Annals of translational medicine最新文献

[This corrects the article DOI: 10.21037/atm-21-4800.].

[This corrects the article DOI: 10.21037/atm-22-1578.].

Background: Transcatheter aortic valve replacement (TAVR) is a guideline recommended minimally invasive cardiovascular procedure used to replace severely stenosis aortic valves. Patients with severe aortic stenosis (AS) and co-existing hypertrophic cardiomyopathy (HCM), a common defect affecting the left ventricle of the heart, have been excluded from TAVR studies due to perceived challenges to optimal valve implantation in this group of patients because of the hypertrophied left ventricle that can result in an abrupt drop in afterload from a newly replaced and more efficient aortic valve. This exclusion has resulted in paucity of data on this patient population. This study aims to review outcomes in patient with HCM undergoing TAVR for severe AS.

Methods: Using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement, we performed a systematic literature search of published outcomes regarding TAVR in HCM patients to provide some insight in this patient population.

Results: Our study showed that TAVR had significantly lower rates of in-hospital mortality, bleeding requiring a blood transfusion, invasive mechanical ventilation, acute kidney injury, vascular complications, and decreased length of stay (LOS) compared to surgical aortic valve replacement (SAVR) in our study population of 836 subjects from 11 publications. Our study is not a randomized controlled trial, which limits its generalization.

Conclusions: In severe AS patients with HCM, TAVR results in better outcomes compared to surgery.

Background: Statins, with their unique ability to stimulate bone formation and soft tissue healing, hold the potential to revolutionize dental care. The present study aims to delve into the profound effects of statins on bone and soft tissue healing in dental extraction sockets, offering a promising future for dental professionals and patients alike.

Methods: This systematic review aimed to understand the role of stains in tissue healing following dental extraction. This study was registered in the International Prospective Register of Ongoing Systematic Reviews (PROSPERO; CRD42022299247). A comprehensive electronic database search yielded 412 manuscripts. After a rigorous screening process, nine manuscripts met the eligibility criteria. The study sample consisted of 403 animals, with eight studies utilizing rat animal models and one conducted on mongrel dogs.

Results: Overall, the application of statin drugs holds promise for improving tissue healing outcomes following tooth extraction. The primary outcome variables across all studies were residual ridge height and width, messenger ribonucleic acid (mRNA) expression of transforming growth factor-beta 1 (TGF-β1), bone morphogenetic protein-2 (BMP-2), and vascular endothelial growth factor (VEGF), bone and gingival healing, inflammatory response, and bone turnover (BT), bone formation in tooth extraction socket, and osteogenic healing in a tooth extraction socket.

Conclusions: The findings of this study underscore the significant potential of statin drugs to enhance tissue healing outcomes following tooth extraction. This discovery opens new and exciting possibilities for improving dentistry patient care, potentially transforming how we approach post-extraction healing.

Background: First performed in 1960, hemicorporectomy, or translumbar amputation, is a rare surgery performed as a last resort for patients with life-threatening diagnoses. While rare, it is associated with significant challenging events for the anesthesiologist. Here we present a challenging hemicorporectomy case which was successfully managed using a multimodal anesthesia approach.

Case description: The patient was a 40-year-old patient presenting for completion of a hemicorporectomy via a left hemipelvectomy for pelvic chondrosarcoma. The patient underwent hemicorporectomy under epidural and total intravenous anesthesia supplemented with ketamine and lidocaine infusion. The surgery lasted 17.5 h and resulted in 28 L of blood loss. The patient noted excellent pain control and was discharged on postoperative day 74 following an uncomplicated hospital course and in-house rehabilitation.

Conclusions: Reviewing the literature, we recognized that there are no standardized anesthesia protocols published for hemicorporectomy. Based on our case report we present a novel anesthesia strategy that addresses almost all major challenges with hemicorporectomies. Our successful strategy suggests that a total intravenous anesthesia with propofol in combination with an epidural and a multimodal pain regimen with rate adjustments based on body mass reduction should be considered as a standard anesthesia protocol for hemicorporectomies. We recommend establishing a state-of-the-art anesthesia guideline for patients undergoing hemicorporectomy and encourage anesthesiologists to publish case reports describing the anesthesia approach for a hemicorporectomy.

Background: Heart rate variability (HRV) has been used as a marker of cardiovascular health and a risk factor for mortality in the adult and paediatric populations, and as an indicator of neonatal sepsis. There has been an increasing interest in using short-term (5 minutes) HRV to identify infants ≤90 days of life with serious bacterial infections. However, there has not been any normative data range reported for short-term HRV indices in this infant population. The aim of this study was to evaluate short-term HRV indices in awake, healthy young infants >48 hours and ≤90 days of life and to establish a reference range. We also aimed to produce a clinical calculator that can be used in this population for evaluation of short-term HRV variables in young infants in the emergency department (ED) setting that can be potentially used in future clinical validation and research.

Methods: We conducted a prospective observational study of short-term HRV analysis of awake, well infants ≤90 days of life in the ED setting.

Results: One hundred and eight infants with complete data [51.9% male, median age 9 days (interquartile range, 4-35 days)] were included. We found that heart rate (HR) is correlated with HRV. Thus, normalisation of HRV parameters was done to remove their dependence on HR. We then provided normative reference range of widely used short-term HRV time-domain, frequency-domain, and non-linear HRV metrics in our cohort.

Conclusions: We established normative values and HRV calculator for evaluation of these short-term HRV variables in young infants in ED settings that can be used for further clinical validation and clinical research.

[This corrects the article DOI: 10.21037/atm-21-5181.].

[This corrects the article DOI: 10.21037/atm-22-377.].

Background and objective: Early recognition and treatment of sepsis in the emergency department (ED) is important. Traditional predictive analytics and clinical decision rules lack accuracy in identifying patients with sepsis. Artificial intelligence (AI) is increasingly prevalent in healthcare and offers application potential in the care of patients with sepsis. This review examines the evidence of AI in diagnosing, managing and prognosticating sepsis in the ED.

Methods: We performed literature search in PubMed, Embase, Google Scholar and Scopus databases for studies published between 1 January 2010 and 30 June 2024 that evaluated the use of AI in adult patients with sepsis in ED, using the following search terms: ("artificial intelligence" OR "machine learning" OR "neural networks, computer" OR "deep learning" OR "natural language processing"), AND ("sepsis" OR "septic shock", AND "emergency services" OR "emergency department"). Independent searches were conducted in duplicate with discrepancies adjudicated by a third member.

Key content and findings: Incorporating multiple variables such as vital signs, free text input, laboratory tests and electrocardiogram was possible with AI compared to traditional models leading to improvement in diagnostic performance. Machine learning (ML) models outperformed traditional scoring tools in both diagnosis and prognosis of sepsis. ML models were able to analyze trends over time and showed utility in predicting mortality, severe sepsis and septic shock. Additionally, real-time ML-assisted alert systems are effective in improving time-to-antibiotic administration and ML algorithms can differentiate sepsis patients into distinct phenotypes to tailor management (especially fluid therapy and critical care interventions), potentially improving outcomes. Existing AI tools for sepsis currently lack generalizability and user acceptance. This is risk of automation bias with loss of clinicians' skills if over-reliance develops.

Conclusions: Overall, AI holds great promise in revolutionizing management of patients with sepsis in the ED as a clinical support tool. However, its application is currently still constrained by inherent limitations. Balanced integration of AI technology with clinician input is essential to harness its full potential and ensure optimal patient outcomes.