External validation of a clinical risk score to predict hospital admission and in-hospital mortality in COVID-19 patients

Understanding contextual and practical factors to inform WHO recommendations on using chest imaging to monitor COVID-19 pulmonary sequelae: a qualitative study exploring stakeholders' perspective

BACKGROUND

A recommendation by the World Health Organization (WHO) was issued about the use of chest imaging to monitor pulmonary sequelae following recovery from COVID-19. This qualitative study aimed to explore the perspective of key stakeholders to understand their valuation of the outcome of the proposition, preferences for the modalities of chest imaging, acceptability, feasibility, impact on equity and practical considerations influencing the implementation of using chest imaging.

METHODS

A qualitative descriptive design using in-depth interviews approach. Key stakeholders included adult patients who recovered from the acute illness of COVID-19, and providers caring for those patients. The Evidence to Decision (EtD) conceptual framework was used to guide data collection of contextual and practical factors related to monitoring using imaging. Data analysis was based on the framework thematic analysis approach.

RESULTS

33 respondents, including providers and patients, were recruited from 15 different countries. Participants highly valued the ability to monitor progression and resolution of long-term sequelae but recommended the avoidance of overuse of imaging. Their preferences for the imaging modalities were recorded along with pros and cons. Equity concerns were reported across countries (e.g., access to resources) and within countries (e.g., disadvantaged groups lacked access to insurance). Both providers and patients accepted the use of imaging, some patients were concerned about affordability of the test. Facilitators included post- recovery units and protocols. Barriers to feasibility included low number of specialists in some countries, access to imaging tests among elderly living in nursing homes, experience of poor coordination of care, emotional exhaustion, and transportation challenges driving to a monitoring site.

CONCLUSION

We were able to demonstrate that there is a high value and acceptability using imaging but there were factors influencing feasibility, equity and some practical considerations associated with implementation. We had a few suggestions to be considered by the expert panel in the formulation of the guideline to facilitate its implementation such as using validated risk score predictive tools for lung complications to recommend the appropriate imaging modality and complementary pulmonary function test.

Electronic health record-based readmission risk model performance for patients undergoing outpatient parenteral antibiotic therapy (OPAT)

BACKGROUND

Outpatient Parenteral Antibiotic Therapy (OPAT) provides coordinated services to deliver parenteral antibiotics outside of the acute care setting. However, the reduction in monitoring and supervision may impact the risks of readmission to the hospital. While identifying those at greatest risk of hospital readmission through use of computer decision support systems could aid in its prevention, validation of such tools in this patient population is lacking.

OBJECTIVE

The primary aim of this study is to determine the ability of the electronic health record-embedded EPIC Unplanned Readmission Model 1 to predict all-cause 30-day hospital unplanned readmissions in discharged patients receiving OPAT through the Duke University Heath System (DUHS) OPAT program. We then explored the impact of OPAT-specific variables on model performance.

METHODS

This retrospective cohort study included patients ≥ 18 years of age discharged to home or skilled nursing facility between July 1, 2019 -February 1, 2020 with OPAT care initiated inpatient and coordinated by the DUHS OPAT program and with at least one Epic readmission score during the index hospitalization. Those with a planned duration of OPAT < 7 days, receiving OPAT administered in a long-term acute care facility (LTAC), or ongoing renal replacement therapy were excluded. The relationship between the primary outcome (unplanned readmission during 30-day post-index discharge) and Epic readmission scores during the index admission (discharge and maximum) was examined using multivariable logistic regression models adjusted for additional predictors. The performance of the models was assessed with the scaled Brier score for overall model performance, the area under the receiver operating characteristics curve (C-index) for discrimination ability, calibration plot for calibration, and Hosmer-Lemeshow goodness-of-fit test for model fit.

RESULTS

The models incorporating maximum or discharge Epic readmission scores showed poor discrimination ability (C-index 0.51, 95% CI 0.45 to 0.58 for both models) in predicting 30-day unplanned readmission in the Duke OPAT cohort. Incorporating additional OPAT-specific variables did not improve the discrimination ability (C-index 0.55, 95% CI 0.49 to 0.62 for the max score; 0.56, 95% CI 0.49 to 0.62 for the discharge score). Although models for predicting 30-day unplanned OPAT-related readmission performed slightly better, discrimination ability was still poor (C-index 0.54, 95% CI 0.45 to 0.62 for both models).

CONCLUSION

EPIC Unplanned Readmission Model 1 scores were not useful in predicting either all-cause or OPAT-related 30-day unplanned readmission in the DUHS OPAT cohort. Further research is required to assess other predictors that can distinguish patients with higher risks of 30-day unplanned readmission in the DUHS OPAT patients.

Telemedicine and Otolaryngology in the COVID-19 Era

OBJECTIVE

The global Coronavirus disease 2019 (COVID-19) pandemic has resulted in an expansion of telemedicine. The purpose of this study is to present our experience with outpatient telemedicine visits within a single institution's Department of Otolaryngology during the initial COVID-19 era.

STUDY DESIGN

Retrospective chart review.

METHODS

This was a single-institution study conducted within the Department of Otolaryngology at an urban tertiary care center. Data on outpatient visits was obtained from billing and scheduling records from January 6 to May 28, 2020. Visits were divided into "pre-shutdown" and "post-shutdown" based on our state's March 23, 2020 COVID-19 shutdown date.

RESULTS

A total of 3447 of 4340 (79.4%) scheduled visits were completed in the pre-shutdown period as compared to 1451 of 1713 (84.7%) in the post-shutdown period. The proportion of telemedicine visits increased (0.7%-81.2%, P < .001). Overall visit completion rate increased following the shutdown (80.2%-84.7%, P < .001). Subspecialties with an increase in visit completion rate were general (76.9%-88.0%, P = .002), otology (77.4%-87.2%, P < .001), and rhinology (80.0%-86.2%, P = .003). Patients with Medicaid and Medicare had higher appointment completion rates following the transition to telemedicine visits (80.7%-85.7%, P = .002; 76.9%-84.7%, P = .001). Older age was associated with decreased appointment cancellation pre-shutdown (OR 0.994 [0.991-0.997], P < .001) but increased appointment cancellation post-shutdown (OR 1.008 [1.001-1.014], P = .015). Mean COVID-19 risk scores were unchanged (P = .654).

CONCLUSIONS

COVID-19 has led to major changes in outpatient practice, with a significant shift from in-person to telemedicine visits following the mandatory shutdown. An associated increase in appointment completion rates was observed, reflecting a promising viable alternative to meet patient needs during this unprecedented time.

Atrial Fibrillation and Cardiovascular Risk Assessment among COVID-19 Patients Using Different Scores

Since the advent of severe acute respiratory syndrome-coronavirus-2 in December 2019, millions of people have been infected and succumbed to death because of this deadly virus. Cardiovascular complications such as thromboembolism and arrhythmia are predominant causes of morbidity and mortality. Different scores previously used for atrial fibrillation (AF) identification or prediction of its complications were investigated by physicians to understand whether those scores can predict in-hospital mortality or AF among patients infected with the severe acute respiratory syndromecoronavirus-2 virus. Using such scores gives hope for early prediction of atrial arrhythmia and in-hospital mortality among coronavirus disease 2019-infected patients. We have discussed the mechanisms of AF and cardiovascular damage in coronavirus disease 2019 patients, different methods of AF prediction, and compared different scores for prediction of in-hospital mortality after this viral infection.

Correlations between Cytokine Levels, Liver Function Markers, and Neuropilin-1 Expression in Patients with COVID-19

Aim: The study evaluated the correlations between cytokine levels, liver function markers, and neuropilin-1 (NRP-1) expression in patients with COVID-19 in Egypt. The study also aimed to evaluate the accuracy sensitivity, specificity, and area under the curve (AUC) of the tested laboratory parameters in identifying COVID-19 infection and its severity. Patients and Methods: Fifty healthy subjects and 100 confirmed patients with COVID-19 were included in this study. COVID-19 patients were separated into two groups based on the severity of their symptoms. Serum ALT, AST, albumin, C-reactive protein (CRP), interleukin (IL)-1β, IL-4, IL-6, IL-18, IL-35, prostaglandin E2 (PGE2), and thromboxane A2 (TXA2) were estimated. We measured the gene expression for nuclear factor-kappa B p50 (NF-κB p50) and nuclear factor-kappa B p65 (NF-κB p65) and NRP-1 in blood samples using quantitative real-time polymerase chain reaction (qRT-PCR). AUC and sensitivity and specificity for cytokine levels and NF-κB p50 and NF-κB p65 and NRP-1 in identifying COVID-19 infection were also determined in both moderate and severe patient groups using receiver-operating characteristic curve (ROC) analysis. Results: All patients with COVID-19 showed higher serum activities of liver enzymes, levels of CRP, IL-1β, IL-4, IL-6, IL-18, IL-35 PGE2, and TXA2, and mRNA expression of NF-κB p50, NF-κB p65, and NRP-1 than healthy subjects. The severe group exhibited a significant increase in serum ALT, AST and IL-6 and a significant decrease in albumin, IL-1β, TXA2, and NF-κB p65 levels compared to the moderate group. In all patients (moderate and severe), all cytokines were positively correlated with NF-κB p50, NF-κB p65 and NRP-1 expression levels. Serum ALT and AST were positively correlated with CRP, cytokines (IL-4, IL-6, IL-18, IL-35 and TXA2), and NF-κB p50 and NF-κB p65 expression levels in both moderate and severe groups. They were also positively correlated with serum IL-1β level in the severe COVID-19 patient group and with NRP-1 expression in the moderate group. Using the logistic regression analysis, the most important four statistically significant predictors associated with COVID-19 infection in the study were found to be IL-6, TAX2, NF-κB p50 and NF-κB p65. ROC analysis of these variables revealed that three of them had AUC > 0.8. In moderate cases, AUC of the serum TXA2 level and NF-κB p65 expression were 0.843 (95% CI 0.517−0.742, p < 0.001) and 0.806 (95% CI 0.739−0.874, p < 0.001), respectively. In the severe group, AUC of serum IL-6 level was 0.844 (95% CI 0.783−0.904, p < 0.001). Moreover, Il-6 had a sensitivity of 100% in both moderate and severe groups. Conclusions: This study concluded that liver injury in patients with COVID-19 may be strongly attributed to the cytokines storm, especially IL-6, which was positively correlated to NF-κB p50, NF-κB p65 and NRP-1 mRNA expression levels. Moreover, ROC analysis revealed that IL-6, TXA2, and NF-κB p65 could be useful in predicting the possibility of infection with COVID-19, and IL-6 could be of possible significance as a good predictor of the severity and disease progress. However, RT-qPCR for SARS-CoV-2 detection is essential to confirm infection and further clinical studies are required to confirm this elucidation.

Neurocognitive screening in patients following SARS-CoV-2 infection: tools for triage

Background

Cognitive complaints are common in patients recovering from Coronavirus Disease 2019 (COVID-19), yet their etiology is often unclear. We assess factors that contribute to cognitive impairment in ambulatory versus hospitalized patients during the sub-acute stage of recovery.

Methods

In this cross-sectional study, participants were prospectively recruited from a hospital-wide registry. All patients tested positive for SARS-CoV-2 infection using a real-time reverse transcriptase polymerase-chain-reaction assay. Patients ≤ 18 years-of-age and those with a pre-existing major neurocognitive disorder were excluded. Participants completed an extensive neuropsychological questionnaire and a computerized cognitive screen via remote telemedicine platform. Rates of subjective and objective neuropsychological impairment were compared between the ambulatory and hospitalized groups. Factors associated with impairment were explored separately within each group.

Results

A total of 102 patients (76 ambulatory, 26 hospitalized) completed the symptom inventory and neurocognitive tests 24 ± 22 days following laboratory confirmation of SARS-CoV-2 infection. Hospitalized and ambulatory patients self-reported high rates of cognitive impairment (27–40%), without differences between the groups. However, hospitalized patients showed higher rates of objective impairment in visual memory (30% vs. 4%; p = 0.001) and psychomotor speed (41% vs. 15%; p = 0.008). Objective cognitive test performance was associated with anxiety, depression, fatigue, and pain in the ambulatory but not the hospitalized group.

Conclusions

Focal cognitive deficits are more common in hospitalized than ambulatory patients. Cognitive performance is associated with neuropsychiatric symptoms in ambulatory but not hospitalized patients. Objective neurocognitive measures can provide essential information to inform neurologic triage and should be included as endpoints in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02817-9.

Diagnostic utility of the Covichem score in predicting COVID-19 disease

BACKGROUND

Identifying which patients with COVİD-19 have a high risk of severe illness is essential to optimizing management and resource utilization strategies.

OBJECTIVES

The aim of this study was to externally validate the diagnostic utility of the Covichem score for predicting COVID-19 disease severity, and secondarily to evaluate its utility in predicting intensive care unit (ICU) admission, and in-hospital mortality.

METHODS

All consecutive COVID-19 patients who presented to the emergency department (ED) were included, and patients' demographic data, comorbidities, vital signs, oxygen requirement, and laboratory results were recorded. We calculated patients' Covichem scores and estimates (using a threshold of 0.5) and evaluated the utility of the Covichem score for predicting disease severity, ICU admission, and mortality.

RESULTS

The median Covichem score was significantly higher for patients with severe illness (Covichem score: 0.170, IQR: 0.298, n = 300 vs. Covichem score: 0.026, IQR: 0.065, n: 191; p < 0.001). Based on their Covichem scores, 12.4% (61/491) of the patients were predicted to experience severe illness (threshold: 0.5), the accuracy of the Covichem score was poor, as the area under curve (AUC) was 48.5% (18.1% sensitivity and 93.8% specificity). When we calculated a new ideal threshold, the AUC reached 82%, but the sensitivity was 79.9% and the specificity was 71.2%.

CONCLUSION

In this external validation of the Covichem score, we found that it performed worse than in the original derivation and validation study, even with the assistance of a new cutoff.

The Pitfalls of Mining for QuantiFERON Gold in Severely Ill COVID-19 Patients

Objective

To assess the proportion of indeterminate QFT-Plus results in patients admitted with severe COVID-19 pneumonia and to evaluate the factors associated with indeterminate QFT-Plus results.

Study design

Retrospective cohort study.

Material & Methods

Data of COVID-19 admissions at Mayo Clinic Florida were extracted between October 13, 2020 and September 20, 2021, and from a pre-pandemic cohort between October 13, 2018 and September 20, 2019. Secondary analysis of the COVID-19 cohort was performed using gradient boosting modeling to generate variable importance and SHAP plots.

Results

Our findings demonstrated more indeterminate QFT-Plus test results among hospitalized patients with severe COVID-19 infection compared to non-COVID patients (139 of 495, 28.1%). Factors associated with indeterminate QFT-Plus tests included elevated C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), and interleukin-6 (IL 6), as well as low leukocytes, lymphocytes, and platelets.

Conclusions

Patients with severe COVID-19 had a higher likelihood of indeterminate QFT-Plus results which were associated with elevated inflammatory markers consistent with severe infection. IGRA screening tests are likely confounded by COVID-19 infection itself, limiting the screening ability for LTBI reactivation. Indeterminate QFT-Plus results may also require follow-up QFT-Plus testing, after patient recovery from COVID-19, increasing cost and complexity of medical decision making and management. Additional risk assessments may be needed in this patient population for LTBI screening in severe COVID-19 infected patients.

Low antispike antibody levels correlate with poor outcomes in COVID-19 breakthrough hospitalizations

BACKGROUND

While COVID-19 immunization programs attempted to reach targeted rates, cases rose significantly since the emergence of the delta variant. This retrospective cohort study describes the correlation between antispike antibodies and outcomes of hospitalized, breakthrough cases during the delta variant surge.

METHODS

All patients with positive SARS-CoV-2 polymerase chain reaction hospitalized at Mayo Clinic Florida from 19 June 2021 to 11 November 2021 were considered for analysis. Cases were analyzed by vaccination status. Breakthrough cases were then analyzed by low and high antibody titers against SARS-CoV-2 spike protein, with a cut-off value of ≥132 U/ml. Outcomes included hospital length of stay (LOS), need for intensive care unit (ICU), mechanical ventilation, and mortality. We used 1:1 nearest neighbor propensity score matching without replacement to assess for confounders.

RESULTS

Among 627 hospitalized patients with COVID-19, vaccine breakthrough cases were older with more comorbidities compared to unvaccinated. After propensity score matching, the unvaccinated patients had higher mortality (27 [28.4%] vs. 12 [12.6%], p = 0.002) and LOS (7 [1.0-57.0] vs. 5 [1.0-31.0] days, p = 0.011). In breakthrough cases, low-titer patients were more likely to be solid organ transplant recipients (16 [34.0%] vs. 9 [12.3%], p = 0.006), with higher need for ICU care (24 [51.1%] vs. 22 [11.0%], p = 0.034), longer hospital LOS (median 6 vs. 5 days, p = 0.013), and higher mortality (10 [21.3%] vs. 5 [6.8%], p = 0.025) than high-titer patients.

CONCLUSIONS

Hospitalized breakthrough cases were more likely to have underlying risk factors than unvaccinated patients. Low-spike antibody titers may serve as an indicator for poor prognosis in breakthrough cases admitted to the hospital.

Predicting progression to severe COVID-19 using the PAINT score

Objectives

One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19.

Methods

A total of 239 hospitalized patients with COVID-19 from two medical centers in China between February 6 and April 6, 2020 were retrospectively included. The prognostic abilities of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analysed using the Cox proportional hazards model and Kaplan–Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19.

Results

Among the 239 patients, 216 (90.38%) patients had mild/moderate disease, and 23 (9.62%) progressed to severe disease. After adjusting for multiple confounding factors, pulmonary disease, age > 75, IgM, CD16⁺/CD56⁺ NK cells and aspartate aminotransferase were independent predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p < 0.001). The PAINT score was validated using a nomogram, bootstrap analysis, calibration curves, decision curves and clinical impact curves, all of which confirmed its high predictive value.

Conclusions

The PAINT score for progression from mild/moderate to severe COVID-19 may be helpful in identifying patients at high risk of progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07466-4.

Accuracy of the Veterans Health Administration COVID-19 (VACO) Index for predicting short-term mortality among 1307 US academic medical centre inpatients and 427 224 US Medicare patients

BACKGROUND

The Veterans Health Administration COVID-19 (VACO) Index predicts 30-day all-cause mortality in patients with COVID-19 using age, sex and pre-existing comorbidity diagnoses. The VACO Index was initially developed and validated in a nationwide cohort of US veterans-we now assess its accuracy in an academic medical centre and a nationwide US Medicare cohort.

METHODS

With measures and weights previously derived and validated in US national Veterans Health Administration (VA) inpatients and outpatients (n=13 323), we evaluated the accuracy of the VACO Index for estimating 30-day all-cause mortality using area under the receiver operating characteristic curve (AUC) and calibration plots of predicted versus observed mortality in inpatients at a single US academic medical centre (n=1307) and in Medicare inpatients and outpatients aged 65+ (n=427 224).

RESULTS

30-day mortality varied by data source: VA 8.5%, academic medical centre 17.5%, Medicare 16.0%. The VACO Index demonstrated similar discrimination in VA (AUC=0.82) and academic medical centre inpatient population (AUC=0.80), and when restricted to patients aged 65+ in VA (AUC=0.69) and Medicare inpatient and outpatient data (AUC=0.67). The Index modestly overestimated risk in VA and Medicare data and underestimated risk in Yale New Haven Hospital data.

CONCLUSIONS

The VACO Index estimates risk of short-term mortality across a wide variety of patients with COVID-19 using data available prior to or at the time of diagnosis. The VACO Index could help inform primary and booster vaccination prioritisation, and indicate who among outpatients testing positive for SARS-CoV-2 should receive greater clinical attention or scarce treatments.

Prediction models for severe manifestations and mortality due to COVID-19: A systematic review

BACKGROUND

Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.

OBJECTIVE

This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.

METHODS

Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.

RESULTS

A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability.

CONCLUSION

Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,¹⁵ and Knight et al.,¹⁸ which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.

Utilization of an Electronic Health Record Integrated Risk Score to Predict Hospitalization Among COVID-19 Patients

Objective:

To evaluate the performance of an Electronic Health Record (EHR) integrated risk score for COVID-19 positive outpatients to predict 30-day risk of hospitalization.

Patients and Methods:

A retrospective observational study of 67 470 patients with COVID-19 confirmed by polymerase chain reaction (PCR) test between March 12, 2020 and February 8, 2021. Risk scores were calculated based on data in the chart at the time of the incident infection.

Results:

The Mayo Clinic COVID-19 risk score consisted of 13 components included age, sex, chronic lung disease, congenital heart disease, congestive heart failure, coronary artery disease, diabetes mellitus, end stage liver disease, end stage renal disease, hypertension, immune compromised, nursing home resident, and pregnant. Univariate analysis showed all components, except pregnancy, have significant (P < .001) association with admission. The Mayo Clinic COVID-19 risk score showed a Receiver Operating Characteristic Area Under Curve (AUC) of 0.837 for the prediction of admission for this large cohort of COVID-19 positive patients.

Conclusion:

The Mayo Clinic COVID-19 risk score is a simple score that is easily integrated into the EHR with excellent predictive performance for severe COVID-19. It can be leveraged to stratify risk for severe COVID-19 at initial contact, when considering therapeutics or in the allocation of vaccine supply.

Clinical Management of Adult Patients with COVID-19 Outside Intensive Care Units: Guidelines from the Italian Society of Anti-Infective Therapy (SITA) and the Italian Society of Pulmonology (SIP)

INTRODUCTION

The Italian Society of Anti-Infective Therapy (SITA) and the Italian Society of Pulmonology (SIP) constituted an expert panel for developing evidence-based guidance for the clinical management of adult patients with coronavirus disease 2019 (COVID-19) outside intensive care units.

METHODS

Ten systematic literature searches were performed to answer ten different key questions. The retrieved evidence was graded according to the Grading of Recommendations Assessment, Development, and Evaluation methodology (GRADE).

RESULTS AND CONCLUSION

The literature searches mostly assessed the available evidence on the management of COVID-19 patients in terms of antiviral, anticoagulant, anti-inflammatory, immunomodulatory, and continuous positive airway pressure (CPAP)/non-invasive ventilation (NIV) treatment. Most evidence was deemed as of low certainty, and in some cases, recommendations could not be developed according to the GRADE system (best practice recommendations were provided in similar situations). The use of neutralizing monoclonal antibodies may be considered for outpatients at risk of disease progression. For inpatients, favorable recommendations were provided for anticoagulant prophylaxis and systemic steroids administration, although with low certainty of evidence. Favorable recommendations, with very low/low certainty of evidence, were also provided for, in specific situations, remdesivir, alone or in combination with baricitinib, and tocilizumab. The presence of many best practice recommendations testified to the need for further investigations by means of randomized controlled trials, whenever possible, with some possible future research directions stemming from the results of the ten systematic reviews.

Return to Work, Demographic Predictors, and Symptomatic Analysis Among Healthcare Workers Presenting for COVID-19 Testing: A Retrospective Cohort From a United States Academic Occupational Medicine Clinic

Introduction

We sought to determine time to return to work (RTW) among healthcare workers (HCWs) with mild/moderate coronavirus disease 2019 (COVID-19) and identify predictors of COVID-19 test positivity and illness duration. 

Methods

A retrospective review of HCWs presenting for COVID-19 testing/evaluation in December 2020 was performed to examine demographics, clinical characteristics, and RTW.

Results

Of 250 exposure incidents, 107 employees (42.80%) tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). No significant differences between COVID-19 positive and negative HCWs were noted in terms of key demographics, including age, gender, and CDC risk scores. Cough (77.57% vs 56.64%, p = 0.001), fatigue (66.36% vs 51.05%, p = 0.015), fever/chills (65.42% vs 37.06%, p < 0.001), myalgia (57.01% vs 35.66%, p = 0.008), and change in smell/taste (38.32% vs 13.29%, p < 0.001) were more prevalent among COVID-19 positive versus negative HCWs. Change in smell/taste (p < 0.001, OR 3.592), cough (p = 0.001, OR 2.966), and fever/chills (p = 0.019, OR 2.107) were independently associated with COVID-19 test positivity. Mean time to RTW from symptom onset was 13.09 days for COVID-19 positive HCWs. Female gender (p = 0.020, + 3.20 days), older age (p = 0.014, + 2.22 days), and myalgia (p = 0.021, + 2.23 days) were predictive of longer illness duration.

Conclusion

Change in taste/smell, cough, and fever/chills were independently associated with COVID-19 test positivity. Among HCWs with mild/moderate COVID-19 infection, the mean time to RTW was approximately 13 days with female gender, older age, and myalgia being predictive of delayed RTW.

The Impact of Disparities in Social Determinants of Health on Hospitalization Rates for Patients with COVID-19 in Michigan (USA)

Importance

The COVID-19 pandemic continues to impact the health-care system in the United States and has brought further light on health disparities within it. However, only a few studies have examined hospitalization risk with regard to social determinants of health.

Objective

We aimed to identify how health disparities affect hospitalization rates among patients with COVID-19.

Design

This observational study included all individuals diagnosed with COVID-19 from February 25, 2020 to December 31, 2020. Uni- and multivariate analyses were utilized to evaluate associations between demographic data and inpatient versus outpatient status for patients with COVID-19.

Setting

Multicenter (8 hospitals), largest size health system in Southeast Michigan, a region highly impacted by the pandemic.

Participants

All outpatients and inpatients with a positive RT-PCR for SARS-CoV-2 on nasopharyngeal swab were included. Exclusion criteria included missing demographic data or status as a non-permanent Michigan resident.

Exposure

Patients who met inclusion and exclusion criteria were divided in 2 groups: outpatients and inpatients.

Main Outcome and Measures

We described the comparative demographics and known disparities associated with hospitalization status.

Results

Of 30,292 individuals who tested positive for SARS-CoV-2, 34.01% were admitted to the hospital. White or Caucasian race was most prevalent (57.49%), and 23.35% were African-American. The most common ethnicity was non-Hispanic or Latino (70.48%). English was the primary language for the majority of patients (91.60%). Private insurance holders made up 71.11% of the sample. Within the hospitalized patients, lower socioeconomic status, African-American race and Hispanic and Latino ethnicity, non-English speaking status, and Medicare and Medicaid were more likely to be admitted to the hospital.

Conclusions and Relevance

Several health disparities were associated with greater rates of hospitalization due to COVID-19. Addressing these inequalities from an individual to system level may improve health-care outcomes for those with health disparities and COVID-19.

Coronavirus Disease 2019 Vaccine-Breakthrough Infections Requiring Hospitalization in Mayo Clinic Florida Through August 2021

We characterized coronavirus disease 2019 (COVID-19) breakthrough cases admitted to a single center in Florida. With the emergence of delta variant, an increased number of hospitalizations was seen due to breakthrough infections. These patients were older and more likely to have comorbidities. Preventive measures should be maintained even after vaccination.

Healthcare Disparities Correlated with In-Hospital Mortality in COVID-19 Patients

Introduction

Increasing age, male gender, African American race, and medical comorbidities have been reported as risk factors for COVID-19 mortality. We aimed to identify health-care disparities associated with increased mortality in COVID-19 patients.

Methods

We performed an observational study of all hospitalized patients with SARS-CoV2 infection from within the largest multicenter healthcare system in Southeast Michigan, from February to December, 2020.

Results

From 11,304 hospitalized patients, 1295 died, representing an in-hospital mortality rate of 11.5%. The mean age of hospitalized patients was 63.77 years-old, with 49.96% being males. Older age (AOR = 1.05, p < 0.0001), male gender (AOR = 1.43, p < 0.0001), divorced status (AOR = 1.25, p = 0.0256), disabled status (AOR = 1.42, p = 0.0091), and homemakers (AOR = 1.96, p = 0.0216) were significantly associated with in-hospital mortality.

Conclusion

Older age, male gender, divorced and disabled status and homemakers were significantly associated with in-hospital mortality if they developed COVID-19. Further research should aim to identify the underlying factors driving these disparities in COVID-19 in-hospital mortality.

Simple demographic characteristics and laboratory findings on admission may predict in-hospital mortality in patients with SARS-CoV-2 infection: development and validation of the covid-19 score

Background

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) constitutes a major health burden worldwide due to high mortality rates and hospital bed shortages. SARS-CoV-2 infection is associated with several laboratory abnormalities. We aimed to develop and validate a risk score based on simple demographic and laboratory data that could be used on admission in patients with SARS-CoV-2 infection to predict in-hospital mortality.

Methods

Three cohorts of patients from different hospitals were studied consecutively (developing, validation, and prospective cohorts). The following demographic and laboratory data were obtained from medical records: sex, age, hemoglobin, mean corpuscular volume (MCV), platelets, leukocytes, sodium, potassium, creatinine, and C-reactive protein (CRP). For each variable, classification and regression tree analysis were used to establish the cut-off point(s) associated with in-hospital mortality outcome based on data from developing cohort and before they were used for analysis in the validation and prospective cohort. The covid-19 score was calculated as a sum of cut-off points associated with mortality outcome.

Results

The developing, validation, and prospective cohorts included 129, 239, and 497 patients, respectively (median age, 71, 67, and 70 years, respectively). The following cut of points associated with in-hospital mortality: age > 56 years, male sex, hemoglobin < 10.55 g/dL, MCV > 92.9 fL, leukocyte count > 9.635 or < 2.64 10³/µL, platelet count, < 81.49 or > 315.5 10³/µL, CRP > 51.14 mg/dL, creatinine > 1.115 mg/dL, sodium < 134.7 or > 145.4 mEq/L, and potassium < 3.65 or > 6.255 mEq/L. The AUC of the covid-19 score for predicting in-hospital mortality was 0.89 (0.84–0.95), 0.850 (0.75–0.88), and 0.773 (0.731–0.816) in the developing, validation, and prospective cohorts, respectively (P < 0.001The mortality of the prospective cohort stratified on the basis of the covid-19 score was as follows: 0–2 points,4.2%; 3 points, 15%; 4 points, 29%; 5 points, 38.2%; 6 and more points, 60%.

Conclusion

The covid-19 score based on simple demographic and laboratory parameters may become an easy-to-use, widely accessible, and objective tool for predicting mortality in hospitalized patients with SARS-CoV-2 infection.

ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores

OBJECTIVES

The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones.

METHODS

Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients.

RESULTS

Median (25-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO₂/FiO₂ ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/).

CONCLUSIONS

An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.

Evaluation of Cytokines as Robust Diagnostic Biomarkers for COVID-19 Detection

Antigen tests or polymerase chain reaction (PCR) amplification are currently COVID-19 diagnostic tools. However, developing complementary diagnosis tools is mandatory. Thus, we performed a plasma cytokine array in COVID-19 patients to identify novel diagnostic biomarkers. A discovery-validation study in two independent prospective cohorts was performed. The discovery cohort included 136 COVID-19 and non-COVID-19 patients recruited consecutively from 24 March to 11 April 2020. Forty-five cytokines' quantification by the MAGPIX system (Luminex Corp., Austin, TX, USA) was performed in plasma samples. The validation cohort included 117 patients recruited consecutively from 15 to 25 April 2020 for validating results by ELISA. COVID-19 patients showed different levels of multiple cytokines compared to non-COVID-19 patients. A single chemokine, IP-10, accurately identified COVID-19 patients who required hospital admission (AUC: 0.962; 95%CI (0.933-0.992); p < 0.001)). The results were validated in an independent cohort by multivariable analysis (OR: 25.573; 95%CI (8.127-80.469); p < 0.001) and AUROC (AUC: 0.900; 95%CI (0.846-0.954); p < 0.001). Moreover, showing IP-10 plasma levels over 173.35 pg/mL identified COVID-19 with higher sensitivity (86.20%) than the first SARS-CoV-2 PCR. Our discover-validation study identified IP-10 as a robust biomarker in clinical practice for COVID-19 diagnosis at hospital. Therefore, IP-10 could be used as a complementary tool in clinical practice, especially in emergency departments.

Development of An Individualized Risk Prediction Model for COVID-19 Using Electronic Health Record Data

Developing an accurate and interpretable model to predict an individual's risk for Coronavirus Disease 2019 (COVID-19) is a critical step to efficiently triage testing and other scarce preventative resources. To aid in this effort, we have developed an interpretable risk calculator that utilized de-identified electronic health records (EHR) from the University of Alabama at Birmingham Informatics for Integrating Biology and the Bedside (UAB-i2b2) COVID-19 repository under the U-BRITE framework. The generated risk scores are analogous to commonly used credit scores where higher scores indicate higher risks for COVID-19 infection. By design, these risk scores can easily be calculated in spreadsheets or even with pen and paper. To predict risk, we implemented a Credit Scorecard modeling approach on longitudinal EHR data from 7,262 patients enrolled in the UAB Health System who were evaluated and/or tested for COVID-19 between January and June 2020. In this cohort, 912 patients were positive for COVID-19. Our workflow considered the timing of symptoms and medical conditions and tested the effects by applying different variable selection techniques such as LASSO and Elastic-Net. Within the two weeks before a COVID-19 diagnosis, the most predictive features were respiratory symptoms such as cough, abnormalities of breathing, pain in the throat and chest as well as other chronic conditions including nicotine dependence and major depressive disorder. When extending the timeframe to include all medical conditions across all time, our models also uncovered several chronic conditions impacting the respiratory, cardiovascular, central nervous and urinary organ systems. The whole pipeline of data processing, risk modeling and web-based risk calculator can be applied to any EHR data following the OMOP common data format. The results can be employed to generate questionnaires to estimate COVID-19 risk for screening in building entries or to optimize hospital resources.

Zoonotic coronavirus epidemics: Severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019

OBJECTIVE

To review the virology, immunology, epidemiology, clinical manifestations, and treatment of the following 3 major zoonotic coronavirus epidemics: severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus disease 2019 (COVID-19).

DATA SOURCES

Published literature obtained through PubMed database searches and reports from national and international public health agencies.

STUDY SELECTIONS

Studies relevant to the basic science, epidemiology, clinical characteristics, and treatment of SARS, MERS, and COVID-19, with a focus on patients with asthma, allergy, and primary immunodeficiency.

RESULTS

Although SARS and MERS each caused less than a thousand deaths, COVID-19 has caused a worldwide pandemic with nearly 1 million deaths. Diagnosing COVID-19 relies on nucleic acid amplification tests, and infection has broad clinical manifestations that can affect almost every organ system. Asthma and atopy do not seem to predispose patients to COVID-19 infection, but their effects on COVID-19 clinical outcomes remain mixed and inconclusive. It is recommended that effective therapies, including inhaled corticosteroids and biologic therapy, be continued to maintain disease control. There are no reports of COVID-19 among patients with primary innate and T-cell deficiencies. The presentation of COVID-19 among patients with primary antibody deficiencies is variable, with some experiencing mild clinical courses, whereas others experiencing a fatal disease. The landscape of treatment for COVID-19 is rapidly evolving, with both antivirals and immunomodulators demonstrating efficacy.

CONCLUSION

Further data are needed to better understand the role of asthma, allergy, and primary immunodeficiency on COVID-19 infection and outcomes.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.