ABSTRACT: In a multicenter observational cohort of patients-admitted to intensive care units (ICU), we assessed whether creatinine elevation prior to dialysis initiation in acute kidney injury (AKI-D) further discriminates risk-adjusted mortality. AKI-D was categorized into four groups (Grp) based on creatinine elevation after ICU admission but before dialysis initiation: Grp I > 0.3 mg/dL to <2-fold increase, Grp II ≥2 times but <3 times increase, Grp III ≥3-fold increase in creatinine, and Grp IV none or <0.3 mg/dl increase. Standardized mortality rates (SMR) were calculated by using a validated risk-adjusted mortality model and expressed with 95% confidence intervals (CI). 2,744 patients developed AKI-D during ICU stay; 36.7%, 20.9%, 31.2%, and 11.2% belonged to groups I, II, III, and IV, respectively. SMR showed a graded increase in Grp I, II, and III (1.40 (95% CI, 1.29-1.42), 1.84 (1.66-2.04), and 2.25 (2.07-2.45)) and was 0.98 (0.78-1.20) in Grp IV. In ICU patients with AKI-D, degree of creatinine elevation prior to dialysis initiation is independently associated with hospital mortality. It is the lowest in those experiencing minor or no elevations in creatinine and may represent reversible fluid-electrolyte disturbances.
International journal of nephrology. 01/2013; 2013:827459.
ABSTRACT: Hyperglycemia during critical illness is common and is associated with increased mortality. Intensive insulin therapy has improved outcomes in some, but not all, intervention trials. It is unclear whether the benefits of treatment differ among specific patient populations. The purpose of the study was to determine the association between hyperglycemia and risk- adjusted mortality in critically ill patients and in separate groups stratified by admission diagnosis. A secondary purpose was to determine whether mortality risk from hyperglycemia varies with intensive care unit type, length of stay, or diagnosed diabetes.
Retrospective cohort study.
One hundred seventy-three U.S. medical, surgical, and cardiac intensive care units.
Two hundred fifty-nine thousand and forty admissions from October 2002 to September 2005; unadjusted mortality rate, 11.2%.
A two-level logistic regression model determined the relationship between glycemia and mortality. Age, diagnosis, comorbidities, and laboratory variables were used to calculate a predicted mortality rate, which was then analyzed with mean glucose to determine the association of hyperglycemia with hospital mortality. Hyperglycemia was associated with increased mortality independent of illness severity. Compared with normoglycemic individuals (70-110 mg/dL), adjusted odds of mortality (odds ratio, [95% confidence interval]) for mean glucose 111-145, 146-199, 200-300, and >300 mg/dL was 1.31 (1.26-1.36), 1.82 (1.74-1.90), 2.13 (2.03-2.25), and 2.85 (2.58-3.14), respectively. Furthermore, the adjusted odds of mortality related to hyperglycemia varied with admission diagnosis, demonstrating a clear association in some patients (acute myocardial infarction, arrhythmia, unstable angina, pulmonary embolism) and little or no association in others. Hyperglycemia was associated with increased mortality independent of intensive care unit type, length of stay, and diabetes.
The association between hyperglycemia and mortality implicates hyperglycemia as a potentially harmful and correctable abnormality in critically ill patients. The finding that hyperglycemia-related risk varied with admission diagnosis suggests differences in the interaction between specific medical conditions and injury from hyperglycemia. The design and interpretation of future trials should consider the primary disease states of patients and the balance of medical conditions in the intensive care unit studied.
Critical care medicine 08/2009; 37(12):3001-9. · 6.37 Impact Factor
ABSTRACT: : To examine the effect of severity of acute kidney injury or renal recovery on risk-adjusted mortality across different intensive care unit settings. Acute kidney injury in intensive care unit patients is associated with significant mortality.
: Retrospective observational study.
: There were 325,395 of 617,927 consecutive admissions to all 191 Veterans Affairs ICUs across the country.
: Large national cohort of patients admitted to Veterans Affairs ICUs and who developed acute kidney injury during their intensive care unit stay.
: Outcome measures were hospital mortality, and length of stay. Acute kidney injury was defined as a 0.3-mg/dL increase in creatinine relative to intensive care unit admission and categorized into Stage I (0.3 mg/dL to <2 times increase), Stage II (> or =2 and <3 times increase), and Stage III (> or =3 times increase or dialysis requirement). Association of mortality and length of stay with acute kidney injury stages and renal recovery was examined. Overall, 22% (n = 71,486) of patients developed acute kidney injury (Stage I: 17.5%; Stage II: 2.4%; Stage III: 2%); 16.3% patients met acute kidney injury criteria within 48 hrs, with an additional 5.7% after 48 hrs of intensive care unit admission. Acute kidney injury frequency varied between 9% and 30% across intensive care unit admission diagnoses. After adjusting for severity of illness in a model that included urea and creatinine on admission, odds of death increased with increasing severity of acute kidney injury. Stage I odds ratio = 2.2 (95% confidence interval, 2.17-2.30); Stage II odds ratio = 6.1 (95% confidence interval, 5.74, 6.44); and Stage III odds ratio = 8.6 (95% confidence interval, 8.07-9.15). Acute kidney injury patients with sustained elevation of creatinine experienced higher mortality risk than those who recovered.
: Admission diagnosis and severity of illness influence frequency and severity of acute kidney injury. Small elevations in creatinine in the intensive care unit are associated with increased risk-adjusted mortality across all intensive care unit settings, whereas renal recovery was associated with a protective effect. Strategies to prevent even mild acute kidney injury or promote renal recovery may improve survival.
Critical care medicine 07/2009; 37(9):2552-8. · 6.37 Impact Factor
ABSTRACT: A valid metric is critical to measure and report intensive care unit (ICU) outcomes and drive innovation in a national system.
To update and validate the Veterans Affairs (VA) ICU severity measure (VA ICU).
A validated logistic regression model was applied to two VA hospital data sets: 36,240 consecutive ICU admissions to a stratified random sample of moderate and large hospitals in 1999-2000 (cohort 1) and 81,964 cases from 42 VA Medical Centers in fiscal years 2002-2004 (cohort 2). The model was updated by adding diagnostic groups and expanding the source of admission variables.
C statistic, Hosmer-Lemeshow goodness-of-fit statistic, and Brier's score measured predictive validity. Coefficients from the 1997 model were applied to predictors (fixed) in a logistic regression model. A 10 x 10 table compared cases with both VA ICU and National Surgical Quality Improvement Performance metrics. The standardized mortality ratios divided observed deaths by the sum of predicted mortality.
The fixed model in both cohorts had predictive validity (cohort 1: C statistic = 0.874, Hosmer-Lemeshow goodness-of-fit C statistic chi-square = 72.5; cohort 2: 0.876, 307), as did the updated model (cohort 2: C statistic = 0.887, Hosmer-Lemeshow goodness-of-fit C statistic chi-square = 39). In 7,411 cases with predictions in both systems, the standardized mortality ratio was similar (1.04 for VA ICU, 1.15 for National Surgical Quality Improvement Performance), and 92% of cases matched (+/-1 decile) when ordered by deciles of mortality. The VA ICU standardized mortality ratio correlates with the National Surgical Quality Improvement Performance standardized mortality ratio (r2 = .74). Variation in discharge and laboratory practices may affect performance measurement.
The VA ICU severity model has face, construct, and predictive validity.
Critical care medicine 05/2008; 36(4):1031-42. · 6.37 Impact Factor
ABSTRACT: To quantify the variability in risk-adjusted mortality and length of stay of Veterans Affairs intensive care units using a computer-based severity of illness measure.
Retrospective cohort study.
A stratified random sample of 34 intensive care units in 17 Veterans Affairs hospitals.
A consecutive sample of 29,377 first intensive care unit admissions from February 1996 through July 1997.
Standardized mortality ratio (observed/expected deaths) and observed minus expected length of stay (OMELOS) with 95% confidence intervals were estimated for each unit using a hierarchical logistic (standardized mortality ratio) or linear (OMELOS) regression model with Markov Chain Monte Carlo simulation. We adjusted for patient characteristics including age, admission diagnosis, comorbid disease, physiology at admission (from laboratory data), and transfer status.
Mortality across the intensive care units for the 12,088 surgical and 17,289 medical cases averaged 11% (range, 2-30%). Length of stay in the intensive care units averaged 4.0 days (range, mean unit length of stay 3.0-5.9). Standardized mortality ratio of the intensive care units varied from 0.62 to 1.27; the standardized mortality ratio and 95% confidence interval were <1 for four intensive care units and >1.0 for seven intensive care units. OMELOS of the intensive care units ranged from -0.89 to 1.34 days. In a random slope hierarchical model, variation in standardized mortality ratio among intensive care units was similar across the range of severity, whereas variation in length of stay increased with severity. Standardized mortality ratio was not associated with OMELOS (Pearson's r = .13).
We identified intensive care units whose indicators for mortality and length of stay differ substantially using a conservative statistical approach with a severity adjustment model based on data available in computerized clinical databases. Computerized risk adjustment employing routinely available data may facilitate research on the utility of intensive care unit profiling and analysis of natural experiments to understand process and outcome links and quality efforts.
Critical Care Medicine 06/2005; 33(5):930-9. · 6.33 Impact Factor
ABSTRACT: Parathyroid hormone (PTH) suppression in patients with end-stage renal disease (ESRD) undergoing maintenance hemodialysis is achieved largely by the use of intravenous calcitriol. Aspects of the utility and efficacy of this therapy remain controversial. It is debated whether oral versus intravenous therapy is more effective. Most existing studies examine the effect of calcitriol in isolation, without adjusting for other factors that might influence PTH levels. Thus, the simultaneous role of factors such as dosing, control of serum calcium and phosphorus, and demographic variables such as age, sex, race, and duration of ESRD is not well understood.
We examined the relationship between the administration of calcitriol and PTH suppression in a cohort of hemodialysis patients at a large urban dialysis facility over a period of 30 months. Hemodialysis patients (n = 155) who received at least 3 months of treatment in this facility were included.
Using a time sensitive multiple linear regression modeling technique, we found that second and subsequent PTH levels were positively correlated with black race (P < 0.0001) and serum phosphate (P < 0.03) and strongly negatively correlated with serum calcium (P< 0.0001) and diabetes (P< 0.0039). Drug dose (in micrograms per kilogram per month) was weakly negatively correlated (P < 0.04). Unlike previous studies, we adjusted for the simultaneous confounding influence of demographic and laboratory variables, as well as for drug dose normalized for body weight.
This analysis suggests that calcitriol therapy in hemodialysis patients is adversely affected by higher phosphate levels and needs to account for such patient characteristics as race and diabetes and such laboratory variables as calcium and phosphate control. Finally, as has been recently suggested by others, the patient's race may require us to aim for different PTH target levels with therapy.
The American Journal of the Medical Sciences 05/2002; 323(4):210-5. · 1.39 Impact Factor