fbpx

Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study

PeerJ. 2024 Jun 12;12:e17428. doi: 10.7717/peerj.17428. eCollection 2024.

ABSTRACT

BACKGROUND: Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 758 million people and resulted in 6.8 million deaths worldwide. Additionally, the progression of the disease may vary from individual to individual, that is, it is essential to identify the clinical parameters that indicate a good prognosis for the patient. Machine learning (ML) algorithms have been used for analyzing complex medical data and identifying prognostic indicators. However, there is still an urgent need for a model to elucidate the predictors related to patient outcomes. Therefore, this research aimed to verify, through ML, the variables involved in the discharge of patients admitted to the ICU due to COVID-19.

METHODS: In this study, 126 variables were collected with information on demography, hospital length stay and outcome, chronic diseases and tumors, comorbidities and risk factors, complications and adverse events, health care, and vital indicators of patients admitted to an ICU in southern Brazil. These variables were filtered and then selected by a ML algorithm known as decision trees to identify the optimal set of variables for predicting patient discharge using logistic regression. Finally, a confusion matrix was performed to evaluate the model’s performance for the selected variables.

RESULTS: Of the 532 patients evaluated, 180 were discharged: female (16.92%), with a central venous catheter (23.68%), with a bladder catheter (26.13%), and with an average of 8.46- and 23.65-days using bladder catheter and submitted to mechanical ventilation, respectively. In addition, the chances of discharge increase by 14% for each additional day in the hospital, by 136% for female patients, 716% when there is no bladder catheter, and 737% when no central venous catheter is used. However, the chances of discharge decrease by 3% for each additional year of age and by 9% for each other day of mechanical ventilation. The performance of the training data presented a balanced accuracy of 0.81, sensitivity of 0.74, specificity of 0.88, and the kappa value was 0.64. The test performance had a balanced accuracy of 0.85, sensitivity 0.75, specificity 0.95, and kappa value of 0.73. The McNemar test found that there were no significant differences in the error rates in the training and test data, suggesting good classification. This work showed that female, the absence of a central venous catheter and bladder catheter, shorter mechanical ventilation, and bladder catheter duration were associated with a greater chance of hospital discharge. These results may help develop measures that lead to a good prognosis for the patient.

PMID:38881861 | PMC:PMC11179634 | DOI:10.7717/peerj.17428

Free CME credits

Both our subscription plans include Free CME/CPD AMA PRA Category 1 credits.

Digital Certificate PDF

On course completion, you will receive a full-sized presentation quality digital certificate.

medtigo Simulation

A dynamic medical simulation platform designed to train healthcare professionals and students to effectively run code situations through an immersive hands-on experience in a live, interactive 3D environment.

medtigo Points

medtigo points is our unique point redemption system created to award users for interacting on our site. These points can be redeemed for special discounts on the medtigo marketplace as well as towards the membership cost itself.
 
  • Registration with medtigo = 10 points
  • 1 visit to medtigo’s website = 1 point
  • Interacting with medtigo posts (through comments/clinical cases etc.) = 5 points
  • Attempting a game = 1 point
  • Community Forum post/reply = 5 points

    *Redemption of points can occur only through the medtigo marketplace, courses, or simulation system. Money will not be credited to your bank account. 10 points = $1.

All Your Certificates in One Place

When you have your licenses, certificates and CMEs in one place, it's easier to track your career growth. You can easily share these with hospitals as well, using your medtigo app.

Our Certificate Courses

Up arrow