Towards a Transportable Causal Network Model Based on Observational Healthcare Data

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods.</p

Towards a Transportable Causal Network Model Based on Observational Healthcare Data

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods

Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients:A Causal Approach

Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models are flexible and expressive enough to capture the dynamics of clinical risk assessment. However, in this setting we are limited to observational data with quality issues, missing values, small sample size and high dimensionality: we cannot reliably learn such models from limited observational data with these sources of bias. Instead, we choose to learn a causal Bayesian network to mitigate the issues above and to leverage the prior knowledge on endometrial cancer available from clinicians and physicians. We introduce a causal discovery algorithm for causal Bayesian networks based on bootstrap resampling, as opposed to the single imputation used in related works. Moreover, we include a context variable to evaluate whether selection bias results in learning spurious associations. Finally, we discuss the strengths and limitations of our findings in light of the presence of missing data that may be missing-not-at-random, which is common in real-world clinical settings.</p

Causal Discovery with Missing Data in a Multicentric Clinical Study

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.</p

Causal Discovery with Missing Data in a Multicentric Clinical Study

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways

Synthesis of the C1-C27 fragment of stambomycin D validates modular polyketide synthase-based stereochemical assignments

The stambomycins are a family of bioactive macrolides isolated from Streptomyces ambofaciens. Aside from two stereocenters installed through cytochrome P450 oxidations, their stereochemistry has been predicted by sequence analysis of the polyketide synthase. We report a synthesis of the C1-C27 fragment of stambomycin D, the spectroscopic data of which correlates well with that of the natural product, further validating predictive sequence analysis as a powerful tool for stereochemical assignment of complex polyketide natural products

Placental pathology in perinatal asphyxia: a case–control study

IntroductionPlacentas of term infants with birth asphyxia are reported to have more lesion such as maternal vascular malperfusion (MVM), fetal vascular malperfusion (FVM) and chorioamnionitis with fetal response (FIR) than those of term infants without birth asphyxia. We compared the placental pathology of asphyxiated newborns, including those who developed hypoxic-ischemic encephalopathy (HIE), with non-asphyxiated controls.MethodsWe conducted a retrospective case–control study of placentas from neonates with a gestational age ≥ 35 weeks, a birthweight ≥ 1,800 g, and no malformations. Cases were asphyxiated newborns (defined as those with an umbilical artery pH ≤ 7.0 or base excess ≤ −12 mMol, 10-minute Apgar score ≤ 5, or the need for resuscitation lasting &gt;10 min) from a previous cohort, with (n=32) and without (n=173) diagnosis of HIE. Controls were non-asphyxiated newborns from low-risk l (n= 50) or high-risk (n= 68) pregnancies. Placentas were analyzed according to the Amsterdam Placental Workshop Group Consensus Statement 2014.ResultsCases had a higher prevalence of nulliparity, BMI&gt;25, thick meconium, abnormal fetal heart monitoring, and acute intrapartum events than controls (p&lt;0.001). MVM and FVM were more frequent among non-asphyxiated than asphyxiated newborns (p&lt;0.001). There was no significant difference in inflammatory lesions or abnormal umbilical insertion site. Histologic meconium-associated changes (MAC) were observed in asphyxiated newborns only (p= 0.039).DiscussionOur results confirm the role of antepartum and intrapartum risk factors in neonatal asphyxia and HIE. No association between neonatal asphyxia and placental lesions was found, except for in the case of MAC. The association between clinical and placental data is crucial to understanding and possibly preventing perinatal asphyxia in subsequent pregnancies

The Clinical Impact of Methotrexate-Induced Stroke-Like Neurotoxicity in Paediatric Departments: An Italian Multi-Centre Case-Series

IntroductionStroke-like syndrome (SLS) is a rare subacute neurological complication of intrathecal or high-dose (&gt;= 500 mg) Methotrexate (MTX) administration. Its clinical features, evoking acute cerebral ischaemia with fluctuating course symptoms and a possible spontaneous resolution, have elicited interest among the scientific community. However, many issues are still open on the underlying pathogenesis, clinical, and therapeutic management and long-term outcome. Materials and MethodsWe retrospectively analyzed clinical, radiological and laboratory records of all patients diagnosed with SLS between 2011 and 2021 at 4 National referral centers for Pediatric Onco-Hematology. Patients with a latency period that was longer than 3 weeks between the last MTX administration of MTX and SLS onset were excluded from the analysis, as were those with unclear etiologies. We assessed symptom severity using a dedicated arbitrary scoring system. Eleven patients were included in the study. ResultsThe underlying disease was acute lymphoblastic leukemia type B in 10/11 patients, while fibroblastic osteosarcoma was present in a single subject. The median age at diagnosis was 11 years (range 4-34), and 64% of the patients were women. Symptoms occurred after a mean of 9.45 days (+/- 0.75) since the last MTX administration and lasted between 1 and 96 h. Clinical features included hemiplegia and/or cranial nerves palsy, paraesthesia, movement or speech disorders, and seizure. All patients underwent neuroimaging studies (CT and/or MRI) and EEG. The scoring system revealed an average of 4.9 points (+/- 2.3), with a median of 5 points (maximum 20 points). We detected a linear correlation between the severity of the disease and age in male patients. ConclusionsSLS is a rare, well-characterized complication of MTX administration. Despite the small sample, we have been able to confirm some of the previous findings in literature. We also identified a linear correlation between age and severity of the disease, which could improve the future clinical management

Maternal and perinatal outcomes following pre-Delta, Delta, and Omicron SARS-CoV-2 variants infection among unvaccinated pregnant women in France and Switzerland: a prospective cohort study using the COVI-PREG registry.

BACKGROUND SARS-CoV-2 positive pregnant women are at higher risk of adverse outcomes, but little evidence is available on how variants impact that risk. We aim to evaluate maternal and perinatal outcomes among unvaccinated pregnant women that tested positive for SARS-CoV-2, stratified by pre-Delta, Delta, and Omicron periods. METHODS This prospective study enrolled women from March 2020 to September 2022. Exposure to the different SARS-CoV-2 variants was defined by their periods of predominance. The primary outcome was severe maternal adverse outcome defined as either intensive care unit admission, acute respiratory distress syndrome, advanced oxygen supplementation, or maternal death. The secondary outcomes were preterm birth and other perinatal outcomes. FINDINGS Overall, 1402, 262, and 391 SARS-CoV-2 positive pregnant women were enrolled during the pre-Delta, Delta, and Omicron periods respectively. Severe maternal adverse outcome was reported in 3.4% (n = 947/1402; 95% confidence intervals (95%CI) 2.5-4.5), 6.5% (n = 7/262; 95%CI 3.8-10.2), and 1.0% (n = 4/391; 95%CI 0.3-2.6) of women during the pre-Delta, Delta, and Omicron periods. The risk of severe maternal adverse outcome was higher during the Delta vs pre-Delta period (adjusted risk ratio (aRR) = 1.8; 95%CI 1.1-3.2) and lower during the Omicron vs pre-Delta period (aRR = 0.3; 95%CI, 0.1-0.8). The risks of hospitalization for COVID-19 were 12.6% (n = 176/1402; 95%CI 10.9-14.4), 17.2% (n = 45/262; 95%CI 12.8-22.3), and 12.5% (n = 49/391; 95%CI 9.4-16.2), during the pre-Delta, Delta, and Omicron period, respectively. Pregnancy complications occurred after SARS-CoV-2 exposure in 30.0% (n = 363/1212; 95%CI 27.4-32.6), 35.2% (n = 83/236; 95%CI 29.1-41.6), and 30.3% (n = 105/347; 95%CI 25.5-35.4) of patients during the pre-Delta, Delta, and Omicron periods, respectively. Stillbirths were reported in 0.5% (n = 6/1159; 95%CI 0.2-1.1), 2.8% (n = 6/210; 95%CI 1.0-6.0), and 0.9% (n = 2/213; 95%CI 0.1-3.4) or patients during the pre-Delta, Delta, and Omicron periods respectively. INTERPRETATION The Delta period was associated with a higher risk of severe maternal adverse outcome and the Omicron period with a lower risk of severe adverse outcome compared to pre-Delta era. The reported risk of hospitalization was high during the Omicron period and should not be trivialized. FUNDING Swiss Federal Office of Public Health, Fondation CHUV

Highlights From the Annual Meeting of the American Epilepsy Society 2022

With more than 6000 attendees between in-person and virtual offerings, the American Epilepsy Society Meeting 2022 in Nashville, felt as busy as in prepandemic times. An ever-growing number of physicians, scientists, and allied health professionals gathered to learn a variety of topics about epilepsy. The program was carefully tailored to meet the needs of professionals with different interests and career stages. This article summarizes the different symposia presented at the meeting. Basic science lectures addressed the primary elements of seizure generation and pathophysiology of epilepsy in different disease states. Scientists congregated to learn about anti-seizure medications, mechanisms of action, and new tools to treat epilepsy including surgery and neurostimulation. Some symposia were also dedicated to discuss epilepsy comorbidities and practical issues regarding epilepsy care. An increasing number of patient advocates discussing their stories were intertwined within scientific activities. Many smaller group sessions targeted more specific topics to encourage member participation, including Special Interest Groups, Investigator, and Skills Workshops. Special lectures included the renown Hoyer and Lombroso, an ILAE/IBE joint session, a spotlight on the impact of Dobbs v. Jackson on reproductive health in epilepsy, and a joint session with the NAEC on coding and reimbursement policies. The hot topics symposium was focused on traumatic brain injury and post-traumatic epilepsy. A balanced collaboration with the industry allowed presentations of the latest pharmaceutical and engineering advances in satellite symposia