Comparison of postoperative pain in children after maintenance anaesthesia with propofol or sevoflurane: a systematic review and meta-analysis.

BACKGROUND: Propofol and sevoflurane are two of the most commonly used anaesthetics for paediatric surgery. Data from some clinical trials suggest that postoperative pain incidence is lower when propofol is used for maintenance of anaesthesia compared with sevoflurane, although this is not clear. METHODS: This meta-analysis compared postoperative pain following maintenance of anaesthesia with propofol or sevoflurane in paediatric surgeries. PubMed Medline, Embase, Scopus, Web of Science and Cochrane Library were searched for randomised controlled trials (RCTs) that compared postoperative pain between sevoflurane and propofol anaesthesia in children. After quality assessment, a meta-analysis was carried out using bias-adjusted inverse heterogeneity methods, heterogeneity using I2 and publication bias using Doi plots. RESULTS: In total, 13 RCTs with 1174 children were included. The overall synthesis suggested nearly two-fold higher odds of overall postoperative pain in the sevoflurane group compared with the propofol group (odds ratio [OR] 1.88, 95% confidence interval [CI] 1.12-3.15, I2=58.2%). Further, children in the sevoflurane group had higher odds of having higher pain scores (OR 3.18, 95% CI 1.83-5.53, I2=20.9%), and a 60% increase in the odds of requiring postoperative rescue analgesia compared with propofol (OR 1.60, 95% CI 0.89-2.88, I2=58.2%). CONCLUSIONS: Children maintained on inhalational sevoflurane had higher odds of postoperative pain compared with those maintained on propofol. The results also suggest that sevoflurane is associated with higher odds of needing postoperative rescue analgesia compared with propofol. REGISTRATION: The protocol for this systematic review and meta-analysis was registered on the International Prospective Register of Systematic Reviews (PROSPERO) with registration ID CRD42023445913.

Impact of phthalate metabolites on vitamin D levels and subclinical inflammation: national health and nutrition examination survey, 2013-2018.

This study explores the association between phthalates and total vitamin D levels and the link between phthalates exposure and subclinical inflammation using monocyte percentage to high-density lipoprotein cholesterol ratio (MHR), utilizing three National Health and Nutrition Examination Survey (NHANES) survey cycles 2013-2018. This study is cross-sectional, utilizing one-time urine samples from randomly selected NHANES participants to assess phthalate metabolites. An inverse association between vitamin D and all Di(2-ethylhexyl) phthalate (DEHP) metabolites was found. The molar sum of DEHP metabolites was inversely associated with vitamin D (ß -2.329; 95% CI -3.937,-0.720). An inverse association was observed between monocarboxynonyl phthalate and vitamin D (ß -0.0278; 95% CI -0.0527,-0.00298). A similar relationship was found between monocarboxyoctyl phthalate and vitamin D (ß -0.0160; 95% CI -0.0242,-0.00775). There was no association between phthalate metabolites and MHR. Stratified analysis showed that the association between phthalate metabolites and MHR may vary according to vitamin D status.

Stand-Alone Tibial Interference Screw Fixation and Tibial Interference Screw Plus Tibial Staple Fixation Produce Comparable Outcomes After Primary Anterior Cruciate Ligament Reconstruction Using Hamstring Autografts.

Purpose: To investigate the impact of adding a metal staple alongside the interference screw in tibial side graft fixation during anterior cruciate ligament reconstruction (ACLR). Methods: All patients treated with autograft hamstring ACLR at our institution between January 2017 and December 2021 with a minimum 1-year follow-up were reviewed retrospectively. Patients treated with a stand-alone interference screw for tibial side fixation were compared with those treated with a combination of interference screw and staple. The primary outcome was failure of the reconstructed graft. Secondary outcomes were operative time, complication rate, and reoperation rate. Results: A total of 497 patients met the study's inclusion and exclusion criteria. A combination of staple and interference screw was used in 167 patients (33.6%), whereas a standalone interference screw was used in 330 patients. There was no significant difference between the 2 groups in terms of operative time, complication rate, or failure rate. The mean follow-up was 23.25 (±13.29) months. Conclusions: Our results demonstrate that augmenting the interference screw with a staple for tibial-side fixation in ACLR does not have a significant impact on operative time, reoperation rate, complications, or failure rates. Level of Evidence: Level III, retrospective cohort study.

Applications of Artificial Intelligence in Thalassemia: A Comprehensive Review.

Thalassemia is an autosomal recessive genetic disorder that affects the beta or alpha subunits of the hemoglobin structure. Thalassemia is classified as a hypochromic microcytic anemia and a definitive diagnosis of thalassemia is made by genetic testing of the alpha and beta genes. Thalassemia carries similar features to the other diseases that lead to microcytic hypochromic anemia, particularly iron deficiency anemia (IDA). Therefore, distinguishing between thalassemia and other causes of microcytic anemia is important to help in the treatment of the patients. Different indices and algorithms are used based on the complete blood count (CBC) parameters to diagnose thalassemia. In this article, we review how effective artificial intelligence is in aiding in the diagnosis and classification of thalassemia.

Applications of Machine Learning in Chronic Myeloid Leukemia.

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by dysregulated growth and the proliferation of myeloid cells in the bone marrow caused by the BCR-ABL1 fusion gene. Clinically, CML demonstrates an increased production of mature and maturing granulocytes, mainly neutrophils. When a patient is suspected to have CML, peripheral blood smears and bone marrow biopsies may be manually examined by a hematologist. However, confirmatory testing for the BCR-ABL1 gene is still needed to confirm the diagnosis. Despite tyrosine kinase inhibitors (TKIs) being the mainstay of treatment for patients with CML, different agents should be used in different patients given their stage of disease and comorbidities. Moreover, some patients do not respond well to certain agents and some need more aggressive courses of therapy. Given the innovations and development that machine learning (ML) and artificial intelligence (AI) have undergone over the years, multiple models and algorithms have been put forward to help in the assessment and treatment of CML. In this review, we summarize the recent studies utilizing ML algorithms in patients with CML. The search was conducted on the PubMed/Medline and Embase databases and yielded 66 full-text articles and abstracts, out of which 11 studies were included after screening against the inclusion criteria. The studies included show potential for the clinical implementation of ML models in the diagnosis, risk assessment, and treatment processes of patients with CML.

Applications of Artificial Intelligence in Thrombocytopenia.

Thrombocytopenia is a medical condition where blood platelet count drops very low. This drop in platelet count can be attributed to many causes including medication, sepsis, viral infections, and autoimmunity. Clinically, the presence of thrombocytopenia might be very dangerous and is associated with poor outcomes of patients due to excessive bleeding if not addressed quickly enough. Hence, early detection and evaluation of thrombocytopenia is essential for rapid and appropriate intervention for these patients. Since artificial intelligence is able to combine and evaluate many linear and nonlinear variables simultaneously, it has shown great potential in its application in the early diagnosis, assessing the prognosis and predicting the distribution of patients with thrombocytopenia. In this review, we conducted a search across four databases and identified a total of 13 original articles that looked at the use of many machine learning algorithms in the diagnosis, prognosis, and distribution of various types of thrombocytopenia. We summarized the methods and findings of each article in this review. The included studies showed that artificial intelligence can potentially enhance the clinical approaches used in the diagnosis, prognosis, and treatment of thrombocytopenia.

Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms.

Philadelphia-negative (Ph-) myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies identified by clonal proliferation of blood cell lineages and encompasses polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The clinical and laboratory features of Philadelphia-negative MPNs are similar, making them difficult to diagnose, especially in the preliminary stages. Because treatment goals and progression risk differ amongst MPNs, accurate classification and prognostication are critical for optimal management. Artificial intelligence (AI) and machine learning (ML) algorithms provide a plethora of possible tools to clinicians in general, and particularly in the field of malignant hematology, to better improve diagnosis, prognosis, therapy planning, and fundamental knowledge. In this review, we summarize the literature discussing the application of AI and ML algorithms in patients with diagnosed or suspected Philadelphia-negative MPNs. A literature search was conducted on PubMed/MEDLINE, Embase, Scopus, and Web of Science databases and yielded 125 studies, out of which 17 studies were included after screening. The included studies demonstrated the potential for the practical use of ML and AI in the diagnosis, prognosis, and genomic landscaping of patients with Philadelphia-negative MPNs.