Predictors of Failure of Spinal Anaesthesia in Elective Surgical Patients: A Prospective Observational Study at a Tertiary Care Hospital in Gwalior, India

Abstract

Background: Spinal anaesthesia is the most commonly employed neuraxial technique for elective lower abdominal, pelvic, and lower extremity surgeries. Despite its high success rate, failure of spinal anaesthesia remains a clinically significant concern, with reported incidence ranging from 1% to 17% globally. Identifying predictors of failure is essential for risk stratification and optimisation of anaesthetic practice.Objective: To determine the incidence and identify predictors of failure of spinal anaesthesia in elective surgical patients at Clearmedi Paridhi Hospital, Gwalior, over a one-year period.Methodology: This prospective observational study was conducted over 12 months (August 2025 – July 2026) among 80 adult patients aged 18–70 years undergoing elective surgery under spinal anaesthesia. Demographic, clinical, and procedural data were collected using a structured proforma. Spinal anaesthesia failure was defined as the need for supplemental analgesia, sedation, or conversion to general anaesthesia. Univariate and multivariable logistic regression analyses were performed to identify independent predictors.Results: The overall incidence of spinal anaesthesia failure was 17.5% (14/80). Partial failure occurred in 10 patients (12.5%) and total failure in 4 patients (5%). Multivariable analysis identified BMI ≥ 30 kg/m² (AOR = 4.21, 95% CI: 1.34–13.22), provider experience < 2 years (AOR = 3.87, 95% CI: 1.18–12.68), number of puncture attempts ≥ 3 (AOR = 5.42, 95% CI: 1.58–18.61), and bloody cerebrospinal fluid (AOR = 6.15, 95% CI: 1.72–21.98) as independent predictors of failure.Conclusion: The incidence of spinal anaesthesia failure in elective surgical patients was 17.5%, with BMI ≥ 30 kg/m², provider inexperience, multiple puncture attempts, and bloody CSF being significant predictors. Targeted interventions including simulation-based training and pre-procedural risk assessment may reduce failure rates.