Predicting In-Hospital Mortality in COVID-19 Older Patients with Specifically Developed Scores

External validation of six COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting

OBJECTIVES

To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care, and nursing homes.

STUDY DESIGN AND SETTING

This retrospective external validation study included 14,092 older individuals of ≥70 years of age with a clinical or polymerase chain reaction-confirmed COVID-19 diagnosis from March 2020 to December 2020. The six validation cohorts include three hospital-based (CliniCo, COVID-OLD, COVID-PREDICT), two primary care-based (Julius General Practitioners Network/Academisch network huisartsgeneeskunde/Network of Academic general Practitioners, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using Prediction model Risk Of Bias ASsessment Tool for quality and risk of bias assessment and considering predictor availability in validation cohorts, we selected six prognostic models predicting mortality risk in adults with COVID-19 infection (GAL-COVID-19 mortality, 4C Mortality Score, National Early Warning Score 2-extended model, Xie model, Wang clinical model, and CURB65 score). All six prognostic models were validated in the hospital cohorts and the GAL-COVID-19 mortality model was validated in all three healthcare settings. The primary outcome was in-hospital mortality for hospitals and 28-day mortality for primary care and nursing home settings. Model performance was evaluated in each validation cohort separately in terms of discrimination, calibration, and decision curves. An intercept update was performed in models indicating miscalibration followed by predictive performance re-evaluation.

MAIN OUTCOME MEASURE

In-hospital mortality for hospitals and 28-day mortality for primary care and nursing home setting.

RESULTS

All six prognostic models performed poorly and showed miscalibration in the older population cohorts. In the hospital settings, model performance ranged from calibration-in-the-large -1.45 to 7.46, calibration slopes 0.24-0.81, and C-statistic 0.55-0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across health-care settings was similar for the GAL-COVID-19 model, with a calibration-in-the-large in the range of -2.35 to -0.15 indicating overestimation, calibration slopes of 0.24-0.81 indicating signs of overfitting, and C-statistic of 0.55-0.71.

CONCLUSION

Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each health-care setting: hospital, primary care, and nursing home settings. Insights into factors influencing predictive model performance in the older population are needed for pandemic preparedness and reliable prognostication of health-related outcomes in this demographic.

Predicting mortality risk in hospitalized COVID-19 patients: an early model utilizing clinical symptoms

BACKGROUND

Despite global efforts to control the COVID-19 pandemic, the emergence of new viral strains continues to pose a significant threat. Accurate patient stratification, optimized resource allocation, and appropriate treatment are crucial in managing COVID-19 cases. To address this, a simple and accurate prognostic tool capable of rapidly identifying individuals at high risk of mortality is urgently needed. Early prognosis facilitates predicting treatment outcomes and enables effective patient management. The aim of this study was to develop an early predictive model for assessing mortality risk in hospitalized COVID-19 patients, utilizing baseline clinical factors.

METHODS

We conducted a descriptive cross-sectional study involving a cohort of 375 COVID-19 patients admitted and treated at the COVID-19 Patient Treatment Center in Military Hospital 175 from October 2021 to December 2022.

RESULTS

Among the 375 patients, 246 and 129 patients were categorized into the survival and mortality groups, respectively. Our findings revealed six clinical factors that demonstrated independent predictive value for mortality in COVID-19 patients. These factors included age greater than 50 years, presence of multiple underlying diseases, dyspnea, acute confusion, saturation of peripheral oxygen below 94%, and oxygen demand exceeding 5 L per minute. We integrated these factors to develop the Military Hospital 175 scale (MH175), a prognostic scale demonstrating significant discriminatory ability with an area under the curve (AUC) of 0.87. The optimal cutoff value for predicting mortality risk using the MH175 score was determined to be ≥ 3 points, resulting in a sensitivity of 96.1%, specificity of 63.4%, positive predictive value of 58%, and negative predictive value of 96.9%.

CONCLUSIONS

The MH175 scale demonstrated a robust predictive capacity for assessing mortality risk in patients with COVID-19. Implementation of the MH175 scale in clinical settings can aid in patient stratification and facilitate the application of appropriate treatment strategies, ultimately reducing the risk of death. Therefore, the utilization of the MH175 scale holds significant potential to improve clinical outcomes in COVID-19 patients.

TRIAL REGISTRATION

An independent ethics committee approved the study (Research Ethics Committee of Military Hospital 175 (No. 3598GCN-HDDD; date: October 8, 2021), which was performed in accordance with the Declaration of Helsinki, Guidelines for Good Clinical Practice.

Association of Serum Krebs von den Lungen-6 (KL-6) Levels and Disease Severity in Patients Hospitalized with COVID-19

Objective

We aimed to evaluate Krebs von den Lungen-6 (KL-6) as a possible biomarker in determining disease severity in patients with moderate and severe COVID-19.

Materials and Methods

This cross-sectional study included moderate or severe COVID-19 patients; critically ill patients who were followed up in the intensive care unit were not included. KL-6 level and routine laboratory test measurements were performed on the first day of admission. The patients were also categorized according to their hyperinflammatory state.

Results

The study included 92 patients, 56 (61%) women. The National Institutes of Health (NIH) score was 2 in 52.2% of the patients and 3 in 47.8%. KL-6 levels did not significantly differ in disease severity (NIH score 2 vs. 3; p=0.15). Median KL-6 values were 52.7 (29.1) in patients with <2 COVID-19 hyperinflammatory syndrome score (cHIS) and 61.7 (32.2) in patients with cHIS ≥ 2 (p= 0.077). KL-6 values tended to be higher among the patients with lower lymphocyte counts, but the difference was not statistically significant (<1000 mm³/L p=0.006 and higher cHIS scores ≥2 p=0.07). KL-6 values were also higher in the patients with diabetes mellitus compared to the remaining patients (p =0.036).

Conclusion

There was no significant association between the serum KL-6 measured at admission and the severity of COVID-19.

Were metabolic and other chronic diseases the driven onset epidemic forces of COVID-19 in Mexico?

The underline hypothesis of this study was that SARS-CoV-2 can infect individuals regardless of health condition, sex, and age in opposition to the classical epidemiological assumption of an identifiable susceptible subpopulation for epidemic development. To address this issue, a population cohort with 24.4 million metadata associated with 226,089 official RT-qPCR positive and 283,450 negative cases, including 27,769 deceased, linked putatively to B.1. and B.1.1. SARS-CoV-2 lineages were analyzed. The analysis baseline was to determine the infection and mortality structure of the diseased cohort at the onset-exponential phase of the first epidemic wave in Mexico under the assumption of limited herd immunity. Individuals with nonchronic diseases (NOCDs) were compared with those exhibiting at least one of 10 chronic diseases (CDs) adjusted by age and sex. Risk factors for infection and mortality were estimated with classification and regression tree (CART) and cluster analysis based on Spearman's matrix of rho-values in RStudio®, complemented with two proposed mortality indices. SARS-CoV-2 infection was independent of health condition (52.8% NOCD vs. 47.2% CDs; p = 0.001-0.009) but influenced by age >46 in one risk analysis scenario (p < 0.001). Sex contributed 9.7% to the overall risk. The independent effect was supported by the health structure of negative cases with a similar tendency but a higher proportion of NOCDs (61.4%, p = 0.007). The infection probability in individuals with one CD was determined by the disease type and age, which was higher in those older individuals (≥56 years) exhibiting diabetes (12.3%, cp = 0.0006), hypertension (10.1%, cp < 0.0001), and obesity (7.8%, cp = 0.001). In contrast, the mortality risk was heavily influenced by CD conditioned by sex and age, accounting for 72.3% of total deaths (p = 0.001-0.008). Significant mortality risk (48%) was comprised of women and men (w, m) aged ≥56 years with diabetes (19% w and 27.9% m, cp < 0.0004), hypertension (11.5% w, cp = 0.0001), and CKD (3.5% w and 5.3% m, cp = 0.0009). Older people with diabetes and hypertension comorbidity increased the risk to 60.5% (p = 0.001). Based on a mortality-weighted index, women were more vulnerable to preexisting metabolic or cardiovascular diseases. These findings support our hypothesis and justify the need for surveillance systems at a communitarian level. This is the first study addressing this fundamental epidemiological question.

Performance of point-of-care severity scores to predict prognosis in patients admitted through the emergency department with COVID-19

BACKGROUND

Identifying COVID-19 patients at the highest risk of poor outcomes is critical in emergency department (ED) presentation. Sepsis risk stratification scores can be calculated quickly for COVID-19 patients but have not been evaluated in a large cohort.

OBJECTIVE

To determine whether well-known risk scores can predict poor outcomes among hospitalized COVID-19 patients.

DESIGNS, SETTINGS, AND PARTICIPANTS

A retrospective cohort study of adults presenting with COVID-19 to 156 Hospital Corporation of America (HCA) Healthcare EDs, March 2, 2020, to February 11, 2021.

INTERVENTION

Quick Sequential Organ Failure Assessment (qSOFA), Shock Index, National Early Warning System-2 (NEWS2), and quick COVID-19 Severity Index (qCSI) at presentation.

MAIN OUTCOME AND MEASURES

The primary outcome was in-hospital mortality. Secondary outcomes included intensive care unit (ICU) admission, mechanical ventilation, and vasopressors receipt. Patients scored positive with qSOFA ≥ 2, Shock Index > 0.7, NEWS2 ≥ 5, and qCSI ≥ 4. Test characteristics and area under the receiver operating characteristics curves (AUROCs) were calculated.

RESULTS

We identified 90,376 patients with community-acquired COVID-19 (mean age 64.3 years, 46.8% female). 17.2% of patients died in-hospital, 28.6% went to the ICU, 13.7% received mechanical ventilation, and 13.6% received vasopressors. There were 3.8% qSOFA-positive, 45.1% Shock Index-positive, 49.8% NEWS2-positive, and 37.6% qCSI-positive at ED-triage. NEWS2 exhibited the highest AUROC for in-hospital mortality (0.593, confidence interval [CI]: 0.588-0.597), ICU admission (0.602, CI: 0.599-0.606), mechanical ventilation (0.614, CI: 0.610-0.619), and vasopressor receipt (0.600, CI: 0.595-0.604).

CONCLUSIONS

Sepsis severity scores at presentation have low discriminative power to predict outcomes in COVID-19 patients and are not reliable for clinical use. Severity scores should be developed using features that accurately predict poor outcomes among COVID-19 patients to develop more effective risk-based triage.

Mortality in COVID-19 older patients hospitalized in a geriatric ward: Is obesity protective?

BACKGROUNDS

To investigate the relationship between obesity and 30-day mortality in a cohort of older hospitalized COVID-19 inpatients.

METHODS

Included patients were aged 70 years or more; hospitalized in acute geriatric wards between March and December 2020; with a positive PCR for COVID-19; not candidate to intensive care unit admission. Clinical data were collected from patients electronic medical records. Data on 30-day mortality were retrieved from the hospital administrative database.

RESULTS

Patients included (N = 294) were on average 83.4 ± 6.7 years old, 50.7% were women, and 21.7% were obese (BMI > 30 kg/m²). At 30-day, 85 (28.9%) patients were deceased. Compared to survivors in bivariable analysis, deceased patients were older (84.6 ± 7.6 vs 83.0 ± 6.3 years), more frequently with very complex health status (63.5% vs 39.7%, P < .001), but less frequently obese (13.4% vs 24.9%, P = .033) at admission. Over their stay, deceased patients more frequently (all P < .001) developed radiologic signs of COVID-19 (84.7% vs 58.9%), anorexia (84.7% vs 59.8%), hypernatremia (40.0% vs 10.5%), delirium (74.1% vs 30.1%), and need for oxygen (87.1% vs 46.4%) compared to survivors. In multivariable analysis that controlled for all markers of poor prognosis identified in bivariable analysis, obese patients remain with 64% (adjOR 0.36, 95%CI 0.14-0.95, P = .038) lower odds to be deceased at 30-day than non-obese patients.

CONCLUSIONS

In this population of older COVID-19 inpatients, an inverse association between obesity and 30-day mortality was observed even after adjusting for all already-known markers of poor prognosis. This result challenges previous observations in younger cohorts and would need to be replicated.

Can lactate levels and lactate kinetics predict mortality in patients with COVID-19 with using qCSI scoring system?

INTRODUCTION

In this study, we aimed to investigate the relationship between blood lactate levels and lactate kinetics (lactate clearance and Δ lactate) for predicting mortality in patients with COVID-19 admitted to the emergency department.

METHODS

This study was performed as a retrospective study that included patients admitted to the emergency department between March 1st, 2020, and January 1st, 2022. Lactate levels were recorded at the first admission (0 h lactate) and the highest blood lactate levels in the first 24 h of follow-up (2nd highest lactate). Lactate kinetics were calculated. Clinical severity was determined according to the quick COVID Severity Index (qCSI).

RESULTS

300 patients were included in the study. Lactate levels at admission were similar in groups with or without mortality, but 2nd highest lactate levels were found to be significantly higher in the group with mortality (p < 0.001). Lactate clearance and ∆ lactate levels were also found to be lower in the mortality group (p < 0.001). Lactate kinetics in patients in the clinically low severity group were lower in the mortality group (p = 0.02 and p = 0.039, respectively). In the low-intermediate and high-intermediate groups, 0-h lactate and 2nd highest lactate levels were found to be higher in the mortality group, and lactate kinetics were similar in the groups with and without mortality. In the group with high clinical severity, 2nd highest lactate levels were found to be higher in the group with mortality (p = 0.010). Lactate kinetics were also found to be significantly lower in the mortality group (p < 0.001). In the high qCSI group, based on ROC analysis, the AUC for 2nd highest lactate levels predicting mortality was 0.642 (95% CI: 0.548-0.728). The optimal cut-off value for mortality was greater than >2.4 mmol/L (60.6% sensitivity, 67.4% specificity). The AUC for lactate clearance was 0.748 (95% CI: 0.659-0.824). The lactate clearance cut-off value was ≤ -177.78% (49.3% sensitivity, 100% specificity). The AUC for ∆ lactate was 0.707 (95% CI: 0.616-0.787). The optimal ∆ lactate cut-off was ≤ -2 mmol/L (45.1% sensitivity, 93.5% specificity).

CONCLUSION

In COVID-19, 2nd highest blood lactate and lactate kinetics were found to be prognostic indicators of the disease. High 2nd highest lactate levels and low lactate kinetics in patients with high clinical severity were guiding physicians regarding the outcome of the disease.

ABC-GOALScl score predicts admission to the intensive care unit and mortality of COVID-19 patients over 60 years of age

BACKGROUND

One of the risk factors for getting seriously ill from COVID-19 and reaching high mortality rates is older age. Older age is also associated with comorbidities, which are risk factors for severe COVID-19 infection. Among the tools that have been evaluated to predict intensive care unit (ICU) admission and mortality is ABC-GOALScl.

AIM

In the present study we validated the utility of ABC-GOALScl to predict in-hospital mortality in subjects over 60 years of age who were positive for SARS-CoV-2 virus at the moment of admission with the purpose of optimizing sanitary resources and offering personalized treatment for these patients.

METHODS

This was an observational, descriptive, transversal, non-interventional and retrospective study of subjects (≥ 60 years of age), hospitalized due to COVID-19 infection at a general hospital in northeastern Mexico. A logistical regression model was used for data analysis.

RESULTS

Two hundred forty-three subjects were included in the study, whom 145 (59.7%) passed away, while 98 (40.3%) were discharged. Average age was 71, and 57.6% were male. The prediction model ABC-GOALScl included sex, body mass index, Charlson comorbidity index, dyspnea, arterial pressure, respiratory frequency, SpFi coefficient (Saturation of oxygen/Fraction of inspired oxygen ratio), serum levels of glucose, albumin, and lactate dehydrogenase; all were measured at the moment of admission. The area under the curve for the scale with respect to the variable of discharge due to death was 0.73 (IC 95% = 0.662-0.792).

CONCLUSION

The ABC-GOALScl scale to predict ICU admission in COVID-19 patients is also useful to predict in-hospital death in COVID-19 patients  ≥ 60 years old.

Evaluation of risk scores as predictors of mortality and hospital length of stay for older COVID-19 patients

Objective

This study was intended to research the sensitivity of the Charlson Comorbidity Index (CCI), COVID-GRAM, and MuLBSTA risk scores for hospital length of stay (LOS) and mortality in older patients hospitalized with coronavirus disease 2019 (COVID-19).

Methods

A total of 217 patients (119 women) were included in the study. The first clinical signs, comorbidities, laboratory and radiology findings, and hospital LOS were recorded for each patient. The CCI, COVID-GRAM, and MuLBSTA risk scores were calculated, and their sensitivities for hospital LOS and mortality were evaluated using receiver operating characteristic (ROC) curve analysis.

Results

Of the hospitalized patients, 59 (27.2%) were followed in the intensive care unit, and mortality developed in 44 (20.3%). The CCI positively correlated with COVID-GRAM and MuLBSTA scores (P < 0.001). COVID-GRAM and MuLBSTA results correlated with LOS and mortality (P < 0.001). According to the ROC curve analysis, the cutoff points for mortality were 5 for CCI, 169 for COVID-GRAM, and 9 for MuLBSTA.

Conclusion

Older patients with comorbidities are the major risk group for severe COVID-19. COVID-GRAM and MuLBSTA scores appear to be sensitive and reliable mortality indicators for these patients.

Clinical and Laboratory Predictors of Mortality in Severe COVID-19 Pneumonia: A Retrospective Study from India

OBJECTIVE

Wide arrays of laboratory parameters have been proposed by many studies for prognosis in COVID-19 patients. In this study, we wanted to determine if the International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score in addition to certain clinical and laboratory parameters would help in predicting mortality. We wanted to determine if a greater severity score on chest x-ray at presentation translated to poor patient outcomes using the COVID-19 chest radiography score.

MATERIAL AND METHODS

This retrospective study was conducted at SDS TRC and Rajiv Gandhi Institute of chest diseases, Bangalore from March 2021 to June 2021. This study included 202 real-time-polymerase chain reaction-positive COVID-19 patients aged above 18 years admitted to the intensive care unit of our hospital. Demographic characteristics and baseline hematological and inflammatory markers (serum C-reactive protein, lactate dehydrogenase, troponin-I, ferritin, and d-dimer) were collected. Radiological severity on a chest x-ray was assessed using the validated COVID-19 chest radiography score. The International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score was assigned to each patient within 24 hours of intensive care unit admission. Outcome studied was in-hospital mortality.

RESULTS

The overall mortality was 54.9% (111 cases). Age more than 50 years, >4 days of symptoms, peripheral oxygen saturation/ fraction of inspired oxygen ratio less than 200, elevated serum lactate dehydrogenase >398.5 IU/L, and hypoalbuminemia (<2.95 g/dL) were detected as independent predictors of mortality. A significant correlation of risk stratification with mortality (P = .057) was seen with International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score. There was no significant correlation between the COVID-19 chest radiography score and mortality.

CONCLUSION

Age >50 years, peripheral oxygen saturation/fraction of inspired oxygen ratio <200, mean symptom duration of >4 days, elevated serum lactate dehydrogenase, and hypoalbuminemia are independent predictors of mortality in severe COVID-19 pneumonia. International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score was different in the survivors and deceased.

Utility of the 4C ISARIC mortality score in hospitalized COVID-19 patients at a large tertiary Saudi Arabian center

Background

The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) 4C mortality score has been used before as a valuable tool for predicting mortality in COVID-19 patients. We aimed to address the utility of the 4C score in a well-defined Saudi population with COVID-19 admitted to a large tertiary referral hospital in Saudi Arabia.

Methods

A retrospective study was conducted that included all adults COVID‑19 patients admitted to the Armed Forces Hospital Southern Region (AFHSR), between January 2021 and September 2022. The receiver operating characteristic (ROC) curve depicted the diagnostic performance of the 4C Score for mortality prediction.

Results

A total of 1,853 patients were enrolled. The ROC curve of the 4C score had an area under the curve of 0.73 (95% CI: 0.702-0.758), p<0.001. The sensitivity and specificity with scores >8 were 80% and 58%, respectively, the positive and negative predictive values were 28% and 93%, respectively. Three hundred and sixteen (17.1%), 638 (34.4%), 814 (43.9%), and 85 (4.6%) patients had low, intermediate, high, and very high values, respectively. There were significant differences between survivors and non-survivors with regard to all variables used in the calculation of the 4C score. Multivariable logistic regression analysis revealed that all components of the 4C score, except gender and O2 saturation, were independent significant predictors of mortality.

Conclusions

Our data support previous international and Saudi studies that the 4C mortality score is a reliable tool with good sensitivity and specificity in the mortality prediction of COVID-19 patients. All components of the 4C score, except gender and O2 saturation, were independent significant predictors of mortality. Within the 4C score, odds ratios increased proportionately with an increase in the score value. Future multi-center prospective studies are warranted.

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.

Prehospital Respiratory Early Warning Score for airway management in-ambulance: A score comparison

BACKGROUND

Prehospital Respiratory Early Warning Scores to estimate the requirement for advanced respiratory support is needed. To develop a prehospital Respiratory Early Warning Score to estimate the requirement for advanced respiratory support.

METHODS

Multicentre, prospective, emergency medical services (EMS)-delivered, longitudinal cohort derivationvalidation study carried out in 59 ambulances and five hospitals across five Spanish provinces. Adults with acute diseases evaluated, supported and discharged to the Emergency Department with high priority were eligible. The primary outcome was the need for invasive or non-invasive respiratory support (NIRS or IRS) in the prehospital scope at the first contact with the patient. The measures included the following: epidemiological endpoints, prehospital vital signs (respiratory rate, pulse oximetry saturation, fraction of inspired oxygen, systolic and diastolic mean blood pressure, heart rate, tympanic temperature and consciousness level by the GCS).

RESULTS

Between 26 Oct 2018 and 26 Oct 2021, we enrolled 5793 cases. For NIRS prediction, the final model of the logistic regression included respiratory rate and pulse oximetry saturation/fraction of inspired oxygen ratio. For the IRS case, the motor response from the Glasgow Coma Scale was also included. The REWS showed an AUC of 0.938 (95% CI: 0.918-0.958), a calibration-in-large of 0.026 and a higher net benefit as compared with the other scores.

CONCLUSIONS

Our results showed that REWS is a remarkably aid for the decision-making process in the management of advanced respiratory support in prehospital care. Including this score in the prehospital scenario could improve patients' care and optimise the resources' management.

Comparison Between Physiological Scores SIPF, CURB-65, and APACHE II as Predictors of Prognosis and Mortality in Hospitalized Patients with COVID-19 Pneumonia: A Multicenter Study, Saudi Arabia

Background

A coronavirus pandemic (COVID-19) is associated with catastrophic effects on the world with high morbidity and mortality. We aimed to evaluate the accuracy of physiological shock index (SIPF) (shock index and hypoxemia), CURB -65, acute physiology, and chronic health assessment II (APACHE II) as predictors of prognosis and in-hospital mortality in patients with COVID-19 pneumonia.

Methods

In Saudi Arabia, a multicenter retrospective study was conducted on hospitalized adult patients confirmed to have COVID-19 pneumonia. Information needed to calculate SIPF, CURB-65, and APACHE II scores were obtained from medical records within 24 hours of admission.

Results

The study included 1131 COVID-19 patients who met the inclusion criteria. They were divided into two groups: (A) the ICU group (n=340; 30.1%) and (B) the ward group (n=791; 69.9%). The most common concomitant diseases of patients at initial ICU admission were hypertension (71.5%) and diabetes (62.4%), and most of them were men (63.8%). The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001). The SIPF score showed a significantly higher ability to predict both ICU admission and mortality in patients with COVID-19 pneumonia compared with APACHE II and CURB -65; (AUC 0.89 vs 0.87; p < 0.001) and (AUC 0.89 vs 0.84; p < 0.001) for ICU admission and (AUC 0.90 vs 0.65; p < 0.001) and (AUC 0.90 vs 0.80; p < 0.001) for mortality, respectively.

Conclusion

The ability of the SIPF score to predict ICU admission and mortality in COVID-19 pneumonia is higher than that of APACHE II and CURB-65. The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001).

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.

Older patients with COVID-19 and neuropsychiatric conditions: A study of risk factors for mortality

BACKGROUND

Little is known about risk factors for mortality in older patients with COVID-19 and neuropsychiatric conditions.

METHODS

We conducted a multicentric retrospective observational study at Assistance Publique-Hôpitaux de Paris. We selected inpatients aged 70 years or older, with COVID-19 and preexisting neuropsychiatric comorbidities and/or new neuropsychiatric manifestations. We examined demographics, comorbidities, functional status, and presentation including neuropsychiatric symptoms and disorders, as well as paraclinical data. Cox survival analysis was conducted to determine risk factors for mortality at 40 days after the first symptoms of COVID-19.

RESULTS

Out of 191 patients included (median age 80 [interquartile range 74-87]), 135 (71%) had neuropsychiatric comorbidities including cognitive impairment (39%), cerebrovascular disease (22%), Parkinsonism (6%), and brain tumors (6%). A total of 152 (79%) patients presented new-onset neuropsychiatric manifestations including sensory symptoms (6%), motor deficit (11%), behavioral (18%) and cognitive (23%) disturbances, gait impairment (11%), and impaired consciousness (18%). The mortality rate at 40 days was 19.4%. A history of brain tumor or Parkinsonism or the occurrence of impaired consciousness were neurological factors associated with a higher risk of mortality. A lower Activities of Daily Living score (hazard ratio [HR] 0.69, 95% confidence interval [CI] 0.58-0.82), a neutrophil-to-lymphocyte ratio ≥ 9.9 (HR 5.69, 95% CI 2.69-12.0), and thrombocytopenia (HR 5.70, 95% CI 2.75-11.8) independently increased the risk of mortality (all p < .001).

CONCLUSION

Understanding mortality risk factors in older inpatients with COVID-19 and neuropsychiatric conditions may be helpful to neurologists and geriatricians who manage these patients in clinical practice.

Nutritional parameters associated with prognosis in non-critically ill hospitalized COVID-19 patients: The NUTRI-COVID19 study

BACKGROUND & AIMS

To investigate the association between the parameters used in nutritional screening assessment (body mass index [BMI], unintentional weight loss [WL] and reduced food intake) and clinical outcomes in non-critically ill, hospitalized coronavirus disease 2019 (COVID-19) patients.

METHODS

This was a prospective multicenter real-life study carried out during the first pandemic wave in 11 Italian Hospitals. In total, 1391 patients were included. The primary end-point was a composite of in-hospital mortality or admission to ICU, whichever came first. The key secondary end-point was in-hospital mortality.

RESULTS

Multivariable models were based on 1183 patients with complete data. Reduced self-reported food intake before hospitalization and/or expected by physicians in the next days since admission was found to have a negative prognostic impact for both the primary and secondary end-point (P < .001 for both). No association with BMI and WL was observed. Other predictors of outcomes were age and presence of multiple comorbidities. A significant interaction between obesity and multi-morbidity (≥2) was detected. Obesity was found to be a risk factor for composite end-point (HR = 1.36 [95%CI, 1.03-1.80]; P = .031) and a protective factor against in-hospital mortality (HR = 0.32 [95%CI, 0.20-0.51]; P < .001) in patients with and without multiple comorbidities, respectively. Secondary analysis (patients, N = 829), further adjusted for high C-reactive protein (>21 mg/dL) and LDH (>430 mU/mL) levels yielded consistent findings.

CONCLUSIONS

Reduced self-reported food intake before hospitalization and/or expected by physicians in the next days since admission was associated with negative clinical outcomes in non-critically ill, hospitalized COVID-19 patients. This simple and easily obtainable parameter may be useful to identify patients at highest risk of poor prognosis, who may benefit from prompt nutritional support. The presence of comorbidities could be the key factor, which may determine the protective or harmful role of a high body mass index in COVID-19.

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

OBJECTIVE

To compare the predictive value of the quick COVID-19 Severity Index (qCSI) and the National Early Warning Score (NEWS) for 90-day mortality amongst COVID-19 patients.

METHODS

Multicenter retrospective cohort study conducted in adult patients transferred by ambulance to an emergency department (ED) with suspected COVID-19 infection subsequently confirmed by a SARS-CoV-2 test (polymerase chain reaction). We collected epidemiological data, clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and use of supplemental oxygen) and hospital variables. The primary outcome was cumulative all-cause mortality during a 90-day follow-up, with mortality assessment monitoring time points at 1, 2, 7, 14, 30 and 90 days from ED attendance. Comparison of performances for 90-day mortality between both scores was carried out by univariate analysis.

RESULTS

From March to November 2020, we included 2,961 SARS-CoV-2 positive patients (median age 79 years, IQR 66-88), with 49.2% females. The qCSI score provided an AUC ranging from 0.769 (1-day mortality) to 0.749 (90-day mortality), whereas AUCs for NEWS ranging from 0.825 for 1-day mortality to 0.777 for 90-day mortality. At all-time points studied, differences between both scores were statistically significant (p < .001).

CONCLUSION

Patients with SARS-CoV-2 can rapidly develop bilateral pneumonias with multiorgan disease; in these cases, in which an evacuation by the EMS is required, reliable scores for an early identification of patients with risk of clinical deterioration are critical. The NEWS score provides not only better prognostic results than those offered by qCSI at all the analyzed time points, but it is also better suited for COVID-19 patients.KEY MESSAGESThis work aims to determine whether NEWS is the best score for mortality risk assessment in patients with COVID-19.AUCs for NEWS ranged from 0.825 for 1-day mortality to 0.777 for 90-day mortality and were significantly higher than those for qCSI in these same outcomes.NEWS provides a better prognostic capacity than the qCSI score and allows for long-term (90 days) mortality risk assessment of COVID-19 patients.

External Validation of COVID-19 Risk Scores during Three Waves of Pandemic in a German Cohort-A Retrospective Study

Several risk scores were developed during the COVID-19 pandemic to identify patients at risk for critical illness as a basic step to personalizing medicine even in pandemic circumstances. However, the generalizability of these scores with regard to different populations, clinical settings, healthcare systems, and new epidemiological circumstances is unknown. The aim of our study was to compare the predictive validity of qSOFA, CRB65, NEWS, COVID-GRAM, and 4C-Mortality score. In a monocentric retrospective cohort, consecutively hospitalized adults with COVID-19 from February 2020 to June 2021 were included; risk scores at admission were calculated. The area under the receiver operating characteristic curve and the area under the precision-recall curve were compared using DeLong's method and a bootstrapping approach. A total of 347 patients were included; 23.6% were admitted to the ICU, and 9.2% died in a hospital. NEWS and 4C-Score performed best for the outcomes ICU admission and in-hospital mortality. The easy-to-use bedside score NEWS has proven to identify patients at risk for critical illness, whereas the more complex COVID-19-specific scores 4C and COVID-GRAM were not superior. Decreasing mortality and ICU-admission rates affected the discriminatory ability of all scores. A further evaluation of risk assessment is needed in view of new and rapidly changing epidemiological evolution.

The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19

BACKGROUND

The COVID-GRAM is a clinical risk rating score for predicting the prognosis of hospitalized COVID-19 infected patients.

AIM

Our study aimed to evaluate the use of the COVID-GRAM score in patients with COVID-19 based on the data from the COronavirus in the LOwer Silesia (COLOS) registry.

MATERIAL AND METHODS

The study group (834 patients of Caucasian patients) was retrospectively divided into three arms according to the risk achieved on the COVID-GRAM score calculated at the time of hospital admission (between February 2020 and July 2021): low, medium, and high risk. The Omnibus chi-square test, Fisher test, and Welch ANOVA were used in the statistical analysis. Post-hoc analysis for continuous variables was performed using Tukey's correction with the Games-Howell test. Additionally, the ROC analysis was performed over time using inverse probability of censorship (IPCW) estimation. The GRAM-COVID score was estimated from the time-dependent area under the curve (AUC).

RESULTS

Most patients (65%) had a low risk of complications on the COVID-GRAM scale. There were 113 patients in the high-risk group (13%). In the medium- and high-risk groups, comorbidities occurred statistically significantly more often, e.g., hypertension, diabetes, atrial fibrillation and flutter, heart failure, valvular disease, chronic kidney disease, and obstructive pulmonary disease (COPD), compared to low-risk tier subjects. These individuals were also patients with a higher incidence of neurological and cardiac complications in the past. Low saturation of oxygen values on admission, changes in C-reactive protein, leukocytosis, hyperglycemia, and procalcitonin level were associated with an increased risk of death during hospitalization. The troponin level was an independent mortality factor. A change from low to medium category reduced the overall survival probability by more than 8 times and from low to high by 25 times. The factor with the strongest impact on survival was the absence of other diseases. The medium-risk patient group was more likely to require dialysis during hospitalization. The need for antibiotics was more significant in the high-risk group on the GRAM score.

CONCLUSION

The COVID-GRAM score corresponds well with total mortality. The factor with the strongest impact on survival was the absence of other diseases. The worst prognosis was for patients who were unconscious during admission. Patients with higher COVID-GRAM score were significantly less likely to return to full health during follow-up. There is a continuing need to develop reliable, easy-to-adopt tools for stratifying the course of SARS-CoV-2 infection.

External validation of the COVID-19 4C mortality score in an urban United States cohort

BACKGROUND

Identifying patients at risk for mortality from COVID-19 is crucial to triage, clinical decision-making, and the allocation of scarce hospital resources. The 4C Mortality Score effectively predicts COVID-19 mortality, but it has not been validated in a United States (U.S.) population. The purpose of this study is to determine whether the 4C Mortality Score accurately predicts COVID-19 mortality in an urban U.S. adult inpatient population.

METHODS

This retrospective cohort study included adult patients admitted to a single-center, tertiary care hospital (Philadelphia, PA) with a positive SARS-CoV-2 PCR from 3/01/2020 to 6/06/2020. Variables were extracted through a combination of automated export and manual chart review. The outcome of interest was mortality during hospital admission or within 30 days of discharge.

RESULTS

This study included 426 patients; mean age was 64.4 years, 43.4% were female, and 54.5% self-identified as Black or African American. All-cause mortality was observed in 71 patients (16.7%). The area under the receiver operator characteristic curve of the 4C Mortality Score was 0.85 (95% confidence interval, 0.79-0.89).

CONCLUSIONS

Clinicians may use the 4C Mortality Score in an urban, majority Black, U.S. inpatient population. The derivation and validation cohorts were treated in the pre-vaccine era so the 4C Score may over-predict mortality in current patient populations. With stubbornly high inpatient mortality rates, however, the 4C Score remains one of the best tools available to date to inform thoughtful triage and treatment allocation.

External validation of risk scores to predict in-hospital mortality in patients hospitalized due to coronavirus disease 2019

BACKGROUND

The coronavirus disease 2019 (COVID-19) presents an urgent threat to global health. Prediction models that accurately estimate mortality risk in hospitalized patients could assist medical staff in treatment and allocating limited resources.

AIMS

To externally validate two promising previously published risk scores that predict in-hospital mortality among hospitalized COVID-19 patients.

METHODS

Two prospective cohorts were available; a cohort of 1028 patients admitted to one of nine hospitals in Lombardy, Italy (the Lombardy cohort) and a cohort of 432 patients admitted to a hospital in Leiden, the Netherlands (the Leiden cohort). The endpoint was in-hospital mortality. All patients were adult and tested COVID-19 PCR-positive. Model discrimination and calibration were assessed.

RESULTS

The C-statistic of the 4C mortality score was good in the Lombardy cohort (0.85, 95CI: 0.82-0.89) and in the Leiden cohort (0.87, 95CI: 0.80-0.94). Model calibration was acceptable in the Lombardy cohort but poor in the Leiden cohort due to the model systematically overpredicting the mortality risk for all patients. The C-statistic of the CURB-65 score was good in the Lombardy cohort (0.80, 95CI: 0.75-0.85) and in the Leiden cohort (0.82, 95CI: 0.76-0.88). The mortality rate in the CURB-65 development cohort was much lower than the mortality rate in the Lombardy cohort. A similar but less pronounced trend was found for patients in the Leiden cohort.

CONCLUSION

Although performances did not differ greatly, the 4C mortality score showed the best performance. However, because of quickly changing circumstances, model recalibration may be necessary before using the 4C mortality score.

COVID-19'da hastane içi mortaliteyi tahmin etmede enflamatuvar temelli parametrelerin ve MELD-XI skorunun 4C mortalite skoru ile karşılaştırılması

Purpose: In this study, we compared the roles of inflammatory parameters such as neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), C-reactive protein/lymphocyte ratio (CLR), monocyte/lymphocyte ratio (MLR), neutrophil/platelet ratio (NPR), neutrophil/monocyte ratio (NMR), CRP/albumin ratio (CAR), BUN/albumin ratio (BAR), MELD-XI score and 4C mortality score in predicting in-hospital mortality risk in COVID-19. Materials and Methods: A total of 117 patients over 18 years old with a PCR-confirmed diagnosis of COVID-19 between June 2020 and February 2021 were retrospectively included. The roles of parameters for independently predicting in-hospital mortality were determined and compared with each other using appropriate statistical methods. Results: Age, chronic kidney disease, diabetes mellitus, acute kidney injury, and length of hospital stay, urea, creatinine, LDH, AST, ferritin, D-dimer, CRP, albumin, Hb, CLR, BAR, CAR, MELD-XI score, and 4C mortality score were significantly correlated to in-hospital mortality. However, only the 4C mortality score and AST independently predicted in-hospital mortality in COVID-19 [OR 2.08 (%95 CI 1.06-2.36), for 4C mortality score, and OR 1.05 (%95 CI 1.00-1.10), for AST]. Conclusion: Unlike other mortality-related inflammatory parameters, the 4C mortality score and AST were independent and strong predictors of mortality in hospitalized COVID-19 patients.

One-Year Report of COVID-19 Impact on Geriatric Patients: a Bio-Psycho-Social Approach

Background

Since December of 2019, coronavirus 19 (COVID-19) has spread rapidly around the world. Our understanding of the infection has grown over the past year, and its impact on older adults is particularly significant. Apart from the direct impacts of COVID-19 infections, it has also led to lockdowns which, in turn, result in isolation and loneliness.

Method

We conducted a literature review of publicly available articles of the COVID-19 pandemic impact on the geriatric population between December 2019 and April 2021, a total of 748 articles.

Results

The review will be presented with the Bio-Psycho-Social model, covering how the biological, psychological, and sociological aspects of health are intertwined and impact older adults. Early studies have also highlighted the prevalence of post-COVID infection symptoms that typically fall under geriatric medicine care. We highlight the bidirectional impact of isolation and COVID-19 infections on geriatric health, as well as discuss pertinent topics such as vaccine efficacy, long-term sequelae of COVID-19 infections, and ageism.

Conclusion

This review seeks to present a one-year report of what is known about COVID-19 and geriatric medicine, as well as provide guidance to practitioners who care for older adults based on the most up-to-date literature.

Comparison of Pneumonia Severity Indices, qCSI, 4C-Mortality Score and qSOFA in Predicting Mortality in Hospitalized Patients with COVID-19 Pneumonia

This is a retrospective and observational study on 1511 patients with SARS-CoV-2, who were diagnosed with COVID-19 by real-time PCR testing and hospitalized due to COVID-19 pneumonia. 1511 patients, 879 male (58.17%) and 632 female (41.83%) with a mean age of 60.1 ± 14.7 were included in the study. Survivors and non-survivors groups were statistically compared with respect to survival, discharge, ICU admission and in-hospital death. Although gender was not statistically significant different between two groups, 80 (60.15%) of the patients who died were male. Mean age was 72.8 ± 11.8 in non-survivors vs. 59.9 ± 14.7 in survivors (p < 0.001). Overall in-hospital mortality was found to be 8.8% (133/1511 cases), and overall ICU admission was 10.85% (164/1511 cases). The PSI/PORT score of the non-survivors group was higher than that of the survivors group (144.38 ± 28.64 versus 67.17 ± 25.63, p < 0.001). The PSI/PORT yielding the highest performance was the best predictor for in-hospital mortality, since it incorporates the factors as advanced age and comorbidity (AUROC 0.971; % 95 CI 0.961−0.981). The use of A-DROP may also be preferred as an easier alternative to PSI/PORT, which is a time-consuming evaluation although it is more comprehensive.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

Clinical update on COVID-19 for the emergency clinician: Presentation and evaluation

INTRODUCTION

Coronavirus disease of 2019 (COVID-19) has resulted in millions of cases worldwide. As the pandemic has progressed, the understanding of this disease has evolved.

OBJECTIVE

This first in a two-part series on COVID-19 updates provides a focused overview of the presentation and evaluation of COVID-19 for emergency clinicians.

DISCUSSION

COVID-19, caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), has resulted in significant morbidity and mortality worldwide. Several variants exist, including a variant of concern known as Delta (B.1.617.2 lineage) and the Omicron variant (B.1.1.529 lineage). The Delta variant is associated with higher infectivity and poor patient outcomes, and the Omicron variant has resulted in a significant increase in infections. While over 80% of patients experience mild symptoms, a significant proportion can be critically ill, including those who are older and those with comorbidities. Upper respiratory symptoms, fever, and changes in taste/smell remain the most common presenting symptoms. Extrapulmonary complications are numerous and may be severe, including the cardiovascular, neurologic, gastrointestinal, and dermatologic systems. Emergency department evaluation includes focused testing for COVID-19 and assessment of end-organ injury. Imaging may include chest radiography, computed tomography, or ultrasound. Several risk scores may assist in prognostication, including the 4C (Coronavirus Clinical Characterisation Consortium) score, quick COVID Severity Index (qCSI), NEWS2, and the PRIEST score, but these should only supplement and not replace clinical judgment.

CONCLUSION

This review provides a focused update of the presentation and evaluation of COVID-19 for emergency clinicians.

A systematic review of the discrimination and absolute mortality predicted by the National Early Warning Scores according to different cut-off values and prediction windows

BACKGROUND

Although early warning scores were intended to simply identify patients in need of life-saving interventions, prediction has become their commonest metric. This review examined variation in the ability of the National Early Warning Scores (NEWS) in adult patients to predict absolute mortality at different times and cut-offs values.

METHOD

Following PRISMA guidelines, all studies reporting NEWS and NEWS2 providing enough information to fulfil the review's aims were included.

RESULTS

From 121 papers identified, the average area under the Receiver Operating Characteristic curve (AUC) for mortality declined from 0.90 at 24-hours to 0.76 at 30-days. Studies with a low overall mortality had a higher AUC for 24-hour mortality, as did general ward patients compared to patients seen earlier in their treatment. 24-hour mortality increased from 1.8% for a NEWS ≥3 to 7.8% for NEWS ≥7. Although 24-hour mortality for NEWS <3 was only 0.07% these deaths accounted for 9% of all deaths within 24-hours; for NEWS <7 24-hour mortality was 0.23%, which accounted for 44% of all 24-hour deaths. Within 30-days of a NEWS recording 22% of all deaths occurred in patients with a NEWS <3, 52% in patients with a NEWS <5, and 75% in patient with a NEWS <7.

CONCLUSION

NEWS reliably identifies patients most and least likely to die within 24-hours, which is what it was designed to do. However, many patients identified to have a low risk of imminent death die within 30-days. NEWS mortality predictions beyond 24-hours are unreliable.

Risk of Seven-Day Worsening and Death: A New Clinically Derived COVID-19 Score

This monocentric, retrospective, two-stage observational study aimed to recognize the risk factors for a poor outcome in patients hospitalized with SARS-CoV-2 infection, and to develop and validate a risk score that identifies subjects at risk of worsening, death, or both. The data of patients with SARS-CoV-2 infection during the first wave of the pandemic were collected and analyzed as a derivation cohort. Variables with predictive properties were used to construct a prognostic score, which was tried out on a validation cohort enrolled during the second wave. The derivation cohort included 494 patients; the median age was 62 and the overall fatality rate was 22.3%. In a multivariable analysis, age, oxygen saturation, neutrophil-to-lymphocyte ratio, C-reactive protein and lactate dehydrogenase were independent predictors of death and composed the score. A cutoff value of 3 demonstrated a sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) of 93.5%, 68.5%, 47.4% and 97.2% for death, and 84.9%, 84.5%, 79.6% and 87.9% for worsening, respectively. The validation cohort included 415 subjects. The score application showed a Se, Sp, PPV and NPV of 93.4%, 61.6%, 29.5% and 98.1% for death, and 81%, 76.3%, 72.1% and 84.1% for worsening, respectively. We propose a new clinical, easy and reliable score to predict the outcome in hospitalized SARS-CoV-2 patients.

Frailty in Elderly Patients with Covid-19: A Narrative Review

Introduction

The SARS CoV-2 pandemic still generates a very high number of affected patients and a significant mortality rate. It is essential to establish objective criteria to stratify COVID-19 death risk. Frailty has been identified as a potential determinant of increased vulnerability in older adults affected by COVID-19, because it may suggest alterations of physical performance and functional autonomy.

Methods

We have conducted a narrative review of the literature on the evidences regarding COVID-19 and the frailty condition. Thirteen observational studies were included.

Conclusion

Data emerging from the studies indicate that older COVID-19 patients with a frailty condition have an increased risk of mortality compared with non-frail patients, and this association is independent of other clinical and demographic factors. A frailty evaluation is required to help clinicians to better stratify the overall risk of death for older patients with COVID-19.

Oropharyngeal Microbiome Profiled at Admission is Predictive of the Need for Respiratory Support Among COVID-19 Patients

The clinical course of infection due to respiratory viruses such as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), the causative agent of Coronavirus Disease 2019 (COVID-19) is thought to be influenced by the community of organisms that colonizes the upper respiratory tract, the oropharyngeal microbiome. In this study, we examined the oropharyngeal microbiome of suspected COVID-19 patients presenting to the Emergency Department and an inpatient COVID-19 unit with symptoms of acute COVID-19. Of 115 enrolled patients, 74 were confirmed COVID-19+ and 50 had symptom duration of 14 days or less; 38 acute COVID-19+ patients (76%) went on to require respiratory support. Although no microbiome features were found to be significantly different between COVID-19+ and COVID-19-patients, when we conducted random forest classification modeling (RFC) to predict the need of respiratory support for the COVID-19+ patients our analysis identified a subset of organisms and metabolic pathways whose relative abundance, when combined with clinical factors (such as age and Body Mass Index), was highly predictive of the need for respiratory support (F1 score 0.857). Microbiome Multivariable Association with Linear Models (MaAsLin2) analysis was then applied to the features identified as predicative of the need for respiratory support by the RFC. This analysis revealed reduced abundance of Prevotella salivae and metabolic pathways associated with lipopolysaccharide and mycolic acid biosynthesis to be the strongest predictors of patients requiring respiratory support. These findings suggest that composition of the oropharyngeal microbiome in COVID-19 may play a role in determining who will suffer from severe disease manifestations.

Importance

The microbial community that colonizes the upper airway, the oropharyngeal microbiome, has the potential to affect how patients respond to respiratory viruses such as SARS-CoV2, the causative agent of COVID-19. In this study, we investigated the oropharyngeal microbiome of COVID-19 patients using high throughput DNA sequencing performed on oral swabs. We combined patient characteristics available at intake such as medical comorbidities and age, with measured abundance of bacterial species and metabolic pathways and then trained a machine learning model to determine what features are predicative of patients needing respiratory support in the form of supplemental oxygen or mechanical ventilation. We found that decreased abundance of some bacterial species and increased abundance of pathways associated bacterial products biosynthesis was highly predictive of needing respiratory support. This suggests that the oropharyngeal microbiome affects disease course in COVID-19 and could be targeted for diagnostic purposes to determine who may need oxygen, or therapeutic purposes such as probiotics to prevent severe COVID-19 disease manifestations.

Validation of ISARIC 4C Mortality and Deterioration Scores in a Mixed Vaccination Status Cohort of Hospitalized Coronavirus Disease 2019 (COVID-19) Patients in Singapore

In this cross-sectional study, we studied performance of the International Severe Acute Respiratory and Emerging Infections Consortium mortality and deterioration scores in a cohort of 410 hospitalized patients (51.2% fully vaccinated). area under the receiver operating characteristic curves were 0.778 and 0.764, respectively, comparable to originally published validation cohorts. Subgroup analysis showed equally good performance in vaccinated and partially or unvaccinated patients.

Clinical Utility of 4C Mortality Scores among Japanese COVID-19 Patients: A Multicenter Study

Background: We analyzed data from COVID-19 patients in Japan to assess the utility of the 4C mortality score as compared with conventional scorings. Methods: In this multicenter study, COVID-19 patients hospitalized between March 2020 and June 2021, over 16 years old, were recruited. The superiority for correctly predicting mortality and severity by applying the receiver operating characteristic (ROC) curve was compared. A Cox regression model was used to compare the length of hospitalization for each risk group of 4C mortality score. Results: Among 206 patients, 21 patients died. The area under the curve (AUC) (95% confidential interval (CI)) of the ROC curve for mortality and severity, respectively, of 4C mortality scores (0.84 (95% CI 0.76–0.92) and 0.85 (95% CI 0.80–0.91)) were higher than those of qSOFA (0.66 (95% CI 0.53–0.78) and 0.67 (95% CI 0.59–0.75)), SOFA (0.70 (95% CI 0.55–0.84) and 0.81 (95% CI 0.74–0.89)), A-DROP (0.78 (95% CI 0.69–0.88) and 0.81 (95% CI 0.74–0.88)), and CURB-65 (0.82 (95% CI 0.74–0.90) and 0.82 (95% CI 0.76–0.88)). For length of hospitalization among survivors, the intermediate- and high- or very high-risk groups had significantly lower hazard ratios, i.e., 0.48 (95% CI 0.30–0.76)) and 0.23 (95% CI 0.13–0.43) for discharge. Conclusions: The 4C mortality score is better for estimating mortality and severity in COVID-19 Japanese patients than other scoring systems.

Performance features and mortality prediction of the 4C Score early in COVID-19 infection: a retrospective study in Saudi Arabia

The ISARIC4C consortium developed and internally validated the 4C Score for prediction of mortality only in hospitalized patients. We aimed to assess the validity of the 4C Score in mortality prediction of patients with COVID-19 who had been home isolated or hospitalized.This retrospective cross-sectional study was performed after the first wave of COVID-19. Data of all PCR-positive COVID-19 patients who had been discharged, hospitalized, or died were retrospectively analyzed. Patients were classified into four risk groups according to the 4C Mortality Score. A total of (506) patients were classified as follows: low (57.1%), intermediate (27.9%), high (13%), and very high (2%) risk groups. Clinical, radiological, and laboratory data were significantly more severe in the high and very high-risk groups compared with other groups (p<0.001 for all). Mortality rate was correctly estimated by the model with 71% sensitivity, 88.6% specificity, and area under the curve of 0.9. The mortality rate was underestimated among the very high-risk group (66.2% vs 90%). The odds of mortality were significantly greater in the presence of hypoxia (OR 2.6, 95% CI 1.5 to 4.6, p<0.001) and high respiratory rate (OR 5.3, 95% CI 1.6 to 17.9, p<0.007), C reactive protein (CRP) (OR 3.5, 95% CI 1.8 to 6.8, p<0.001), and blood urea nitrogen (BUN) (OR 1.9, 95% CI 1.3 to 3.1, p<0.002). Other components of the model had non-significant predictions. In conclusion, the 4C Mortality Score has good sensitivity and specificity in early risk stratification and mortality prediction of patient with COVID-19. Within the model, only hypoxia, tachypnea, high BUN, and CRP were the independent mortality predictors with the possibility of overlooking other important predictors.

Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care

OBJECTIVES

Existing UK prognostic models for patients admitted to the hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death and intensive therapy unit (ITU) admission) in UK secondary care and externally validate the existing 4C score.

DESIGN

Candidate predictors included demographic variables, symptoms, physiological measures, imaging and laboratory tests. Final models used logistic regression with stepwise selection.

SETTING

Model development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset.

PARTICIPANTS

Patients with COVID-19 admitted to UHB January-August 2020 were included.

MAIN OUTCOME MEASURES

Death and ITU admission within 28 days of admission.

RESULTS

1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating characteristic curve (AUROC) for mortality was 0.791 (95% CI 0.761 to 0.822) in UHB and 0.767 (95% CI 0.754 to 0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95% CI 0.883 to 0.929) in UHB and 0.811 (95% CI 0.795 to 0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the International Severe Acute Respiratory and Emerging Infection Consortium 4C score in the UHB dataset was 0.753 (95% CI 0.720 to 0.785).

CONCLUSIONS

The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and performed at least as well as the existing 4C score using only routinely collected patient information. The models can be integrated into electronic medical records systems to calculate each individual patient's probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.

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.

External validation of 4c ISARIC mortality score in critically ill COVID-19 patients from Saudi Arabia

Background:

ISARIC mortality score is a risk stratification tool that helps predict the in-hospital mortality of COVID-19 patients. However, this tool was developed and validated in a British population, and thus, the external validation of this tool in local populations is important.

Objectives:

External validation of the ISARIC mortality score in COVID-19 patients from a large Saudi Arabian intensive care unit (ICU).

Methods:

This is a retrospective study that included all adult patients with COVID-19 admitted to the ICU of King Saud Medical City, Riyadh, Saudi Arabia, from March 2020 to June 2021. Patients who were pregnant or had pulmonary tuberculosis/human immunodeficiency virus were excluded along with patients with missing variables. Data were collected to calculate the ISARIC mortality score and then fitting receiver operator characteristic curve against patients’ outcome.

Results:

A total of 1493 critically ill COVID-19 patients were included. The mortality was 38%, the area under the curve of the score was 0.81 (95% confidence interval [CI]: 0.79–0.83, P < 0.001) and the cutoff value correctly classified 72.7% of the cohort. The cutoff value of >9 had sensitivity of 70.5% (95% CI: 66.6–74.3); specificity, 73.97% (95% CI: 71–76.8); positive predictive value, 62.4% (95% CI: 59.5–65.2) and negative predictive value, 80.2% (95% CI: 78.2–82.4).

Conclusion:

The ISARIC score was found to have excellent predictive ability for mortality in critically ill COVID-19 patients in our Saudi Arabian cohort. A cutoff score of >9 was the optimal criterion.

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.

ISARIC-4C Mortality Score overestimates risk of death due to COVID-19 in Australian ICU patients: a validation cohort study

Objective: To assess the performance of the UK International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) Coronavirus Clinical Characterisation Consortium (4C) Mortality Score for predicting mortality in Australian patients with coronavirus disease 2019 (COVID-19) requiring intensive care unit (ICU) admission. Design: Multicentre, prospective, observational cohort study. Setting: 78 Australian ICUs participating in the SPRINT-SARI (Short Period Incidence Study of Severe Acute Respiratory Infection) Australia study of COVID-19. Participants: Patients aged 16 years or older admitted to participating Australian ICUs with polymerase chain reaction (PCR)-confirmed COVID-19 between 27 February and 10 October 2020. Main outcome measures: ISARIC-4C Mortality Score, calculated at the time of ICU admission. The primary outcome was observed versus predicted in-hospital mortality (by 4C Mortality and APACHE II). Results: 461 patients admitted to a participating ICU were included. 149 (32%) had complete data to calculate a 4C Mortality Score without imputation. Overall, 61/461 patients (13.2%) died, 16.9% lower than the comparable ISARIC-4C cohort in the United Kingdom. In patients with complete data, the median (interquartile range [IQR]) 4C Mortality Score was 10.0 (IQR, 8.0-13.0) and the observed mortality was 16.1% (24/149) versus 22.9% median predicted risk of death. The 4C Mortality Score discriminatory performance measured by the area under the receiver operating characteristic curve (AUROC) was 0.79 (95% CI, 0.68-0.90), similar to its performance in the original ISARIC-4C UK cohort (0.77) and not superior to APACHE II (AUROC, 0.81; 95% CI, 0.75-0.87). Conclusions: When calculated at the time of ICU admission, the 4C Mortality Score consistently overestimated the risk of death for Australian ICU patients with COVID-19. The 4C Mortality Score may need to be individually recalibrated for use outside the UK and in different hospital settings.

Comparison of mortality risk evaluation tools efficacy in critically ill COVID-19 patients

Background

As the COVID-19 pandemic continues, the number of patients admitted to the intensive care unit (ICU) is still increasing. The aim of our article is to estimate which of the conventional ICU mortality risk scores is the most accurate at predicting mortality in COVID-19 patients and to determine how these scores can be used in combination with the 4C Mortality Score.

Methods

This was a retrospective study of critically ill COVID-19 patients treated in tertiary reference COVID-19 hospitals during the year 2020. The 4C Mortality Score was calculated upon admission to the hospital. The Simplified Acute Physiology Score (SAPS) II, Acute Physiology and Chronic Health Evaluation (APACHE) II, and Sequential Organ Failure Assessment (SOFA) scores were calculated upon admission to the ICU. Patients were divided into two groups: ICU survivors and ICU non-survivors.

Results

A total of 249 patients were included in the study, of which 63.1% were male. The average age of all patients was 61.32 ± 13.3 years. The all-cause ICU mortality ratio was 41.4% (n = 103). To determine the accuracy of the ICU mortality risk scores a ROC-AUC analysis was performed. The most accurate scale was the APACHE II, with an AUC value of 0.772 (95% CI 0.714–0.830; p < 0.001). All of the ICU risk scores and 4C Mortality Score were significant mortality predictors in the univariate regression analysis. The multivariate regression analysis was completed to elucidate which of the scores can be used in combination with the independent predictive value. In the final model, the APACHE II and 4C Mortality Score prevailed. For each point increase in the APACHE II, mortality risk increased by 1.155 (OR 1.155, 95% CI 1.085–1.229; p < 0.001), and for each point increase in the 4C Mortality Score, mortality risk increased by 1.191 (OR 1.191, 95% CI 1.086–1.306; p < 0.001), demonstrating the best overall calibration of the model.

Conclusions

The study demonstrated that the APACHE II had the best discrimination of mortality in ICU patients. Both the APACHE II and 4C Mortality Score independently predict mortality risk and can be used concomitantly.

Mental and neurological disorders and risk of COVID-19 susceptibility, illness severity and mortality: A systematic review, meta-analysis and call for action

BACKGROUND

Coronavirus disease 2019 (COVID-19) has evolved into a worldwide pandemic, and has been found to be closely associated with mental and neurological disorders. We aimed to comprehensively quantify the association between mental and neurological disorders, both pre-existing and subsequent, and the risk of susceptibility, severity and mortality of COVID-19.

METHODS

In this systematic review and meta-analysis, we searched PubMed, Web of Science, Embase, PsycINFO, and Cochrane library databases for studies published from the inception up to January 16, 2021 and updated at July 7, 2021. Observational studies including cohort and case-control, cross-sectional studies and case series that reported risk estimates of the association between mental or neurological disorders and COVID-19 susceptibility, illness severity and mortality were included. Two researchers independently extracted data and conducted the quality assessment. Based on I2 heterogeneity, we used a random effects model to calculate pooled odds ratios (OR) and 95% confidence intervals (95% CI). Subgroup analyses and meta-regression analysis were also performed. This study was registered on PROSPERO (registration number: CRD 42021230832).

FINDING

A total of 149 studies (227,351,954 participants, 89,235,737 COVID-19 patients) were included in this analysis, in which 27 reported morbidity (132,727,798), 56 reported illness severity (83,097,968) and 115 reported mortality (88,878,662). Overall, mental and neurological disorders were associated with a significant high risk of infection (pre-existing mental: OR 1·67, 95% CI 1·12-2·49; and pre-existing neurological: 2·05, 1·58-2·67), illness severity (mental: pre-existing, 1·40, 1·25-1·57; sequelae, 4·85, 2·53-9·32; neurological: pre-existing, 1·43, 1·09-1·88; sequelae, 2·17, 1·45-3·24), and mortality (mental: pre-existing, 1·47, 1·26-1·72; neurological: pre-existing, 2·08, 1·61-2·69; sequelae, 2·03, 1·66-2·49) from COVID-19. Subgroup analysis revealed that association with illness severity was stronger among younger COVID-19 patients, and those with subsequent mental disorders, living in low- and middle-income regions. Younger patients with mental and neurological disorders were associated with higher mortality than elders. For type-specific mental disorders, susceptibility to contracting COVID-19 was associated with pre-existing mood disorders, anxiety, and attention-deficit hyperactivity disorder (ADHD); illness severity was associated with both pre-existing and subsequent mood disorders as well as sleep disturbance; and mortality was associated with pre-existing schizophrenia. For neurological disorders, susceptibility was associated with pre-existing dementia; both severity and mortality were associated with subsequent delirium and altered mental status; besides, mortality was associated with pre-existing and subsequent dementia and multiple specific neurological diseases. Heterogeneities were substantial across studies in most analysis.

INTERPRETATION

The findings show an important role of mental and neurological disorders in the context of COVID-19 and provide clues and directions for identifying and protecting vulnerable populations in the pandemic. Early detection and intervention for neurological and mental disorders are urgently needed to control morbidity and mortality induced by the COVID-19 pandemic. However, there was substantial heterogeneity among the included studies, and the results should be interpreted with caution. More studies are needed to explore long-term mental and neurological sequela, as well as the underlying brain mechanisms for the sake of elucidating the causal pathways for these associations.

FUNDING

This study is supported by grants from the National Key Research and Development Program of China, the National Natural Science Foundation of China, Special Research Fund of PKUHSC for Prevention and Control of COVID-19, and the Fundamental Research Funds for the Central Universities.

Performance of the CURB-65, ISARIC-4C and COVID-GRAM scores in terms of severity for COVID-19 patients

BACKGROUND

In the COVID-19 pandemic, difficulties have been experienced in the provision of healthcare services because of excessive patient admissions to hospitals and emergency departments. It has become important to use clear and objective criteria for the early diagnosis of patients with high-risk classification and clinical worsening risk.

OBJECTIVE

The aim of this study was to assess the prognostic accuracy of CURB-65, ISARIC-4C and COVID-GRAM scores in patients hospitalised for COVID-19 and to compare the scoring systems in terms of predicting in-hospital mortality and intensive care unit requirement.

METHODS

The files of all COVID-19 patients over the age of 18 who were admitted to the emergency department and hospitalised between September 1, 2020 and December 1, 2020 were retrospectively scanned. The area under the receiver operating characteristic curve and Youden J Index were used to compare scoring systems for predicting in-hospital mortality and intensive care requirement.

RESULTS

There were 481 patients included in this study. The median age of the patients was 67 (52-79). In terms of in-hospital mortality, the AUC of CURB-65, ISARIC-4C and COVID-GRAM were 0.846, 0.784 and 0.701 respectively. In terms of intensive care requirement, the AUC of CURB-65, ISARIC-4C and COVID-GRAM were 0.898, 0.797 and 0.684 respectively. In our study, Youden's J indexes of CURB-65, ISARIC-4C and COVID-GRAM scores were found to be 0.59, 0.27 and 0.01 respectively, for mortality prediction of COVID-19 patients. Whereas Youden's J indexes were found to be 0.63, 0.26 and 0.01 respectively for determining intensive care requirement.

CONCLUSIONS

Among the scoring systems assessed, CURB-65 score had better performance in predicting in-hospital mortality and ICU requirement in COVID-19 patients. ISARIC-4C has been found successful in identifying low-risk patients and the use of the ISARIC-4C score with CURB-65 increases the accuracy of risk assessment.

Prediction of mortality in COVID-19 through combing CT severity score with NEWS, qSOFA, or peripheral perfusion index

INTRODUCTION

The assessment of disease severity and the prediction of clinical outcomes at early disease stages can contribute to decreased mortality in patients with Coronavirus disease 2019 (COVID-19). This study was conducted to develop and validate a multivariable risk prediction model for mortality with using a combination of computed tomography severity score (CT-SS), national early warning score (NEWS), and quick sequential (sepsis-related) organ failure assessment (qSOFA) in COVID-19 patients.

METHODS

We retrospectively collected medical data from 655 adult COVID-19 patients admitted to our hospital between July and November 2020. Data on demographics, clinical characteristics, and laboratory and radiological findings measured as part of standard care at admission were used to calculate NEWS, qSOFA score, CT-SS, peripheral perfusion index (PPI) and shock index (SI). Logistic regression and Cox proportional hazard models were used to predict mortality, which was our primary outcome. The predictive accuracy of distinct scoring systems was evaluated by the receiver-operating characteristic (ROC) curve analysis.

RESULTS

The median age was 50.0 years [333 males (50.8%), 322 females (49.2%)]. Higher NEWS and SI was associated with time-to-death within 90-days, whereas higher age, CT-SS and lower PPI were significantly associated with time-to-death within both 14 days and 90 days in the adjusted Cox regression model. The CT-SS predicted different mortality risk levels within each stratum of NEWS and qSOFA and improved the discrimination of mortality prediction models. Combining CT-SS with NEWS score yielded more accurate 14 days (DBA: -0.048, p = 0.002) and 90 days (DBA: -0.066, p < 0.001) mortality prediction.

CONCLUSION

Combining severity tools such as CT-SS, NEWS and qSOFA improves the accuracy of predicting mortality in patients with COVID-19. Inclusion of these tools in decision strategies might provide early detection of high-risk groups, avoid delayed medical attention, and improve patient outcomes.

Comorbidity-adjusted NEWS predicts mortality in suspected patients with COVID-19 from nursing homes: Multicentre retrospective cohort study

Aims

To assess the prognostic accuracy of comorbidity‐adjusted National Early Warning Score in suspected Coronavirus disease 2019 patients transferred from nursing homes by the Emergency Department.

Design

Multicentre retrospective cohort study.

Methods

Patients transferred by high‐priority ambulances from nursing homes to Emergency Departments with suspected severe acute respiratory syndrome coronavirus 2 infection, from March 12 to July 31 2020, were considered. Included variables were: clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and supplemental oxygen use), the presence of comorbidities and confirmatory analytical diagnosis of severe acute respiratory syndrome coronavirus 2 infection. The primary outcome was a 2‐day mortality rate. The discriminatory capability of the National Early Warning Score was assessed by the area under the receiver operating characteristic curve in two different cohorts, the validation and the revalidation, which were randomly selected from the main cohort.

Results

A total of 337 nursing homes, 10 advanced life support units, 51 basic life support units and 8 hospitals in Spain entailing 1,324 patients (median age 87 years) was involved in this study. Two‐day mortality was 11.5% (152 cases), with a positivity rate of severe acute respiratory syndrome coronavirus 2 of 51.2%, 77.7% of hospitalization from whom 1% was of intensive care unit admission. The National Early Warning Score results for the revalidation cohort presented an AUC of 0.771, and of 0.885, 0.778 and 0.730 for the low‐, medium‐ and high‐level groups of comorbidities.

Conclusion

The comorbidity‐adjusted National Early Warning Score provides a good short‐term prognostic criterion, information that can help in the decision‐making process to guide the best strategy for each older adult, under the current pandemic.

Impact

What problem did the study address?

Under the current coronavirus disease 2019 pandemic, targeting older adults at high risk of deterioration in nursing homes remains challenging.

What were the main findings?

Comorbidity‐adjusted National Early Warning Score helps to forecast the risk of clinical deterioration more accurately.

Where and on whom will the research have impact?

A high NEWS, with a low level of comorbidity is associated with optimal predictive performance, making these older adults likely to benefit from continued follow up and potentially hospital referral under the current coronavirus disease 2019 pandemic.

Chest Radiograph Scoring Alone or Combined with Other Risk Scores for Predicting Outcomes in COVID-19

Background

Radiographic severity may help predict patient deterioration and outcomes from COVID-19 pneumonia.

Purpose

To assess the reliability and reproducibility of three chest radiograph reporting systems (radiographic assessment of lung edema [RALE], Brixia, and percentage opacification) in patients with proven SARS-CoV-2 infection and examine the ability of these scores to predict adverse outcomes both alone and in conjunction with two clinical scoring systems, National Early Warning Score 2 (NEWS2) and International Severe Acute Respiratory and Emerging Infection Consortium: Coronavirus Clinical Characterization Consortium (ISARIC-4C) mortality.

Materials and Methods

This retrospective cohort study used routinely collected clinical data of patients with polymerase chain reaction–positive SARS-CoV-2 infection admitted to a single center from February 2020 through July 2020. Initial chest radiographs were scored for RALE, Brixia, and percentage opacification by one of three radiologists. Intra- and interreader agreement were assessed with intraclass correlation coefficients. The rate of admission to the intensive care unit (ICU) or death up to 60 days after scored chest radiograph was estimated. NEWS2 and ISARIC-4C mortality at hospital admission were calculated. Daily risk for admission to ICU or death was modeled with Cox proportional hazards models that incorporated the chest radiograph scores adjusted for NEWS2 or ISARIC-4C mortality.

Results

Admission chest radiographs of 50 patients (mean age, 74 years ± 16 [standard deviation]; 28 men) were scored by all three radiologists, with good interreader reliability for all scores, as follows: intraclass correlation coefficients were 0.87 for RALE (95% CI: 0.80, 0.92), 0.86 for Brixia (95% CI: 0.76, 0.92), and 0.72 for percentage opacification (95% CI: 0.48, 0.85). Of 751 patients with a chest radiograph, those with greater than 75% opacification had a median time to ICU admission or death of just 1–2 days. Among 628 patients for whom data were available (median age, 76 years [interquartile range, 61–84 years]; 344 men), opacification of 51%–75% increased risk for ICU admission or death by twofold (hazard ratio, 2.2; 95% CI: 1.6, 2.8), and opacification greater than 75% increased ICU risk by fourfold (hazard ratio, 4.0; 95% CI: 3.4, 4.7) compared with opacification of 0%–25%, when adjusted for NEWS2 score.

Conclusion

Brixia, radiographic assessment of lung edema, and percentage opacification scores all reliably helped predict adverse outcomes in SARS-CoV-2 infection.

© RSNA, 2021

Online supplemental material is available for this article.

See also the editorial by Little in this issue.

Frailty Assessment in the Emergency Department for Risk Stratification of COVID-19 Patients Aged ≥80 Years

OBJECTIVES

To evaluate, in a cohort of adults aged ≥80 years, the overlapping effect of clinical severity, comorbidities, cognitive impairment, and frailty, for the in-hospital death risk stratification of COVID-19 older patients since emergency department (ED) admission.

DESIGN

Single-center prospective observational cohort study.

SETTING AND PARTICIPANTS

The study was conducted in the ED of a teaching hospital that is a referral center for COVID-19 in central Italy. We enrolled all patients with aged ≥80 years old consecutively admitted to the ED between April 2020 and March 2021.

METHODS

Clinical variables assessed in the ED were evaluated for the association with all-cause in-hospital death. Evaluated parameters were severity of disease, frailty, comorbidities, cognitive impairment, delirium, and dependency in daily life activities. Cox regression analysis was used to identify independent risk factors for poor outcomes.

RESULTS

A total of 729 patients aged ≥80 years were enrolled [median age 85 years (interquartile range 82-89); 346 were males (47.3%)]. According to the Clinical Frailty Scale, 61 (8.4%) were classified as fit, 417 (57.2%) as vulnerable, and 251 (34.4%) as frail. Severe disease [hazard ratio (HR) 1.87, 95% confidence interval (CI) 1.31-2.59], ≥3 comorbidities (HR 1.54, 95% CI 1.11-2.13), male sex (HR 1.46, 95% CI 1.14-1.87), and frailty (HR 6.93, 95% CI 1.69-28.27) for vulnerable and an overall HR of 12.55 (95% CI 2.96-53.21) for frail were independent risk factors for in-hospital death.

CONCLUSIONS AND IMPLICATIONS

The ED approach to older patients with COVID-19 should take into account the functional and clinical characteristics of patients being admitted. A sole evaluation based on the clinical severity and the presence of comorbidities does not reflect the complexity of this population. A comprehensive evaluation based on clinical severity, multimorbidity, and frailty could effectively predict the clinical risk of in-hospital death for patients with COVID-19 aged ≥80 years at the time of ED presentation.

Does chronic oral anticoagulation reduce in-hospital mortality among COVID-19 older patients?

Background

Patients hospitalized with COVID-19 experienced an increased risk of venous thromboembolism.

Aims

To evaluate the effect of chronic oral anticoagulation (OAC) therapy, both with vitamin K antagonists (VKAs) or direct oral anticoagulants (DOACs), on prognosis of COVID-19 older patients.

Methods

Single-center prospective study conducted in the Emergency Department (ED) of a teaching hospital, referral center for COVID-19 in central Italy. We evaluated all the patients ≥ 65 years, consecutively admitted to our ED for confirmed COVID-19. We compared the clinical outcome of those who were on chronic OAC at ED admission with those who did not, using a propensity score matched paired cohort of controls. The primary study endpoint was all-cause in-hospital death. Patients were matched for age, sex, clinical comorbidities, and clinical severity at presentation (based on NEWS ≥ 6). Study parameters were assessed for association to all-cause in-hospital death by a multivariate Cox regression analysis to identify independent risk factor for survival.

Results

Although overall mortality was slightly higher for anticoagulated patients compared to controls (63.3% vs 43.5%, p = 0.012), the multivariate adjusted hazard ratio (HR) for death was not significant (HR = 1.56 [0.78–3.12]; p = 0.208). Both DOACs (HR 1.46 [0.73–2.92]; p = 0.283) and VKAs (HR 1.14 [0.48–2.73]; p = 0.761) alone did not affect overall survival in our cohort.

Conclusions

Among older patients hospitalized for COVID-19, chronic OAC therapy was not associated with a reduced risk of in-hospital death. Moreover, our data suggest similar outcome both for patients on VKAs or in patients on DOACs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40520-021-01924-w.

Predicting hospital mortality in COVID-19 hemodialysis patients with developed scores

Various risk scores such as COVID-GRAM Critical Illness Risk Score (COVID-GRAM), quick COVID-19 Severity Index (qCSI), and systemic immune-inflammation index (SII) have been developed to determine critical illness in hospitalized patients. None of these risk scoring systems was evaluated in HD patients who indeed carry the highest risk of developing critical illnesses. We aimed to evaluate, in hemodialysis (HD) patients with COVID-19, the performance of these scoring systems for the need of intensive care unit (ICU) and mortality. The qCSI, COVID-GRAM, and SII scores of the patients at admission to hospital were calculated and grouped according to the scoring results. The primary outcome of the study was mortality and need of ICU. Critical illness was described as a composition of admission to the ICU, invasive ventilation, or death. It was determined that when the qCSI is over 6.5, the need for ICU increased 13.8 times and mortality increased 21.3 times. When the COVID-GRAM score is >157, the ICU need increased 14.7 times and the mortality increased 33.7 times. We found that the need for ICU increased 4.2 times and mortality increased 3.1 times when the SII score was >1145. These tests, which can be easily calculated, could be used to estimate the risk of developing critical illness among COVID-19 HD patients. Estimating the risk of critical illness could help to reduce mortality in HD patients.

A Gastroenterologist' clinical experience in COVID 19 and in-hospital mortality and length of stay analysis

OBJECTIVES

COVID19 pandemic has forced physicians from different specialties to assist cases overload. Our aim is to assess gastroenterologist's assistance in COVID-19 by assessing mortality, ICU admission, and length of stay, and seek for risk factors for in-hospital mortality and longer hospital stay.

METHODS

A total of 41 COVID-19 patients assisted by gastroenterologist (GI cohort) and 137 assisted by pulmonologist, internal medicine practitioners, and infectious disease specialists (COVID expert cohort) during October-November 2020 were prospectively collected. Clinical, demographic, imaging, and laboratory markers were collected and compared between both cohorts. Bivariate analysis and logistic regression were performed to search for risk factors of mortality and longer hospital stays.

RESULTS

A total of 27 patients died (15.1%), 11 were admitted to ICU (6.1%). There were no differences between cohorts in mortality (14.6% vs 15.4%;p = 0.90), ICU admission (12.1% vs 4%;p = 0.13), and length of stay (6.67 ± 4 vs 7.15 ± 4.5 days; p = 0.58). PaO2/FiO2 on admission (OR 0.991;CI95% 0.984-0.998) and age > 70 (OR 17.54;CI95% 3.93-78.22) were independently related to mortality. Age > 70, history of malignancy, diabetes, and cardiovascular disease were related to longer hospital stays (p < 0.001, p = 0.03, p = 0.04, p = 0.02 respectively).

CONCLUSIONS

COVID-19 assistance was similar between gastroenterologist and COVID experts when assessing mortality, ICU admission, and length of stay. Age>70 and decreased PaO2/FiO2 on admission were independent risk factors of mortality. Age and several comorbidities were related to longer hospital stay.

"Impact of pentaglobin in severe COVID 19 pneumonia- a prospective study."

Background

The current COVID-19 pandemic has become a global public health crisis and presents a serious challenge in treatment of severe COVID pneumonia patients. With an imperative need for an effective treatment, we aimed to study the effectiveness of Pentaglobin, an intravenous immunoglobin in the treatment of severe Covid-19 pneumonia patients.

Methods

This is an open-label non-randomised controlled study. Patients in the study group (n = 17) received Pentaglobin in addition to standard therapy and the control group (n = 19) received only the standard of care treatment. Severity of illness were quantified by severity scores and inflammatory laboratory parameters were compared between the two groups.

Results

The average length of hospital stay in pentaglobin group were 12.35 ± 6.98 days compared to 10.94 ± 4.62 days in standard treatment group with mean difference of 1.4 days (p value = 0.4). Pentaglobin did not provide an added advantage in terms of reducing the duration of hospital stay. There was no significant difference between both the groups in terms of requirement of invasive ventilation (p = 0.56) and mortality (p = 0.86). CT Severity score (OR = 1.39 95% CI = 1.09–1.77, P = 0.01), APACHE II score (OR = 1.16 95% CI = 0.99–1.35, P = 0.05) and the SOFA score (OR = 2.11 95% CI = 1.13–3.93, P = 0.02) were independent predictors of mortality.

Conclusion

The administration of pentaglobin in COVID −19 patients has no significant effect in reducing the risk of mechanical ventilation or death, in disease worsening or in reduction of inflammation.