In a recent article published in Scientific Reports, researchers explored a novel approach to assess postoperative infection risk in patients with upper urinary tract calculus (UUTC), commonly known as kidney stones. They aimed to develop a predictive model that uses infrared spectroscopy data to analyze the chemical composition of kidney stones, identifying high-risk patients and guiding treatment decisions.
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Background
UUTC is a common urological condition characterized by the formation of mineral deposits in the kidneys. It has become increasingly prevalent worldwide, leading to significant health problems due to the associated pain, discomfort, and potential complications. The main symptoms include fever, kidney pain, and blood in the urine, which can severely affect both mental and physical health.
Treatment options vary based on the size and location of the stone and include conservative therapy, percutaneous nephrolithotomy (PCNL), extracorporeal shock wave lithotripsy, retrograde intrarenal surgery (RIRS), open surgery, or laparoscopic surgery. However, postoperative complications, particularly urinary tract infections, remain a major concern.
Infrared spectroscopy is a non-invasive technique widely used in biology and medicine. It works by detecting the absorption and emission of electromagnetic radiation at specific wavelengths, producing a unique spectral signature for different molecules. This technique has been applied to analyze the chemical composition of biological samples, including kidney stones, and shows promise in predicting the risk of postoperative infections.
About the Research
In this paper, the authors investigated the potential of using infrared spectroscopy data to predict postoperative infections in UUTC patients. They hypothesized that the transmittance of organic components within the stones could be linked to the risk of postoperative infections.
The study analyzed data from 480 patients who underwent PCNL or RIRS for UUTC. It included 328 patients from Fujian Hospital as the construction cohort and 152 patients as the validation cohort.
The researchers used Fourier Transform Infrared Spectroscopy (FTIR) to analyze the infrared spectral data of kidney stones by measuring the transmittance values of 3,736 infrared wavenumbers for each patient.
They applied several statistical methods, including univariate analysis, multicollinearity screening, and Lasso regression, to identify the most relevant wavenumbers linked to postoperative infections. From this analysis, they identified four infection-related wavenumbers and combined them into a novel marker called the IR-infection score.
To further enhance the predictive power of the marker, the authors developed a nomogram. This graphical tool combines the IR infection score with other clinically relevant factors, including gender, procalcitonin (PCT) levels, white blood cell count (UWBC), and urine culture results. This tool aimed to provide a comprehensive assessment of postoperative infection risk in UUTC patients.
Research Findings
The outcomes showed a significant link between the IR-infection score and the risk of postoperative infection in UUTC patients. The IR-infection score achieved an area under the receiver operating characteristic curve (AUC) of 0.707, indicating moderate predictive accuracy. The nomogram, which included the IR-infection score and other clinical factors, demonstrated improved accuracy, with an AUC of 0.873.
The authors identified 653 infection-related infrared wavenumbers, including WN_1261.22, WN_1479.13, WN_1320.03, and WN_1966.07. These wavenumbers were linked to organic compounds in kidney stones, which are known to increase the risk of postoperative infections.
The IR-infection score outperformed traditional indicators, such as the presence of infection stones and white blood cell count, suggesting its value in identifying high-risk patients and guiding treatment decisions.
Applications
This research has significant implications for urology. The IR-infection score and nomogram can help predict postoperative infection risk in UUTC patients, enabling early intervention and potentially improving patient outcomes.
Given that infrared spectroscopy is already used to analyze stone composition, integrating the IR-infection score into clinical workflows is feasible. Clinicians can use these tools to identify high-risk patients, allowing for tailored treatment strategies, such as more aggressive antibiotic prophylaxis or closer monitoring for signs of infection.
Conclusion
The novel infrared spectroscopy marker effectively assessed postoperative infection risk in UUTC patients. The IR-infection score and nomogram show promising predictive accuracy and could improve patient care by enabling early identification of high-risk patients and guiding treatment decisions.
The researchers recommend validating these findings in larger, multi-institutional studies and further exploring the specific organic components associated with postoperative infections to refine predictive models.
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Journal Reference
Lin, J., et al. (2024). A novel infrared spectroscopy marker for assessing the postoperative infection risk in patients with upper urinary tract calculus. Sci Rep. DOI: 10.1038/s41598-024-69720-w, https://www.nature.com/articles/s41598-024-69720-w
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