Dataset Information

Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective 'before/after' cohort study.

ABSTRACT:

Objectives

Antibiotic resistance has risen dramatically over the past years. For individual patients, adequate initial antibiotic therapy is essential for clinical outcome. Computer-assisted decision support systems (CDSSs) are advocated to support implementation of rational anti-infective treatment strategies based on guidelines. The aim of this study was to evaluate long-term effects after implementation of a CDSS.

Design

This prospective 'before/after' cohort study was conducted over four observation periods within 5 years. One preinterventional period (pre) was compared with three postinterventional periods: directly after intensive implementation efforts (post1), 2 years (post2) and 3 years (post3) after implementation.

Setting

Five anaesthesiological-managed intensive care units (ICU) (one cardiosurgical, one neurosurgical, two interdisciplinary and one intermediate care) at a university hospital.

Participants

Adult patients with an ICU stay of >48 h were included in the analysis. 1316 patients were included in the analysis for a total of 12,965 ICU days.

Intervention

Implementation of a CDSS.

Outcome measures

The primary end point was percentage of days with guideline adherence during ICU treatment. Secondary end points were antibiotic-free days and all-cause mortality compared for patients with low versus high guideline adherence.

Main results

Adherence to guidelines increased from 61% prior to implementation to 92% in post1, decreased in post2 to 76% and remained significantly higher compared with baseline in post3, with 71% (p=0.178). Additionally, antibiotic-free days increased over study periods. At all time periods, mortality for patients with low guideline adherence was higher with 12.3% versus 8% (p=0.014) and an adjusted OR of 1.56 (95% CI 1.05 to 2.31).

Conclusions

Implementation of computerised regional adapted guidelines for antibiotic therapy is paralleled with improved adherence. Even without further measures, adherence stayed high for a longer period and was paralleled by reduced antibiotic exposure. Improved guideline adherence was associated with reduced ICU mortality.

Trial registration number

ISRCTN54598675.

SUBMITTER: Nachtigall I

PROVIDER: S-EPMC4275685 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Publications

Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective 'before/after' cohort study.

Nachtigall I I Tafelski S S Deja M M Halle E E Grebe M C MC Tamarkin A A Rothbart A A Uhrig A A Meyer E E Musial-Bright L L Wernecke K D KD Spies C C

BMJ open 20141222 12

<h4>Objectives</h4>Antibiotic resistance has risen dramatically over the past years. For individual patients, adequate initial antibiotic therapy is essential for clinical outcome. Computer-assisted decision support systems (CDSSs) are advocated to support implementation of rational anti-infective treatment strategies based on guidelines. The aim of this study was to evaluate long-term effects after implementation of a CDSS.<h4>Design</h4>This prospective 'before/after' cohort study was conducte ...[more]

PMID: 25534209

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Project description: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.

Dataset Information

Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective 'before/after' cohort study.

Objectives

Design

Setting

Participants

Intervention

Outcome measures

Main results

Conclusions

Trial registration number

Publications

Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective 'before/after' cohort study.

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