TY - JOUR
T1 - The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring
T2 - a cluster randomised stepped-wedge trial
AU - Bakker, Tinka
AU - Klopotowska, Joanna E.
AU - Dongelmans, Dave A.
AU - Eslami, Saeid
AU - Vermeijden, Wytze J.
AU - Hendriks, Stefaan
AU - ten Cate, Julia
AU - Karakus, Attila
AU - Purmer, Ilse M.
AU - van Bree, Sjoerd H.W.
AU - Spronk, Peter E.
AU - Hoeksema, Martijn
AU - de Jonge, Evert
AU - de Keizer, Nicolette F.
AU - Abu-Hanna, Ameen
AU - SIMPLIFY study group
AU - van Balen, Dorieke E.M.
AU - Schutte, Peter F.
AU - Sigtermans, Marnix J.
AU - Kuck, Emile M.
AU - van Kan, Erik J.M.
AU - van der Steen, Marijke S.
AU - Bosma, Liesbeth E.
AU - Nowitzky, Ralph O.
AU - Beishuizen, Albertus
AU - Movig, Kris L.L.
AU - Wesselink, Elsbeth M.
AU - Lammers, Rick J.W.
AU - Lau, Cedric
AU - Masselink, Joost B.
AU - Bosman, Rob J.
AU - de Lange, Dylan W.
AU - van Marum, Rob J.
AU - van der Sijs, Heleen
AU - Franssen, Eric J.F.
AU - Kieft, Hans
AU - van den Bergh, Walter M.
AU - Bult, Wouter
AU - Renes, Maurits H.
AU - de Feiter, Peter W.
AU - Wils, Evert Jan
AU - Hunfeld, Nicole G.M.
AU - Mulder, Froukje
AU - Duyvendak, Michiel
N1 - Funding Information: This study was funded by ZonMw (dossier number 80–83600–98–40140). We thank all participating ICUs and Itémedical for making this study possible. From Itémedical we specifically thank Johan Vogelaar for the data extractions, and Johan Vogelaar, Rick Lin, and Jan Heeremans for installing MiM+. From the participating ICUs, we specifically thank Pita van Dalen from Zaans Medisch Centrum; Peter Schutte from Antoni van Leeuwenhoek; Dick van Blokland from Ziekenhuis Gelderse Vallei; Lilian Taal from Gelre ziekenhuizen; Bjorn Schrauwen and Dominic de Pater from Albert Schweitzer ziekenhuis; Arjaan Korpershoek and Dirk Schoenaker from Diakonessenhuis; Ellen van Geest from Hagaziekenhuis; Jordy Baven and Rene Sterk from Leids Universitair Medisch Centrum; and Wim Addink from Medisch Spectrum Twente, for implementing or making adjustments to, or both, the MiM decision support system. We express our appreciation for: Marie-José Roos Blom for performing the computerised randomisation; Jan Hendrik Leopold for assisting the analysis by optimising the potential DDI detection algorithm; Stephanie Medlock for advice on computerised decision support system research; Rebecca Holman and Koos Zwinderman for their advice on the statistical analysis for this study; Birgit Damoiseaux-Volman for her help with the graphical illustrations for the SIMPLIFY project; Leonora van Dorp-Grandia, the product manager of pharmacotherapy at Z-Index, and Marianne le Comte, from the Royal Dutch Pharmacist's Association, for their advice and assistance on using the G-Standaard potential DDI database; and Lilian Vloet, President of Family and Patient Centered Intensive Care, for her support in this and preceding studies as a representative of patients in the ICU and their family members. Funding Information: This study was funded by ZonMw (dossier number 80–83600–98–40140). We thank all participating ICUs and Itémedical for making this study possible. From Itémedical we specifically thank Johan Vogelaar for the data extractions, and Johan Vogelaar, Rick Lin, and Jan Heeremans for installing MiM+. From the participating ICUs, we specifically thank Pita van Dalen from Zaans Medisch Centrum; Peter Schutte from Antoni van Leeuwenhoek; Dick van Blokland from Ziekenhuis Gelderse Vallei; Lilian Taal from Gelre ziekenhuizen; Bjorn Schrauwen and Dominic de Pater from Albert Schweitzer ziekenhuis; Arjaan Korpershoek and Dirk Schoenaker from Diakonessenhuis; Ellen van Geest from Hagaziekenhuis; Jordy Baven and Rene Sterk from Leids Universitair Medisch Centrum; and Wim Addink from Medisch Spectrum Twente, for implementing or making adjustments to, or both, the MiM decision support system. We express our appreciation for: Marie-José Roos Blom for performing the computerised randomisation; Jan Hendrik Leopold for assisting the analysis by optimising the potential DDI detection algorithm; Stephanie Medlock for advice on computerised decision support system research; Rebecca Holman and Koos Zwinderman for their advice on the statistical analysis for this study; Birgit Damoiseaux-Volman for her help with the graphical illustrations for the SIMPLIFY project; Leonora van Dorp-Grandia, the product manager of pharmacotherapy at Z-Index, and Marianne le Comte, from the Royal Dutch Pharmacist's Association, for their advice and assistance on using the G-Standaard potential DDI database; and Lilian Vloet, President of Family and Patient Centered Intensive Care, for her support in this and preceding studies as a representative of patients in the ICU and their family members. Publisher Copyright: © 2024 Elsevier Ltd
PY - 2024/2/3
Y1 - 2024/2/3
N2 - Background: Drug–drug interactions (DDIs) can harm patients admitted to the intensive care unit (ICU). Yet, clinical decision support systems (CDSSs) aimed at helping physicians prevent DDIs are plagued by low-yield alerts, causing alert fatigue and compromising patient safety. The aim of this multicentre study was to evaluate the effect of tailoring potential DDI alerts to the ICU setting on the frequency of administered high-risk drug combinations. Methods: We implemented a cluster randomised stepped-wedge trial in nine ICUs in the Netherlands. Five ICUs already used potential DDI alerts. Patients aged 18 years or older admitted to the ICU with at least two drugs administered were included. Our intervention was an adapted CDSS, only providing alerts for potential DDIs considered as high risk. The intervention was delivered at the ICU level and targeted physicians. We hypothesised that showing only relevant alerts would improve CDSS effectiveness and lead to a decreased number of administered high-risk drug combinations. The order in which the intervention was implemented in the ICUs was randomised by an independent researcher. The primary outcome was the number of administered high-risk drug combinations per 1000 drug administrations per patient and was assessed in all included patients. This trial was registered in the Netherlands Trial Register (identifier NL6762) on Nov 26, 2018, and is now closed. Findings: In total, 10 423 patients admitted to the ICU between Sept 1, 2018, and Sept 1, 2019, were assessed and 9887 patients were included. The mean number of administered high-risk drug combinations per 1000 drug administrations per patient was 26·2 (SD 53·4) in the intervention group (n=5534), compared with 35·6 (65·0) in the control group (n=4353). Tailoring potential DDI alerts to the ICU led to a 12% decrease (95% CI 5–18%; p=0·0008) in the number of administered high-risk drug combinations per 1000 drug administrations per patient, after adjusting for clustering and prognostic factors. Interpretation: This cluster randomised stepped-wedge trial showed that tailoring potential DDI alerts to the ICU setting significantly reduced the number of administered high-risk drug combinations. Our list of high-risk drug combinations can be used in other ICUs, and our strategy of tailoring alerts based on clinical relevance could be applied to other clinical settings. Funding: ZonMw.
AB - Background: Drug–drug interactions (DDIs) can harm patients admitted to the intensive care unit (ICU). Yet, clinical decision support systems (CDSSs) aimed at helping physicians prevent DDIs are plagued by low-yield alerts, causing alert fatigue and compromising patient safety. The aim of this multicentre study was to evaluate the effect of tailoring potential DDI alerts to the ICU setting on the frequency of administered high-risk drug combinations. Methods: We implemented a cluster randomised stepped-wedge trial in nine ICUs in the Netherlands. Five ICUs already used potential DDI alerts. Patients aged 18 years or older admitted to the ICU with at least two drugs administered were included. Our intervention was an adapted CDSS, only providing alerts for potential DDIs considered as high risk. The intervention was delivered at the ICU level and targeted physicians. We hypothesised that showing only relevant alerts would improve CDSS effectiveness and lead to a decreased number of administered high-risk drug combinations. The order in which the intervention was implemented in the ICUs was randomised by an independent researcher. The primary outcome was the number of administered high-risk drug combinations per 1000 drug administrations per patient and was assessed in all included patients. This trial was registered in the Netherlands Trial Register (identifier NL6762) on Nov 26, 2018, and is now closed. Findings: In total, 10 423 patients admitted to the ICU between Sept 1, 2018, and Sept 1, 2019, were assessed and 9887 patients were included. The mean number of administered high-risk drug combinations per 1000 drug administrations per patient was 26·2 (SD 53·4) in the intervention group (n=5534), compared with 35·6 (65·0) in the control group (n=4353). Tailoring potential DDI alerts to the ICU led to a 12% decrease (95% CI 5–18%; p=0·0008) in the number of administered high-risk drug combinations per 1000 drug administrations per patient, after adjusting for clustering and prognostic factors. Interpretation: This cluster randomised stepped-wedge trial showed that tailoring potential DDI alerts to the ICU setting significantly reduced the number of administered high-risk drug combinations. Our list of high-risk drug combinations can be used in other ICUs, and our strategy of tailoring alerts based on clinical relevance could be applied to other clinical settings. Funding: ZonMw.
UR - http://www.scopus.com/inward/record.url?scp=85183033909&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/S0140-6736(23)02465-0
DO - https://doi.org/10.1016/S0140-6736(23)02465-0
M3 - Article
C2 - 38262430
SN - 0140-6736
VL - 403
SP - 439
EP - 449
JO - The Lancet
JF - The Lancet
IS - 10425
ER -