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Takkavatakarn K, Oh W, Cheng E, Nadkarni GN, Chan L. Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4. BMC Nephrol 2023; 24:376. [PMID: 38114923 PMCID: PMC10731874 DOI: 10.1186/s12882-023-03424-7] [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: 10/04/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
INTRODUCTION End-stage kidney disease (ESKD) is associated with increased morbidity and mortality. Identifying patients with stage 4 CKD (CKD4) at risk of rapid progression to ESKD remains challenging. Accurate prediction of CKD4 progression can improve patient outcomes by improving advanced care planning and optimizing healthcare resource allocation. METHODS We obtained electronic health record data from patients with CKD4 in a large health system between January 1, 2006, and December 31, 2016. We developed and validated four models, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network (ANN), to predict ESKD at 3 years. We utilized area under the receiver operating characteristic curve (AUROC) to evaluate model performances and utilized Shapley additive explanation (SHAP) values and plots to define feature dependence of the best performance model. RESULTS We included 3,160 patients with CKD4. ESKD was observed in 538 patients (21%). All approaches had similar AUROCs; ANN yielded the highest AUROC (0.77; 95%CI 0.75 to 0.79) and LASSO regression (0.77; 95%CI 0.75 to 0.79), followed by random forest (0.76; 95% CI 0.74 to 0.79), and XGBoost (0.76; 95% CI 0.74 to 0.78). CONCLUSIONS We developed and validated several models for near-term prediction of kidney failure in CKD4. ANN, random forest, and XGBoost demonstrated similar predictive performances. Using this suite of models, interventions can be customized based on risk, and population health and resources appropriately allocated.
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Affiliation(s)
- Kullaya Takkavatakarn
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand
| | - Wonsuk Oh
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ella Cheng
- The Cooper Union for the Advancement of Science and Art, New York, NY, USA
| | - Girish N Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Zhu Y, Bi D, Saunders M, Ji Y. Prediction of chronic kidney disease progression using recurrent neural network and electronic health records. Sci Rep 2023; 13:22091. [PMID: 38086905 PMCID: PMC10716428 DOI: 10.1038/s41598-023-49271-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 12/06/2023] [Indexed: 12/18/2023] Open
Abstract
Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.
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Affiliation(s)
- Yitan Zhu
- Computing, Environment and Life Sciences, Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA.
| | - Dehua Bi
- Department of Public Health Sciences, The University of Chicago, 5841 South Maryland Ave, MC 2000, Chicago, IL, 60637, USA
| | - Milda Saunders
- Department of Medicine, The University of Chicago, 5841 South Maryland Ave, MC 2007, Chicago, IL, 60637, USA
| | - Yuan Ji
- Department of Public Health Sciences, The University of Chicago, 5841 South Maryland Ave, MC 2000, Chicago, IL, 60637, USA.
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3
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Lei N, Zhang X, Wei M, Lao B, Xu X, Zhang M, Chen H, Xu Y, Xia B, Zhang D, Dong C, Fu L, Tang F, Wu Y. Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2022; 22:205. [PMID: 35915457 PMCID: PMC9341041 DOI: 10.1186/s12911-022-01951-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 07/18/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Kidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been established in nephrology. However, their accuracy have been inconsistent. Therefore, we conducted a systematic review and meta-analysis to investigate the diagnostic accuracy of ML algorithms for kidney disease progression. METHODS We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, the Chinese Biomedicine Literature Database, Chinese National Knowledge Infrastructure, Wanfang Database, and the VIP Database for diagnostic studies on ML algorithms' accuracy in predicting kidney disease prognosis, from the establishment of these databases until October 2020. Two investigators independently evaluate study quality by QUADAS-2 tool and extracted data from single ML algorithm for data synthesis using the bivariate model and the hierarchical summary receiver operating characteristic (HSROC) model. RESULTS Fifteen studies were left after screening, only 6 studies were eligible for data synthesis. The sample size of these 6 studies was 12,534, and the kidney disease types could be divided into chronic kidney disease (CKD) and Immunoglobulin A Nephropathy, with 5 articles using end-stage renal diseases occurrence as the primary outcome. The main results indicated that the area under curve (AUC) of the HSROC was 0.87 (0.84-0.90) and ML algorithm exhibited a strong specificity, 95% confidence interval and heterogeneity (I2) of (0.87, 0.84-0.90, [I2 99.0%]) and a weak sensitivity of (0.68, 0.58-0.77, [I2 99.7%]) in predicting kidney disease deterioration. And the the results of subgroup analysis indicated that ML algorithm's AUC for predicting CKD prognosis was 0.82 (0.79-0.85), with the pool sensitivity of (0.64, 0.49-0.77, [I2 99.20%]) and pool specificity of (0.84, 0.74-0.91, [I2 99.84%]). The ML algorithm's AUC for predicting IgA nephropathy prognosis was 0.78 (0.74-0.81), with the pool sensitivity of (0.74, 0.71-0.77, [I2 7.10%]) and pool specificity of (0.93, 0.91-0.95, [I2 83.92%]). CONCLUSION Taking advantage of big data, ML algorithm-based prediction models have high accuracy in predicting kidney disease progression, we recommend ML algorithms as an auxiliary tool for clinicians to determine proper treatment and disease management strategies.
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Affiliation(s)
- Nuo Lei
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xianlong Zhang
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Mengting Wei
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Beini Lao
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xueyi Xu
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Min Zhang
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huifen Chen
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yanmin Xu
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bingqing Xia
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Dingjun Zhang
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chendi Dong
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lizhe Fu
- Chronic Disease Management Department, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Fang Tang
- Chronic Disease Management Department, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yifan Wu
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
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Lim DKE, Boyd JH, Thomas E, Chakera A, Tippaya S, Irish A, Manuel J, Betts K, Robinson S. Prediction models used in the progression of chronic kidney disease: A scoping review. PLoS One 2022; 17:e0271619. [PMID: 35881639 PMCID: PMC9321365 DOI: 10.1371/journal.pone.0271619] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 07/04/2022] [Indexed: 11/19/2022] Open
Abstract
Objective
To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).
Design
Scoping review.
Data sources
Medline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022.
Study selection
All English written studies that are published in peer-reviewed journals in any country, that developed at least a statistical or computational model that predicted the risk of CKD progression.
Data extraction
Eligible studies for full text review were assessed on the methods that were used to predict the progression of CKD. The type of information extracted included: the author(s), title of article, year of publication, study dates, study location, number of participants, study design, predicted outcomes, type of prediction model, prediction variables used, validation assessment, limitations and implications.
Results
From 516 studies, 33 were included for full-text review. A qualitative analysis of the articles was compared following the extracted information. The study populations across the studies were heterogenous and data acquired by the studies were sourced from different levels and locations of healthcare systems. 31 studies implemented supervised models, and 2 studies included unsupervised models. Regardless of the model used, the predicted outcome included measurement of risk of progression towards end-stage kidney disease (ESKD) of related definitions, over given time intervals. However, there is a lack of reporting consistency on details of the development of their prediction models.
Conclusions
Researchers are working towards producing an effective model to provide key insights into the progression of CKD. This review found that cox regression modelling was predominantly used among the small number of studies in the review. This made it difficult to perform a comparison between ML algorithms, more so when different validation methods were used in different cohort types. There needs to be increased investment in a more consistent and reproducible approach for future studies looking to develop risk prediction models for CKD progression.
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Affiliation(s)
- David K. E. Lim
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
- * E-mail:
| | - James H. Boyd
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
- La Trobe University, Melbourne, Bundoora, VIC, Australia
| | - Elizabeth Thomas
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
- Medical School, The University of Western Australia, Perth, WA, Australia
| | - Aron Chakera
- Medical School, The University of Western Australia, Perth, WA, Australia
- Renal Unit, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Sawitchaya Tippaya
- Curtin Institute for Computation, Curtin University, Perth, WA, Australia
| | | | | | - Kim Betts
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
| | - Suzanne Robinson
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
- Deakin Health Economics, Deakin University, Burwood, VIC, Australia
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Chuah A, Walters G, Christiadi D, Karpe K, Kennard A, Singer R, Talaulikar G, Ge W, Suominen H, Andrews TD, Jiang S. Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease. Front Med (Lausanne) 2022; 9:837232. [PMID: 35372378 PMCID: PMC8965763 DOI: 10.3389/fmed.2022.837232] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 02/18/2022] [Indexed: 11/30/2022] Open
Abstract
Background and Objectives Chronic kidney disease progression to ESKD is associated with a marked increase in mortality and morbidity. Its progression is highly variable and difficult to predict. Methods This is an observational, retrospective, single-centre study. The cohort was patients attending hospital and nephrology clinic at The Canberra Hospital from September 1996 to March 2018. Demographic data, vital signs, kidney function test, proteinuria, and serum glucose were extracted. The model was trained on the featurised time series data with XGBoost. Its performance was compared against six nephrologists and the Kidney Failure Risk Equation (KFRE). Results A total of 12,371 patients were included, with 2,388 were found to have an adequate density (three eGFR data points in the first 2 years) for subsequent analysis. Patients were divided into 80%/20% ratio for training and testing datasets. ML model had superior performance than nephrologist in predicting ESKD within 2 years with 93.9% accuracy, 60% sensitivity, 97.7% specificity, 75% positive predictive value. The ML model was superior in all performance metrics to the KFRE 4- and 8-variable models. eGFR and glucose were found to be highly contributing to the ESKD prediction performance. Conclusions The computational predictions had higher accuracy, specificity and positive predictive value, which indicates the potential integration into clinical workflows for decision support.
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Affiliation(s)
- Aaron Chuah
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University (ANU), Canberra, ACT, Australia
| | - Giles Walters
- Department of Renal Medicine, The Canberra Hospital, Garran, ACT, Australia
| | - Daniel Christiadi
- Department of Renal Medicine, The Canberra Hospital, Garran, ACT, Australia
| | - Krishna Karpe
- Department of Renal Medicine, The Canberra Hospital, Garran, ACT, Australia
| | - Alice Kennard
- Department of Renal Medicine, The Canberra Hospital, Garran, ACT, Australia
| | - Richard Singer
- Department of Renal Medicine, The Canberra Hospital, Garran, ACT, Australia
| | - Girish Talaulikar
- Department of Renal Medicine, The Canberra Hospital, Garran, ACT, Australia
| | - Wenbo Ge
- School of Computing, Australian National University, ACT, Australia
| | - Hanna Suominen
- School of Computing, Australian National University, ACT, Australia.,Department of Computing, University of Turku, Turku, Finland
| | - T Daniel Andrews
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University (ANU), Canberra, ACT, Australia.,Centre for Personalised Immunology, Australian National University (ANU), Canberra, ACT, Australia
| | - Simon Jiang
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University (ANU), Canberra, ACT, Australia.,Department of Renal Medicine, The Canberra Hospital, Garran, ACT, Australia.,Centre for Personalised Immunology, Australian National University (ANU), Canberra, ACT, Australia
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6
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Poonia RC, Gupta MK, Abunadi I, Albraikan AA, Al-Wesabi FN, Hamza MA, B T. Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease. Healthcare (Basel) 2022; 10:healthcare10020371. [PMID: 35206985 PMCID: PMC8871759 DOI: 10.3390/healthcare10020371] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 11/17/2022] Open
Abstract
Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits.
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Affiliation(s)
- Ramesh Chandra Poonia
- Department of Computer Science, CHRIST (Deemed to be University), Bangalore 560029, India; (R.C.P.); (T.B.)
| | - Mukesh Kumar Gupta
- Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur 302017, India;
| | - Ibrahim Abunadi
- Department of Information Systems, Prince Sultan University, P.O. Box No. 66833 Rafha Street, Riyadh 11586, Saudi Arabia;
| | - Amani Abdulrahman Albraikan
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Fahd N. Al-Wesabi
- Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha 61421, Saudi Arabia
- Correspondence: ; Tel.: +966-534227096
| | - Manar Ahmed Hamza
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia;
| | - Tulasi B
- Department of Computer Science, CHRIST (Deemed to be University), Bangalore 560029, India; (R.C.P.); (T.B.)
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Ramspek CL, de Jong Y, Dekker FW, van Diepen M. Towards the best kidney failure prediction tool: a systematic review and selection aid. Nephrol Dial Transplant 2021; 35:1527-1538. [PMID: 30830157 PMCID: PMC7473808 DOI: 10.1093/ndt/gfz018] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 01/15/2019] [Indexed: 12/14/2022] Open
Abstract
Background Prediction tools that identify chronic kidney disease (CKD) patients at a high risk of developing kidney failure have the potential for great clinical value, but limited uptake. The aim of the current study is to systematically review all available models predicting kidney failure in CKD patients, organize empirical evidence on their validity and ultimately provide guidance in the interpretation and uptake of these tools. Methods PubMed and EMBASE were searched for relevant articles. Titles, abstracts and full-text articles were sequentially screened for inclusion by two independent researchers. Data on study design, model development and performance were extracted. The risk of bias and clinical usefulness were assessed and combined in order to provide recommendations on which models to use. Results Of 2183 screened studies, a total of 42 studies were included in the current review. Most studies showed high discriminatory capacity and the included predictors had large overlap. Overall, the risk of bias was high. Slightly less than half the studies (48%) presented enough detail for the use of their prediction tool in practice and few models were externally validated. Conclusions The current systematic review may be used as a tool to select the most appropriate and robust prognostic model for various settings. Although some models showed great potential, many lacked clinical relevance due to being developed in a prevalent patient population with a wide range of disease severity. Future research efforts should focus on external validation and impact assessment in clinically relevant patient populations.
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Affiliation(s)
- Chava L Ramspek
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ype de Jong
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Friedo W Dekker
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Merel van Diepen
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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8
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Senteio CR, Callahan MB. Supporting quality care for ESRD patients: the social worker can help address barriers to advance care planning. BMC Nephrol 2020; 21:55. [PMID: 32075587 PMCID: PMC7031953 DOI: 10.1186/s12882-020-01720-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 02/11/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Advance Care Planning (ACP) is essential for preparation for end-of-life. It is a means through which patients clarify their treatment wishes. ACP is a patient-centered, dynamic process involving patients, their families, and caregivers. It is designed to 1) clarify goals of care, 2) increase patient agency over their care and treatments, and 3) help prepare for death. ACP is an active process; the end-stage renal disease (ESRD) illness trajectory creates health circumstances that necessitate that caregivers assess and nurture patient readiness for ACP discussions. Effective ACP enhances patient engagement and quality of life resulting in better quality of care. MAIN BODY Despite these benefits, ACP is not consistently completed. Clinical, technical, and social barriers result in key challenges to quality care. First, ACP requires caregivers to have end-of-life conversations that they lack the training to perform and often find difficult. Second, electronic health record (EHR) tools do not enable the efficient exchange of requisite psychosocial information such as treatment burden, patient preferences, health beliefs, priorities, and understanding of prognosis. This results in a lack of information available to enable patients and their families to understand the impact of illness and treatment options. Third, culture plays a vital role in end-of-life conversations. Social barriers include circumstances when a patient's cultural beliefs or value system conflicts with the caregiver's beliefs. Caregivers describe this disconnect as a key barrier to ACP. Consistent ACP is integral to quality patient-centered care and social workers' training and clinical roles uniquely position them to support ACP. CONCLUSION In this debate, we detail the known barriers to completing ACP for ESRD patients, and we describe its benefits. We detail how social workers, in particular, can support health outcomes by promoting the health information exchange that occurs during these sensitive conversations with patients, their family, and care team members. We aim to inform clinical social workers of this opportunity to enhance quality care by engaging in ACP. We describe research to help further elucidate barriers, and how researchers and caregivers can design and deliver interventions that support ACP to address this persistent challenge to quality end-of-life care.
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Affiliation(s)
- Charles R Senteio
- School of Communication and Information, Rutgers University, 4 Huntington Street, New Brunswick, NJ, 08901, USA.
| | - Mary Beth Callahan
- Dallas Nephrology Associates, 411 North Washington Street, Suite #7000, Dallas, TX, 75246, USA
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9
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Bull LM, Lunt M, Martin GP, Hyrich K, Sergeant JC. Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods. Diagn Progn Res 2020; 4:9. [PMID: 32671229 PMCID: PMC7346415 DOI: 10.1186/s41512-020-00078-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 04/28/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers. METHODS MEDLINE, Embase and Web of Science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method. RESULTS The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised estimating equations, landmark analysis, two-stage modelling, joint-modelling, trajectory classification and machine learning. Each of these frameworks satisfies at least one of three aims: to better represent the predictor-outcome relationship over time, to infer a covariate value at a pre-specified time and to account for the effect of covariate change. CONCLUSIONS The applicability of identified methods depends on the motivation for including longitudinal information and the method's compatibility with the clinical context and available patient data, for both model development and risk estimation in practice.
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Affiliation(s)
- Lucy M. Bull
- grid.5379.80000000121662407Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- grid.5379.80000000121662407Centre for Biostatistics, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Mark Lunt
- grid.5379.80000000121662407Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Glen P. Martin
- grid.5379.80000000121662407Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Kimme Hyrich
- grid.5379.80000000121662407Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- grid.498924.aNational Institute for Health Research Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Jamie C. Sergeant
- grid.5379.80000000121662407Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- grid.5379.80000000121662407Centre for Biostatistics, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
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10
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Shahid AH, Singh M. Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.05.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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