External validation of a 2-year all-cause mortality prediction tool developed using machine learning in patients with stage 4-5 chronic kidney disease.

BACKGROUND: Chronic kidney disease (CKD) is associated with increased mortality. Individual mortality prediction could be of interest to improve individual clinical outcomes. Using an independent regional dataset, the aim of the present study was to externally validate the recently published 2-year all-cause mortality prediction tool developed using machine learning. METHODS: A validation dataset of stage 4 or 5 CKD outpatients was used. External validation performance of the prediction tool at the optimal cutoff-point was assessed by the area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and specificity. A survival analysis was then performed using the Kaplan-Meier method. RESULTS: Data of 527 outpatients with stage 4 or 5 CKD were analyzed. During the 2 years of follow-up, 91 patients died and 436 survived. Compared to the learning dataset, patients in the validation dataset were significantly younger, and the ratio of deceased patients in the validation dataset was significantly lower. The performance of the prediction tool at the optimal cutoff-point was: AUC-ROC = 0.72, accuracy = 63.6%, sensitivity = 72.5%, and specificity = 61.7%. The survival curves of the predicted survived and the predicted deceased groups were significantly different (p < 0.001). CONCLUSION: The 2-year all-cause mortality prediction tool for patients with stage 4 or 5 CKD showed satisfactory discriminatory capacity with emphasis on sensitivity. The proposed prediction tool appears to be of clinical interest for further development.

Prediction of all-cause mortality for chronic kidney disease patients using four models of machine learning.

BACKGROUND: The prediction tools developed from general population data to predict all-cause mortality are not adapted to chronic kidney disease (CKD) patients, because this population displays a higher mortality risk. This study aimed to create a clinical prediction tool with good predictive performance to predict the 2-year all-cause mortality of stage 4 or stage 5 CKD patients. METHODS: The performance of four different models (deep learning, random forest, Bayesian network, logistic regression) to create four prediction tools was compared using a 10-fold cross validation. The model that offered the best performance for predicting mortality in the Photo-Graphe 3 cohort was selected and then optimized using synthetic data and a selected number of explanatory variables. The performance of the optimized prediction tool to correctly predict the 2-year mortality of the patients included in the Photo-Graphe 3 database were then assessed. RESULTS: Prediction tools developed using the Bayesian network and logistic regression tended to have the best performances. Although not significantly different from logistic regression, the prediction tool developed using the Bayesian network was chosen because of its advantages and then optimized. The optimized prediction tool that was developed using synthetic data and the seven variables with the best predictive value (age, erythropoietin-stimulating agent, cardiovascular history, smoking status, 25-hydroxy vitamin D, parathyroid hormone and ferritin levels) had satisfactory internal performance. CONCLUSIONS: A Bayesian network was used to create a seven-variable prediction tool to predict the 2-year all-cause mortality in patients with stage 4-5 CKD. Prior to external validation, the proposed prediction tool can be used at: https://dev.hed.cc/?a=jpfauvel&n=2022-05%20Modele%20Bayesien%2020000%20Mortalite%207%20variables%20Naif%20Zou%20online(1).neta for research purposes.

Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning Tool: The UniverSel Study.

There is a need for a reliable and validated method to estimate dietary potassium intake in chronic kidney disease (CKD) patients to improve prevention of cardiovascular complications. This study aimed to develop a clinical tool to estimate potassium intake using 24-h urinary potassium excretion as a surrogate of dietary potassium intake in this high-risk population. Data of 375 adult CKD-patients routinely collecting their 24-h urine were included to develop a prediction tool to estimate potassium diet. The prediction tool was built from a random sample of 80% of patients and validated on the remaining 20%. The accuracy of the prediction tool to classify potassium diet in the three classes of potassium excretion was 74%. Surprisingly, the variables related to potassium consumption were more related to clinical characteristics and renal pathology than to the potassium content of the ingested food. Artificial intelligence allowed to develop an easy-to-use tool for estimating patients' diets in clinical practice. After external validation, this tool could be extended to all CKD-patients for a better clinical and therapeutic management for the prevention of cardiovascular complications.

A Clinical Decision Support Tool to Find the Best Initial Intravenous Cyclosporine Regimen in Pediatric Hematopoietic Stem Cell Transplantation.

To optimize cyclosporine A (CsA) dosing regimen in pediatric patients undergoing hematopoietic stem cell transplantation (HSCT), we aimed to provide clinicians with a validated decision support tool for determining the most suitable first dose of intravenous CsA. We used a 10-year monocentric data set of pediatric patients undergoing HSCT. Discretization of all variables was performed according to literature or thanks to algorithms using Shannon entropy (from information theory) or equal width intervals. The first 8 years were used to build the Bayesian network model. This model underwent a 10-fold cross-validation, and then a prospective validation with data of the last 2 years. There were 3.3% and 4.1% of missing values in the training and the validation data set, respectively. After prospective validation, the Tree-Augmented Naïve Bayesian network shows interesting prediction performances with an average area under the receiver operating characteristic curve of 0.804, 32.8% of misclassified patients, a true-positive rate of 0.672, and a false-positive rate of 0.285. This validated model allows good predictions to propose an optimized and personalized initial CsA dose for pediatric patients undergoing HSCT. The clinical impact of its use should be further evaluated.

Blood pressure control in patients with chronic kidney disease according to office and home blood pressures.

OBJECTIVES: The aim of this study was to assess blood pressure (BP) control in patients with chronic kidney disease (CKD) according to office and home BP and to assess the prevalence of normal BP, white-coat uncontrolled hypertension (WUCH), masked uncontrolled hypertension (MUCH) and elevated BP. METHODS: Patients with renal failure with or without proteinuria were included in this multicenter observational study. Office BP was first measured by the physician using a self-monitoring BP device (three automatic readings), then by the patient at home (morning and evening) over 3 consecutive days. WUCH was defined as a systolic BP (SBP)/diastolic BP (DBP) ≥140/90 mmHg in the clinic and SBP/DBP<135/85 mmHg at home. MUCH was defined as SBP/DBP <140/90 mmHg in the clinic and SBP/DBP ≥135/85 mmHg at home. RESULTS: Among the 243 included subjects, data of 225 patients were analyzed. Mean estimated glomerular filtration rate was 37.7 ± 15.7 mL/min/1.73 m and mean office SBP/DBP was 154 ± 19/83 ± 13 mmHg. Mean office SBP/DBP was significantly higher than home SBP/DBP (+9.0 ± 15.1/+7.0 ± 10.0 mmHg, P < 0.01). Normal BP (office and home BP), WUCH, MUCH and elevated BP (office and home BP) rates were 12.0, 14.2, 6.7 and 67.1%, respectively. The patients were taking, on average, 2.8 ± 1.5 antihypertensive drugs/day. CONCLUSION: BP control in patients with CKD was poor. Routine use of 'out-of-office' BP measurement, in addition to office BP by which we can identify patients with WUCH or MUCH, should be recommended based on the current findings.

A decision support tool to find the best cyclosporine dose when switching from intravenous to oral route in pediatric stem cell transplant patients.

PURPOSE: Managing the pharmacokinetic variability of immunosuppressive drugs after pediatric hematopoietic stem cell transplantation (HSCT) is a clinical challenge. Thus, the aim of our study was to design and validate a decision support tool predicting the best first cyclosporine oral dose to give when switching from intravenous route. METHODS: We used 10-years pediatric HSCT patients' dataset from 2008 to 2018. A tree-augmented naïve Bayesian network model (method belonging to artificial intelligence) was built with data from the first eight-years, and validated with data from the last two. RESULTS: The Bayesian network model obtained showed good prediction performances, both after a 10-fold cross-validation and external validation, with respectively an AUC-ROC of 0.89 and 0.86, a percentage of misclassified patients of 28.7% and 35.2%, a true positive rate of 0.71 and 0.65, and a false positive rate of 0.12 and 0.14 respectively. CONCLUSION: The final model allows the prediction of the most likely cyclosporine oral dose to reach the therapeutic target specified by the clinician. The clinical impact of using this model needs to be prospectively warranted. Respecting the decision support tool terms of use is necessary as well as remaining critical about the prediction by confronting it with the clinical context.

Intra-individual Pharmacokinetic Variability of Intravenous Busulfan in Hematopoietic Stem Cell-Transplanted Children.

BACKGROUND: Busulfan therapeutic drug monitoring (TDM) is necessary to better achieve the target exposure in children before hematopoietic stem cell transplantation (HSCT). However, TDM-based dosing may be challenging if intra-individual pharmacokinetic variability (also denoted inter-occasion variability [IOV]) occurs during therapy. OBJECTIVES: The objectives of this study were to describe and quantify busulfan IOV in children, and to investigate its potential determinants. METHODS: We performed a new analysis of published data from children who received intravenous busulfan over 4 days before HSCT. We calculated individual pharmacokinetic parameters on each day of therapy using a published population pharmacokinetic model of busulfan and analyzed their changes. Population estimation of IOV was also performed with non-linear mixed effects (NLME) modeling. Potential predictors of significant decrease in busulfan clearance (CL) were assessed by using machine learning approaches. RESULTS: IOV could be assessed in 136 children. Between day (D) 1 and D2, most patients (80%) experienced a decrease in busulfan CL, with a median change of - 7.9%. However, both large decreases (minimum, - 48.5%) and increases in CL (maximum, + 44%) were observed. Over D1-D3 of therapy, mean CL significantly decreased (- 15%), with a decrease of ≥ 20% in 22% of patients. Some patients also showed unstable CL from day to day. NLME modeling of IOV provided a coefficient of variation of 10.6% and 13.1% for volume of distribution (Vd) and CL, respectively. Some determinants of significant decreases in busulfan CL were identified, but predictive performance of the models was limited. CONCLUSIONS: Significant busulfan intra-individual variability may occur in children who receive a HSCT and is hardly predictable. The main risk is busulfan overexposure. Performing TDM repeatedly over therapy appears to be the best way to accurately estimate busulfan exposure and perform precision dosing.

Prediction of all-cause mortality in haemodialysis patients using a Bayesian network.

BACKGROUND: All-cause mortality in haemodialysis (HD) is high, reaching 15.6% in the first year according to the European Renal Association. METHODS: A new clinical tool to predict all-cause mortality in HD patients is proposed. It uses a post hoc analysis of data from the prospective cohort study Photo-Graph V3. A total of 35 variables related to patient characteristics, laboratory values and treatments were used as predictors of all-cause mortality. The first step was to compare the results obtained using a logistic regression to those obtained by a Bayesian network. The second step aimed to increase the performance of the best prediction model using synthetic data. Finally, a compromise between performance and ergonomics was proposed by reducing the number of variables to be entered in the prediction tool. RESULTS: Among the 9010 HD patients included in the Photo-Graph V3 study, 4915 incident patients with known medical status at 2 years were analysed. All-cause mortality at 2 years was 34.1%. The Bayesian network provided the most reliable prediction. The final optimized models that used 14 variables had areas under the receiver operating characteristic curves of 0.78 ± 0.01, sensitivity of 72 ± 2%, specificity of 69 ± 2%, predictive positive value of 70 ± 1% and negative predictive value of 71 ± 2% for the prediction of all-cause mortality. CONCLUSIONS: Using artificial intelligence methods, a new clinical tool to predict all-cause mortality in incident HD patients is proposed. The latter can be used for research purposes before its external validation at: https://www.hed.cc/? a=twoyearsallcausemortalityhemod&n=2-years%20All-cause%20Mortality%20Hemodialysis.neta.

Prediction of docetaxel toxicity in older cancer patients: a Bayesian network approach.

Chemotherapy is an essential therapy in the fight against cancer. Polypathology and polymedication are often encountered in elderly patients, making this population especially at risk for adverse drug reactions, and particularly with cytotoxic drugs. The objective of this study was to build a model to predict high-grade toxicity in elderly patients treated with docetaxel. Data from the trial TAX-108 have been used to create the model. The variable to predict was the occurrence of grade 3 or 4 toxicity. The explanatory variables entered in the model were anthropometric and biological characteristics of patients at inclusion; fragility criteria (SMAF, CIRS-G, performance status); location of the primary tumor; chemotherapy history, radiotherapy or surgery; weekly dose of docetaxel, cumulative dose administered. A Bayesian network model was developed using a global search procedure and an Expectation-Maximization algorithm. A 10-fold cross-validation was performed. A toxicity of grade 3 or higher was observed in 54% of patients. The variables providing the most information were the primary site (19.4%), the dose per course (17.5%), and albuminemia (13.1%). The area under the curve of the model obtained after cross-validation was 74 ± 1.4%. The model built allows classifying correctly 71.21 ± 0.9% of patients in our sample in the cross-validation procedure. The sensitivity and specificity of the model were 75 and 67%, respectively, and the positive and negative predictive values were 73 and 69%. The encouraging results from this first study show that Bayesian networks could help assess the benefit-risk ratio of chemotherapy in elderly patients.

Variables linked to hepatitis B vaccination success in non-dialyzed chronic kidney disease patients: Use of a bayesian model.

BACKGROUND: Hepatitis B vaccination is recommended for chronic kidney disease (CKD) patients before starting dialysis. We performed an analyis aimed to describe the clinical and biological parameters related to the success of vaccination in CKD patients before starting dialysis. METHODS: We extracted data of 170 non-dialyzed patients who were offered hepatitis B vaccination from a register. They received a first vaccination of 40µg followed by boosters after one, two and six months. Patients were considered protected if their hepatitis B antibody level was >10IU/L, three months apart. A logistic regression and a Bayesian model were used to describe the relationships between variables and the success of vaccination. RESULTS: Vaccination protected 50.6% of the patients. Model adjustment to the data was higher using the Bayesian model compared to the logistic regression (with area under the ROC curve of 0.955±0.007 vs 0.775±0.066 respectively). The Bayesian model's robustness studied using a 10 fold cross validation showed a percentage of misclassified subjects of 12.4±1.8%, a sensitivity of 87.7±0.3%, a specificity of 87.5±0.3%, a positive predictive value of 87.8±0.3% and negative predictive value of 87.4±0.2%. As classified by the Bayesian model, the variables most related to successful vaccination were, in descending order: age, eGFR, protidemia, albuminemia, cause of renal failure, gender, previous vaccination and weight. CONCLUSION: The Bayesian network confirmed that both kidney function and nutritional status of patients are important factors to explain the success of vaccination against hepatitis B in CKD patients before dialysis. For research purposes, before an external validation, the network can be used online at www.hed.cc/?s=Bhepatitis&n=ReseauhepatiteBsup10.neta.

Time in Therapeutic Range of Oral Vitamin K Antagonists in Hospitalized Elderly Patients.

BACKGROUND: Oral vitamin K antagonists (VKAs) are commonly used in older adults. To ensure the efficiency and safety of these drugs, the international normalized ratio (INR) must be monitored. The time in therapeutic range (TTR) is an internationally recommended assessment of the anticoagulation quality. OBJECTIVE: Our study aimed to assess the TTR of VKAs in a hospitalized geriatric population and identify factors associated with low TTR. METHODS: This was a multicenter retrospective study of data from 1899 patients with a mean age of 87 years between 2013 and 2015 in the geriatric units of four French hospitals. The data collection consisted of 2450 VKA prescriptions. We excluded prescriptions with a duration of < 7 days, monitoring with fewer than two INR values and patients with prosthetic heart valves. TTR was assessed using the Rosendaal method. Factors associated with a low TTR (< 50%) were assessed using a non-parametric method. RESULTS: The mean TTR observed in this population was 42.6%. The TTR was < 50% for 62.5% of the patients included in this study. Significant associations were found between TTR < 50% and aspartate transaminase (AST), alkaline phosphatase (ALT), thyroid-stimulating hormone (TSH), prescription duration, fluconazole instauration, hemoglobin, and C-reactive protein (CRP). CONCLUSIONS: Both our results and those in the literature indicate that TTR in geriatric populations is lower than that in the general population. Most patients had an insufficient TTR, exposing them to an increased risk of thromboembolic and hemorrhagic events. These data provide a perspective on poor-quality anticoagulation and illustrates the difficulty of using VKAs in geriatric patients.

Combined effect of renal function and serum potassium level in sudden cardiac death in aging hypertensive subjects.

In patients with chronic kidney disease, serum potassium level is a factor influencing sudden cardiac death (SCD). The aim of our analysis was to study the combined effect of serum potassium level and renal function on the onset of SCD in elderly hypertensive subjects. Data from the 3620 hypertensive patients aged over 70 years were extracted from three randomized clinical trials included in the INDANA database. During a mean follow up of 4.5 years, 81 patients (2.24%) died from SCD. Mean serum potassium levels and prevalence of chronic kidney disease were not different in patients who died from SCD. In addition to serum potassium and creatinine levels, 14 clinical and biological variables linked to cardiovascular diseases recorded at baseline were analyzed using a Bayesian network. The area under the receiver operating characteristic curve of the Bayesian model reached 0.91. Bayesian inference was used to simulate the combined effects of serum potassium and creatinine levels on SCD. Our analysis, using simulated data from Bayesian model, showed that the estimated probabilities of SCD was significantly increased in case of hyperkalemia (>5.0 mmol/l) and in case of hypokalemia (<3.5 mmol/l) and in case of chronic kidney disease. Combined effects of serum potassium level and renal function revealed that chronic kidney disease increased the probability of SCD whatever the serum potassium level. Our results using a Bayesian model confirm the deleterious effects of hypokalemia, hyperkalemia and chronic kidney disease on SCD in elderly hypertensive patients.

Bayesian Networks: A New Approach to Predict Therapeutic Range Achievement of Initial Cyclosporine Blood Concentration After Pediatric Hematopoietic Stem Cell Transplantation.

BACKGROUND: Pediatric hematopoietic stem cell transplantation (HSCT) allows the treatment of numerous diseases, both malignant and non-malignant. Cyclosporine, a narrow therapeutic index drug, is the major immunosuppressant used to prevent graft-versus-host disease (GVHD), but may also cause severe adverse effects in case of overdosing. OBJECTIVE: The objective of this study is to predict the initial cyclosporine residual blood concentration value after pediatric HSCT, and consequently the dose necessary to reach the therapeutic range, using a mathematical individual predictive model. METHODS: Clinical and biological data collected from the graft infusion for 2 months after transplantation in 155 pediatric patients undergoing HSCT between 2008 and 2016 were used to generate synthetic data for 1000 subjects which were used to build a Bayesian network model. We compared the characteristics and sensitivity to clinical or biological missing data of this model with four other methods. RESULTS: The tree-augmented Naïve Bayesian network showed the best characteristics, with no missing data (area under the curve of the receiving operator characteristics curve [AUC-ROC] of 0.89 ± 0.02), 18.9 ± 2.6% of patients misclassified, and positive and negative predictive values of 85.9 ± 3.4% and 74.2 ± 5.1%, respectively, and this trend is found in the synthetic dataset from no to 10% missing data. The most relevant variables that could influence whether the initial residual cyclosporine concentration is in the therapeutic range are the last dose before measurement and the mean dose before measurement. CONCLUSIONS: We developed and cross-validated an online Bayesian network to predict the first cyclosporine concentration after pediatric HSCT. This model allows simulation of different dosing regimens, and enables the best dosing regimen to reach the therapeutic range immediately after transplantation to be found, minimizing the risk of adverse effects and GVHD occurrence.

Population pharmacokinetics of teicoplanin administered by subcutaneous or intravenous route and simulation of optimal loading dose regimen.

Objectives: To investigate the population pharmacokinetics of teicoplanin in patients treated by the subcutaneous (sc) and/or intravenous (iv) route. Patients and methods: Non-linear mixed-effects modelling described teicoplanin concentrations from 98 patients with infection caused by Gram-positive cocci. Monte Carlo simulations were performed to evaluate the probability of target attainment (PTA) of various dosage regimens. Results: Teicoplanin concentrations were best described by a two-compartment model with clearance predicted by estimated glomerular filtration rate. Estimated absorption rate constant (between-subject variability) was 0.039 h-1 (77%), clearance was 0.305 L/h (28%), central volume was 10.3 L (49%), inter-compartmental clearance was 4.42 L/h (66%) and peripheral volume was 97.4 L (51%). The sc route was associated with lower initial Cmin and AUC (day 3: loading phase) compared with the iv route. This difference appeared to vanish after 14 days, with comparable simulated PTAs based on the Cmin and AUC for all tested dosages (400, 600, 800 and 1000 mg every 12 h). However, a loading dose regimen with five administrations of either 400 or 600 mg was not sufficient to achieve the target Cmin (≥15 mg/L) for both routes. Also, PTAs for higher MIC (≥1.0 mg/L) were poor with all regimens for both routes. Conclusions: This is the first study examining the pharmacokinetic/pharmacodynamic implications of using the sc route for teicoplanin. Subcutaneous administration is associated with lower Cmin and AUC values after the loading phase compared with iv administration. Therefore, iv administration should be preferred in the first few days of therapy. This study also shows that loading doses of teicoplanin higher than currently recommended should be used to improve PTA.

Is Trough Concentration of Vancomycin Predictive of the Area Under the Curve? A Clinical Study in Elderly Patients.

BACKGROUND: Current guidelines suggest that vancomycin trough concentrations (Cmin) between 15 and 20 mg/L should be achieved to optimize vancomycin exposure and effect. The objective of this study was to analyze the correlation between vancomycin Cmin and the area under the concentration-time curve (AUC) and assess the ability to predict an AUC target of 400 mg·h/L based on Cmin. METHODS: A retrospective analysis of vancomycin therapeutic drug monitoring data collected in 95 elderly patients treated with intermittent intravenous vancomycin was performed. For each patient, individual pharmacokinetic parameters of vancomycin and AUC24 were estimated from concentration measurements using a Bayesian approach. The relationship between vancomycin Cmin and AUC was studied using global and local correlation analysis as well as logistic regression with Receiver Operating Characteristic curve analysis. RESULTS: The overall correlation between AUC24 and Cmin was significant but moderate (R = 0.51). When vancomycin Cmin was greater than 15 mg/L, the corresponding AUC24 was >400 mg·h/L in 95% of cases. However, AUC24 values >400 mg·h/L were obtained with Cmin < 15 mg/L in more than 30% of the cases. The logistic regression analysis identified a Cmin value of 10.8 mg/L as the optimal predictor of AUC24 > 400 mg·h/L. CONCLUSIONS: The results of this study indicate that the recommended target range of 15-20 mg/L for vancomycin Cmin seems acceptable for controlling vancomycin exposure, although a value of approximately 11 mg/L appears to be optimal and may be safer.

Pharmacokinetics of Vancomycin in Elderly Patients Aged over 80 Years.

Since the 1950s, vancomycin has remained a reference treatment for severe infections caused by Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus Vancomycin is a nephrotoxic and ototoxic drug mainly eliminated through the kidneys. It has a large interindividual pharmacokinetic variability, which justifies monitoring its plasma concentrations in patients. This is especially important in patients aged over 80 years, who frequently have renal impairment. However, the pharmacokinetics of vancomycin in this population is very poorly described in the literature. The objective of this work was to propose a model able to predict the pharmacokinetics of vancomycin in very elderly people. First, a population pharmacokinetic model was carried out using the algorithm NPAG (nonparametric adaptive grid) on a database of 70 hospitalized patients aged over 80 years and treated with vancomycin. An external validation then was performed on 41 patients, and the predictive capabilities of the model were assessed. The model had two compartments and six parameters. Body weight and creatinine clearance significantly influenced vancomycin volume of distribution and body clearance, respectively. The means (± standard deviations) of vancomycin volume of distribution and clearance were 36.3 ± 15.2 liter and 2.0 ± 0.9 liter/h, respectively. In the validation group, the bias and precision were -0.75 mg/liter and 8.76 mg/liter for population predictions and -0.39 mg/liter and 2.68 mg/liter for individual predictions. In conclusion, a pharmacokinetic model of vancomycin in a very elderly population has been created and validated for predicting plasma concentrations of vancomycin.

A Bayesian Model to Describe Factors Influencing Trough Levels of Vancomycin in Hemodialysis Patients.

BACKGROUND/AIMS: In hemodialysis patients, there is a marked inter-individual variability in the pharmacokinetics of vancomycin. This retrospective study was carried out to design a model describing the parameters that may influence the trough concentrations of vancomycin (TCV) in hemodialysis patients. METHODS: A Bayesian model was constructed from data obtained during 314 hemodialysis sessions performed in 31 hemodialysis patients receiving vancomycin. The model's validity was assessed by goodness of fit. A bootstrap resampling method was used to calculate bias and accuracy for 80 predicted and observed TCV. RESULTS: A total of 31 patients underwent dialysis 3 times a week for a mean duration of 4 h. Their mean age was 69 ± 12 years. The vancomycin infusion was started 30 min before the scheduled end of the dialysis session at a flow rate of 1,000 mg/h. The mean TCV of the study population was 16.1 ± 3.2 mg/l. The area under receiver operating characteristic curve of the constructed model was 95.2%. In the validation sample (80 randomly selected TCV), the observed mean TCV was 15.8 ± 3.6 mg/l, whereas the mean TCV predicted by the model was 15.7 ± 3.0 mg/l. If the mean bias was low between the predicted and observed TCV (-0.1 mg/l), SD was high (3.43 mg/l). The variables most closely linked to TCV were in descending order: weight after dialysis, weight before dialysis, the dose of vancomycin administered during the previous dialysis session and creatinine concentration before dialysis. CONCLUSION: This simple model describes patient-related and dialysis-related parameters that mainly influence TCV. Before its use in clinical practice, this model should be validated prospectively.

Are ciprofloxacin dosage regimens adequate for antimicrobial efficacy and prevention of resistance? Pseudomonas aeruginosa bloodstream infection in elderly patients as a simulation case study.

The aim of this work was to define the optimal dosage (OD) of ciprofloxacin in order to prevent the emergence of bacterial resistance of Pseudomonas aeruginosa in a geriatric population with a bloodstream infection. A thousand pharmacokinetic profiles were simulated with a ciprofloxacin pharmacokinetic model from the literature. Three dosing regimens were tested for five days: once daily (QD), twice daily (BID), and thrice daily (TID). First of all, effective dosages (ED) of ciprofloxacin were defined as those achieving a target AUC24 /MIC ≥ 125. Then, these ED were simulated in order to calculate the percentage of time spent within the mutant selection window (TMSW ) and to select optimal dosage (OD) defined as those achieving TMSW ≤ 20%. Based on the AUC24 /MIC, for low MICs (0.125 µg/mL), all dosing regimens recommended by French guidelines were effective. For intermediate MICs (0.25 and 0.5 µg/mL), simulated doses higher than those recommended were needed to achieve the efficacy target. About prevention of resistance for low MICs, dosages recommended were only effective in patients with creatinine clearance (CLCR ) ≥ 60 mL/min. For intermediate MICs, dosages higher than recommended were needed to achieve the optimality target. This study shows that current ciprofloxacin dosing guidelines have not been optimized to prevent the emergence of bacterial resistance, especially in geriatric patients with mild to severe renal impairment. To achieve both efficacy and prevention of resistance, ciprofloxacin dosages greater than those recommended would be needed. Tolerance of such higher doses needs to be evaluated in clinical studies.

Reliability of a Bayesian network to predict an elevated aldosterone-to-renin ratio.

BACKGROUND: Resistant hypertension is common, mainly idiopathic, but sometimes related to primary aldosteronism. Thus, most hypertension specialists recommend screening for primary aldosteronism. AIMS: To optimize the selection of patients whose aldosterone-to-renin ratio (ARR) is elevated from simple clinical and biological characteristics. METHODS: Data from consecutive patients referred between 1 June 2008 and 30 May 2009 were collected retrospectively from five French 'European excellence hypertension centres' institutional registers. Patients were included if they had at least one of: onset of hypertension before age 40 years, resistant hypertension, history of hypokalaemia, efficient treatment by spironolactone, and potassium supplementation. An ARR>32 ng/L and aldosterone>160 ng/L in patients treated without agents altering the renin-angiotensin system was considered as elevated. Bayesian network and stepwise logistic regression were used to predict an elevated ARR. RESULTS: Of 334 patients, 89 were excluded (31 for incomplete data, 32 for taking agents that alter the renin-angiotensin system and 26 for other reasons). Among 245 included patients, 110 had an elevated ARR. Sensitivity reached 100% or 63.3% using Bayesian network or logistic regression, respectively, and specificity reached 89.6% or 67.2%, respectively. The area under the receiver-operating-characteristic curve obtained with the Bayesian network was significantly higher than that obtained by stepwise regression (0.93±0.02 vs. 0.70±0.03; P<0.001). CONCLUSION: In hypertension centres, Bayesian network efficiently detected patients with an elevated ARR. An external validation study is required before use in primary clinical settings.

Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.

OBJECTIVES: Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribution of a causal Bayesian network in this clinical setting. A logistic regression was performed as a standard control approach to check the robustness of the causal Bayesian network approach. SETTING: EPIDOS is a multicenter study, conducted in an ambulatory care setting in five French cities between 1992 and 1996 and updated in 2010. The study included 7598 women aged 75 years or older, in which fractures were assessed quarterly during 4 years. A causal Bayesian network and a logistic regression were performed on EPIDOS data to describe major variables involved in hip fractures occurrences. RESULTS: Both models had similar association estimations and predictive performances. They detected gait speed and mineral bone density as variables the most involved in the fracture process. The causal Bayesian network showed that gait speed and bone mineral density were directly connected to fracture and seem to mediate the influence of all the other variables included in our model. The logistic regression approach detected multiple interactions involving psychotropic drug use, age and bone mineral density. CONCLUSION: Both approaches retrieved similar variables as predictors of hip fractures. However, Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture. According to the latter results, intervention focusing concomitantly on gait speed and bone mineral density may be necessary for an optimal prevention of hip fracture occurrence in elderly people.