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.

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.

Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients.

The COVID-19 pandemic has prompted an international effort to develop and repurpose medications and procedures to effectively combat the disease. Several groups have focused on the potential treatment utility of angiotensin-converting-enzyme inhibitors (ACEIs) and angiotensin-receptor blockers (ARBs) for hypertensive COVID-19 patients, with inconclusive evidence thus far. We couple electronic medical record (EMR) and registry data of 3,643 patients from Spain, Italy, Germany, Ecuador, and the US with a machine learning framework to personalize the prescription of ACEIs and ARBs to hypertensive COVID-19 patients. Our approach leverages clinical and demographic information to identify hospitalized individuals whose probability of mortality or morbidity can decrease by prescribing this class of drugs. In particular, the algorithm proposes increasing ACEI/ARBs prescriptions for patients with cardiovascular disease and decreasing prescriptions for those with low oxygen saturation at admission. We show that personalized recommendations can improve patient outcomes by 1.0% compared to the standard of care when applied to external populations. We develop an interactive interface for our algorithm, providing physicians with an actionable tool to easily assess treatment alternatives and inform clinical decisions. This work offers the first personalized recommendation system to accurately evaluate the efficacy and risks of prescribing ACEIs and ARBs to hypertensive COVID-19 patients.

From predictions to prescriptions: A data-driven response to COVID-19.

The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemic's spread and inform social distancing policies. Fourth, we propose an optimization model to re-allocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and vaccine trial location planning at Janssen Pharmaceuticals, and have been integrated into the US Center for Disease Control's pandemic forecast.

COVID-19 mortality risk assessment: An international multi-center study.

Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84-0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.

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.