Excess Mortality Attributable to Acute Kidney Injury in the ICU* Kenneth B. Christopher, MD The Nathan E. Hellman Memcrial Labcratcry Renal Divisicn, Brigham and Women's Hcspital Harvard Medical SchocI Besten, MA
particular exposures. PAR is dependent on the relative risk and prevalence of the exposure (AKI) and also is dependent on the underlying risk in those without the exposure (non-AKI) (12). The study has notable strengths. The FINNAKI trial was a nationwide, nonselected patient cohort study, with meticulously recorded prospective data collected in 17 ICUs and cute kidney injury (AKI) is a common and devastatmanaged with the Finnish Intensive Care Consortium which coordinates a national intensive care benchmarking program ing consequence of critical illness and associated with (8-10). The investigators have previously shown that the increased morbidity and mortality (1). Depending FINNAKI data were systematically obtained with maintenance on how it is defined, the prevalence of AKI ranges from 35% of prespecified definitions in a careful effort to reduce errors, to 70% of ICU patients and roughly 10% of those with AKI minimize missing data, and decrease potential ascertainment wifl need renal replacement therapy (2). Death in patients with AKI is associated with the magnitude of renal injury bias (8-10). The KDIGO Clinical Practice Guideline for AKI criteria (11), although limited for clinical use, is felt to be (3, 4). In the critically ill, AKI commonly occurs as a part of appropriate for epidemiological studies of AKI and for clinical multiple organ dysfunction syndrome, which is associated trial design ( 13). In the FINNAKI trial dataset, the investigators with poor outcome (5). previously compared the AKI Network with the KDIGO CliniAlthough quality of life and long-term survival have been cal Practice Guideline for AKI criteria and reported an identiexplored in critically ill patients with AKI (3,5), the number of cal classification (8). The study likely has sufficient numbers deaths that might be avoided if AKI could be prevented is not of patients to ensure the adequate reliability of the estimates known. Given the heightened mortality in critically ill patients with AKI (6), in this issue of Critical Care Medicine, Vaara et al presented ( « = 2,719,90-day mortality rate - 39.2%). All-cause mortality as a primary endpoint is an unbiased and clinically (7) performed a novel study utilizing a sequentially propenrelevant outcome. sity-matched analysis of data collected from the multicenter Finnish AKI study (FINNAKI) (8-10) to determine the excess The present study may have several important limitations. mortality attributable to AKI in the critically ill. Including variables without considering their probable causal Vaara et al (7) studied 2,719 medical or surgical ICU relationships with the exposure and outcome may result in patients admitted to one of 16 ICUs with 40% of the cohort more rather than less bias. In other words, adjustment varideveloping AKI in the first 5 ICU days as defined by the Kidney ables should preferably be selected based on a prespecified Disease: Improving Global Outcomes (KDIGO) Clinical Praccausal structure rather than statistical significance alone. The tice Guideline for AKI criteria (11). The 90-day mortality rate utilization of the supplemental SPSS algorithm "FUZZY" for was 39.2%. Following sequentially propensity-matched analycase-control matching is appropriate, but it is not clear if Vaara sis, Vaara et al (7) determined the population attributable risk et al (7) employed exact matching or tolerated a "fuzz" fac(PAR) as a more direct estimation of the decrease in mortality tor for each matching variable. In the study, the nonmatched among general ICU population had none of them developed patients with AKI had higher illness severity compared with AKI. They show that in their study cohort, the reduction in the analyzed matched patients with AKI, which potentially 90-day mortality would be 19.6% had none of the ICU patients reduces the generalizability to all critical care patients. Furtherdeveloped AKI. more, generalizability of the study sample to ICU populations in other areas of the world may be limited by the lack of data The PAR is a gauge of the amount of an outcome or condion race/ethnicity. tion that can be attributed to an exposure under study. PAR can suggest the impact on the outcome (potential gain) with Importantly, in the original FINNAKI cohort of 2,901 a reduction (or elimination) of exposure. PAR can sharpen patients, baseline serum/plasma creatinine was available in only public health policy focus by highlighting the importance of 1,847 of cases (64%). If baseline creatinine was not available, Vaara et al estimated it using the modification of diet in renal disease equation assuming a giomeruiar filtration rate (GFR) of *See also p. 878. 75mL/min/1.73 m^ (8). In the ICU, the estimation of baseline Key Words: acute kidney injury; critical illness; mortality; outcome; creatinine assuming a GFR of 75mL/min/1.73 m' is controverpopulation attributable risk sial, as it may greatly overestimate the proportion of patients The author has disclosed that he does not have any potential conflicts of with AKI (14). Furthermore, although 90-day mortality is a interest. simple and robust endpoint to evaluate mortality following hosCopyright © 2013 by the Society of Critical Care Medicine and Lippincott pital discharge, it may not be sufficient to accurately measure the Williams & Wilkins attributable mortality from an episode of critical illness or AKI. DOI: 10.1097/CCM.0000000000000088
A
992
vwvw.ccmjournal.org
April 2014 • Volume 42 • Number 4
Editorials
There is always the possibility that there are other predictor variables that were not included in the model under study that have equal or greater impact on the outcome variable. To this point, it is possible that the model used may not be appropriately adjusted by not controlling for variables that affect bias or the causal relation between exposure and outcome. It is possible that other unmeasured variables influence mortality independently of AKI, which may have biased estimates. Despite the author's adjustment for multiple potential confounders in their model, there may be residual confounding of unmeasured variables leading to observed differences in outcomes. Specifically, AKI may simply be a reflection of the overall poor condition of the patient, for which the authors are unable to fully adjust. Furthermore, measures of functional status, health literacy, social support, and medication adherence are not evaluated which are recognized as predictors of poor outcome in survivors of hospital care (15). Although observational studies cannot prove causation, the authors have performed a well-designed study with adjustment for known key confounders and minimal bias that provides important public health information. With increasing societal and political interest in reducing the cost of healthcare delivery, models that can predict mortality may be useful for targeting interventions aimed at improving outcomes. The authors' estimation of the PAR identifies the impact of AKI on 90-day mortality in the critically ill, underscores the importance of studying biomarkers to detect early kidney injury, further motivates AKI prevention efforts, and should encourage closer follow-up of critical ulness survivors who suffered an episode of AKI.
REFERENCES 1. Barrantes F, Tian J, Vazquez R, et al: Acute kidney injury criteria predict outcomes of critically ill patients. Crit Care Med 2008; 36:1397-1403
Critical Care Medicine
2. Hoste EA, Clermont G, Kersten A, et al: RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: A cohort analysis. Crit Care 2006; 10:R73 3. Maynard SE, Whittle J, Chelluri L, et al: Quality of life and dialysis decisions in critically ill patients with acute renal failure. Intensive Care Med 2003; 29:1589-1593 4. Garzotto F, Piccinni P, Cruz D, et al; NEFROINT Investigation Group: RIFLE-based data collection/management system applied to a prospective cohort multicenter Italian study on the epidemiology of acute kidney injury in the intensive care unit. Blood Purif 2011 ; 31:159-171 5. Korkeila M, Ruokonen E, Takala J: Costs of care, long-term prognosis and quality of life in patients requiring renal replacement therapy during intensive care. Intensive Care Med 2000; 26:1824-1831 6. Gammelager H, Christiansen CF, Johansen MB, et al: Cne-year mortality among Danish intensive care patients with acute kidney injury: A cohort study. Crit Care 201 2; 16:R124 7 Vaara ST Pettilä V, Kaukonen K-M, et al; the Finnish Acute Kidney Injury Study Group: The Attributable Mortality of Acute Kidney Injury: A Sequentially Matched Analysis. Crit Care Med 2014; 42:878-885 8. Nisula S, Kaukonen KM, Vaara ST et al; FINNAKI Study Group: Incidence, risk factors and 90-day mortality of patients with acute kidney injury in Finnish intensive care units: The FINNAKI study. Intensive Care Med 2013; 39:420-428 9. Vaara ST, Korhonen AM, Kaukonen KM, et al; The FINNAKI Study Group: Fluid overload is associated with an increased risk for 90-day mortality in critically ill patients with renal replacement therapy: Data from the prospective FINNAKI study. Crit Care 201 2; 1 6:R197 10. Poukkanen M, Vaara ST Pettilä V, et al; FINNAKI Study Group: Acute kidney injury in patients with severe sepsis in Finnish intensive care units. Acta Anaesthesiol Scand 2013; 57:863-872 11. Kidney Disease: Improving Global Outcomes (KDIGC) Acute Kidney Injury Work Group: KDIGC clinical practice guideline for acute kidney injury. Kidney Int 2012; 2(Suppl 1):1-138 12. Roderick PJ: Assessing the impact of chronic kidney disease on individuals and populations: Use of relative and absolute measures. Nephrol Dial Transplant 201 2; 27(Suppl 3):iii39-42 13. Palevsky PM, Liu KD, Brophy PD, et al: KDCQI US commentary on the 201 2 KDIGO clinical practice guideline for acute kidney injury. Am J Kidney Dis 2013; 61:649-672 14. Pickering JW, Endre ZH: Back-calculating baseline creatinine with MDRD misclassifies acute kidney injury in the intensive care unit. Clin J Am Soc Nephrol 2010; 5:1165-11 73 15. Coleman EA, Min SJ, Chomiak A, et al: Posthospital care transitions: Patterns, complications, and risk identification. Health Serv Res 2004; 39:1449-1465
www.ccmjournal.org
993
Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
particular exposures. PAR is dependent on the relative risk and prevalence of the exposure (AKI) and also is dependent on the underlying risk in those without the exposure (non-AKI) (12). The study has notable strengths. The FINNAKI trial was a nationwide, nonselected patient cohort study, with meticulously recorded prospective data collected in 17 ICUs and cute kidney injury (AKI) is a common and devastatmanaged with the Finnish Intensive Care Consortium which coordinates a national intensive care benchmarking program ing consequence of critical illness and associated with (8-10). The investigators have previously shown that the increased morbidity and mortality (1). Depending FINNAKI data were systematically obtained with maintenance on how it is defined, the prevalence of AKI ranges from 35% of prespecified definitions in a careful effort to reduce errors, to 70% of ICU patients and roughly 10% of those with AKI minimize missing data, and decrease potential ascertainment wifl need renal replacement therapy (2). Death in patients with AKI is associated with the magnitude of renal injury bias (8-10). The KDIGO Clinical Practice Guideline for AKI criteria (11), although limited for clinical use, is felt to be (3, 4). In the critically ill, AKI commonly occurs as a part of appropriate for epidemiological studies of AKI and for clinical multiple organ dysfunction syndrome, which is associated trial design ( 13). In the FINNAKI trial dataset, the investigators with poor outcome (5). previously compared the AKI Network with the KDIGO CliniAlthough quality of life and long-term survival have been cal Practice Guideline for AKI criteria and reported an identiexplored in critically ill patients with AKI (3,5), the number of cal classification (8). The study likely has sufficient numbers deaths that might be avoided if AKI could be prevented is not of patients to ensure the adequate reliability of the estimates known. Given the heightened mortality in critically ill patients with AKI (6), in this issue of Critical Care Medicine, Vaara et al presented ( « = 2,719,90-day mortality rate - 39.2%). All-cause mortality as a primary endpoint is an unbiased and clinically (7) performed a novel study utilizing a sequentially propenrelevant outcome. sity-matched analysis of data collected from the multicenter Finnish AKI study (FINNAKI) (8-10) to determine the excess The present study may have several important limitations. mortality attributable to AKI in the critically ill. Including variables without considering their probable causal Vaara et al (7) studied 2,719 medical or surgical ICU relationships with the exposure and outcome may result in patients admitted to one of 16 ICUs with 40% of the cohort more rather than less bias. In other words, adjustment varideveloping AKI in the first 5 ICU days as defined by the Kidney ables should preferably be selected based on a prespecified Disease: Improving Global Outcomes (KDIGO) Clinical Praccausal structure rather than statistical significance alone. The tice Guideline for AKI criteria (11). The 90-day mortality rate utilization of the supplemental SPSS algorithm "FUZZY" for was 39.2%. Following sequentially propensity-matched analycase-control matching is appropriate, but it is not clear if Vaara sis, Vaara et al (7) determined the population attributable risk et al (7) employed exact matching or tolerated a "fuzz" fac(PAR) as a more direct estimation of the decrease in mortality tor for each matching variable. In the study, the nonmatched among general ICU population had none of them developed patients with AKI had higher illness severity compared with AKI. They show that in their study cohort, the reduction in the analyzed matched patients with AKI, which potentially 90-day mortality would be 19.6% had none of the ICU patients reduces the generalizability to all critical care patients. Furtherdeveloped AKI. more, generalizability of the study sample to ICU populations in other areas of the world may be limited by the lack of data The PAR is a gauge of the amount of an outcome or condion race/ethnicity. tion that can be attributed to an exposure under study. PAR can suggest the impact on the outcome (potential gain) with Importantly, in the original FINNAKI cohort of 2,901 a reduction (or elimination) of exposure. PAR can sharpen patients, baseline serum/plasma creatinine was available in only public health policy focus by highlighting the importance of 1,847 of cases (64%). If baseline creatinine was not available, Vaara et al estimated it using the modification of diet in renal disease equation assuming a giomeruiar filtration rate (GFR) of *See also p. 878. 75mL/min/1.73 m^ (8). In the ICU, the estimation of baseline Key Words: acute kidney injury; critical illness; mortality; outcome; creatinine assuming a GFR of 75mL/min/1.73 m' is controverpopulation attributable risk sial, as it may greatly overestimate the proportion of patients The author has disclosed that he does not have any potential conflicts of with AKI (14). Furthermore, although 90-day mortality is a interest. simple and robust endpoint to evaluate mortality following hosCopyright © 2013 by the Society of Critical Care Medicine and Lippincott pital discharge, it may not be sufficient to accurately measure the Williams & Wilkins attributable mortality from an episode of critical illness or AKI. DOI: 10.1097/CCM.0000000000000088
A
992
vwvw.ccmjournal.org
April 2014 • Volume 42 • Number 4
Editorials
There is always the possibility that there are other predictor variables that were not included in the model under study that have equal or greater impact on the outcome variable. To this point, it is possible that the model used may not be appropriately adjusted by not controlling for variables that affect bias or the causal relation between exposure and outcome. It is possible that other unmeasured variables influence mortality independently of AKI, which may have biased estimates. Despite the author's adjustment for multiple potential confounders in their model, there may be residual confounding of unmeasured variables leading to observed differences in outcomes. Specifically, AKI may simply be a reflection of the overall poor condition of the patient, for which the authors are unable to fully adjust. Furthermore, measures of functional status, health literacy, social support, and medication adherence are not evaluated which are recognized as predictors of poor outcome in survivors of hospital care (15). Although observational studies cannot prove causation, the authors have performed a well-designed study with adjustment for known key confounders and minimal bias that provides important public health information. With increasing societal and political interest in reducing the cost of healthcare delivery, models that can predict mortality may be useful for targeting interventions aimed at improving outcomes. The authors' estimation of the PAR identifies the impact of AKI on 90-day mortality in the critically ill, underscores the importance of studying biomarkers to detect early kidney injury, further motivates AKI prevention efforts, and should encourage closer follow-up of critical ulness survivors who suffered an episode of AKI.
REFERENCES 1. Barrantes F, Tian J, Vazquez R, et al: Acute kidney injury criteria predict outcomes of critically ill patients. Crit Care Med 2008; 36:1397-1403
Critical Care Medicine
2. Hoste EA, Clermont G, Kersten A, et al: RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: A cohort analysis. Crit Care 2006; 10:R73 3. Maynard SE, Whittle J, Chelluri L, et al: Quality of life and dialysis decisions in critically ill patients with acute renal failure. Intensive Care Med 2003; 29:1589-1593 4. Garzotto F, Piccinni P, Cruz D, et al; NEFROINT Investigation Group: RIFLE-based data collection/management system applied to a prospective cohort multicenter Italian study on the epidemiology of acute kidney injury in the intensive care unit. Blood Purif 2011 ; 31:159-171 5. Korkeila M, Ruokonen E, Takala J: Costs of care, long-term prognosis and quality of life in patients requiring renal replacement therapy during intensive care. Intensive Care Med 2000; 26:1824-1831 6. Gammelager H, Christiansen CF, Johansen MB, et al: Cne-year mortality among Danish intensive care patients with acute kidney injury: A cohort study. Crit Care 201 2; 16:R124 7 Vaara ST Pettilä V, Kaukonen K-M, et al; the Finnish Acute Kidney Injury Study Group: The Attributable Mortality of Acute Kidney Injury: A Sequentially Matched Analysis. Crit Care Med 2014; 42:878-885 8. Nisula S, Kaukonen KM, Vaara ST et al; FINNAKI Study Group: Incidence, risk factors and 90-day mortality of patients with acute kidney injury in Finnish intensive care units: The FINNAKI study. Intensive Care Med 2013; 39:420-428 9. Vaara ST, Korhonen AM, Kaukonen KM, et al; The FINNAKI Study Group: Fluid overload is associated with an increased risk for 90-day mortality in critically ill patients with renal replacement therapy: Data from the prospective FINNAKI study. Crit Care 201 2; 1 6:R197 10. Poukkanen M, Vaara ST Pettilä V, et al; FINNAKI Study Group: Acute kidney injury in patients with severe sepsis in Finnish intensive care units. Acta Anaesthesiol Scand 2013; 57:863-872 11. Kidney Disease: Improving Global Outcomes (KDIGC) Acute Kidney Injury Work Group: KDIGC clinical practice guideline for acute kidney injury. Kidney Int 2012; 2(Suppl 1):1-138 12. Roderick PJ: Assessing the impact of chronic kidney disease on individuals and populations: Use of relative and absolute measures. Nephrol Dial Transplant 201 2; 27(Suppl 3):iii39-42 13. Palevsky PM, Liu KD, Brophy PD, et al: KDCQI US commentary on the 201 2 KDIGO clinical practice guideline for acute kidney injury. Am J Kidney Dis 2013; 61:649-672 14. Pickering JW, Endre ZH: Back-calculating baseline creatinine with MDRD misclassifies acute kidney injury in the intensive care unit. Clin J Am Soc Nephrol 2010; 5:1165-11 73 15. Coleman EA, Min SJ, Chomiak A, et al: Posthospital care transitions: Patterns, complications, and risk identification. Health Serv Res 2004; 39:1449-1465
www.ccmjournal.org
993
Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
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