RESUMEN
Background: This study strives to provide a current and thorough assessment of the comparative efficacy and safety between equiosmolar quantities of hypertonic saline (HS) and mannitol in facilitating brain relaxation for patients undergoing elective craniotomies. Methods: This systematic review and meta-analysis, following preferred reporting items for systematic reviews and meta-analyses guidelines, compared the efficacy and safety of equiosmolar concentrations of mannitol and HS in elective craniotomies. PubMed, Scopus, Cochrane Library, ScienceDirect, and Proquest databases were searched using keywords related to mannitol, HS, and craniotomy. Results were analyzed through a random-effects model using Mantel-Haenszel risk ratio and standard mean difference. P < 0.05 was considered significant. Results: Thirteen randomized controlled trials encompassing 965 patients (516 in the HS group and 448 in the mannitol group) were analyzed. The quality of studies was moderate-to-high, and no significant publication bias was observed. The primary outcome, brain relaxation, favored HS over mannitol without significant heterogeneity. Mannitol was associated with increased urine output compared to HS, irrespective of dose, with high heterogeneity. HS was linked to significantly reduced fluid input, confirmed by subgroup analysis with lower heterogeneity. No significant difference was found in serum osmolality between the two agents. Serum sodium (Na+) levels favored HS, whereas arterial blood Na+ levels also favored HS despite considerable heterogeneity. Maximum mean arterial pressure was higher with HS, but it displayed significant heterogeneity. Maximum central venous pressure showed no significant difference between the two agents, with moderate heterogeneity. Conclusion: HS appears more effective than mannitol in achieving brain relaxation, and it may offer advantages in fluid management and Na+ balance. Clinicians should consider these findings when selecting hyperosmotic agents for neurosurgical procedures. Further research is needed to address heterogeneity in certain outcomes and guide clinical practice.
RESUMEN
Personalized medicine entails tailoring medical care to an individual's unique genomic and molecular characteristics. AI holds significant promise in advancing the field of personalized medicine. The challenge lies in effectively analyzing vast amounts of data to create tailored treatment approaches. The incorporation of AI into personalized treatment will require healthcare infrastructure adjustments. Upon patients' arrival, their personal data and clinical information (including images, electrophysiology findings, genetic data, blood pressure, medical notes, etc.) are gathered into the AI system with their consent. Subsequently, the AI system utilizes this patient-specific data to offer healthcare recommendations, aiding healthcare professionals in their clinical decision-making. Results and insights from these recommendations, whether accurate or not, are logged and fed back into the AI system to enhance its precision.