Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED

Goto, Tadahiro; Camargo, Carlos A.; Faridi, Mohammad Kamal; Yun, Brian J.; Hasegawa, Kohei

doi:10.1016/j.ajem.2018.06.062

Cited by 121 publications

(88 citation statements)

References 20 publications

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“…The primary outcome was critical care according to the previous studies. 5 , 13 , 19 , 24 Critical care—as an indicator for high-severity medical need—was defined as either direct admission to an ICU or in-hospital death. 5 , 13 , 19 , 24 Timely ED management for patients who require admission to the ICU has been consistently related to improvement in patient outcomes.…”

Section: Methodsmentioning

confidence: 99%

“…Machine learning approaches have attracted attention because of their superior ability to predict patient outcomes compared with traditional approaches in various settings and disease conditions (eg, sepsis and unplanned transfers to the intensive care unit [ICU]). 9 , 10 , 11 , 12 , 13 , 14 , 15 The advantages of machine learning approaches include their ability to process complex nonlinear relationships between predictors and yield more stable predictions. 16 For example, a recent 2-center retrospective study using one of the machine learning approaches reported an improved triage classification in a general ED population.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

et al. 2019

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Key Points Question Do machine learning approaches improve the ability to predict clinical outcomes and disposition of children at emergency department triage? Findings In this prognostic study of a nationally representative sample of 52 037 emergency department visits by children, machine learning–based triage models had better discrimination ability for clinical outcomes and disposition compared with the conventional triage approaches, with a higher sensitivity for the critical care outcome and higher specificity for the hospitalization outcome. Meaning Machine learning may improve the prediction ability of triage approaches and could be used to reduce undertriage of critically ill children and to improve resource allocation in emergency departments.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

et al. 2019

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“…19,33 However, in our study regarded, severe exacerbations were main target, whose definition included exacerbations requiring hospitalization or ED visits, accordant with the GOLD guideline, 1 rather than only focused on hospitalized exacerbations. Statistically, there were 3206 34 Also, in the Confronting COPD International Survey, 12.8% of patients reported at least one hospitalization and 13.8% required ED care due to COPD in the prior year. 35 Therefore, emergency department visits because of COPD exacerbations should not be ignored.…”

Section: Discussionmentioning

confidence: 99%

<p>A Nomogram for Predicting Severe Exacerbations in Stable COPD Patients</p>

Chen¹,

Wang²,

Hu³

et al. 2020

COPD

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Objective: To develop a practicable nomogram aimed at predicting the risk of severe exacerbations in COPD patients at three and five years. Methods: COPD patients with prospective follow-up data were extracted from Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) obtained from National Heart, Lung and Blood Institute (NHLBI) Biologic Specimen and Data Repository Information Coordinating Center. We comprehensively considered the demographic characteristics, clinical data and inflammation marker of disease severity. Cox proportional hazard regression was performed to identify the best combination of predictors on the basis of the smallest Akaike Information Criterion. A nomogram was developed and evaluated on discrimination, calibration, and clinical efficacy by the concordance index (C-index), calibration plot and decision curve analysis, respectively. Internal validation of the nomogram was assessed by the calibration plot with 1000 bootstrapped resamples. Results: Among 1711 COPD patients, 523 (30.6%) suffered from at least one severe exacerbation during follow-up. After stepwise regression analysis, six variables were determined including BMI, severe exacerbations in the prior year, comorbidity index, postbronchodilator FEV 1 % predicted, and white blood cells. Nomogram to estimate patients' likelihood of severe exacerbations at three and five years was established. The C-index of the nomogram was 0.74 (95%CI: 0.71-0.76), outperforming ADO, BODE and DOSE risk score. Besides, the calibration plot of three and five years showed great agreement between nomogram predicted possibility and actual risk. Decision curve analysis indicated that implementation of the nomogram in clinical practice would be beneficial and better than aforementioned risk scores. Conclusion: Our new nomogram was a useful tool to assess the probability of severe exacerbations at three and five years for COPD patients and could facilitate clinicians in stratifying patients and providing optimal therapies.

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“…Variable selection before modeling can help simplify the network structure, and guarantee the causal rationality. Nowdays, LASSO is widely applied as an effective tool for variable selection in BNs and other machine learning modeling [27,28].…”

Section: Discussionmentioning

confidence: 99%

Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies

Chen

Wang

et al. 2019

Preprint

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Background: This study aims to explore a novel machine-learning algorithm, Bayesian networks (BNs), to delineate the interrelationships between acute kidney injury (AKI) and its associated risk factors among patients with hematologic malignancies (HM), to assess the prediction ability of BNs model, and to infer the probability of AKI under different clinical settings. Methods: From 1 October 2014 to 30 September 2015, 2501 hospitalized patients diagnosed with HM in Zhongshan Hospital, Fudan University, Shanghai of China, were recruited in this retrospective study. Data on demographics, comorbidities, and baseline clinical lab records were exported from the electronic medical records. The group-LASSO (gLASSO) regression was performed to select the candidate predictors of AKI, which were further presented in BNs analysis for interrelationship exploration and disease prediction. Results: Among 2395 eligible patients, 370 episodes were diagnosed with AKI (15.4%). Patients with multiple myeloma (24.1%) and leukemia (23.9%) shared a higher AKI incidence than lymphoma (13.4%). Screened by the gLASSO regression, variables as age, gender, diabetes, hemopathy category, anti-tumor treatment, hemoglobin, serum creatinine (SCr), estimated glomerular filtration rate (eGFR), serum uric acid, serum sodium and potassium level were found with significant associations with AKI occurrence. BNs model revealed a complex interrelationship between these factors, among which there were direct connections between AKI and age, hemoglobin, eGFR, serum sodium and potassium. Moreover, HM category and anti-tumor treatment were indirectly linked to AKI through hemoglobin and eGFR; diabetes was connected with AKI through serum sodium level. BNs inferences indicated that when poor GFR, anemia and hyponatremia occurred simultaneously, the probability of AKI might reach 78.5%. The area under the receiver operating characteristic curve (AUC) of BNs model was 0.835, higher than that in logistic score model (0.763) and showed a robust performance in 10-fold cross-validation. Conclusion: AKI is common in HM hospitalized patients and is affected by multiple risk factors. The application of Bayesian networks and gLASSO regression can provide a novel strategy to explore the potential risk factors integrally and deep into their interrelationships, in accordance with the tide of e-alert and big-data for AKI early detection.

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Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED

Cited by 121 publications

References 20 publications

Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

<p>A Nomogram for Predicting Severe Exacerbations in Stable COPD Patients</p>

Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies

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