Journal of cardiovascular medicine (Hagerstown, Md.) · 2012
Comparison of renal predictors for in-hospital and postdischarge mortality after hospitalized heart failure.
Сравнительное исследованиеСтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Singh Gurjit, Peterson Edward L, Wells Karen, Williams L Keoki, Lanfear David E
Аннотация
OBJECTIVES: Abnormal baseline and acutely worsening renal function (WRF) during heart failure hospitalization are associated with worse outcomes. However, which renal criterion is most predictive of in-hospital and postdischarge mortality is uncertain.
METHODS: We analyzed patients hospitalized for heart failure between 1 January 2000 and 30 June 2008. Preexisting end-stage renal disease was excluded. Blood urea nitrogen (BUN), creatinine, and modification of diet in renal disease-estimated glomerular filtration rate (eGFR) at admission and during hospitalization were tested for association with in-hospital and 1-year mortality. Logistic regression and conditional receiver operating curves were used to compare criteria in terms of association with mortality.
RESULTS: Among 7394 patients, 204 died in-hospital and 1652 within 1 year. Admission BUN was the strongest correlate for both in-hospital and postdischarge mortality [area under the curve (AUC) = 0.724 and 0.656; P < 0.001 vs. creatinine/eGFR], showing 4.6-fold and 3.0-fold mortality, respectively. Adjusting for baseline BUN, subsequent changes in creatinine and BUN performed similarly for in-hospital death (model AUC 0.812; P < 0.001 vs. eGFR) and postdischarge death (all similar, model AUC = 0.661). Optimally predictive thresholds of WRF in hospital were dependent on the baseline renal function and did not always correspond to common definitions.
CONCLUSION: Among hospitalized heart failure patients, baseline BUN is the renal index most strongly associated with in-hospital and 1-year mortality. WRF definitions that use BUN or creatinine have similar discriminative ability overall, but commonly used thresholds are suboptimal for predicting mortality; optimal thresholds varied with baseline renal function and time horizon.
METHODS: We analyzed patients hospitalized for heart failure between 1 January 2000 and 30 June 2008. Preexisting end-stage renal disease was excluded. Blood urea nitrogen (BUN), creatinine, and modification of diet in renal disease-estimated glomerular filtration rate (eGFR) at admission and during hospitalization were tested for association with in-hospital and 1-year mortality. Logistic regression and conditional receiver operating curves were used to compare criteria in terms of association with mortality.
RESULTS: Among 7394 patients, 204 died in-hospital and 1652 within 1 year. Admission BUN was the strongest correlate for both in-hospital and postdischarge mortality [area under the curve (AUC) = 0.724 and 0.656; P < 0.001 vs. creatinine/eGFR], showing 4.6-fold and 3.0-fold mortality, respectively. Adjusting for baseline BUN, subsequent changes in creatinine and BUN performed similarly for in-hospital death (model AUC 0.812; P < 0.001 vs. eGFR) and postdischarge death (all similar, model AUC = 0.661). Optimally predictive thresholds of WRF in hospital were dependent on the baseline renal function and did not always correspond to common definitions.
CONCLUSION: Among hospitalized heart failure patients, baseline BUN is the renal index most strongly associated with in-hospital and 1-year mortality. WRF definitions that use BUN or creatinine have similar discriminative ability overall, but commonly used thresholds are suboptimal for predicting mortality; optimal thresholds varied with baseline renal function and time horizon.
Предметные рубрики MeSH
AgedAged, 80 and overBiomarkersBlood Urea NitrogenCreatinineFemaleGlomerular Filtration RateHeart FailureHospital MortalityHospitalizationHumansKidneyKidney DiseasesKidney Function TestsLinear ModelsLogistic ModelsMaleMichiganMiddle AgedPatient DischargePredictive Value of TestsPrognosisROC CurveRetrospective StudiesRisk AssessmentRisk FactorsTime Factors