical Center and Department of Pediatrics, University of CincinnatiCincinnatiOhio
| | - Ruth H. Keogh
- Department of Medical StatisticsLondon School of Hygiene and Tropical MedicineLondonUK
| | - John P. Pestian
- Division of Biomedical InformaticsCincinnati Children's Hospital Medical Center, and Department of Pediatrics, University of CincinnatiCincinnatiOhio
| | - Michael Seid
- James M. Anderson Center for Health Systems Excellence and Department of PediatricsUniversity of CincinnatiCincinnatiOhio
| | - Peter J. Diggle
- CHICASLancaster Medical School Lancaster University Lancaster, UK and Health Data Research UKLondonUK
| | - John P. Clancy
- Division of Pulmonary MedicineCincinnati Children's Hospital Medical Center and Department of Pediatrics, University of CincinnatiCincinnatiOhio
- Cystic Fibrosis FoundationBethesdaMaryland
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