Communications medicine最新文献

{"title":"Machine learning evaluation of intensified conditioning on haematopoietic stem cell transplantation in adult acute lymphoblastic leukemia patients","authors":"Tomoyasu Jo, Kosuke Inoue, Tomoaki Ueda, Makoto Iwasaki, Yu Akahoshi, Satoshi Nishiwaki, Hiroki Hatsusawa, Tetsuya Nishida, Naoyuki Uchida, Ayumu Ito, Masatsugu Tanaka, Satoru Takada, Toshiro Kawakita, Shuichi Ota, Yuta Katayama, Satoshi Takahashi, Makoto Onizuka, Yuta Hasegawa, Keisuke Kataoka, Yoshinobu Kanda, Takahiro Fukuda, Ken Tabuchi, Yoshiko Atsuta, Yasuyuki Arai","doi":"10.1038/s43856-024-00680-y","DOIUrl":"10.1038/s43856-024-00680-y","url":null,"abstract":"The advantage of intensified myeloablative conditioning (MAC) over standard MAC has not been determined in haematopoietic stem cell transplantation (HSCT) for adult acute lymphoblastic leukemia (ALL) patients. To evaluate heterogeneous effects of intensified MAC among individuals, we analyzed the registry database of adult ALL patients between 2000 and 2021. After propensity score matching, we applied a machine-learning Bayesian causal forest algorithm to develop a prediction model of individualized treatment effect (ITE) of intensified MAC on reduction in overall mortality at 1 year after HSCT. Among 2440 propensity score-matched patients, our model shows heterogeneity in the association between intensified MAC and 1-year overall mortality. Individuals in the high-benefit group (n = 1220), defined as those with ITEs greater than the median, are more likely to be younger, male, and to have higher refined Disease Risk Index (rDRI), T-cell phenotype, and grafts from related donors than those in the low-benefit group (n = 1220). The high-benefit approach (applying intensified MAC to individuals in the high-benefit group) shows the largest reduction in overall mortality at 1 year (risk difference [95% confidence interval], +5.94 percentage points [0.88 to 10.51], p = 0.011). In contrast, the high-risk approach (targeting patients with high or very high rDRI) does not achieve statistical significance (risk difference [95% confidence interval], +3.85 percentage points [−1.11 to 7.90], p = 0.063). These findings suggest that the high-benefit approach, targeting patients expected to benefit from intensified MAC, has the capacity to maximize HSCT effectiveness using intensified MAC. People with acute lymphoblastic leukemia (ALL), a blood cancer, can be treated by being transplanted with stem cells from other healthy people. However, in some people the cancer grows back after treatment. Intensified treatment, which combines additional chemotherapy with standard conditioning (called intensified myeloablative conditioning, intensified MAC), prior to transplant can reduce relapse but it remains unclear which patients will benefit most from this approach. We used a computational approach to analyse results from Japanese cancer patients. We identified a group of patients whose likelihood of death within 1 year was reduced by intensified MAC. Targeting these patients with intensified MAC could maximize treatment effectiveness and improve transplant outcomes. Jo, Inoue et al. use a machine-learning Bayesian causal forest algorithm to evaluate the effect of intensified myeloablative conditioning (MAC) on mortality following haematopoietic stem cell transplantation (HSCT). Intensified myeloablative conditioning (MAC) has heterogeneous effects on reducing mortality.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11589779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early nasal microbiota and subsequent respiratory tract infections in infants with cystic fibrosis","authors":"Ruth Steinberg, Nadja Mostacci, Elisabeth Kieninger, Bettina Frauchiger, Carmen Casaulta, Jakob Usemann, Alexander Moeller, Daniel Trachsel, Isabelle Rochat, Sylvain Blanchon, Dominik Mueller-Suter, Barbara Kern, Maura Zanolari, Urs Frey, Kathryn A. Ramsey, Markus Hilty, Philipp Latzin, Insa Korten, SCILD study group, BILD study group","doi":"10.1038/s43856-024-00616-6","DOIUrl":"10.1038/s43856-024-00616-6","url":null,"abstract":"Respiratory tract infections (RTIs) drive lung function decline in children with cystic fibrosis (CF). While the respiratory microbiota is clearly associated with RTI pathogenesis in infants without CF, data on infants with CF is scarce. We compared nasal microbiota development between infants with CF and controls and assessed associations between early-life nasal microbiota, RTIs, and antibiotic treatment in infants with CF. We included 50 infants with CF and 30 controls from two prospective birth cohorts followed throughout the first year of life. We collected 1511 biweekly nasal swabs and analyzed the microbiota after amplifying the V3–V4 region of the 16S rRNA gene. We conducted structured weekly interviews to assess respiratory symptoms and antibiotic treatment. We calculated generalized additive mixed models and permutational analysis of variance. Here, we show that the nasal microbiota is already altered before the first RTI or antibiotic treatment in infants with CF. Microbiota diversity differs between infants with CF and controls following RTIs and/or antibiotic treatment. CF infants with lower α-diversity have a higher number of subsequent RTIs. Early nasal microbiota alterations may reflect predisposition or predispose to RTIs in infants with CF, and further change after RTIs and antibiotic treatment. This highlights the potential of targeting the nasal microbiota in CF-related RTI management, while also questioning current practices in the era of novel modulator therapies. Cystic fibrosis (CF) is an inherited condition which can increase the risk of developing respiratory tract infections (RTIs). We investigated the microorganisms present in the respiratory tract of infants from birth to the age of one. We found that infants with CF had differences in the microorganisms present immediately after birth compared to infants without CF. These differences increased after development of RTIs and following antibiotic treatment. Our results suggest that infants with CF could potentially benefit from treatments that modify microorganisms present in their respiratory tract prior to development of any RTI, or from different antibiotics to those used by infants without CF. Steinberg et al. explore the associations between the nasal microbiota, respiratory tract infections (RTIs) and antibiotics in infants with cystic fibrosis (CF) and controls during the first year of life. Infants with CF have a different microbiota before their first RTI or antibiotic treatment, with lower diversity linked to higher number of RTIs.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00616-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morbidity of SARS-CoV-2 in the evolution to endemicity and in comparison with influenza","authors":"Istvan Bartha, Cyrus Maher, Victor Lavrenko, Yi-Pei Chen, Qiqing Tao, Julia di Iulio, Keith Boundy, Elizabeth Kinter, Wendy Yeh, Davide Corti, Amalio Telenti","doi":"10.1038/s43856-024-00633-5","DOIUrl":"10.1038/s43856-024-00633-5","url":null,"abstract":"There are three possible SARS-CoV-2 post-pandemic scenarios: (i) ongoing severity, (ii) influenza-like severity, and (iii) a transition to an endemic disease with lesser morbidity similar to that of other human coronaviruses. To assess a possible evolution of the pandemic under the three scenarios, we use data from the US National Covid Cohort Collaborative, CDC COVID-NET, and CDC Fluview and from the WastewaterSCAN Dashboard. We include influenza disease and treatment response as benchmark. The US National Covid Cohort Collaborative allows the quantification of viral-specific morbidity using electronic health records from 424,165 SARS-CoV-2 cases, 53,846 influenza cases, and 199,971 uninfected control subjects from 2021–2022. Evolution of hospitalization rates is estimated from the correlation between national SARS-CoV-2 and influenza hospitalization data and viral gene copies in wastewater. Our findings reveal that medically attended SARS-CoV-2 infections exhibit similar morbidity to influenza [indicative of scenario (ii)], but SARS-CoV-2 hospitalization rates are one order of magnitude lower than influenza when considering virus concentration in wastewater [indicative of scenario (iii)]. Moreover, SARS-CoV-2 displays a more favorable response to antiviral therapy. Our analysis confirms a rapid decline in SARS-CoV-2 morbidity as it transitions to an endemic state. The impact of SARS-CoV-2 infections has changed over time since the start of the pandemic. We use information about deaths and hospitalization from COVID-19 and combine it with data obtained from monitoring wastewater to study how patterns of infection have changed over time and how this compares with the impact of influenza. We show that recently there has been a marked decrease in SARS-CoV-2 infections leading to hospitalization, in contrast to stable rates of hospitalization for people infected with influenza. Our results suggest that SARS-CoV-2 is currently a persistent, i.e., endemic disease with less severe impact on most people who are infected. This information is helpful for hospitals and public health departments that monitor and prepare for infectious disease outbreaks. Bartha et al. investigate the evolution of SARS-CoV-2 towards an endemic state. Real world data on over 600,000 individuals and from wastewater surveillance show loss of SARS-CoV-2 virulence and patterns of morbidity similar to influenza.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-9"},"PeriodicalIF":5.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00633-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep reinforcement learning extracts the optimal sepsis treatment policy from treatment records","authors":"Yunho Choi, Songmi Oh, Jin Won Huh, Ho-Taek Joo, Hosu Lee, Wonsang You, Cheng-mok Bae, Jae-Hun Choi, Kyung-Joong Kim","doi":"10.1038/s43856-024-00665-x","DOIUrl":"10.1038/s43856-024-00665-x","url":null,"abstract":"Sepsis is one of the most life-threatening medical conditions. Therefore, many clinical trials have been conducted to identify optimal treatment strategies for sepsis. However, finding reliable strategies remains challenging due to limited-scale clinical tests. Here we tried to extract the optimal sepsis treatment policy from accumulated treatment records. In this study, with our modified deep reinforcement learning algorithm, we stably generated a patient treatment artificial intelligence model. As training data, 16,744 distinct admissions in tertiary hospitals were used and tested with separate datasets. Model performance was tested by t test and visualization of estimated survival rates. We also analyze model behavior using the confusion matrix, important feature extraction by a random forest decision tree, and treatment behavior comparison to understand how our treatment model achieves high performance. Here we show that our treatment model’s policy achieves a significantly higher estimated survival rate (up to 10.03%). We also show that our models’ vasopressor treatment was quite different from that of physicians. Here, we identify that blood urea nitrogen, age, sequential organ failure assessment score, and shock index are the most different factors in dealing with sepsis patients between our model and physicians. Our results demonstrate that the patient treatment model can extract potential optimal sepsis treatment policy. We also extract core information about sepsis treatment by analyzing its policy. These results may not apply directly in clinical settings because they were only tested on a database. However, they are expected to serve as important guidelines for further research. Sepsis is one of the most life-threatening medical conditions. It can be challenging to select the best treatment strategy for individual patients. We developed a computational model to identify optimal treatment strategies and applied it to a large amount of data obtained from patients with sepsis. We identified particular types of information about the patients that can be used to decide on the best medication and dose to treat sepsis. Further development of our treatment model could potentially enable it to be used to improve the survival of patients with sepsis. Also, the results we obtained could be used to improve the general guidance followed when treating people with sepsis. Choi et al. develop and validate a sepsis treatment model based on deep reinforcement learning using patients’ treatment records. The model increases patients’ estimated survival rate and analysis of the treatment model’s strategies also suggests potential future sepsis research directions.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-11"},"PeriodicalIF":5.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00665-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate patient alignment without unnecessary imaging using patient-specific 3D CT images synthesized from 2D kV images","authors":"Yuzhen Ding, Jason M. Holmes, Hongying Feng, Baoxin Li, Lisa A. McGee, Jean-Claude M. Rwigema, Sujay A. Vora, William W. Wong, Daniel J. Ma, Robert L. Foote, Samir H. Patel, Wei Liu","doi":"10.1038/s43856-024-00672-y","DOIUrl":"10.1038/s43856-024-00672-y","url":null,"abstract":"In radiotherapy, 2D orthogonally projected kV images are used for patient alignment when 3D-on-board imaging (OBI) is unavailable. However, tumor visibility is constrained due to the projection of patient’s anatomy onto a 2D plane, potentially leading to substantial setup errors. In treatment room with 3D-OBI such as cone beam CT (CBCT), the field of view (FOV) of CBCT is limited with unnecessarily high imaging dose. A solution to this dilemma is to reconstruct 3D CT from kV images obtained at the treatment position. We propose a dual-models framework built with hierarchical ViT blocks. Unlike a proof-of-concept approach, our framework considers kV images acquired by 2D imaging devices in the treatment room as the solo input and can synthesize accurate, full-size 3D CT within milliseconds. We demonstrate the feasibility of the proposed approach on 10 patients with head and neck (H&N) cancer using image quality (MAE: < 45HU), dosimetric accuracy (Gamma passing rate ((2%/2 mm/10%): > 97%) and patient position uncertainty (shift error: < 0.4 mm). The proposed framework can generate accurate 3D CT faithfully mirroring patient position effectively, thus substantially improving patient setup accuracy, keeping imaging dose minimal, and maintaining treatment veracity. Effective and accurate imaging guidance is critical for precise patient alignment, accurate tumor tracking, accurate delivery of radiation therapy and to protect organs that should not be irradiated. However, high-quality imaging guidance usually can only be provided following detailed imaging using a large amount of radiation. We propose a computational method that can generate the full size 3D images required as image guidance from X-Ray images. We demonstrated its utility using data from 10 people with head and neck cancer. Our proposed approach can be used by existing treatment machines to improve the accuracy of patient alignment and hence ensure more accurate treatment of patients. Ding et al. propose a deep learning-based model for fast and accurate 3D CT reconstruction given 2D kV (X-Ray) images as the solo inputs. The experimental results and analysis indicate that the proposed framework can be used for accurate and robust patient alignment with minimum imaging dose.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00672-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A retrospective two-center cohort study of the bidirectional relationship between depression and tinnitus-related distress","authors":"Cosima F. Lukas, Birgit Mazurek, Petra Brueggemann, Markus Junghöfer, Orlando Guntinas–Lichius, Christian Dobel","doi":"10.1038/s43856-024-00678-6","DOIUrl":"10.1038/s43856-024-00678-6","url":null,"abstract":"Tinnitus can cause considerable psychological distress among patients, particularly if comorbidities occur. Despite a strong relationship between tinnitus-related distress and depression, the underlying mechanisms represent a long-standing question. By investigating the co-development of tinnitus-related distress and depressiveness throughout therapy, we capture the dynamic interplay of both conditions and uncover underlying common features mediating their link. Large datasets from two different day clinics in Germany have been analyzed using a regularization method for predictor selection (analysis 1) and latent growth curve modeling (LCM; analysis 2). Tinnitus-related distress was assessed using the Tinnitus Questionnaire (TQ). All patients have been experiencing chronic subjective tinnitus with a minimum mean severity level of TQ grade 2. Treatment at both day clinics involved tinnitus management according to clinical guidelines with minor idiosyncratic differences. Analysis 1 was performed on a dataset of 500 patients who received the Jena Interdisciplinary Treatment for Tinnitus (JITT) for 5 consecutive days between 2013 and 2017. Analysis 2 was performed on a second dataset, which included 1016 patients treated at the Tinnitus Center of the Charité Universitätsmedizin Berlin for 7 days between 2011 and 2015. Here, we show a substantial bidirectional relationship between tinnitus-related distress and depression severity while emphasizing the role of somatic symptoms and perceived stress in the experience and maintenance of tinnitus awareness. The LCM provides adequate model fit (CFI = 0.993, SRMR = 0.016). Our results indicate enhanced therapy success in depression when tinnitus-related distress is addressed and vice versa. The combined treatment of tinnitus and depression is proposed for future treatment strategies. Tinnitus, also described as ringing in the ears, can lead to considerable psychological distress and often occurs with depression. This study aimed to explore the relationship between tinnitus-related distress and depression. We analyzed data from two German day clinics to understand how tinnitus-related distress and depression interact during therapy. The main finding is a strong bidirectional relationship between tinnitus-related distress and depression. Physical complaints and stress explain part of this association. The study highlights the importance of addressing both tinnitus-related distress and depressive mood in a combined treatment. It suggests that reducing distress in one condition can enhance improvement in the other. This insight can make treatment better for individuals with chronic tinnitus and depression. Lukas et al. investigate the co-development of tinnitus-related distress and depressiveness throughout treatment. The strong bidirectional relationship indicates a combined treatment of tinnitus and depression, suggesting enhanced treatment success in tinnitus-related distress when depression is addressed and ","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00678-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Underestimated risk of secondary complications in pathogenic and glucose-elevating GCK variant carriers with type 2 diabetes","authors":"Kelly M. Schiabor Barrett, Natalie Telis, Lisa M. McEwen, Evanette K. Burrows, Basil Khuder, Daniel P. Judge, Pamala A. Pawloski, Joseph J. Grzymski, Nicole L. Washington, Alexandre Bolze, Elizabeth T. Cirulli","doi":"10.1038/s43856-024-00663-z","DOIUrl":"10.1038/s43856-024-00663-z","url":null,"abstract":"Natural HbA1c levels in GCK Maturity-onset diabetes of the young (GCK-MODY) patients often sit above the diagnostic threshold for type 2 diabetes (T2D). Treatments to lower HbA1c levels show reduced effectiveness in these individuals, yet in case studies to date, GCK-MODY patients often evade secondary T2D complications. Given these deviations, genetic screening of GCK may be clinically useful, but population studies are needed to more broadly understand T2D-related complications in GCK variant carriers. To identify GCK variant carriers at the population level, we used both ACMG/AMP variant interpretation for GCK-MODY pathogenicity and a state-of-the-art variant interpretation strategy based on functional and statistical evidence to predict glucose elevations. Presence of pathogenic and glucose-elevating GCK variants was assessed in two cohorts (n~535,000). We identified 442 individuals with GCK variants predicted to increase glucose (~1/1200), with 150 (34%) of these individuals harboring variants reaching a pathogenic interpretation. In a retrospective analysis, we show that in addition to elevated HbA1c, pathogenic variant carriers are 10x as likely, and all other glucose-elevating GCK variant carriers are 3x as likely, to receive a T2D diagnosis compared to non-GCK carriers. Surprisingly, carriers of pathogenic and glucose-elevating GCK variants with T2D develop T2D-related complications at rates more than double that of individuals without T2D, comparable to non-GCK individuals with T2D. This population-level assessment shows secondary complications in individuals with pathogenic and glucose-elevating GCK variants and T2D and suggests that genotyping for these variants should be considered in a precision medicine approach for T2D treatment and prevention. This study investigates the role of the GCK gene in Type 2 Diabetes (T2D). Variations in the GCK gene are known to cause a form of diabetes that is characterized by early onset and stable blood sugar levels, with a low risk of the type of complications often experienced by people with other causes of T2D. However, our study of a large number of people living in both the United Kingdom and the United States shows that some specific variations in the GCK gene give people a higher risk of developing T2D with the typical secondary complications. This suggests that knowledge of GCK variation in a person should be considered when optimizing T2D prevention and treatment strategies. Schiabor Barrett et al evaluate variants in GCK, a gene associated with Monogenic Diabetes of the Young (MODY), in two population cohorts with healthcare records. They find that participants with pathogenic MODY and other glucose-elevating variants are at risk for Type 2 Diabetes (T2D) and those with T2D show secondary complications of T2D.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-12"},"PeriodicalIF":5.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00663-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales","authors":"Emily S. Nightingale, Swaminathan Subramanian, Ashley R. Schwarzer, Lloyd A. C. Chapman, Purushothaman Jambulingam, Mary M. Cameron, Oliver J. Brady, Graham F. Medley, Tim C. D. Lucas","doi":"10.1038/s43856-024-00659-9","DOIUrl":"10.1038/s43856-024-00659-9","url":null,"abstract":"As cases of visceral leishmaniasis (VL) in India dwindle, there is motivation to monitor elimination progress on a finer geographic scale than sub-district (block). Low-incidence projections across geographically- and demographically- heterogeneous communities are difficult to act upon, and equitable elimination cannot be achieved if local pockets of incidence are overlooked. However, maintaining consistent surveillance at this scale is resource-intensive and not sustainable in the long-term. We analysed VL incidence across 45,000 villages in Bihar state, exploring spatial autocorrelation and associations with local environmental conditions in order to assess the feasibility of inference at this scale. We evaluated a statistical disaggregation approach to infer finer spatial variation from routinely-collected, block-level data, validating against observed village-level incidence. This disaggregation approach does not estimate village-level incidence more accurately than a baseline assumption of block-homogeneity. Spatial auto-correlation is evident on a block-level but weak between neighbouring villages within the same block, possibly suggesting that longer-range transmission (e.g., due to population movement) may be an important contributor to village-level heterogeneity. Increasing the range of reactive interventions to neighbouring villages may not improve their efficacy in suppressing transmission, but maintaining surveillance and diagnostic capacity in areas distant from recently observed cases - particularly along routes of population movement from endemic regions - could reduce reintroduction risk in currently unaffected villages. The reactive, spatially-targeted approach to VL surveillance limits interpretability of data observed at the village level, and hence the feasibility of routinely drawing and validating inference at this scale. Near elimination, it is important to understand how the remaining cases of disease are distributed on a local level. However, surveillance data are more easily collated according to larger administrative units. We investigated whether village-level patterns of visceral leishmaniasis (VL) incidence could be inferred from administrative-level data using a statistical modelling approach. We found strong similarity in incidence between neighbouring administrative units but not between neighbouring villages, and model predictions did not correspond well to observed village-level case data. This could suggest that longer-range transmission contributes more to the village-level pattern of incidence than short in this near-elimination context, which should be considered in intervention planning. However, increased surveillance effort in assumed high-risk villages makes interpretation of data at this level challenging. Nightingale et al. compare block-homogeneity and statistical disaggregation approaches to analyse visceral leishmaniasis incidence across 45,000 villages in Bihar state. Village-level incidence is no","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00659-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ursodeoxycholic acid and severe COVID-19 outcomes in a cohort study using the OpenSAFELY platform","authors":"Ruth E. Costello, Karen M. J. Waller, Rachel Smith, George F. Mells, Angel Y. S. Wong, Anna Schultze, Viyaasan Mahalingasivam, Emily Herrett, Bang Zheng, Liang-Yu Lin, Brian MacKenna, Amir Mehrkar, Sebastian C. J. Bacon, Ben Goldacre, Laurie A. Tomlinson, John Tazare, Christopher T. Rentsch, the OpenSAFELY collaborative, the LH&W NCS (or CONVALESCENCE) Collaborative","doi":"10.1038/s43856-024-00664-y","DOIUrl":"10.1038/s43856-024-00664-y","url":null,"abstract":"Biological evidence suggests ursodeoxycholic acid (UDCA)—a common treatment of cholestatic liver disease—may prevent severe COVID-19 outcomes. We aimed to compare the hazard of COVID-19 hospitalisation or death between UDCA users versus non-users in a population with primary biliary cholangitis (PBC) or primary sclerosing cholangitis (PSC). With the approval of NHS England, we conducted a population-based cohort study using primary care records between 1 March 2020 and 31 December 2022, linked to death registration data and hospital records through the OpenSAFELY-TPP platform. Cox proportional hazards regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between time-varying UDCA exposure and COVID-19 related hospitalisation or death, stratified by geographical region and considering models unadjusted and fully adjusted for pre-specified confounders. We identify 11,305 eligible individuals, 640 were hospitalised or died with COVID-19 during follow-up, 400 (63%) events among UDCA users. After confounder adjustment, UDCA is associated with a 21% relative reduction in the hazard of COVID-19 hospitalisation or death (HR 0.79, 95% CI 0.67–0.93), consistent with an absolute risk reduction of 1.35% (95% CI 1.07%–1.69%). We found evidence that UDCA is associated with a lower hazard of COVID-19 related hospitalisation and death, support calls for clinical trials investigating UDCA as a preventative measure for severe COVID-19 outcomes. Costello et al. assess the impact of ursodeoxycholic acid (UDCA) treatment on COVID-19-related outcomes among people with chronic primary biliary cirrhosis and primary sclerosing cholangitis. Using a population-based cohort, they show that treatment with UDCA was associated with a reduced risk of COVID-19-related hospitalisation or death. Ursodeoxycholic acid is a drug used to treat liver disease. It has been proposed that it may prevent severe COVID-19 outcomes, however previous studies of this have had inconsistent results. We used electronic health records from people in the UK and identified people with two liver diseases: primary biliary cholangitis and primary sclerosing cholangitis. We looked at differences in hospitalisation and death between people taking UDCA and people who were not taking it. We found UDCA reduced the risk of severe COVID-19 outcomes by one-fifth. This suggests UDCA may help prevent serious COVID-19. Further clinical studies of UCDA should be undertaken, particularly in other groups with high risk or hospitalisation and death from COVID.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00664-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using UK Biobank data to establish population-specific atlases from whole body MRI","authors":"Sophie Starck, Vasiliki Sideri-Lampretsa, Jessica J. M. Ritter, Veronika A. Zimmer, Rickmer Braren, Tamara T. Mueller, Daniel Rueckert","doi":"10.1038/s43856-024-00670-0","DOIUrl":"10.1038/s43856-024-00670-0","url":null,"abstract":"Reliable reference data in medical imaging is largely unavailable. Developing tools that allow for the comparison of individual patient data to reference data has a high potential to improve diagnostic imaging. Population atlases are a commonly used tool in medical imaging to facilitate this. Constructing such atlases becomes particularly challenging when working with highly heterogeneous datasets, such as whole-body images, which contain significant anatomical variations. In this work, we propose a pipeline for generating a standardised whole-body atlas for a highly heterogeneous population by partitioning the population into anatomically meaningful subgroups. Using magnetic resonance images from the UK Biobank dataset, we create six whole-body atlases representing a healthy population average. We furthermore unbias them, and this way obtain a realistic representation of the population. In addition to the anatomical atlases, we generate probabilistic atlases that capture the distributions of abdominal fat (visceral and subcutaneous) and five abdominal organs across the population (liver, spleen, pancreas, left and right kidneys). Our pipeline effectively generates high-quality, realistic whole-body atlases with clinical applicability. The probabilistic atlases show differences in fat distribution between subjects with medical conditions such as diabetes and cardiovascular diseases and healthy subjects in the atlas space. With this work, we make the constructed anatomical and label atlases publically available, with the expectation that they will support medical research involving whole-body MR images. Medical imaging requires examples of healthy images to be available for comparison with individual patient data. This comparison is important to detect changes that are indicative of disease and enable diagnosis. Population atlases consist of healthy images that can be used for comparisons. The images should match population characteristics as much as possible. However, building these atlases can be difficult, especially if the images used to compile the atlas show differences. In this study, we provide a method to create a standardised whole-body atlas using whole-body (neck to knee) magnetic resonance images. We produce a set of atlases that represent a healthy population. These atlases have been made publicly available and should assist medical researchers and improve healthcare outcomes for patients. Starck and Sideri-Lampretsa et al. propose a pipeline for generating whole-body atlases from a heterogeneous population by dividing it into anatomically meaningful subgroups. They demonstrate the use of these atlases for studying differences between healthy individuals and those with conditions such as diabetes or cardiovascular disease.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}