The role of a noninvasive index 'Spo2/ Fio2' in predicting mortality among patients with COVID-19 pneumonia

Prognostic significance of oxygen saturation/fraction of inspired oxygen 3 days after initiation of tocilizumab treatment in patients with COVID-19

BACKGROUND

Tocilizumab is effective in treating severe coronavirus disease 2019 (COVID-19). However, the specific time point it acts as a valid indicator of treatment efficacy remains unclear. This study aimed to determine the optimal day for assessing the prognostic value of the oxygen saturation/fraction of inspired oxygen (SpO2/FiO2) and ratio of respiratory rate-oxygenation (ROX) index in patients receiving tocilizumab for COVID-19.

METHODS

All patients admitted to our hospital from March 2020 to July 2021 who received tocilizumab for COVID-19 were retrospectively identified from hospital charts. Biodata, medical history, and laboratory tests results were obtained from medical records. The prognostic values of the SpO2/FiO2 and ROX index for predicting mortality were assessed. Cox proportional hazard and receiver operating characteristic curve models were utilized.

RESULTS

Of the 84 included patients, 34 died within 7 days after discharge. The patients who recovered had a mean age of 65 years and were younger than those who died. The multivariate analysis indicated that multiple comorbidities, cancer history, CURB-65 score, neutrophil and lymphocyte counts, neutrophil/lymphocyte ratio, and lactate dehydrogenase levels were higher in those who died compared with those who survived. No significant differences were found in dyspnea or total bilirubin levels between the two groups. The SpO2/FiO2 at 3 days post-tocilizumab initiation was strongly associated with survival.

CONCLUSIONS

The SpO2/FiO2 on day 3 post-tocilizumab initiation was a predictor of COVID-19 prognosis, which could be employed in determining clinical decisions. Prompt alternative interventions should be considered when this ratio does not improve.

The SpO2/FiO2 Ratio Combined with Prognostic Scores for Pneumonia and COVID-19 Increases Their Accuracy in Predicting Mortality of COVID-19 Patients

Background: Identifying high-risk COVID-19 patients is critical for emergency department decision-making. Our study's primary objective was to identify new independent predictors of mortality and their predictive utility in combination with traditional pneumonia risk assessment scores and new risk scores for COVID-19 developed during the pandemic. Methods: A retrospective study was performed in two Italian University Hospitals. A multivariable logistic model was used to locate independent parameters associated with mortality. Results: Age, PaO2/FiO2, and SpO2/FiO2 ratios were found to be independent parameters associated with mortality. This study found that the Pneumonia Severity Index (PSI) was superior to many of the risk scores developed during the pandemic, for example, the International Severe Acute Respiratory Infection Consortium Coronavirus Clinical Characterisation Consortium (ISARIC 4C) (AUC 0.845 vs. 0.687, p < 0.001), and to many of the risk scores already in use, for example, the National Early Warning Score 2 (NEWS2) (AUC 0.845 vs. 0.589, p < 0.001). Furthermore, our study found that the Pneumonia Severity Index had a similar performance to other risk scores, such as CRB-65 (AUC 0.845 vs. 0.823, p = 0.294). Combining the PaO2/FiO2 or SpO2/FiO2 ratios with the risk scores analyzed improved the prognostic accuracy. Conclusions: Adding the SpO2/FiO2 ratio to the traditional, validated, and already internationally known pre-pandemic prognostic scores seems to be a valid and rapid alternative to the need for developing new prognostic scores. Future research should focus on integrating these markers into existing pneumonia scores to improve their prognostic accuracy.

Predictors of Poor Outcomes in Chronic Obstructive Pulmonary Disease (COPD) Patients Admitted to the Emergency Department With COVID-19: A Prospective Study

Objectives Long-term consequences of COVID-19 in the post-pandemic era are still being investigated. Despite the growing data on COVID-19, there remains a lack of information regarding predictors of poor outcomes among chronic obstructive pulmonary disease (COPD) patients with COVID-19.  Methods A single-center prospective cohort study was conducted with a total of 172 adult COPD patients with COVID-19 pneumonia included. Univariable and multivariable analyses were conducted to define independent factors associated with ICU admission, need for mechanical ventilation, or all-cause mortality in 30 days following COVID-19 pneumonia. receiver operating characteristic (ROC) analyses evaluated the diagnostic performance of the independent predictors. Results Out of all the patients, 73 (42.4%) experienced poor outcomes. Lower forced expiratory volume in the first second (FEV1) (OR= 0.949, p= 0.004), higher radiological severity score (OR= 1.15, p= 0.004), and lower respiratory rate oxygenation (ROX) index (OR= 0.867, p<0.001) were independently associated with poor outcomes. ROX index was found a better predictor of poor outcome than oxygen saturation (SaO2)/fraction of inspired oxygen (FiO2) and partial pressure of oxygen (PaO2)/FiO2 ratio (area under the curve (AUC)=0.80 vs. 0.73, p=0.01; AUC= 0. 80 vs. 0.63 p=0.001). A significant decline in FEV1 values compared to baseline values was observed (55.9 ±12.9 vs. 62.1±10.0; p<0.001). Conclusion Lower baseline FEV1, higher COVID-19 radiological severity score, and lower ROX index are strongly associated with poor outcomes in COPD patients with COVID-19.

Comparison of Standard and Prone-Position Electrocardiograms in COVID-19 Patients With Pulmonary Complications: Correlations and Implications

BACKGROUND

Previous research highlighted variability in electrocardiogram (ECG) readings across patient positions, particularly in the context of COVID-19 patients with pulmonary complications requiring prone positioning as part of the treatment.

OBJECTIVE

This study aimed to elucidate the effects of prone positioning on ECG parameters and explore its association with the severity of COVID-19.

METHODS

A prospective cohort study involved 60 patients diagnosed with COVID-19 and presenting pulmonary complications. ECGs were recorded in both supine and prone positions, and analyzed for various parameters including heart rate, QRS axis, and QTc interval. Clinical severity was assessed using APACHE II scores and SpO2/FiO2 ratios.

RESULTS

Prone positioning led to an increase in heart rate (mean difference: 2.100, 95% CI: 0.471-3.729, p = 0.012), with minor shifts in the QRS axis. Heart rate and QRS axis demonstrated strong positive correlations between positions, with Pearson's correlation coefficients of 0.927 and 0.894, respectively. The study also found a significant association between prolonged QTc intervals in the prone position and elevated APACHE II scores, with a relative risk of 10.75 (95% CI: 1.82-63.64, p = 0.008).

CONCLUSIONS

The prone positioning caused minor yet significant changes in heart rate and QRS axis. The correlation of prolonged QTc intervals in the prone position with higher APACHE II scores suggests the prognostic relevance of prone ECG in COVID-19 patients. However, further research is needed to fully understand the clinical implications and mechanisms of these findings.

Can peripheral perfusion index predict disease mortality in COVID-19 patients in the emergency department

Background

Coronavirus disease 2019 (COVID-19) pneumonia remains a major public health concern. The prognostic efficacy of Peripheral Perfusion Index (PPI) has been researched in different pathologies such as trauma and sepsis. We hypothesized that PPI may serve as predictor of mortality in hospitalized patients with COVID-19 infection. This study aimed to describe the association between PPI at admission and COVID-19 mortality, a new mortality prediction tool.

Methods

This retrospective, observational study was conducted at a tertiary care center in Turkey. Adult patients diagnosed with COVID-19 infection were enrolled in this study between Februrary 15, 2022 to April 15, 2023. Patient demographic and clinical data including vital signs, laboratory parameters and PPI on admission were collected from an electronic database. PPI was measured using Philips G30E patient monitor system. The primary outcome was in-hospital mortality.

Results

In total, 200 patients with COVID-19 infection were included and 42 (21 %) in-hospital deaths were identified. For all parameters of study, age, oxygen saturation, respiratory rate, PPI, urea, creatinine, White Blood Cell (WBC), and High-sensitive cardiac Troponin T (hs-cTnT) values were significantly different between survivors vs non-survivors. hs-cTnT >21,25 pg/mL[HR:2.823 (95 % CI:1.211-6583)], PPI <2,15 [HR:2485 (95 % CI:1.194-5.175)], Oxygen saturation <87 % [HR:2258 (95 % CI:1.191-4.282)], and WBC >9680 x103/ml [HR:2.124 (95 % CI:1.083-4.163)] were independent predictors of in-hospital mortality.

Conclusions

This study identified the factors affecting in-hospital mortality among COVID-19 patients. Importantly, besides many parameter, PPI at admission was significantly associated with COVID-19 mortality and could be a feasible marker in emergency department to identify high risk patients.

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

Comparison of Prognostic Scores for Patients with COVID-19 Presenting with Dyspnea in the Emergency Department

BACKGROUND

Easy-to-use bedside risk assessment is crucial for patients with COVID-19 in the overcrowded emergency department (ED).

OBJECTIVE

The aim of this study was to explore the prognostic ability of ratio of percutaneous oxygen saturation (SpO2) to fraction of inspired oxygen (FiO2) (S/F); ratio of SpO2/FiO2 to respiratory rate (ROX); National Early Warning Score (NEWS); quick Sequential Organ Failure Assessment (qSOFA); and confusion, respiratory rate, blood pressure, and age ≥ 65 years (CRB-65) in patients with COVID-19 presenting with dyspnea to the ED.

METHODS

In this retrospective observational study, clinical and demographic details of patients with COVID-19 were obtained at ED admission. S/F, ROX, NEWS, CRB-65, and qSOFA scores were calculated at the time of ED arrival. Accuracy of these five indices to predict the need for invasive mechanical ventilation (IMV) within 48 h, intensive care unit (ICU) admission, and early (7-day) mortality were determined using receiver operating characteristic curves.

RESULTS

A total of 375 patients were included in this study. Fifty patients (13.3%) required IMV within 48 h and 58 patients (15.5%) were transferred to the ICU. Seven-day mortality was 6.7% and 28-day mortality was 18.1%. Among all five scores determined from patient data on ED admission, ROX, S/F, and NEWS presented greater discriminatory performance than CRB-65 and qSOFA in predicting IMV within 48 h, ICU admission, and early mortality.

CONCLUSIONS

Emergency physicians can effectively use S/F, ROX, and NEWS scores for rapid risk stratification of patients with COVID-19 infection. Moreover, from the perspective of simplicity and ease of calculation, we recommend the use of the S/F ratio.

A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data

The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the development of machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed by the NHSx, was introduced in 2021. We present an extension to the original cleaning pipeline for the clinical data of the database. It has been adjusted to correct additional systematic inconsistencies in the raw data such as patient sex, oxygen levels and date values. The most important changes will be discussed in this paper, whilst the code and further explanations are made publicly available on GitLab. The suggested cleaning will allow global users to work with more consistent data for the development of machine learning tools without being an expert. In addition, it highlights some of the challenges when working with clinical multi-center data and includes recommendations for similar future initiatives.

NOS3 (rs61722009) gene variants testing in prediction of COVID-19 pneumonia severity

BACKGROUND

There is a hypothesis that a sufficient level of endothelial nitric oxide synthase is important for reliable protection against COVID-19. Theoretical ideas about the NOS3 gene demonstrated that it can have an effect on links of the complications pathogenesis in COVID-associated pneumonia. We determined the goal - to investigate the association of the NOS3 gene variants with the occurrence of the disease and its clinical course in patients of the intensive care unit.

METHODS

The study group included 117 patients with a diagnosis of severe "viral COVID-19 pneumonia". Determination of NOS3 gene variants was performed using the PCR method. The probability of differences in the quantitative results were determined using ANOVA or Kruskal-Wallis test (depend of normality of studied parameters).

RESULTS

Our results indicate that the presence of the NOS3 gene 4a allele increase the risk of complicated COVID-19-associated pneumonia (χ2 = 18.84, p = 0.00001, OR = 3.53 (1.95-6.39)). It was showed, that carriers of the 4aa genotype had a significantly higher ratio of SpO2/FiO2 on the first and second days after hospitalization (p = 0.017 and p = 0.03, respectively). Patients with the 4aa genotype also had the acid-base imbalances, as showed by indicators of base deficiency and standard bicarbonate, which were beyond the reference values. Potassium and sodium concentrations on the first and second day after hospitalization were also significantly lower in patients with 4aa genotype (p = 0.009 and p = 0.048, respectively), for whom, in the same time, the concentrations of C-reactive protein and total bilirubin were significantly higher (p = 0.002 and p = 0.033, respectively).

CONCLUSIONS

Our results confirmed that the rs61722009 variant of the NOS3 gene is associated with an increased risk of severe СOVID-19-associated pneumonia and its adverse clinical course with potential progression of kidney and liver damage, and occurrence risk of systemic inflammatory response syndrome. These results require further research for the new metabolic strategy formation, in order to prevent the severe COVID-19 associated pneumonia and its complications.

Efficacy of blood urea nitrogen-to-albumin ratio for predicting prognostic outcomes of inpatients with COVID-19: A meta-analysis

BACKGROUND

The associations between blood urea nitrogen (BUN)/albumin ratio and poor prognosis in patients with diagnosis of coronavirus disease 2019 (COVID-19) remain to be clarified.

METHODS

A search based on 4 electronic databases (i.e., EMBASE, Google scholar, MEDLINE, and Cochrane Library) was performed on June 23, 2022. The association of BUN/Albumin ratio with poor prognostic outcomes, defined as patients with mortality/severe illnesses, were analyzed.

RESULTS

Results from analysis of 7 cohort studies (3600 individuals with COVID-19) published between 2020 and 2022 showed a higher BUN/Albumin ratio in the poor-prognosis group (Mean difference:  = 2.838, 95% confidence interval: 2.015-3.66, P < .001, I2 = 92.5%) than the good-prognosis group. Additional investigation into the connection between BUN/Albumin ratio as a binary variable (i.e., high or low) and the risk of poor outcome also supported an association between a higher BUN/Albumin ratio and a poor prognostic risk (odd ratio = 3.009, 95% confidence interval: 1.565-5.783, P = .001, I2 = 93.7%, 5 studies). Merged analysis of poor prognosis produced a sensitivity of 0.76, specificity of 0.72, and area under curve of 0.81.

CONCLUSION

This meta-analysis demonstrated a positive correlation between BUN/albumin ratio and poor outcome in patients with COVID-19. Additional large-scale prospective studies are needed to verify our findings.

Comorbid Asthma Increased the Risk for COVID-19 Mortality in Asia: A Meta-Analysis

We aimed to explore the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in Asia by using a meta-analysis. Electronic databases were systematically searched for eligible studies. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by using a random-effect model. An inconsistency index (I2) was utilized to assess the statistical heterogeneity. A total of 103 eligible studies with 198,078 COVID-19 patients were enrolled in the meta-analysis; our results demonstrated that comorbid asthma was significantly related to an increased risk for COVID-19 mortality in Asia (pooled OR = 1.42, 95% CI: 1.20−1.68; I2 = 70%, p < 0.01). Subgroup analyses by the proportion of males, setting, and sample sizes generated consistent findings. Meta-regression indicated that male proportion might be the possible sources of heterogeneity. A sensitivity analysis exhibited the reliability and stability of the overall results. Both Begg’s analysis (p = 0.835) and Egger’s analysis (p = 0.847) revealed that publication bias might not exist. In conclusion, COVID-19 patients with comorbid asthma might bear a higher risk for mortality in Asia, at least among non-elderly individuals.

Association of Prognostic Nutritional Index with Severity and Mortality of Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis

The associations of prognostic nutritional index (PNI) with disease severity and mortality in patients with coronavirus disease 2019 (COVID-19) remain unclear. Electronic databases, including MEDLINE, EMBASE, Google scholar, and Cochrane Library, were searched from inception to 10 May 2022. The associations of PNI with risk of mortality (primary outcome) and disease severity (secondary outcome) were investigated. Merged results from meta-analysis of 13 retrospective studies (4204 patients) published between 2020 and 2022 revealed a lower PNI among patients in the mortality group [mean difference (MD): −8.65, p < 0.001] or severity group (MD: −5.19, p < 0.001) compared to those in the non-mortality or non-severity groups. A per-point increase in PNI was associated with a reduced risk of mortality [odds ratio (OR) = 0.84, 95% CI: 0.79 to 0.9, p < 0.001, I2 = 67.3%, seven studies] and disease severity (OR = 0.84, 95% CI: 0.77 to 0.92, p < 0.001, I2 = 83%, five studies). The pooled diagnostic analysis of mortality yielded a sensitivity of 0.76, specificity of 0.71, and area under curve (AUC) of 0.79. Regarding the prediction of disease severity, the sensitivity, specificity, and AUC were 0.8, 0.61, and 0.65, respectively. In conclusion, this study demonstrated a negative association between PNI and prognosis of COVID-19. Further large-scale trials are warranted to support our findings.