Prediction of recovery from multiple organ dysfunction syndrome in pediatric sepsis patients.

MOTIVATION: Sepsis is a leading cause of death and disability in children globally, accounting for ∼3 million childhood deaths per year. In pediatric sepsis patients, the multiple organ dysfunction syndrome (MODS) is considered a significant risk factor for adverse clinical outcomes characterized by high mortality and morbidity in the pediatric intensive care unit. The recent rapidly growing availability of electronic health records (EHRs) has allowed researchers to vastly develop data-driven approaches like machine learning in healthcare and achieved great successes. However, effective machine learning models which could make the accurate early prediction of the recovery in pediatric sepsis patients from MODS to a mild state and thus assist the clinicians in the decision-making process is still lacking. RESULTS: This study develops a machine learning-based approach to predict the recovery from MODS to zero or single organ dysfunction by 1 week in advance in the Swiss Pediatric Sepsis Study cohort of children with blood-culture confirmed bacteremia. Our model achieves internal validation performance on the SPSS cohort with an area under the receiver operating characteristic (AUROC) of 79.1% and area under the precision-recall curve (AUPRC) of 73.6%, and it was also externally validated on another pediatric sepsis patients cohort collected in the USA, yielding an AUROC of 76.4% and AUPRC of 72.4%. These results indicate that our model has the potential to be included into the EHRs system and contribute to patient assessment and triage in pediatric sepsis patient care. AVAILABILITY AND IMPLEMENTATION: Code available at https://github.com/BorgwardtLab/MODS-recovery. The data underlying this article is not publicly available for the privacy of individuals that participated in the study. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dexmedetomidine leading to profound bradycardia in a pediatric liver transplant recipient.

Dexmedetomidine, an α2 -agonist, is used in the PICU for its sedative properties as it minimally affects respiratory status. However, hemodynamic instability is one of its known side effects. There is limited published experience with its use in pediatric liver transplant. We present a case of a 9-month-old infant who received a deceased donor liver transplantation for biliary atresia and received an IV dexmedetomidine infusion for sedation starting at 20 hours post-operatively. The patient received an IV bolus of 0.08 mcg/kg followed by an increase to 1 mcg/kg/hour. She was also receiving a fentanyl infusion for sedation at the time of dexmedetomidine initiation. Approximately 3 hours after initiation, she developed bradycardia as low as 30 beats-per-minute with an associated sinus pause of 7 seconds. She was given chest compressions by the bedside nurse briefly before arousing and becoming agitated. Evaluation of other etiologies for the patient's bradycardia was unrevealing. Thus, bradycardia was attributed to dexmedetomidine therapy which was discontinued without recurrence. Hemodynamic instability, specifically bradycardia, is known to occur with dexmedetomidine administration. As this medication is primarily metabolized by the liver, its use immediately after transplantation, when liver function is still recovering, may be associated with an increased risk of side effects. Understanding risk factors for bradycardia and hemodynamic instability early after liver transplantation, particularly with dexmedetomidine, is critical to allow clinicians to identify the patients for higher risk for dexmedetomidine side effects.

In defence of the entity of Macaronesia as a biogeographical region.

Since its coinage ca. 1850 AD by Philip Barker Webb, the biogeographical region of Macaronesia, consisting of the North Atlantic volcanic archipelagos of the Azores, Madeira with the tiny Selvagens, the Canaries and Cabo Verde, and for some authors different continental coastal strips, has been under dispute. Herein, after a brief introduction on the terminology and purpose of regionalism, we recover the origins of the Macaronesia name, concept and geographical adscription, as well as its biogeographical implications and how different authors have positioned themselves, using distinct terrestrial or marine floristic and/or faunistic taxa distributions and relationships for accepting or rejecting the existence of this biogeographical region. Four main issues related to Macaronesia are thoroughly discussed: (i) its independence from the Mediterranean phytogeographical region; (ii) discrepancies according to different taxa analysed; (iii) its geographical limits and the role of the continental enclave(s), and, (iv) the validity of the phytogeographical region level. We conclude that Macaronesia has its own identity and a sound phytogeographical foundation, and that this is mainly based on three different floristic components that are shared by the Macaronesian core (Madeira and the Canaries) and the outermost archipelagos (Azores and Cabo Verde). These floristic components are: (i) the Palaeotropical-Tethyan Geoflora, formerly much more widely distributed in Europe and North Africa and currently restricted to the three northern archipelagos (the Azores, Madeira and the Canaries); (ii) the African Rand Flora, still extant in the coastal margins of Africa and Arabia, and present in the southern archipelagos (Madeira, the Canaries and Cabo Verde), and (iii) the Macaronesian neoendemic floristic component, represented in all the archipelagos, a result of allopatric diversification promoted by isolation of Mediterranean ancestors that manage to colonize Central Macaronesia and, from there, the outer archipelagos. Finally, a differentiating floristic component recently colonized the different archipelagos from the nearest continental coast, providing them with different biogeographic flavours.

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.

Introduction: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. Methods: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Results: Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Conclusions: Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.

Transportability of bacterial infection prediction models for critically ill patients.

OBJECTIVE: Bacterial infections (BIs) are common, costly, and potentially life-threatening in critically ill patients. Patients with suspected BIs may require empiric multidrug antibiotic regimens and therefore potentially be exposed to prolonged and unnecessary antibiotics. We previously developed a BI risk model to augment practices and help shorten the duration of unnecessary antibiotics to improve patient outcomes. Here, we have performed a transportability assessment of this BI risk model in 2 tertiary intensive care unit (ICU) settings and a community ICU setting. We additionally explored how simple multisite learning techniques impacted model transportability. METHODS: Patients suspected of having a community-acquired BI were identified in 3 datasets: Medical Information Mart for Intensive Care III (MIMIC), Northwestern Medicine Tertiary (NM-T) ICUs, and NM "community-based" ICUs. ICU encounters from MIMIC and NM-T datasets were split into 70/30 train and test sets. Models developed on training data were evaluated against the NM-T and MIMIC test sets, as well as NM community validation data. RESULTS: During internal validations, models achieved AUROCs of 0.78 (MIMIC) and 0.81 (NM-T) and were well calibrated. In the external community ICU validation, the NM-T model had robust transportability (AUROC 0.81) while the MIMIC model transported less favorably (AUROC 0.74), likely due to case-mix differences. Multisite learning provided no significant discrimination benefit in internal validation studies but offered more stability during transport across all evaluation datasets. DISCUSSION: These results suggest that our BI risk models maintain predictive utility when transported to external cohorts. CONCLUSION: Our findings highlight the importance of performing external model validation on myriad clinically relevant populations prior to implementation.

Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care.

Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting.

Pediatric Critical Care in the Twenty-first Century and Beyond.

Pediatric critical care addresses prevention, diagnosis, and treatment of organ dysfunction in the setting of increasingly complex patients, therapies, and environments. Soon burgeoning data science will enable all aspects of intensive care: driving facilitated diagnostics, empowering a learning health-care environment, promoting continuous advancement of care, and informing the continuum of critical care outside the intensive care unit preceding and following critical illness/injury. Although novel technology will progressively objectify personalized critical care, humanism, practiced at the bedside, defines the essence of pediatric critical care now and in the future.

Clinical decision support tools for paediatric sepsis in resource-poor settings: an international qualitative study.

OBJECTIVE: New paediatric sepsis criteria are being developed by an international task force. However, it remains unknown what type of clinical decision support (CDS) tools will be most useful for dissemination of those criteria in resource-poor settings. We sought to design effective CDS tools by identifying the paediatric sepsis-related decisional needs of multidisciplinary clinicians and health system administrators in resource-poor settings. DESIGN: Semistructured qualitative focus groups and interviews with 35 clinicians (8 nurses, 27 physicians) and 5 administrators at health systems that regularly provide care for children with sepsis, April-May 2022. SETTING: Health systems in Africa, Asia and Latin America, where sepsis has a large impact on child health and healthcare resources may be limited. PARTICIPANTS: Participants had a mean age of 45 years, a mean of 15 years of experience, and were 45% female. RESULTS: Emergent themes were related to the decisional needs of clinicians caring for children with sepsis and to the needs of health system administrators as they make decisions about which CDS tools to implement. Themes included variation across regions and institutions in infectious aetiologies of sepsis and available clinical resources, the need for CDS tools to be flexible and customisable in order for implementation to be successful, and proposed features and format of an ideal paediatric sepsis CDS tool. CONCLUSION: Findings from this study will directly contribute to the design and implementation of CDS tools to increase the uptake and impact of the new paediatric sepsis criteria in resource-poor settings.