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Tam S, Al-Antary N, Adjei Boakye E, Springer K, Poisson LM, Su WT, Grewal J, Zatirka T, Ryan M, Movsas B, Chang SS. Differences in Patient-Reported Outcome Measures in Patients With Cancer Six Months Before Death. JCO Oncol Pract 2024:OP2300720. [PMID: 39250724 DOI: 10.1200/op.23.00720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 06/21/2024] [Accepted: 08/02/2024] [Indexed: 09/11/2024] Open
Abstract
PURPOSE Patient-reported outcome measures (PROMs) provide a direct report of the patient's perspective, complementary to clinician assessment. Currently, understanding the real-time changes in PROM scores near the end of life remains limited. This study evaluated differences in mean PROM scores between patients with cancer within 6 months before death compared with surviving patients with cancer. METHODS This retrospective case-control study uses the National Institutes of Health's Patient-Reported Outcomes Measurement Information System computer adaptive testing instruments to assess pain interference, physical function, fatigue, and depression. Patients dying within 6 months of PROM completion were selected as cases and matched to controls 1:3 by age at PROM completion, sex, cancer disease site, and cancer stage at diagnosis. Generalized estimating equation models assessed the difference in mean PROM score in cases compared with controls. RESULTS A total of 461 cases and 1,270 controls from September 2020 to January 2023 were included. After adjustment for ethnicity, Charlson Comorbidity Index, and census tract median household income, significant differences in mean scores were demonstrated. Physical function domain showed the largest difference, with cases averaging 6.52 points lower than controls (95% CI, -8.25 to -4.80). Fatigue and pain interference domains showed a rise in PROMs scores by 4.83 points (95% CI, 2.94 to 6.72) and 4.33 points (95% CI, 2.53 to 6.12), respectively. CONCLUSION Compared with controls, patients dying within 6 months of PROM completion demonstrated worse PROM scores in the four domains assessed. These findings suggest the utility of routinely collected PROMs as a real-time indicator of the terminal stage of life among patients with cancer to allow for earlier intervention with supportive oncology services.
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Affiliation(s)
- Samantha Tam
- Department of Otolaryngology-Head and Neck Surgery, Henry Ford Hospital, Detroit, MI
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI
| | - Nada Al-Antary
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI
| | - Eric Adjei Boakye
- Department of Otolaryngology-Head and Neck Surgery, Henry Ford Hospital, Detroit, MI
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI
| | - Kylie Springer
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI
| | - Laila M Poisson
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI
| | - Wan-Ting Su
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI
| | - Jeewanjot Grewal
- Department of Otolaryngology-Head and Neck Surgery, Henry Ford Hospital, Detroit, MI
| | - Theresa Zatirka
- Division of Clinical and Quality Transformation, Transformation Consulting, Henry Ford Health, Detroit, MI
| | - Michael Ryan
- Division of Supportive Oncology Services, Henry Ford Health, Detroit, MI
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI
| | - Steven S Chang
- Department of Otolaryngology-Head and Neck Surgery, Henry Ford Hospital, Detroit, MI
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI
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Wójcik Z, Dimitrova V, Warrington L, Velikova G, Absolom K. Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures. JCO Clin Cancer Inform 2024; 8:e2300264. [PMID: 38669610 PMCID: PMC11161248 DOI: 10.1200/cci.23.00264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/14/2024] [Accepted: 03/01/2024] [Indexed: 04/28/2024] Open
Abstract
PURPOSE Adverse effects of chemotherapy often require hospital admissions or treatment management. Identifying factors contributing to unplanned hospital utilization may improve health care quality and patients' well-being. This study aimed to assess if patient-reported outcome measures (PROMs) improve performance of machine learning (ML) models predicting hospital admissions, triage events (contacting helpline or attending hospital), and changes to chemotherapy. MATERIALS AND METHODS Clinical trial data were used and contained responses to three PROMs (European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire [QLQ-C30], EuroQol Five-Dimensional Visual Analogue Scale [EQ-5D], and Functional Assessment of Cancer Therapy-General [FACT-G]) and clinical information on 508 participants undergoing chemotherapy. Six feature sets (with following variables: [1] all available; [2] clinical; [3] PROMs; [4] clinical and QLQ-C30; [5] clinical and EQ-5D; [6] clinical and FACT-G) were applied in six ML models (logistic regression [LR], decision tree, adaptive boosting, random forest [RF], support vector machines [SVMs], and neural network) to predict admissions, triage events, and chemotherapy changes. RESULTS The comprehensive analysis of predictive performances of the six ML models for each feature set in three different methods for handling class imbalance indicated that PROMs improved predictions of all outcomes. RF and SVMs had the highest performance for predicting admissions and changes to chemotherapy in balanced data sets, and LR in imbalanced data set. Balancing data led to the best performance compared with imbalanced data set or data set with balanced train set only. CONCLUSION These results endorsed the view that ML can be applied on PROM data to predict hospital utilization and chemotherapy management. If further explored, this study may contribute to health care planning and treatment personalization. Rigorous comparison of model performance affected by different imbalanced data handling methods shows best practice in ML research.
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Affiliation(s)
- Zuzanna Wójcik
- UKRI Centre for Doctoral Training in Artificial Intelligence for Medical Diagnosis and Care, University of Leeds, Leeds, United Kingdom
| | - Vania Dimitrova
- School of Computing, University of Leeds, Leeds, United Kingdom
| | - Lorraine Warrington
- Leeds Institute of Medical Research, University of Leeds, St James's University Hospital, Leeds, United Kingdom
| | - Galina Velikova
- Leeds Institute of Medical Research, University of Leeds, St James's University Hospital, Leeds, United Kingdom
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Kate Absolom
- Leeds Institute of Medical Research, University of Leeds, St James's University Hospital, Leeds, United Kingdom
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
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Collins GS, Whittle R, Bullock GS, Logullo P, Dhiman P, de Beyer JA, Riley RD, Schlussel MM. Open science practices need substantial improvement in prognostic model studies in oncology using machine learning. J Clin Epidemiol 2024; 165:111199. [PMID: 37898461 DOI: 10.1016/j.jclinepi.2023.10.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/06/2023] [Accepted: 10/20/2023] [Indexed: 10/30/2023]
Abstract
OBJECTIVE To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING We conducted a systematic review, searching the MEDLINE database between December 1, 2022, and December 31, 2022, for studies developing a multivariable prognostic model using machine learning methods (as defined by the authors) in oncology. Two authors independently screened records and extracted open science practices. RESULTS We identified 46 publications describing the development of a multivariable prognostic model. The adoption of open science principles was poor. Only one study reported availability of a study protocol, and only one study was registered. Funding statements and conflicts of interest statements were common. Thirty-five studies (76%) provided data sharing statements, with 21 (46%) indicating data were available on request to the authors and seven declaring data sharing was not applicable. Two studies (4%) shared data. Only 12 studies (26%) provided code sharing statements, including 2 (4%) that indicated the code was available on request to the authors. Only 11 studies (24%) provided sufficient information to allow their model to be used in practice. The use of reporting guidelines was rare: eight studies (18%) mentioning using a reporting guideline, with 4 (10%) using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis statement, 1 (2%) using Minimum Information About Clinical Artificial Intelligence Modeling and Consolidated Standards Of Reporting Trials-Artificial Intelligence, 1 (2%) using Strengthening The Reporting Of Observational Studies In Epidemiology, 1 (2%) using Standards for Reporting Diagnostic Accuracy Studies, and 1 (2%) using Transparent Reporting of Evaluations with Nonrandomized Designs. CONCLUSION The adoption of open science principles in oncology studies developing prognostic models using machine learning methods is poor. Guidance and an increased awareness of benefits and best practices of open science are needed for prediction research in oncology.
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Affiliation(s)
- Gary S Collins
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom.
| | - Rebecca Whittle
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Garrett S Bullock
- Department of Orthopaedic Surgery, Wake Forest School of Medicine, Winston-Salem, NC, USA; Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, United Kingdom
| | - Patricia Logullo
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Paula Dhiman
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Jennifer A de Beyer
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Michael M Schlussel
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
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Khalili H, Rismani M, Nematollahi MA, Masoudi MS, Asadollahi A, Taheri R, Pourmontaseri H, Valibeygi A, Roshanzamir M, Alizadehsani R, Niakan A, Andishgar A, Islam SMS, Acharya UR. Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Sci Rep 2023; 13:960. [PMID: 36653412 PMCID: PMC9849475 DOI: 10.1038/s41598-023-28188-w] [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: 08/01/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.
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Affiliation(s)
- Hosseinali Khalili
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Maziyar Rismani
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | | | - Mohammad Sadegh Masoudi
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Arefeh Asadollahi
- Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Reza Taheri
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Hossein Pourmontaseri
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran.,Bitab Knowledge Enterprise, Fasa University of Medical Sciences, Fasa, Iran
| | - Adib Valibeygi
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, 74617-81189, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Amin Niakan
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Aref Andishgar
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia.,Cardiovascular Division, The George Institute for Global Health, Newtown, Australia.,Sydney Medical School, University of Sydney, Camperdown, Australia
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.,Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan
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