1
|
Hsu CC, Kao Y, Hsu CC, Chen CJ, Hsu SL, Liu TL, Lin HJ, Wang JJ, Liu CF, Huang CC. Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time. BMC Endocr Disord 2023; 23:234. [PMID: 37872536 PMCID: PMC10594858 DOI: 10.1186/s12902-023-01437-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 08/22/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Hyperglycemic crises are associated with high morbidity and mortality. Previous studies have proposed methods to predict adverse outcomes of patients in hyperglycemic crises; however, artificial intelligence (AI) has never been used to predict adverse outcomes. We implemented an AI model integrated with the hospital information system (HIS) to clarify whether AI could predict adverse outcomes. METHODS We included 2,666 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. The patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from the electronic medical records were collected. The performance of the multilayer perceptron (MLP), logistic regression, random forest, Light Gradient Boosting Machine (LightGBM), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms was compared. We selected the best algorithm to construct an AI model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. The outcomes between the non-AI and AI groups were compared after implementing the HIS and predicting the hyperglycemic crisis death (PHD) score. RESULTS The MLP had the best performance in predicting the three adverse outcomes, compared with the random forest, logistic regression, SVM, KNN, and LightGBM models. The areas under the curves (AUCs) using the MLP model were 0.852 for sepsis or septic shock, 0.743 for ICU admission, and 0.796 for all-cause mortality. Furthermore, we integrated the AI predictive model with the HIS to assist decision making in real time. No significant differences in ICU admission or all-cause mortality were detected between the non-AI and AI groups. The AI model performed better than the PHD score for predicting all-cause mortality (AUC 0.796 vs. 0.693). CONCLUSIONS A real-time AI predictive model is a promising method for predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies recruiting more patients are warranted.
Collapse
Affiliation(s)
- Chin-Chuan Hsu
- Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan
| | - Yuan Kao
- Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan
- Graduate Institute of Medical Sciences, College of Health Sciences, Chang Jung Christian University, Tainan, Taiwan
| | - Chien-Chin Hsu
- Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen university, Kaohsiung, Taiwan
| | - Chia-Jung Chen
- Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Shu-Lien Hsu
- Department of Nursing, Chi Mei Medical Center, Tainan, Taiwan
| | - Tzu-Lan Liu
- Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Hung-Jung Lin
- Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen university, Kaohsiung, Taiwan
- Department of Emergency Medicine, Taipei Medical University, Taipei, Taiwan
| | - Jhi-Joung Wang
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
- Department of Anesthesiology, National Defense Medical Center, Taipei, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan.
| | - Chien-Cheng Huang
- Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan.
- School of Medicine, College of Medicine, National Sun Yat-sen university, Kaohsiung, Taiwan.
- Department of Emergency Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
| |
Collapse
|
2
|
Heerink JS, Oudega R, Hopstaken R, Koffijberg H, Kusters R. Clinical decision rules in primary care: necessary investments for sustainable healthcare. Prim Health Care Res Dev 2023; 24:e34. [PMID: 37129072 PMCID: PMC10156469 DOI: 10.1017/s146342362300021x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023] Open
Abstract
Clinical judgement in primary care is more often decisive than in the hospital. Clinical decision rules (CDRs) can help general practitioners facilitating the work-through of differentials that follows an initial suspicion, resulting in a concrete 'course of action': a 'rule-out' without further testing, a need for further testing, or a specific treatment. However, in daily primary care, the use of CDRs is limited to only a few isolated rules. In this paper, we aimed to provide insight into the laborious path required to implement a viable CDR. At the same time, we noted that the limited use of CDRs in primary care cannot be explained by implementation barriers alone. Through the case study of the Oudega rule for the exclusion of deep vein thrombosis, we concluded that primary care CDRs come out best if they are tailor-made, taking into consideration the specific context of primary health care. Current CDRs should be evaluated frequently, and future decision rules should anticipate the latest developments such as the use of point-of-care (POC) tests. Hence, such new powerful diagnostic CDRs could improve and expand the possibilities for patient-oriented primary care.
Collapse
Affiliation(s)
- Jorn S Heerink
- Department of Clinical Chemistry and Haematology, Jeroen Bosch Hospital, 's-Hertogenbosch, the Netherlands
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands
| | - Ruud Oudega
- Department of Clinical Chemistry and Haematology, Jeroen Bosch Hospital, 's-Hertogenbosch, the Netherlands
- Julius Centre for Health Sciences and General Practice, University Medical CentreUtrecht, the Netherlands
| | | | - Hendrik Koffijberg
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands
| | - Ron Kusters
- Department of Clinical Chemistry and Haematology, Jeroen Bosch Hospital, 's-Hertogenbosch, the Netherlands
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands
| |
Collapse
|