Shock index as a predictor of mortality among the Covid-19 patients

Predicting mortality among patients with severe COVID-19 pneumonia based on admission vital sign indices: a retrospective cohort study

BACKGROUND

Coronavirus disease 2019 (COVID-19) pneumonia remains a major public health concern. Vital sign indices-shock index (SI; heart rate [HR]/systolic blood pressure [SBP]), shock index age (SIA, SI × age), MinPulse (MP; maximum HR-HR), Pulse max index (PMI; HR/maximum HR), and blood pressure-age index (BPAI; SBP/age)-are better predictors of mortality in patients with trauma compared to traditional vital signs. We hypothesized that these vital sign indices may serve as predictors of mortality in patients with severe COVID-19 pneumonia. This study aimed to describe the association between vital sign indices at admission and COVID-19 pneumonia mortality and to modify the CURB-65 with the best performing vital sign index to establish a new mortality prediction tool.

METHODS

This retrospective study was conducted at a tertiary care center in southern Thailand. Adult patients diagnosed with COVID-19 pneumonia were enrolled in this study between January 2020 and July 2022. Patient demographic and clinical data on admission were collected from an electronic database. The area under the receiver operating characteristic (AUC) curve analysis was used to assess the predictive power of the resultant multivariable logistic regression model after univariate and multivariate analyses of variables with identified associations with in-hospital mortality.

RESULTS

In total, 251 patients with COVID-19 pneumonia were enrolled in this study. The in-hospital mortality rate was 27.9%. Non-survivors had significantly higher HR, respiratory rate, SIA, and PMI and lower MP and BPAI than survivors. A cutoff value of 51 for SIA (AUC, 0.663; specificity, 80%) was used to predict mortality. When SIA was introduced as a modifier for the CURB-65 score, the new score (the CURSIA score) showed a higher AUC than the Acute Physiology and Chronic Health Evaluation II and CURB-65 scores (AUCs: 0.785, 0.780, and 0.774, respectively) without statistical significance.

CONCLUSIONS

SIA and CURSIA scores were significantly associated with COVID-19 pneumonia mortality. These scores may contribute to better patient triage than traditional vital signs.

Shock index in the emergency department as a predictor for mortality in COVID-19 patients: A systematic review and meta-analysis

Background

The shock index (SI) ratio serves as a straightforward predictor to identify patients who are either at risk of or experiencing shock. COVID-19 patients with shock face increased mortality risk and reduced chances of recovery. This review aims to determine the role of SI in the emergency department (ED) to predict COVID-19 patient outcomes.

Methods

The systematic search was conducted in PubMed, ProQuest, Scopus, and ScienceDirect on June 16, 2023. We included observational studies evaluating SI in ED and COVID-19 patient outcomes. Random-effect meta-analysis was done to generate odds ratios of SI as the predictor of intensive care unit (ICU) admission and mortality. The sensitivity and specificity of SI in predicting these outcomes were also pooled, and a summary receiver operating characteristics (sROC) curve was generated.

Results

A total of eight studies involving 4557 participants were included in the pooled analysis. High SI was found to be associated with an increased risk of ICU admission (OR 5.81 [95%CI: 1.18-28.58], p = 0.03). Regarding mortality, high SI was linked to higher rates of in-hospital (OR 7.45 [95%CI: 2.44-22.74], p = 0.0004), within 30-day (OR 7.34 [95%CI: 5.27-10.21], p < 0.00001), and overall (OR 7.52 [95%CI: 3.72-15.19], p < 0.00001) mortality. The sensitivity and specificity of SI for predicting ICU admission were 76.2% [95%CI: 54.6%-89.5%] and 64.3% [95%CI: 19.6%-93.0%], respectively. In terms of overall mortality, the sensitivity and specificity were 54.0% (95%CI: 34.3%-72.6%) and 85.9% (95%CI: 75.8%-92.3%), respectively, with only subtle changes for in-hospital and within 30-day mortality. Adjustment of SI cut-off to >0.7 yielded improved sensitivity (95%CI: 78.0% [59.7%-89.4%]) and specificity (95%CI: 76.8% [41.7%-93.9%]) in predicting overall mortality.

Conclusion

SI in emergency room may be a simple and useful triage instrument for predicting ICU admission and mortality in COVID-19 patients. Future well-conducted studies are still needed to corroborate the findings of this study.

Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis

Background

Multiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and to assess their clinical outcomes. Our secondary objective was to demonstrate the clinical applicability of this method by developing an interpretable model for phenotype assignment.

Methods

We analyzed data from 547 patients hospitalized with COVID-19 at a Canadian academic hospital. We processed the data by applying a factor analysis of mixed data (FAMD) and compared four clustering algorithms: k-means, partitioning around medoids (PAM), and divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 h of admission to train our algorithm. We conducted a survival analysis to compare the clinical outcomes across phenotypes. With the data split into training and validation sets (75/25 ratio), we developed a decision-tree-based model to facilitate the interpretation and assignment of the observed phenotypes.

Results

Agglomerative hierarchical clustering was the most robust algorithm. We identified three clinical phenotypes: 79 patients (14%) in Cluster 1, 275 patients (50%) in Cluster 2, and 203 (37%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Intensive care unit (ICU) admission and mechanical ventilation risks were the highest in Cluster 1. Using only two to four decision rules, the classification and regression tree (CART) phenotype assignment model achieved an AUC of 84% (81.5-86.5%, 95 CI) on the validation set.

Conclusions

We conducted a multidimensional phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. We also demonstrated the clinical usability of this approach, as phenotypes can be accurately assigned using a simple decision tree. Further research is still needed to properly incorporate these phenotypes in the management of patients with COVID-19.

Clinical prediction rule for bacterial arthritis: Chi-squared automatic interaction detector decision tree analysis model

Objectives:

Differences in demographic factors, symptoms, and laboratory data between bacterial and non-bacterial arthritis have not been defined. We aimed to identify predictors of bacterial arthritis, excluding synovial testing.

Methods:

This retrospective cross-sectional survey was performed at a university hospital. All patients included received arthrocentesis from January 1, 2010, to December 31, 2020. Clinical information was gathered from medical charts from the time of synovial fluid sample collection. Factors potentially predictive of bacterial arthritis were analyzed using the Student’s t-test or chi-squared test, and the chi-squared automatic interaction detector decision tree analysis. The resulting subgroups were divided into three groups according to the risk of bacterial arthritis: low-risk, intermediate-risk, or high-risk groups.

Results:

A total of 460 patients (male/female = 229/231; mean ± standard deviation age, 70.26 ± 17.66 years) were included, of whom 68 patients (14.8%) had bacterial arthritis. The chi-squared automatic interaction detector decision tree analysis revealed that patients with C-reactive protein > 21.09 mg/dL (incidence of septic arthritis: 48.7%) and C-reactive protein ⩽ 21.09 mg/dL plus 27.70 < platelet count ⩽ 30.70 × 10⁴/μL (incidence: 36.1%) were high-risk groups.

Conclusions:

Our results emphasize that patients categorized as high risk of bacterial arthritis, and appropriate treatment could be initiated as soon as possible.

The hypoxia-age-shock index at triage to predict the outcomes of Covid-19 patients

STUDY OBJECTIVE

The coronavirus disease 2019 (COVID-19) outbreak has caused a severe burden on medical professionals, as the rapid disposition of patients is important. Therefore, we aimed to develop a new clinical assessment tool based on the shock index (SI) and age-shock index (ASI). We proposed the hypoxia-age-shock index (HASI) and determined the usability of triage for COVID-19 infected patients in the first scene.

METHODS

The predictive power for three indexes on mortality, intensive care unit (ICU) admission, and endotracheal intubation rate was evaluated using the receiver operating curve (ROC). We used DeLong's method for comparing the ROCs.

RESULTS

The area under the curve (AUC) for ROC on mortality for SI, ASI, and HASI were 0.546, 0.771, and 0.773, respectively. The AUC on ICU admission mortality for SI, ASI, and HASI were 0.581, 0.700, and 0.743, respectively. The AUC for intubation for SI, ASI, and HASI were 0.592, 0.708, and 0.757, respectively. The AUC differences between HASI and SI showed statistically significant (P = 0.001) results on mortality, ICU admission, and intubation. Additionally, statistically significant results were found for the AUC difference between the HASI and ASI on ICU admission and intubation (P = 0.001 and P = 0.004, respectively).

CONCLUSION

HASI can provide a better prediction compared to ASI on ICU admission and endotracheal intubation. HASI was more sensitive in mortality, ICU admission, and intubation prediction than the ASI.

Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees

Background and objective

A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis.

Material and method

This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease.

Results

Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients.

Conclusions

This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.

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.

Shock Index Is a Validated Prediction Tool for the Short-Term Survival of Advanced Cancer Patients Presenting to the Emergency Department

Advanced cancer patients who are not expected to survive past the short term can benefit from early initiation of palliative care in the emergency department (ED). This discussion, however, requires accurate prognostication of their short-term survival. We previously found in our retrospective study that shock index (SI) is an ideal risk stratification tool in predicting the 60-day mortality risk of advanced cancer patients presenting to the ED. This study is a follow-up prospective validation study conducted from January 2019 to April 2021. A total of 410 advanced cancer patients who presented to the ED of a medical centre and could be followed-up feasibly were recruited. Univariate and multivariable logistic regression analyses were performed with receiver operator calibrating (ROC) curve analysis. Non-survivors had significantly lower body temperatures, higher pulse rates, higher respiratory rates, lower blood pressures, and higher SI. Each 0.1 increment of SI increased the odds of 60-day mortality by 1.591. Area under ROC curve was 0.7819. At optimal cut-off of 0.94, SI had 66.10% accuracy. These results were similar to our previous study, thus validating the use of SI in predicting the 60-day mortality of advanced cancer patients presenting to the ED. Identified patients may be offered palliative care.

Prognostic Performance of Shock Index, Diastolic Shock Index, Age Shock Index, and Modified Shock Index in COVID-19 Pneumonia

OBJECTIVE

We aimed to compare the prognostic accuracy of shock indexes in terms of mortality in patients hospitalized with coronavirus disease 2019 (COVID-19) pneumonia.

METHODS

Hospitalized patients whose COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) test results were positive, had thoracic computed tomography (CT) scan performed, and had typical thoracic CT findings for COVID-19 were included in the study.

RESULTS

Eight hundred one patients were included in the study. Chronic obstructive pulmonary disease, congestive heart failure, chronic neurological diseases, chronic renal failure, and a history of malignancy were found to be chronic diseases that were significantly associated with mortality in patients with COVID-19 pneumonia. White blood cell, neutrophil, lymphocyte, C reactive protein, creatinine, sodium, aspartate aminotransferase, alanine aminotransferase, total bilirubin, high sensitive troponin, d-dimer, hemoglobin, and platelet had a statistically significant relationship with in-hospital mortality in patients with COVID-19 pneumonia. The area under the curve (AUC) values of shock index (SI), age shock index (aSI), diastolic shock index (dSI), and modified shock index (mSI) calculated to predict mortality were 0.772, 0.745, 0.737, 0.755, and Youden Index J (YJI) values were 0.523, 0.396, 0.436, 0.452, respectively.

CONCLUSIONS

The results of this study show that SI, dSI, mSI, and aSI are effective in predicting in-hospital mortality.

What is the impact and efficacy of routine immunological, biochemical and hematological biomarkers as predictors of COVID-19 mortality?

It remains important to investigate the changing and impact of routine blood values (RBVs) in order to predict mortality and follow an appropriate treatment in COVID-19 patients. In the study, the importance of RBVs in the mortality of patients with COVID-19 was investigated. The changes in the biochemical, hematological, and immunological parameters of patients who recovered (n = 4364) and died (n = 233) from COVID-19 over time and their relationship with the mortality of the disease were evaluated retrospectively. Odds ratios of the parameters affecting one-month mortality were calculated by running multiple-logistic-regression analysis. The cut off values and diagnostic efficiencies of the parameters that posed a risk for mortality were obtained via receiver operating curve analysis. It was determined that the C-reactive protein (CRP), D-dimer, procalcitonin, erythrocyte-sedimentation-rate (ESR), troponin values were at abnormal levels until death occurred in the patients who died. In addition, the procalcitonin levels were consistently high in patients who died. The patients who died generally had a sustained increase in their leukocyte and neutrophil levels and biochemical variables, and an ongoing decrease in lymphopenia and eosinopenia levels. Although significant changes were observed in liver function tests, cardiac troponin, hemogram values, kidney function tests and parameters related to inflammation in deceased patients, high ESR, international-normalized-ratio (INR), prothrombin-time (PT), CRP, D-dimer, ferritin and red-cell-distribution width (RDW) values, respectively, were the most effective predictive mortality risk biomarkers of COVID-19. In addition, neutrophilia, leukocytosis, thrombocytopenia, erythrocytopenia were other risk predictors of mortality. Indicators was found in this study can be successfully used to predict mortality from COVID-19.

Shock Index in COVID Era

The health care burden and risks to health care workers imposed by novel coronavirus disease 2019 (COVID-19) mandated the need for a simple, noninvasive, objective, and parsimonious risk stratification system predicting the level of care, need for definitive airway, and titration of the ongoing patient care. Shock index (SI = heart rate/systolic blood pressure) has been evaluated in emergency triage, sepsis, and trauma settings including different age group of patients. The ever accumulating girth of evidences demonstrated a superior predictive value of SI over other hemodynamic parameters. Inclusion of respiratory and/or neurological parameters and adjustment of the cutoffs appropriate to patient age increase the predictability in the trauma and sepsis scenario. Being reproducible, dynamic, and simple, SI can be a valuable patient risk stratification tool in this ongoing era of COVID-19 pandemic.

Usefulness of the neutrophil-to-lymphocyte ratio in predicting the severity of COVID-19 patients: a retrospective cohort study

BACKGROUND

Quick and accurate identification of critically ill patients ensures appropriate and correct use of medical resources. In situations that threaten public health, like pandemics, rapid and effective methods are needed for early disease detection among critically ill patients.

OBJECTIVE

To determine the relationship between the neutrophil-to-lymphocyte ratio (NLR) of coronavirus disease-19 (COVID-19) patients upon admission to the emergency department (ED) and these patients' prognosis.

DESIGN AND SETTING

Retrospective cohort study among COVID-19 patients in the ED of a tertiary-level hospital.

METHODS

Data on patients' age, gender, vital signs, chronic diseases, laboratory tests and clinical outcomes were collected from electronic medical records. Receiver operating characteristic (ROC) curve analysis was performed. The area under the curve (AUC) was used to assess the accuracy of NLR for predicting in-hospital mortality risk and intensive care unit (ICU) requirement. The Youden J index (YJI) was used to determine optimal threshold values.

RESULTS

1,175 patients were included. Their median age was 63 years (IQR, 48-75). With an NLR cutoff value of 5.14, the sensitivity, specificity, PPV, AUC and YJI for ICU requirement were calculated as 77.87%, 74.08%, 92.4%, 0.811 and 0.5194, respectively. With the same cutoff value, the sensitivity, specificity, AUC and YJI for in-hospital mortality were 77.27%, 75.82%, 0.815 and 0.5309, respectively. In addition, advanced age, leukocytosis, anemia and lymphopenia were found to be associated with poor prognosis.

CONCLUSION

The NLR, which is a widely available simple parameter, can provide rapid insights regarding early recognition of critical illness and prognosis among COVID-19 patients.

Comparison of 4 Different Threshold Values of Shock Index in Predicting Mortality of COVID-19 Patients

OBJECTIVE

The object of this study was to examine the accuracy in prehospital shock index (SI) for predicting intensive care unit (ICU) requirement and 30-d mortality among from coronavirus disease 2019 (COVID-19) patients transported to the hospital by ambulance.

METHODS

All consecutive patients who were the age ≥18 y, transported to the emergency department (ED) by ambulance with a suspected or confirmed COVID-19 in the prehospital frame were included in the study. Four different cutoff points were compared (0.7, 0.8, 0.9, and 1.0) to examine the predictive performance of both the mortality and ICU requirement of the SI. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) was used to evaluate each cut-off value discriminatory for predicting 30-d mortality and ICU admission.

RESULTS

The total of 364 patients was included in this study. The median age in the study population was 69 y (range, 55-80 y), of which 196 were men and 168 were women. AUC values for 30-d mortality outcome were calculated as 0.672, 0.674, 0.755, and 0.626, respectively, for threshold values of 0.7, 0.8, 0.9 and 1.0. ICU admission was more likely for the patients with prehospital SI > 0.9. Similarly, the mortality rate was higher in patients with prehospital SI > 0.9.

CONCLUSIONS

Early triage of COVID-19 patients will ensure efficient use of health-care resources. The SI could be a helpful, fast, and powerful tool for predicting mortality status and ICU requirements of adult COVID-19 patients. It was concluded that the most useful threshold value for the shock index in predicting the prognosis of COVID-19 patients is 0.9.

Comparison of C-reactive protein and C-reactive protein-to-albumin ratio in predicting mortality among geriatric coronavirus disease 2019 patients

OBJECTIVE

The aim of this study was to evaluate and compare C-reactive protein and C-reactive protein-to-albumin ratio performances in predicting mortality of geriatric patients who visited the emergency department.

METHODS

The data of patients with COVID-19 and aged 65 years and above, who visited emergency department during the study period, were retrospectively analyzed. The data were obtained from an electronic-based hospital information system. The area under the receiver operating characteristic curve and the area under the curve were used to assess each cutoff value discriminatory for predicting mortality.

RESULTS

The mean age of the population included in this study was 76 (71-82) years, while 52.7% were males. The sensitivity, specificity, and area under the curve values for C-reactive protein in terms of mortality were calculated as 71.01, 52.34, and 0.635%, respectively, while the sensitivity, specificity, and area under the curve values for C-reactive protein-to-albumin ratio were calculated as 75.74, 47.66, and 0.645%, respectively (p<0.001). In the pairwise comparison for the receiver operating characteristic curves of C-reactive protein and C-reactive protein-to-albumin ratio, no statistically significant difference was found.

CONCLUSIONS

Geriatric patients are the "most vulnerable" patient group against the COVID-19. In this study, both C-reactive protein and C-reactive protein-to-albumin ratio were found to be successful in predicting mortality for geriatric COVID-19 patients.

Prediction of diagnosis and prognosis of COVID-19 disease by blood gas parameters using decision trees machine learning model: a retrospective observational study

The coronavirus disease 2019 (COVID-19) epidemic went down in history as a pandemic caused by corona-viruses that emerged in 2019 and spread rapidly around the world. The different symptoms of COVID-19 made it difficult to understand which variables were more influential on the diagnosis, course and mortality of the disease. Machine learning models can accurately assess hidden patterns among risk factors by analyzing large-datasets to quickly predict diagnosis, prognosis and mortality of diseases. Because of this advantage, the use of machine learning models as decision support systems in health services is increasing. The aim of this study is to determine the diagnosis and prognosis of COVID-19 disease with blood-gas data using the Chi-squared Automatic Interaction Detector (CHAID) decision-tree-model, one of the machine learning methods, which is a subfield of artificial intelligence. This study was carried out on a total of 686 patients with COVID-19 (n = 343) and non-COVID-19 (n = 343) treated at Erzincan-Mengücek-Gazi-Training and Research-Hospital between April 1, 2020 and March 1, 2021. Arterial blood gas values of all patients were obtained from the hospital registry system. While the total-accuracyratio of the decision-tree-model was 65.0% in predicting the prognosis of the disease, it was 68.2% in the diagnosis of the disease. According to the results obtained, the low ionized-calcium value (< 1.10 mM) significantly predicted the need for intensive care of COVID-19 patients. At admission, low-carboxyhemoglobin (< 1.00%), high-pH (> 7.43), low-sodium (< 135.0 mM), hematocrit (< 40.0%), and methemoglobin (< 1.30%) values are important biomarkers in the diagnosis of COVID-19 and the results were promising. The findings in the study may aid in the early-diagnosis of the disease and the intensive-care treatment of patients who are severe. The study was approved by the Ministry of Health and Erzincan University Faculty of Medicine Clinical Research Ethics Committee.

Forecasting of Oxidant/Antioxidant levels of COVID-19 patients by using Expert models with biomarkers used in the Diagnosis/Prognosis of COVID-19

Background

Early detection of oxidant-antioxidant levels and special care in severe patients are important in combating the COVID-19 epidemic. However, this process is costly and time consuming. Therefore, there is a need for faster, reliable and economical methods.

Methods

In this study, antioxidant/oxidant levels of patients were estimated by Expert-models using biomarkers, which are effective in the diagnosis/prognosis of COVID-19 disease. For this purpose, Expert-models were trained and created between the white-blood-cell-count (WBC), lymphocyte-count (LYM), C-reactive-protein (CRP), D-dimer, ferritin values of 35 patients with COVID-19 and antioxidant/oxidant parameter values of the same patients. Error criteria and R² ratio were taken into account for the performance of the models. The validity of the all models was checked by the Box-Jenkis-method.

Results

Antioxidant/Oxidant levels were estimated with 95% confidence-coefficient using the values of WBC, LYM, CRP, D-dimer, ferritin of different 500 patients diagnosed with COVID-19 with the trained models. The error rate of all models was low and the coefficients of determination were sufficient. In the first data set, there was no significant difference between measured antioxidant/oxidant levels and predicted antioxidant/oxidant levels. This result showed that the models are accurate and reliable. In determining antioxidant/oxidant levels, LYM and ferritin biomarkers had the most effect on models, while WBC and CRP biomarkers had the least effect. The antioxidant/oxidant parameter estimated with the highest accuracy was Native-Thiol divided by Total-Thiol.

Conclusions

The results showed that the antioxidant/oxidant levels of infected patients can be estimated accurately and reliably with LYM, ferritin, D-dimer, WBC, CRP biomarkers in the COVID-19 outbreak.

Predicting Intensive Care Unit Admissions for COVID-19 Patients in the Emergency Department

Objective:

Determining the parameters that can predict the requirement of intensive care unit (ICU) admissions among the coronavirus disease 2019 (COVID-19) patients presented to the emergency departments (EDs).

Methods:

In adult consecutive patients admitted (March 15 - April 15, 2020) to the ED of a state hospital for COVID-19, we retrospectively analyzed demographic data, symptoms, laboratory tests, and chest computed tomography (CT) on arrival.

Results:

We included 458 patients [213 (46.5%) females, median age 48 y]. Body temperature, respiration rate, C-reactive protein (CRP), D-dimer, ferritin values, and the number of comorbidities were significantly higher in patients admitted to the ICU than others. Also, diffuse infiltration in chest CT is more common in patients who need ICU follow-up. As a result of the binary regression analysis, a statistically significant correlation was found between the presence of dyspnea (odds ratio [OR]: 12.55), tachypnea (relative risk [RR] ≥ 18) (OR: 14.54), multiple comorbidities (≥2) (OR: 23.39), diffuse infiltration in CT (OR: 14.52), and CRP (≥45 mg/L) (OR: 4.71); and the need for ICU admission.

Conclusion:

It has been concluded that the presence of dyspnea and tachypnea, elevated CRP, presence of multiple comorbidities, and diffuse infiltration in CT may predict the need for ICU admissions of the patients, who presented to the EDs.

The Usefulness of Shock Index and Modified Shock Index in Predicting the Outcome of COVID-19 Patients

Objective:

The aim of this study is to investigate the accuracy of shock index (SI) and modified shock index (mSI) in predicting the intensive care unit (ICU) requirement and in-hospital mortality among coronavirus disease (COVID-19) patients who are admitted to the emergency department (ED). Likewise, the effects of patients’ conditions such as age, gender, and comorbidity on prognosis will be analyzed.

Methods:

The files were retrospectively scanned for all COVID-19 patients over the age of 18 years who were admitted to the ED and hospitalized between January 1, 2021, and March 15, 2021. The area under the receiver operating characteristic curve and the area under the curve (AUC) were used to assess each scoring system discriminatory for predicting in-hospital mortality and ICU admission.

Results:

There were 464 patients included in this study. The mean age of the patients was 62.4 ± 16.7, of which 245 were men and 219 were women. The most common comorbidity in patients was hypertension (200; 43.1%), followed by chronic obstructive pulmonary disease (174; 37.5%), and coronary artery disease (154; 33.2%). In terms of in-hospital mortality, the AUC of SI, and mSI were 0.719 and 0.739, respectively. In terms of an ICU requirement, the AUC of SI, and mSI were 0.704 and 0.729, respectively.

Conclusion:

In this study, it was concluded that SI and mSI are useful in predicting in-hospital mortality and ICU requirement in COVID-19 patients. In addition, another important result of the study is that advanced age, male gender, and hypertension may be associated with a poor prognosis.

Acute Limb Ischemia in Hospitalized COVID-19 Patients

Background

COVID-19 is a multisystemic disorder. Hematologic and cardiovascular involvement of COVID-19 causes thromboembolic events across multiple organs which mainly manifest as venous thromboembolism, and rarely, peripheral arterial thromboembolic events. In-situ thrombosis of a healthy, non-atherosclerotic native artery is rare, and COVID-19 has been reported to be a cause of this phenomenon. We aimed to report our institutional experience with COVID-19 patients who developed acute limb ischemia (ALI) during hospitalization or after discharge.

Methods

This was a single-center cross-sectional study. Records of all patients ≥18 years of age admitted to a tertiary center with a confirmed diagnosis of COVID-19 infection between September 1 and December 31, 2020 were retrospectively examined. Data regarding patient demographics, co-morbidities and outcomes were collected. Patients were followed-up during index hospitalization and for 30 days postdischarge. Acute limb ischemia was diagnosed by means of duplex ultrasound and computed tomography angiography in the presence of a clinical suspicion.

Results

A total of 681 consecutive patients (38.5% women) were hospitalized with a confirmed diagnosis of COVID-19 during the study period. Median age was 63 years (IQR, 52–74). In-hospital mortality occurred in 94 (13.8%) patients. Ninety (13.2%) patients required intensive care unit admission at some point of their hospital stay. Six (0.9%) patients (one woman) with a median age of 62 years experienced ALI (IQR, 59–64.3). All patients were receiving low molecular weight heparin when they developed ALI. The median of duration between COVID-19 diagnosis and ALI symptom onset was 13 days (IQR, 11.3–14). Three patients underwent emergent surgical thrombectomy combined with systemic anticoagulation, and 3 received systemic anticoagulation alone. Two patients with ALI did not survive to hospital discharge. Among survivors, 1 patient underwent bilateral major amputations, and another underwent a minor amputation within 1 month of hospital discharge. Symptoms of ALI completely resolved in 2 patients without sequelae.

Conclusions

COVID-19 is a multisystemic disorder with involvement of hematologic and cardiovascular systems. Despite widespread use of thromboprophylaxis, hospitalized patients with COVID-19 are at increased risk of ALI, and subsequent limb loss or even death.