A biological profile for diagnosis and outcome of COVID-19 patients

Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods

COVID-19 has a higher rate of morbidity and mortality among dialysis patients than the general population. Identifying infected patients early with the support of predictive models helps dialysis centers implement concerted procedures (e.g., temperature screenings, universal masking, isolation treatments) to control the spread of SARS-CoV-2 and mitigate outbreaks. We collect data from multiple sources, including demographics, clinical, treatment, laboratory, vaccination, socioeconomic status, and COVID-19 surveillance. Previous early prediction models, such as logistic regression, SVM, and XGBoost, require sophisticated feature engineering and need improved prediction performance. We create deep learning models, including Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), to predict SARS-CoV-2 infections during incubation. Our study shows deep learning models with minimal feature engineering can identify those infected patients more accurately than previously built models. Our Long Short-Term Memory (LSTM) model consistently performed well, with an AUC exceeding 0.80, peaking at 0.91 in August 2021. The CNN model also demonstrated strong results with an AUC above 0.75. Both models outperformed previous best XGBoost models by over 0.10 in AUC. Prediction accuracy declined as the pandemic evolved, dropping to approximately 0.75 between September 2021 and January 2022. Maintaining a 20% false positive rate, our LSTM and CNN models identified 66% and 64% of positive cases among patients, significantly outperforming XGBoost models at 42%. We also identify key features for dialysis patients by calculating the gradient of the output with respect to the input features. By closely monitoring these factors, dialysis patients can receive earlier diagnoses and care, leading to less severe outcomes. Our research highlights the effectiveness of deep neural networks in analyzing longitudinal data, especially in predicting COVID-19 infections during the crucial incubation period. These deep network approaches surpass traditional methods relying on aggregated variable means, significantly improving the accurate identification of SARS-CoV-2 infections.

Evaluation of Biochemical Characteristics in a Retrospective Cohort of COVID-19 Patients

BACKGROUND

Coronavirus disease 2019 (COVID-19) has had a significant impact on global health and healthcare systems. This retrospective study aimed to assess the association between biochemical parameters and outcomes in COVID-19 patients in Jazan, Saudi Arabia.

METHODS

After establishing the inclusion criteria and obtaining ethical approval, data from 156 reverse transcriptase-polymerase chain reaction (RT-PCR)-confirmed COVID-19 patients were collected from electronic medical records from a general hospital in Samtah, Jazan, from April 2020 to October 2021. The collected data included patient demographics and liver, kidney, heart, and electrolyte function marker levels. Descriptive, inferential, and principal component analyses were conducted.

RESULTS

Survival rates varied according to age and body mass index (BMI). Statistical analysis demonstrated that the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), sodium (Na), potassium (K), blood urea nitrogen (BUN), creatinine (Cr), creatine kinase (CK), CK myocardial band (MB), and lactate dehydrogenase (LDH) were significantly higher (P < 0.05) than the reference values, as assessed using the one-sample t-test. Principal component analysis (PCA) also revealed an underlying pattern in the variation of these biochemical markers. These findings suggest that certain biochemical parameters may serve as useful indicators for monitoring the condition of COVID-19 patients.

CONCLUSION

This retrospective study in Jazan, Saudi Arabia highlights the association between biochemical parameters and outcomes in COVID-19 patients. Elevated levels of markers of liver, kidney, heart, and electrolyte function suggest organ damage and dysregulation. The pattern identified through PCA provides insights into disease severity. Monitoring these parameters may serve as valuable indicators for assessing COVID-19 patients. Further research is needed to validate these findings, explore their potential for personalized treatment strategies, and improve patient outcomes during the ongoing pandemic.

The Characteristics and Laboratory Findings of SARS-CoV-2 Infected Patients during the First Three COVID-19 Waves in Portugal-A Retrospective Single-Center Study

Background and Objectives: Given the wide spectrum of clinical and laboratory manifestations of the coronavirus disease 2019 (COVID-19), it is imperative to identify potential contributing factors to patients' outcomes. However, a limited number of studies have assessed how the different waves affected the progression of the disease, more so in Portugal. Therefore, our main purpose was to study the clinical and laboratory patterns of COVID-19 in an unvaccinated population admitted to the intensive care unit, identifying characteristics associated with death, in each of the first three waves of the pandemic. Materials and Methods: This study included 337 COVID-19 patients admitted to the intensive care unit of a single-center hospital in Lisbon, Portugal, between March 2020 and March 2021. Comparisons were made between three COVID-19 waves, in the second (n = 325) and seventh (n = 216) days after admission, and between discharged and deceased patients. Results: Deceased patients were considerably older (p = 0.021) and needed greater ventilatory assistance (p = 0.023), especially in the first wave. Differences between discharged and deceased patients' biomarkers were minimal in the first wave, on both analyzed days. In the second wave significant differences emerged in troponins, lactate dehydrogenase, procalcitonin, C-reactive protein, and white blood cell subpopulations, as well as platelet-to-lymphocyte and neutrophil-to-lymphocyte ratios (all p < 0.05). Furthermore, in the third wave, platelets and D-dimers were also significantly different between patients' groups (all p < 0.05). From the second to the seventh days, troponins and lactate dehydrogenase showed significant decreases, mainly for discharged patients, while platelet counts increased (all p < 0.01). Lymphocytes significantly increased in discharged patients (all p < 0.05), while white blood cells rose in the second (all p < 0.001) and third (all p < 0.05) waves among deceased patients. Conclusions: This study yields insights into COVID-19 patients' characteristics and mortality-associated biomarkers during Portugal's first three COVID-19 waves, highlighting the importance of considering wave variations in future research due to potential significant outcome differences.

It's in the blood: a review of the hematological system in SARS-CoV-2-associated COVID-19

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global healthcare crisis. While SARS-CoV-2-associated COVID-19 affects primarily the respiratory system, patients with COVID-19 frequently develop extrapulmonary manifestations. Notably, changes in the hematological system, including lymphocytopenia, neutrophilia and significant abnormalities of hemostatic markers, were observed early in the pandemic. Hematological manifestations have since been recognized as important parameters in the pathophysiology of SARS-CoV-2 and in the management of patients with COVID-19. In this narrative review, we summarize the state-of-the-art regarding the hematological and hemostatic abnormalities observed in patients with SARS-CoV-2-associated COVID-19, as well as the current understanding of the hematological system in the pathophysiology of acute and chronic SARS-CoV-2-associated COVID-19.

Etiology, clinical characteristics, and risk factors associated with severe influenza-like illnesses in Mexican adults

Objective

The aim of this study was to determine the risk factors associated with severe influenza-like illness (ILI) in Mexican adults that could be useful to clinicians when assessing patients with ILI.

Methods

Data from adult patients enrolled from 2010 through 2014 in ILI002 - a prospective hospital-based observational cohort study - were analyzed. Etiology and clinical characteristics were compared between cases of severe ILI (defined as hospitalization and/or death) and cases of non-severe ILI.

Results

Overall, 1428 (39.0%) out of a total 3664 cases of ILI were classified as severe. Adjusted analyses showed a higher risk of severe ILI associated with signs and symptoms related to lower tract infection, i.e. cough with sputum (odds ratio (OR) 2.037, 95% confidence interval (CI) 1.206-3.477; P = 0.008), dyspnea (OR 5.044, 95% CI 2.99-8.631; and shortness of breath (OR 5.24, 95% CI 3.0839.124; P < 0.001), and with increases in lactate dehydrogenase (OR 4.426, 95% CI 2.321-8.881; P < 0.001) and C-reactive protein (OR 3.618, 95% CI 2.5955.196; P < 0.001). Further, there was an increased risk of severe ILI with a longer time between symptom onset and inclusion (OR 1.108, 95% CI 1.049-1.172; P < 0.001) and with chronic steroid use (OR 14.324, 95% CI 8.059-26.216; P < 0.001).

Conclusions

Respiratory viruses can cause severe ILI. The results of this study highlight the importance of evaluating data compatible with lower tract involvement and previous use of immunosuppressants at baseline, because patients meeting these conditions may develop severe illness.

Diagnostic value of procalcitonin, hypersensitive C-reactive protein and neutrophil-to-lymphocyte ratio for bloodstream infections in pediatric tumor patients

OBJECTIVES

Bloodstream infection (BSI) is one of the major causes of death in pediatric tumor patients. Blood samples are relatively easy to obtain and thus provide a ready source of infection-related biological markers for the prompt evaluation of infection risk.

METHODS

A total of 259 pediatric tumor patients were included from May 2019 to March 2022. Patients were divided into BSI group (n=70) and control group (n=189). Clinical and biological data were collected using electronic medical records. Differences in biological markers between BSI group and control group and differences before and during infection in BSI group were analyzed.

RESULTS

The infected group showed higher levels of procalcitonin (PCT) and hypersensitive C-reactive-protein (hsCRP), and lower prealbumin (PA) than the uninfected group. Area under the receiver-operating curve (ROC) curves (AUC) of PCT, hsCRP and NLR (absolute neutrophil count to the absolute lymphocyte count) were 0.756, 0.617 and 0.612. The AUC of other biomarkers was ≤0.6. In addition, PCT, hsCRP, NLR and fibrinogen (Fg) were significantly increased during infection, while PA and lymphocyte (LYM) were significantly decreased. Antibiotic resistant of Gram-positive bacteria to CHL, SXT, OXA and PEN was lower than that of Coagulase-negative Staphylococcus. Resistant of Gram-positive bacteria to CHL was lower, while to SXT was higher than that of Gram-negative bacteria.

CONCLUSIONS

This study explored the utility of biomarkers to assist in diagnosis and found that the PCT had the greatest predictive value for infection in pediatric tumor patients with BSI. Additionally, the PCT, hsCRP, NLR, PA, LYM and Fg were changed by BSI.

The Dynamics of the Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios Predict Progression to Septic Shock and Death in Patients with Prolonged Intensive Care Unit Stay

Background and objectives: The prognoses of patients experiencing a prolonged stay in the intensive care unit (ICU) are often significantly altered by hospital-acquired infections (HAIs), the early detection of which might be cumbersome. The aim of this study was to investigate the roles of the neutrophil-to-lymphocyte (NLR), derived-NRL (d-NLR), platelet-to-lymphocyte (PLR), and lymphocyte-to-C-reactive protein (LCR) ratios in predicting the progression to septic shock and death. Materials and Methods: A retrospective analysis of a consecutive series of ninety COVID-19 patients with prolonged hospitalization (exceeding 15 days) admitted to the ICU was conducted. The prevalence of culture-proven HAIs throughout their hospital stays was documented. NLR, dNLR, PLR, and LCR were recorded on admission, day 7, and day 14 to assess their discriminative prowess for detecting further progression to septic shock or death. Results: The prevalence of HAIs was 76.6%, 50% of patients met the criteria for septic shock, and 50% died. The median time to the first positive culture was 13.5 days and 20.5 days for developing septic shock. Mechanical ventilation was a key contributing factor to HAI, septic shock, and mortality. On admission and day 7 NLR, dNLR, PLR, and LCR values had no prognostic relevance for events occurring late during hospitalization. However, day-14 NLR, dNLR, and PLR were independent predictors for progression to septic shock and mortality and have shown good discriminative capabilities. The AUCs for septic shock were 0.762, 0.764, and 0.716, while the values for predicting in-hospital death were 0.782, 0.778, and 0.758, respectively. Conclusions: NLR, dNLR, and PLR are quick, easy-to-use, cheap, effective biomarkers for the detection of a more severe disease course, of the late development of HAIs, and of the risk of death in critically ill patients requiring a prolonged ICU stay.

Studying C-reactive protein and D-dimer levels in blood may prevent severe complications: A study in Bangladeshi COVID-19 patients

The ongoing COVID-19 pandemic has been a scientific, medical and social challenge. Since clinical course of this disease is largely unpredictable and can develop rapidly causing severe complications, it is important to identify laboratory biomarkers, which may help to classify patient's severity during initial stage. Previous studies have suggested C-reactive protein (inflammatory) and D-dimer (biochemical) as an effective biomarker. The differential severity in patients across the world and our limited understanding in the progression of the disease calls for a multi-country analysis for biomarkers. Therefore, we have analyzed these biomarkers among 228 Bangladeshi COVID-19 patients. We observed significant association of COVID-19 severity with these two biomarkers. Thus, we suggest to use these biomarkers for Bangladeshi COVID-19 patients for better disease monitoring. Such validated preventive measures may decrease the case fatality ratio substantially.

Evaluation of Hematological Parameters in Predicting Intensive Care Unit Admission in COVID-19 Patients

Hematological parameters like total leukocyte count (TLC), neutrophil, lymphocyte, and absolute eosinophil counts (AEC), and neutrophil-to-lymphocyte ratio (NLR) are known to predict the severity of novel coronavirus disease 2019 (COVID-19) patients. In the present study, we aimed to study the role of complete blood count parameters in triaging these patients requiring intensive care unit (ICU) admission. A retrospective study was done over a period of 2 months. Patients, who were ≥ 18 years of age with COVID-19 confirmed on SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) and whose routine hematology counts were sent within 24 h of admission, were included in the study. Cut-off values of 47.5 years for age, 11.3 × 10⁹/L for TLC, and 9.1 for NLR were predictive of disease severity among COVID-19 patients. Relative neutrophilia ≥ 70% (p < 0.007), relative lymphopenia ≤ 20% (p < 0.002), AEC ≤ 40/cumm (p < 0.001), and NLR ≥ 9.1 (p < 0.001) were significantly associated with ICU admission. Routine hematological parameters are cost-effective and fast predictive markers for severe COVID-19 patients, especially in resource-constrained health care settings to utilize limited ICU resources more effectively.

Comparative analysis of neutrophil to lymphocyte ratio and derived neutrophil to lymphocyte ratio with respect to outcomes of in-hospital coronavirus disease 2019 patients: A retrospective study

Introduction and objectives

In patients with coronavirus disease 2019 (COVID-19), several abnormal hematological biomarkers have been reported. The current study aimed to find out the association of neutrophil to lymphocyte ratio (NLR) and derived NLR (dNLR) with COVID-19. The objective was to compare the accuracy of both of these markers in predicting the severity of the disease.

Materials and methods

The study was conducted in a single-center having patients with COVID-19 with a considerable hospital stay. NLR is easily calculated by dividing the absolute neutrophil count (ANC) with the absolute lymphocyte count (ALC) {ANC/ALC}, while dNLR is calculated by ANC divided by total leukocyte count minus ANC {ANC/(WBC-ANC)}. Medians and interquartile ranges (IQR) were represented by box plots. Multivariable logistic regression was performed obtaining an odds ratio (OR), 95% CI, and further adjusted to discover the independent predictors and risk factors associated with elevated NLR and dNLR.

Results

A total of 1,000 patients with COVID-19 were included. The baseline NLR and dNLR were 5.00 (2.91–10.46) and 4.00 (2.33–6.14), respectively. A cut-off value of 4.23 for NLR and 2.63 for dNLR were set by receiver operating characteristic (ROC) analysis. Significant associations of NLR were obtained by binary logistic regression for dependent outcome variables as ICU stay (p < 0.001), death (p < 0.001), and invasive ventilation (p < 0.001) while that of dNLR with ICU stay (p = 0.002), death (p < 0.001), and invasive ventilation (p = 0.002) on multivariate analysis when adjusted for age, gender, and a wave of pandemics. Moreover, the indices were found correlating with other inflammatory markers such as C-reactive protein (CRP), D-dimer, and procalcitonin (PCT).

Conclusion

Both markers are equally reliable and sensitive for predicting in-hospital outcomes of patients with COVID-19. Early detection and predictive analysis of these markers can allow physicians to risk assessment and prompt management of these patients.

SARS-CoV-2 and human retroelements: a case for molecular mimicry?

Background

The factors driving the late phase of COVID-19 are still poorly understood. However, autoimmunity is an evolving theme in COVID-19’s pathogenesis. Additionally, deregulation of human retroelements (RE) is found in many viral infections, and has also been reported in COVID-19.

Results

Unexpectedly, coronaviruses (CoV) – including SARS-CoV-2 – harbour many RE-identical sequences (up to 35 base pairs), and some of these sequences are part of SARS-CoV-2 epitopes associated to COVID-19 severity. Furthermore, RE are expressed in healthy controls and human cells and become deregulated after SARS-CoV-2 infection, showing mainly changes in long interspersed nuclear element (LINE1) expression, but also in endogenous retroviruses.

Conclusion

CoV and human RE share coding sequences, which are targeted by antibodies in COVID-19 and thus could induce an autoimmune loop by molecular mimicry.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01040-2.

Neutrophil-to-Lymphocyte Ratio as a Predictor of Disease Severity and Mortality in Coronavirus Disease 2019: Prospective Study From Central India

Background: Clinical presentation of coronavirus disease 2019 (COVID-19) varies from an asymptomatic state to severe disease characterized by acute respiratory distress syndrome, respiratory failure, thrombosis, and multi-organ dysfunction syndrome. The neutrophil-to-lymphocyte ratio (NLR) has been reviewed as one of the laboratory factors that have been proposed to predict the severity of disease and mortality in COVID-19 pandemic.

Aim and objectives: To evaluate the association between NLR and the disease severity and mortality in COVID-19.

Materials and methods: After approval from Institutional Ethics Committee, this prospective cohort study was carried out in a tertiary-care teaching medical institute of Central India. COVID-19 patients of the age group 18 years and above admitted during the study period were included. Cases were categorized into four groups as asymptomatic (Group A), mild (Group B), moderate (Group C), and severe (Group D) based on clinical symptoms, respiratory rate, oxygen saturation, and chest imaging. NLR was calculated by doing a complete blood count at the time of hospitalization by the Mindray BC-6000 auto hematology analyzer. The outcome of the disease was classified as recovery and death during hospitalization. Receiver operating characteristic (ROC) curve analysis was used to assess the ability of NLR at admission to predict severe COVID-19 or mortality. Ordinal regression analysis was used to assess the impact of NLR on disease severity and mortality.

Results: Mean NLR was significantly higher in the severe COVID-19 group as compared to the mild/moderate group and in deceased as compared to discharged cases. ROC curve analysis revealed NLR to be an excellent predictor of disease severity as well as a prognostic parameter for risk of death. NLR was found to be a significant independent positive predictor for contracting the severe disease (Odd’s ratio 1.396, 95% CI=1.112-1.753, p=0.004) and mortality (Odd’s ratio 1.276, 95% CI=1.085-1.499, p=0.003).

Conclusion: High NLR was significantly associated with the disease severity and mortality in COVID-19.

Survey of laboratory medicine's national response to the Covid-19 pandemic in the Republic of Ireland

BACKGROUND

The global SARS-CoV-2 pandemic placed Irish Laboratory Medicine services under sustained and massive strain. Rapid reconfiguration was required to introduce new assays at high capacity for diagnosis and monitoring of COVID-19, while maintaining existing services.

AIM

The aim of this national survey was to capture Laboratory Medicine's response across the Republic of Ireland during the first wave of the COVID-19 pandemic.

METHODS

An electronic survey developed using Microsoft Forms® was emailed on 5 October 2020 to 53 local representatives of the PeriAnalytic and Laboratory Medicine Society (PALMSoc), reaching 38 separate pathology departments in the country.

RESULTS

A total of 45 responses from 38 laboratories were received (72% response rate) representing a range of departments and disciplines. Most laboratories (63%) introduced new tests, and in a time frame of less than 6 weeks (80%). Point-of-care testing (POCT) played a significant role in the response to COVID-19, with almost half of respondents (47%) reporting that additional equipment was introduced. Maintenance of the Quality Management System (QMS) proved challenging, with 60% of respondents indicating that not all aspects were sustained. When asked about changes to staff rostering, 98% of respondents reported that changes were made. All adjustments were made despite staffing challenges; only 18% of respondents described the staffing levels in their department as 100% prior to the onset of the first wave.

CONCLUSIONS

This study confirms an agile and resilient response to the COVID-19 pandemic from Ireland's Laboratory Medicine services despite many economic and staffing challenges.

Analyzing Correlation of Clinical Severity of COVID-19 with Other Biochemical Parameters: A Retrospective Study from Pakistan

The third wave of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing damage all over the world, especially in Pakistan and India. Although vaccines are available and preventive measures are being taken, but SARS-CoV-2 is unstoppable. Currently, there are around 841,636 positive cases in Pakistan and 18,429 deaths, whereas, in India, both are high. From April 8th to 12th, 2021, nasopharyngeal swabs of 190 patients were submitted to PRL (PACP) lab for the SARS-CoV-2 testing, and blood samples were collected at the Mayo Hospital lab for ferritin, D-dimers, lactate dehydrogenase (LDH), and C-reactive protein (CRP) testing. This study observed that coronavirus disease 2019 (COVID-19) was more likely in individuals aged 51-60 than 61-70. In addition, our study found that COVID-19 patients exhibited a statistically significant increase in levels of ferritin, D-dimers, LDH, and CRP. In addition, this study found that COVID-19 patients had significantly higher levels of ferritin, D-dimers, LDH, and CRP. Our study revealed that SARS-CoV-2 relapsed. Furthermore, we concluded that these biochemical parameters are useful indicators for severity of COVID-19.

Novel Systemic Inflammation Markers to Predict COVID-19 Prognosis

Coronavirus disease 2019 (COVID-19) has resulted in a global pandemic, challenging both the medical and scientific community for the development of novel vaccines and a greater understanding of the effects of the SARS-CoV-2 virus. COVID-19 has been associated with a pronounced and out-of-control inflammatory response. Studies have sought to understand the effects of inflammatory response markers to prognosticate the disease. Herein, we aimed to review the evidence of 11 groups of systemic inflammatory markers for risk-stratifying patients and prognosticating outcomes related to COVID-19. Numerous studies have demonstrated the effectiveness of neutrophil to lymphocyte ratio (NLR) in prognosticating patient outcomes, including but not limited to severe disease, hospitalization, intensive care unit (ICU) admission, intubation, and death. A few markers outperformed NLR in predicting outcomes, including 1) systemic immune-inflammation index (SII), 2) prognostic nutritional index (PNI), 3) C-reactive protein (CRP) to albumin ratio (CAR) and high-sensitivity CAR (hsCAR), and 4) CRP to prealbumin ratio (CPAR) and high-sensitivity CPAR (hsCPAR). However, there are a limited number of studies comparing NLR with these markers, and such conclusions require larger validation studies. Overall, the evidence suggests that most of the studied markers are able to predict COVID-19 prognosis, however NLR seems to be the most robust marker.

Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study

Objectives: To characterize the temporal characteristics of clinical variables with time lock to mortality and build a predictive model of mortality associated with COVID-19 using clinical variables. Design: Retrospective cohort study of the temporal characteristics of clinical variables with time lock to mortality. Setting: Stony Brook University Hospital (New York) and Tongji Hospital. Patients: Patients with confirmed positive for severe acute respiratory syndrome coronavirus-2 using polymerase chain reaction testing. Patients from the Stony Brook University Hospital data were used for training (80%, N = 1,002) and testing (20%, N = 250), and 375 patients from the Tongji Hospital (Wuhan, China) data were used for testing. Intervention: None. Measurements and Main Results: Longitudinal clinical variables were analyzed as a function of days from outcome with time-lock-to-day of death (non-survivors) or discharge (survivors). A predictive model using the significant earliest predictors was constructed. Performance was evaluated using receiver operating characteristics area under the curve (AUC). The predictive model found lactate dehydrogenase, lymphocytes, procalcitonin, D-dimer, C-reactive protein, respiratory rate, and white-blood cells to be early predictors of mortality. The AUC for the zero to 9 days prior to outcome were: 0.99, 0.96, 0.94, 0.90, 0.82, 0.75, 0.73, 0.77, 0.79, and 0.73, respectively (Stony Brook Hospital), and 1.0, 0.86, 0.88, 0.96, 0.91, 0.62, 0.67, 0.50, 0.63, and 0.57, respectively (Tongji Hospital). In comparison, prediction performance using hospital admission data was poor (AUC = 0.59). Temporal fluctuations of most clinical variables, indicative of physiological and biochemical instability, were markedly higher in non-survivors compared to survivors (p < 0.001). Conclusion: This study identified several clinical markers that demonstrated a temporal progression associated with mortality. These variables accurately predicted death within a few days prior to outcome, which provides objective indication that closer monitoring and interventions may be needed to prevent deterioration.

Can biomarkers of extracellular matrix remodelling and wound healing be used to identify high risk patients infected with SARS-CoV-2?: lessons learned from pulmonary fibrosis

Pulmonary fibrosis has been identified as a main factor leading to pulmonary dysfunction and poor quality of life in post-recovery Severe Acute Respiratory Syndrome (SARS) survivor's consequent to SARS-Cov-2 infection. Thus there is an urgent medical need for identification of readily available biomarkers that in patients with SARS-Cov-2 infection are able to; (1) identify patients in most need of medical care prior to admittance to an intensive care unit (ICU), and; (2) identify patients post-infection at risk of developing persistent fibrosis of lungs with subsequent impaired quality of life and increased morbidity and mortality. An intense amount of research have focused on wound healing and Extracellular Matrix (ECM) remodelling of the lungs related to lung function decline in pulmonary fibrosis (PF). A range of non-invasive serological biomarkers, reflecting tissue remodelling, and fibrosis have been shown to predict risk of acute exacerbations, lung function decline and mortality in PF and other interstitial lung diseases (Sand et al. in Respir Res 19:82, 2018). We suggest that lessons learned from such PF studies of the pathological processes leading to lung function decline could be used to better identify patients infected with SARS-Co-V2 at most risk of acute deterioration or persistent fibrotic damage of the lung and could consequently be used to guide treatment decisions.