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Bertsimas D, Margonis GA, Sujichantararat S, Koulouras A, Ma Y, Antonescu CR, Brennan MF, Martín-Broto J, Tang S, Rutkowski P, Kreis ME, Beyer K, Wang J, Bylina E, Sobczuk P, Gutierrez A, Jadeja B, Tap WD, Chi P, Singer S. Interpretable artificial intelligence to optimise use of imatinib after resection in patients with localised gastrointestinal stromal tumours: an observational cohort study. Lancet Oncol 2024; 25:1025-1037. [PMID: 38976997 DOI: 10.1016/s1470-2045(24)00259-6] [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: 08/22/2023] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND Current guidelines recommend use of adjuvant imatinib therapy for many patients with gastrointestinal stromal tumours (GISTs); however, its optimal treatment duration is unknown and some patient groups do not benefit from the therapy. We aimed to apply state-of-the-art, interpretable artificial intelligence (ie, predictions or prescription logic that can be easily understood) methods on real-world data to establish which groups of patients with GISTs should receive adjuvant imatinib, its optimal treatment duration, and the benefits conferred by this therapy. METHODS In this observational cohort study, we considered for inclusion all patients who underwent resection of primary, non-metastatic GISTs at the Memorial Sloan Kettering Cancer Center (MSKCC; New York, NY, USA) between Oct 1, 1982, and Dec 31, 2017, and who were classified as intermediate or high risk according to the Armed Forces Institute of Pathology Miettinen criteria and had complete follow-up data with no missing entries. A counterfactual random forest model, which used predictors of recurrence (mitotic count, tumour size, and tumour site) and imatinib duration to infer the probability of recurrence at 7 years for a given patient under each duration of imatinib treatment, was trained in the MSKCC cohort. Optimal policy trees (OPTs), a state-of-the-art interpretable AI-based method, were used to read the counterfactual random forest model by training a decision tree with the counterfactual predictions. The OPT recommendations were externally validated in two cohorts of patients from Poland (the Polish Clinical GIST Registry), who underwent GIST resection between Dec 1, 1981, and Dec 31, 2011, and from Spain (the Spanish Group for Research in Sarcomas), who underwent resection between Oct 1, 1987, and Jan 30, 2011. FINDINGS Among 1007 patients who underwent GIST surgery in MSKCC, 117 were included in the internal cohort; for the external cohorts, the Polish cohort comprised 363 patients and the Spanish cohort comprised 239 patients. The OPT did not recommend imatinib for patients with GISTs of gastric origin measuring less than 15·9 cm with a mitotic count of less than 11·5 mitoses per 5 mm2 or for those with small GISTs (<5·4 cm) of any site with a count of less than 11·5 mitoses per 5 mm2. In this cohort, the OPT cutoffs had a sensitivity of 92·7% (95% CI 82·4-98·0) and a specificity of 33·9% (22·3-47·0). The application of these cutoffs in the two external cohorts would have spared 38 (29%) of 131 patients in the Spanish cohort and 44 (35%) of 126 patients in the Polish cohort from unnecessary treatment with imatinib. Meanwhile, the risk of undertreating patients in these cohorts was minimal (sensitivity 95·4% [95% CI 89·5-98·5] in the Spanish cohort and 92·4% [88·3-95·4] in the Polish cohort). The OPT tested 33 different durations of imatinib treatment (<5 years) and found that 5 years of treatment conferred the most benefit. INTERPRETATION If the identified patient subgroups were applied in clinical practice, as many as a third of the current cohort of candidates who do not benefit from adjuvant imatinib would be encouraged to not receive imatinib, subsequently avoiding unnecessary toxicity on patients and financial strain on health-care systems. Our finding that 5 years is the optimal duration of imatinib treatment could be the best source of evidence to inform clinical practice until 2028, when a randomised controlled trial with the same aims is expected to report its findings. FUNDING National Cancer Institute.
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Affiliation(s)
- Dimitris Bertsimas
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Georgios Antonios Margonis
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Angelos Koulouras
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yu Ma
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cristina R Antonescu
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Murray F Brennan
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Javier Martín-Broto
- Medical Oncology Department, Fundación Jimenez Diaz University Hospital, Madrid, Spain; Medical Oncology Department, Hospital General de Villalba, Madrid, Spain; Instituto de Investigacion Sanitaria Fundacion Jimenez Diaz, Madrid, Spain
| | - Seehanah Tang
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Piotr Rutkowski
- Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Martin E Kreis
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Katharina Beyer
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jane Wang
- Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Elzbieta Bylina
- Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Pawel Sobczuk
- Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Antonio Gutierrez
- Medical Oncology Department, Fundación Jimenez Diaz University Hospital, Madrid, Spain; Medical Oncology Department, Hospital General de Villalba, Madrid, Spain; Instituto de Investigacion Sanitaria Fundacion Jimenez Diaz, Madrid, Spain
| | - Bhumika Jadeja
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - William D Tap
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ping Chi
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Samuel Singer
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Zhao B, Nguyen VK, Xu M, Colacino JA, Jolliet O. Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality. Sci Rep 2024; 14:15566. [PMID: 38971926 PMCID: PMC11227534 DOI: 10.1038/s41598-024-66261-0] [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: 02/08/2024] [Accepted: 07/01/2024] [Indexed: 07/08/2024] Open
Abstract
Understanding the combined effects of risk factors on all-cause mortality is crucial for implementing effective risk stratification and designing targeted interventions, but such combined effects are understudied. We aim to use survival-tree based machine learning models as more flexible nonparametric techniques to examine the combined effects of multiple physiological risk factors on mortality. More specifically, we (1) study the combined effects between multiple physiological factors and all-cause mortality, (2) identify the five most influential factors and visualize their combined influence on all-cause mortality, and (3) compare the mortality cut-offs with the current clinical thresholds. Data from the 1999-2014 NHANES Survey were linked to National Death Index data with follow-up through 2015 for 17,790 adults. We observed that the five most influential factors affecting mortality are the tobacco smoking biomarker cotinine, glomerular filtration rate (GFR), plasma glucose, sex, and white blood cell count. Specifically, high mortality risk is associated with being male, active smoking, low GFR, elevated plasma glucose levels, and high white blood cell count. The identified mortality-based cutoffs for these factors are mostly consistent with relevant studies and current clinical thresholds. This approach enabled us to identify important cutoffs and provide enhanced risk prediction as an important basis to inform clinical practice and develop new strategies for precision medicine.
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Affiliation(s)
- Bu Zhao
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA.
| | - Vy Kim Nguyen
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ming Xu
- School of Environment, Tsinghua University, Beijing, China
| | - Justin A Colacino
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Olivier Jolliet
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.
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3
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Young DL, Hannum SM, Engels R, Colantuoni E, Friedman LA, Hoyer EH. Dynamic Prediction of Post-Acute Care Needs for Hospitalized Medicine Patients. J Am Med Dir Assoc 2024; 25:104939. [PMID: 38387858 DOI: 10.1016/j.jamda.2024.01.008] [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: 05/10/2023] [Revised: 10/05/2023] [Accepted: 01/10/2024] [Indexed: 02/24/2024]
Abstract
OBJECTIVES Use patient demographic and clinical characteristics at admission and time-varying in-hospital measures of patient mobility to predict patient post-acute care (PAC) discharge. DESIGN Retrospective cohort analysis of electronic medical records. SETTING AND PARTICIPANTS Patients admitted to the two participating Hospitals from November 2016 through December 2019 with ≥72 hours in a general medicine service. METHODS Discharge location (PAC vs home) was the primary outcome, and 2 time-varying measures of patient mobility, Activity Measure for Post-Acute Care (AM-PAC) Mobility "6-clicks" and Johns Hopkins Highest Level of Mobility, were the primary predictors. Other predictors included demographic and clinical characteristics. For each day of hospitalization, we predicted discharge to PAC using the demographic and clinical characteristics and most recent mobility data within a random forest (RF) for survival, longitudinal, and multivariate (RF-SLAM) data. A regression tree for the daily predicted probabilities of discharge to PAC was constructed to represent a global summary of the RF. RESULTS There were 23,090 total patients and compared to PAC, those discharged home were younger (64 vs 71), had shorter length of stay (5 vs 8 days), higher AM-PAC at admission (43 vs 32), and average AM-PAC throughout hospitalization (45 vs 35). AM-PAC was the most important predictor, followed by age, and whether the patient lives alone. The area under the hospital day-specific receiver operating characteristic curve ranged from 0.76 to 0.79 during the first 5 days. The global summary tree explained 75% of the variation in predicted probabilities for PAC from the RF. Sensitivity (75%), specificity (70%), and accuracy (72%) were maximized at a PAC probability threshold of 40%. CONCLUSIONS AND IMPLICATIONS Daily assessment of patient mobility should be part of routine practice to help inform care planning by hospital teams. Our prediction model could be used as a valuable tool by multidisciplinary teams in the discharge planning process.
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Affiliation(s)
- Daniel L Young
- Department of Physical Therapy, University of Nevada, Las Vegas, Las Vegas, NV, USA; Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, MD, USA.
| | - Susan M Hannum
- Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rebecca Engels
- Division of Hospital Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elizabeth Colantuoni
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Lisa Aronson Friedman
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Outcomes After Critical Illness and Surgery (OACIS) Group, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Erik H Hoyer
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, MD, USA; Division of Hospital Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Burton J, Farrell S, Mäntylä Noble PJ, Al Moubayed N. Explainable text-tabular models for predicting mortality risk in companion animals. Sci Rep 2024; 14:14217. [PMID: 38902282 PMCID: PMC11190214 DOI: 10.1038/s41598-024-64551-1] [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: 11/22/2023] [Accepted: 06/10/2024] [Indexed: 06/22/2024] Open
Abstract
As interest in using machine learning models to support clinical decision-making increases, explainability is an unequivocal priority for clinicians, researchers and regulators to comprehend and trust their results. With many clinical datasets containing a range of modalities, from the free-text of clinician notes to structured tabular data entries, there is a need for frameworks capable of providing comprehensive explanation values across diverse modalities. Here, we present a multimodal masking framework to extend the reach of SHapley Additive exPlanations (SHAP) to text and tabular datasets to identify risk factors for companion animal mortality in first-opinion veterinary electronic health records (EHRs) from across the United Kingdom. The framework is designed to treat each modality consistently, ensuring uniform and consistent treatment of features and thereby fostering predictability in unimodal and multimodal contexts. We present five multimodality approaches, with the best-performing method utilising PetBERT, a language model pre-trained on a veterinary dataset. Utilising our framework, we shed light for the first time on the reasons each model makes its decision and identify the inclination of PetBERT towards a more pronounced engagement with free-text narratives compared to BERT-base's predominant emphasis on tabular data. The investigation also explores the important features on a more granular level, identifying distinct words and phrases that substantially influenced an animal's life status prediction. PetBERT showcased a heightened ability to grasp phrases associated with veterinary clinical nomenclature, signalling the productivity of additional pre-training of language models.
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Affiliation(s)
- James Burton
- Department of Computer Science, Durham University, Durham, UK.
| | - Sean Farrell
- Department of Computer Science, Durham University, Durham, UK
| | - Peter-John Mäntylä Noble
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Noura Al Moubayed
- Department of Computer Science, Durham University, Durham, UK
- Evergreen Life Ltd, Manchester, UK
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Chen Z, Berger JS, Castellucci LA, Farkouh M, Goligher EC, Hade EM, Hunt BJ, Kornblith LZ, Lawler PR, Leifer ES, Lorenzi E, Neal MD, Zarychanski R, Heath A. A comparison of computational algorithms for the Bayesian analysis of clinical trials. Clin Trials 2024:17407745241247334. [PMID: 38752434 DOI: 10.1177/17407745241247334] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
BACKGROUND Clinical trials are increasingly using Bayesian methods for their design and analysis. Inference in Bayesian trials typically uses simulation-based approaches such as Markov Chain Monte Carlo methods. Markov Chain Monte Carlo has high computational cost and can be complex to implement. The Integrated Nested Laplace Approximations algorithm provides approximate Bayesian inference without the need for computationally complex simulations, making it more efficient than Markov Chain Monte Carlo. The practical properties of Integrated Nested Laplace Approximations compared to Markov Chain Monte Carlo have not been considered for clinical trials. Using data from a published clinical trial, we aim to investigate whether Integrated Nested Laplace Approximations is a feasible and accurate alternative to Markov Chain Monte Carlo and provide practical guidance for trialists interested in Bayesian trial design. METHODS Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded. RESULTS The average overlap of the 95% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96% and 97.6% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77%, 85.6% and 91.3%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS. CONCLUSION Integrated Nested Laplace Approximations could reduce the computational complexity of Bayesian analysis in clinical trials as it is easy to implement in R, substantially faster than Markov Chain Monte Carlo methods implemented in JAGS and stan, and provides near identical approximations to the posterior distributions for the treatment effect. Integrated Nested Laplace Approximations was less accurate when estimating the posterior distribution for the variance of hierarchical effects, particularly for the proportional odds model, and future work should determine if the Integrated Nested Laplace Approximations algorithm can be adjusted to improve this estimation.
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Affiliation(s)
- Ziming Chen
- The Hospital for Sick Children, Toronto, ON, Canada
| | | | - Lana A Castellucci
- Department of Medicine, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
| | | | | | | | | | | | | | - Eric S Leifer
- National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | | | - Matthew D Neal
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Anna Heath
- The Hospital for Sick Children, Toronto, ON, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Statistical Science, University College London, London, UK
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6
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Lee SY. Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review. BMC Med Res Methodol 2024; 24:110. [PMID: 38714936 PMCID: PMC11077897 DOI: 10.1186/s12874-024-02235-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024] Open
Abstract
Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.
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Affiliation(s)
- Se Yoon Lee
- Department of Statistics, Texas A &M University, 3143 TAMU, College Station, TX, 77843, USA.
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7
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Yu D, Yang J, Wang B, Li Z, Wang K, Li J, Zhu C. New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model. Cancer Immunol Immunother 2024; 73:112. [PMID: 38693422 PMCID: PMC11063021 DOI: 10.1007/s00262-024-03684-8] [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: 01/01/2024] [Accepted: 03/18/2024] [Indexed: 05/03/2024]
Abstract
OBJECTIVE The high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients. METHODS Differentially expressed genes were identified using single-cell RNA sequencing (scRNA-seq) and gene set enrichment analysis (GESA). A random forest model was constructed based on these genes, and its effectiveness was validated through prognostic analysis. Further, analyses of immune cell infiltration, immune checkpoints, and the random forest model provided deeper insights. RESULTS High METTL1 expression was found to correlate with improved survival rates in gastric cancer patients (P = 0.042), and the random forest model, based on METTL1 and associated prognostic genes, achieved a significant predictive performance (AUC = 0.863). It showed associations with various immune cell types and negative correlations with CTLA4 and PDCD1 immune checkpoints. Experiments in vitro and in vivo demonstrated that METTL1 enhances gastric cancer cell activity by suppressing T cell proliferation and upregulating CTLA4 and PDCD1. CONCLUSION The random forest model, based on scRNA-seq, shows high predictive value for survival and immunotherapy sensitivity in gastric cancer patients. This study underscores the potential of METTL1 as a biomarker in enhancing the efficacy of gastric cancer immunotherapy.
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Affiliation(s)
- Dajun Yu
- Jinan University, Guangzhou, Guangdong, China.
- Department of Radiation Oncology, The Second Clinical Medical College (Shenzhen People's Hospital) of Jinan University, Shenzhen, Guangdong, China.
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China.
| | - Jie Yang
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China
| | - BinBin Wang
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China
| | - Zhixiang Li
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China
| | - Kai Wang
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China
| | - Jing Li
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China
| | - Chao Zhu
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China
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Kolk MZH, Ruipérez-Campillo S, Alvarez-Florez L, Deb B, Bekkers EJ, Allaart CP, Van Der Lingen ALCJ, Clopton P, Išgum I, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator. EBioMedicine 2024; 99:104937. [PMID: 38118401 PMCID: PMC10772563 DOI: 10.1016/j.ebiom.2023.104937] [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: 06/30/2023] [Revised: 10/20/2023] [Accepted: 12/12/2023] [Indexed: 12/22/2023] Open
Abstract
BACKGROUND Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. FUNDING This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).
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Affiliation(s)
- Maarten Z H Kolk
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology Zurich (ETHz), Gloriastrasse 35, Zurich, Switzerland; ITACA Institute, Universtitat Politècnica de València, Camino de Vera S/n, Valencia, Spain
| | - Laura Alvarez-Florez
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Erik J Bekkers
- Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands
| | - Cornelis P Allaart
- Department of Cardiology, Amsterdam UMC, Location VU Medical Center, De Boelelaan 1118, Amsterdam, the Netherlands
| | | | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Reinoud E Knops
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fleur V Y Tjong
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands.
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Xiao Z, Song Q, Wei Y, Fu Y, Huang D, Huang C. Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients. Transl Cancer Res 2023; 12:3581-3590. [PMID: 38192980 PMCID: PMC10774032 DOI: 10.21037/tcr-23-316] [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: 03/01/2023] [Accepted: 10/18/2023] [Indexed: 01/10/2024]
Abstract
Background The Cox regression model is not sufficiently accurate to predict the survival prognosis of nasopharyngeal carcinoma (NPC) patients. It is impossible to calculate and rank the importance of impact factors due to the low predictive accuracy of the Cox regression model. So, we developed a system. Using the SEER (The Surveillance, Epidemiology, and End Results) database data on NPC patients, we proposed the use of random survival forest (RSF) and survival-support vector machine (SVM) from the machine learning methods to develop a survival prediction system specifically for NPC patients. This approach aimed to make up for the insufficiency of the Cox regression model. We also used the Cox regression model to validate the development of the nomogram and compared it with machine learning methods. Methods A total of 1,683 NPC patients were extracted from the SEER database from January 2010 to December 2015. We used R language for modeling work, established the nomogram of survival prognosis of NPC patients by Cox regression model, ranked the correlation of influencing factors by RSF model VIMP (variable important) method, developed a survival prognosis system for NPC patients based on survival-SVM, and used C-index for model evaluation and performance comparison. Results Although the Cox regression models can be developed to predict the prognosis of NPC patients, their accuracy was lower than that of machine learning methods. When we substituted the data for the Cox model, the C-index for the training set was only 0.740, and the C-index for the test set was 0.721. In contrast, the C index of the survival-SVM model was 0.785. The C-index of the RSF model was 0.729. The importance ranking of each variable could be obtained according to the VIMP method. Conclusions The prediction results from the Cox model are not as good as those of the RSF method and survival-SVM based on the machine learning method. For the survival prognosis of NPC patients, the machine learning method can be considered for clinical application.
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Affiliation(s)
- Zhiwei Xiao
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, China
| | - Qiong Song
- Key Laboratory of Longevity and Aging-related Diseases of Chinese Ministry of Education, Center for Translational Medicine, Guangxi Medical University, Nanning, China
| | - Yuekun Wei
- School of Information and Management, Guangxi Medical University, Nanning, China
| | - Yong Fu
- Life Sciences Institute, Guangxi Medical University, Nanning, China
| | - Daizheng Huang
- Life Sciences Institute, Guangxi Medical University, Nanning, China
| | - Chao Huang
- School of Information and Management, Guangxi Medical University, Nanning, China
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10
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Wang X, George SL. Futility monitoring for randomized clinical trials with non-proportional hazards: An optimal conditional power approach. Clin Trials 2023; 20:603-612. [PMID: 37366172 PMCID: PMC10751393 DOI: 10.1177/17407745231181908] [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: 06/28/2023]
Abstract
BACKGROUND Standard futility analyses designed for a proportional hazards setting may have serious drawbacks when non-proportional hazards are present. One important type of non-proportional hazards occurs when the treatment effect is delayed. That is, there is little or no early treatment effect but a substantial later effect. METHODS We define optimality criteria for futility analyses in this setting and propose simple search procedures for deriving such rules in practice. RESULTS We demonstrate the advantages of the optimal rules over commonly used rules in reducing the average number of events, the average sample size, or the average study duration under the null hypothesis with minimal power loss under the alternative hypothesis. CONCLUSION Optimal futility rules can be derived for a non-proportional hazards setting that control the loss of power under the alternative hypothesis while maximizing the gain in early stopping under the null hypothesis.
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Affiliation(s)
- Xiaofei Wang
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Stephen L George
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA
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11
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Huang Y, Li J, Li M, Aparasu RR. Application of machine learning in predicting survival outcomes involving real-world data: a scoping review. BMC Med Res Methodol 2023; 23:268. [PMID: 37957593 PMCID: PMC10641971 DOI: 10.1186/s12874-023-02078-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/20/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare. METHODS PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC). RESULTS Of 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6-0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%). CONCLUSIONS The ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.
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Affiliation(s)
- Yinan Huang
- Department of Pharmacy Administration, School of Pharmacy, University of Mississippi, University, MS, 38677, USA
| | - Jieni Li
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, 77204, USA
| | - Mai Li
- Department of Industrial Engineering, Cullen College of Engineering, University of Houston, Houston, TX, USA
| | - Rajender R Aparasu
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, 77204, USA.
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12
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Yin C, Tang D, Zhang F, Tang Q, Feng Y, He Z. Students learning performance prediction based on feature extraction algorithm and attention-based bidirectional gated recurrent unit network. PLoS One 2023; 18:e0286156. [PMID: 37878591 PMCID: PMC10599562 DOI: 10.1371/journal.pone.0286156] [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: 02/28/2023] [Accepted: 05/10/2023] [Indexed: 10/27/2023] Open
Abstract
With the development of information technology construction in schools, predicting student grades has become a hot area of application in current educational research. Using data mining to analyze the influencing factors of students' performance and predict their grades can help students identify their shortcomings, optimize teachers' teaching methods and enable parents to guide their children's progress. However, there are no models that can achieve satisfactory predictions for education-related public datasets, and most of these weakly correlated factors in the datasets can still adversely affect the predictive effect of the model. To solve this issue and provide effective policy recommendations for the modernization of education, this paper seeks to find the best grade prediction model based on data mining. Firstly, the study uses the Factor Analyze (FA) model to extract features from the original data and achieve dimension reduction. Then, the Bidirectional Gate Recurrent Unit (BiGRU) model and attention mechanism are utilized to predict grades. Lastly, Comparing the prediction results of ablation experiments and other single models, such as linear regression (LR), back propagation neural network (BP), random forest (RF), and Gate Recurrent Unit (GRU), the FA-BiGRU-attention model achieves the best prediction effect and performs equally well in different multi-step predictions. Previously, problems with students' grades were only detected when they had already appeared. However, the methods presented in this paper enable the prediction of students' learning in advance and the identification of factors affecting their grades. Therefore, this study has great potential to provide data support for the improvement of educational programs, transform the traditional education industry, and ensure the sustainable development of national talents.
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Affiliation(s)
- Chengxin Yin
- Institute of Vocational Education, Chengdu Aeronautic Polytechnic, Chengdu, Asia, China
- Faculty of Education, Beijing Normal University, Beijing, Asia, China
| | - Dezhao Tang
- College of Information Engineering, Sichuan Agricultural University, Yaan, Asia, China
| | - Fang Zhang
- College of Information Engineering, Sichuan Agricultural University, Yaan, Asia, China
| | - Qichao Tang
- College of Information Engineering, Sichuan Agricultural University, Yaan, Asia, China
| | - Yang Feng
- College of Information Engineering, Sichuan Agricultural University, Yaan, Asia, China
| | - Zhen He
- Faculty of Education, Beijing Normal University, Beijing, Asia, China
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13
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Wang C. Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm. PLoS One 2023; 18:e0292557. [PMID: 37862380 PMCID: PMC10588863 DOI: 10.1371/journal.pone.0292557] [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: 04/28/2023] [Accepted: 09/23/2023] [Indexed: 10/22/2023] Open
Abstract
This study leverages advanced data mining and machine learning techniques to delve deeper into the impact of sports activities on physical health and provide a scientific foundation for informed sports selection and health promotion. Guided by the Elastic Net algorithm, a sports performance assessment model is meticulously constructed. In contrast to the conventional Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, this model seeks to elucidate the factors influencing physical health indicators due to sports activities. Additionally, the incorporation of the Random Forest algorithm facilitates a comprehensive evaluation of sports performance across distinct dimensions: wrestling-type sports, soccer-type sports, skill-based sports, and school physical education. Employing the Top-K criterion for evaluation and juxtaposing it with the high-performance Support Vector Machine (SVM) algorithm, the accuracy is scrutinized under three distinct criteria: Top-3, Top-5, and Top-10. The pivotal innovation of this study resides in the amalgamation of the Elastic Net and Random Forest algorithms, permitting a holistic contemplation of the influencing factors of diverse sports activities on physical health indicators. Through this integrated methodology, the research achieves a more precise assessment of the effects of sports activities, unveiling a range of impacts various sports have on physical health. Consequently, a more refined assessment tool for sports performance detection and health development is established. Capitalizing on the Elastic Net algorithm, this research optimizes model construction during the pivotal feature selection phase, effectively capturing the crucial influencing factors associated with different sports activities. Concurrently, the integration of the Random Forest algorithm augments the predictive prowess of the model, enabling the sports performance assessment model to comprehensively unveil the extent of impact stemming from various sports activities. This study stands as a noteworthy contribution to the arena of sports performance assessment, offering substantial insights and advancements to both sports health and research methodologies.
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Affiliation(s)
- Caixia Wang
- Department of Primary Education, Jiaozuo Normal College, Jiaozuo, Henan, China
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14
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Veeramani A, Zhang AS, Blackburn AZ, Etzel CM, DiSilvestro KJ, McDonald CL, Daniels AH. An Artificial Intelligence Approach to Predicting Unplanned Intubation Following Anterior Cervical Discectomy and Fusion. Global Spine J 2023; 13:1849-1855. [PMID: 35132907 PMCID: PMC10556901 DOI: 10.1177/21925682211053593] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
STUDY DESIGN Level III retrospective database study. OBJECTIVES The purpose of this study is to determine if machine learning algorithms are effective in predicting unplanned intubation following anterior cervical discectomy and fusion (ACDF). METHODS The National Surgical Quality Initiative Program (NSQIP) was queried to select patients who had undergone ACDF. Machine learning analysis was conducted in Python and multivariate regression analysis was conducted in R. C-Statistics area under the curve (AUC) and prediction accuracy were used to measure the classifier's effectiveness in distinguishing cases. RESULTS In total, 54 502 patients met the study criteria. Of these patients, .51% underwent an unplanned re-intubation. Machine learning algorithms accurately classified between 72%-100% of the test cases with AUC values of between .52-.77. Multivariable regression indicated that the number of levels fused, male sex, COPD, American Society of Anesthesiologists (ASA) > 2, increased operating time, Age > 65, pre-operative weight loss, dialysis, and disseminated cancer were associated with increased risk of unplanned intubation. CONCLUSIONS The models presented here achieved high accuracy in predicting risk factors for re-intubation following ACDF surgery. Machine learning analysis may be useful in identifying patients who are at a higher risk of unplanned post-operative re-intubation and their treatment plans can be modified to prophylactically prevent respiratory compromise and consequently unplanned re-intubation.
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Affiliation(s)
- Ashwin Veeramani
- Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Andrew S Zhang
- Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Amy Z. Blackburn
- Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Christine M. Etzel
- Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Kevin J. DiSilvestro
- Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Christopher L. McDonald
- Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Alan H. Daniels
- Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
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15
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Kim R, Lin T, Pang G, Liu Y, Tungate AS, Hendry PL, Kurz MC, Peak DA, Jones J, Rathlev NK, Swor RA, Domeier R, Velilla MA, Lewandowski C, Datner E, Pearson C, Lee D, Mitchell PM, McLean SA, Linnstaedt SD. Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure. Psychol Med 2023; 53:4952-4961. [PMID: 35775366 DOI: 10.1017/s003329172200191x] [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: 11/06/2022]
Abstract
BACKGROUND Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS. METHODS Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale - Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample). RESULTS Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms. CONCLUSIONS These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.
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Affiliation(s)
- Raphael Kim
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
| | - Tina Lin
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
| | - Gehao Pang
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Genetics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| | - Andrew S Tungate
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
| | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Michael C Kurz
- Department of Emergency Medicine, University of Alabama, Birmingham, AL, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jeffrey Jones
- Department of Emergency Medicine, Spectrum Health Butterworth Campus, Grand Rapids, MI, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, Baystate State Health System, Springfield, MA, USA
| | - Robert A Swor
- Department of Emergency Medicine, Beaumont Hospital, Royal Oak, MI, USA
| | - Robert Domeier
- Department of Emergency Medicine, St Joseph Mercy Health System, Ann Arbor, MI, USA
| | | | | | - Elizabeth Datner
- Department of Emergency Medicine, Albert Einstein Medical Center, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Detroit Receiving, Detroit, MI, USA
| | - David Lee
- Department of Emergency Medicine, North Shore University Hospital, Manhasset, NY, USA
| | - Patricia M Mitchell
- Department of Emergency Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Samuel A McLean
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
- Department of Emergency Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
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16
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Alaimo L, Moazzam Z, Woldesenbet S, Lima HA, Endo Y, Munir MM, Azap L, Ruzzenente A, Guglielmi A, Pawlik TM. Artificial intelligence to investigate predictors and prognostic impact of time to surgery in colon cancer. J Surg Oncol 2023; 127:966-974. [PMID: 36840925 DOI: 10.1002/jso.27224] [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: 02/02/2023] [Accepted: 02/18/2023] [Indexed: 02/26/2023]
Abstract
BACKGROUND AND OBJECTIVES The role of time to surgery (TTS) for long-term outcomes in colon cancer (CC) remains ill-defined. We sought to utilize artificial intelligence (AI) to characterize the drivers of TTS and its prognostic impact. METHODS The National Cancer Database was utilized to identify patients diagnosed with non-metastatic CC between 2004 and 2018. AI models were employed to rank the importance of several sociodemographic, facility, and tumor characteristics in determining TTS, and postoperative survival. RESULTS Among 518 983 patients, 137 902 (26.6%) received intraoperative diagnosis of CC (TTS = 0), while 381 081 (74.4%) underwent elective surgery (TTS > 0) with median TTS of 19.0 days (interquartile range [IQR]: 7.0-33.0). An AI model, identified tumor stage, receipt of adequate lymphadenectomy, histologic grade, lymphovascular invasion, and insurance status as the most important variables associated with TTS = 0. Conversely, the type and location of treating facility and receipt of adjuvant therapy were among the most important variables for TTS > 0. Notably, TTS was among the most important variables associated with survival, and TTS > 3 weeks was associated with an incremental increase in mortality risk. CONCLUSIONS The identification of factors associated with TTS can help stratify patients most likely to suffer poor outcomes due to prolonged TTS, as well as guide quality improvement initiatives related to timely surgical care.
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Affiliation(s)
- Laura Alaimo
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA
- Department of Surgery, University of Verona, Verona, Italy
| | - Zorays Moazzam
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Selamawit Woldesenbet
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Henrique A Lima
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Yutaka Endo
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Muhammad M Munir
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Lovette Azap
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA
| | | | | | - Timothy M Pawlik
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA
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17
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Arad D, Rosenfeld A, Magnezi R. Factors contributing to preventing operating room "never events": a machine learning analysis. Patient Saf Surg 2023; 17:6. [PMID: 37004090 PMCID: PMC10067209 DOI: 10.1186/s13037-023-00356-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 03/09/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND A surgical "Never Event" is a preventable error occurring immediately before, during or immediately following surgery. Various factors contribute to the occurrence of major Never Events, but little is known about their quantified risk in relation to a surgery's characteristics. Our study uses machine learning to reveal and quantify risk factors with the goal of improving patient safety and quality of care. METHODS We used data from 9,234 observations on safety standards and 101 root-cause analyses from actual, major "Never Events" including wrong site surgery and retained foreign item, and three random forest supervised machine learning models to identify risk factors. Using a standard 10-cross validation technique, we evaluated the models' metrics, measuring their impact on the occurrence of the two types of Never Events through Gini impurity. RESULTS We identified 24 contributing factors in six surgical departments: two had an impact of > 900% in Urology, Orthopedics, and General Surgery; six had an impact of 0-900% in Gynecology, Urology, and Cardiology; and 17 had an impact of < 0%. Combining factors revealed 15-20 pairs with an increased probability in five departments: Gynecology, 875-1900%; Urology, 1900-2600%; Cardiology, 833-1500%; Orthopedics,1825-4225%; and General Surgery, 2720-13,600%. Five factors affected wrong site surgery's occurrence (-60.96 to 503.92%) and five affected retained foreign body (-74.65 to 151.43%): two nurses (66.26-87.92%), surgery length < 1 h (85.56-122.91%), and surgery length 1-2 h (-60.96 to 85.56%). CONCLUSIONS Using machine learning, we could quantify the risk factors' potential impact on wrong site surgeries and retained foreign items in relation to a surgery's characteristics, suggesting that safety standards should be adjusted to surgery's characteristics based on risk assessment in each operating room. . TRIAL REGISTRATION NUMBER MOH 032-2019.
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Affiliation(s)
- Dana Arad
- Department of Management, Health Management Program, Faculty of Sciences, Bar-Ilan University, Ramat Gan, Israel.
- Patient Safety Division, Ministry of Health, Ramat Gan, Israel.
| | - Ariel Rosenfeld
- Department of Information Science, Bar-Ilan University, Ramat Gan, Israel
| | - Racheli Magnezi
- Department of Management, Health Management Program, Faculty of Sciences, Bar-Ilan University, Ramat Gan, Israel
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Kim Y, Chamseddine I, Cho Y, Kim JS, Mohan R, Shusharina N, Paganetti H, Lin S, Yoon HI, Cho S, Grassberger C. Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions. Neoplasia 2023; 39:100889. [PMID: 36931040 PMCID: PMC10025955 DOI: 10.1016/j.neo.2023.100889] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/10/2022] [Accepted: 02/13/2023] [Indexed: 03/17/2023]
Abstract
The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT. In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation. The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map. This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan.
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Affiliation(s)
- Yejin Kim
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ibrahim Chamseddine
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yeona Cho
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University Health System, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Radhe Mohan
- Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Nadya Shusharina
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven Lin
- Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Hong In Yoon
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University Health System, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Seungryong Cho
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
| | - Clemens Grassberger
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Segmental motor recovery after cervical spinal cord injury relates to density and integrity of corticospinal tract projections. Nat Commun 2023; 14:723. [PMID: 36759606 PMCID: PMC9911610 DOI: 10.1038/s41467-023-36390-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 01/27/2023] [Indexed: 02/11/2023] Open
Abstract
Cervical spinal cord injury (SCI) causes extensive impairments for individuals which may include dextrous hand function. Although prior work has focused on the recovery at the person-level, the factors determining the recovery of individual muscles are poorly understood. Here, we investigate the muscle-specific recovery after cervical spinal cord injury in a retrospective analysis of 748 individuals from the European Multicenter Study about Spinal Cord Injury (NCT01571531). We show associations between corticospinal tract (CST) sparing and upper extremity recovery in SCI, which improves the prediction of hand muscle strength recovery. Our findings suggest that assessment strategies for muscle-specific motor recovery in acute spinal cord injury are improved by accounting for CST sparing, and complement person-level predictions.
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20
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Davidov O, Rudas T. On the use of historical estimates. Stat Pap (Berl) 2023; 65:1-34. [PMID: 36643817 PMCID: PMC9821390 DOI: 10.1007/s00362-022-01375-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 11/08/2022] [Indexed: 01/08/2023]
Abstract
The use of historical, i.e., already existing, estimates in current studies is common in a wide variety of application areas. Nevertheless, despite their routine use, the uncertainty associated with historical estimates is rarely properly accounted for in the analysis. In this communication, we review common practices and then provide a mathematical formulation and a principled frequentist methodology for addressing the problem of drawing inferences in the presence of historical estimates. Three distinct variants are investigated in detail; the corresponding limiting distributions are found and compared. The design of future studies, given historical data, is also explored and relations with a variety of other well-studied statistical problems discussed.
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Affiliation(s)
- Ori Davidov
- Department of Statistics, University of Haifa, Mount Carmel, 3498838 Haifa, Israel
| | - Tamás Rudas
- Department of Statistics, Faculty of Social Sciences, Eötvös Loránd University, Pázmány Péter sétány 1/A, Budapest, Hungary
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21
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Ferro M, Crocetto F, Barone B, del Giudice F, Maggi M, Lucarelli G, Busetto GM, Autorino R, Marchioni M, Cantiello F, Crocerossa F, Luzzago S, Piccinelli M, Mistretta FA, Tozzi M, Schips L, Falagario UG, Veccia A, Vartolomei MD, Musi G, de Cobelli O, Montanari E, Tătaru OS. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review. Ther Adv Urol 2023; 15:17562872231164803. [PMID: 37113657 PMCID: PMC10126666 DOI: 10.1177/17562872231164803] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/04/2023] [Indexed: 04/29/2023] Open
Abstract
Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
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Affiliation(s)
| | - Felice Crocetto
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Francesco del Giudice
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation
Unit, Department of Emergency and Organ Transplantation, University of Bari,
Bari, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | | | - Michele Marchioni
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti,
Italy
| | - Francesco Cantiello
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Fabio Crocerossa
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Stefano Luzzago
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Mattia Piccinelli
- Cancer Prognostics and Health Outcomes Unit,
Division of Urology, University of Montréal Health Center, Montréal, QC,
Canada
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Tozzi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Luigi Schips
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
| | | | - Alessandro Veccia
- Urology Unit, Azienda Ospedaliera
Universitaria Integrata Verona, University of Verona, Verona, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology,
George Emil Palade University of Medicine, Pharmacy, Science and Technology
of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of
Vienna, Vienna, Austria
| | - Gennaro Musi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca’
Granda – Ospedale Maggiore Policlinico, Department of Clinical Sciences and
Community Health, University of Milan, Milan, Italy
| | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral
Studies (IOSUD), George Emil Palade University of Medicine, Pharmacy,
Science and Technology of Târgu Mures, Târgu Mures, Romania
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22
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Tsimpida D, Panagioti M, Kontopantelis E. Forty years on: a new national study of hearing in England and implications for global hearing health policy. Int J Audiol 2023; 62:62-70. [PMID: 35080184 DOI: 10.1080/14992027.2021.2022791] [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: 01/05/2023]
Abstract
OBJECTIVE We aimed to update the prevalence estimates of hearing loss in older adults in England using a nationally representative sample of adults aged 50 years old and older. DESIGN A comparative cross-sectional study design was implemented. Hearing loss was defined as ≥35 dB HL at 3.0 kHz, as measured via Hearcheck in the better-hearing ear. STUDY SAMPLE We compared the estimates based on the English census in 2015 to estimates from psychoacoustic hearing data available for 8,263 participants in the English Longitudinal Study of Ageing (ELSA) Wave 7 (2014-2015). RESULTS Marked regional variability in hearing loss prevalence was revealed among participants with similar age profiles. The regional differences in hearing outcomes reached up to 13.53% in those belonging to the 71-80 years old group; the prevalence of hearing loss was 49.22% in the North East of England (95%CI 48.0-50.4), versus 35.69% in the South East (95%CI 34.8-36.50). CONCLUSION A socio-spatial approach in planning sustainable models of hearing care based on the actual populations' needs and not on age demographics might offer a viable opportunity for healthier lives. Regular assessment of the extent and causality of the population's different audiological needs within the country is strongly supported.
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Affiliation(s)
- Dialechti Tsimpida
- Institute for Health Policy and Organisation (IHPO), Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Maria Panagioti
- NIHR Greater Manchester Patient Safety Translational Research Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Evangelos Kontopantelis
- Institute for Health Policy and Organisation (IHPO), Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
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23
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Lee JK. The roles of individual differences in time perspective, promotion focus, and innovativeness: Testing technology acceptance model. CURRENT PSYCHOLOGY 2022; 42:1-13. [PMID: 36415452 PMCID: PMC9672636 DOI: 10.1007/s12144-022-04016-8] [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] [Accepted: 11/05/2022] [Indexed: 11/19/2022]
Abstract
The goal of this study is to examine the roles of Zimbardo's time perspective along with other individual differences such as promotion focus and innovativeness in perceived ease of use, perceived usefulness, and attitude toward SNSs (social networking sites) in the technology acceptance model (TAM). A total of 234 participants joined this online study in South Korea. As predicted, past positive time perspective (TP) positively affected promotion focus and innovativeness, whereas past negative TP negatively affected them. Present hedonic TP positively affected innovativeness, and present fatalistic TP negatively affected promotion focus each. Future TP also positively related to promotion focus and innovativeness. In addition, simple and serial mediation effects of perceived ease of use and perceived usefulness independently and sequentially mediated the impact of TP on attitude toward SNSs. By considering TP along with promotion focus and innovativeness in conjunction with beliefs in the TAM, this study identifies psychological underpinnings of how individual differences affect technology adoption attitude and behavior. Research implications and future research suggestions will be discussed in detail.
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Affiliation(s)
- Jin Kyun Lee
- School of Advertising & Public Relations, Hongik University, B303-1, 2639, Sejong-Ro, Jochiwon-Eup, Sejong-Si, South Korea 30016
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24
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Hunter E, Kelleher JD. A review of risk concepts and models for predicting the risk of primary stroke. Front Neuroinform 2022; 16:883762. [DOI: 10.3389/fninf.2022.883762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 10/31/2022] [Indexed: 11/17/2022] Open
Abstract
Predicting an individual's risk of primary stroke is an important tool that can help to lower the burden of stroke for both the individual and society. There are a number of risk models and risk scores in existence but no review or classification designed to help the reader better understand how models differ and the reasoning behind these differences. In this paper we review the existing literature on primary stroke risk prediction models. From our literature review we identify key similarities and differences in the existing models. We find that models can differ in a number of ways, including the event type, the type of analysis, the model type and the time horizon. Based on these similarities and differences we have created a set of questions and a system to help answer those questions that modelers and readers alike can use to help classify and better understand the existing models as well as help to make necessary decisions when creating a new model.
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25
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Xu Y, Greene TH, Bress AP, Bellows BK, Zhang Y, Zhang Z, Kolm P, Weintraub WS, Moran AS, Shen J. An efficient approach for optimizing the cost-effective individualized treatment rule using conditional random forest. Stat Methods Med Res 2022; 31:2122-2136. [PMID: 35912490 DOI: 10.1177/09622802221115876] [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: 11/17/2022]
Abstract
Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness analyses. Similar as in comparative effectiveness studies, health economic evaluations that consider subject-level heterogeneity produce individualized treatment rules that are often more cost-effective than one-size-fits-all treatment. Thus, it is of great interest to develop statistical tools for learning such a cost-effective individualized treatment rule under the causal inference framework that allows proper handling of potential confounding and can be applied to both trials and observational studies. In this paper, we use the concept of net-monetary-benefit to assess the trade-off between health benefits and related costs. We estimate cost-effective individualized treatment rule as a function of patients' characteristics that, when implemented, optimizes the allocation of limited healthcare resources by maximizing health gains while minimizing treatment-related costs. We employ the conditional random forest approach and identify the optimal cost-effective individualized treatment rule using net-monetary-benefit-based classification algorithms, where two partitioned estimators are proposed for the subject-specific weights to effectively incorporate information from censored individuals. We conduct simulation studies to evaluate the performance of our proposals. We apply our top-performing algorithm to the NIH-funded Systolic Blood Pressure Intervention Trial to illustrate the cost-effectiveness gains of assigning customized intensive blood pressure therapy.
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Affiliation(s)
- Yizhe Xu
- Department of Population Health Sciences, 7060University of Utah, SLC, UT, USA
| | - Tom H Greene
- Department of Population Health Sciences, 7060University of Utah, SLC, UT, USA
| | - Adam P Bress
- Department of Population Health Sciences, 7060University of Utah, SLC, UT, USA
| | | | - Yue Zhang
- Department of Population Health Sciences, 7060University of Utah, SLC, UT, USA
| | - Zugui Zhang
- 5973Christiana Care Health System, Newark, DE, USA
| | - Paul Kolm
- Department of Medicine, 121577MedStar Health Research Institute, Washington, DC, USA
| | - William S Weintraub
- Department of Medicine, 121577MedStar Health Research Institute, Washington, DC, USA
| | - Andrew S Moran
- 21611Columbia University Medical Center, New York, NY, USA
| | - Jincheng Shen
- Department of Population Health Sciences, 7060University of Utah, SLC, UT, USA
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26
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Zhu GL, Fang XL, Yang KB, Tang LL, Ma J. Development and validation of a joint model for dynamic prediction of overall survival in nasopharyngeal carcinoma based on longitudinal post-treatment plasma cell-free Epstein-Barr virus DNA load. Oral Oncol 2022; 134:106140. [PMID: 36183501 DOI: 10.1016/j.oraloncology.2022.106140] [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: 06/02/2022] [Revised: 08/24/2022] [Accepted: 09/20/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To develop and validate a joint model for dynamic prediction of overall survival (OS) in nasopharyngeal carcinoma (NPC) based on longitudinal post-treatment plasma cell-free Epstein-Barr virus (cfEBV) DNA load. PATIENTS AND METHODS We analyzed 695 patients with non-metastatic NPC and detectable post-treatment cfEBV DNA load who did not receive adjuvant therapy. We fitted the trajectories of post-treatment cfEBV DNA load as a function of time into a linear mixed-effect model and fitted a Cox regression model with covariates including age, T and N stages, and lactate dehydrogenase level. Finally, we combined both via joint modeling to develop and validate our dynamic model. RESULTS A strong positive correlation was found between the individual longitudinal post-treatment cfEBV DNA load and the risk of death from any cause (P < 0.001). We developed a joint model capable of providing subject-specific dynamic prediction of conditional OS based on the evolution of the individual plasma cfEBV DNA load trajectory. The joint model showed reliable performance in both training and validation cohorts, with a large area under the curve (interquartile range [IQR]: training cohort, 0.775-0.850; validation cohort, 0.826-0.900) and low prediction errors (IQR: training cohort, 0.017-0.078; validation cohort, 0.034 -0.071). An increasing amount of data on cfEBV DNA load was associated with better model performance. CONCLUSION Our model provided reliable subject-specific dynamic prediction of conditional OS, which could help guide individualized post-treatment surveillance, risk stratification, and management of NPC in the future.
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Affiliation(s)
- Guang-Li Zhu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, PR China; Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, PR China
| | - Xue-Liang Fang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, PR China; Research Units of New Technologies of Endoscopic Surgery in Skull Base Tumor, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Kai-Bin Yang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, PR China
| | - Ling-Long Tang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, PR China.
| | - Jun Ma
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, PR China.
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27
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Xie E, Sung E, Saad E, Trayanova N, Wu KC, Chrispin J. Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death. Front Cardiovasc Med 2022; 9:884767. [PMID: 36072882 PMCID: PMC9441865 DOI: 10.3389/fcvm.2022.884767] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.
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Affiliation(s)
- Eric Xie
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eric Sung
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Elie Saad
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Natalia Trayanova
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Katherine C. Wu
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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28
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Bofill Roig M, Krotka P, Burman CF, Glimm E, Gold SM, Hees K, Jacko P, Koenig F, Magirr D, Mesenbrink P, Viele K, Posch M. On model-based time trend adjustments in platform trials with non-concurrent controls. BMC Med Res Methodol 2022; 22:228. [PMID: 35971069 PMCID: PMC9380382 DOI: 10.1186/s12874-022-01683-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 07/12/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial's efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends. METHODS We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model. RESULTS A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a step function when patients are allocated to arms by block randomisation. However, if time trends differ between arms or are not additive to treatment effects in the scale of the model, the type 1 error rate may be inflated. CONCLUSIONS The efficiency gained by using step function models to incorporate non-concurrent controls can outweigh potential risks of biases, especially in settings with small sample sizes. Such biases may arise if the model assumptions of equality and additivity of time trends are not satisfied. However, the specifics of the trial, scientific plausibility of different time trends, and robustness of results should be carefully considered.
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Affiliation(s)
- Marta Bofill Roig
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Pavla Krotka
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Carl-Fredrik Burman
- Statistical Innovation, Data Science & Artificial Intelligence, AstraZeneca, Gothenburg, Sweden
| | - Ekkehard Glimm
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
- Institute of Biometry and Medical Informatics, University of Magdeburg, Magdeburg, Germany
| | - Stefan M Gold
- Klinik für Psychiatrie und Psychotherapie, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Medizinische Klinik m.S. Psychosomatik, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Zentrum für Molekulare Neurobiologie, Universitätsklinikum Hamburg Eppendorf, Hamburg, Germany
| | - Katharina Hees
- Section of Biostatistics, Paul-Ehrlich-Institut, Langen, Germany
| | - Peter Jacko
- Berry Consultants, Abingdon, UK
- Lancaster University, Lancaster, UK
| | - Franz Koenig
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Dominic Magirr
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
| | - Peter Mesenbrink
- Analytics Global Drug Development, Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
| | | | - Martin Posch
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
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29
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Remiro-Azócar A. Two-stage matching-adjusted indirect comparison. BMC Med Res Methodol 2022; 22:217. [PMID: 35941551 PMCID: PMC9358807 DOI: 10.1186/s12874-022-01692-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/19/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Anchored covariate-adjusted indirect comparisons inform reimbursement decisions where there are no head-to-head trials between the treatments of interest, there is a common comparator arm shared by the studies, and there are patient-level data limitations. Matching-adjusted indirect comparison (MAIC), based on propensity score weighting, is the most widely used covariate-adjusted indirect comparison method in health technology assessment. MAIC has poor precision and is inefficient when the effective sample size after weighting is small. METHODS A modular extension to MAIC, termed two-stage matching-adjusted indirect comparison (2SMAIC), is proposed. This uses two parametric models. One estimates the treatment assignment mechanism in the study with individual patient data (IPD), the other estimates the trial assignment mechanism. The first model produces inverse probability weights that are combined with the odds weights produced by the second model. The resulting weights seek to balance covariates between treatment arms and across studies. A simulation study provides proof-of-principle in an indirect comparison performed across two randomized trials. Nevertheless, 2SMAIC can be applied in situations where the IPD trial is observational, by including potential confounders in the treatment assignment model. The simulation study also explores the use of weight truncation in combination with MAIC for the first time. RESULTS Despite enforcing randomization and knowing the true treatment assignment mechanism in the IPD trial, 2SMAIC yields improved precision and efficiency with respect to MAIC in all scenarios, while maintaining similarly low levels of bias. The two-stage approach is effective when sample sizes in the IPD trial are low, as it controls for chance imbalances in prognostic baseline covariates between study arms. It is not as effective when overlap between the trials' target populations is poor and the extremity of the weights is high. In these scenarios, truncation leads to substantial precision and efficiency gains but induces considerable bias. The combination of a two-stage approach with truncation produces the highest precision and efficiency improvements. CONCLUSIONS Two-stage approaches to MAIC can increase precision and efficiency with respect to the standard approach by adjusting for empirical imbalances in prognostic covariates in the IPD trial. Further modules could be incorporated for additional variance reduction or to account for missingness and non-compliance in the IPD trial.
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Affiliation(s)
- Antonio Remiro-Azócar
- Medical Affairs Statistics, Bayer plc, 400 South Oak Way, Reading, UK. .,Department of Statistical Science, University College London, 1-19 Torrington Place, London, UK.
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Yao W, Frydman H, Larocque D, Simonoff JS. Ensemble methods for survival function estimation with time-varying covariates. Stat Methods Med Res 2022; 31:2217-2236. [PMID: 35895510 DOI: 10.1177/09622802221111549] [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: 11/16/2022]
Abstract
Survival data with time-varying covariates are common in practice. If relevant, they can improve on the estimation of a survival function. However, the traditional survival forests-conditional inference forest, relative risk forest and random survival forest-have accommodated only time-invariant covariates. We generalize the conditional inference and relative risk forests to allow time-varying covariates. We also propose a general framework for estimation of a survival function in the presence of time-varying covariates. We compare their performance with that of the Cox model and transformation forest, adapted here to accommodate time-varying covariates, through a comprehensive simulation study in which the Kaplan-Meier estimate serves as a benchmark, and performance is compared using the integrated L2 difference between the true and estimated survival functions. In general, the performance of the two proposed forests substantially improves over the Kaplan-Meier estimate. Taking into account all other factors, under the proportional hazard setting, the best method is always one of the two proposed forests, while under the non-proportional hazard setting, it is the adapted transformation forest. K-fold cross-validation is used as an effective tool to choose between the methods in practice.
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Affiliation(s)
- Weichi Yao
- 5894New York University, New York, NY, USA
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31
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Yeung MK. Frontal cortical activation during emotional and non-emotional verbal fluency tests. Sci Rep 2022; 12:8497. [PMID: 35589939 PMCID: PMC9120192 DOI: 10.1038/s41598-022-12559-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/06/2022] [Indexed: 01/19/2023] Open
Abstract
There has been growing recognition of the utility of combining the verbal fluency test and functional near-infrared spectroscopy (fNIRS) to assess brain functioning and to screen for psychiatric disorders. Recently, an emotional analogue of the semantic fluency test (SFT) has been developed that taps partly different processes from conventional verbal fluency tests. Nevertheless, neural processing during the emotional SFT remains elusive. Here, fNIRS was used to compare frontal cortical activation during emotional and non-emotional SFTs. The goal was to determine whether the emotional SFT activated overlapping yet distinct frontal cortical regions compared with the conventional, non-emotional SFT. Forty-three healthy young adults performed the emotional and non-emotional SFTs while hemodynamic changes in the bilateral frontopolar, dorsomedial, dorsolateral, ventrolateral, and posterolateral frontal cortices were measured by fNIRS. There were significant increases in oxyhemoglobin concentration and significant decreases in deoxyhemoglobin concentration (i.e., activation) in frontopolar, dorsolateral, and ventrolateral frontal regions during both the non-emotional and emotional SFTs. Also, complementary analyses conducted on changes in the two chromophores using classical and Bayesian hypothesis testing suggested that comparable frontal cortical regions were activated while performing the two tests. This similarity in activation occurred in a context where non-emotional and emotional SFT performances exhibited differential relationships with the overall level of negative mood symptoms. In conclusion, frontal cortical activation during the emotional SFT is similar to that during the conventional, non-emotional SFT. Given that there is evidence for discriminant validity for the emotional SFT, the neural mechanisms underlying the uniqueness of this test warrant further investigation.
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Affiliation(s)
- Michael K Yeung
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China. .,University Research Facility in Behavioral and Systems Neuroscience, The Hong Kong Polytechnic University, Hong Kong, China.
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Wang Z, Bowring MG, Rosen A, Garibaldi B, Zeger S, Nishimura A. Learning and Predicting from Dynamic Models for COVID-19 Patient Monitoring. Stat Sci 2022; 37:251-265. [DOI: 10.1214/22-sts861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Zitong Wang
- Zitong Wang is a PhD student, Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Mary Grace Bowring
- Mary Grace Bowring is an MD/PhD student, Departments of Biomedical Engineering and Biostatistics, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Antony Rosen
- Antony Rosen is Mary Betty Stevens Professor of Medicine and Vice Dean for Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Brian Garibaldi
- Brian Garibaldi is Associate Professor, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Scott Zeger
- Scott L. Zeger (co-senior author) is John C. Malone Professor, Department of Biostatistics and Medicine, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Akihiko Nishimura
- Akihiko (Aki) Nishimura (co-senior author) is Assistant Professor, Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
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Jørgensen TSH, Allore H, Elman MR, Nagel C, Quiñones AR. The importance of chronic conditions for potentially avoidable hospitalizations among non-Hispanic Black and non-Hispanic White older adults in the US: a cross-sectional observational study. BMC Health Serv Res 2022; 22:468. [PMID: 35397539 PMCID: PMC8994911 DOI: 10.1186/s12913-022-07849-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 03/28/2022] [Indexed: 11/26/2022] Open
Abstract
Background Non-Hispanic (NH) Black older adults experience substantially higher rates of potentially avoidable hospitalization compared to NH White older adults. This study explores the top three chronic conditions preceding hospitalization and potentially avoidable hospitalization among NH White and NH Black Medicare beneficiaries in the United States. Methods Data on 4993 individuals (4,420 NH White and 573 NH Black individuals) aged ≥ 65 years from 2014 Medicare claims were linked with sociodemographic data from previous rounds of the Health and Retirement Study. Conditional inference random forests were used to rank the importance of chronic conditions in predicting hospitalization and potentially avoidable hospitalization separately for NH White and NH Black beneficiaries. Multivariable logistic regression with the top three chronic diseases for each outcome adjusted for sociodemographic characteristics were conducted to quantify the associations. Results In total, 22.1% of NH White and 24.9% of NH Black beneficiaries had at least one hospitalization during 2014. Among those with hospitalization, 21.3% of NH White and 29.6% of NH Black beneficiaries experienced at least one potentially avoidable hospitalization. For hospitalizations, chronic kidney disease, heart failure, and atrial fibrillation were the top three contributors among NH White beneficiaries and acute myocardial infarction, chronic obstructive pulmonary disease (COPD), and chronic kidney disease were the top three contributors among NH Black beneficiaries. These chronic conditions were associated with increased odds of hospitalization for both groups. For potentially avoidable hospitalizations, asthma, COPD, and heart failure were the top three contributors among NH White beneficiaries and fibromyalgia/chronic pain/fatigue, COPD, and asthma were the top three contributors among NH Black beneficiaries. COPD and heart failure were associated with increased odds of potentially avoidable hospitalization among NH White beneficiaries, whereas only COPD was associated with increased odds of potentially avoidable hospitalizations among NH Black beneficiaries. Conclusion Having at least one hospitalization and at least one potentially avoidable hospitalization was more prevalent among NH Black than NH White Medicare beneficiaries. This suggests greater opportunity for increasing prevention efforts among NH Black beneficiaries. The importance of COPD for potentially avoidable hospitalizations further highlights the need to focus on prevention of exacerbations for patients with COPD, possibly through greater access to primary care and continuity of care. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-07849-y.
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Decarie A, Cressman EK. Improved proprioception does not benefit visuomotor adaptation. Exp Brain Res 2022; 240:1499-1514. [PMID: 35366069 PMCID: PMC8975733 DOI: 10.1007/s00221-022-06352-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 03/04/2022] [Indexed: 12/24/2022]
Abstract
Visuomotor adaptation arises when reaching in an altered visual environment, where one's seen hand position does not match their felt (i.e., proprioceptive) hand position in space. Here, we asked if proprioceptive training benefits visuomotor adaptation, and if these benefits arise due to implicit (unconscious) or explicit (conscious strategy) processes. Seventy-two participants were divided equally into 3 groups: proprioceptive training with feedback (PTWF), proprioceptive training no feedback (PTNF), and Control (CTRL). The PTWF and PTNF groups completed passive proprioceptive training, where a participant's hand was moved to an unknown reference location and they judged the felt position of their unseen hand relative to their body midline on every trial. The PTWF group received verbal feedback with respect to their response accuracy on the middle 60% of trials, whereas the PTNF did not receive any feedback during training. The CTRL group did not complete proprioceptive training and instead sat quietly during this time. Following proprioceptive training or time delay, all three groups reached when seeing a cursor that was rotated 30° clockwise relative to their hand motion. The experiment ended with participants completing a series of no-cursor reaches to assess implicit and explicit adaptation. Results indicated that the PTWF group improved the accuracy of their sense of felt hand position following proprioceptive training. However, this improved proprioceptive acuity (i.e., the accuracy of their sense of felt hand) did not benefit visuomotor adaptation, as all three groups showed similar visuomotor adaptation across rotated reach training trials. Visuomotor adaptation arose implicitly, with minimal explicit contribution for all three groups. Together, these results suggest that passive proprioceptive training does not benefit, nor hinder, the extent of implicit visuomotor adaptation established immediately following reach training with a 30° cursor rotation.
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Affiliation(s)
- Amelia Decarie
- School of Human Kinetics, University of Ottawa, Ottawa, Canada.
| | - Erin K Cressman
- School of Human Kinetics, University of Ottawa, Ottawa, Canada
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35
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Mazzanti A, Trancuccio A, Kukavica D, Pagan E, Wang M, Mohsin M, Peterson D, Bagnardi V, Zareba W, Priori SG. Independent validation and clinical implications of the risk prediction model for long QT syndrome (1-2-3-LQTS-Risk): comment-Authors' reply. Europace 2022; 24:698-699. [PMID: 35303087 DOI: 10.1093/europace/euac013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 01/28/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Andrea Mazzanti
- Molecular Cardiology, Istituti Clinici Scientifici Maugeri, IRCCS, Via Maugeri, 10, Pavia 27100, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Molecular Cardiology, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Alessandro Trancuccio
- Molecular Cardiology, Istituti Clinici Scientifici Maugeri, IRCCS, Via Maugeri, 10, Pavia 27100, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Molecular Cardiology, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Deni Kukavica
- Molecular Cardiology, Istituti Clinici Scientifici Maugeri, IRCCS, Via Maugeri, 10, Pavia 27100, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Molecular Cardiology, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Eleonora Pagan
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
| | - Meng Wang
- Department of Computational Biology and Biostatistics, University of Rochester, Rochester, NY, USA
| | - Muhammed Mohsin
- Molecular Cardiology, Istituti Clinici Scientifici Maugeri, IRCCS, Via Maugeri, 10, Pavia 27100, Italy
| | - Derick Peterson
- Department of Computational Biology and Biostatistics, University of Rochester, Rochester, NY, USA
| | - Vincenzo Bagnardi
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
| | - Wojciech Zareba
- Cardiology Unit of the Department of Medicine, University of Rochester Medical Center, Rochester, 265 Crittenden Blvd., CU 420653, NY 14642, USA
| | - Silvia G Priori
- Molecular Cardiology, Istituti Clinici Scientifici Maugeri, IRCCS, Via Maugeri, 10, Pavia 27100, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Molecular Cardiology, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
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Chang DS, Chen WL, Wang R. Impact of the bidirectional relationship between communication and cognitive efficacy on orthopedic patient adherence behavior. BMC Health Serv Res 2022; 22:199. [PMID: 35164761 PMCID: PMC8845262 DOI: 10.1186/s12913-022-07575-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 02/01/2022] [Indexed: 02/03/2023] Open
Abstract
Background There is growing interest in patient autonomy, and communication between physicians and patients has become the essential cornerstone for improving the quality of healthcare services. Previous research has concentrated on the direct effect of physician-patient communication on service outcomes. In the present study, we examined the influence among constructs in the service process and the impact on healthcare outcomes. The present study used behavioral theory to expand the process aspect of the Donabedian healthcare service quality structure-process-outcome model to examine the impact of cognitive changes and communication feedback on patients’ adherence behavior. In addition, the moderating effect of hospital facility levels is examined. Methods A conceptual model was developed and tested using a questionnaire administered to patients in eight hospitals. A total of 397 respondents returned usable surveys, with a response rate of 92.11%. Structural equation modeling was used to analyze the data in two steps that involved a measurement model and a structural model. The former was applied to estimate the Cronbach’s alphas, intercorrelations of factors, and descriptive statistics; the latter was used to test the hypothesized relationships of the constructs. Results The results identified three mediators of the healthcare process within the healthcare services framework: physician-patient communication, cognitive efficacy, and adherence behavior. Physician-patient communication influenced cognitive efficacy (β = 0.16, p < 0.001), and cognitive efficacy influenced physician-patient communication (β = 0.18, p < 0.001). The effect of this bidirectional relationship on adherence behavior was positive (β = 0.38, p < 0.001). The healthcare structure influenced healthcare outcomes via these three healthcare process constructs. The adherence behavior of patients who were treated in the medical center has greater influences by the structure and physician-patient communication than it was treated in the regional hospitals. Conclusions This study revealed a complex pattern in relationships among process constructs for healthcare services. The findings of this study acknowledge the important potential interrelationships among the healthcare service constructs to improve the quality of healthcare outcomes. Trial registration CRREC104107. Date: 22/01/2016. Prospectively Registered. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-07575-5.
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Affiliation(s)
- Dong-Shang Chang
- Department of Business Administration, National Central University, Taoyuan, Taiwan
| | - Wil-Lie Chen
- School of Nursing, China Medical University, Taichung, Taiwan.
| | - Rouwen Wang
- Department of Business Administration, National Central University, Taoyuan, Taiwan
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Bayramli I, Castro V, Barak-Corren Y, Madsen EM, Nock MK, Smoller JW, Reis BY. Temporally informed random forests for suicide risk prediction. J Am Med Inform Assoc 2021; 29:62-71. [PMID: 34725687 PMCID: PMC8714280 DOI: 10.1093/jamia/ocab225] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/20/2021] [Accepted: 10/04/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE Suicide is one of the leading causes of death worldwide, yet clinicians find it difficult to reliably identify individuals at high risk for suicide. Algorithmic approaches for suicide risk detection have been developed in recent years, mostly based on data from electronic health records (EHRs). Significant room for improvement remains in the way these models take advantage of temporal information to improve predictions. MATERIALS AND METHODS We propose a temporally enhanced variant of the random forest (RF) model-Omni-Temporal Balanced Random Forests (OT-BRFs)-that incorporates temporal information in every tree within the forest. We develop and validate this model using longitudinal EHRs and clinician notes from the Mass General Brigham Health System recorded between 1998 and 2018, and compare its performance to a baseline Naive Bayes Classifier and 2 standard versions of balanced RFs. RESULTS Temporal variables were found to be associated with suicide risk: Elevated suicide risk was observed in individuals with a higher total number of visits as well as those with a low rate of visits over time, while lower suicide risk was observed in individuals with a longer period of EHR coverage. RF models were more accurate than Naive Bayesian classifiers at predicting suicide risk in advance (area under the receiver operating curve = 0.824 vs. 0.754, respectively). The proposed OT-BRF model performed best among all RF approaches, yielding a sensitivity of 0.339 at 95% specificity, compared to 0.290 and 0.286 for the other 2 RF models. Temporal variables were assigned high importance by the models that incorporated them. DISCUSSION We demonstrate that temporal variables have an important role to play in suicide risk detection and that requiring their inclusion in all RF trees leads to increased predictive performance. Integrating temporal information into risk prediction models helps the models interpret patient data in temporal context, improving predictive performance.
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Affiliation(s)
- Ilkin Bayramli
- Predictive Medicine Group, Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, USA
- Harvard University, Cambridge, Massachusetts, USA
| | - Victor Castro
- Mass General Brigham Research Information Science and Computing, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yuval Barak-Corren
- Predictive Medicine Group, Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Emily M Madsen
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Psychiatry, Center for Precision Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Matthew K Nock
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Psychology, Harvard University, Cambridge, Massachusetts, USA
- Mental Health Research Program, Franciscan Children’s, Brighton, Massachusetts, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Psychiatry, Center for Precision Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Ben Y Reis
- Predictive Medicine Group, Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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Balis LE, Adjei KK, Nyame S, Opoku Mensah J, Asante KP. Stakeholder perspectives on adapting and disseminating Ghana's physical activity guidelines: a qualitative study. BMC Public Health 2021; 21:2266. [PMID: 34895182 PMCID: PMC8666022 DOI: 10.1186/s12889-021-12250-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 11/10/2021] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Ghana is facing the public health "double burden" of both communicable and chronic diseases. To combat increased chronic disease prevalence, physical activity promotion efforts are necessary. The Ministry of Health (MOH) developed physical activity guidelines in 2009, but community members are unaware of the guidelines and sample activities (e.g., ballroom dancing) are not culturally appropriate. The purposes of this study were to investigate 1) dissemination of the physical activity guidelines through MOH and Ghana Health Service (GHS) and 2) culturally appropriate physical activities. METHODS Data were collected in urban and rural areas of Ghana through focus groups (N = 2) with community representatives and in-depth interviews (N = 15) with GHS health workers. Focus group and interview questions included recommended types of physical activity; interview questions included dissemination factors based on Diffusion of Innovations. The research team analyzed the data through an inductive, grounded theory approach. RESULTS Together, the focus groups and in-depth interviews generated 942 meaning units coded into themes of Physical Activity Perceptions (N = 337 meaning units), Suggested Physical Activities (N = 317), and Dissemination and Implementation Factors (N = 290). Participants had positive perceptions of physical activity but expressed concerns over individual abilities; barriers included the built environment and a lack of time. Suggested physical activities included walking, jogging, football, and dancing for adults; traditional games and football for youth, and walking and daily chores for older adults. Participants noted that guideline implementation was influenced by leadership engagement at multiple levels, relative advantage, and compatibility. Respondents suggested implementation strategies to resolve barriers, including involving partner organizations and developing an implementation plan. Participants were largely unaware of the physical activity guidelines; typical dissemination methods included written materials and the internet. CONCLUSIONS The results of this study suggest that physical activity guidelines should include familiar physical activities such as traditional games. Results also suggest that public health workers within GHS experience challenges in disseminating the physical activity guidelines. Adapting, disseminating, and implementing physical activity guidelines is a necessary step in increasing physical activity levels and preventing chronic diseases. These results contribute to understanding translation of physical activity policy to practice.
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Affiliation(s)
- Laura E. Balis
- Pacific Institute for Research and Evaluation, Louisville, KY USA
- University of Wyoming Extension, Lander, WY USA
| | - Kwame Kesse Adjei
- Kintampo Health Research Centre, Ghana Health Service, Kintampo, Ghana
| | - Solomon Nyame
- Kintampo Health Research Centre, Ghana Health Service, Kintampo, Ghana
| | | | - Kwaku Poku Asante
- Kintampo Health Research Centre, Ghana Health Service, Kintampo, Ghana
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Loucera C, Peña-Chilet M, Esteban-Medina M, Muñoyerro-Muñiz D, Villegas R, Lopez-Miranda J, Rodriguez-Baño J, Túnez I, Bouillon R, Dopazo J, Quesada Gomez JM. Real world evidence of calcifediol or vitamin D prescription and mortality rate of COVID-19 in a retrospective cohort of hospitalized Andalusian patients. Sci Rep 2021; 11:23380. [PMID: 34862422 PMCID: PMC8642445 DOI: 10.1038/s41598-021-02701-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/19/2021] [Indexed: 12/12/2022] Open
Abstract
COVID-19 is a major worldwide health problem because of acute respiratory distress syndrome, and mortality. Several lines of evidence have suggested a relationship between the vitamin D endocrine system and severity of COVID-19. We present a survival study on a retrospective cohort of 15,968 patients, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020. Based on a central registry of electronic health records (the Andalusian Population Health Database, BPS), prescription of vitamin D or its metabolites within 15-30 days before hospitalization were recorded. The effect of prescription of vitamin D (metabolites) for other indication previous to the hospitalization was studied with respect to patient survival. Kaplan-Meier survival curves and hazard ratios support an association between prescription of these metabolites and patient survival. Such association was stronger for calcifediol (Hazard Ratio, HR = 0.67, with 95% confidence interval, CI, of [0.50-0.91]) than for cholecalciferol (HR = 0.75, with 95% CI of [0.61-0.91]), when prescribed 15 days prior hospitalization. Although the relation is maintained, there is a general decrease of this effect when a longer period of 30 days prior hospitalization is considered (calcifediol HR = 0.73, with 95% CI [0.57-0.95] and cholecalciferol HR = 0.88, with 95% CI [0.75, 1.03]), suggesting that association was stronger when the prescription was closer to the hospitalization.
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Affiliation(s)
- Carlos Loucera
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013, Seville, Spain
- Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013, Seville, Spain
| | - María Peña-Chilet
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013, Seville, Spain
- Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013, Seville, Spain
- Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, 41013, Seville, Spain
| | - Marina Esteban-Medina
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013, Seville, Spain
- Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013, Seville, Spain
| | - Dolores Muñoyerro-Muñiz
- Subdirección Técnica Asesora de Gestión de la Información, Servicio Andaluz de Salud, Seville, Spain
| | - Román Villegas
- Subdirección Técnica Asesora de Gestión de la Información, Servicio Andaluz de Salud, Seville, Spain
| | - Jose Lopez-Miranda
- Internal Medicine Department, IMIBIC/Reina Sofia University Hospital/University of Cordoba, 14004, Córdoba, Spain
- CIBER Fisiopatologia Obesidad y Nutricion (CIBEROBN), Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Jesus Rodriguez-Baño
- Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013, Seville, Spain
- Unidad Clínica de Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Hospital Universitario Virgen Macarena, Seville, Spain
- Departamento de Medicina, Universidad de Sevilla, Seville, Spain
| | - Isaac Túnez
- Departamento de Bioquimica y Biología Molecular, Facultad de Medicina y Enfermería, Universidad de Córdoba, Córdoba, Spain
- Instituto Maimónides de Investigacion Biomédica de Córdoba (IMIBIC), 14004, Córdoba, Spain
- G. Técnico de Expertos de Andalucía para Estudios de Suplementos e Intervención Nutricional Frente a Covid-19, SGIDIS, Consejería de Salud y Familias, Junta de Andalucia, Seville, Spain
- Secretaria General de Investigación, Desarrollo e Innovación en Salud, Consejería de Salud y Familias de la Junta de Andalucía, Seville, Spain
| | - Roger Bouillon
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KULeuven, Herestraat, 3000, Leuven, Belgium
| | - Joaquin Dopazo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013, Seville, Spain.
- Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013, Seville, Spain.
- Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, 41013, Seville, Spain.
- FPS/ELIXIR-ES, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013, Seville, Spain.
| | - Jose Manuel Quesada Gomez
- Instituto Maimónides de Investigacion Biomédica de Córdoba (IMIBIC), 14004, Córdoba, Spain.
- CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Hospital Universitario Reina Sofía, Universidad de Córdoba, Menéndez Pidal s/n, 14004, Córdoba, Spain.
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Abstract
Supplemental Digital Content is available in the text. Accurately measuring the risk of pressure injury remains the most important step for effective prevention and intervention. Time-dependent risk factors for pressure injury development in the adult intensive care unit setting are not well understood.
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Xie F, Ning Y, Yuan H, Goldstein BA, Ong MEH, Liu N, Chakraborty B. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. J Biomed Inform 2021; 125:103959. [PMID: 34826628 DOI: 10.1016/j.jbi.2021.103959] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 11/18/2021] [Accepted: 11/19/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created ad-hoc using a few manually selected variables based on clinician's knowledge, suggesting an unmet need for a robust and efficient generic score-generating method. METHODS AutoScore was previously developed as an interpretable machine learning score generator, integrating both machine learning and point-based scores in the strong discriminability and accessibility. We have further extended it to the time-to-event outcomes and developed AutoScore-Survival, for generating time-to-event scores with right-censored survival data. Random survival forest provided an efficient solution for selecting variables, and Cox regression was used for score weighting. We implemented our proposed method as an R package. We illustrated our method in a study of 90-day survival prediction for patients in intensive care units and compared its performance with other survival models, the random survival forest, and two traditional clinical scores. RESULTS The AutoScore-Survival-derived scoring system was more parsimonious than survival models built using traditional variable selection methods (e.g., penalized likelihood approach and stepwise variable selection), and its performance was comparable to survival models using the same set of variables. Although AutoScore-Survival achieved a comparable integrated area under the curve of 0.782 (95% CI: 0.767-0.794), the integer-valued time-to-event scores generated are favorable in clinical applications because they are easier to compute and interpret. CONCLUSIONS Our proposed AutoScore-Survival provides a robust and easy-to-use machine learning-based clinical score generator to studies of time-to-event outcomes. It gives a systematic guideline to facilitate the future development of time-to-event scores for clinical applications.
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Affiliation(s)
- Feng Xie
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Yilin Ning
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Han Yuan
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Benjamin Alan Goldstein
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States
| | - Marcus Eng Hock Ong
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| | - Nan Liu
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore, Singapore; Health Services Research Centre, Singapore Health Services, Singapore, Singapore.
| | - Bibhas Chakraborty
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States; Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore
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Pickett KL, Suresh K, Campbell KR, Davis S, Juarez-Colunga E. Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker. BMC Med Res Methodol 2021; 21:216. [PMID: 34657597 PMCID: PMC8520610 DOI: 10.1186/s12874-021-01375-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 08/21/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient's biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their longitudinal nature, using only baseline information. Dynamic prediction incorporates longitudinal information to produce updated survival predictions during follow-up. Existing methods for dynamic prediction include joint modeling, which often suffers from computational complexity and poor performance under misspecification, and landmarking, which has a straightforward implementation but typically relies on a proportional hazards model. Random survival forests (RSF), a machine learning algorithm for time-to-event outcomes, can capture complex relationships between the predictors and survival without requiring prior specification and has been shown to have superior predictive performance. METHODS We propose an alternative approach for dynamic prediction using random survival forests in a landmarking framework. With a simulation study, we compared the predictive performance of our proposed method with Cox landmarking and joint modeling in situations where the proportional hazards assumption does not hold and the longitudinal marker(s) have a complex relationship with the survival outcome. We illustrated the use of the RSF landmark approach in two clinical applications to assess the performance of various RSF model building decisions and to demonstrate its use in obtaining dynamic predictions. RESULTS In simulation studies, RSF landmarking outperformed joint modeling and Cox landmarking when a complex relationship between the survival and longitudinal marker processes was present. It was also useful in application when there were several predictors for which the clinical relevance was unknown and multiple longitudinal biomarkers were present. Individualized dynamic predictions can be obtained from this method and the variable importance metric is useful for examining the changing predictive power of variables over time. In addition, RSF landmarking is easily implementable in standard software and using suggested specifications requires less computation time than joint modeling. CONCLUSIONS RSF landmarking is a nonparametric, machine learning alternative to current methods for obtaining dynamic predictions when there are complex or unknown relationships present. It requires little upfront decision-making and has comparable predictive performance and has preferable computational speed.
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Affiliation(s)
- Kaci L Pickett
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, 80045 Colorado USA
| | - Krithika Suresh
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, 80045 Colorado USA
- Adult and Child Consortium for Health Outcomes and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, 80045 Colorado USA
| | - Kristen R Campbell
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, 80045 Colorado USA
| | - Scott Davis
- Division of Renal Diseases and Hypertension, University of Colorado Anschutz Medical Campus, Aurora, 80045 Colorado USA
| | - Elizabeth Juarez-Colunga
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, 80045 Colorado USA
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, 80045 Colorado USA
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43
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Luo X, Gandhi P, Zhang Z, Shao W, Han Z, Chandrasekaran V, Turzhitsky V, Bali V, Roberts AR, Metzger M, Baker J, La Rosa C, Weaver J, Dexter P, Huang K. Applying interpretable deep learning models to identify chronic cough patients using EHR data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 210:106395. [PMID: 34525412 DOI: 10.1016/j.cmpb.2021.106395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD code specific for chronic cough makes it challenging to identify such patients from electronic health records (EHRs). For clinical and research purposes, computational methods using EHR data are urgently needed to identify chronic cough cases. This research aims to investigate the data representations and deep learning algorithms for chronic cough prediction. METHODS Utilizing real-world EHR data from a large academic healthcare system from October 2005 to September 2015, we investigated Natural Language Representation of the EHR data and systematically evaluated deep learning and traditional machine learning models to predict chronic cough patients. We built these machine learning models using structured data (medication and diagnosis) and unstructured data (clinical notes). RESULTS The sensitivity and specificity of a transformer-based deep learning algorithm, specifically BERT with attention model, was 0.856 and 0.866, respectively, using structured data (medication and diagnosis). Sensitivity and specificity improved to 0.952 and 0.930 when we combined structured data with symptoms extracted from clinical notes. We further found that the attention mechanism of deep learning models can be used to extract important features that drive the prediction decisions. Compared with our previously published rule-based algorithm, the deep learning algorithm can identify more chronic cough patients with structured data. CONCLUSIONS By applying deep learning models, chronic cough patients can be reliably identified for prospective or retrospective research through medication and diagnosis data, widely available in EHR and electronic claims data, thus improving the generalizability of the patient identification algorithm. Deep learning models can identify chronic cough patients with even higher sensitivity and specificity when structured and unstructured EHR data are utilized. We anticipate language-based data representation and deep learning models developed in this research could also be productively used for other disease prediction and case identification.
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Affiliation(s)
- Xiao Luo
- Purdue School of Engineering and Technology, IUPUI, 799W Michigan St, Indianapolis, IN 46202, United States.
| | - Priyanka Gandhi
- Purdue School of Engineering and Technology, IUPUI, 799W Michigan St, Indianapolis, IN 46202, United States.
| | - Zuoyi Zhang
- Indiana University School of Medicine, 340W 10th St #6200, Indianapolis, IN 46202, United States.
| | - Wei Shao
- Indiana University School of Medicine, 340W 10th St #6200, Indianapolis, IN 46202, United States.
| | - Zhi Han
- Indiana University School of Medicine, 340W 10th St #6200, Indianapolis, IN 46202, United States; Regenstrief Institute, 1101W 10th Street, Indianapolis, IN, 46202, United States.
| | - Vasu Chandrasekaran
- Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States.
| | - Vladimir Turzhitsky
- Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States.
| | - Vishal Bali
- Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States.
| | - Anna R Roberts
- Regenstrief Institute, 1101W 10th Street, Indianapolis, IN, 46202, United States.
| | - Megan Metzger
- Regenstrief Institute, 1101W 10th Street, Indianapolis, IN, 46202, United States.
| | - Jarod Baker
- Regenstrief Institute, 1101W 10th Street, Indianapolis, IN, 46202, United States.
| | - Carmen La Rosa
- Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States.
| | - Jessica Weaver
- Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States.
| | - Paul Dexter
- Indiana University School of Medicine, 340W 10th St #6200, Indianapolis, IN 46202, United States; Regenstrief Institute, 1101W 10th Street, Indianapolis, IN, 46202, United States; Eskenazi Health, 720 Eskenazi Ave, Indianapolis, IN 46202, United States.
| | - Kun Huang
- Indiana University School of Medicine, 340W 10th St #6200, Indianapolis, IN 46202, United States; Regenstrief Institute, 1101W 10th Street, Indianapolis, IN, 46202, United States.
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Wu WT, Li YJ, Feng AZ, Li L, Huang T, Xu AD, Lyu J. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res 2021; 8:44. [PMID: 34380547 PMCID: PMC8356424 DOI: 10.1186/s40779-021-00338-z] [Citation(s) in RCA: 168] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 08/03/2021] [Indexed: 02/07/2023] Open
Abstract
Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.
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Affiliation(s)
- Wen-Tao Wu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China.,School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China
| | - Yuan-Jie Li
- Department of Human Anatomy, Histology and Embryology, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China
| | - Ao-Zi Feng
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China
| | - Li Li
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China
| | - Tao Huang
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China
| | - An-Ding Xu
- Department of Neurology, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China.
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Tianhe District, 613 W. Huangpu Avenue, Guangzhou, 510632, Guangdong, China.
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Moradian H, Yao W, Larocque D, Simonoff JS, Frydman H. Dynamic estimation with random forests for discrete‐time survival data. CAN J STAT 2021. [DOI: 10.1002/cjs.11639] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Hoora Moradian
- Department of‐10 Decision Sciences HEC Montréal Montréal Québec Canada
| | - Weichi Yao
- Department of Technology, Operations, and Statistics Stern School of Business, New York University New York New York USA
| | - Denis Larocque
- Department of‐10 Decision Sciences HEC Montréal Montréal Québec Canada
| | - Jeffrey S. Simonoff
- Department of Technology, Operations, and Statistics Stern School of Business, New York University New York New York USA
| | - Halina Frydman
- Department of Technology, Operations, and Statistics Stern School of Business, New York University New York New York USA
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Malpani R, Petty CW, Bhatt N, Staib LH, Chapiro J. Use of Artificial Intelligence in Non-Oncologic Interventional Radiology: Current State and Future Directions. DIGESTIVE DISEASE INTERVENTIONS 2021; 5:331-337. [PMID: 35005333 PMCID: PMC8740955 DOI: 10.1055/s-0041-1726300] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in non-oncologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, post-procedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current 'black box' of AI research and help bridge the gap between the research laboratory and clinical practice.
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Affiliation(s)
- Rohil Malpani
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Christopher W. Petty
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Neha Bhatt
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Lawrence H. Staib
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
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Practical Considerations and Recommendations for Master Protocol Framework: Basket, Umbrella and Platform Trials. Ther Innov Regul Sci 2021; 55:1145-1154. [PMID: 34160785 PMCID: PMC8220876 DOI: 10.1007/s43441-021-00315-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 06/07/2021] [Indexed: 11/05/2022]
Abstract
Master protocol, categorized as basket trial, umbrella trial or platform trial, is an innovative clinical trial framework that aims to expedite clinical drug development, enhance trial efficiency, and eventually bring medicines to patients faster. Despite a clear uptake on the advantages in the concepts and designs, master protocols are still yet to be widely used. Part of that may be due to the fact that the master protocol framework comes with the need for new statistical designs and considerations for analyses and operational challenges. In this article, we provide an overview of the master protocol framework, unify the definitions with some examples, review the statistical methods for the designs and analyses, and focus our discussions on some practical considerations and recommendations of master protocols to help practitioners better design and implement such studies.
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48
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Wongvibulsin S, Garibaldi BT, Antar AAR, Wen J, Wang MC, Gupta A, Bollinger R, Xu Y, Wang K, Betz JF, Muschelli J, Bandeen-Roche K, Zeger SL, Robinson ML. Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19. Ann Intern Med 2021; 174:777-785. [PMID: 33646849 PMCID: PMC7934337 DOI: 10.7326/m20-6754] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission. OBJECTIVE To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization. DESIGN Retrospective observational cohort study. SETTINGS Five hospitals in Maryland and Washington, D.C. PATIENTS Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. MEASUREMENTS A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization. RESULTS Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively. LIMITATION The SCARP tool was developed by using data from a single health system. CONCLUSION Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. PRIMARY FUNDING SOURCE Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.
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Affiliation(s)
- Shannon Wongvibulsin
- Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.)
| | - Brian T Garibaldi
- Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.)
| | - Annukka A R Antar
- Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.)
| | - Jiyang Wen
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.)
| | - Mei-Cheng Wang
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.)
| | - Amita Gupta
- Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.)
| | - Robert Bollinger
- Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.)
| | - Yanxun Xu
- Johns Hopkins University, Baltimore, Maryland (Y.X., K.W.)
| | - Kunbo Wang
- Johns Hopkins University, Baltimore, Maryland (Y.X., K.W.)
| | - Joshua F Betz
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.)
| | - John Muschelli
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.)
| | - Karen Bandeen-Roche
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.)
| | - Scott L Zeger
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.)
| | - Matthew L Robinson
- Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.)
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Ghazaleh N, Houghton R, Palermo G, Schobel SA, Wijeratne PA, Long JD. Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease. Front Neurol 2021; 12:678484. [PMID: 34093422 PMCID: PMC8176643 DOI: 10.3389/fneur.2021.678484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 04/26/2021] [Indexed: 12/01/2022] Open
Abstract
Huntington's disease (HD) is characterised by a triad of cognitive, behavioural, and motor symptoms which lead to functional decline and loss of independence. With potential disease-modifying therapies in development, there is interest in accurately measuring HD progression and characterising prognostic variables to improve efficiency of clinical trials. Using the large, prospective Enroll-HD cohort, we investigated the relative contribution and ranking of potential prognostic variables in patients with manifest HD. A random forest regression model was trained to predict change of clinical outcomes based on the variables, which were ranked based on their contribution to the prediction. The highest-ranked variables included novel predictors of progression—being accompanied at clinical visit, cognitive impairment, age at diagnosis and tetrabenazine or antipsychotics use—in addition to established predictors, cytosine adenine guanine (CAG) repeat length and CAG-age product. The novel prognostic variables improved the ability of the model to predict clinical outcomes and may be candidates for statistical control in HD clinical studies.
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Affiliation(s)
| | | | | | | | - Peter A Wijeratne
- Department of Computer Science, Centre for Medical Imaging Computing, University College London, London, United Kingdom.,Department of Neurodegenerative Disease, Huntington's Disease Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jeffrey D Long
- Department of Psychiatry, University of Iowa, Iowa City, IA, United States.,Department of Biostatistics, University of Iowa, Iowa City, IA, United States
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50
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Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11104572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
This paper focuses on the statistical analysis of mimetic muscle rehabilitation after head and neck surgery causing facial paresis in patients after head and neck surgery. Our work deals with an evaluation problem of mimetic muscle rehabilitation that is observed by a Kinect stereo-vision camera. After a specific brain surgery, patients are often affected by face palsy, and rehabilitation to renew mimetic muscle innervation takes several months. It is important to be able to observe the rehabilitation process in an objective way. The most commonly used House–Brackmann (HB) scale is based on the clinician’s subjective opinion. This paper compares different methods of supervised learning classification that should be independent of the clinician’s opinion. We compare a parametric model (based on logistic regression), non-parametric model (based on random forests), and neural networks. The classification problem that we have studied combines a limited dataset (it contains only 122 measurements of 93 patients) of complex observations (each measurement consists of a collection of time curves) with an ordinal response variable. To balance the frequencies of the considered classes in our data set, we reclassified the samples from HB4 to HB3 and HB5 to HB6—it means that only four HB grades are used for classification algorithm. The parametric statistical model was found to be the most suitable thanks to its stability, tractability, and reasonable performance in terms of both accuracy and precision.
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