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Sheehan KA, Shin S, Hall E, Mak DYF, Lapointe-Shaw L, Tang T, Marwaha S, Gandell D, Rawal S, Inouye S, Verma AA, Razak F. Characterizing medical patients with delirium: A cohort study comparing ICD-10 codes and a validated chart review method. PLoS One 2024; 19:e0302888. [PMID: 38739670 PMCID: PMC11090329 DOI: 10.1371/journal.pone.0302888] [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: 07/27/2023] [Accepted: 04/15/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Delirium is a major cause of preventable mortality and morbidity in hospitalized adults, but accurately determining rates of delirium remains a challenge. OBJECTIVE To characterize and compare medical inpatients identified as having delirium using two common methods, administrative data and retrospective chart review. METHODS We conducted a retrospective study of 3881 randomly selected internal medicine hospital admissions from six acute care hospitals in Toronto and Mississauga, Ontario, Canada. Delirium status was determined using ICD-10-CA codes from hospital administrative data and through a previously validated chart review method. Baseline sociodemographic and clinical characteristics, processes of care and outcomes were compared across those without delirium in hospital and those with delirium as determined by administrative data and chart review. RESULTS Delirium was identified in 6.3% of admissions by ICD-10-CA codes compared to 25.7% by chart review. Using chart review as the reference standard, ICD-10-CA codes for delirium had sensitivity 24.1% (95%CI: 21.5-26.8%), specificity 99.8% (95%CI: 99.5-99.9%), positive predictive value 97.6% (95%CI: 94.6-98.9%), and negative predictive value 79.2% (95%CI: 78.6-79.7%). Age over 80, male gender, and Charlson comorbidity index greater than 2 were associated with misclassification of delirium. Inpatient mortality and median costs of care were greater in patients determined to have delirium by ICD-10-CA codes (5.8% greater mortality, 95% CI: 2.0-9.5 and $6824 greater cost, 95%CI: 4713-9264) and by chart review (11.9% greater mortality, 95%CI: 9.5-14.2% and $4967 greater cost, 95%CI: 4415-5701), compared to patients without delirium. CONCLUSIONS Administrative data are specific but highly insensitive, missing most cases of delirium in hospital. Mortality and costs of care were greater for both the delirium cases that were detected and missed by administrative data. Better methods of routinely measuring delirium in hospital are needed.
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Affiliation(s)
- Kathleen A. Sheehan
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Saeha Shin
- St. Michael’s Hospital, Unity Health Network, Toronto, ON, Canada
| | - Elise Hall
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Unity Health Network, Toronto, ON, Canada
| | - Denise Y. F. Mak
- St. Michael’s Hospital, Unity Health Network, Toronto, ON, Canada
| | - Lauren Lapointe-Shaw
- Department of Medicine, University of Toronto, Toronto ON, Canada
- Department of Medicine, University Health Network, Toronto, ON, Canada
| | - Terence Tang
- Department of Medicine, University of Toronto, Toronto ON, Canada
- Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada
| | - Seema Marwaha
- Department of Medicine, University of Toronto, Toronto ON, Canada
- Department of Medicine, Unity Health Network, Toronto, ON, Canada
| | - Dov Gandell
- Department of Medicine, University of Toronto, Toronto ON, Canada
- Department of Medicine, Sunnybrook Heatlh Sciences Centre, Toronto, ON, Canada
| | - Shail Rawal
- Department of Medicine, University of Toronto, Toronto ON, Canada
- Department of Medicine, University Health Network, Toronto, ON, Canada
| | - Sharon Inouye
- Aging Brain Center, Hebrew Senior Life, Boston, MA, United States of America
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
| | - Amol A. Verma
- Department of Medicine, University of Toronto, Toronto ON, Canada
- Department of Medicine, Unity Health Network, Toronto, ON, Canada
| | - Fahad Razak
- Department of Medicine, University of Toronto, Toronto ON, Canada
- Department of Medicine, Unity Health Network, Toronto, ON, Canada
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Vaidya YP, Shumway SJ. Artificial intelligence: The future of cardiothoracic surgery. J Thorac Cardiovasc Surg 2024:S0022-5223(24)00371-4. [PMID: 38685465 DOI: 10.1016/j.jtcvs.2024.04.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/02/2024]
Affiliation(s)
- Yash Pradeep Vaidya
- Department of Cardiothoracic Surgery, University of Minnesota, Minneapolis, Minn.
| | - Sara Jane Shumway
- Department of Cardiothoracic Surgery, University of Minnesota, Minneapolis, Minn
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Weidmann AE, Watson EW. Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups. Int J Clin Pharm 2024:10.1007/s11096-024-01707-z. [PMID: 38594470 DOI: 10.1007/s11096-024-01707-z] [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: 01/02/2024] [Accepted: 01/22/2024] [Indexed: 04/11/2024]
Abstract
The advent of artificial intelligence (AI) technologies has taken the world of science by storm in 2023. The opportunities of this easy to access technology for clinical pharmacy research are yet to be fully understood. The development of a custom-made large language model (LLM) (DELSTAR) trained on a wide range of internationally recognised scientific publication databases, pharmacovigilance sites and international product characteristics to help identify and summarise medication related information on delirium, as a proof-of-concept model, identified new facilitators and barriers for robust clinical pharmacy practice research. This technology holds great promise for the development of much more comprehensive prescribing guidelines, practice support applications for clinical pharmacy, increased patient and prescribing safety and resultant implications for healthcare costs. The challenge will be to ensure its methodologically robust use and the detailed and transparent verification of its information accuracy.
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Affiliation(s)
- Anita Elaine Weidmann
- Department of Clinical Pharmacy, Institute of Pharmacy, Innsbruck University, Innrain 80, 6020, Innsbruck, Austria.
| | - Edward William Watson
- Department of Media and Learning Technology, Innsbruck University, Innrain 52, 6020, Innsbruck, Austria
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Yang T, Yang H, Liu Y, Liu X, Ding YJ, Li R, Mao AQ, Huang Y, Li XL, Zhang Y, Yu FX. Postoperative delirium prediction after cardiac surgery using machine learning models. Comput Biol Med 2024; 169:107818. [PMID: 38134752 DOI: 10.1016/j.compbiomed.2023.107818] [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: 07/01/2023] [Revised: 11/03/2023] [Accepted: 12/03/2023] [Indexed: 12/24/2023]
Abstract
OBJECTIVE Postoperative delirium (POD) is a common postoperative complication in elderly patients, especially those undergoing cardiac surgery, which seriously affects the short- and long-term prognosis of patients. Early identification of risk factors for the development of POD can help improve the perioperative management of surgical patients. In the present study, five machine learning models were developed to predict patients at high risk of delirium after cardiac surgery and their performance was compared. METHODS A total of 367 patients who underwent cardiac surgery were retrospectively included in this study. Using single-factor analysis, 21 risk factors for POD were selected for inclusion in machine learning. The dataset was divided using 10-fold cross-validation for model training and testing. Five machine learning models (random forest (RF), support vector machine (SVM), radial based kernel neural network (RBFNN), K-nearest neighbour (KNN), and Kernel ridge regression (KRR)) were compared using area under the receiver operating characteristic curve (AUC-ROC), accuracy (ACC), sensitivity (SN), specificity (SPE), and Matthews coefficient (MCC). RESULTS Among 367 patients, 105 patients developed POD, the incidence of delirium was 28.6 %. Among the five ML models, RF had the best performance in ACC (87.99 %), SN (69.27 %), SPE (95.38 %), MCC (70.00 %) and AUC (0.9202), which was far superior to the other four models. CONCLUSION Delirium is common in patients after cardiac surgery. This analysis confirms the importance of the computational ML models in predicting the occurrence of delirium after cardiac surgery, especially the outstanding performance of the RF model, which has practical clinical applications for early identification of patients at risk of developing POD.
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Affiliation(s)
- Tan Yang
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Hai Yang
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yan Liu
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Xiao Liu
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yi-Jie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 324000 Quzhou, Zhejiang, China
| | - Run Li
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - An-Qiong Mao
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yue Huang
- Department of Anesthesiology, Zigong First People's Hospital, Zi Gong, 644099, Sichuan, China
| | - Xiao-Liang Li
- Department of Cardiothoracic Surgery, First Peoples Hospital of Neijiang, Nei Jiang, 641000, Sichuan, China
| | - Ying Zhang
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
| | - Feng-Xu Yu
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
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Miyazawa Y, Katsuta N, Nara T, Nojiri S, Naito T, Hiki M, Ichikawa M, Takeshita Y, Kato T, Okumura M, Tobita M. Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records. PLoS One 2024; 19:e0296760. [PMID: 38241284 PMCID: PMC10798448 DOI: 10.1371/journal.pone.0296760] [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: 06/20/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024] Open
Abstract
COVID-19 has a range of complications, from no symptoms to severe pneumonia. It can also affect multiple organs including the nervous system. COVID-19 affects the brain, leading to neurological symptoms such as delirium. Delirium, a sudden change in consciousness, can increase the risk of death and prolong the hospital stay. However, research on delirium prediction in patients with COVID-19 is insufficient. This study aimed to identify new risk factors that could predict the onset of delirium in patients with COVID-19 using machine learning (ML) applied to nursing records. This retrospective cohort study used natural language processing and ML to develop a model for classifying the nursing records of patients with delirium. We extracted the features of each word from the model and grouped similar words. To evaluate the usefulness of word groups in predicting the occurrence of delirium in patients with COVID-19, we analyzed the temporal changes in the frequency of occurrence of these word groups before and after the onset of delirium. Moreover, the sensitivity, specificity, and odds ratios were calculated. We identified (1) elimination-related behaviors and conditions and (2) abnormal patient behavior and conditions as risk factors for delirium. Group 1 had the highest sensitivity (0.603), whereas group 2 had the highest specificity and odds ratio (0.938 and 6.903, respectively). These results suggest that these parameters may be useful in predicting delirium in these patients. The risk factors for COVID-19-associated delirium identified in this study were more specific but less sensitive than the ICDSC (Intensive Care Delirium Screening Checklist) and CAM-ICU (Confusion Assessment Method for the Intensive Care Unit). However, they are superior to the ICDSC and CAM-ICU because they can predict delirium without medical staff and at no cost.
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Affiliation(s)
- Yusuke Miyazawa
- Department of Healthcare Innovation, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Narimasa Katsuta
- Department of Psychiatry, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Tamaki Nara
- Department of Healthcare Innovation, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
- Clinical Research and Trial Center, Juntendo University, Tokyo, Japan
| | - Shuko Nojiri
- Department of Healthcare Innovation, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
- Clinical Research and Trial Center, Juntendo University, Tokyo, Japan
| | - Toshio Naito
- Department of General Medicine, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Makoto Hiki
- Department of Emergency and Disaster Medicine, Juntendo University Faculty of Medicine, Tokyo, Japan
- Department of Cardiovascular Biology and Medicine, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Masako Ichikawa
- Department of Emergency and Disaster Medicine, Juntendo University Faculty of Medicine, Tokyo, Japan
- Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yoshihide Takeshita
- Department of Psychiatry, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Tadafumi Kato
- Department of Psychiatry, Juntendo University Faculty of Medicine, Tokyo, Japan
| | | | - Morikuni Tobita
- Department of Healthcare Innovation, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
- Clinical Research and Trial Center, Juntendo University, Tokyo, Japan
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6
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Zhao X, Li J, Xie X, Fang Z, Feng Y, Zhong Y, Chen C, Huang K, Ge C, Shi H, Si Y, Zou J. Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study. J Psychosom Res 2024; 176:111553. [PMID: 37995429 DOI: 10.1016/j.jpsychores.2023.111553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 11/12/2023] [Accepted: 11/12/2023] [Indexed: 11/25/2023]
Abstract
OBJECTIVE Postoperative delirium (POD) is strongly associated with poor early and long-term prognosis in cardiac surgery patients with cardiopulmonary bypass (CPB). This study aimed to develop dynamic prediction models for POD after cardiac surgery under CPB using machine learning (ML) algorithms. METHODS From July 2021 to June 2022, clinical data were collected from patients undergoing cardiac surgery under CPB at Nanjing First Hospital. A dataset from the same center (October 2022 to November 2022) was also used for temporal external validation. We used ML and deep learning to build models in the training set, optimized parameters in the test set, and finally validated the best model in the validation set. The SHapley Additive exPlanations (SHAP) method was introduced to explain the best models. RESULTS Of the 885 patients enrolled, 221 (25.0%) developed POD. 22 (22.0%) of 100 validation cohort patients developed POD. The preoperative and postoperative artificial neural network (ANN) models exhibited optimal performance. The validation results demonstrated satisfactory predictive performance of the ANN model, with area under the receiver operator characteristic curve (AUROC) values of 0.776 and 0.684 for the preoperative and postoperative models, respectively. Based on the ANN algorithm, we constructed dynamic, highly accurate, and interpretable web risk calculators for POD. CONCLUSIONS We successfully developed online interpretable dynamic ANN models as clinical decision aids to identify patients at high risk of POD before and after cardiac surgery to facilitate early intervention or care.
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Affiliation(s)
- Xiuxiu Zhao
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Junlin Li
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xianhai Xie
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhaojing Fang
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yue Feng
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yi Zhong
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Chen Chen
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Kaizong Huang
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Chun Ge
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Hongwei Shi
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yanna Si
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
| | - Jianjun Zou
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China.
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Nagata C, Hata M, Miyazaki Y, Masuda H, Wada T, Kimura T, Fujii M, Sakurai Y, Matsubara Y, Yoshida K, Miyagawa S, Ikeda M, Ueno T. Development of postoperative delirium prediction models in patients undergoing cardiovascular surgery using machine learning algorithms. Sci Rep 2023; 13:21090. [PMID: 38036664 PMCID: PMC10689441 DOI: 10.1038/s41598-023-48418-5] [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: 05/23/2023] [Accepted: 11/27/2023] [Indexed: 12/02/2023] Open
Abstract
Associations between delirium and postoperative adverse events in cardiovascular surgery have been reported and the preoperative identification of high-risk patients of delirium is needed to implement focused interventions. We aimed to develop and validate machine learning models to predict post-cardiovascular surgery delirium. Patients aged ≥ 40 years who underwent cardiovascular surgery at a single hospital were prospectively enrolled. Preoperative and intraoperative factors were assessed. Each patient was evaluated for postoperative delirium 7 days after surgery. We developed machine learning models using the Bernoulli naive Bayes, Support vector machine, Random forest, Extra-trees, and XGBoost algorithms. Stratified fivefold cross-validation was performed for each developed model. Of the 87 patients, 24 (27.6%) developed postoperative delirium. Age, use of psychotropic drugs, cognitive function (Mini-Cog < 4), index of activities of daily living (Barthel Index < 100), history of stroke or cerebral hemorrhage, and eGFR (estimated glomerular filtration rate) < 60 were selected to develop delirium prediction models. The Extra-trees model had the best area under the receiver operating characteristic curve (0.76 [standard deviation 0.11]; sensitivity: 0.63; specificity: 0.78). XGBoost showed the highest sensitivity (AUROC, 0.75 [0.07]; sensitivity: 0.67; specificity: 0.79). Machine learning algorithms could predict post-cardiovascular delirium using preoperative data.Trial registration: UMIN-CTR (ID; UMIN000049390).
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Affiliation(s)
- Chie Nagata
- Division of Health Sciences, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Masahiro Hata
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Yuki Miyazaki
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Hirotada Masuda
- Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Tamiki Wada
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Tasuku Kimura
- SANKEN (The Institution of Scientific and Industrial Research), Osaka University, Ibaraki, Osaka, Japan
| | - Makoto Fujii
- Division of Health Sciences, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yasushi Sakurai
- SANKEN (The Institution of Scientific and Industrial Research), Osaka University, Ibaraki, Osaka, Japan
| | - Yasuko Matsubara
- SANKEN (The Institution of Scientific and Industrial Research), Osaka University, Ibaraki, Osaka, Japan
| | - Kiyoshi Yoshida
- Division of Health Sciences, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shigeru Miyagawa
- Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Takayoshi Ueno
- Division of Health Sciences, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
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Matsumoto K, Nohara Y, Sakaguchi M, Takayama Y, Fukushige S, Soejima H, Nakashima N, Kamouchi M. Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study. JMIR Perioper Med 2023; 6:e50895. [PMID: 37883164 PMCID: PMC10636625 DOI: 10.2196/50895] [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: 07/16/2023] [Revised: 09/24/2023] [Accepted: 09/29/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications. OBJECTIVE The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model. METHODS The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method. RESULTS A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept -0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance. CONCLUSIONS The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.
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Affiliation(s)
| | - Yasunobu Nohara
- Big Data Science and Technology, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
| | - Mikako Sakaguchi
- Department of Nursing, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Yohei Takayama
- Department of Nursing, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Syota Fukushige
- Department of Inspection, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Hidehisa Soejima
- Institute for Medical Information Research and Analysis, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Naoki Nakashima
- Medical Information Center, Kyushu University Hospital, Fukuoka, Japan
| | - Masahiro Kamouchi
- Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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Shahidi F, Rennert-May E, D'Souza AG, Crocker A, Faris P, Leal J. Machine learning risk estimation and prediction of death in continuing care facilities using administrative data. Sci Rep 2023; 13:17708. [PMID: 37853045 PMCID: PMC10584843 DOI: 10.1038/s41598-023-43943-9] [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: 05/04/2023] [Accepted: 09/30/2023] [Indexed: 10/20/2023] Open
Abstract
In this study, we aimed to identify the factors that were associated with mortality among continuing care residents in Alberta, during the coronavirus disease 2019 (COVID-19) pandemic. We achieved this by leveraging and linking various administrative datasets together. Then, we examined pre-processing methods in terms of prediction performance. Finally, we developed several machine learning models and compared the results of these models in terms of performance. We conducted a retrospective cohort study of all continuing care residents in Alberta, Canada, from March 1, 2020, to March 31, 2021. We used a univariable and a multivariable logistic regression (LR) model to identify predictive factors of 60-day all-cause mortality by estimating odds ratios (ORs) with a 95% confidence interval. To determine the best sensitivity-specificity cut-off point, the Youden index was employed. We developed several machine learning models to determine the best model regarding performance. In this cohort study, increased age, male sex, symptoms, previous admissions, and some specific comorbidities were associated with increased mortality. Machine learning and pre-processing approaches offer a potentially valuable method for improving risk prediction for mortality, but more work is needed to show improvement beyond standard risk factors.
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Affiliation(s)
- Faezehsadat Shahidi
- Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada
| | - Elissa Rennert-May
- Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Department of Medicine, University of Calgary, Calgary, AB, Canada
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
- Department of Microbiology, Immunology, and Infectious Diseases, University of Calgary, Calgary, AB, Canada
- Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
- AMR - One Health Consortium, University of Calgary, Calgary, AB, Canada
| | - Adam G D'Souza
- Centre for Health Informatics, University of Calgary, Calgary, AB, Canada
- Analytics, Alberta Health Services, Calgary, AB, Canada
| | - Alysha Crocker
- Clinical Information Systems, Alberta Health Services, Calgary, AB, Canada
| | - Peter Faris
- Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Analytics, Alberta Health Services, Calgary, AB, Canada
| | - Jenine Leal
- Community Health Sciences, University of Calgary, Calgary, AB, Canada.
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
- Department of Microbiology, Immunology, and Infectious Diseases, University of Calgary, Calgary, AB, Canada.
- AMR - One Health Consortium, University of Calgary, Calgary, AB, Canada.
- Infection Prevention and Control, Alberta Health Services, Calgary, AB, Canada.
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10
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Tian Y, Ji B, Diao X, Wang C, Wang W, Gao Y, Wang S, Zhou C, Zhang Q, Gao S, Xu X, Liu J, Wang J, Wang Y. Dynamic predictive scores for cardiac surgery-associated agitated delirium: a single-center retrospective observational study. J Cardiothorac Surg 2023; 18:219. [PMID: 37415226 DOI: 10.1186/s13019-023-02339-6] [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: 09/04/2022] [Accepted: 06/29/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Prevention, screening, and early treatment are the aims of postoperative delirium management. The scoring system is an objective and effective tool to stratify potential delirium risk for patients undergoing cardiac surgery. METHODS Patients who underwent cardiac surgery between January 1, 2012, and January 1, 2019, were enrolled in our retrospective study. The patients were divided into a derivation cohort (n = 45,744) and a validation cohort (n = 11,436). The AD predictive systems were formulated using multivariate logistic regression analysis at three time points: preoperation, ICU admittance, and 24 h after ICU admittance. RESULTS The prevalence of AD after cardiac surgery in the whole cohort was 3.6% (2,085/57,180). The dynamic scoring system included preoperative LVEF ≤ 45%, serum creatinine > 100 µmol/L, emergency surgery, coronary artery disease, hemorrhage volume > 600 mL, intraoperative platelet or plasma use, and postoperative LVEF ≤ 45%. The area under the receiver operating characteristic curve (AUC) values for AD prediction were 0.68 (preoperative), 0.74 (on the day of ICU admission), and 0.75 (postoperative). The Hosmer‒Lemeshow test indicated that the calibration of the preoperative prediction model was poor (P = 0.01), whereas that of the pre- and intraoperative prediction model (P = 0.49) and the pre, intra- and postoperative prediction model (P = 0.35) was good. CONCLUSIONS Using perioperative data, we developed a dynamic scoring system for predicting the risk of AD following cardiac surgery. The dynamic scoring system may improve the early recognition of and the interventions for AD.
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Affiliation(s)
- Yu Tian
- Department of Anesthesiology, Beijing AnZhen Hospital, Capital Medical University, Beijing, China
| | - Bingyang Ji
- Department of Cardiopulmonary Bypass, Fuwai Hospital,National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaolin Diao
- Department of Information Center, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chunrong Wang
- Department of Anesthesiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beilishi Road 167, Xicheng District, Beijing, 100037, China
| | - Weiwei Wang
- Department of Information Center, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuchen Gao
- Department of Anesthesiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beilishi Road 167, Xicheng District, Beijing, 100037, China
| | - Sudena Wang
- Department of Anesthesiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beilishi Road 167, Xicheng District, Beijing, 100037, China
| | - Chun Zhou
- Department of Cardiopulmonary Bypass, Fuwai Hospital,National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qiaoni Zhang
- Department of Cardiopulmonary Bypass, Fuwai Hospital,National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sizhe Gao
- Department of Cardiopulmonary Bypass, Fuwai Hospital,National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinyi Xu
- Department of Information Center, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jia Liu
- Department of Surgery Intensive Care Unit, Beijing Shijitan Hospital, Capital Medical University, No. 10 Tieyi Road, Yangfangdian, Haidian District, Beijing, China
| | - Jianhui Wang
- Department of Anesthesiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beilishi Road 167, Xicheng District, Beijing, 100037, China.
| | - Yuefu Wang
- Department of Surgery Intensive Care Unit, Beijing Shijitan Hospital, Capital Medical University, No. 10 Tieyi Road, Yangfangdian, Haidian District, Beijing, China.
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Cho E, Kim S, Heo SJ, Shin J, Hwang S, Kwon E, Lee S, Kim S, Kang B. Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation. Sci Rep 2023; 13:8073. [PMID: 37202454 DOI: 10.1038/s41598-023-35194-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 05/14/2023] [Indexed: 05/20/2023] Open
Abstract
The behavioral and psychological symptoms of dementia (BPSD) are challenging aspects of dementia care. This study used machine learning models to predict the occurrence of BPSD among community-dwelling older adults with dementia. We included 187 older adults with dementia for model training and 35 older adults with dementia for external validation. Demographic and health data and premorbid personality traits were examined at the baseline, and actigraphy was utilized to monitor sleep and activity levels. A symptom diary tracked caregiver-perceived symptom triggers and the daily occurrence of 12 BPSD classified into seven subsyndromes. Several prediction models were also employed, including logistic regression, random forest, gradient boosting machine, and support vector machine. The random forest models revealed the highest area under the receiver operating characteristic curve (AUC) values for hyperactivity, euphoria/elation, and appetite and eating disorders; the gradient boosting machine models for psychotic and affective symptoms; and the support vector machine model showed the highest AUC. The gradient boosting machine model achieved the best performance in terms of average AUC scores across the seven subsyndromes. Caregiver-perceived triggers demonstrated higher feature importance values across the seven subsyndromes than other features. Our findings demonstrate the possibility of predicting BPSD using a machine learning approach.
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Affiliation(s)
- Eunhee Cho
- Mo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, 50-1, Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Sujin Kim
- Department of Nursing, Yong-In Arts and Science University, Gyeonggi-do, Korea
| | - Seok-Jae Heo
- Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea
| | - Jinhee Shin
- College of Nursing, Woosuk University, Jeollabuk-do, Korea
| | - Sinwoo Hwang
- Korea Armed Forces Nursing Academy, Daejeon, Korea
| | - Eunji Kwon
- Korea Armed Forces Nursing Academy, Daejeon, Korea
| | | | | | - Bada Kang
- Mo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, 50-1, Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
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12
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Hosseinzadeh Kasani P, Lee JE, Park C, Yun CH, Jang JW, Lee SA. Evaluation of nutritional status and clinical depression classification using an explainable machine learning method. Front Nutr 2023; 10:1165854. [PMID: 37229464 PMCID: PMC10203418 DOI: 10.3389/fnut.2023.1165854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/27/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction Depression is a prevalent disorder worldwide, with potentially severe implications. It contributes significantly to an increased risk of diseases associated with multiple risk factors. Early accurate diagnosis of depressive symptoms is a critical first step toward management, intervention, and prevention. Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. Despite the challenges to better understanding the association between nutritional risk factors and the occurrence of depression, assessing the interplay of these markers through supervised machine learning remains to be fully explored. Methods This study aimed to determine the ability of machine learning-based decision support methods to identify the presence of depression using publicly available health data from the Korean National Health and Nutrition Examination Survey. Two exploration techniques, namely, uniform manifold approximation and projection and Pearson correlation, were performed for explanatory analysis among datasets. A grid search optimization with cross-validation was performed to fine-tune the models for classifying depression with the highest accuracy. Several performance measures, including accuracy, precision, recall, F1 score, confusion matrix, areas under the precision-recall and receiver operating characteristic curves, and calibration plot, were used to compare classifier performances. We further investigated the importance of the features provided: visualized interpretation using ELI5, partial dependence plots, and local interpretable using model-agnostic explanations and Shapley additive explanation for the prediction at both the population and individual levels. Results The best model achieved an accuracy of 86.18% for XGBoost and an area under the curve of 84.96% for the random forest model in original dataset and the XGBoost algorithm with an accuracy of 86.02% and an area under the curve of 85.34% in the quantile-based dataset. The explainable results revealed a complementary observation of the relative changes in feature values, and, thus, the importance of emergent depression risks could be identified. Discussion The strength of our approach is the large sample size used for training with a fine-tuned model. The machine learning-based analysis showed that the hyper-tuned model has empirically higher accuracy in classifying patients with depressive disorder, as evidenced by the set of interpretable experiments, and can be an effective solution for disease control.
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Affiliation(s)
- Payam Hosseinzadeh Kasani
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Jung Eun Lee
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Chihyun Park
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, Republic of Korea
| | - Cheol-Heui Yun
- Department of Agricultural Biotechnology, Seoul National University, Seoul, Republic of Korea
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea
- Department of Neurology, Kangwon National University School of Medicine, Chuncheon, Republic of Korea
| | - Sang-Ah Lee
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
- Department of Preventive Medicine, College of Medicine, Kangwon National University, Chuncheon, Republic of Korea
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Mueller B, Street WN, Carnahan RM, Lee S. Evaluating the performance of machine learning methods for risk estimation of delirium in patients hospitalized from the emergency department. Acta Psychiatr Scand 2023; 147:493-505. [PMID: 36999191 PMCID: PMC10147581 DOI: 10.1111/acps.13551] [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: 07/02/2022] [Revised: 03/06/2023] [Accepted: 03/23/2023] [Indexed: 04/01/2023]
Abstract
INTRODUCTION Delirium is a cerebral dysfunction seen commonly in the acute care setting. It is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) and inpatient care by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions in the hospital setting. OBJECTIVE Our objective was to leverage electronic health records to identify a clinically valuable risk estimation model for prevalent delirium in patients being transferred from the ED to inpatient units. METHODS This was a retrospective cohort study to develop and validate a risk model to detect delirium using patient data available from prior visits and ED encounter. Electronic health records were extracted for patients hospitalized from the ED between January 1, 2014, and December 31, 2020. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded within 72 h of hospitalization. Six machine learning models were developed to estimate the risk of delirium using clinical variables including demographic features, physiological measurements, medications administered, lab results, and diagnoses. RESULTS A total of 28,531 patients met the inclusion criteria with 8057 (28.4%) having a positive delirium screening within the outcome observation period. Machine learning models were compared using the area under the receiver operating curve (AUC). The gradient boosted machine achieved the best performance with an AUC of 0.839 (95% CI, 0.837-0.841). At a 90% sensitivity threshold, this model achieved a specificity of 53.5% (95% CI 53.0%-54.0%) a positive predictive value of 43.5% (95% CI 43.2%-43.9%), and a negative predictive value of 93.1% (95% CI 93.1%-93.2%). A random forest model and L1-penalized logistic regression also demonstrated notable performance with AUCs of 0.837 (95% CI, 0.835-0.838) and 0.831 (95% CI, 0.830-0.833) respectively. CONCLUSION This study demonstrated the use of machine learning algorithms to identify a combination of variables that enables an estimation of risk of positive delirium screens early in hospitalization to develop prevention or management protocols.
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Affiliation(s)
- Brianna Mueller
- Tippie College of Business, The University of Iowa, Iowa City, Iowa, USA
| | - W Nick Street
- Tippie College of Business, The University of Iowa, Iowa City, Iowa, USA
| | - Ryan M Carnahan
- Department of Epidemiology, The University of Iowa College of Public Health, Iowa City, Iowa, USA
| | - Sangil Lee
- Department of Emergency Medicine, The University of Iowa, Iowa City, Iowa, USA
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14
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Jiang Z, Cai Y, Liu S, Ye P, Yang Y, Lin G, Li S, Xu Y, Zheng Y, Bao Z, Nie S, Gu W. Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis. Front Aging Neurosci 2023; 14:1109485. [PMID: 36688167 PMCID: PMC9853194 DOI: 10.3389/fnagi.2022.1109485] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 12/16/2022] [Indexed: 01/07/2023] Open
Abstract
Objectives The abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms. Methods Resting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test. Results We found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus. Conclusion The decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR. Clinical Trial Registration : Chinese Clinical Trial Registry, ChiCTR-DCD-15006096.
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Affiliation(s)
- Zhaoshun Jiang
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yuxi Cai
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Songbin Liu
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Pei Ye
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yifeng Yang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Shihong Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yan Xu
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yangjing Zheng
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Zhijun Bao
- Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Department of Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Research Center on Aging and Medicine, Fudan University, Shanghai, China
| | - Shengdong Nie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China,Shengdong Nie,
| | - Weidong Gu
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China,*Correspondence: Weidong Gu,
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15
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Mulkey M, Albanese T, Kim S, Huang H, Yang B. Delirium detection using GAMMA wave and machine learning: A pilot study. Res Nurs Health 2022; 45:652-663. [PMID: 36321335 PMCID: PMC9649882 DOI: 10.1002/nur.22268] [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: 01/24/2022] [Revised: 09/13/2022] [Accepted: 09/22/2022] [Indexed: 11/11/2022]
Abstract
Delirium occurs in as many as 80% of critically ill older adults and is associated with increased long-term cognitive impairment, institutionalization, and mortality. Less than half of delirium cases are identified using currently available subjective assessment tools. Electroencephalogram (EEG) has been identified as a reliable objective measure but has not been feasible. This study was a prospective pilot proof-of-concept study, to examine the use of machine learning methods evaluating the use of gamma band to predict delirium from EEG data derived from a limited lead rapid response handheld device. Data from 13 critically ill participants aged 50 or older requiring mechanical ventilation for more than 12 h were enrolled. Across the three models, accuracy of predicting delirium was 70 or greater. Stepwise discriminant analysis provided the best overall method. While additional research is needed to determine the best cut points and efficacy, use of a handheld limited lead rapid response EEG device capable of monitoring all five cerebral lobes of the brain for predicting delirium hold promise.
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Affiliation(s)
- Malissa Mulkey
- College of Nursing, University of South Carolina, Columbia, South Carolina, USA
| | - Thomas Albanese
- College of Engineering and Technology, East Carolina University, Greenville, North Carolina, USA
| | - Sunghan Kim
- College of Engineering and Technology, East Carolina University, Greenville, North Carolina, USA
| | - Huyanting Huang
- Department of Computer and Information Technology, Purdue University, West Lafayette, Indiana, USA
| | - Baijain Yang
- Department of Computer and Information Technology, Purdue University, West Lafayette, Indiana, USA
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16
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Song Y, Yang X, Luo Y, Ouyang C, Yu Y, Ma Y, Li H, Lou J, Liu Y, Chen Y, Cao J, Mi W. Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study. CNS Neurosci Ther 2022; 29:158-167. [PMID: 36217732 PMCID: PMC9804041 DOI: 10.1111/cns.13991] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/16/2022] [Accepted: 09/23/2022] [Indexed: 02/06/2023] Open
Abstract
AIMS To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients. METHOD This was a retrospective study of perioperative medical data from patients undergoing non-cardiac and non-neurology surgery over 65 years old from January 2014 to August 2019. Forty-six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and precision. RESULTS In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765-0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%. CONCLUSIONS The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model.
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Affiliation(s)
- Yu‐xiang Song
- Department of AnesthesiologyThe First Medical Center of Chinese PLA General HospitalBeijingChina,Medical School of Chinese People's Liberation ArmyBeijingChina
| | - Xiao‐dong Yang
- Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
| | - Yun‐gen Luo
- Department of AnesthesiologyThe First Medical Center of Chinese PLA General HospitalBeijingChina,Medical School of Chinese People's Liberation ArmyBeijingChina
| | - Chun‐lei Ouyang
- Department of AnesthesiologyThe First Medical Center of Chinese PLA General HospitalBeijingChina
| | - Yao Yu
- Department of AnesthesiologyThe First Medical Center of Chinese PLA General HospitalBeijingChina
| | - Yu‐long Ma
- Department of AnesthesiologyThe First Medical Center of Chinese PLA General HospitalBeijingChina
| | - Hao Li
- Department of AnesthesiologyThe First Medical Center of Chinese PLA General HospitalBeijingChina
| | - Jing‐sheng Lou
- Department of AnesthesiologyThe First Medical Center of Chinese PLA General HospitalBeijingChina
| | - Yan‐hong Liu
- Department of AnesthesiologyThe First Medical Center of Chinese PLA General HospitalBeijingChina
| | - Yi‐qiang Chen
- Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
| | - Jiang‐bei Cao
- Department of AnesthesiologyThe First Medical Center of Chinese PLA General HospitalBeijingChina
| | - Wei‐dong Mi
- Department of AnesthesiologyThe First Medical Center of Chinese PLA General HospitalBeijingChina
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Mumtaz H, Saqib M, Ansar F, Zargar D, Hameed M, Hasan M, Muskan P. The future of Cardiothoracic surgery in Artificial intelligence. Ann Med Surg (Lond) 2022; 80:104251. [PMID: 36045824 PMCID: PMC9422274 DOI: 10.1016/j.amsu.2022.104251] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 12/23/2022] Open
Abstract
Humans' great and quick technological breakthroughs in the previous decade have undoubtedly influenced how surgical procedures are executed in the operating room. AI is becoming incredibly influential for surgical decision-making to help surgeons make better projections about the implications of surgical operations by considering different sources of data such as patient health conditions, disease natural history, patient values, and finance. Although the application of artificial intelligence in healthcare settings is rapidly increasing, its mainstream application in clinical practice remains limited. The use of machine learning algorithms in thoracic surgery is extensive, including different clinical stages. By leveraging techniques such as machine learning, computer vision, and robotics, AI may play a key role in diagnostic augmentation, operative management, pre-and post-surgical patient management, and upholding safety standards. AI, particularly in complex surgical procedures such as cardiothoracic surgery, may be a significant help to surgeons in executing more intricate surgeries with greater success, fewer complications, and ensuring patient safety, while also providing resources for robust research and better dissemination of knowledge. In this paper, we present an overview of AI applications in thoracic surgery and its related components, including contemporary projects and technology that use AI in cardiothoracic surgery and general care. We also discussed the future of AI and how high-tech operating rooms will use human-machine collaboration to improve performance and patient safety, as well as its future directions and limitations. It is vital for the surgeons to keep themselves acquainted with the latest technological advancement in AI order to grasp this technology and easily integrate it into clinical practice when it becomes accessible. This review is a great addition to literature, keeping practicing and aspiring surgeons up to date on the most recent advances in AI and cardiothoracic surgery. This literature review tells about the role of Artificial Intelligence in Cardiothoracic Surgery. Discussed the future of AI and how high-tech operating rooms will use human-machine collaboration to improve performance and patient safety, as well as its future directions and limitations. Vital for the surgeons to keep themselves acquainted with the latest technological advancement in AI order to grasp this technology and easily integrate it into clinical practice when it becomes accessible.
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18
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Xie Q, Wang XL, Pei JH, Wu YP, Guo Q, Su YJ, Yan H, Nan RL, Chen HX, Dou XM. Machine Learning-Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc 2022; 23:1655-1668.e6. [DOI: 10.1016/j.jamda.2022.06.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 05/22/2022] [Accepted: 06/18/2022] [Indexed: 10/16/2022]
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19
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Lapp L, Bouamrane MM, Roper M, Kavanagh K, Schraag S. Definition and Classification of Postoperative Complications After Cardiac Surgery: A Pilot Delphi Study (Preprint). JMIR Perioper Med 2022; 5:e39907. [PMID: 36222812 PMCID: PMC9607909 DOI: 10.2196/39907] [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: 05/27/2022] [Revised: 09/02/2022] [Accepted: 09/22/2022] [Indexed: 11/13/2022] Open
Abstract
Background Postoperative complications following cardiac surgery are common and represent a serious burden to health services and society. However, there is a lack of consensus among experts on what events should be considered as a “complication” and how to assess their severity. Objective This study aimed to consult domain experts to pilot the development of a definition and classification system for complications following cardiac surgery with the goal to allow the progression of standardized clinical processes and systems in cardiac surgery. Methods We conducted a Delphi study, which is a well-established method to reach expert consensus on complex topics. We sent 2 rounds of surveys to domain experts, including cardiac surgeons and anesthetists, to define and classify postoperative complications following cardiac surgery. The responses to open-ended questions were analyzed using a thematic analysis framework. Results In total, 71 and 37 experts’ opinions were included in the analysis in Round 1 and Round 2 of the study, respectively. Cardiac anesthetists and cardiac critical care specialists took part in the study. Cardiac surgeons did not participate. Experts agreed that a classification for postoperative complications for cardiac surgery is useful, and consensus was reached for the generic definition of a postoperative complication in cardiac surgery. Consensus was also reached on classification of complications according to the following 4 levels: “Mild,” “Moderate,” “Severe,” and “Death.” Consensus was also reached on definitions for “Mild” and “Severe” categories of complications. Conclusions Domain experts agreed on the definition and classification of complications in cardiac surgery for “Mild” and “Severe” complications. The standardization of complication identification, recording, and reporting in cardiac surgery should help the development of quality benchmarks, clinical audit, care quality assessment, resource planning, risk management, communication, and research.
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Affiliation(s)
- Linda Lapp
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Matt-Mouley Bouamrane
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Marc Roper
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Kimberley Kavanagh
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, United Kingdom
| | - Stefan Schraag
- Department of Anaesthesia and Perioperative Medicine, Golden Jubilee National Hospital, Clydebank, United Kingdom
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Kim JH, Hua M, Whittington RA, Lee J, Liu C, Ta CN, Marcantonio ER, Goldberg TE, Weng C. A machine learning approach to identifying delirium from electronic health records. JAMIA Open 2022; 5:ooac042. [PMID: 35663114 PMCID: PMC9152701 DOI: 10.1093/jamiaopen/ooac042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 05/01/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
The identification of delirium in electronic health records (EHRs) remains difficult due to inadequate assessment or under-documentation. The purpose of this research is to present a classification model that identifies delirium using retrospective EHR data. Delirium was confirmed with the Confusion Assessment Method for the Intensive Care Unit. Age, sex, Elixhauser comorbidity index, drug exposures, and diagnoses were used as features. The model was developed based on the Columbia University Irving Medical Center EHR data and further validated with the Medical Information Mart for Intensive Care III dataset. Seventy-six patients from Surgical/Cardiothoracic ICU were included in the model. The logistic regression model achieved the best performance in identifying delirium; mean AUC of 0.874 ± 0.033. The mean positive predictive value of the logistic regression model was 0.80. The model promises to identify delirium cases with EHR data, thereby enable a sustainable infrastructure to build a retrospective cohort of delirium. Delirium is a commonly observed complication in hospitalized patients, especially with intensive care. While signs and symptoms of delirium could be observed and well managed during the hospital stay, less is known about the long-term complication of delirium after discharge. In order to monitor the long-term sequelae of delirium, the correct identification of delirium patients is crucial. Currently, the retrospective identification of delirium patients is limited due to the under-coding of delirium diagnosis in electronic health records. We proposed a simple machine-learning model to retrospectively identify patients who experienced delirium during their intensive care unit stay. The model could be used to identify missed delirium cases and the establishment of a delirium cohort for long-term monitoring and surveillance.
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Affiliation(s)
- Jae Hyun Kim
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - May Hua
- Department of Anesthesiology, Columbia University Medical Center, New York Presbyterian Hospital, New York, New York, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, USA
| | - Robert A Whittington
- Department of Anesthesiology, Columbia University Medical Center, New York Presbyterian Hospital, New York, New York, USA
| | - Junghwan Lee
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Casey N Ta
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Edward R Marcantonio
- Harvard Medical School, Boston, Massachusetts, USA
- Division of General Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Terry E Goldberg
- Department of Anesthesiology, Columbia University Medical Center, New York Presbyterian Hospital, New York, New York, USA
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
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21
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Hong L, Shen X, Shi Q, Song X, Chen L, Chen W, Chen S, Xue Y, Zhang C, Zhou J. Association Between Hypernatremia and Delirium After Cardiac Surgery: A Nested Case-Control Study. Front Cardiovasc Med 2022; 9:828015. [PMID: 35355967 PMCID: PMC8959150 DOI: 10.3389/fcvm.2022.828015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background The association between hypernatremia and delirium after cardiac surgery has rarely been investigated. This study aimed to determine whether hypernatremia increases the risk of delirium after exposure. Materials and Methods From April 2016 to June 2021, 7,831 consecutive patients receiving cardiac surgery were screened for potential enrollment. The primary outcome was postoperative delirium (POD). For the respective case of delirium, 10 controls were matched according to the index date within the nested case-control design. Hypernatremia exposure was defined as serum sodium > 145 mmol/L within 7 days before the index date. A generalized estimation equation was performed to assess excess risks for POD associated with hypernatremia, adjusted for demographics and clinical variables. Results About 7,277 patients were included in the final analyses. About 669 (9.2%) patients with POD were assigned to the case group, and 6,690 controls were identified from the whole population. About 66.5% of the cases and 36.3% of the controls had hypernatremia exposure. After being adjusted to certain well-recognized confounding factors, hypernatremia showed a significant correlation with increased risk of delirium after cardiac surgery (adjusted OR, 1.73; 95% CI, 1.41~2.12). An e-value analysis suggested the robustness to unmeasured confounding. Conclusions Hypernatremia was associated with an increased risk of delirium after cardiac surgery. This finding could have implications for risk stratification, early detection, and management of delirium in patients receiving cardiac surgery.
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Affiliation(s)
- Liang Hong
- Department of Intensive Care Unit, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiao Shen
- Department of Intensive Care Unit, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Qiankun Shi
- Department of Intensive Care Unit, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiaochun Song
- Department of Intensive Care Unit, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Lihai Chen
- Department of Anesthesia, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wenxiu Chen
- Department of Intensive Care Unit, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Shangyu Chen
- Department of Intensive Care Unit, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yingyin Xue
- Department of Intensive Care Unit, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Cui Zhang
- Department of Intensive Care Unit, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Cui Zhang
| | - Jifang Zhou
- School of International Business, China Pharmaceutical University, Nanjing, China
- Jifang Zhou
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22
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Fan Y, Dong J, Wu Y, Shen M, Zhu S, He X, Jiang S, Shao J, Song C. Development of machine learning models for mortality risk prediction after cardiac surgery. Cardiovasc Diagn Ther 2022; 12:12-23. [PMID: 35282663 PMCID: PMC8898685 DOI: 10.21037/cdt-21-648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 12/28/2021] [Indexed: 02/12/2024]
Abstract
BACKGROUND We developed machine learning models that combine preoperative and intraoperative risk factors to predict mortality after cardiac surgery. METHODS Machine learning involving random forest, neural network, support vector machine, and gradient boosting machine was developed and compared with the risk scores of EuroSCORE I and II, Society of Thoracic Surgeons (STS), as well as a logistic regression model. Clinical data were collected from patients undergoing adult cardiac surgery at the First Medical Centre of Chinese PLA General Hospital between December 2008 and December 2017. The primary outcome was post-operative mortality. Model performance was estimated using several metrics, including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). The visualization algorithm was implemented using Shapley's additive explanations. RESULTS A total of 5,443 patients were enrolled during the study period. The mean EuroSCORE II score was 3.7%, and the actual in-hospital mortality rate was 2.7%. For predicting operative mortality after cardiac surgery, the AUC scores were 0.87, 0.79, 0.81, and 0.82 for random forest, neural network, support vector machine, and gradient boosting machine, compared with 0.70, 0.73, 0.71, and 0.74 for EuroSCORE I and II, STS, and logistic regression model. Shapley's additive explanations analysis of random forest yielded the top-20 predictors and individual-level explanations for each prediction. CONCLUSIONS Machine learning models based on available clinical data may be superior to clinical scoring tools in predicting postoperative mortality in patients following cardiac surgery. Explanatory models show the potential to provide personalized risk profiles for individuals by accounting for the contribution of influencing factors. Additional prospective multicenter studies are warranted to confirm the clinical benefit of these machine learning-driven models.
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Affiliation(s)
- Yunlong Fan
- Medical School of Chinese PLA, Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Junfeng Dong
- Department of Organ Transplantation, Changzhen Hospital, Navy Medical University, Shanghai, China
| | - Yuanbin Wu
- Medical School of Chinese PLA, Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Ming Shen
- Department of Cardiology, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Siming Zhu
- Medical School of Chinese PLA, Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Xiaoyi He
- Medical School of Chinese PLA, Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Shengli Jiang
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | | | - Chao Song
- Medical School of Chinese PLA, Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China
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23
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Rellum SR, Schuurmans J, van der Ven WH, Eberl S, Driessen AHG, Vlaar APJ, Veelo DP. Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review. J Thorac Dis 2022; 13:6976-6993. [PMID: 35070381 PMCID: PMC8743411 DOI: 10.21037/jtd-21-765] [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: 05/01/2021] [Accepted: 08/27/2021] [Indexed: 12/27/2022]
Abstract
Background Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients. Methods We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted. Results Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results. Conclusions ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.
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Affiliation(s)
- Santino R Rellum
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands.,Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Jaap Schuurmans
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands.,Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Ward H van der Ven
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands.,Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Susanne Eberl
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Antoine H G Driessen
- Department of Cardiothoracic Surgery, Heart Center, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Alexander P J Vlaar
- Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Denise P Veelo
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
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24
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Postoperative delirium prediction using machine learning models and preoperative electronic health record data. BMC Anesthesiol 2022; 22:8. [PMID: 34979919 PMCID: PMC8722098 DOI: 10.1186/s12871-021-01543-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 12/09/2021] [Indexed: 12/14/2022] Open
Abstract
Background Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression. Methods This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models (“clinician-guided” and “ML hybrid”), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded. Results POD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816–0.863] and for XGBoost was 0.851 [95% CI 0.827–0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734–0.793], p < 0.001) and ML hybrid (AUC-ROC 0.824 [0.800–0.849], p < 0.001) regression models and AWOL-S (AUC-ROC 0.762 [95% CI 0.713–0.812], p < 0.001). Neural Net, XGBoost, and ML hybrid models demonstrated excellent calibration, while calibration of the clinician-guided and AWOL-S models was moderate; they tended to overestimate delirium risk in those already at highest risk. Conclusion Using pragmatically collected EHR data, two ML models predicted POD in a broad perioperative population with high discrimination. Optimal application of the models would provide automated, real-time delirium risk stratification to improve perioperative management of surgical patients at risk for POD. Supplementary Information The online version contains supplementary material available at 10.1186/s12871-021-01543-y.
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25
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Jiang Z, Cai Y, Zhang X, Lv Y, Zhang M, Li S, Lin G, Bao Z, Liu S, Gu W. Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning. Front Aging Neurosci 2021; 13:715517. [PMID: 34867266 PMCID: PMC8633536 DOI: 10.3389/fnagi.2021.715517] [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: 05/27/2021] [Accepted: 10/25/2021] [Indexed: 01/14/2023] Open
Abstract
Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR. Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).
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Affiliation(s)
- Zhaoshun Jiang
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
| | - Yuxi Cai
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
| | - Xixue Zhang
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
| | - Yating Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Mengting Zhang
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
| | - Shihong Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Zhijun Bao
- Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China.,Department of Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Research Center on Aging and Medicine, Fudan University, Shanghai, China
| | - Songbin Liu
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
| | - Weidong Gu
- Department of Anesthesiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.,Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China
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26
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Von Gerich H, Moen H, Block LJ, Chu CH, DeForest H, Hobensack M, Michalowski M, Mitchell J, Nibber R, Olalia MA, Pruinelli L, Ronquillo CE, Topaz M, Peltonen LM. Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. Int J Nurs Stud 2021; 127:104153. [DOI: 10.1016/j.ijnurstu.2021.104153] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 11/23/2021] [Accepted: 12/01/2021] [Indexed: 12/20/2022]
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27
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Son CS, Kang WS, Lee JH, Moon KJ. Machine Learning to Identify Psychomotor Behaviors of Delirium for Patients in Long-Term Care Facility. IEEE J Biomed Health Inform 2021; 26:1802-1814. [PMID: 34596563 DOI: 10.1109/jbhi.2021.3116967] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This study aimed to develop accurate and explainable machine learning models for three psychomotor behaviors of delirium for hospitalized adult patients. A prospective pilot study was conducted with 33 participants admitted to a long-term care facility between August 10 and 25, 2020. During the pilot study, we collected 560 cases that included 33 clinical variables and the survey items from the short confusion assessment method (S-CAM), and developed a mobile-based application. Multiple machine learning algorithms, including four rule-mining algorithms (C4.5, CBA, MCAR, and LEM2) and four other statistical learning algorithms (LR, ANNs, SVMs with three kernel functions, and random forest), were validated by paired Wilcoxon signed-rank tests on both macro-averaged F1 and weighted average F1-measures during the 10-times stratified 2-fold cross-validation. The LEM2 algorithm achieved the best prediction performance (macro-averaged F1-measure of 49.35%; weighted average F1-measure of 96.55%), correctly identifying adult patients at delirium risk. In the pairwise comparison between predictive powers observed from independent models, the LEM2 model showed a medium or large effect size between 0.4925 and 0.8766 when compared with LR, ANN, SVM with RBF, and MCAR models. We have confirmed that acute consciousness in S-CAM assessment is closely associated with different predictors for screening three psychomotor behaviors of delirium: 1) education level, dementia type or its level, sleep disorder, dehydration, and infection in mixed-type delirium; 2) gender, education level, dementia type, dehydration, bedsores, and foley catheter in hyperactive delirium; and 3) pain, sleep disorder, and haloperidol use in hypoactive delirium.
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28
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Li Q, Zhao Y, Chen Y, Yue J, Xiong Y. Developing a machine learning model to identify delirium risk in geriatric internal medicine inpatients. Eur Geriatr Med 2021; 13:173-183. [PMID: 34553310 DOI: 10.1007/s41999-021-00562-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 09/06/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE To develop a machine learning model that predicts delirium risk in geriatric internal medicine inpatients. METHODS A prospective cohort study of internal medicine wards in a tertiary care hospital in China. Blinded observers assessed delirium using the Confusion Assessment Method (CAM). The data set was randomly divided into a training set (70%) and a test set (30%). The model was trained on the training set using the decision tree and the five-fold cross-validation, and then the model performance was evaluated on the test set. Under-sampling was used to address the class imbalance. The discriminatory power of the model was measured by the area under the receiver operating characteristic curve (AUC) and F1 score. The data set comprised 740 patients from March 2016 to January 2017. RESULTS The training set included 518 patients; the median (IQR) age was 84 (79-87) years; 364 (70.3%) were men; 71 (13.7%) with delirium. The test set included 222 patients; the median (IQR) age was 84.5 (79-87) years; 163 (73.4%) were men; 30 (13.5%) with delirium. In total, the data set included 740 hospital admissions with a median (IQR) age of 84 (79-87) years, 527 (71.2%) were men, and 101 (13.6%) with delirium. From 32 potential predictors, we included five variables in the predictive model: depression, cognitive impairment, types of drugs, nutritional status, and activity of daily life (ADL). The mean AUC on the training set was 0.967, the AUC and F1 score on the test set was 0.950 and 0.810, respectively. The model achieved 93.3% sensitivity, 94.3% specificity, 71.8% positive predictive value, 98.9% negative predictive value, and 94.1% accuracy on the test set. CONCLUSION This machine learning model may allow more precise targeting of delirium prevention and could support clinical decision making in geriatric internal medicine wards.
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Affiliation(s)
- Qinzheng Li
- School of Mechanical Engineering, Sichuan University, Chengdu, China
| | - Yanli Zhao
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Chen
- Department of Applied Mechanics, Sichuan University, Chengdu, China.,Medical Big Data Center, Sichuan University, Chengdu, China
| | - Jirong Yue
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
| | - Yan Xiong
- School of Mechanical Engineering, Sichuan University, Chengdu, China.
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29
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Abstract
PURPOSE OF REVIEW Artificial intelligence is the ability for machines to perform intelligent tasks. Artificial intelligence is already penetrating many aspects of medicine including cardiac surgery. Here, we offer a platform introduction to artificial intelligence for cardiac surgeons to understand the implementations of this transformative tool. RECENT FINDINGS Artificial intelligence has contributed greatly to the automation of cardiac imaging, including echocardiography, cardiac computed tomography, cardiac MRI and most recently, in radiomics. There are also several artificial intelligence based clinical prediction tools that predict complex outcomes after cardiac surgery. Waveform analysis, specifically, automated electrocardiogram analysis, has seen significant strides with promise in wearables and remote monitoring. Experimentally, artificial intelligence has also entered the operating room in the form of augmented reality and automated robotic surgery. SUMMARY Artificial intelligence has many potential exciting applications in cardiac surgery. It can streamline physician workload and help make medicine more human again by placing the physician back at the bedside. Here, we offer cardiac surgeons an introduction to this transformative tool so that they may actively participate in creating clinically relevant implementations to improve our practice.
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30
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Chua SJ, Wrigley S, Hair C, Sahathevan R. Prediction of delirium using data mining: A systematic review. J Clin Neurosci 2021; 91:288-298. [PMID: 34373042 DOI: 10.1016/j.jocn.2021.07.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 06/18/2021] [Accepted: 07/18/2021] [Indexed: 12/19/2022]
Abstract
Delirium remains a significant cause of morbidity, mortality and economic burden to society. "Big data" refers to data of significantly large volume, obtained from a variety of resources, which is created and processed at high velocity. We conducted a systematic review and meta-analysis exploring whether big data could predict the incidence of delirium of patients in the inpatient setting. Medline, Embase, the Cochrane Library, Web of Science, CINAHL, clinicaltrials.gov, who.int and IEEE Xplore were searched using MeSH terms "big data", "data mining", "delirium" and "confusion" up to 30th September 2019. We included both randomised and observational studies. The primary outcome of interest was development of delirium and the secondary outcomes of interest were type of statistical methods used, variables included in the mining algorithms and clinically important outcomes such as mortality and length of hospital stay. The quality of studies was graded using the CHARMs checklist. Six retrospective single centre observational studies were included (n = 178,091), of which 17, 574 participants developed delirium. Studies were of generally of low to moderate quality. The most commonly studied method was random forest, followed by support vector machine and artificial neural networks. The model with best performance for delirium prediction was random forest, with area under receiver operating curve (AUROC) ranging from 0.78 to 0.91. Sensitivity ranged from 0.59 to 0.81 and specificity ranged from 0.73 to 0.92. Our systematic review suggests that machine-learning techniques can be utilised to predict delirium.
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Affiliation(s)
- S J Chua
- Ballarat Health Services, Ballarat, Australia.
| | - S Wrigley
- Ballarat Health Services, Ballarat, Australia
| | - C Hair
- Ballarat Health Services, Ballarat, Australia
| | - R Sahathevan
- Ballarat Health Services, Ballarat, Australia; School of Medicine, Deakin University, Geelong, Australia; Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
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31
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Binkheder S, Aldekhyyel R, Almulhem J. Health informatics publication trends in Saudi Arabia: a bibliometric analysis over the last twenty-four years. J Med Libr Assoc 2021; 109:219-239. [PMID: 34285665 PMCID: PMC8270356 DOI: 10.5195/jmla.2021.1072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE Understanding health informatics (HI) publication trends in Saudi Arabia may serve as a framework for future research efforts and contribute toward meeting national "e-Health" goals. The authors' intention was to understand the state of the HI field in Saudi Arabia by exploring publication trends and their alignment with national goals. METHODS A scoping review was performed to identify HI publications from Saudi Arabia in PubMed, Embase, and Web of Science. We analyzed publication trends based on topics, keywords, and how they align with the Ministry of Health's (MOH's) "digital health journey" framework. RESULTS The total number of publications included was 242. We found 1 (0.4%) publication in 1995-1999, 11 (4.5%) publications in 2000-2009, and 230 (95.0%) publications in 2010-2019. We categorized publications into 3 main HI fields and 4 subfields: 73.1% (n=177) of publications were in clinical informatics (85.1%, n=151 medical informatics; 5.6%, n=10 pharmacy informatics; 6.8%, n=12 nursing informatics; 2.3%, n=4 dental informatics); 22.3% (n=54) were in consumer health informatics; and 4.5% (n=11) were in public health informatics. The most common keyword was "medical informatics" (21.5%, n=52). MOH framework-based analysis showed that most publications were categorized as "digitally enabled care" and "digital health foundations." CONCLUSIONS The years of 2000-2009 may be seen as an infancy stage of the HI field in Saudi Arabia. Exploring how the Saudi Arabian MOH's e-Health initiatives may influence research is valuable for advancing the field. Data exchange and interoperability, artificial intelligence, and intelligent health enterprises might be future research directions in Saudi Arabia.
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Affiliation(s)
- Samar Binkheder
- , Assistant Professor of Biomedical and Health Informatics, College of Medicine, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Raniah Aldekhyyel
- , Assistant Professor of Biomedical and Health Informatics, College of Medicine, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Jwaher Almulhem
- , Assistant Professor of Biomedical and Health Informatics, College of Medicine, King Saud University, Riyadh, Kingdom of Saudi Arabia
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Coombes CE, Coombes KR, Fareed N. A novel model to label delirium in an intensive care unit from clinician actions. BMC Med Inform Decis Mak 2021; 21:97. [PMID: 33750375 PMCID: PMC7941123 DOI: 10.1186/s12911-021-01461-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 03/02/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance. METHODS EHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forests; Naïve Bayes; and Support Vector Machines) were fit and ranked by Area Under the Curve (AUC) scores. We compared our best model with two models previously proposed in the literature for goodness of fit, precision, and through biological validation. RESULTS Our best performing model with threshold reclassification for predicting delirium was based on a multiple logistic regression using the 31 clinical actions (AUC 0.83). Our model out performed other proposed models by biological validation on clinically meaningful, delirium-associated outcomes. CONCLUSIONS Hurdles in identifying accurate labels in large-scale datasets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models.
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Affiliation(s)
- Caitlin E Coombes
- College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Kevin R Coombes
- Department of Biomedical Informatics, The Ohio State University College of Medicine, 460 Medical Center Dr., 512 Institute of Behavioral Medicine Research, Columbus, OH, 43210, USA
| | - Naleef Fareed
- Department of Biomedical Informatics, The Ohio State University College of Medicine, 460 Medical Center Dr., 512 Institute of Behavioral Medicine Research, Columbus, OH, 43210, USA.
- Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
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