Artikel
What medication actions follow patient monitoring alarms in intensive care units? A retrospective analysis
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Veröffentlicht: | 15. September 2023 |
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Introduction: In intensive care units (ICUs) patients' vital signs are constantly monitored and personnel is alarmed if the measurements exceed given thresholds. Up to 94% of the alarms require no intervention, causing stress and alarm fatigue in the staff [1]. Numerous Research projects aim to reduce this high rate of non-actionable alarms [2].
Medication management is the main action after an alarm aiming to adjust patients' vitals [3]. Medication records in the hospital information system are therefore invaluable for machine learning (ML) approaches to tackle non-actionable alarms. We aim to examine a large dataset for existing relationships between different alarm types and medication actions in their temporal proximity, or whether manual documentation error noise or other data quality issues obscure them.
Methods: During this retrospective, observational study (IRB approval EA1/127/18), we collected alarm and patient data from 19 adult ICUs of a large German hospital between 2019 and 2021 and assembled them in a database with a modified MIMIC-IV schema [4]. This analysis focuses on medication actions in the context of red alarms (highly critical) of five physical alarm conditions (PAC) [5]: low oxygen saturation (SpO2), low or high heart frequency (HF) and low or high blood pressure (BP). Medication actions were defined as start or stop of an administration, increase or reduction of a dosage within a time window of 15 minutes after an alarm. We filtered out regular actions prior to the analysis. For each PAC, we queried medication actions and calculated the cumulated percentage (cut-off set at 70%). Medications appearing within at least one PAC medication list were included in our descriptive analysis.
Results: For 35,004 patients (40,865 stays), we counted 3,987,543 medication actions and 7,949,606 alarm start log entries. Red alarms make 9.93%, the remainder are yellow (less critical). We counted 661 different substances applied in the time window. Using the 70% cutoff, 43 substances remained across all PACs (Table 1 [Tab. 1]).
Some medication action distributions are highly skewed towards a single PAC, while others have a rather flat distribution across PACs (e.g. with norepinephrine, heparin). Overall distribution shows a distinct non-random structure.
Discussion: Some distributions are clinically explicable- e.g., amiodarone, magnesium, salbutamol, or urapidil; these medications were most likely applied due to medical indication after an alarm. The administration of substances such as norepinephrine, fluids, or propofol is often routine – which explains their more even distribution – but their variance may represent clinically meaningful responses to specific PACs. Some other substances seem to occur randomly due to routine applications. The start and increase actions show a clearer pattern, compared to the stop and decrease actions, which are more random and sometimes contradictory.
Conclusion: To our knowledge, this is the first analysis examining medication actions in the context of patient monitoring alarms. In addition to providing clinical insights, it showed that there is a sensible non-random structure in the collected data. In a next step the dataset will be used for ML to predict actionable alarms.
Grant: BMBF 16SV8559.
The authors declare that they have no competing interests.
The authors declare that a positive ethics committee vote has been obtained.
References
- 1.
- Lawless ST. Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994 Jun;22(6):981–5.
- 2.
- Chromik J, Klopfenstein SAI, et al. Computational Approaches to Alleviate Alarm Fatigue: A Systematic Literature Review. Front Digit Health. 2022;4:154. DOI: 10.3389/fdgth.2022.843747
- 3.
- Görges M, Markewitz BA, Westenskow DR. Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesth Analg. 2009;108(5):1546-1552. DOI: 10.1213/ane.0b013e31819bdfbb
- 4.
- Giesa N, Heeren P, Klopfenstein SAI, et al. MIMIC-IV as a Clinical Data Schema. Stud Health Technol Inform. 2022;294:559-560. DOI: 10.3233/SHTI22052
- 5.
- International Electrotechnical Commission. IEC 60601-1-8:2006. Medical electrical equipment - Part 1-8: General requirements for basic safety and essential performance - Collateral Standard: General requirements, tests and guidance for alarm systems in medical electrical equipment and medical electrical systems. Geneva: IEC; 2006.