Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score

Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia

OBJECTIVE

Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays.

METHODS

Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorableclinical course, were collected. The number of affected lung fields for the two CXRs was assessed using the AI tool.

RESULTS

One hundred fourteen patients (57.4±14.2 years, 65-57%-men) were retrospectively collected. Fifteen (13.2%) required ventilatory support. Progression of pneumonic extension ≥0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26s of radiological time.

CONCLUSIONS

Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.

Prevalence of overweight and obesity and their effect on COVID-19 severity and hospitalization among younger than 50 years versus older than 50 years population: A systematic review and meta-analysis

Cohort studies have shown that both overweight and obesity have their impact by increasing hospitalization with COVID-19. We conducted a systematic literature search in PubMed, Google Scholar, and MedRxiv databases following the PRISMA guidelines. Statistical analyses were performed using STATA software version 16 MP (Stata Corp, College Station, TX, USA) and Med Calc software version 22.009(Med Calc software Ltd, Ostend, Belgium). The primary outcome was to measure the prevalence of overweight and obesity and their impact on the risk of hospitalization among COVID-19 patients under and above 50 years of age. In total, 184 studies involving 2,365,377 patients were included. The prevalence of overweight was highest among those younger than 50 years of age over those older than 50 years of age, (26.33% vs. 30.46%), but there was no difference in obesity (36.30% vs. 36.02%). Overall, the pooled prevalence of overweight and obesity among hospitalized COVID-19 patients was 31.0% and 36.26%, respectively. Compared with normal weight, the odds of hospitalization with overweight (odds ratio [OR] 2.186, 95% confidence interval [CI] [1.19, 3.99], p < 0.01) and obesity (OR 3.069, 95% CI [1.67, 5.61], p < 0.001) in those younger than 50 years and obesity (OR 3.977, 95% CI [2.75, 5.73], p < 0.001) in the older than 50 years age group were significantly high. The increased prevalence of overweight and obesity among the under 50 years age group and obesity among the older than 50 years age group significantly increased the rate of COVID-19 infections, severity and hospitalization.

Ventilatory associated barotrauma in COVID-19 patients: A multicenter observational case control study (COVI-MIX-study)

BACKGROUND

The risk of barotrauma associated with different types of ventilatory support is unclear in COVID-19 patients. The primary aim of this study was to evaluate the effect of the different respiratory support strategies on barotrauma occurrence; we also sought to determine the frequency of barotrauma and the clinical characteristics of the patients who experienced this complication.

METHODS

This multicentre retrospective case-control study from 1 March 2020 to 28 February 2021 included COVID-19 patients who experienced barotrauma during hospital stay. They were matched with controls in a 1:1 ratio for the same admission period in the same ward of treatment. Univariable and multivariable logistic regression (OR) were performed to explore which factors were associated with barotrauma and in-hospital death.

RESULTS

We included 200 cases and 200 controls. Invasive mechanical ventilation was used in 39.3% of patients in the barotrauma group, and in 20.1% of controls (p<0.001). Receiving non-invasive ventilation (C-PAP/PSV) instead of conventional oxygen therapy (COT) increased the risk of barotrauma (OR 5.04, 95% CI 2.30 - 11.08, p<0.001), similarly for invasive mechanical ventilation (OR 6.24, 95% CI 2.86-13.60, p<0.001). High Flow Nasal Oxygen (HFNO), compared with COT, did not significantly increase the risk of barotrauma. Barotrauma frequency occurred in 1.00% [95% CI 0.88-1.16] of patients; these were older (p=0.022) and more frequently immunosuppressed (p=0.013). Barotrauma was shown to be an independent risk for death (OR 5.32, 95% CI 2.82-10.03, p<0.001).

CONCLUSIONS

C-PAP/PSV compared with COT or HFNO increased the risk of barotrauma; otherwise HFNO did not. Barotrauma was recorded in 1.00% of patients, affecting mainly patients with more severe COVID-19 disease. Barotrauma was independently associated with mortality.

TRIAL REGISTRATION

this case-control study was prospectively registered in clinicaltrial.gov as NCT04897152 (on 21 May 2021).

COVID-19-Associated Pulmonary Embolism: Review of the Pathophysiology, Epidemiology, Prevention, Diagnosis, and Treatment

COVID-19 is associated with endothelial activation in the setting of a potent inflammatory reaction and a hypercoagulable state. The end result of this thromboinflammatory state is an excess in thrombotic events, in particular venous thromboembolism. Pulmonary embolism (PE) has been of special interest in patients with COVID-19 given its association with respiratory deterioration, increased risk of intensive care unit admission, and prolonged hospital stay. The pathophysiology and clinical characteristics of COVID-19-associated PE may differ from the conventional non-COVID-19-associated PE. In addition to embolic events from deep vein thrombi, in situ pulmonary thrombosis, particularly in smaller vascular beds, may be relevant in patients with COVID-19. Appropriate prevention of thrombotic events in COVID-19 has therefore become of critical interest. Several changes in viral biology, vaccination, and treatment management during the pandemic may have resulted in changes in incidence trends. This review provides an overview of the pathophysiology, epidemiology, clinical characteristics, and risk factors of COVID-19-associated PE. Furthermore, we briefly summarize the results from randomized controlled trials of preventive antithrombotic therapies in COVID-19, focusing on their findings related to PE. We discuss the acute treatment of COVID-19-associated PE, which is substantially similar to the management of conventional non-COVID-19 PE. Ultimately, we comment on the current knowledge gaps in the evidence and the future directions in the treatment and follow-up of COVID-19-associated PE, including long-term management, and its possible association with long-COVID.

Quantitative LC-MS study of compounds found predictive of COVID-19 severity and outcome

INTRODUCTION

Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients.

OBJECTIVES

In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome.

METHODS

A targeted LC-MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3',4'-didehydro-3'-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients' levels.

RESULTS & CONCLUSION

Finally, we demonstrate the added value of the kynurenic acid/tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.

The new combination of oxygen saturation with age shock index predicts the outcome of COVID-19 pneumonia

Introduction

Emergency departments around the world have been struggling to deal with patients with COVID-19 and presumed COVID-19. Triaging patients who need further medical support is the key matter to emergency physicians as the delay of proper treatment may worsen the results. The aim of this study was to validate the ability of age shock index and hypoxia-age-shock index at the time of presentation to the emergency department to predict case fatality in patients with COVID-19 pneumonia.

Methods

We only included patients who had COVID-19-associated pneumonia who needed in-hospital treatment. The vital signs and oxygen saturation used in the study were collected, especially from the triage sector, before patients were given supplemental oxygen.

Results

A total of 241 patients enrolled in the study. The case fatality rate was 27%. The median age of the study samples was 78 (66-86) years with 133 male and 108 female patients. Hypoxia-age-shock index showed the best performance in analysis (odds ratio 15.1, 95% confidence interval: 5.1-44.4; adjusted odds ratio 8.6, 95% confidence interval: 2.8-26.8).

Conclusion

The hypoxia-age-shock index was a strong predictor for in-hospital mortality of COVID-19 pneumonia. Furthermore, when it was compared with age shock index, hypoxia-age-shock index showed better performance in predicting fatality of the disease.

Association of Immune Semaphorins with COVID-19 Severity and Outcomes

Semaphorins have recently been recognized as crucial modulators of immune responses. In the pathogenesis of COVID-19, the activation of immune responses is the key factor in the development of severe disease. This study aimed to determine the association of serum semaphorin concentrations with COVID-19 severity and outcomes. Serum semaphorin concentrations (SEMA3A, -3C, -3F, -4D, -7A) were measured in 80 hospitalized adult patients with COVID-19 (moderate (n = 24), severe (n = 32), critical, (n = 24)) and 40 healthy controls. While SEMA3C, SEMA3F and SEMA7A serum concentrations were significantly higher in patients with COVID-19, SEMA3A was significantly lower. Furthermore, SEMA3A and SEMA3C decreased with COVID-19 severity, while SEMA3F and SEMA7A increased. SEMA4D showed no correlation with disease severity. Serum semaphorin levels show better predictive values than CRP, IL-6 and LDH for differentiating critical from moderate/severe COVID-19. SEMA3F and SEMA7A serum concentrations were associated with the time to recovery, requirement of invasive mechanical ventilation, development of pulmonary thrombosis and nosocomial infections, as well as with in-hospital mortality. In conclusion, we provide the first evidence that SEMA3A, SEMA3C, SEMA3F and SEMA7A can be considered as new biomarkers of COVID-19 severity.

High-Flow Nasal Cannula oxygen therapy in COVID-19: retrospective analysis of clinical outcomes - single center experience

Background

High-Flow Nasal Cannula (HFNC) oxygen therapy emerged as the therapy of choice in COVID-19-related pneumonia and moderate to severe acute hypoxemic respiratory failure (AHRF). HFNC oxygen therapy in COVID-19 has been recommended based its use to treat AHRF of other etiologies, and studies on assessing outcomes in COVID-19 patients are highly needed. This study aimed to examine outcomes in COVID-19 patients with pneumonia and severe AHRF treated with HFNC.

Materials and methods

The study included 235 COVID-19 patients with pneumonia treated with HFNC. Data extracted from medical records included demographic characteristics, comorbidities, laboratory parameters, clinical and oxygenation status, clinical complications, as well as the length of hospital stay. Patients were segregated into two groups based on their oxygen therapy needs: HDU group, those who exclusively required HFNC and ICU group, those whose oxygen therapy needed to be escalated at some point of hospital stay. The primary outcome was the need for respiratory support escalation (noninvasive or invasive mechanical ventilation) and the secondary outcome was the in-hospital all-cause mortality.

Results

The primary outcome was met in 113 (48%) of patients. The overall mortality was 70%, significantly higher in the ICU group [102 (90.2%) vs. 62 (50.1%), p < 0.001]. The rate of intrahospital infections was significantly higher in the ICU group while there were no significant differences in the length of hospital stay between the groups. The ICU group exhibited significant increases in D-dimer, NLR, and NEWS values, accompanied by a significant decrease in the SaO2/FiO2 ratio. The multivariable COX proportional regression analysis identified malignancy, higher levels of 4C Mortality Score and NEWS2 as significant predictors of mortality.

Conclusion

High-Flow Nasal Cannula oxygen therapy is a safe type of respiratory support in patients with COVID-19 pneumonia and acute hypoxemic respiratory failure with significantly less possibility for emergence of intrahospital infections. In 52% of patients, HFNC was successful in treating AHRF in COVID-19 patients. Overall, mortality in COVID-19 pneumonia with AHRF is still very high, especially in patients treated with noninvasive/invasive mechanical ventilation.

Leukopenia and leukocytosis as strong predictors of COVID-19 severity: A cross-sectional study of the hematologic abnormalities and COVID-19 severity in hospitalized patients

Background and Aims

Predicting severe disease is important in provocative decision-making for the management of patients with the coronavirus disease 2019 (COVID-19); However, there are still some controversies about the COVID-19's severity predicting factors. This study aimed to investigate the relationships between clinical and laboratory findings regarding COVID-19's severity in patients admitted to a tertiary hospital in Mashhad, Iran.

Methods

A cross-sectional study was conducted on patients with documented COVID-19 infection based on the reverse transcription-polymerase chain reaction test. Clinical symptoms, vital signs, and medical history of the patients were recorded from their medical records. Laboratory findings and computed tomography (CT) study findings were documented. Disease severity was defined based on CT scan findings.

Results

A total of 564 patients (58.8 ± 16.8 years old) were evaluated. The frequency of severe disease was 70.4%. There was a significant difference in heart rate (p = 0.0001), fever (p = 0.002), dyspnea (p = 0.0001), chest pain (p = 0.0001), diarrhea (p = 0.021), arthralgia (p = 0.0001), and chills (p = 0.044) as well as lymphopenia (p = 0.014), white blood cell count (p = 0.001), neutrophil count (p < 0.0001), lymphocyte count (p < 0.0001), and prothrombin time (p = 0.001) between disease severity groups. Predictors of severe COVID-19 were pulse rate (crude odds ratio [cOR] = 1.014, 95% confidence interval [CI] for cOR: 1.001, 1.027) and leukopenia (cOR = 3.910, 95% CI for cOR: 1.294, 11.809). Predictors for critical COVID-19 were pulse rate (cOR = 1.075, 95% CI for cOR: 1.046, 1.104), fever (cOR = 2.516, 95%CI for cOR: 1.020, 6.203), dyspnea (cOR = 4.190, 95% CI for cOR: 1.227, 14.306), and leukocytosis (cOR = 3.866, 95% CI for cOR: 1.815, 8.236).

Conclusions

Leukopenia and leukocytosis have the strongest correlation with the COVID-19 severity. These findings could be a valuable guild for clinicians in COVID-19 patient management in the inpatient setting.

Using clinical risk models to predict outcomes: what are we predicting and why?

Clinical risk prediction models can support decision making in emergency medicine, but directing intervention towards high-risk patients may involve a flawed assumption. This concepts paper examines prognostic clinical risk prediction and specifically describes the potential impact of treatment effects in model development studies. Treatment effects may lead to models failing to achieve the aim of identifying the patients most likely to benefit from intervention, and may instead identify patients who are unlikely to benefit from intervention. The paper provides practical advice to help clinicians who wish to use clinical prediction scores to assist clinical judgement rather than dictate clinical decision making.

Validation of a risk prediction model for COVID-19: the PERIL prospective cohort study

Aim: This study aims to perform an external validation of a recently developed prognostic model for early prediction of the risk of progression to severe COVID-19. Patients & methods/materials: Patients were recruited at their initial diagnosis at two facilities within Hamad Medical Corporation in Qatar. 356 adults were included for analysis. Predictors for progression of COVID-19 were all measured at disease onset and first contact with the health system. Results: The C statistic was 83% (95% CI: 78%-87%) and the calibration plot showed that the model was well-calibrated. Conclusion: The published prognostic model for the progression of COVID-19 infection showed satisfactory discrimination and calibration and the model is easy to apply in clinical practice.d.

A Transcriptomic Severity Classifier IMX-SEV-3b to Predict Mortality in Intensive Care Unit Patients with COVID-19: A Prospective Observational Pilot Study

The prediction of disease outcomes in COVID-19 patients in the ICU is of critical importance, and the examination of host gene expressions is a promising tool. The 29-host mRNA Inflam-matix-Severity-3b (IMX-SEV-3b) classifier has been reported to predict mortality in emergency department COVID-19 patients and surgical ICU patients. The accuracy of the IMX-SEV-3b in predicting mortality in COVID-19 patients admitted to the ICU is yet unknown. Our aim was to investigate the accuracy of the IMX-SEV-3b in predicting the ICU mortality of COVID-19 patients. In addition, we assessed the predictive performance of routinely measured biomarkers and the Sequential Organ Failure Assessment (SOFA) score as well. This was a prospective observational study enrolling COVID-19 patients who received mechanical ventilation on the ICU of the Erasmus MC, the Netherlands. The IMX-SEV-3b scores were generated by amplifying 29 host response genes from blood collected in PAXgene® Blood RNA tubes. A severity score was provided, ranging from 0 to 1 for increasing disease severity. The primary outcome was the accuracy of the IMX-SEV-3b in predicting ICU mortality, and we calculated the AUROC of the IMX-SEV-3b score, the biomarkers C-reactive protein (CRP), D-dimer, ferritin, leukocyte count, interleukin-6 (IL-6), lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), procalcitonin (PCT) and the SOFA score. A total of 53 patients were included between 1 March and 30 April 2020, with 47 of them being included within 72 h of their admission to the ICU. Of these, 18 (34%) patients died during their ICU stay, and the IMX-SEV-3b scores were significantly higher in non-survivors compared to survivors (0.65 versus 0.57, p = 0.05). The Area Under the Receiver Operating Characteristic Curve (AUROC) for prediction of ICU mortality by the IMX-SEV-3b was 0.65 (0.48-0.82). The AUROCs of the biomarkers ranged from 0.52 to 0.66, and the SOFA score had an AUROC of 0.81 (0.69-0.93). The AUROC of the pooled biomarkers CRP, D-dimer, ferritin, leukocyte count, IL-6, LDH, NLR and PCT for prediction of ICU mortality was 0.81 (IQR 0.69-0.93). Further validation in a larger interventional trial of a point-of-care version of the IMX-SEV-3b classifier is warranted to determine its value for patient management.

Non-Contact Measurement of Blood Oxygen Saturation Using Facial Video Without Reference Values

The continuous measurement of percutaneous oxygen saturation (SpO2) enables diseases that cause hypoxemia to be detected early and patients' conditions to be monitored. Currently, SpO2 is mainly measured using a pulse oximeter, which, owing to its simplicity, can be used in clinical settings and at home. However, the pulse oximeter requires a sensor to be in contact with the skin; therefore, prolonged use of the pulse oximeter for neonates or patients with sensitive skin may cause local inflammation or stress due to restricted movement. In addition, owing to COVID-19, there has been a growing demand for the contactless measurement of SpO2. Several studies on measuring SpO2 without contact used skin video images have been conducted. However, in these studies, the SpO2 values were estimated using a linear regression model or a look-up table that required reference values obtained using a contact-type pulse oximeter. In this study, we propose a new technique for the contactless measurement of SpO2 that does not require reference values. Specifically, we used certain approaches that reduced the influence of non-pulsating components and utilized different light wavelengths of video images that penetrated subcutaneously to different depths. We experimentally investigated the accuracy of SpO2 measurements using the proposed methods. The results indicate that the proposed methods were more accurate than the conventional method.

The influence of explainable vs non-explainable clinical decision support systems on rapid triage decisions: a mixed methods study

BACKGROUND

During the COVID-19 pandemic, a variety of clinical decision support systems (CDSS) were developed to aid patient triage. However, research focusing on the interaction between decision support systems and human experts is lacking.

METHODS

Thirty-two physicians were recruited to rate the survival probability of 59 critically ill patients by means of chart review. Subsequently, one of two artificial intelligence systems advised the physician of a computed survival probability. However, only one of these systems explained the reasons behind its decision-making. In the third step, physicians reviewed the chart once again to determine the final survival probability rating. We hypothesized that an explaining system would exhibit a higher impact on the physicians' second rating (i.e., higher weight-on-advice).

RESULTS

The survival probability rating given by the physician after receiving advice from the clinical decision support system was a median of 4 percentage points closer to the advice than the initial rating. Weight-on-advice was not significantly different (p = 0.115) between the two systems (with vs without explanation for its decision). Additionally, weight-on-advice showed no difference according to time of day or between board-qualified and not yet board-qualified physicians. Self-reported post-experiment overall trust was awarded a median of 4 out of 10 points. When asked after the conclusion of the experiment, overall trust was 5.5/10 (non-explaining median 4 (IQR 3.5-5.5), explaining median 7 (IQR 5.5-7.5), p = 0.007).

CONCLUSIONS

Although overall trust in the models was low, the median (IQR) weight-on-advice was high (0.33 (0.0-0.56)) and in line with published literature on expert advice. In contrast to the hypothesis, weight-on-advice was comparable between the explaining and non-explaining systems. In 30% of cases, weight-on-advice was 0, meaning the physician did not change their rating. The median of the remaining weight-on-advice values was 50%, suggesting that physicians either dismissed the recommendation or employed a "meeting halfway" approach. Newer technologies, such as clinical reasoning systems, may be able to augment the decision process rather than simply presenting unexplained bias.

Rationale for combined therapies in severe-to-critical COVID-19 patients

An unprecedented global social and economic impact as well as a significant number of fatalities have been brought on by the coronavirus disease 2019 (COVID-19), produced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Acute SARS-CoV-2 infection can, in certain situations, cause immunological abnormalities, leading to an anomalous innate and adaptive immune response. While most patients only experience mild symptoms and recover without the need for mechanical ventilation, a substantial percentage of those who are affected develop severe respiratory illness, which can be fatal. The absence of effective therapies when disease progresses to a very severe condition coupled with the incomplete understanding of COVID-19's pathogenesis triggers the need to develop innovative therapeutic approaches for patients at high risk of mortality. As a result, we investigate the potential contribution of promising combinatorial cell therapy to prevent death in critical patients.

ANCOC Score to Predict Mortality in Different SARS-CoV-2 Variants and Vaccination Status

BACKGROUND

More than three years after the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic outbreak, hospitals worldwide are still affected by coronavirus disease 19 (COVID-19). The availability of a clinical score that can predict the risk of death from the disease at the time of diagnosis and that can be used even if population characteristics change and the virus mutates can be a useful tool for emergency physicians to make clinical decisions. During the first COVID-19 waves, we developed the ANCOC (age, blood urea nitrogen, C-reactive protein, oxygen saturation, comorbidities) score, a clinical score based on five main parameters (age, blood urea nitrogen, C-reactive protein, oxygen saturation, comorbidities) that accurately predicts the risk of death in patients infected with SARS-CoV-2. A score of less than -1 was associated with 0% mortality risk, whereas a score of 6 was associated with 100% risk of death, with an overall accuracy of 0.920. The aim of our study is to internally validate the ANCOC score and evaluate whether it can predict 60-day mortality risk independent of vaccination status and viral variant.

METHODS

We retrospectively enrolled 843 patients admitted to the emergency department (ED) of our hospital with a diagnosis of COVID-19. A total of 515 patients were admitted from July 2021 to September 2021, when the Delta variant was prevalent, and 328 in January 2022, when the Omicron 1 variant was predominant. All patients included in the study had a diagnosis of COVID-19 confirmed by polymerase chain reaction (PCR) on an oropharyngeal swab. Demographic data, comorbidities, vaccination data, and various laboratory, radiographic, and blood gas parameters were collected from all patients to determine differences between the two waves. ANCOC scores were then calculated for each patient, ranging from -6 to 6.

RESULTS

Patients infected with the Omicron variant were significantly older and had a greater number of comorbidities, of which hypertension and chronic obstructive pulmonary disease (COPD) were the most common. Immunization was less common in Delta patients than in Omicron patients (34% and 56%, respectively). To assess the accuracy of mortality prediction, we constructed a receiver operating characteristic (ROC) curve and found that the area under the ROC curve was greater than 0.8 for both variants. These results suggest that the ANCOC score is able to predict 60-day mortality regardless of viral variant and whether the patient is vaccinated or not.

CONCLUSION

In a population with increasingly high vaccination rates, several parameters may be considered prognostic for the risk of fatal outcomes. This study suggests that the ANCOC score can be very useful for the clinician in an emergency setting to quickly understand the patient's evolution and provide proper attention and the most appropriate treatments.

Daily disease severity in patients with COVID-19 admitted to the hospital: The SCODA (severity of coronavirus disease assessment) score

BACKGROUND

A multitude of diagnostic and predictive algorithms have been designed for COVID-19. However, currently no score can accurately quantify and track day-to-day disease severity in hospitalised patients with COVID-19. We aimed to design such a score to improve pathophysiological insight in COVID-19.

METHODS

Development of the Severity of COronavirus Disease Assessment (SCODA) score was based on the 4C Mortality score but patient demographic variables that remain constant during admission were excluded. Instead, parameters associated with breathing and oxygenation were added to reflect the daily condition. The SCODA score was subsequently applied to the BEAT-COVID cohort to describe COVID-19 severity over time and to determine the timing of clinical recovery for each patient, an important marker in pathophysiological studies. The BEAT-COVID study included patients with PCR confirmed COVID-19 who were hospitalized between April 2020 and March 2021 in the Leiden University Medical Center, The Netherlands.

RESULTS

The SCODA score consists of 6 clinical and 2 routine lab parameters. 191 patients participated in the BEAT-COVID study. Median age was 66, and 74.4% was male. The modal timepoint at which recovery was clinically initiated occurred on days 8 and 24 since symptom onset for non-ICU and ICU-patients, respectively.

CONCLUSIONS

We developed a daily score which can be used to track disease severity of patients admitted due to COVID-19. This score is useful for improving insight in COVID-19 pathophysiology, its clinical course and to evaluate interventions. In a future stage this score can also be used in other (emerging) infectious respiratory diseases.

Physical function and mental health trajectories in COVID-19 patients following invasive mechanical ventilation: a prospective observational study

This prospective observational cohort study was performed to investigate the physical function and mental health trajectories of novel coronavirus disease 2019 (COVID-19) patients requiring invasive mechanical ventilation (IMV) after discharge from the intensive care unit (ICU). The study population consisted of 64 patients (median age, 60 years; 85.9% male; median IMV duration, 9 days). At ICU discharge, 28.1% of the patients had Medical Research Council (MRC) sum score < 48 points, and prolonged IMV was significantly associated with lower MRC sum score and handgrip strength. Symptoms were similar between groups at ICU discharge, and the symptoms most commonly reported as moderate-to-severe were impaired well-being (52%), anxiety (43%), tiredness (41%), and depression (35%). Although muscle strength and mobility status were significantly improved after ICU discharge, Edmonton Symptom Assessment System score did not improve significantly in the prolonged IMV group. EuroQol five-dimension five-level summary index was significantly lower in the prolonged than short IMV group at 6 months after ICU discharge. We found substantial negative physical function and mental health consequences in the majority of surviving COVID-19 patients requiring IMV, with prolonged period of IMV showing greater negative effects not only immediately but also at 6 months after discharge from the ICU.

Validation of extracorporeal membrane oxygenation mortality prediction and severity of illness scores in an international COVID-19 cohort

BACKGROUND

Veno-venous extracorporeal membrane oxygenation (V-V ECMO) is a lifesaving support modality for severe respiratory failure, but its resource-intensive nature led to significant controversy surrounding its use during the COVID-19 pandemic. We report the performance of several ECMO mortality prediction and severity of illness scores at discriminating survival in a large COVID-19 V-V ECMO cohort.

METHODS

We validated ECMOnet, PRESET (PREdiction of Survival on ECMO Therapy-Score), Roch, SOFA (Sequential Organ Failure Assessment), APACHE II (acute physiology and chronic health evaluation), 4C (Coronavirus Clinical Characterisation Consortium), and CURB-65 (Confusion, Urea nitrogen, Respiratory Rate, Blood Pressure, age >65 years) scores on the ISARIC (International Severe Acute Respiratory and emerging Infection Consortium) database. We report discrimination via Area Under the Receiver Operative Curve (AUROC) and Area under the Precision Recall Curve (AURPC) and calibration via Brier score.

RESULTS

We included 1147 patients and scores were calculated on patients with sufficient variables. ECMO mortality scores had AUROC (0.58-0.62), AUPRC (0.62-0.74), and Brier score (0.286-0.303). Roch score had the highest accuracy (AUROC 0.62), precision (AUPRC 0.74) yet worst calibration (Brier score of 0.3) despite being calculated on the fewest patients (144). Severity of illness scores had AUROC (0.52-0.57), AURPC (0.59-0.64), and Brier Score (0.265-0.471). APACHE II had the highest accuracy (AUROC 0.58), precision (AUPRC 0.64), and best calibration (Brier score 0.26).

CONCLUSION

Within a large international multicenter COVID-19 cohort, the evaluated ECMO mortality prediction and severity of illness scores demonstrated inconsistent discrimination and calibration highlighting the need for better clinically applicable decision support tools.

Thrombocytopenia is associated with a dysregulated host response in severe COVID-19

BACKGROUND

Thrombocytopenia is associated with increased mortality in COVID-19 patients.

OBJECTIVE

To determine the association between thrombocytopenia and alterations in host response pathways implicated in disease pathogenesis in patients with severe COVID-19.

PATIENTS/METHODS

We studied COVID-19 patients admitted to a general hospital ward included in a national (CovidPredict) cohort derived from 13 hospitals in the Netherlands. In a subgroup, 43 host response biomarkers providing insight in aberrations in distinct pathophysiological domains (coagulation and endothelial cell function; inflammation and damage; cytokines and chemokines) were determined in plasma obtained at a single time point within 48 h after admission. Patients were stratified in those with normal platelet counts (150-400 × 109/L) and those with thrombocytopenia (<150 × 109/L).

RESULTS

6.864 patients were enrolled in the national cohort, of whom 1.348 had thrombocytopenia and 5.516 had normal platelets counts; the biomarker cohort consisted of 429 patients, of whom 85 with thrombocytopenia and 344 with normal platelet counts. Plasma D-dimer levels were not different in thrombocytopenia, although patients with moderate-severe thrombocytopenia (<100 × 109/L) showed higher D-dimer levels, indicating enhanced coagulation activation. Patients with thrombocytopenia had lower plasma levels of many proinflammatory cytokines and chemokines, and antiviral mediators, suggesting involvement of platelets in inflammation and antiviral immunity. Thrombocytopenia was associated with alterations in endothelial cell biomarkers indicative of enhanced activation and a relatively preserved glycocalyx integrity.

CONCLUSION

Thrombocytopenia in hospitalized patients with severe COVID-19 is associated with broad host response changes across several pathophysiological domains. These results suggest a role of platelets in the immune response during severe COVID-19.

Association Between Vaccination Status and Outcomes in Patients Admitted to the ICU With COVID-19

OBJECTIVES

Although COVID-19 vaccines can reduce the need for intensive care unit admission in COVID-19, their effect on outcomes in critical illness remains unclear. We evaluated outcomes in vaccinated patients admitted to the ICU with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the association between vaccination and booster status on clinical outcomes.

DESIGN

Retrospective cohort.

SETTING AND PATIENTS

All patients were admitted to an ICU between January 2021 (after vaccination was available) and July 2022 with a diagnosis of COVID-19 based on a SARS-CoV-2 polymerase chain reaction test in Alberta, Canada.

INTERVENTIONS

None.

MEASUREMENT

The propensity-matched primary outcome of all-cause in-hospital mortality was compared between vaccinated and unvaccinated patients, and vaccinated patients were stratified by booster dosing. Secondary outcomes were mechanical ventilation (MV) duration ICU length of stay (LOS).

MAIN RESULTS

The study included 3,293 patients: 743 (22.6%) were fully vaccinated (54.6% with booster), 166 (5.0%) were partially vaccinated, and 2,384 (72.4%) were unvaccinated. Unvaccinated patients were more likely to require invasive MV (78.4% vs 68.2%), vasopressor use (71.1% vs 66.6%), and extracorporeal membrane oxygenation (2.1% vs 0.5%). In a propensity-matched analysis, in-hospital mortality was similar (31.8% vs 34.0%, adjusted odds ratio [OR], 1.25; 95% CI, 0.97-1.61), but median duration MV (7.6 vs 4.7 d; p < 0.001) and ICU LOS (6.6 vs 5.2 d; p < 0.001) were longer in unvaccinated compared to fully vaccinated patients. Among vaccinated patients, greater than or equal to 1 booster had lower in-hospital mortality (25.5% vs 40.9%; adjusted OR, 0.50; 95% CI, 0.0.36-0.68) and duration of MV (3.8 vs 5.6 d; p = 0.025).

CONCLUSIONS

Nearly one in four patients admitted to the ICU with COVID-19 after widespread COVID-19 vaccine availability represented a vaccine-breakthrough case. Mortality risk remains substantial in vaccinated patients and similar between vaccinated and unvaccinated patients after the onset of critical illness. However, COVID-19 vaccination is associated with reduced ICU resource utilization and booster dosing may increase survivability from COVID-19-related critical illness.

Longitudinal Lung Function Assessment of Patients Hospitalized With COVID-19 Using 1H and 129Xe Lung MRI

BACKGROUND

Microvascular abnormalities and impaired gas transfer have been observed in patients with COVID-19. The progression of pulmonary changes in these patients remains unclear.

RESEARCH QUESTION

Do patients hospitalized with COVID-19 without evidence of architectural distortion on structural imaging exhibit longitudinal improvements in lung function measured by using 1H and 129Xe MRI between 6 and 52 weeks following hospitalization?

STUDY DESIGN AND METHODS

Patients who were hospitalized with COVID-19 pneumonia underwent a pulmonary 1H and 129Xe MRI protocol at 6, 12, 25, and 51 weeks following hospital admission in a prospective cohort study between November 2020 and February 2022. The imaging protocol was as follows: 1H ultra-short echo time, contrast-enhanced lung perfusion, 129Xe ventilation, 129Xe diffusion-weighted, and 129Xe spectroscopic imaging of gas exchange.

RESULTS

Nine patients were recruited (age 57 ± 14 [median ± interquartile range] years; six of nine patients were male). Patients underwent MRI at 6 (n = 9), 12 (n = 9), 25 (n = 6), and 51 (n = 8) weeks following hospital admission. Patients with signs of interstitial lung damage were excluded. At 6 weeks, patients exhibited impaired 129Xe gas transfer (RBC to membrane fraction), but lung microstructure was not increased (apparent diffusion coefficient and mean acinar airway dimensions). Minor ventilation abnormalities present in four patients were largely resolved in the 6- to 25-week period. At 12 weeks, all patients with lung perfusion data (n = 6) showed an increase in both pulmonary blood volume and flow compared with 6 weeks, although this was not statistically significant. At 12 weeks, significant improvements in 129Xe gas transfer were observed compared with 6-week examinations; however, 129Xe gas transfer remained abnormally low at weeks 12, 25, and 51.

INTERPRETATION

129Xe gas transfer was impaired up to 1 year following hospitalization in patients who were hospitalized with COVID-19 pneumonia, without evidence of architectural distortion on structural imaging, whereas lung ventilation was normal at 52 weeks.

Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry

COVID-19 is responsible for high mortality, but robust machine learning-based predictors of mortality are lacking. To generate a model for predicting mortality in patients hospitalized with COVID-19 using Gradient Boosting Decision Trees (GBDT). The Spanish SEMI-COVID-19 registry includes 24,514 pseudo-anonymized cases of patients hospitalized with COVID-19 from 1 February 2020 to 5 December 2021. This registry was used as a GBDT machine learning model, employing the CatBoost and BorutaShap classifier to select the most relevant indicators and generate a mortality prediction model by risk level, ranging from 0 to 1. The model was validated by separating patients according to admission date, using the period 1 February to 31 December 2020 (first and second waves, pre-vaccination period) for training, and 1 January to 30 November 2021 (vaccination period) for the test group. An ensemble of ten models with different random seeds was constructed, separating 80% of the patients for training and 20% from the end of the training period for cross-validation. The area under the receiver operating characteristics curve (AUC) was used as a performance metric. Clinical and laboratory data from 23,983 patients were analyzed. CatBoost mortality prediction models achieved an AUC performance of 84.76 (standard deviation 0.45) for patients in the test group (potentially vaccinated patients not included in model training) using 16 features. The performance of the 16-parameter GBDT model for predicting COVID-19 hospital mortality, although requiring a relatively large number of predictors, shows a high predictive capacity.

Endothelial Damage, Neutrophil Extracellular Traps and Platelet Activation in COVID-19 vs. Community-Acquired Pneumonia: A Case-Control Study

COVID-19 has been a diagnostic and therapeutic challenge. It has marked a paradigm shift when considering other types of pneumonia etiology. We analyzed the biomarkers related to endothelial damage and immunothrombosis in COVID-19 in comparison to community-acquired pneumonia (CAP) through a case-control study of 358 patients with pneumonia (179 hospitalized with COVID-19 vs. 179 matched hospitalized with CAP). Endothelial damage markers (endothelin and proadrenomedullin), neutrophil extracellular traps (NETs) (citrullinated-3 histone, cell-free DNA), and platelet activation (soluble P-selectin) were measured. In-hospital and 1-year follow-up outcomes were evaluated. Endothelial damage, platelet activation, and NET biomarkers are significantly higher in CAP compared to COVID-19. In-hospital mortality in COVID-19 was higher compared to CAP whereas 1-year mortality and cardiovascular complications were higher in CAP. In the univariate analysis (OR 95% CIs), proADM and endothelin were associated with in-hospital mortality (proADM: CAP 3.210 [1.698-6.070], COVID-19 8.977 [3.413-23.609]; endothelin: CAP 1.014 [1.006-1.022], COVID-19 1.024 [1.014-1.034]), in-hospital CVE (proADM: CAP 1.623 [1.080-2.439], COVID-19 2.146 [1.186-3.882]; endothelin: CAP 1.005 [1.000-1.010], COVID-19 1.010 [1.003-1.018]), and 1-year mortality (proADM: CAP 2.590 [1.644-4.080], COVID-19 13.562 [4.872-37.751]; endothelin: CAP 1.008 [1.003-1.013], COVID-19 1.026 [1.016-1.037]). In conclusion, COVID-19 and CAP showed different expressions of endothelial damage and NETs. ProADM and endothelin are associated with short- and long-term mortality.

Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm

Analyzing the COVID-19 pandemic is a critical factor in developing effective policies to deal with similar challenges in the future. However, many parameters (e.g., the actual number of infected people, the effectiveness of vaccination) are still subject to considerable debate because they are unobservable. To model a pandemic and estimate unobserved parameters, researchers use compartmental models. Most often, in such models, the transition rates are considered as constants, which allows simulating only one epidemiological wave. However, multiple waves have been reported for COVID-19 caused by different strains of the virus. This paper presents an approach based on the reconstruction of real distributions of transition rates using genetic algorithms, which makes it possible to create a model that describes several pandemic peaks. The model is fitted on registered COVID-19 cases in four countries with different pandemic control strategies (Germany, Sweden, UK, and US). Mean absolute percentage error (MAPE) was chosen as the objective function, the MAPE values of 2.168%, 2.096%, 1.208% and 1.703% were achieved for the listed countries, respectively. Simulation results are consistent with the empirical statistics of medical studies, which confirms the quality of the model. In addition to observables such as registered infected, the output of the model contains variables that cannot be measured directly. Among them are the proportion of the population protected by vaccines, the size of the exposed compartment, and the number of unregistered cases of COVID-19. According to the results, at the peak of the pandemic, between 14% (Sweden) and 25% (the UK) of the population were infected. At the same time, the number of unregistered cases exceeds the number of registered cases by 17 and 3.4 times, respectively. The average duration of the vaccine induced immune period is shorter than claimed by vaccine manufacturers, and the effectiveness of vaccination has declined sharply since the appearance of the Delta and Omicron strains. However, on average, vaccination reduces the risk of infection by about 65-70%.

Hemodynamic, Oxygenation and Lymphocyte Parameters Predict COVID-19 Mortality

The mortality of COVID-19 patients has left the world devastated. Many scoring systems have been developed to predict the mortality of COVID-19 patients, but several scoring components cannot be carried out in limited health facilities. Herein, the authors attempted to create a new and easy scoring system involving mean arterial pressure (MAP), PF Ratio, or SF ratio-respiration rate (SF Ratio-R), and lymphocyte absolute, which were abbreviated as MPL or MSLR functioning, as a predictive scoring system for mortality within 30 days for COVID-19 patients. Of 132 patients with COVID-19 hospitalized between March and November 2021, we followed up on 96 patients. We present bivariate and multivariate analyses as well as the area under the curve (AUC) and Kaplan-Meier charts. From 96 patients, we obtained an MPL score of 3 points: MAP < 75 mmHg, PF Ratio < 200, and lymphocyte absolute < 1500/µL, whereas the MSLR score was 6 points: MAP < 75 mmHg, SF Ratio < 200, lymphocyte absolute < 1500/µL, and respiration rate 24/min. The MPL cut-off point is 2, while the MSLR is 4. MPL and MSLR have the same sensitivity (79.1%) and specificity (75.5%). The AUC value of MPL vs. MSLR was 0.802 vs. 0.807. The MPL ≥ 2 and MSLR ≥ 4 revealed similar predictions for survival within 30 days (p < 0.05). Conclusion: MPL and MSLR scores are potential predictors of mortality in COVID-19 patients within 30 days in a resource-limited country.

CT Scan-Derived Muscle, But Not Fat, Area Independently Predicts Mortality in COVID-19

BACKGROUND

COVID-19 has demonstrated a highly variable disease course, from asymptomatic to severe illness and eventually death. Clinical parameters, as included in the 4C Mortality Score, can predict mortality accurately in COVID-19. Additionally, CT scan-derived low muscle and high adipose tissue cross-sectional areas (CSAs) have been associated with adverse outcomes in COVID-19.

RESEARCH QUESTION

Are CT scan-derived muscle and adipose tissue CSAs associated with 30-day in-hospital mortality in COVID-19, independent of 4C Mortality Score?

STUDY DESIGN AND METHODS

This was a retrospective cohort analysis of patients with COVID-19 seeking treatment at the ED of two participating hospitals during the first wave of the pandemic. Skeletal muscle and adipose tissue CSAs were collected from routine chest CT-scans at admission. Pectoralis muscle CSA was demarcated manually at the fourth thoracic vertebra, and skeletal muscle and adipose tissue CSA was demarcated at the first lumbar vertebra level. Outcome measures and 4C Mortality Score items were retrieved from medical records.

RESULTS

Data from 578 patients were analyzed (64.6% men; mean age, 67.7 ± 13.5 years; 18.2% 30-day in-hospital mortality). Patients who died within 30 days demonstrated lower pectoralis CSA (median, 32.6 [interquartile range (IQR), 24.3-38.8] vs 35.4 [IQR, 27.2-44.2]; P = .002) than survivors, whereas visceral adipose tissue CSA was higher (median, 151.1 [IQR, 93.6-219.7] vs 112.9 [IQR, 63.7-174.1]; P = .013). In multivariate analyses, low pectoralis muscle CSA remained associated with 30-day in-hospital mortality when adjusted for 4C Mortality Score (hazard ratio, 0.98; 95% CI, 0.96-1.00; P = .038).

INTERPRETATION

CT scan-derived low pectoralis muscle CSA is associated significantly with higher 30-day in-hospital mortality in patients with COVID-19 independently of the 4C Mortality Score.

Severity-onset prediction of COVID-19 via artificial-intelligence analysis of multivariate factors

Progression to a severe condition remains a major risk factor for the COVID-19 mortality. Robust models that predict the onset of severe COVID-19 are urgently required to support sensitive decisions regarding patients and their treatments. In this study, we developed a multivariate survival model based on early-stage CT images and other physiological indicators and biomarkers using artificial-intelligence analysis to assess the risk of severe COVID-19 onset. We retrospectively enrolled 338 adult patients admitted to a hospital in China (severity rate, 31.9%; mortality rate, 0.9%). The physiological and pathological characteristics of the patients with severe and non-severe outcomes were compared. Age, body mass index, fever symptoms upon admission, coexisting hypertension, and diabetes were the risk factors for severe progression. Compared with the non-severe group, the severe group demonstrated abnormalities in biomarkers indicating organ function, inflammatory responses, blood oxygen, and coagulation function at an early stage. In addition, by integrating the intuitive CT images, the multivariable survival model showed significantly improved performance in predicting the onset of severe disease (mean time-dependent area under the curve = 0.880). Multivariate survival models based on early-stage CT images and other physiological indicators and biomarkers have shown high potential for predicting the onset of severe COVID-19.

External validation of the ISARIC 4C Mortality Score to predict in-hospital mortality among patients with COVID-19 in a Canadian intensive care unit: a single-centre historical cohort study

PURPOSE

With uncertain prognostic utility of existing predictive scoring systems for COVID-19-related illness, the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) 4C Mortality Score was developed by the International Severe Acute Respiratory and Emerging Infection Consortium as a COVID-19 mortality prediction tool. We sought to externally validate this score among critically ill patients admitted to an intensive care unit (ICU) with COVID-19 and compare its discrimination characteristics to that of the Acute Physiology and Chronic Health Evaluation (APACHE) II and Sequential Organ Failure Assessment (SOFA) scores.

METHODS

We enrolled all consecutive patients admitted with COVID-19-associated respiratory failure between 5 March 2020 and 5 March 2022 to our university-affiliated and intensivist-staffed ICU (Jewish General Hospital, Montreal, QC, Canada). After data abstraction, our primary outcome of in-hospital mortality was evaluated with an objective of determining the discriminative properties of the ISARIC 4C Mortality Score, using the area under the curve of a logistic regression model.

RESULTS

A total of 429 patients were included, 102 (23.8%) of whom died in hospital. The receiver operator curve of the ISARIC 4C Mortality Score had an area under the curve of 0.762 (95% confidence interval [CI], 0.717 to 0.811), whereas those of the SOFA and APACHE II scores were 0.705 (95% CI, 0.648 to 0.761) and 0.722 (95% CI, 0.667 to 0.777), respectively.

CONCLUSIONS

The ISARIC 4C Mortality Score is a tool that had a good predictive performance for in-hospital mortality in a cohort of patients with COVID-19 admitted to an ICU for respiratory failure. Our results suggest a good external validity of the 4C score when applied to a more severely ill population.

Developing Prediction Models for COVID-19 Outcomes: A Valuable Tool for Resource-Limited Hospitals

Purpose

Coronavirus disease is a global pandemic with millions of confirmed cases and hundreds of thousands of deaths worldwide that continues to create a significant burden on the healthcare systems. The aim of this study was to determine the patient clinical and paraclinical profiles that associate with COVID-19 unfavourable outcome and generate a prediction model that could separate between high-risk and low-risk groups.

Patients and Methods

The present study is a multivariate observational retrospective study. A total of 483 patients, residents of the municipality of Timișoara, the biggest city in the Western Region of Romania, were included in the study group that was further divided into 3 sub-groups in accordance with the disease severity form.

Results

Increased age (cOR=1.09, 95% CI: 1.06-1.11, p<0.001), cardiovascular diseases (cOR=3.37, 95% CI: 1.96-6.08, p<0.001), renal disease (cOR=4.26, 95% CI: 2.13-8.52, p<0.001), and neurological disorder (cOR=5.46, 95% CI: 2.71-11.01, p<0.001) were all independently significantly correlated with an unfavourable outcome in the study group. The severe form increases the risk of an unfavourable outcome 19.59 times (95% CI: 11.57-34.10, p<0.001), while older age remains an independent risk factor even when disease severity is included in the statistical model. An unfavourable outcome was positively associated with increased values for the following paraclinical parameters: white blood count (WBC; cOR=1.10, 95% CI: 1.05-1.15, p<0.001), absolute neutrophil count (ANC; cOR=1.15, 95% CI: 1.09-1.21, p<0.001) and C-reactive protein (CRP; cOR=1.007, 95% CI: 1.004-1.009, p<0.001). The best prediction model including age, ANC and CRP achieved a receiver operating characteristic (ROC) curve with the area under the curve (AUC) = 0.845 (95% CI: 0.813-0.877, p<0.001); cut-off value = 0.12; sensitivity = 72.3%; specificity = 83.9%.

Conclusion

This model and risk profiling may contribute to a more precise allocation of limited healthcare resources in a clinical setup and can guide the development of strategies for disease management.

Fibrosis-4 (FIB-4) index as a predictor for mechanical ventilation and 30-day mortality across COVID-19 variants

Background

The Fibrosis-4 (FIB-4) index, a simple index that includes age, liver enzymes, and platelet count has been studied as a tool to identify patients at a risk of requiring mechanical ventilation due to its high negative predictive value. It is unknown if FIB-4 remains useful to predict the severity of respiratory disease requiring mechanical ventilation amongst new Coronavirus disease 2019 (COVID-19) variants and whether a relationship also exists between FIB-4 and 30-day mortality. The main objective was to determine if FIB-4 can predict mechanical ventilation requirements and 30-day mortality from COVID-19 across variants including Alpha, Delta, and Omicron.

Methods

This was a population-based, retrospective cohort analysis of 232,364 hospitalized patients in the National COVID-19 Cohort Collaborative between the age of 18-90 who tested positive for COVID-19 between April 27, 2020 and June 25, 2022. The primary outcome was association between FIB-4 and need for mechanical ventilation. Secondary measures included the association of FIB-4 with 30-day mortality.

Results

A FIB-4 > 2.67 had 1.8 times higher odds of requiring mechanical ventilation across all variants of COVID-19 (OR 1.81; 95% CI: [1.76, 1.86]). The area under the ROC curve showed high diagnostic accuracy with values ranging between 0.79 (Omicron wave) and 0.97 (delta wave). Increased FIB-4 was associated with 30-day mortality across the variates.

Conclusion

The FIB-4 was consistently associated with both increased utilization of mechanical ventilation and 30-day mortality among COVID-19 patients across all waves in both adjusted and unadjusted models. This provides a simple tool for risk-stratification for front-line health care professionals.

Using cardiovascular risk indices to predict mortality in Covid-19 patients with acute respiratory distress syndrome: a cross sectional study

Covid-19 patients who require admission to an intensive care unit (ICU) have a higher risk of mortality. Several risk factors for severe Covid-19 infection have been identified, including cardiovascular risk factors. Therefore, the aim was to investigate the association between cardiovascular (CV) risk and major adverse cardiovascular events (MACE) and mortality of Covid-19 ARDS patients admitted to an ICU. A prospective cross-sectional study was conducted in a university hospital in Graz, Austria. Covid-19 patients who were admitted to an ICU with a paO2/fiO2 ratio < 300 were included in this study. Standard lipid profile was measured at ICU admission to determine CV risk. 31 patients with a mean age of 68 years were recruited, CV risk was stratified using Framingham-, Procam- and Charlson Comorbidity Index (CCI) score. A total of 10 (32.3%) patients died within 30 days, 8 patients (25.8%) suffered from MACE during ICU stay. CV risk represented by Framingham-, Procam- or CCI score was not associated with higher rates of MACE. Nevertheless, higher CV risk represented by Procam score was significantly associated with 30- day mortality (13.1 vs. 6.8, p = 0.034). These findings suggest that the Procam score might be useful to estimate the prognosis of Covid-19 ARDS patients.

Utility of Presepsin and Interferon-λ3 for Predicting Disease Severity and Clinical Outcomes in COVID-19 Patients

We explored the utility of novel biomarkers, presepsin and interferon-λ3 (IFN-λ3), for predicting disease severity and clinical outcomes in hospitalized Coronavirus (COVID-19) patients. In a total of 55 patients (non-critical, n = 16; critical, n = 39), presepsin and IFN-λ3 were compared with sequential organ failure assessment (SOFA) scores and age. Disease severity and clinical outcomes (in-hospital mortality, intensive care unit admission, ventilator use, and kidney replacement therapy) were analyzed using receiver operating characteristic (ROC) curves. In-hospital mortality was also analyzed using the Kaplan-Meier method with hazard ratios (HR). SOFA scores, age, presepsin, and IFN-λ3 predicted disease severity comparably (area under the curve [AUC], 0.67-0.73). SOFA score and IFN-λ3 predicted clinical outcomes comparably (AUC, 0.68-0.88 and 0.66-0.74, respectively). Presepsin predicted in-hospital mortality (AUC = 0.74). The combination of presepsin and IFN-λ3 showed a higher mortality risk than SOFA score or age (HR [95% confidence interval, CI], 6.7 [1.8-24.1]; 3.6 [1.1-12.1]; 2.8 [0.8-9.6], respectively) and mortality rate further increased when presepsin and IFN-λ3 were added to SOFA scores or age (8.5 [6.8-24.6], 4.2 [0.9-20.6], respectively). In the elderly (≥65 years), in-hospital mortality rate was significantly higher when both presepsin and IFN-λ3 levels increased than when either one or no biomarker level increased (88.9% vs. 14.3%, p < 0.001). Presepsin and IFN-λ3 predicted disease severity and clinical outcomes in hospitalized COVID-19 patients. Both biomarkers, whether alone or added to the clinical assessment, could be useful for managing COVID-19 patients, especially the elderly.

Individual-level precision diagnosis for coronavirus disease 2019 related severe outcome: an early study in New York

Because of inadequate information provided by the on-going population level risk analyses for Coronavirus disease 2019 (COVID-19), this study aimed to evaluate the risk factors and develop an individual-level precision diagnostic method for COVID-19 related severe outcome in New York State (NYS) to facilitate early intervention and predict resource needs for patients with COVID-19. We analyzed COVID-19 related hospital encounter and hospitalization in NYS using Statewide Planning and Research Cooperative System hospital discharge dataset. Logistic regression was performed to evaluate the risk factors for COVID-19 related mortality. We proposed an individual-level precision diagnostic method by taking into consideration of the different weights and interactions of multiple risk factors. Age was the greatest risk factor for COVID-19 related fatal outcome. By adding other demographic variables, dyspnea or hypoxemia and multiple chronic co-morbid conditions, the model predictive accuracy was improved to 0.85 (95% CI 0.84-0.85). We selected cut-off points for predictors and provided a general recommendation to categorize the levels of risk for COVID-19 related fatal outcome, which can facilitate the individual-level diagnosis and treatment, as well as medical resource prediction. We further provided a use case of our method to evaluate the feasibility of public health policy for monoclonal antibody therapy.

Evaluation of a remote monitoring service for patients with COVID-19 discharged from University College London Hospital

INTRODUCTION

In May 2020 a virtual ward for COVID-19 patients seen at University College London Hospital (UCLH) was established. The aim of this study was to see if specific factors can be used to predict the risk of deterioration and need for Emergency Department (ED) reattendance or admission.

METHODS

We performed a service evaluation of the COVID-19 virtual ward service at UCLH between 24/10/2020 and 12/2/2021. 649 patients were included with data collected on vital signs, basic measurements, and blood tests from their initial ED attendance, allowing calculation of ISARIC-4C mortality scores. Outcomes of interest were ED reattendance, facilitation of this by virtual ward physician, level of care if admitted, and death within 28 days of the first COVID-19 virtual ward appointment. Analysis was performed using Mann-Whitney U tests.

RESULTS

Reattendance rate to ED was 17.3% (112/649) of which 8% (51/649) were admitted. Half of ED reattendances were facilitated by the virtual ward service. Overall mortality was 0.92%. Patients who reattended ED, facilitated by the virtual ward service, had a higher mean CRP (53.63 vs 41.67 mg/L), presented to ED initially later in their COVID-19 illness (8 vs 6.5 days) and had a higher admission rate (61 vs 39%). The mean ISARIC-4C score was higher in the reattendance group compared to the non-reattendance group (3.87 vs 3.48, difference of 0.179, p = 0.003). The mean ISARIC-4C score was higher in the admission group than the non-reattendance group (5.56 vs 3.48, difference of 0.115, p = 0.003).

CONCLUSION

Identification of patient risk factors for reattendance following a diagnosis of COVID-19 in ED can be used to design a service to safely manage patients remotely. We found that the ISARIC -4C mortality score was associated with risk of hospital admission and could be used to identify those requiring more active remote follow up.

Immunologic and vascular biomarkers of mortality in critical COVID-19 in a South African cohort

Introduction

Biomarkers predicting mortality among critical Coronavirus disease 2019 (COVID-19) patients provide insight into the underlying pathophysiology of fatal disease and assist with triaging of cases in overburdened settings. However, data describing these biomarkers in Sub-Saharan African populations are sparse.

Methods

We collected serum samples and corresponding clinical data from 87 patients with critical COVID-19 on day 1 of admission to the intensive care unit (ICU) of a tertiary hospital in Cape Town, South Africa, during the second wave of the COVID-19 pandemic. A second sample from the same patients was collected on day 7 of ICU admission. Patients were followed up until in-hospital death or hospital discharge. A custom-designed 52 biomarker panel was performed on the Luminex® platform. Data were analyzed for any association between biomarkers and mortality based on pre-determined functional groups, and individual analytes.

Results

Of 87 patients, 55 (63.2%) died and 32 (36.8%) survived. We found a dysregulated cytokine response in patients who died, with elevated levels of type-1 and type-2 cytokines, chemokines, and acute phase reactants, as well as reduced levels of regulatory T cell cytokines. Interleukin (IL)-15 and IL-18 were elevated in those who died, and levels reduced over time in those who survived. Procalcitonin (PCT), C-reactive protein, Endothelin-1 and vascular cell adhesion molecule-1 were elevated in those who died.

Discussion

These results show the pattern of dysregulation in critical COVID-19 in a Sub-Saharan African cohort. They suggest that fatal COVID-19 involved excessive activation of cytotoxic cells and the NLRP3 (nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3) inflammasome. Furthermore, superinfection and endothelial dysfunction with thrombosis might have contributed to mortality. HIV infection did not affect the outcome. A clinically relevant biosignature including PCT, pH and lymphocyte percentage on differential count, had an 84.8% sensitivity for mortality, and outperformed the Luminex-derived biosignature.

Age-related changes in plasma biomarkers and their association with mortality in COVID-19

BACKGROUND

Coronavirus disease 2019 (COVID-19)-induced mortality occurs predominantly in older patients. Several immunomodulating therapies seem less beneficial in these patients. The biological substrate behind these observations is unknown. The aim of this study was to obtain insight into the association between ageing, the host response and mortality in patients with COVID-19.

METHODS

We determined 43 biomarkers reflective of alterations in four pathophysiological domains: endothelial cell and coagulation activation, inflammation and organ damage, and cytokine and chemokine release. We used mediation analysis to associate ageing-driven alterations in the host response with 30-day mortality. Biomarkers associated with both ageing and mortality were validated in an intensive care unit and external cohort.

RESULTS

464 general ward patients with COVID-19 were stratified according to age decades. Increasing age was an independent risk factor for 30-day mortality. Ageing was associated with alterations in each of the host response domains, characterised by greater activation of the endothelium and coagulation system and stronger elevation of inflammation and organ damage markers, which was independent of an increase in age-related comorbidities. Soluble tumour necrosis factor receptor 1, soluble triggering receptor expressed on myeloid cells 1 and soluble thrombomodulin showed the strongest correlation with ageing and explained part of the ageing-driven increase in 30-day mortality (proportion mediated: 13.0%, 12.9% and 12.6%, respectively).

CONCLUSIONS

Ageing is associated with a strong and broad modification of the host response to COVID-19, and specific immune changes likely contribute to increased mortality in older patients. These results may provide insight into potential age-specific immunomodulatory targets in COVID-19.

The Association between Acute Cardiac Injury and Outcomes of Hospitalized Patients with COVID-19: Long-Term Follow-up Results from the Sina Hospital COVID-19 Registry, Iran

Background

The present study aimed to investigate the association between acute cardiac injury (ACI) and outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19) in Iran.

Methods

The current cohort study enrolled all consecutive hospitalized patients with COVID-19 (≥ 18 y) who had serum high-sensitivity cardiac troponin-I (hs-cTnT) measurements on admission between March 2020 and March 2021. ACI was determined as hs-cTnT levels exceeding the 99th percentile of normal values. Data on demographics, comorbidities, clinical and laboratory characteristics, and outcomes were collected from Web-based electronic health records.

Results

The study population consisted of 1413 hospitalized patients with COVID-19, of whom 319 patients (22.58%) presented with ACI. The patients with ACI had a significantly higher mortality rate than those without ACI (48.28% vs 15.63%; P<0.001) within a mean follow-up of 218.86 days from symptom onset. ACI on admission was independently associated with mortality (HR, 1.44; P=0.018). In multivariable logistic regression, age (OR, 1.034; P<0.001), preexisting cardiac disease (OR, 1.49; P=0.035), preexisting malignancy (OR, 2.01; P=0.030), oxygen saturation reduced to less than 90% (OR, 2.15; P<0.001), leukocytosis (OR, 1.45; P=0.043), lymphopenia (OR, 1.49; P=0.020), reduced estimated glomerular filtration rates (eGFRs) (OR, 0.99; P=0.008), and treatment with intravenous immunoglobulin during hospitalization (OR, 4.03; P=0.006) were independently associated with ACI development.

Conclusion

ACI occurrence on admission was associated with long-term mortality in our hospitalized patients with COVID-19. The finding further underscores the significance of evaluating ACI occurrence on admission, particularly in individuals more prone to ACI, including older individuals and those with preexisting comorbidities, reduced oxygen saturation, and increased inflammatory responses.

Validity of prognostic models of critical COVID-19 is variable. A systematic review with external validation

OBJECTIVES

To identify prognostic models which estimate the risk of critical COVID-19 in hospitalized patients and to assess their validation properties.

STUDY DESIGN AND SETTING

We conducted a systematic review in Medline (up to January 2021) of studies developing or updating a model that estimated the risk of critical COVID-19, defined as death, admission to intensive care unit, and/or use of mechanical ventilation during admission. Models were validated in two datasets with different backgrounds (HM [private Spanish hospital network], n = 1,753, and ICS [public Catalan health system], n = 1,104), by assessing discrimination (area under the curve [AUC]) and calibration (plots).

RESULTS

We validated 18 prognostic models. Discrimination was good in nine of them (AUCs ≥ 80%) and higher in those predicting mortality (AUCs 65%-87%) than those predicting intensive care unit admission or a composite outcome (AUCs 53%-78%). Calibration was poor in all models providing outcome's probabilities and good in four models providing a point-based score. These four models used mortality as outcome and included age, oxygen saturation, and C-reactive protein among their predictors.

CONCLUSION

The validity of models predicting critical COVID-19 by using only routinely collected predictors is variable. Four models showed good discrimination and calibration when externally validated and are recommended for their use.

External validation of triage tools for adults with suspected COVID-19 in a middle-income setting: an observational cohort study

BACKGROUND

Tools proposed to triage ED acuity in suspected COVID-19 were derived and validated in higher income settings during early waves of the pandemic. We estimated the accuracy of seven risk-stratification tools recommended to predict severe illness in the Western Cape, South Africa.

METHODS

An observational cohort study using routinely collected data from EDs across the Western Cape, from 27 August 2020 to 11 March 2022, was conducted to assess the performance of the PRIEST (Pandemic Respiratory Infection Emergency System Triage) tool, NEWS2 (National Early Warning Score, version 2), TEWS (Triage Early Warning Score), the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS (Pandemic Medical Early Warning Score) in suspected COVID-19. The primary outcome was intubation or non-invasive ventilation, death or intensive care unit admission at 30 days.

RESULTS

Of the 446 084 patients, 15 397 (3.45%, 95% CI 34% to 35.1%) experienced the primary outcome. Clinical decision-making for inpatient admission achieved a sensitivity of 0.77 (95% CI 0.76 to 0.78), specificity of 0.88 (95% CI 0.87 to 0.88) and the negative predictive value (NPV) of 0.99 (95% CI 0.99 to 0.99). NEWS2, PMEWS and PRIEST scores achieved good estimated discrimination (C-statistic 0.79 to 0.82) and identified patients at risk of adverse outcomes at recommended cut-offs with moderate sensitivity (>0.8) and specificity ranging from 0.41 to 0.64. Use of the tools at recommended thresholds would have more than doubled admissions, with only a 0.01% reduction in false negative triage.

CONCLUSION

No risk score outperformed existing clinical decision-making in determining the need for inpatient admission based on prediction of the primary outcome in this setting. Use of the PRIEST score at a threshold of one point higher than the previously recommended best approximated existing clinical accuracy.

[Risk factors for mortality from COVID-19 Omicron variant: Retrospective analysis in elderly from the Canary Islands]

BACKGROUND AND AIMS

Since the beginning of the COVID-19 pandemic, the elderly population has had the highest rates of complications and mortality. This study aimed to determine the influence of different risk factors on deaths due to the Omicron variant in the Canary Islands.

MATERIALS AND METHODS

A retrospective observational study of 16,998 cases of COVID-19 over 40 years of age was conducted in the Canary Islands between August 1, 2022, and January 31, 2023. We extracted sociodemographic data (age and sex) and clinical data (death, vaccination history, hospital admission, previous diseases, and treatments).

RESULTS

Among the deaths, there was a higher proportion of males aged over 70 years, with diabetes, cardiovascular, renal, respiratory, and systemic diseases, and nursing home residents. Significant differences were observed in the number of doses of the vaccine. The multiple regression model showed that male sex (OR [95% CI]=1.92 [1.42-2.58]), age (70-79 years, 9.11 [4.27-19.43]; 80-89 years, 21.72 [10.40-45.36]; 90-99 years, 66.24 [31.03-141.38]; 100 years or older, 69.22 [12.97-369.33]), being unvaccinated (6.96, [4.01-12.08]), or having the last dose administered at least 12 months before the diagnosis (2.38, [1.48-3.81]) were significantly associated with mortality.

CONCLUSIONS

Multiple factors may increase the risk of mortality due to COVID-19 in the elderly population. In our study, we found that only three predictors can effectively explain the variability: older age, male sex, and not being vaccinated or last vaccination date prior to one year.

An Australian COVID-19 respiratory care unit experience

BACKGROUND

Coronavirus disease (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with a heterogeneous presentation ranging from severe pneumonitis to asymptomatic infection. International studies have demonstrated the utility of respiratory care units (RCUs) to facilitate the delivery of non-invasive ventilation techniques to patients with COVID-19 pneumonitis.

AIMS

This study aims to describe the patient characteristics, flow and outcomes of admissions to the Royal Melbourne Hospital (RMH) COVID-19 RCU (CRCU) during its initial period of operation.

METHODS

Single-centre retrospective cohort study, all patients admitted to CRCU between 17 September and 10 December 2021 were included in this study. Patient demographics, including comorbidities and limitations of medical treatment, were analysed. Admission source and discharge destination were reviewed. Length of stay was recorded. Finally, in-hospital and CRCU mortality were analysed.

RESULTS

Ninety-seven patients, comprising 111 admissions, occurred during the study period with median age of 65 years (48% female). Most patients were admitted from and discharged to the ward. Twenty patients died in hospital (21%), with age, 4C score, comorbidity and presence of obstructive lung disease predicting mortality (area under the curve (AUC) 0.85, P < 0.001). Mortality was significantly higher in those over 65 years of age compared to those under 65 (P < 0.001), or those deemed not for intubation compared to those for intubation (P = 0.0019).

CONCLUSIONS

This study demonstrates the feasibility of operating a CRCU within an Australian tertiary healthcare setting.

Is Age the Most Important Risk Factor in COVID-19 Patients? The Relevance of Comorbidity Burden: A Retrospective Analysis of 10,551 Hospitalizations

Purpose

To assess the contribution of age and comorbidity to the risk of critical illness in hospitalized COVID-19 patients using increasingly exhaustive tools for measuring comorbidity burden.

Patients and Methods

We assessed the effect of age and comorbidity burden in a retrospective, multicenter cohort of patients hospitalized due to COVID-19 in Catalonia (North-East Spain) between March 1, 2020, and January 31, 2022. Vaccinated individuals and those admitted within the first of the six COVID-19 epidemic waves were excluded from the primary analysis but were included in secondary analyses. The primary outcome was critical illness, defined as the need for invasive mechanical ventilation, transfer to the intensive care unit (ICU), or in-hospital death. Explanatory variables included age, sex, and four summary measures of comorbidity burden on admission extracted from three indices: the Charlson index (17 diagnostic group codes), the Elixhauser index and count (31 diagnostic group codes), and the Queralt DxS index (3145 diagnostic group codes). All models were adjusted by wave and center. The proportion of the effect of age attributable to comorbidity burden was assessed using a causal mediation analysis.

Results

The primary analysis included 10,551 hospitalizations due to COVID-19; of them, 3632 (34.4%) experienced critical illness. The frequency of critical illness increased with age and comorbidity burden on admission, irrespective of the measure used. In multivariate analyses, the effect size of age decreased with the number of diagnoses considered to estimate comorbidity burden. When adjusting for the Queralt DxS index, age showed a minimal contribution to critical illness; according to the causal mediation analysis, comorbidity burden on admission explained the 98.2% (95% CI 84.1-117.1%) of the observed effect of age on critical illness.

Conclusion

Comorbidity burden (when measured exhaustively) explains better than chronological age the increased risk of critical illness observed in patients hospitalized with COVID-19.

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model-SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease.

Who to escalate during a pandemic? A retrospective observational study about decision-making during the COVID-19 pandemic in the UK

BACKGROUND

Optimal decision-making regarding who to admit to critical care in pandemic situations remains unclear. We compared age, Clinical Frailty Score (CFS), 4C Mortality Score and hospital mortality in two separate COVID-19 surges based on the escalation decision made by the treating physician.

METHODS

A retrospective analysis of all referrals to critical care during the first COVID-19 surge (cohort 1, March/April 2020) and a late surge (cohort 2, October/November 2021) was undertaken. Patients with confirmed or high clinical suspicion of COVID-19 infection were included. A senior critical care physician assessed all patients regarding their suitability for potential intensive care unit admission. Demographics, CFS, 4C Mortality Score and hospital mortality were compared depending on the escalation decision made by the attending physician.

RESULTS

203 patients were included in the study, 139 in cohort 1 and 64 in cohort 2. There were no significant differences in age, CFS and 4C scores between the two cohorts. Patients deemed suitable for escalation by clinicians were significantly younger with significantly lower CFS and 4C scores compared with patients who were not deemed to benefit from escalation. This pattern was observed in both cohorts. Mortality in patients not deemed suitable for escalation was 61.8% in cohort 1 and 47.4% in cohort 2 (p<0.001).

CONCLUSIONS

Decisions who to escalate to critical care in settings with limited resources pose moral distress on clinicians. 4C score, age and CFS did not change significantly between the two surges but differed significantly between patients deemed suitable for escalation and those deemed unsuitable by clinicians. Risk prediction tools may be useful in a pandemic to supplement clinical decision-making, even though escalation thresholds require adjustments to reflect changes in risk profile and outcomes between different pandemic surges.

High Fever, Wide Distribution of Viral Pneumonia, and Pleural Effusion are More Critical Findings at the First Visit in Predicting the Prognosis of COVID-19: A Single Center, retrospective, Propensity Score-Matched Case-Control Study

Introduction

Currently, infection control measures for SARS-COV2 are being relaxed, and it is important in daily clinical practice to decide which findings to focus on when managing patients with similar background factors.

Methods

We retrospectively evaluated 66 patients who underwent blood tests (complete blood count, blood chemistry tests, and coagulation tests) and thin slice CT between January 1 and May 31, 2020, and performed a propensity score-matched case-control study. Cases and controls were a severe respiratory failure group (non-rebreather mask, nasal high-flow, and positive-pressure ventilation) and a non-severe respiratory failure group, matched at a ratio of 1:3 by propensity scores constructed by age, sex, and medical history. We compared groups for maximum body temperature up to diagnosis, blood test findings, and CT findings in the matched cohort. Two-tailed P-values <0.05 were considered statistically significant.

Results

Nine cases and 27 controls were included in the matched cohort. Significant differences were seen in maximum body temperature up to diagnosis (p=0.0043), the number of shaded lobes (p=0.0434), amount of ground-glass opacity (GGO) in the total lung field (p=0.0071), amounts of GGO (p=0.0001), and consolidation (p=0.0036) in the upper lung field, and pleural effusion (p=0.0117).

Conclusion

High fever, the wide distribution of viral pneumonia, and pleural effusion may be prognostic indicators that can be easily measured at diagnosis in COVID-19 patients with similar backgrounds.

[Risk Factors for COVID-19 Mortality]

The COVID-19 pandemic has changed the world significantly within the last two years and has put a major burden on health care systems worldwide. Due to the imbalance between the number of patients requiring treatment and the shortage of necessary healthcare resources, a new mode of triage had to be established. The allocation of resources and definition of treatment priorities could be supported by taking the actual short-term mortality risk of patients with COVID-19 into account. We therefore analyzed the current literature for criteria to predict mortality in COVID-19.

C reactive protein utilisation, a biomarker for early COVID-19 treatment, improves lenzilumab efficacy: results from the randomised phase 3 'LIVE-AIR' trial

OBJECTIVE

COVID-19 severity is correlated with granulocyte macrophage colony-stimulating factor (GM-CSF) and C reactive protein (CRP) levels. In the phase three LIVE-AIR trial, lenzilumab an anti-GM-CSF monoclonal antibody, improved the likelihood of survival without ventilation (SWOV) in COVID-19, with the greatest effect in participants having baseline CRP below a median of 79 mg/L. Herein, the utility of baseline CRP to guide lenzilumab treatment was assessed.

DESIGN

A subanalysis of the randomised, blinded, controlled, LIVE-AIR trial in which lenzilumab or placebo was administered on day 0 and participants were followed through Day 28.

PARTICIPANTS

Hospitalised COVID-19 participants (N=520) with SpO2 ≤94% on room air or requiring supplemental oxygen but not invasive mechanical ventilation.

INTERVENTIONS

Lenzilumab (1800 mg; three divided doses, q8h, within 24 hours) or placebo infusion alongside corticosteroid and remdesivir treatments.

MAIN OUTCOME MEASURES

The primary endpoint was the time-to-event analysis difference in SWOV through day 28 between lenzilumab and placebo treatments, stratified by baseline CRP.

RESULTS

SWOV was achieved in 152 (90%; 95% CI 85 to 94) lenzilumab and 144 (79%; 72 to 84) placebo-treated participants with baseline CRP <150 mg/L (HR: 2.54; 95% CI 1.46 to 4.41; p=0.0009) but not with CRP ≥150 mg/L (HR: 1.04; 95% CI 0.51 to 2.14; p=0.9058). A statistically significant interaction between CRP and lenzilumab treatment was observed (p=0.044). Grade ≥3 adverse events with lenzilumab were comparable to placebo in both CRP strata. No treatment-emergent serious adverse events were attributed to lenzilumab.

CONCLUSION

Hospitalised hypoxemic patients with COVID-19 with baseline CRP <150 mg/L derived the greatest clinical benefit from treatment with lenzilumab.

TRIAL REGISTRATION NUMBER

NCT04351152; ClinicalTrials.gov.

Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients

To support physicians in clinical decision process on patients affected by Coronavirus Disease 2019 (COVID-19) in areas with a low vaccination rate, we devised and evaluated the performances of several machine learning (ML) classifiers fed with readily available clinical and laboratory data. Our observational retrospective study collected data from a cohort of 779 COVID-19 patients presenting to three hospitals of the Lazio-Abruzzo area (Italy). Based on a different selection of clinical and respiratory (ROX index and PaO2/FiO2 ratio) variables, we devised an AI-driven tool to predict safe discharge from ED, disease severity and mortality during hospitalization. To predict safe discharge our best classifier is an RF integrated with ROX index that reached AUC of 0.96. To predict disease severity the best classifier was an RF integrated with ROX index that reached an AUC of 0.91. For mortality prediction the best classifier was an RF integrated with ROX index, that reached an AUC of 0.91. The results obtained thanks to our algorithms are consistent with the scientific literature an accomplish significant performances to forecast safe discharge from ED and severe clinical course of COVID-19.