Pediatric Cervical Spine Injury Following Blunt Trauma in Children Younger Than 3 Years: The PEDSPINE II Study.

Importance: There is variability in practice and imaging usage to diagnose cervical spine injury (CSI) following blunt trauma in pediatric patients. Objective: To develop a prediction model to guide imaging usage and to identify trends in imaging and to evaluate the PEDSPINE model. Design, Setting, and Participants: This cohort study included pediatric patients (<3 years years) following blunt trauma between January 2007 and July 2017. Of 22 centers in PEDSPINE, 15 centers, comprising level 1 and 2 stand-alone pediatric hospitals, level 1 and 2 pediatric hospitals within an adult hospital, and level 1 adult hospitals, were included. Patients who died prior to obtaining cervical spine imaging were excluded. Descriptive analysis was performed to describe the population, use of imaging, and injury patterns. PEDSPINE model validation was performed. A new algorithm was derived using clinical criteria and formulation of a multiclass classification problem. Analysis took place from January to October 2022. Exposure: Blunt trauma. Main Outcomes and Measures: Primary outcome was CSI. The primary and secondary objectives were predetermined. Results: The current study, PEDSPINE II, included 9389 patients, of which 128 (1.36%) had CSI, twice the rate in PEDSPINE (0.66%). The mean (SD) age was 1.3 (0.9) years; and 70 patients (54.7%) were male. Overall, 7113 children (80%) underwent cervical spine imaging, compared with 7882 (63%) in PEDSPINE. Several candidate models were fitted for the multiclass classification problem. After comparative analysis, the multinomial regression model was chosen with one-vs-rest area under the curve (AUC) of 0.903 (95% CI, 0.836-0.943) and was able to discriminate between bony and ligamentous injury. PEDSPINE and PEDSPINE II models' ability to identify CSI were compared. In predicting the presence of any injury, PEDSPINE II obtained a one-vs-rest AUC of 0.885 (95% CI, 0.804-0.934), outperforming the PEDSPINE score (AUC, 0.845; 95% CI, 0.769-0.915). Conclusion and Relevance: This study found wide clinical variability in the evaluation of pediatric trauma patients with increased use of cervical spine imaging. This has implications of increased cost, increased radiation exposure, and a potential for overdiagnosis. This prediction tool could help to decrease the use of imaging, aid in clinical decision-making, and decrease hospital resource use and cost.

Integrated multimodal artificial intelligence framework for healthcare applications.

Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approach uses generalizable data pre-processing and machine learning modeling stages that can be readily adapted for research and deployment in healthcare environments. We evaluate our HAIM framework by training and characterizing 14,324 independent models based on HAIM-MIMIC-MM, a multimodal clinical database (N = 34,537 samples) containing 7279 unique hospitalizations and 6485 patients, spanning all possible input combinations of 4 data modalities (i.e., tabular, time-series, text, and images), 11 unique data sources and 12 predictive tasks. We show that this framework can consistently and robustly produce models that outperform similar single-source approaches across various healthcare demonstrations (by 6-33%), including 10 distinct chest pathology diagnoses, along with length-of-stay and 48 h mortality predictions. We also quantify the contribution of each modality and data source using Shapley values, which demonstrates the heterogeneity in data modality importance and the necessity of multimodal inputs across different healthcare-relevant tasks. The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.