Deterioration Of COVID-19 Research Articles

Along with PaO2/FiO2 ratio and lymphopenia, low HLA-DR monocytes are the only additional parameter that independently predicts the clinical course of undifferentiated SARS-CoV-2 patients in emergency departments.

Because one-third of patients deteriorate after their admission to the emergency department, assessing the prognosis of COVID-19 patients is of great importance. However, to date, only lymphopenia and the partial pressure of oxygen/fraction of inspired oxygen (PaO2/FiO2) ratio have been reported as partly predictive of COVID-19-related further deterioration, and their association has not been evaluated. We asked whether other key biomarkers of SARS-CoV-2 immunologic defects-increase in circulating immature granulocytes, loss of monocyte HLA-DR (mHLA-DR) expression, and monocyte differentiation blockade-could also predict further COVID-19 deterioration. A series of 284 consecutive COVID-19 patients, with the sole inclusion criterion of being an adult, were prospectively enrolled at emergency department admission (day 0) of 2 different hospitals: 1 for the exploratory cohort (180 patients) and 1 for the confirmatory cohort (104 patients). Deterioration was assessed over the next 7 days. Neither increased immature granulocyte levels nor monocyte differentiation blockade predicted patient worsening. Among more than 30 clinical, biological, and radiological parameters, the value of decreased P/F ratio and lymphopenia for prediction of further COVID-19 deterioration was strongly confirmed, and the loss of mHLA-DR was the only additional independent marker. Combined together in a simple OxyLymphoMono score, the 3 variables perfectly predicted patients who did not worsen and correctly predicted worsening in 59% of cases. By highlighting lymphocyte and monocyte defects as preceding COVID-19 deterioration, these results point on early immunosuppression in COVID-19 deterioration. Combining P/F ratio, lymphopenia, and loss of mHLA-DR together in a simple and robust score could offer a pragmatic method for COVID-19 patient stratification.

Endothelial dysfunction in COVID-19 with smoking

Endothelial dysfunction is one of the mechanisms that most likely to play a role in the occurrence of poor clinical outcomes in patients with COVID-19. The hyperinflammatory and pro-coagulation state in COVID-19 suggests an important role for the endothelium, both as an effector contributing to inflammation and thrombosis, as well as a target organ. This state of endothelial dysfunction is also believed to be exacerbated by a history of previous smoking. Tobacco can increase the severity of infectious diseases such as influenza, by increasing viral replication through suppression of antiviral mechanisms, and changes in cytokine patterns in cells also increase ACE2 expression. Smoking is associated with endothelial dysfunction because it can increase the concentration of free radicals and proinflammatory cytokines. So, it is very possible that SARS-CoV-2 will damage the endothelium of smokers who have previously been injured as a result of smoking habits. The literature that we used in this study was obtained through searching engines from the Google Scholar and PubMed databases, with the inclusion criteria being literature that used English. We managed to take 1,613 literature articles, then we selected 81 articles that were most relevant to the discussion of our study. In this study we conclude that endothelial dysfunction may play a role in the mechanism of clinical deterioration in COVID-19 and may be exacerbated by a history of previous smoking.

The landscape of abnormal pathway activation confers COVID-19 patients' molecular sequelae earlier than clinical phenotype

Rationale: The 2019 coronavirus disease (COVID-19) pandemic poses a significant threat to human health. After SARS-CoV-2 infection, major clinical concerns are organ damage and possible sequelae. Methods: In this study, we analyzed serum multi-omics data based on population-level, including healthy cohort, non-COVID-19 and COVID-19 covered different severity cohorts. We applied the pseudo-SpatioTemporal Consistency Alignment (pST-CA) strategy to correct for individualized disease course differences, and developed pseudo-deterioration timeline model and pseudo-recovery timeline model based on the "severe index" and "course index". Further, we comprehensively analyzed and discussed the dynamic damage signaling in COVID-19 deterioration and/or recovery, as well as the potential risk of sequelae. Results: The deterioration and course models based on the pST-CA strategy can effectively map the activation of blood molecular signals on cellular, pathway, functional and disease phenotypes in COVID-19 deterioration and throughout the disease course. The models revealed the neurological, cardiovascular, and hepatic toxicity present in SARS-CoV-2. The abundance of differentially expressed proteins and the activity of upstream regulators were comprehensively analyzed and evaluated to predict possible target drugs for SARS-CoV-2. On molecular docking simulation analysis, it was further demonstrated that blocking CEACAM1 is a potential therapeutic target for SARS-CoV-2. Conclusions: Clinically, the risk of organ failure and death in COVID-19 patients rises with increasing number of infections. Individualized sequelae prediction for patients and assessment of individualized intervenable targets and available drugs in combination with the upstream regulator analysis results are of great clinical value.

Relationship between Anti-SARS-CoV-2 S Abs and IFN-λ3 Levels in the Administration of Oxygen following COVID-19 Vaccination.

Although the effectiveness of vaccination at preventing hospitalization and severe coronavirus disease (COVID-19) has been reported in numerous studies, the detailed mechanism of innate immunity occurring in host cells by breakthrough infection is unclear. One hundred forty-six patients were included in this study. To determine the effects of vaccination and past infection on innate immunity following SARS-CoV-2 infection, we analyzed the relationship between anti-SARS-CoV-2 S Abs and biomarkers associated with the deterioration of COVID-19 (IFN-λ3, C-reactive protein, lactate dehydrogenase, ferritin, procalcitonin, and D-dimer). Anti-S Abs were classified into two groups according to titer: high titer (≥250 U/ml) and low titer (<250 U/ml). A negative correlation was observed between anti-SARS-CoV-2 S Abs and IFN-λ3 levels (r = -0.437, p < 0.001). A low titer of anti-SARS-CoV-2 S Abs showed a significant association with oxygen demand in patients, excluding aspiration pneumonia. Finally, in a multivariate analysis, a low titer of anti-SARS-CoV-2 S Abs was an independent risk factor for oxygen demand, even after adjusting for age, sex, body mass index, aspiration pneumonia, and IFN-λ3 levels. In summary, measuring anti-SARS-CoV-2 S Abs and IFN-λ3 may have clinical significance for patients with COVID-19. To predict the oxygen demand of patients with COVID-19 after hospitalization, it is important to evaluate the computed tomography findings to determine whether the pneumonia is the result of COVID-19 or aspiration pneumonia.

LY6E level associated with smoking as risk for lung cancer patients susceptible to COVID-19

Studies revealed that cancer patients seemed more susceptible to COVID-19 and the clinical symptoms were serious. A recent study depicted that lymphocyte antigen 6 complex, locus E (LY6E) might inhibit coronavirus entry into cells by interfering with the membrane fusion process mediated through spike protein, and potently restricting the SARS-CoV-2 (Severe acute respiratory syndrome-associated coronavirus 2) infection. LY6E mRNA level in lung cancer was detected by publicly available datasets. Patient-specific features were used to analyze the potential factors that could affect LY6E level. Analysis of association between LY6E level and immune infiltration was also performed. In present study, it was found that smoker with lung adenocarcinoma showed lower LY6E level than non-smoker (p &lt; 0.05). In LUSC (lung squamous cell carcinoma) patients, reformed smokers showed higher LY6E than smokers (p &lt; 0.05). These results suggested that smoke can be a risk susceptible to COVID-19 in lung cancer patients. Further studies exhibited that LY6E was positively associated with immune cell infiltration in lung adenocarcinoma, indicating that LY6E may influence the infection severity of COVID-19 in lung cancer patients. In summary, smoke may downregulate LY6E level and exacerbate infection and deterioration of COVID-19 in lung cancer patients.

Multicenter validation of Early Warning Scores for detection of clinical deterioration in COVID-19 hospitalized patients

ObjectiveInvestigate the predictive value of NEWS2, NEWS-C, and COVID-19 Severity Index for predicting intensive care unit (ICU) transfer in the next 24h. DesignRetrospective multicenter study. SettingTwo third-level hospitals in Argentina. PatientsAll adult patients with confirmed COVID-19, admitted on general wards, excluding patients with non-intubated orders. InterventionsPatients were divided between those who were admitted to ICU and non-admitted. We calculated the three scores for each day of hospitalization. VariablesWe evaluate the calibration and discrimination of the three scores for the outcome ICU admission within 24, 48h, and at hospital admission. ResultsWe evaluate 13,768 days of hospitalizations on general medical wards of 1318 patients. Among these, 126 (9.5%) were transferred to ICU. The AUROC of NEWS2 was 0.73 (95%CI 0.68–0.78) 24h before ICU admission, and 0.52 (95%CI 0.47–0.57) at hospital admission. The AUROC of NEWS-C was 0.73 (95%CI 0.68–0.78) and 0.52 (95%CI 0.47–0.57) respectively, and the AUROC of COVID-19 Severity Index was 0.80 (95%CI 0.77–0.84) and 0.61 (95%CI 0.58–0.66) respectively. COVID-19 Severity Index presented better calibration than NEWS2 and NEWS-C. ConclusionCOVID-19 Severity index has better calibration and discrimination than NEWS2 and NEWS-C to predict ICU transfer during hospitalization.

Staff and patient experience of the implementation and delivery of a virtual health care home monitoring service for COVID-19 in Melbourne, Australia

BackgroundProvision of virtual health care (VHC) home monitoring for patients who are experiencing mild to moderate COVID-19 illness is emerging as a central strategy for reducing pressure on acute health systems. Understanding the enablers and challenges in implementation and delivery of these programs is important for future implementation and re-design. The aim of this study was to explore the perspectives of staff involved with the implementation and delivery, and the experience of patients managed by, a VHC monitoring service in Melbourne, Australia during the COVID-19 pandemic.MethodsA descriptive qualitative approach informed by naturalist inquiry was used. Staff interviews were analysed using the Consolidated Framework for Implementation Research (CFIR). Patient experience was captured using a survey and descriptive statistics were used to describe categorical responses while content analysis was used to analyse free text responses as they related to the CFIR. Finally, data from the interviews and patient experience were triangulated to see if patient experience validated data from staff interviews.ResultsAll 15 staff were interviewed, and 271 patients were surveyed (42%). A total of four final overarching themes emerged: service implementation enablers, service delivery benefits for patients, fragmentation of care, and workforce strengths. 19 subthemes aligned with 18 CFIR constructs from staff and patient data.ConclusionRapid implementation was enabled through shared resources, dividing implementation tasks between senior personnel, engaging furloughed healthcare staff in design and delivery, and having a flexible approach that allowed for ongoing improvements. Benefits for patients included early identification of COVID-19 deterioration, as well as provision of accurate and trustworthy information to isolate safely at home. The main challenges were the multiple agencies involved in patient monitoring, which may be addressed in the future by attributing responsibility for monitoring to a single agency.

Abstract 5279: Prognostic serum biomarkers in cancer patients with COVID-19: A systematic review

Abstract Purpose: COVID-19 has led to 4 million deaths worldwide since 2019. COVID-19 patients with cancers likely express biomarker changes in circulation. While many biomarker studies focused on COVID-19 diagnosis and prognosis, the panel of biomarkers used in SARS-CoV-2 infected cancer patients for COVID-19 severity and prognosis are largely unclear. Therefore, this systematic review aims to determine what biomarkers have been measured in cancer patients with COVID-19 and their prognostic utility. Methods: A systematic literature review in PubMed, Embase, and Scopus was performed on June 16th, 2021. The search keywords coronavirus, neoplasm, biomarkers, and disease progression were used to filter out 17 eligible studies, which were then carefully evaluated. Results: A total of 4,168 patients from 17 eligible articles were included in this study. Sixteen types of cancer and 60 biomarkers were identified. The majority of changed biomarkers in the cancer patients with COVID-19 compared to the healthy group and non-cancer patients with COVID-19 were biochemical and inflammatory markers. The up-regulated markers, including CRP, D-dimer, ferritin, IL-2R, IL-6, LDH, and PCT, were identified in eligible studies. Albumin and hemoglobin were significantly down-regulated in cancer patients with COVID-19. Additionally, we observed that the SARS-CoV-2 infected cancer patients with lower levels of CRP, ferritin, and LDH successfully survived from antiviral drug and immunotherapy for COVID-19 treatments. Conclusion: Several important clinical biomarkers, such as CRP, ferritin, and LDH, may serve as the prognostic markers to predict the outcomes following COVID-19 treatment and monitor the deterioration of COVID-19 in cancer patients. Citation Format: Yun-Ju Lai, Te-An Lee, Shih-Han Wang, Chia-Wei Li, Louise D. McCullough, Dhimiter Bello. Prognostic serum biomarkers in cancer patients with COVID-19: A systematic review [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5279.

Timing of Initiation of Methylprednisolone Pulse Therapy in Patients with COVID-19

High-dose methylprednisolone pulse therapy is widely used in patients with severe COVID-19. This therapy is known to have sufficient clinical effectiveness, but the optimal administration method is not known. In this study, we assessed the deterioration of oxygenation after methylprednisolone pulse therapy in patients with COVID-19 according to disease severity (oxygen requirement) at initiation of therapy. Ninety-nine patients with COVID-19 who received methylprednisolone pulse therapy at Saitama Medical University Hospital in Japan between October 2020 and October 2021 were retrospectively reviewed. Clinical outcomes were compared according to the fraction of inspired oxygen as a measure of disease severity at initiation of methylprednisolone pulse therapy. Based on the FIO2 level at initiation of methylprednisolone pulse therapy, patients were classified into an early treatment group (FIO2 ≤ 0.39; n = 21), a middle treatment group (FIO2 0.40–0.69; n = 38), and a late treatment group (FIO2 ≥ 0.70; n = 40). The frequency of administration of mechanical ventilation and the days of oxygen therapy in the middle group were lower than in the other groups. The frequency of adverse events was also lower in the middle group. Both late and early methylprednisolone pulse therapy may lead to further deterioration of COVID-19 and an increase in adverse events.

Feasibility and accuracy of the 40-steps desaturation test to determine outcomes in a cohort of patients presenting to hospital with and without COVID-19

Desaturation on exercise has been suggested as a predictive feature for deterioration in COVID-19. The objective of this paper was to determine the feasibility and validity for the 40-steps desaturation test.A prospective observational cohort study was undertaken in patients assessed in hospital prior to discharge. One-hundred and fifty-two participants were screened between November 2020 and February 2021, and 64 were recruited to perform a 40-steps desaturation test. Patients who were able to perform the test were younger and less frail. Four patients were readmitted to hospital and one patient deteriorated within 30 days but no patient died.The majority of patients showed little change in saturations during the test, even with pre-existing respiratory pathology. Change in saturations, respiratory rate, heart rate and breathlessness were not predictive of death or readmission to hospital within 30 days. Of 13 patients who had a desaturation of 3% or more during exercise, none was readmitted to hospital within 30 days.Not enough patients with COVID-19 could be recruited to the study to provide evidence for the safety of the test in this patient group.The 40-steps desaturation test requires further evaluation to assess clinical utility.

Neutrophil Lymphocyte Ratio as a Marker of In-Hospital Deterioration in COVID-19: Observations From a Resource Constraint Setting.

Introduction and Objectives:The study was conducted to assess the association of neutrophil lymphocyte ratio (NLR) in COVID-19 and to identify the cut-off value that predicts mortality, need of respiratory support and admission to high-dependency or intensive care.Methods:A retrospective observational study was conducted to collect demographic data, clinical variables, the neutrophil-lymphocyte ratio on-admission and the outcome of confirmed COVID-19 patients admitted to a tertiary care center in Sri Lanka.Results:There were 208 patients with a median age of 56 years (IQR 43-67) and 98 (47.1%) males. The median neutrophil count was 4.07 × 103/µL (IQR 2.97-6.79) and the median lymphocyte count was 1.74 × 103/µL (IQR 1.36-4.75). The calculated NLR ranged from 0.12 to 48.28 with a median value of 2.32 (IQR 1.37-4.76). A NLR value >3.6 predicted development of severe disease requiring respiratory support, transfer to a high-dependency or an intensive care unit and/or succumbing to the illness with a sensitivity 80% and specificity 80% (area under the curve 0.8, 95% CI 0.72-0.88, P < .0001). The adjusted odds ratio of NLR > 3.6 on predicting severe disease was 11.1, 95% CI 4.5- 27.0, P < .0001.Conclusions:A NLR > 3.6 is a useful variable to be included in risk prediction scores in Sri Lanka.

ROX Index as a Predictor of Intubation Risk and 28-Day Mortality in COVID-19 Patients with Acute Respiratory Distress Syndrome

Background: The main concern when treating COVID-19 acute respiratory distress syndrome (ARDS) during high flow nasal cannula (HFNC) is delayed intubation, thus increasing the risk of prior mortality. This study aims to analyze the prognostic ability of the Respiratory Rate Oxygenation (ROX) index as a predictor for intubation and 28-days mortality in COVID-19 patients. Methods: A retrospective analysis of COVID-19 patients admitted to Intensive Care Unit (ICU) Dr. Soetomo General Hospital in Surabaya from July to December 2020 with ARDS. The ROX indices were recorded at the 1st hour, 2nd, 4th, 6th, 12th, 18th, 24th, and 48th hours of treatment in ICU with HFNC. Identification of ROX association with HFNC failure led to intubation and 28-days mortality was through Cox proportional hazards regression. The most specific cut-off of the ROX index for predicting intubation and 28-days mortality was assessed. Result: Among 78 patients who met the inclusion criteria, 26 (33.3%) patients required intubation. Out of 26 patients, six patients were extubated and had survived. The 28-days mortality rate is 20 (25.6%) patients. The ROX index at 12th hours (ROX-12) ≤ 4.85 (AUC 0.857; p&lt;0.001; HR 4.7) and the ROX index at 48th hours (ROX-48) ≤ 5.68 (AUC 0.858; p&lt;0.001; HR 5.4) can accurately predict the need for intubation. ROX-12 ≤ 4.745 is a predictor of 28-days mortality (AUC 0.85, p&lt;0.001; HR 10.2). Conclusion: ROX index predicts the risk of intubation and 28-days mortality especially ROX-12 and ROX-48. Utilization of ROX index for rapid assessment of the respiratory deterioration in COVID-19 is recommended.

Clinical Characteristics and Laboratory Testing of Patients With COVID19: A Retrospective Analysis

Objective:To explore the early warning signs of deterioration of patients Methods: The data of thirty-six patients who were admitted to Handan Infectious Hospital was collected. The clinical features and laboratory testing were retrospectively. The initial laboratory testing included blood chemistries, dimer, coagulation function, etc. The patients were divided into mild/common severe/critical group. Results: The lymphocyte count, monocyte count, hemoglobin, albumin levels in severe/critical group were lower compared with those in group, while the fibrinogen was higher. The lymphocyte count and monocyte positively correlated with hemoglobin, pre-albumin respectively. Conclusion: patients with lower initial prealbumin and hemoglobin level were more likely severe conditions. Decreased prealbumin and hemoglobin, combined with and monocyte count, could be the early warning signs of deterioration of COVID-19.

350. Joint Modeling of EHR and CXR Data to Predict COVID-19 Deterioration

BackgroundInfectious respiratory-track pathogens are a common trigger of healthcare capacity strain, e.g. the COVID19 pandemic. Patient risk stratification models to identify low-risk patients can help improve patient care processes and allocate limited resources. Many existing deterioration indices are based entirely on structured data from the Electronic Health Record (EHR) and ignore important information from other data sources. However, chest radiographs have been demonstrated to be helpful in predicting the progress of respiratory diseases. We developed a joint EHR and chest x-ray (CXR) model method and applied it to identify low-risk COVID19+ patients within the first 48 hours of hospital admission. MethodsAll COVID19+ patients admitted to a large urban hospital between March 2020 and February 2021 were included. We trained an image model using large public chest radiograph datasets and fine-tuned this model to predict acute dyspnea using a cohort from the same hospital. We then combined this image model with two existing EHR deterioration indices to predict the risk of a COVID19+ patient being intubated, receiving a nasal cannula, or being treated with a vasopressor. We evaluated models’ ability to identify low-risk patients by using the positive predictive value (PPV). ResultsThe image-augmented deterioration index was able to identify 12% of 716 COVID-19+ patients as low risk with 0.95 positive predictive value in the first 48 hours of admission. In contrast, when used individually, the EHR and CXR models each identified roughly 3% of the patients with a PPV of 0.95.Predicting Low Risk Patients Aggregated predictions for COVID19 positive patients within the first 48 hours of admission, shown with exponential weight moving average and 95% CIs. Each plot shows the number of patients flagged as low-risk by lowest aggregated prediction and the resulting accuracy for that fraction of patients. The bottom plot compares the MCURES fused model to the MCURES model. The top plot compares the EDI fused model to the EDI model.ConclusionOur multi-modal models were able to identify far more patients at low-risk of COVID19 deterioration than models trained on either modality alone. This indicates the importance of combining structured data with chest X-rays when creating a deterioration index performance for infectious respiratory-track diseases.Disclosures All Authors: No reported disclosures

The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized&#x0D; Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained.&#x0D; Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting.&#x0D; Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

Multicenter validation of Early Warning Scores for detection of clinical deterioration in COVID-19 hospitalized patients.

ObjectiveInvestigate the predictive value of NEWS2, NEWS-C, and COVID-19 Severity Index for predicting intensive care unit (ICU) transfer in the next 24 h.DesignRetrospective multicenter study.SettingTwo third-level hospitals in Argentina.PatientsAll adult patients with confirmed COVID-19, admitted on general wards, excluding patients with non-intubated orders.InterventionsPatients were divided between those who were admitted to ICU and non-admitted. We calculated the three scores for each day of hospitalization.VariablesWe evaluate the calibration and discrimination of the three scores for the outcome ICU admission within 24, 48 h, and at hospital admission.ResultsWe evaluate 13,768 days of hospitalizations on general medical wards of 1318 patients. Among these, 126 (9.5%) were transferred to ICU. The AUROC of NEWS2 was 0.73 (95%CI 0.68–0.78) 24 h before ICU admission, and 0.52 (95%CI 0.47–0.57) at hospital admission. The AUROC of NEWS-C was 0.73 (95%CI 0.68–0.78) and 0.52 (95%CI 0.47–0.57) respectively, and the AUROC of COVID-19 Severity Index was 0.80 (95%CI 0.77–0.84) and 0.61 (95%CI 0.58–0.66) respectively. COVID-19 Severity Index presented better calibration than NEWS2 and NEWS-C.ConclusionCOVID-19 Severity index has better calibration and discrimination than NEWS2 and NEWS-C to predict ICU transfer during hospitalization.

Risk factors associated with hospital transfer among mild or asymptomatic COVID-19 patients in isolation facilities in Tokyo: a case-control study

The Tokyo Metropolitan Government has been implementing facility-based isolation of asymptomatic/mild coronavirus disease (COVID-19) patients to facilitate timely hospital referral. However, there are only a few published studies in prehospital settings, and the factors associated with hospital transfer are unclear. Our study identified the factors associated with COVID-19 deterioration in a prehospital setting. This case-control study assessed the risk factors for hospital transfer from isolation facilities and the need for ambulance transport due to deterioration among COVID-19 patients, using multivariate logistic regression analysis. In total, 10590 patients (median age 34 years), with male predominance (61.1%), were included. 367 (3.5%) were transferred to hospital, of whom 44 (12.0%) required ambulance transport. Hypertension, diabetes, and bronchial asthma were prevalent in 704 (6.6%), 195 (1.8%), and 305 (2.9%) patients, respectively. After adjustment, older age, male sex, higher body mass index (BMI), and comorbidities (including diabetes, inflammatory bowel disease, and bronchial asthma) were associated with hospital transfer. Older age, male sex, and higher BMI significantly increased the risk of transfer by ambulance. Our results may be beneficial for the development of intervention measures for probable future COVID-19 waves.

Risk factors for clinical deterioration in patients admitted for COVID-19: A case-control study

IntroductionThere is controversy regarding the best predictors of clinical deterioration in COVID-19. ObjectiveThis work aims to identify predictors of risk factors for deterioration in patients hospitalized due to COVID-19. Methods designNested case-control study within a cohort. Setting: 13 acute care centers of the Osakidetza-Basque Health Service. Participants: patients hospitalized for COVID-19 with clinical deterioration—defined as onset of severe ARDS, ICU admission, or death—were considered cases. Two controls were matched to each case based on age. Sociodemographic data; comorbidities; baseline treatment; symptoms; date of onset; previous consultations; and clinical, analytical, and radiological variables were collected. An explanatory model of clinical deterioration was created by means of conditional logistic regression. ResultsA total of 99 cases and 198 controls were included. According to the logistic regression analysis, the independent variables associated with clinical deterioration were: emergency department O2 saturation ≤90% (OR 16.6; 95%CI 4–68), pathological chest X-ray (OR 5.6; 95%CI 1.7–18.4), CRP > 100 mg/dL (OR 3.62; 95%CI 1.62–8), thrombocytopenia with <150,000 platelets (OR 4; 95%CI 1.84–8.6); and a medical history of acute myocardial infarction (OR 15.7; 95%CI, 3.29–75.09), COPD (OR 3.05; 95%CI 1.43–6.5), or HT (OR 2.21; 95%CI 1.11–4.4). The model’s AUC was 0.86. On the univariate analysis, female sex and presence of dry cough and sore throat were associated with better clinical progress, but were not found to be significant on the multivariate analysis. ConclusionThe variables identified could be useful in clinical practice for the detection of patients at high risk of poor outcomes.

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

PurposeWhile more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.MethodsWe developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).ResultsThe LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.ConclusionWe present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

Therapeutic Agents Rounding Up the Immunopathology of COVID-19

COVID-19 pandemic has caused more than 3 million deaths globally during the past year. The direct attack from SARS-CoV-2 and hyperactivated immune response contribute to the progress and deterioration of COVID-19. After the virus invades, the activation and release of cytokines/chemokines cause “cytokine storm”, leading to acute respiratory distress syndrome (ARDS) and multiple organs dysfunction syndrome (MODS). Eliminating virus and blocking cytokines are important checkpoints of COVID-19 therapy, and several agents targeting immunopathology, including interferons, thymosin, glucocorticoids and immunoglobulin, have shown therapeutic effects in severe patients with COVID-19. Herein, we reviewed the practice evidences and concluded that several agents rounding up the immunopathology of COVID-19 may be the alternative approaches under the scenario of the lacking of effective antiviral drugs.