RESUMEN
Background: C-section is usually performed under spinal anesthesia also known as a subarachnoid block (SAB) over general anesthesia. Because of the lesser amount of dose used, there is a lower risk of local anesthetic toxicity and minimal transfer of drugs to the fetus. Obstetric patients have a higher risk of having post-dural puncture headache (PDPH). PDPH occurs due to leakage of the cerebrospinal fluid (CSF) through the hole created by a spinal needle. There are many elements affecting the frequency of PDPH, these elements can also consist of age, female sex, needle size, and types, pregnancy, preceding records of PDPH, median-paramedian distinction in approach, a puncture level. PDPH is commonly in the form of a frontal, occipital, or retro-orbital headache that starts in 12-72 h after the dural puncture and will increase when standing and decrease when lying down or resting. We aimed to learn about headache frequency between elective and emergency lower segment cesarean section using 26-G Quincke spinal needle in full-term pregnant patients. Objectives: To study the incidence of PDPH using the 26G Quincke spinal needle. To analyze the causal factors/determinants such as adequate preloading of fluids, size of spinal needle, number of pricks, and technique of lumbar puncture effects on the incidence of PDPH. Methodology: This study is a prospective questionnaire-based comparative observational study using the convenience sampling method. The patients were interviewed with a structured questionnaire at the Symbiosis University Hospital and Research Centre, Lavale, Pune. The patients observed for the study were between 20 and 40 of age group, posted for emergency or elective lower segment cesarean section, with body mass index (BMI) less than 14.5 to 24.9 and with ASA I and II grades. Patients with any comorbidities, recurrent headaches, obesity, and spine deformity were excluded. According to the review of the literature and with the help of a formula, the sample size was calculated as 20; 10 patients for elective LSCS, and 10 patients for emergency LSCS. Results: Out of 20 patients, 10 patients were posted for elective LSCS, and the rest 10 patients were for emergency LSCS under spinal anesthesia. The incidence of PDPH was found only in 2 out of 10 emergency LSCS patients, and no patients from elective LSCS cases showed up with the incidence of PDPH.
RESUMEN
This review article examines the utility of artificial intelligence (AI) in anesthesia, with a focus on recent developments and future directions in the field. A total of 19,300 articles were available on the given topic after searching in the above mentioned databases, and after choosing the custom range of years from 2015 to 2023 as an inclusion component, only 12,100 remained. 5,720 articles remained after eliminating non-full text. Eighteen papers were identified to meet the inclusion criteria for the review after applying the inclusion and exclusion criteria. The applications of AI in anesthesia after studying the articles were in favor of the use of AI as it enhanced or equaled human judgment in drug dose decision and reduced mortality by early detection. Two studies tried to formulate prediction models, current techniques, and limitations of AI; ten studies are mainly focused on pain and complications such as hypotension, with a P value of <0.05; three studies tried to formulate patient outcomes with the help of AI; and three studies are mainly focusing on how drug dose delivery is calculated (median: 1.1% ± 0.5) safely and given to the patients with applications of AI. In conclusion, the use of AI in anesthesia has the potential to revolutionize the field and improve patient outcomes. AI algorithms can accurately predict patient outcomes and anesthesia dosing, as well as monitor patients during surgery in real time. These technologies can help anesthesiologists make more informed decisions, increase efficiency, and reduce costs. However, the implementation of AI in anesthesia also presents challenges, such as the need to address issues of bias and privacy. As the field continues to evolve, it will be important to carefully consider the ethical implications of AI in anesthesia and ensure that these technologies are used in a responsible and transparent manner.