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Can 24-hour urine stone risk profiles predict urinary stone composition?
J Endourol 2014; 28(6):735-8JE

Abstract

BACKGROUND AND PURPOSE

Distinguishing calcium oxalate from uric acid stones is critical to identify those patients who may benefit from dissolution therapy and can also help direct preventive measures for stone growth. We aim to study whether 24-hour urine analysis may predict the urinary stone composition.

PATIENTS AND METHODS

We retrospectively identified patients with calcium oxalate and uric acid stone compositions who also had a 24-hour urine collection within 3 months of stone analysis. Patients with calcium phosphate, cystine, and other stone compositions were excluded. Subjects were divided based on their stone type (calcium oxalate vs uric acid stones) and were compared according to demographic data and 24-hour urine analysis. Logistic regression analysis was performed to assess the association between stone composition and covariates. A nomogram was then constructed to predict uric acid stones over calcium oxalate stones.

RESULTS

Of the 1163 patients identified, 1054 (90.6%) had calcium oxalate stones and 109 (9.4%) had uric acid stones. On logistic regression, body mass index (BMI) (odds ratio [OR] 1.351, 95% confidence interval [CI] 1.133-1.609; P<0.001), urinary sodium (OR 1.021, 95% CI 1.004-1.037; P=0.013), calcium (OR 0.987, 95% CI 0.979-0.996; P=0.003), oxalate (OR 0.890, 95% CI 0.804-0.985; P=0.024), and uric acid (OR 0.989, 95% CI 0.982-0.997; P=0.005) were significant predictors for urinary stone composition. The nomogram with the highest concordance index (c-index=0.855) was obtained using age, BMI, urinary sodium, calcium, oxalate, and uric acid as variables.

CONCLUSION

Distinguishing uric acid from calcium oxalate stones can be performed with relative accuracy using parameters from the 24-hour urine stone risk profile and the patient's BMI and age.

Authors+Show Affiliations

Stevan B. Streem Center for Endourology & Stone Disease, Glickman Urological & Kidney Institute , The Cleveland Clinic, Cleveland, Ohio.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

24460026

Citation

Torricelli, Fabio C M., et al. "Can 24-hour Urine Stone Risk Profiles Predict Urinary Stone Composition?" Journal of Endourology, vol. 28, no. 6, 2014, pp. 735-8.
Torricelli FC, De S, Liu X, et al. Can 24-hour urine stone risk profiles predict urinary stone composition? J Endourol. 2014;28(6):735-8.
Torricelli, F. C., De, S., Liu, X., Calle, J., Gebreselassie, S., & Monga, M. (2014). Can 24-hour urine stone risk profiles predict urinary stone composition? Journal of Endourology, 28(6), pp. 735-8. doi:10.1089/end.2013.0769.
Torricelli FC, et al. Can 24-hour Urine Stone Risk Profiles Predict Urinary Stone Composition. J Endourol. 2014;28(6):735-8. PubMed PMID: 24460026.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Can 24-hour urine stone risk profiles predict urinary stone composition? AU - Torricelli,Fabio C M, AU - De,Shubha, AU - Liu,Xiaobo, AU - Calle,Juan, AU - Gebreselassie,Surafel, AU - Monga,Manoj, Y1 - 2014/02/14/ PY - 2014/1/28/entrez PY - 2014/1/28/pubmed PY - 2014/9/11/medline SP - 735 EP - 8 JF - Journal of endourology JO - J. Endourol. VL - 28 IS - 6 N2 - BACKGROUND AND PURPOSE: Distinguishing calcium oxalate from uric acid stones is critical to identify those patients who may benefit from dissolution therapy and can also help direct preventive measures for stone growth. We aim to study whether 24-hour urine analysis may predict the urinary stone composition. PATIENTS AND METHODS: We retrospectively identified patients with calcium oxalate and uric acid stone compositions who also had a 24-hour urine collection within 3 months of stone analysis. Patients with calcium phosphate, cystine, and other stone compositions were excluded. Subjects were divided based on their stone type (calcium oxalate vs uric acid stones) and were compared according to demographic data and 24-hour urine analysis. Logistic regression analysis was performed to assess the association between stone composition and covariates. A nomogram was then constructed to predict uric acid stones over calcium oxalate stones. RESULTS: Of the 1163 patients identified, 1054 (90.6%) had calcium oxalate stones and 109 (9.4%) had uric acid stones. On logistic regression, body mass index (BMI) (odds ratio [OR] 1.351, 95% confidence interval [CI] 1.133-1.609; P<0.001), urinary sodium (OR 1.021, 95% CI 1.004-1.037; P=0.013), calcium (OR 0.987, 95% CI 0.979-0.996; P=0.003), oxalate (OR 0.890, 95% CI 0.804-0.985; P=0.024), and uric acid (OR 0.989, 95% CI 0.982-0.997; P=0.005) were significant predictors for urinary stone composition. The nomogram with the highest concordance index (c-index=0.855) was obtained using age, BMI, urinary sodium, calcium, oxalate, and uric acid as variables. CONCLUSION: Distinguishing uric acid from calcium oxalate stones can be performed with relative accuracy using parameters from the 24-hour urine stone risk profile and the patient's BMI and age. SN - 1557-900X UR - https://www.unboundmedicine.com/medline/citation/24460026/Can_24_hour_urine_stone_risk_profiles_predict_urinary_stone_composition L2 - https://www.liebertpub.com/doi/full/10.1089/end.2013.0769?url_ver=Z39.88-2003&amp;rfr_id=ori:rid:crossref.org&amp;rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -