Clinical decision rules in emergency care

Lim, Swee Han

doi:10.11622/smedj.2018042

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…Clinical decision rules (CDRs) like the PHD score can help with critical decision-making regarding patient health [ 37 – 39 ], but they have limitations. CDRs are designed to simplify complexity, and they should be externally validated in diverse settings to ensure applicability [ 37 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time

Hsu,

Kao,

Hsu

et al. 2023

BMC Endocr Disord

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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.

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Section: Discussionmentioning

confidence: 99%

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time

Hsu,

Kao,

Hsu

et al. 2023

BMC Endocr Disord

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“…Clinical decision rules (CDRs), like the PHD score, are developed to help physicians in critical decision making for the management and disposition of patients [11][12][13]. The CDRs are psychologically appealing because they are developed to create order out of disorder.…”

Section: Discussionmentioning

confidence: 99%

“…However, the CDRs strive to structure and simplify the complexity [11]. Although CDRs have the above strengths, some limitations need to be clari ed before implementing them in clinical practice [11,12]. First, the CDRs should be externally validated in diverse settings before clinical use.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence prediction of adverse outcomes in emergency department patients with hyperglycemic crises in real-time

Hsu¹,

Kao²,

Hsu³

et al. 2020

Preprint

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Background Hyperglycemic crises are associated with high morbidity and mortality. Previous studies proposed methods for predicting adverse outcome in hyperglycemic crises, artificial intelligence (AI) has however never been tried. We implemented an AI prediction model integrated with hospital information system (HIS) to clarify this issue. Methods We included 3,715 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. Patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from their electronic medical records were collected, and multilayer perceptron (MLP) was used to construct an AI prediction model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. Comparisons of the performance among random forest, logistic regression, support vector machine (SVM), K-nearest neighbor (KNN), Light Gradient Boosting Machine (LightGBM), and MLP algorithms were also done. Results Using the MLP model, the areas under the curves (AUCs) were 0.808 for sepsis or septic shock, 0.688 for ICU admission, and 0.770 for all-cause mortality. MLP had the best performance in predicting sepsis or septic shock and all-cause mortality, compared with logistic regression, SVM, KNN, and LightGBM. Furthermore, we integrated the AI prediction model with the HIS to assist physicians for decision making in real-time. Conclusions A real-time AI prediction model is a promising method to assist physicians in predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies on the impact on clinical practice and patient outcome are warranted.

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“…Traditionally, CDRs have been implemented primarily in the emergency room, cardiology and paediatrics. Within these clinical settings, in the majority of cases, they serve as a screening tool for safely ruling out a disease outcome prior to imaging (Carmelli et al, 2018;Ebell, 2010;Le Marechal et al, 2013;Lim, 2018;Monahan et al, 2017;Phillips, 2010a;Pugh, 2016;Reed, 2006), next to other clinical tasks of CDRs: diagnosis, prognosis, treatment and prevention (Ebell, 2010).…”

Section: Decision Rules Within Primary Carementioning

confidence: 99%

Clinical decision rules in primary care: necessary investments for sustainable healthcare

Heerink

Oudega

Hopstaken

et al. 2023

Prim Health Care Res Dev

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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.

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Clinical decision rules in emergency care

Cited by 3 publications

References 8 publications

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time

Artificial intelligence prediction of adverse outcomes in emergency department patients with hyperglycemic crises in real-time

Clinical decision rules in primary care: necessary investments for sustainable healthcare

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