Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic.

Importance: The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents. Objective: To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic. Design, Setting, and Participants: This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children's hospitals in the US and France. Main Outcomes and Measures: Change in the monthly proportion of mental health condition-associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis. Results: There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5%] female) and 11 101 during the pandemic period (7603 [68.5%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4%]), depression (5065 [48.0%]), and suicidality or self-injury (4673 [44.2%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55%; 95% CI, 0.26%-0.84%), depression (0.50%; 95% CI, 0.19%-0.79%), and suicidality or self-injury (0.38%; 95% CI, 0.08%-0.68%). There was an estimated 0.60% increase (95% CI, 0.31%-0.89%) overall in the monthly proportion of mental health-associated hospitalizations following onset of the pandemic compared with the prepandemic period. Conclusions and Relevance: In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children's hospitals to care for adolescents with mental health conditions during the pandemic and beyond.

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality.

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study.

BACKGROUND: Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. OBJECTIVE: The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. METHODS: From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as "admitted with COVID-19" (incidental) versus specifically admitted for COVID-19 ("for COVID-19"). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. RESULTS: EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity. CONCLUSIONS: A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study.

BACKGROUND: Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. OBJECTIVE: The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. METHODS: From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as "admitted with COVID-19" (incidental) versus specifically admitted for COVID-19 ("for COVID-19"). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. RESULTS: EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity. CONCLUSIONS: A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data.

OBJECTIVE: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. MATERIALS AND METHODS: Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. RESULTS: The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. DISCUSSION: We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. CONCLUSIONS: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

Evaluating sources of bias in observational studies of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker use during COVID-19: beyond confounding.

Concerns over ACE inhibitor or ARB use to treat hypertension during COVID-19 remain unresolved. Although studies using more robust methodologies provided some clarity, sources of bias persist and it remains critical to quickly address this question. In this review, we discuss pernicious sources of bias using a causal model framework, including time-varying confounder, collider, information, and time-dependent bias, in the context of recently published studies. We discuss causal inference methodologies that can address these issues, including causal diagrams, time-to-event analyses, sensitivity analyses, and marginal structural modeling. We discuss effect modification and we propose a role for causal mediation analysis to estimate indirect effects via mediating factors, especially components of the renin--angiotensin system. Thorough knowledge of these sources of bias and the appropriate methodologies to address them is crucial when evaluating observational studies to inform patient management decisions regarding whether ACE inhibitors or ARBs are associated with greater risk from COVID-19.

Severe Acute Respiratory Syndrome Coronavirus 2, COVID-19, and the Renin-Angiotensin System: Pressing Needs and Best Research Practices.

The coronavirus disease 2019 (COVID-19) pandemic is associated with significant morbidity and mortality throughout the world, predominantly due to lung and cardiovascular injury. The virus responsible for COVID-19-severe acute respiratory syndrome coronavirus 2-gains entry into host cells via ACE2 (angiotensin-converting enzyme 2). ACE2 is a primary enzyme within the key counter-regulatory pathway of the renin-angiotensin system (RAS), which acts to oppose the actions of Ang (angiotensin) II by generating Ang-(1-7) to reduce inflammation and fibrosis and mitigate end organ damage. As COVID-19 spans multiple organ systems linked to the cardiovascular system, it is imperative to understand clearly how severe acute respiratory syndrome coronavirus 2 may affect the multifaceted RAS. In addition, recognition of the role of ACE2 and the RAS in COVID-19 has renewed interest in its role in the pathophysiology of cardiovascular disease in general. We provide researchers with a framework of best practices in basic and clinical research to interrogate the RAS using appropriate methodology, especially those who are relatively new to the field. This is crucial, as there are many limitations inherent in investigating the RAS in experimental models and in humans. We discuss sound methodological approaches to quantifying enzyme content and activity (ACE, ACE2), peptides (Ang II, Ang-[1-7]), and receptors (types 1 and 2 Ang II receptors, Mas receptor). Our goal is to ensure appropriate research methodology for investigations of the RAS in patients with severe acute respiratory syndrome coronavirus 2 and COVID-19 to ensure optimal rigor and reproducibility and appropriate interpretation of results from these investigations.

Coronavirus Disease 2019 and Hypertension: The Role of Angiotensin-Converting Enzyme 2 and the Renin-Angiotensin System.

Hypertension emerged from early reports as a potential risk factor for worse outcomes for persons with coronavirus disease 2019 (COVID-19). Among the putative links between hypertension and COVID-19 is a key counter-regulatory component of the renin-angiotensin system (RAS): angiotensin-converting enzyme 2 (ACE2). ACE2 facilitates entry of severe acute respiratory syndrome coronavirus 2, the virus responsible for COVID-19, into host cells. Because RAS inhibitors have been suggested to increase ACE2 expression, health-care providers and patients have grappled with the decision of whether to discontinue these medications during the COVID-19 pandemic. However, experimental models of analogous viral pneumonias suggest RAS inhibitors may exert protective effects against acute lung injury. We review how RAS and ACE2 biology may affect outcomes in COVID-19 through pulmonary and other systemic effects. In addition, we briefly detail the data for and against continuation of RAS inhibitors in persons with COVID-19 and summarize the current consensus recommendations from select specialty organizations.

Relationship Between ACE2 and Other Components of the Renin-Angiotensin System.

PURPOSE OF THE REVIEW: Angiotensin-converting enzyme 2 (ACE2) is a key counter-regulatory component of the renin-angiotensin system. Here, we briefly review the mechanistic and target organ effects related to ACE2 activity, and the importance of ACE2 in SARS-CoV-2 infection. RECENT FINDINGS: ACE2 converts angiotensin (Ang) II to Ang-(1-7), which directly opposes the vasoconstrictive, proinflammatory, and prothrombotic effects of Ang II. ACE2 also facilitates SARS-CoV-2 viral entry into host cells. Drugs that interact with the renin-angiotensin system may impact ACE2 expression and COVID-19 pathogenesis; however, the magnitude and direction of these effects are unknown at this time. High quality research is needed to improve our understanding of how agents that act on the renin-angiotensin system impact ACE2 and COVID-19-related disease outcomes.

Electrolyte imbalances in patients with severe coronavirus disease 2019 (COVID-19).

BACKGROUND: Early studies have reported various electrolyte abnormalities at admission in patients who progress to the severe form of coronavirus disease 2019 (COVID-19). As electrolyte imbalance may not only impact patient care, but provide insight into the pathophysiology of COVID-19, we aimed to analyse all early data reported on electrolytes in COVID-19 patients with and without severe form. METHODS: An electronic search of Medline (PubMed interface), Scopus and Web of Science was performed for articles comparing electrolytes (sodium, potassium, chloride and calcium) between COVID-19 patients with and without severe disease. A pooled analysis was performed to estimate the weighted mean difference (WMD) with 95% confidence interval. RESULTS: Five studies with a total sample size of 1415 COVID-19 patients. Sodium was significantly lower in patients with severe COVID-19 (WMD: -0.91 mmol/L [95% CI: -1.33 to -0.50 mmol/L]). Similarly, potassium was also significantly lower in COVID-19 patients with severe disease (WMD: -0.12 mmol/L [95% CI: -0.18 to -0.07 mmol/L], I2=33%). For chloride, no statistical differences were observed between patients with severe and non-severe COVID-19 (WMD: 0.30 mmol/L [95% CI: -0.41 to 1.01 mmol/L]). For calcium, a statistically significant lower concentration was noted in patients with severe COVID-19 (WMD: -0.20 mmol/L [95% CI: -0.25 to -0.20 mmol/L]). CONCLUSIONS: This pooled analysis confirms that COVID-19 severity is associated with lower serum concentrations of sodium, potassium and calcium. We recommend electrolytes be measured at initial presentation and serially monitored during hospitalization in order to establish timely and appropriate corrective actions.