Search

Background Models developed to predict hospital-acquired acute kidney injury (HA-AKI) in non-critically ill patients have a low sensitivity, do not include dynamic changes of risk factors and do not allow the establishment of a time relationship between exposure to risk factors and AKI. We developed and externally validated a predictive model of HA-AKI integrating electronic health databases and recording the exposure to risk factors prior to the detection of AKI. Methods The study set was 36 852 non-critically ill hospitalized patients admitted from January to December 2017. Using stepwise logistic analyses, including demography, chronic comorbidities and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI. This model was then externally validated in 21 545 non-critical patients admitted to the validation centre in the period from June 2017 to December 2018. Results The incidence of AKI in the study set was 3.9%. Among chronic comorbidities, the highest odds ratios (ORs) were conferred by chronic kidney disease, urologic disease and liver disease. Among acute complications, the highest ORs were associated with acute respiratory failure, anaemia, systemic inflammatory response syndrome, circulatory shock and major surgery. The model showed an area under the curve (AUC) of 0.907 [95% confidence interval (CI) 0.902–0.908), a sensitivity of 82.7 (95% CI 80.7–84.6) and a specificity of 84.2 (95% CI 83.9–84.6) to predict HA-AKI, with an adequate goodness-of-fit for all risk categories (χ2 = 6.02, P = 0.64). In the validation set, the prevalence of AKI was 3.2%. The model showed an AUC of 0.905 (95% CI 0.904–0.910), a sensitivity of 81.2 (95% CI 79.2–83.1) and a specificity of 82.5 (95% CI 82.2–83) to predict HA-AKI and had an adequate goodness-of-fit for all risk categories (χ2 = 4.2, P = 0.83). An online tool (predaki.amalfianalytics.com) is available to calculate the risk of AKI in other hospital environments. Conclusions By using electronic health data records, our study provides a model that can be used in clinical practice to obtain an accurate dynamic and updated assessment of the individual risk of HA-AKI during the hospital admission period in non-critically ill patients.

Objectives Idiopathic focal segmental glomerulosclerosis (FSGS) has been linked to immunological and inflammatory response dysregulations. The aim of this study was to find endotypes of FSGS patients using a cluster (CL) analysis based on inflammatory and immunological variables, and to analyse whether a certain endotype is associated with response to treatment with corticosteroids. Methods This prospective observational study included patients with idiopathic FSGS diagnosed by kidney biopsy. Serum levels of soluble interleukin (IL)-1 receptor, tumoural necrosis factor alpha, Interferon gamma (IFNγ), IL-6, IL-17, IL-12, IL-23, IL-13, IL-4, IL-5, IL-6, haemopexin (Hx), haptoglobin (Hgl), soluble urokinase-type plasminogen activator receptor (suPAR) and urinary CD80 (uCD80) were measured with enzyme-linked immunosorbent assay or nephelometry. T-helper lymphocyte populations and T-regulatory lymphocytes were analysed by flow cytometry. A factorial analysis followed by a k-means CL analysis was performed. Results A total of 79 FSGS patients were included. Three CLs were identified. CL1 (27.8%) included IL-12, IL-17, IL-23 and a T helper 17 (Th17) pattern. CL2 (20.2%) included IL-4, IL-5, IL-13, immunoglobulin E and Th2 pattern. CL3 (51.8%) included IL-6, Hx, Hgl, suPAR and uCD80. There were no differences in age, gender, kidney function, albumin or proteinuria among CLs. About 42/79 patients (53.1%) showed cortico-resistance. The prevalence of cortico-resistance was significantly lower in CL2 (4/16, 25%) than in CL1 (16/26, 72.7%) and CL3 (22/41, 53.7%) (P = 0.018), with no significant differences between CLs 1 and 3 (P = 0.14). Conclusions Patients with FSGS and indistinguishable clinical presentation at diagnosis were classified in three distinct CLs according to predominant Th17, Th2 and acute inflammatory responses that display differences in clinical response to treatment with corticosteroids., Graphical Abstract

Glomerulonefritis creixent; Implants mamaris de silici; Esclerosi sistèmica Crescentic glomerulonephritis; Silicon breast implants; Systemic sclerosis Glomerulonefritis creciente; Implantes mamarios de silicio; Esclerosis sistémica The relationship between silicon breast implants (SBIs) and autoimmune/inflammatory syndrome induced by adjuvants (ASIA) has been extensively analysed, with discordant results. We present a 45-year-old woman with confirmed systemic exposure to SBI who developed systemic sclerosis (SSc) followed by anti-neutrophil cytoplasmic antibody anti-myeloperoxidase vasculitis with renopulmonary syndrome. The novelty of our case is, first, confirmation of systemic exposure to SBI and, second, chronologic development of not one, but two severe autoimmune diseases. Controversy may still remain regarding SBIs and ASIA because it is unclear that previous studies confirmed systemic exposure to silicon in their cohort of patients. Grant number: PI18/00356 - Instituto de Salud Carlos III - FEDER "Una manera de hacer Europa" - CERCA Programme/Generalitat de Catalunya - IRBLleida - Fundació Dr. Pifarré

Background The Madrid Acute Kidney Injury Prediction Score (MAKIPS) is a recently described tool capable of performing automatic calculations of the risk of hospital-acquired acute kidney injury (HA-AKI) using data from from electronic clinical records that could be easily implemented in clinical practice. However, to date, it has not been externally validated. The aim of our study was to perform an external validation of the MAKIPS in a hospital with different characteristics and variable case mix. Methods This external validation cohort study of the MAKIPS was conducted in patients admitted to a single tertiary hospital between April 2018 and September 2019. Performance was assessed by discrimination using the area under the receiver operating characteristics curve and calibration plots. Results A total of 5.3% of the external validation cohort had HA-AKI. When compared with the MAKIPS cohort, the validation cohort showed a higher percentage of men as well as a higher prevalence of diabetes, hypertension, cardiovascular disease, cerebrovascular disease, anaemia, congestive heart failure, chronic pulmonary disease, connective tissue diseases and renal disease, whereas the prevalence of peptic ulcer disease, liver disease, malignancy, metastatic solid tumours and acquired immune deficiency syndrome was significantly lower. In the validation cohort, the MAKIPS showed an area under the curve of 0.798 (95% confidence interval 0.788–0.809). Calibration plots showed that there was a tendency for the MAKIPS to overestimate the risk of HA-AKI at probability rates ˂0.19 and to underestimate at probability rates between 0.22 and 0.67. Conclusions The MAKIPS can be a useful tool, using data that are easily obtainable from electronic records, to predict the risk of HA-AKI in hospitals with different case mix characteristics.

BackgroundHeight-adjusted total kidney volume (htTKV) is considered as the best predictor of kidney function in patients with autosomal dominant polycystic kidney disease (ADPKD), but its limited predictive capacity stresses the need to find new biomarkers of ADPKD progression. The aim of this study was to investigate urinary biomarkers of ADPKD progression.MethodsThis observational study included ADPKD patients, and two comparator groups of ischaemic and non-ischaemic kidney injury: benign nephroangiosclerosis patients and non-ischaemic chronic kidney disease (CKD) patients. Proteinuria, htTKV and urinary levels of molecules are associated with ischaemia and/or tubular injury. The slope of estimated glomerular filtration rate (eGFR) was used as a dependent variable in univariate and multivariate models of kidney function decline.ResultsThe study included 130 patients with ADPKD, 55 with nephroangiosclerosis and 40 with non-ischaemic CKD. All patients had increased urinary concentrations of biomarkers associated with tubular lesions (liver fatty acid-binding protein, kidney injury molecule-1, β2-microglobulin) and molecules overexpressed under ischaemic conditions [hypoxia-inducible factor-1α, vascular endothelial growth factor (VEGF) and monocyte chemoattractant protein-1 (MCP-1)]. These biomarkers correlated positively with htTKV and negatively with the eGFR slope. htTKV was the single best predictor of the eGFR slope variability in univariate analyses. However, a multivariate model including urinary levels of β2-microglobulin, MCP-1 and VEGF improved the capacity to predict the decline of eGFR in ADPKD patients compared with htTKV alone.ConclusionsThe urinary levels of molecules associated with either renal ischaemia (VEGF and MCP-1) or tubular damage (β2-microglobulin) are associated with renal function deterioration in ADPKD patients, and are, therefore, candidates as biomarkers of ADPKD progression.