The severity of acute kidney injury predicts progression to chronic kidney disease
Authors: Lakhmir S. Chawla, Richard L. Amdur, Susan Amodeo, Paul L. Kimmel, Carlos E. Palant
Journal: Kidney International (2011)

Acute kidney injury (AKI) is associated with progression to advanced chronic kidney disease (CKD). We tested whether patients who survive AKI and are at higher risk for CKD progression can be identified during their hospital admission, thus providing opportunities to intervene. This was assessed in patients in the Department of Veterans Affairs Healthcare System hospitalized with a primary diagnosis indicating AKI (ICD9 codes 584.xx). In the exploratory phase, three multivariate prediction models for progression to stage 4 CKD were developed. In the confirmatory phase, the models were validated in 11,589 patients admitted for myocardial infarction or pneumonia during the same time frame that had RIFLE codes R, I, or F and complete data for all predictor variables. Of the 5351 patients in the AKI group, 728 entered stage 4 CKD after hospitalization. Models 1, 2, and 3 were all significant with ‘c’ statistics of 0.82, 0.81, and 0.77, respectively. In model validation, all three were highly significant when tested in the confirmatory patients, with moderate to large effect sizes and good predictive accuracy (‘c’ 0.81–0.82). Patients with AKI who required dialysis and then recovered were at especially high risk for progression to CKD. Hence, the severity of AKI is a robust predictor of progression to CKD. Acute kidney injury (AKI) is associated with progression to advanced chronic kidney disease (CKD). We tested whether patients who survive AKI and are at higher risk for CKD progression can be identified during their hospital admission, thus providing opportunities to intervene. This was assessed in patients in the Department of Veterans Affairs Healthcare System hospitalized with a primary diagnosis indicating AKI (ICD9 codes 584.xx). In the exploratory phase, three multivariate prediction models for progression to stage 4 CKD were developed. In the confirmatory phase, the models were validated in 11,589 patients admitted for myocardial infarction or pneumonia during the same time frame that had RIFLE codes R, I, or F and complete data for all predictor variables. Of the 5351 patients in the AKI group, 728 entered stage 4 CKD after hospitalization. Models 1, 2, and 3 were all significant with ‘c’ statistics of 0.82, 0.81, and 0.77, respectively. In model validation, all three were highly significant when tested in the confirmatory patients, with moderate to large effect sizes and good predictive accuracy (‘c’ 0.81–0.82). Patients with AKI who required dialysis and then recovered were at especially high risk for progression to CKD. Hence, the severity of AKI is a robust predictor of progression to CKD. Acute kidney injury (AKI) is a disorder that complicates the hospital course of patients by exposing them to circulatory overload, hyperkalemia, metabolic acidosis, neurological complications, and increased mortality risk.1.Liangos O. Wald R. O’Bell J.W. et al.Epidemiology and outcomes of acute renal failure in hospitalized patients: a national survey.Clin J Am Soc Nephrol. 2006; 1: 43-51Crossref PubMed Scopus (434) Google Scholar, 2.Star R.A. Treatment of acute renal failure.Kidney Int. 1998; 54: 1817-1831Abstract Full Text Full Text PDF PubMed Scopus (676) Google Scholar, 3.Uchino S. Kellum J.A. Bellomo R. et al.Acute renal failure in critically ill patients: a multinational, multicenter study.JAMA. 2005; 294: 813-818Crossref PubMed Scopus (3123) Google Scholar, 4.Waikar S.S. Liu K.D. Chertow G.M. Diagnosis, epidemiology and outcomes of acute kidney injury.Clin J Am Soc Nephrol. 2008; 3: 844-861Crossref PubMed Scopus (382) Google Scholar, 5.Waikar S.S. Curhan G.C. Wald R. et al.Declining mortality in patients with acute renal failure, 1988 to 2002.J Am Soc Nephrol. 2006; 17: 1143-1150Crossref PubMed Scopus (467) Google Scholar, 6.Xue J.L. Daniels F. Star R.A. et al.Incidence and mortality of acute renal failure in medicare beneficiaries, 1992 to 2001.J Am Soc Nephrol. 2006; 17: 1135-1142Crossref PubMed Scopus (619) Google Scholar The incidence of AKI is increasing, and will nearly double over the next decade as the population ages. In spite of recent improvements in diagnosis and therapy, the mortality of AKI remains unacceptably high.5.Waikar S.S. Curhan G.C. Wald R. et al.Declining mortality in patients with acute renal failure, 1988 to 2002.J Am Soc Nephrol. 2006; 17: 1143-1150Crossref PubMed Scopus (467) Google Scholar, 6.Xue J.L. Daniels F. Star R.A. et al.Incidence and mortality of acute renal failure in medicare beneficiaries, 1992 to 2001.J Am Soc Nephrol. 2006; 17: 1135-1142Crossref PubMed Scopus (619) Google Scholar As a result of AKI, patients experience increased hospital length of stay and may require dialysis. Population-based studies show that even small acute changes in serum creatinine (SC) concentration are associated with poor outcomes.7.Chertow G.M. Burdick E. Honour M. et al.Acute kidney injury, mortality, length of stay, and costs in hospitalized patients.J Am Soc Nephrol. 2005; 16: 3365-3370Crossref PubMed Scopus (2483) Google Scholar, 8.Lassnigg A. Schmidlin D. Mouhieddine M. et al.Minimal changes of serum creatinine predict prognosis in patients after cardiothoracic surgery: a prospective cohort study.J Am Soc Nephrol. 2004; 15: 1597-1605Crossref PubMed Scopus (1084) Google Scholar, 9.Levy M.M. Macias W.L. Vincent J.L. et al.Early changes in organ function predict eventual survival in severe sepsis.Crit Care Med. 2005; 33: 2194-2201Crossref PubMed Scopus (284) Google Scholar, 10.Silbiger S. Neugarten J. Gender and human chronic renal disease.Gend Med. 2008; 5: S3-S10Abstract Full Text PDF PubMed Scopus (159) Google Scholar After accounting for other predictors, AKI is an independent risk factor for death.11.Chertow G.M. Levy E.M. Hammermeister K.E. et al.Independent association between acute renal failure and mortality following cardiac surgery.Am J Med. 1998; 104: 343-348Abstract Full Text Full Text PDF PubMed Scopus (1034) Google Scholar, 12.Coca S.G. Bauling P. Schifftner T. et al.Contribution of acute kidney injury toward morbidity and mortality in burns: a contemporary analysis.Am J Kidney Dis. 2007; 49: 517-523Abstract Full Text Full Text PDF PubMed Scopus (115) Google Scholar Large population-based studies have demonstrated that patients who survive an episode of AKI are at considerable risk for progressing to advanced stages of chronic kidney disease (CKD).13.Amdur R.L. Chawla L.S. Amodeo S. et al.Outcomes following diagnosis of acute renal failure in U.S. veterans: focus on acute tubular necrosis.Kidney Int. 2009; 76: 1089-1097Abstract Full Text Full Text PDF PubMed Scopus (237) Google Scholar, 14.Wald R. Quinn R.R. Luo J. et al.Chronic dialysis and death among survivors of acute kidney injury requiring dialysis.JAMA. 2009; 302: 1179-1185Crossref PubMed Scopus (550) Google Scholar, 15.Lo L.J. Go A.S. Chertow G.M. et al.Dialysis-requiring acute renal failure increases the risk of progressive chronic kidney disease.Kidney Int. 2009; 76: 893-899Abstract Full Text Full Text PDF PubMed Scopus (443) Google Scholar, 16.Ishani A. Xue J.L. Himmelfarb J. et al.Acute kidney injury increases risk of ESRD among elderly.J Am Soc Nephrol. 2009; 20: 223-228Crossref PubMed Scopus (877) Google Scholar The population-based incidence of AKI is estimated at 2147 cases per million population per year.17.Ali T. Khan I. Simpson W. et al.Incidence and outcomes in acute kidney injury: a comprehensive population-based study.J Am Soc Nephrol. 2007; 18: 1292-1298Crossref PubMed Scopus (660) Google Scholar Given the US population of 300 million, each year there will be ∼600,000 cases of AKI. If 20% of such patients progress to advanced CKD a year, AKI would be responsible for 120,000 cases of advanced CKD. These data are sobering and demonstrate the continuity of disease from AKI to CKD to end-stage renal disease (ESRD). Specifically, we have shown that up to 20% of patients with an in-patient diagnosis of acute tubular necrosis (ATN) progress to CKD stage IV or greater within 18–24 months.13.Amdur R.L. Chawla L.S. Amodeo S. et al.Outcomes following diagnosis of acute renal failure in U.S. veterans: focus on acute tubular necrosis.Kidney Int. 2009; 76: 1089-1097Abstract Full Text Full Text PDF PubMed Scopus (237) Google Scholar However, within this same population, there exists a subset of patients who appear to achieve near total recovery, and so seem not to progress to advanced stages of CKD. Therefore, it is likely that some patients who develop AKI are at higher risk for CKD progression than others. We hypothesized that if the patients who survive AKI that are at highest risk for progression to CKD can be well characterized, opportunities to implement renoprotective interventions might be realized. It is possible that if these patients at high risk for CKD progression can be subjected to close follow-up by nephrologists, the natural history of this progression might be favorably altered. In order for this to occur, identification of these patients is critical. In this study, we set out to further risk stratify AKI patients who are more likely to progress to CKD, and develop a prediction tool to help clinicians identify these patients at the time of hospital discharge for nephrological outpatient follow-up. There were 5351 patients in the AKI population. These subjects had a mean 10.2±13.9 SC evaluations during the year before hospitalization, 9.8±7.7 SC evaluations during their index hospitalization, and 15.0±24.0 after hospitalization. In this group, 728 (13.6%) entered CKD4 after hospitalization. Of this same group the maximum RIFLE score was: 299 (5.6%) had no RIFLE score, 845 (15.8%) had R, 1345 (25.1%) had I, and 2862 (53.5%) had F. Among the 15,917 control (CON) subjects, the mean number of SC evaluations was 7.4±11.3 during the year before admission, 10.0±8.5 during their index hospitalization, and 14.8±23.7 after the index hospitalization. In the CON group, 1348 subjects (8.5%) entered CKD4 after hospitalization. There were 12,057 CON subjects RIFLE scores with R (75.7%), 2495 with I (15.7%), and 1365 with F (8.6%) during their hospitalization. Among CON subjects, 11,589 had complete data for all predictor variables, and were included in the analysis. Among demographic variables, age was associated with progression to CKD4 (each year of age raised the odds of entering CKD by 1% (95% confidence interval (CI) 1–2%; P<0.001), but gender and race were not (Table 1). Need for dialysis varied between those who entered CKD4 and those who did not (P<0.0001), with the CKD4 patients more likely to have required dialysis during their index hospitalization. Having ATN (as opposed to ARF) diagnosis was associated with progression to CKD4, raising the odds by 60% (95% CI 21–111%, P<0.001).Table 1Univariate relationships with CKD4VariableNo CKD4 (n=4623)CKD4 (n=728)Total (N=5351)Univariate odds ratio (95% CI)n (column %)n (column %)Race African American1444 (31.2)212 (29.1)1656 (31.0) Hispanic275 (6.0)57 (7.8)332 (6.2) Caucasian2830 (61.2)446 (61.3)3276 (61.2) Other74 (1.6)13 (1.8)87 (1.6)Gender Male4519 (97.9)713 (98.6)5232 (98.0)1.50 (0.78–2.89) NS Female95 (2.1)10 (1.4)105 (2.0)DM pre-admission Yes1767 (38.2)257 (35.3)2024 (37.8)1.13 (0.96–1.33), NS No2856 (61.8)471 (64.7)3327 (62.2)Dialysis***P<0.0001. Never4578 (99.0)690 (94.8)5268 (98.5) During hospitalization39 (0.8)18 (2.5)57 (1.1) Post hospitalization6 (0.1)20 (2.8)26 (0.5)Hospital complexityaVA hospital complexity is an administrative rating where ‘1’ indicates treatment of high-risk patients, presence of specialty providers, high volume, complex ICU services, and research. 1A2493 (54.0)392 (53.9)2885 (53.9) 1B831 (18.0)126 (17.3)957 (17.9) 1C662 (14.3)98 (13.5)760 (14.2) 2469 (10.2)81 (11.1)550 (10.3) 3166 (3.6)31 (4.3)197 (3.7)Teaching hospital Yes4080 (88.3)630 (86.5)4710 (88.1)0.85 (0.68–1.07), NS No541 (11.7)98 (13.5)639 (12.0)ATN diagnosis**P<0.001 Yes276 (6.0)67 (9.2)343 (6.4)1.60 (1.21–2.11) No4347 (94.0)661 (90.8)5008 (93.6)Mean±s.d.Age**P<0.00166.1±12.267.8±12.666.3±12.31.01 (1.005–1.02)Alb-Base***P<0.0001.3.7±0.63.3±0.73.61±0.60.38 (0.33–0.43)Alb-Hosp***P<0.0001.3.3±0.72.7±0.73.24±0.80.31 (0.27–0.36)Hgb-Base***P<0.0001.12.9±1.912.4±1.812.9±1.90.86 (0.82–0.89)Hgb-Hosp***P<0.0001.11.7±1.910.8±1.811.6±1.90.74 (0.71–0.78)Residency slots35.3±18.535.8±19.635.3±18.71.00 (1.00–1.01)Baseline eGFR80.4±17.381.6±18.180.6±17.41.00 (1.00–1.01)Time at risk (years)***P<0.0001.2.35±1.622.79±1.672.41±1.61.18 (1.12–1.23)Abbreviations: Alb-Base, baseline serum albumin; Alb-Hosp, serum albumin during hospitalization; ATN, acute tubular necrosis; CI, confidence interval; CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; Hgb-Base, baseline serum hemoglobin; Hgb-Hosp, serum hemoglobin during hospitalization; ICU, intensive care unit; NS, not significant.* P<0.01** P<0.001*** P<0.0001.a VA hospital complexity is an administrative rating where ‘1’ indicates treatment of high-risk patients, presence of specialty providers, high volume, complex ICU services, and research. Open table in a new tab Abbreviations: Alb-Base, baseline serum albumin; Alb-Hosp, serum albumin during hospitalization; ATN, acute tubular necrosis; CI, confidence interval; CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; Hgb-Base, baseline serum hemoglobin; Hgb-Hosp, serum hemoglobin during hospitalization; ICU, intensive care unit; NS, not significant. * P<0.01 In the exploratory phase of the study, we tested various CKD4 prediction models in the AKI population. This prediction model included: sex, age, race, diagnosis of ATN, time at risk (T), presence of diabetes mellitus (DM), estimated glomerular filtration rate (eGFR)-pre, SC-Hosp, renal replacement therapy (RRT), teaching hospital, baseline serum albumin concentrations (Alb-Base), serum albumin during hospitalization (Alb-Hosp), baseline hemoglobin, and mean-hemoglobin during hospitalization. The final model was significant (P<0.0001), had moderate effect size (D=64), and good prediction accuracy (area under the receiver operating characteristic (ROC) curve=0.82; Table 2, Figure 1a). Model parameters were:Table 2Results model 1 for CKD4VariableEstimates.e.PAdjusted OR95% CIIntercept0.21410.4370.62NANAAge0.01740.00470.00021.021.01–1.03Time at risk0.12230.04050.00261.131.04–1.22DM0.10670.05850.0681.240.98–1.56SC-Hosp0.40750.0286<0.00011.501.42–1.59African American−0.18610.06380.00360.690.54–0.89Alb-Base−0.58650.1033<0.00010.560.45–0.68Alb-Hosp−0.89420.0947<0.00010.410.34–0.49Abbreviations: Alb-Base, baseline serum albumin; Alb-Hosp, serum albumin during hospitalization; CI, confidence interval; CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; NA, not applicable; OR, odds ratio; RRT, renal replacement therapy; SC-Hosp, serum creatinine during hospitalization.Predictors that were tested included sex, age, race, ATN, time-at-risk, DM, eGFR-Base, SC-Hosp, RRT, teaching hospital, Alb-Base, Alb-Hosp, mean-hemoglobin-pre, mean-hemoglobin-during.Forward selection was used with P<0.1 required to enter the equation. There were 3180 subjects with all data non-missing, who were included in the analysis, 474 of whom (14.9%) reached CKD4. Somer's D was 0.64, c=0.82, and likelihood ratio χ2 was 565.8 (d.f.=7, P<0.0001). Open table in a new tab Abbreviations: Alb-Base, baseline serum albumin; Alb-Hosp, serum albumin during hospitalization; CI, confidence interval; CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; NA, not applicable; OR, odds ratio; RRT, renal replacement therapy; SC-Hosp, serum creatinine during hospitalization. Predictors that were tested included sex, age, race, ATN, time-at-risk, DM, eGFR-Base, SC-Hosp, RRT, teaching hospital, Alb-Base, Alb-Hosp, mean-hemoglobin-pre, mean-hemoglobin-during. Forward selection was used with P<0.1 required to enter the equation. There were 3180 subjects with all data non-missing, who were included in the analysis, 474 of whom (14.9%) reached CKD4. Somer's D was 0.64, c=0.82, and likelihood ratio χ2 was 565.8 (d.f.=7, P<0.0001). y=0.2141+0.0174*age−0.1861*AA+0.1223*T+0.1067*DM+0.4075*(SC-Hosp)−0.5865*(Alb-Base)−0.8942*(Alb-Hosp) The results indicated that each year of age added 2%, having DM added 24%, and each mg/dl unit increase for SC during hospitalization (SC-Hosp) added 50% to the odds of reaching CKD4, African Americans (AAs) had a 31% lower risk, each gm/dl unit increase in Alb-Base reduced 44%, and each gm/dl unit increase of Alb-Hosp reduced 59% from the odds of reaching CKD4. Using the optimal cut-point for the risk score,18.Gonen M. Analyzing Receiver Operating Characteristic Curves with SAS. SAS Institute, Cary, NC2007Google Scholar this model had sensitivity of 0.75 and specificity of 0.73 for predicting CKD4, and the odds ratio (OR) for a risk score greater than the cut-point was 8.25 (95% CI 6.66–10.23; Table 3).Table 3Performance of model 1 in the derivation and cross-validation samples, at various cut-pointsIndicatorCut-point−3.0−2.5−2.0−1.5−1−0.50In AKI (derivation sample) Sensitivity0.960.900.770.640.460.310.20 Specificity0.370.540.700.830.910.950.97 PPV0.200.250.310.380.460.530.57 NPV0.980.970.950.930.910.890.88 Rel risk (pos)11.737.895.935.615.044.834.63 OR (pos)14.5010.178.108.508.469.089.38In CON (cross-validation sample) Sensitivity0.880.720.540.350.180.090.04 Specificity0.380.590.780.910.970.991.00 PPV0.110.140.180.250.350.480.64 NPV0.970.960.950.940.930.920.92 Rel risk (pos)4.113.453.624.104.896.218.03 OR (pos)4.513.844.215.146.9610.9520.62Abbreviations: AKI, acute kidney injury; CON, control; NPV, negative predictive value; OR, odds ratio; PPV, positive predictive value. Open table in a new tab Abbreviations: AKI, acute kidney injury; CON, control; NPV, negative predictive value; OR, odds ratio; PPV, positive predictive value. Predictors tested included age, Alb-Hosp, SC-Hosp, and time at risk. All variables were entered into the equation. This model was significant (P<0.0001), with moderate effect size (D=0.61), and good prediction accuracy (area under the ROC curve=0.81; Figure 1b). Model parameters were: y=−0.8249+0.0162*age−0.1064*T+0.3655*(SC-Hosp)−1.1468*(Alb-Hosp) According to this model, each year of age raised the odds of reaching CKD4 by 2%, each 1.0 mg/dl SC increase raised the odds by 44%, and each 1.0 gm/dl albumin increase reduced the odds by 68% (Table 4). Risk scores were computed for each AKI subject using the above equation. Using this risk score cut-point, the model had a sensitivity of 0.72 and specificity of 0.74. The odds of reaching CKD4 for those with a risk score above the cut-point were 7.27 worse (95% CI 5.98–8.84; Table 5) compared with those with risk scores below the cut-point.Table 4Results for model 2 for CKD4VariableEstimates.e.PAdjusted OR95% CIIntercept−0.82490.36610.0243NANAAge0.01620.0041<0.00011.021.01–1.03Time at risk0.10640.03410.00181.111.04–1.19SC-Hosp0.36550.0234<0.00011.441.38–1.51Alb-Hosp−1.14680.0707<0.00010.320.28–0.37Abbreviations: Alb-Hosp, serum albumin during hospitalization; CI, confidence interval; CKD, chronic kidney disease; NA, not applicable; OR, odds ratio; SC-Hosp, serum creatinine during hospitalization.Predictors tested included age, Alb-Hosp, SC-Hosp, time-at-risk. All variables were entered into the equation. There were 4095 subjects with all data non-missing, who were included in the analysis, 581 of whom (14.2%) reached CKD4. Somer's D was 0.61, c=0.81, and likelihood ratio χ2 was 643.2 (d.f.=4, P<0.0001). Open table in a new tab Table 5Performance of model 2 in the derivation and cross-validation samples, at various cut-pointsIndicatorCut-point−3.0−2.5−2.0−1.5−1−0.50In AKI (derivation sample) Sensitivity0.960.900.770.620.460.300.17 Specificity0.330.510.680.820.910.950.98 PPV0.190.230.280.360.450.510.57 NPV0.980.970.950.930.910.890.88 Rel risk (pos)9.757.505.335.074.954.764.66 OR (pos)11.829.497.047.358.128.759.52In CON (cross-validation sample) Sensitivity0.910.770.580.380.200.090.03 Specificity0.300.530.740.890.970.991.00 PPV0.110.130.170.240.370.530.80 NPV0.970.960.950.940.930.920.92 Rel risk (pos)4.083.493.493.915.196.729.78 OR (pos)4.463.874.014.827.6413.1146.02Abbreviations: AKI, acute kidney injury; CON, control; NPV, negative predictive value; OR, odds ratio; PPV, positive predictive value. Open table in a new tab Abbreviations: Alb-Hosp, serum albumin during hospitalization; CI, confidence interval; CKD, chronic kidney disease; NA, not applicable; OR, odds ratio; SC-Hosp, serum creatinine during hospitalization. Predictors tested included age, Alb-Hosp, SC-Hosp, time-at-risk. All variables were entered into the equation. There were 4095 subjects with all data non-missing, who were included in the analysis, 581 of whom (14.2%) reached CKD4. Somer's D was 0.61, c=0.81, and likelihood ratio χ2 was 643.2 (d.f.=4, P<0.0001). Abbreviations: AKI, acute kidney injury; CON, control; NPV, negative predictive value; OR, odds ratio; PPV, positive predictive value. Predictors entered into the equation included age, time at risk, eGFR-Base, RRT, Alb-Hosp, and RIF score. This model was significant (P<0.0001), and had acceptable prediction accuracy (D=0.54; area under ROC curve=0.77, Figure 1c). The prediction model parameters were: y=−0.0394+0.00959*age+0.1165*T−0.00562*(GFR-Base)+0.4384*RRT+0.6326*RIF−1.1214*(Alb-Hosp) In this model, each year of age added 1%, each 1 point increase in RIF score added 88%, and having dialysis during admission added 140% to the odds of reaching CKD4, each 1 point increase in GFR-Base reduced the odds by 1%, and each 1-point increase in Alb-Hosp reduced the odds of reaching CKD4 by 67% (Table 6). Those with a risk score above the cut-point had odds of reaching CKD4 that were 5.75 (95% CI 4.72–7.00; Table 7) times higher than those with scores below the cut-point.Table 6Results for model 3 for CKD4VariableEstimates.e.PAdjusted OR95% CIIntercept−0.03940.51920.94NANAAge0.00960.00410.0191.011.00–1.02Time at risk0.11650.03280.00041.121.05–1.20eGFR-Base−0.00560.00290.0520.990.99–1.00RRT0.43840.16040.00632.401.28–4.51RIF score0.63260.0725<0.00011.881.63–2.17Alb-Hosp−1.12140.0682<0.00010.330.29–0.37Abbreviations: Alb-Hosp, serum albumin during hospitalization; CI, confidence interval; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; NA, not applicable; OR, odds ratio. Predictors entered into the equation included age, time-at-risk, eGFR-Base, RRT, Alb-Hosp, and RIF score (which was coded none=0, R=1, I=2, F=3). There were 4150 subjects with all data non-missing, who were included in the analysis, 582 of whom (14.0%) reached CKD4. Somer's D was 0.54, c=0.77, and likelihood ratio χ2 was 484.5 (d.f.=6, P<0.0001). Open table in a new tab Table 7Performance of model 3 in the derivation and cross-validation samples, at various cut-pointsIndicatorCut-point−3.0−2.5−2.0−1.5−1−0.50In AKI (derivation sample) Sensitivity0.980.950.890.770.550.360.16 Specificity0.150.300.480.640.800.910.97 PPV0.160.180.220.260.310.390.43 NPV0.980.970.960.940.920.900.88 Rel risk (pos)8.156.826.054.683.633.813.49 OR (pos)9.508.107.465.974.795.635.40In CON (cross-validation sample) Sensitivity0.960.870.750.620.440.260.12 Specificity0.210.420.630.790.910.960.99 PPV0.100.120.160.220.300.400.54 NPV0.980.970.960.960.950.930.92 Rel risk (pos)6.194.324.555.035.606.057.11 OR (pos)6.784.785.226.157.599.4614.35Abbreviations: AKI, acute kidney injury; CON, control; NPV, negative predictive value; OR, odds ratio; PPV, positive predictive value. Open table in a new tab Abbreviations: Alb-Hosp, serum albumin during hospitalization; CI, confidence interval; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; NA, not applicable; OR, odds ratio. Predictors entered into the equation included age, time-at-risk, eGFR-Base, RRT, Alb-Hosp, and RIF score (which was coded none=0, R=1, I=2, F=3). There were 4150 subjects with all data non-missing, who were included in the analysis, 582 of whom (14.0%) reached CKD4. Somer's D was 0.54, c=0.77, and likelihood ratio χ2 was 484.5 (d.f.=6, P<0.0001). Abbreviations: AKI, acute kidney injury; CON, control; NPV, negative predictive value; OR, odds ratio; PPV, positive predictive value. In the CON sample, model 1 was significant (likelihood ratio χ2=1219.8, P<0.0001), with moderate effect size (D=0.62), and good prediction accuracy (c=0.81; Figure 2a). ORs for this model indicated that each additional year of age raised the odds of entering CKD4 by 1%, having DM raised the odds by 21%, each 1.0 mg/dl increase in SC-Hosp raised the odds by 582%, being AA reduced the odds by 36%, each additional 1.0 g/dl unit of Alb-Base reduced the odds by 17%, and each additional 1.0 g/dl unit of Alb-Hosp reduced the odds by 43%. Risk scores were computed for each CON subject using the model 1 equation (Table 8). Using a cut-point of -2.45, sensitivity was 0.71, specificity was 0.61, and the OR for CKD4 was 3.85 (95% CI 3.29–4.52). These results indicate little loss of prediction accuracy in the validation cohort of patients without a primary AKI diagnosis compared with the derivation sample of patients that had a primary AKI diagnosis, in the absence of CKD.Table 8Results of model cross-validation in CON sampleVariableEstimates.e.PAdjusted OR95% CIModel 1 Intercept−4.03480.4116<0.0001NANA Age0.01080.00380.00411.011.003–1.02 Time at risk0.16300.0298<0.00011.181.11–1.25 DM0.09430.04300.02851.211.02–1.43 SC-Hosp1.91990.0740<0.00016.825.90–7.89 African American−0.44180.1068<0.00010.640.52–0.79 Alb-Base−0.19030.08670.02810.830.70–0.98 Alb-Hosp−0.56140.0743<0.00010.570.49–0.66Model 2 Intercept−4.53270.3247<0.0001NANA Age0.01170.00340.00061.011.01–1.02 Time at risk0.15060.0255<0.00011.161.11–1.22 SC-Hosp1.83940.0646<0.00016.295.55–7.14 Alb-Hosp−0.63160.0565<0.00010.530.48–0.59Model 3 InterceptNANANANANA Age0.00580.00340.0881.011.00–1.01 Time at risk0.17890.0251<0.00011.201.14–1.26 RIF (0–3)1.48810.0464<0.00014.434.04–4.85 GFR-base−0.02630.0023<0.00010.970.97–0.98 Alb-Hosp−0.53500.0561<0.00010.590.53–0.65 RRT1.98680.3955<0.000153.1811.28–250.64Abbreviations: AKI, acute kidney injury; Alb-Base, baseline serum albumin; Alb-Hosp, serum albumin during hospitalization; CON, control; DM, diabetes mellitus; NA, not applicable; OR, odds ratio; SC-Hosp, serum creatinine during hospitalization.For each model, the predictor variables used in the AKI sample were entered in a single step in the CON sample.Model 1 Likelihood ratio χ2 (LRC)=1219.8, d.f.=7, P<0.0001; Somer's D=0.62, c=0.81.Model 2 LRC=1470.2, d.f.=4, P<0.0001; Somer's D=0.63, c=0.81.Model 3 LRC=10810.0, d.f.=6, P<0.0001; Somer's D=0.64, c=0.82. Open table in a new tab Abbreviations: AKI, acute kidney injury; Alb-Base, baseline serum albumin; Alb-Hosp, serum albumin during hospitalization; CON, control; DM, diabetes mellitus; NA, not applicable; OR, odds ratio; SC-Hosp, serum creatinine during hospitalization. For each model, the predictor variables used in the AKI sample were entered in a single step in the CON sample. Model 1 Likelihood ratio χ2 (LRC)=1219.8, d.f.=7, P<0.0001; Somer's D=0.62, c=0.81. Model 2 LRC=1470.2, d.f.=4, P<0.0001; Somer's D=0.63, c=0.81. Model 3 LRC=10810.0, d.f.=6, P<0.0001; Somer's D=0.64, c=0.82. Model 2 was significant in the CON population (P<0.0001), it had moderate effect size (D=0.63), and good prediction accuracy (c=0.81; Figure 2b). The model indicated that each year of age added 1%, and each 1.0 g/dl increase in SC-Hosp added 529% to the odds of reaching CKD4. Each 1.0 g/dl increase in Alb-Hosp reduced the odds by 47% (Table 8). Using a risk score cut-point of -2.2, the model had sensitivity of 0.66, specificity of 0.66, and the odds of having CKD4 were 3.76 (95% CI 3.28–4.32) for those with risk scores above, as opposed to below the cut-point. When model 3 was tested in CON subjects, RRT was found to be highly collinear with the intercept, so this model was evaluated without the intercept. It was significant (P<0.0001), had moderate effect size (D=0.64) and good prediction accuracy (c=0.82; Figure 2c). This model indicated that each year of age added 1%, and each 1 point increase in RIF added 343% to the odds of reaching CKD4, each 1 point increase in GFR-Base reduced the odds of reaching CKD4 by 3% and each 1.0 g/dl increase of mean albumin during hospitalization reduced the odds by 41% (Table 8). Having required RRT during admission raised the risk of entering CKD4 by 5218%. Risk scores were computed using model 3 in the CON subjects. Using a cut-point of -1.8, the model had sensitivity 0.70, specificity 0.70, and OR=5.62 for those with risk scores above, as opposed to below, the cut-point (95% CI 4.87–6.49). These were very similar to those obtained in the validation cohort when compared with the derivation sample. Before the era of CKD staging, it was generally accepted that patients who recovered from AKI had ‘good’ renal outcomes as assessed by a low incidence of end stage renal disease (ESRD).19.Liano F. Felipe C. Tenorio M.T. et al.Long-term outcome of acute tubular necrosis: a contribution to its natural history.Kidney Int. 2007; 71: 679-686Abstract Full Text Full Text PDF PubMed Scopus (115) Google Scholar These previous long-term studies were hampered by different definitions, variable follow-up times, and low overall numbers. From 2008 through 2010, four studies assessing four different