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Mudumbai SC, Gabriel RA, Howell S, Tan JM, Freundlich RE, O’Reilly Shah V, Kendale S, Poterack K, Rothman BS. Public Health Informatics and the Perioperative Physician: Looking to the Future. Anesth Analg 2024; 138:253-272. [PMID: 38215706 PMCID: PMC10825795 DOI: 10.1213/ane.0000000000006649] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.
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Affiliation(s)
- Seshadri C. Mudumbai
- Anesthesiology and Perioperative Care Service, Veterans Affairs Palo Alto Health Care System
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine
| | - Rodney A. Gabriel
- Department of Anesthesiology, University of California, San Diego, California
| | | | - Jonathan M. Tan
- Department of Anesthesiology Critical Care Medicine, Children’s Hospital Los Angeles
- Department of Anesthesiology, Keck School of Medicine at the University of Southern California
- Spatial Sciences Institute at the University of Southern California
| | - Robert E. Freundlich
- Department of Anesthesiology, Surgery, and Biomedical Informatics, Vanderbilt University Medical Center
| | | | - Samir Kendale
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center
| | - Karl Poterack
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic
| | - Brian S. Rothman
- Department of Anesthesiology, Surgery, and Biomedical Informatics, Vanderbilt University Medical Center
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2
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Miele L, Dajko M, Savino MC, Capocchiano ND, Calvez V, Liguori A, Masciocchi C, Vetrone L, Mignini I, Schepis T, Marrone G, Biolato M, Cesario A, Patarnello S, Damiani A, Grieco A, Valentini V, Gasbarrini A. Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves. Intern Emerg Med 2023; 18:1415-1427. [PMID: 37491564 PMCID: PMC10412472 DOI: 10.1007/s11739-023-03310-y] [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] [Received: 01/05/2023] [Accepted: 05/10/2023] [Indexed: 07/27/2023]
Abstract
Increased values of the FIB-4 index appear to be associated with poor clinical outcomes in COVID-19 patients. This study aimed to develop and validate predictive mortality models, using data upon admission of hospitalized patients in four COVID-19 waves between March 2020 and January 2022. A single-center cohort study was performed on consecutive adult patients with Covid-19 admitted at the Fondazione Policlinico Gemelli IRCCS (Rome, Italy). Artificial intelligence and big data processing were used to retrieve data. Patients and clinical characteristics of patients with available FIB-4 data derived from the Gemelli Generator Real World Data (G2 RWD) were used to develop predictive mortality models during the four waves of the COVID-19 pandemic. A logistic regression model was applied to the training and test set (75%:25%). The model's performance was assessed by receiver operating characteristic (ROC) curves. A total of 4936 patients were included. Hypertension (38.4%), cancer (12.15%) and diabetes (16.3%) were the most common comorbidities. 23.9% of patients were admitted to ICU, and 12.6% had mechanical ventilation. During the study period, 762 patients (15.4%) died. We developed a multivariable logistic regression model on patient data from all waves, which showed that the FIB-4 score > 2.53 was associated with increased mortality risk (OR = 4.53, 95% CI 2.83-7.25; p ≤ 0.001). These data may be useful in the risk stratification at the admission of hospitalized patients with COVID-19.
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Affiliation(s)
- Luca Miele
- Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168 Rome, Italy
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Marianxhela Dajko
- Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168 Rome, Italy
| | - Maria Chiara Savino
- Department Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | - Nicola D. Capocchiano
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Valentino Calvez
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Antonio Liguori
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Carlotta Masciocchi
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Lorenzo Vetrone
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Irene Mignini
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Tommaso Schepis
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Giuseppe Marrone
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Marco Biolato
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Alfredo Cesario
- Gemelli Digital Medicine and Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Stefano Patarnello
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Andrea Damiani
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Antonio Grieco
- Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168 Rome, Italy
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Vincenzo Valentini
- Department Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | - Antonio Gasbarrini
- Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168 Rome, Italy
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Gemelli against COVID Group
- Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168 Rome, Italy
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
- Department Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Gemelli Digital Medicine and Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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Banerjee J, Taroni JN, Allaway RJ, Prasad DV, Guinney J, Greene C. Machine learning in rare disease. Nat Methods 2023:10.1038/s41592-023-01886-z. [PMID: 37248386 DOI: 10.1038/s41592-023-01886-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/22/2023] [Indexed: 05/31/2023]
Abstract
High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types involved in disease phenotypes. Machine learning (ML) can be a useful tool for extracting disease-relevant patterns from high-dimensional datasets. However, depending upon the complexity of the biological question, machine learning often requires many samples to identify recurrent and biologically meaningful patterns. Rare diseases are inherently limited in clinical cases, leading to few samples to study. In this Perspective, we outline the challenges and emerging solutions for using ML for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data. We propose that the method community prioritize the development of ML techniques for rare disease research.
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Affiliation(s)
| | - Jaclyn N Taroni
- Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Philadelphia, PA, USA
| | | | | | | | - Casey Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
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Mooney SD. Technology Platforms and Approaches for Building and Evaluating Machine Learning Methods in Healthcare. J Appl Lab Med 2023; 8:194-202. [PMID: 36610427 PMCID: PMC10729736 DOI: 10.1093/jalm/jfac113] [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: 06/17/2022] [Accepted: 10/18/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND Artificial intelligence (AI) methods are becoming increasingly commonly implemented in healthcare as decision support, business intelligence tools, or, in some cases, Food and Drug Administration-approved clinical decision-makers. Advanced lab-based diagnostic tools are increasingly becoming AI driven. The path from data to machine learning methods is an active area for research and quality improvement, and there are few established best practices. With data being generated at an unprecedented rate, there is a need for processes that enable data science investigation that protect patient privacy and minimize other business risks. New approaches for data sharing are being utilized that lower these risks. CONTENT In this short review, clinical and translational AI governance is introduced along with approaches for securely building, sharing, and validating accurate and fair models. This is a constantly evolving field, and there is much interest in collecting data using standards, sharing data, building new models, evaluating models, sharing models, and, of course, implementing models into practice. SUMMARY AI is an active area of research and development broadly for healthcare and laboratory testing. Robust data governance and machine learning methodological governance are required. New approaches for data sharing are enabling the development of models and their evaluation. Evaluation of methods is difficult, particularly when the evaluation is performed by the team developing the method, and should ideally be prospective. New technologies have enabled standardization of platforms for moving analytics and data science methods.
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Affiliation(s)
- Sean D Mooney
- Institute for Medical Data Science and Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
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Yan C, Yan Y, Wan Z, Zhang Z, Omberg L, Guinney J, Mooney SD, Malin BA. A Multifaceted benchmarking of synthetic electronic health record generation models. Nat Commun 2022; 13:7609. [PMID: 36494374 PMCID: PMC9734113 DOI: 10.1038/s41467-022-35295-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
Synthetic health data have the potential to mitigate privacy concerns in supporting biomedical research and healthcare applications. Modern approaches for data generation continue to evolve and demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a systematic benchmarking framework to appraise key characteristics with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic health data and further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context.
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Affiliation(s)
- Chao Yan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yao Yan
- Sage Bionetworks, Seattle, WA, USA
| | - Zhiyu Wan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ziqi Zhang
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | | | - Justin Guinney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
- Tempus Labs, Chicago, IL, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
| | - Bradley A Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
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Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database. BMC Med Inform Decis Mak 2022; 22:288. [DOI: 10.1186/s12911-022-01995-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 09/14/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Hospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance.
Methods
This population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance.
Results
Of the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance.
Conclusion
The performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.
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Computational interpretation of human genetic variation. Hum Genet 2022; 141:1545-1548. [PMID: 36149496 DOI: 10.1007/s00439-022-02483-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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