Quality evaluation of JAMA Patient Pages on diabetes using the Ensuring Quality Information for Patient (EQIP) tool.

CONTEXT: Many medical journals provide patient information leaflets on the correct use of medicines and/or appropriate lifestyles. Only a few studies have assessed the quality of this patient-specific literature. OBJECTIVE: The purpose of this study was to evaluate the quality of JAMA Patient Pages on diabetes using the Ensuring Quality Information for Patient (EQIP) tool. METHOD: A multidisciplinary group of 10 medical doctors analyzed all diabetes-related Patient Pages published by JAMA from 1998 to 2010 using the EQIP tool. Inter-rater reliability was assessed using the percentage of observed total agreement (p(o)). A quality score between 0 and 1 (the higher score indicating higher quality) was calculated for each item on every page as a function of raters' answers to the EQIP checklist. A mean score per item and a mean score per page were then calculated. DATA SUMMARY: We found 8 Patient Pages on diabetes on the JAMA web site. The overall quality score of the documents ranged between 0.55 (Managing Diabetes and Diabetes) and 0.67 (weight and diabetes). p(o) was at least moderate (>50%) for 15 of the 20 EQIP items. Despite generally favorable quality scores, some items received low scores. The worst scores were for the item assessing provision of an empty space to customize information for individual patients (score=0.01, p(o)=95%) and patients involvement in document drafting (score=0.11, p(o)=79%). CONCLUSIONS: The Patient Pages on diabetes published by JAMA were found to present weak points that limit their overall quality and may jeopardize their efficacy. We therefore recommend that authors and publishers of written patient information comply with published quality criteria. Further research is needed to evaluate the quality and efficacy of existing written health care information.

The well-being model: a new drug interaction model for positive and negative effects.

BACKGROUND: Drugs are routinely combined in anesthesia and pain management to obtain an enhancement of the desired effects. However, a parallel enhancement of the undesired effects might take place as well, resulting in a limited therapeutic usefulness. Therefore, when addressing the question of optimal drug combinations, side effects must be taken into account. METHODS: By extension of a previously published interaction model, the authors propose a method to study drug interactions considering also their side effects. A general outcome parameter identified as patient's well-being is defined by superposition of positive and negative effects. Well-being response surfaces are computed and analyzed for varying drugs pharmacodynamics and interaction types. In particular, the existence of multiple maxima and of optimal drug combinations is investigated for the combination of two drugs. RESULTS: Both drug pharmacodynamics and interaction type affect the well-being surface and the deriving optimal combinations. The effect of the interaction parameters can be explained in terms of synergy and antagonism and remains unchanged for varying pharmacodynamics. For all simulations performed for the combination of two drugs, the presence of more than one maximum was never observed. CONCLUSIONS: The model is consistent with clinical knowledge and supports previously published experimental results on optimal drug combinations. This new framework improves understanding of the characteristics of drug combinations used in clinical practice and can be used in clinical research to identify optimal drug dosing.

On-line estimation of propofol pharmacodynamic parameters.

A novel advisory system, the Anesthesia Advisory Display (AAD) has been recently developed. It displays information about actual and future predictions of anesthetic drug concentrations mapped to clinical end-points such as BIS, basing on the actual infusion rate combined with population pharmacokinetic and interaction pharmacodynamic (PKPD) models. According to the large variability observed among patients in clinical end-points to the same dosing history, a population model could fail in predicting the real patient's behavior: an "individualized" model is then necessary. An on-line estimation of the model's parameter for the single patients has been developed, basing on the extended Kalman filter algorithm. The method has been tested on data of 40 patients from a previous clinical study and the prediction of the individual models have been compared with the population model's.

A new model for drug interactions and optimal drug dosing.

Drugs are routinely combined in anesthesia and pain management to obtain an enhancement of the desired effects. However, a parallel enhancement of the undesired effects might take place as well, resulting in a limited therapeutic usefulness. Therefore, when addressing the question of optimal drug combinations, side effects must be taken into account. We propose a new method to study drug interactions considering also their side effects and to identify optimal drug dosing. The model is consistent with clinical knowledge and can explain previously published experimental results, improving our understanding of the characteristics of drug combinations used in clinical practice.

Optimal infusion policy of intravenous morphine and ketamine - A Mixed-Integer Linear Programming application.

Recently, a model for drug interactions considering also side effects has been proposed. According to this model, the effect compartment concentration range maximizing the global well-being of the patient can be identified. This optimal range represents the set which should be targeted by drug infusion. In this work, we apply this novel model to the clinically relevant combination of intravenous morphine and ketamine. The optimal range is identified and its center used as the reference value for controller design. The control problem can be formulated as consisting of mixed continuous and discrete parts. By solving the optimal control problem, the optimal infusion policy is identified minimizing the drug consumption.

Combinations of bupivacaine, fentanyl, and clonidine for lumbar epidural postoperative analgesia: a novel optimization procedure.

BACKGROUND: The authors developed and applied a method to optimize the combination of bupivacaine, fentanyl, and clonidine for continuous postoperative lumbar epidural analgesia. METHODS: One hundred eighteen patients undergoing knee or hip surgery participated in the study. Postoperative epidural analgesia during 48 h after surgery was optimized under restrictions dictated by side effects. Initially, eight combinations of bupivacaine, fentanyl, and clonidine (expressed as drug concentration in the solution administered) were empirically chosen and investigated. To determine subsequent combinations, an optimization model was applied until three consecutive steps showed no decrease in pain score. For the first time in a clinical investigation, a regression model was applied when the optimization procedure led to combinations associated with unacceptable side effects. RESULTS: The authors analyzed 12 combinations with an allowed bupivacaine concentration range of 0-2.5 mg/ml, a fentanyl concentration range of 0-5 microg/ml, and a clonidine concentration range of 0-5 microg/ml. The best combinations of bupivacaine, fentanyl, and clonidine concentrations were 1.0 mg/ml-1.4 microg/ml-0.5 microg/ml, 0.9 mg/ml-3.0 microg/ml-0.3 microg/ml, 0.6 mg/ml-2.5 microg/ml-0.8 microg/ml, and 1.0 mg/ml-2.4 microg/ml-1.0 microg/ml, respectively, all producing a similarly low pain score. The incidence of side effects was low. The application of the regression model to combinations associated with high incidence of motor block successfully directed the optimization procedure to combinations within the therapeutic range. CONCLUSIONS: The results support further study of the combinations of bupivacaine, fentanyl, and clonidine mentioned above for postoperative analgesia after knee and hip surgery. This novel optimization method may be useful in clinical research.