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Haimovich AD, Burke RC, Nathanson LA, Rubins D, Taylor RA, Kross EK, Ouchi K, Shapiro NI, Schonberg MA. Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients. JAMA Netw Open 2024; 7:e2414213. [PMID: 38819823 PMCID: PMC11143461 DOI: 10.1001/jamanetworkopen.2024.14213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/31/2024] [Indexed: 06/01/2024] Open
Abstract
Importance Emergency department (ED) visits by older adults with life-limiting illnesses are a critical opportunity to establish patient care end-of-life preferences, but little is known about the optimal screening criteria for resource-constrained EDs. Objectives To externally validate the Geriatric End-of-Life Screening Tool (GEST) in an independent population and compare it with commonly used serious illness diagnostic criteria. Design, Setting, and Participants This prognostic study assessed a cohort of patients aged 65 years and older who were treated in a tertiary care ED in Boston, Massachusetts, from 2017 to 2021. Patients arriving in cardiac arrest or who died within 1 day of ED arrival were excluded. Data analysis was performed from August 1, 2023, to March 27, 2024. Exposure GEST, a logistic regression algorithm that uses commonly available electronic health record (EHR) datapoints and was developed and validated across 9 EDs, was compared with serious illness diagnoses as documented in the EHR. Serious illnesses included stroke/transient ischemic attack, liver disease, cancer, lung disease, and age greater than 80 years, among others. Main Outcomes and Measures The primary outcome was 6-month mortality following an ED encounter. Statistical analyses included area under the receiver operating characteristic curve, calibration analyses, Kaplan-Meier survival curves, and decision curves. Results This external validation included 82 371 ED encounters by 40 505 unique individuals (mean [SD] age, 76.8 [8.4] years; 54.3% women, 13.8% 6-month mortality rate). GEST had an external validation area under the receiver operating characteristic curve of 0.79 (95% CI, 0.78-0.79) that was stable across years and demographic subgroups. Of included encounters, 53.4% had a serious illness, with a sensitivity of 77.4% (95% CI, 76.6%-78.2%) and specificity of 50.5% (95% CI, 50.1%-50.8%). Varying GEST cutoffs from 5% to 30% increased specificity (5%: 49.1% [95% CI, 48.7%-49.5%]; 30%: 92.2% [95% CI, 92.0%-92.4%]) at the cost of sensitivity (5%: 89.3% [95% CI, 88.8-89.9]; 30%: 36.2% [95% CI, 35.3-37.1]). In a decision curve analysis, GEST outperformed serious illness criteria across all tested thresholds. When comparing patients referred to intervention by GEST with serious illness criteria, GEST reclassified 45.1% of patients with serious illness as having low risk of mortality with an observed mortality rate 8.1% and 2.6% of patients without serious illness as having high mortality risk with an observed mortality rate of 34.3% for a total reclassification rate of 25.3%. Conclusions and Relevance The findings of this study suggest that both serious illness criteria and GEST identified older ED patients at risk for 6-month mortality, but GEST offered more useful screening characteristics. Future trials of serious illness interventions for high mortality risk in older adults may consider transitioning from diagnosis code criteria to GEST, an automatable EHR-based algorithm.
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Affiliation(s)
- Adrian D. Haimovich
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Ryan C. Burke
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Larry A. Nathanson
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - David Rubins
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - R. Andrew Taylor
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Erin K. Kross
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle
- Cambia Palliative Care Center of Excellence at UW Medicine, Seattle, Washington
| | - Kei Ouchi
- Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Nathan I. Shapiro
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Mara A. Schonberg
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
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Haimovich AD, Xu W, Wei A, Schonberg MA, Hwang U, Taylor RA. Automatable end-of-life screening for older adults in the emergency department using electronic health records. J Am Geriatr Soc 2023; 71:1829-1839. [PMID: 36744550 PMCID: PMC10258151 DOI: 10.1111/jgs.18262] [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: 10/04/2022] [Revised: 12/20/2022] [Accepted: 01/08/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND Emergency department (ED) visits are common at the end-of-life, but the identification of patients with life-limiting illness remains a key challenge in providing timely and resource-sensitive advance care planning (ACP) and palliative care services. To date, there are no validated, automatable instruments for ED end-of-life screening. Here, we developed a novel electronic health record (EHR) prognostic model to screen older ED patients at high risk for 6-month mortality and compare its performance to validated comorbidity indices. METHODS This was a retrospective, observational cohort study of ED visits from adults aged ≥65 years who visited any of 9 EDs across a large regional health system between 2014 and 2019. Multivariable logistic regression that included clinical and demographic variables, vital signs, and laboratory data was used to develop a 6-month mortality predictive model-the Geriatric End-of-life Screening Tool (GEST) using five-fold cross-validation on data from 8 EDs. Performance was compared to the Charlson and Elixhauser comorbidity indices using area under the receiver-operating characteristic curve (AUROC), calibration, and decision curve analyses. Reproducibility was tested against data from the remaining independent ED within the health system. We then used GEST to investigate rates of ACP documentation availability and code status orders in the EHR across risk strata. RESULTS A total of 431,179 encounters by 123,128 adults were included in this study with a 6-month mortality rate of 12.2%. Charlson (AUROC (95% CI): 0.65 (0.64-0.69)) and Elixhauser indices (0.69 (0.68-0.70)) were outperformed by GEST (0.82 (0.82-0.83)). GEST displayed robust performance across demographic subgroups and in our independent validation site. Among patients with a greater than 30% mortality risk using GEST, only 5.0% had ACP documentation; 79.0% had a code status previously ordered, of which 70.7% were full code. In decision curve analysis, GEST provided greater net benefit than the Charlson and Elixhauser scores. CONCLUSIONS Prognostic models using EHR data robustly identify high mortality risk older adults in the ED for whom code status, ACP, or palliative care interventions may be of benefit. Although all tested methods identified patients approaching the end-of-life, GEST was most performant. These tools may enable resource-sensitive end-of-life screening in the ED.
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Affiliation(s)
- Adrian D Haimovich
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Wenxin Xu
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Andrew Wei
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Mara A Schonberg
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Ula Hwang
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA
- Geriatric Research, Education and Clinical Center, James J. Peters VAMC, Bronx, New York, USA
| | - R Andrew Taylor
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA
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3
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Mavragani A, Hardy F, Tucker K, Hopper A, Marchã MJM, Navaratnam AV, Briggs TWR, Yates J, Day J, Wheeler A, Eve-Jones S, Gray WK. Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data. Interact J Med Res 2022; 11:e41520. [PMID: 36423306 PMCID: PMC9746678 DOI: 10.2196/41520] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/22/2022] [Accepted: 11/24/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Older adults have worse outcomes following hospitalization with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes. OBJECTIVE We aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed for machine learning algorithms. METHODS This was a retrospective study that used the Hospital Episode Statistics administrative data set from March 1, 2020, to February 28, 2021, for hospitalized patients in England aged 65 years or older. The data set was split into separate training (70%), test (15%), and validation (15%) data sets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domains of frailty were identified using the Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, and International Statistical Classification of Disease, 10th Edition codes recorded during the admission. Features were selected, preprocessed, and input into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed; the first model included the demographic, hospital-related, and disease-related items described above, as well as individual GFS domains and CCI items. The second model was similar to the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy. RESULTS In total, 215,831 patients were included. The model using the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model using the HFRS had similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures, and pressure ulcers/weight loss. The most important comorbidity items in the CCI were cancer, heart failure, and renal disease. CONCLUSIONS The physical manifestations of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who need additional support during hospitalization with COVID-19.
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Affiliation(s)
| | - Flavien Hardy
- Getting It Right First Time programme, National Health Service England and National Health Service Improvement, London, United Kingdom
| | - Katie Tucker
- Innovation and Intelligent Automation Unit, Royal Free London National Health Service Foundation Trust, London, United Kingdom
| | - Adrian Hopper
- Getting It Right First Time programme, National Health Service England and National Health Service Improvement, London, United Kingdom.,Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Maria J M Marchã
- Science and Technology Facilities Council Distributed Research Utilising Advanced Computing High Performance Computing Facility, University College London, London, United Kingdom
| | - Annakan V Navaratnam
- University College London Hospitals National Health Service Foundation Trust, London, United Kingdom
| | - Tim W R Briggs
- Getting It Right First Time programme, National Health Service England and National Health Service Improvement, London, United Kingdom.,Royal National Orthopaedic Hospital National Health Service Trust, London, United Kingdom
| | - Jeremy Yates
- Department of Computer Science, University College London, London, United Kingdom
| | - Jamie Day
- Getting It Right First Time programme, National Health Service England and National Health Service Improvement, London, United Kingdom
| | - Andrew Wheeler
- Getting It Right First Time programme, National Health Service England and National Health Service Improvement, London, United Kingdom
| | - Sue Eve-Jones
- Getting It Right First Time programme, National Health Service England and National Health Service Improvement, London, United Kingdom
| | - William K Gray
- Getting It Right First Time programme, National Health Service England and National Health Service Improvement, London, United Kingdom
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Cohen AA, Leung DL, Legault V, Gravel D, Blanchet FG, Côté AM, Fülöp T, Lee J, Dufour F, Liu M, Nakazato Y. Synchrony of biomarker variability indicates a critical transition: Application to mortality prediction in hemodialysis. iScience 2022; 25:104385. [PMID: 35620427 PMCID: PMC9127602 DOI: 10.1016/j.isci.2022.104385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/22/2022] [Accepted: 05/05/2022] [Indexed: 12/03/2022] Open
Abstract
Critical transition theory suggests that complex systems should experience increased temporal variability just before abrupt state changes. We tested this hypothesis in 763 patients on long-term hemodialysis, using 11 biomarkers collected every two weeks and all-cause mortality as a proxy for critical transitions. We find that variability-measured by coefficients of variation (CVs)-increases before death for all 11 clinical biomarkers, and is strikingly synchronized across all biomarkers: the first axis of a principal component analysis on all CVs explains 49% of the variance. This axis then generates powerful predictions of mortality (HR95 = 9.7, p < 0.0001, where HR95 is a scale-invariant metric of hazard ratio; AUC up to 0.82) and starts to increase markedly ∼3 months prior to death. Our results provide an early warning sign of physiological collapse and, more broadly, a quantification of joint system dynamics that opens questions of how system modularity may break down before critical transitions.
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Affiliation(s)
- Alan A. Cohen
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
- Research Center on Aging, Sherbrooke, Quebec J1H 4C4, Canada
- Research Center of Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Diana L. Leung
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Véronique Legault
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Dominique Gravel
- Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada
| | - F. Guillaume Blanchet
- Research Center on Aging, Sherbrooke, Quebec J1H 4C4, Canada
- Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada
- Département de mathématique, Université de Sherbrooke, Sherbrooke, Québec J1K 2R1, Canada
- Département des Sciences de la Santé Communautaires, Université de Sherbrooke, Sherbrooke, Québec J1H 5N4, Canada
| | - Anne-Marie Côté
- Department of Medicine, Nephrology Division, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Tamàs Fülöp
- Research Center on Aging, Sherbrooke, Quebec J1H 4C4, Canada
- Department of Medicine, Geriatric Division, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Juhong Lee
- InfoCentre, Centre intégré universitaire de santé et de services sociaux de l’Estrie – Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Frédérik Dufour
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
- Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada
| | - Mingxin Liu
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Yuichi Nakazato
- Division of Nephrology, Hakuyukai Medical Corporation, Yuai Nisshin Clinic, 2-1914-6 Nisshin-cho, Kita-ku, Saitama-City, Saitama 331-0823, Japan
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5
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Chi S, Guo A, Heard K, Kim S, Foraker R, White P, Moore N. Development and Structure of an Accurate Machine Learning Algorithm to Predict Inpatient Mortality and Hospice Outcomes in the Coronavirus Disease 2019 Era. Med Care 2022; 60:381-386. [PMID: 35230273 PMCID: PMC8989608 DOI: 10.1097/mlr.0000000000001699] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has challenged the accuracy and racial biases present in traditional mortality scores. An accurate prognostic model that can be applied to hospitalized patients irrespective of race or COVID-19 status may benefit patient care. RESEARCH DESIGN This cohort study utilized historical and ongoing electronic health record features to develop and validate a deep-learning model applied on the second day of admission predicting a composite outcome of in-hospital mortality, discharge to hospice, or death within 30 days of admission. Model features included patient demographics, diagnoses, procedures, inpatient medications, laboratory values, vital signs, and substance use history. Conventional performance metrics were assessed, and subgroup analysis was performed based on race, COVID-19 status, and intensive care unit admission. SUBJECTS A total of 35,521 patients hospitalized between April 2020 and October 2020 at a single health care system including a tertiary academic referral center and 9 community hospitals. RESULTS Of 35,521 patients, including 9831 non-White patients and 2020 COVID-19 patients, 2838 (8.0%) met the composite outcome. Patients who experienced the composite outcome were older (73 vs. 61 y old) with similar sex and race distributions between groups. The model achieved an area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.88, 0.91) and an average positive predictive value of 0.46 (0.40, 0.52). Model performance did not differ significantly in White (0.89) and non-White (0.90) subgroups or when grouping by COVID-19 status and intensive care unit admission. CONCLUSION A deep-learning model using large-volume, structured electronic health record data can effectively predict short-term mortality or hospice outcomes on the second day of admission in the general inpatient population without significant racial bias.
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Affiliation(s)
- Stephen Chi
- Division of Pulmonary and Critical Care Medicine
| | - Aixia Guo
- Institute for Informatics, Washington University in St. Louis
| | | | - Seunghwan Kim
- Division of General Medical Sciences, School of Medicine, Washington University in St. Louis
| | - Randi Foraker
- Institute for Informatics, Washington University in St. Louis
| | - Patrick White
- Division of Palliative Medicine, Department of Medicine, Washington University in St. Louis
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Harish KB, Price WN, Aphinyanaphongs Y. Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges. JMIR Form Res 2022; 6:e33970. [PMID: 35404258 PMCID: PMC9039816 DOI: 10.2196/33970] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/05/2022] [Accepted: 01/19/2022] [Indexed: 12/12/2022] Open
Abstract
Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and devices, a longstanding culture of open-source products in other industries, and the beginnings of machine learning–friendly regulatory pathways together allow for the development and deployment of open-source machine learning models. Such tools have distinct advantages including enhanced product integrity, customizability, and lower cost, leading to increased access. However, significant questions regarding engineering concerns about implementation infrastructure and model safety, a lack of incentives from intellectual property protection, and nebulous liability rules significantly complicate the ability to develop such open-source models. Ultimately, the reconciliation of open-source machine learning and the proprietary information–driven health care environment requires that policymakers, regulators, and health care organizations actively craft a conducive market in which innovative developers will continue to both work and collaborate.
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Affiliation(s)
- Keerthi B Harish
- Grossman School of Medicine, New York University, New York, NY, United States
| | - W Nicholson Price
- Law School, University of Michigan, Ann Arbor, MI, United States.,Centre for Advanced Studies In Biomedical Innovation Law, University of Copenhagen, Copenhagen, Denmark
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7
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Taseen R, Ethier JF. Expected clinical utility of automatable prediction models for improving palliative and end-of-life care outcomes: Toward routine decision analysis before implementation. J Am Med Inform Assoc 2021; 28:2366-2378. [PMID: 34472611 PMCID: PMC8510333 DOI: 10.1093/jamia/ocab140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/15/2021] [Accepted: 06/21/2021] [Indexed: 11/22/2022] Open
Abstract
Objective The study sought to evaluate the expected clinical utility of automatable prediction models for increasing goals-of-care discussions (GOCDs) among hospitalized patients at the end of life (EOL). Materials and Methods We built a decision model from the perspective of clinicians who aim to increase GOCDs at the EOL using an automated alert system. The alternative strategies were 4 prediction models—3 random forest models and the Modified Hospital One-year Mortality Risk model—to generate alerts for patients at a high risk of 1-year mortality. They were trained on admissions from 2011 to 2016 (70 788 patients) and tested with admissions from 2017-2018 (16 490 patients). GOCDs occurring in usual care were measured with code status orders. We calculated the expected risk difference (beneficial outcomes with alerts minus beneficial outcomes without alerts among those at the EOL), the number needed to benefit (number of alerts needed to increase benefit over usual care by 1 outcome), and the net benefit (benefit minus cost) of each strategy. Results Models had a C-statistic between 0.79 and 0.86. A code status order occurred during 2599 of 3773 (69%) hospitalizations at the EOL. At a risk threshold corresponding to an alert prevalence of 10%, the expected risk difference ranged from 5.4% to 10.7% and the number needed to benefit ranged from 5.4 to 10.9 alerts. Using revealed preferences, only 2 models improved net benefit over usual care. A random forest model with diagnostic predictors had the highest expected value, including in sensitivity analyses. Discussion Prediction models with acceptable predictive validity differed meaningfully in their ability to improve over usual decision making. Conclusions An evaluation of clinical utility, such as by using decision curve analysis, is recommended after validating a prediction model because metrics of model predictiveness, such as the C-statistic, are not informative of clinical value.
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Affiliation(s)
- Ryeyan Taseen
- Respiratory Division, Department of Medicine, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec, Canada.,Centre Interdisciplinaire de Recherche en Informatique de la Santé, University of Sherbrooke, Sherbrooke, Quebec, Canada.,Groupe de Recherche Interdisciplinaire en Informatique de la Santé, University of Sherbrooke, Sherbrooke, Quebec, Canada
| | - Jean-François Ethier
- Centre Interdisciplinaire de Recherche en Informatique de la Santé, University of Sherbrooke, Sherbrooke, Quebec, Canada.,Groupe de Recherche Interdisciplinaire en Informatique de la Santé, University of Sherbrooke, Sherbrooke, Quebec, Canada.,General Internal Medicine Division, Department of Medicine, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec, Canada
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8
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Sreedher G, Ho ML, Smith M, Udayasankar UK, Risacher S, Rapalino O, Greer MLC, Doria AS, Gee MS. Magnetic resonance imaging quality control, quality assurance and quality improvement. Pediatr Radiol 2021; 51:698-708. [PMID: 33772641 DOI: 10.1007/s00247-021-05043-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 01/22/2021] [Accepted: 03/01/2021] [Indexed: 12/01/2022]
Abstract
Quality in MR imaging is a comprehensive process that encompasses scanner performance, clinical processes for efficient scanning and reporting, as well as data-driven improvement involving measurement of key performance indicators. In this paper, the authors review this entire process. This article provides a framework for establishing a successful MR quality program. The collective experiences of the authors across a spectrum of pediatric hospitals is summarized here.
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Affiliation(s)
- Gayathri Sreedher
- Department of Radiology, Akron Children's Hospital, One Perkins Square, Akron, OH, 44308, USA.
| | - Mai-Lan Ho
- Department of Radiology, Nationwide Children's Hospital, Columbus, OH, USA
| | - Mark Smith
- Department of Radiology, Nationwide Children's Hospital, Columbus, OH, USA
| | - Unni K Udayasankar
- Department of Medical Imaging, University of Arizona College of Medicine, Phoenix, AZ, USA
| | - Seretha Risacher
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Otto Rapalino
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Mary-Louise C Greer
- Department of Diagnostic Imaging, The Hospital for Sick Children, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Andrea S Doria
- Department of Diagnostic Imaging, The Hospital for Sick Children, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Michael S Gee
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
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