Date Published: April 30, 2019
Publisher: Public Library of Science
Author(s): Liesbeth B. E. Bosma, Nienke van Rein, Nicole G. M. Hunfeld, Ewout W. Steyerberg, Piet H. G. J. Melief, Patricia M. L. A. van den Bemt, Shane Patman.
Discharge from the intensive care unit (ICU) is a high-risk process, leading to numerous potentially harmful medication transfer errors (PH-MTE). PH-MTE could be prevented by medication reconciliation by ICU pharmacists, but resources are scarce, which renders the need for predicting which patients are at risk for PH-MTE. The aim of this study was to develop a prognostic multivariable model in patients discharged from the ICU to predict who is at increased risk for PH-MTE after ICU discharge, using predictors of PH-MTE that are readily available at the time of ICU discharge.
Data for this study were derived from the Transfer ICU Medication reconciliation study, which included ICU patients and scored MTE at discharge of the ICU. The potential harm of every MTE was estimated with a validated score, where after MTE with potential for harm were indicated as PH-MTE. Predictors for PH-MTE at ICU discharge were identified using LASSO regression. The c statisticprovided a measure of the overall discriminative ability of the prediction model and the prediction model was internally validated by bootstrap resampling. Based on sensitivity and specificity, the cut-off point of the prediction model was determined.
The cohort contained 258 patients and six variables were identified as predictors for PH-MTE: length of ICU admission, number of home medications and patient taking one of the following medication groups at home: vitamin/mineral supplements, cardiovascular medication, psycholeptic/analeptic medication and medication for obstructive airway disease. The c of the final prediction model was 0.73 (95%CI 0.67–0.79) and decreased to 0.62 according to bootstrap resampling. At a cut-off score of two the prediction model yielded a sensitivity of 70% and a specificity of 61%.
A multivariable prediction model was developed to identify patients at risk for PH-MTE after ICU discharge. The model contains predictors that are available on the day of ICU discharge. Once external validation and evaluation of this model in daily practice has been performed, its incorporation into clinical practice could potentially allow institutions to identify patients at risk for PH-MTE after ICU discharge, on the day of ICU discharge, thus allowing for efficient, patient-specific allocation of clinical pharmacy services.
Dutch trial register: NTR4159, 5 September 2013, retrospectively registered.
Discharging patients from an Intensive care unit (ICU) is a high-risk process prone to medication transfer errors (MTE) with a high potential for adverse drug events (ADE) . Possible causes of MTE are multifactorial, relating to the system, the patient and the healthcare staff [2–4]. For instance, the vulnerability of the ICU discharge process may be caused by a lack of standardized discharge procedures, a significant reduction in monitoring of the patient, the number, complexity and acuity of the patient’s medical conditions and finally the involvement of several healthcare providers (multi-professional and inter-specialty) [5,6]. A large Canadian population-based cohort study  showed that being a post-ICU-patient was associated with MTE due to unintended discontinuation of chronic home medication at hospital discharge. Moreover, patients who experienced unintended discontinuation of antiplatelet drugs, anticoagulants or statins had a higher risk of adverse events (i.e. death, emergency department visit or emergency hospitalization).
This is the first study that developed a prediction model for PH-MTE at ICU discharge. The developed model contained six predictors that can easily be obtained at the moment the ICU discharge process starts: length of ICU admission, number of medications in use at home and patients taking one of the following medication groups at home: vitamin and mineral supplements, cardiovascular medication, psycholeptic/analeptic medication and medication for obstructive airway disease. The c-statistic of the model was 0.73 (95%CI 0.67–0.79) and decreased to 0.62 after bootstrapping. In addition, sensitivity and specificity of the prediction model was best at a cut-off value of two, namely 70% and 61%. With this cut-off value 60% of the ICU patients were selected as being at increased risk for PH-MTE. This 40% reduction in workload is substantial, increasing the probability that implementation of medication reconciliation at ICU discharge will become feasible.
The discharge from the ICU is a high-risk process, leading to numerous PH-MTE after ICU discharge. We developed a model to predict patients at risk for PH-MTE at ICU discharge. Once external validation and evaluation of this model in daily practice has been performed, its incorporation into clinical practice could potentially allow institutions to identify patients at risk for PH-MTE after ICU discharge on the day of ICU discharge, thus allowing for efficient, patient-specific allocation of clinical pharmacy services.