Outcome prediction of acute renal failure in medical intensive care

Jh, Schaefer; Jochimsen, Friederike; Keller, Franz; Wegscheider, Karl; Distler, A.

doi:10.1007/bf01708404

Cited by 193 publications

(112 citation statements)

References 19 publications

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“…No information on vital status was available in 31 (3.6%). Of the 820 remaining, data sufficient to calculate the generic (APACHE II [26], APACHE III [27], SAPS II [28], LODS [29], Multiple Organ Dysfunction [MOD] [30], Brussels [31], Sequential Organ Failure Assessment [SOFA] [32]) and diseasespecific (see Appendix; Liañ o et al [10], Schaefer et al [19], ANP Study [24], Lohr et al [20], and Steuvenberg Hospital Acute Renal Failure [SHARF] [22]) severity of illness scores were available in 605 patients (73.7%), which comprised the analytic sample. Of the 605 patients, 262 (43.3%) were at University of California San Diego Medical Center, 167 (27.6%) were at the San Diego Veterans Affairs Medical Center, 104 (17.2%) were at the Navy Hospital, and 72 (11.9%) at University of California Irvine Medical Center.…”

Section: Study Cohortmentioning

confidence: 99%

Refining Predictive Models in Critically Ill Patients with Acute Renal Failure

Mehta

Pascual²,

Gruta³

et al. 2002

Journal of the American Society of Nephrology

323

211

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Abstract. Mortality rates in acute renal failure remain extremely high, and risk-adjustment tools are needed for quality improvement initiatives and design (stratification) and analysis of clinical trials. A total of 605 patients with acute renal failure in the intensive care unit during 1989-1995 were evaluated, and demographic, historical, laboratory, and physiologic variables were linked with in-hospital death rates using multivariable logistic regression. Three hundred and fourteen (51.9%) patients died in-hospital. The following variables were significantly associated with in-hospital death: age (odds ratio [OR], 1.02 per yr), male gender (OR, 2.36), respiratory (OR, 2.62), liver (OR, 3.06), and hematologic failure (OR, 3.40), creatinine (OR, 0.71 per mg/dl), blood urea nitrogen (OR, 1.02 per mg/dl), log urine output (OR, 0.64 per log ml/d), and heart rate (OR, 1.01 per beat/min). The area under the receiver operating characteristic curve was 0.83, indicating good model discrimination. The model was superior in all performance metrics to six generic and four acute renal failurespecific predictive models. A disease-specific severity of illness equation was developed using routinely available and specific clinical variables. Cross-validation of the model and additional bedside experience will be needed before it can be effectively applied across centers, particularly in the context of clinical trials.Acute renal failure (ARF) in critically ill patients is associated with a distressingly high mortality rate (1-3). Despite improvements in intensive care and dialytic technology, particularly with continuous renal replacement therapies, we have not observed meaningful improvements in patient survival over the past three decades (4 -8). In most series, more than 50% of patients with hospital-acquired ARF die before hospital discharge; of those who survive, between 10 and 33% require long-term dialysis (9 -11).Over the past decade, several clinical trials have been conducted, aiming to reduce ARF-associated mortality (12)(13)(14). Most of these studies have unfortunately proved unsuccessful, including relatively large, well-designed trials using pharmacologic agents with strong preclinical data (e.g., atrial natriuretic peptide [ANP]). Among the difficulties in design and analysis of clinical trials in ARF are the lack of a standardized definition of ARF, the heterogeneity of ARF, comorbidity and severity of illness directly influencing mortality, and large variations in the process of care. -24). Moreover, the timing of evaluation (e.g., consultation, initial dialysis procedure) and the population to which the index was applied (e.g., all patients with ARF, only dialyzed patients, etc.) have differed across studies. To prepare for future clinical trials in ARF, it is essential that valid, generalizable models for risk adjustment be developed, both for stratification in patient selection and for covariate adjustment in the event of imbalanced randomization.We evaluated 851 consecutive cases of ARF in the intensive...

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Section: Study Cohortmentioning

confidence: 99%

Refining Predictive Models in Critically Ill Patients with Acute Renal Failure

Mehta

Pascual²,

Gruta³

et al. 2002

Journal of the American Society of Nephrology

323

211

View full text Add to dashboard Cite

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“…Multivariate logistic regression analysis was used to generate the score. Using their population, they compared the AUROC and Hosmer-Lemeshow goodness-of-fit of their score with several general severity scores and kidney specific severity scores [19, 20, 23, 24, 26, 29]. They found that their score had the best discrimination (AUROC of 0.832) and calibration ability among all scores they tested.…”

Section: Aki-specific Severity Scoresmentioning

confidence: 99%

“…Multiple AKI severity scores have been published in the literature and are listed in table 1[16,17,18,19,20,21,22,23,24,25,26,27]. Most of these scores were developed in a single center, or if multicentric, they were confined to a single country.…”

Section: Aki-specific Severity Scoresmentioning

confidence: 99%

“…External validation studies have shown that no AKI severity scores have good outcome prediction ability [9,10,11,12]. Douma et al[ 9] evaluated the scores by Rasmussen et al [18], Lohr et al [19], Schaefer et al [20] and Liano et al [21] in 238 patients who received RRT in the ICU. The AUROC of these scores ranged from 0.63 to 0.78, with Liano’s score being the highest.…”

Section: Aki-specific Severity Scoresmentioning

confidence: 99%

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Outcome Prediction for Patients with Acute Kidney Injury

Uchino¹

2008

Nephron Clin Pract

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Background/Aims: To review the currently available severity scores to predict outcome of acute kidney injury (AKI) patients, to discuss the problems with such scores, and to provide information for the development of more accurate AKI severity scores in the future. Methods: Literature review and multivariate analysis using a large international database for AKI. Results: Although general severity scores have good discrimination and calibration abilities to predict outcome of critically ill patients, the accuracy of these systems for AKI patients has been questioned. To improve prediction ability, multiple AKI severity scores have been published in the literature. However, most of these scores were developed and tested in a single center, or if multicentric, they were confined to a single country. Seven variables (mechanical ventilation, bilirubin, age, oliguria, hypotension, sepsis and platelet count) are often found as common risk factors in these severity scores and should be included in future AKI severity scores. Although several studies have consistently reported that both low creatinine and high urea at the start of RRT are related to worse outcome in AKI patients, they might not improve prediction ability. Conclusion: Using available information and a large database collected internationally, a more accurate score for AKI is likely to be developed.

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“…Schaefer JH et al 25 found out the outcome predictors of acute kidney injury in ICU setting. They reported that the mortality rate was 56.7% in AKI patients in ICU.…”

Section: Discussionmentioning

confidence: 99%

A Study on Acute Kidney Injury in Intensive Care Setting

Elavazhagan¹

2016

IOSR

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Outcome prediction of acute renal failure in medical intensive care

Cited by 193 publications

References 19 publications

Refining Predictive Models in Critically Ill Patients with Acute Renal Failure

Refining Predictive Models in Critically Ill Patients with Acute Renal Failure

Outcome Prediction for Patients with Acute Kidney Injury

A Study on Acute Kidney Injury in Intensive Care Setting

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