A simplified pneumonia severity index (PSI) for clinical outcome prediction in COVID-19.

BACKGROUND: The Pneumonia Score Index (PSI) was developed to estimate the risk of dying within 30 days of presentation for community-acquired pneumonia patients and is a strong predictor of 30-day mortality after COVID-19. However, three of its required 20 variables (skilled nursing home, altered mental status and pleural effusion) are not discreetly available in the electronic medical record (EMR), resulting in manual chart review for these 3 factors. The goal of this study is to compare a simplified 17-factor version (PSI-17) to the original (denoted PSI-20) in terms of prediction of 30-day mortality in COVID-19. METHODS: In this retrospective cohort study, the hospitalized patients with confirmed SARS-CoV-2 infection between 2/28/20-5/28/20 were identified to compare the predictive performance between PSI-17 and PSI-20. Correlation was assessed between PSI-17 and PSI-20, and logistic regressions were performed for 30-day mortality. The predictive abilities were compared by discrimination, calibration, and overall performance. RESULTS: Based on 1,138 COVID-19 patients, the correlation between PSI-17 and PSI-20 was 0.95. Univariate logistic regression showed that PSI-17 had performance similar to PSI-20, based on AUC, ICI and Brier Score. After adjusting for confounding variables by multivariable logistic regression, PSI-17 and PSI-20 had AUCs (95% CI) of 0.85 (0.83-0.88) and 0.86 (0.84-0.89), respectively, indicating no significant difference in AUC at significance level of 0.05. CONCLUSION: PSI-17 and PSI-20 are equally effective predictors of 30-day mortality in terms of several performance metrics. PSI-17 can be obtained without the manual chart review, which allows for automated risk calculations within an EMR. PSI-17 can be easily obtained and may be a comparable alternative to PSI-20.

Using Large Language Models to Annotate Complex Cases of Social Determinants of Health in Longitudinal Clinical Records.

Social Determinants of Health (SDoH) are an important part of the exposome and are known to have a large impact on variation in health outcomes. In particular, housing stability is known to be intricately linked to a patient's health status, and pregnant women experiencing housing instability (HI) are known to have worse health outcomes. Most SDoH information is stored in electronic health records (EHRs) as free text (unstructured) clinical notes, which traditionally required natural language processing (NLP) for automatic identification of relevant text or keywords. A patient's housing status can be ambiguous or subjective, and can change from note to note or within the same note, making it difficult to use existing NLP solutions. New developments in NLP allow researchers to prompt LLMs to perform complex, subjective annotation tasks that require reasoning that previously could only be attempted by human annotators. For example, large language models (LLMs) such as GPT (Generative Pre-trained Transformer) enable researchers to analyze complex, unstructured data using simple prompts. We used a secure platform within a large healthcare system to compare the ability of GPT-3.5 and GPT-4 to identify instances of both current and past housing instability, as well as general housing status, from 25,217 notes from 795 pregnant women. Results from these LLMs were compared with results from manual annotation, a named entity recognition (NER) model, and regular expressions (RegEx). We developed a chain-of-thought prompt requiring evidence and justification for each note from the LLMs, to help maximize the chances of finding relevant text related to HI while minimizing hallucinations and false positives. Compared with GPT-3.5 and the NER model, GPT-4 had the highest performance and had a much higher recall (0.924) than human annotators (0.702) in identifying patients experiencing current or past housing instability, although precision was lower (0.850) compared with human annotators (0.971). In most cases, the evidence output by GPT-4 was similar or identical to that of human annotators, and there was no evidence of hallucinations in any of the outputs from GPT-4. Most cases where the annotators and GPT-4 differed were ambiguous or subjective, such as "living in an apartment with too many people". We also looked at GPT-4 performance on de-identified versions of the same notes and found that precision improved slightly (0.936 original, 0.939 de-identified), while recall dropped (0.781 original, 0.704 de-identified). This work demonstrates that, while manual annotation is likely to yield slightly more accurate results overall, LLMs, when compared with manual annotation, provide a scalable, cost-effective solution with the advantage of greater recall. At the same time, further evaluation is needed to address the risk of missed cases and bias in the initial selection of housing-related notes. Additionally, while it was possible to reduce confabulation, signs of unusual justifications remained. Given these factors, together with changes in both LLMs and charting over time, this approach is not yet appropriate for use as a fully-automated process. However, these results demonstrate the potential for using LLMs for computer-assisted annotation with human review, reducing cost and increasing recall. More efficient methods for obtaining structured SDoH data can help accelerate inclusion of exposome variables in biomedical research, and support healthcare systems in identifying patients who could benefit from proactive outreach.

Maternal-fetal outcomes in patients with immune-mediated inflammatory diseases, with consideration of comorbidities: a retrospective cohort study in a large U.S. healthcare system.

Background: Immune-mediated inflammatory diseases (IMIDs) are likely to complicate maternal health. However, literature on patients with IMIDs undergoing pregnancy is scarce and often overlooks the presence of comorbidities. We aimed to evaluate the impact of IMIDs on adverse pregnancy outcomes after assessing and addressing any discrepancies in the distribution of covariates associated with adverse pregnancy outcomes between patients with and without IMIDs. Methods: We conducted a retrospective cohort study using data from an integrated U.S. community healthcare system that provides care across Alaska, California, Montana, Oregon, New Mexico, Texas, and Washington. We used a database containing all structured data from electronic health record (EHRs) and analyzed the cohort of pregnant people who had live births from January 1, 2013, through December 31, 2022. We investigated 12 selected IMIDs: psoriasis, inflammatory bowel disease, rheumatoid arthritis, spondyloarthritis, multiple sclerosis, systemic lupus erythematosus, psoriatic arthritis, antiphospholipid syndrome, Sjögren's syndrome, vasculitides, sarcoidosis, and systemic sclerosis. We characterized patients with IMIDs prior to pregnancy (IMIDs group) based on pregnancy/maternal characteristics, comorbidities, and pre-pregnancy/prenatal immunomodulatory medications (IMMs) prescription patterns. We 1:1 propensity score matched the IMIDs cohort with people who had no IMID diagnoses prior to pregnancy (non-IMIDs cohort). Outcome measures were preterm birth (PTB), low birth weight (LBW), small for gestational age (SGA), and caesarean section. Findings: Our analytic cohort had 365,075 people, of which 5784 were in the IMIDs group and 359,291 were in the non-IMIDs group. The prevalence rate of pregnancy of at least 20 weeks duration in people with a previous IMID diagnosis has doubled in the past ten years. 17% of the IMIDs group had at least one prenatal IMM prescription. Depending on the type of IMM, 48%-70% of the patients taking IMMs before pregnancy continued them throughout pregnancy. Overall, patients with one or more of these 12 IMIDs had increased risk of PTB (Relative risk (RR) = 1.1 [1.0, 1.3]; p = 0.08), LBW (RR = 1.2 [1.0, 1.4]; p = 0.02), SGA (RR = 1.1 [1.0, 1.2]; p = 0.03), and caesarean section (RR = 1.1 [1.1, 1.2], p < 0.0001) compared to a matched cohort of people without IMIDs. When adjusted for comorbidities, patients with rheumatoid arthritis (PTB RR = 1.2, p = 0.5; LBW RR = 1.1, p = 0.6) and/or inflammatory bowel disease (PTB RR = 1.2, p = 0.3; LBW RR = 1.0, p = 0.8) did not have significantly increased risk for PTB and LBW. Interpretation: For patients who have been pregnant for 20 weeks or greater, the association between IMIDs and adverse pregnancy outcomes depends on both the nature of the IMID and the presence of comorbidities. Because this study was limited to pregnancies resulting in live births, results must be interpreted together with other studies on early pregnancy loss and stillbirth in patient with IMIDs. Funding: National Institutes of Health.

Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system.

BACKGROUND: In the context of immune-mediated inflammatory diseases (IMIDs), COVID-19 outcomes are incompletely understood and vary considerably depending on the patient population studied. We aimed to analyse severe COVID-19 outcomes and to investigate the effects of the pandemic time period and the risks associated with individual IMIDs, classes of immunomodulatory medications (IMMs), chronic comorbidities, and COVID-19 vaccination status. METHODS: In this retrospective cohort study, clinical data were derived from the electronic health records of an integrated health-care system serving patients in 51 hospitals and 1085 clinics across seven US states (Providence St Joseph Health). Data were observed for patients (no age restriction) with one or more IMID and for unmatched controls without IMIDs. COVID-19 was identified with a positive nucleic acid amplification test result for SARS-CoV-2. Two timeframes were analysed: March 1, 2020-Dec 25, 2021 (pre-omicron period), and Dec 26, 2021-Aug 30, 2022 (omicron-predominant period). Primary outcomes were hospitalisation, mechanical ventilation, and mortality in patients with COVID-19. Factors, including IMID diagnoses, comorbidities, long-term use of IMMs, and COVID-19 vaccination status, were analysed with multivariable logistic regression (LR) and extreme gradient boosting (XGB). FINDINGS: Of 2 167 656 patients tested for SARS-CoV-2, 290 855 (13·4%) had confirmed COVID-19: 15 397 (5·3%) patients with IMIDs and 275 458 (94·7%) without IMIDs. In the pre-omicron period, 169 993 (11·2%) of 1 517 295 people who were tested for COVID-19 tested positive, of whom 23 330 (13·7%) were hospitalised, 1072 (0·6%) received mechanical ventilation, and 5294 (3·1%) died. Compared with controls, patients with IMIDs and COVID-19 had higher rates of hospitalisation (1176 [14·6%] vs 22 154 [13·7%]; p=0·024) and mortality (314 [3·9%] vs 4980 [3·1%]; p<0·0001). In the omicron-predominant period, 120 862 (18·6%) of 650 361 patients tested positive for COVID-19, of whom 14 504 (12·0%) were hospitalised, 567 (0·5%) received mechanical ventilation, and 2001 (1·7%) died. Compared with controls, patients with IMIDs and COVID-19 (7327 [17·3%] of 42 249) had higher rates of hospitalisation (13 422 [11·8%] vs 1082 [14·8%]; p<0·0001) and mortality (1814 [1·6%] vs 187 [2·6%]; p<0·0001). Age was a risk factor for worse outcomes (adjusted odds ratio [OR] from 2·1 [95% CI 2·0-2·1]; p<0·0001 to 3·0 [2·9-3·0]; p<0·0001), whereas COVID-19 vaccination (from 0·082 [0·080-0·085]; p<0·0001 to 0·52 [0·50-0·53]; p<0·0001) and booster vaccination (from 2·1 [2·0-2·2]; p<0·0001 to 3·0 [2·9-3·0]; p<0·0001) status were associated with better outcomes. Seven chronic comorbidities were significant risk factors during both time periods for all three outcomes: atrial fibrillation, coronary artery disease, heart failure, chronic kidney disease, chronic obstructive pulmonary disease, chronic liver disease, and cancer. Two IMIDs, asthma (adjusted OR from 0·33 [0·32-0·34]; p<0·0001 to 0·49 [0·48-0·51]; p<0·0001) and psoriasis (from 0·52 [0·48-0·56] to 0·80 [0·74-0·87]; p<0·0001), were associated with a reduced risk of severe outcomes. IMID diagnoses did not appear to be significant risk factors themselves, but results were limited by small sample size, and vasculitis had high feature importance in LR. IMMs did not appear to be significant, but less frequently used IMMs were limited by sample size. XGB outperformed LR, with the area under the receiver operating characteristic curve for models across different time periods and outcomes ranging from 0·77 to 0·92. INTERPRETATION: Our results suggest that age, chronic comorbidities, and not being fully vaccinated might be greater risk factors for severe COVID-19 outcomes in patients with IMIDs than the use of IMMs or the IMIDs themselves. Overall, there is a need to take age and comorbidities into consideration when developing COVID-19 guidelines for patients with IMIDs. Further research is needed for specific IMIDs (including IMID severity at the time of SARS-CoV-2 infection) and IMMs (considering dosage and timing before a patient's first COVID-19 infection). FUNDING: Pfizer, Novartis, Janssen, and the National Institutes of Health.

Timing of selective serotonin reuptake inhibitor use and risk for preterm birth and related adverse events: with a consideration of the COVID-19 pandemic period.

OBJECTIVE: There is uncertainty around the safety of SSRIs for treating depression during pregnancy. Nevertheless, the use of SSRIs has been gradually increasing, especially during the COVID-19 pandemic period. We aimed to (1) characterize maternal depression rate and use of SSRIs in a recent 10-year period, (2) address confounding by indication, as well as socioeconomic and environmental factors, and (3) evaluate associations of the timing of SSRI exposure in pregnancy with risk for preterm birth (PTB), low birthweight (LBW), and small for gestational age (SGA) infants among women with depression before pregnancy. METHODS: We conducted propensity score-adjusted regression to calculate odds ratios (ORs) of PTB, LBW, and SGA. We accounted for maternal/pregnancy characteristics, comorbidity, depression severity, time of delivery, social vulnerability, and rural residence. RESULTS: There were 50.3% and 40.3% increases in the prevalence rate of prenatal depression and prenatal SSRI prescription rate during the pandemic. We identified women with depression ≤180 days before pregnancy (n = 8406). Women with no SSRI order during pregnancy (n = 3760) constituted the unexposed group. The late SSRI exposure group consisted of women with an SSRI order after the first trimester (n = 3759). The early-only SSRI exposure group consisted of women with SSRI orders only in the first trimester (n = 887). The late SSRI exposure group had an increased risk of PTB of OR = 1.5 ([1.2,1.8]) and LBW of OR = 1.5 ([1.2,2.0]), relative to the unexposed group. Associations between late SSRI exposure and risk of PTB/LBW were similar among a subsample of patients who delivered during the pandemic. CONCLUSIONS: These findings suggest an association between PTB/LBW and SSRI exposure is dependent on exposure timing during pregnancy. Small for gestational age is not associated with SSRI exposure.

Generally-healthy individuals with aberrant bowel movement frequencies show enrichment for microbially-derived blood metabolites associated with reduced kidney function.

Bowel movement frequency (BMF) has been linked to changes in the composition of the human gut microbiome and to many chronic conditions, like metabolic disorders, neurodegenerative diseases, chronic kidney disease (CKD), and other intestinal pathologies like irritable bowel syndrome and inflammatory bowel disease. Lower BMF (constipation) can lead to compromised intestinal barrier integrity and a switch from saccharolytic to proteolytic fermentation within the microbiota, giving rise to microbially-derived toxins that may make their way into circulation and cause damage to organ systems. However, the connections between BMF, gut microbial metabolism, and the early-stage development and progression of chronic disease remain underexplored. Here, we examined the phenotypic impact of BMF variation in a cohort of generally-healthy, community dwelling adults with detailed clinical, lifestyle, and multi-omic data. We showed significant differences in microbially-derived blood plasma metabolites, gut bacterial genera, clinical chemistries, and lifestyle factors across BMF groups that have been linked to inflammation, cardiometabolic health, liver function, and CKD severity and progression. We found that the higher plasma levels of 3-indoxyl sulfate (3-IS), a microbially-derived metabolite associated with constipation, was in turn negatively associated with estimated glomerular filtration rate (eGFR), a measure of kidney function. Causal mediation analysis revealed that the effect of BMF on eGFR was significantly mediated by 3-IS. Finally, we identify self-reported diet, lifestyle, and psychological factors associated with BMF variation, which indicate several common-sense strategies for mitigating constipation and diarrhea. Overall, we suggest that aberrant BMF is an underappreciated risk factor in the development of chronic diseases, even in otherwise healthy populations.

An approach for collaborative development of a federated biomedical knowledge graph-based question-answering system: Question-of-the-Month challenges.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph-based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly "Question-of-the-Month (QotM) Challenge" series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

Maternal-fetal outcomes in patients with immune mediated inflammatory diseases, with consideration of comorbidities: a retrospective cohort study in a large U.S. healthcare system.

Background: Immune-mediated inflammatory diseases (IMIDs) are likely to complicate maternal health. However, literature data on patients with IMIDs undergoing pregnancy is scarce and often overlooks the impact of comorbidities. Methods: We investigated 12 selected IMIDs: psoriasis, inflammatory bowel disease, rheumatoid arthritis, spondyloarthritis, multiple sclerosis, systemic lupus erythematosus, psoriatic arthritis, antiphospholipid syndrome, Sjögren's syndrome, vasculitis, sarcoidosis, systemic sclerosis. We characterized patients with IMIDs prior to pregnancy (IMIDs group) based on pregnancy/maternal characteristics, comorbidities, and pre-pregnancy/prenatal immunomodulatory medications (IMMs) prescription patterns. We 1:1 propensity score matched the IMIDs cohort with people who had no IMID diagnoses prior to pregnancy (non-IMIDs cohort). Outcome measures were preterm birth (PTB), low birth weight (LBW), small for gestational age (SGA), and cesarean section. Findings: The prevalence rate of pregnancy occurring with people with a previous IMID diagnosis has doubled in the past ten years. We identified 5,784 patients with IMIDs. 17% of the IMIDs group had at least one prenatal IMM prescription. Depending on the type of IMM, from 48% to 70% of the patients taking IMMs before pregnancy continued them throughout pregnancy. Patients with IMIDs had similar but slightly increased risks of PTB (Relative risk (RR)=1·1[1·0, 1·3]), LBW (RR=1·2 [1·0,1·4]), SGA (RR=1·1 [1·0,1·2]), and cesarean section (RR=1·1 [1·1,1·2]) compared to a matched cohort of people without IMIDs. Out of the 12 selected IMIDs, three for PTB, one for LBW, two for SGA, and six for cesarean section had results supporting increased risk. Interpretation: The association between IMIDs and the increased risk of adverse pregnancy outcomes depend on both the nature of the IMID and the presence of comorbidities.

Effect of COVID-19 vaccination and booster on maternal-fetal outcomes: a retrospective cohort study.

BACKGROUND: COVID-19 in pregnant people increases the risk for poor maternal-fetal outcomes. However, COVID-19 vaccination hesitancy remains due to concerns over the vaccine's potential effects on maternal-fetal outcomes. Here we examine the impact of COVID-19 vaccination and boosters on maternal SARS-CoV-2 infections and birth outcomes. METHODS: This was a retrospective multicentre cohort study on the impact of COVID-19 vaccination on maternal-fetal outcomes for people who delivered (n=106 428) at Providence St Joseph Health across seven western US states from Jan 26, 2021 to Oct 26, 2022. Cohorts were defined by vaccination status at delivery: vaccinated (n=35 926; two or more doses of mRNA-1273 Moderna or BNT162b2 Pfizer-BioNTech), unvaccinated (n=55 878), unvaccinated propensity score matched (n=16 771), boosted (n=10 927; three or more doses), vaccinated unboosted (n=13 243; two doses only), and vaccinated unboosted with propensity score matching (n=4414). We built supervised machine learning classification models, which we used to determine which people were more likely to be vaccinated or boosted at delivery. The primary outcome was maternal SARS-CoV-2 infection. COVID-19 vaccination status at delivery, COVID-19-related health care, preterm birth, stillbirth, and very low birthweight were evaluated as secondary outcomes. FINDINGS: Vaccinated people were more likely to conceive later in the pandemic, have commercial insurance, be older, live in areas with lower household composition vulnerability, and have a higher BMI than unvaccinated people. Boosted people were more likely to have more days since receiving the second COVID-19 vaccine dose, conceive earlier in the pandemic, have commercial insurance, be older, and live in areas with lower household composition vulnerability than vaccinated unboosted people. Vaccinated pregnant people had lower rates of COVID-19 during pregnancy (4·0%) compared with unvaccinated matched people (5·3%; p<0·0001). COVID-19 rates were even lower in boosted people (3·2%) compared with vaccinated unboosted matched people (5·6%; p<0·0001). Vaccinated people were also less likely to have a preterm birth (7·9%; p<0·0001), stillbirth (0·3%; p<0·0002), or very low birthweight neonate (1·0%; p<0·0001) compared with unvaccinated matched people (preterm birth 9·4%; stillbirth 0·6%; very low birthweight 1·5%). Boosted people were less likely to have a stillbirth (0·3%; p<0·025) and have no differences in rates of preterm birth (7·6%; p=0·090) or very low birthweight neonates (0·8%; p=0·092) compared with vaccinated unboosted matched people (stillbirth 0·5%; preterm birth 8·4%; very low birthweight 1·1%). INTERPRETATION: COVID-19 vaccination protects against adverse maternal-fetal outcomes, with booster doses conferring additional protection. Pregnant people should be high priority for vaccination and stay up to date with their COVID-19 vaccination schedule. FUNDING: National Institute for Child Health & Human Development and the William O and K Carole Ellison Foundation.

Risk factors for severe COVID-19 outcomes: a study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US healthcare system.

Background: COVID-19 outcomes, in the context of immune-mediated inflammatory diseases (IMIDs), are incompletely understood. Reported outcomes vary considerably depending on the patient population studied. It is essential to analyse data for a large population, while considering the effects of the pandemic time period, comorbidities, long term use of immunomodulatory medications (IMMs), and vaccination status. Methods: In this retrospective case-control study, patients of all ages with IMIDs were identified from a large U.S. healthcare system. COVID-19 infections were identified based on SARS-CoV-2 NAAT test results. Controls without IMIDs were selected from the same database. Severe outcomes were hospitalisation, mechanical ventilation (MV), and death. We analysed data from 1 March 2020 to 30 August 2022, looking separately at both pre-Omicron and Omicron predominant periods. Factors including IMID diagnoses, comorbidities, long term use of IMMs, and vaccination and booster status were analysed using multivariable logistic regression (LR) and extreme gradient boosting (XGB). Findings: Out of 2 167 656 patients tested for SARS-CoV-2, there were 290 855 with confirmed COVID-19 infection: 15 397 patients with IMIDs and 275 458 controls (patients without IMIDs). Age and most chronic comorbidities were risk factors for worse outcomes, whereas vaccination and boosters were protective. Patients with IMIDs had higher rates of hospitalisation and mortality compared with controls. However, in multivariable analyses, few IMIDs were rarely risk factors for worse outcomes. Further, asthma, psoriasis and spondyloarthritis were associated with reduced risk. Most IMMs had no significant association, but less frequently used IMM drugs were limited by sample size. XGB outperformed LR, with the AUROCs for models across different time periods and outcomes ranging from 0·77 to 0·92. Interpretation: For patients with IMIDs, as for controls, age and comorbidities were risk factors for worse COVID-19 outcomes, whereas vaccinations were protective. Most IMIDs and immunomodulatory therapies were not associated with more severe outcomes. Interestingly, asthma, psoriasis and spondyloarthritis were associated with less severe COVID-19 outcomes than those expected for the population overall. These results can help inform clinical, policy and research decisions. Funding: Pfizer, Novartis, Janssen, NIH.

Adoption of a National Prophylactic Anticoagulation Guideline for Hospitalized Pregnant Women With COVID-19: Retrospective Cohort Study.

BACKGROUND: Both COVID-19 and pregnancy are associated with hypercoagulability. Due to the increased risk for thrombosis, the United States National Institute of Health's recommendation for prophylactic anticoagulant use for pregnant patients has expanded from patients hospitalized for severe COVID-19 manifestation to all patients hospitalized for the manifestation of COVID-19 (no guideline: before December 26, 2020; first update: December 27, 2022; second update: February 24, 2022-present). However, no study has evaluated this recommendation. OBJECTIVE: The objective of this study was to characterize prophylactic anticoagulant use among hospitalized pregnant people with COVID-19 from March 20, 2020, to October 19, 2022. METHODS: This was a retrospective cohort study in large US health care systems across 7 states. The cohort of interest was pregnant patients who were hospitalized with COVID-19, without previous coagulopathy or contraindication to anticoagulants (n=2767). The treatment group consisted of patients prescribed prophylactic dose anticoagulation between 2 days before and 14 days after COVID-19 treatment onset (n=191). The control group was patients with no anticoagulant exposure between 14 days before and 60 days after COVID-19 treatment onset (n=2534). We ascertained the use of prophylactic anticoagulants with attention to the updates in guidelines and emerging SARS-CoV-2 variants. We propensity score matched the treatment and control group 1:1 on the most important features contributing to the prophylactic anticoagulant administration status classification. Outcome measures included coagulopathy, bleeding, COVID-19-related complications, and maternal-fetal health outcomes. Additionally, the inpatient anticoagulant administration rate was validated in a nationwide population from Truveta, a collective of 700 hospitals across the United States. RESULTS: The overall administration rate of prophylactic anticoagulants was 7% (191/2725). It was lowest after the second guideline update (no guideline: 27/262, 10%; first update: 145/1663, 8.72%; second update: 19/811, 2.3%; P<.001) and during the omicron-dominant period (Wild type: 45/549, 8.2%; Alpha: 18/129, 14%; Delta: 81/507, 16%; and Omicron: 47/1551, 3%; P<.001). Models developed on retrospective data showed that the variable most associated with the administration of inpatient prophylactic anticoagulant was comorbidities prior to SARS-CoV-2 infection. The patients who were administered prophylactic anticoagulant were also more likely to receive supplementary oxygen (57/191, 30% vs 9/188, 5%; P<.001). There was no statistical difference in a new diagnosis of coagulopathy, bleeding, or maternal-fetal health outcomes between those who received treatment and the matched control group. CONCLUSIONS: Most hospitalized pregnant patients with COVID-19 did not receive prophylactic anticoagulants across health care systems as recommended by guidelines. Guideline-recommended treatment was administered more frequently to patients with greater COVID-19 illness severity. Given the low rate of administration and differences between treated and untreated cohorts, efficacy could not be assessed.

Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention.

Multiomic profiling can reveal population heterogeneity for both health and disease states. Obesity drives a myriad of metabolic perturbations and is a risk factor for multiple chronic diseases. Here we report an atlas of cross-sectional and longitudinal changes in 1,111 blood analytes associated with variation in body mass index (BMI), as well as multiomic associations with host polygenic risk scores and gut microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program (Arivale). Machine learning model predictions of BMI from blood multiomics captured heterogeneous phenotypic states of host metabolism and gut microbiome composition better than BMI, which was also validated in an external cohort (TwinsUK). Moreover, longitudinal analyses identified variable BMI trajectories for different omics measures in response to a healthy lifestyle intervention; metabolomics-inferred BMI decreased to a greater extent than actual BMI, whereas proteomics-inferred BMI exhibited greater resistance to change. Our analyses further identified blood analyte-analyte associations that were modified by metabolomics-inferred BMI and partially reversed in individuals with metabolic obesity during the intervention. Taken together, our findings provide a blood atlas of the molecular perturbations associated with changes in obesity status, serving as a resource to quantify metabolic health for predictive and preventive medicine.

The impact of maternal SARS-CoV-2 infection and COVID-19 vaccination on maternal-fetal outcomes.

The rapidly evolving COVID-19 pandemic has resulted in an upsurge of scientific productivity to help address the global health crisis. One area of active research is the impact of COVID-19 on pregnancy. Here, we provide an epidemiological overview about what is known about the effects of maternal SARS-CoV-2 infection and COVID-19 vaccination on maternal-fetal outcomes, and identify gaps in knowledge. Pregnant people are at increased risk for severe COVID-19, and maternal SARS-CoV-2 infection increases the risk of negative maternal-fetal outcomes. Despite this elevated risk, there have been high rates of vaccine hesitancy, heightened by the initial lack of safety and efficacy data for COVID-19 vaccination in pregnancy. In response, retrospective cohort studies were performed to examine the impact of COVID-19 vaccination during pregnancy. Here, we report the vaccine's efficacy during pregnancy and its impact on maternal-fetal outcomes, as well as an overview of initial studies on booster shots in pregnancy. We found that pregnant people are at risk for more severe COVID-19 outcomes, maternal SARS-CoV-2 infection is associated with worse birth outcomes, COVID-19 vaccine hesitancy remains prevalent in the pregnant population, and COVID-19 vaccination and boosters promote better maternal-fetal outcomes. The results should help reduce vaccine hesitancy by alleviating concerns about the safety and efficacy of administering the COVID-19 vaccine during pregnancy. Overall, this review provides an introduction to COVID-19 during pregnancy. It is expected to help consolidate current knowledge, accelerate research of COVID-19 during pregnancy and inform clinical, policy, and research decisions regarding COVID-19 vaccination in pregnant people.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

The effect of COVID-19 vaccination and booster on maternal-fetal outcomes: a retrospective multicenter cohort study.

Background: COVID-19 infection in pregnant people has previously been shown to increase the risk for poor maternal-fetal outcomes. Despite this, there has been a lag in COVID-19 vaccination in pregnant people due to concerns over the potential effects of the vaccine on maternal-fetal outcomes. Here we examine the impact of COVID-19 vaccination and booster on maternal COVID-19 breakthrough infections and birth outcomes. Methods: This was a retrospective multicenter cohort study on the impact of COVID-19 vaccination on maternal-fetal outcomes for people that delivered (n=86,833) at Providence St. Joseph Health across Alaska, California, Montana, Oregon, New Mexico, Texas, and Washington from January 26, 2021 through July 11, 2022. Cohorts were defined by vaccination status at time of delivery: unvaccinated (n=48,492), unvaccinated propensity score matched (n=26,790), vaccinated (n=26,792; two doses of mRNA-1273 Moderna or BNT162b2 Pfizer-BioNTech), and/or boosted (n=7,616). The primary outcome was maternal COVID-19 infection. COVID-19 vaccination status at delivery, COVID-19 infection-related health care, preterm birth (PTB), stillbirth, very low birth weight (VLBW), and small for gestational age (SGA) were evaluated as secondary outcomes. Findings: Vaccinated pregnant people were significantly less likely to have a maternal COVID-19 infection than unvaccinated matched (p<0.0001) pregnant people. During a maternal COVID-19 infection, vaccinated pregnant people had similar rates of hospitalization (p=0.23), but lower rates of supplemental oxygen (p<0.05) or vasopressor (p<0.05) use than those in an unvaccinated matched cohort. Compared to an unvaccinated matched cohort, vaccinated people had significantly lower stillbirth rate (p<0.01) as well as no difference in rate of PTB (p=0.35), SGA (p=0.79), or rate of VLBW (>1,500 g; 0.31). Vaccinated people who were boosted had significantly lower rates of maternal COVID-19 infections (p<0.0001), COVID-19 related hospitalization (p<0.05), PTB (p<0.05), stillbirth (p<0.01), SGA (p<0.05), and VLBW (p<0.01), compared to vaccinated people that did not receive a third booster dose five months after completing the initial vaccination series. Interpretation: COVID-19 vaccination protects against adverse maternal-fetal outcomes with booster doses conferring additional protection against COVID-19 infection. It is therefore important for pregnant people to have high priority status for vaccination, and for them to stay current with their COVID-19 vaccination schedule. Funding: This study was funded by the National Institute for Child Health & Human Development and the William O. and K. Carole Ellison Foundation.

Progress toward a universal biomedical data translator.

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

Risk factors for severe COVID-19 differ by age for hospitalized adults.

Risk stratification for hospitalized adults with COVID-19 is essential to inform decisions about individual patients and allocation of resources. So far, risk models for severe COVID outcomes have included age but have not been optimized to best serve the needs of either older or younger adults. Models also need to be updated to reflect improvements in COVID-19 treatments. This retrospective study analyzed data from 6906 hospitalized adults with COVID-19 from a community health system across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalization, using clinical data available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. For the seven-day interval, models for age ≥ 18 and < 50 years reached AUROC 0.81 (95% CI 0.71-0.91) and models for age ≥ 50 years reached AUROC 0.82 (95% CI 0.77-0.86). Models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients including age, BMI, vital signs, and laboratory results. In addition, for hospitalized patients, sex and chronic comorbidities had lower predictive value than vital signs and laboratory results.

The effect of maternal SARS-CoV-2 infection timing on birth outcomes: a retrospective multicentre cohort study.

BACKGROUND: The impact of maternal SARS-CoV-2 infection remains unclear. In this study, we evaluated the risk of maternal SARS-CoV-2 infection on birth outcomes and how this is modulated by the pregnancy trimester in which the infection occurs. We also developed models to predict gestational age at delivery for people following a SARS-CoV-2 infection during pregnancy. METHODS: We did a retrospective cohort study of the impact of maternal SARS-CoV-2 infection on birth outcomes. We used clinical data from Providence St Joseph Health electronic health records for pregnant people who delivered in the USA at the Providence, Swedish, or Kadlec sites in Alaska, California, Montana, Oregon, or Washington. The SARS-CoV-2 positive cohort included people who had a positive SARS-CoV-2 PCR-based test during pregnancy, subdivided by trimester of infection. No one in this cohort had been vaccinated for COVID-19 at time of infection. The SARS-CoV-2 negative cohort were people with at least one negative SARS-CoV-2 PCR-based test and no positive tests during pregnancy. Cohorts were matched on common covariates impacting birth outcomes, and univariate and multivariate analysis were done to investigate risk factors and predict outcomes. The primary outcome was gestational age at delivery with annotation of preterm birth classification. We trained multiple supervised learning models on 24 features of the SARS-CoV-2 positive cohort to evaluate performance and feature importance for each model and discuss the impact of SARS-CoV-2 infection on gestational age at delivery. FINDINGS: Between March 5, 2020, and July 4, 2021, 73 666 pregnant people delivered, 18 335 of whom had at least one SARS-CoV-2 test during pregnancy before Feb 14, 2021. We observed 882 people infected with SARS-CoV-2 during their pregnancy (first trimester n=85; second trimester n=226; and third trimester n=571) and 19 769 people who have never tested positive for SARS-CoV-2 and received at least one negative SARS-CoV-2 test during their pregnancy. SARS-CoV-2 infection indicated an increased risk of preterm delivery (p<0·05) and stillbirth (p<0·05), accounted for primarily by first and second trimester SARS-CoV-2 infections. Gestational age at SARS-CoV-2 infection was correlated with gestational age at delivery (p<0·01) and had the greatest impact on predicting gestational age at delivery. The people in this study had mild or moderate SARS-CoV-2 infections and acute COVID-19 severity was not correlated with gestational age at delivery (p=0·31). INTERPRETATION: These results suggest that pregnant people would benefit from increased monitoring and enhanced prenatal care after first or second trimester SARS-CoV-2 infection, regardless of acute COVID-19 severity. FUNDING: US National Institutes of Health.

Remdesivir and Mortality in Patients With Coronavirus Disease 2019.

BACKGROUND: The impact of remdesivir (RDV) on mortality rates in coronavirus disease 2019 (COVID-19) is controversial, and the mortality effect in subgroups of baseline disease severity has been incompletely explored. The purpose of this study was to assess the association of RDV with mortality rates in patients with COVID-19. METHODS: In this retrospective cohort study we compared persons receiving RDV with those receiving best supportive care (BSC). Patients hospitalized between 28 February and 28 May 2020 with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection were included with the development of COVID-19 pneumonia on chest radiography and hypoxia requiring supplemental oxygen or oxygen saturation ≤94% with room air. The primary outcome was overall survival, assessed with time-dependent Cox proportional hazards regression and multivariable adjustment, including calendar time, baseline patient characteristics, corticosteroid use, and random effects for hospital. RESULTS: A total of 1138 patients were enrolled, including 286 who received RDV and 852 treated with BSC, 400 of whom received hydroxychloroquine. Corticosteroids were used in 20.4% of the cohort (12.6% in RDV and 23% in BSC). Comparing persons receiving RDV with those receiving BSC, the hazard ratio (95% confidence interval) for death was 0.46 (.31-.69) in the univariate model (P < .001) and 0.60 (.40-.90) in the risk-adjusted model (P = .01). In the subgroup of persons with baseline use of low-flow oxygen, the hazard ratio (95% confidence interval) for death in RDV compared with BSC was 0.63 (.39-1.00; P = .049). CONCLUSION: Treatment with RDV was associated with lower mortality rates than BSC. These findings remain the same in the subgroup with baseline use of low-flow oxygen.

Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19.

A better understanding of the metabolic alterations in immune cells during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may elucidate the wide diversity of clinical symptoms experienced by individuals with coronavirus disease 2019 (COVID-19). Here, we report the metabolic changes associated with the peripheral immune response of 198 individuals with COVID-19 through an integrated analysis of plasma metabolite and protein levels as well as single-cell multiomics analyses from serial blood draws collected during the first week after clinical diagnosis. We document the emergence of rare but metabolically dominant T cell subpopulations and find that increasing disease severity correlates with a bifurcation of monocytes into two metabolically distinct subsets. This integrated analysis reveals a robust interplay between plasma metabolites and cell-type-specific metabolic reprogramming networks that is associated with disease severity and could predict survival.