Machine Learning Classifier Models: The Future for Acute Respiratory Distress Syndrome Phenotyping?

McNicholas, Bairbre; Madden, Michael G.; Laffey, John G.

doi:10.1164/rccm.202006-2388ed

Cited by 9 publications

(8 citation statements)

References 13 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The general limitations of the use of ML in the field of medicine apply to the prediction models developed for ARDS as well. ML models may have greater explanatory power than the linear statistical methods, but when the models are used in situations that extrapolate beyond the scope of training data, they can give rise to “black box” models that do not support clinical comprehension [ 10 ]. Careful choice of appropriate ML algorithms and diligent and meticulous evaluation of models may help curb the problems [ 10 ].…”

Section: Reviewmentioning

confidence: 99%

“…ML models may have greater explanatory power than the linear statistical methods, but when the models are used in situations that extrapolate beyond the scope of training data, they can give rise to “black box” models that do not support clinical comprehension [ 10 ]. Careful choice of appropriate ML algorithms and diligent and meticulous evaluation of models may help curb the problems [ 10 ]. Clinicians also fear the possibility of “alarm fatigue” which creates an unsafe patient environment because a life-threatening event may be missed due to sensory overload created by alerts, especially in the ICU setting [ 40 ].…”

Section: Reviewmentioning

confidence: 99%

“…ML models can, however, give extreme results. On one hand, ML models can outperform traditional statistical models by discovering the complex non-linear relationship between patient and disease state, while, on the other hand, it can result in erratic predictions if used in situations beyond the stretch of training data [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?

Bhattarai

Gupta

Ali

et al. 2021

Cureus

View full text Add to dashboard Cite

Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease. Clinical methods alone can result in the under-recognition of this heterogeneous syndrome. The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms. Most of the work in the field of ML in ARDS has been in the development of prediction models that have comparable efficacies to that of traditional models. Prediction algorithms have been useful in identifying new variables that may be important to consider in the future, supplementing the unknown information with the help of available noninvasive parameters, as well as predicting mortality. Phenotype identification using an unsupervised ML algorithm has been pivotal in classifying the heterogeneous population into more homogenous classes. Big data generated from ventilators in the form of ventilator waveform analysis and images in the form of radiomics have also been leveraged for the identification of the syndrome and can be incorporated into a clinical decision support system. Although the results are promising, lack of generalizability, “black box” nature of algorithms and concerns about “alarm fatigue” should be addressed for more mainstream adoption of these models.

show abstract

Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?

Bhattarai

Gupta

Ali

et al. 2021

Cureus

View full text Add to dashboard Cite

show abstract

“…Acute respiratory distress syndrome (ARDS), clinically defined by the Berlin definition [1], is a heterogenous syndrome characterized by acute hypoxic respiratory failure that can be caused by a wide variety of insults [2]. ARDS is a clinically heterogenous syndrome with diverse populations, multiple etiologies, and a broad definition which might explain the absence of benefit in most randomized controlled trials (RCTs) assessing various treatment strategies [3]. Identifying specific ARDS phenotypes could lead to more favorable clinical trials and personalized ARDS management [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Longitudinal phenotypes in patients with acute respiratory distress syndrome: a multi-database study

Chen

Xie

et al. 2022

Crit Care

View full text Add to dashboard Cite

Background Previously identified phenotypes of acute respiratory distress syndrome (ARDS) have been limited by a disregard for temporal dynamics. We aimed to identify longitudinal phenotypes in ARDS to test the prognostic and predictive enrichment of longitudinal phenotypes, and to develop simplified models for phenotype identification. Methods We conducted a multi-database study based on the Chinese Database in Intensive Care (CDIC) and four ARDS randomized clinical trials (RCTs). We employed latent class analysis (LCA) to identify longitudinal phenotypes using 24-hourly data from the first four days of invasive ventilation. We used the Cox regression model to explore the association between time-varying respiratory parameters and 28-day mortality across phenotypes. Phenotypes were validated in four RCTs, and the heterogeneity of treatment effect (HTE) was investigated. We also constructed two multinomial logistical regression analyses to develop the probabilistic models. Findings A total of 605 ARDS patients in CDIC were enrolled. The three-class LCA model was identified and had the optimal fit, as follows: Class 1 (n = 400, 66.1% of the cohort) was the largest phenotype over all study days, and had fewer abnormal values, less organ dysfunction and the lowest 28-day mortality rate (30.5%). Class 2 (n = 102, 16.9% of the cohort) was characterized by pulmonary mechanical dysfunction and had the highest proportion of poorly aerated lung volume, the 28-day mortality rate was 47.1%. Class 3 (n = 103, 17% of the cohort) was correlated with extra-pulmonary dysfunction and had the highest 28-day mortality rate (56.3%). Time-varying mechanical power was more significantly associated with 28-day mortality in Class 2 patients compared to other phenotypes. Similar phenotypes were identified in four RCTs. A significant HTE between phenotypes and treatment strategies was observed in the ALVEOLI (high PEEP vs. low PEEP) and the FACTT trials (conservative vs. liberal fluid management). Two parsimonious probabilistic models were constructed to identify longitudinal phenotypes. Interpretation We identified and validated three novel longitudinal phenotypes for ARDS patients, with both prognostic and predictive enrichment. The phenotypes of ARDS can be accurately identified with simple classifier models, except for Class 3.

show abstract

“…While traditional statistical modelling has been used to combine multiple attributes to predict outcomes for patients with COVID-19 (10)(11), modern machine learning (ML) has added advantage in its capability to discover more complex interactions between correlated attributes, giving ML greater power to discriminate outcomes. Such methods have shown promise for predicting acute respiratory distress syndrome (ARDS) in diseases other than COVID-19 (12)(13). Multiple studies have demonstrated the potential utility of ML for predicting poor outcomes in COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Predicting Prognosis in COVID-19 Patients using Machine Learning and Readily Available Clinical Data

Campbell¹,

Wilson

Röder³

et al. 2021

Preprint

View full text Add to dashboard Cite

RationalePrognostic tools for aiding in the treatment of hospitalized COVID-19 patients could help improve outcome by identifying patients at higher or lower risk of severe disease.ObjectivesThe study objective was to develop models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission.MethodsHierarchical ensemble classification models were trained on a set of 229 patients hospitalized with COVID-19 to predict severe outcomes, including ICU admission, development of ARDS, or intubation, using easily attainable attributes including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation. Each test stratifies patients into groups of increasing risk. An additional cohort of 330 patients was used for blinded, independent validation. Shapley value analysis evaluated which attributes contributed most to the models’ predictions of risk.Measurements and Main ResultsTest performance was assessed using precision (positive predictive value) and recall (sensitivity) of the final risk groups. All test cut-offs were fixed prior to blinded validation. In both development and validation, the tests achieved precision in the lowest risk groups near or above 0.9. The proportion of patients with severe outcomes significantly increased across increasing risk groups. While the importance of attributes varied by test and patient, CRP, LDH, and D-dimer were often found to be important in the assignment of risk label.ConclusionsRisk of severe outcomes for patients hospitalized with COVID-19 infection can be assessed using machine learning-based models based on attributes routinely collected at hospital admission.

show abstract

Machine Learning Classifier Models: The Future for Acute Respiratory Distress Syndrome Phenotyping?

Cited by 9 publications

References 13 publications

Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?

Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?

Longitudinal phenotypes in patients with acute respiratory distress syndrome: a multi-database study

Predicting Prognosis in COVID-19 Patients using Machine Learning and Readily Available Clinical Data

Contact Info

Product

Resources

About