Stone Free Success Rate by Using Modified Guy’s Scoring System in Patients Undergoing Percutaneous Nephrolithotomy
DOI:
https://doi.org/10.61919/jhrr.v3i2.236Keywords:
Renal Stone Disease, Percutaneous Nephrolithotomy, Stone-Free Rate, Modified Guy’s Stone Score, UrologyAbstract
Background: Renal stone disease (RSD) is a prevalent condition globally, with significant impacts on patient health and healthcare systems. The complexity of RSD management, particularly in achieving high stone-free rates (SFR) with minimal complications, necessitates ongoing research into effective treatment modalities. Percutaneous nephrolithotomy (PCNL) has emerged as a standard treatment, but predicting its success pre-operatively remains a challenge.
Objective: The study aimed to evaluate the efficacy of the Modified Guy’s Stone Score (M-GSS) in predicting the SFR in patients undergoing PCNL, and to analyze the relationship between various patient and stone characteristics and the success of the procedure.
Methods: This descriptive case series study involved 161 patients undergoing PCNL at a tertiary care center. Data on patient demographics, stone characteristics, and operative details were collected. Stones were classified using the M-GSS, and PCNL outcomes were assessed in terms of SFR. Statistical analysis was performed using SPSS 23.0, with a focus on the correlation between M-GSS grades and SFR.
Results: The overall SFR was 59.6%. The majority of patients (38.5%) were classified as M-GSS grade 1. The mean stone size was 2.74 ± 1.6 cm, and the mean operative time was 73.4 ± 14.6 minutes. There was no significant difference in SFR across different M-GSS grades (p=0.236). Stone size was the only factor significantly affecting the SFR (p=0.0001).
Conclusion: The study indicates that while M-GSS is a useful tool for preoperative assessment in PCNL, stone size remains a crucial determinant of SFR. The findings suggest the need for further research, particularly larger, multi-center randomized studies, to validate these results and refine preoperative patient counseling and outcome prediction strategies in PCNL.
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Copyright (c) 2023 Saeed Ahmed Khan, Shahzad Ali, Naresh Kumar Valecha, Abdul Mujeeb, Khizer Ali, Afaque Hyder Shah
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