Integrating electronic health data records to develop and validate a predictive model of hospital-acquired acute kidney injury in non-critically ill patients.

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.

Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients.

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.

Drug-related acute renal failure in hospitalised patients.

INTRODUCTION: The information available on the incidence and the characteristics of patients with acute renal failure (ARF) related to drugs is scarce. OBJECTIVES: To estimate the incidence of drug-related ARF in hospitalised patients and to compare their characteristics with those of patients with ARF due to other causes. MATERIAL AND METHODS: We selected a prospective cohort of patients with ARF during hospital admission (July 2010-July 2011). Information on patients' demographics, medical antecedents, ARF risk factors, ARF severity according to the RIFLE classification and hospital drug administration was collected. We analysed the relationship of drugs with the ARF episodes using Spanish Pharmacovigilance System methods and algorithm. RESULTS: A total of 194 cases had an episode of hospital-acquired ARF. The median age of patients was 72 years [IQR 20]; 60% were men. The ARF incidence during hospitalization was 9.6 per 1,000 admissions. According to the RIFLE classification, a risk of kidney damage or kidney injury was present in 77.8% of cases. In 105 (54.1%) cases, ARF was drug-related; the drugs most frequently involved were diuretics, agents acting on the renin-angiotensin system, immunosuppressants, ß-blocking agents, calcium channel blockers, contrast media and non-steroid anti-inflammatory drugs. Patients with drug-related ARF had more multi-morbidity, fewer ARF risk factors and lower mortality. CONCLUSIONS: 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.

Assessing glomerular filtration rate in hospitalized patients: a comparison between CKD-EPI and four cystatin C-based equations.

BACKGROUND 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 = 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). CONCLUSIONS: These 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.

Hiperplasia gingival en restauraciones clase II con amalgama y resina en pacientes de la clínica UNFV / Gingival hiperplasia in class II restorations with amalgam and resin in UNFV dental clinic patients

Se realizó un estudio clínico, descriptivo, transversal de observación y prospectivo en 21 pacientes de ambos sexos que asistieron a la clínica dental de la Universidad Nacional Federico Villarreal a los cuales se le realizó el índice de placa (I.P.) e índice de Angeulopoulus (I.A.). Encontrándose un promedio de 1.8 en el índice de placa (I.P.) y en el grado II el 54.2 por ciento según el índice de Angeulopoulus (I.A.) en pacientes en ambos sexos. Los resultados mostraron que el índice de placa (I.P.) 1.8 corresponde a un resultado de malo y el índice de Angeulopoulus (I.A.) fue de grado II el que alcanzó el mayor porcentaje. En el grado II la hiperplasia se extiende hasta el tercio medio de la corona del diente. Se presentaron con mayor frecuencia en el sexo femenino 62.5 por ciento (I.A.). La de mayor edad fue de 62 años y la de menor edad de 18 años en el sexo femenino. En el sexo masculino la de mayor edad fue de 68 años y la de menor de 22 años. Se concluye que los resultados mostraron presencia de hiperplasia gingival en restauraciones Clase II con amalgama, siendo el de mayor grado (IA) el II con el 54.2 por ciento.