COVID-19 Severity Index: A predictive score for hospitalized patients

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.

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

Symptom severity in an outbreak of SARS-CoV-2 at a university student gala in the Omicron era, Chicago, Illinois, April 2022

Objective: In April 2022, the University of Illinois Chicago (UIC) COVID-19 Contact Tracing & Epidemiology Program identified an outbreak associated with an indoor student gala. This study's aims were to characterize COVID-19 transmission dynamics and measure symptom severity among cases.Participants: The study population included UIC-affiliated gala attendees. Outbreak-associated cases tested positive for COVID-19 between April 2 and April 11, 2022. Attendees who did not test positive or develop symptoms within ten days of the event were classified as contacts.Methods: We ascertained cases through phone-based contact tracing and a survey and evaluated symptom severity using a novel classification system.Results: Among 307 UIC students registered to attend the gala, the minimum attack rate was 14.0%. Approximately 56% of cases were mildly symptomatic, and 38.9% reported severe symptoms.Conclusions: Our findings align with prior research documenting heightened transmissibility of Omicron-variant-related strains and highlight the need for nuanced symptom assessment methodologies.

A multiparametric score for assessing the individual risk of severe Covid-19 among patients with Multiple Sclerosis

Background

Many risk factors for the development of severe forms of Covid-19 have been identified, some applying to the general population and others specific to Multiple Sclerosis (MS) patients. However, a score for quantifying the individual risk of severe Covid-19 in patients with MS is not available. The aim of this study was to construct such score and to evaluate its performance.

Methods

Data on patients with MS infected with Covid-19 in Italy, Turkey and South America were extracted from the Musc-19 platform. After imputation of missing values, data were separated into training data set (70%) and validation data set (30%). Univariable logistic regression models were performed in the training dataset to identify the main risk factors to be included in the multivariable logistic regression analyses. To select the most relevant variables we applied three different approaches: (1) multivariable stepwise, (2) Lasso regression, (3) Bayesian model averaging. Three scores were defined as the linear combination of the coefficients estimated in the models multiplied by the corresponding value of the variables and higher scores were associated to higher risk of severe Covid-19 course. The performances of the three scores were compared in the validation dataset based on the area under the ROC curve (AUC) and an optimal cut-off was calculated in the training dataset for the score with the best performance. The probability of showing a severe Covid-19 course was calculated based on the score with the best performance.

Results

3852 patients were included in the study (2696 in the training dataset and 1156 in the validation data set). 17% of the patients required hospitalization and risk factors for severe Covid-19 course were older age, male sex, living in Turkey or South America instead of living in Italy, presence of comorbidities, progressive MS, longer disease duration, higher Expanded Disability Status Scale, Methylprednisolone use and anti-CD20 treatment. The score with the best performance was the one derived using the Lasso selection approach (AUC= 0.72) and it was built with the following variables: age, sex, country, BMI, presence of comorbidities, EDSS, methylprednisolone use, treatment. An excel spreadsheet to calculate the score and the probability of severe Covid-19 is available at the following link: https://osf.io/ac47u/?view_only=691814d57b564a34b3596e4fcdcf8580.

Conclusions

The originality of this study consists in building a useful tool to quantify the individual risk for Covid-19 severity based on patient's characteristics. Due to the modest predictive ability and to the need of external validation, this tool is not ready for being fully used in clinical practice to make important decisions or interventions. However, it can be used as an additional instrument to identify high-risk patients and persuade them to take important measures to prevent Covid-19 infection (i.e. getting vaccinated against Covid-19, adhering to social distancing, and using of personal protection equipment).