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Hiemstra FW, Stenvers DJ, Kalsbeek A, de Jonge E, van Westerloo DJ, Kervezee L. Daily variation in blood glucose levels during continuous enteral nutrition in patients on the intensive care unit: a retrospective observational study. EBioMedicine 2024; 104:105169. [PMID: 38821022 PMCID: PMC11177052 DOI: 10.1016/j.ebiom.2024.105169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND The circadian timing system coordinates daily cycles in physiological functions, including glucose metabolism and insulin sensitivity. Here, the aim was to characterise the 24-h variation in glucose levels in critically ill patients during continuous enteral nutrition after controlling for potential sources of bias. METHODS Time-stamped clinical data from adult patients who stayed in the Intensive Care Unit (ICU) for at least 4 days and received enteral nutrition were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Linear mixed-effects and XGBoost modelling were used to determine the effect of time of day on blood glucose values. FINDINGS In total, 207,647 glucose measurements collected during enteral nutrition were available from 6,929 ICU patients (3,948 males and 2,981 females). Using linear mixed-effects modelling, time of day had a significant effect on blood glucose levels (p < 0.001), with a peak of 9.6 [9.5-9.6; estimated marginal means, 95% CI] mmol/L at 10:00 in the morning and a trough of 8.6 [8.5-8.6] mmol/L at 02:00 at night. A similar impact of time of day on glucose levels was found with the XGBoost regression model. INTERPRETATION These results revealed marked 24-h variation in glucose levels in ICU patients even during continuous enteral nutrition. This 24-h pattern persists after adjustment for potential sources of bias, suggesting it may be the result of endogenous biological rhythmicity. FUNDING This work was supported by a VENI grant from the Netherlands Organisation for Health Research and Development (ZonMw), an institutional project grant, and by the Dutch Research Council (NWO).
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Affiliation(s)
- Floor W Hiemstra
- Department of Intensive Care, Leiden University Medical Center, Albinusdreef 2, Leiden 2333 ZA, the Netherlands; Group of Circadian Medicine, Department of Cell and Chemical Biology, Leiden University Medical Center, Albinusdreef 2, Leiden 2333 ZA, the Netherlands
| | - Dirk Jan Stenvers
- Department of Endocrinology and Metabolism, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands; Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands; Department of Endocrinology and Metabolism, Amsterdam UMC Location Vrije Universiteit, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
| | - Andries Kalsbeek
- Department of Endocrinology and Metabolism, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands; Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands; Netherlands Institute for Neuroscience (NIN), Royal Dutch Academy of Arts and Sciences (KNAW), Meibergdreef 47, Amsterdam 1105 BA, the Netherlands; Laboratory of Endocrinology, Department of Laboratory Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
| | - Evert de Jonge
- Department of Intensive Care, Leiden University Medical Center, Albinusdreef 2, Leiden 2333 ZA, the Netherlands
| | - David J van Westerloo
- Department of Intensive Care, Leiden University Medical Center, Albinusdreef 2, Leiden 2333 ZA, the Netherlands
| | - Laura Kervezee
- Group of Circadian Medicine, Department of Cell and Chemical Biology, Leiden University Medical Center, Albinusdreef 2, Leiden 2333 ZA, the Netherlands.
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Wassell M, Vitiello A, Butler-Henderson K, Verspoor K, Pollard H. Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders. JOURNAL OF OCCUPATIONAL REHABILITATION 2024:10.1007/s10926-024-10196-w. [PMID: 38739344 DOI: 10.1007/s10926-024-10196-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/07/2024] [Indexed: 05/14/2024]
Abstract
PURPOSE Electronic Health Records (EHRs) can contain vast amounts of clinical information that could be reused in modelling outcomes of work-related musculoskeletal disorders (WMSDs). Determining the generalizability of an EHR dataset is an important step in determining the appropriateness of its reuse. The study aims to describe the EHR dataset used by occupational musculoskeletal therapists and determine whether the EHR dataset is generalizable to the Australian workers' population and injury characteristics seen in workers' compensation claims. METHODS Variables were considered if they were associated with outcomes of WMSDs and variables data were available. Completeness and external validity assessment analysed frequency distributions, percentage of records and confidence intervals. RESULTS There were 48,434 patient care plans across 10 industries from 2014 to 2021. The EHR collects information related to clinical interventions, health and psychosocial factors, job demands, work accommodations as well as workplace culture, which have all been shown to be valuable variables in determining outcomes to WMSDs. Distributions of age, duration of employment, gender and region of birth were mostly similar to the Australian workforce. Upper limb WMSDs were higher in the EHR compared to workers' compensation claims and diagnoses were similar. CONCLUSION The study shows the EHR has strong potential to be used for further research into WMSDs as it has a similar population to the Australian workforce, manufacturing industry and workers' compensation claims. It contains many variables that may be relevant in modelling outcomes to WMSDs that are not typically available in existing datasets.
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Affiliation(s)
- M Wassell
- School of Computing Technologies, RMIT University, Melbourne, Australia.
| | - A Vitiello
- School of Health, Medical and Applied Sciences, Central Queensland University, Queensland, Australia
| | - K Butler-Henderson
- STEM|Health and Biomedical Sciences, RMIT University, Melbourne, Australia
| | - K Verspoor
- School of Computing Technologies, RMIT University, Melbourne, Australia
| | - H Pollard
- Faculty of Health Sciences, Durban University of Technology, Durban, South Africa
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Jabeen S, Rahman M, Siddique AB, Hasan M, Matin R, Rahman QSU, AKM TH, Alim A, Nadia N, Mahmud M, Islam J, Islam MS, Haider MS, Dewan F, Begum F, Barua U, Anam MT, Islam A, Razzak KSB, Ameen S, Hossain AT, Nahar Q, Ahmed A, El Arifeen S, Rahman AE. Introducing a digital emergency obstetric and newborn care register for indoor obstetric patient management: An implementation research in selected public health care facilities of Bangladesh. J Glob Health 2024; 14:04075. [PMID: 38722093 PMCID: PMC11082830 DOI: 10.7189/jogh.14.04075] [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: 05/12/2024] Open
Abstract
Background Digital health records have emerged as vital tools for improving health care delivery and patient data management. Acknowledging the gaps in data recording by a paper-based register, the emergency obstetric and newborn care (EmONC) register used in the labour ward was digitised. In this study, we aimed to assess the implementation outcome of the digital register in selected public health care facilities in Bangladesh. Methods Extensive collaboration with stakeholders facilitated the development of an android-based electronic register from the paper-based register in the labour rooms of the selected district and sub-district level public health facilities of Bangladesh. We conducted a study to assess the implementation outcome of introducing the digital EmONC register in the labour ward. Results The digital register demonstrated high usability with a score of 83.7 according to the system usability scale, and health care providers found it highly acceptable, with an average score exceeding 95% using the technology acceptance model. The adoption rate reached an impressive 98% (95% confidence interval (CI) = 98-99), and fidelity stood at 90% (95% CI = 88-91) in the digital register, encompassing more than 80% of data elements. Notably, fidelity increased significantly over the implementation period of six months. The digital system proved a high utility rate of 89% (95% CI = 88-91), and all outcome variables exceeded the predefined benchmark. Conclusions The implementation outcome assessment underscores the potential of the digital register to enhance maternal and newborn health care in Bangladesh. Its user-friendliness, improved data completeness, and high adoption rates indicate its capacity to streamline health care data management and improve the quality of care.
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Affiliation(s)
- Sabrina Jabeen
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Mahiur Rahman
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | | | - Mehedi Hasan
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Rubaiya Matin
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | | | | | - Azizul Alim
- Directorate General of Health Services, Ministry of Health and Family Welfare, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh
| | - Nuzhat Nadia
- Directorate General of Health Services, Ministry of Health and Family Welfare, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh
| | - Mustufa Mahmud
- Directorate General of Health Services, Ministry of Health and Family Welfare, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh
| | - Jahurul Islam
- Directorate General of Health Services, Ministry of Health and Family Welfare, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh
| | - Muhammad Shariful Islam
- Directorate General of Health Services, Ministry of Health and Family Welfare, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh
| | - Mohammad Sabbir Haider
- Directorate General of Health Services, Ministry of Health and Family Welfare, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh
| | - Farhana Dewan
- Obstetrical and Gynaecological Society of Bangladesh, Dhaka, Bangladesh
| | - Ferdousi Begum
- Obstetrical and Gynaecological Society of Bangladesh, Dhaka, Bangladesh
| | - Uchchash Barua
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | | | - Abirul Islam
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | | | - Shafiqul Ameen
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | | | - Quamrun Nahar
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Anisuddin Ahmed
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Shams El Arifeen
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
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Kervezee L, Dashti HS, Pilz LK, Skarke C, Ruben MD. Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges. PLOS DIGITAL HEALTH 2024; 3:e0000511. [PMID: 38781189 PMCID: PMC11115276 DOI: 10.1371/journal.pdig.0000511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
A wealth of data is available from electronic health records (EHR) that are collected as part of routine clinical care in hospitals worldwide. These rich, longitudinal data offer an attractive object of study for the field of circadian medicine, which aims to translate knowledge of circadian rhythms to improve patient health. This narrative review aims to discuss opportunities for EHR in studies of circadian medicine, highlight the methodological challenges, and provide recommendations for using these data to advance the field. In the existing literature, we find that data collected in real-world clinical settings have the potential to shed light on key questions in circadian medicine, including how 24-hour rhythms in clinical features are associated with-or even predictive of-health outcomes, whether the effect of medication or other clinical activities depend on time of day, and how circadian rhythms in physiology may influence clinical reference ranges or sampling protocols. However, optimal use of EHR to advance circadian medicine requires careful consideration of the limitations and sources of bias that are inherent to these data sources. In particular, time of day influences almost every interaction between a patient and the healthcare system, creating operational 24-hour patterns in the data that have little or nothing to do with biology. Addressing these challenges could help to expand the evidence base for the use of EHR in the field of circadian medicine.
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Affiliation(s)
- Laura Kervezee
- Group of Circadian Medicine, Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Hassan S. Dashti
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Luísa K. Pilz
- Department of Anesthesiology and Intensive Care Medicine CCM / CVK, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- ECRC Experimental and Clinical Research Center, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Carsten Skarke
- Institute for Translational Medicine and Therapeutics (ITMAT), University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Chronobiology and Sleep Institute (CSI), University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Marc D. Ruben
- Divisions of Pulmonary and Sleep Medicine and Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
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Zhang H, Clark AS, Hubbard RA. A Quantitative Bias Analysis Approach to Informative Presence Bias in Electronic Health Records. Epidemiology 2024; 35:349-358. [PMID: 38630509 PMCID: PMC11027938 DOI: 10.1097/ede.0000000000001714] [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] [Indexed: 04/19/2024]
Abstract
Accurate outcome and exposure ascertainment in electronic health record (EHR) data, referred to as EHR phenotyping, relies on the completeness and accuracy of EHR data for each individual. However, some individuals, such as those with a greater comorbidity burden, visit the health care system more frequently and thus have more complete data, compared with others. Ignoring such dependence of exposure and outcome misclassification on visit frequency can bias estimates of associations in EHR analysis. We developed a framework for describing the structure of outcome and exposure misclassification due to informative visit processes in EHR data and assessed the utility of a quantitative bias analysis approach to adjusting for bias induced by informative visit patterns. Using simulations, we found that this method produced unbiased estimates across all informative visit structures, if the phenotype sensitivity and specificity were correctly specified. We applied this method in an example where the association between diabetes and progression-free survival in metastatic breast cancer patients may be subject to informative presence bias. The quantitative bias analysis approach allowed us to evaluate robustness of results to informative presence bias and indicated that findings were unlikely to change across a range of plausible values for phenotype sensitivity and specificity. Researchers using EHR data should carefully consider the informative visit structure reflected in their data and use appropriate approaches such as the quantitative bias analysis approach described here to evaluate robustness of study findings.
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Affiliation(s)
- Hanxi Zhang
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Amy S Clark
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rebecca A Hubbard
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
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Qureshi AI, Baskett WI, Lodhi A, Gomez F, Arora N, Chandrasekaran PN, Siddiq F, Gomez CR, Shyu CR. Assessment of Blood Pressure and Heart Rate Related Variables in Acute Stroke Patients Receiving Intravenous Antihypertensive Medication Infusions. Neurocrit Care 2024:10.1007/s12028-024-01974-8. [PMID: 38649651 DOI: 10.1007/s12028-024-01974-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 03/07/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND We performed an analysis of a large intensive care unit electronic database to provide preliminary estimates of various blood pressure parameters in patients with acute stroke receiving intravenous (IV) antihypertensive medication and determine the relationship with in-hospital outcomes. METHODS We identified the relationship between pre-treatment and post-treatment systolic blood pressure (SBP) and heart rate (HR)-related variables and in-hospital mortality and acute kidney injury in patients with acute stroke receiving IV clevidipine, nicardipine, or nitroprusside using data provided in the Medical Information Mart for Intensive Care (MIMIC) IV database. RESULTS A total of 1830 patients were treated with IV clevidipine (n = 64), nicardipine (n = 1623), or nitroprusside (n = 143). The standard deviations [SDs] of pre-treatment SBP (16.3 vs. 13.7, p ≤ 0.001) and post-treatment SBP (15.4 vs. 14.4, p = 0.004) were higher in patients who died compared with those who survived, particularly in patients with intracerebral hemorrhage (ICH). The mean SBP was significantly lower post treatment compared with pre-treatment values for clevidipine (130.7 mm Hg vs. 142.5 mm Hg, p = 0.006), nicardipine (132.8 mm Hg vs. 141.6 mm Hg, p ≤ 0.001), and nitroprusside (126.2 mm Hg vs. 139.6 mm Hg, p ≤ 0.001). There were no differences in mean SDs post treatment compared with pre-treatment values for clevidipine (14.5 vs. 13.5, p = 0.407), nicardipine (14.2 vs. 14.6, p = 0.142), and nitroprusside (14.8 vs. 14.8, p = 0.997). The SDs of pre-treatment and post-treatment SBP were not significantly different in patients with ischemic stroke treated with IV clevidipine, nicardipine, or nitroprusside or for patients with ICH treated with IV clevidipine or nitroprusside. However, patients with ICH treated with IV nicardipine had a significantly higher SD of post-treatment SBP (13.1 vs. 14.2, p = 0.0032). CONCLUSIONS We found that SBP fluctuations were associated with in-hospital mortality in patients with acute stroke. IV antihypertensive medication reduced SBP but did not reduce SBP fluctuations in this observational study. Our results highlight the need for optimizing therapeutic interventions to reduce SBP fluctuations in patients with acute stroke.
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Affiliation(s)
- Adnan I Qureshi
- Zeenat Qureshi Stroke Institute, ZQSI, St. Cloud, MN, USA.
- Department of Neurology, University of Missouri, Columbia, MO, USA.
| | - William I Baskett
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Abdullah Lodhi
- Zeenat Qureshi Stroke Institute, ZQSI, St. Cloud, MN, USA
| | - Francisco Gomez
- Department of Neurology, University of Missouri, Columbia, MO, USA
| | - Niraj Arora
- Department of Neurology, University of Missouri, Columbia, MO, USA
| | | | - Farhan Siddiq
- Division of Neurosurgery, University of Missouri, Columbia, MO, USA
| | - Camilo R Gomez
- Department of Neurology, University of Missouri, Columbia, MO, USA
| | - Chi-Ren Shyu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
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Cogan AM, Roberts P, Mallinson T. Using Electronic Health Record Data for Occupational Therapy Health Services Research: Invited Commentary. OTJR-OCCUPATION PARTICIPATION AND HEALTH 2024:15394492241246544. [PMID: 38622903 DOI: 10.1177/15394492241246544] [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: 04/17/2024]
Abstract
Health services research (HSR) is a field of study that examines how social factors, financing systems, organizational structures and processes, health technologies, and personal behaviors affect access to health care, the quality and cost of health care, and health and well-being. HSR approaches can help build the occupational therapy evidence base, particularly in relation to population health. Data from electronic health record (EHR) systems provide a rich resource for applying HSR approaches to examine the value of occupational therapy services. Transparency about data preparation procedures is important for interpreting results. Based on our findings, we describe a six-step cleaning protocol for preparing EHR and billing data from an inpatient rehabilitation facility for research and provide recommendations for the field based on our experience. Using and reporting similar strategies across studies will improve efficiency and transparency, and facilitate comparability of results.
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Affiliation(s)
| | - Pamela Roberts
- University of Southern California, Los Angeles, USA
- Cedars-Sinai, Los Angeles, CA, USA
- California Rehabilitation Institute, Los Angeles, USA
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Jain S, Krumholz HM. Patient Privacy and Data Provenance in Pulmonary and Critical Care Research Using Big Data. Ann Am Thorac Soc 2024; 21:538-540. [PMID: 38259228 PMCID: PMC10995548 DOI: 10.1513/annalsats.202305-497ip] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/22/2024] [Indexed: 01/24/2024] Open
Affiliation(s)
- Snigdha Jain
- Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut; and
| | - Harlan M. Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
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Li Y, Wu K, Yang H, Wang J, Chen Q, Ding X, Zhao Q, Xiao S, Yang L. Surgical prediction of neonatal necrotizing enterocolitis based on radiomics and clinical information. Abdom Radiol (NY) 2024; 49:1020-1030. [PMID: 38285178 DOI: 10.1007/s00261-023-04157-9] [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] [Received: 10/21/2023] [Revised: 12/11/2023] [Accepted: 12/14/2023] [Indexed: 01/30/2024]
Abstract
PURPOSE To assess the predictive value of radiomics for surgical decision-making in neonatal necrotizing enterocolitis (NEC) when abdominal radiographs (ARs) do not suggest an absolute surgical indication for free pneumoperitoneum. METHODS In this retrospective study, we finally included 171 newborns with NEC and obtained their ARs and clinical data. The dataset was randomly divided into a training set (70%) and a test set (30%). We developed machine learning models for predicting surgical treatment using clinical features and radiomic features, respectively, and combined these features to build joint models. We assessed predictive performance of the different models by receiver operating characteristic curve (ROC) analysis and compared area under curve (AUC) using the Delong test. Decision curve analysis (DCA) was used to assess the potential clinical benefit of the models to patients. RESULTS There was no significant difference in AUC between the clinical model and the four radiomic models (P > 0.05). The XGBoost joint model had better predictive efficacy and stability (AUC, training set: 0.988, test set: 0.959). Its AUC in the test set was significantly higher than that of the clinical model (P < 0.05). DCA showed that the XGBoost joint model achieved higher net clinical benefit compared to the clinical model in the threshold probability range (0.2-0.6). CONCLUSION Radiomic features based on AR are objective and reproducible. The joint model combining radiomic features and clinical signs has good surgical predictive efficacy and may be an important method to help primary neonatal surgeons assess the surgical risk of NEC neonates.
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Affiliation(s)
- Yongteng Li
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Kai Wu
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Huirong Yang
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Jianjun Wang
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Qinming Chen
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Xiaoting Ding
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Qianyun Zhao
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Shan Xiao
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Liucheng Yang
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
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Joseph A, Baslet G, O'Neal MA, Polich G, Gonsalvez I, Christoforou AN, Dworetzky BA, Spagnolo PA. Prevalence of autoimmune diseases in functional neurological disorder: influence of psychiatric comorbidities and biological sex. J Neurol Neurosurg Psychiatry 2024:jnnp-2023-332825. [PMID: 38514177 DOI: 10.1136/jnnp-2023-332825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/27/2024] [Indexed: 03/23/2024]
Abstract
BACKGROUND Functional neurological disorder (FND) is a common and disabling neuropsychiatric condition, which disproportionally affects women compared with men. While the etiopathogenesis of this disorder remains elusive, immune dysregulation is emerging as one potential mechanism. To begin to understand the role of immune dysfunctions in FND, we assessed the prevalence of several common autoimmune diseases (ADs) in a large cohort of patients with FND and examined the influence of psychiatric comorbidities and biological sex. METHODS Using a large biorepository database (Mass General Brigham Biobank), we obtained demographic and clinical data of a cohort of 643 patients diagnosed with FND between January 2015 and December 2021. The proportion of ADs was calculated overall, by sex and by the presence of psychiatric comorbidities. RESULTS The overall prevalence of ADs in our sample was 41.9%, with connective tissue and autoimmune endocrine diseases being the most commonly observed ADs. Among patients with FND and ADs, 27.7% had ≥2 ADs and 8% met criteria for multiple autoimmune syndrome. Rates of ADs were significantly higher in subjects with comorbid major depressive disorder and post-traumatic stress disorder (p= 0.02). Women represented the largest proportion of patients with concurrent ADs, both in the overall sample and in the subgroups of interest (p's < 0.05). CONCLUSIONS This study is unique in providing evidence of an association between FND and ADs. Future studies are needed to investigate the mechanisms underlying this association and to understand whether FND is characterised by distinct dysregulations in immune response.
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Affiliation(s)
- Anna Joseph
- Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Gaston Baslet
- Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Mary A O'Neal
- Harvard Medical School, Boston, Massachusetts, USA
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Ginger Polich
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital Boston, Boston, Massachusetts, USA
| | - Irene Gonsalvez
- Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Andrea N Christoforou
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital Boston, Boston, Massachusetts, USA
| | - Barbara A Dworetzky
- Harvard Medical School, Boston, Massachusetts, USA
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Primavera A Spagnolo
- Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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11
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Boßelmann CM, Ivaniuk A, St John M, Taylor SC, Krishnaswamy G, Milinovich A, Leu C, Gupta A, Pestana-Knight EM, Najm I, Lal D. Healthcare utilization and clinical characteristics of genetic epilepsy in electronic health records. Brain Commun 2024; 6:fcae090. [PMID: 38524155 PMCID: PMC10959483 DOI: 10.1093/braincomms/fcae090] [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: 10/29/2023] [Revised: 02/05/2024] [Accepted: 03/12/2024] [Indexed: 03/26/2024] Open
Abstract
Understanding the clinical characteristics and medical treatment of individuals affected by genetic epilepsies is instrumental in guiding selection for genetic testing, defining the phenotype range of these rare disorders, optimizing patient care pathways and pinpointing unaddressed medical need by quantifying healthcare resource utilization. To date, a matched longitudinal cohort study encompassing the entire spectrum of clinical characteristics and medical treatment from childhood through adolescence has not been performed. We identified individuals with genetic and non-genetic epilepsies and onset at ages 0-5 years by linkage across the Cleveland Clinic Health System. We used natural language processing to extract medical terms and procedures from longitudinal electronic health records and tested for cross-sectional and temporal associations with genetic epilepsy. We implemented a two-stage design: in the discovery cohort, individuals were stratified as being 'likely genetic' or 'non-genetic' by a natural language processing algorithm, and controls did not receive genetic testing. The validation cohort consisted of cases with genetic epilepsy confirmed by manual chart review and an independent set of controls who received negative genetic testing. The discovery and validation cohorts consisted of 503 and 344 individuals with genetic epilepsy and matched controls, respectively. The median age at the first encounter was 0.1 years and 7.9 years at the last encounter, and the mean duration of follow-up was 8.2 years. We extracted 188,295 Unified Medical Language System annotations for statistical analysis across 9659 encounters. Individuals with genetic epilepsy received an earlier epilepsy diagnosis and had more frequent and complex encounters with the healthcare system. Notably, the highest enrichment of encounters compared with the non-genetic groups was found during the transition from paediatric to adult care. Our computational approach could validate established comorbidities of genetic epilepsies, such as behavioural abnormality and intellectual disability. We also revealed novel associations for genitourinary abnormalities (odds ratio 1.91, 95% confidence interval: 1.66-2.20, P = 6.16 × 10-19) linked to a spectrum of underrecognized epilepsy-associated genetic disorders. This case-control study leveraged real-world data to identify novel features associated with the likelihood of a genetic aetiology and quantified the healthcare utilization of genetic epilepsies compared with matched controls. Our results strongly recommend early genetic testing to stratify individuals into specialized care paths, thus improving the clinical management of people with genetic epilepsies.
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Affiliation(s)
- Christian M Boßelmann
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Alina Ivaniuk
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Mark St John
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Sara C Taylor
- Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | | | - Alex Milinovich
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Costin Leu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, WC1N 3BG, UK
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Center for Neurogenetics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ajay Gupta
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | | | - Imad Najm
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Dennis Lal
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Center for Neurogenetics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and M.I.T., Cambridge, MA 02142, USA
- Cologne Center for Genomics (CCG), University of Cologne, 50931 Cologne, Germany
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Elbers P, Thoral P, Bos LDJ, Greco M, Wendel-Garcia PD, Ercole A. The ESICM datathon and the ESICM and ICMx data science strategy. Intensive Care Med Exp 2024; 12:29. [PMID: 38472595 DOI: 10.1186/s40635-024-00615-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 03/07/2024] [Indexed: 03/14/2024] Open
Affiliation(s)
- Paul Elbers
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam Public Health (APH), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick Thoral
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam Public Health (APH), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Lieuwe D J Bos
- Department of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Massimiliano Greco
- Department of Biomedical Sciences, Department of Anesthesiology and Intensive Care, Humanitas University, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Pedro D Wendel-Garcia
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland.
| | - Ari Ercole
- Division of Anaesthesia and Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK
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13
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Mazzotti DR. Multimodal integration of sleep electroencephalogram, brain imaging, and cognitive assessments: approaches using noisy clinical data. Sleep 2024; 47:zsad305. [PMID: 38019853 PMCID: PMC10851849 DOI: 10.1093/sleep/zsad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Indexed: 12/01/2023] Open
Affiliation(s)
- Diego R Mazzotti
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
- Division of Pulmonary Critical Care and Sleep Medicine, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
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Andresen K, Hinojosa-Campos M, Podmore B, Drysdale M, Qizilbash N, Cunnington M. Validity of Routine Health Data To Identify Safety Outcomes of Interest For Covid-19 Vaccines and Therapeutics in the Context of the Emerging Pandemic: A Comprehensive Literature Review. Drug Healthc Patient Saf 2024; 16:1-17. [PMID: 38192299 PMCID: PMC10771726 DOI: 10.2147/dhps.s415292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 08/15/2023] [Indexed: 01/10/2024] Open
Abstract
Introduction Regulatory guidance encourages transparent reporting of information on the quality and validity of electronic health record data being used to generate real-world benefit-risk evidence for vaccines and therapeutics. We aimed to provide an overview of the availability of validated diagnostic algorithms for selected safety endpoints for Coronavirus disease 2019 (COVID-19) vaccines and therapeutics in the context of the emerging pandemic prior to December 2020. Methods We reviewed the literature up to December 2020 to identify validation studies for various safety events of interest, including myocardial infarction, arrhythmia, myocarditis, acute cardiac injury, vasculitis/vasculopathy, venous thromboembolism, stroke, respiratory distress syndrome (RDS), pneumonitis, cytokine release syndrome (CRS), multiple organ dysfunction syndrome, and renal failure. We included studies published between 2015 and 2020 that were considered high quality assessed with QUADAS and that reported positive predictive values (PPVs). Results Out of 43 identified studies, we found that diagnostic algorithms for cardiovascular outcomes were supported by the highest number of validation studies (n=17). Accurate algorithms are available for myocardial infarction (median PPV 80%; IQR 22%), arrhythmia (PPV range >70%), venous thromboembolism (median PPV: 73%) and ischaemic stroke (PPV range ≥85%). We found a lack of validation studies for less common respiratory and cardiac safety outcomes of interest (eg, pneumonitis and myocarditis), as well as for COVID-specific complications (CRS, RDS). Conclusion There is a need for better understanding of barriers to conducting validation studies, including data governance restrictions. Regulatory guidance should promote embedding validation within real-world EHR research used for decision-making.
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Affiliation(s)
- Kirsty Andresen
- OXON Epidemiology, London, UK
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - Bélène Podmore
- OXON Epidemiology, London, UK
- London School of Hygiene and Tropical Medicine, London, UK
- OXON Epidemiology, Madrid, Spain
| | | | - Nawab Qizilbash
- OXON Epidemiology, London, UK
- London School of Hygiene and Tropical Medicine, London, UK
- OXON Epidemiology, Madrid, Spain
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Beridze G, Abbadi A, Ars J, Remelli F, Vetrano DL, Trevisan C, Pérez LM, López-Rodríguez JA, Calderón-Larrañaga A. Patterns of multimorbidity in primary care electronic health records: A systematic review. JOURNAL OF MULTIMORBIDITY AND COMORBIDITY 2024; 14:26335565231223350. [PMID: 38298757 PMCID: PMC10829499 DOI: 10.1177/26335565231223350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/12/2023] [Indexed: 02/02/2024]
Abstract
Background Multimorbidity, the coexistence of multiple chronic conditions in an individual, is a complex phenomenon that is highly prevalent in primary care settings, particularly in older individuals. This systematic review summarises the current evidence on multimorbidity patterns identified in primary care electronic health record (EHR) data. Methods Three databases were searched from inception to April 2022 to identify studies that derived original multimorbidity patterns from primary care EHR data. The quality of the included studies was assessed using a modified version of the Newcastle-Ottawa Quality Assessment Scale. Results Sixteen studies were included in this systematic review, none of which was of low quality. Most studies were conducted in Spain, and only one study was conducted outside of Europe. The prevalence of multimorbidity (i.e. two or more conditions) ranged from 14.0% to 93.9%. The most common stratification variable in disease clustering models was sex, followed by age and calendar year. Despite significant heterogeneity in clustering methods and disease classification tools, consistent patterns of multimorbidity emerged. Mental health and cardiovascular patterns were identified in all studies, often in combination with diseases of other organ systems (e.g. neurological, endocrine). Discussion These findings emphasise the frequent coexistence of physical and mental health conditions in primary care, and provide useful information for the development of targeted preventive and management strategies. Future research should explore mechanisms underlying multimorbidity patterns, prioritise methodological harmonisation to facilitate the comparability of findings, and promote the use of EHR data globally to enhance our understanding of multimorbidity in more diverse populations.
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Affiliation(s)
- Giorgi Beridze
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
| | - Ahmad Abbadi
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
| | - Joan Ars
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- RE-FiT Barcelona Research group, Vall d'Hebron Institute of Research (VHIR) and Parc Sanitari Pere Virgili, Barcelona, Spain
- Medicine Department, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Francesca Remelli
- Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Davide L Vetrano
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Caterina Trevisan
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Laura-Mónica Pérez
- RE-FiT Barcelona Research group, Vall d'Hebron Institute of Research (VHIR) and Parc Sanitari Pere Virgili, Barcelona, Spain
| | - Juan A López-Rodríguez
- Research Unit, Primary Health Care Management, Madrid, Spain
- Department of Medical Specialties and Public Health, Faculty of Health Sciences Rey Juan Carlos University, Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), Carlos III Health Institute, Madrid, Spain
| | - Amaia Calderón-Larrañaga
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- Stockholm Gerontology Research Center, Stockholm, Sweden
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), Carlos III Health Institute, Madrid, Spain
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Grzenda A, Widge AS. Electronic health records and stratified psychiatry: bridge to precision treatment? Neuropsychopharmacology 2024; 49:285-290. [PMID: 37667021 PMCID: PMC10700348 DOI: 10.1038/s41386-023-01724-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/24/2023] [Accepted: 08/27/2023] [Indexed: 09/06/2023]
Abstract
The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR data's power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed.
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Affiliation(s)
- Adrienne Grzenda
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.
- Olive View-UCLA Medical Center, Sylmar, CA, USA.
| | - Alik S Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
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Prasanna A, Jing B, Plopper G, Miller KK, Sanjak J, Feng A, Prezek S, Vidyaprakash E, Thovarai V, Maier EJ, Bhattacharya A, Naaman L, Stephens H, Watford S, Boscardin WJ, Johanson E, Lienau A. Synthetic Health Data Can Augment Community Research Efforts to Better Inform the Public During Emerging Pandemics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.11.23298687. [PMID: 38168217 PMCID: PMC10760275 DOI: 10.1101/2023.12.11.23298687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The COVID-19 pandemic had disproportionate effects on the Veteran population due to the increased prevalence of medical and environmental risk factors. Synthetic electronic health record (EHR) data can help meet the acute need for Veteran population-specific predictive modeling efforts by avoiding the strict barriers to access, currently present within Veteran Health Administration (VHA) datasets. The U.S. Food and Drug Administration (FDA) and the VHA launched the precisionFDA COVID-19 Risk Factor Modeling Challenge to develop COVID-19 diagnostic and prognostic models; identify Veteran population-specific risk factors; and test the usefulness of synthetic data as a substitute for real data. The use of synthetic data boosted challenge participation by providing a dataset that was accessible to all competitors. Models trained on synthetic data showed similar but systematically inflated model performance metrics to those trained on real data. The important risk factors identified in the synthetic data largely overlapped with those identified from the real data, and both sets of risk factors were validated in the literature. Tradeoffs exist between synthetic data generation approaches based on whether a real EHR dataset is required as input. Synthetic data generated directly from real EHR input will more closely align with the characteristics of the relevant cohort. This work shows that synthetic EHR data will have practical value to the Veterans' health research community for the foreseeable future.
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Affiliation(s)
| | - Bocheng Jing
- Northern California Institute for Research and Education
- San Francisco VA Medical Center
| | | | | | | | | | | | | | | | | | | | | | | | - Sean Watford
- Booz Allen Hamilton
- Currently U.S. Environmental Protection Agency
| | - W John Boscardin
- University of California, San Francisco, Department of Medicine
- University of California, San Francisco, Department of Epidemiology & Biostatistics
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Carrasco-Ribelles LA, Llanes-Jurado J, Gallego-Moll C, Cabrera-Bean M, Monteagudo-Zaragoza M, Violán C, Zabaleta-del-Olmo E. Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review. J Am Med Inform Assoc 2023; 30:2072-2082. [PMID: 37659105 PMCID: PMC10654870 DOI: 10.1093/jamia/ocad168] [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] [Received: 05/05/2023] [Revised: 08/02/2023] [Accepted: 08/11/2023] [Indexed: 09/04/2023] Open
Abstract
OBJECTIVE To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes. METHODS This review included studies in any language that: EHR was at least one of the data sources, collected longitudinal data, used an AI technique capable of handling longitudinal data, and predicted any health-related outcomes. We searched MEDLINE, Scopus, Web of Science, and IEEE Xplorer from inception to January 3, 2022. Information on the dataset, prediction task, data preprocessing, feature selection, method, validation, performance, and implementation was extracted and summarized using descriptive statistics. Risk of bias and completeness of reporting were assessed using a short form of PROBAST and TRIPOD, respectively. RESULTS Eighty-one studies were included. Follow-up time and number of registers per patient varied greatly, and most predicted disease development or next event based on diagnoses and drug treatments. Architectures generally were based on Recurrent Neural Networks-like layers, though in recent years combining different layers or transformers has become more popular. About half of the included studies performed hyperparameter tuning and used attention mechanisms. Most performed a single train-test partition and could not correctly assess the variability of the model's performance. Reporting quality was poor, and a third of the studies were at high risk of bias. CONCLUSIONS AI models are increasingly using longitudinal data. However, the heterogeneity in reporting methodology and results, and the lack of public EHR datasets and code sharing, complicate the possibility of replication. REGISTRATION PROSPERO database (CRD42022331388).
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Affiliation(s)
- Lucía A Carrasco-Ribelles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, 08034, Spain
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, 08303, Spain
| | - José Llanes-Jurado
- Instituto de Investigación e Innovación en Bioingeniería (i3B), Universitat Politècnica de València (UPV), València, 46022, Spain
| | - Carlos Gallego-Moll
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, 08303, Spain
| | - Margarita Cabrera-Bean
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, 08034, Spain
| | - Mònica Monteagudo-Zaragoza
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
| | - Concepción Violán
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, 08303, Spain
- Direcció d’Atenció Primària Metropolitana Nord, Institut Català de Salut, Badalona, 08915, Spain
- Fundació Institut d’Investigació en ciències de la salut Germans Trias i Pujol (IGTP), Badalona, 08916, Spain
- Fundació UAB, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193, Spain
| | - Edurne Zabaleta-del-Olmo
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
- Gerència Territorial de Barcelona, Institut Català de la Salut, Carrer de Balmes 22, Barcelona, 08007, Spain
- Nursing Department, Faculty of Nursing, Universitat de Girona, Girona, 17003, Spain
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Spector-Bagdady K, Armoundas AA, Arnaout R, Hall JL, Yeager McSwain B, Knowles JW, Price WN, Rawat DB, Riegel B, Wang TY, Wiley K, Chung MK. Principles for Health Information Collection, Sharing, and Use: A Policy Statement From the American Heart Association. Circulation 2023; 148:1061-1069. [PMID: 37646159 PMCID: PMC10912036 DOI: 10.1161/cir.0000000000001173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
The evolution of the electronic health record, combined with advances in data curation and analytic technologies, increasingly enables data sharing and harmonization. Advances in the analysis of health-related and health-proxy information have already accelerated research discoveries and improved patient care. This American Heart Association policy statement discusses how broad data sharing can be an enabling driver of progress by providing data to develop, test, and benchmark innovative methods, scalable insights, and potential new paradigms for data storage and workflow. Along with these advances come concerns about the sensitive nature of some health data, equity considerations about the involvement of historically excluded communities, and the complex intersection of laws attempting to govern behavior. Data-sharing principles are therefore necessary across a wide swath of entities, including parties who collect health information, funders, researchers, patients, legislatures, commercial companies, and regulatory departments and agencies. This policy statement outlines some of the key equity and legal background relevant to health data sharing and responsible management. It then articulates principles that will guide the American Heart Association's engagement in public policy related to data collection, sharing, and use to continue to inform its work across the research enterprise, as well as specific examples of how these principles might be applied in the policy landscape. The goal of these principles is to improve policy to support the use or reuse of health information in ways that are respectful of patients and research participants, equitable in impact in terms of both risks and potential benefits, and beneficial across broad and demographically diverse communities in the United States.
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Shau WY, Setia S, Chen YJ, Ho TY, Prakash Shinde S, Santoso H, Furtner D. Integrated Real-World Study Databases in 3 Diverse Asian Health Care Systems in Taiwan, India, and Thailand: Scoping Review. J Med Internet Res 2023; 25:e49593. [PMID: 37615085 PMCID: PMC10520767 DOI: 10.2196/49593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/28/2023] [Accepted: 08/24/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND The use of real-world data (RWD) warehouses for research in Asia is on the rise, but current trends remain largely unexplored. Given the varied economic and health care landscapes in different Asian countries, understanding these trends can offer valuable insights. OBJECTIVE We sought to discern the contemporary landscape of linked RWD warehouses and explore their trends and patterns in 3 Asian countries with contrasting economies and health care systems: Taiwan, India, and Thailand. METHODS Using a systematic scoping review methodology, we conducted an exhaustive literature search on PubMed with filters for the English language and the past 5 years. The search combined Medical Subject Heading terms and specific keywords. Studies were screened against strict eligibility criteria to identify eligible studies using RWD databases from more than one health care facility in at least 1 of the 3 target countries. RESULTS Our search yielded 2277 studies, of which 833 (36.6%) met our criteria. Overall, single-country studies (SCS) dominated at 89.4% (n=745), with cross-country collaboration studies (CCCS) being at 10.6% (n=88). However, the country-wise breakdown showed that of all the SCS, 623 (83.6%) were from Taiwan, 81 (10.9%) from India, and 41 (5.5%) from Thailand. Among the total studies conducted in each country, India at 39.1% (n=133) and Thailand at 43.1% (n=72) had a significantly higher percentage of CCCS compared to Taiwan at 7.6% (n=51). Over a 5-year span from 2017 to 2022, India and Thailand experienced an annual increase in RWD studies by approximately 18.2% and 13.8%, respectively, while Taiwan's contributions remained consistent. Comparative effectiveness research (CER) was predominant in Taiwan (n=410, or 65.8% of SCS) but less common in India (n=12, or 14.8% of SCS) and Thailand (n=11, or 26.8% of SCS). CER percentages in CCCS were similar across the 3 countries, ranging from 19.2% (n=10) to 29% (n=9). The type of RWD source also varied significantly across countries, with India demonstrating a high reliance on electronic medical records or electronic health records at 55.6% (n=45) of SCS and Taiwan showing an increasing trend in their use over the period. Registries were used in 26 (83.9%) CCCS and 31 (75.6%) SCS from Thailand but in <50% of SCS from Taiwan and India. Health insurance/administrative claims data were used in most of the SCS from Taiwan (n=458, 73.5%). There was a consistent predominant focus on cardiology/metabolic disorders in all studies, with a noticeable increase in oncology and infectious disease research from 2017 to 2022. CONCLUSIONS This review provides a comprehensive understanding of the evolving landscape of RWD research in Taiwan, India, and Thailand. The observed differences and trends emphasize the unique economic, clinical, and research settings in each country, advocating for tailored strategies for leveraging RWD for future health care research and decision-making. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/43741.
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Affiliation(s)
- Wen-Yi Shau
- Regional Medical Affairs, Pfizer Corporation Hong Kong Limited, Hong Kong, Hong Kong
| | - Sajita Setia
- Executive Office, Transform Medical Communications Limited, Wanganui, New Zealand
| | - Ying-Jan Chen
- Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
| | - Tsu-Yun Ho
- Medical Affairs Office, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Salil Prakash Shinde
- Regional Medical Affairs, Pfizer Corporation Hong Kong Limited, Hong Kong, Hong Kong
| | - Handoko Santoso
- Regional Medical Affairs, Pfizer Corporation Hong Kong Limited, Hong Kong, Hong Kong
| | - Daniel Furtner
- Executive Office, Transform Medical Communications Limited, Wanganui, New Zealand
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Dakhil ZA. Routine electronic health record-based clinical trials: what should an early-career trialist know? Eur Heart J 2023; 44:3207-3211. [PMID: 37525523 DOI: 10.1093/eurheartj/ehad437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/02/2023] Open
Affiliation(s)
- Zainab Atiyah Dakhil
- Department of Cardiology, Ibn Al-Bitar Cardiac Centre, Al-Salhiya, Baghdad, Iraq
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Anik FI, Sakib N, Shahriar H, Xie Y, Nahiyan HA, Ahamed SI. Unraveling a blockchain-based framework towards patient empowerment: A scoping review envisioning future smart health technologies. SMART HEALTH (AMSTERDAM, NETHERLANDS) 2023; 29:100401. [PMID: 37200573 PMCID: PMC10102703 DOI: 10.1016/j.smhl.2023.100401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/15/2023] [Accepted: 04/10/2023] [Indexed: 05/20/2023]
Abstract
The COVID-19 pandemic shows us how crucial patient empowerment can be in the healthcare ecosystem. Now, we know that scientific advancement, technology integration, and patient empowerment need to be orchestrated to realize future smart health technologies. In that effort, this paper unravels the Good (advantages), Bad (challenges/limitations), and Ugly (lacking patient empowerment) of the blockchain technology integration in the Electronic Health Record (EHR) paradigm in the existing healthcare landscape. Our study addresses four methodically-tailored and patient-centric Research Questions, primarily examining 138 relevant scientific papers. This scoping review also explores how the pervasiveness of blockchain technology can help to empower patients in terms of access, awareness, and control. Finally, this scoping review leverages the insights gleaned from this study and contributes to the body of knowledge by proposing a patient-centric blockchain-based framework. This work will envision orchestrating three essential elements with harmony: scientific advancement (Healthcare and EHR), technology integration (Blockchain Technology), and patient empowerment (access, awareness, and control).
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Affiliation(s)
- Fahim Islam Anik
- Department of Mechanical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
| | - Nazmus Sakib
- Department of Information Technology, Kennesaw State University, GA, USA
| | - Hossain Shahriar
- Department of Information Technology, Kennesaw State University, GA, USA
| | - Yixin Xie
- Department of Information Technology, Kennesaw State University, GA, USA
| | - Helal An Nahiyan
- Department of Mechanical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
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23
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Edwards TL, Greene CA, Piekos JA, Hellwege JN, Hampton G, Jasper EA, Velez Edwards DR. Challenges and Opportunities for Data Science in Women's Health. Annu Rev Biomed Data Sci 2023; 6:23-45. [PMID: 37040736 PMCID: PMC10877578 DOI: 10.1146/annurev-biodatasci-020722-105958] [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] [Indexed: 04/13/2023]
Abstract
The intersection of women's health and data science is a field of research that has historically trailed other fields, but more recently it has gained momentum. This growth is being driven not only by new investigators who are moving into this area but also by the significant opportunities that have emerged in new methodologies, resources, and technologies in data science. Here, we describe some of the resources and methods being used by women's health researchers today to meet challenges in biomedical data science. We also describe the opportunities and limitations of applying these approaches to advance women's health outcomes and the future of the field, with emphasis on repurposing existing methodologies for women's health.
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Affiliation(s)
- Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Catherine A Greene
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jacqueline A Piekos
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jacklyn N Hellwege
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Gabrielle Hampton
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Elizabeth A Jasper
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Precision Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Digna R Velez Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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24
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Salem AM, Dvergsten E, Karovic S, Maitland ML, Gopalakrishnan M. Model-based approach to identify predictors of paclitaxel-induced myelosuppression in "real-world" administration. CPT Pharmacometrics Syst Pharmacol 2023; 12:929-940. [PMID: 37101403 PMCID: PMC10349185 DOI: 10.1002/psp4.12963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 03/11/2023] [Accepted: 03/22/2023] [Indexed: 04/28/2023] Open
Abstract
Taxanes are currently the most frequently used chemotherapeutic agents in cancer care, where real-world use has focused on minimizing adverse events and standardizing the delivery. Myelosuppression is a well-characterized, adverse pharmacodynamic effect of taxanes. Electronic health records (EHRs) comprise data collected during routine clinical care that include patients with heterogeneous demographic, clinical, and treatment characteristics. Application of pharmacokinetic/pharmacodynamic (PK/PD) modeling to EHR data promises new insights on the real-world use of taxanes and strategies to improve therapeutic outcomes especially for populations who are typically excluded from clinical trials, including the elderly. This investigation: (i) leveraged previously published PK/PD models developed with clinical trial data and addressed challenges to fit EHR data, and (ii) evaluated predictors of paclitaxel-induced myelosuppression. Relevant EHR data were collected from patients treated with paclitaxel-containing chemotherapy at Inova Schar Cancer Institute between 2015 and 2019 (n = 405). Published PK models were used to simulate mean individual exposures of paclitaxel and carboplatin, which were linearly linked to absolute neutrophil count (ANC) using a published semiphysiologic myelosuppression model. Elderly patients (≥70 years) constituted 21.2% of the dataset and 2274 ANC measurements were included in the analysis. The PD parameters were estimated and matched previously reported values. The baseline ANC and chemotherapy regimen were significant predictors of paclitaxel-induced myelosuppression. The nadir ANC and use of supportive treatments, such as growth factors and antimicrobials, were consistent across age quantiles suggesting age had no effect on paclitaxel-induced myelosuppression. In conclusion, EHR data could complement clinical trial data in answering key therapeutic questions.
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Affiliation(s)
- Ahmed M. Salem
- Center for Translational MedicineUniversity of Maryland School of PharmacyBaltimoreMarylandUSA
| | | | | | - Michael L. Maitland
- Inova Schar Cancer InstituteFairfaxVirginiaUSA
- University of Virginia Comprehensive Cancer CenterCharlottesvilleVirginiaUSA
| | - Mathangi Gopalakrishnan
- Center for Translational MedicineUniversity of Maryland School of PharmacyBaltimoreMarylandUSA
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25
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McElroy SJ, Lueschow SR. State of the art review on machine learning and artificial intelligence in the study of neonatal necrotizing enterocolitis. Front Pediatr 2023; 11:1182597. [PMID: 37303753 PMCID: PMC10250644 DOI: 10.3389/fped.2023.1182597] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/25/2023] [Indexed: 06/13/2023] Open
Abstract
Necrotizing Enterocolitis (NEC) is one of the leading causes of gastrointestinal emergency in preterm infants. Although NEC was formally described in the 1960's, there is still difficulty in diagnosis and ultimately treatment for NEC due in part to the multifactorial nature of the disease. Artificial intelligence (AI) and machine learning (ML) techniques have been applied by healthcare researchers over the past 30 years to better understand various diseases. Specifically, NEC researchers have used AI and ML to predict NEC diagnosis, NEC prognosis, discover biomarkers, and evaluate treatment strategies. In this review, we discuss AI and ML techniques, the current literature that has applied AI and ML to NEC, and some of the limitations in the field.
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Affiliation(s)
- Steven J. McElroy
- Department of Pediatrics, University of California Davis, Sacramento, CA, United States
| | - Shiloh R. Lueschow
- Stead Family Department of Pediatrics, University of Iowa, Iowa City, IA, United States
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Tsakiroglou M, Evans A, Pirmohamed M. Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis. Front Genet 2023; 14:1100352. [PMID: 36968610 PMCID: PMC10036914 DOI: 10.3389/fgene.2023.1100352] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.
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Affiliation(s)
- Maria Tsakiroglou
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- *Correspondence: Maria Tsakiroglou,
| | - Anthony Evans
- Computational Biology Facility, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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Koroukian SM, Booker BD, Vu L, Schumacher FR, Rose J, Cooper GS, Selfridge JE, Markt SC. Receipt of Targeted Therapy and Survival Outcomes in Patients With Metastatic Colorectal Cancer. JAMA Netw Open 2023; 6:e2250030. [PMID: 36656585 PMCID: PMC9857024 DOI: 10.1001/jamanetworkopen.2022.50030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
IMPORTANCE Professional society guidelines recommend treating patients with metastatic colorectal cancer with targeted therapies, including epithelial growth factor receptor (EGFR) inhibitors and vascular endothelial growth factor (VEGF) inhibitors, depending on the presence or absence of certain mutations. Since most studies of first-line targeted therapies have been limited by sample size, there is a need for larger studies using data from routine clinical care. OBJECTIVES To identify factors associated with receipt of first-line targeted therapies among patients with metastatic colorectal cancer for whom RAS or BRAF mutation data in the tumor were available and investigate whether targeted therapy is associated with survival. DESIGN, SETTING, AND PARTICIPANTS This cohort study used deidentified data from an electronic health record-derived database to include patients from 800 sites of patient care across the US who were diagnosed with de novo metastatic colorectal cancer between January 1, 2013, and March 31, 2020 (n = 9134). MAIN OUTCOMES AND MEASURES Receipt of first-line targeted therapy, categorized as ever having received EGFR inhibitors, VEGF inhibitors, or neither. The secondary outcome was overall survival. RESULTS The study population included 9134 patients. The median age at diagnosis was 62 years (IQR, 53-71 years), 5019 (54.9%) were male, and 5692 (62.3%) were White. The median follow-up period was 15 months. Overall, 713 patients (7.8%) received EGFR inhibitors and 5081 patients (55.6%) received VEGF inhibitors as part of their first-line treatment. Among patients with RAS wild-type (RAS-WT) tumors, 625 patients (15.5%) received EGFR inhibitors and 2053 patients (50.9%) received VEGF inhibitors. In patients with RAS mutant (RAS-Mut) tumors, 50 patients (1.1%) received EGFR inhibitors and 2682 patients (59.7%) received VEGF inhibitors; among those with BRAF-mutant (BRAF-Mut) tumors, 38 patients (6.3%) received EGFR inhibitors and 346 patients (57.2%) received VEGF inhibitors. More than one-third of the patients (36.6%) received neither EGFR inhibitors nor VEGF inhibitors. Compared with patients younger than age 40 years, those aged 80 years or older had significantly lower odds to receive targeted therapies (EGFR or VEGF inhibitors in patients with RAS-WT tumors: adjusted odds ratio [aOR], 0.53; 95% CI, 0.36-0.79; and VEGF inhibitors in patients with RAS-Mut tumors: aOR, 0.62; 95% CI, 0.42-0.90). Improved survival was associated with EGFR inhibitor therapy in patients with RAS-WT tumors (adjusted hazard ratio [aHR], 0.85; 95% CI, 0.74-0.98). Unlike in clinical trials, however, no survival benefit was noted with use of VEGF inhibitors among patients with RAS-WT (aHR, 1.00; 95% CI, 0.91-1.11) or RAS-Mut (aHR, 1.01; 95% CI, 0.93-1.10) tumors. CONCLUSIONS AND RELEVANCE The findings of this study showed mixed results on survival benefits associated with targeted therapy. In addition, given that some of the results differed from those of randomized clinical trials, this study highlights the importance of using data originating from routine clinical care.
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Affiliation(s)
- Siran M. Koroukian
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Benjamin D. Booker
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Long Vu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Fredrick R. Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Johnie Rose
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
- Center for Community Health Integration, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Gregory S. Cooper
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
- Department of Internal Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - J. Eva Selfridge
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
- Division of Solid Tumor Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Sarah C. Markt
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
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28
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Chen S, Wang Y, Mueller C. Code-Based Algorithms for Identifying Dementia in Electronic Health Records: Bridging the Gap Between Theory and Practice. J Alzheimers Dis 2023; 95:941-943. [PMID: 37718822 DOI: 10.3233/jad-230887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Code-based algorithms are crucial tools in the detection of dementia using electronic health record data, with broad applications in medical research and healthcare. Vassilaki et al.'s study explores the efficacy of code-based algorithms in dementia detection using electronic health record data, achieving approximately 70% sensitivity and positive predictive value. Despite the promising results, the algorithms fail to detect around 30% of dementia cases, highlighting challenges in distinguishing cognitive decline factors. The study emphasizes the need for algorithmic improvements and further exploration across diverse healthcare systems and populations, serving as a critical step toward bridging gaps in dementia care and understanding.
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Affiliation(s)
- Shanquan Chen
- International Centre for Evidence in Disability, London School of Hygiene & Tropical Medicine, London, UK
| | - Yuqi Wang
- Department of Computer Science, University College London, London, UK
| | - Christoph Mueller
- King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
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29
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Donnelly C, Janssen A, Vinod S, Stone E, Harnett P, Shaw T. A Systematic Review of Electronic Medical Record Driven Quality Measurement and Feedback Systems. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:ijerph20010200. [PMID: 36612522 PMCID: PMC9819986 DOI: 10.3390/ijerph20010200] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 06/09/2023]
Abstract
Historically, quality measurement analyses utilize manual chart abstraction from data collected primarily for administrative purposes. These methods are resource-intensive, time-delayed, and often lack clinical relevance. Electronic Medical Records (EMRs) have increased data availability and opportunities for quality measurement. However, little is known about the effectiveness of Measurement Feedback Systems (MFSs) in utilizing EMR data. This study explores the effectiveness and characteristics of EMR-enabled MFSs in tertiary care. The search strategy guided by the PICO Framework was executed in four databases. Two reviewers screened abstracts and manuscripts. Data on effect and intervention characteristics were extracted using a tailored version of the Cochrane EPOC abstraction tool. Due to study heterogeneity, a narrative synthesis was conducted and reported according to PRISMA guidelines. A total of 14 unique MFS studies were extracted and synthesized, of which 12 had positive effects on outcomes. Findings indicate that quality measurement using EMR data is feasible in certain contexts and successful MFSs often incorporated electronic feedback methods, supported by clinical leadership and action planning. EMR-enabled MFSs have the potential to reduce the burden of data collection for quality measurement but further research is needed to evaluate EMR-enabled MFSs to translate and scale findings to broader implementation contexts.
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Affiliation(s)
- Candice Donnelly
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2006, Australia
| | - Anna Janssen
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2006, Australia
| | - Shalini Vinod
- Liverpool Cancer Therapy Centre, South Western Sydney Local Health District, Liverpool, NSW 2170, Australia
- South West Sydney Clinical Campuses, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Emily Stone
- Department of Thoracic Medicine and Lung Transplantation, St Vincent’s Hospital, Darlinghurst, NSW 2010, Australia
- School of Clinical Medicine, University of New South Wales, Randwick, NSW 2031, Australia
| | - Paul Harnett
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2006, Australia
- Crown Princess Mary Cancer Centre, Western Sydney Local Health District, Westmead, NSW 2145, Australia
| | - Tim Shaw
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2006, Australia
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