Recovery Trajectories in Children Requiring 3 or More Days of Invasive Ventilation.

OBJECTIVES: To characterize health-related quality of life (HRQL) and functional recovery trajectories and risk factors for prolonged impairments among critically ill children receiving greater than or equal to 3 days of invasive ventilation. DESIGN: Prospective cohort study. SETTING: Quaternary children's hospital PICU. PATIENTS: Children without a preexisting tracheostomy who received greater than or equal to 3 days of invasive ventilation, survived hospitalization, and completed greater than or equal to 1 postdischarge data collection. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We evaluated 144 children measuring HRQL using proxy-report Pediatric Quality of Life Inventory and functional status using the Functional Status Scale (FSS) reflecting preillness baseline, PICU and hospital discharge, and 1, 3, 6, and 12 months after hospital discharge. They had a median age of 5.3 years (interquartile range, 1.1-13.0 yr), 58 (40%) were female, 45 (31%) had a complex chronic condition, and 110 (76%) had normal preillness FSS scores. Respiratory failure etiologies included lung disease ( n = 49; 34%), neurologic failure ( n = 23; 16%), and septic shock ( n = 22; 15%). At 1-month postdischarge, 68 of 122 (56%) reported worsened HRQL and 35 (29%) had a new functional impairment compared with preillness baseline. This improved at 3 months to 54 (46%) and 24 (20%), respectively, and remained stable through the remaining 9 months of follow-up. We used interaction forests to evaluate relative variable importance including pairwise interactions and found that therapy consultation within 3 days of intubation was associated with better HRQL recovery in older patients and those with better preillness physical HRQL. During the postdischarge year, 76 patients (53%) had an emergency department visit or hospitalization, and 62 (43%) newly received physical, occupational, or speech therapy. CONCLUSIONS: Impairments in HRQL and functional status as well as health resource use were common among children with acute respiratory failure. Early therapy consultation was a modifiable characteristic associated with shorter duration of worsened HRQL in older patients.

Association between monoclonal antibody therapy, vaccination, and longer-term symptom resolution after acute COVID-19.

Effective therapies for reducing post-acute sequelae of COVID-19 (PASC) symptoms are lacking. Evaluate the association between monoclonal antibody (mAb) treatment or COVID-19 vaccination with symptom recovery in COVID-19 participants. The longitudinal survey-based cohort study was conducted from April 2021 to January 2022 across a multihospital Colorado health system. Adults ≥18 years with a positive SARS-CoV-2 test were included. Primary exposures were mAb treatment and COVID-19 vaccination. The primary outcome was time to symptom resolution after SARS-CoV-2 positive test date. The secondary outcome was hospitalization within 28 days of a positive SARS-CoV-2 test. Analysis included 1612 participants, 539 mAb treated, and 486 with ≥2 vaccinations. Time to symptom resolution was similar between mAb treated versus untreated patients (adjusted hazard ratio (aHR): 0.90, 95% CI: 0.77-1.04). Time to symptom resolution was shorter for patients who received ≥2 vaccinations compared to those unvaccinated (aHR: 1.56, 95% CI: 1.31-1.88). 28-day hospitalization risk was lower for patients receiving mAb therapy (adjusted odds ratio [aOR]: 0.31, 95% CI: 0.19-0.50) and ≥2 vaccinations (aOR: 0.33, 95% CI: 0.20-0.55), compared with untreated or unvaccinated status. Analysis included 1612 participants, 539 mAb treated, and 486 with ≥2 vaccinations. Time to symptom resolution was similar between mAb treated versus untreated patients (adjusted hazard ratio (aHR): 0.90, 95% CI: 0.77-1.04). Time to symptom resolution was shorter for patients who received ≥2 vaccinations compared to those unvaccinated (aHR: 1.56, 95% CI: 1.31-1.88). 28-day hospitalization risk was lower for patients receiving mAb therapy (adjusted odds ratio [aOR]: 0.31, 95% CI: 0.19-0.50) and ≥2 vaccinations (aOR: 0.33, 95% CI: 0.20-0.55), compared with untreated or unvaccinated status. COVID-19 vaccination, but not mAb therapy, was associated with a shorter time to symptom resolution. Both were associated with lower 28-day hospitalization.

Real-world evaluation of early remdesivir in high-risk COVID-19 outpatients during Omicron including BQ.1/BQ.1.1/XBB.1.5.

BACKGROUND: A trial performed among unvaccinated, high-risk outpatients with COVID-19 during the delta period showed remdesivir reduced hospitalization. We used our real-world data platform to determine the effectiveness of remdesivir on reducing 28-day hospitalization among outpatients with mild-moderate COVID-19 during an Omicron period including BQ.1/BQ.1.1/XBB.1.5. METHODS: We did a propensity-matched, retrospective cohort study of non-hospitalized adults with SARS-CoV-2 infection between April 7, 2022, and February 7, 2023. Electronic healthcare record data from a large health system in Colorado were linked to statewide vaccination and mortality data. We included patients with a positive SARS-CoV-2 test or outpatient remdesivir administration. Exclusion criteria were other SARS-CoV-2 treatments or positive SARS-CoV-2 test more than seven days before remdesivir. The primary outcome was all-cause hospitalization up to day 28. Secondary outcomes included 28-day COVID-related hospitalization and 28-day all-cause mortality. RESULTS: Among 29,270 patients with SARS-CoV-2 infection, 1,252 remdesivir-treated patients were matched to 2,499 untreated patients. Remdesivir was associated with lower 28-day all-cause hospitalization (1.3% vs. 3.3%, adjusted hazard ratio (aHR) 0.39 [95% CI 0.23-0.67], p < 0.001) than no treatment. All-cause mortality at 28 days was numerically lower among remdesivir-treated patients (0.1% vs. 0.4%; aOR 0.32 [95% CI 0.03-1.40]). Similar benefit of RDV treatment on 28-day all-cause hospitalization was observed across Omicron periods, aOR (95% CI): BA.2/BA2.12.1 (0.77[0.19-2.41]), BA.4/5 (0.50[95% CI 0.50-1.01]), BQ.1/BQ.1.1/XBB.1.5 (0.21[95% CI 0.08-0.57]. CONCLUSION: Among outpatients with SARS-CoV-2 during recent Omicron surges, remdesivir was associated with lower hospitalization than no treatment, supporting current National Institutes of Health Guidelines.

Association of an In-Hospital Desirability of Outcomes Ranking Scale With Postdischarge Health-Related Quality of Life: A Secondary Analysis of the Life After Pediatric Sepsis Evaluation.

OBJECTIVES: To develop a desirability of outcome ranking (DOOR) scale for use in children with septic shock and determine its correlation with a decrease in 3-month postadmission health-related quality of life (HRQL) or death. DESIGN: Secondary analysis of the Life After Pediatric Sepsis Evaluation prospective study. SETTING: Twelve U.S. PICUs, 2013-2017. PATIENTS: Children (1 mo-18 yr) with septic shock. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We applied a 7-point pediatric critical care (PCC) DOOR scale: 7: death; 6: extracorporeal life support; 5: supported by life-sustaining therapies (continuous renal replacement therapy, vasoactive, or invasive ventilation); 4: hospitalized with or 3: without organ dysfunction; 2: discharged with or 1: without new morbidity to patients by assigning the highest applicable score on specific days post-PICU admission. We analyzed Spearman rank-order correlations (95% CIs) between proximal outcomes (PCC-DOOR scale on days 7, 14, and 21, ventilator-free days, cumulative 28-day Pediatric Logistic Organ Dysfunction-2 (PELOD-2) scores, and PICU-free days) and 3-month decrease in HRQL or death. HRQL was measured by Pediatric Quality of Life Inventory 4.0 or Functional Status II-R for patients with developmental delay. Patients who died were assigned the worst possible HRQL score. PCC-DOOR scores were applied to 385 patients, median age 6 years (interquartile range 2, 13) and 177 (46%) with a complex chronic condition(s). Three-month outcomes were available for 245 patients (64%) and 42 patients (17%) died. PCC-DOOR scale on days 7, 14, and 21 demonstrated fair correlation with the primary outcome (-0.42 [-0.52, -0.31], -0.47 [-0.56, -0.36], and -0.52 [-0.61, -0.42]), similar to the correlations for cumulative 28-day PELOD-2 scores (-0.51 [-0.59, -0.41]), ventilator-free days (0.43 [0.32, 0.53]), and PICU-free days (0.46 [0.35, 0.55]). CONCLUSIONS: The PCC-DOOR scale is a feasible, practical outcome for pediatric sepsis trials and demonstrates fair correlation with decrease in HRQL or death at 3 months.

Biomarker Assessment of a High-Risk, Data-Driven Pediatric Sepsis Phenotype Characterized by Persistent Hypoxemia, Encephalopathy, and Shock.

OBJECTIVES: Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. We sought to the determine reproducibility of the data-driven "persistent hypoxemia, encephalopathy, and shock" (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk strata. DESIGN: We retrained and validated a random forest classifier using organ dysfunction subscores in the 2012-2018 electronic health record (EHR) dataset used to derive the PHES phenotype. We used this classifier to assign phenotype membership in a test set consisting of prospectively (2003-2023) enrolled pediatric septic shock patients. We compared profiles of the PERSEVERE family of biomarkers among those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk strata. SETTING: Twenty-five PICUs across the United States. PATIENTS: EHR data from 15,246 critically ill patients with sepsis-associated MODS split into derivation and validation sets and 1,270 pediatric septic shock patients in the test set of whom 615 had complete biomarker data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The area under the receiver operator characteristic curve of the modified classifier to predict PHES phenotype membership was 0.91 (95% CI, 0.90-0.92) in the EHR validation set. In the test set, PHES phenotype membership was associated with both increased adjusted odds of complicated course (adjusted odds ratio [aOR] 4.1; 95% CI, 3.2-5.4) and 28-day mortality (aOR of 4.8; 95% CI, 3.11-7.25) after controlling for age, severity of illness, and immunocompromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and were more likely to be stratified as high risk based on PERSEVERE biomarkers predictive of death and persistent MODS. CONCLUSIONS: The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlapped with higher risk strata based on prospectively validated biomarker approaches.

International Consensus Criteria for Pediatric Sepsis and Septic Shock.

Importance: Sepsis is a leading cause of death among children worldwide. Current pediatric-specific criteria for sepsis were published in 2005 based on expert opinion. In 2016, the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) defined sepsis as life-threatening organ dysfunction caused by a dysregulated host response to infection, but it excluded children. Objective: To update and evaluate criteria for sepsis and septic shock in children. Evidence Review: The Society of Critical Care Medicine (SCCM) convened a task force of 35 pediatric experts in critical care, emergency medicine, infectious diseases, general pediatrics, nursing, public health, and neonatology from 6 continents. Using evidence from an international survey, systematic review and meta-analysis, and a new organ dysfunction score developed based on more than 3 million electronic health record encounters from 10 sites on 4 continents, a modified Delphi consensus process was employed to develop criteria. Findings: Based on survey data, most pediatric clinicians used sepsis to refer to infection with life-threatening organ dysfunction, which differed from prior pediatric sepsis criteria that used systemic inflammatory response syndrome (SIRS) criteria, which have poor predictive properties, and included the redundant term, severe sepsis. The SCCM task force recommends that sepsis in children be identified by a Phoenix Sepsis Score of at least 2 points in children with suspected infection, which indicates potentially life-threatening dysfunction of the respiratory, cardiovascular, coagulation, and/or neurological systems. Children with a Phoenix Sepsis Score of at least 2 points had in-hospital mortality of 7.1% in higher-resource settings and 28.5% in lower-resource settings, more than 8 times that of children with suspected infection not meeting these criteria. Mortality was higher in children who had organ dysfunction in at least 1 of 4-respiratory, cardiovascular, coagulation, and/or neurological-organ systems that was not the primary site of infection. Septic shock was defined as children with sepsis who had cardiovascular dysfunction, indicated by at least 1 cardiovascular point in the Phoenix Sepsis Score, which included severe hypotension for age, blood lactate exceeding 5 mmol/L, or need for vasoactive medication. Children with septic shock had an in-hospital mortality rate of 10.8% and 33.5% in higher- and lower-resource settings, respectively. Conclusions and Relevance: The Phoenix sepsis criteria for sepsis and septic shock in children were derived and validated by the international SCCM Pediatric Sepsis Definition Task Force using a large international database and survey, systematic review and meta-analysis, and modified Delphi consensus approach. A Phoenix Sepsis Score of at least 2 identified potentially life-threatening organ dysfunction in children younger than 18 years with infection, and its use has the potential to improve clinical care, epidemiological assessment, and research in pediatric sepsis and septic shock around the world.

Development and Validation of the Phoenix Criteria for Pediatric Sepsis and Septic Shock.

Importance: The Society of Critical Care Medicine Pediatric Sepsis Definition Task Force sought to develop and validate new clinical criteria for pediatric sepsis and septic shock using measures of organ dysfunction through a data-driven approach. Objective: To derive and validate novel criteria for pediatric sepsis and septic shock across differently resourced settings. Design, Setting, and Participants: Multicenter, international, retrospective cohort study in 10 health systems in the US, Colombia, Bangladesh, China, and Kenya, 3 of which were used as external validation sites. Data were collected from emergency and inpatient encounters for children (aged <18 years) from 2010 to 2019: 3 049 699 in the development (including derivation and internal validation) set and 581 317 in the external validation set. Exposure: Stacked regression models to predict mortality in children with suspected infection were derived and validated using the best-performing organ dysfunction subscores from 8 existing scores. The final model was then translated into an integer-based score used to establish binary criteria for sepsis and septic shock. Main Outcomes and Measures: The primary outcome for all analyses was in-hospital mortality. Model- and integer-based score performance measures included the area under the precision recall curve (AUPRC; primary) and area under the receiver operating characteristic curve (AUROC; secondary). For binary criteria, primary performance measures were positive predictive value and sensitivity. Results: Among the 172 984 children with suspected infection in the first 24 hours (development set; 1.2% mortality), a 4-organ-system model performed best. The integer version of that model, the Phoenix Sepsis Score, had AUPRCs of 0.23 to 0.38 (95% CI range, 0.20-0.39) and AUROCs of 0.71 to 0.92 (95% CI range, 0.70-0.92) to predict mortality in the validation sets. Using a Phoenix Sepsis Score of 2 points or higher in children with suspected infection as criteria for sepsis and sepsis plus 1 or more cardiovascular point as criteria for septic shock resulted in a higher positive predictive value and higher or similar sensitivity compared with the 2005 International Pediatric Sepsis Consensus Conference (IPSCC) criteria across differently resourced settings. Conclusions and Relevance: The novel Phoenix sepsis criteria, which were derived and validated using data from higher- and lower-resource settings, had improved performance for the diagnosis of pediatric sepsis and septic shock compared with the existing IPSCC criteria.

Open source and reproducible and inexpensive infrastructure for data challenges and education.

Data sharing is necessary to maximize the actionable knowledge generated from research data. Data challenges can encourage secondary analyses of datasets. Data challenges in biomedicine often rely on advanced cloud-based computing infrastructure and expensive industry partnerships. Examples include challenges that use Google Cloud virtual machines and the Sage Bionetworks Dream Challenges platform. Such robust infrastructures can be financially prohibitive for investigators without substantial resources. Given the potential to develop scientific and clinical knowledge and the NIH emphasis on data sharing and reuse, there is a need for inexpensive and computationally lightweight methods for data sharing and hosting data challenges. To fill that gap, we developed a workflow that allows for reproducible model training, testing, and evaluation. We leveraged public GitHub repositories, open-source computational languages, and Docker technology. In addition, we conducted a data challenge using the infrastructure we developed. In this manuscript, we report on the infrastructure, workflow, and data challenge results. The infrastructure and workflow are likely to be useful for data challenges and education.

phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and criteria.

Objectives: The publication of the Phoenix criteria for pediatric sepsis and septic shock initiates a new era in clinical care and research of pediatric sepsis. Tools to consistently and accurately apply the Phoenix criteria to electronic health records (EHRs) is one part of building a robust and internally consistent body of research across multiple research groups and datasets. Materials and Methods: We developed the phoenix R package and Python module to provide researchers with intuitive and simple functions to apply the Phoenix criteria to EHR data. Results: The phoenix R package and Python module enable researchers to apply the Phoenix criteria to EHR datasets and derive the relevant indicators, total scores, and sub-scores. Discussion: The transition to the Phoenix criteria marks a major change in the conceptual definition of pediatric sepsis. Applicable across differentially resourced settings, the Phoenix criteria should help improve clinical care and research. Conclusion: The phoenix R package and Python model are freely available on CRAN, PyPi, and GitHub. These tools enable the consistent and accurate application of the Phoenix criteria to EHR datasets.

Intracranial pressure-flow relationships in traumatic brain injury patients expose gaps in the tenets of models and pressure-oriented management.

Background: The protocols and therapeutic guidance established for treating traumatic brain injuries (TBI) in neurointensive care focus on managing cerebral blood flow (CBF) and brain tissue oxygenation based on pressure signals. The decision support process relies on assumed relationships between cerebral perfusion pressure (CPP) and blood flow, pressure-flow relationships (PFRs), and shares this framework of assumptions with mathematical intracranial hemodynamic models. These foundational assumptions are difficult to verify, and their violation can impact clinical decision-making and model validity. Method: A hypothesis- and model-driven method for verifying and understanding the foundational intracranial hemodynamic PFRs is developed and applied to a novel multi-modality monitoring dataset. Results: Model analysis of joint observations of CPP and CBF validates the standard PFR when autoregulatory processes are impaired as well as unmodelable cases dominated by autoregulation. However, it also identifies a dynamical regime -or behavior pattern- where the PFR assumptions are wrong in a precise, data-inferable way due to negative CPP-CBF coordination over long timescales. This regime is of both clinical and research interest: its dynamics are modelable under modified assumptions while its causal direction and mechanistic pathway remain unclear. Conclusions: Motivated by the understanding of mathematical physiology, the validity of the standard PFR can be assessed a) directly by analyzing pressure reactivity and mean flow indices (PRx and Mx) or b) indirectly through the relationship between CBF and other clinical observables. This approach could potentially help personalize TBI care by considering intracranial pressure and CPP in relation to other data, particularly CBF. The analysis suggests a threshold using clinical indices of autoregulation jointly generalizes independently set indicators to assess CA functionality. These results support the use of increasingly data-rich environments to develop more robust hybrid physiological-machine learning models.

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research.

Refined management of mechanically ventilation is an obvious target for improving patient outcomes, but is impeded by the nature of data for study and hypothesis generation. The connections between clinical outcomes and temporal development of iatrogenic injuries current lung-protective ventilator settings remain poorly understood. Analysis of lung-ventilator system (LVS) evolution at relevant timescales is frustrated by data volume and multiple sources of heterogeneity. This work motivates, presents, and validates a computational pipeline for resolving LVS systems into the joint evolution of data-conditioned model parameters and ventilator information. Applied to individuals, the workflow yields a concise low-dimensional representation of LVS behavior expressed in phenotypic breath waveforms suitable for analysis. The effectiveness of this approach is demonstrated through application to multi-day observational series of 35 patients. Individual patient analyses reveal multiple types of patient-oriented dynamics and breath behavior to expose the complexity of LVS evolution; less than 10% of phenotype changes related to ventilator settings changes. Dynamics are shown to including both stable and unstable phenotype transitions as well as both discrete and continuous changes unrelated to ventilator settings. At a cohort scale, 721 phenotypes constructed from individual data are condensed into a set of 16 groups that empirically organize around certain settings (positive end-expository pressure and ventilator mode) and structurally similar pressure-volume loop characterizations. Individual and cohort scale phenotypes, which may be refined by hypothesis-specific constructions, provide a common framework for ongoing temporal analysis and investigation of LVS dynamics.

Effectiveness of subcutaneous monoclonal antibody treatment in emergency department outpatients with COVID-19.

Objectives: To evaluate whether subcutaneous neutralizing monoclonal antibody (mAb) treatment given in the emergency department (ED) setting was associated with reduced hospitalizations, mortality, and severity of disease when compared to nontreatment among mAb-eligible patients with coronavirus disease 2019 (COVID-19). Methods: This retrospective observational cohort study of ED patients utilized a propensity score-matched analysis to compare patients who received subcutaneous casirivimab and imdevimab mAb to nontreated COVID-19 control patients in November-December 2021. The primary outcome was all-cause hospitalization within 28 days, and secondary outcomes were 90-day hospitalization, 28- and 90-day mortality, and ED length of stay (LOS). Results: Of 1340 patients included in the analysis, 490 received subcutaneous casirivimab and imdevimab, and 850 did not received them. There was no difference observed for 28-day hospitalization (8.4% vs. 10.6%; adjusted odds ratio [aOR] 0.79, 95% confidence intervals [CI] 0.53-1.17) or 90-day hospitalization (11.6% vs. 12.5%; aOR 0.93, 95% CI 0.65-1.31). However, mortality at both the 28-day and 90-day timepoints was substantially lower in the treated group (28-day 0.6% vs. 3.1%; aOR 0.18, 95% CI 0.08-0.41; 90-day 0.6% vs. 3.9%; aOR 0.14, 95% CI 0.06-0.36). Among hospitalized patients, treated patients had shorter hospital LOS (5.7 vs. 11.4 days; adjusted rate ratio [aRR] 0.47, 95% CI 0.33-0.69), shorter intensive care unit LOS (3.8 vs. 10.2 days; aRR 0.22, 95% CI 0.14-0.35), and the severity of hospitalization was lower (aOR 0.45, 95% CI 0.21-0.97) compared to untreated. Conclusions: Among ED patients who presented for symptomatic COVID-19 during the Delta variant phase, ED subcutaneous casirivimab/imdevimab treatment was not associated with a decrease in hospitalizations. However, treatment was associated with lower mortality at 28 and 90 days, hospital LOS, and overall severity of illness.

Digital solutions in paediatric sepsis: current state, challenges, and opportunities to improve care around the world.

The digitisation of health care is offering the promise of transforming the management of paediatric sepsis, which is a major source of morbidity and mortality in children worldwide. Digital technology is already making an impact in paediatric sepsis, but is almost exclusively benefiting patients in high-resource health-care settings. However, digital tools can be highly scalable and cost-effective, and-with the right planning-have the potential to reduce global health disparities. Novel digital solutions, from wearable devices and mobile apps, to electronic health record-embedded decision support tools, have an unprecedented opportunity to transform paediatric sepsis research and care. In this Series paper, we describe the current state of digital solutions in paediatric sepsis around the world, the advances in digital technology that are enabling the development of novel applications, and the potential effect of advances in artificial intelligence in paediatric sepsis research and clinical care.

Real-world use of nirmatrelvir-ritonavir in COVID-19 outpatients during BQ.1, BQ.1.1., and XBB.1.5 predominant omicron variants in three U.S. health systems: a retrospective cohort study.

Background: Ritonavir-boosted Nirmatrelvir (NMV-r), a protease inhibitor with in vitro activity against SARS-CoV-2, can reduce risk of progression to severe COVID-19 among high-risk individuals infected with earlier variants, but less is known about its effectiveness against omicron variants BQ.1/BQ.1.1/XBB.1.5. We sought to evaluate effectiveness of NMV-r in BQ.1/BQ.1.1/XBB.1.5 omicron variants by comparing hospitalisation rates to NMV-r treated patients during a previous omicron phase and to contemporaneous untreated patients. Methods: We conducted a retrospective observational cohort study of non-hospitalised adult patients with SARS-CoV-2 infection using real-world data from three health systems in Colorado and Utah, and compared hospitalisation rates in NMV-r-treated patients in a BA.2/BA.2.12.1/BA.4/BA.5 variant-predominant (first) phase (April 3, 2022-November 12, 2022), with a BQ.1/BQ.1.1/XBB.1.5 variant-predominant (second) phase (November 13, 2022-March 7, 2023). In the primary analysis, we used Firth logistic regression with a two-segment (phase) linear time model, and pre-specified non-inferiority bounds for the mean change between segments. In a pre-specified secondary analysis, we inferred NMV-r effectiveness in a cohort of treated and untreated patients infected during the second phase. For both analyses, the primary outcome was 28-day all-cause hospitalisation. Subgroup analyses assessed treatment effect heterogeneity. Findings: In the primary analysis, 28-day all-cause hospitalisation rates in NMV-r treated patients in the second phase (n = 12,061) were non-inferior compared to the first phase (n = 25,075) (198 [1.6%] vs. 345 [1.4%], adjusted odds ratio (aOR): 0.76 [95% CI 0.54-1.06]), with consistent results among secondary endpoints and key subgroups. Secondary cohort analyses revealed additional evidence for NMV-r effectiveness, with reduced 28-day hospitalisation rates among treated patients compared to untreated patients during a BQ.1/BQ.1.1/XBB.1.5 predominant phase (198/12,061 [1.6%] vs. 376/10,031 [3.7%], aOR 0.34 [95% CI 0.30-0.38), findings robust to additional sensitivity analyses. Interpretation: Real-world evidence from major US healthcare systems suggests ongoing NMV-r effectiveness in preventing hospitalisation during a BQ.1/BQ.1.1/XBB.1.5-predominant phase in the U.S, supporting its continued use in similar patient populations. Funding: U.S. National Institutes of Health.

An open source knowledge graph ecosystem for the life sciences.

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.