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.

Toward an Optimized Staging System for Pancreatic Ductal Adenocarcinoma: A Clinically Interpretable, Artificial Intelligence-Based Model.

PURPOSE: The American Joint Committee on Cancer (AJCC) eighth edition schema for pancreatic ductal adenocarcinoma treats T and N stage as independent factors and uses positive lymph nodes (PLNs) to define N stage, despite data favoring lymph node ratio (LNR). We used artificial intelligence-based techniques to compare PLN with LNR and investigate interactions between tumor size and nodal status. METHODS: Patients who underwent pancreatic ductal adenocarcinoma resection between 2000 and 2017 at six institutions were identified. LNR and PLN were compared through shapley additive explanations (SHAP) analysis, with the best predictor used to define nodal status. We trained optimal classification trees (OCTs) to predict 1-year and 3-year risk of death, incorporating only tumor size and nodal status as variables. The OCTs were compared with the AJCC schema and similarly trained XGBoost models. Variable interactions were explored via SHAP. RESULTS: Two thousand eight hundred seventy-four patients comprised the derivation and 1,231 the validation cohort. SHAP identified LNR as a superior predictor. The OCTs outperformed the AJCC schema in the derivation and validation cohorts (1-year area under the curve: 0.681 v 0.603; 0.638 v 0.586, 3-year area under the curve: 0.682 v 0.639; 0.675 v 0.647, respectively) and performed comparably with the XGBoost models. We identified interactions between LNR and tumor size, suggesting that a negative prognostic factor partially overrides the effect of a concurrent favorable factor. CONCLUSION: Our findings highlight the superiority of LNR and the importance of interactions between tumor size and nodal status. These results and the potential of the OCT methodology to combine them into a powerful, visually interpretable model can help inform future staging systems.

Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach.

PURPOSE: Severe and febrile neutropenia present serious hazards to patients with cancer undergoing chemotherapy. We seek to develop a machine learning-based neutropenia prediction model that can be used to assess risk at the initiation of a chemotherapy cycle. MATERIALS AND METHODS: We leverage rich electronic medical records (EMRs) data from a large health care system and apply machine learning methods to predict severe and febrile neutropenic events. We outline the data curation process and challenges posed by EMRs data. We explore a range of algorithms with an emphasis on model interpretability and ease of use in a clinical setting. RESULTS: Our final proposed model demonstrates an out-of-sample area under the receiver operating characteristic curve of 0.865 (95% CI, 0.830 to 0.891) in the prediction of neutropenic events on the basis of only 20 clinical features. The model validates known risk factors and offers insight into potential novel clinical indicators and treatment characteristics that elevate risk. It relies on factors that are directly extractable from EMRs, provided a tool can be easily integrated into existing workflows. A cost-based analysis provides insight into optimal risk thresholds and offers a framework for tailoring algorithms to individual hospital needs. CONCLUSION: A better understanding of neutropenic risk on an individual level enables a more informed approach to patient monitoring and treatment decisions.

Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach.

BACKGROUND: Cervical spine injuries (CSI) are a major concern in young pediatric trauma patients. The consequences of missed injuries and difficulties in injury clearance for non-verbal patients have led to a tendency to image young children. Imaging, particularly computed tomography (CT) scans, presents risks including radiation-induced carcinogenesis. In this study we leverage machine learning methods to develop highly accurate clinical decision rules to predict pediatric CSI. METHODS: The PEDSPINE I registry was used to investigate CSI in blunt trauma patients under the age of three. Predictive models were built using Optimal Classification Trees, a novel machine learning approach offering high accuracy and interpretability, as well as other widely used machine learning methods. RESULTS: The final Optimal Classification Trees model predicts injury based on overall Glasgow Coma Score (GCS) and patient age. This model has a sensitivity of 93.3% and specificity of 82.3% on the full dataset. It has comparable or superior performance to other machine learning methods as well as existing clinical decision rules. CONCLUSIONS: This study developed a decision rule that achieves high injury identification while reducing unnecessary imaging. It demonstrates the value of machine learning in improving clinical decision protocols for pediatric trauma. TYPE OF STUDY: Retrospective Prognosis Study. LEVEL OF EVIDENCE: II.