Incidence and Outcomes of Cytomegalovirus Reactivation after Chimeric Antigen Receptor T Cell Therapy.

Cytomegalovirus (CMV) reactivation is a major complication among seropositive allogeneic hematopoietic cell transplantation (HCT) recipients; however, data regarding CMV reactivation after chimeric antigen receptor T (CAR T) cell therapy are limited. In this single-center retrospective study, we report the incidence and outcomes of 95 adult CMV seropositive patients who received CAR T cell therapy between February 2018 and February 2023. CMV outcomes were CMV reactivation (any viremia) and clinically significant CMV infection (cs-CMV, viremia requiring antiviral treatment). Thirty-one patients (33%) had evidence of CMV reactivation (any viremia), and 10 patients (11%) had cs-CMV. The median time from CAR T cell infusion to CMV reactivation was 19 days (IQR, 9-31). The cumulative incidence of CMV (any viremia) was significantly higher among patients with grade 3-4 cytokine release syndrome (67 vs. 28%; P=0.01), and those who received corticosteroids (39 vs. 21%; P=0.03), anakinra (56 vs. 28%; P=0.02), or ≥2 immunosuppressants (41 vs. 21%; P=0.02). Receipt of corticosteroids (18 vs. 0%; P=0.004), tocilizumab (14 vs. 0%; P=0.04), anakinra (33 vs. 7%; P=0.008), and ≥2 immunosuppressants (20 vs. 0%; P=0.001) were all associated with cs-CMV. Receiving ≥2 immunosuppressants was associated with a 2-fold increase in CMV reactivation in multivariate analyses (aOR 2.27, 95%CI 1.1-4.8, P=0.03). Overall, 1-year mortality was significantly higher in those with CMV reactivation (57% vs. 23%, P=0.001). Immunosuppression, particularly corticosteroids, for the management of CAR T cell toxicities is a major risk factor for CMV reactivation. CMV preventive strategies in high-risk CAR T recipients might improve outcomes.

Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion.

Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm. Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code. Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code). Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients. The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID's genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.

Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study.

BACKGROUND: Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear. OBJECTIVE: This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions. METHODS: Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic. RESULTS: Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems. CONCLUSIONS: This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

Wait Time Advantage for Transplant Candidates With HIV Who Accept Kidneys From Donors With HIV Under the HOPE Act.

BACKGROUND: Kidney transplant (KT) candidates with HIV face higher mortality on the waitlist compared with candidates without HIV. Because the HIV Organ Policy Equity (HOPE) Act has expanded the donor pool to allow donors with HIV (D + ), it is crucial to understand whether this has impacted transplant rates for this population. METHODS: Using a linkage between the HOPE in Action trial (NCT03500315) and Scientific Registry of Transplant Recipients, we identified 324 candidates listed for D + kidneys (HOPE) compared with 46 025 candidates not listed for D + kidneys (non-HOPE) at the same centers between April 26, 2018, and May 24, 2022. We characterized KT rate, KT type (D + , false-positive [FP; donor with false-positive HIV testing], D - [donor without HIV], living donor [LD]) and quantified the association between HOPE enrollment and KT rate using multivariable Cox regression with center-level clustering; HOPE was a time-varying exposure. RESULTS: HOPE candidates were more likely male individuals (79% versus 62%), Black (73% versus 35%), and publicly insured (71% versus 52%; P < 0.001). Within 4.5 y, 70% of HOPE candidates received a KT (41% D + , 34% D - , 20% FP, 4% LD) versus 43% of non-HOPE candidates (74% D - , 26% LD). Conversely, 22% of HOPE candidates versus 39% of non-HOPE candidates died or were removed from the waitlist. Median KT wait time was 10.3 mo for HOPE versus 60.8 mo for non-HOPE candidates ( P < 0.001). After adjustment, HOPE candidates had a 3.30-fold higher KT rate (adjusted hazard ratio = 3.30, 95% confidence interval, 2.14-5.10; P < 0.001). CONCLUSIONS: Listing for D + kidneys within HOPE trials was associated with a higher KT rate and shorter wait time, supporting the expansion of this practice for candidates with HIV.

Sharing and Reusing Computable Phenotype Definitions.

Background: A scalable approach for the sharing and reuse of human-readable and computer-executable phenotype definitions can facilitate the reuse of electronic health records for cohort identification and research studies. Description: We developed a tool called Sharephe for the Informatics for Integrating Biology and the Bedside (i2b2) platform. Sharephe consists of a plugin for i2b2 and a cloud-based searchable repository of computable phenotypes, has the functionality to import to and export from the repository, and has the ability to link to supporting metadata. Discussion: The i2b2 platform enables researchers to create, evaluate, and implement phenotypes without knowing complex query languages. In an initial evaluation, two sites on the Evolve to Next-Gen ACT (ENACT) network used Sharephe to successfully create, share, and reuse phenotypes. Conclusion: The combination of a cloud-based computable repository and an i2b2 plugin for accessing the repository enables investigators to store and retrieve phenotypes from anywhere and at any time and to collaborate across sites in a research network.

Breakthrough invasive fungal infections on isavuconazole prophylaxis in hematologic malignancy & hematopoietic stem cell transplant patients.

BACKGROUND: Isavuconazole (ISA) is a newer antifungal used in patients with history of hematologic malignancies and hematopoietic transplant and cellular therapies (HM/TCT). Although it has a more favorable side-effect profile, breakthrough invasive fungal infections (bIFIs) while on ISA have been reported. METHODS: In this single-center retrospective study evaluating HM/TCT patients who received prophylactic ISA for ≥7 days, we evaluated the incidence and potential risk factors for bIFIs. RESULTS: We evaluated 106 patients who received prophylactic ISA. The patients were predominantly male (60.4%) with median age of 65 (range: 21-91) years. Acute myeloid leukemia (48/106, 45.3%) was the most common HM, with majority having relapsed and/or refractory disease (43/106, 40.6%) or receiving ongoing therapy (38/106, 35.8%). Nineteen patients (17.9%) developed bIFIs-nine proven [Fusarium (3), Candida (2), Mucorales plus Aspergillus (2), Mucorales (1), Colletotrichum (1)], four probable invasive pulmonary Aspergillus, and six possible infections. Twelve patients were neutropenic for a median of 28 (8-253) days prior to bIFI diagnosis. ISA levels checked within 7 days of bIFI diagnosis (median: 3.65 µg/mL) were comparable to industry-sponsored clinical trials. All-cause mortality among the bIFI cases was 47.4% (9/19).We also noted clinically significant cytomegalovirus co-infection in 5.3% (1/19). On univariate analysis, there were no significant differences in baseline comorbidities and potential risk factors between the two groups. CONCLUSION: ISA prophylaxis was associated with a significant cumulative incidence of bIFIs. Despite the appealing side-effect and drug-interaction profile of ISA, clinicians must be vigilant about the potential risk for bIFIs.

Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study.

Background: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. Methods: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. Findings: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. Interpretation: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. Funding: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.

Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium.

Background: Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infection. It remains unclear how MIS-C phenotypes vary across SARS-CoV-2 variants. We aimed to investigate clinical characteristics and outcomes of MIS-C across SARS-CoV-2 eras. Methods: We performed a multicentre observational retrospective study including seven paediatric hospitals in four countries (France, Spain, U.K., and U.S.). All consecutive confirmed patients with MIS-C hospitalised between February 1st, 2020, and May 31st, 2022, were included. Electronic Health Records (EHR) data were used to calculate pooled risk differences (RD) and effect sizes (ES) at site level, using Alpha as reference. Meta-analysis was used to pool data across sites. Findings: Of 598 patients with MIS-C (61% male, 39% female; mean age 9.7 years [SD 4.5]), 383 (64%) were admitted in the Alpha era, 111 (19%) in the Delta era, and 104 (17%) in the Omicron era. Compared with patients admitted in the Alpha era, those admitted in the Delta era were younger (ES -1.18 years [95% CI -2.05, -0.32]), had fewer respiratory symptoms (RD -0.15 [95% CI -0.33, -0.04]), less frequent non-cardiogenic shock or systemic inflammatory response syndrome (SIRS) (RD -0.35 [95% CI -0.64, -0.07]), lower lymphocyte count (ES -0.16 × 109/uL [95% CI -0.30, -0.01]), lower C-reactive protein (ES -28.5 mg/L [95% CI -46.3, -10.7]), and lower troponin (ES -0.14 ng/mL [95% CI -0.26, -0.03]). Patients admitted in the Omicron versus Alpha eras were younger (ES -1.6 years [95% CI -2.5, -0.8]), had less frequent SIRS (RD -0.18 [95% CI -0.30, -0.05]), lower lymphocyte count (ES -0.39 × 109/uL [95% CI -0.52, -0.25]), lower troponin (ES -0.16 ng/mL [95% CI -0.30, -0.01]) and less frequently received anticoagulation therapy (RD -0.19 [95% CI -0.37, -0.04]). Length of hospitalization was shorter in the Delta versus Alpha eras (-1.3 days [95% CI -2.3, -0.4]). Interpretation: Our study suggested that MIS-C clinical phenotypes varied across SARS-CoV-2 eras, with patients in Delta and Omicron eras being younger and less sick. EHR data can be effectively leveraged to identify rare complications of pandemic diseases and their variation over time. Funding: None.

A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation.

OBJECTIVE: Patients who receive most care within a single healthcare system (colloquially called a "loyalty cohort" since they typically return to the same providers) have mostly complete data within that organization's electronic health record (EHR). Loyalty cohorts have low data missingness, which can unintentionally bias research results. Using proxies of routine care and healthcare utilization metrics, we compute a per-patient score that identifies a loyalty cohort. MATERIALS AND METHODS: We implemented a computable program for the widely adopted i2b2 platform that identifies loyalty cohorts in EHRs based on a machine-learning model, which was previously validated using linked claims data. We developed a novel validation approach, which tests, using only EHR data, whether patients returned to the same healthcare system after the training period. We evaluated these tools at 3 institutions using data from 2017 to 2019. RESULTS: Loyalty cohort calculations to identify patients who returned during a 1-year follow-up yielded a mean area under the receiver operating characteristic curve of 0.77 using the original model and 0.80 after calibrating the model at individual sites. Factors such as multiple medications or visits contributed significantly at all sites. Screening tests' contributions (eg, colonoscopy) varied across sites, likely due to coding and population differences. DISCUSSION: This open-source implementation of a "loyalty score" algorithm had good predictive power. Enriching research cohorts by utilizing these low-missingness patients is a way to obtain the data completeness necessary for accurate causal analysis. CONCLUSION: i2b2 sites can use this approach to select cohorts with mostly complete EHR data.

Clinical Algorithms with Race: An Online Database.

Some clinical algorithms incorporate a person's race, ethnicity, or both as an input variable or predictor in determining diagnoses, prognoses, treatment plans, or risk assessments. Inappropriate use of race and ethnicity in clinical algorithms at the point of care may exacerbate health disparities and promote harmful practices of race-based medicine. This article describes a comprehensive search of online resources, the scientific literature, and the FDA Drug Label Information that uncovered 39 race-based risk calculators, six laboratory test results with race-based reference ranges, one race-based therapy recommendation, and 15 medications with race-based recommendations. These clinical algorithms based on race are freely accessible through an online database. This resource aims to raise awareness about the use of race-based clinical algorithms and track the progress made toward eradicating the inappropriate use of race. The database will be actively updated to include clinical algorithms based on race that were previously omitted, along with additional characteristics of these algorithms.

A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework.

Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients.

A Retrospective, Observational Study of 12 Cases of Expanded-Access Customized Phage Therapy: Production, Characteristics, and Clinical Outcomes.

BACKGROUND: Antimicrobial resistance (AMR) is undermining modern medicine, a problem compounded by bacterial adaptation to antibiotic pressures. Phages are viruses that infect bacteria. Their diversity and evolvability offer the prospect of their use as a therapeutic solution. Reported are outcomes of customized phage therapy for patients with difficult-to-treat antimicrobial resistant infections. METHODS: We retrospectively assessed 12 cases of customized phage therapy from a phage production center. Phages were screened, purified, sequenced, characterized, and Food and Drug Administration-approved via the IND (investigational new drug) compassionate-care route. Outcomes were assessed as favorable or unfavorable by microbiologic and clinical standards. Infections were device-related or systemic. Other experiences such as time to treatment, antibiotic synergy, and immune responses were recorded. RESULTS: Fifty requests for phage therapy were received. Customized phages were generated for 12 patients. After treatment, 42% (5/12) of cases showed bacterial eradication and 58% (7/12) showed clinical improvement, with two-thirds of all cases (66%) showing favorable responses. No major adverse reactions were observed. Antibiotic-phage synergy in vitro was observed in most cases. Immunological neutralization of phages was reported in 5 cases. Several cases were complicated by secondary infections. Complete characterization of the phages (morphology, genomics, and activity) and their production (methods, sterility, and endotoxin tests) are reported. CONCLUSIONS: Customized phage production and therapy was safe and yielded favorable clinical or microbiological outcomes in two-thirds of cases. A center or pipeline dedicated to tailoring the phages against a patient's specific AMR bacterial infection may be a viable option where standard treatment has failed.

Successful restoration of protective immunity against measles, mumps, and rubella following MMR vaccination in adult hematopoietic cell transplant recipients.

BACKGROUND: The optimal number of doses as well as the role for measurement of postvaccination titers after measles, mumps, rubella (MMR) vaccination in adult hematopoietic cell transplantation (HCT) recipients remains unknown. METHODS: In the present study, we assessed humoral immunity against measles, mumps and rubella before and after MMR vaccination in 187 adults who received at least one dose of the MMR vaccine after HCT. RESULTS: Among those with baseline titers, posttransplant prevaccination seroprotection rates were 56%, 30%, and 54% for measles, mumps, and rubella, respectively; and significantly lower in allogeneic versus autologous HCT recipients for measles (39% vs. 80%, p = .0001), mumps (22% vs. 41%; p = .02) and rubella (48% vs. 62%, p = .12). Among those who were seronegative at baseline, seroconversion rates after one dose of MMR were 69%, 56%, and 97% for measles, mumps, and rubella, respectively. Seronegative patients after one dose of MMR (i.e., nonresponders) seroconverted for measles and mumps after a second MMR vaccine dose. CONCLUSION: Our findings demonstrate successful restoration of protective immunity against measles, mumps, and rubella after vaccination in adult HCT recipients; one dose of MMR elicited protective titers in the majority of patients, and a second vaccine dose was immunogenic in nonresponders.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

Early administration of SARS-CoV-2 monoclonal antibody reduces the risk of mortality in hematologic malignancy and hematopoietic cell transplant patients with COVID-19.

BACKGROUND: Data on severe acute respiratory distress syndrome coronavirus 2 monoclonal antibody (SARS-CoV-2-specific mAb) use in hematologic malignancy and hematopoietic cell transplantation (HM/HCT) patients are limited. Here, we describe our experience with the use of casirivimab-imdevimab or bamlanivimab for the treatment of coronavirus disease 2019 (COVID-19) in HM/HCT patients. METHODS: This was a retrospective chart review at the University of Miami Hospital and Sylvester Comprehensive Cancer Center for HM/HCT patients with COVID-19 who received casirivimab-imdevimab or bamlanivimab from November 21, 2020, to September 30, 2021. Outcomes measured were mortality, hospital admission, and infusion reaction to SARS-CoV-2-specific mAbs. RESULTS: We identified 59 HM/HCT patients with mild to moderate COVID-19 who received casirivimab-imdevimab or bamlanivimab. Median age was 57 years (interquartile range [IQR]: 45-65). Among the 59 patients, 25 (42%) received cellular therapy: 14 (24%) had undergone allogeneic HCT, nine (15%) autologous HCT, and two (3%) received chimeric antigen receptor T-cell therapy. The median time from COVID-19 symptom onset to SARS-CoV-2-specific mAb administration was 4 (IQR: 3-6) days. Forty-six (78%) patients received SARS-CoV-2-specific mAbs as outpatients and 13 (22%) patients received SARS-CoV-2-specific mAbs during hospitalization. Among patients who received SARS-CoV-2-specific mAbs as outpatients, only four (9%) visited the emergency department at days 10, 11, 15, and 35 after SARS-CoV-2-specific mAb administration. None of these four patients required hospital admission. Among the hospitalized patients, five (38%) were admitted to the hospital with neutropenic fever, four (31%) were already hospitalized for transplantation and cellular therapy, three (23%) were admitted for monitoring of COVID-19 symptoms, and one (8%) was admitted with acute kidney injury. Three hospitalized patients (23%) died at 14, 35, and 59 days after SARS-CoV-2-specific mAb administration; two of these three deaths were attributed to COVID-19 infection. One patient developed an immediate infusion reaction to bamlanivimab, and no infusion reactions were reported to casirivimab-imdevimab use. CONCLUSION: During the alpha and delta variant surges, early administration of bamlanivimab or casirivimab-imdevimab prevented hospitalization and death when given in the outpatient setting. Among patients who received mAbs at or after hospital admission, the risk of COVID-19 disease progression and death remains significant. Larger studies of the use of mAb therapy to treat COVID-19 in this population are needed.

Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium.

PURPOSE: In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population. METHODS: A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS. RESULTS: Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%). CONCLUSION: Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study.

Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53-3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI. Interpretation: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding: Authors are supported by various funders, with full details stated in the acknowledgement section.