Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation

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.

Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data

Background

Epidermal growth factor receptor (EGFR) T790M mutation is the standard predictive biomarker for third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) treatment. While not all T790M-positive patients respond to third-generation EGFR-TKIs and have a good prognosis, it necessitates novel tools to supplement EGFR genotype detection for predicting efficacy and stratifying EGFR-mutant patients with various prognoses. Mixture-of-experts (MoE) is designed to disassemble a large model into many small models. Meanwhile, it is also a model ensembling method that can better capture multiple patterns of intrinsic subgroups of enrolled patients. Therefore, the combination of MoE and Cox algorithm has the potential to predict efficacy and stratify survival in non-small cell lung cancer (NSCLC) patients with EGFR mutations.

Methods

We utilized the electronic medical record (EMR) and pharmacokinetic parameters of 326 T790M-mutated NSCLC patients, including 283 patients treated with Abivertinib in phase I (n=177, for training) and II (n=106, for validation) clinical trials and an additional validation cohort 2 comprising 43 patients treated with BPI-7711. Furthermore, 18 patients underwent whole-exome sequencing for biological interpretation of CoxMoE. We evaluated the predictive performance for therapeutic response using the area under the curve (AUC) and the Concordance index (C-index) for progression-free survival (PFS).

Results

CoxMoE exhibited AUCs of 0.73-0.83 for predicting efficacy defined by best overall response (BoR) and achieved C-index values of 0.64-0.65 for PFS prediction in training and validating cohorts. The PFS of 198 patients with a low risk [median, 6.0 (range, 1.0-23.3) months in the abivertinib treated cohort; median 16.5 (range, 1.4-27.4) months in BPI-7711 treated cohort] of being non-responder increased by 43% [hazard ratio (HR), 0.56; 95% confidence interval (CI), 0.40-0.78; P=0.0013] and 50% (HR, 0; 95% CI, 0-0; P=0.01) compared to those at high-risk [median, 4.2 (range, 1.0-35) months in the abivertinib treated cohort; median, 11.0 (range, 1.4-25.1) months in BPI-7711 treated cohort]. Additionally, activated partial thromboplastin time (APTT), creatinine clearance (Ccr), monocyte, and steady-state plasma trough concentration utilited to construct model were found significantly associated with drug resistance and aggressive tumor pathways. A robust correlation was observed between APTT and Ccr with PFS (log-rank test; P<0.01) and treatment response (Wilcoxon test; P<0.05), respectively.

Conclusions

CoxMoE offers a valuable approach for patient selection by forecasting therapeutic response and PFS utilizing laboratory tests and pharmacokinetic parameters in the setting of early-phase clinical trials. Simultaneously, CoxMoE could predict the efficacy of third-generation EGFR-TKI non-invasively for T790M-positive NSCLC patients, thereby complementing existing EGFR genotype detection.

Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza

Objective

Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, of those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for deterioration from those who can be safely discharged.

Methods

We developed random forest machine learning (ML) models to estimate needs for critical care within 24 h and inpatient care within 72 h in ED patients with influenza. Predictor data were limited to those recorded prior to ED disposition decision: demographics, ED complaint, medical problems, vital signs, supplemental oxygen use, and laboratory results. Our study population was comprised of adults diagnosed with influenza at one of five EDs in our university health system between January 1, 2017 and May 18, 2022; visits were divided into two cohorts to facilitate model development and validation. Prediction performance was assessed by the area under the receiver operating characteristic curve (AUC) and the Brier score.

Results

Among 8032 patients with laboratory-confirmed influenza, incidence of critical care needs was 6.3% and incidence of inpatient care needs was 19.6%. The most common reasons for ED visit were symptoms of respiratory tract infection, fever, and shortness of breath. Model AUCs were 0.89 (95% CI 0.86-0.93) for prediction of critical care and 0.90 (95% CI 0.88-0.93) for inpatient care needs; Brier scores were 0.026 and 0.042, respectively. Importantpredictors included shortness of breath, increasing respiratory rate, and a high number of comorbid diseases.

Conclusions

ML methods can be used to accurately predict clinical deterioration in ED patients with influenza and have potential to support ED disposition decision-making.

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.

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.

Comparison of eight prehospital early warning scores in life-threatening acute respiratory distress: a prospective, observational, multicentre, ambulance-based, external validation study

BACKGROUND

A myriad of early warning scores (EWSs) exist, yet there is a need to identify the most clinically valid score to be used in prehospital respiratory assessments to estimate short-term and midterm mortality, intensive-care unit admission, and airway management in life-threatening acute respiratory distress.

METHODS

This is a prospective, observational, multicentre, ambulance-based, external validation study performed in 44 ambulance services and four hospitals across three Spanish provinces (ie, Salamanca, Segovia, and Valladolid). We identified adults (ie, those aged 18 years and older) discharged to the emergency department with suspected acute respiratory distress. The primary outcome was 2-day all-cause in-hospital mortality, for all the patients or according to prehospital respiratory conditions, including dyspnoea, chronic obstructive pulmonary disease (COPD), COVID-19, other infections, and other conditions (asthma exacerbation, haemoptysis, and bronchoaspirations). 30-day mortality, intensive-care unit admission, and invasive and non-invasive mechanical ventilation were secondary outcomes. Eight EWSs, namely, the National Early Warning Score 2, the Modified Rapid Emergency Medicine Score, the Rapid Acute Physiology Score, the Quick Sequential Organ Failure Assessment Score, the CURB-65 Severity Score for Community-Acquired Pneumonia, the BAP-65 Score for Acute Exacerbation of COPD, the Quick COVID-19 Severity Index, and the Modified Sequential Organ Failure Assessment (mSOFA), were explored to determine their predictive validity through calibration, clinical net benefit as determined through decision curve analysis, and discrimination analysis (area under the curve of the receiver operating characteristic [AUROC], compared with Delong's test).

FINDINGS

Between Jan 1, 2020, and Nov 31, 2022, 902 patients were enrolled. The global 2-day mortality rate was 87 (10%); in proportion to various respiratory conditions, the rates were 35 (40%) for dyspnoea, nine (10%) for COPD, 13 (15%) for COVID-19, 28 (32%) for other infections, and two (2%) for others conditions. mSOFA showed the best calibration, a higher net benefit, and the best discrimination (AUROC 0·911, 95% CI 0·86-0·95) for predicting 2-day mortality, and its discrimination was statistically significantly more accurate (p<0·0001) compared with the other scores. The performance of mSOFA for predicting 2-day mortality was higher than the other scores when considering the prehospital respiratory conditions, and was also higher for the secondary outcomes, except for non-invasive mechanical ventilation.

INTERPRETATION

Our results showed that mSOFA outperformed other EWSs. The inclusion of mSOFA in prehospital decision making will entail a quick identification of patients in acute respiratory distress at high risk of deterioration, allowing prioritisation of resources and patient care.

FUNDING

Gerencia Regional de Salud, Public Health System of Castilla y León (GRS Spain).

TRANSLATION

For the Spanish translation of the abstract see Supplementary Materials section.

Prognostic accuracy of eight triage scores in suspected COVID-19 in an Emergency Department low-income setting: An observational cohort study

Introduction

Previous studies deriving and validating triage scores for patients with suspected COVID-19 in Emergency Department settings have been conducted in high- or middle-income settings. We assessed eight triage scores' accuracy for death or organ support in patients with suspected COVID-19 in Sudan.

Methods

We conducted an observational cohort study using Covid-19 registry data from eight emergency unit isolation centres in Khartoum State, Sudan. We assessed performance of eight triage scores including: PRIEST, LMIC-PRIEST, NEWS2, TEWS, the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS in suspected COVID-19. A composite primary outcome included death, ventilation or ICU admission.

Results

In total 874 (33.84 %, 95 % CI:32.04 % to 35.69 %) of 2,583 patients died, required intubation/non-invasive ventilation or HDU/ICU admission . All risk-stratification scores assessed had worse estimated discrimination in this setting, compared to studies conducted in higher-income settings: C-statistic range for primary outcome: 0.56-0.64. At previously recommended thresholds NEWS2, PRIEST and LMIC-PRIEST had high estimated sensitivities (≥0.95) for the primary outcome. However, the high baseline risk meant that low-risk patients identified at these thresholds still had a between 8 % and 17 % risk of death, ventilation or ICU admission.

Conclusion

None of the triage scores assessed demonstrated sufficient accuracy to be used clinically. This is likely due to differences in the health care system and population (23 % of patients died) compared to higher-income settings in which the scores were developed. Risk-stratification scores developed in this setting are needed to provide the necessary accuracy to aid triage of patients with suspected COVID-19.

The effect of limited healthcare access on poor outcomes among hospitalized COVID-19 patients in Honduras: A single center cohort study

Background

The COVID-19 pandemic has had a severe impact on the Latin American subcontinent, particularly in areas with limited hospital resources and a restricted Intensive Care Unit (ICU) capacity. This study aimed to provide a comprehensive description of the clinical characteristics, outcomes, and factors associated with survival of COVID-19 hospitalized patients in Honduras.

Research question

What were the characteristics and outcomes of COVID-19 patients in a large referral center in Honduras?

Study design and methods

This study employed a retrospective cohort design conducted in a single center in San Pedro Sula, Honduras, between October 2020 to March 2021. All hospitalized cases of confirmed COVID-19 during this timeframe were included in the analysis. Univariable and multivariable survival analysis were performed using Kaplan-Meier curves and Cox proportional hazards model aiming to identify factors associated with decreased 30 day in-hospital survival, using a priori-selected factors.

Results

A total of 929 confirmed cases were identified in this cohort, with males accounting for 55.4 % of cases. The case fatality rate among the hospitalized patients was found to be 50.1 % corresponding to 466 deaths. Patients with comorbidities such as hypertension, diabetes, obesity, chronic kidney disease, chronic obstructive pulmonary disease and cardiovascular disease had a higher likelihood of mortality. Additionally, non-survivors had a significantly longer time from illness onset to hospital admission compared to survivors (8.2 days vs 4.7 days). Among the cohort, 306 patients (32.9 %) met criteria for ICU admission. However, due to limited capacity, only 60 patients (19·6 %) were admitted to the ICU. Importantly, patients that were unable to receive level-appropriate care had lower likelihood of survival compared to those who received level-appropriate care (hazard ratio: 1.84).

Interpretation

This study represents, the largest investigation of in-hospital COVID-19 cases in Honduras and Central America. The findings highlight a substantial case fatality rate among hospitalized patients. In this study, patients who couldn't receive level-appropriate care (ICU admission) had a significantly lower likelihood of survival when compared to those who did. These results underscore the significant impact of healthcare access during the pandemic, particularly in low- and middle-income countries.

Predictors of Intensive Care Unit Admissions in Patients Presenting with Coronavirus Disease 2019

Background  Increased mortality rates among coronavirus disease 2019 (COVID-19) positive patients admitted to intensive care units (ICUs) highlight a compelling need to establish predictive criteria for ICU admissions. The aim of our study was to identify criteria for recognizing patients with COVID-19 at elevated risk for ICU admission. Methods  We identified patients who tested positive for COVID-19 and were hospitalized between March and May 2020. Patients' data were manually abstracted through review of electronic medical records. An ICU admission prediction model was derived from a random sample of half the patients using multivariable logistic regression. The model was validated with the remaining half of the patients using c-statistic. Results  We identified 1,094 patients; 204 (18.6%) were admitted to the ICU. Correlates of ICU admission were age, body mass index (BMI), quick Sequential Organ Failure Assessment (qSOFA) score, arterial oxygen saturation to fraction of inspired oxygen ratio, platelet count, and white blood cell count. The c-statistic in the derivation subset (0.798, 95% confidence interval [CI]: 0.748, 0.848) and the validation subset (0.764, 95% CI: 0.706, 0.822) showed excellent comparability. At 22% predicted probability for ICU admission, the derivation subset estimated sensitivity was 0.721, (95% CI: 0.637, 0.804) and specificity was 0.763, (95% CI: 0.722, 0.804). Our pilot predictive model identified the combination of age, BMI, qSOFA score, and oxygenation status as significant predictors for ICU admission. Conclusion  ICU admission among patients with COVID-19 can be predicted by age, BMI, level of hypoxia, and severity of illness.

Fully independent validation of eleven prognostic scores predicting progression to critically ill condition in hospitalized patients with COVID-19

INTRODUCTION

COVID-19 remains an important threat to global health and maintains the challenge of COVID-19 hospital care. To assist decision making regarding COVID-19 hospital care many instruments to predict COVID-19 progression to critical condition were developed and validated.

OBJECTIVE

To validate eleven COVID-19 progression prediction scores for critically ill hospitalized patients in a Brazilian population.

METHODOLOGY

Observational study with retrospective follow-up, including 301 adults confirmed for COVID-19 sequentially. Participants were admitted to non-critical units for treatment of the disease, between January and April 2021 and between September 2021 and February 2022. Eleven prognostic scores were applied using demographic, clinical, laboratory and imaging data collected in the first 48 of the hospital admission. The outcomes of greatest interest were as originally defined for each score. The analysis plan was to apply the instruments, estimate the outcome probability reproducing the original development/validation of each score, then to estimate performance measures (discrimination and calibration) and decision thresholds for risk classification.

RESULTS

The overall outcome prevalence was 41.8 % on 301 participants. There was a greater risk of the occurrence of the outcomes in older and male patients, and a linear trend with increasing comorbidities. Most of the patients studied were not immunized against COVID-19. Presence of concomitant bacterial infection and consolidation on imaging increased the risk of outcomes. College of London COVID-19 severity score and the 4C Mortality Score were the only with reasonable discrimination (ROC AUC 0.647 and 0.798 respectively) and calibration. The risk groups (low, intermediate and high) for 4C score were updated with the following thresholds: 0.239 and 0.318 (https://pedrobrasil.shinyapps.io/INDWELL/).

CONCLUSION

The 4C score showed the best discrimination and calibration performance among the tested instruments. We suggest different limits for risk groups. 4C score use could improve decision making and early therapeutic management at hospital care.

Development and validation of the COVID-19 Hospitalized Patient Deterioration Index

OBJECTIVES

To develop a COVID-19-specific deterioration index for hospitalized patients: the COVID Hospitalized Patient Deterioration Index (COVID-HDI). This index builds on the proprietary Epic Deterioration Index, which was not developed for predicting respiratory deterioration events among patients with COVID-19.

STUDY DESIGN

A retrospective observational cohort was used to develop and validate the COVID-HDI model to predict respiratory deterioration or death among hospitalized patients with COVID-19. Deterioration events were defined as death or requiring high-flow oxygen, bilevel positive airway pressure, mechanical ventilation, or intensive-level care within 72 hours of run time. The sample included hospitalized patients with COVID-19 diagnoses or positive tests at Kaiser Permanente Southern California between May 3, 2020, and October 17, 2020.

METHODS

Machine learning models and 118 candidate predictors were used to generate benchmark performance. Logit regression with least absolute shrinkage and selection operator and physician input were used to finalize the model. Split-sample cross-validation was used to train and test the model.

RESULTS

The area under the receiver operating curve was 0.83. COVID-HDI identifies patients at low risk (negative predictive value [NPV] > 98.5%) and borderline low risk (NPV > 95%) of an event. Of all patients, 74% were identified as being at low or borderline low risk at some point during their hospitalization and could be considered for discharge with or without home monitoring. A high-risk group with a positive predictive value of 51% included 12% of patients. Model performance remained high in a recent cohort of patients.

CONCLUSIONS

COVID-HDI is a parsimonious, well-calibrated, and accurate model that may support clinical decision-making around discharge and escalation of care.

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.

Head-to-head comparison of six warning scores to predict mortality and clinical impairment in COVID-19 patients in emergency department

The aim was to evaluate the ability of six risk scores (4C, CURB65, SEIMC, mCHOSEN, QuickCSI, and NEWS2) to predict the outcome of patients with COVID-19 during the sixth pandemic wave in Spain. A retrospective observational study was performed to review the electronic medical records in patients ≥ 18 years of age who consulted consecutively in an emergency department with COVID-19 diagnosis throughout 2 months during the sixth pandemic wave. Clinical-epidemiological variables, comorbidities, and their respective outcomes, such as 30-day in-hospital mortality and clinical deterioration risk (a combined outcome considering: mechanical ventilation, intensive care unit admission, and/or 30-day in-hospital mortality), were calculated. The area under the curve for each risk score was calculated, and the resulting curves were compared by the Delong test, concluding with a decision curve analysis. A total of 626 patients (median age 79 years; 49.8% female) fulfilled the inclusion criteria. Two hundred and ninety-three patients (46.8%) had two or more comorbidities. Clinical deterioration risk criteria were present in 10.1% (63 cases), with a 30-day in-hospital mortality rate of 6.2% (39 cases). Comparison of the results showed that score 4C presented the best results for both outcome variables, with areas under the curve for mortality and clinical deterioration risk of 0.931 (95% CI 0.904-0.957) and 0.871 (95% CI 0.833-0.910) (both p < 0.001). The 4C Mortality Score proved to be the best score for predicting mortality or clinical deterioration risk among patients with COVID-19 attended in the emergency department in the following 30 days.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

TRIAL REGISTRATION

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

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

Introduction

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

Methods

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

Results

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

Conclusion

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

Total Psoas Area and Psoas Density Assessment in COVID-19 Patients Using CT Imaging - Could Muscle Mass Alteration During Intensive Care Hospitalization be Determined?

Background

Since its debut, as reported by the first published studies, COVID-19 has been linked to life-threatening conditions that needed vital assistance and admission to the intensive care unit. Skeletal muscle is a core element in an organism's health due to its ability to keep energy balance and homeostasis. Many patients with prolonged hospitalization are characterized by a greater probability prone to critical illness myopathy or intensive care unit-acquired weakness.

Objective

The main aim of this study was to assess the skeletal muscle in a COVID-19 cohort of critically ill patients by measuring the psoas area and density.

Material and methods

This is a retrospective study that included critically ill adult patients, COVID-19 positive, mechanically ventilated, with an ICU stay of over 24 hours, and who had 2 CT scans eligible for psoas muscle evaluation. In these patients, correlations between different severity scores and psoas CT scans were sought, along with correlations with the outcome of the patients.

Results

Twenty-two patients met the inclusion criteria. No statistically significant differences were noticed regarding the psoas analysis by two blinded radiologists. Significant correlations were found between LOS in the hospital and in ICU with psoas area and Hounsfield Units for the first CT scan performed. With reference to AUC-ROC and outcome, it is underlined that AUC-ROC is close to 0.5 values, for both the psoas area and HU, indicating that the model had no class separation capacity.

Conclusion

The study suggested that over a short period, the psoas muscle area, and the psoas HU decline, for both the left and the right sight, in adult COVID-19 patients in ICU conditions, yet not statistically significant. Although more than two-thirds of the patients had a negative outcome, it was not possible to demonstrate an association between the SARS-COV2 infection and psoas muscle impairment. These findings highlight the need for further larger investigations.

Assessment of the Diaphragm Thickness Decrease in Critically Ill COVID-19 Patients: Could Computed Tomography Be of Aid Regarding Diaphragm Muscle Mass?

INTRODUCTION

The diaphragm has a significant clinical value on respiratory performance. There is little literature on the use of thorax computed tomography for the purpose of identifying alterations in diaphragm thickness in critically ill patients diagnosed with COVID-19. The present study aims to investigate dynamic changes in muscle thickness and its association with clinical outcomes.

METHODS

A single-center retrospective observational study was conducted in a tertiary intensive care unit (ICU). The study comprised adult patients with severe COVID-19 who were admitted to the ICU and underwent two thorax CT scans. We measured diaphragmatic thickness at the level of the celiac truncus.

RESULTS

The average reduction in thickness of the dynamic diaphragm was found to be -0.58 mm for the right diaphragm and -0.54 mm for the left diaphragm. The diaphragm thickness exhibited a substantial decrease on both the right and left sides in both CT scans (p=0.02). A negative correlation coefficient was observed for both the right and left diaphragm. The criterion indicating a poor prognosis for the right diaphragm was a value greater than -0.175, whereas it was more significant for the left diaphragm than -0.435. The cut-off values indicated a high risk of prolonged mechanical ventilation and an increased risk of ICU mortality.

CONCLUSION

 CT diaphragm evaluation in mechanically ventilated COVID-19 patients has the possibility of becoming a reliable tool for predicting muscle modifications.

Comparison of different prognostic scores in estimating short- and long-term mortality in COVID-19 patients above 60 years old in a university hospital in Belgium

BACKGROUND AND OBJECTIVES

Multiple scoring systems were used for risk stratification in COVID-19 patients. The objective was to determine among 6 scores which performed the best in predicting short-and long-term mortality in hospitalized COVID-19 patients ≥ 60 years.

METHODS

An observational, retrospective cohort study conducted between 21/10/2020 and 20/01/2021. 6 scores were calculated (Clinical Frailty Scale (CFS), Charlson Comorbidity Index (CCI), 4C Mortality Score (4CMS), NEWS score (NEWS), quick-SOFA score (qSOFA), and Quick COVID-19 Severity Index (qCSI)). We included unvaccinated hospitalized patients with COVID-19 ≥ 60 years old in Brugmann hospital, detected by PCR and/or suggestive CT thorax images. Old and nosocomial infections, and patients admitted immediately at the intensive care unit were excluded.

RESULTS

199 patients were included, mean age was 76.2 years (60-99). 47.2% were female. 56 patients (28%) died within 1 year after the first day of hospitalization. The 4CMS predicted the best intrahospital, 30 days and 6 months mortality, with area under the ROC curve (AUROC) 0.695 (0.58-0.81), 0.76 (0.65-0.86) and 0.72 (0.63-0.82) respectively. The CCI came right after with respectively AUROC of 0.69 (0.59-0.79), 0.74 (0.65-0.83) and 0.71 (0.64-0.8). To predict mortality at 12 months after hospitalization, the CCI had the highest AUROC with 0.77 (0.69-0.85), before the 4CMS with 0.69 (0.60-0.79).

DISCUSSION

Among 6 scores, the 4CMS was the best to predict intrahospital, 30-day and 6-month mortality. To predict mortality at 12 months, CCI had the best performance before 4CMS. This reflects the importance of considering comorbidities for short- and long-term mortality after COVID 19.

REGISTRATION

This study was approved by the ethical committee of Brugmann University Hospital (reference CE 2020/228).

Effect of Optimizing Medical Rehabilitation System for Patients with Coronavirus Disease 2019 Using the Functional Resonance Analysis Method

Objectives

Coronavirus infection 2019 (COVID-19) is an indication for rehabilitation medicine, especially in severe cases. However, there has been no system analysis of safe and continuous provision of medical rehabilitation for COVID-19 patients. The aim of this study was to confirm the effectiveness of rehabilitation for severe COVID-19 and to analyze the optimization of the medical rehabilitation system using the Functional Resonance Analysis Method (FRAM).

Methods

The subject of the analysis was the medical rehabilitation system itself, which had been implemented by the Rehabilitation Center of our hospital in response to the increased number of COVID-19 patients. In the FRAM analysis, Functions were identified, and their relationships were examined. Functions were established using a hierarchical cross-check by the authors. Patient outcomes resulting from optimization of the rehabilitation system were length of hospital stay, patient independence in daily living, and rehabilitation-related medical costs, and these were statistically validated.

Results

In repeated optimizations of the rehabilitation system, the main issues were "handling of infected patients and isolation of usual clinical practice," "staff rotation," and "remote consultation". The modification of the medical rehabilitation system was associated with shorter hospital stays, shorter periods of time without prescription, faster improvement in independence of daily living, and lower rehabilitation-related medical costs.

Conclusions

Optimization at each stage of medical rehabilitation resulted in positive effects on patient outcomes. FRAM is useful for identifying and the optimization of key functions.

Higher incidence of new atrial fibrillation in hospitalised COVID-19 patients compared to lower respiratory tract infection, however, less patients prescribed anticoagulants at discharge

Infection contributes to developing cardiac arrhythmias, such as atrial fibrillation (AF), which causes over 25% of ischaemic stroke. We analysed a hospital coding database of patients hospitalised with Coronavirus 2019 (COVID-19) ± AF or a lower respiratory tract infection (LRTI) ± AF, to compare the incidence of first-diagnosed or 'new' AF (nAF) between COVID-19 and LRTI, as well as risk factors associated with developing nAF during COVID-19. In total, 2,243 patients with LRTI and 488 patients with COVID-19 were included. nAF was diagnosed in significantly more patients with COVID-19 compared with those with LRTI (7.0% vs 3.6%, p=0.003); however, significantly fewer patients with COVID-19 were discharged on anticoagulation medication (26.3% vs 56.4%, p=0.02). Patients who developed nAF during COVID-19 were older (p<0.001), had congestive cardiac failure (p=0.004), ischaemic heart disease (IHD) or peripheral vascular disease (PVD) (p<0.001) and a higher CHA2DS2-VASc score (p=0.02), compared with patients with COVID-19 patients who did not develop nAF. Older age (Odds ratio (OR) 1.03, p=0.007) and IHD/PVD (OR 2.87, p=0.01) increased the odds of developing nAF with COVID-19.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting

BACKGROUND

Uneven vaccination and less resilient health care systems mean hospitals in LMICs are at risk of being overwhelmed during periods of increased COVID-19 infection. Risk-scores proposed for rapid triage of need for admission from the emergency department (ED) have been developed in higher-income settings during initial waves of the pandemic.

METHODS

Routinely collected data for public hospitals in the Western Cape, South Africa from the 27th August 2020 to 11th March 2022 were used to derive a cohort of 446,084 ED patients with suspected COVID-19. The primary outcome was death or ICU admission at 30 days. The cohort was divided into derivation and Omicron variant validation sets. We developed the LMIC-PRIEST score based on the coefficients from multivariable analysis in the derivation cohort and existing triage practices. We externally validated accuracy in the Omicron period and a UK cohort.

RESULTS

We analysed 305,564 derivation, 140,520 Omicron and 12,610 UK validation cases. Over 100 events per predictor parameter were modelled. Multivariable analyses identified eight predictor variables retained across models. We used these findings and clinical judgement to develop a score based on South African Triage Early Warning Scores and also included age, sex, oxygen saturation, inspired oxygen, diabetes and heart disease. The LMIC-PRIEST score achieved C-statistics: 0.82 (95% CI: 0.82 to 0.83) development cohort; 0.79 (95% CI: 0.78 to 0.80) Omicron cohort; and 0.79 (95% CI: 0.79 to 0.80) UK cohort. Differences in prevalence of outcomes led to imperfect calibration in external validation. However, use of the score at thresholds of three or less would allow identification of very low-risk patients (NPV ≥0.99) who could be rapidly discharged using information collected at initial assessment.

CONCLUSION

The LMIC-PRIEST score shows good discrimination and high sensitivity at lower thresholds and can be used to rapidly identify low-risk patients in LMIC ED settings.

Automatable end-of-life screening for older adults in the emergency department using electronic health records

BACKGROUND

Emergency department (ED) visits are common at the end-of-life, but the identification of patients with life-limiting illness remains a key challenge in providing timely and resource-sensitive advance care planning (ACP) and palliative care services. To date, there are no validated, automatable instruments for ED end-of-life screening. Here, we developed a novel electronic health record (EHR) prognostic model to screen older ED patients at high risk for 6-month mortality and compare its performance to validated comorbidity indices.

METHODS

This was a retrospective, observational cohort study of ED visits from adults aged ≥65 years who visited any of 9 EDs across a large regional health system between 2014 and 2019. Multivariable logistic regression that included clinical and demographic variables, vital signs, and laboratory data was used to develop a 6-month mortality predictive model-the Geriatric End-of-life Screening Tool (GEST) using five-fold cross-validation on data from 8 EDs. Performance was compared to the Charlson and Elixhauser comorbidity indices using area under the receiver-operating characteristic curve (AUROC), calibration, and decision curve analyses. Reproducibility was tested against data from the remaining independent ED within the health system. We then used GEST to investigate rates of ACP documentation availability and code status orders in the EHR across risk strata.

RESULTS

A total of 431,179 encounters by 123,128 adults were included in this study with a 6-month mortality rate of 12.2%. Charlson (AUROC (95% CI): 0.65 (0.64-0.69)) and Elixhauser indices (0.69 (0.68-0.70)) were outperformed by GEST (0.82 (0.82-0.83)). GEST displayed robust performance across demographic subgroups and in our independent validation site. Among patients with a greater than 30% mortality risk using GEST, only 5.0% had ACP documentation; 79.0% had a code status previously ordered, of which 70.7% were full code. In decision curve analysis, GEST provided greater net benefit than the Charlson and Elixhauser scores.

CONCLUSIONS

Prognostic models using EHR data robustly identify high mortality risk older adults in the ED for whom code status, ACP, or palliative care interventions may be of benefit. Although all tested methods identified patients approaching the end-of-life, GEST was most performant. These tools may enable resource-sensitive end-of-life screening in the ED.

Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department

Background

The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA).

Materials and methods

A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models' performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models' performance. Bootstrap samples with multiple imputations were used for internal validation.

Results

The mean age of the patients was 64 years (IQR:50-76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration.

Conclusion

The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors.

How to cite this article

Rahmatinejad Z, Hoseini B, Reihani H, Hanna AA, Pourmand A, Tabatabaei SM, et al. Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department. Indian J Crit Care Med 2023;27(6):416-425.

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

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

Barotrauma during Noninvasive Respiratory Support in COVID-19 Pneumonia Outside ICU: The Ancillary COVIMIX-2 Study

BACKGROUND

Noninvasive respiratory support (NIRS) has been extensively used during the COVID-19 surge for patients with acute respiratory failure. However, little data are available about barotrauma during NIRS in patients treated outside the intensive care unit (ICU) setting.

METHODS

COVIMIX-2 was an ancillary analysis of the previous COVIMIX study, a large multicenter observational work investigating the frequencies of barotrauma (i.e., pneumothorax and pneumomediastinum) in adult patients with COVID-19 interstitial pneumonia. Only patients treated with NIRS outside the ICU were considered. Baseline characteristics, clinical and radiological disease severity, type of ventilatory support used, blood tests and mortality were recorded.

RESULTS

In all, 179 patients were included, 60 of them with barotrauma. They were older and had lower BMI than controls (p < 0.001 and p = 0.045, respectively). Cases had higher respiratory rates and lower PaO₂/FiO₂ (p = 0.009 and p < 0.001). The frequency of barotrauma was 0.3% [0.1-1.3%], with older age being a risk factor for barotrauma (OR 1.06, p = 0.015). Alveolar-arterial gradient (A-a) DO₂ was protective against barotrauma (OR 0.92 [0.87-0.99], p = 0.026). Barotrauma required active treatment, with drainage, in only a minority of cases. The type of NIRS was not explicitly related to the development of barotrauma. Still, an escalation of respiratory support from conventional oxygen therapy, high flow nasal cannula to noninvasive respiratory mask was predictive for in-hospital death (OR 15.51, p = 0.001).

CONCLUSIONS

COVIMIX-2 showed a low frequency for barotrauma, around 0.3%. The type of NIRS used seems not to increase this risk. Patients with barotrauma were older, with more severe systemic disease, and showed increased mortality.

Performance of CURB-65 and ISARIC 4C mortality scores for hospitalized patients with confirmed COVID-19 infection in Saudi Arabia

Background

The COVID-19 pandemic continues with new waves that could persist with the arrival of new SARS-CoV-2 variants. Therefore, the availability of validated and effective triage tools is the cornerstone for proper clinical management. Thus, this study aimed to assess the validity of the ISARIC-4C score as a triage tool for hospitalized COVID-19 patients in Saudi Arabia and to compare its performance with the CURB-65 score.

Material and methods

This retrospective observational cohort study was conducted between March 2020 and May 2021 at KFHU, Saudi Arabia, using 542 confirmed COVID-19 patient data on the variables relevant to the application of the ISARIC-4C mortality score and the CURB-65 score. Chi-square and t-tests were employed to study the significance of the CURB-65 score and the ISARIC-4C score variables considering the ICU requirements and the mortality of COVID-19 hospitalized patients. In addition, logistic regression was used to predict the variables related to COVID-19 mortality. In addition, the diagnostic accuracy of both scores was validated by calculating sensitivities, specificities, positive predictive value, negative predictive value, and Youden's J indices (YJI).

Results

ROC analysis showed an AUC value of 0.834 [95% CI; 0.800-0.865]) for the CURB-65 score and 0.809 [95% CI; 0.773-0.841]) for the ISARIC-4C score. The sensitivity for CURB-65 and ISARIC-4C is 75% and 85.71%, respectively, while the specificity was 82.31% and 62.66%, respectively. The difference between AUCs was 0.025 (95% [CI; -0.0203-0.0704], p = 0.2795).

Conclusion

Study results support external validation of the ISARIC-4C score in predicting the mortality risk of hospitalized COVID-19 patients in Saudi Arabia. In addition, the CURB-65 and ISARIC-4C scores showed comparable performance with good consistent discrimination and are suitable for clinical utility as triage tools for hospitalized COVID-19 patients.

[Validation of a symptom scale for COVID-19 patients in ambulatory care]

Background

A symptom scale can be useful for the standardization of clinical evaluations and follow-up of COVID-19 patients in ambultaroy care. Scale development should be accompanied by an assessment of its reliablility and validity.

Objective

To develop and measure the psychometric characteristics of a COVID-19 symptom scale to be answered by either healthcare personnel or adult patients in ambulatory care.

Material and methods

The scale was developed by an expert panel using the Delphi method. We evaluated inter-rater reliability, where we defined a good correlation if Spearman's Rho was ≥ 0.8; test-retest, where we defined a good correlation if Spearman's Rho was ≥ 0.7; factor analysis using principal component methodology; and discriminant validity using Mann-Whitney's U test. A p < 0.05 was considered statistically significant.

Results

We obtained an 8 symptom scale, each symptom is scored from 0-4, with a total minimum score of 0 and a maximum of 32 points. Inter-rater reliability was 0.995 (n = 31), test-retest showed correlation of 0.88 (n = 22), factor analysis detected 4 factors (n = 40) and discriminant capacity of healthy versus sick adults was significant (p < 0.0001, n = 60).

Conclusions

We obtained a reliable and valid Spanish (from Mexico) symptom scale for COVID-19 ambulatory care, answerable by patients and health care staff.

Validation of the qSOFA and CRB-65 in SARS-CoV-2-infected community-acquired pneumonia

Rationale

Prognostic accuracy of the quick sequential organ failure assessment (qSOFA) and CRB-65 (confusion, respiratory rate, blood pressure and age (≥65 years)) risk scores have not been widely evaluated in patients with SARS-CoV-2-positive compared to SARS-CoV-2-negative community-acquired pneumonia (CAP). The aim of the present study was to validate the qSOFA(-65) and CRB-65 scores in a large cohort of SARS-CoV-2-positive and SARS-CoV-2-negative CAP patients.

Methods

We included all cases with CAP hospitalised in 2020 from the German nationwide mandatory quality assurance programme and compared cases with SARS-CoV-2 infection to cases without. We excluded cases with unclear SARS-CoV-2 infection state, transferred to another hospital or on mechanical ventilation during admission. Predefined outcomes were hospital mortality and need for mechanical ventilation.

Results

Among 68 594 SARS-CoV-2-positive patients, hospital mortality (22.7%) and mechanical ventilation (14.9%) were significantly higher when compared to 167 880 SARS-CoV-2-negative patients (15.7% and 9.2%, respectively). All CRB-65 and qSOFA criteria were associated with both outcomes, and age dominated mortality prediction in SARS-CoV-2 (risk ratio >9). Scores including the age criterion had higher area under the curve (AUCs) for mortality in SARS-CoV-2-positive patients (e.g. CRB-65 AUC 0.76) compared to SARS-CoV-2 negative patients (AUC 0.68), and negative predictive value was highest for qSOFA-65=0 (98.2%). Sensitivity for mechanical ventilation prediction was poor with all scores (AUCs 0.59-0.62), and negative predictive values were insufficient (qSOFA-65=0 missed 1490 out of 10 198 patients (∼15%) with mechanical ventilation). Results were similar when excluding frail and palliative patients.

Conclusions

Hospital mortality and mechanical ventilation rates were higher in SARS-CoV-2-positive than SARS-CoV-2-negative CAP. For SARS-CoV-2-positive CAP, the CRB-65 and qSOFA-65 scores showed adequate prediction of mortality but not of mechanical ventilation.

Performance of point-of-care severity scores to predict prognosis in patients admitted through the emergency department with COVID-19

BACKGROUND

Identifying COVID-19 patients at the highest risk of poor outcomes is critical in emergency department (ED) presentation. Sepsis risk stratification scores can be calculated quickly for COVID-19 patients but have not been evaluated in a large cohort.

OBJECTIVE

To determine whether well-known risk scores can predict poor outcomes among hospitalized COVID-19 patients.

DESIGNS, SETTINGS, AND PARTICIPANTS

A retrospective cohort study of adults presenting with COVID-19 to 156 Hospital Corporation of America (HCA) Healthcare EDs, March 2, 2020, to February 11, 2021.

INTERVENTION

Quick Sequential Organ Failure Assessment (qSOFA), Shock Index, National Early Warning System-2 (NEWS2), and quick COVID-19 Severity Index (qCSI) at presentation.

MAIN OUTCOME AND MEASURES

The primary outcome was in-hospital mortality. Secondary outcomes included intensive care unit (ICU) admission, mechanical ventilation, and vasopressors receipt. Patients scored positive with qSOFA ≥ 2, Shock Index > 0.7, NEWS2 ≥ 5, and qCSI ≥ 4. Test characteristics and area under the receiver operating characteristics curves (AUROCs) were calculated.

RESULTS

We identified 90,376 patients with community-acquired COVID-19 (mean age 64.3 years, 46.8% female). 17.2% of patients died in-hospital, 28.6% went to the ICU, 13.7% received mechanical ventilation, and 13.6% received vasopressors. There were 3.8% qSOFA-positive, 45.1% Shock Index-positive, 49.8% NEWS2-positive, and 37.6% qCSI-positive at ED-triage. NEWS2 exhibited the highest AUROC for in-hospital mortality (0.593, confidence interval [CI]: 0.588-0.597), ICU admission (0.602, CI: 0.599-0.606), mechanical ventilation (0.614, CI: 0.610-0.619), and vasopressor receipt (0.600, CI: 0.595-0.604).

CONCLUSIONS

Sepsis severity scores at presentation have low discriminative power to predict outcomes in COVID-19 patients and are not reliable for clinical use. Severity scores should be developed using features that accurately predict poor outcomes among COVID-19 patients to develop more effective risk-based triage.

COVID-19 severity scale for claims data research

OBJECTIVE

To create and validate a methodology to assign a severity level to an episode of COVID-19 for retrospective analysis in claims data.

DATA SOURCE

Secondary data obtained by license agreement from Optum provided claims records nationally for 19,761,754 persons, of which, 692,094 persons had COVID-19 in 2020.

STUDY DESIGN

The World Health Organization (WHO) COVID-19 Progression Scale was used as a model to identify endpoints as measures of episode severity within claims data. Endpoints used included symptoms, respiratory status, progression to levels of treatment and mortality.

DATA COLLECTION/EXTRACTION METHODS

The strategy for identification of cases relied upon the February 2020 guidance from the Centers for Disease Control and Prevention (CDC).

PRINCIPAL FINDINGS

A total of 709,846 persons (3.6%) met the criteria for one of the nine severity levels based on diagnosis codes with 692,094 having confirmatory diagnoses. The rates for each level varied considerably by age groups, with the older age groups reaching higher severity levels at a higher rate. Mean and median costs increased as severity level increased. Statistical validation of the severity scales revealed that the rates for each level varied considerably by age group, with the older ages reaching higher severity levels (p < 0.001). Other demographic factors such as race and ethnicity, geographic region, and comorbidity count had statistically significant associations with severity level of COVID-19.

CONCLUSION

A standardized severity scale for use with claims data will allow researchers to evaluate episodes so that analyses can be conducted on the processes of intervention, effectiveness, efficiencies, costs and outcomes related to COVID-19.

Can lactate levels and lactate kinetics predict mortality in patients with COVID-19 with using qCSI scoring system?

INTRODUCTION

In this study, we aimed to investigate the relationship between blood lactate levels and lactate kinetics (lactate clearance and Δ lactate) for predicting mortality in patients with COVID-19 admitted to the emergency department.

METHODS

This study was performed as a retrospective study that included patients admitted to the emergency department between March 1st, 2020, and January 1st, 2022. Lactate levels were recorded at the first admission (0 h lactate) and the highest blood lactate levels in the first 24 h of follow-up (2nd highest lactate). Lactate kinetics were calculated. Clinical severity was determined according to the quick COVID Severity Index (qCSI).

RESULTS

300 patients were included in the study. Lactate levels at admission were similar in groups with or without mortality, but 2nd highest lactate levels were found to be significantly higher in the group with mortality (p < 0.001). Lactate clearance and ∆ lactate levels were also found to be lower in the mortality group (p < 0.001). Lactate kinetics in patients in the clinically low severity group were lower in the mortality group (p = 0.02 and p = 0.039, respectively). In the low-intermediate and high-intermediate groups, 0-h lactate and 2nd highest lactate levels were found to be higher in the mortality group, and lactate kinetics were similar in the groups with and without mortality. In the group with high clinical severity, 2nd highest lactate levels were found to be higher in the group with mortality (p = 0.010). Lactate kinetics were also found to be significantly lower in the mortality group (p < 0.001). In the high qCSI group, based on ROC analysis, the AUC for 2nd highest lactate levels predicting mortality was 0.642 (95% CI: 0.548-0.728). The optimal cut-off value for mortality was greater than >2.4 mmol/L (60.6% sensitivity, 67.4% specificity). The AUC for lactate clearance was 0.748 (95% CI: 0.659-0.824). The lactate clearance cut-off value was ≤ -177.78% (49.3% sensitivity, 100% specificity). The AUC for ∆ lactate was 0.707 (95% CI: 0.616-0.787). The optimal ∆ lactate cut-off was ≤ -2 mmol/L (45.1% sensitivity, 93.5% specificity).

CONCLUSION

In COVID-19, 2nd highest blood lactate and lactate kinetics were found to be prognostic indicators of the disease. High 2nd highest lactate levels and low lactate kinetics in patients with high clinical severity were guiding physicians regarding the outcome of the disease.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Rheumatoid factor is associated with severe COVID ‐19

AIM

Coronavirus disease 2019 (COVID-19) has been proposed as triggering autoimmunity. The aim of this study was to evaluate the presence and clinical significance of autoantibodies in patients with COVID-19.

METHODS

We retrospectively collected data from 245 patients who were hospitalized for COVID-19. All patients were tested for the presence of antinuclear antibody (ANA), rheumatoid factor (RF), anti-citrullinated peptide antibody (ACPA), and anti-cytoplasmic neutrophil antibody (ANCA). Risk factors for death and critical COVID-19, defined as the need for invasive mechanical ventilation or extracorporeal membrane oxygenation, were analyzed.

RESULTS

Ninety (36.7%) patients tested positive for ANA, and 51 (20.8%) patients tested positive for RF. Three patients each (1.2%) tested positive for ACPA and ANCA. RF-positive patients had higher rates of invasive mechanical ventilation and death than RF-negative patients (70.6% vs 28.4%, P < 0.001 and 45.1% vs 18.6%, P < 0.001, respectively). Underlying lung disease, kidney disease, heart disease, quick COVID severity index (qCSI), and lactate dehydrogenase (LDH) were associated with in-hospital death. RF (odds ratio [OR] 7.31, 95% CI 2.50-21.37, P < 0.001), qCSI (OR 1.42, 95% CI 1.19-1.69, P < 0.001), and LDH (OR 1.004, 95% CI 1.002-1.005, P < 0.001) were associated with critical COVID-19. Combination of RF, qCSI, and LDH showed good prognostic value (area under the curve = 0.903, P < 0.001) for critical COVID-19.

CONCLUSIONS

ANA and RF were frequently detected in COVID-19 patients. RF could be a risk factor for critical COVID-19. The results of this study suggest immune dysfunction contributes to the complications of COVID-19.

Using Non-Invasive Respiratory Monitoring for COVID-19 Pulmonary Embolism Diagnosis

With the high incidence rate of pulmonary embolism (PE) and pneumonia reported in hospitalized patients with COVID-19, the ability to determine the dominant etiology for severe respiratory distress quickly and accurately is crucial to a patient's well-being. Traditionally, D-dimer blood tests and diagnostic imaging studies would be utilized to determine the presence of a PE or a venous thromboembolism. However, COVID-19 places patients in a prothrombotic state and performing diagnostic imaging studies on all patients with COVID-19 would be impractical, making the need for a simple and reliable method to determine the likelihood of PE or venous thromboembolism a priority for emergency departments. The authors believe the use of non-invasive respiratory monitoring technology to assess lung function in hospitalized patients with COVID-19 can aid in discerning the dominant hypoxia etiology and tailoring of their treatment. Here, the authors outline a case and method of using non-invasive respiratory monitoring of lung function in the successful diagnosis of a PE in a 62-year-old patient with COVID-19.

An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems

BACKGROUND

We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores.

METHODS

This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis.

RESULTS

Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores.

CONCLUSION

The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.

ABC-GOALScl score predicts admission to the intensive care unit and mortality of COVID-19 patients over 60 years of age

BACKGROUND

One of the risk factors for getting seriously ill from COVID-19 and reaching high mortality rates is older age. Older age is also associated with comorbidities, which are risk factors for severe COVID-19 infection. Among the tools that have been evaluated to predict intensive care unit (ICU) admission and mortality is ABC-GOALScl.

AIM

In the present study we validated the utility of ABC-GOALScl to predict in-hospital mortality in subjects over 60 years of age who were positive for SARS-CoV-2 virus at the moment of admission with the purpose of optimizing sanitary resources and offering personalized treatment for these patients.

METHODS

This was an observational, descriptive, transversal, non-interventional and retrospective study of subjects (≥ 60 years of age), hospitalized due to COVID-19 infection at a general hospital in northeastern Mexico. A logistical regression model was used for data analysis.

RESULTS

Two hundred forty-three subjects were included in the study, whom 145 (59.7%) passed away, while 98 (40.3%) were discharged. Average age was 71, and 57.6% were male. The prediction model ABC-GOALScl included sex, body mass index, Charlson comorbidity index, dyspnea, arterial pressure, respiratory frequency, SpFi coefficient (Saturation of oxygen/Fraction of inspired oxygen ratio), serum levels of glucose, albumin, and lactate dehydrogenase; all were measured at the moment of admission. The area under the curve for the scale with respect to the variable of discharge due to death was 0.73 (IC 95% = 0.662-0.792).

CONCLUSION

The ABC-GOALScl scale to predict ICU admission in COVID-19 patients is also useful to predict in-hospital death in COVID-19 patients  ≥ 60 years old.

Development of a Risk Score for AKI onset in COVID-19 Patients: COV-AKI Score

PURPOSE

Acute Kidney Injury (AKI) in COVID-19 patients is associated with increased morbidity and mortality. In the present study, we aimed to develop a prognostic score to predict AKI development in these patients.

MATERIALS AND METHODS

This was a retrospective observational study of 2334 COVID 19 patients admitted to 23 different hospitals in Brazil, between January 10th and August 30rd, 2020. The primary outcome of AKI was defined as any increase in serum creatinine (SCr) by 0.3 mg/dL within 48 h or a change in SCr by ≥ 1.5 times of baseline within 1 week, based on Kidney Disease Improving Global Outcomes (KDIGO) guidelines. All patients aged ≥ 18 y/o admitted with confirmed SARS-COV-2 infection were included. Discrimination of variables was calculated by the Receiver Operator Characteristic Curve (ROC curve) utilizing area under curve. Some continuous variables were categorized through ROC curve. The cutoff points were calculated using the value with the best sensitivity and specificity.

RESULTS

A total of 1131 patients with COVID-19 admitted to the ICU were included. Patients mean age was 52 ± 15,8 y/o., with a prevalence of males 60% (n = 678). The risk of AKI was 33% (n = 376), 78% (n = 293) of which did not require dialysis. Overall mortality was 11% (n = 127), while for AKI patients, mortality rate was 21% (n = 80). Variables selected for the logistic regression model and inclusion in the final prognostic score were the following: age, diabetes, ACEis, ARBs, chronic kidney disease and hypertension.

CONCLUSION

AKI development in COVID 19 patients is accurately predicted by common clinical variables, allowing early interventions to attenuate the impact of AKI in these patients.

The Role of Hemogram-derived Ratios in COVID-19 Severity Stratification in a Primary Healthcare Facility

Background

Coronavirus disease 2019 (COVID-19) can cause a wide clinical spectrum, ranging from asymptomatic to severe disease with a high mortality rate. In view of the current pandemic and the increasing influx of patients into healthcare facilities, there is a need to identify simple and reliable tools for stratifying patients.

Objective

Study aimed to analyze whether hemogram-derived ratios (HDRs) can be used to identify patients with a risk of developing a severe clinical form and admission to hospital.

Methods

This cross-sectional and observational study included 500 patients with a confirmed diagnosis of COVID-19. Data on clinical features and laboratory parameters were collected from medical records and 13 HDRs were calculated and analyzed. Descriptive and inferential statistics were included in the analysis.

Results

Of the 500 patients, 43.8% had a severe form of the disease. Lymphocytopenia, monocytopenia, higher C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR) were found in severe patients (p < 0.05). Significantly higher neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), neutrophil-to-platelet ratio (NPR), neutrophil-to-lymphocyte-to-platelet ratio (NLPR) and CRP-to-lymphocyte ratio (CRP/Ly) values were found in severe patients (p < 0.001). In addition, they have statistically significant prognostic potential (p < 0.001). The area under the curve (AUC) for CRP/Ly, dNLR, NLPR, NLR, and NPR were 0.693, 0.619, 0.619, 0.616, and 0.603, respectively. The sensitivity and specificity were 65.7% and 65.6% for CRP/Ly, 51.6% and 70.8 for dNLR, 61.6% and 57.3% for NLPR, 40.6% and 80.4% for NLR, and 48.8% and 69.1% for NPR.

Conclusion

The results of the study suggest that NLR, dNLR, CRP/Ly, NPR, and NLPR can be considered as potentially useful markers for stratifying patients with a severe form of the disease. HDRs derived from routine blood tests results should be included in common laboratory practice since they are readily available, easy to calculate, and inexpensive.

What is the role of proton pump inhibitors consumption on the clinical presentation and severity of COVID-19 infection?

OBJECTIVE

Proton pump inhibitors (PPI) are among the most prescribed drugs worldwide; therefore, assessing their effect on COVID-19 infection symptoms and severity is of great importance. This study was designed to evaluate the role of previous PPI consumption on the clinical presentation and severity of COVID-19.

PATIENTS AND METHODS

All adult COVID-19 patients were eligible in this observational cross-sectional study. The patients' demographic and clinical data, history of PPI consumption, and comorbid disease were recorded. Charlson comorbidity index (CCI) and quick COVID-19 severity index (qCSI) score were calculated for each patient. IBM SPSS version 25 was used for statistical analysis.

RESULTS

Totally 670 patients completed the study (PPI users=121). The average severity (qCSI) score of PPI user patients with comorbidity score of zero was significantly higher than non-users (P-value=0.001). Mortality rate was 6.6% and 3.8% in PPI-users and non-users respectively (P-value=0.117). PPI users were significantly more symptomatic compared to non-users (P-value=0.001).

CONCLUSION

We found that PPI users were meaningfully more symptomatic and had a higher severity (qCSI) score. Rational prescription of PPIs should be considered by physicians during and after the pandemic.

A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms

BACKGROUND

Accurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflammatory profiles.

METHODS

1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative symptom scores (FLU-PRO Plus) were included in this analysis. We employed machine-learning approaches to identify symptom clusters and compared risk of hospitalization, long-term symptoms, as well as peak CRP and IL-6 concentrations.

RESULTS

We identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 ("Nasal cluster") is highly correlated with reporting runny/stuffy nose and sneezing, cluster 2 ("Sensory cluster") is highly correlated with loss of smell or taste, and cluster 3 ("Respiratory/Systemic cluster") is highly correlated with the respiratory (cough, trouble breathing, among others) and systemic (body aches, chills, among others) domain symptoms. Participants in the Respiratory/Systemic cluster were twice as likely as those in the Nasal cluster to have been hospitalized, and 1.5 times as likely to report that they had not returned-to-activities, which remained significant after controlling for confounding covariates (P < 0.01). Respiratory/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset (P = 0.03). We observed higher peak CRP and IL-6 in the Respiratory/Systemic cluster (P < 0.01).

CONCLUSIONS

We identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.

Outcome prediction in hospitalized COVID-19 patients: Comparison of the performance of five severity scores

Background

The aim of our study was to externally validate the predictive capability of five developed coronavirus disease 2019 (COVID-19)-specific prognostic tools, including the COVID-19 Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC), Shang COVID severity score, COVID-intubation risk score-neutrophil/lymphocyte ratio (IRS-NLR), inflammation-based score, and ventilation in COVID estimator (VICE) score.

Methods

The medical records of all patients hospitalized for a laboratory-confirmed COVID-19 diagnosis between May 2021 and June 2021 were retrospectively analyzed. Data were extracted within the first 24 h of admission, and five different scores were calculated. The primary and secondary outcomes were 30-day mortality and mechanical ventilation, respectively.

Results

A total of 285 patients were enrolled in our cohort. Sixty-five patients (22.8%) were intubated with ventilator support, and the 30-day mortality rate was 8.8%. The Shang COVID severity score had the highest numerical area under the receiver operator characteristic (AUC-ROC) (AUC 0.836) curve to predict 30-day mortality, followed by the SEIMC score (AUC 0.807) and VICE score (AUC 0.804). For intubation, both the VICE and COVID-IRS-NLR scores had the highest AUC (AUC 0.82) compared to the inflammation-based score (AUC 0.69). The 30-day mortality increased steadily according to higher Shang COVID severity scores and SEIMC scores. The intubation rate exceeded 50% in the patients stratified by higher VICE scores and COVID-IRS-NLR score quintiles.

Conclusion

The discriminative performances of the SEIMC score and Shang COVID severity score are good for predicting the 30-day mortality of hospitalized COVID-19 patients. The COVID-IRS-NLR and VICE showed good performance for predicting invasive mechanical ventilation (IMV).

Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers

COVID-19 mortality prediction Background COVID-19 has become a major global public health problem, despite prevention and efforts. The daily number of COVID-19 cases rapidly increases, and the time and financial costs associated with testing procedure are burdensome. Method To overcome this, we aim to identify immunological and metabolic biomarkers to predict COVID-19 mortality using a machine learning model. We included inpatients from Hong Kong's public hospitals between January 1, and September 30, 2020, who were diagnosed with COVID-19 using RT-PCR. We developed three machine learning models to predict the mortality of COVID-19 patients based on data in their electronic medical records. We performed statistical analysis to compare the trained machine learning models which are Deep Neural Networks (DNN), Random Forest Classifier (RF) and Support Vector Machine (SVM) using data from a cohort of 5,059 patients (median age = 46 years; 49.3% male) who had tested positive for COVID-19 based on electronic health records and data from 532,427 patients as controls. Result We identified top 20 immunological and metabolic biomarkers that can accurately predict the risk of mortality from COVID-19 with ROC-AUC of 0.98 (95% CI 0.96-0.98). Of the three models used, our result demonstrate that the random forest (RF) model achieved the most accurate prediction of mortality among COVID-19 patients with age, glomerular filtration, albumin, urea, procalcitonin, c-reactive protein, oxygen, bicarbonate, carbon dioxide, ferritin, glucose, erythrocytes, creatinine, lymphocytes, PH of blood and leukocytes among the most important biomarkers identified. A cohort from Kwong Wah Hospital (131 patients) was used for model validation with ROC-AUC of 0.90 (95% CI 0.84-0.92). Conclusion We recommend physicians closely monitor hematological, coagulation, cardiac, hepatic, renal and inflammatory factors for potential progression to severe conditions among COVID-19 patients. To the best of our knowledge, no previous research has identified important immunological and metabolic biomarkers to the extent demonstrated in our study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44247-022-00001-0.

Randomized Controlled Trial Evaluating the Benefit of a Novel Clinical Decision Support System for the Management of COVID-19 Patients in Home Quarantine: A Study Protocol

(1) Background: We present the protocol of a randomized controlled trial designed to evaluate the benefit of a novel clinical decision support system for the management of patients with COVID-19. (2) Methods: The study will recruit up to 500 participants (250 cases and 250 controls). Both groups will receive the conventional telephone follow-up protocol by primary care and will also be provided with access to a mobile application, in which they will be able to report their symptoms three times a day. In addition, patients in the active group will receive a wearable smartwatch and a pulse oximeter at home for real-time monitoring. The measured data will be visualized by primary care and emergency health service professionals, allowing them to detect in real time the progression and complications of the disease in order to promote early therapeutic interventions based on their clinical judgement. (3) Results: Ethical approval for this study was obtained from the Drug Research Ethics Committee of the Valladolid East Health Area (CASVE-NM-21-516). The results obtained from this study will form part of the thesis of two PhD students and will be disseminated through publication in a peer-reviewed journal. (4) Conclusions: The implementation of this telemonitoring system can be extrapolated to patients with other similar diseases, such as chronic diseases, with a high prevalence and need for close monitoring.

Utility of the 4C ISARIC mortality score in hospitalized COVID-19 patients at a large tertiary Saudi Arabian center

Background

The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) 4C mortality score has been used before as a valuable tool for predicting mortality in COVID-19 patients. We aimed to address the utility of the 4C score in a well-defined Saudi population with COVID-19 admitted to a large tertiary referral hospital in Saudi Arabia.

Methods

A retrospective study was conducted that included all adults COVID‑19 patients admitted to the Armed Forces Hospital Southern Region (AFHSR), between January 2021 and September 2022. The receiver operating characteristic (ROC) curve depicted the diagnostic performance of the 4C Score for mortality prediction.

Results

A total of 1,853 patients were enrolled. The ROC curve of the 4C score had an area under the curve of 0.73 (95% CI: 0.702-0.758), p<0.001. The sensitivity and specificity with scores >8 were 80% and 58%, respectively, the positive and negative predictive values were 28% and 93%, respectively. Three hundred and sixteen (17.1%), 638 (34.4%), 814 (43.9%), and 85 (4.6%) patients had low, intermediate, high, and very high values, respectively. There were significant differences between survivors and non-survivors with regard to all variables used in the calculation of the 4C score. Multivariable logistic regression analysis revealed that all components of the 4C score, except gender and O2 saturation, were independent significant predictors of mortality.

Conclusions

Our data support previous international and Saudi studies that the 4C mortality score is a reliable tool with good sensitivity and specificity in the mortality prediction of COVID-19 patients. All components of the 4C score, except gender and O2 saturation, were independent significant predictors of mortality. Within the 4C score, odds ratios increased proportionately with an increase in the score value. Future multi-center prospective studies are warranted.

"Predictors of in-hospital mortality in adult cancer patients with COVID-19 infection presenting to the emergency department: A retrospective study"

BACKGROUND

Adult cancer patients are at higher risk of morbidity and mortality following COVID-19 infection. Being on the front lines, it is crucial for emergency physicians to identify those who are at higher risk of mortality. The aim of our study was to determine the predictors of in-hospital mortality in COVID-19 positive cancer patients who present to the emergency department.

METHODS

This is a retrospective cohort study conducted on adult cancer patients who presented to the ED of the American university of Beirut medical center from February 21, 2020, till February 21, 2021, and were found to have COVID-19 infection. Relevant data was extracted and analyzed. The association between different variables and in-hospital mortality was tested using Student's t test and Fisher's exact test or Pearson's Chi-square where appropriate. Logistic regression was applied to factors with p <0.2 in the univariate models.

RESULTS

The study included 89 distinct patients with an average age of 66 years (± 13.6). More than half of them were smokers (52.8%) and had received chemotherapy within 1 month of presentation (52.8%). About one third of the patients died (n = 31, 34.8%). Mortality was significantly higher in patients who had recently received chemotherapy (67.7% vs 44.8%, p = .039), a history of congestive heart failure (CHF)(p = .04), higher levels of CRP (p = 0.048) and/or PCT(p<0.04) or were tachypneic in the ED (P = 0.016).

CONCLUSIONS

Adult cancer patients with COVID-19 infection are at higher risks of mortality if they presented with tachypnea, had a recent chemotherapy, history of CHF, high CRP, and high procalcitonin levels at presentation.

Prehospital Respiratory Early Warning Score for airway management in-ambulance: A score comparison

BACKGROUND

Prehospital Respiratory Early Warning Scores to estimate the requirement for advanced respiratory support is needed. To develop a prehospital Respiratory Early Warning Score to estimate the requirement for advanced respiratory support.

METHODS

Multicentre, prospective, emergency medical services (EMS)-delivered, longitudinal cohort derivationvalidation study carried out in 59 ambulances and five hospitals across five Spanish provinces. Adults with acute diseases evaluated, supported and discharged to the Emergency Department with high priority were eligible. The primary outcome was the need for invasive or non-invasive respiratory support (NIRS or IRS) in the prehospital scope at the first contact with the patient. The measures included the following: epidemiological endpoints, prehospital vital signs (respiratory rate, pulse oximetry saturation, fraction of inspired oxygen, systolic and diastolic mean blood pressure, heart rate, tympanic temperature and consciousness level by the GCS).

RESULTS

Between 26 Oct 2018 and 26 Oct 2021, we enrolled 5793 cases. For NIRS prediction, the final model of the logistic regression included respiratory rate and pulse oximetry saturation/fraction of inspired oxygen ratio. For the IRS case, the motor response from the Glasgow Coma Scale was also included. The REWS showed an AUC of 0.938 (95% CI: 0.918-0.958), a calibration-in-large of 0.026 and a higher net benefit as compared with the other scores.

CONCLUSIONS

Our results showed that REWS is a remarkably aid for the decision-making process in the management of advanced respiratory support in prehospital care. Including this score in the prehospital scenario could improve patients' care and optimise the resources' management.

"Underneath the visible" - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department

Objectives

Patient risk stratification is the cornerstone of COVID-19 disease management; that has impacted health systems globally. We evaluated the performance of the Brescia-COVID Respiratory Severity Scale (BCRSS), CALL (Co-morbid, age, Lymphocyte and Lactate dehydrogenase) Score, and World Health Organization (WHO) guidelines in Emergency department (ED) on arrival, as predictors of outcomes; Intensive care unit (ICU) admission and in-hospital mortality.

Methods

A two-month retrospective chart review of 88 adult patients with confirmed COVID-19 pneumonia; requiring emergency management was conducted at ED, Indus Hospital and Health Network (IHHN), Karachi, Pakistan, (April 1 to May 31, 2020). The sensitivity, specificity, receiver operator characteristic curve (ROC) and area under the curve (AUC) for the scores were obtained to assess their predictive capability for outcomes.

Results

The in-hospital mortality rate was 48.9 % with 59.1 % ICU admissions and with a mean age at presentation of 56 ± 13 years. Receiver operator curve for BCRSS depicted good predicting capability for in hospital mortality [AUC 0.81(95% CI 0.71-0.91)] and ICU admission [AUC 0.73(95%CI 0.62-0.83)] amongst all models of risk assessment.

Conclusion

BCRSS depicted better prediction of in-hospital mortality and ICU admission. Prospective studies using this tool are needed to assess its utility in predicting high-risk patients and guide treatment escalation in LMIC's.

Evaluation of the effectiveness of quick COVID-19 Severity Index and COVID-GRAM Critical Illness Risk Score in determining mortality and severity in COVID-19

Background/Aim: With the COVID-19 pandemic, the increase in the number of patients admitted to the emergency department has led to an increase in the need for intensive care and mechanical ventilation. Methods that can predict the development of serious disease will allow for a more accurate use of resources. This study was conducted to test the ability of the Quick COVID-19 Severity Index and the COVID-GRAM Critical Illness Risk Score to predict serious disease development and mortality. Methods: This is a prospective cohort study. Among the patients admitted to the emergency department, those hospitalized due to COVID-19 were included in the study. The Quick COVID-19 Severity Index and COVID-GRAM Critical Illness Risk Scores of the patients were calculated, and the ability of these scores to predict serious illness and mortality was investigated. Results: A total of 556 patients were included in this study. Development of critical illness, described as the need for non-invasive / invasive ventilation or the need for intensive care unit admission, was found significant when the Quick COVID-19 Severity Index was above 5 and the COVID-GRAM Critical Illness Risk Score showed high risk (AUC: 0.927; P < 0.001, AUC: 0.986; P < 0.001, respectively). A Quick COVID-19 Severity Index over 6 and COVID-GRAM Critical Illness Risk Score indicating high risk were found to be associated with mortality (AUC: 0.918, P < 0.001, AUC: 0.982, P < 0.001, respectively). Conclusion: Both the Quick COVID-19 Severity Index and the COVID-GRAM Critical Illness Risk Score can be used to assess severity in COVID-19 patients in the emergency room. However, the COVID-GRAM Critical Illness Risk Score was more successful in differentiating low- and high-risk patients.