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

A Predefined Rule-Based Multi-Factor Risk Stratification Is Associated With Improved Outcomes at a Rural Primary Care Practice

This study built a predefined rule-based risk stratification paradigm using 19 factors in a primary care setting that works with rural communities. The factors include medical and nonmedical variables. The nonmedical variables represent 3 demographic attributes and one other factor represents transportation availability. Medical variables represent major clinical variables such as blood pressure and BMI. Many risk stratification models are found in the literature but few integrate medical and nonmedical variables, and to our knowledge, no such model is designed specifically for rural communities. The data used in this study contain the associated variables of all medical visits in 2021. Data from 2022 were used to evaluate the model. After our risk stratification model and several interventions were adopted in 2022, the percentage of patients with high or medium risk of deteriorating health outcomes dropped from 34.9% to 24.4%, which is a reduction of 30%. The medium-complex patient population size, which had been 29% of all patients, decreased by about 4% to 5.7%. According to the analysis, the total risk score showed a strong correlation with 3 risk factors: dual diagnoses, the number of seen providers, and PHQ9 (0.63, 0.54, and 0.45 correlation coefficients, respectively).

Accuracy of telephone triage for predicting adverse outcomes in suspected COVID-19: an observational cohort study

OBJECTIVE

To assess accuracy of telephone triage in identifying need for emergency care among those with suspected COVID-19 infection and identify factors which affect triage accuracy.

DESIGN

Observational cohort study.

SETTING

Community telephone triage provided in the UK by Yorkshire Ambulance Service NHS Trust (YAS).

PARTICIPANTS

40 261 adults who contacted National Health Service (NHS) 111 telephone triage services provided by YAS between 18 March 2020 and 29 June 2020 with symptoms indicating COVID-19 infection were linked to Office for National Statistics death registrations and healthcare data collected by NHS Digital.

OUTCOME

Accuracy of triage disposition was assessed in terms of death or need for organ support up to 30 days from first contact.

RESULTS

Callers had a 3% (1200/40 261) risk of serious adverse outcomes (death or organ support). Telephone triage recommended self-care or non-urgent assessment for 60% (24 335/40 261), with a 1.3% (310/24 335) risk of adverse outcomes. Telephone triage had 74.2% sensitivity (95% CI: 71.6 to 76.6%) and 61.5% specificity (95% CI: 61% to 62%) for the primary outcome. Multivariable analysis suggested respiratory comorbidities may be overappreciated, and diabetes underappreciated as predictors of deterioration. Repeat contact with triage service appears to be an important under-recognised predictor of deterioration with 2 contacts (OR 1.77, 95% CI: 1.14 to 2.75) and 3 or more contacts (OR 4.02, 95% CI: 1.68 to 9.65) associated with false negative triage.

CONCLUSION

Patients advised to self-care or receive non-urgent clinical assessment had a small but non-negligible risk of serious clinical deterioration. Repeat contact with telephone services needs recognition as an important predictor of subsequent adverse outcomes.

Coronavirus Disease 2019: Past, Present, and Future

Severe acute respiratory syndrome coronavirus 2 is one of the most impactful diseases experienced in the past century. While the official national health emergency concluded in May of 2023, coronavirus disease 2019 (COVID-19) continues to mutate. As the summer of 2023, all countries were experiencing a new surge of cases from the EG.5 Omicron variant. Additionally, a new genetically distinct Omicron descendant BA2.86 had been detected in multiple countries including the United States. This article seeks to offer lessons learned from the pandemic, summarize best evidence for current management of patients with COVID-19, and give insights into future directions with this disease.

AB2CO risk score for in-hospital mortality of COVID-19 patients admitted to intensive care units

PURPOSE

To develop a mortality risk score for COVID-19 patients admitted to intensive care units (ICU), and to compare it with other existing scores.

MATERIALS AND METHODS

It is a retrospective observational study, including consecutive adult patients with laboratory-confirmed COVID-19 admitted to ICUs of 18 hospitals from nine Brazilian cities, from 09/2021 to 07/2022. Potential predictors were selected based on the literature review. Generalized Additive Models were used to examine outcomes and predictors. LASSO regression was used to derive the mortality score.

RESULTS

From 558 patients, median age was 69 years (IQR 58-78), 56.3% were men, 19.7% required mechanical ventilation (MV), and 44.8% died. The final model comprised six variables: age, pO2/FiO2, respiratory function (respiratory rate or if in MV), chronic obstructive pulmonary disease, and obesity. The AB2CO had an AUROC of 0.781 (95% CI 0.744 to 0.819), good overall performance (Brier score=0.191) and an excellent calibration (slope=1.063, intercept=0.015, p-value=0.834). The model was compared with other scores and displayed better discrimination ability than the majority of them.

CONCLUSIONS

The AB2CO score is a fast and easy tool to be used upon ICU admission.

Post-COVID-19 cognitive symptoms in patients assisted by a teleassistance service: a retrospective cohort study

Introduction

Four years after the onset of the COVID-19 pandemic, the frequency of long-term post-COVID-19 cognitive symptoms is a matter of concern given the impact it may have on the work and quality of life of affected people.

Objective

To evaluate the incidence of post-acute COVID-19 cognitive symptoms, as well as the associated risk factors.

Methods

Retrospective cohort, including outpatients with laboratory-confirmed COVID-19 and who were assisted by a public telehealth service provided by the Telehealth Network of Minas Gerais (TNMG), during the acute phase of the disease, between December/2020 and March/2022. Data were collected through a structured questionnaire, applied via phone calls, regarding the persistence of COVID-19 symptoms after 12 weeks of the disease. Cognitive symptoms were defined as any of the following: memory loss, problems concentrating, word finding difficulties, and difficulty thinking clearly.

Results

From 630 patients who responded to the questionnaire, 23.7% presented cognitive symptoms at 12 weeks after infection. These patients had a higher median age (33 [IQR 25-46] vs. 30 [IQR 24-42] years-old, p = 0.042) with a higher prevalence in the female sex (80.5% vs. 62.2%, p < 0.001) when compared to those who did not present cognitive symptoms, as well as a lower prevalence of smoking (8.7% vs. 16.2%, p = 0.024). Furthermore, patients with persistent cognitive symptoms were more likely to have been infected during the second wave of COVID-19 rather than the third (31.0% vs. 21.3%, p = 0.014). Patients who needed to seek in-person care during the acute phase of the disease were more likely to report post-acute cognitive symptoms (21.5% vs. 9.3%, p < 0,001). In multivariate logistic regression analysis, cognitive symptoms were associated with female sex (OR 2.24, CI 95% 1.41-3.57), fatigue (OR 2.33, CI 95% 1.19-4.56), depression (OR 5.37, CI 95% 2.19-13.15) and the need for seek in-person care during acute COVID-19 (OR 2.23, CI 95% 1.30-3.81).

Conclusion

In this retrospective cohort of patients with mostly mild COVID-19, cognitive symptoms were present in 23.7% of patients with COVID-19 at 12 weeks after infection. Female sex, fatigue, depression and the need to seek in-person care during acute COVID-19 were the risk factors independently associated with this condition.

A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care

The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks-outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care-which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling.

A Systematic Review of Predictor Composition, Outcomes, Risk of Bias, and Validation of COVID-19 Prognostic Scores

BACKGROUND

Numerous prognostic scores have been published to support risk stratification for patients with coronavirus disease 2019 (COVID-19).

METHODS

We performed a systematic review to identify the scores for confirmed or clinically assumed COVID-19 cases. An in-depth assessment and risk of bias (ROB) analysis (Prediction model Risk Of Bias ASsessment Tool [PROBAST]) was conducted for scores fulfilling predefined criteria ([I] area under the curve [AUC)] ≥ 0.75; [II] a separate validation cohort present; [III] training data from a multicenter setting [≥2 centers]; [IV] point-scale scoring system).

RESULTS

Out of 1522 studies extracted from MEDLINE/Web of Science (20/02/2023), we identified 242 scores for COVID-19 outcome prognosis (mortality 109, severity 116, hospitalization 14, long-term sequelae 3). Most scores were developed using retrospective (75.2%) or single-center (57.1%) cohorts. Predictor analysis revealed the primary use of laboratory data and sociodemographic information in mortality and severity scores. Forty-nine scores were included in the in-depth analysis. The results indicated heterogeneous quality and predictor selection, with only five scores featuring low ROB. Among those, based on the number and heterogeneity of validation studies, only the 4C Mortality Score can be recommended for clinical application so far.

CONCLUSIONS

The application and translation of most existing COVID scores appear unreliable. Guided development and predictor selection would have improved the generalizability of the scores and may enhance pandemic preparedness in the future.

Plasma steroid concentrations reflect acute disease severity and normalise during recovery in people hospitalised with COVID-19

OBJECTIVE

Endocrine systems are disrupted in acute illness, and symptoms reported following coronavirus disease 2019 (COVID-19) are similar to those found with clinical hormone deficiencies. We hypothesised that people with severe acute COVID-19 and with post-COVID symptoms have glucocorticoid and sex hormone deficiencies.

DESIGN/PATIENTS

Samples were obtained for analysis from two UK multicentre cohorts during hospitalisation with COVID-19 (International Severe Acute Respiratory Infection Consortium/World Health Organisation [WHO] Clinical Characterization Protocol for Severe Emerging Infections in the UK study), and at follow-up 5 months after hospitalisation (Post-hospitalisation COVID-19 study).

MEASUREMENTS

Plasma steroids were quantified by liquid chromatography-mass spectrometry. Steroid concentrations were compared against disease severity (WHO ordinal scale) and validated symptom scores. Data are presented as geometric mean (SD).

RESULTS

In the acute cohort (n = 239, 66.5% male), plasma cortisol concentration increased with disease severity (cortisol 753.3 [1.6] vs. 429.2 [1.7] nmol/L in fatal vs. least severe, p < .001). In males, testosterone concentrations decreased with severity (testosterone 1.2 [2.2] vs. 6.9 [1.9] nmol/L in fatal vs. least severe, p < .001). In the follow-up cohort (n = 198, 62.1% male, 68.9% ongoing symptoms, 165 [121-192] days postdischarge), plasma cortisol concentrations (275.6 [1.5] nmol/L) did not differ with in-hospital severity, perception of recovery, or patient-reported symptoms. Male testosterone concentrations (12.6 [1.5] nmol/L) were not related to in-hospital severity, perception of recovery or symptom scores.

CONCLUSIONS

Circulating glucocorticoids in patients hospitalised with COVID-19 reflect acute illness, with a marked rise in cortisol and fall in male testosterone. These findings are not observed 5 months from discharge. The lack of association between hormone concentrations and common post-COVID symptoms suggests steroid insufficiency does not play a causal role in this condition.

Human Bronchial Epithelial Cell Transcriptome Changes in Response to Serum from Patients with Different Status of Inflammation

PURPOSE

To investigate the transcriptome of human bronchial epithelial cells (HBEC) in response to serum from patients with different degrees of inflammation.

METHODS

Serum from 19 COVID-19 patients obtained from the Hannover Unified Biobank was used. At the time of sampling, 5 patients had a WHO Clinical Progression Scale (WHO-CPS) score of 9 (severe illness). The remaining 14 patients had a WHO-CPS of below 9 (range 1-7), and lower illness. Multiplex immunoassay was used to assess serum inflammatory markers. The culture medium of HBEC was supplemented with 2% of the patient's serum, and the cells were cultured at 37 °C, 5% CO2 for 18 h. Subsequently, cellular RNA was used for RNA-Seq.

RESULTS

Patients with scores below 9 had significantly lower albumin and serum levels of E-selectin, IL-8, and MCP-1 than patients with scores of 9. Principal component analysis based on 500 "core genes" of RNA-seq segregated cells into two subsets: exposed to serum from 4 (I) and 15 (II) patients. Cells from a subset (I) treated with serum from 4 patients with a score of 9 showed 5566 differentially expressed genes of which 2793 were up- and 2773 downregulated in comparison with cells of subset II treated with serum from 14 patients with scores between 1 and 7 and one with score = 9. In subset I cells, a higher expression of TLR4 and CXCL8 but a lower CDH1, ACE2, and HMOX1, and greater effects on genes involved in metabolic regulation, cytoskeletal organization, and kinase activity pathways were observed.

CONCLUSION

This simple model could be useful to characterize patient serum and epithelial cell properties.

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

OBJECTIVES

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

STUDY DESIGN AND SETTING

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

MAIN OUTCOME MEASURE

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

RESULTS

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

CONCLUSION

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

Circulating haematopoietic stem cells and long-term outcomes of COVID-19

BACKGROUND AND AIMS

An acute depletion of circulating haematopoietic stem/progenitor cells (HSPCs) occurs during COVID-19, especially among patients with a poorer disease course. We herein examined whether HSPCs levels at hospital admission for COVID-19 predict 1-year mortality and the long-COVID syndrome.

MATERIALS AND METHODS

Patients hospitalized for COVID-19 in an infectious disease ward were consecutively enrolled. Circulating HSPC levels were assessed by flow cytometry as cells expressing CD34 and/or CD133. Follow-up was performed for 12 months after hospitalization through the review of electronic medical records and demographic local registers.

RESULTS

The study included 100 patients, 36 of whom reported symptoms of long-COVID and 20 died during follow-up. The reduction of 1-SD of HSPCs was associated with a 3- to 5-fold increase in the risk of 1-year mortality. Age, admission hyperglycaemia, C-reactive protein peak, liver enzymes, the need of high-flow oxygen and/or invasive ventilation were predictors of mortality at univariate analysis. Among pre-existing comorbidities, coronary heart disease and chronic kidney disease, but not diabetes, were associated with 1-year mortality. In multivariate analyses, HSPCs remained significantly associated with 1-year mortality independently of confounders. The development of pneumonia an in-hospital treatment with glucocorticoids and convalescent plasma were associated with long-COVID symptoms at follow-up. HSPCs, diabetes and other comorbidities were not predictors of long-COVID.

CONCLUSIONS

In a cohort of patients hospitalized for COVID-19, lower HSPC levels at the time of admission were independent predictors of 1-year mortality. However, COVID-19 severity, but not HSPC level, was significantly associated with the development of long-COVID symptoms.

Evaluation of a virtual ward model of care and readmission characteristics during the COVID-19 pandemic within an Australian tertiary hospital

BACKGROUND

Virtual ward (VW) models of care established during the coronavirus disease 2019 (COVID-19) pandemic provided safe and equitable provision of ambulatory care for low-risk patients; however, little is known about patients who require escalation of care to hospitals from VWs.

AIM

To assess our VW model of care and describe the characteristics of patients admitted to the hospital from the VW.

METHODS

Observational study of all patients admitted to a tertiary hospital COVID-19 VW between 1 December 2021 and 30 June 2022. Utilisation and epidemiological characteristics were assessed for all patients while additional demographics, assessments, treatments and outcomes were assessed for patients admitted to the hospital from the VW.

RESULTS

Of 9494 patient admissions, 269 (2.83%) patients identified as Aboriginal and Torres Strait Islander and 1774 (18.69%) were unvaccinated. The median length of stay was 5.10 days and the mean Index of Relative Socio-economic Advantage and Disadvantage decile was 5.73. One hundred sixty (1.69%) patients were admitted to the hospital from the VW, of which 25 were adults admitted to medical wards. Of this cohort, prominent comorbidities were obesity, hypertension, asthma and frailty, while the main symptoms on admission to the VW were cough, fatigue, nausea and sore throat. High Pandemic Respiratory Infection Emergency System Triage (PRIEST), Veterans Health Administration COVID-19 (VACO), COVID Home Safely Now (CHOSEN) and 4C mortality scores existed for those readmitted.

CONCLUSIONS

This VW model of care was both safe and effective when applied to a broad socioeconomic population during the COVID-19 pandemic. While readmission to the hospital was low, this study identified key characteristics of such presentations, which may assist future triaging, escalation and resource allocation within VWs during the COVID-19 pandemic and beyond.

A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning

Introduction

The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians.

Methods

Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity.

Results

Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively.

Conclusion

Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.

Platelet and large platelet ratios are useful in predicting severity of COVID-19

The role of platelets in coronavirus disease (COVID-19) severity requires further exploration. To determine whether the platelet index is useful in predicting COVID-19 severity, we compared the platelet index in patients with higher and lower oxygen requirements (≥ 4 L/min vs. < 4 L/min) and patients without COVID-19. We also analyzed the time course of the platelet index in each group. A total of 285 patients with COVID-19 and 36 without COVID-19 who were hospitalized at Fussa Hospital were analyzed. After matching for oxygen requirement at admission, multivariate analysis was performed. Platelets (≤ 16.6 × 104/μL) and platelet-large cell ratio (P-LCR) (≥ 27.8%) were significant factors influencing severity. Based on these factors, we created the Fussa platelet score, and the group with a Fussa platelet score ≥ 2 was significantly more likely to reach the 4 L/min oxygen requirement (event-free survival: Fussa platelet score ≥ 2 versus < 2, P < 0.00000001). Analysis of platelet index by time period showed a significant increase from 6-10 days after onset. The Fussa platelet score can be measured quickly, easily, and inexpensively in a clinic and may be useful in determining need for transfer to a critical care hospital.

COVID-19: The Development and Validation of a New Mortality Risk Score

Background: The coronavirus disease 2019 (COVID-19) pandemic has found the whole world unprepared for its correct management. Italy was the first European country to experience the spread of the SARS-CoV-2 virus at the end of February 2020. As a result of hospital overcrowding, the quality of care delivered was not always optimal. A substantial number of patients admitted to non-ICU units could have been treated at home. It would have been extremely useful to have a score that, based on personal and clinical characteristics and simple blood tests, could have predicted with sufficient reliability the probability that a patient had or did not have a disease that could have led to their death. This study aims to develop a scoring system to identify which patients with COVID-19 are at high mortality risk upon hospital admission, to expedite and enhance clinical decision making. Methods: A retrospective analysis was performed to develop a multivariable prognostic prediction model. Results: Derivation and external validation cohorts were obtained from two Italian University Hospital databases, including 388 (10.31% deceased) and 1357 (7.68% deceased) patients with confirmed COVID-19, respectively. A multivariable logistic model was used to select seven variables associated with in-hospital death (age, baseline oxygen saturation, hemoglobin value, white blood cell count, percentage of neutrophils, platelet count, and creatinine value). Calibration and discrimination were satisfactory with a cumulative AUC for prediction mortality of 0.924 (95% CI: 0.893-0.944) in derivation cohorts and 0.808 (95% CI: 0.886-0.828) in external validation cohorts. The risk score obtained was compared with the ISARIC 4C Mortality Score, and with all the other most important scores considered so far, to evaluate the risk of death of patients with COVID-19. It performed better than all the above scores to evaluate the predictability of dying. Its sensitivity, specificity, and AUC were higher than the other COVID-19 scoring systems when the latter were calculated for the 388 patients in our derivation cohort. Conclusions: In conclusion, the CZ-COVID-19 Score may help all physicians by identifying those COVID-19 patients who require more attention to provide better therapeutic regimens or, on the contrary, by identifying those patients for whom hospitalization is not necessary and who could therefore be sent home without overcrowding healthcare facilities. We developed and validated a new risk score based on seven variables for upon-hospital admission of COVID-19 patients. It is very simple to calculate and performs better than all the other similar scores to evaluate the predictability of dying.

Derivation and validation of a risk-stratification model for patients with probable or proven COVID-19 in EDs: the revised HOME-CoV score

BACKGROUND

The HOME-CoV (Hospitalisation or Outpatient ManagEment of patients with SARS-CoV-2 infection) score is a validated list of uniquely clinical criteria indicating which patients with probable or proven COVID-19 can be treated at home. The aim of this study was to optimise the score to improve its ability to discriminate between patients who do and do not need admission.

METHODS

A revised HOME-CoV score was derived using data from a previous prospective multicentre study which evaluated the original Home-CoV score. Patients with proven or probable COVID-19 attending 34 EDs in France, Monaco and Belgium between April and May 2020 were included. The population was split into a derivation and validation sample corresponding to the observational and interventional phases of the original study. The main outcome was non-invasive or invasive ventilation or all-cause death within 7 days following inclusion. Two threshold values were defined using a sensitivity of >0.9 and a specificity of >0.9 to identify low-risk and high-risk patients, respectively. The revised HOME-CoV score was then validated by retrospectively applying it to patients in the same EDs with proven or probable COVID-19 during the interventional phase. The revised HOME-CoV score was also tested against original HOME-CoV, qCSI, qSOFA, CRB65 and SMART-COP in this validation cohort.

RESULTS

There were 1696 patients in the derivation cohort, of whom 65 (3.8%) required non-invasive ventilation or mechanical ventilation or died within 7 days and 1304 patients in the validation cohort, of whom 22 (1.7%) had a progression of illness. The revised score included seven clinical criteria. The area under the curve (AUC) was 87.6 (95% CI 84.7 to 90.6). The cut-offs to define low-risk and high-risk patients were <2 and >3, respectively. In the validation cohort, the AUC was 85.8 (95% CI 80.6 to 91.0). A score of <2 qualified 73% of patients as low risk with a sensitivity of 0.77 (0.55-0.92) and a negative predictive value of 0.99 (0.99-1.00).

CONCLUSION

The revised HOME-CoV score, which does not require laboratory testing, may allow accurate risk stratification and safely qualify a significant proportion of patients with probable or proven COVID-19 for home treatment.

Effects of acute phase intensive electrical muscle stimulation in COVID-19 patients requiring invasive mechanical ventilation: an observational case-control study

We investigated the effects of acute-phase intensive electrical muscle stimulation (EMS) on physical function in COVID-19 patients with respiratory failure requiring invasive mechanical ventilation (IMV) in the intensive care unit (ICU). Consecutive COVID-19 patients requiring IMV admitted to a university hospital ICU between January and April 2022 (EMS therapy group) or between March and September 2021 (age-matched historical control group) were included in this retrospective observational case-control study. EMS was applied to both upper and lower limb muscles for up to 2 weeks in the EMS therapy group. The study population consisted of 16 patients undergoing EMS therapy and 16 age-matched historical controls (median age, 71 years; 81.2% male). The mean period until initiation of EMS therapy after ICU admission was 3.2 ± 1.4 days. The EMS therapy group completed a mean of 6.2 ± 3.7 EMS sessions, and no adverse events occurred. There were no significant differences between the two groups in Medical Research Council sum score (51 vs. 53 points, respectively; P = 0.439) or ICU mobility scale at ICU discharge. Addition of upper and lower limb muscle EMS therapy to an early rehabilitation program did not result in improved physical function at ICU discharge in severe COVID-19 patients.

Cell-free DNA methylation reveals cell-specific tissue injury and correlates with disease severity and patient outcomes in COVID-19

BACKGROUND

The recently identified methylation patterns specific to cell type allows the tracing of cell death dynamics at the cellular level in health and diseases. This study used COVID-19 as a disease model to investigate the efficacy of cell-specific cell-free DNA (cfDNA) methylation markers in reflecting or predicting disease severity or outcome.

METHODS

Whole genome methylation sequencing of cfDNA was performed for 20 healthy individuals, 20 cases with non-hospitalized COVID-19 and 12 cases with severe COVID-19 admitted to intensive care unit (ICU). Differentially methylated regions (DMRs) and gene ontology pathway enrichment analyses were performed to explore the locus-specific methylation difference between cohorts. The proportion of cfDNA derived from lung and immune cells to a given sample (i.e. tissue fraction) at cell-type resolution was estimated using a novel algorithm, which reflects lung injuries and immune response in COVID-19 patients and was further used to evaluate clinical severity and patient outcome.

RESULTS

COVID‑19 patients had globally reduced cfDNA methylation level compared with healthy controls. Compared with non-hospitalized COVID-19 patients, the cfDNA methylation pattern was significantly altered in severe patients with the identification of 11,156 DMRs, which were mainly enriched in pathways related to immune response. Markedly elevated levels of cfDNA derived from lung and more specifically alveolar epithelial cells, bronchial epithelial cells, and lung endothelial cells were observed in COVID-19 patients compared with healthy controls. Compared with non-hospitalized patients or healthy controls, severe COVID-19 had significantly higher cfDNA derived from B cells, T cells and granulocytes and lower cfDNA from natural killer cells. Moreover, cfDNA derived from alveolar epithelial cells had the optimal performance to differentiate COVID-19 with different severities, lung injury levels, SOFA scores and in-hospital deaths, with the area under the receiver operating characteristic curve of 0.958, 0.941, 0.919 and 0.955, respectively.

CONCLUSION

Severe COVID-19 has a distinct cfDNA methylation signature compared with non-hospitalized COVID-19 and healthy controls. Cell type-specific cfDNA methylation signature enables the tracing of COVID-19 related cell deaths in lung and immune cells at cell-type resolution, which is correlated with clinical severities and outcomes, and has extensive application prospects to evaluate tissue injuries in diseases with multi-organ dysfunction.

Proteome analysis develops novel plasma proteins classifier in predicting the mortality of COVID-19

COVID-19 has been a global concern for 3 years, however, consecutive plasma protein changes in the disease course are currently unclear. Setting the mortality within 28 days of admission as the main clinical outcome, plasma samples were collected from patients in discovery and independent validation groups at different time points during the disease course. The whole patients were divided into death and survival groups according to their clinical outcomes. Proteomics and pathway/network analyses were used to find the differentially expressed proteins and pathways. Then, we used machine learning to develop a protein classifier which can predict the clinical outcomes of the patients with COVID-19 and help identify the high-risk patients. Finally, a classifier including C-reactive protein, extracellular matrix protein 1, insulin-like growth factor-binding protein complex acid labile subunit, E3 ubiquitin-protein ligase HECW1 and phosphatidylcholine-sterol acyltransferase was determined. The prediction value of the model was verified with an independent patient cohort. This novel model can realize early prediction of 28-day mortality of patients with COVID-19, with the area under curve 0.88 in discovery group and 0.80 in validation group, superior to 4C mortality and E-CURB65 scores. In total, this work revealed a potential protein classifier which can assist in predicting the outcomes of COVID-19 patients and providing new diagnostic directions.

A risk stratification model for high-flow nasal cannula use in patients with coronavirus disease 2019 in Japan: A single-center retrospective observational cohort study

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has put a strain on the healthcare system, and sudden changes in disease status during home treatment have become a serious issue. Therefore, prediction of disease severity and allocation of sufficient medical resources, including high-flow nasal cannula (HFNC), to patients in need are important. We aimed to determine risk factors for the need of HFNC use in COVID-19.

METHODS

This was a single-center retrospective observational cohort study including all eligible hospitalized adult patients aged ≥18 years diagnosed with COVID-19 between April 14, 2020 and August 5, 2021 who were treated in the study hospital. The primary outcome is the need for HFNC. Nineteen potential predictive variables, including patient characteristics at hospital admission, were screened using least absolute shrinkage and selection operator and logistic regression to construct a predictive risk score. Accuracy of the risk score was determined using area under the receiver operating characteristic curve.

RESULTS

The study cohort included 148 patients. The rate of the need for HFNC was 22.9%. Among the 19 potential variables, percutaneous oxygen saturation (SpO2) <92% (odds ratio [OR] 7.50, 95% confidence interval [CI] 2.806-20.82) and IL-6 (OR 1.021, 95% CI 1.010-1.033) were included in developing the risk score, which was termed interleukin (IL)-6-based COVID-19 severity (IBC-S) score.

CONCLUSIONS

The IBC-S score, an easy-to-use risk score based on parameters available at the time of hospital admission, predicted the need for HFNC in patients with COVID-19. The IBC-S score based on interleukin-6 and SpO2 might aid in determining patients who should be transported to a tertiary medical institution or an isolation facility.

4C mortality score and COVID-19 mortality risk score: an analysis in four different age groups of an Italian population

To evaluate the prognostic stratification ability of 4C Mortality Score and COVID-19 Mortality Risk Score in different age groups. Retrospective study, including all patients, presented to the Emergency Department of the University Hospital Careggi, between February, 2020 and May, 2021, and admitted for SARS-CoV2. Patients were divided into four subgroups based on the quartiles of age distribution: patients < 57 years (G1, n = 546), 57-71 years (G2, n = 508), 72-81 years (G3, n = 552), and > 82 years (G4, n = 578). We calculated the 4C Mortality Score and COVID-19 Mortality Risk Score. The end-point was in-hospital mortality. In the whole population (age 68 ± 16 years), the mortality rate was 19% (n = 424), and increased with increasing age (G1: 4%, G2: 11%, G3: 22%, and G4: 39%, p < 0.001). Both scores were higher among non-survivors than survivors in all subgroups (4C-MS, G1: 6 [3-7] vs 3 [2-5]; G2: 10 [7-11] vs 7 [5-8]; G3: 11 [10-14] vs 10 [8-11]; G4: 13 [12-15] vs 11 [10-13], all p < 0.001; COVID-19 MRS, G1: 8 [7-9] vs 9 [9-11], G2: 10 [8-11] vs 11 [10-12]; G3: 11 [10-12] vs 12 [11-13]; G4: 11 [10-13] vs 13 [12-14], all p < 0.01). The ability of both scores to identify patients at higher risk of in-hospital mortality, was similar in different age groups (4C-MS: G1 0.77, G2 0.76, G3 0.68, G4 0.72; COVID-19 MRS: G1 0.67, G2 0.69, G3 0.69, G4 0.72, all p for comparisons between subgroups = NS). Both scores confirmed their good performance in predicting in-hospital mortality in all age groups, despite their different mortality rate.

Determinants of COVID-19 Infection Among Employees of an Italian Financial Institution

BACKGROUND

Understanding the trend of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is becoming crucial. Previous studies focused on predicting COVID-19 trends, but few papers have considered models for disease estimation and progression based on large real-world data.

METHODS

We used de-identified data from 60,938 employees of a major financial institution in Italy with daily COVID-19 status information between 31 March 2020 and 31 August 2021. We consider six statuses: (i) concluded case, (ii) confirmed case, (iii) close contact, (iv) possible-probable contact, (v) possible contact, and (vi) no-COVID-19 or infection. We conducted a logistic regression to assess the odds ratio (OR) of transition to confirmed COVID-19 case at each time point. We also fitted a general model for disease progression via the multi-state transition probability model at each time point, with lags of 7 and 15 days.

RESULTS

Employment in a branch versus in a central office was the strongest predictor of case or contact status, while no association was detected with gender or age. The geographic prevalence of possible-probable contacts and close contacts was predictive of the subsequent risk of confirmed cases. The status with the highest probability of becoming a confirmed case was concluded case (12%) in April 2020, possible-probable contact (16%) in November 2020, and close contact (4%) in August 2021. The model based on transition probabilities predicted well the rate of confirmed cases observed 7 or 15 days later.

CONCLUSION

Data from industry-based surveillance systems may effectively predict the risk of subsequent infection.

Viral and Host Factors Are Associated With Mortality in Hospitalized Patients With COVID-19

BACKGROUND

Persistent mortality in adults hospitalized due to acute COVID-19 justifies pursuit of disease mechanisms and potential therapies. The aim was to evaluate which virus and host response factors were associated with mortality risk among participants in Therapeutics for Inpatients with COVID-19 (TICO/ACTIV-3) trials.

METHODS

A secondary analysis of 2625 adults hospitalized for acute SARS-CoV-2 infection randomized to 1 of 5 antiviral products or matched placebo in 114 centers on 4 continents. Uniform, site-level collection of participant baseline clinical variables was performed. Research laboratories assayed baseline upper respiratory swabs for SARS-CoV-2 viral RNA and plasma for anti-SARS-CoV-2 antibodies, SARS-CoV-2 nucleocapsid antigen (viral Ag), and interleukin-6 (IL-6). Associations between factors and time to mortality by 90 days were assessed using univariate and multivariable Cox proportional hazards models.

RESULTS

Viral Ag ≥4500 ng/L (vs <200 ng/L; adjusted hazard ratio [aHR], 2.07; 1.29-3.34), viral RNA (<35 000 copies/mL [aHR, 2.42; 1.09-5.34], ≥35 000 copies/mL [aHR, 2.84; 1.29-6.28], vs below detection), respiratory support (<4 L O2 [aHR, 1.84; 1.06-3.22]; ≥4 L O2 [aHR, 4.41; 2.63-7.39], or noninvasive ventilation/high-flow nasal cannula [aHR, 11.30; 6.46-19.75] vs no oxygen), renal impairment (aHR, 1.77; 1.29-2.42), and IL-6 >5.8 ng/L (aHR, 2.54 [1.74-3.70] vs ≤5.8 ng/L) were significantly associated with mortality risk in final adjusted analyses. Viral Ag, viral RNA, and IL-6 were not measured in real-time.

CONCLUSIONS

Baseline virus-specific, clinical, and biological variables are strongly associated with mortality risk within 90 days, revealing potential pathogen and host-response therapeutic targets for acute COVID-19 disease.

Host Response Changes and Their Association with Mortality in COVID-19 Patients with Lymphopenia

Rationale: Lymphopenia in coronavirus disease (COVID-19) is associated with increased mortality. Objectives: To explore the association between lymphopenia, host response aberrations, and mortality in patients with lymphopenic COVID-19. Methods: We determined 43 plasma biomarkers reflective of four pathophysiological domains: endothelial cell and coagulation activation, inflammation and organ damage, cytokine release, and chemokine release. We explored if decreased concentrations of lymphocyte-derived proteins in patients with lymphopenia were associated with an increase in mortality. We sought to identify host response phenotypes in patients with lymphopenia by cluster analysis of plasma biomarkers. Measurements and Main Results: A total of 439 general ward patients with COVID-19 were stratified by baseline lymphocyte counts: normal (>1.0 × 109/L; n = 167), mild lymphopenia (>0.5 to ⩽1.0 × 109/L; n = 194), and severe lymphopenia (⩽0.5 × 109/L; n = 78). Lymphopenia was associated with alterations in each host response domain. Lymphopenia was associated with increased mortality. Moreover, in patients with lymphopenia (n = 272), decreased concentrations of several lymphocyte-derived proteins (e.g., CCL5, IL-4, IL-13, IL-17A) were associated with an increase in mortality (at P < 0.01 or stronger significance levels). A cluster analysis revealed three host response phenotypes in patients with lymphopenia: "hyporesponsive" (23.2%), "hypercytokinemic" (36.4%), and "inflammatory-injurious" (40.4%), with substantially differing mortality rates of 9.5%, 5.1%, and 26.4%, respectively. A 10-biomarker model accurately predicted these host response phenotypes in an external cohort with similar mortality distribution. The inflammatory-injurious phenotype showed a remarkable combination of relatively high inflammation and organ damage markers with high antiinflammatory cytokine levels yet low proinflammatory cytokine levels. Conclusions: Lymphopenia in COVID-19 signifies a heterogenous group of patients with distinct host response features. Specific host responses contribute to lymphopenia-associated mortality in COVID-19, including reduced CCL5 levels.

The association between vitamin D deficiency and the clinical outcomes of hospitalized COVID-19 patients

Background

Vitamin D deficiency is an emerging public health problem that affects more than one billion people worldwide. Vitamin D has been shown to be effective in preventing and reducing the severity of viral respiratory diseases, including influenza. However, the role of vitamin D in COVID-19 infection remains controversial. This study aimed to analyze the association of vitamin D deficiency on the clinical outcome of hospitalized COVID-19 patients.

Methods

A prospective cohort study was conducted among hospitalized COVID-19 patients at two COVID-19 referral hospitals in Indonesia from October 2021 until February 2022.

Results

The median serum 25(OH)D level in 191 hospitalized COVID-19 patients was 13.6 [IQR=10.98] ng/mL. The serum 25(OH)D levels were significantly lower among COVID-19 patients with vitamin D deficiency who had cardiovascular disease (p-value=0.04), the use of a ventilator (p-value=0.004), more severe COVID-19 cases (p-value=0.047), and mortality (p-value=0.002). Furthermore, serum 25(OH)D levels were significantly different between patients with mild and severe COVID-19 cases (p-value=0.019). Serum 25(OH)D levels in moderate and severe COVID-19 cases were significantly different (p-value=0.031). Lower serum 25(OH)D levels were significantly associated with an increased number of comorbidities (p-value=0.03), the severity of COVID-19 (p-value=0.002), and the use of mechanical ventilation (p-value=0.032). Mortality was found in 7.3% of patients with deficient vitamin D levels. However, patients with either sufficient or insufficient vitamin D levels did not develop mortality.

Conclusions

COVID-19 patients with vitamin D deficiency were significantly associated with having cardiovascular disease, mortality, more severe COVID-19 cases, and the used of mechanical ventilation. Lower serum 25(OH)D levels were associated with an increased number of comorbidities, COVID-19 severity, and the use of mechanical-ventilation. Thus, we suggest hospitalized COVID-19 patients to reach a sufficient vitamin D status to improve the clinical outcome of the disease.

Performance of oxygenation indices and risk scores to predict invasive mechanical ventilation and mortality in COVID-19

BACKGROUND

Information on the performance of oxygenation indices (OIs) and risk scores in patients requiring invasive mechanical ventilation (IMV) is limited. We determine the performance of the OIs and risk scores in hospitalized patients with COVID-19 to predict the requirement of IMV and death at 28 days after admission.

METHODS

A retrospective study of diagnostic tests in patients admitted to the emergency department, hospitalization, and intensive care unit diagnosed with COVID-19. The receiver operating characteristic curve (ROC-curve) were built with the OIs and risk scores to predict IMV and mortality.

RESULTS

A total of 1402 subjects entered the final analysis, of whom 19.5% (274/1402) received IMV and 23.0% (323/1402) died at 28 days. The ROC-curve of the delta PaO2/FiO2 ratio for the requirement of IMV and mortality at 28-day was 0.589 (95% CI: 0.546-0.632) and 0.567 (95% CI: 0.526-0.608), respectively. PaO2/FiO2 ≤ 300 shows a ROC curve of 0.669 (95% CI: 0.628-0.711) to predict IMV. PaO2/FiO2 ≤ 300 and 4 C mortality score in mortality at 28 days showed an ROC-curve of 0.624 (95% CI: 0.582-0.667) and 0.706 (95% CI: 0.669-0.742), respectively.

CONCLUSION

PaO2/FiO2 ≤ 300, 4 C mortality score ≥ 8, SOFA score ≥ 4 y SaO2/FiO2 ≤ 300 were weak predictors of the IMV requirement from admission, and 4 C mortality score ≥ 8 was weak predictors of the mortality from admission in patients with pulmonary involvement by COVID-19.

Analysis of acute COVID-19 including chronic morbidity: protocol for the deep phenotyping National Pandemic Cohort Network in Germany (NAPKON-HAP)

BACKGROUND

The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) pandemic causes a high burden of acute and long-term morbidity and mortality worldwide despite global efforts in containment, prophylaxis, and therapy. With unprecedented speed, the global scientific community has generated pivotal insights into the pathogen and the host response evoked by the infection. However, deeper characterization of the pathophysiology and pathology remains a high priority to reduce morbidity and mortality of coronavirus disease 2019 (COVID-19).

METHODS

NAPKON-HAP is a multi-centered prospective observational study with a long-term follow-up phase of up to 36 months post-SARS-CoV-2 infection. It constitutes a central platform for harmonized data and biospecimen for interdisciplinary characterization of acute SARS-CoV-2 infection and long-term outcomes of diverging disease severities of hospitalized patients.

RESULTS

Primary outcome measures include clinical scores and quality of life assessment captured during hospitalization and at outpatient follow-up visits to assess acute and chronic morbidity. Secondary measures include results of biomolecular and immunological investigations and assessment of organ-specific involvement during and post-COVID-19 infection. NAPKON-HAP constitutes a national platform to provide accessibility and usability of the comprehensive data and biospecimen collection to global research.

CONCLUSION

NAPKON-HAP establishes a platform with standardized high-resolution data and biospecimen collection of hospitalized COVID-19 patients of different disease severities in Germany. With this study, we will add significant scientific insights and provide high-quality data to aid researchers to investigate COVID-19 pathophysiology, pathology, and chronic morbidity.

The effect of ciprofloxacin prophylaxis during haematopoietic cell transplantation on infection episodes, exposure to treatment antimicrobials and antimicrobial resistance: a single-centre retrospective cohort study

Objectives

Fluroquinolone prophylaxis during haematopoietic cell transplantation (HCT) remains contentious. We aimed to determine its effectiveness and association with exposure to treatment antimicrobials and antimicrobial resistance.

Methods

All admission episodes for HCT (N = 400 , 372 unique patients) in a tertiary centre between January 2020 and December 2022 were studied. Allogeneic HCT (allo-HCT) recipients received prophylaxis with ciprofloxacin during chemotherapy-induced neutropenia, while autologous HCT (auto-HCT) recipients did not.

Results

Allo-HCT was performed for 43.3% (173/400) of patients, auto-HCT for 56.7% (227/400). Allo-HCT was associated with an average of 1.01 fewer infection episodes per 100 admission days (95% CI 0.62-1.40, P < 0.001) compared with auto-HCT. In allo-HCT, the total exposure to all antimicrobials was higher [+24.8 days of therapy (DOT)/100 admission days, P < 0.001], as was exposure to ciprofloxacin (+40.5 DOT/100 admission days, P < 0.001). By contrast, exposure to meropenem (-4.5 DOT/100 admission days, P = 0.02), piperacillin/tazobactam (-5.2 DOT/100 admission days, P < 0.001), aminoglycosides (-4.5 DOT/100 admission days, P < 0.001) and glycopeptides (-6.4 DOT/100 admission days, P < 0.001) was reduced. Enterobacteriaceae isolated during allo-HCT were more resistant to ciprofloxacin (65.5%, 19/29 versus 6.1%, 2/33, P < 0001), ceftriaxone (65.5%, 19/29 versus 9.1%, 3/33, P < 0.001), other antimicrobial classes. Vancomycin-resistant enterococci were more common in allo-HCT recipients (11%, 19/173 versus 0.9%, 2/227, P < 0.001). Inpatient mortality during allo- and auto-HCT was 9.8% (17/173) and 0.4% (1/227). respectively (P < 0.001).

Conclusions

Ciprofloxacin prophylaxis in allo-HCT was associated with fewer infection episodes and reduced exposure to treatment antimicrobials. Mortality in auto-HCT remained low. A significant burden of antimicrobial resistance was detected in allo-HCT recipients.

A Retrospective Study on the Outcome of Coronavirus Disease 2019 (COVID-19) Patients Admitted to a District General Hospital and Predictors of High Mortality

BACKGROUND

The clinical features and severity of coronavirus disease 2019 (COVID-19) vary between patients and countries. Patients with certain conditions are predisposed to poor outcomes compared with those without medical conditions, such as diabetes, dementia, and hypertension (HTN).

METHODS

The aim of this retrospective study was to assess factors associated with higher mortality in patients with COVID-19 infections and to identify the reason for hospital admission in these patients. The study was performed on patients admitted between 1 and 31 March 2020. Data collection was done retrospectively from electronic medical records.

RESULTS

There were 269 patient admissions during this period, of which 147 were included in this audit. The mean age of COVID-19-positive patients was 62.8 years and 65.9 years for COVID-19-negative patients during this period. Forty-seven patients requiring hospital admission were COVID-19 positive and 93 were COVID-19 negative. There were no COVID-19 swabs in the seven patients included in the audit. Approximately 50% of the COVID-19-positive patients presented with fever and shortness of breath (sob), followed by dyspnea and cough (seven patients). The most common comorbidity was HTN, followed by type 2 diabetes mellitus (T2DM) and ischemic heart disease (IHD). The survival rate was 72.3% in COVID-19-positive patients and 80% in COVID-19-negative patients. The average length of stay was 14.4 days for COVID-19-positive survivors compared to 7.8 days for COVID-19-negative survivors. Most patients who tested positive for COVID-19 infection received oseltamivir vaccination and antibiotics. The presence of HTN, diabetes mellitus (DM), age, and organ failure was associated with a high mortality risk.

CONCLUSION

Our study supports the findings of previous studies that diabetes, HTN, coronary artery disease, old age, and organ failure were associated with high mortality in patients admitted to hospitals with COVID-19 infections.

A Simple Risk Formula for the Prediction of COVID-19 Hospital Mortality

SARS-CoV-2 respiratory infection is associated with significant morbidity and mortality in hospitalized patients. We aimed to assess the risk factors for hospital mortality in non-vaccinated patients during the 2021 spring wave in the Czech Republic. A total of 991 patients hospitalized between January 2021 and March 2021 with a PCR-confirmed SARS-CoV-2 acute respiratory infection in two university hospitals and five rural hospitals were included in this analysis. After excluding patients with unknown outcomes, 790 patients entered the final analyses. Out of 790 patients included in the analysis, 282/790 (35.7%) patients died in the hospital; 162/790 (20.5) were male and 120/790 (15.2%) were female. There were 141/790 (18%) patients with mild, 461/790 (58.3%) with moderate, and 187/790 (23.7%) with severe courses of the disease based mainly on the oxygenation status. The best-performing multivariate regression model contains only two predictors-age and the patient's state; both predictors were rendered significant (p < 0.0001). Both age and disease state are very significant predictors of hospital mortality. An increase in age by 10 years raises the risk of hospital mortality by a factor of 2.5, and a unit increase in the oxygenation status raises the risk of hospital mortality by a factor of 20.

Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI-processed radiological parameter upon admission: a multicentre study

Limited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095-0.826), dyspnoea (OR: 1.684; 95%CI 1.049-2.705), loss of consciousness (OR: 4.593; 95%CI 1.702-12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900-0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967-0.996), neutrophil % (OR: 1.034; 95%CI 1.013-1.055), serum urea (OR: 1.018; 95%CI 1.010-1.026), affected lung area score (OR: 1.026; 95%CI 1.014-1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774-0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661-0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations.

Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study

BACKGROUND

Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability.

OBJECTIVE

The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers.

METHODS

We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods.

RESULTS

Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910).

CONCLUSIONS

RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.

Lymphocyte and neutrophil count combined with intestinal bacteria abundance predict the severity of COVID-19

IMPORTANCE

The 2019 coronavirus disease (COVID-19) patients had a unique profile of gut bacteria. In this study, we characterized the intestinal bacteria in our COVID-19 cohorts and found that there was an increased incidence of severe cases in COVID-19 patients with decreased lymphocytes and increased neutrophils. Levels of lymphocytes and neutrophils and abundances of intestinal bacteria correlated with the severity of COVID-19.

Proteomic Evolution from Acute to Post-COVID-19 Conditions

Many COVID-19 survivors have post-COVID-19 conditions, and females are at a higher risk. We sought to determine (1) how protein levels change from acute to post-COVID-19 conditions, (2) whether females have a plasma protein signature different from that of males, and (3) which biological pathways are associated with COVID-19 when compared to restrictive lung disease. We measured protein levels in 74 patients on the day of admission and at 3 and 6 months after diagnosis. We determined protein concentrations by multiple reaction monitoring (MRM) using a panel of 269 heavy-labeled peptides. The predicted forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO) were measured by routine pulmonary function testing. Proteins associated with six key lipid-related pathways increased from admission to 3 and 6 months; conversely, proteins related to innate immune responses and vasoconstriction-related proteins decreased. Multiple biological functions were regulated differentially between females and males. Concentrations of eight proteins were associated with FVC, %, and they together had c-statistics of 0.751 (CI:0.732-0.779); similarly, concentrations of five proteins had c-statistics of 0.707 (CI:0.676-0.737) for DLCO, %. Lipid biology may drive evolution from acute to post-COVID-19 conditions, while activation of innate immunity and vascular regulation pathways decreased over that period. (ProteomeXchange identifiers: PXD041762, PXD029437).

The National Early Warning Score 2 with Age and Body Mass Index (NEWS2 Plus) to Determine Patients with Severe COVID-19 Pneumonia

(1) Background: Early identification of severe coronavirus disease 2019 (COVID-19) pneumonia at the initial phase of hospitalization is very crucial. To address this, we validated and updated the National Early Warning Score 2 (NEWS2) for this purpose. (2) Methods: We conducted a study on adult patients with COVID-19 infection in Chiang Mai, Thailand, between May 2021 and October 2021. (3) Results: From a total of 725 COVID-19 adult patients, 350 (48.3%) patients suffered severe COVID-19 pneumonia. In determining severe COVID-19 pneumonia, NEWS2 and NEWS2 + Age + BMI (NEWS2 Plus) showed the C-statistic values of 0.798 (95% CI, 0.767-0.830) and 0.821 (95% CI, 0.791-0.850), respectively. The C-statistic values of NEWS2 Plus were significantly improved compared to those of NEWS2 alone (p = 0.012). Utilizing a cut-off point of five, NEWS2 Plus exhibited better sensitivity and negative predictive value than the traditional NEWS2, with values of 99.7% vs. 83.7% and 98.9% vs. 80.7%, respectively. (4) Conclusions: The incorporation of age and BMI into the traditional NEWS2 score enhanced the efficacy of determining severe COVID-19 pneumonia. Physicians can rely on NEWS2 Plus (NEWS2 + Age + BMI) as a more effective decision-making tool for triaging COVID-19 patients during early hospitalization.

Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges

BACKGROUND

Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data.

METHODS

Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status.

RESULTS

A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71-0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74-0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables.

CONCLUSIONS

We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making.

Multivariate joint model under competing risks to predict death of hospitalized patients for SARS-CoV-2 infection

During the coronavirus disease 2019 (COVID-19) pandemic, several clinical prognostic scores have been proposed and evaluated in hospitalized patients, relying on variables available at admission. However, capturing data collected from the longitudinal follow-up of patients during hospitalization may improve prediction accuracy of a clinical outcome. To answer this question, 327 patients diagnosed with COVID-19 and hospitalized in an academic French hospital between January and July 2020 are included in the analysis. Up to 59 biomarkers were measured from the patient admission to the time to death or discharge from hospital. We consider a joint model with multiple linear or nonlinear mixed-effects models for biomarkers evolution, and a competing risks model involving subdistribution hazard functions for the risks of death and discharge. The links are modeled by shared random effects, and the selection of the biomarkers is mainly based on the significance of the link between the longitudinal and survival parts. Three biomarkers are retained: the blood neutrophil counts, the arterial pH, and the C-reactive protein. The predictive performances of the model are evaluated with the time-dependent area under the curve (AUC) for different landmark and horizon times, and compared with those obtained from a baseline model that considers only information available at admission. The joint modeling approach helps to improve predictions when sufficient information is available. For landmark 6 days and horizon of 30 days, we obtain AUC [95% CI] 0.73 [0.65, 0.81] and 0.81 [0.73, 0.89] for the baseline and joint model, respectively (p = 0.04). Statistical inference is validated through a simulation study.

Safety and Outcome of High-Flow Nasal Oxygen Therapy Outside ICU Setting in Hypoxemic Patients With COVID-19

OBJECTIVE

High-flow nasal oxygen (HFNO) therapy is frequently applied outside ICU setting in hypoxemic patients with COVID-19. However, safety concerns limit more widespread use. We aimed to assess the safety and clinical outcomes of initiation of HFNO therapy in COVID-19 on non-ICU wards.

DESIGN

Prospective observational multicenter pragmatic study.

SETTING

Respiratory wards and ICUs of 10 hospitals in The Netherlands.

PATIENTS

Adult patients treated with HFNO for COVID-19-associated hypoxemia between December 2020 and July 2021 were included. Patients with treatment limitations were excluded from this analysis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Outcomes included intubation and mortality rate, duration of hospital and ICU stay, severity of respiratory failure, and complications. Using propensity-matched analysis, we compared patients who initiated HFNO on the wards versus those in ICU. Six hundred eight patients were included, of whom 379 started HFNO on the ward and 229 in the ICU. The intubation rate in the matched cohort ( n = 214 patients) was 53% and 60% in ward and ICU starters, respectively ( p = 0.41). Mortality rates were comparable between groups (28-d [8% vs 13%], p = 0.28). ICU-free days were significantly higher in ward starters (21 vs 17 d, p < 0.001). No patient died before endotracheal intubation, and the severity of respiratory failure surrounding invasive ventilation and clinical outcomes did not differ between intubated ward and ICU starters (respiratory rate-oxygenation index 3.20 vs 3.38; Pa o2 :F io2 ratio 65 vs 64 mm Hg; prone positioning after intubation 81 vs 78%; mortality rate 17 vs 25% and ventilator-free days at 28 d 15 vs 13 d, all p values > 0.05).

CONCLUSIONS

In this large cohort of hypoxemic patients with COVID-19, initiation of HFNO outside the ICU was safe, and clinical outcomes were similar to initiation in the ICU. Furthermore, the initiation of HFNO on wards saved time in ICU without excess mortality or complicated course. Our results indicate that HFNO initiation outside ICU should be further explored in other hypoxemic diseases and clinical settings aiming to preserve ICU capacity and healthcare costs.

Stratification of COVID-19 Patients with Moderate-to-Severe Hypoxemic Respiratory Failure for Response to High-Flow Nasal Cannula: A Retrospective Observational Study

Background and Objectives: In patients with COVID-19, high-flow nasal cannula (HFNC) and continuous positive airway pressure (CPAP) are widely applied as initial treatments for moderate-to-severe acute hypoxemic respiratory failure. The aim of the study was to assess which respiratory supports improve 28-day mortality and to identify a predictive index of treatment response. Materials and Methods: This is a single-center retrospective observational study including 159 consecutive adult patients with COVID-19 and moderate-to-severe hypoxemic acute respiratory failure. Results: A total of 159 patients (82 in the CPAP group and 77 in the HFNC group) were included in the study. Mortality within 28 days was significantly lower with HFNC compared to CPAP (16.8% vs. 50%), while ICU admission and tracheal intubation within 28 days were significantly higher with CPAP compared to HFNC treatment (32% vs. 13%). We identified an index for survival in HFNC by including three variables easily available at admission (LDH, age, and respiratory rate) and the PaO2/FiO2 ratio at 48 h. The index showed high discrimination for survival with an AUC of 0.88, a negative predictive value of 86%, and a positive predictive value of 95%. Conclusions: Treatment with HFNC appears to be associated with greater survival and fewer ICU admission than CPAP. LDH, respiratory rate, age, and PaO2/FiO2 at 48 h were independently associated with survival and an index based on these variables allows for the prediction of treatment success and the assessment of patient allocation to the appropriate intensity of care after 48 h. Further research is warranted to determine effects on other outcomes and to assess the performance of the index in larger cohorts.

Vitamin C deficiency in critically ill COVID-19 patients admitted to intensive care unit

Objectives

To determine vitamin C plasma kinetics, through the measurement of vitamin C plasma concentrations, in critically ill Coronavirus infectious disease 2019 (COVID-19) patients, identifying eventually the onset of vitamin C deficiency.

Design

Prospective, observational, single-center study.

Setting

Intensive Care Unit (ICU), Vall d'Hebron University Hospital, Barcelona. Study period from November 12th, 2020, to February 24th, 2021.

Patients

Patients who had a severe hypoxemic acute respiratory failure due to COVID-19 were included.

Interventions

Plasma vitamin C concentrations were measured on days 1, 5, and 10 of ICU admission. There were no vitamin C enteral nor parenteral supplementation. The supportive treatment was performed following the standard of care or acute respiratory distress syndrome (ARDS) patients.

Measurement

Plasma vitamin C concentrations were analyzed using an ultra-performance liquid chromatography (UPLC) system with a photodiode array detector (wavelength set to 245 nm). We categorized plasmatic levels of vitamin C as follows: undetectable: < 1,5 mg/L, deficiency: <2 mg/L. Low plasma concentrations: 2-5 mg/L; (normal plasma concentration: > 5 mg/L).

Main results

Forty-three patients were included (65% men; mean age 62 ± 10 years). The median Sequential Organ Failure Assessment (SOFA) score was 3 (1-4), and the Acute Physiology and Chronic Health disease Classification System (APACHE II) score was 13 (10-22). Five patients had shock. Bacterial coinfection was documented in 7 patients (16%). Initially all patients required high-flow oxygen therapy, and 23 (53%) further needed invasive mechanical ventilation during 21 (± 10) days. The worst PaO2/FIO2 registered was 93 (± 29). ICU and hospital survival were 77 and 74%, respectively. Low or undetectable levels remained constant throughout the study period in the vast majority of patients.

Conclusion

This observational study showed vitamin C plasma levels were undetectable on ICU admission in 86% of patients with acute respiratory failure due to COVID-19 pneumonia requiring respiratory support. This finding remained consistent throughout the study period.

Head-to-head comparison of 19 prediction models for short-term outcome in medical patients in the emergency department: a retrospective study

INTRODUCTION

Prediction models for identifying emergency department (ED) patients at high risk of poor outcome are often not externally validated. We aimed to perform a head-to-head comparison of the discriminatory performance of several prediction models in a large cohort of ED patients.

METHODS

In this retrospective study, we selected prediction models that aim to predict poor outcome and we included adult medical ED patients. Primary outcome was 31-day mortality, secondary outcomes were 1-day mortality, 7-day mortality, and a composite endpoint of 31-day mortality and admission to intensive care unit (ICU).The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). Finally, the prediction models with the highest performance to predict 31-day mortality were selected to further examine calibration and appropriate clinical cut-off points.

RESULTS

We included 19 prediction models and applied these to 2185 ED patients. Thirty-one-day mortality was 10.6% (231 patients), 1-day mortality was 1.4%, 7-day mortality was 4.4%, and 331 patients (15.1%) met the composite endpoint. The RISE UP and COPE score showed similar and very good discriminatory performance for 31-day mortality (AUC 0.86), 1-day mortality (AUC 0.87), 7-day mortality (AUC 0.86) and for the composite endpoint (AUC 0.81). Both scores were well calibrated. Almost no patients with RISE UP and COPE scores below 5% had an adverse outcome, while those with scores above 20% were at high risk of adverse outcome. Some of the other prediction models (i.e. APACHE II, NEWS, WPSS, MEWS, EWS and SOFA) showed significantly higher discriminatory performance for 1-day and 7-day mortality than for 31-day mortality.

CONCLUSIONS

Head-to-head validation of 19 prediction models in medical ED patients showed that the RISE UP and COPE score outperformed other models regarding 31-day mortality.

Analysis of blood and nasal epithelial transcriptomes to identify mechanisms associated with control of SARS-CoV-2 viral load in the upper respiratory tract

OBJECTIVES

The amount of SARS-CoV-2 detected in the upper respiratory tract (URT viral load) is a key driver of transmission of infection. Current evidence suggests that mechanisms constraining URT viral load are different from those controlling lower respiratory tract viral load and disease severity. Understanding such mechanisms may help to develop treatments and vaccine strategies to reduce transmission. Combining mathematical modelling of URT viral load dynamics with transcriptome analyses we aimed to identify mechanisms controlling URT viral load.

METHODS

COVID-19 patients were recruited in Spain during the first wave of the pandemic. RNA sequencing of peripheral blood and targeted NanoString nCounter transcriptome analysis of nasal epithelium were performed and gene expression analysed in relation to paired URT viral load samples collected within 15 days of symptom onset. Proportions of major immune cells in blood were estimated from transcriptional data using computational differential estimation. Weighted correlation network analysis (adjusted for cell proportions) and fixed transcriptional repertoire analysis were used to identify associations with URT viral load, quantified as standard deviations (z-scores) from an expected trajectory over time.

RESULTS

Eighty-two subjects (50% female, median age 54 years (range 3-73)) with COVID-19 were recruited. Paired URT viral load samples were available for 16 blood transcriptome samples, and 17 respiratory epithelial transcriptome samples. Natural Killer (NK) cells were the only blood cell type significantly correlated with URT viral load z-scores (r = -0.62, P = 0.010). Twenty-four blood gene expression modules were significantly correlated with URT viral load z-score, the most significant being a module of genes connected around IFNA14 (Interferon Alpha-14) expression (r = -0.60, P = 1e-10). In fixed repertoire analysis, prostanoid-related gene expression was significantly associated with higher viral load. In nasal epithelium, only GNLY (granulysin) gene expression showed significant negative correlation with viral load.

CONCLUSIONS

Correlations between the transcriptional host response and inter-individual variations in SARS-CoV-2 URT viral load, revealed many molecular mechanisms plausibly favouring or constraining viral replication. Existing evidence corroborates many of these mechanisms, including likely roles for NK cells, granulysin, prostanoids and interferon alpha-14. Inhibition of prostanoid production and administration of interferon alpha-14 may be attractive transmission-blocking interventions.

COVID-19 subphenotypes at hospital admission are associated with mortality: a cross-sectional study

BACKGROUND

We have an incomplete understanding of COVID-19 characteristics at hospital presentation and whether underlying subphenotypes are associated with clinical outcomes and therapeutic responses.

METHODS

For this cross-sectional study, we extracted electronic health data from adults hospitalized between 1 March and 30 August 2020 with a PCR-confirmed diagnosis of COVID-19 at five New York City Hospitals. We obtained clinical and laboratory data from the first 24 h of the patient's hospitalization. Treatment with tocilizumab and convalescent plasma was assessed over hospitalization. The primary outcome was mortality; secondary outcomes included intubation, intensive care unit (ICU) admission and length of stay (LOS). First, we employed latent class analysis (LCA) to identify COVID-19 subphenotypes on admission without consideration of outcomes and assigned each patient to a subphenotype. We then performed robust Poisson regression to examine associations between COVID-19 subphenotype assignment and outcome. We explored whether the COVID-19 subphenotypes had a differential response to tocilizumab and convalescent plasma therapies.

RESULTS

A total of 4620 patients were included. LCA identified six subphenotypes, which were distinct by level of inflammation, clinical and laboratory derangements and ranged from a hypoinflammatory subphenotype with the fewest derangements to a hyperinflammatory with multiorgan dysfunction subphenotypes. Multivariable regression analyses found differences in risk for mortality, intubation, ICU admission and LOS, as compared to the hypoinflammatory subphenotype. For example, in multivariable analyses the moderate inflammation with fever subphenotype had 3.29 times the risk of mortality (95% CI 2.05, 5.28), while the hyperinflammatory with multiorgan failure subphenotype had 17.87 times the risk of mortality (95% CI 11.56, 27.63), as compared to the hypoinflammatory subphenotype. Exploratory analyses suggested that subphenotypes may differential respond to convalescent plasma or tocilizumab therapy.

CONCLUSION

COVID-19 subphenotype at hospital admission may predict risk for mortality, ICU admission and intubation and differential response to treatment.KEY MESSAGEThis cross-sectional study of COVID patients admitted to the Mount Sinai Health System, identified six distinct COVID subphenotypes on admission. Subphenotypes correlated with ICU admission, intubation, mortality and differential response to treatment.

Safety implications of remote assessments for suspected COVID-19: qualitative study in UK primary care

BACKGROUND

The introduction of remote triage and assessment early in the pandemic raised questions about patient safety. We sought to capture patients and clinicians' experiences of the management of suspected acute COVID-19 and generate wider lessons to inform safer care.

SETTING AND SAMPLE

UK primary healthcare. A subset of relevant data was drawn from five linked in-pandemic qualitative studies. The data set, on a total of 87 participants recruited via social media, patient groups and snowballing, comprised free text excerpts from narrative interviews (10 survivors of acute COVID-19), online focus groups (20 patients and 30 clinicians), contributions to a Delphi panel (12 clinicians) and fieldnotes from an online workshop (15 patients, clinicians and stakeholders).

METHODS

Data were uploaded onto NVivo. Coding was initially deductive and informed by WHO and Institute of Medicine frameworks of quality and safety. Further inductive analysis refined our theorisation using a wider range of theories-including those of risk, resilience, crisis management and social justice.

RESULTS

In the early weeks of the pandemic, patient safety was compromised by the driving logic of 'stay home' and 'protect the NHS', in which both patients and clinicians were encouraged to act in a way that helped reduce pressure on an overloaded system facing a novel pathogen with insufficient staff, tools, processes and systems. Furthermore, patients and clinicians observed a shift to a more transactional approach characterised by overuse of algorithms and decision support tools, limited empathy and lack of holistic assessment.

CONCLUSION

Lessons from the pandemic suggest three key strategies are needed to prevent avoidable deaths and inequalities in the next crisis: (1) strengthen system resilience (including improved resourcing and staffing; support of new tools and processes; and recognising primary care's role as the 'risk sink' of the healthcare system); (2) develop evidence-based triage and scoring systems; and (3) address social vulnerability.

Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19

PURPOSE

As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatment and the appropriate allocation of limited healthcare resources remain unresolved issues. In this study we constructed a deep-learning (DL) model to predict the need for oxygen supplementation using clinical information and chest CT images of patients with COVID-19.

MATERIALS AND METHODS

We retrospectively enrolled 738 patients with COVID-19 for whom clinical information (patient background, clinical symptoms, and blood test findings) was available and chest CT imaging was performed. The initial data set was divided into 591 training and 147 evaluation data. We developed a DL model that predicted oxygen supplementation by integrating clinical information and CT images. The model was validated at two other facilities (n = 191 and n = 230). In addition, the importance of clinical information for prediction was assessed.

RESULTS

The proposed DL model showed an area under the curve (AUC) of 89.9% for predicting oxygen supplementation. Validation from the two other facilities showed an AUC > 80%. With respect to interpretation of the model, the contribution of dyspnea and the lactate dehydrogenase level was higher in the model.

CONCLUSIONS

The DL model integrating clinical information and chest CT images had high predictive accuracy. DL-based prediction of disease severity might be helpful in the clinical management of patients with COVID-19.

Validation of the SACOV-19 score for identifying patients at risk of complicated or more severe COVID-19: a prospective study

PURPOSE

Identification of patients at risk of complicated or more severe COVID-19 is of pivotal importance, since these patients might require monitoring, antiviral treatment, and hospitalization. In this study, we prospectively evaluated the SACOV-19 score for its ability to predict complicated or more severe COVID-19.

METHODS

In this prospective multicenter study, we included 124 adult patients with acute COVID-19 in three German hospitals, who were diagnosed in an early, uncomplicated stage of COVID-19 within 72 h of inclusion. We determined the SACOV-19 score at baseline and performed a follow-up at 30 days.

RESULTS

The SACOV-19 score's AUC was 0.816. At a cutoff of > 3, it predicted deterioration to complicated or more severe COVID-19 with a sensitivity of 94% and a specificity of 55%. It performed significantly better in predicting complicated COVID-19 than the random tree-based SACOV-19 predictive model, the CURB-65, 4C mortality, or qCSI scores.

CONCLUSION

The SACOV-19 score is a feasible tool to aid decision making in acute COVID-19.

Assessment of the ABC2-SPH risk score to predict invasive mechanical ventilation in COVID-19 patients and comparison to other scores

Background

Predicting the need for invasive mechanical ventilation (IMV) is important for the allocation of human and technological resources, improvement of surveillance, and use of effective therapeutic measures. This study aimed (i) to assess whether the ABC2-SPH score is able to predict the receipt of IMV in COVID-19 patients; (ii) to compare its performance with other existing scores; (iii) to perform score recalibration, and to assess whether recalibration improved prediction.

Methods

Retrospective observational cohort, which included adult laboratory-confirmed COVID-19 patients admitted in 32 hospitals, from 14 Brazilian cities. This study was conducted in two stages: (i) for the assessment of the ABC2-SPH score and comparison with other available scores, patients hospitalized from July 31, 2020, to March 31, 2022, were included; (ii) for ABC2-SPH score recalibration and also comparison with other existing scores, patients admitted from January 1, 2021, to March 31, 2022, were enrolled. For both steps, the area under the receiving operator characteristic score (AUROC) was calculated for all scores, while a calibration plot was assessed only for the ABC2-SPH score. Comparisons between ABC2-SPH and the other scores followed the Delong Test recommendations. Logistic recalibration methods were used to improve results and adapt to the studied sample.

Results

Overall, 9,350 patients were included in the study, the median age was 58.5 (IQR 47.0-69.0) years old, and 45.4% were women. Of those, 33.5% were admitted to the ICU, 25.2% received IMV, and 17.8% died. The ABC2-SPH score showed a significantly greater discriminatory capacity, than the CURB-65, STSS, and SUM scores, with potentialized results when we consider only patients younger than 80 years old (AUROC 0.714 [95% CI 0.698-0.731]). Thus, after the ABC2-SPH score recalibration, we observed improvements in calibration (slope = 1.135, intercept = 0.242) and overall performance (Brier score = 0.127).

Conclusion

The ABC2-SPHr risk score demonstrated a good performance to predict the need for mechanical ventilation in COVID-19 hospitalized patients under 80 years of age.

Evaluation of pragmatic oxygenation measurement as a proxy for Covid-19 severity

Choosing optimal outcome measures maximizes statistical power, accelerates discovery and improves reliability in early-phase trials. We devised and evaluated a modification to a pragmatic measure of oxygenation function, the [Formula: see text] ratio. Because of the ceiling effect in oxyhaemoglobin saturation, [Formula: see text] ratio ceases to reflect pulmonary oxygenation function at high [Formula: see text] values. We found that the correlation of [Formula: see text] with the reference standard ([Formula: see text]/[Formula: see text] ratio) improves substantially when excluding [Formula: see text] and refer to this measure as [Formula: see text]. Using observational data from 39,765 hospitalised COVID-19 patients, we demonstrate that [Formula: see text] is predictive of mortality, and compare the sample sizes required for trials using four different outcome measures. We show that a significant difference in outcome could be detected with the smallest sample size using [Formula: see text]. We demonstrate that [Formula: see text] is an effective intermediate outcome measure in COVID-19. It is a non-invasive measurement, representative of disease severity and provides greater statistical power.

Prevalence and prognostic value of neurological affections in hospitalized patients with moderate to severe COVID-19 based on objective assessments

Neurological manifestations of coronavirus disease 2019 (COVID-19) have been frequently described. In this prospective study of hospitalized COVID-19 patients without a history of neurological conditions, we aimed to analyze their prevalence and prognostic value based on established, standardized and objective methods. Patients were investigated using a multimodal electrophysiological approach, accompanied by neuropsychological and neurological examinations. Prevalence rates of central (CNS) and peripheral (PNS) nervous system affections were calculated and the relationship between neurological affections and mortality was analyzed using Firth logistic regression models. 184 patients without a history of neurological diseases could be enrolled. High rates of PNS affections were observed (66% of 138 patients receiving electrophysiological PNS examination). CNS affections were less common but still highly prevalent (33% of 139 examined patients). 63% of patients who underwent neuropsychological testing (n = 155) presented cognitive impairment. Logistic regression models revealed pathology in somatosensory evoked potentials as an independent risk factor of mortality (Odds Ratio: 6.10 [1.01-65.13], p = 0.049). We conclude that hospitalized patients with moderate to severe COVID-19 display high rates of PNS and CNS affection, which can be objectively assessed by electrophysiological examination. Electrophysiological assessment may have a prognostic value and could thus be helpful to identify patients at risk for deterioration.

Ventilatory support and inflammatory peptides in hospitalised patients with COVID-19: A prospective cohort trial

PURPOSE

Several studies have shown that SARS-CoV-2 can induce a massive release of cytokines which contributes to disease severity and mortality. Therefore, cytokine levels in the serum may help to predict disease severity and survival in COVID-19 patients.

METHODS

In this prospective trial, 88 patients who were hospitalised for COVID-19 were enrolled. Blood samples for serum peptide measurements were taken at the time closest to hospitalisation, at day 5, 9 and 13 (±1). The concentrations of cytokines (IL-1α, IL-1β, IL-1RA, IL-6, L-7, L-10, IFN-γ and TNF-α), chemokines (CCL-3, CCL-4 and CCL-7) and growth factors (G-CSF, GM-CSF and VEGF) were assessed and correlated with the type of ventilation, occurrence of consolidations on imaging and the level of care.

RESULTS

COVID-19 patients (median age 68 years, IQR 55-77) stayed in hospital between 5-171 days. Compared to patients in the general care unit, patients in the intermediate care unit (IMCU) and intensive care unit (ICU) presented significantly elevated serum IL-6 (p = 0.004) and lower IFN-γ levels (p = 0.005), respectively. The peak inspiratory pressure in ventilated patients correlated positively with IL-1RA, G-CSF and inversely with IFN-γ serum levels (all p<0.05). VEGF serum levels inversely correlated with the fraction of inspired oxygen in patients receiving high-flow nasal canula oxygen therapy (p = 0.047). No significant correlation between serum concentrations of the measured peptides and the type of ventilation, occurrence of radiological consolidations or in-hospital mortality has been observed.

CONCLUSION

IL1-RA, IL-6, IFN-γ, G-CSF, CCL-7 and VEGF serum levels could prove helpful as biomarkers to assess disease severity and the need for intensive care in COVID-19 patients.