A Simplified Comorbidity Evaluation Predicting Clinical Outcomes Among Patients With Coronavirus Disease 2019

Adverse COVID-19 outcomes in American Veterans with age-related macular degeneration: a case-control study

OBJECTIVES

Prior studies suggest that patients with age-related macular degeneration (AMD) have poorer COVID-19 outcomes. This study aims to evaluate whether AMD is associated with adverse COVID-19 outcomes in a large clinical database.

DESIGN

Case-control study.

SETTING

We obtained demographic and clinical data from a national US Veterans Affairs (VA) database for all Veterans aged 50 years or older with positive COVID-19 testing prior to 2 May 2021.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome measure was hospitalisation. Secondary outcome measures were intensive care unit admission, mechanical ventilation and death. Potential associations between AMD and outcome measures occurring within 60 days of COVID-19 diagnosis were evaluated using multiple logistic regression analyses.

RESULTS

Of the 171 325 patients in the study cohort, 7913 (5%) had AMD and 2152 (1%) had severe AMD, defined as advanced atrophic or exudative AMD disease coding. Multiple logistic regression adjusting for age, Charlson Comorbidity Index, sex, race, ethnicity and COVID-19 timing showed that an AMD diagnosis did not significantly increase the odds of hospitalisation (p=0.11). Using a Bonferroni-adjusted significance level of 0.006, AMD and severe AMD also were not significant predictors for the secondary outcomes, except for AMD being modestly protective for death (p=0.002).

CONCLUSIONS

After adjusting for other variables, neither AMD nor severe AMD was a risk factor for adverse COVID-19 outcomes in the VA healthcare system. These findings indicate that an AMD diagnosis alone should not alter recommended ophthalmic management based on COVID-19 adverse outcome risk.

Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida

Introduction

The SARS-CoV-2 (COVID-19) pandemic has created substantial health and economic burdens in the US and worldwide. As new variants continuously emerge, predicting critical clinical events in the context of relevant individual risks is a promising option for reducing the overall burden of COVID-19. This study aims to train an AI-driven decision support system that helps build a model to understand the most important features that predict the "mortality" of patients hospitalized with COVID-19.

Methods

We conducted a retrospective analysis of "5,371" patients hospitalized for COVID-19-related symptoms from the South Florida Memorial Health Care System between March 14th, 2020, and January 16th, 2021. A data set comprising patients' sociodemographic characteristics, pre-existing health information, and medication was analyzed. We trained Random Forest classifier to predict "mortality" for patients hospitalized with COVID-19.

Results

Based on the interpretability of the model, age emerged as the primary predictor of "mortality", followed by diarrhea, diabetes, hypertension, BMI, early stages of kidney disease, smoking status, sex, pneumonia, and race in descending order of importance. Notably, individuals aged over 65 years (referred to as "older adults"), males, Whites, Hispanics, and current smokers were identified as being at higher risk of death. Additionally, BMI, specifically in the overweight and obese categories, significantly predicted "mortality". These findings indicated that the model effectively learned from various categories, such as patients' sociodemographic characteristics, pre-hospital comorbidities, and medications, with a predominant focus on characterizing pre-hospital comorbidities. Consequently, the model demonstrated the ability to predict "mortality" with transparency and reliability.

Conclusion

AI can potentially provide healthcare workers with the ability to stratify patients and streamline optimal care solutions when time is of the essence and resources are limited. This work sets the platform for future work that forecasts patient responses to treatments at various levels of disease severity and assesses health disparities and patient conditions that promote improved health care in a broader context. This study contributed to one of the first predictive analyses applying AI/ML techniques to COVID-19 data using a vast sample from South Florida.

Coronavirus disease 2019 pandemic associated with anxiety and depression among Non-Hispanic whites with chronic conditions in the US

OBJECTIVES

During the coronavirus 2019 (COVID-19) pandemic, increased anxiety and depression were reported, with mixed findings among individuals of different races/ethnicities. This study examines whether anxiety and depression increased during the COVID-19 pandemic compared to the pre-COVD-19 period among different racial/ethnic groups in the US.

METHODS

The Health Information National Trend Surveys 5 (HINTS 5) Cycle 4 data was analyzed. We used the time when the survey was administered as the pre-COVID-19 period (before March 11, 2020, weighted N = 77,501,549) and during the COVID-19 period (on and after March 11, 2020, weighted N = 37,222,019). The Patient Health Questionnaire (PHQ) was used to measure anxiety/depression and further compared before and during COVID-19. Separate multivariable logistic regression analyses were used to determine the association of the COVID-19 pandemic with anxiety/depression after adjusting for age, sex, insurance, income, and education.

RESULT

A higher percentage of Non-Hispanic whites (NHW) with chronic conditions reported anxiety (24.3% vs. 11.5%, p = 0.0021) and depression (20.7% vs. 9.3%, p = 0.0034) during COVID-19 than pre-COVID-19. The adjusted odds ratio (AOR) of anxiety and depression for NHWs with chronic conditions during the COVID-19 pandemic was 2.02 (95% confidence interval of 1.10-3.73, p = 0.025) and 2.33 (1.17-4.65, p = 0.018) compared to NHWs who participated in the survey before the COVID-19.

LIMITATIONS

Limited to the NHW US population. PHQ can only be used as the initial screening tool.

CONCLUSION

The COVID-19 pandemic was associated with an increased prevalence of anxiety and depression among NHW adults with chronic conditions, but not among people of color.

Validation of a simplified comorbidity evaluation predicting clinical outcomes among patients with coronavirus disease 2019 – A multicenter retrospective observation study

Objectives

We compared and validated the performance accuracy of simplified comorbidity evaluation compared to the Charlson Comorbidity Index (CCI) predicting COVID-19 severity. In addition, we also determined whether risk prediction of COVID-19 severity changed during different COVID-19 pandemic outbreaks.

Methods

We enrolled all patients whose SARS-CoV-2 PCR tests were performed at six different hospital Emergency Departments in 2020. Patients were divided into three groups based on the various COVID-19 outbreaks in the US (first wave: March–May 2020, second wave: June–September 2020, and third wave: October–December 2020). A simplified comorbidity evaluation was used as an independent risk factor to predict clinical outcomes using multivariate logistic regressions.

Results

A total of 22,248 patients were included, for which 7023 (32%) patients tested COVID-19 positive. Higher percentages of COVID-19 patients with more than three chronic conditions had worse clinical outcomes (i.e., hospital and intensive care unit admissions, receiving invasive mechanical ventilations, and in-hospital mortality) during all three COVID-19 outbreak waves.

Conclusions

This simplified comorbidity evaluation was validated to be associated with COVID clinical outcomes. Such evaluation did not perform worse when compared with CCI to predict in-hospital mortality.

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.