Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients

Introduction Antibiotic dosing in critically ill patients is challenging because their pharmacokinetics (PK) are altered and may change rapidly with disease progression. Standard dosing frequently leads to inadequate PK exposure. Therapeutic drug monitoring (TDM) offers a potential solution but requires sampling and PK knowledge, which delays decision support. It is our philosophy that antibiotic dosing support should be directly available at the bedside through deep integration into the electronic health record (EHR) system. Therefore we developed AutoKinetics, a clinical decision support system (CDSS) for real time, model informed precision antibiotic dosing. Objective To provide a detailed description of the design, development, validation, testing, and implementation of AutoKinetics. Methods We created a development framework and used workflow analysis to facilitate integration into popular EHR systems. We used a development cycle to iteratively adjust and expand AutoKinetics functionalities. Furthermore, we performed a literature review to select and integrate pharmacokinetic models for five frequently prescribed antibiotics for sepsis. Finally, we tackled regulatory challenges, in particular those related to the Medical Device Regulation under the European regulatory framework. Results We developed a SQL-based relational database as the backend of AutoKinetics. We developed a data loader to retrieve data in real time. We designed a clinical dosing algorithm to find a dose regimen to maintain antibiotic pharmacokinetic exposure within clinically relevant safety constraints. If needed, a loading dose is calculated to minimize the time until steady state is achieved. Finally, adaptive dosing using Bayesian estimation is applied if plasma levels are available. We implemented support for five extensively used antibiotics following model development, calibration, and validation. We integrated AutoKinetics into two popular EHRs (Metavision, Epic) and developed a user interface that provides textual and visual feedback to the physician. Conclusion We successfully developed a CDSS for real time model informed precision antibiotic dosing at the bedside of the critically ill. This holds great promise for improving sepsis outcome. Therefore, we recently started the Right Dose Right Now multi-center randomized control trial to validate this concept in 420 patients with severe sepsis and septic shock.

[1]  H. Mulla,et al.  Personalised dosing of medicines for children , 2017, The Journal of pharmacy and pharmacology.

[2]  J. Roberts,et al.  Therapeutic drug monitoring of &bgr;-lactam antibiotics in the critically ill: direct measurement of unbound drug concentrations to achieve appropriate drug exposures , 2018, The Journal of antimicrobial chemotherapy.

[3]  P. Elbers,et al.  Clinically relevant pharmacokinetic knowledge on antibiotic dosing among intensive care professionals is insufficient: a cross-sectional study , 2019, Critical Care.

[4]  Robert J Bauer,et al.  NONMEM Tutorial Part I: Description of Commands and Options, With Simple Examples of Population Analysis , 2019, CPT: pharmacometrics & systems pharmacology.

[5]  M. Levy,et al.  Empiric Antibiotic Treatment Reduces Mortality in Severe Sepsis and Septic Shock From the First Hour: Results From a Guideline-Based Performance Improvement Program* , 2014, Critical care medicine.

[6]  J. Mouton,et al.  Meropenem Clinical Pharmacokinetics , 1995, Clinical pharmacokinetics.

[7]  J. Lipman,et al.  Antibiotic dose optimization in critically ill patients. , 2015, Medicina intensiva.

[8]  R. Bax,et al.  The human pharmacokinetics of cefotaxime and its metabolites and the role of renal tubular secretion on their elimination , 1985, Journal of Pharmacokinetics and Biopharmaceutics.

[9]  Paul W G Elbers,et al.  Right dose, right now: using big data to optimize antibiotic dosing in the critically ill. , 2014, Anaesthesiology intensive therapy.

[10]  J. Kline,et al.  Association between timing of antibiotic administration and mortality from septic shock in patients treated with a quantitative resuscitation protocol* , 2011, Critical care medicine.

[11]  R. Bellomo,et al.  The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). , 2016, JAMA.

[12]  P. V. D. van der Voort,et al.  External Evaluation of Population Pharmacokinetic Models of Vancomycin in Large Cohorts of Intensive Care Unit Patients , 2019, Antimicrobial Agents and Chemotherapy.

[13]  J. Rello,et al.  DALI: defining antibiotic levels in intensive care unit patients: are current β-lactam antibiotic doses sufficient for critically ill patients? , 2014, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[14]  H. O. Oudemans-van Straaten,et al.  Unexplained mortality differences between septic shock trials: a systematic analysis of population characteristics and control-group mortality rates , 2018, Intensive Care Medicine.

[15]  M. Lustik,et al.  Optimization of time to initial vancomycin target trough improves clinical outcomes , 2015, SpringerPlus.

[16]  M. Samtani,et al.  Pharmacokinetic-Pharmacodynamic-Model-Guided Doripenem Dosing in Critically Ill Patients , 2010, Antimicrobial Agents and Chemotherapy.

[17]  Karl Aberer,et al.  TUCUXI: An Intelligent System for Personalized Medicine from Individualization of Treatments to Research Databases and Back , 2017, BCB.

[18]  B. Melton.  Systematic Review of Medical Informatics–Supported Medication Decision Making , 2017, Biomedical informatics insights.

[19]  Michael N Neely,et al.  Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions. , 2014, The Lancet. Infectious diseases.

[20]  J. D. De Waele,et al.  Personalized antibiotic dosing for the critically ill , 2019, Intensive Care Medicine.

[21]  A H Thomson,et al.  Cost-Effectiveness of Therapeutic Drug Monitoring: A Systematic Review , 2005, Therapeutic drug monitoring.

[22]  T. Buclin,et al.  Benchmarking Therapeutic Drug Monitoring Software: A Review of Available Computer Tools , 2012, Clinical Pharmacokinetics.

[23]  E. Tacconelli,et al.  The pharmacokinetics and pharmacodynamics of vancomycin in clinical practice: evidence and uncertainties. , 2013, The Journal of antimicrobial chemotherapy.

[24]  C. Runge.  Ueber die numerische Auflösung von Differentialgleichungen , 1895 .

[25]  User-Centered Design: Clothing The EMR Emperor , 2012 .

[26]  K. Wood,et al.  Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock* , 2006, Critical care medicine.

[27]  P. McNamara,et al.  Pharmacokinetics of ceftriaxone following intravenous administration of a 3 g dose , 2004, European Journal of Clinical Pharmacology.

[28]  M. Doogue,et al.  Bayesian therapeutic drug monitoring software: past, present and future , 2018, International Journal of Pharmacokinetics.

[29]  J. Roberts,et al.  Antibiotic dose optimization in critically ill patients / Optimización de la dosis de antibióticos en pacientes críticamente enfermos , 2015 .

[30]  J. Beney,et al.  Clinical and economic outcomes of pharmaceutical services related to antibiotic use: a literature review , 2007, Pharmacy World & Science.

[31]  D. Yealy,et al.  Sepsis‐3 Septic Shock Criteria and Associated Mortality Among Infected Hospitalized Patients Assessed by a Rapid Response Team , 2018, Chest.

[32]  Sarah P. Slight,et al.  A systematic review of the types and causes of prescribing errors generated from using computerized provider order entry systems in primary and secondary care , 2017, AMIA.

[33]  Richard Beale,et al.  Surviving Sepsis Campaign: International guidelines for management of severe sepsis and septic shock: 2008 , 2008 .

[34]  J. Rotschafer,et al.  Clinical Pharmacokinetics of Ciprofloxacin , 1990, Clinical pharmacokinetics.

[35]  M. Puskarich,et al.  The Impact of Timing of Antibiotics on Outcomes in Severe Sepsis and Septic Shock: A Systematic Review and Meta-Analysis* , 2015, Critical care medicine.

[36]  Brian T. Tsuji,et al.  The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections , 2017, Intensive Care Medicine.

[37]  T. van Gelder,et al.  Clinical applications of population pharmacokinetic models of antibiotics: Challenges and perspectives , 2018, Pharmacological research.

[38]  J. Rello,et al.  Risk factors for target non-attainment during empirical treatment with β-lactam antibiotics in critically ill patients , 2014, Intensive Care Medicine.

[39]  C. Seifert,et al.  Timing and appropriateness of initial antibiotic therapy in newly presenting septic patients. , 2013, The American journal of emergency medicine.

[40]  J. Roberts,et al.  How do we use therapeutic drug monitoring to improve outcomes from severe infections in critically ill patients? , 2014, BMC Infectious Diseases.

[41]  A. Huttner,et al.  Therapeutic Drug Monitoring of Beta-Lactams and Other Antibiotics in the Intensive Care Unit: Which Agents, Which Patients and Which Infections? , 2018, Drugs.

[42]  Felicia M Bowens,et al.  Health information technology: integration of clinical workflow into meaningful use of electronic health records. , 2010, Perspectives in health information management.

[43]  Michael P. Rogers.  Python Tutorial , 2009 .

[44]  J. Rello,et al.  Does contemporary vancomycin dosing achieve therapeutic targets in a heterogeneous clinical cohort of critically ill patients? Data from the multinational DALI study , 2014, Critical Care.

[45]  F. Jaimes,et al.  Antimicrobial agent prescription: a prospective cohort study in patients with sepsis and septic shock , 2018, Tropical medicine & international health : TM & IH.

[46]  Francesco Balena,et al.  Programming Microsoft Visual Basic 6.0 , 1999 .

[47]  Joyce Polessi,et al.  The impact of each action in the Surviving Sepsis Campaign measures on hospital mortality of patients with severe sepsis/septic shock Impacto de cada ação dos pacotes da Surviving Sepsis Campaign na mortalidade hospitalar de pacientes portadores de sepse grave/choque séptico , 2008 .

[48]  Anne Miller,et al.  Integrating computerized clinical decision support systems into clinical work: A meta-synthesis of qualitative research , 2015, Int. J. Medical Informatics.

[49]  Adil Rafiq Rather,et al.  The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) , 2015 .

[50]  Albert Wambecq,et al.  Rational Runge-Kutta methods for solving systems of ordinary differential equations , 2005, Computing.

[51]  Munish Goyal,et al.  Impact of time to antibiotics on survival in patients with severe sepsis or septic shock in whom early goal-directed therapy was initiated in the emergency department* , 2010, Critical care medicine.

[52]  Stephan Harbarth,et al.  Conserving antibiotics for the future: new ways to use old and new drugs from a pharmacokinetic and pharmacodynamic perspective. , 2011, Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy.