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      Adaptive threshold-based alarm strategies for continuous vital signs monitoring

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          Abstract

          Continuous vital signs monitoring in post-surgical ward patients may support early detection of clinical deterioration, but novel alarm approaches are required to ensure timely notification of abnormalities and prevent alarm-fatigue. The current study explored the performance of classical and various adaptive threshold-based alarm strategies to warn for vital sign abnormalities observed during development of an adverse event. A classical threshold-based alarm strategy used for continuous vital signs monitoring in surgical ward patients was evaluated retrospectively. Next, (combinations of) six methods to adapt alarm thresholds to personal or situational factors were simulated in the same dataset. Alarm performance was assessed using the overall alarm rate and sensitivity to detect adverse events. Using a wireless patch-based monitoring system, 3999 h of vital signs data was obtained in 39 patients. The clinically used classical alarm system produced 0.49 alarms/patient/day, and alarms were generated for 11 out of 18 observed adverse events. Each of the tested adaptive strategies either increased sensitivity to detect adverse events or reduced overall alarm rate. Combining specific strategies improved overall performance most and resulted in earlier presentation of alarms in case of adverse events. Strategies that adapt vital sign alarm thresholds to personal or situational factors may improve early detection of adverse events or reduce alarm rates as compared to classical alarm strategies. Accordingly, further investigation of the potential of adaptive alarms for continuous vital signs monitoring in ward patients is warranted.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10877-021-00666-4.

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          Validation of a modified Early Warning Score in medical admissions.

          The Early Warning Score (EWS) is a simple physiological scoring system suitable for bedside application. The ability of a modified Early Warning Score (MEWS) to identify medical patients at risk of catastrophic deterioration in a busy clinical area was investigated. In a prospective cohort study, we applied MEWS to patients admitted to the 56-bed acute Medical Admissions Unit (MAU) of a District General Hospital (DGH). Data on 709 medical emergency admissions were collected during March 2000. Main outcome measures were death, intensive care unit (ICU) admission, high dependency unit (HDU) admission, cardiac arrest, survival and hospital discharge at 60 days. Scores of 5 or more were associated with increased risk of death (OR 5.4, 95%CI 2.8-10.7), ICU admission (OR 10.9, 95%CI 2.2-55.6) and HDU admission (OR 3.3, 95%CI 1.2-9.2). MEWS can be applied easily in a DGH medical admission unit, and identifies patients at risk of deterioration who require increased levels of care in the HDU or ICU. A clinical pathway could be created, using nurse practitioners and/or critical care physicians, to respond to high scores and intervene with appropriate changes in clinical management.
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            Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

            Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database.
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              Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries

              Background: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. Methods: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. Results: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2–7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. Conclusions: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. Study registration: ISRCTN51817007
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                Author and article information

                Contributors
                m.c.vanrossum@utwente.nl
                Journal
                J Clin Monit Comput
                J Clin Monit Comput
                Journal of Clinical Monitoring and Computing
                Springer Netherlands (Dordrecht )
                1387-1307
                1573-2614
                11 February 2021
                11 February 2021
                2022
                : 36
                : 2
                : 407-417
                Affiliations
                [1 ]GRID grid.6214.1, ISNI 0000 0004 0399 8953, Biomedical Signals and Systems, , University of Twente, ; P.O. Box 217, 7500 AE Enschede, The Netherlands
                [2 ]GRID grid.6214.1, ISNI 0000 0004 0399 8953, Cardiovascular and Respiratory Physiology, , University of Twente, ; P.O. Box 217, 7500 AE Enschede, The Netherlands
                [3 ]GRID grid.7692.a, ISNI 0000000090126352, Department of Anesthesiology, , University Medical Center Utrecht, ; Heidelberglaan 100, 3584 CG Utrecht, the Netherlands
                [4 ]GRID grid.6214.1, ISNI 0000 0004 0399 8953, Technical Medicine, , University of Twente, ; P.O. Box 217, 7500 AE Enschede, The Netherlands
                [5 ]GRID grid.509540.d, ISNI 0000 0004 6880 3010, Department of Anesthesiology, , Amsterdam University Medical Center, ; Location AMC, H1-148, P.O. Box 22660, 1100 DD Amsterdam, The Netherlands
                Author information
                http://orcid.org/0000-0002-1144-3793
                Article
                666
                10.1007/s10877-021-00666-4
                9123069
                33575922
                53cbb080-5e24-4a35-90a9-58a7042471b9
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 November 2020
                : 27 January 2021
                Categories
                Original Research
                Custom metadata
                © Springer Nature B.V. 2022

                Medicine
                vital signs,clinical alarms,physiological monitoring,clinical deterioration,telemonitoring

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