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Background Post-COVID conditions encompass a range of long-term symptoms after SARS-CoV-2 infection. The potential clinical and economic burden in the United States is unclear. We evaluated diagnoses, medications, healthcare use, and medical costs before and after acute COVID-19 illness in US patients at high risk of severe COVID-19. Methods Eligible adults were diagnosed with COVID-19 from April 1 to May 31, 2020, had ≥ 1 condition placing them at risk of severe COVID-19, and were enrolled in Optum’s de-identified Clinformatics ® Data Mart Database for ≥ 12 months before and ≥ 13 months after COVID-19 diagnosis. Percentages of diagnoses, medications, resource use, and costs were calculated during baseline (12 months preceding diagnosis) and the post-acute phase (12 months after the 30-day acute phase of COVID-19). Data were stratified by age and COVID-19 severity. Results The cohort included 19,558 patients (aged 18–64 y, n  = 9381; aged ≥ 65 y, n  = 10,177). Compared with baseline, patients during the post-acute phase had increased percentages of blood disorders (16.3%), nervous system disorders (11.1%), and mental and behavioral disorders (7.7%), along with increases in related prescriptions. Overall, there were substantial increases in inpatient and outpatient healthcare utilization, along with a 23.0% increase in medical costs. Changes were greatest among older patients and those admitted to the intensive care unit for acute COVID-19 but were also observed in younger patients and those who did not require COVID-19 hospitalization. Conclusions There is a significant clinical and economic burden of post-COVID conditions among US individuals at high risk for severe COVID-19.

The COVID-19 pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has substantially impacted healthcare utilization worldwide. The objective of this retrospective analysis of a large hospital discharge database was to compare all-cause and cause-specific hospitalizations during the first six months of the pandemic in the United States with the same months in the previous four years. Data were collected from all hospitals in the Premier Healthcare Database (PHD) and PHD Special Release reporting hospitalizations from January through July for each year from 2016 through 2020. Hospitalization trends were analyzed stratified by age group, major diagnostic categories (MDCs), and geographic region. The analysis included 286 hospitals from all 9 US Census divisions. The number of all-cause hospitalizations per month was relatively stable from 2016 through 2019 and then fell by 21% (57,281 fewer hospitalizations) between March and April 2020, particularly in hospitalizations for non-respiratory illnesses. From April onward there was a rise in the number of monthly hospitalizations per month. Hospitalizations per month, nationally and in each Census division, decreased for 20 of 25 MDCs between March and April 2020. There was also a decrease in hospitalizations per month for all age groups between March and April 2020 with the greatest decreases in hospitalizations observed for patients 50-64 and ≥65 years of age. Rates of hospitalization declined substantially during the first months of the COVID-19 pandemic, suggesting delayed routine, elective, and emergency care in the United States. These lapses in care for illnesses not related to COVID-19 may lead to increases in morbidity and mortality for other conditions. Thus, in the current stage of the pandemic, clinicians and public-health officials should work, not only to prevent SARS-CoV-2 transmission, but also to ensure that care for non-COVID-19 conditions is not delayed.

Background Patients recovering from SARS-CoV-2 infection and acute COVID-19 illness can experience a range of long-term post-acute effects. The potential clinical and economic burden of these outcomes in the USA is unclear. We evaluated diagnoses, medications, healthcare utilization, and medical costs before and after acute COVID-19 illness in US patients who were not at high risk of severe COVID-19. Methods This study included eligible adults who were diagnosed with COVID-19 from April 1 to May 31, 2020, who were 18 − 64 years of age, and enrolled within Optum’s de-identified Clinformatics® Data Mart Database for 12 months before and 13 months after COVID-19 diagnosis. Patients with any condition or risk factor placing them at high risk of progression to severe COVID-19 were excluded. Percentages of diagnoses, medications, healthcare utilization, and costs were calculated during baseline (12 months preceding diagnosis) and the post-acute phase (12 months after the 30-day acute phase of COVID-19). Data were stratified into 3 cohorts according to disposition during acute COVID-19 illness (i.e., not hospitalized, hospitalized without intensive care unit [ICU] admission, or admitted to the ICU). Results The study included 3792 patients; 56.5% of patients were men, 44% were White, and 94% did not require hospitalization. Compared with baseline, patients during the post-acute phase had percentage increases in the diagnosis of the following disorders: blood (166%), endocrine and metabolic (123%), nervous system (115%), digestive system (76%), and mental and behavioral (75%), along with increases in related prescriptions. Substantial increases in all measures of healthcare utilization were observed among all 3 cohorts. Total medical costs increased by 178% during the post-acute phase. Those who were hospitalized with or without ICU admission during the acute phase had the greatest increases in comorbidities and healthcare resource utilization. However, the burden was apparent across all cohorts. Conclusions As evidenced by resource use in the post-acute phase, COVID-19 places a significant long-term clinical and economic burden among US individuals, even among patients whose acute infection did not merit hospitalization.

Alopecia areata (AA) is an autoimmune disease characterized by the development of non-scarring alopecia. The prevalence is not well known, and estimates vary considerably with no recent estimates in the United States (US). The objective of this study was to define the current AA point prevalence estimate among the general population in the US overall and by severity. We administered an online, cross-sectional survey to a representative sample of the US population. Participants self-screening as positive for AA using the Alopecia Assessment Tool (ALTO) also completed the Severity of Alopecia Tool (SALT) to measure the severity of disease as a percent of scalp hair loss. Self-reported AA participants were invited to upload photographs for adjudication of AA by 3 clinicians. The average age of participants was 43 years. Approximately half of the participants (49.2%) were male, and the majority were white (77.1%) and not of Hispanic origin (93.2%). Among the 511 self-reported AA participants, 104 (20.4%) uploaded photographs for clinician evaluation. Clinician-adjudicated point prevalence of AA was 0.21% (95% CI: 0.17%, 0.25%) overall, 0.12% (95% CI: 0.09%, 0.15%) for \"mild\" disease (≤50% SALT score), and 0.09% (95% CI: 0.06%, 0.11%) for \"moderate to severe\" disease (>50% SALT score) with 0.04% (95% CI: 0.02%, 0.06%) for the alopecia totalis/alopecia universalis (100% SALT score) \"moderate to severe\" subgroup. The average SALT score was 44.4% overall, 8.8% for \"mild\", and 93.4% for \"moderate to severe\". This study suggests that the current AA prevalence in the US is similar to the upper estimates from the 1970s at approximately 0.21% (700,000 persons) with the current prevalence of \"moderate to severe\" disease at approximately 0.09% (300,000 persons). Given this prevalence and the substantial impact of AA on quality of life, the burden of AA within the US is considerable.

ObjectiveTo characterise subphenotypes of self-reported symptoms and outcomes (SRSOs) in postacute sequelae of COVID-19 (PASC).DesignProspective, observational cohort study of subjects with PASC.SettingAcademic tertiary centre from five clinical referral sources.ParticipantsAdults with COVID-19 ≥20 days before enrolment and presence of any new self-reported symptoms following COVID-19.ExposuresWe collected data on clinical variables and SRSOs via structured telephone interviews and performed standardised assessments with validated clinical numerical scales to capture psychological symptoms, neurocognitive functioning and cardiopulmonary function. We collected saliva and stool samples for quantification of SARS-CoV-2 RNA via quantitative PCR.Outcomes measuresDescription of PASC SRSOs burden and duration, derivation of distinct PASC subphenotypes via latent class analysis (LCA) and relationship with viral load.ResultsWe analysed baseline data for 214 individuals with a study visit at a median of 197.5 days after COVID-19 diagnosis. Participants reported ever having a median of 9/16 symptoms (IQR 6–11) after acute COVID-19, with muscle-aches, dyspnoea and headache being the most common. Fatigue, cognitive impairment and dyspnoea were experienced for a longer time. Participants had a lower burden of active symptoms (median 3 (1–6)) than those ever experienced (p<0.001). Unsupervised LCA of symptoms revealed three clinically active PASC subphenotypes: a high burden constitutional symptoms (21.9%), a persistent loss/change of smell and taste (20.6%) and a minimal residual symptoms subphenotype (57.5%). Subphenotype assignments were strongly associated with self-assessments of global health, recovery and PASC impact on employment (p<0.001) as well as referral source for enrolment. Viral persistence (5.6% saliva and 1% stool samples positive) did not explain SRSOs or subphenotypes.ConclusionsWe identified three distinct PASC subphenotypes. We highlight that although most symptoms progressively resolve, specific PASC subpopulations are impacted by either high burden of constitutional symptoms or persistent olfactory/gustatory dysfunction, requiring prospective identification and targeted preventive or therapeutic interventions.

Background Invasive mucormycosis (IM) is a rare and often life-threatening fungal infection, for which clinical and epidemiological understanding is lacking. Electronic health record (EHR) data can be utilized to elucidate large populations of patients with IM to address this unmet need. This study aimed to descriptively assess data on patients with IM using the Optum® EHR dataset. Methods US patient data from the Optum® deidentified EHR dataset (2007–2019) were analyzed to identify patients with IM. Patients with hematologic malignancies (HM), at high risk of IM, were selected and sorted by IM diagnosis (ICD9 117.7; ICD10 B46). Demographics, comorbidities/other diagnoses, and treatments were analyzed in patients with IM. Results In total, 1133 patients with HM and IM were identified. Most were between 40 and 64 years of age, Caucasian, and from the Midwest. Essential primary hypertension (50.31%) was the most common comorbidity. Of the 1133 patients, only 33.72% were prescribed an antifungal treatment. The most common antifungal treatments were fluconazole (24.27%) and posaconazole (16.33%), which may have been prophylactic, and any AmB (15.62%). Conclusions A large population of patients with IM were identified, highlighting the potential of analyzing EHR data to investigate epidemiology, diagnosis, and the treatment of apparently rare diseases.

Background and Objective Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia results in substantial morbidity and mortality. As current treatments often lead to unsatisfactory outcomes, evidence guiding alternative treatment options is needed. This study evaluated real-world clinical outcomes of ceftaroline fosamil for the treatment of MRSA bacteremia. Methods This retrospective study included adults hospitalized with MRSA bacteremia between 2011 and 2019. Patients were classified according to treatment with ceftaroline fosamil (ceftaroline), vancomycin, or daptomycin: Group 1, ceftaroline; Group 2, vancomycin or daptomycin (without ceftaroline); Group 3, combination therapy with ≥ 2 of these three agents. Clinical outcomes were compared using propensity-score-adjusted odds ratios (ORs) from logistic regression models. Results Overall, 24,479 patients were included (Group 1, n  = 532; Group 2, n  = 21,555; Group 3, n  = 2392). Mean age was 59.6, 60.8, and 57.4 years in Groups 1, 2, and 3, respectively. Mean post-index treatment length of stay was 8.8, 8.8, and 8.0 days, respectively. The most frequent line of therapy was ceftaroline first-line (42.1%), vancomycin or daptomycin first-line (95.4%), and combination therapy third-line or later (67.8%) in Groups 1, 2, and 3, respectively. Compared with Group 2, Groups 1 and 3 had similar favorable clinical responses {odds ratio [OR] = 1.18 [95% confidence interval (CI) 0.98–1.44], p  = 0.08; OR = 1.20 [95% CI 0.97–1.47], p  = 0.09, respectively} and were less likely to switch treatment (both p  < 0.001). Compared with Group 2, Group 1 was more likely to undergo 30-day all-cause readmission [OR = 1.38 (95% CI 1.06–1.80), p  = 0.02], whereas this was less likely for Group 3 [OR = 0.77 (95% CI 0.58–1.00), p  = 0.05]. Conclusions Patients receiving ceftaroline more often had favorable clinical responses than those receiving vancomycin or daptomycin monotherapy. In the absence of large-scale randomized controlled trials, these real-world data provide insights into the potential role of ceftaroline for treating MRSA bacteremia.

Diagnosis and treatment of invasive mold infections (IMI) can be challenging and IMI is a significant source of morbidity and mortality. Invasive aspergillosis (IA) and invasive mucormycosis (IM) are two of the most common mold infections. A better understanding of patient comorbidities and risk factors that predispose IMI may help clinicians to refine the difficult diagnostic and treatment process. A systematic literature review (SLR) was conducted (January 2008-October 2019) for studies reporting comorbidities/risk factors of patients with IA or IM (Phase I), followed by an analysis on the Optum US EHR database of prominent risk factor cohorts based on SLR findings and expert opinion (Phase II). From the four identified patient cohorts: 1) patients undergoing solid organ transplant (SOT) and patients with 2) hematologic cancers, 3) diabetes, or 4) lung disease, rates of IA, IM, or concurrent IA and IM; patient comorbidities; and Charlson Comorbidity Index (CCI) scores were reported. The SLR included 88 studies, and 46 were used to select comorbidities/risk factors cohorts in IA and IM patients. The most important comorbidities/risk factors in IA and IM patients were diabetes, lung disease, hematological malignances, and SOT. In the Optum database (N=101,340,454 patients), IA rates were highest in lung transplant (10.81%) patients and IM rates were highest in intestine transplant (0.83%) patients, lung transplant (0.43%), and hematopoietic stem cell transplant (0.49%). CCI scores were elevated in all mold infection groups compared to the total Optum cohort. The current study describes patient comorbidity and risk factors associated with IA and IM. These data can be used to refine clinical decision-making regarding when to suspect mold infections. Future research should focus on identifying whether patients respond differently to various antifungal treatments to determine if strategic recommendations should be made for certain patient groups.

Importance A newInternational Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)diagnosis code (U09.9 Post COVID-19 condition, unspecified) was introduced by the Centers for Disease Control and Prevention on October 1, 2021. Objective To examine the use of the U09.9 code and describe concurrently diagnosed conditions to understand physician use of this code in clinical practice. Design, Setting, and Participants This cohort study of US patients with anICD-10-CMcode for post–COVID-19 condition used deidentified patient-level claims data aggregated by HealthVerity. Children and adolescents (aged 0-17 years) and adults (aged 18-64 and ≥65 years) with a post–COVID-19 condition code were identified between October 1, 2021, and January 31, 2022. To identify a prior COVID-19 diagnosis, 3 months of continuous enrollment (CE) before the post–COVID-19 diagnosis date was required. Main Outcomes and Measures Presence of theICD-10-CMU09.9 code. Results There were 56 143 patients (7723 female patients [61.2%]; mean [SD] age, 47.6 [19.2] years) with a post–COVID-19 diagnosis code, with cases increasing in mid-December 2021 following the trajectory of the Omicron case wave by 3 to 4 weeks. The analysis cohort included 12 622 patients after the 3-month preindex CE criteria was applied. Among this cohort, the median (IQR) age was 49 (35-61) years; however, 1080 (8.6%) were pediatric patients. The U09.9 code was used most often in the outpatient setting, although 305 older adults (14.0%) were inpatients. Only 698 patients (5.5%) had at least 1 of the 5 codes listed as possible concurrent conditions in the coding guidance. Only 8879 patients (70.4%) had a documented acute COVID-19 diagnosis code (569 [52.7%] among children), and the median (IQR) time between acute COVID-19 and post–COVID-19 diagnosis codes was 56 (21-200) days. The most common concurrently coded conditions varied by age; children experienced COVID-19–like symptoms (eg, 207 [19.2%] had cough and 115 [10.6%] had breathing abnormalities), while 459 older adults aged 65 years or older (21.1%) experienced respiratory failure and 189 (8.7%) experienced viral pneumonia. Conclusions and Relevance This retrospective cohort study found patients with a post–COVID-19ICD-10-CMdiagnosis code following the acute phase of COVID-19 disease among patients of all ages in clinical practice in the US. The use of the U09.9 code encompassed a wide range of conditions. It will be important to monitor how the use of this code changes as the pandemic continues to evolve.