Background

In response to the COVID-19 pandemic and associated adoption of scarce resource allocation (SRA) policies, we sought to rapidly deploy a novel survey to ascertain community values and preferences for SRA and to test the utility of a brief intervention to improve knowledge of and values alignment with a new SRA policy. Given social distancing and precipitous evolution of the pandemic, Internet-enabled recruitment was deemed the best method to engage a community-based sample. We quantify the efficiency and acceptability of this Internet-based recruitment for engaging a trial cohort and describe the approach used for implementing a health-related trial entirely online using off-the-shelf tools.

Methods

We recruited 1971 adult participants (≥ 18 years) via engagement with community partners and organizations and outreach through direct and social media messaging. We quantified response rate and participant characteristics of our sample, examine sample representativeness, and evaluate potential non-response bias.

Results

Recruitment was similarly derived from direct referral from partner organizations and broader social media based outreach, with extremely low study entry from organic (non-invited) search activity. Of social media platforms, Facebook was the highest yield recruitment source. Bot activity was present but minimal and identifiable through meta-data and engagement behavior. Recruited participants differed from broader populations in terms of sex, ethnicity, and education, but had similar prevalence of chronic conditions. Retention was satisfactory, with entrance into the first follow-up survey for 61% of those invited.

Conclusions

We demonstrate that rapid recruitment into a longitudinal intervention trial via social media is feasible, efficient, and acceptable. Recruitment in conjunction with community partners representing target populations, and with outreach across multiple platforms, is recommended to optimize sample size and diversity. Trial implementation, engagement tracking, and retention are feasible with off-the-shelf tools using preexisting platforms.

Trial registration

ClinicalTrials.gov NCT04373135 . Registered on May 4, 2020.

Background

Detecting delirium with standardized assessment tools such as the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) is important, but such detection is frequently hampered by poor documentation and inappropriate "unable to assess" responses (in noncomatose patients).

Objective

To identify patient, clinical, and workplace factors that may impede or facilitate appropriate delirium assessment through use of the CAM-ICU, specifically documentation and inappropriate "unable to assess" responses.

Methods

An electronic health record-based data set was used to quantify CAM-ICU documentation and inappropriate "unable to assess" responses during 24 months. Associated patient (eg, age), clinical (eg, diagnosis), and workplace (eg, geographic location within the ICU, shift) factors were evaluated with multivariable regression.

Results

Of 28 586 CAM-ICU documentation opportunities, 66% were documented; 16% of documentations in alert or lightly sedated patients had inappropriate "unable to assess" responses. Night shift was associated with lower CAM-ICU documentation rates (P = .001), whereas physical restraints and location on side B (rather than side A) of the ICU were associated with higher documentation rates (P < .05 for both). Age older than 80 years, non-White race, intubation, and physical restraints were associated with more inappropriate "unable to assess" responses (all P < .05), as was infusion of propofol, midazolam, dexmedetomidine, or fentanyl (all P < .05).

Conclusion

Data from electronic health records can identify patient, clinical, and workplace factors associated with CAM-ICU documentation and inappropriate "unable to assess" responses, which can help target quality improvement efforts related to delirium assessment.

Background

The mechanism by which various classes of medication reduce COPD exacerbation risk remains unknown. We hypothesized a correlation between reduced exacerbation risk and improvement in airway patency as measured according to FEV₁.

Methods

By systematic review, COPD trials were identified that reported therapeutic changes in predose FEV₁ (dFEV₁) and occurrence of moderate to severe exacerbations. Using meta-regression analysis, a model was generated with dFEV₁ as the moderator variable and the absolute difference in exacerbation rate (RD), ratio of exacerbation rates (RRs), or hazard ratio (HR) as dependent variables.

Results

The analysis of RD and RR included 119,227 patients, and the HR analysis included 73,475 patients. For every 100-mL change in predose FEV₁, the HR decreased by 21% (95% CI, 17-26; P < .001; R² = 0.85) and the absolute exacerbation rate decreased by 0.06 per patient per year (95% CI, 0.02-0.11; P = .009; R² = 0.05), which corresponded to an RR of 0.86 (95% CI, 0.81-0.91; P < .001; R² = 0.20). The relationship with exacerbation risk remained statistically significant across multiple subgroup analyses.

Conclusions

A significant correlation between increased FEV₁ and lower COPD exacerbation risk suggests that airway patency is an important mechanism responsible for this effect.

Rationale: Comorbidity is a significant driver of health status and healthcare utilization in chronic obstructive pulmonary disease (COPD). Obstructive sleep apnea (OSA) portends poorer outcomes, whereas obesity is protective. Objectives: We describe the prevalence and influence of these comorbidities on COPD readmissions. Methods: We collated discharge records for COPD exacerbations spanning 2010-2016 from the Nationwide Readmissions Database using Medicare's Hospital Readmissions Reduction Program criteria, with OSA-COPD overlap identified by concomitant diagnosis code for OSA. We used mixed-effects logistic regression to predict readmission odds. A cross-sectional mediation analysis was performed to evaluate the extent that OSA attenuated obesity's impact on readmission. Results: Of 1,662,983 qualifying COPD discharges, 19.1% carried a diagnosis of obesity and 12.9% had OSA, with both diagnoses present in 7.8%. In unadjusted analyses, obesity (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.03-1.05; P < 0.001) and OSA (OR, 1.11; 95% CI, 1.10-1.13; P < 0.001) had increased readmission odds. In models adjusted for patient and hospital characteristics, 71% of readmission risk from obesity was attributable to OSA. When additionally adjusted for Charlson Comorbidity Index, we found that OSA remained a significant risk factor (OR, 1.05; 95% CI, 1.03-1.06; P < 0.001), whereas obesity remained protective (OR, 0.96; 95% CI, 0.94-0.97; P < 0.001) even after accounting for OSA. Conclusions: A significant proportion of patients with COPD suffer comorbid OSA and obesity with resultant readmission risk. Interestingly, obesity's protective effect attenuates readmission odds from OSA. Taken together, OSA and aggregate comorbidity influence readmissions in patients with COPD. Testing for and treating OSA-COPD overlap may provide a mechanism to reduce avoidable readmissions.

Background

Since January 2020, COVID-19 has affected more than 100 million people in the U.S. Previous studies on racial and ethnic disparities related to characteristics and outcomes of COVID-19 patients have been insightful. However, appropriate epidemiologic age-standardization of the disease burden and disparities for hospitalization data are lacking.

Objective

To identify and describe racial and ethnic disparities for primary COVID-19 hospitalizations in the U.S. in 2020.

Methods

In this nationally representative observational study, we use the National Inpatient Sample to quantify racial and ethnic disparities in COVID-19 hospitalizations. Descriptive statistics for patient characteristics, common comorbidities, age-standardized hospitalization rates, inpatient complications, and mortality among COVID-19 hospitalizations were contrasted by race and ethnicity.

Results

There were 1,058,815 primary COVID-19 hospitalizations in 2020. Of those, 47.2% were female, with median age of 66 (IQR, 54, 77). Overall inpatient mortality rate was 11.1%. When compared to White patients, Black, Hispanic, and Native American patients had higher age-standardized hospitalization rate ratios of 2.42 (95% CI 2.40-2.43), 2.26 (2.25-2.28), and 2.51 (2.46-2.56), respectively. Non-White patients had increased age-adjusted rates for procedures and complications. Factors associated with inpatient mortality include age, male sex, Hispanic or Native American race or ethnicity, lower income, Medicaid, heart failure, arrhythmias, coagulopathy, and chronic liver disease.

Conclusions

Marginalized populations in the U.S. had over twice the COVID-19 hospitalization rate relative to White patients. Age-adjusted mortality rates were highest for Black, Hispanic, and Native American patients. Careful consideration for vulnerable populations is encouraged during highly communicable respiratory pandemics.

Rationale

Tobacco use is a risk factor for COVID-19 adverse outcomes. Despite health implications, data conflict regarding COVID-19 and tobacco consumption. We present results from a survey of health behaviors during the pandemic to identify how COVID-19 influenced tobacco behaviors.

Methods

A nationally administered, internet-based survey was deployed between May-September 2020. Of respondents, we analyzed participants who reported current smoking and/or vaping. Our primary outcome of interest was change in tobacco or vape use using measures from the Behavioral Risk Factor Surveillance System, as well as whether participants reported that these changes were related to COVID-19. Our principal exposures were previously psychometrically evaluated measures of anxiety, depression, and novel perceived COVID-19 threat scale with additional adjustment for age. We employed multinomial logistic regression to determine associations between these factors and tobacco consumption.

Results

We identified 500 respondents who reported ever smoking in their lifetime, 150 of which reported currently smoking at the time of the survey. Of 220 participants who reported any use of vapes, 110 reported currently vaping. Increased perceived threat of COVID-19 was associated with both increased (aRR_increase 1.75, 95% CI [1.07-2.86], P = 0.03) and decreased (aRR_decrease 1.72 [1.04-2.85], P = 0.03) tobacco consumption relative to no change. There were no significant relationships found between perceived threat of COVID-19 and vaping behavior.

Conclusions

As perceived COVID-19 threat increased, people were more likely to increase or decrease their smoking as opposed to continue at the same amount of use, even after controlling for anxiety and depression, both of which are known to affect smoking in either direction. Further study into motivators of changing tobacco consumption behaviors, and how barriers to care from safer-at-home policies and changes in care delivery moderate change in tobacco use will aid planning tobacco reduction interventions during the ongoing and future respiratory viral pandemics.

Trial registration

This manuscript is derived from baseline survey data obtained in the "Understanding Community Considerations, Opinions, Values, Impacts, and Decisions in COVID-19" study.

Clinicaltrials

gov registration NCT04373135, registered 04/30/2020.

Spirometry is the current gold standard for diagnosing and monitoring the progression of Chronic Obstructive Pulmonary Disease (COPD). However, many current and former smokers who do not meet established spirometric criteria for the diagnosis of this disease have symptoms and clinical courses similar to those with diagnosed COPD. Large longitudinal observational studies following individuals at risk of developing COPD offer us additional insight into spirometric patterns of disease development and progression. Analysis of forced expiratory maneuver changes over time may allow us to better understand early changes predictive of progressive disease. This review discusses the theoretical ability of spirometry to capture fine pathophysiologic changes in early airway disease, highlights the shortcomings of current diagnostic criteria, and reviews existing evidence for spirometric measures which may be used to better detect early airflow impairment.

Background

Readmissions after exacerbations of chronic obstructive pulmonary disease (COPD) are penalized under the Hospital Readmissions Reduction Program (HRRP). Understanding attributable diagnoses at readmission would improve readmission reduction strategies.

Objectives

Determine factors that portend 30-day readmissions attributable to COPD versus non-COPD diagnoses among patients discharged following COPD exacerbations.

Design, setting, and participants

We analyzed COPD discharges in the Nationwide Readmissions Database from 2010 to 2016 using inclusion and readmission definitions in HRRP.

Main outcomes and measures

We evaluated readmission odds for COPD versus non-COPD returns using a multilevel, multinomial logistic regression model. Patient-level covariates included age, sex, community characteristics, payer, discharge disposition, and Elixhauser Comorbidity Index. Hospital-level covariates included hospital ownership, teaching status, volume of annual discharges, and proportion of Medicaid patients.

Results

Of 1,622,983 (a weighted effective sample of 3,743,164) eligible COPD hospitalizations, 17.25% were readmitted within 30 days (7.69% for COPD and 9.56% for other diagnoses). Sepsis, heart failure, and respiratory infections were the most common non-COPD return diagnoses. Patients readmitted for COPD were younger with fewer comorbidities than patients readmitted for non-COPD. COPD returns were more prevalent the first two days after discharge than non-COPD returns. Comorbidity was a stronger driver for non-COPD (odds ratio [OR] 1.19) than COPD (OR 1.04) readmissions.

Conclusion

Thirty-day readmissions following COPD exacerbations are common, and 55% of them are attributable to non-COPD diagnoses at the time of return. Higher burden of comorbidity is observed among non-COPD than COPD rehospitalizations. Readmission reduction efforts should focus intensively on factors beyond COPD disease management to reduce readmissions considerably by aggressively attempting to mitigate comorbid conditions.

Background

Readmissions following exacerbations of chronic obstructive pulmonary disease (COPD) are prevalent and costly. Multimorbidity is common in COPD and understanding how comorbidity influences readmission risk will enable health systems to manage these complex patients.

Objectives

We compared two commonly used comorbidity indices published by Charlson and Elixhauser regarding their ability to estimate readmission odds in COPD and determine which one provided a superior model.

Methods

We analyzed discharge records for COPD from the Nationwide Readmissions Database spanning 2010 to 2016. Inclusion and readmission criteria from the Hospital Readmissions Reduction Program were utilized. Elixhauser and Charlson Comorbidity Index scores were calculated from published methodology. A mixed-effects logistic regression model with random intercepts for hospital clusters was fit for each comorbidity index, including year, patient-level, and hospital-level covariates to estimate odds of thirty-day readmissions. Sensitivity analyses included testing age inclusion thresholds and model stability across time.

Results

In analysis of 1.6 million COPD discharges, readmission odds increased by 9% for each half standard deviation increase of Charlson Index scores and 13% per half standard deviation increase of Elixhauser Index scores. Model fit was slightly better for the Elixhauser Index using information criteria. Model parameters were stable in our sensitivity analyses.

Conclusions

Both comorbidity indices provide meaningful information in prediction readmission odds in COPD with slightly better model fit in the Elixhauser model. Incorporation of comorbidity information into risk prediction models and hospital discharge planning may be informative to mitigate readmissions.

Background

While cannabis is known to have immunomodulatory properties, the clinical consequences of its use on outcomes in COVID-19 have not been extensively evaluated. We aimed to assess whether cannabis users hospitalized for COVID-19 had improved outcomes compared to non-users.

Methods

We conducted a retrospective analysis of 1831 patients admitted to two medical centers in Southern California with a diagnosis of COVID-19. We evaluated outcomes including NIH COVID-19 Severity Score, need for supplemental oxygen, ICU (intensive care unit) admission, mechanical ventilation, length of hospitalization, and in-hospital death for cannabis users and non-users. Cannabis use was reported in the patient's social history. Propensity matching was used to account for differences in age, body-mass index, sex, race, tobacco smoking history, and comorbidities known to be risk factors for COVID-19 mortality between cannabis users and non-users.

Results

Of 1831 patients admitted with COVID-19, 69 patients reported active cannabis use (4% of the cohort). Active users were younger (44 years vs. 62 years, p < 0.001), less often diabetic (23.2% vs 37.2%, p < 0.021), and more frequently active tobacco smokers (20.3% vs. 4.1%, p < 0.001) compared to non-users. Notably, active users had lower levels of inflammatory markers upon admission than non-users-CRP (C-reactive protein) (3.7 mg/L vs 7.6 mg/L, p < 0.001), ferritin (282 μg/L vs 622 μg/L, p < 0.001), D-dimer (468 ng/mL vs 1140 ng/mL, p = 0.017), and procalcitonin (0.10 ng/mL vs 0.15 ng/mL, p = 0.001). Based on univariate analysis, cannabis users had significantly better outcomes compared to non-users as reflected in lower NIH scores (5.1 vs 6.0, p < 0.001), shorter hospitalization (4 days vs 6 days, p < 0.001), lower ICU admission rates (12% vs 31%, p < 0.001), and less need for mechanical ventilation (6% vs 17%, p = 0.027). Using propensity matching, differences in overall survival were not statistically significant between cannabis users and non-users, nevertheless ICU admission was 12 percentage points lower (p = 0.018) and intubation rates were 6 percentage points lower (p = 0.017) in cannabis users.

Conclusions

This retrospective cohort study suggests that active cannabis users hospitalized with COVID-19 had better clinical outcomes compared with non-users, including decreased need for ICU admission or mechanical ventilation. However, our results need to be interpreted with caution given the limitations of a retrospective analysis. Prospective and observational studies will better elucidate the effects cannabis use in COVID-19 patients.