Validation of automated data abstraction for SCCM discovery VIRUS COVID-19 registry: practical EHR export pathways (VIRUS-PEEP)

Background

The gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities.

Objective

This study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients.

Materials and methods

This observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen's kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson's correlation coefficient and Bland-Altman plots. The strength of agreement was defined as almost perfect (0.81-1.00), substantial (0.61-0.80), and moderate (0.41-0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00-0.30), low (0.30-0.50), moderate (0.50-0.70), high (0.70-0.90), and extremely high (0.90-1.00).

Measurements and main results

The cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%.

Conclusion and relevance

Our study confirms the feasibility and validity of an automated process to gather data from the EHR.

"I Don't Want to Go to Work": A Mixed-Methods Analysis of Healthcare Worker Experiences from the Front- and Side-Lines of COVID-19

During the COVID-19 pandemic, healthcare workers (HCW) were categorized as "essential" and "non-essential", creating a division where some were "locked-in" a system with little ability to prepare for or control the oncoming crisis. Others were "locked-out" regardless of whether their skills might be useful. The purpose of this study was to systematically gather data over the course of the COVID-19 pandemic from HCW through an interprofessional lens to examine experiences of locked-out HCW. This convergent parallel mixed-methods study captured perspectives representing nearly two dozen professions through a survey, administered via social media, and video blogs. Analysis included logistic regression models of differences in outcome measures by professional category and Rapid Identification of Themes from Audio recordings (RITA) of video blogs. We collected 1299 baseline responses from 15 April 2020 to 16 March 2021. Of those responses, 12.1% reported no signs of burnout, while 21.9% reported four or more signs. Qualitative analysis identified four themes: (1) professional identity, (2) intrinsic stressors, (3) extrinsic factors, and (4) coping strategies. There are some differences in the experiences of locked-in and locked-out HCW. This did not always lead to differing reports of moral distress and burnout, and both groups struggled to cope with the realities of the pandemic.

A Path to Real-World Evidence in Critical Care Using Open-Source Data Harmonization Tools

COVID-19 highlighted the need for use of real-world data (RWD) in critical care as a near real-time resource for clinical, research, and policy efforts. Analysis of RWD is gaining momentum and can generate important evidence for policy makers and regulators. Extracting high quality RWD from electronic health records (EHRs) requires sophisticated infrastructure and dedicated resources. We sought to customize freely available public tools, supporting all phases of data harmonization, from data quality assessments to de-identification procedures, and generation of robust, data science ready RWD from EHRs. These data are made available to clinicians and researchers through CURE ID, a free platform which facilitates access to case reports of challenging clinical cases and repurposed treatments hosted by the National Center for Advancing Translational Sciences/National Institutes of Health in partnership with the Food and Drug Administration. This commentary describes the partnership, rationale, process, use case, impact in critical care, and future directions for this collaborative effort.

Outpatient Opioid Dispensing Patterns for SC Medicaid Children 1-36 Months Old

OBJECTIVES

We sought to identify the most common diagnostic categories linked to dispensed opioid prescriptions among children 1-36 months old and changes in patterns over the years 2000 to 2017.

METHODS

This study used South Carolina's Medicaid claims data of pediatric dispensed outpatient opioid prescriptions between 2000 and 2017. The major opioid-related diagnostic category (indication) for each prescription was identified using visit primary diagnoses and the Clinical Classification System (AHRQ-CCS) software. The variables of interest were the rate of opioid prescriptions per 1,000 visits for each diagnostic category and the relative percentage of opioid prescriptions assigned to each category compared to all categories.

RESULTS

Six major diagnostic categories were identified; Diseases of the respiratory system (RESP), Congenital anomalies (CONG), Injury (INJURY), Diseases of the nervous system and sense organs (NEURO), Diseases of the digestive system (GI), and Diseases of the genitourinary system (GU). The overall rate of dispensed opioid prescriptions per category declined significantly for four diagnostic categories throughout the study period, RESP by 15.13, INJURY by 8.49, NEURO by 7.33, and GI by 5.93. Two categories increased during the same time, CONG (by 9.47) and GU (by 6.98). RESP was the most prevalent category linked to a dispensed opioid prescription within 2010-2012 (almost 25%) but CONG was the most prevalent by 2014 (17.77%).

CONCLUSIONS FOR PRACTICE

Among Medicaid children 1-36 months old, annual dispensed opioid prescription rates declined for most major diagnostic categories (RESP, INJURY, NEURO, and GI). Future studies should explore alternatives to current opioid dispensing practices for GU and CONG cases.

Challenges and opportunities for medical referrals at a mobile community health clinic serving sexual and gender minorities in rural South Carolina: a qualitative approach

BACKGROUND

Sexual and gender minorities (SGM) in the Southern United States face challenges in accessing sexual and gender affirming health care. Alternative care models, like inclusive mobile clinics, help mitigate barriers to care for SGM. There is limited data in the literature on the experience of medical referral processes for SGM individuals accessing services from mobile health clinics.

AIMS AND OBJECTIVES

The purpose of this study is to describe the medical referral experiences of SGM clients and their providers at a mobile health clinic in the Southern United States.

METHODS

We recruited English-speaking individuals who provided care or received care from the mobile health clinic in South Carolina between June 2019 and August 2020. Participants completed a brief demographic survey and a virtual in-depth, semi-structured individual interview. Data analysis was conducted using an iterative process to generate codes, categories, and themes. Data collection and analysis were terminated once thematic saturation was achieved.

RESULTS

The findings from this study indicated that the mobile health clinic had an inconsistent referral process that was largely dependent on providers' knowledge. Furthermore, clients and providers expressed individual barriers to the referral process, such as financial barriers, and opportunities to improve the referral process, such as an opt-in follow-up from the mobile clinic and increased mobile clinic resources.

CONCLUSION

The findings in this study underscore the importance of having mobile clinics create a structured referral process that all medical providers are familiar with, and the value of hiring patient navigators that can support and refer clients to care that goes beyond the mobile health clinic setting.

Admission Code Status and End-of-life Care for Hospitalized Patients With COVID-19

CONTEXT

The COVID-19 pandemic has highlighted variability in intensity of care. We aimed to characterize intensity of care among hospitalized patients with COVID-19.

OBJECTIVES

Examine the prevalence and predictors of admission code status, palliative care consultation, comfort-measures-only orders, and cardiopulmonary resuscitation (CPR) among patients hospitalized with COVID-19.

METHODS

This cross-sectional study examined data from an international registry of hospitalized patients with COVID-19. A proportional odds model evaluated predictors of more aggressive code status (i.e., Full Code) vs. less (i.e., Do Not Resuscitate, DNR). Among decedents, logistic regression was used to identify predictors of palliative care consultation, comfort measures only, and CPR at time of death.

RESULTS

We included 29,923 patients across 179 sites. Among those with admission code status documented, Full Code was selected by 90% (n = 15,273). Adjusting for site, Full Code was more likely for patients who were of Black or Asian race (ORs 1.82, 95% CIs 1.5-2.19; 1.78, 1.15-3.09 respectively, relative to White race), Hispanic ethnicity (OR 1.89, CI 1.35-2.32), and male sex (OR 1.16, CI 1.0-1.33). Of the 4951 decedents, 29% received palliative care consultation, 59% transitioned to comfort measures only, and 29% received CPR, with non-White racial and ethnic groups less likely to receive comfort measures only and more likely to receive CPR.

CONCLUSION

In this international cohort of patients with COVID-19, Full Code was the initial code status in the majority, and more likely among patients who were Black or Asian race, Hispanic ethnicity or male. These results provide direction for future studies to improve these disparities in care.

Association of latitude and altitude with adverse outcomes in patients with COVID-19: The VIRUS registry

BACKGROUND

The coronavirus disease 2019 (COVID-19) course may be affected by environmental factors. Ecological studies previously suggested a link between climatological factors and COVID-19 fatality rates. However, individual-level impact of these factors has not been thoroughly evaluated yet.

AIM

To study the association of climatological factors related to patient location with unfavorable outcomes in patients.

METHODS

In this observational analysis of the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study: COVID-19 Registry cohort, the latitudes and altitudes of hospitals were examined as a covariate for mortality within 28 d of admission and the length of hospital stay. Adjusting for baseline parameters and admission date, multivariable regression modeling was utilized. Generalized estimating equations were used to fit the models.

RESULTS

Twenty-two thousand one hundred eight patients from over 20 countries were evaluated. The median age was 62 (interquartile range: 49-74) years, and 54% of the included patients were males. The median age increased with increasing latitude as well as the frequency of comorbidities. Contrarily, the percentage of comorbidities was lower in elevated altitudes. Mortality within 28 d of hospital admission was found to be 25%. The median hospital-free days among all included patients was 20 d. Despite the significant linear relationship between mortality and hospital-free days (adjusted odds ratio (aOR) = 1.39 (1.04, 1.86), P = 0.025 for mortality within 28 d of admission; aOR = -1.47 (-2.60, -0.33), P = 0.011 for hospital-free days), suggesting that adverse patient outcomes were more common in locations further away from the Equator; the results were no longer significant when adjusted for baseline differences (aOR = 1.32 (1.00, 1.74), P = 0.051 for 28-day mortality; aOR = -1.07 (-2.13, -0.01), P = 0.050 for hospital-free days). When we looked at the altitude’s effect, we discovered that it demonstrated a non-linear association with mortality within 28 d of hospital admission (aOR = 0.96 (0.62, 1.47), 1.04 (0.92, 1.19), 0.49 (0.22, 0.90), and 0.51 (0.27, 0.98), for the altitude points of 75 MASL, 125 MASL, 400 MASL, and 600 MASL, in comparison to the reference altitude of 148 m.a.s.l, respectively. P = 0.001). We detected an association between latitude and 28-day mortality as well as hospital-free days in this worldwide study. When the baseline features were taken into account, however, this did not stay significant.

CONCLUSION

Our findings suggest that differences observed in previous epidemiological studies may be due to ecological fallacy rather than implying a causal relationship at the patient level.

HIV Preexposure Prophylaxis Care Continuum Among Individuals Receiving Medication for Opioid Use Disorder, South Carolina, 2020-2021

We implemented the HIV preexposure prophylaxis (PrEP) care continuum among individuals receiving medication for opioid use disorder (MOUD). We screened HIV-negative MOUD participants for PrEP eligibility by assessing injection drug use risk factors and sexual behaviors. Implementation of the PrEP care continuum was challenging; less than a third of MOUD participants were aware of PrEP, and very few initiated PrEP. Findings should promote the development of effective interventions to increase engagement in PrEP during MOUD treatment. (Am J Public Health. 2022;112(1):34-37. https://doi.org/10.2105/AJPH.2021.306566).

Metabolic Syndrome and Acute Respiratory Distress Syndrome in Hospitalized Patients With COVID-19

IMPORTANCE

Obesity, diabetes, and hypertension are common comorbidities in patients with severe COVID-19, yet little is known about the risk of acute respiratory distress syndrome (ARDS) or death in patients with COVID-19 and metabolic syndrome.

OBJECTIVE

To determine whether metabolic syndrome is associated with an increased risk of ARDS and death from COVID-19.

DESIGN, SETTING, AND PARTICIPANTS

This multicenter cohort study used data from the Society of Critical Care Medicine Discovery Viral Respiratory Illness Universal Study collected from 181 hospitals across 26 countries from February 15, 2020, to February 18, 2021. Outcomes were compared between patients with metabolic syndrome (defined as ≥3 of the following criteria: obesity, prediabetes or diabetes, hypertension, and dyslipidemia) and a control population without metabolic syndrome. Participants included adult patients hospitalized for COVID-19 during the study period who had a completed discharge status. Data were analyzed from February 22 to October 5, 2021.

EXPOSURES

Exposures were SARS-CoV-2 infection, metabolic syndrome, obesity, prediabetes or diabetes, hypertension, and/or dyslipidemia.

MAIN OUTCOMES AND MEASURES

The primary outcome was in-hospital mortality. Secondary outcomes included ARDS, intensive care unit (ICU) admission, need for invasive mechanical ventilation, and length of stay (LOS).

RESULTS

Among 46 441 patients hospitalized with COVID-19, 29 040 patients (mean [SD] age, 61.2 [17.8] years; 13 059 [45.0%] women and 15713 [54.1%] men; 6797 Black patients [23.4%], 5325 Hispanic patients [18.3%], and 16 507 White patients [57.8%]) met inclusion criteria. A total of 5069 patients (17.5%) with metabolic syndrome were compared with 23 971 control patients (82.5%) without metabolic syndrome. In adjusted analyses, metabolic syndrome was associated with increased risk of ICU admission (adjusted odds ratio [aOR], 1.32 [95% CI, 1.14-1.53]), invasive mechanical ventilation (aOR, 1.45 [95% CI, 1.28-1.65]), ARDS (aOR, 1.36 [95% CI, 1.12-1.66]), and mortality (aOR, 1.19 [95% CI, 1.08-1.31]) and prolonged hospital LOS (median [IQR], 8.0 [4.2-15.8] days vs 6.8 [3.4-13.0] days; P < .001) and ICU LOS (median [IQR], 7.0 [2.8-15.0] days vs 6.4 [2.7-13.0] days; P < .001). Each additional metabolic syndrome criterion was associated with increased risk of ARDS in an additive fashion (1 criterion: 1147 patients with ARDS [10.4%]; P = .83; 2 criteria: 1191 patients with ARDS [15.3%]; P < .001; 3 criteria: 817 patients with ARDS [19.3%]; P < .001; 4 criteria: 203 patients with ARDS [24.3%]; P < .001).

CONCLUSIONS AND RELEVANCE

These findings suggest that metabolic syndrome was associated with increased risks of ARDS and death in patients hospitalized with COVID-19. The association with ARDS was cumulative for each metabolic syndrome criteria present.

Risk Factors for Critical Coronavirus Disease 2019 and Mortality in Hospitalized Young Adults: An Analysis of the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS) Coronavirus Disease 2019 Registry

IMPORTANCE

Even with its proclivity for older age, coronavirus disease 2019 has been shown to affect all age groups. However, there remains a lack of research focused primarily on the young adult population.

OBJECTIVES

To describe the epidemiology and outcomes of coronavirus disease 2019 and identify the risk factors associated with critical illness and mortality in hospitalized young adults.

DESIGN SETTINGS AND PARTICIPANTS

A retrospective cohort study of the Society of Critical Care Medicine's Viral Infection and Respiratory Illness Universal Study registry. Patients 18-40 years old, hospitalized from coronavirus disease 2019 from March 2020 to April 2021, were included in the analysis.

MAIN OUTCOMES AND MEASURES

Critical illness was defined as a composite of mortality and 21 predefined interventions and complications. Multivariable logistic regression was used to assess associations with critical illness and mortality.

RESULTS

Data from 4,005 patients (152 centers, 19 countries, 18.6% non-U.S. patients) were analyzed. The median age was 32 years (interquartile range, 27-37 yr); 51% were female, 29.4% Hispanic, and 42.9% had obesity. Most patients (63.2%) had comorbidities, the most common being hypertension (14.5%) and diabetes (13.7%). Hospital and ICU mortality were 3.2% (129/4,005) and 8.3% (109/1,313), respectively. Critical illness occurred in 25% (n = 996), and 34.3% (n = 1,376) were admitted to the ICU. Older age (p = 0.03), male sex (adjusted odds ratio, 1.83 [95% CI, 1.2-2.6]), and obesity (adjusted odds ratio, 1.6 [95% CI, 1.1-2.4]) were associated with hospital mortality. In addition to the above factors, the presence of any comorbidity was associated with critical illness from coronavirus disease 2019. Multiple sensitivity analyses, including analysis with U.S. patients only and patients admitted to high-volume sites, showed similar risk factors.

CONCLUSIONS

Among hospitalized young adults, obese males with comorbidities are at higher risk of developing critical illness or dying from coronavirus disease 2019.