The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
A Risk Prediction Score for Kidney Failure or Mortality in Rhabdomyolysis
Open accessAbstract
IMPORTANCE: Rhabdomyolysis ranges in severity from asymptomatic elevations in creatine phosphokinase levels to a life-threatening disorder characterized by severe acute kidney injury requiring hemodialysis or continuous renal replacement therapy (RRT). OBJECTIVE: To develop a risk prediction tool to identify patients at greatest risk of RRT or in-hospital mortality. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of 2371 patients admitted between January 1, 2000, and March 31, 2011, to 2 large teaching hospitals in Boston, Massachusetts, with creatine phosphokinase levels in excess of 5000 U/L within 3 days of admission. The derivation cohort consisted of 1397 patients from Massachusetts General Hospital, and the validation cohort comprised 974 patients from Brigham and Women's Hospital. MAIN OUTCOMES AND MEASURES: The composite of RRT or in-hospital mortality. RESULTS: The causes and outcomes of rhabdomyolysis were similar between the derivation and validation cohorts. In total, the composite outcome occurred in 19.0% of patients (8.0% required RRT and 14.1% died during hospitalization). The highest rates of the composite outcome were from compartment syndrome (41.2%), sepsis (39.3%), and following cardiac arrest (58.5%). The lowest rates were from myositis (1.7%), exercise (3.2%), and seizures (6.0%). The independent predictors of the composite outcome were age, female sex, cause of rhabdomyolysis, and values of initial creatinine, creatine phosphokinase, phosphate, calcium, and bicarbonate. We developed a risk-prediction score from these variables in the derivation cohort and subsequently applied it in the validation cohort. The C statistic for the prediction model was 0.82 (95% CI, 0.80-0.85) in the derivation cohort and 0.83 (0.80-0.86) in the validation cohort. The Hosmer-Lemeshow P values were .14 and .28, respectively. In the validation cohort, among the patients with the lowest risk score (<5), 2.3% died or needed RRT. Among the patients with the highest risk score (>10), 61.2% died or needed RRT. CONCLUSIONS AND RELEVANCE: Outcomes from rhabdomyolysis vary widely depending on the clinical context. The risk of RRT or in-hospital mortality in patients with rhabdomyolysis can be estimated using commonly available demographic, clinical, and laboratory variables on admission.
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Cites
KDIGO Clinical Practice Guidelines for Acute Kidney Injury
Khwaja, A. (2012) · Nephron Clinical Practice · doi:10.1159/000339789 · Cited by 8,014
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Mehran, R., Aymong, E. D., Nikolsky, E., Lasic, Z., Iakovou, I., Fahy, M., Mintz, G. S., Lansky, A. J., Moses, J. W., Stone, G. W., et al. (2004) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2004.06.068 · Cited by 2,044
Rhabdomyolysis and Acute Kidney Injury
Bosch, X., Poch, E., & Grau, J. M. (2009) · New England Journal of Medicine · doi:10.1056/nejmra0801327 · Cited by 1,488
The Spectrum of Rhabdomyolysis
GABOW, P. A., KAEHNY, W. D., & KELLEHER, S. P. (1982) · Medicine · doi:10.1097/00005792-198205000-00002 · Cited by 1,078
Rhabdomyolysis: An Evaluation of 475 Hospitalized Patients
Melli, G., Chaudhry, V., & Cornblath, D. R. (2005) · Medicine · doi:10.1097/01.md.0000188565.48918.41 · Cited by 629
Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review
Minne, L., Abu-Hanna, A., & de Jonge, E. (2008) · Critical Care · doi:10.1186/cc7160 · Cited by 547
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Factors Predictive of Acute Renal Failure in Rhabdomyolysis
Ward, M. M. (1988) · Archives of Internal Medicine · doi:10.1001/archinte.148.7.1553 · Cited by 464
Waikar, S. S., Wald, R., Chertow, G. M., Curhan, G. C., Winkelmayer, W. C., Liangos, O., Sosa, M.-A., & Jaber, B. L. (2006) · Journal of the American Society of Nephrology · doi:10.1681/asn.2006010073 · Cited by 460
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Cited by
Mehta, R. L., Cerdá, J., Burdmann, E. A., Tonelli, M., García-García, G., Jha, V., Susantitaphong, P., Rocco, M., Vanholder, R., Sever, M. S., et al. (2015) · The Lancet · doi:10.1016/s0140-6736(15)60126-x · Cited by 1,154
Calibration drift in regression and machine learning models for acute kidney injury
Davis, S. E., Lasko, T. A., Chen, G., Siew, E. D., & Matheny, M. E. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx030 · Cited by 312
Sutherland, S. M., Chawla, L. S., Kane-Gill, S. L., Hsu, R. K., Kramer, A. A., Goldstein, S. L., Kellum, J. A., Ronco, C., Bagshaw, S. M., & on behalf of the 15 ADQI Consensus Group (2016) · Canadian Journal of Kidney Health and Disease · doi:10.1186/s40697-016-0099-4 · Cited by 115
Metadata
- Authors
- McMahon, G. M., Zeng, X., & Waikar, S. S.
- Venue
- JAMA Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2013-10-28
- Volume
- 173
- Issue
- 19
- Pages
- 1821
- ISSN
- 2168-6106 (ISSN-L)
- OpenAlex
- W2151444324
- Cited by
- 323
- References
- 20
- FWCI
- 12.87
- Type
- journal article
- Language
- en
- Open access
- green
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