Diagnostic utility of the Covichem score in predicting COVID-19 disease

Unveiling COVID-19 Secrets: Harnessing Cytokines as Powerful Biomarkers for Diagnosis and Predicting Severity

Introduction

In coronavirus disease (COVID-19), inflammation takes center stage, with a cascade of cytokines released, contributing to both inflammation and lung damage. The objective of this study is to identify biomarkers for diagnosing and predicting the severity of COVID-19.

Materials and Methods

Cytokine levels were determined in the serum from venous blood samples collected from 100 patients with COVID-19 and 50 healthy controls. COVID-19 patients classified based on the Modified Early Warning (MEWS) score. Cytokine concentrations were determined with a multiplex ELISA kit (Bio-Plex Pro™ Human Cytokine Screening Panel).

Results

The concentrations of all analyzed cytokines were elevated in the serum of COVID-19 patients relative to the control group, but no significant differences were observed in interleukin-9 (IL-9) and IL-12 p70 levels. In addition, the concentrations of IL-1α, IL-1β, IL-1ra, IL-2Rα, IL-6, IL-12 p40, IL-18, and tumor necrosis factor alpha (TNFα) were significantly higher in symptomatic patients with accompanying pneumonia without respiratory failure (stage 2) than in asymptomatic/mildly symptomatic patients (stage 1).

Conclusion

The study revealed that IL-1ra, IL-2Rα, IL-6, IL-8, IL-12 p40, IL-16, and IL-18 levels serve as potential diagnostic biomarkers in COVID-19 patients. Furthermore, elevated IL-1α levels proved to be valuable in assessing the severity of COVID-19.

Outcome prediction in hospitalized COVID-19 patients: Comparison of the performance of five severity scores

Background

The aim of our study was to externally validate the predictive capability of five developed coronavirus disease 2019 (COVID-19)-specific prognostic tools, including the COVID-19 Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC), Shang COVID severity score, COVID-intubation risk score-neutrophil/lymphocyte ratio (IRS-NLR), inflammation-based score, and ventilation in COVID estimator (VICE) score.

Methods

The medical records of all patients hospitalized for a laboratory-confirmed COVID-19 diagnosis between May 2021 and June 2021 were retrospectively analyzed. Data were extracted within the first 24 h of admission, and five different scores were calculated. The primary and secondary outcomes were 30-day mortality and mechanical ventilation, respectively.

Results

A total of 285 patients were enrolled in our cohort. Sixty-five patients (22.8%) were intubated with ventilator support, and the 30-day mortality rate was 8.8%. The Shang COVID severity score had the highest numerical area under the receiver operator characteristic (AUC-ROC) (AUC 0.836) curve to predict 30-day mortality, followed by the SEIMC score (AUC 0.807) and VICE score (AUC 0.804). For intubation, both the VICE and COVID-IRS-NLR scores had the highest AUC (AUC 0.82) compared to the inflammation-based score (AUC 0.69). The 30-day mortality increased steadily according to higher Shang COVID severity scores and SEIMC scores. The intubation rate exceeded 50% in the patients stratified by higher VICE scores and COVID-IRS-NLR score quintiles.

Conclusion

The discriminative performances of the SEIMC score and Shang COVID severity score are good for predicting the 30-day mortality of hospitalized COVID-19 patients. The COVID-IRS-NLR and VICE showed good performance for predicting invasive mechanical ventilation (IMV).

Detecting Patient Deterioration Early Using Continuous Heart rate and Respiratory rate Measurements in Hospitalized COVID-19 Patients

BACKGROUND

Presenting symptoms of COVID-19 patients are unusual compared with many other illnesses. Blood pressure, heart rate, and respiratory rate may stay within acceptable ranges as the disease progresses. Consequently, intermittent monitoring does not detect deterioration as it is happening. We investigated whether continuously monitoring heart rate and respiratory rate enables earlier detection of deterioration compared with intermittent monitoring, or introduces any risks.

METHODS

When available, patients admitted to a COVID-19 ward received a wireless wearable sensor which continuously measured heart rate and respiratory rate. Two intensive care unit (ICU) physicians independently assessed sensor data, indicating when an intervention might be necessary (alarms). A third ICU physician independently extracted clinical events from the electronic medical record (EMR events). The primary outcome was the number of true alarms. Secondary outcomes included the time difference between true alarms and EMR events, interrater agreement for the alarms, and severity of EMR events that were not detected.

RESULTS

In clinical practice, 48 (EMR) events occurred. None of the 4 ICU admissions were detected with the sensor. Of the 62 sensor events, 13 were true alarms (also EMR events). Of these, two were related to rapid response team calls. The true alarms were detected 39 min (SD = 113) before EMR events, on average. Interrater agreement was 10%. Severity of the 38 non-detected events was similar to the severity of 10 detected events.

CONCLUSION

Continuously monitoring heart rate and respiratory rate does not reliably detect deterioration in COVID-19 patients when assessed by ICU physicians.