SummaryBackground and objectives A specific method is required for estimating glomerular filtration rate GFR in hospitalized patients. Our objective was to validate the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and four cystatin C (CysC)-based equations in this setting.Design, setting, participants, & measurements This was an epidemiologic, cross-sectional study in a random sample of hospitalized patients (n ϭ 3114). We studied the accuracy of the CKD-EPI and four CysC-based equations-based on (1) CysC alone or (2) adjusted by gender; (3) age, gender, and race; and (4) age, gender, race, and creatinine, respectively-compared with GFR measured by iohexol clearance (mGFR). Clinical, biochemical, and nutritional data were also collected.Results The CysC equation 3 significantly overestimated the GFR (bias of 7.4 ml/min per 1.73 m 2 ). Most of the error in creatinine-based equations was attributable to calculated muscle mass, which depended on patient's nutritional status. In patients without malnutrition or reduced body surface area, the CKD-EPI equation adequately estimated GFR. Equations based on CysC gave more precise mGFR estimates when malnutrition, extensive reduction of body surface area, or loss of muscle mass were present (biases of 1 and 1.3 ml/min per 1.73 m 2 for equations 2 and 4, respectively, versus 5.9 ml/min per 1.73 m 2 for CKD-EPI). ConclusionsThese results suggest that the use of equations based on CysC and gender, or CysC, age, gender, and race, is more appropriate in hospitalized patients to estimate GFR, since these equations are much less dependent on patient's nutritional status or muscle mass than the CKD-EPI equation.

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Background Models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill patients have a low sensitivity, do not include dynamic changes of risk factors and do not allow to establish 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 Study set: 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 center 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, were conferred by chronic kidney disease, urologic and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, anemia, systemic inflammatory response syndrome (SIRS), circulatory shock and major surgery. The model showed an AUC of 0.907(95% CI 0.902 to 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 (Chi2:6.02, p:0.64). In the validation set, 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 (Chi2:4.2, 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 along the hospital admission period in non-critically ill patients.

Half of ARF episodes during hospitalisation were drug related. Patients with drug-related ARF had higher cardiovascular morbidity than those with ARF related to other causes, but they had a lower frequency of ARF risk factors and mortality.

Background. The current models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill fail to identify the patients at risk of severe HA-AKI stage 3. Objective. To develop and externally validate a model to predict the individual probability of developing HA-AKI stage 3 through the integration of electronic health databases. Methods. Study set: 165,893 non-critically ill hospitalized patients. Using stepwise logistic regression analyses, including demography, chronic comorbidities, and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI stage 3. This model was then externally validated in 43,569 non-critical patients admitted to the validation center. Results. The incidence of HA-AKI stage 3 in the study set was 0.6%. Among chronic comorbidities, the highest odds ratios were conferred by ischemic heart disease, ischemic cerebrovascular disease, chronic congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, major surgery and exposure to nephrotoxic drugs. The model showed an AUC of 0.906 (95% CI 0.904 to 0.908), a sensitivity of 89.1 (95% CI 87.0–91.0) and a specificity of 80.5 (95% CI 80.2–80.7) to predict HA-AKI stage 3, but tended to overestimate the risk at low-risk categories with an adequate goodness-of-fit for all risk categories (Chi2:16.4, p: 0.034). In the validation set, incidence of HA-AKI stage 3 was 0.62%. The model showed an AUC of 0.861 (95% CI 0.859–0.863), a sensitivity of 83.0 (95% CI 80.5–85.3) and a specificity of 76.5 (95% CI 76.2–76.8) to predict HA-AKI stage 3 with an adequate goodness of fit for all risk categories (Chi2:15.42, p: 0.052). Conclusions. Our study provides a model that can be used in clinical practice to obtain an accurate dynamic assessment of the individual risk of HA-AKI stage 3 along the hospital stay period in non-critically ill patients.

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