Absolute Hounsfield unit measurement on noncontrast computed tomography cannot accurately predict struvite stone composition

A nomogram clinical prediction model for predicting urinary infection stones: development and validation in a retrospective study

PURPOSE

This study aimed to develop a nomogram prediction model to predict the exact probability of urinary infection stones before surgery in order to better deal with the clinical problems caused by infection stones and take effective treatment measures.

METHODS

We retrospectively collected the clinical data of 390 patients who were diagnosed with urinary calculi by imaging examination and underwent postoperative stone analysis between August 2018 and August 2023. The patients were randomly divided into training group (n = 312) and validation group (n = 78) using the "caret" R package. The clinical data of the patients were evaluated. Univariate and multivariate logistic regression analysis were used to screen out the independent influencing factors and construct a nomogram prediction model. The receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA) and clinical impact curves were used to evaluate the discrimination, accuracy, and clinical application efficacy of the prediction model.

RESULTS

Gender, recurrence stones, blood uric acid value, urine pH, and urine bacterial culture (P < 0.05) were independent predictors of infection stones, and a nomogram prediction model ( https://zhaoyshenjh.shinyapps.io/DynNomInfectionStone/ ) was constructed using these five parameters. The area under the ROC curve of the training group was 0.901, 95% confidence interval (CI) (0.865-0.936), and the area under the ROC curve of the validation group was 0.960, 95% CI (0.921-0.998). The results of the calibration curve for the training group showed a mean absolute error of 0.015 and the Hosmer-Lemeshow test P > 0.05. DCA and clinical impact curves showed that when the threshold probability value of the model was between 0.01 and 0.85, it had the maximum net clinical benefit.

CONCLUSIONS

The nomogram developed in this study has good clinical predictive value and clinical application efficiency can help with risk assessment and decision-making for infection stones in diagnosing and treating urolithiasis.

In-vivo or in-vitro stone attenuation: what is more valuable for the prediction of renal stone composition in non-contrast-enhanced abdominal computed tomography?

OBJECTIVES

To compare the efficacy of in-vivo and in-vitro stone attenuation in the prediction of stone composition using non-contrast-enhanced abdominal computed tomography (NCCT).

METHODS

This study included a total of 104 patients with renal stones who received percutaneous nephrolithotomy treatment for renal stones between December 2016 and December 2019 and underwent NCCT before the procedure. Preoperative (in-vivo) and postoperative (in-vitro) kidney stone attenuations were compared using the NCCT images of the patients. Renal stone fragments were analysed with the infrared spectrophotometer method.

RESULTS

The mean age of the 104 patients was 49.5 (interquartile range: 37-61) years. According to the receiver operating characteristics analysis, the cut-off values for the prediction of uric acid stones were determined to be 556 HU for the in-vivo and 774 HU for the in-vitro attenuation measurement. Sensitivity and specificity were 100% and 96.6%, respectively, for the in-vivo and 90.9 and 91%, respectively, for the in-vitro images. The cut-off values for the prediction of calcium stones were determined to be 824 HU and 1065 HU for the in-vivo and in-vitro attenuation measurements, respectively. Sensitivity and specificity were 97.3 and 96% for the in-vivo and 96 and 96% for the in-vitro images.

CONCLUSIONS

In-vivo stone attenuation measurement in NCCT was slightly superior to in-vitro measurement due to the reduction in the composition and size of the stone. Our findings show that NCCT in-vivo stone attenuation might differentiate uric acid and calcium stones from the other stone types.

Development and Validation of the Prediction Model of Sepsis in Patients After Percutaneous Nephrolithotomy and Sepsis Progresses to Septic Shock

Background: To study the predictors of sepsis and the progression of sepsis to septic shock in patients after percutaneous nephrolithotomy (PCNL) and to establish and validate predictive models. Methods: The patients were assigned to either the development cohort or the validation cohort depending on their hospital. In the development cohort, univariate and multivariate logistic regression analyses were used to screen independent risk factors for sepsis after PCNL and sepsis progression to septic shock. Nomogram prediction models were established according to the related independent risk factors. Areas under the receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) were used to estimate the discrimination, calibration, and clinical usefulness of the prediction models, respectively. The two sets of models were further validated on the validation cohort. Results: In the development cohort, the risk factors for sepsis after PCNL were diabetes, urine nitrite, staghorn calculi, HU value, albumin-globulin ratio, and high-sensitivity C-reactive protein/albumin ratio. The pre- and postoperative white blood cell counts were risk factors for the progression of sepsis to septic shock. The area under the ROC curve value for predicting sepsis risk was 0.891 and that for predicting septic shock risk was 0.981 in the development cohort; in the validation cohort, these values were 0.893 and 0.996, respectively. In the development cohort, the calibration test p values in the sepsis and septic shock cohorts were 0.946 and 0.634, respectively; in the validation cohort, these values were 0.739 and 0.208, respectively. DCA of the model in the sepsis and septic shock cohorts showed threshold probabilities of 10%-90% in the development cohort; in the validation cohort, these values were 10%-90%. Conclusion: The individualized nomogram prediction models can help improve the early identification of patients who are at higher risk of developing sepsis after PCNL and the progression of sepsis to septic shock to avoid further damage.

The combination of mean and maximum Hounsfield Unit allows more accurate prediction of uric acid stones

Based on mean Hounsfield Unit (HuMean), we aimed to evaluate the additional use of standard deviation of Hounsfield Unit (HuStd), minimum Hounsfield Unit (HuMin), and maximum Hounsfield Unit (HuMax) in noncontrast computed tomography (NCCT) to evaluate uric acid (UA) stones more accurately. The data of patients who underwent the NCCT examination and infrared spectroscopy in our hospital from August 2017 to December 2021 were analyzed retrospectively. Based on CT scans, the HuMean, HuStd, HuMin, and HuMax of all patients were measured. The patients were divided into groups according to the stone composition. The attenuation value of mixed stones was in the middle of their pure stones. Except for Str, statistically significant differences between UA stones and other pure stones were observed for HuMean, HuStd, HuMin, and HuMax. A moderate correlation was found between HuMean, HuStd, HuMin, and HuMax and UA stones (r_s showed -0.585, -0.409, -0.492, and -0.577, respectively). Receiver operator characteristic (ROC) curve showed that the area under the curve (AUC) of HuMean and HuMax were higher than those of HuStd and HuMin (AUC = 0.896, AUC = 0.891 vs. AUC = 0.777, AUC = 0.833). Higher AUC (0.904), specificity (0.899) and positive predictive value (PPV) (0.712) can be obtained by combining HuMean and HuMax in the diagnosis of UA stones. In conclusion, HuMean and HuMax can better predict UA stones than HuStd and HuMin. The combined use of HuMean and HuMax can lead to higher accuracy.

Development of a nomogram predicting the infection stones in kidney for better clinical management: A retrospective study

PURPOSE

To establish the first comprehensive nomogram for prediction of infection stones before treatment for better perioperative treatment and post-operative prevention of infection stones.

METHODS

A total number of 461 patients with kidney stones who underwent mini-percutaneous nephrolithotomy (mPCNL) and flexible ureteroscopy (FURS) between January 2019 to March 2021 were retrospectively analyzed. Univariable analysis and multivariable logistic regression analysis were conducted to identify the predictors for infection stones. Furthermore, the nomogram was established as a predicted model for infection stones.

RESULTS

Among 461 patients with infrared spectroscopy stone analysis, 100 (21.70%) had infection stones and 361 (78.31%) had noninfection stones. Multivariate logistic regression analysis indicated that female (OR 2.816, 95% CI 1.148-6.909, P = 0.024), recurrent kidney stones (OR 8.263, 95% CI 2.295-29.745, P = 0.001), stone burden (OR 6.872, 95% CI 2.973-15.885, P < 0.001), Hounsfield units (HU) (OR 15.208, 95% CI 6.635-34.860, P < 0.001), positive preoperative bladder urine culture (PBUC) (OR 4.899, 95% CI 1.911-12.560, P = 0.001), positive urine leukocyte esterase (ULE) (OR 3.144, 95% CI 1.114-8.870, P = 0.030), urine pH (OR 2.692, 95% CI 1.573-4.608, P < 0.001) and positive urine turbidity (OR 3.295, 95% CI 1.207-8.998, P = 0.020) were predictors for infection stone.

CONCLUSIONS

For patients with kidney stones, female, recurrent kidney stones, stone burden (>601 mm2), HU (750-1000), positive PBUC, positive ULE, urine pH and positive urine turbidity were predictors for infection stone. We established the first comprehensive model for identifying infection stones in vivo, which is extremely useful for the management of infection stones.

New Imaging Techniques in the Management of Stone Disease

Recent advances in computed tomography, X-ray-based imaging, and ultrasonography have improved the accuracy of urinary stone detection and differentiation of stone composition while minimizing radiation exposure. Dual-energy computed tomography and digital tomosynthesis show promise in predicting mineral composition to optimize medical and surgical therapy. Electromagnetic tracking may enhance the use of ultrasonography to achieve percutaneous renal access for nephrolithotomy. This article reviews innovations in imaging technology in the contemporary management of urinary stone disease.

Clinical utility of computed tomography Hounsfield characterization for percutaneous nephrolithotomy: a cross-sectional study

Background

Computed Tomography (CT) is considered the gold-standard for the pre-operative evaluation of urolithiasis. However, no Hounsfield (HU) variable capable of differentiating stone types has been clearly identified. The aim of this study is to assess the predictive value of HU parameters on CT for determining stone composition and outcomes in percutaneous nephrolithotomy (PCNL).

Methods

Seventy seven consecutive cases of PCNL between 2011 and 2016 were divided into 4 groups: 40 (52%) calcium, 26 (34%) uric acid, 5 (6%) struvite and 6 (8%) cystine stones. All images were reviewed by a single urologist using abdomen/bone windows to evaluate: stone volume, core (HUC), periphery HU and their absolute difference. HU density (HUD) was defined as the ratio between mean HU and the stone’s largest diameter. ROC curves assessed the predictive power of HU for determining stone composition/stone-free rate (SFR).

Results

No differences were found based on the viewing window (abdomen vs bone). Struvite stones had values halfway between hyperdense (calcium) and low-density (cystine/uric acid) calculi for all parameters except HUD, which was the lowest. All HU variables for medium-high density stones were greater than low-density stones (p < 0.001). HUC differentiated the two groups (cut-off 825 HU; specificity 90.6%, sensitivity 88.9%). HUD distinguished calcium from struvite (mean ± SD 51 ± 16 and 28 ± 12 respectively; p = 0.02) with high sensitivity (82.5%) and specificity (80%) at a cut-off of 35 HU/mm. Multivariate analysis revealed HUD ≥ 38.5 HU/mm to be an independent predictor of SFR (OR = 3.1, p = 0.03). No relationship was found between HU values and complication rate.

Conclusions

HU parameters help predict stone composition to select patients for oral chemolysis. HUD is an independent predictor of residual fragments after PCNL and may be fundamental to categorize it, driving the imaging choice at follow-up.

Electronic supplementary material

The online version of this article (10.1186/s12894-017-0296-1) contains supplementary material, which is available to authorized users.

Case Discussion: Kidney stone in a patient with an ileal conduit

Kidney stones in patients with ileal conduit and multiple sclerosis are secondary to postoperative anatomical changes and ascending urinary tract infections by urea-splitting bacteria. PNL is the preferred treatment option in patients with urinary diversion and infectious renal stones.

[S2k guidelines on diagnostics, therapy and metaphylaxis of urolithiasis (AWMF 043/025) : Compendium]

Every tenth German citizen will suffer from at least one urinary calculus during the lifetime. The diagnostics, treatment and follow-up treatment of urolithiasis are, therefore, part of the daily routine practice for all urologists in hospitals and private practices as well as in many other disciplines, such as general practitioners, internists, nephrologists and pediatricians. Although the diagnostics and therapy have experienced substantial alterations over the last 10 years, the possibilities of metabolic diagnostics and secondary prevention for patients at risk are, unfortunately and unjustly, in many places very poorly represented. The present S2k guidelines, which for the first time were established in an interdisciplinary consensus process, represent the current practical recommendations and, whenever possible, use tables and algorithms in order to facilitate easy reference in the routine daily work. Last but not least, this greatly simplifies the measures for metaphylaxis.

Novel Methods of Determining Urinary Calculi Composition: Petrographic Thin Sectioning of Calculi and Nanoscale Flow Cytometry Urinalysis

Accurate determination of urinary stone composition has significant bearing on understanding pathophysiology, choosing treatment modalities and preventing recurrence. A need exists for improved methods to determine stone composition. Urine of 31 patients with known renal calculi was examined with nanoscale flow cytometry and the calculi collected during surgery subsequently underwent petrographic thin sectioning with polarized and fluorescent microscopy. Fluorescently labeled bisphosphonate probes (Alendronate-fluorescein/Alendronate-Cy5) were developed for nanoscale flow cytometry to enumerate nanocrystals that bound the fluorescent probes. Petrographic sections of stones were also imaged by fluorescent and polarized light microscopy with composition analysis correlated to alendronate +ve nanocrystal counts in corresponding urine samples. Urine samples from patients with Ca²⁺ and Mg²⁺ based calculi exhibited the highest alendronate +ve nanocrystal counts, ranging from 100–1000 nm in diameter. This novel urine based assay was in agreement with composition determined by petrographic thin sections with Alendronate probes. In some cases, high alendronate +ve nanocrystal counts indicated a Ca²⁺ or Mg²⁺ composition, as confirmed by petrographic analysis, overturning initial spectrophotometric diagnosis of stone composition. The combination of nanoscale flow cytometry and petrographic thin sections offer an alternative means for determining stone composition. Nanoscale flow cytometry of alendronate +ve nanocrystals alone may provide a high-throughput means of evaluating stone burden.

Usefulness of hounsfield unit and density in the assessment and treatment of urinary stones

Computed tomography (CT) is widely used to examine stones in the urinary system. In addition to the size and location of the stone and the overall health of the kidney, CT can also assess the density of the stone in Hounsfield units (HU). The HU, or Hounsfield density, measured by CT, is related to the density of the tissue or stone. A number of studies have assessed the use of HU in urology. HUs have been used to predict the type and opacity of stones during diagnosis, and the efficacy has been assessed using methods including extracorporeal shock wave lithotripsy (ESWL), percutaneous nephrolithotomy (PCNL), ureterorenoscopic ureterolithotripsy (URSL), and medical expulsive treatment (MET). Previous studies have focused on the success rate of HU for predicting the type of stone and of ESWL treatment. Understanding the composition of the stone plays a key role in determining the most appropriate treatment modality. The most recent reports have suggested that the HU value and its variants facilitate prediction of stone composition. However, the inclusion of data regarding urine, such as pH and presence of crystals, increases the predictive accuracy. HUs, which now form part of the clinical guidelines, allow us to predict the success of ESWL; therefore, they should be taken into account when ESWL is considered as a treatment option. However, there are currently insufficient data available regarding the value of HU for assessing the efficacy of PCNL, URSL, and MET. Studies performed to date suggest that these values would make a significant contribution to the diagnosis and treatment of urinary system stones. However, more data are required to assess this further.

Renal struvite stones--pathogenesis, microbiology, and management strategies

Infection stones-which account for 10-15% of all urinary calculi-are thought to form in the presence of urease-producing bacteria. These calculi can cause significant morbidity and mortality if left untreated or treated inadequately; optimal treatment involves complete stone eradication in conjunction with antibiotic therapy. The three key principles of treating struvite stones are: removal of all stone fragments, the use of antibiotics to treat the infection, and prevention of recurrence. Several methods to remove stone fragments have been described in the literature, including the use of urease inhibitors, acidification therapy, dissolution therapy, extracorporeal shockwave lithotripsy, ureteroscopy, percutaneous nephrolithotomy (PCNL), and anatrophic nephrolithotomy. PCNL is considered to be the gold-standard approach to treating struvite calculi, but adjuncts might be used when deemed necessary. When selecting antibiotics to treat infection, it is necessary to acquire a stone culture or, at the very least, urine culture from the renal pelvis at time of surgery, as midstream urine cultures do not always reflect the causative organism.

Predicting urinary stone composition based on single-energy noncontrast computed tomography: the challenge of cystine

OBJECTIVE

To study several measurements from a single-energy noncontrast computed tomography (NCCT) that may distinguish calcium oxalate, uric acid, and cystine stones.

METHODS

Patients with pure urinary stones who had at least 1 single-energy NCCT before the stone composition analysis from January 2008 to December 2012 were enrolled in this study. The analyzed data comprised stone size, volume, core Hounsfield unit (HU), periphery HU, absolute and relative HU differences between core and periphery, and HU density. After these measurements, an NCCT bone window was subjectively evaluated to study the homogeneity of each stone from core to periphery. The Spearman correlation test was used to determine the correlation between HU values and stone size and volume for each group.

RESULTS

A total of 113 patients were found with pure urinary stones who also had a corresponding NCCT. There were 36, 47, and 30 patients in the calcium oxalate, uric acid, and cystine groups, respectively. The core HU, periphery HU, absolute and relative HU differences, and HU density were significantly different among the 3 groups (P<.001). Stone size and volume had a positive correlation with core and periphery HUs only for calcium oxalate and cystine stones. The subjective evaluation of the urinary calculi revealed a different pattern for each stone composition.

CONCLUSION

Single-energy NCCT may predict calcium oxalate stones with a high degree of accuracy. There is an overlap in radiographic profiles of cystine and uric acid stones, making a definitive differentiation more challenging.