One-on-one comparison between qCSI and NEWS scores for mortality risk assessment in patients with COVID-19

A model based on PT-INR and age serves as a promising predictor for evaluating mortality risk in patients with SARS-CoV-2 infection

COVID-19 caused by the coronavirus SARS-CoV-2 has resulted in a global pandemic. Considering some patients with COVID-19 rapidly develop respiratory distress and hypoxemia, early assessment of the prognosis for COVID-19 patients is important, yet there is currently a lack of research on a comprehensive multi-marker approach for disease prognosis assessment. Here, we utilized a large sample of hospitalized individuals with COVID-19 to systematically compare the clinical characteristics at admission and developed a nomogram model that was used to predict prognosis. In all cases, those with pneumonia, older age, and higher PT-INR had a poor prognosis. Besides, pneumonia patients with older age and higher PT-INR also had a poor prognosis. A nomogram model incorporating presence of pneumonia, age and PT-INR could evaluate the prognosis in all patients with SARS-CoV-2 infections well, while a nomogram model incorporating age and PT-INR could evaluate the prognosis in those with pneumonia well. Together, our study establishes a prognostic prediction model that aids in the timely identification of patients with poor prognosis and helps facilitate the improvement of treatment strategies in clinical practice in the future.

NEWS-2 Accuracy in Predicting Mortality and Severe Morbidity Among Hospitalized COVID-19 Patients: A Prospective Cohort Study

Background: Early risk stratification tools for COVID-19 patients have been indicated yet there are few data about their ability to effectively detect clinical deterioration among COVID-19 patients. Objectives: To evaluate the NEWS-2 to predict severe morbidity and mortality for COVID-19 patients admitted to hospitals. Methods: We conducted a prospective cohort study among adult COVID-19 patients with a confirmed diagnosis who were admitted to the inpatient units at COVID-19 Martyrs Medical Military Complex Hospital, from 1 March 2022, until 29 February 2023. NEWS-2 scores were measured at admission and 6, 12, 24, and 48 h after their admission to the hospital using receiver operating characteristic (ROC) curves. Results: Overall, 192 adult COVID-19 patients aged 25-94 years (mean = 62.1, SD = 13.9) were enrolled. Of those, 49.0% were males, 47.4% were vaccinated, and 53.6% had diabetes. The 192 enrolled patients were classified into NEWS-2 score categories, with almost 13% (12.5%) falling into the high-risk category already upon admission. The mean NEWS-2 scores were excellent predictors of mechanical ventilation, admission to the ICU, and mortality, as indicated by an AUROC of 0.94 (95% CI: 0.88-1.00, p < 0.001), 0.91 (95% CI: 0.87-0.96, p < 0.001), and 0.96 (95% CI: 0.92-1.00, p < 0.001), respectively. Significant differences in mean NEWS-2 scores were found between the participating patients, both with and without comorbidity in the course of the patient's stay in the ICU, and mortality (p = 0.004, p = 0.043, respectively). Positive correlations of the high NEWS-2 scores were revealed using a multiple linear regression model, indicating the necessity of administering non-invasive ventilatory assistance (p = 0.013), hospitalization for a minimum of six days (p = 0.013), and admission to the ICU (p = 0.006). Nonetheless, there was a negative association between mortality and the NEWS-2 score (p < 0.001). Conclusions: The NEWS-2 had moderate sensitivity and specificity in predicting the deterioration of patients with COVID-19 whereas there was high sensitivity and specificity in predicting the mortality for patients with COVID-19, both with and without comorbidity. Our findings support the utility of NEWS-2 monitoring as a sensitive approach for initially assessing COVID-19 patients. It could be helpful to enhance the accuracy of predictive performance by supplementing the score parameters by adding biological parameters in addition to clinical judgment.

Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments

The evolution of the COVID-19 pandemic has been associated with variations in clinical presentation and severity. Similarly, prediction scores may suffer changes in their diagnostic accuracy. The aim of this study was to test the 30-day mortality predictive validity of the 4C and SEIMC scores during the sixth wave of the pandemic and to compare them with those of validation studies. This was a longitudinal retrospective observational study. COVID-19 patients who were admitted to the Emergency Department of a Spanish hospital from December 15, 2021, to January 31, 2022, were selected. A side-by-side comparison with the pivotal validation studies was subsequently performed. The main measures were 30-day mortality and the 4C and SEIMC scores. A total of 27,614 patients were considered in the study, including 22,361 from the 4C, 4,627 from the SEIMC and 626 from our hospital. The 30-day mortality rate was significantly lower than that reported in the validation studies. The AUCs were 0.931 (95% CI: 0.90-0.95) for 4C and 0.903 (95% CI: 086-0.93) for SEIMC, which were significantly greater than those obtained in the first wave. Despite the changes that have occurred during the coronavirus disease 2019 (COVID-19) pandemic, with a reduction in lethality, scorecard systems are currently still useful tools for detecting patients with poor disease risk, with better prognostic capacity.

Predictive value of hematocrit, serum albumin level difference, and fibrinogen-to-albumin ratio for COVID-19-associated acute respiratory failure

Background

Acute respiratory failure is the main clinical manifestation and a major cause of death in patients with COVID-19. However, few reports on its prevention and control have been published because of the need for laboratory predictive indicators. This study aimed to evaluate the predictive value of hematocrit level, serum albumin level difference, and fibrinogen-to-albumin ratio for COVID-19-associated acute respiratory failure.

Material and methods

A total of 120 patients with COVID-19 from the First Affiliated Hospital of Anhui Medical University were selected between December 2022 and March 2023. Patients were divided into acute respiratory failure and non-acute respiratory failure groups and compared patient-related indicators between them using univariate and multivariate logistic regression analyses. Receiver operating characteristic analysis was performed to determine the discrimination accuracy.

Results

In total, 48 and 72 patients were enrolled in the acute respiratory failure and non-acute respiratory failure groups, respectively. The Quick COVID-19 Severity Index scores, fibrinogen-to-albumin ratio, hematocrit and serum albumin level difference, fibrinogen, and hematocrit levels were significantly higher in the acute respiratory failure group than in the non-acute respiratory failure group. A Quick COVID-19 Severity Index >7, fibrinogen-to-albumin ratio >0.265, and hematocrit and serum albumin level difference >12.792 had a 96.14 % positive predictive rate and a 94.06 % negative predictive rate.

Conclusion

Both fibrinogen-to-albumin ratio and hematocrit and serum albumin level difference are risk factors for COVID-19-associated acute respiratory failure. The Quick COVID-19 Severity Index score combined with fibrinogen-to-albumin ratio, and hematocrit and serum albumin level difference predict high and low risks with better efficacy and sensitivity than those of the Quick COVID-19 Severity Index score alone; therefore, these parameters can be used collectively as a risk stratification method for assessing patients with COVID-19.

Validation of oxygen saturations measured in the community by emergency medical services as a marker of clinical deterioration in patients with confirmed COVID-19: a retrospective cohort study

OBJECTIVES

To evaluate oxygen saturation and vital signs measured in the community by emergency medical services (EMS) as clinical markers of COVID-19-positive patient deterioration.

DESIGN

A retrospective data analysis.

SETTING

Patients were conveyed by EMS to two hospitals in Hampshire, UK, between 1 March 2020 and 31 July 2020.

PARTICIPANTS

A total of 1080 patients aged ≥18 years with a COVID-19 diagnosis were conveyed by EMS to the hospital.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary study outcome was admission to the intensive care unit (ICU) within 30 days of conveyance, with a secondary outcome representing mortality within 30 days of conveyance. Receiver operating characteristic (ROC) analysis was performed to evaluate, in a retrospective fashion, the efficacy of different variables in predicting patient outcomes.

RESULTS

Vital signs measured by EMS staff at the first point of contact in the community correlated with patient 30-day ICU admission and mortality. Oxygen saturation was comparably predictive of 30-day ICU admission (area under ROC (AUROC) 0.753; 95% CI 0.668 to 0.826) to the National Early Warning Score 2 (AUROC 0.731; 95% CI 0.655 to 0.800), followed by temperature (AUROC 0.720; 95% CI 0.640 to 0.793) and respiration rate (AUROC 0.672; 95% CI 0.586 to 0.756).

CONCLUSIONS

Initial oxygen saturation measurements (on air) for confirmed COVID-19 patients conveyed by EMS correlated with short-term patient outcomes, demonstrating an AUROC of 0.753 (95% CI 0.668 to 0.826) in predicting 30-day ICU admission. We found that the threshold of 93% oxygen saturation is prognostic of adverse events and of value for clinician decision-making with sensitivity (74.2% CI 0.642 to 0.840) and specificity (70.6% CI 0.678 to 0.734).

Head-to-head comparison of six warning scores to predict mortality and clinical impairment in COVID-19 patients in emergency department

The aim was to evaluate the ability of six risk scores (4C, CURB65, SEIMC, mCHOSEN, QuickCSI, and NEWS2) to predict the outcome of patients with COVID-19 during the sixth pandemic wave in Spain. A retrospective observational study was performed to review the electronic medical records in patients ≥ 18 years of age who consulted consecutively in an emergency department with COVID-19 diagnosis throughout 2 months during the sixth pandemic wave. Clinical-epidemiological variables, comorbidities, and their respective outcomes, such as 30-day in-hospital mortality and clinical deterioration risk (a combined outcome considering: mechanical ventilation, intensive care unit admission, and/or 30-day in-hospital mortality), were calculated. The area under the curve for each risk score was calculated, and the resulting curves were compared by the Delong test, concluding with a decision curve analysis. A total of 626 patients (median age 79 years; 49.8% female) fulfilled the inclusion criteria. Two hundred and ninety-three patients (46.8%) had two or more comorbidities. Clinical deterioration risk criteria were present in 10.1% (63 cases), with a 30-day in-hospital mortality rate of 6.2% (39 cases). Comparison of the results showed that score 4C presented the best results for both outcome variables, with areas under the curve for mortality and clinical deterioration risk of 0.931 (95% CI 0.904-0.957) and 0.871 (95% CI 0.833-0.910) (both p < 0.001). The 4C Mortality Score proved to be the best score for predicting mortality or clinical deterioration risk among patients with COVID-19 attended in the emergency department in the following 30 days.

Outpatient anti-spike monoclonal antibody administration is associated with decreased morbidity and mortality among patients with cancer and COVID-19

Patients with cancer have many comorbidities that increase their risk of death from Coronavirus disease 2019 (COVID-19). Anti-spike monoclonal antibodies (mAbs) reduce the risk of hospitalization or death from COVID-19 in the general population. To our knowledge, no studies have focused on the clinical efficacy of mAbs compared to no outpatient treatment exclusively among patients with solid tumors and hematologic malignancies, who are often excluded from clinical trials. We studied patients with cancer who had COVID-19 between 11.9.2020 and 7.21.2022 and received mAbs in an outpatient setting. We compared hospitalization and mortality rates to those of patients with cancer concurrently diagnosed with COVID-19, who were eligible for mAbs, but did not receive any outpatient treatment. 63 patients received mAbs and 89 no outpatient treatment. Administration of mAbs was associated with lower 90-day hospitalization (20.6% vs. 60.7%, p <0.001), all-cause (6.3% vs. 19.1%, p 0.025) and COVID-19-attributed (3.2% vs. 14.6%, p 0.019) mortality rates, and lower peak O2 requirements (ordinal Odds Ratio [OR] = 0.33, 95% Confidence Intervals [CI] = 0.20-0.53). Administration of mAbs (aHR 0.21, p <0.001), age (≥ 60 years, adjusted Hazard Ratio [aHR] 1.86, p=0.033), and metastases (aHR 0.41, p 0.007) were independently associated with hospitalization. mAb treatment remained significantly associated with all-cause (aHR 0.27, p 0.019) and COVID-19-attributed (aHR 0.19, p 0.031) mortality, after adjustment for other factors. mAb administration was associated with improved clinical outcomes among vulnerable patients with cancer and COVID-19. With no mAbs approved currently for treatment against the prevalent circulating variants, the development of new mAbs should be a research priority.

Assessment of risk scores to predict mortality of COVID-19 patients admitted to the intensive care unit

Objectives

To assess the ABC2-SPH score in predicting COVID-19 in-hospital mortality, during intensive care unit (ICU) admission, and to compare its performance with other scores (SOFA, SAPS-3, NEWS2, 4C Mortality Score, SOARS, CURB-65, modified CHA2DS2-VASc, and a novel severity score).

Materials and methods

Consecutive patients (≥ 18 years) with laboratory-confirmed COVID-19 admitted to ICUs of 25 hospitals, located in 17 Brazilian cities, from October 2020 to March 2022, were included. Overall performance of the scores was evaluated using the Brier score. ABC2-SPH was used as the reference score, and comparisons between ABC2-SPH and the other scores were performed by using the Bonferroni method of correction. The primary outcome was in-hospital mortality.

Results

ABC2-SPH had an area under the curve of 0.716 (95% CI 0.693-0.738), significantly higher than CURB-65, SOFA, NEWS2, SOARS, and modified CHA2DS2-VASc scores. There was no statistically significant difference between ABC2-SPH and SAPS-3, 4C Mortality Score, and the novel severity score.

Conclusion

ABC2-SPH was superior to other risk scores, but it still did not demonstrate an excellent predictive ability for mortality in critically ill COVID-19 patients. Our results indicate the need to develop a new score, for this subset of patients.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Machine learning models for predicting severe COVID-19 outcomes in hospitals

The aim of this observational retrospective study is to improve early risk stratification of hospitalized Covid-19 patients by predicting in-hospital mortality, transfer to intensive care unit (ICU) and mechanical ventilation from electronic health record data of the first 24 h after admission. Our machine learning model predicts in-hospital mortality (AUC = 0.918), transfer to ICU (AUC = 0.821) and the need for mechanical ventilation (AUC = 0.654) from a few laboratory data of the first 24 h after admission. Models based on dichotomous features indicating whether a laboratory value exceeds or falls below a threshold perform nearly as good as models based on numerical features. We devise completely data-driven and interpretable machine-learning models for the prediction of in-hospital mortality, transfer to ICU and mechanical ventilation for hospitalized Covid-19 patients within 24 h after admission. Numerical values of. CRP and blood sugar and dichotomous indicators for increased partial thromboplastin time (PTT) and glutamic oxaloacetic transaminase (GOT) are amongst the best predictors.

Outpatient anti-spike monoclonal antibody administration is associated with decreased morbidity and mortality among patients with cancer and COVID-19

BACKGROUND

Patients with cancer have many comorbidities that increase their risk of death from Coronavirus disease 2019 (COVID-19). Anti-spike monoclonal antibodies (mAbs) reduce the risk of hospitalization or death from COVID-19 in the general population. To our knowledge, no studies have focused on the clinical efficacy of mAbs compared to no outpatient treatment exclusively among patients with solid tumors and hematologic malignancies, who are often excluded from clinical trials.

METHODS

We studied patients with cancer who had COVID-19 between 11.9.2020 and 7.21.2022 and received mAbs in an outpatient setting. We compared hospitalization and mortality rates to those of patients with cancer concurrently diagnosed with COVID-19, who were eligible for mAbs, but did not receive any outpatient treatment.

RESULTS

63 patients received mAbs and 89 no outpatient treatment. Administration of mAbs was associated with lower 90-day hospitalization (20.6% vs. 60.7%, p<0.001), all-cause (6.3% vs. 19.1%, p=0.025) and COVID-19-attributed (3.2% vs. 14.6%, p=0.019) mortality rates, and lower peak O2 requirements (ordinal Odds Ratio [OR]=0.33, 95%Confidence Intervals [CI]=0.20-0.53). Administration of mAbs (aHR 0.21, p<0.001), age (≥ 60 years, adjusted Hazard Ratio [aHR] 1.86, p=0.033), and metastases (aHR 0.41, p=0.007) were independently associated with hospitalization. mAb treatment remained significantly associated with all-cause (aHR 0.27, p=0.019) and COVID-19-attributed (aHR 0.19, p=0.031) mortality, after adjustment for other factors.

CONCLUSIONS

mAb administration was associated with improved clinical outcomes among vulnerable patients with cancer and COVID-19. With no mAbs approved currently for treatment against the prevalent circulating variants, the development of new mAbs should be a research priority.

Assessment of the disease severity in patients hospitalized for COVID-19 based on the National Early Warning Score (NEWS) using statistical and machine learning methods: An electronic health records database analysis

BACKGROUND

The coronavirus disease 2019 (COVID-19) was a cause of concern in the healthcare system and increased the need for disease severity indicators. However, they still vary in use to evaluate in-hospital outcomes and severity. The National Early Warning Score (NEWS) is routinely used to evaluate patient health status at the hospital. Further research is needed to ensure if NEWS can be a good instrument for an overall health status assessment with or without additional information like laboratory tests, intensive care needs, and history of chronic diseases.

OBJECTIVE

To evaluate if NEWS can be an indicator to measure COVID-19 patient status in-hospital.

METHODS

We used the fully anonymized Electronic Health Records (EHR) characterizing patients admitted to the hospital with COVID-19. Data was obtained from Vilnius University Hospital Santaros Klinikos EHR system (SANTA-HIS) from 01-03-2020 to 31-12-2022. The study sample included 3875 patients. We created several statistical and machine learning models for discrimination between in-hospital death/discharge for evaluation NEWS as a disease severity measure for COVID-19 patients. In these models, two variable sets were considered: median NEWS and its combination with clinical parameters and medians of laboratory test results. Assessment of models' performance was based on the scoring metrics: accuracy, sensitivity, specificity, area under the ROC curve (AUC), and F1-score.

RESULTS

Our analysis revealed that NEWS predictive ability for describing patient health status during the stay in the hospital can be increased by adding the patient's age at hospitalization, gender, clinical and laboratory variables (0.853 sensitivity, 0.992 specificity and F1-score - 0.859) in comparison with single NEWS (0.603, 0.995, 0.719, respectively). A comparison of different models showed that stepwise logistic regression was the best method for in-hospital mortality classification. Our findings suggest employing models like ours for advisory routine usage.

CONCLUSION

Our model demonstrated incremental value for COVID-19 patient's status evaluation.

Evaluation of the effectiveness of quick COVID-19 Severity Index and COVID-GRAM Critical Illness Risk Score in determining mortality and severity in COVID-19

Background/Aim: With the COVID-19 pandemic, the increase in the number of patients admitted to the emergency department has led to an increase in the need for intensive care and mechanical ventilation. Methods that can predict the development of serious disease will allow for a more accurate use of resources. This study was conducted to test the ability of the Quick COVID-19 Severity Index and the COVID-GRAM Critical Illness Risk Score to predict serious disease development and mortality. Methods: This is a prospective cohort study. Among the patients admitted to the emergency department, those hospitalized due to COVID-19 were included in the study. The Quick COVID-19 Severity Index and COVID-GRAM Critical Illness Risk Scores of the patients were calculated, and the ability of these scores to predict serious illness and mortality was investigated. Results: A total of 556 patients were included in this study. Development of critical illness, described as the need for non-invasive / invasive ventilation or the need for intensive care unit admission, was found significant when the Quick COVID-19 Severity Index was above 5 and the COVID-GRAM Critical Illness Risk Score showed high risk (AUC: 0.927; P < 0.001, AUC: 0.986; P < 0.001, respectively). A Quick COVID-19 Severity Index over 6 and COVID-GRAM Critical Illness Risk Score indicating high risk were found to be associated with mortality (AUC: 0.918, P < 0.001, AUC: 0.982, P < 0.001, respectively). Conclusion: Both the Quick COVID-19 Severity Index and the COVID-GRAM Critical Illness Risk Score can be used to assess severity in COVID-19 patients in the emergency room. However, the COVID-GRAM Critical Illness Risk Score was more successful in differentiating low- and high-risk patients.