DÄ internationalArchive22/2019The Incidence of Acute Kidney Injury and Associated Hospital Mortality

Original article

The Incidence of Acute Kidney Injury and Associated Hospital Mortality

A retrospective cohort study of over 100 000 patients at Berlin‘s Charité hospital

Dtsch Arztebl Int 2019; 116: 397-404. DOI: 10.3238/arztebl.2019.0397

Khadzhynov, D; Schmidt, D; Hardt, J; Rauch, G; Gocke, P; Eckardt, K; Schmidt-Ott, K M

Background: Studies from multiple countries have shown that acute kidney injury (AKI) in hospitalized patients is associated with mortality and morbidity. There are no reliable data at present on the incidence and mortality of AKI episodes among hospitalized patients in Germany. The utility of administrative codings of AKI for the identification of AKI episodes is also unclear.

Methods: In an exploratory approach, we retrospectively analyzed all episodes of AKI over a period of 3.5 years (2014–2017) on the basis of routinely obtained serum creatinine measurements in 103 161 patients whose creatinine had been measured at least twice and who had been in the hospital for at least two days. We used the “Kidney Disease: Improving Global Outcomes” (KDIGO) criteria for AKI. In parallel, we assessed the administrative coding of discharge diagnoses of the same patients with codes from the International Classification of Diseases (ICD-10-GM).

Results: Among 185 760 hospitalizations, stage 1 AKI occurred in 25 417 cases (13.7%), stage 2 in 8503 cases (4.6%), and stage 3 in 5881 cases (3.1%). AKI cases were associated with length of hospital stay, renal morbidity, and overall mortality, and this association was stage-dependent. The in-hospital mortality was 5.1% for patients with stage 1 AKI, 13.7% for patients with stage 2 AKI, and 24.8% for patients with stage 3 AKI. An administrative coding for acute kidney injury (N17) was present in only 28.8% (11 481) of the AKI cases that were identified by creatinine criteria. Like the AKI cases overall, those that were identified by creatinine criteria but were not coded as AKI had significantly higher mortality, and this association was stage-dependent.

Conclusion: AKI episodes are common among hospitalized patients and are associated with considerable morbidity and mortality, yet they are inadequately documented and probably often escape the attention of the treating physicians.

LNSLNS

Acute kidney injury (AKI) is a frequent (8% to 22% of all hospital patients) and relevant clinical event (1, 2). It is characterized by a rapid worsening of kidney function to varying degrees and is associated with a 1.4- to 15.4-fold increase in the risk of mortality (36). Patients with AKI are also at considerable risk for the development or worsening of chronic kidney disease (710).

AKI is currently defined and staged depending on changes in serum creatinine and urine excretion according to the KDIGO AKI guidelines (Table 1) (10, 11). According to international guidelines, the diagnosis of AKI should prompt a number of steps. These include identifying and treating triggering factors, adjusting drug doses to impaired renal function, avoiding nephrotoxins, monitoring of hemodynamics as well as fluid and electrolyte balance, and nephrological follow-up. A recently published study showed that the systematic implementation of these measures was associated with an absolute reduction in the risk of mortality of 16.6% (12). The main precondition for the prompt implementation of these measures is early diagnosis of AKI. However, a number of studies indicate that AKI episodes remained undetected in clinical routine in the majority of cases (57% to 99%) (1315).

Diagnosis and staging of acute kidney injury according to KDIGO (10)*
Diagnosis and staging of acute kidney injury according to KDIGO (10)*
Table 1
Diagnosis and staging of acute kidney injury according to KDIGO (10)*

Epidemiological studies often identify AKI episodes from administrative hospital data (1618). However, the extent to which administrative data reflect the actual incidence of AKI is controversial. Studies conducted in the US suggest that administrative data identify only a proportion of clinical AKI episodes (13, 14, 19). The usefulness of administrative data to identify AKI episodes in the hospital sector in Germany is currently unclear. In line with case coding according to the ICD-10-GM, acute kidney injury is coded as N17. Since 2011, the German Society of Nephrology (Deutsche Gesellschaft für Nephrologie) has recommended coding acute kidney injury (N17) if a 50% increase in creatinine within 7 days or a ≥0.3-mg/dL rise in creatinine within 48 h is observed (20). It is unclear to what extent these recommendations are implemented in coding practice.

This study retrospectively analyzed the presence or absence of AKI episodes according to the KDIGO creatinine criteria over a 3.5-year period (2014–2017) on the basis of serial creatinine concentration measurements in clinical routine during all hospital stays. In addition, patient characteristics, comorbidities, AKI codings (i.e., with the N17 code), as well as fatalities were recorded on the basis of administrative data for all hospital stays. This made it possible to determine the incidence and outcomes of creatinine-based AKI episodes and to calculate the proportion of coded AKI episodes in relation to the estimated true incidence of AKI in the defined study population.

Methods

This explorative retrospective study included adult patients treated on an inpatient basis at the Charité University Hospital Berlin, Germany, between 1 January 2014 and 30 June 2017. The exclusion criteria are shown in the study flow chart (eFigure).

The definition and staging of acute kidney injury (AKI) was performed according to the KDIGO creatinine criteria (Table 1) (11). Figure 1 provides a schematic representation of the various scenarios of AKI episodes and the associated determination of baseline creatinine (21, 22).

Possible scenarios of AKI episodes
Possible scenarios of AKI episodes
Figure 1
Possible scenarios of AKI episodes

Mortality data were obtained from hospital data. As such, only fatalities that occurred at the Charité University Hospital Berlin were recorded. Descriptive analyses and Kaplan-Meier curves were stratified by AKI stage. Uni- and multivariate Cox regression analyses were performed to identify predictors of mortality. The study population, the extraction of administrative data, the definition of AKI stages, as well as the variables and statistical analyses, are described in detail in the eMethods Section.

Results

Incidence of creatinine-based AKI episodes, patient characteristics, and follow-up parameters

We analyzed the data on 103 161 adult patients treated on at least two consecutive days at the Charité University Hospital in Berlin during the period 2014–2017 (eFigure). Including all available follow-up visits, the average observation period per patient was 248 days. AKI episodes and their staging were identified on the basis of serial creatinine measurements and using the KDIGO criteria. In a first step, we analyzed basic patient characteristics in relation to the maximum AKI stage observed. A total of 32 238 patients fulfilled the criteria for AKI at at least one point in time during the observation period (31.3% of the total population). AKI stage 1 (≥ 0.3 mg/dL or 1.5-fold increase in creatinine) was identified in 19 009 patients (18.4%), AKI stage 2 (≥ two-fold increase in creatinine) in 7499 patients (7.3%), and AKI stage 3 (≥ three-fold increase in creatine or a rise to ≥ 4.0 mg/dL) was seen in 5730 patients (5.6%) (Table 2). Patients with AKI had a higher average age compared to patients without AKI. There were more men than women among the AKI patients, whereas non-AKI patients were more evenly distributed in terms of sex. Patients with AKI had comorbidities such as diabetes mellitus, heart failure, coronary heart disease, and hypertension more frequently than did patients without AKI. Baseline estimated glomerular filtration rate (eGFR) was slightly lower in patients with AKI compared to patients without AKI. Recurrent AKI episodes were seen significantly more frequently in patients that had had stage-3 AKI at least once.

Patient-related analysis: demography and comorbidities according to KDIGO stage of acute kidney injury
Patient-related analysis: demography and comorbidities according to KDIGO stage of acute kidney injury
Table 2
Patient-related analysis: demography and comorbidities according to KDIGO stage of acute kidney injury

In a next step, we performed case-related analyses in which the 185 760 hospital cases of the patients under study were analyzed separately in order to identify primary and secondary diagnoses as well as procedures associated with AKI episodes (Table 3). AKI episodes were observed in 21.4% of all hospital cases. It was found that cases associated with AKI more frequently had a primary diagnosis of acute coronary syndrome, acute respiratory disease, or cancer, and more frequently had sepsis as a primary or secondary diagnosis. In AKI cases, surgical procedures and mechanical ventilation were more frequently coded, with a clear association with the KDIGO stage. Renal replacement procedures were coded as stage-3 AKI in a substantial number of cases, as intermittent hemodialysis in 29.9%, and as continuous renal replacement procedures in 21.4%. With regard to short-term hospital outcomes, patients with AKI had significantly longer stays as well as increased hospital mortality (Table 3). Patients with AKI also more often required renal replacement therapy at the time of hospital discharge. To analyze long-term outcomes, we investigated mortality data on a case-related basis for the available follow-up period (starting from the first documented hospital stay during the observation period). Kaplan-Meier survival curves showed progressively worse long-term survival from stage 1 to stage 3 (Figure 2a).

Stage-dependent long-term survival following creatinine-based acute kidney injury (stages 1–3 according to KDIGO)
Stage-dependent long-term survival following creatinine-based acute kidney injury (stages 1–3 according to KDIGO)
Figure 2
Stage-dependent long-term survival following creatinine-based acute kidney injury (stages 1–3 according to KDIGO)
Case-related analysis: primary and secondary diagnoses, procedures, and hospital outcomes according to KDIGO stage of AKI
Case-related analysis: primary and secondary diagnoses, procedures, and hospital outcomes according to KDIGO stage of AKI
Table 3
Case-related analysis: primary and secondary diagnoses, procedures, and hospital outcomes according to KDIGO stage of AKI

Univariate Cox regression analysis confirmed the association between AKI and mortality. Likewise in the multivariate Cox regression model, a significant association remained between AKI and mortality when adjusted for age, male sex, comorbidities, baseline eGFR, sepsis, and mechanical ventilation (hazard ratio [HR] = 4.71; 95% confidence interval [CI]: [4.42; 5.00]) (Table 4).

Cox regression analysis for the prediction of mortality: univariate and multivariate regression analyses
Cox regression analysis for the prediction of mortality: univariate and multivariate regression analyses
Table 4
Cox regression analysis for the prediction of mortality: univariate and multivariate regression analyses

In summary, associations with cardiovascular comorbidities, severe disease courses, unfavorable renal outcomes, as well as long-term and short-term mortality were seen depending on the KDIGO stage of AKI. It is of note that even stage 1 AKI is associated with an increase in unfavorable outcomes.

Administrative coding of acute kidney injury

We investigated the presence or absence of administrative coding with N17 (ICD-10-GM) among discharge diagnoses (N = 185 760 cases) on a case-related basis. The overall analysis showed that only 18.4% (N = 4670) of KDIGO stage-1 AKI episodes (according to serial creatinine analysis) were appropriately coded. With increasing severity of AKI the proportion of appropriately coded cases increased to 35.9% (AKI stage 2; N=3054) and 63.9% (AKI Stage 3; N=3757), respectively (Table 3). We repeated these analyses separately for the individual years between 2014 and 2017 and found a gradual overall increase in the frequency of coding of AKI (Table 3). Thus, for example, coding as stage-1 AKI increased from 13.4% in 2014 to 23.8% in 2017. This may reflect a rise in the implementation of coding recommendations.

In summary, these data yield clear evidence of administrative undercoding of AKI, which is evident even at higher stages of AKI. In order to investigate whether coding is consciously or unconsciously performed only in cases of more clinically relevant AKI episodes, we analyzed whether non-coded AKI episodes are also associated with increased long-term mortality and whether there was a link between AKI stage and mortality in those patients whose AKI episodes were not coded (N = 97 126)(Figure 2b). Also in this subgroup, a clear association was found between KDIGO stage of AKI and long-term mortality. This observation suggests that even clinically relevant episodes of AKI fall through the net of administrative coding.

Discussion

AKI: an underestimated risk factor

Early identification of comorbidities and risk factors is of crucial importance in hospitalized patients in order to:

  • Optimize care
  • Minimize the risks of inpatient treatment
  • Deploy resources efficiently
  • Optimize the long-term prognosis.

Acute kidney failure, even if severe, was long considered as largely “harmless” and reversible as long as the patient survived the conditions under which it developed. No particular importance was attributed to non-“dialysis-dependent” renal function impairment. Studies conducted over the last 15 years have refuted this view and shown that even comparatively mild temporary renal function decline is associated with an unfavorable prognosis; for example, a rise in creatinine of 0.3–0.4 mg/dL was associated with a 1.7-fold increase in the risk of mortality (3).

The present study underlines the importance of AKI as a risk factor in hospitalized patients on the basis of our retrospective analysis of a 3.5-year period at the Charité University Hospital in Berlin. Almost a third of all inpatients treated for at least 2 days—and more than a fifth of all hospital stays—during this period were associated with episodes of AKI. The fact that altogether less than 30% of all cases of AKI were administratively coded (18%–64% depending on the stage of AKI) suggests that the clinical recognition of AKI is still inadequate.

International comparison

The stage-dependent association observed here between AKI episodes and short- and long-term hospital mortality confirms US studies (3, 5, 23, 24). As in international studies (3, 5, 25), we also observed a pronounced stage-dependent association between AKI and increasing length of hospital stay and dialysis dependence at the time of discharge.

Furthermore, our detailed analysis of primary and secondary diagnoses and procedures confirmed previously described links between AKI episodes and:

  • Sepsis (26, 27)
  • Mechanical ventilation (26, 27)
  • Cardiovascular disease (28)
  • Cancer (29)
  • Liver disease (30, 31)
  • Surgical interventions (32).

In summary, these comparisons show that AKI represents a remarkably consistent risk factor across disciplines and irrespective of the sometimes significant differences in inpatient hospital care in different healthcare systems.

Insufficient AKI coding

Our study found clear evidence of undercoding among the almost 40 000 cases involving creatinine-based AKI, particularly in stage-1 and -2 AKI. This underdocumentation likely points to a failure to identify all AKI episodes in clinical routine. However, since our analysis was based purely on administrative data and not on patient records, this lack of administrative AKI coding is not necessarily due to a failure on the physician‘s part to make the diagnosis. However, coding is generally carried out by skilled documentation personnel using medical reports, suggesting that a significant proportion of AKI episodes do indeed remain unrecognized. This is particularly relevant given that there was also a clear link between AKI episodes and mortality in the subgroup of AKI cases that were not administratively documented.

Grams et al. also reported that administrative billing codes have low sensitivity (<20%) in terms of the identification of patients with creatinine-based AKI in a US population (19). A retrospective cohort study conducted at a university hospital in the US demonstrated that only 43% of patients with AKI (defined as a doubling of creatinine) had this correspondingly noted in their medical records (14). Interestingly, Wilson et al.‘s study showed in an unadjusted analysis that formal documentation of AKI was associated with higher mortality, a phenomenon that was also observed in our cohort (Figure 2a, 2b). An unlikely explanation for this observation would be that recognized AKI episodes were associated with clinically counterproductive interventions that led to higher mortality. The far more likely explanation is that non-coded AKI episodes are associated with lower case severity and lower mortality. In fact, Wilson et al. showed that the association was inversed after adjustment for disease severity, whereupon non-documented AKI was linked to increased mortality. Studies in other countries also found non-recognition rates of AKI of over 70% (15), with evidence that the time of AKI recognition was likewise associated with hospital mortality.

The potential for automated analysis of routine clinical data

Our investigation is based on the use of an algorithm to identify AKI episodes using creatinine measurements taken in the routine clinical setting. Other studies have previously used similar AKI algorithms (9, 3336). The majority of these algorithms are designed to identify emerging AKI on the basis of rising creatinine levels. In order to perform a comprehensive analysis of AKI episodes, we developed an expanded algorithm that additionally identifies falls in serum creatinine levels over the course of a hospital stay, thereby indicating resolution of an AKI episode.

Taking AKI as an example, the discrepancy between documented and non-documented creatinine-based AKI episodes suggests that a comparatively simple, automated “digital” evaluation can yield important additional information. At the same time, this evaluation demonstrates the potential of using stored medical data to identify disease characteristics, risk associations, and treatment practice. Merely by combining the analysis of a single laboratory parameter (creatinine concentration) from one database with clinical data and coding data (primary diagnoses, secondary diagnoses, and procedures) from another administrative database, we were able to make a comprehensive status description.

Strengths and weaknesses of the study

Study strengths include the size of the cohort, which, by means of an objective database query, led to the identification of more than 39 000 AKI episodes in approximately 186 000 cases over a period of 3.5 years. Added to that is the fact that all creatinine values were measured in a central laboratory, which is particularly relevant given the possible variation in methods between laboratories. Another strength is the fact that both the emergence as well as the resolution of AKI episodes were recorded using a specially designed algorithm.

Weaknesses include the retrospective nature of the analysis, meaning that unidentified confounders might have affected the results. Furthermore, AKI episodes were recorded exclusively on the basis of creatinine criteria determined in the context of clinical routine. Other AKI criteria such as urine excretion or initiation of renal replacement therapy were not taken into account. Since AKI episodes are due to changes in creatinine levels, the analysis was restricted to patients with at least two creatinine measurements. This methodological approach may have resulted in a certain selection bias in favor of more severely ill patients, meaning that the AKI incidence in the entire hospital population may be overestimated. On the other hand, it remains unclear how many additional AKI episodes occurred but were not recorded since creatinine measurements were not taken. This could lead to an underestimation of the actual number of AKI episodes. A further limitation lies in the fact that the analyses on patient survival were based purely on hospital mortality data at the Charité University Hospital Berlin; deaths in the outpatient setting or in other hospitals were not recorded. Furthermore, irrespective of the multivariate analysis, the statistical links observed do not necessarily imply causality. Since this was a monocentric analysis, it is also possible that the observations made here cannot be extrapolated to other centers and hospitals with different treatment mandates.

Conflict of interests
The authors state that there are no conflicts of interest.

Manuscript submitted on 11 December 2018, revised version accepted on 1 April 2019.

Translated from the original German by Christine Rye.

Correspondence address
Dr. med. Dmytro Khadzhynov

Medizinische Klinik mit Schwerpunkt Nephrologie und Internistische Intensivmedizin

Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany

dmytro.khadzhynov@charite.de

Cite this as
Khadzhynov D, Schmidt D, Hardt J, Rauch G, Gocke P, Eckardt KU, Schmidt-Ott KM: The incidence of acute kidney injury and associated hospital mortality—a retrospective cohort study of over 100 000 patients at Berlin‘s Charité hospital. Dtsch Arztebl Int 2019; 116: 397– 404. DOI: 10.3238/arztebl.2019.0397

Supplementary material
eMethods Section, eFigure:
www.aerzteblatt-international.de/19m0397

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Department of Nephrology and Medical Intensive Care Medicine, Charité—Universitätsmedizin Berlin and Berlin Institute of Health, Berlin:
Dr. med. Dmytro Khadzhynov,
Prof. Dr. med. Kai-Uwe Eckardt,
Prof. Dr. med. Kai M. Schmidt-Ott
Business Division IT, Department of Research and Teaching, Charité—Universitätsmedizin Berlin, Berlin: Dipl.-Inf. Danilo Schmidt
Institute of Biometry and Clinical Epidemiology, Charité—Universitätsmedizin Berlin and Berlin Institute of Health, Berlin: Dipl.-Psych. Juliane Hardt,
Prof. Dr. rer. nat.
Geraldine Rauch
Biostatistics, Clinical Research Unit, Berlin Institute of Health, Berlin: Dipl.-Psych. Juliane Hardt
Administrative Office for Digital Transformation, Charité—Universitätsmedizin Berlin, Berlin: Dr. med. Peter Gocke
* These two authors share first authorship.
Possible scenarios of AKI episodes
Possible scenarios of AKI episodes
Figure 1
Possible scenarios of AKI episodes
Stage-dependent long-term survival following creatinine-based acute kidney injury (stages 1–3 according to KDIGO)
Stage-dependent long-term survival following creatinine-based acute kidney injury (stages 1–3 according to KDIGO)
Figure 2
Stage-dependent long-term survival following creatinine-based acute kidney injury (stages 1–3 according to KDIGO)
Key messages
Diagnosis and staging of acute kidney injury according to KDIGO (10)*
Diagnosis and staging of acute kidney injury according to KDIGO (10)*
Table 1
Diagnosis and staging of acute kidney injury according to KDIGO (10)*
Patient-related analysis: demography and comorbidities according to KDIGO stage of acute kidney injury
Patient-related analysis: demography and comorbidities according to KDIGO stage of acute kidney injury
Table 2
Patient-related analysis: demography and comorbidities according to KDIGO stage of acute kidney injury
Case-related analysis: primary and secondary diagnoses, procedures, and hospital outcomes according to KDIGO stage of AKI
Case-related analysis: primary and secondary diagnoses, procedures, and hospital outcomes according to KDIGO stage of AKI
Table 3
Case-related analysis: primary and secondary diagnoses, procedures, and hospital outcomes according to KDIGO stage of AKI
Cox regression analysis for the prediction of mortality: univariate and multivariate regression analyses
Cox regression analysis for the prediction of mortality: univariate and multivariate regression analyses
Table 4
Cox regression analysis for the prediction of mortality: univariate and multivariate regression analyses
1.Sawhney S, Marks A, Fluck N, Levin A, Prescott G, Black C: Intermediate and long-term outcomes of survivors of acute kidney injury episodes: a large population-based cohort study. Am J Kidney Dis 2017; 69: 18–28 CrossRef MEDLINE PubMed Central
2.Wang HE, Muntner P, Chertow GM, Warnock DG: Acute kidney injury and mortality in hospitalized patients. Am J Nephrol 2012; 35: 349–55 CrossRef MEDLINE PubMed Central
3.Chertow GM, Burdick E, Honour M, Bonventre JV, Bates DW: Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. J Am Soc Nephrol 2005; 16: 3365–70 CrossRef MEDLINE
4.Chertow GM, Soroko SH, Paganini EP, et al.: Mortality after acute renal failure: models for prognostic stratification and risk adjustment. Kidney Int 2006; 70: 1120–6 CrossRef MEDLINE
5.Liangos O, Wald R, O‘Bell JW, Price L, Pereira BJ, Jaber BL: Epidemiology and outcomes of acute renal failure in hospitalized patients: a national survey. Clin J Am Soc Nephrol 2006; 1: 43–51 CrossRef MEDLINE
6.Uchino S, Bellomo R, Goldsmith D, Bates S, Ronco C: An assessment of the RIFLE criteria for acute renal failure in hospitalized patients. Crit Care Med 2006; 34: 1913–7 CrossRef MEDLINE
7.Chawla LS, Amdur RL, Amodeo S, Kimmel PL, Palant CE: The severity of acute kidney injury predicts progression to chronic kidney disease. Kidney Int 2011; 79: 1361–9 CrossRef MEDLINE PubMed Central
8.Coca SG, Singanamala S, Parikh CR: Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int 2012; 81: 442–8 CrossRef MEDLINE PubMed Central
9.Heung M, Steffick DE, Zivin K, et al.: Acute kidney injury recovery pattern and subsequent risk of CKD: an analysis of veterans health administration data. Am J Kidney Dis 2016; 67: 742–52 CrossRef MEDLINE
10.Levey AS, Levin A, Kellum JA: Definition and classification of kidney diseases. Am J Kidney Dis 2013; 61: 686–8 CrossRef MEDLINE
11.Kidney Disease: Improving global outcomes (KDIGO) acute kidney injury work group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int 2012 (Suppl): 1–138.
12.Meersch M, Schmidt C, Hoffmeier A, et al.: Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: the PrevAKI randomized controlled trial. Intensive Care Med 2017; 43: 1551–61 CrossRef CrossRef MEDLINE PubMed Central
13.Bihorac A, Yavas S, Subbiah S, et al.: Long-term risk of mortality and acute kidney injury during hospitalization after major surgery. Ann Surg 2009; 249: 851–8 CrossRef MEDLINE
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