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A machine learning model on Real World Data for predicting progression to Acute Respiratory Distress Syndrome (ARDS)
Nicola Lazzarini1, Avgoustinos Filippoupolitis1, Pedro Manzione2
1Real World Analytics & AI, IQVIA, Cambridge, United Kingdom.
Insights
This study developed a machine learning model to predict severe COVID-19 outcomes, specifically Acute Respiratory Distress Syndrome (ARDS). The model utilizes patient claims history and performs comparably to clinical experts, aiding early screening for high-risk patients.
Area of Science:
- Computational biology
- Medical informatics
- Epidemiology
Background:
- Identifying patients at high risk for severe COVID-19 is critical for effective management and survival.
- Severe COVID-19 is often characterized by the development of Acute Respiratory Distress Syndrome (ARDS).
Purpose of the Study:
- To develop and evaluate a machine learning model for predicting severe COVID-19 outcomes (ARDS) using historical patient data.
- To identify key risk factors contributing to COVID-19 disease progression.
Main Methods:
- A cohort of 289,351 COVID-19 patients was analyzed using administrative claims data.
- Historical diagnoses (817) were collected to predict ARDS within four months of infection.
- A Gradient Boosting Decision Tree model was developed and validated.
Main Results:
- The Gradient Boosting Decision Tree model achieved an AUC of 0.695 and an AUPRC of 0.0730.
- The model demonstrated a 40% performance improvement over a baseline classifier.
- The model's predictive performance was comparable to or exceeded that of a panel of clinical experts.
Conclusions:
- A machine learning model utilizing patient claims history can effectively predict ARDS in COVID-19 patients.
- The identified risk factors are well-established in the literature and can aid early screening.
- The model offers a promising tool for optimizing care and improving outcomes for COVID-19 patients.
Introduction:
Identifying COVID-19 patients that are most likely to progress to a severe infection is crucial for optimizing care management and increasing the likelihood of survival. This study presents a machine learning model that predicts severe cases of COVID-19, defined as the presence of Acute Respiratory Distress Syndrome (ARDS) and highlights the different risk factors that play a significant role in disease progression.
Methods:
A cohort composed of 289,351 patients diagnosed with COVID-19 in April 2020 was created using US administrative claims data from Oct 2015 to Jul 2020. For each patient, information about 817 diagnoses, were collected from the medical history ahead of COVID-19 infection. The primary outcome of the study was the presence of ARDS in the 4 months following COVID-19 infection. The study cohort was randomly split into training set used for model development, test set for model evaluation and validation set for real-world performance estimation.
Results:
We analyzed three machine learning classifiers to predict the presence of ARDS. Among the algorithms considered, a Gradient Boosting Decision Tree had the highest performance with an AUC of 0.695 (95% CI, 0.679-0.709) and an AUPRC of 0.0730 (95% CI, 0.0676 - 0.0823), showing a 40% performance increase in performance against a baseline classifier. A panel of five clinicians was also used to compare the predictive ability of the model to that of clinical experts. The comparison indicated that our model is on par or outperforms predictions made by the clinicians, both in terms of precision and recall.
Conclusion:
This study presents a machine learning model that uses patient claims history to predict ARDS. The risk factors used by the model to perform its predictions have been extensively linked to the severity of the COVID-19 in the specialized literature. The most contributing diagnosis can be easily retrieved in the patient clinical history and can be used for an early screening of infected patients. Overall, the proposed model could be a promising tool to deploy in a healthcare setting to facilitate and optimize the care of COVID-19 patients.
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