Citations

Introduction A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review aims to compile all up-to-date studies addressing such algorithms’ use in clinical practice. Methods We searched PubMed/MEDLINE, EMBASE, CINAHL, and Web of Science using the keywords “machine learning,” “opioid,” and “prediction.” The results were limited to human studies with full-text availability in English. We included all peer-reviewed journal articles that addressed an ML model to predict PPO use by adult patients. Results Fifteen studies were included with a sample size ranging from 381 to 112898, primarily orthopedic-surgery-related. Most authors define a prolonged misuse of opioids if it extends beyond 90 days postoperatively. Input variables ranged from 9 to 23 and were primarily preoperative. Most studies developed and tested at least two algorithms and then enhanced the best-performing model for use retrospectively on electronic medical records. The best-performing models were decision-tree-based boosting algorithms in 5 studies with AUC ranging from .81 to .66 and Brier scores ranging from .073 to .13, followed second by logistic regression classifiers in 5 studies. The topmost contributing variable was preoperative opioid use, followed by depression and antidepressant use, age, and use of instrumentation. Conclusions ML algorithms have demonstrated promising potential as a decision-supportive tool in predicting prolonged opioid use in post-surgical patients. Further validation studies would allow for their confident incorporation into daily clinical practice.

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare’s path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

Traumatic brain injury (TBI) remains a major cause of morbidity and death among the pediatric population. Timely diagnosis, however, remains a complex task because of the lack of standardized methods that permit its accurate identification. The aim of this study was to determine whether serum levels of brain injury biomarkers can be used as a diagnostic and prognostic tool in this pathology. This prospective, observational study collected and analyzed the serum concentration of neuronal injury biomarkers at enrollment, 24h and 48h post-injury, in 34 children ages 0–18 with pTBI and 19 healthy controls (HC). Biomarkers included glial fibrillary acidic protein (GFAP), neurofilament protein L (NfL), ubiquitin-C-terminal hydrolase (UCH-L1), S-100B, tau and tau phosphorylated at threonine 181 (p-tau181). Subjects were stratified by admission Glasgow Coma Scale score into two categories: a combined mild/moderate (GCS 9–15) and severe (GCS 3–8). Glasgow Outcome Scale-Extended (GOS-E) Peds was dichotomized into favorable (≤4) and unfavorable (≥5) and outcomes. Data were analyzed utilizing Prism 9 and R statistical software. The findings were as follows: 15 patients were stratified as severe TBI and 19 as mild/moderate per GCS. All biomarkers measured at enrollment were elevated compared with HC. Serum levels for all biomarkers were significantly higher in the severe TBI group compared with HC at 0, 24, and 48h. The GFAP, tau S100B, and p-tau181 had the ability to differentiate TBI severity in the mild/moderate group when measured at 0h post-injury. Tau serum levels were increased in the mild/moderate group at 24h. In addition, NfL and p-tau181 showed increased serum levels at 48h in the aforementioned GCS category. Individual biomarker performance on predicting unfavorable outcomes was measured at 0, 24, and 48h across different GOS-E Peds time points, which was significant for p-tau181 at 0h at all time points, UCH-L1 at 0h at 6–9 months and 12 months, GFAP at 48h at 12 months, NfL at 0h at 12 months, tau at 0h at 12 months and S100B at 0h at 12 months. We concluded that TBI leads to increased serum neuronal injury biomarkers during the first 0–48h post-injury. A biomarker panel measuring these proteins could aid in the early diagnosis of mild to moderate pTBI and may predict neurological outcomes across the injury spectrum.

Background: The use of bone morphogenic protein and mesenchymal stem cells has shown promise in promoting bone regeneration in calvarial defects. However, a systematic review of the available literature is needed to evaluate the efficacy of this approach. Methods: We comprehensively searched electronic databases using MeSH terms related to skull defects, bone marrow mesenchymal stem cells, and bone morphogenic proteins. Eligible studies included animal studies that used BMP therapy and mesenchymal stem cells to promote bone regeneration in calvarial defects. Reviews, conference articles, book chapters, and non-English language studies were excluded. Two independent investigators conducted the search and data extraction. Results: Twenty-three studies published between 2010 and 2022 met our inclusion criteria after a full-text review of the forty-five records found in the search. Eight of the 23 studies used mice as models, while 15 used rats. The most common mesenchymal stem cell was bone marrow-derived, followed by adipose-derived. BMP-2 was the most popular. Stem cells were embedded in Scaffold (13), Transduction (7), and Transfection (3), and they were delivered BMP to cells. Each treatment used 2 × 104–1 × 107 mesenchymal stem cells, averaging 2.26 × 106. Most BMP-transduced MSC studies used lentivirus. Conclusions: This systematic review examined BMP and MSC synergy in biomaterial scaffolds or alone. BMP therapy and mesenchymal stem cells in calvarial defects, alone, or with a scaffold regenerated bone. This method treats skull defects in clinical trials. The best scaffold material, therapeutic dosage, administration method, and long-term side effects need further study.

Background: Multiple myeloma (MM) is the second most common hematologic malignancy, with 34,470 estimated new cases in 2022. High-dose therapy followed by autologous hematopoietic cell transplantation (auto-HCT) remains a standard treatment for MM even in the era of novel therapies. This is usually performed in hospital-based settings, either in the inpatient or outpatient units. Advanced Care at Home (ACH) represents a virtual hybrid hospital-at-home program that combines a virtual provider-staffed command center with a vendor-mediated supply chain capable of delivering high-acuity care in the comfort of the patients’ own homes. In our program, we used the existing ACH platform to deliver post-HCT care for recipients of auto-HCT. Patients and methods: Four patients (female = 2, 50%) with MM, with a median age of 60 (range, 40–74) years, were admitted to the inpatient Blood and Marrow Transplant (BMT) unit. The conditioning regimen consisted of melphalan 200 mg/m 2 , administered on day –2. All patients received stem cell infusion (day 0) in the inpatient setting, with a median dose of 3.64 (range, 2.92–8.22) × 10 6 /kg CD34 cells. Results: Patients were discharged to their homes after completing the infusion on day 0 or day +1 at the latest. Postinfusion care was provided by the ACH team in coordination with the BMT team. The median time intervals to absolute neutrophil count and platelet engraftment were 12 (range, 11–13) and 11 (range, 9–16) days, respectively. All patients were successfully discharged from the ACH program at a median of day +14 (range, day +14 to day +15). Conclusions: Our results highlight the feasibility of delivering post-HCT care for auto-HCT recipients in the home setting and confirm the generalizability of this approach.

Background: The use of artificial intelligence (AI) in health care has grown exponentially with the promise of facilitating biomedical research and enhancing diagnosis, treatment, monitoring, disease prevention, and health care delivery. We aim to examine the current state, limitations, and future directions of AI in thyroidology. Summary: AI has been explored in thyroidology since the 1990s, and currently, there is an increasing interest in applying AI to improve the care of patients with thyroid nodules (TNODs), thyroid cancer, and functional or autoimmune thyroid disease. These applications aim to automate processes, improve the accuracy and consistency of diagnosis, personalize treatment, decrease the burden for health care professionals, improve access to specialized care in areas lacking expertise, deepen the understanding of subtle pathophysiologic patterns, and accelerate the learning curve of less experienced clinicians. There are promising results for many of these applications. Yet, most are in the validation or early clinical evaluation stages. Only a few are currently adopted for risk stratification of TNODs by ultrasound and determination of the malignant nature of indeterminate TNODs by molecular testing. Challenges of the currently available AI applications include the lack of prospective and multicenter validations and utility studies, small and low diversity of training data sets, differences in data sources, lack of explainability, unclear clinical impact, inadequate stakeholder engagement, and inability to use outside of the research setting, which might limit the value of their future adoption. Conclusions: AI has the potential to improve many aspects of thyroidology; however, addressing the limitations affecting the suitability of AI interventions in thyroidology is a prerequisite to ensure that AI provides added value for patients with thyroid disease.