Vital Signs Prediction for COVID-19 Patients in ICU

Effects of love glove application on vital signs for COVID-19 patients in the intensive care unit

AIM

To evaluate the effects of love glove application on vital signs for COVID-19 patients in the intensive care unit.

DESIGN

A single-group pretest-posttest quasi-experimental design was used. TREND Statement Checklist was followed during the present study.

METHODS

The study was conducted on 30 intubated/extubated adult patients. The gloves were filled with warm water and air to prevent pressure injuries. Then they were tied together and applied to both hands of the patient for 30 min. The patient's vital signs were recorded before and after the application. A Wilcoxon signed-rank test was performed.

RESULTS

It was determined that respiratory rate, systolic blood pressure, diastolic blood pressure and oxygen saturation were significantly affected after the application of the love glove. The application of love gloves is a cheap and non-pharmacological method with no side effects.

PATIENT OR PUBLIC CONTRIBUTION

Patients were involved in the design and conduct of this study.

Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan–Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548–49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000–3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141–2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895–2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.