Machine learning model from a Spanish cohort for prediction of SARS-COV-2 mortality risk and critical patients

Abstract

Patients affected by SARS-COV-2 have collapsed healthcare systems around the world. Consequently, different challenges arise regarding the prediction of hospital needs, optimization of resources, diagnostic triage tools and patient evolution, as well as tools that allow us to analyze which are the factors that determine the severity of patients. Currently, it is widely accepted that one of the problems since the pandemic appeared was to detect (i) who patients were about to need Intensive Care Unit (ICU) and (ii) who ones were about not overcome the disease. These critical patients collapsed Hospitals to the point that many surgeries around the world had to be cancelled. Therefore, the aim of this paper is to provide a Machine Learning (ML) model that helps us to prevent when a patient is about to be critical. Although we are in the era of data, regarding the SARS-COV-2 patients, there are currently few tools and solutions that help medical professionals to predict the evolution of patients in order to improve their treatment and the needs of critical resources at hospitals. Moreover, most of these tools have been created from small populations and/or Chinese populations, which carries a high risk of bias. In this paper, we present a model, based on ML techniques, based on 5378 Spanish patients’ data from which a quality cohort of 1201 was extracted to train the model. Our model is capable of predicting the probability of death of patients with SARS-COV-2 based on age, sex and comorbidities of the patient. It also allows what-if analysis, with the inclusion of comorbidities that the patient may develop during the SARS-COV-2 infection. For the training of the model, we have followed an agnostic approach. We explored all the active comorbidities during the SARS-COV-2 infection of the patients with the objective that the model weights the effect of each comorbidity on the patient’s evolution according to the data available. The model has been validated by using stratified cross-validation with k = 5 to prevent class imbalance. We obtained robust results, presenting a high hit rate, with 84.16% accuracy, 83.33% sensitivity, and an Area Under the Curve (AUC) of 0.871. The main advantage of our model, in addition to its high success rate, is that it can be used with medical records in order to predict their diagnosis, allowing the critical population to be identified in advance. Furthermore, it uses the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD 9-CM) standard. In this sense, we should also emphasize that those hospitals using other encodings can add an intermediate layer business to business (B2B) with the aim of making transformations to the same international format.

Establishment of Difficult Caudal Epidural Blockade Prediction Model

“…We took advantage of new statistical methodologies that incorporated machine and deep learning into prediction models to develop an objective risk scoring model, which is widely used in predicting the evolution of the disease in patients or the risk of mortality in patients [ 20 , 21 ]. We hypothesized that palpation and other objective factors can predict difficult CEB well.…”

Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning

“…A previous model developed from a large VHA cohort of 7,635,064 (both infected and non-infected) with an observation window from May 21 to November 2, 2020 predicted 30-day mortality with a validation AUC of 0.836 (95% CI, 82.0%-85.3%) [ 9 ]. In addition, a recent study of 1,201 patients who contracted SARS-CoV-2 in Spain in 2020 predicted 30-day mortality with an AUC of 0.872 [ 25 ]. Commonly identified covariates in prior studies, advanced age and higher medical co-morbidity indices, were associated with higher risks for the adverse outcomes of interest in our models [ 9 – 11 ].…”

Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers

“…Existing AI-based applications for diagnosis using comorbidities have been shown to perform successfully [8] , [10] , [14] , [39] , [40] . Although similar results [8] , [10] , [39] have been obtained, our main contribution is that we extend the scope by identifying patients who will require ICU admission. Regarding [40] we improved the presented results in terms of AUC (11.12%), sensitivity (3.05%) and accuracy (3.27%).…”

“…Analysis of pre-existing comorbidities has been proven to be valuable in predicting COVID-19 outcomes [8] , [39] , [40] , diagnosis [10] , even in survival analysis on censored data [41] . However, their applicability might be hampered due to the fact that: i) comorbidities prevalence varies along countries and regions [42] due to socio-political factors, health equity issues, and environmental threats [43] , [44] ; ii) there are discrepancies and variability in data collection systems as well as in the version of international classification of diseases (ICD) used across different institutions and countries, hindering meaningful comparison or introducing research bias [42] , [45] by producing skewed results as a consequence of the relationship between some comorbidities and death rates [46] ; iii) inclusion of pre-existing comorbidities analysis is required [37] ; iv) Achieving a high level of digital transformation maturity is necessary [42] , [47] to ensure models robustness and usability.…”

“…The proposed model builds on the results of an earlier study [39] , which allows us to develop an AI-based predictive model aimed at the identification of those patients at higher risk of dying in the event of COVID-19 infection based on demographic factors and comorbidities. In this study, we go further by analysing the impact of biochemical and haematology parameters, obtained from routine blood tests, on the model's performance.…”