Context: Spinal anesthesia is the widely preferred type of anesthesia for caesarean section. Determining the ideal intrathecal local anaesthetic dose to attain the desired block is a major clinical challenge. Anthropometric measurements can have an impact on the rate at which spinal anesthesia spreads. Aims: The main aim of the study was to study the correlation of anthropometric measurements and the spread of spinal anesthesia for caesarean section. Methods and Materials: This prospective observational study was conducted in a single tertiary care hospital, over a study period of 1 year. The study included 136 parturients undergoing elective caesarean section under spinal anesthesia. The patient’s height, weight, abdominal circumference (AC), vertebral column length (VCL), hip, and shoulder length were measured. The primary outcomes measured were the time taken for sensory blockade and the maximum level achieved after administration of subarachnoid block using 2 cc of 0.5% hyperbaric bupivacaine. Statistical Analysis Used: Statistical analysis was done using Microsoft Word, Excel, and MedCalc software, applying the suitable method. Results: AC and body mass index (BMI) showed statistically significant positive correlation with the maximum sensory block level (mean ± standard deviation (SD): T4 ± 1 dermatomes), time to reach T4 level (mean ± SD: 4.3 ± 1.1 min), and time to achieve maximum sensory level (mean ± SD: 6.8 ± 1.5 min) ( P < 0.05). Hip/shoulder ratio (HSR) showed weaker but significant correlation with time to reach T4 level ( r = 0.27, P = 0.0263). No significant correlation was found between VCL and measured block parameters. Hypotension occurred in 36 patients (26.5%) and was positively associated with higher AC ( P < 0.05). No bradycardia episodes were noted. Conclusion: This study found that AC and BMI had a strong correlation with the cephalad spread of sensory blockade. The HSR showed a weak correlation only with the time taken to reach T4 sensory level, while VCL showed no predictive value. These easily measurable variables may help titrate local anesthetic doses and explain variations in spinal anesthesia spread.

Intrapartum management of babies with life-threatening airway obstruction is challenging and requires meticulous planning to achieve a positive outcome. The inability to maintain a patent airway after birth carries the risk of neonatal hypoxia leading to death or long-term disability. In these cases, the mother is anesthetized for cesarean section and the baby is partially delivered out; and airway is secured while the baby is still on placental support. Important elements in the maternal management include maintenance of uterine relaxation to prevent placental separation, and then using uterotonics to avoid postpartum hemorrhage. Airway management in the baby has to follow a preplanned algorithmic protocol for dealing with the different contingencies encountered. The successful long-term outcome also necessitates meticulous management in the neonatal period, and afterward. This is a case report of ex utero intrapartum treatment procedure for a large cervical mass, illustrating the importance of multidisciplinary planning of equipment and management pathways, execution of the procedure, and then postnatal and long-term management of the child. We also include a review of the Indian experience and discuss the reasons for poor long-term outcomes in four out of nine babies reported in the literature.

Klippel–Feil syndrome (KFS) is a congenital anomaly characterized by cervical spine fusion, restricted neck movement, and other systemic abnormalities, posing significant challenges during perioperative management. We present the case of a 29-year-old primigravida with KFS scheduled for elective lower segment cesarean section (LSCS) at 38 weeks of gestation. Our patient exhibited classical features of KFS with bilateral sensorineural hearing loss leading to mutism, hypothyroidism, ventricular septal defect, and thoracic scoliosis. Considering the risks associated with general anesthesia compounded by pregnancy-related airway changes, combined spinal and epidural anesthesia was planned. Awake fiber-optic intubation was kept as a backup plan. The surgery was uneventful and the patient recovered without any complications. This case underscores the importance of a multidisciplinary approach, detailed preoperative evaluation, and tailored anesthetic planning to address the complex challenges in parturient with KFS, ensuring optimal perioperative outcomes.

Purpose: Phenylephrine (PE) is the vasopressor of choice for spinal induced hypotension. In recent studies, norepinephrine (NE) has been used to manage hypotension following spinal anesthesia. However, its effect on neonatal acid–base status in preeclamptic mothers has not been well established. The aim of this study is to compare the neonatal acid–base outcomes of bolus doses of PE versus NE for the prevention of spinal anesthesia-induced hypotension in nonlaboring preeclamptic patients undergoing cesarean section. Methods: A randomized, double-blind study was conducted on 50 nonlaboring patients diagnosed with preeclampsia posted for cesarean section under spinal anesthesia. Patients were randomized to receive bolus doses of either PE or NE following hypotension. Neonatal umbilical artery blood gas analysis and maternal hemodynamics were compared. All the statistical tests were two sided and performed at a significance level of 0.05. Results: The study did not reveal any difference in the primary outcome of umbilical artery pH when using PE or NE in patients with preeclampsia (P 0.90). The secondary outcomes were also comparable. Conclusion: Our study shows that PE and NE are comparable in terms of fetal umbilical artery blood pH in nonlaboring preeclamptic patients undergoing cesarean section under spinal anesthesia.

Hypertrophic obstructive cardiomyopathy (HOCM) is an autosomal dominant genetic disease with an incidence of 0.1–0.5% in pregnant women.[1] It is characterized by an asymmetric hypertrophy of the septum with mitral systolic anterior motion leading to dynamic left ventricular outflow tract (LVOT) obstruction. Even in asymptomatic patient heart failure is a possibility due to dynamic LVOT obstruction aggravated by surgical stress, increased plasma volume expansion due to pregnancy, and associated anesthetic factors. We would like to report an oxytocin-induced myocardial ischemia (MI) in a pregnant woman diagnosed with HOCM who was posted for emergency Cesarean Section (CS). Her pregnancy was uneventful and incidentally detected to have HOCM with LVOT gradient of 60 mmHg along with moderate mitral regurgitation. She was not on any cardiac medications and had no limitations in ordinary physical activity (NYHA grade I). She was in spontaneous labor and planned for emergency CS due to an inadequate pelvis. Surgical anesthesia was successfully initiated with epidural local anesthetic and she was hemodynamically stable with phenylephrine infusion (20 µg/min). After delivery, oxytocin infusion was started at the rate of 2 IU/hour (0.03 IU/min) and increased to 4 IU/hour due to uterine atony. ST depression was noted in lead II and V5 within a few minutes of the commencement of oxytocin and the patient became agitated and restless with pulmonary edema and severe hypotension resistant to phenylephrine. The airway was secured, noradrenaline infusion (5 µg/min) was initiated for the management of hypotension, and furosemide was administered for pulmonary edema. Since oxytocin-induced MI is the suspected cause of the hemodynamic instability, the oxytocin infusion was discontinued and further uterine tone was maintained with rectal misoprostol. She was shifted to ICU and serial monitoring of cardiac troponin was done. She was hemodynamically stable and extubated after 12 hours and discharged after 5 days. Autotransfusion induced cardiac failure could have also caused such changes; yet the occurrence of symptoms, such as the hemodynamic instability associated with ST changes occurred after initiation of oxytocin. Hence, we consider oxytocin could be the triggering agent, and the hemodynamic instability is due to oxytocin-induced MI. Though there are cases of oxytocin-induced MI in literature, such occurrence in pregnancy associated with HOCM is limited. Oxytocin-induced MI has been reported in healthy women who were administered high doses of up to 10 IU of oxytocin.[2] But in actual clinical practice the dose does not exceed more than 3 IU for bolus administration. In a study using incremental bolus doses of 0.05–0.5 IU oxytocin in cardiac disease patients for CS, no major adverse events were observed except for the occurrence of dyspnea after 0.1 IU of oxytocin in a case of aortic stenosis.[3] Oxytocin is preferably administered as an infusion, especially in patients with cardiac disease, to avoid systemic adverse effects like hypotension and tachycardia. Oxytocin-induced MI is primarily due to the hypotension and tachycardia caused by systemic vasodilation, yet another mechanism postulated is oxytocin-induced coronary artery spasm.[4] In the case of HOCM, where there is diastolic dysfunction and already compromised coronary perfusion, severe myocardial injury occurs, even with minimal doses of oxytocin. We would like to highlight that even in HOCM patients with well-preserved myocardial function (NYHA I), trivial doses of oxytocin can induce MI and cardiac failure. Though the occurrence of MI in HOCM patients is multifactorial, like surgical stress, hypotension, autotransfusion, and uterotonics; strict vigilance, anticipation, and monitoring are required in all these high-risk patients. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Neurofibromatosis (NF1) is a relatively common multi-system genetic disorder. NF1 increases the risk of hypertension, and vasculopathy is a common finding. Extracranial vertebral artery aneurysm, though rare, can rupture and cause fatal hemorrhage. Pregnancy in NF1 is known to be associated with hypertension, preeclampsia, Hemolysis, elevated liver enzymes, low platelets (HELLP) syndrome. We report anesthesia management of a primigravida with 30 weeks of gestation with neurofibromatosis presenting with severe preeclampsia and acute onset vertebral artery aneurysm, who underwent urgent cesarean section. We opted for general anesthesia over regional due to the risk of aneurysm rupture and technical difficulty with vigilant hemodynamic control. Postoperative course was complicated with HELLP and sepsis but was successfully managed. Knowledge, careful preoperative evaluation, appropriate technique, and management of anesthesia, and close postoperative monitoring are of utmost importance.

Context: Mephentermine by intramuscular route prevents post-spinal anesthesia hypotension. However, the intravenous route had not been tried earlier for this prophylactic purpose. Aims: Comparing the safety and efficacy of intravenous and intramuscular routes of mephentermine in preventing maternal hypotension during cesarean section (CS). The primary objective was to compare the incidences of hypotension. Methods and Material: ASA II term parturients posted for CS were randomized into three groups. Group I received intravenous mephentermine 10 mg just before spinal and 1 ml normal saline (NS) by intramuscular route 10 minutes before spinal. Group M received intramuscular mephentermine 15 mg 10 minutes before spinal and 1 ml NS by the intravenous route just before spinal. Group C received 1 ml NS intramuscularly 10 minutes before spinal and 1 ml NS by the intravenous route just before spinal. The vital parameters, episodes of hypotension, adverse events, and neonatal parameters were assessed. Results: During midterm analysis, it was found that Group I had significantly higher adverse effects, so data collection was stopped, with a total intake of 25 patients in Group M, 22 patients in Group I, and 20 patients in Group C, respectively. The incidence of hypotension was less with the use of IM (8%) and IV (22.7%) mephentermine compared to the control group (P value: <0.05). Group I had significantly more adverse events like headache (59%), chest pain (36.36%), and ST changes compared to other groups (P value: <0.001). Conclusions: We conclude that the intramuscular route is better for preventing post-spinal hypotension in cesarean section with lesser maternal side effects and a better Apgar score.

Early definitions of artificial intelligence (AI) started with Alan Turing's question in his landmark paper on computing machinery and intelligence—"Can machines think?".[1] In the last two decades, AI and its real-world implementation have made great strides. A more current understanding of AI delineates four categories: human-like behavior, rational decision-making, internal thought processes, and external intelligent expression. Each, some, or all of these domains can be developed to achieve a chosen objective.[2] The three components of AI that are utilized in the medical field are machine learning (ML), neural networks, and deep learning. ML implies making machines smarter by enabling them to learn, predict, and adapt from past behavior. Neural networks and deep learning are components of AI that teach computers to process data similar to the workings of the human brain. A network consists of a sheet of "nodes" that receive and process information; if the result crosses a "threshold," it passes on data to the next sheet of nodes. This process can be designed to continue through multiple layers (sheets of nodes) to further refine output and simultaneously give feedback to improve its own processes. Deep learning models can pick up complex patterns in media, writing, and other data to provide insights and predictions. There are four main areas where AI can become important and useful in obstetric anesthesia and analgesia. These are optimizing pre-procedure parturient assessment (especially airway), faster and higher success rates in epidural and spinal analgesia or anesthesia, predicting and preventing hemodynamic crises, ensuring optimal drug administration, and refining pain management.[3,4] All these can improve the quality and delivery of obstetric anesthesia. AI can play an important role in preoperative risk stratification and airway assessment of parturients presenting for analgesia or surgery to an anesthesiologist. AI has been used for prediction and risk assessment in a variety of surgical procedures.[5] ML algorithms using maternal and ultrasonographic parameters obtained during pregnancy have been used to predict the possibility of an emergent cesarean section during active labor.[6,7] In the future, perhaps all pregnant women could be appropriately counseled if studies can use similar algorithms across different races and ethnicities.[6] Another ML-based algorithm MySurgeryRisk is able to predict the risk for eight postoperative complications—acute kidney injury, sepsis, venous thromboembolism, intensive care unit admission for more than 48 hours, need for mechanical ventilation for more than 48 hours, wound, neurological, and cardiovascular complications.[8] If a similar ML-based algorithm is made with data from parturients, it could prove very useful to predict the need for an expert anesthesiologist or intensivist opinion, the possibility of developing postpartum hemorrhage, or other serious complications after anesthesia. This would enable the timely shifting of patients to higher care centers and decrease maternal morbidity and mortality. A thorough literature search does not reveal quality studies regarding the use of AI for risk stratification of patients undergoing labor pain relief interventions or cesarean sections. Airway assessment and difficult airway in a parturient are very important concerns for all anesthesiologists. Pregnant patients may need a general anesthetic for non-obstetric procedures during pregnancy or delivery-related procedures. It is common knowledge that airway management in this subpopulation may be complicated by physiologic changes in pregnancy, the high risk of aspiration, and the need to avoid any morbidity to both mother and unborn baby. In clinical practice, standard airway assessment tools such as the Mallampati score are used; however, clinical assessment alone is not a good predictor of difficult intubation in parturients.[9] Ultrasound has been used for both prediction and airway management in anesthesia. A recent systemic review of clinical evidence concluded that many ultrasound parameters alone or in combination might be useful in predicting difficult laryngoscopy or difficult intubation (although data on difficult mask ventilation were scarce).[10] There have also been reports of ultrasound usage in comparing airway assessment in healthy parturients, patients with preeclampsia, and nonpregnant patients. In one of the first studies regarding this, the authors concluded that airway parameters as assessed by an ultrasound differ significantly between pregnant and nonpregnant patients. Parturients had a significantly higher hyomental distance, anterior neck soft tissue thickness at the hyoid and vocal cord level, and oral cavity height, while the tongue thickness and mandibular condylar movements were significantly lower than in nonpregnant patients. Similar results were reported in patients with preeclampsia.[11,12] These are early findings, and each of these studies was conducted on less than 100 women. Applying such data to AI-based algorithms could play a vital role in airway assessment and prediction of difficult airway in pregnancy. The most challenging aspect of this is likely to be the need for massive data collection. There have been a few reports where convolutional neural networks have used images of patients and compared them to existing data for difficult airway risk assessment.[13,14] Once such AI algorithms are broadly available, they could be very useful for obstetric anesthesiologists. One of the greatest challenges that an anesthesiologist faces is that of performing spinal or epidural anesthesia in parturients, especially when obese. Other than obesity, the difficulty is increased by pregnancy-induced changes in the spine, difficulty in positioning, and the decreased ability to keep still during the procedure due to painful contractions during labor. It is not uncommon nowadays to routinely use ultrasound for regional and neuraxial blocks due to its real-time, low-cost, and radiation-free capabilities. However, nerve tracking and accurate needle localization remain an ongoing challenge due to noise interference, artifacts, and anatomic variability.[15-17] AI techniques have been developed to help in determining the needle insertion point using automated spinal landmarks based on ultrasound imaging of the lumbar spine. Using this, a very high (80%) first-attempt success rate has been reported.[18] Furthermore, AI in the form of convolutional neural networks has been designed to study and learn from large data sets of epidural ultrasound anatomy. This information has been used in real time to optimize the ultrasound image, overlay labeled anatomy onto the patient or on-screen (augmented reality), estimate epidural space depth, and even guide needle placement. This has been shown to reduce procedure time and needle redirections and provide better success rates than the palpation method.[19,20] Another use of AI is in estimating optimal drug doses, such as when administering vasopressors and calculating spinal local anesthetic doses. Closed-loop systems are likely to be used more often in the future. In a closed-loop system, a target is set by the operator, which is automatically maintained by varying the amount of medication administered based on patient information, drug properties, and monitored values. This has several advantages: greater precision in achieving targeted outcomes, reducing the possibility of human error, and freeing the anesthesiologist to focus on higher-level tasks. These have been used successfully in the treatment of hypotension during obstetric anesthesia; blood pressure below a set threshold starts a vasopressor infusion, which is automatically stopped, reduced, or increased based on response.[21-23] The results have been very encouraging, but the devices have still not gained popularity. The intrathecal local anesthetic dose for cesarean section varies and is usually dependent not only on various anatomical and physiological parameters of the parturient but also on the personal preferences of anesthesiologists. A recent study was one of the first to use an ML algorithm for calculating the intrathecal hyperbaric bupivacaine dosage based on patients' physical variables before cesarean section. The authors developed a decision model using abdominal girth and vertebral column length to predict the dose of intrathecal bupivacaine that would provide optimal levels of spinal anesthetic blockade for a cesarean section.[24] Future studies and clinical implementation may determine whether such dose calculations are of any clinical significance. Labor analgesia with patient-controlled epidurals (PCEA) can be augmented using AI algorithms to increase or decrease basal infusion rates in proportion to changes in patient demand. This can be taken one step further by maintaining baseline levels using calculated volume intermittent boluses instead of a continuous infusion; this has been shown to increase epidural spread and analgesic efficacy. A similar system has been designed for use with patient-controlled intravenous remifentanil labor analgesia, with safeguards that reduce or stop the infusion if there are adverse respiratory or hemodynamic effects.[23] Safety is a cornerstone of anesthesia practice, and AI is a core component of new clinical tools being developed for predicting adverse intraoperative events. Most important among these are hypoxemia and hypotension during a cesarean section, which can affect not only the mother but also the baby. The literature has innumerable reports regarding the best vasopressor, its dose, the mode—bolus or infusion—and the timing to be used to treat post-neuraxial hypotension. However, the prediction of this hypotension and hypoxemia using AI in obstetric anesthesia is still to be explored. The initial AI-related reports were from data collected from nonpregnant subjects. An ensemble model-based ML tool, named "Prescience," has been used to predict intraoperative hypoxemia. This model was unique in that it was able to delineate the risk factors that contributed to its prediction, thus avoiding the "black box effect" (when AI provides useful information but does not explain how or why).[25] The system was trained using minute-by-minute data from the electronic medical records encompassing fifty thousand surgeries and improved the performance of anesthesiologists when providing interpretable hypoxemia risks and contributing factors. More recently, a transferable embedding method (one that transforms time-series signals into input features for prediction with ML models) with data from intensive care unit (ICU) and operation rooms has been evaluated to predict multiple outcomes: hypocapnia, hypoxemia, hypertension, hypotension, and the administration of phenylephrine and epinephrine.[26] The results were very encouraging in both reports. In one of the few studies on parturients, the authors compared the capabilities of a single hidden neural network layer of 12 nodes to those of a discrete feature discrimination approach in predicting significant hypotension under spinal anesthesia during cesarean section.[27] The results were very encouraging, but again more data need to be collected. A few clinical trials implementing AI prediction of post-induction hypotension in cesarean sections under spinal anesthesia are being conducted presently, and one is still recruiting patients.[28] Initial AI-based studies on predicting the incidence and duration of intraoperative hypotension and subsequent effectiveness of treatment with vasopressors have shown promise, but more research is needed to assess the real-world usefulness of these in clinical practice and their impact on postoperative outcomes. These principles can be greatly beneficial in emergency obstetric anesthesia as there is often no time for effective clinical evaluation. Other methods used by AI are an interpretation of various arterial blood pressure waveform points to detect early cardiovascular deterioration. Noninvasive (finger cuff) pulse contour analysis by neural networks has been able to warn for hypotension following spinal anesthesia in obstetric patients and has guided pressor use.[27] Additional anesthetic complications such as postoperative nausea and vomiting can also be anticipated.[28] The main basis of an efficient AI system is data acquisition, and getting this from parturients is likely to have many ethical and legal issues. Informed consent, privacy, ownership, transparency, data quality, and data protection are important concerns that still need to be addressed. This would require not only collaboration between medical bodies but also a commitment from political heads of different countries. Furthermore, industries need to manufacture and market AI-based medical technology to increase availability. Overall, a big push to AI would be required for it to become meaningful in the developing world, where data acquisition is still in its infancy. India can play an important role in acquiring these obstetric data, as it continues to have a high fertility rate. Ideal AI-based equipment to complement the obstetric anesthesiologist should cover five domains: firstly, a difficult airway prediction system that analyses the patient's facial images and compares it to a database; secondly, a device that assists neuraxial anesthesia by detecting and labeling anatomical landmarks while also guiding needle placement in real time; thirdly, a prediction system for hemodynamic instability after neuraxial blockade along with closed-loop systems that auto-infuse the optimal amount of vasopressors titrated to response; and lastly, a closed-loop system for epidural or intravenous labor analgesia incorporating pain scores, hemodynamic stability, respiratory effects, and fetal status. It is also imperative to teach the principles of AI as part of the anesthesia resident curriculum—for future anesthesiologists—and to have AI-related continuing medical education for the present generation of anesthesiologists. In conclusion, AI in anesthesia will surely complement traditional methods of clinical decision-making and treatment in a few years, but for an obstetric anesthesiologist it is still a long wait!

One dilemma for anesthesiologists managing obstetric patients may be ever-pervasive. Can obstetric anesthesiologists be neonatal anesthesiologists to effectively participate in neonatal resuscitation? Self-assessment by obstetric anesthesiologists in the following core avenues may address and somewhat resolve this dilemma: Fellowship training in obstetric anesthesiology Neonatal Resuscitation Program® (NRP®) Anesthesiologist-led vs. neonatologist-led Elective participation vs. emergent participation Availability of airway management tools and monitors Credentialing and billing Neonatal resuscitation may have to be one of the core milestones (entrustable professional activities) during fellowship training in obstetric anesthesiology, which is yet to get its own subspecialty certification from the American Board of Anesthesiology. Even among those caring for obstetric patients based solely on acquired experience in clinical skills without formal fellowship training, obstetric anesthesiologists may have to maintain provider status at NRP® by the American Academy of Pediatrics. While maintaining NRP® provider status every two years, they may have to participate in booster training as individuals and team members involved in neonatal resuscitation during each of those 2 years according to American Heart Association Guidelines.[1] Even in the absence of formal training certifications, they may have to understand that neonatologist-led neonatal resuscitation teams may have forgone any expected participation by neonatal anesthesiologists because of their ever-pervasive shortage. Even if obstetric anesthesiologists are welcome in lieu of neonatal anesthesiologists to participate in neonatal resuscitation, they may have to self-realize the specific skills they are bringing to the table. They may be called emergently after failed attempts at neonatal resuscitation or may be involved preemptively for anticipated difficulty during neonatal resuscitation, assuming that other anesthesia providers are available to manage the anesthetized mothers of neonates being resuscitated. Assuming that calls to obstetric anesthesiologists if any are always emergent with all those calls specifically for failed airway management,[2] they may have to self-acknowledge about the last time they managed pediatric patients’ airways electively, let alone neonatal patients’ airways emergently. Even if they have recent experience with pediatric airway management, they may have to explore whether pediatric airway management tools and monitors are available during neonatal resuscitation just as they are available in the pediatric operating rooms.[3] With clinical skills becoming more attuned to continuous waveform capnography and video laryngoscopy for any age patient’s airway management, they may have to self-acknowledge their success rates in neonatal resuscitation in the absence of their inseparably beloved airway management tools and monitors. Assuming that neonatal resuscitation teams have evolved difficult airway algorithms for pediatric patients into difficult airway algorithms specific for neonates,[4] they may have to self-assess whether the absence of continuous waveform capnography may preclude the objective direct monitoring of success in using oropharyngeal airway, bilateral jaw thrust, and laryngeal mask airway during failed endotracheal intubations, considering that neonatal resuscitation teams may have been traditionally monitoring the success of neonatal ventilation indirectly only via the changes in monitored heart rates of neonates being resuscitated.[5] They may have to self-assess whether the absence of video laryngoscopy on top of absent continuous waveform capnography may make them question the position of the endotracheal tube, risking premature and unnecessary extubation when neonates being resuscitated may be just transiently suffering bronchospasm and/or regional dynamic airway collapse distal to the intratracheal tip of an appropriately placed endotracheal tube. Last but not least, obstetric anesthesiologists may have to be credentialed as neonatal anesthesiologists and by their healthcare institutions to ensure their medicolegal protection during participation in neonatal resuscitation at their healthcare institutions where after they may be correspondingly billed for their participation in neonatal resuscitation, electively and/or emergently. Summarily, the core avenues for self-assessment may be too many, thus potentially inducing too many hesitations among obstetric anesthesiologists. Anyhow, a good start will be obstetric anesthesiologists playing active roles in the education, innovation, and evaluation of administered neonatal resuscitations at their healthcare institutions before neonatologist-led neonatal resuscitation teams may welcome them in the role of neonatal anesthesiologists to actively participate within and maybe even lead the neonatal resuscitation teams. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Sir, Posterior reversible encephalopathy syndrome (PRES) is a rare acute reversible neurologic disorder. PRES is commonly observed in females and its cause is unclear. The patient usually presents with headache, nausea, vomiting, visual disturbances, impairment in the level of consciousness, and seizures. Mental derangement could vary from restlessness, agitation, confusion, increased sleep, stupor, and coma. They mostly present with multiple seizures.[1] We present a case of a 28-year-old parturient P2L2, with a previous history of uneventful full-term normal delivery and no comorbidities, who underwent full-term normal vaginal delivery, followed by postpartum sterilization (PPS) under local anesthesia on day 3. On day 6, she presented to the local hospital with bifrontal headache, vomiting, up rolling of eyes, high blood pressure, and five episodes of generalized tonic–clonic seizures and was referred for further management. On arrival, she was drowsy with a Glasgow coma scale-E3V4M5 and her blood pressure was 180/110 mmHg. She was managed in the intensive care unit (ICU) with intravenous (IV) magnesium sulfate 4 g in 100 mL administered as a bolus over 20 min followed by 2 g/h for the next 24 h, IV levetiracetam 1 g twice daily, and oral nimodipine 60 mg fourth hourly for 5 days. The serum magnesium level was monitored. Magnetic resonance imaging (MRI)-T2 weighted fluid attenuated inversion recovery (FLAIR) sequence of the brain revealed features suggestive of PRES and high parietal subarachnoid hemorrhage [Figure 1]. There was no evidence of central venous sinus thrombosis on the MRI venogram. A gynecology consultation was sought for fever, chills, and discharge from the PPS site. Meningitis was ruled out by normal cerebrospinal fluid analysis. Pus culture from the PPS site was positive for Klebsiella and Escherichia coli. Antibiotics (intravenous piperacillin 4.5 g 8th hourly and tazobactam 2 g twice daily) were initiated. She improved symptomatically over the next 3 days, her headache subsided, her blood pressure was controlled, and she did not have any further seizures. She was discharged from the hospital by day 7 without any neurological deficits.Figure 1: (a) Abnormal T2 FLAIR hyperintensity involving cortical and subcortical locations of bilateral parieto-occipital, frontal regions with focal sulcal blooming in the right high parietal region. (b) Abnormal T2 FLAIR hyperintense signals without any diffusion restriction in the peritrigonal parietal white matter on the left side and to a lesser extent bilateral occipital regionsDelay in diagnosis and management of PRES could result in permanent neurological damage. Multiple theories are used to explain the etiology of PRES. It is believed that acute increases in the mean arterial pressure result in blood–brain barrier dysfunction causing vascular leakage of plasma and macromolecules into the cerebral white matter. However, hypertension is found to be absent in about one-fourth of the patients who develop PRES.[2] It could also be due to endothelial dysfunction from various endogenous or exogenous toxins.[3] The risk factors for the development of PRES include sudden elevation of blood pressure, infarction, renal dysfunction, preeclampsia, eclampsia, autoimmune disorders, solid organ transplantation, and chemotherapeutic and immunosuppressive drugs.[4] Preeclampsia and eclampsia are commonly observed in parturients with PRES and most cases are managed as eclampsia without any neuroimaging. Other causes of seizures in the peripartum period such as intracerebral hemorrhage, intracranial tumors, thrombosis, head injury, meningitis, and encephalitis have to be ruled out. Neuroimaging is essential for the diagnosis of PRES. MRI brain is diagnostic. Typical MRI findings are symmetrical white matter edema in the parieto-occipital regions. Vasoconstriction is common in vascular imaging. Atypical features with involvement of anterior cerebral regions, deep white matter, and brainstem are also observed. Management includes the withdrawal of the offending agent or primary etiology, control of blood pressure, anticonvulsive therapy, supportive care with antibiotics, hydration, and correction of electrolyte disturbances.[5] Clinical improvement is always observed with control of blood pressure. In the case of PRES induced by chemotherapeutic or immunosuppressants, tapering or discontinuation of the drug is performed to reverse the process. Hypomagnesemia is a common finding in PRES and could be a possible etiological factor.[6] Hence, magnesium therapy should be initiated as soon as PRES is suspected. It treats both seizures and hypertension. PRES usually resolves clinically and radiographically within days. To conclude, seizures during the postoperative period need to be evaluated by MRI to rule out PRES, and a multidisciplinary approach with the involvement of a neurologist is required to avoid permanent neurological damage. Declaration of patient consent The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.