Methods

Study design and data collection.

All adult (18 years and more) admissions to a tertiary care university hospital (Centro Hospitalar Universitário de São João), in Porto, Portugal, between January 1, 2013, and December 31, 2015, were considered for this study (Figure 1). Admissions to maternity and gynaecological departments, patients with a history of renal transplant, on chronic renal replacement therapy (RRT) or hospitalised due to end-stage renal disease were excluded. We also eliminated admissions with no inpatient serum creatinine (SCr) measured. For patients with multiple hospitalisations during the study period, we included only one randomly selected admission.
Using institutional electronic medical records, we retrieved patients’ demographics, admission and discharge data, discharge diagnoses and procedures coded according to the International Classification of Diseases, Ninth Revision, Clinical Modification, and laboratory results, including SCr with the date and time of biological specimen sampling. All clinical routine SCr measurements were isotope-dilution mass spectrometry-aligned. Comorbidities were identified, and Charlson Index was automatically calculated on the basis of patients’ diagnoses of hospitalisations in the previous five years and certain secondary diagnoses of the index hospitalisation. Patients’ vital status after discharge was ascertained based on electronic medical records.
This study was approved by the institutional Ethics Committee (Comissão de Ética para a Saúde do Centro Hospitalar Universitário de São João, reference number 365-15 dated: 29-12-2015) with a waiver of informed consents obtained because of the observational nature of the study.

Definitions

The baseline SCr was defined as the median of ambulatory measurements at the same hospital between 7 and 365 days before admission(11). When no preadmission SCr was available, missing values were imputed using random-forest model controlled for patients’ age, sex, admission unit and type (medical, surgical and intensive care; emergency and elective), comorbid conditions (history of myocardial infarction, chronic heart failure, cardiovascular disease, dementia, chronic liver disease, pulmonary disease, diabetes mellitus, chronic kidney disease (CKD), hypertension, cancer, peripheral vascular disease and rheumatologic disease), total Charlson Index and first inpatient SCr. To calculate the estimated glomerular filtration rate (eGFR), we used Chronic Kidney Disease Epidemiology Collaboration formula(12).
AKI was identified using the Kidney Disease: Improving Global Outcomes definition (13, 14) if at least one of the criteria was met: i) SCr ≥ 1.5 times higher than the baseline SCr within first 7 days after admission; ii) SCr ≥1.5 times higher than the lowest inpatient SCr creatinine within 7 days and iii) SCr ≥0.3 mg /dL higher than the lowest value within 48 hours, and with the increase sustained for more than 24 hours. Urine output data were not available. We considered one AKI episode per admission and the time of first SCr measurement meeting the criteria was recorded as AKI onset. Patients with AKI apparent within 24 hours of admission were designated community-acquired AKI (CA-AKI), while patients in whom the syndrome developed afterwards were denoted as hospital-acquired AKI (HA-AKI). Based on the ratio of peak inpatient SCr relative to the baseline value, we categorised AKI severity into three stages: Stage 1 - ratio 1.5 to 2 or increase in SCr of 0.3 mg/dL; Stage 2 - ratio 2 to 3 and Stage 3 - ratio ≥3 or increase in SCr above ≥4.0 mg/dL or receipt of RRT.

Statistical analysis

Patients with CA-AKI and HA-AKI were compared with respect to baseline characteristics, clinical presentation and outcomes. Normally distributed continuous variables are reported as means and their standard deviations (SD) or as medians with 25th and 75th percentiles (P25, P75) otherwise. Categorical variables are presented as counts with percentages.
We estimated cumulative incidence functions for the length of hospital stay, in-hospital and 6th-month mortalities and compared them among groups of patients with No AKI, CA-AKI and HA-AKI, and across severity stages, with differences being evaluated by log-rank tests. Discharge and in-hospital death were considered to be the competing risk outcomes, and time to these events was presented in days counted from the syndrome onset. We used multivariable Cox proportional hazard regression to examine the effect of the presence of CA- or HA-AKI and its’ severity on the risk of outcomes. There were significant interactions between AKI type (CA- and HA-AKI) and AKI stage, therefore in the Cox model, we created dummies to treat each combination AKI type - AKI stage separately.
To estimate the hazard function for AKI incidence over time in patients free of AKI at admission, we tested two parametric survival regression models with:
exponential distribution of time to an event, T~exp (λ), assuming constant hazard function over time and given by
\(h\left(t\right)=\ \lambda\)
where hazard function h(t) denotes the probability of AKI onset on day t , given that the patient remains AKI free to the beginning of day t and λ is a scale parameter,
Weibull distribution of time to an event, T~Weibull (λ, p), allowing monotonic increase or decrease in hazards over time and given by
\begin{equation} h\left(t\right)=\text{λpt}^{p-1}\nonumber \\ \end{equation}
where p is the shape parameter (the hazard function is increasing when p > 1, and decreasing otherwise).
We plotted the survival functions of the two models against the Kaplan-Meier curve to determine the most suited distribution of time to AKI occurrence. We used Akaike’s information criterion (AIC) to evaluate which model better fitted to our data.
All analyses were performed using R version 3.5.3 (R Foundation for Statistical Computing, Vienna, Austria) and packages: ‘random forest’ , ‘survival’ , ‘survminer’ and ‘cmprsk’ .