Trends and opportunities in computable clinical phenotyping: A scoping review.

Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1 % (N = 121) of all studies, and International Classification of Diseases codes were heavily used in 55.4 % (N = 77) of all studies, however, only 25.9 % (N = 36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.

An Evaluation of Pretrained BERT Models for Comparing Semantic Similarity Across Unstructured Clinical Trial Texts.

Processing unstructured clinical texts is often necessary to support certain tasks in biomedicine, such as matching patients to clinical trials. Among other methods, domain-specific language models have been built to utilize free-text information. This study evaluated the performance of Bidirectional Encoder Representations from Transformers (BERT) models in assessing the similarity between clinical trial texts. We compared an unstructured aggregated summary of clinical trials reviewed at the Johns Hopkins Molecular Tumor Board with the ClinicalTrials.gov records, focusing on the titles and eligibility criteria. Seven pretrained BERT-Based models were used in our analysis. Of the six biomedical-domain-specific models, only SciBERT outperformed the original BERT model by accurately assigning higher similarity scores to matched than mismatched trials. This finding is promising and shows that BERT and, likely, other language models may support patient-trial matching.

Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.

Background: High flow nasal cannula (HFNC) is commonly used as non-invasive respiratory support in critically ill children. There are limited data to inform consensus on optimal device parameters, determinants of successful patient response, and indications for escalation of support. Clinical scores, such as the respiratory rate-oxygenation (ROX) index, have been described as a means to predict HFNC non-response, but are limited to evaluating for escalations to invasive mechanical ventilation (MV). In the presence of apparent HFNC non-response, a clinician may choose to increase the HFNC flow rate to hypothetically prevent further respiratory deterioration, transition to an alternative non-invasive interface, or intubation for MV. To date, no models have been assessed to predict subsequent escalations of HFNC flow rates after HFNC initiation. Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations. Methods: We performed a retrospective, cohort study assessing children admitted for acute respiratory failure under 24 months of age placed on HFNC in the Johns Hopkins Children's Center pediatric intensive care unit from January 2019 through January 2020. We excluded encounters with gaps in recorded clinical data, encounters in which MV treatment occurred prior to HFNC, and cases electively intubated in the operating room. The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. In an exploratory fashion, model feature importance rankings were assessed by comparing Shapley values. Results: Our gradient boosting model with a time window of 8 h and lead time of 1 h before HFNC flow rate escalation achieved an AUROC with a 95% confidence interval of 0.810 ± 0.003. In comparison, the ROX index achieved an AUROC of 0.525 ± 0.000. Conclusion: In this single-center, retrospective cohort study assessing children under 24 months of age receiving HFNC for acute respiratory failure, tree-based machine learning models outperformed the ROX index in predicting subsequent flow rate escalations. Further validation studies are needed to ensure generalizability for bedside application.

Management of CRPS secondary to preganglionic C8 nerve root avulsion: A case report and literature review.

BACKGROUND: Cervical nerve root avulsion is a well-documented result of high-velocity motor vehicle accidents (MVAs). In up to 21% of cases, preganglionic cervical root avulsion can result in a complex regional pain syndrome (CRPS) impacting the quality of life for patients already impaired by motor, sensory, and autonomic dysfunction. The optimal treatment strategies include repeated stellate ganglion blocks (SBGs). CASE DESCRIPTION: A 43-year-old male sustained a high-velocity MVA resulting in the left C8 nerve root avulsion. This resulted in weakness in the C8 distribution, tactile allodynia, and dysesthesias. The magnetic resonance imaging demonstrated an abnormal signal ventral to the C8-T1 level. As the patient was not considered a candidate for surgical intervention secondary to the attendant brachial plexus injury, a C7-C8 epidural steroid injection was performed; this did not provide improvement. Before placing a spinal cord stimulator, the patient underwent a series of six ultrasound-guided SBGs performed 2 weeks apart; there was 75% improvement in pain and strength. Six years later, the patient continues to do well while receiving SBGs 4 times a year. CONCLUSION: A preganglionic cervical nerve root avulsion should not be a contraindication for a stellate ganglion block in a patient with established CRPS.

Recent decline in the use of invasive neurocritical care monitoring for traumatic brain injury: A case report.

BACKGROUND: In this article, we discuss the dramatic decline in the utilization of invasive cranial monitoring of patients with traumatic brain injury (TBI). CASE DESCRIPTION: A 52-year-old male presented with a severe TBI following a motor vehicle accident. The initial computed tomography scan showed a subdural hematoma, and the patient underwent a craniotomy. However, preoperatively, intraoperatively, and postoperatively, the critical care team never utilized invasive cranial monitoring. Therefore, when the patient expired several weeks later due to multiorgan failure, his death was in part attributed to the neurocritical care specialists' failure to employ invasive cranial monitoring techniques. CONCLUSION: Evidence-based and defensive medicine, cost containment, and a lack of leadership have contributed to neurocritical care specialists' increased failure to utilize invasive hemodynamic and neurological monitoring for TBI.