Benchmarking therapeutic drug monitoring software: A systematic evaluation of available computer tools

Introduction: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on measurement of blood concentrations. Maintaining concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. In the last decades computer programs have been developed to assist clinicians in this assignment. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Method: Literature and Internet search was performed to identify software. All programs were tested on common personal computer. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software's characteristics. Numbers of drugs handled vary widely and 8 programs offer the ability to the user to add its own drug model. 10 computer programs are able to compute Bayesian dosage adaptation based on a blood concentration (a posteriori adjustment) while 9 are also able to suggest a priori dosage regimen (prior to any blood concentration measurement), based on individual patient covariates, such as age, gender, weight. Among those applying Bayesian analysis, one uses the non-parametric approach. The top 2 software emerging from this benchmark are MwPharm and TCIWorks. Other programs evaluated have also a good potential but are less sophisticated (e.g. in terms of storage or report generation) or less user-friendly.¦Conclusion: Whereas 2 integrated programs are at the top of the ranked listed, such complex tools would possibly not fit all institutions, and each software tool must be regarded with respect to individual needs of hospitals or clinicians. Interest in computing tool to support therapeutic monitoring is still growing. Although developers put efforts into it the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capacity of data storage and report generation.

Benchmarking therapeutic drug monitoring software: A systematic evaluation of available computer tools

Objectives: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on blood concentrations measurement. Maintaining concentrations within a target range requires pharmacokinetic (PK) and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Methods: Literature and Internet were searched to identify software. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software characteristics. Numbers of drugs handled vary from 2 to more than 180, and integration of different population types is available for some programs. Nevertheless, 8 programs offer the ability to add new drug models based on population PK data. 10 computer tools incorporate Bayesian computation to predict dosage regimen (individual parameters are calculated based on population PK models). All of them are able to compute Bayesian a posteriori dosage adaptation based on a blood concentration while 9 are also able to suggest a priori dosage regimen, only based on individual patient covariates. Among those applying Bayesian analysis, MM-USC*PACK uses a non-parametric approach. The top 2 programs emerging from this benchmark are MwPharm and TCIWorks. Others programs evaluated have also a good potential but are less sophisticated or less user-friendly.¦Conclusions: Whereas 2 software packages are ranked at the top of the list, such complex tools would possibly not fit all institutions, and each program must be regarded with respect to individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Although interest in TDM tools is growing and efforts were put into it in the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capability of data storage and automated report generation.

Childhood adversities as specific contributors to the co-occurrence of posttraumatic stress and alcohol use disorders.

There is much evidence that alcohol use disorders (AUD) often co-occur with posttraumatic stress disorders (PTSD), and that the comorbid condition is associated with a more severe clinical profile than that of PTSD without AUD. However, little is known about the role of childhood adversities as specific risk factors for the development of AUD in individuals presenting with PTSD. The aim of the study was to explore whether specific stressors from the spectrum of trauma and childhood adversities contribute to the development of AUD among subjects with PTSD. From a large community sample, of N=140 individuals with PTSD, N=24 (17.14%) received an additional diagnosis of AUD with an onset after the onset of PTSD. Those with comorbid PTSD/AUD and those with PTSD only were compared regarding type and features of their trauma, childhood adversities and psychiatric comorbidity. Compared to PTSD alone, PTSD/AUD was associated with higher levels of stress in terms of childhood adversities; in particular, sexual abuse below the age of 16, but also with having been brought up in a foster home. PTSD/AUD was also associated with an earlier age of adverse events. Treatment of AUD should include standardized assessments of trauma, especially of trauma experienced during childhood.

Childhood adversities as specific contributors to the co-occurrence of posttraumatic stress and alcohol use disorders.

There is much evidence that alcohol use disorders (AUD) often co-occur with posttraumatic stress disorders (PTSD), and that the comorbid condition is associated with a more severe clinical profile than that of PTSD without AUD. However, little is known about the role of childhood adversities as specific risk factors for the development of AUD in individuals presenting with PTSD. The aim of the study was to explore whether specific stressors from the spectrum of trauma and childhood adversities contribute to the development of AUD among subjects with PTSD. From a large community sample, of N=140 individuals with PTSD, N=24 (17.14%) received an additional diagnosis of AUD with an onset after the onset of PTSD. Those with comorbid PTSD/AUD and those with PTSD only were compared regarding type and features of their trauma, childhood adversities and psychiatric comorbidity. Compared to PTSD alone, PTSD/AUD was associated with higher levels of stress in terms of childhood adversities; in particular, sexual abuse below the age of 16, but also with having been brought up in a foster home. PTSD/AUD was also associated with an earlier age of adverse events. Treatment of AUD should include standardized assessments of trauma, especially of trauma experienced during childhood.