Citations

Summary Chimeric antigen receptor T‐cell (CAR‐T) therapy and bispecific T‐cell engagers (BsAb) have emerged as promising immunotherapeutic modalities in patients with relapsed and/or refractory multiple myeloma (RRMM). However, there is limited data on the safety and efficacy of CAR‐T and BsAb therapies in MM patients with a prior history of allogeneic transplantation (allo‐HCT). Thirty‐three MM patients with prior allo‐HCT received CAR‐T ( n = 24) or BsAb ( n = 9) therapy. CAR‐T therapy demonstrated an ORR of 92% (67% ≥ CR), and 73% were MRD negative. BsAb therapy resulted in an ORR of 44% (44% ≥ CR) and 44% MRD negative. Safety analysis showed grade ≥3 AEs in 92% of CAR‐T and 56% of BsAb patients. Cytokine release syndrome (CRS) occurred in 83% of CAR‐T and 78% of BsAb recipients, while immune effector cell‐associated neurotoxicity syndrome (ICANS) was observed in three CAR‐T patients. Infections of grade ≥3 were reported in 50% of CAR‐T and 44% of BsAb recipients. No exacerbation of graft‐versus‐host disease occurred except in one BsAb recipient. CAR‐T and BsAb therapies appear to be feasible, safe and provide deep and durable responses in MM patients with prior allo‐HCT.

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

Since hemorrhage is a leading cause of preventable death in both civilian and military settings, the development of advanced decision support monitoring capabilities is necessary to promote improved clinical outcomes. The emergence of lower body negative pressure (LBNP) has provided a bioengineering technology for inducing progressive reductions in central blood volume shown to be accurate as a model for the study of the early compensatory stages of hemorrhage. In this context, the specific aim of this study was to provide for the first time a systematic technical evaluation to meet a commonly accepted engineering standard based on the FDA-recognized Standard for Assessing Credibility of Modeling through Verification and Validation (V&V) for Medical Devices (ASME standard V&V 40) specifically highlighting LBNP as a valuable resource for the safe study of hemorrhage physiology in humans. As an experimental tool, evidence is presented that LBNP is credible, repeatable, and validated as an analog for the study of human hemorrhage physiology compared to actual blood loss. The LBNP tool can promote the testing and development of advanced monitoring algorithms and evaluating wearable sensors with the goal of improving clinical outcomes during use in emergency medical settings.

Abstract Background Obesity is associated with an increased risk of developing recurrent atrial fibrillation (AF) after catheter ablation (CA). However, the current data on weight loss interventions show inconsistent results in preventing the recurrence of AF after CA. Methods We conducted a systematic search in MEDLINE and EMBASE to identify studies that reported the outcome of recurrence of AF after CA in obese patients undergoing weight interventions. The subgroup analysis included: (1) Weight loss versus no weight loss, (2) >10% weight loss versus <10% weight loss, (3) <10% weight loss versus no weight loss, (4) Follow‐up <12 months, and (5) Follow‐up >12 months after CA. Mantel–Haenszel risk ratios with a 95% confidence interval (CI) were calculated using a random effects model and for heterogeneity, I 2 statistics were reported. Results A total of 10 studies (one randomized controlled trial and nine observational studies) comprising 1851 patients were included. The recurrence of AF was numerically reduced in the weight loss group (34.5%) versus no weight loss group (58.2%), but no statistically significant difference was observed (risk ratio [RR] = 0.76; 95% CI: 0.49–1.18, p = .22). However, there was a statistically significant reduction in recurrence of AF with weight loss versus no weight loss at follow‐up >12 months after CA (RR = 0.47; 95% CI: 0.32–0.68, p < .0001). At follow‐up >12 months after CA, both >10% weight loss versus <10% weight loss (RR = 0.49; 95% CI: 0.31–0.80, p = .004) and <10% weight loss versus no weight loss (RR = 0.39; 95% CI: 0.31–0.49, p < .00001) were associated with a statistically significant reduction in recurrent AF. Conclusion In patients with AF undergoing CA, weight loss is associated with reducing recurrent AF at > 12 months after ablation and these benefits are consistently seen with both >10% and <10% weight loss. The benefits of weight loss in preventing recurrent AF after CA should be examined in larger studies with extended follow‐up duration.

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.