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.

External validation of the Madrid Acute Kidney Injury Prediction Score.

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.

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.

Laparoscopic sleeve gastrectomy prevents the deterioration of renal function in morbidly obese patients over 40 years.

BACKGROUND: Chronic kidney disease (CKD) risk has been associated with elevated body mass index (BMI), especially in morbidly obese subjects. Aging and obesity can play a synergic effect on accelerating the renal function deterioration. Bariatric surgery (mainly gastric bypass or biliopancreatic diversion) has demonstrated an improvement on renal function, but little is known about the potential effect of sleeve gastrectomy on renal function. METHODS: A prospective observational study was performed. Between 2009 and 2013, 50 morbidly obese patients over 40 years underwent a laparoscopic sleeve gastrectomy (LSG) at our institution. Renal function was evaluated by serum creatinine, urea, and estimated glomerular filtration rate (eGFR), calculated using the MDRD-4 formula. All the variables were obtained at three times: on the first visit to the surgeon's office (baseline), the day before surgery (preoperative), and 12 months after surgery. RESULTS: Fifty patients underwent a LSG, 44 females (88%) and 6 males (12%), with a mean age 49.2 ± 6.4 years and mean BMI of 48.4 ± 7.7 kg/m(2). MDRD-4 values presented a significant reduction (69.4 ml/min/m(2) at baseline vs 62.5 ml/min/m(2) preoperatively; CI95% (2.2-11.3 ml/min/m(2)); p = 0.01). Comparing pre- and postoperative values, a significant reduction could be determined in creatinine (0.89 mg/dl preoperatively vs 0.71 mg/dl postoperatively; p = 0.01), urea (36.1 mg/dl preoperatively vs 29.8 mg/dl postoperatively; p = 0.023), and a significant increase in MDRD-4 (62.5 ml/min/m(2) preoperatively vs 77.6 ml/min/m(2) postoperatively; p < 0.001). CONCLUSION: In patients over 40 years, renal function is going to deteriorate as long as the excess of weight persists. Laparoscopic sleeve gastrectomy has shown to improve the renal function 12 months after surgery.

Mineral metabolism parameters throughout chronic kidney disease stages 1-5--achievement of K/DOQI target ranges.

BACKGROUND: Dialysis Outcomes and Practice Patterns Study has shown that the proportion of haemodialysis patients with adequate mineral metabolism parameters according to the Kidney Disease Outcome Quality Initiative (K/DOQI) guidelines is very low. The adequacy of such parameters in relation to the recommended ranges in patients with different chronic kidney disease (CKD) stages has not been reported. The objective of this study is to provide an in-depth description of mineral metabolism in the early stages of CKD in a European population, and to compare it with current recommendations for stages 3-5 (K/DOQI guidelines). METHODS: A total of 1836 patients were classified into stages 1-5 according to K/DOQI guidelines. The following clinical and biochemical data were recorded: age, gender, CKD aetiology, presence of diabetes, serum creatinine, creatinine clearance, serum phosphate, calcium, CaxP product and intact parathyroid hormone (PTH). RESULTS: A decrease in 1,25-dihydroxyvitamin D and an increase in PTH are the earliest mineral metabolism alterations in CKD, while serum calcium and phosphate are altered later in the course of CKD. The percentages of patients with serum levels within the recommended K/DOQI guidelines for stages 3, 4 and 5 were as follows: serum calcium: 90.7, 85.6 and 55; serum phosphate: 90.9, 77.1 and 70.3; iPTH 42.4, 24.6 and 46.8 and Ca x P product 99.9, 99.6 and 83.8, respectively. The percentages of patients who had all four parameters within the recommended ranges were 34.9, 18.4 and 21.6 for stages 3, 4 and 5, respectively. CONCLUSION: Mineral metabolism disturbances start early in the course of CKD. The first alterations to take place are a 1,25-dihydroxyvitamin D decrease, a 24 h urine phosphate decrease and a PTH elevation, which show significant level variation when the glomerular filtration rate falls below 60 ml/min. K/DOQI recommended levels for mineral metabolism parameters are difficult to accomplish, in particular for PTH levels.

Higher impact of mineral metabolism on cardiovascular mortality in a European hemodialysis population.

BACKGROUND: Cardiovascular disease is the main cause of morbidity and mortality among hemodialysis patients. Chronic renal failure influences a number of factors that cause accelerated atherogenesis, with calcium, phosphorus, and PTH playing key roles. Several studies have demonstrated the influence of these factors on all-cause and cardiovascular mortality in the American hemodialysis population. In the present study we evaluated the variables that influence long-term cardiovascular mortality in a European hemodialysis population. METHODS: One hundred and forty-three hemodialysis patients were followed for six years. Several Cox models were used to study the influence of demographic and biochemical data, and comorbid conditions in cardiovascular survival, with a particular interest in mineral metabolism. RESULTS: There was an increased risk of cardiovascular death in patients with serum P>6.5 mg/dL (risk ratio [RR], 2.5), PTH>50 pmol/L (RR, 3.9), Ca x P>52 (RR, 2.8), BB or Bb genotype (RR, 3.8), and in diabetics. CONCLUSION: There is a stronger influence of mineral metabolism on cardiovascular death among European patients when compared to the American population.