Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes

Social determinants of health predict readmission following COVID-19 hospitalization: a health information exchange-based retrospective cohort study

Introduction

Since February 2020, over 104 million people in the United States have been diagnosed with SARS-CoV-2 infection, or COVID-19, with over 8.5 million reported in the state of Texas. This study analyzed social determinants of health as predictors for readmission among COVID-19 patients in Southeast Texas, United States.

Methods

A retrospective cohort study was conducted investigating demographic and clinical risk factors for 30, 60, and 90-day readmission outcomes among adult patients with a COVID-19-associated inpatient hospitalization encounter within a regional health information exchange between February 1, 2020, to December 1, 2022.

Results and discussion

In this cohort of 91,007 adult patients with a COVID-19-associated hospitalization, over 21% were readmitted to the hospital within 90  days (n = 19,679), and 13% were readmitted within 30  days (n = 11,912). In logistic regression analyses, Hispanic and non-Hispanic Asian patients were less likely to be readmitted within 90  days (adjusted odds ratio [aOR]: 0.8, 95% confidence interval [CI]: 0.7-0.9, and aOR: 0.8, 95% CI: 0.8-0.8), while non-Hispanic Black patients were more likely to be readmitted (aOR: 1.1, 95% CI: 1.0-1.1, p = 0.002), compared to non-Hispanic White patients. Area deprivation index displayed a clear dose-response relationship to readmission: patients living in the most disadvantaged neighborhoods were more likely to be readmitted within 30 (aOR: 1.1, 95% CI: 1.0-1.2), 60 (aOR: 1.1, 95% CI: 1.2-1.2), and 90  days (aOR: 1.2, 95% CI: 1.1-1.2), compared to patients from the least disadvantaged neighborhoods. Our findings demonstrate the lasting impact of COVID-19, especially among members of marginalized communities, and the increasing burden of COVID-19 morbidity on the healthcare system.

Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned

Severe cases of COVID-19 often necessitate escalation to the Intensive Care Unit (ICU), where patients may face grave outcomes, including mortality. Chest X-rays play a crucial role in the diagnostic process for evaluating COVID-19 patients. Our collaborative efforts with Michigan Medicine in monitoring patient outcomes within the ICU have motivated us to investigate the potential advantages of incorporating clinical information and chest X-ray images for predicting patient outcomes. We propose an analytical workflow to address challenges such as the absence of standardized approaches for image pre-processing and data utilization. We then propose an ensemble learning approach designed to maximize the information derived from multiple prediction algorithms. This entails optimizing the weights within the ensemble and considering the common variability present in individual risk scores. Our simulations demonstrate the superior performance of this weighted ensemble averaging approach across various scenarios. We apply this refined ensemble methodology to analyze post-ICU COVID-19 mortality, an occurrence observed in 21% of COVID-19 patients admitted to the ICU at Michigan Medicine. Our findings reveal substantial performance improvement when incorporating imaging data compared to models trained solely on clinical risk factors. Furthermore, the addition of radiomic features yields even larger enhancements, particularly among older and more medically compromised patients. These results may carry implications for enhancing patient outcomes in similar clinical contexts.

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality

As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.

Patient-reported health outcomes of SARS-CoV-2-tested patients presenting to emergency departments: a propensity score-matched prospective cohort study

OBJECTIVE

This study aimed to compare the long-term physical and mental health outcomes of matched severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive and SARS-CoV-2-negative patients controlling for seasonal effects.

STUDY DESIGN

This was a retrospective cohort study.

METHODS

This study enrolled patients presenting to emergency departments participating in the Canadian COVID-19 Emergency Department Rapid Response Network. We enrolled consecutive eligible consenting patients who presented between March 1, 2020, and July 14, 2021, and were tested for SARS-CoV-2. Research assistants randomly selected four site and date-matched SARS-CoV-2-negative controls for every SARS-CoV-2-positive patient and interviewed them at least 30 days after discharge. We used propensity scores to match patients by baseline characteristics and used linear regression to compare Veterans RAND 12-item physical health component score (PCS) and mental health component scores (MCS), with higher scores indicating better self-reported health.

RESULTS

We included 1170 SARS-CoV-2-positive patients and 3716 test-negative controls. The adjusted mean difference for PCS was 0.50 (95% confidence interval [CI]: -0.36, 1.36) and -1.01 (95% CI: -1.91, -0.11) for MCS. Severe disease was strongly associated with worse PCS (β = -7.4; 95% CI: -9.8, -5.1), whereas prior mental health illness was strongly associated with worse MCS (β = -5.4; 95% CI: -6.3, -4.5).

CONCLUSION

Physical health, assessed by PCS, was similar between matched SARS-CoV-2-positive and SARS-CoV-2-negative patients, whereas mental health, assessed by MCS, was worse during a time when the public experienced barriers to care. These results may inform the development and prioritization of support programs for patients.

Influence of social deprivation index on in-hospital outcomes of COVID-19

While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospitalized with confirmed COVID-19 between March 1, 2020 and February 8, 2021 from the INSIGHT Clinical Research Network (CRN). To compute the SDI (exposure variable), we linked clinical data using patient's residential zip-code with social data at zip-code tabulation area. SDI is a composite of seven socioeconomic characteristics determinants at the zip-code level. For this analysis, we categorized SDI into quintiles. The two outcomes of interest were in-hospital intubation and mortality. For each outcome, we examined logistic regression and random forests to determine incremental value of SDI in predicting outcomes. We studied 30,016 included COVID-19 patients. In a logistic regression model for intubation, a model including demographics, comorbidity, and vitals had an Area under the receiver operating characteristic curve (AUROC) = 0.73 (95% CI 0.70-0.75); the addition of SDI did not improve prediction [AUROC = 0.73 (95% CI 0.71-0.75)]. In a logistic regression model for in-hospital mortality, demographics, comorbidity, and vitals had an AUROC = 0.80 (95% CI 0.79-0.82); the addition of SDI in Model 2 did not improve prediction [AUROC = 0.81 (95% CI 0.79-0.82)]. Random forests revealed similar findings. SDI did not provide incremental improvement in predicting in-hospital intubation or mortality. SDI plays an important role on who acquires COVID-19 and its severity; but once hospitalized, SDI appears less important.

Long-haul COVID: healthcare utilization and medical expenditures 6 months post-diagnosis

Background

Despite extensive evidence that COVID-19 symptoms may persist for up to a year, their long-term implications for healthcare utilization and costs 6 months post-diagnosis remain relatively unexplored. We examine patient-level association of COVID-19 diagnosis association of COVID-19 diagnosis with average monthly healthcare utilization and medical expenditures for up to 6 months, explore heterogeneity across age groups and determine for how many months post-diagnosis healthcare utilization and costs of COVID-19 patients persist above pre-diagnosis levels.

Methods

This population-based retrospective cohort study followed COVID-19 patients’ healthcare utilization and costs from January 2019 through March 2021 using claims data provided by the COVID-19 Research Database. The patient population includes 250,514 individuals infected with COVID-19 during March-September 2020 and whose last recorded claim was not hospitalization with severe symptoms. We measure the monthly number and costs of total visits and by telemedicine, preventive, urgent care, emergency, immunization, cardiology, inpatient or surgical services and established patient or new patient visits.

Results

The mean (SD) total number of monthly visits and costs pre-diagnosis were .4783 (4.0839) and 128.06 (1182.78) dollars compared with 1.2078 (8.4962) visits and 351.67 (2473.63) dollars post-diagnosis. COVID-19 diagnosis associated with .7269 (95% CI, 0.7088 to 0.7449 visits; P < .001) more total healthcare visits and an additional $223.60 (95% CI, 218.34 to 228.85; P < .001) in monthly costs. Excess monthly utilization and costs for individuals 17 years old and under subside after 5 months to .070 visits and $2.77, persist at substantial levels for all other groups and most pronounced among individuals age 45–64 (.207 visits and $73.43) and 65 years or older (.133 visits and $60.49).

Conclusions

This study found that COVID-19 diagnosis was associated with increased healthcare utilization and costs over a six-month post-diagnosis period. These findings imply a prolonged burden to the US healthcare system from medical encounters of COVID-19 patients and increased spending.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08387-3.

Risk Factors for Admission Within a Hospital-Based COVID-19 Home Monitoring Program

Background

Despite increasing vaccination rates, coronavirus disease 2019 (COVID-19) continues to overwhelm heath systems worldwide. Few studies follow outpatients diagnosed with COVID-19 to understand risks for subsequent admissions. We sought to identify hospital admission risk factors in individuals with COVID-19 to guide outpatient follow-up and prioritization for novel therapeutics.

Methods

We prospectively designed data collection templates and remotely monitored patients after a COVID-19 diagnosis, then retrospectively analyzed data to identify risk factors for 30-day admission for those initially managed outpatient and for 30-day re-admissions for those monitored after an initial COVID-19 admission. We included all patients followed by our COVID-19 follow-up monitoring program from April 2020 to February 2021.

Results

Among 4070 individuals followed by the program, older age (adjusted odds ratio [aOR], 1.05; 95% CI, 1.03-1.06), multiple comorbidities (1-2: aOR, 5.88; 95% CI, 2.07-16.72; ≥3: aOR, 20.40; 95% CI, 7.23-57.54), presence of fever (aOR, 2.70; 95% CI, 1.65-4.42), respiratory symptoms (aOR, 2.46; 95% CI, 1.53-3.94), and gastrointestinal symptoms (aOR, 2.19; 95% CI, 1.53-3.94) at initial contact were associated with increased risk of COVID-19-related 30-day admission among those initially managed outpatient. Loss of taste/smell was associated with decreased admission risk (aOR, 0.46; 95% CI, 0.25-0.85). For postdischarge patients, older age was also associated with increased re-admission risk (aOR, 1.04; 95% CI, 1.01-1.06).

Conclusions

This study reveals that in addition to older age and specific comorbidities, the number of high-risk conditions, fever, respiratory symptoms, and gastrointestinal symptoms at diagnosis all increased odds of COVID-19-related admission. These data could enhance patient prioritization for early treatment interventions and ongoing surveillance.