Metapaths: similarity search in heterogeneous knowledge graphs via meta-paths.

SUMMARY: Heterogeneous knowledge graphs (KGs) have enabled the modeling of complex systems, from genetic interaction graphs and protein-protein interaction networks to networks representing drugs, diseases, proteins, and side effects. Analytical methods for KGs rely on quantifying similarities between entities, such as nodes, in the graph. However, such methods must consider the diversity of node and edge types contained within the KG via, for example, defined sequences of entity types known as meta-paths. We present metapaths, the first R software package to implement meta-paths and perform meta-path-based similarity search in heterogeneous KGs. The metapaths package offers various built-in similarity metrics for node pair comparison by querying KGs represented as either edge or adjacency lists, as well as auxiliary aggregation methods to measure set-level relationships. Indeed, evaluation of these methods on an open-source biomedical KG recovered meaningful drug and disease-associated relationships, including those in Alzheimer's disease. The metapaths framework facilitates the scalable and flexible modeling of network similarities in KGs with applications across KG learning. AVAILABILITY AND IMPLEMENTATION: The metapaths R package is available via GitHub at https://github.com/ayushnoori/metapaths and is released under MPL 2.0 (Zenodo DOI: 10.5281/zenodo.7047209). Package documentation and usage examples are available at https://www.ayushnoori.com/metapaths.

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.

Clinical utility of a decision-making aid for upper limb neurorehabilitation: applying the Hypertonicity Intervention Planning Model across cultures.

BACKGROUND: The Hypertonicity Intervention Planning Model (HIPM) is a decision-making aid which guides clinical reasoning in individualizing upper limb (UL) neurorehabilitation. AIM: To examine the HIPM's clinical utility across cultures, using therapists' perceptions of its usefulness and challenges when applied in clinical practice. METHODS: Interpretive description methodology guided qualitative data collection and analysis because it produces clinically practical applications. Forty-four occupational therapists working in Australia or Singapore participated. Three group discussions were conducted using a modified nominal group technique. RESULTS: Three themes were: (1) The HIPM guides systematic clinical decision-making for assessment, goal-setting, and intervention; (2) Utility was influenced by systemic or organizational supports and barriers including availability of time, resources, and funding; organizational readiness to change; multidisciplinary and transorganizational collaboration; (3) Therapists' skills and confidence to apply the HIPM, and openness to changing practice, influenced utility. CONCLUSIONS: Therapists strongly support HIPM use for structuring and communicating clinical reasoning in UL neurorehabilitation. However, organizational support is key to optimizing clinical utility. Incorporating decision-making aids into documentation and referral processes may strengthen multidisciplinary and transorganizational teamwork, enhancing clinical use. Different training tiers to suit therapist experience levels, refresher courses, and supplementary resources may improve therapists' skills and confidence, thereby boosting utility.

SurvMaximin: Robust federated approach to transporting survival risk prediction models.

OBJECTIVE: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/31400.].

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

BACKGROUND: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. OBJECTIVE: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. METHODS: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. RESULTS: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. CONCLUSIONS: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Genome-wide meta-analysis associates GPSM1 with type 2 diabetes, a plausible gene involved in skeletal muscle function.

Genome-wide association studies (GWASs) have identified many genetic variations associated with type 2 diabetes mellitus (T2DM) in Asians, but understanding the functional genetic variants that influence traits is often a complex process. In this study, fine mapping and other analytical strategies were performed to investigate the effects of G protein signaling modulator 1 (GPSM1) on insulin resistance in skeletal muscle. A total of 128 single-nucleotide polymorphisms (SNPs) within GPSM1 were analysed in 21,897 T2DM cases and 32,710 healthy controls from seven GWASs. The SNP rs28539249 in intron 9 of GPSM1 showed a nominally significant association with T2DM in Asians (OR = 1.07, 95% CI = 1.04-1.10, P < 10-4). The GPSM1 mRNA was increased in skeletal muscle and correlated with T2DM traits across obese mice model. An eQTL for the cis-acting regulation of GPSM1 expression in human skeletal muscle was identified for rs28539249, and the increased GPSM1 expression related with T2DM traits within GEO datasets. Another independent Asian cohort showed that rs28539249 is associated with the skeletal muscle expression of CACFD1, GTF3C5, SARDH, and FAM163B genes, which are functionally enriched for endoplasmic reticulum stress (ERS) and unfolded protein response (UPR) pathways. Moreover, rs28539249 locus was predicted to disrupt regulatory regions in human skeletal muscle with enriched epigenetic marks and binding affinity for CTCF. Supershift EMSA assays followed luciferase assays demonstrated the CTCF specifically binding to rs28539249-C allele leading to decreased transcriptional activity. Thus, the post-GWAS annotation confirmed the Asian-specific association of genetic variant in GPSM1 with T2DM, suggesting a role for the variant in the regulation in skeletal muscle.

Understanding the professional satisfaction of hospital trainees in Australia.

CONTEXT: Ensuring that specialty trainees are professionally satisfied is not only important from the point of view of trainee well-being, but is also critical if health systems are to retain doctors. Despite this, little systematic research in specialist trainees has identified policy-amenable factors correlated with professional satisfaction. This study examined factors associated with trainee professional satisfaction in a national Australian cohort. METHODS: This study used 2008-2015 data from the Medicine in Australia: Balancing Employment and Life (MABEL) survey, a national study of doctor demographics, characteristics and professional and personal satisfaction. Our study examined specialist trainees using a repeat cross-sectional method pooling first responses across all waves. A multivariate logistic regression analysis was used to assess correlates with professional satisfaction. RESULTS: The three factors most strongly correlated with professional satisfaction were feeling well supported and supervised by consultants (odds ratio [OR] 2.59, 95% confidence interval [CI] 2.42-2.77), having sufficient study time (OR 1.54, 95% CI 1.40-1.70) and self-rated health status (OR 1.65, 95% CI 1.53-1.80). Those working >56 hours per week were significantly less professionally satisfied (OR 0.76, 95% CI 0.70-0.84) compared with those working the median work hours (45-50 hours per week). Those earning in the lower quintiles, those earlier in their training and those who had studied at overseas universities were also significantly less likely to be satisfied. CONCLUSIONS: Our study suggests that good clinical supervision and support, appropriate working hours and supported study time directly impact trainee satisfaction, potentially affecting the quality of clinical care delivered by trainees. Furthermore, the needs of junior trainees, overseas graduates and those working >56 hours per week should be given particular consideration when developing well-being and training programmes.

Nausea and vomiting of pregnancy: Effects on quality of life and day-to-day function.

AIMS: To investigate the effect of nausea and vomiting of pregnancy (NVP) on quality of life (QoL) and activities of daily living/socioeconomic function in a contemporary Australian setting. MATERIALS AND METHODS: Observational, single centre prospective cohort study using validated survey instruments in pregnant women at 9-16 weeks gestation at a tertiary metropolitan women's hospital in Sydney, Australia. QoL measured by the Short-Form Health Survey (SF-12) was compared between those with and without NVP. NVP severity scores were correlated with QoL scores, work patterns and medication use. RESULTS: Of 116 participants, 72% had NVP, with no baseline (including mental health) differences between women with or without NVP. As classified by modified Pregnancy-Unique-Quantified-Emesis (PUQE) survey, 42% had mild symptoms, 55% moderate and 1% severe. SF-12 Physical Component Summary (PCS) scores were significantly lower for those with NVP (P < 0.001), but not Mental Component Summary (MCS) scores (P = 0.11). Decreasing QoL was associated with increasing NVP severity (P < 0.001), most markedly in the physical domain (P < 0.001). Only 39% of women used any NVP treatment and 15% pharmacotherapy. Most used treatments were vitamin B6 , ginger, metoclopramide and natural remedies. Significantly more women with NVP required time off work (45% vs 16%, P = 0.003). CONCLUSIONS: NVP is a physically morbid disease, affecting most pregnancies. NVP has a significant detrimental impact on QoL, especially physical QoL and work function. Despite this, we found low treatment utilisation, even in those with moderate/severe symptoms. Women should be encouraged to seek assistance for NVP and further education is required to improve practitioner awareness and management.

"The biggest problem we've ever had to face": how families manage driving cessation with people with dementia.

BACKGROUND: It is recognized that people with dementia are likely to need to stop driving at some point following diagnosis. Driving cessation can lead to negative outcomes for people with dementia and their family caregivers (FC), who often experience family conflict and tension throughout the process. Family experiences surrounding driving cessation have begun to be explored but warrant further examination. METHODS: Using a descriptive phenomenological approach, semi-structured interviews were undertaken with key stakeholders, including 5 retired drivers with dementia, 12 FC, and 15 health professionals (HP). Data were analyzed inductively to explore the needs and experiences of people with dementia and FC. RESULTS: The data revealed a range of possible interactions between people with dementia and FC. These were organized into a continuum of family dynamics according to levels of collaboration and conflict: in it together, behind the scenes, active negotiations, and at odds. At the in it together end of the continuum, people with dementia and FC demonstrated collaborative approaches and minimal conflict in managing driving cessation. At the at odds end, they experienced open conflict and significant tension in their interactions. Contextual factors influencing family dynamics were identified, along with the need for individualized approaches to support. CONCLUSIONS: The continuum of family dynamics experienced during driving cessation may help clinicians better understand and respond to complex family needs. Interventions should be tailored to families' distinctive needs with consideration of their unique contextual factors influencing dynamics, to provide sensitive and responsive support for families managing driving cessation.

Managing nausea and vomiting in pregnancy in a primary care setting.

ERRATUM.

Leveraging Large Language Models to Analyze Continuous Glucose Monitoring Data: A Case Study.

Continuous glucose monitors (CGM) provide patients and clinicians with valuable insights about glycemic control that aid in diabetes management. The advent of large language models (LLMs), such as GPT-4, has enabled real-time text generation and summarization of medical data. Further, recent advancements have enabled the integration of data analysis features in chatbots, such that raw data can be uploaded and analyzed when prompted. Studying both the accuracy and suitability of LLM-derived data analysis performed on medical time series data, such as CGM data, is an important area of research. The objective of this study was to assess the strengths and limitations of using an LLM to analyze raw CGM data and produce summaries of 14 days of data for patients with type 1 diabetes. This study used simulated CGM data from 10 different cases. We first evaluated the ability of GPT-4 to compute quantitative metrics specific to diabetes found in an Ambulatory Glucose Profile (AGP). Then, using two independent clinician graders, we evaluated the accuracy, completeness, safety, and suitability of qualitative descriptions produced by GPT-4 across five different CGM analysis tasks. We demonstrated that GPT-4 performs well across measures of accuracy, completeness, and safety when producing summaries of CGM data across all tasks. These results highlight the capabilities of using an LLM to produce accurate and safe narrative summaries of medical time series data. We highlight several limitations of the work, including concerns related to how GPT-4 may misprioritize highlighting instances of hypoglycemia and hyperglycemia. Our work serves as a preliminary study on how generative language models can be integrated into diabetes care through CGM analysis, and more broadly, the potential to leverage LLMs for streamlined medical time series analysis.

Where does the time go? Temporal patterns of pumping behaviors in mothers of very preterm infants vary by sociodemographic and clinical factors.

Background: Mothers of very preterm (<32 weeks gestational age [GA]) infants are breast pump dependent and have shorter duration of milk provision than mothers of term infants. The opportunity (i.e., time) cost of pumping and transporting mother's own milk (MOM) from home to the NICU may be a barrier. There is a paucity of data regarding how much time mothers actually spend pumping. Objective: To investigate the variation in pumping behavior by postpartum week, maternal characteristics, and infant GA. Methods: Prospectively collected pump log data from mothers enrolled in ReDiMOM (Reducing Disparity in Mother's Own Milk) randomized, controlled trial included pumping date and start time and end time of each pumping session for the first 10 weeks postpartum or until the infant was discharged from the NICU, whichever occurred first. Outcomes included number of daily pumping sessions, number of minutes spent pumping per day, and pumping behaviors during 24-h periods, aggregated to the postpartum week. Medians (interquartile ranges) were used to describe outcomes overall, and by maternal characteristics and infant GA. Results: Data included 13,994 pump sessions from 75 mothers. Maternal characteristics included 55% Black, 35% Hispanic, and 11% White and 44% <30 years old. The majority (56%) of infants were born at GA 28-31 weeks. Mothers pumped an average of less than 4 times per day, peaking in postpartum week 2. After accounting for mothers who stopped pumping, there was a gradual decrease in daily pumping minutes between postpartum weeks 2 (89 min) and 10 (46 min). Black mothers pumped fewer times daily than non-Black mothers after the first 2 weeks postpartum. Conclusion: On average mothers pumped less intensively than the minimum recommendation of 8 times and 100 min per day. However, these pumping behaviors represent significant maternal opportunity costs that should be valued by the institution and society at large.

Polygenic risk scores for autoimmune related diseases are significantly different in cancer exceptional responders.

A small number of cancer patients respond exceptionally well to therapies and survive significantly longer than patients with similar diagnoses. Profiling the germline genetic backgrounds of exceptional responder (ER) patients, with extreme survival times, can yield insights into the germline polymorphisms that influence response to therapy. As ERs showed a high incidence in autoimmune diseases, we hypothesized the differences in autoimmune disease risk could reflect the immune background of ERs and contribute to better cancer treatment responses. We analyzed the germline variants of 51 ERs using polygenic risk score (PRS) analysis. Compared to typical cancer patients, the ERs had significantly elevated PRSs for several autoimmune-related diseases: type 1 diabetes, hypothyroidism, and psoriasis. This indicates that an increased genetic predisposition towards these autoimmune diseases is more prevalent among the ERs. In contrast, ERs had significantly lower PRSs for developing inflammatory bowel disease. The left-skew of type 1 diabetes score was significant for exceptional responders. Variants on genes involved in the T1D PRS model associated with cancer drug response are more likely to co-occur with other variants among ERs. In conclusion, ERs exhibited different risks for autoimmune diseases compared to typical cancer patients, which suggests that changes in a patient's immune set point or immune surveillance specificity could be a potential mechanistic link to their exceptional response. These findings expand upon previous research on immune checkpoint inhibitor-treated patients to include those who received chemotherapy or radiotherapy.

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.

Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.

Electronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system. We characterize patterns of missing data in laboratory measurements collected at the University of Pennsylvania Hospital System from long-term COVID-19 patients and focus on the changes in these patterns between 2020 and 2021. We investigate how these patterns are associated with comorbidities such as acute respiratory distress syndrome (ARDS), and 90-day mortality in ARDS patients. This work displays how knowledge and experience can change the way clinicians and hospitals manage a novel disease. It can also provide insight into best practices when it comes to patient monitoring to improve outcomes.

Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.

Electronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system. We characterize patterns of missing data in laboratory measurements collected at the University of Pennsylvania Hospital System from long-term COVID-19 patients and focus on the changes in these patterns between 2020 and 2021. We investigate how these patterns are associated with comorbidities such as acute respiratory distress syndrome (ARDS), and 90-day mortality in ARDS patients. This work displays how knowledge and experience can change the way clinicians and hospitals manage a novel disease. It can also provide insight into best practices when it comes to patient monitoring to improve outcomes.

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.

Volatile sulphur compounds and pathways of L-methionine catabolism in Williopsis yeasts.

Volatile sulphur compounds (VSCs) are important to the food industry due to their high potency and presence in many foods. This study assessed for the first time VSC production and pathways of L: -methionine catabolism in yeasts from the genus Williopsis with a view to understanding VSC formation and their potential flavour impact. Five strains of Williopsis saturnus (var. saturnus, var. subsufficiens, var. suavolens, var. sargentensis and var. mrakii) were screened for VSC production in a synthetic medium supplemented with L: -methionine. A diverse range of VSCs were produced including dimethyl disulphide, dimethyl trisulphide, 3-(methylthio)-1-propanal (methional), 3-(methylthio)-1-propanol (methionol), 3-(methylthio)-1-propene, 3-(methylthio)-1-propyl acetate, 3-(methylthio)-1-propanoic acid (methionic acid) and ethyl 3-(methylthio)-1-propanoate, though the production of these VSCs varied between yeast strains. W. saturnus var. saturnus NCYC22 was selected for further studies due to its relatively high VSC production. VSC production was characterised step-wise with yeast strain NCYC22 in coconut cream at different L: -methionine concentrations (0.00-0.20%) and under various inorganic sulphate (0.00-0.20%) and nitrogen (ammonia) supplementation (0.00-0.20%), respectively. Optimal VSC production was obtained with 0.1% of L: -methionine, while supplementation of sulphate had no significant effect. Nitrogen supplementation showed a dramatic inhibitory effect on VSC production. Based on the production of VSCs, the study suggests that the Ehrlich pathway of L: -methionine catabolism is operative in W. saturnus yeasts and can be manipulated by adjusting certain nutrient parameters to control VSC production.