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Objectives: Malnutrition is a prevalent condition affecting 30-50% of hospitalized patients. Malnutrition is linked to impairments in health outcomes and increased economic burden on healthcare systems. We assessed the prevalence and burden of malnutrition by examining demographic characteristics, Disease Related Group (DRG) payments and associated claims among Medicare inpatients (65+ years) with and without COVID-19. Method(s): Hospital inpatient COVID-19 claims from the Centers for Medicare & Medicaid Services (CMS) Inpatient Prospective Payment System (IPPS) between October 2020 - September 2021 were analyzed. The International Classification of Diseases, Tenth Revision, and Clinical Modification (ICD-10-CM) were used for malnutrition diagnoses. Demographic variables were compared based on the COVID-19 status;economic burden was analyzed by DRG payment of malnutrition cases with and without COVID-19. Result(s): Among 7,394,657 Medicare inpatient claims, only 12% had a documented malnutrition diagnosis. Of these patients, 1.2% had COVID-19. Regardless of COVID-19 status, malnourished patients averaged 75 years of age, and were predominantly female (54%) and White (78%) followed by Black (14%), and Hispanic (2%). Sepsis, kidney failure, and urinary tract infection (UTI) were the most common primary diagnoses in malnourished patients, regardless of COVID-19 status. Malnourished patients with COVID-19 had significantly higher DRG payments ($27,407 vs. $18,327) and increased cost of outlier payment ($3,208 vs. $2,049) compared to those without COVID-19, regardless of other diagnoses. Conclusion(s): Malnutrition diagnosis was confirmed in only 12% of the Medicare inpatients, thus suggesting that malnutrition continues to be underdiagnosed and undertreated - evidenced by high rates of hospitalizations/claims and payments in both COVID-19 and non-COVID-19 cases. It is imperative for hospitals to implement nutrition-focused protocols to identify, diagnose and address malnutrition among all Medicare inpatients regardless of COVID-19 status (and especially among patients with sepsis, kidney failure, and UTI). Nutrition-focused protocols can effectively improve patient health outcomes and reduce healthcare costs.Copyright © 2023
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Objectives: Developing a control group of a clinical trial using real world data is desirable and ethically sound despite challenges pertaining to internal validity. To examine the internal validity, we reproduced the control group in a Randomized Controlled Trial (RCT) using Electric Health Record (EHR) data and evaluated the difference between the outcome of the original trial and that of the reproduced analysis. Method(s): We selected an RCT, REMDACTA trial, that was performed to evaluate the efficacy of tocilizumab plus remdesivir against placebo plus remdesivir in patients with severe COVID-19 pneumonia. We reproduced its control group (patients with severe COVID-19 pneumonia taking only remdesivir), using Japanese EHR data, Millennial Medical Record provided by Life Data Initiative. Target patients for the main analysis were those prescribed remdesivir within 2 days after admission and diagnosed with COVID-19 (defined by ICD-10 code, U07.1) and/or with COVID-19 pneumonia (defined by diagnosis name). Patients in the sub analysis included only those with COVID-19 pneumonia diagnosis. Among the target patients, those undergoing image inspection, oxygen administration, and not taking any medicines for pneumonia before the first remdesivir prescription were eligible for the analyses. Median length of stay was compared in the reproduced group and in REMDACTA trial. Result(s): The database included 676 and 110 target patients for the main and sub analyses, respectively. However, only 57 and 14 patients met the eligibility criteria for the main and sub analyses, respectively. The reduction in the number of patients is attributed to the criteria of image inspection and oxygen administration. Median length of stay in the reproduced group were 13 and 11 days in the main and sub analyses, respectively. In REMDACTA trial, 95% CI of median time was 11.0-16.0. Conclusion(s): We successfully reproduced the median time of the control group by EHR data.Copyright © 2023
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Objectives: The COVID-19 pandemic disrupted many facets of healthcare including patients delaying medical care for potentially life-threatening conditions. This study sought to compare specific key outcomes related to ischemic stroke that occurred before and during the COVID-19 pandemic. We assessed mortality rates, morbidity rates, and the administration of thrombolytics in patients with ischemic stroke admitted to emergency departments (ED) in the Stroke Belt, a region of the United States with historically worse stroke outcomes. Method(s): Cerner Real-World Data was used to identify patients residing in the Stroke Belt (Southeastern United States) who were admitted to the ED with ICD-10 codes indicating acute ischemic stroke. We determined in-hospital and 30-day mortality rates, morbidity rates (physical disability tracked 1-year post-ischemic stroke), and administration of thrombolytics for acute ischemic stroke patients before the COVID-19 pandemic (March 2019-February 2020) and during the pandemic (March 2020-February 2021). Result(s): In the defined period prior to COVID-19, 2,338 patients presented to the ED with ischemic strokes (49.5% male;mean age 64.8, SD:15.23;69.6% white). During COVID-19, 2,052 ischemic stroke patients presented to the ED (50.9% male;mean age 65.8, SD:15.04;71.5% white). Our analyses show a significant decrease in thrombolytic administration during the pandemic compared to before the pandemic (12.2% and 14.5%, respectively;p<0.05). There was no significant difference in rates of in-hospital mortality, 30-day mortality, or morbidity following ischemic strokes. Conclusion(s): The findings of our study suggest a reduction in ischemic stroke related ED encounters during the COVID-19 period, but no differences were observed in mortality and morbidity rates in ischemic stroke compared to before the pandemic. Future studies are required to determine if these trends were true in other regions of the United States, as well as to investigate other potential covariates linked to outcomes before and during the COVID-19 pandemic.Copyright © 2023
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Objectives: Post COVID-19 conditions or long COVID continues to burden the healthcare system. With the introduction of new code in October 2021 to appropriately capture this condition (U09.9), we have enough data to understand the detailed demographic and clinical characterization of the patients with long COVID. As this new clinical entity continues to evolve, our study will provide insights for care management and planning. Method(s): We conducted a retrospective cohort study from a large deidentified database of US health insurance claims. The study population included all individuals with at least one ICD-10 code for COVID (U07.1) between June 1, 2021, and November 30, 2022. Individuals with at least one ICD-10 code for long COVID (U09.9), at least 7 days after COVID diagnosis were termed "Long COVID" patients. Index date was defined as the first long COVID diagnosis date. We also assessed the most prevalent diagnosis codes within the 30 days pre- and post-index to understand top symptoms. Result(s): A cohort of 253,145 patients (62% female patients;38% male patients) were identified. Among this cohort, 3.2% were pediatric patients aged 0 - 17 years;73.3 % aged 18 - 64 years and 23.5 % aged 65+ years. Most prevalent symptoms that increased in the 30 day pre- and post-index: Nervous system symptoms (6 fold), fatigue (7 fold), Dyspnea (4.3 fold), esophagitis (1.6 fold) chronic kidney disease (1.3 fold) among others. Conclusion(s): Our findings indicate that long COVID is more prevalent in females, with fatigue and dyspnea emerging as top symptoms. These findings are consistent with the published literature. However, we uncovered additional symptoms such as nervous system symptoms, chronic kidney disease among others. Additional analysis is planned to evaluate the association of these symptoms with sociodemographic features to understand the health inequity aspects of long COVID.Copyright © 2023
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Objectives: This analysis was conducted to develop a comprehensive list of ICD-10 CM codes for underlying conditions identified by the CDC as being associated with high-risk of developing severe COVID-19 and assessed the consistency of these codes when applied to large US based datasets. Method(s): The comprehensive list of ICD 10-CM codes for CDC-defined high-risk underlying conditions were mapped from CDC references and FDA Sentinel code lists. These codes were subsequently applied to Optum's de-identified Clinformatics Data Mart Database (claims) and the Optum de-identified Electronic Health Record (EHR) database across 3 years (2018, 2019 and 2020) among continuously enrolled subjects >= 12 years of age to determine the performance and consistency in identifying these high-risk underlying conditions annually over these years. Result(s): A total of 10,276 ICD-10 codes were mapped to 21 underlying conditions. Within the claims data, 62.7% of subjects >= 12 years had >= 1 CDC-defined high-risk condition (excluding age) with 26.6% of patients >= 65 years while in the EHR data 38% had >= 1 high-risk underlying condition (excluding age) with 14.4% >= 65 years. These results were similar and consistent in both datasets across all years. Patients aged 12-64 years in the claims data had a higher rate of >=1 high risk underlying condition relative to the EHR data, 49.3% and 34%, respectively. The top 5 conditions among the >= 65 were identical across both databases: hypertension, immunocompromised status, heart conditions, diabetes (type 1 or 2), and overweight/obesity. The top 5 conditions among the 12-64 age group were also similar among the databases and included: immunocompromised status, hypertension, overweight/obesity, smoking (current or former), and mental health conditions. Conclusion(s): These findings present a comprehensive list of codes that can be used by researchers, clinicians and policy makers to identify and characterize patients that may be at high-risk for severe COVID-19 outcomes.Copyright © 2023
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About 10 % of all symptomatic COVID-19 patients suffer from long-lasting health complaints. Fatigue, cognitive and emotional disorders are the most frequent neuropsychiatric symptoms. Evidence-based therapies for these post-covid impairments are still lacking. Here, we examined the feasibility of a newly developed group-therapy program for patients with fatigue, emotional and cognitive disorders following COVID-19. 24 patients with ICD-10 diagnosis of F06.8 and U0.09 participated in the group therapy on average 13 month after their acute COVID-19 infection. Before and after the group therapy they underwent a comprehensive clinical and neuropsychological assessment. The group therapy was held online and consisted of 8 weekly sessions with psychotherapeutic and psychoeducational elements regarding fatigue and pacing, mindfulness, psychiatric disorders, cognition as well as physical activity after COVID-19. Participation in the group was high with an average of 7.25 of 8 visited sessions. Mean overall group satisfaction was 7.78 out of 10 points. Patients improved in their self-reported fatigue, daily living skills, depression and subjective cognitive abilities as well as in their objective performance in neuropsychological tests of attention during the study time. The newly developed group therapy program for patients with fatigue and emotional and cognitive disorders following an infection with SARS-CoV-2 was well accepted and evaluated and is feasible in an online setting. Copyright © 2023. Thieme. All rights reserved.
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Objectives: Glycogen Storage Disease Type Ia (GSDIa) is a rare inherited disorder resulting in acute hypoglycemia due to impaired release of glucose from glycogen. Despite dietary management practices to prevent hypoglycemia in patients with GSDIa, complications still occur in children and throughout adulthood. This retrospective cohort study compared the prevalence of complications in adults and children with GSDIa. Method(s): Using ICD-10 diagnosis codes, the IQVIA Pharmetrics Plus database was searched for patients with >=2 GSDI claims (E74.01) from January 2016 through February 2020, with >=12 months continuous enrollment beginning prior to March 2019 (for one year of follow-up before COVID-19), and no inflammatory bowel disease diagnoses (indicative of GSDIb). Complication prevalence in adults and children with GSDIa was summarized descriptively. Result(s): In total, 557 patients with GSDIa were identified (adults, 67%;male, 63%), including 372 adults (median age, 41 years) and 185 children (median age, 7 years). Complications occurring only in adults were atherosclerotic heart disease (8.6%), pulmonary hypertension (3.0%), primary liver cancer (1.9%), dialysis (0.8%), and focal segmental glomerulosclerosis (0.3%). Other complications with the greatest prevalence in adults/children included gout (11.8%/0.5%), insomnia (10.0%/1.1%), osteoarthritis (22.0%/2.7%), severe chronic kidney disease (4.3%/0.5%), malignant neoplasm (10.8%/1.6%), hypertension (49.7%/8.7%), acute kidney failure (15.3%/2.7%), pancreatitis (3.0%/0.5%), gallstones (7.8%/1.6%), benign neoplasm (37.4%/8.1%), hepatocellular adenoma (7.0%/1.6%), neoplasm (41.1%/9.7%), and hyperlipidemia (45.2%/10.8%). Complications with the greatest prevalence in children/adults included poor growth (22.2%/1.9%), gastrostomy (29.7%/3.2%), kidney hypertrophy (2.7%/0.8%), seizure (1.6%/0.5%), hypoglycemia (27.0%/11.3%), hepatomegaly (28.7%/15.9%), kidney transplant (1.6%/1.1%), diarrhea (26.5%/18.6%), nausea and/or vomiting (43.8%/35.8%), acidosis (20.0%/17.2%), and anemia due to enzyme disorders (43.8%/40.6%). Conclusion(s): GSDIa is associated with numerous, potentially serious complications. Compared with children, adults with GSDIa had a greater prevalence of chronic complications, potentially indicating the progressive nature of disease. Children with GSDIa had more acute complications related to suboptimal metabolic control.Copyright © 2023
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Objectives: To estimate the reclassification of COVID-19 related ICD-10 codes from admission to discharge using Real-World Data (RWD) from the 2020 Mexican Ministry of Health (MoH) hospitals discharge dataset. Method(s): In this retrospective study, we analyzed all COVID-19 related discharges in the 2020 MoH open database, according to ten ICD-10 codes that the WHO associated with COVID-19. Reclassification was defined as those COVID-19 related cases who were discharged with a different ICD-10 code compared to their ICD-10 admission code. Result(s): From a total of 1,937,360 discharges reported in the MoH's 2020 database 63,740 (3.3%), mostly men (60.8%), with a median age of 56 years, were discharged with a COVID-19 related ICD-10 code and 12,945 of these were reclassified (20.3%). Although "2019-nCoV acute respiratory disease" (U071) had the greatest frequency of reclassified discharges (12,013, 22.3%), the "other coronavirus as the cause of diseases classified elsewhere" (B972) was associated with the greatest reclassification proportion (68, 74.7%) followed by "pneumonia case confirmed as due to COVID-19" (J128) (26.0%). Codes with lower percentages were "acute respiratory distress syndrome due to COVID-19" (J80X) and "acute respiratory failure due to COVID-19" (J960) with 6.3% and 3.8%, respectively. From 63,740 discharges, 50.7% were due to clinical improvement, followed by death (38.2%), transfer to another unit (5.2%) and voluntary discharge (3.3%). The J960 code had the highest mortality (67%) followed by codes J80X (59.7%) and U071 (35.5%). Conclusion(s): In our RWD analysis, we identified that 1 in 5 COVID-19 discharges were admitted with different diagnoses, highlighting the enormous challenges faced by the Mexican MoH during the global health crisis to establish an accurate COVID-19 diagnosis and coding. Given that this is the first reclassification analysis in Mexico, the conduction of future studies is essential to gain more insights on the consequences of reclassification at a health system level.Copyright © 2023
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Objectives: The symptoms of patients with post-acute COVID-19 syndrome are heterogenous, impact multiple systems, and are often non-specific. To better understand the symptomatic profile of this population, this study used real-world data and unsupervised machine learning techniques to identify distinct groupings of long COVID patients. Method(s): Children/adolescents (age 0-17) and adults (age 18-64 and >=65) with >=2 primary diagnoses for U09.9 "Post COVID-19 condition" from 10/01/2021 (ICD-10 code introduction) until 03/31/2022 were selected from Optum's de-identified Clinformatics Data Mart Database, with the first diagnosis deemed index. Included patients had >=1 diagnosis for COVID-19 at least 4 weeks before index and continuous enrollment during the 12 months prior to index. Diagnoses recorded +/-2 weeks from index that were not present prior to the initial COVID-19 diagnosis were captured and used as patient features for k-means clustering. Final cluster assignments were selected based on silhouette coefficient and clinical relevancy of groupings. Result(s): 3,587 patients met eligibility criteria, yielding three clusters. Concurrent symptom domains surrounding index included breathing, fatigue, pain, cognitive, and cardiovascular diagnoses. The first cluster (N=2,578, 71.8%) was characterized by patients with only a single symptom domain (33% breathing, 33% cardiovascular, 20% fatigue, 11% cognitive). The second cluster (N=651, 18.1%) all presented with breathing symptoms accompanied by one additional domain (cardiovascular 40%, fatigue 28%, pain 18%). The final cluster (N=358, 9.9%) experienced breathing symptoms accompanied by two additional domains (fatigue and cardiovascular 34%, cardiovascular and cognitive 34%). Cluster 3 was slightly older than clusters 1 or 2 (mean age 66 vs. 58 years, respectively). Conclusion(s): Unsupervised machine learning identified distinct groups of long COVID patients, which may help inform multidisciplinary care needs. Our analysis suggests that many patients with long COVID may experience symptoms from only a single domain, and multi-system illness may generally include breathing complications accompanied by fatigue and/or cardiovascular complications.Copyright © 2023
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Objectives: While persistent and relapsing symptoms of COVID-19 are increasingly documented, limited data exist on the post-acute population. The objective of this analysis is to identify the characteristics of patients diagnosed with long COVID using real-world data. Method(s): Children/adolescents (age 0-17) and adults (age 18-39, 40-64 and >=65) with >=2 primary diagnoses for U09.9 "Post COVID-19 condition" from 10/01/2021 (ICD-10 code introduction) until 03/31/2022 were selected from Optum's de-identified Clinformatics Data Mart Database, with the first diagnosis deemed index. Included patients had >=1 diagnosis for COVID-19 and continuous enrollment 12 months prior to index (baseline). To ensure alignment with most institutional definitions, >=4 weeks between initial COVID-19 infection and index was required. Diagnoses recorded +/-2 weeks from index that were not present prior to the initial COVID-19 diagnosis were summarized. Newly prescribed treatments and total medical costs were evaluated during the month following index (continuous enrollment required). Result(s): 3,587 patients met eligibility criteria (mean age 59.02, 57.56% female) with a median time from initial COVID-19 infection to long COVID diagnosis of 83 days (IQR: 46-201 days). The most common concurrent diagnoses included breathing complications such as dyspnea (20.38%) and respiratory failure (15.23%);malaise and fatigue (15.31%);symptoms related to cognitive functioning/anxiety (11.35%);and chest pain (7.67%). Children/adolescents had the highest prevalence of chest pain, while patients >=65 years of age had the highest prevalence of issues with coordination. The average total medical cost during the month following long COVID diagnosis was $4,267 (SD $14,662), with common prescriptions including albuterol (4.42%), prednisone (3.51%), and methylprednisolone (2.01%). Conclusion(s): This retrospective analysis confirms clinically documented symptoms of long COVID in a large, real-world population. Once more data become available, further research on the long term economic and clinical outcomes among patients diagnosed with post-acute COVID-19 syndrome are warranted.Copyright © 2023
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Objectives: Post-COVID conditions (PCC) are increasingly reported in people who had COVID. Certain racial or socioeconomic groups may be at greater risk for PCC and less likely to seek care. We examined the uptake of the new ICD-10-CM diagnosis code for PCC in routine clinical practice in the United States and how it varied by race and payer group. Method(s): Using the Optum de-identified Electronic Health Record (EHR) dataset, we identified patients with an ICD-10-CM code for PCC (U09.9) between October 1, 2021, through March 31, 2022, with 6 months of prior EHR activity. The earliest diagnosis defined the index date. All concurrent diagnoses were measured on the index date. Prior COVID diagnosis was assessed using all available data before the index date. Result(s): There were 23,647 patients: 9.9% were African American, 12.1% had Medicaid, and 2.4% were uninsured. There was an overrepresentation of white patients among those with PCC (78.6% compared with 69.6% of the overall EHR in 2021). More African American (24.1%), Medicaid (23.1%), and uninsured (27.5%) patients were diagnosed in the inpatient setting or emergency department than whites (14.0%) and commercially insured patients (10.0%). Among racial groups, African Americans had the highest percentage of documented prior COVID diagnosis at 63.6%. Of concurrent diagnoses, shortness of breath and acute respiratory failure with hypoxia were higher among African Americans (13.9% and 6.1%, respectively) than whites (11.5% and 4.3%, respectively). The same pattern was seen when comparing Medicaid and uninsured to commercial payors. Conclusion(s): The PCC code was used differently across racial groups and payor types and captures varying manifestations of PCC. The differences in diagnosis locations underscore the importance of using data capturing all care settings when conducting studies using this code. Subgroup analyses are important for future studies using U09.9 due to variability in code application.Copyright © 2023
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Objectives: To describe the use of extracorporeal membrane oxygenation (ECMO) among hospitalized coronavirus disease 2019 (H-COVID-19) patients in a linked closed claims (CC) and open claims (OC) database. Method(s): This analysis identified H-COVID-19 patients between April 2020 (Q2 2020) and June 2022 (Q2 2022) in CHRONOS, a linked CC and OC database. The index event was the date of hospitalization, defined as an inpatient claim within 21 days of a COVID-19 diagnosis in the CC. The occurrence of ECMO 30 days after index was identified using CC data alone and then CC and OP data in combination to assess missing data. Study exclusions included patients under the age of 18, a first COVID-19 diagnosis that did not result in hospitalization, and less than 12-months of continuous enrollment in the CC before index. Study criteria were defined by the presence of an ICD-10-CM, ICD-10-PCS, or CPT code on a claim. Results are reported as percentages and 95% confidence intervals. Result(s): Of 321,687 patients with H-COVID-19, the mean age was 50.1 (SD:12.8) with the highest proportion of hospitalizations occurring in Q3 2021 (19.4%). Overall, 0.50% (0.48%-0.52%) of patients in the CC data received ECMO, increasing to 0.61% (0.58%-0.64%) with the inclusion of OC data. The use of ECMO to treat H-COVID-19 patients decreased between Q2 2021 and Q2 2022, with the highest rates occurring in Q2 of 2020 (0.78%) and Q2 2021 (0.80%). The addition of OC data increased rates to 1.12% and 0.89% in Q2 of 2020 and Q2 2021. Conclusion(s): Although use of ECMO decreased in the later months of the pandemic, it represents a substantial burden. The current analysis demonstrates that CC data, often sourced from payers, may underestimate the use of ECMO in real-world settings. Opportunities exist to mitigate issues of missing data by linking CC, OC, and other real-world data sources.Copyright © 2023
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Introduction: COVID-19 vaccination substantially reduces morbidity and mortality associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severe illness. However, despite effective COVID-19 vaccines many questions remain about the efficacy of vaccines and the durability and robustness of immune responses, especially in immunocompromised persons. The NCI-funded Serological Sciences Network (SeroNet) is a coordinated effort including 11 sites to advance research on the immune response to SARS-CoV-2 infection and COVID-19 vaccination among diverse and vulnerable populations. The goals of the Pooling Project are: (1) to conduct real-world data (RWD) analyses using electronic medical records (EMR) data from four health care systems (Kaiser Permanente Northern California, Northwell Health, Veterans Affairs-Case Western, and Cedars-Sinai) to determine vaccine effectiveness in (a) cancer patients;(b) autoimmune diseases and (c) solid organ transplant recipients (SOTR);(2) to conduct meta-analyses of prospective cohort studies from eight SeroNet institutions (Cedars-Sinai, Johns Hopkins, Northwell Health, Emory University, University of Minnesota, Mount Sinai, Yale University) to determine post-vaccine immune responses in (a) lung cancer patients;(b) hematologic cancers/hematopoietic stem cell transplant (HSCT) recipients;(c) SOTR;(d) lupus. Method(s): For our RWD analyses, data is extracted from EMR using standardized algorithms using ICD-10 codes to identify immunocompromised persons (hematologic and solid organ malignancy;SOTR;autoimmune disease, including inflammatory bowel disease, rheumatoid arthritis, and SLE). We use common case definitions to extract data on demographic, laboratory values, clinical co morbidity, COVID-19 vaccination, SARS-CoV-2 infection and severe COVID-19, and diseasespecific variables. In addition, we pool individual-level data from prospective cohorts enrolling patients with cancer and other immunosuppressed conditions from across network. Surveys and biospecimens from serology and immune profiling are collected at pre-specified timepoints across longitudinal cohorts. Result(s): Currently, we have EMR data extracted from 4 health systems including >715,000 cancer patients, >9,500 SOTR and >180,000 with autoimmune conditions. Prospective cohorts across the network have longitudinal data on >450 patients with lung cancer, >1,200 patients with hematologic malignancies, >400 SOTR and >400 patients with lupus. We will report results examining vaccine effectiveness for prevention of SARS-CoV-2 infection, severe COVID-19 and post-acute sequelae of COVID-19 (PAS-C or long COVID) in cancer patients compared to other immunocompromised conditions. Conclusion(s): Our goal is to inform public health guidelines on COVID-19 vaccine and boosters to reduce SARS-CoV-2 infection and severe illness in immunocompromised populations.
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Objectives: The World Health Organization has declared COVID-19 a global pandemic in March 2020. Multiple COVID-19 waves are putting tremendous stress on healthcare systems. Evidence showed that high-flow nasal canula (HFNC) reduced the need for mechanical ventilation and shortened the time to clinical recovery among patients with severe COVID-19. This study aimed to assess the effect of using HFNC compared to non-invasive mechanical ventilation (NIV), on adult patients with COVID-19. Method(s): This retrospective study included patients hospitalized due to COVID-19 between October 2020 to December 2021 with appropriate ICD-10 diagnosis recorded in a commercially available, all-payer administrative database across 300+ hospitals. The identified patients were divided in two cohorts, one being the patients treated with HFNC as the first line respiratory support and another with NIV. Outcomes included all-cause mortality rate and length of stay. Multivariable analyses were performed to adjust for baseline characteristics. Result(s): Out of 16,534 eligible patients, 4,334 patients received HFNC as the first line respiratory support, whereas 12,200 received NIV. The all-cause mortality rate was 20.24% and 37.14% in the HFNC and NIV group, respectively. After adjusting for baseline characteristics, the all-cause mortality rate in the HFNC group was lower compared to NIV (odds ratio [OR], 0.51;95% confidence interval [CI], 0.47-0.55;p<0.001). The total length of stay was around 15 days for all patients. No different was observed between groups ( mean difference 0.3 days;95% CI, -0.27 - 0.92 days;p>0.05). Conclusion(s): Patients treated with HFNC showed lower mortality rates compared to NIV for hospitalized COVID-19 patients. However, further studies are still needed to better elucidate the clinical and economic benefit of HFNC in COVID-19 patients.Copyright © 2023
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Background: Atrial fibrillation (AF) is the most common arrhythmia in the United States. Concomitant Covid-19 infection and the outcomes of AF are unknown. Objective(s): The study's goals were to analyze the outcomes of AF during the Covid-19 pandemic. Method(s): We conducted a retrospective cohort study based on the 2020 National Inpatient Sample (NIS) of Adults (>18 years) hospitalized for AF as the primary admitting diagnosis based on the ICD-10 codes and stratified these groups into concomitant covid-19 infection vs. non-covid-19 infection. All-cause mortality was our primary outcome, while the rate of ICU admission, length of stay, hospital charges were our secondary outcomes. Temporal trends were assessed using logistic regression. Result(s): In 2020, there were 1,994,985 admissions for atrial fibrillation, out of whom 104,495 (5.3%) had concomitant Covid-19. In the 104,495 AF admissions with covid-19, the mean age was 75y and 56.8% were males. Our results, image 1, showed AF with and without concomitant Covid-19 had similar rates of comorbid conditions including HTN, DM, OSA, CAD. HFrEF, and ESRD. AF patients with Covid-19 infection had a lower prevalence of smoking (31.83% vs. 39.4%, p<.001) and alcohol use (2% vs. 4.2%, p<.001). AF patients from both groups had similar rates of stroke (1.6% vs. 1.0%, p<.001). New AF patients with concomitant Covid-19 had worsening in-hospital outcomes such as shock (12.8% vs. 3.7%, p<.001), admission to the ICU (18.1% vs. 6.4%, p<0.001), higher all-cause mortality (21.8% vs. 3.9%, p<0.001), a longer length of stay (9.96 days vs 6.08 days, p<.001), and total hospital costs ($114,387 vs. $85,830, p<.0001). The incidence of AF catheter ablation on initial hospital admission for AF Covid-19 was lower compared to the AF non-covid-19 patients (.08% vs. 1.39%, p<.001). Conclusion(s): In 2020, Covid-19 infection was an independent predictor of higher all-cause mortality, length of stay, and costs in patients admitted for atrial fibrillation. In addition, these patients were less likely to get catheter ablation on hospital admission. [Formula presented]Copyright © 2023
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Objectives: Female athletes have a higher risk of concussion than their male counterparts but there is a need to investigate these differences in a general population. We assessed reported concussions during a pre-COVID time period in a claims database to compare the prevalence across sexes and ages. Method(s): Using the MerativeTM MarketScan Commercial and Medicare Databases and Treatment Pathways tool, patients were included in the current study if they had continuous enrollment in the database from January 1, 2018 - December 31, 2019 and were between the ages of 14-65. The prevalence of concussions, identified using ICD-10-DX codes, was calculated for all cohorts (14-17, 18-23, >=24 years) and compared statistically between males and females. A secondary analysis, restricted to those with at least one year of continuous enrollment after the initial concussion, was conducted to determine if the proportion of patients who had a diagnosis code for a concussion at least 30 days after the first concussion differed across cohorts. Result(s): Approximately 9 million patients met the inclusion criteria for the study. A total of 66,098 patients had a claim for a concussion during the study period (56% female). The prevalence of concussions was 0.7% and 0.8% among males and females, respectively (p-value<.0001). Females were more likely to be diagnosed with a concussion than males in all age cohorts, with prevalence decreasing with age. Among patients with at least one concussion, 11.7% of males and 16.9% of females had a diagnosis code for a concussion at least 30 days after the initial code (p-value<.0001). Conclusion(s): This study supports previous research done in athletic populations and provides evidence that in a general population the prevalence of concussions is higher among females across all age groups. Further research is needed to investigate why the risk of concussion differs across sexes.Copyright © 2023
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Objectives: Assess real-world evidence data on the prevalence and impact of long COVID (LC) to establish a baseline for the value of potential therapeutic interventions. Method(s): This study was a retrospective, longitudinal analysis of administrative claims from multiple payer channels spanning 4/1/2020-6/30/2022. Inclusion criteria: 1) ICD-10-CM diagnosis code of COVID-19 (U07.1) on or after 4/1/2020 (COVID-19 diagnosis date=index date), 2) 18+ years of age on index, and 3) at least -365/+30 days of continuous plan enrollment surrounding index. Employing a conservative LC definition, patients were classified as LC if they presented at least 1 claim >= 28 days following the index date which included both a COVID-19 diagnosis and >=1 of 8 LC-related symptoms. LC and non-LC patients were compared on demographics, COVID-19 symptoms, healthcare utilization, and medical costs. Descriptive statistics were presented for outcomes, and bivariate tests of significance were used to assess differences between cohorts. Result(s): Of 4,938,801 medically attended COVID-19 patients meeting inclusion criteria, 386,153 (7.8%) qualified as LC. The LC patients were older (Mean(SD) = 67.0(19.0) vs. 51.0(20.7)), were more likely to be female (65.1% vs. 60.4%), were in poorer health (Deyo-Charlson Comorbidity Index=3.51(3.24) vs. 1.47(2.45)), and presented greater baseline total medical expenditures ($39,769($60,401) vs. $15,275($35,640);p < 0.0001). On index, LC patients had a higher rate of LC-related symptoms, and in the 180-day post-index period, LC patients incurred increased total medical costs ($38,874($54,098) vs. $7,319($18,439);p < 0.001) and greater use of inpatient and outpatient medical services. Conclusion(s): Patients with LC presented elevated rates of symptoms and incurred 5-fold greater medical costs post-index compared to non-LC patients. This study is one of the first to longitudinally quantify the cost and symptom burden of LC in a real-world setting and helps to establish a baseline for the value of potential therapeutic interventions.Copyright © 2023
ABSTRACT
Objectives: Due to large sample sizes, electronic medical records (EMR) databases have the potential to provide pivotal insights into patients diagnosed with rare, orphan, or emerging diseases. This study aimed to explore the patient profile of African and American trypanosomiasis, both vector-borne parasitic diseases, pre-and post the COVID-19 pandemic using the TriNetX Network. Method(s): From Jan 1, 2018 - Nov 30, 2019 (pre-COVID) and Jan 1, 2020 - Nov 30, 2021 (post-COVID) patients were queried from the TriNetX Global health research network, inclusive of 88 million patients from the United States (US), Europe, the Middle East, Africa, Latin America, and Asia Pacific. Eligible patients with an ICD-10 diagnosis code of African trypanosomiasis or American trypanosomiasis were identified (2280 patients on 22-Dec-2022) and analyzed separately, pre- and post-COVID. Result(s): We identified 340 patients pre- and 960 patients post-COVID with African trypanosomiasis and 960 patients pre- and 190 patients post-COVID with American trypanosomiasis. Most patients resided in the US. Pre-COVID African trypanosomiasis patients had a mean age of 38 and were 59% female while post-COVID patients had a mean age of 34 and were 57% female. Pre-COVID American trypanosomiasis patients had a mean age of 49 and were 57% female while post-COVID patients had a mean age of 49 and were 53% female. Top co-diagnoses included diseases of the respiratory (85%, 84%) and nervous systems (82%, 79%) for patients with African trypanosomiasis and diseases of the digestive (69%, 54%) and circulatory systems (68%, 61%) for patients with American trypanosomiasis in both the pre- and post-COVID cohorts, respectively. Conclusion(s): Using real-world EMR data we were able to obtain patient profiles for a rare disease (African trypanosomiasis) and a common, emerging disease (American trypanosomiasis). This informationsupportsutilizing EMR data for describing patient populations in rare, orphan, or emerging diseases, which may aid drug development for these indications.Copyright © 2023
ABSTRACT
Background: Although over 100 million pregnant women worldwide are at risk of infection with SARS-CoV-2, little data exists on the impact of COVID-19 and related treatments on maternal/neonatal health. Objective(s): (1) To quantify the prevalence of medication use in pregnancy to treat COVID-19, and (2) To quantify and compare the risk of adverse pregnancy/neonatal outcomes in those with and without COVID-19. Method(s): In the Canadian Mother-Child population-based cohort (CAMCCO), two sub-cohorts were identified using prospective data collection of medical services, prescription drugs, hospitalization archives data, and COVID-19 surveillance testing program (02/28/2020- 2021). The first cohort included all pregnant women during the study period regardless of pregnancy status (delivery, induced/planned or spontaneous abortion);this cohort was further stratified on COVID-19 status. The second cohort included all nonpregnant women (aged 15-45) with a positive COVID-19 test. COVID-19 in pregnant or nonpregnant women was assessed using COVID-19 test results or ICD-10CM code U07.1 from hospital data. COVID-19 severity was categorized based on hospital admission. Women were considered exposed to COVID-19 medications if they filled at least one prescription for a medicine included in the WHO list in the 30 days pre- or 30 days post-COVID-19 positive test/diagnosis. Considering potential confounders, association between COVID-19 during pregnancy, treated vs not, and perinatal outcomes were quantified using log-binomial regression models. Result(s): 150,345 pregnant women (3,464 (2.3%) had COVID-19), and 112,073 nonpregnant women with COVID-19 diagnoses were included. Pregnant women with COVID-19 were more likely to have severe infections compared to nonpregnant women with COVID-19 (11.4% vs 1.6%, p<0.001). The most frequent medications used in pregnancy to treat COVID-19 were antibacterials (13.96%), psychoanaleptics (7.35%), and medicines for obstructive airway disease (3.20%). In pregnancy COVID-19 was associated with spontaneous abortions (adjRR 1.76, 95%CI 1.37, 2.25), gestational diabetes (adjRR 1.52, 95%CI 1.18, 1.97), prematurity (adjRR 1.30, 95%CI 1.01, 1.67), NICU admissions (adjRR 1.32, 95%CI 1.10, 1.59);COVID-19 severity was increasing these risks but exposures to COVID-19 medications reduced all risks. Conclusion(s): COVID-19 severity was higher in pregnancy. Antibacterials, psychoanaleptics, and medicines for obstructive airway disease were the most used overall. COVID-19 was associated with adverse outcomes for mothers and newborns.
ABSTRACT
Objectives: To describe and compare real-world outcomes for patients with mild-to-moderate COVID-19 at high risk for progression to severe COVID-19, treated with sotrovimab versus untreated. Method(s): Electronic health records from the National COVID Cohort Collaborative were used to identify US patients (aged >=12 years) diagnosed with COVID-19 (positive test or ICD-10: U07.1) in an ambulatory setting (26 May 2021-30 April 2022) who met Emergency Use Authorization high-risk criteria. Patients receiving the monoclonal antibody (mAb) sotrovimab within 10 days of diagnosis were assigned to the sotrovimab cohort with an index date on the day of infusion. Untreated patients (no evidence of early mAb treatment or prophylaxis mAb or oral antiviral treatment) were assigned to the untreated cohort with an imputed index date based on the time distribution between diagnosis and sotrovimab infusion for the sotrovimab cohort. The primary endpoint was hospitalization or death (both all-cause) within 29 days of index, reported as descriptive rates and adjusted (via inverse-probability-of-treatment weighting [IPTW]) odds ratios (OR) and 95% confidence intervals (CI). Result(s): Of nearly 2.9 million patients diagnosed with COVID-19 during the analysis time period, 4,992 met the criteria for the sotrovimab cohort and 541,325 were included in the untreated cohort. Patients in the sotrovimab cohort were older (60 versus 54 years), more likely to be male (40% versus 38%) and White (85% versus 75%), and met more EUA criteria (3 versus 2) versus the untreated cohort. The 29-day hospitalization or mortality rates were 3.5% (176/4,992) and 4.5% (24,163/541,325) in the sotrovimab and untreated cohorts respectively (unadjusted OR [95% CI]: 0.77 [0.67,0.90];p=0.001;IPTW-adjusted OR [95% CI]: 0.74 [0.61,0.91];p=0.004). Conclusion(s): Sotrovimab demonstrated clinical effectiveness in preventing severe outcomes (hospitalization, mortality) between 26 May 2021-30 April 2022, which included the Delta variant and early surge of Omicron BA.1/BA.2. Funding(s): GSK (Study 219020)Copyright © 2023