02409nas a2200361   4500000000100000008004100001653001000042653001100052653001100063653000900074653002200083653000900105653001600114653001700130653003000147653002400177653001400201653001000215653002400225653002300249653001500272653001800287100001900305700001600324700001800340700002400358700001700382245006600399300001100465490000700476520155000483022001402033       2005                            d10aAdult10aFemale10aHumans10aAged10aFollow-Up Studies10aMale10aMiddle Aged10aRisk Factors10aPredictive Value of Tests10aProspective Studies10aROC Curve10aIndia10aAcute Kidney Injury10aBiological Markers10aCreatinine10aSurvival Rate1 aDharan Kishore1 aJohn George1 aAntonisamy B.1 aKirubakaran Meshach1 aJacob Chakko00aPrediction of mortality in acute renal failure in the tropics  a289-960 v273 a<p>
Despite significant improvements in medical care, acute renal failure (ARF) remains a high risk for mortality. It is important to be able to predict the outcome in these patients in view of the emotional and ethical needs of the patients and to address questions of efficiency and quality of care. We analyzed the risk factors predicting mortality prospectively in a group of 265 patients using univariate and multiple logistic regression analysis. A prognostic model was evolved that included 10 variables. The model showed good discrimination [(receiver operating characteristic (ROC) area=0.91) and correctly classified 88.30% of patients. The variables significantly associated with mortality were coma odds ratio (OR)=9.8], oliguria (OR=4.9), jaundice (OR=3.7), hypotension (OR=3.1), assisted ventilation (OR=2.3), hospital acquired ARF (OR=2.3), sepsis (OR=2.2), and hypoalbuminemia (OR=1.7). Age and male gender were included in the model as they are clinically important. The score was validated in the same sample by boot strapping. It was also validated in a prospective sample of 194 patients. The model was calibrated by the Hosmer-Lemeshow goodness-of-fit test. It was compared with two generic illness scores and one specific ARF score and was found to be superior to them. The model was verified in different subgroups of ARF like hospital acquired, community acquired, intensive care settings, nonintensive care settings, due to sepsis, due to nonsepsis etiologies, and showed good predictability and discrimination.
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