Ain Shams Journal of Anesthesiology (Mar 2023)

Serial perioperative optic nerve sheath measurements for early diagnosis of the transurethral resection of prostate syndrome: an open label pilot study

  • Bharti Chauhan,
  • Pamposh Raina,
  • Ravi Kant Dogra,
  • Jyoti Pathania

DOI
https://doi.org/10.1186/s42077-023-00316-8
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 8

Abstract

Read online

Abstract Background Ultrasound imaging of optic sheath nerve diameter [ONSD] is reported to reflect changes consistent with intracranial pressure changes seen in traumatic brain injury and also in documented serum hyponatremia. We hypothesized that hyponatremia and hypervolemia seen during trans urethral resection of prostate [TURP] surgery may also have some association with different ONSD readings from the baseline perioperatively, resulting in early detection of TURP syndrome. In this prospective observational study, 50 adult male patients scheduled for TURP surgery meeting inclusion criteria were included and the ONSD measurements were serially recorded perioperatively. Patients with measurements ≥ 5.2 mm with either clinical symptoms or electrolyte changes suggested TURP syndrome were taken as true positive. Results The sensitivity, specificity, area under the curve, positive predictive, and negative predictive value at 95%CI of ONSD for early detection of TURP syndrome was {100% [15.81 to 100.00%], 91.67% [80.02 to 97.68%], 0.96 [0.86 to 0.99%], 33.33% [4.33 to 77.72%], 100% [91.96 to 100.00%]} with a diagnostic accuracy of 95.83%. In univariate logistic regressions, the duration of surgery had a positive association with TURP syndrome [odd ratio 1.066, β coefficient 0.064, p = 0.015]. In multivariate logistic regression, we could not validate the association between these factors and TURP syndrome [p > 0.050]. Conclusions The ONSD measurements have good diagnostic accuracy for detecting TURP syndrome, but we advocate more multi-centric studies with large sample sizes to validate this association in the multivariate regression model.

Keywords