The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study
Abstract Intensive care units (ICU) are often overflooded with alarms from monitoring devices which constitutes a hazard to both staff and patients. To date, the suggested solutions to excessive monitoring alarms have remained on a research level. We aimed to identify patient characteristics that af...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-26261-4 |
| _version_ | 1798005384730902528 |
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| author | Zeena-Carola Sinno Denys Shay Jochen Kruppa Sophie A.I. Klopfenstein Niklas Giesa Anne Rike Flint Patrick Herren Franziska Scheibe Claudia Spies Carl Hinrichs Axel Winter Felix Balzer Akira-Sebastian Poncette |
| author_facet | Zeena-Carola Sinno Denys Shay Jochen Kruppa Sophie A.I. Klopfenstein Niklas Giesa Anne Rike Flint Patrick Herren Franziska Scheibe Claudia Spies Carl Hinrichs Axel Winter Felix Balzer Akira-Sebastian Poncette |
| author_sort | Zeena-Carola Sinno |
| collection | DOAJ |
| description | Abstract Intensive care units (ICU) are often overflooded with alarms from monitoring devices which constitutes a hazard to both staff and patients. To date, the suggested solutions to excessive monitoring alarms have remained on a research level. We aimed to identify patient characteristics that affect the ICU alarm rate with the goal of proposing a straightforward solution that can easily be implemented in ICUs. Alarm logs from eight adult ICUs of a tertiary care university-hospital in Berlin, Germany were retrospectively collected between September 2019 and March 2021. Adult patients admitted to the ICU with at least 24 h of continuous alarm logs were included in the study. The sum of alarms per patient per day was calculated. The median was 119. A total of 26,890 observations from 3205 patients were included. 23 variables were extracted from patients' electronic health records (EHR) and a multivariable logistic regression was performed to evaluate the association of patient characteristics and alarm rates. Invasive blood pressure monitoring (adjusted odds ratio (aOR) 4.68, 95%CI 4.15–5.29, p < 0.001), invasive mechanical ventilation (aOR 1.24, 95%CI 1.16–1.32, p < 0.001), heart failure (aOR 1.26, 95%CI 1.19–1.35, p < 0.001), chronic renal failure (aOR 1.18, 95%CI 1.10–1.27, p < 0.001), hypertension (aOR 1.19, 95%CI 1.13–1.26, p < 0.001), high RASS (aOR 1.22, 95%CI 1.18–1.25, p < 0.001) and scheduled surgical admission (aOR 1.22, 95%CI 1.13–1.32, p < 0.001) were significantly associated with a high alarm rate. Our study suggests that patient-specific alarm management should be integrated in the clinical routine of ICUs. To reduce the overall alarm load, particular attention regarding alarm management should be paid to patients with invasive blood pressure monitoring, invasive mechanical ventilation, heart failure, chronic renal failure, hypertension, high RASS or scheduled surgical admission since they are more likely to have a high contribution to noise pollution, alarm fatigue and hence compromised patient safety in ICUs. |
| first_indexed | 2024-04-11T12:38:21Z |
| format | Article |
| id | doaj.art-003dc2280de3403bbd65d68ea1c16035 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T12:38:21Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-003dc2280de3403bbd65d68ea1c160352022-12-22T04:23:33ZengNature PortfolioScientific Reports2045-23222022-12-011211710.1038/s41598-022-26261-4The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort studyZeena-Carola Sinno0Denys Shay1Jochen Kruppa2Sophie A.I. Klopfenstein3Niklas Giesa4Anne Rike Flint5Patrick Herren6Franziska Scheibe7Claudia Spies8Carl Hinrichs9Axel Winter10Felix Balzer11Akira-Sebastian Poncette12Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical InformaticsHarvard T.H. Chan School of Public Health, Department of EpidemiologyHochschule Osnabrück, University of Applied SciencesCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical InformaticsCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical InformaticsCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical InformaticsCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical InformaticsCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of NeurologyCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care MedicineCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Nephrology and Medical Intensive CareCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of SurgeryCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical InformaticsCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical InformaticsAbstract Intensive care units (ICU) are often overflooded with alarms from monitoring devices which constitutes a hazard to both staff and patients. To date, the suggested solutions to excessive monitoring alarms have remained on a research level. We aimed to identify patient characteristics that affect the ICU alarm rate with the goal of proposing a straightforward solution that can easily be implemented in ICUs. Alarm logs from eight adult ICUs of a tertiary care university-hospital in Berlin, Germany were retrospectively collected between September 2019 and March 2021. Adult patients admitted to the ICU with at least 24 h of continuous alarm logs were included in the study. The sum of alarms per patient per day was calculated. The median was 119. A total of 26,890 observations from 3205 patients were included. 23 variables were extracted from patients' electronic health records (EHR) and a multivariable logistic regression was performed to evaluate the association of patient characteristics and alarm rates. Invasive blood pressure monitoring (adjusted odds ratio (aOR) 4.68, 95%CI 4.15–5.29, p < 0.001), invasive mechanical ventilation (aOR 1.24, 95%CI 1.16–1.32, p < 0.001), heart failure (aOR 1.26, 95%CI 1.19–1.35, p < 0.001), chronic renal failure (aOR 1.18, 95%CI 1.10–1.27, p < 0.001), hypertension (aOR 1.19, 95%CI 1.13–1.26, p < 0.001), high RASS (aOR 1.22, 95%CI 1.18–1.25, p < 0.001) and scheduled surgical admission (aOR 1.22, 95%CI 1.13–1.32, p < 0.001) were significantly associated with a high alarm rate. Our study suggests that patient-specific alarm management should be integrated in the clinical routine of ICUs. To reduce the overall alarm load, particular attention regarding alarm management should be paid to patients with invasive blood pressure monitoring, invasive mechanical ventilation, heart failure, chronic renal failure, hypertension, high RASS or scheduled surgical admission since they are more likely to have a high contribution to noise pollution, alarm fatigue and hence compromised patient safety in ICUs.https://doi.org/10.1038/s41598-022-26261-4 |
| spellingShingle | Zeena-Carola Sinno Denys Shay Jochen Kruppa Sophie A.I. Klopfenstein Niklas Giesa Anne Rike Flint Patrick Herren Franziska Scheibe Claudia Spies Carl Hinrichs Axel Winter Felix Balzer Akira-Sebastian Poncette The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study Scientific Reports |
| title | The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study |
| title_full | The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study |
| title_fullStr | The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study |
| title_full_unstemmed | The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study |
| title_short | The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study |
| title_sort | influence of patient characteristics on the alarm rate in intensive care units a retrospective cohort study |
| url | https://doi.org/10.1038/s41598-022-26261-4 |
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