The Diagnosis-Wide Landscape of Hospital-Acquired AKI

Abstract

Background and objectives: The exploration of electronic hospital records offers a unique opportunity to describe in-depth the prevalence of conditions associated with diagnoses at an unprecedented level of comprehensiveness. We used a diagnosis-wide approach, adapted from phenome-wide association studies (PheWAS), to perform an exhaustive analysis of all diagnoses associated with hospital-acquired AKI (HA-AKI) in a French urban tertiary academic hospital over a period of 10 years.

Design, setting, participants, & measurements: We retrospectively extracted all diagnoses from an i2b2 (Informatics for Integrating Biology and the Bedside) clinical data warehouse for patients who stayed in this hospital between 2006 and 2015 and had at least two plasma creatinine measurements performed during the first week of their stay. We then analyzed the association between HA-AKI and each International Classification of Diseases (ICD)-10 diagnostic category to draw a comprehensive picture of diagnoses associated with AKI. Hospital stays for 126,736 unique individuals were extracted.

Results: Hemodynamic impairment and surgical procedures are the main factors associated with HA-AKI and five clusters of diagnoses were identified: sepsis, heart diseases, polytrauma, liver disease, and cardiovascular surgery. The ICD-10 code corresponding to AKI (N17) was recorded in 30% of the cases with HA-AKI identified, and in this situation, 20% of the diagnoses associated with HA-AKI corresponded to kidney diseases such as tubulointerstitial nephritis, necrotizing vasculitis, or myeloma cast nephropathy. Codes associated with HA-AKI that demonstrated the greatest increase in prevalence with time were related to influenza, polytrauma, and surgery of neoplasms of the genitourinary system.

Conclusions: Our approach, derived from PheWAS, is a valuable way to comprehensively identify and classify all of the diagnoses and clusters of diagnoses associated with HA-AKI. Our analysis delivers insights into how diagnoses associated with HA-AKI evolved over time. On the basis of ICD-10 codes, HA-AKI appears largely underestimated in this academic hospital.

Keywords: Acute Kidney Injury; Heart Diseases; Hemodynamics; Hospital Records; Humans; Influenza, Human; International Classification of Diseases; Length of Stay; Liver Diseases; Multiple Trauma; Neoplasms; Nephritis, Interstitial; Prevalence; Retrospective Studies; Sepsis; Urogenital System; acute renal failure; clinical nephrology; creatinine; hospitalization; vasculitis.

Copyright © 2017 by the American Society of Nephrology.

Figures

Figure 1.

ICD-10 codes positively and significantly associated with HA-AKI have a specific distribution pattern. A total of 1730 ICD-10 diagnoses were reported within the whole cohort; of these diagnoses, 217 were present in only one or two patients and were, therefore, excluded from the analysis. Multiple comparisons were performed on 1513 patients (1730−217), leading to a P value considered statistically significant of (0.05/1513)=3.3×10−5 after Bonferroni correction. The scatter plot represents the 208 ICD-10 codes significantly (P<3.3×10−5) and positively (OR>1) associated with HA-AKI, and classified according to diagnosis family. HA-AKI, hospital-acquired AKI; ICD-10, International Classification of Diseases–10.

Figure 2.

Some diagnoses have a specific evolution of association pattern over time. (A) Scatter plots of the −log10 (P value) of the 251 ICD-10 diagnosis codes significantly associated with HA-AKI according to the observation period (2006–2010 and 2011–2016). Gray dots denote diagnoses with a P value >0.05 in 2005–2010 and <3.3×10−5 in 2011–2015. (B) Histogram representing the ORs of the 17 ICD-10 diagnoses significantly associated with HA-AKI in 2011–2015 but not in 2006–2010. HA-AKI, hospital-acquired AKI; ICD-10, International Classification of Diseases–10; OR, odds ratio.

Figure 3.

Clusters of diagnoses associated with AKI can be identified. Heatmap representation of the hierarchic cluster analysis of the Jaccard distances of diagnoses in the HA-AKI population. Diagnoses for which the sum of the dissimilarity coefficients was lower than four are represented on the heatmap. Five major clusters are highlighted. HA-AKI, hospital-acquired AKI.

Figure 4.

ICD10 codes associated with N17 in patients with HA-AKI are enriched in diagnoses related to kidney diseases. Plot of the ORs for the 51 ICD-10 codes associated with N17 with a P value <6×10−5 in the cohort of 8479 individuals with HA-AKI. ORs were calculated as follow: in the group of individuals with HA-AKI, ([ICD-10+/ICD-10] in individuals with N17 code) divided by ([ICD-10+/ICD-10−] in individuals without N17 code). A total of 1201 ICD-10 codes were identified in this cohort, of which 384 were present in one or two patients and excluded. Multiple comparisons were performed on 817 patients (1201−384), leading to a P value considered statistically significant of (0.05/817)=6×10−5 after Bonferroni correction. Gray dots correspond to a diagnosis related to a kidney disease. HA-AKI, hospital-acquired AKI; ICD-10, International Classification of Diseases–10; OR, odds ratio