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Qian F, van den Boom W, See KC. The new global definition of acute respiratory distress syndrome: insights from the MIMIC-IV database. Intensive Care Med 2024; 50:608-609. [PMID: 38483560 DOI: 10.1007/s00134-024-07383-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2024] [Indexed: 04/16/2024]
Affiliation(s)
- Fang Qian
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 12, Singapore, 119228, Singapore
| | - Willem van den Boom
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 12, Singapore, 119228, Singapore.
| | - Kay Choong See
- Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Hospital, 1E Kent Ridge Road, NUHS Tower Block, Level 10, 119074, Singapore, Singapore
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2
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Qian F, van den Boom W, See KC. Different oxygenation targets for stable COPD and acute exacerbations in the ICU. Author's reply. Intensive Care Med 2023; 49:1430-1432. [PMID: 37750903 DOI: 10.1007/s00134-023-07229-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/08/2023] [Indexed: 09/27/2023]
Affiliation(s)
- Fang Qian
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, 12 Science Drive 2, #13-01, Singapore, 117549, Singapore
| | - Willem van den Boom
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, 12 Science Drive 2, #13-01, Singapore, 117549, Singapore.
| | - Kay Choong See
- Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Hospital, Singapore, Singapore
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3
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Qian F, van den Boom W, See KC. Real-world evidence challenges controlled hypoxemia guidelines for critically ill patients with chronic obstructive pulmonary disease. Intensive Care Med 2023; 49:1133-1135. [PMID: 37462696 DOI: 10.1007/s00134-023-07166-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 07/07/2023] [Indexed: 09/14/2023]
Affiliation(s)
- Fang Qian
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, 12 Science Drive 2, #13-01, Singapore, 117549, Singapore
| | - Willem van den Boom
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, 12 Science Drive 2, #13-01, Singapore, 117549, Singapore.
| | - Kay Choong See
- Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Hospital, Singapore, Singapore
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4
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Franzolini B, Cremaschi A, van den Boom W, De Iorio M. Bayesian clustering of multiple zero-inflated outcomes. Philos Trans A Math Phys Eng Sci 2023; 381:20220145. [PMID: 36970823 PMCID: PMC10041346 DOI: 10.1098/rsta.2022.0145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 09/15/2022] [Indexed: 06/18/2023]
Abstract
Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
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Affiliation(s)
- Beatrice Franzolini
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Andrea Cremaschi
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Willem van den Boom
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Maria De Iorio
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
- Department of Statistical Science, University College London, London, UK
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5
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Feng SV, van den Boom W, De Iorio M, Thng GJ, Chan JKY, Chen HY, Tan KH, Kee MZL. Joint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach. J Appl Stat 2023; 51:388-405. [PMID: 38283054 PMCID: PMC10810649 DOI: 10.1080/02664763.2022.2154329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 09/23/2022] [Indexed: 01/14/2023]
Abstract
Maternal depression and anxiety through pregnancy have lasting societal impacts. It is thus crucial to understand the trajectories of its progression from preconception to postnatal period, and the risk factors associated with it. Within the Bayesian framework, we propose to jointly model seven outcomes, of which two are physiological and five non-physiological indicators of maternal depression and anxiety over time. We model the former two by a Gaussian process and the latter by an autoregressive model, while imposing a multidimensional Dirichlet process prior on the subject-specific random effects to account for subject heterogeneity and induce clustering. The model allows for the inclusion of covariates through a regression term. Our findings reveal four distinct clusters of trajectories of the seven health outcomes, characterising women's mental health progression from before to after pregnancy. Importantly, our results caution against the loose use of hair corticosteroids as a biomarker, or even a causal factor, for pregnancy mental health progression. Additionally, the regression analysis reveals a range of preconception determinants and risk factors for depressive and anxiety symptoms during pregnancy.
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Affiliation(s)
| | - Willem van den Boom
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Agency for Science, Technology and Research, Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - Maria De Iorio
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Agency for Science, Technology and Research, Singapore Institute for Clinical Sciences, Singapore, Singapore
- Department of Statistical Science, University College London, London, UK
| | - Gladi J. Thng
- Agency for Science, Technology and Research, Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - Jerry K. Y. Chan
- Department of Reproductive Medicine, KK Women's and Children's Hospital, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Helen Y. Chen
- Duke-NUS Medical School, Singapore, Singapore
- Department of Psychological Medicine, KK Women's and Children's Hospital, Singapore, Singapore
| | - Kok Hian Tan
- Duke-NUS Medical School, Singapore, Singapore
- Department of Maternal Fetal Medicine, KK Women's and Children's Hospital, Singapore, Singapore
| | - Michelle Z. L. Kee
- Agency for Science, Technology and Research, Singapore Institute for Clinical Sciences, Singapore, Singapore
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6
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Young AL, van den Boom W, Schroeder RA, Krishnamoorthy V, Raghunathan K, Wu HT, Dunson DB. Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data. PLoS One 2023; 18:e0284904. [PMID: 37099536 PMCID: PMC10132663 DOI: 10.1371/journal.pone.0284904] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 04/11/2023] [Indexed: 04/27/2023] Open
Abstract
Given a large clinical database of longitudinal patient information including many covariates, it is computationally prohibitive to consider all types of interdependence between patient variables of interest. This challenge motivates the use of mutual information (MI), a statistical summary of data interdependence with appealing properties that make it a suitable alternative or addition to correlation for identifying relationships in data. MI: (i) captures all types of dependence, both linear and nonlinear, (ii) is zero only when random variables are independent, (iii) serves as a measure of relationship strength (similar to but more general than R2), and (iv) is interpreted the same way for numerical and categorical data. Unfortunately, MI typically receives little to no attention in introductory statistics courses and is more difficult than correlation to estimate from data. In this article, we motivate the use of MI in the analyses of epidemiologic data, while providing a general introduction to estimation and interpretation. We illustrate its utility through a retrospective study relating intraoperative heart rate (HR) and mean arterial pressure (MAP). We: (i) show postoperative mortality is associated with decreased MI between HR and MAP and (ii) improve existing postoperative mortality risk assessment by including MI and additional hemodynamic statistics.
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Affiliation(s)
- Alexander L Young
- Department of Statistics, Harvard University, Cambridge, Massachusetts, United States of America
| | - Willem van den Boom
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Rebecca A Schroeder
- Department of Anesthesiology, Duke University, Durham, North Carolina, United States of America
| | - Vijay Krishnamoorthy
- Department of Anesthesiology, Duke University, Durham, North Carolina, United States of America
| | - Karthik Raghunathan
- Department of Anesthesiology, Duke University, Durham, North Carolina, United States of America
| | - Hau-Tieng Wu
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
| | - David B Dunson
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
- Department of Statistical Science, Duke University, Durham, North Carolina, United States of America
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7
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van den Boom W, Beskos A, De Iorio M. The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2050250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
| | - Alexandros Beskos
- Department of Statistical Science, University College London
- Alan Turing Institute, UK
| | - Maria De Iorio
- Yong Loo Lin School of Medicine, National University of Singapore
- Department of Statistical Science, University College London
- Singapore Institute for Clinical Sciences, A*STAR
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8
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van den Boom W, De Iorio M, Tallarita M. Bayesian inference on the number of recurrent events: A joint model of recurrence and survival. Stat Methods Med Res 2021; 31:139-153. [PMID: 34812661 DOI: 10.1177/09622802211048059] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The number of recurrent events before a terminating event is often of interest. For instance, death terminates an individual's process of rehospitalizations and the number of rehospitalizations is an important indicator of economic cost. We propose a model in which the number of recurrences before termination is a random variable of interest, enabling inference and prediction on it. Then, conditionally on this number, we specify a joint distribution for recurrence and survival. This novel conditional approach induces dependence between recurrence and survival, which is often present, for instance, due to frailty that affects both. Additional dependence between recurrence and survival is introduced by the specification of a joint distribution on their respective frailty terms. Moreover, through the introduction of an autoregressive model, our approach is able to capture the temporal dependence in the recurrent events trajectory. A non-parametric random effects distribution for the frailty terms accommodates population heterogeneity and allows for data-driven clustering of the subjects. A tailored Gibbs sampler involving reversible jump and slice sampling steps implements posterior inference. We illustrate our model on colorectal cancer data, compare its performance with existing approaches and provide appropriate inference on the number of recurrent events.
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Affiliation(s)
- Willem van den Boom
- Yale-NUS College, 37580National University of Singapore, Singapore.,Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore
| | - Maria De Iorio
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore.,Yong Loo Lin School of Medicine, 37580National University of Singapore, Singapore.,Department of Statistical Science, 4919University College London, UK
| | - Marta Tallarita
- Department of Statistical Science, 4919University College London, UK
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9
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Lysaght T, Ballantyne A, Toh HJ, Lau A, Ong S, Schaefer O, Shiraishi M, van den Boom W, Xafis V, Tai ES. Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore. J Pers Med 2021; 11:921. [PMID: 34575698 PMCID: PMC8465970 DOI: 10.3390/jpm11090921] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/13/2021] [Accepted: 09/13/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Precision medicine (PM) programs typically use broad consent. This approach requires maintenance of the social license and public trust. The ultimate success of PM programs will thus likely be contingent upon understanding public expectations about data sharing and establishing appropriate governance structures. There is a lack of data on public attitudes towards PM in Asia. METHODS The aim of the research was to measure the priorities and preferences of Singaporeans for sharing health-related data for PM. We used adaptive choice-based conjoint analysis (ACBC) with four attributes: uses, users, data sensitivity and consent. We recruited a representative sample of n = 1000 respondents for an in-person household survey. RESULTS Of the 1000 respondents, 52% were female and majority were in the age range of 40-59 years (40%), followed by 21-39 years (33%) and 60 years and above (27%). A total of 64% were generally willing to share de-identified health data for IRB-approved research without re-consent for each study. Government agencies and public institutions were the most trusted users of data. The importance of the four attributes on respondents' willingness to share data were: users (39.5%), uses (28.5%), data sensitivity (19.5%), consent (12.6%). Most respondents found it acceptable for government agencies and hospitals to use de-identified data for health research with broad consent. Our sample was consistent with official government data on the target population with 52% being female and majority in the age range of 40-59 years (40%), followed by 21-39 years (33%) and 60 years and above (27%). CONCLUSIONS While a significant body of prior research focuses on preferences for consent, our conjoint analysis found consent was the least important attribute for sharing data. Our findings suggest the social license for PM data sharing in Singapore currently supports linking health and genomic data, sharing with public institutions for health research and quality improvement; but does not support sharing with private health insurers or for private commercial use.
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Affiliation(s)
- Tamra Lysaght
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore; (T.L.); (A.B.); (S.O.); (O.S.); (M.S.); (V.X.)
| | - Angela Ballantyne
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore; (T.L.); (A.B.); (S.O.); (O.S.); (M.S.); (V.X.)
- Department of Primary Health Care & General Practice, University of Otago, Wellington 6021, New Zealand
| | - Hui Jin Toh
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore; (T.L.); (A.B.); (S.O.); (O.S.); (M.S.); (V.X.)
| | - Andrew Lau
- Projective Insights Consultants, Singapore 590003, Singapore;
| | - Serene Ong
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore; (T.L.); (A.B.); (S.O.); (O.S.); (M.S.); (V.X.)
| | - Owen Schaefer
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore; (T.L.); (A.B.); (S.O.); (O.S.); (M.S.); (V.X.)
| | - Makoto Shiraishi
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore; (T.L.); (A.B.); (S.O.); (O.S.); (M.S.); (V.X.)
| | - Willem van den Boom
- Yale-NUS College, National University of Singapore, Singapore 138527, Singapore;
| | - Vicki Xafis
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore; (T.L.); (A.B.); (S.O.); (O.S.); (M.S.); (V.X.)
| | - E Shyong Tai
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore;
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore
- Precision Health Research, Singapore 139234, Singapore
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10
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van den Boom W, Hoy M, Sankaran J, Liu M, Chahed H, Feng M, See KC. Response. Chest 2021; 158:1287-1288. [PMID: 32892869 DOI: 10.1016/j.chest.2020.04.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 04/17/2020] [Indexed: 10/23/2022] Open
Affiliation(s)
| | - Michael Hoy
- School of Electrical Engineering, Nanyang Technological University, Singapore
| | - Jagadish Sankaran
- Department of Biological Sciences, National University of Singapore, Singapore
| | - Mengru Liu
- School of Information Systems, Singapore Management University, Singapore
| | - Haroun Chahed
- Yale-NUS College, National University of Singapore, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore
| | - Kay Choong See
- Division of Respiratory and Critical Care Medicine, University Medicine Cluster, National University Health System, Singapore
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11
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van den Boom W, Hoy M, Sankaran J, Liu M, Chahed H, Feng M, See KC. The Search for Optimal Oxygen Saturation Targets in Critically Ill Patients: Observational Data From Large ICU Databases. Chest 2019; 157:566-573. [PMID: 31589844 DOI: 10.1016/j.chest.2019.09.015] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 07/28/2019] [Accepted: 09/08/2019] [Indexed: 10/25/2022] Open
Abstract
BACKGROUND Although low oxygen saturations are generally regarded as deleterious, recent studies in ICU patients have shown that a liberal oxygen strategy increases mortality. However, the optimal oxygen saturation target remains unclear. The goal of this study was to determine the optimal range by using real-world data. METHODS Replicate retrospective analyses were conducted of two electronic medical record databases: the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care III database (MIMIC). Only patients with at least 48 h of oxygen therapy were included. Nonlinear regression was used to analyze the association between median pulse oximetry-derived oxygen saturation (Spo2) and hospital mortality. We derived an optimal range of Spo2 and analyzed the association between the percentage of time within the optimal range of Spo2 and hospital mortality. All models adjusted for age, BMI, sex, and Sequential Organ Failure Assessment score. Subgroup analyses included ICU types, main diagnosis, and comorbidities. RESULTS The analysis identified 26,723 patients from eICU-CRD and 8,564 patients from MIMIC. The optimal range of Spo2 was 94% to 98% in both databases. The percentage of time patients were within the optimal range of Spo2 was associated with decreased hospital mortality (OR of 80% vs 40% of the measurements within the optimal range, 0.42 [95% CI, 0.40-0.43] for eICU-CRD and 0.53 [95% CI, 0.50-0.55] for MIMIC). This association was consistent across subgroup analyses. CONCLUSIONS The optimal range of Spo2 was 94% to 98% and should inform future trials of oxygen therapy.
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Affiliation(s)
- Willem van den Boom
- Yale-NUS College, National University Health System, National University of Singapore, Singapore.
| | - Michael Hoy
- School of Electrical Engineering, Nanyang Technological University
| | - Jagadish Sankaran
- the Department of Biological Sciences, National University Health System, National University of Singapore, Singapore
| | - Mengru Liu
- School of Information Systems, Singapore Management University, Singapore
| | - Haroun Chahed
- Yale-NUS College, National University Health System, National University of Singapore, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore
| | - Kay Choong See
- Division of Respiratory and Critical Care Medicine, University Medicine Cluster, National University Health System
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12
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van den Boom W, Mao C, Schroeder RA, Dunson DB. Extrema-weighted feature extraction for functional data. Bioinformatics 2018; 34:2457-2464. [PMID: 29506206 DOI: 10.1093/bioinformatics/bty120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 02/27/2018] [Indexed: 11/14/2022] Open
Abstract
Motivation Although there is a rich literature on methods for assessing the impact of functional predictors, the focus has been on approaches for dimension reduction that do not suit certain applications. Examples of standard approaches include functional linear models, functional principal components regression and cluster-based approaches, such as latent trajectory analysis. This article is motivated by applications in which the dynamics in a predictor, across times when the value is relatively extreme, are particularly informative about the response. For example, physicians are interested in relating the dynamics of blood pressure changes during surgery to post-surgery adverse outcomes, and it is thought that the dynamics are more important when blood pressure is significantly elevated or lowered. Results We propose a novel class of extrema-weighted feature (XWF) extraction models. Key components in defining XWFs include the marginal density of the predictor, a function up-weighting values at extreme quantiles of this marginal, and functionals characterizing local dynamics. Algorithms are proposed for fitting of XWF-based regression and classification models, and are compared with current methods for functional predictors in simulations and a blood pressure during surgery application. XWFs find features of intraoperative blood pressure trajectories that are predictive of postoperative mortality. By their nature, most of these features cannot be found by previous methods. Availability and implementation The R package 'xwf' is available at the CRAN repository: https://cran.r-project.org/package=xwf. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Callie Mao
- Department of Statistical Science, Duke University, Durham, NC, USA
| | - Rebecca A Schroeder
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC, USA
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
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13
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van den Boom W, Schroeder RA, Manning MW, Setji TL, Fiestan GO, Dunson DB. Effect of A1C and Glucose on Postoperative Mortality in Noncardiac and Cardiac Surgeries. Diabetes Care 2018; 41:782-788. [PMID: 29440113 DOI: 10.2337/dc17-2232] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 01/16/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Hemoglobin A1c (A1C) is used in assessment of patients for elective surgeries because hyperglycemia increases risk of adverse events. However, the interplay of A1C, glucose, and surgical outcomes remains unclarified, with often only two of these three factors considered simultaneously. We assessed the association of preoperative A1C with perioperative glucose control and their relationship with 30-day mortality. RESEARCH DESIGN AND METHODS Retrospective analysis on 431,480 surgeries within the Duke University Health System determined the association of preoperative A1C with perioperative glucose (averaged over the first 3 postoperative days) and 30-day mortality among 6,684 noncardiac and 6,393 cardiac surgeries with A1C and glucose measurements. A generalized additive model was used, enabling nonlinear relationships. RESULTS A1C and glucose were strongly associated. Glucose and mortality were positively associated for noncardiac cases: 1.0% mortality at mean glucose of 100 mg/dL and 1.6% at mean glucose of 200 mg/dL. For cardiac procedures, there was a striking U-shaped relationship between glucose and mortality, ranging from 4.5% at 100 mg/dL to a nadir of 1.5% at 140 mg/dL and rising again to 6.9% at 200 mg/dL. A1C and 30-day mortality were not associated when controlling for glucose in noncardiac or cardiac procedures. CONCLUSIONS Although A1C is positively associated with perioperative glucose, it is not associated with increased 30-day mortality after controlling for glucose. Perioperative glucose predicts 30-day mortality, linearly in noncardiac and nonlinearly in cardiac procedures. This confirms that perioperative glucose control is related to surgical outcomes but that A1C, reflecting antecedent glycemia, is a less useful predictor.
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Affiliation(s)
| | - Rebecca A Schroeder
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC
| | - Michael W Manning
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC
| | - Tracy L Setji
- Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, Duke University School of Medicine, Durham, NC
| | - Gic-Owens Fiestan
- Department of Neurobiology, Duke University School of Medicine, Durham, NC
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC
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