Alert burden when monitoring patients’ vital signs continuously at home

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Meyhoff, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8924964/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Purpose Continuous monitoring of vital signs after hospital discharge aims to detect clinical deterioration. However, the utility may be challenged by high alert frequencies. The current study aimed to assess the impact of evidence-based augmented algorithms on alert frequency and the ability to detect complications. Methods Adult patients (≥ 18 years) discharged after acute medical admission were monitored continuously using wearable devices that measured heart rate, respiratory rate, blood pressure, and oxygen saturation. The primary outcome was the number of alerts per patient per day. We compared outcomes across three filtering strategies: (1) no filtering, (2) artefact removal, and (3) filtering with artefact removal and clinical criteria based upon severity and duration. Results Ninety-eight patients were enrolled; the total vital sign alert frequency was reduced from a median of 67 [IQR 33–103] alerts/patient/day after artefact removal to 5 [IQR 1–13] alerts/patient/day following application of the clinical criteria filters (p < 0.001). The number of any vital sign alert following the three filtering approaches was 74 [IQR 36–125], 67 [IQR 33–103], and 5 [IQR 1–13] alerts/patient/day, respectively, p < 0.001. Conclusions Artefact removal and the application of filters based on severity and event duration significantly reduced the frequency of alerts by 84% in patients continuously monitored at home after hospital discharge. Vital signs Continuous monitoring remote monitoring vital signs monitoring Figures Figure 1 Figure 2 Figure 3 Introduction Continuous monitoring of vital signs in hospitalized patients outperforms manual intermittent monitoring regarding the detection of vital sign deviations, and a significant reduction in adverse events and healthcare costs has been documented [ 1 – 3 ]. Home monitoring of recently discharged patients is emerging as a relevant extension of hospital care, offering the possibility of similar timely recognition of post-discharge deterioration and avoidance of preventable readmissions [ 4 , 5 ]. Continuous vital sign monitoring with wearable devices provides clinicians with real-time data on patients’ physiological status and can trigger alerts in response to abnormal vital signs [ 2 , 6 ]. However, alarm fatigue is a well-recognized barrier to the effective use of continuous monitoring systems [ 7 ]. Studies from intensive care units have shown that up to 80–90% of alarms are non-actionable, leading to desensitization among clinical staff and delayed responses to true deterioration [ 8 ]. This highlights the importance of designing alarm systems that detect true deterioration without generating excessive noise. Monitoring in home settings is technically feasible [ 9 ] but differs fundamentally from the hospital environment: patients are typically more mobile, interact less with healthcare personnel, demonstrate greater physiological variability due to daily activities, and have different adherence to wearing the devices [ 10 ]. These factors may contribute to an increased likelihood of data artefacts and false alerts when patients’ vital signs are monitored remotely [ 11 ]. Without appropriate filtering, continuous vital sign monitoring at home carries an inherent risk of generating large volumes of non-actionable alarms, placing unnecessary burden on the attending healthcare provider [ 6 , 9 , 12 ]. Various strategies - such as artefact removal, adaptive thresholds, and delay algorithms - have been developed to reduce alarm burden, but these have primarily been designed and validated in in-hospital settings [ 13 , 14 ], and there is limited evidence to support their applicability in patients recovering at home. Existing studies are based on data from healthy volunteers or simulated scenarios [ 15 – 17 ]; hence, it cannot be assumed that alarm dynamics effective in hospitals will perform similarly in the home. This underscores the need to investigate such strategies specifically in the home setting. The aim of this study was to assess the impact of evidence-based augmented algorithms on alert frequency and the ability to detect complications. We hypothesized that application of these algorithms would reduce alert frequency compared with both unfiltered data and data with artefact removal. Materials and methods Study design This was an explorative cohort study analysing data from two prospective studies (NCT05223504 and NCT05536206). Both studies were part of the Wireless Assessment of Respiratory and circulatory Distress at Home (WARD-HOME) project. All eligible patients provided written informed consent before enrolment. The studies were approved by the regional ethics committee (H-20009132, including subsequent amendment 91724) before data collection and analysis. This sub-analysis was registered at ClinicalTrials.gov (NCT07096648) before data extraction. Setting Patient recruitment was conducted between August 2021 and July 2023 at Bispebjerg Hospital, Copenhagen, Denmark. Overall inclusion criteria were adult patients (≥ 18 years) admitted with an acute medical disease and scheduled for discharge within five days from inclusion. Exclusion criteria were allergy to plaster/silicone, pacemaker or implantable cardioverter defibrillator device, inability to give informed consent, or deemed not able to open the front door when visited by the investigator. Patients were enrolled in the studies during the final days of hospitalization, and the study period was defined as starting when patients entered their home environment. All patients in our study adhered to standard discharge criteria and were not discharged earlier due to study participation Up to three wearable and wireless monitoring devices were applied to the patient either before discharge or in the patient’s home shortly thereafter, with the intent of a maximum of eight days of monitoring. Patients were taught how to replace the devices following personal hygiene and how to change batteries after two days of monitoring. Daily follow-up, either in-person or by telephone, was conducted to ensure correct device placement, assess signal quality, and confirm patient well-being. Patients were included in the analyses if monitoring lasted for a minimum of six hours. Variables Demographic data were obtained from the electronic medical record and included height, weight, former and current diagnoses, and smoking history. Continuous monitoring of vital signs was performed using up to three CE- and FDA-approved devices measuring heart rate (HR), respiratory rate (RR), systolic and diastolic blood pressure (SBP and DBP), and peripheral oxygen saturation (SpO₂). All devices were connected via Bluetooth to a gateway tablet (Isansys Lifecare, Oxfordshire, United Kingdom), which transmitted data through an encrypted 4G connection to the hospital’s secure internal server. HR and RR were measured using a single-lead electrocardiogram (ECG) patch (Lifetouch, Isansys, Oxfordshire, United Kingdom), which recorded ECG signals for detection of QRS complexes and R peaks. The ECG was only transmitted every 10. second to reduce battery consumption of the devices. SpO₂ was measured using a wrist-worn pulse oximeter (WristOx 3150, Nonin Medical Inc., Minnesota, USA), capturing SpO₂ values with each pulse beat. SBP and DBP were measured using an automated cuff-based device (TM-2441, A&D Medical, Tokyo, Japan) at 60-minute intervals. Patient outcomes were assessed from the electronic medical record 30 days after discharge to identify any serious adverse events (SAEs) occurring in the period following hospitalization. All adverse events were classified according to a predefined outcome manual that specified a total of 35 outcomes, grouped into six different categories: neurologic, respiratory, cardiovascular, infectious, other complications, mortality, ICU-admission, and readmissions. Exposure variables The exposure variables were vital sign signal processing according to these three approaches: Raw signals: No artefact removal and no signals considered of bad quality. The signal with raw N-N intervals was discretized for each minute, and mean values were calculated. Alerts were considered present every time a vital sign crossed the predefined thresholds for more than 1 minute of duration. Application of artefact removal: We used data generated with a frequency of 1 minute from the Isansys data. Alerts were generated and reissued every 10 minutes a deviation lasted. Application of artefact removal and WARD-Clinical Support System (WARD-CSS) filters: Clinical duration and severity criteria added to the previously described artefact removal procedure, followed by a two-dimensional alert rule combining the magnitude of a deviation with its duration (Table 1 ). Alerts are only reissued if they are triggered after a 10-minute cooldown period. Outcomes The primary outcome was the total number of vital sign alerts per patient per day. Secondary outcomes were the number of alerts per specific vital sign parameter per patient per day, frequency of any vital sign alert per hour, including the hour of day with the highest frequency of alerts (peak alert time), and the number of alerts occurring in the 24 hours preceding an SAE (for patients having an SAE within 30 days after discharge). Data analysis Outcome data are described using non-parametric statistics and are presented as medians with interquartile ranges (IQR) and means with standard deviations (SD). Alert differences are presented as mean differences and relative reductions with percentages and 95% confidence intervals (CI). No formal sample size calculation was performed as the study was exploratory with a fixed patient cohort. Differences in alert counts between groups were assessed using the Wilcoxon rank-sum test. A p-value < 0.05 was considered statistically significant. Data were analysed using R version 4.2.2 (R Core Team, Vienna, Austria). Results Of the 110 enrolled patients, ten patients were monitored for less than six hours, and two patients withdrew consent before monitoring was initiated. Thus, 98 were monitored at home for at least six hours between August 2021 and July 2023. The total monitoring time was 8,372 hours, corresponding to a median of 71 [IQR 55–124] hours per patient. Median age was 66 years, 48% were women, and 52% had chronic obstructive pulmonary disease (Table 2 ). Primary outcome The frequency of any vital sign alerts was 74 [IQR 36–125] alerts/patient/day before any filtering. The frequency of any vital sign alerts was a median of 67 [IQR 33–103] alerts/patient/day after artefact removal, which was reduced to 5 [ 1 – 13 ] alerts/patient/day following application of the clinical criteria filters (p < 0.001). This was equivalent to a relative reduction by 84% (95% CI: 81–87) (Fig. 2 ), (Table 3 ). Secondary outcomes and specific vital sign alerts For several variables – including bradycardia, hypotension, and severe desaturation – the median number of alerts was 0 both before and after artefact removal and clinical criteria filters (Table 4 ). The total number of alerts had a peak around 19:00 (Fig. 3 ). Respiration and peripheral oxygen saturation Alert frequency for tachypnoea decreased from 7 [IQR 1–23] to 0 [IQR 0–1], corresponding to a mean difference of -15 (95% CI: -18 to -12) alerts per patient per day (Table 4 ). All four SpO 2 alert thresholds had fewer alerts following application of clinical criteria filters, most pronounced for SpO 2 <92%, where the number of alerts was reduced from median 16 [IQR 3–38] to 0 [IQR 0–1] alerts per patient per day, corresponding to a mean difference of -23 (95% CI: -25 to -20) alerts per patient per day after application of artefact removal and clinical criteria filters. When both clinical and artefact filters were applied, alert frequency was significantly reduced across all saturation thresholds (SpO 2 <88%, SpO 2 <85%, and SpO 2 <80%) (Table 4 ). Heart rate Median alert frequency for both sinus tachycardia and sinus bradycardia was 0. Alert counts were significantly reduced across all threshold categories following application of both artefact removal and clinical criteria filters. The median number of alerts for sinus tachycardia ≥ 110 bpm decreased with a mean difference of -9 (95% CI: -11 to -7) alerts per patient per day (Table 4 ). Blood pressure Alerts for systolic blood pressure 180 mmHg were also reduced when artefact removal and clinical criteria filters were applied. However, these events had no significant difference in median frequency between the different filtering approaches (Table 4 ). Complications vs alerts Fourteen patients suffered an SAE within 30 days after initiation of monitoring, of whom three patients had their SAE (one unspecified and two respiratory) during monitoring or up to 24 hours after the end of monitoring. The absolute number of alerts in the 24 hours preceding the SAE was 17, 22, and 34 before any filtering. After application of the WARD-CSS filters, these counts were reduced to 0, 3, and 8 alerts. Discussion In this explorative study, we found application of evidence-based augmented algorithms to result in statistically significant alert reduction compared with both unfiltered signals and artefact removal alone. The frequency of clinically non-actionable alerts generated during home monitoring was reduced by 84% from 74 alerts to 5 alerts per patient per day when applying filters based on a 2-dimensional combination of clinically relevant thresholds and event duration. This finding is consistent with previous studies demonstrating that algorithmic filtering can effectively reduce alert burden without substantially compromising sensitivity. Kjærgaard et al. [ 6 ] performed a similar study on 716 in-hospital patients admitted for severe medical disease or major surgery and reported that the addition of the WARD-CSS filtering reduced the frequency of false alarms by 86%. The alert frequencies in our study in the home setting were generally lower, illustrated by an overall median frequency of 5 alerts/patient/day. It is therefore reasonable to assume that our cohort was less affected by illness, demonstrated higher activity levels, and was closer to their habitual physiological state. In a randomized controlled trial by Alcoceba-Herrero et al. [ 18 ], 95% of more than 60,000 alerts for deviating vital signs generated by 100 continuously monitored patients at home were technical and automatically resolved without clinician involvement, while the remaining 5% clinically relevant alerts were managed by healthcare professionals. Notably, none of these led to unnecessary escalation, underscoring the potential of algorithmic systems to maintain patient safety while reducing alert burden. In our study, the median alert frequencies for many vital sign deviations were low, often 0, indicating that only a minority of patients generated alerts, while a few patients contributed largely to the total alert burden. This is as expected, as the majority of discharged patients should be stable with improving physiology, and the asymmetric distribution indicates that the generation of alerts is driven by individual factors such as comorbidity or patient mobility rather than by identical physiological variation across the population. Such heterogeneity supports the concept of individualized monitoring and adaptive alert strategies. Schmid et al. [ 19 ] introduced adaptive time delays and demonstrated a reduction of clinically irrelevant alerts by 73% while improving the positive predictive value of true alerts. In a cohort of 60 surgical patients, Van Rossum et al. [ 14 ] demonstrated that adaptive threshold strategies, including patient-specific baselines with individual physiological variability, can reduce false alerts while maintaining sensitivity to true clinical deterioration. However, individualized alarm thresholds are not without challenges, as their implementation may be influenced by caregiver bias, require continuous recalibration, and demand transparency in how thresholds are adjusted [ 14 , 20 ]. Application of clinical filtering requires a strong scientific documentation – otherwise, important, serious complications may be overlooked. The concept of reducing alert frequency is primarily motivated by the need to prevent alert fatigue and to avoid unnecessary escalation triggered by non-actionable alerts [ 21 – 23 ]. Alert fatigue refers to the desensitization that occurs when caregivers are repeatedly exposed to frequent or clinically irrelevant alerts, which may lead to delayed or missed responses to true deterioration [ 24 ]. This phenomenon is well described in hospital settings, where studies have shown that most monitor alarms are either false or non-actionable, leading to staff inattention and decreased trust in monitoring systems [ 25 , 26 ]. Similar concerns may arise in remote monitoring environments, where clinical decisions must be made at a distance and without physical examination of the patient. In such contexts, excessive alert load not only increases workload but also risks the lack of confidence in system reliability and long-term adherence to remote monitoring systems [ 27 , 28 ]. The primary strength of this study was the real-world setting, reflecting true conditions of home monitoring after hospital discharge. All continuous data were collected in patient homes and included variations in patient mobility, adherence, and signal quality that are typically excluded from simulated scenarios. This enhances the external validity and clinical relevance of the findings. While the overall number of alerts in our study decreased after application of artefact removal and clinical filters, some parameters demonstrated an increase in alert frequency after artefact removal alone. This finding may reflect improved signal quality, allowing detection of true physiological deviations that were previously masked by signal noise. Thus, an increase in alerts after filtering does not necessarily indicate poorer performance but rather enhanced sensitivity to physiological events. As with all studies, ours has limitations. More than half of the study population were older patients with chronic respiratory disease, and findings may not be directly generalizable to a younger or postoperative population. As participants were clinically stable at inclusion and adhered to standard discharge criteria, the monitoring system was not challenged by early discharge, which may have resulted in lower alert frequencies. Finally, low adherence to blood pressure monitoring substantially limits the interpretability of blood pressure-related alerts. Conclusion Artefact removal and the application of filters based on clinically relevant thresholds and event duration significantly reduced the frequency of alerts in patients continuously monitored for vital signs at home after hospital discharge. These findings provide a promising path towards clinically feasible continuous monitoring at home that may improve the ability to detect complications after hospital discharge to allow for earlier interventions. Declarations Conflict of interest statement: EKA and CSM: Founders of a spin-out company, WARD24/7 ApS, with the aim of pursuing the regulatory and commercial activities of the WARD-project (Wireless Assessment of Respiratory and circulatory Distress, a project developing a clinical support system for continuous wireless monitoring of vital signs). WARD24/7 ApS has obtained license agreement for any WARD-project software and patents. One patent has been filed: “Wireless Assessment of Respiratory and circulatory Distress (WARD), EP 21184712.4 and EP 21205557.8”. Ethics approval statement: The was performed in line with the principles of the Declaration of Helsinki. The study was approved by the Danish Research Ethics Committees (case no. 20009132/91724, 09/2022). Competing Interests EKA and CSM: Founders of a spin-out company, WARD24/7 ApS, with the aim of pursuing the regulatory and commercial activities of the WARD-project (Wireless Assessment of Respiratory and circulatory Distress, a project developing a clinical support system for continuous wireless monitoring of vital signs). WARD24/7 ApS has obtained license agreement for any WARD-project software and patents. One patent has been filed: “Wireless Assessment of Respiratory and circulatory Distress (WARD), EP 21184712.4 and EP 21205557.8”. Funding statement: This study was funded by The Agency for Digital Government, Denmark. The funding source did not have any role in the study design, in the collection, analysis and interpretation of data, or in the writing and decision to submit this manuscript. Author Contribution All authors contributed to the study conception and design. Project administration, methodology, data collection and analysis were performed by E.S. Data analysis, review and editing were performed by J.M. H.N were the site lead and performed review and editing. Funding acquisition, methodology and supervision were performed by C.S.M and E.K.A. The original draft of the manuscript was written by ES, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript Data Availability Data is available upon request References Larsen AT, Sopina L, Aasvang EK, Meyhoff CS, Kristensen SR, Kjellberg J. 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JMIR Med Inf. 2025;13. https://doi.org/10.2196/66066 . Royal College of Physicians London. Natl Early Warning Score (NEWS) 2. 2012. Tables Table 1 – Overview of alerts used in the WARD-CSS system Alert Threshold Duration before alert Desaturation SpO 2 ≤ 92% SpO 2 ≤ 88% SpO 2 ≤ 85% SpO 2 ≤ 80% ≥60 min. ≥10 min. ≥5 min ≥1 min Tachypnoea Respiratory rate ≥24 brpm ≥5 min Bradypnea Respiratory rate ≤5 brpm ≥1 min Sinus tachycardia Heart rate ≥ 111 bpm Heart rate > 130 bpm ≥60 min ≥30 min Bradycardia Heart rate < 30 bpm Heart rate = 30-40 bpm ≥1 min ≥5 min Hypotension Systolic blood pressure <70 mmHg Systolic blood pressure <90 mmHg ≥1 measurement ≥30 min. Hypertension Systolic blood pressure ≥180 mmHg Systolic blood pressure ≥220 mmHg ≥60 min. ≥1 measurement Based on the NEWS threshold and the hypothesis of increasing harm with longer deviations (duration-severity criteria). Bpm, beats per minute. Brpm, breaths per minute. SpO 2, peripheral oxygen saturation. NEWS: National Early Warning Score [29]. Table 2 – Baseline characteristics N=98 Age, years 65.7 [20.5] Female sex 47 (48%) Height, cm 173 [9.5] Weight, kg 77 [18] Smoking status Former 43 (44) Current 25 (26) Comorbidity Atrial fibrillation 37 (38) COPD 51 (52) Congestive heart failure 2 (2) Myocardial infarction 4 (4.1) Diabetes 14 (14) Reason for admission Anemia 6 (6.1) Asthma in exacerbation 1 (1.0) Covid-19 7 (7.1) Diabetes 1 (1.0) AECOPD 24 (25) Pneumonia 15 (15) Urinary tract infection 2 (2.0) Other 39 (40) Data is presented as n (%) and means [SD]. COPD: chronic obstructive pulmonary disease. AECOPD: acute exacerbation of chronic obstructive pulmonary disease. Table 3. Frequency of any alert after filtration by the three criteria Relative reduction No artifact removal (1) Alerts after artefact removal (2) Alerts with artefact removal and clinical filters (3) Filter 2 compared to filter 1* Filter 3 compared to filter 1** P* P** All alerts/pt/day 74 [36 - 125] 67 [33 - 103] 5 [1 - 13] -18% (-27% to -9.5%) -84% (-87% to -81%) 0.04 < 0.001 Median [IQR] number of alerts per patient per day without any filtering and following artefact removal and application of clinical criteria filters based on deviation severity and duration. Relative values are presented as % (95% CI). A p-value <0.05 is considered statistically significant. Table 4 – Number of specific alerts per 24 hours in the three filtering groups With no artefact removal (1) With artefact removal (2) With artefact removal and clinical filters (3) Filter 2 compared to filter 1* *P Filter 3 compared to filter 1 **P Desaturation SpO2 ≤ 92% 16 [3 - 38] 15 [3 – 30] 0 [0 - 1] -6 (- 9 to -3) 0.01 -23 (-25 to -20) <0.001 24 (21 - 26) 19 (17 - 20) 1 (0.9 - 1.2) SpO2 ≤ 88% 2 [0 - 9] 4 [0 - 13] 0 [0 - 1] 1 (-1 to 3) 0.001 -7 (-8 to -5) <0.001 7.8 (6.3 - 9.2) 9.1 (7.8 - 10.4) 1.4 (1.1 - 1.7) SpO2 ≤ 85% 0 [0 - 4] 2 [0 - 6] 0 [0 - 1] 1 (-1 to 2) <0.001 -3.0 (-4to -2) <0.001 4.2 (3.1 - 5.3) 5.1 (4.3 - 6.0) 1.3 (1 - 1.7) SpO2 ≤ 80% 0 [0 - 1] 1.6 (0.9 - 2.4) 0 [0 - 2] 1.8 (1.2 - 2.3) 0 [0 - 2] 1.8 (1.2 - 2.3) -0 (-1 to 1 <0.001 -0 (-1 to 1) 0.005 Bradypnea RR < 5 brpm 0 [0 - 0] 0.7 (0.4 - 1) 0 [0 - 0] 0.7 (0.4 - 0.9) 0 [0 - 0] 0.7 (0.4 - 0.9) -0 (-0 to 0) 0.11 -0 (-0 to 0) 0.11 Tachypnea RR ≥ 25 brpm 7 [1- 23] 17 (14 - 20) 6 [1 - 17] 12 (10 - 13) 0 [0 - 1] 2.1 (1.5 - 2.7) -6 (-9 to -3) 0.23 -15 (-18 to -12) <0.001 Sinus bradycardia HR < 30 bpm 0 [0 - 1] 0 [0 - 0] 0 [0 - 0] -1 (-1 to -1) <0.001 -1 (-1 to -1) <0.001 1.2 (0.9 - 1.5) 0.1 (0 - 0.1) 0 (0 - 0) HR= 30-40 bpm 0 [0 - 0] 0 [0 - 0] 0 [0 - 0] -0 (-0 to 0) 0.15 -0 (-0 to -0) <0.001 0.2 (0.1 - 0.3) 0.2 (0.1 - 0.3) 0 (0 - 0) Sinus tachycardia HR ≥111 bpm 1 [0 - 10] 3 [0 - 10] 0 [0 - 0] -2 (-4 to 0) 0.04 -9 (-11 to -7) 130 bpm Hypotension 0 [0 - 1] 2 (1.4 - 2.7) 0 [0 - 1] 1.6 (1.2 - 2) 0 [0 - 0] 0 (0 - 0.1) -0 (-1 to 0) 0.68 -2.0 (-3 to -1) <0.001 SBP <70 mmHg 0 [0 - 0] 0 [0 - 0] 0 [0 - 0] 0 (-0 to 0) 0.1 0 (-0 to 0) 0.92 0 (0 - 0.1) 0 (0 - 0.1) 0 (0 - 0.1) SBP <90 mmHg 0 [0 - 0] 0.5 (0.3 - 0.6) 0 [0 - 0] 0.5 (0.4 - 0.6) 0 [0 - 0] 0.2 (0.2 - 0.3) 0 (-0 to 0) 0.1 0 (-0 to 0) <0.001 Hypertension SBP ≥180 mmHg 0 [0 - 0] 0 [0 - 0] 0 [0 - 0] 0 (-0 to 0) 0.7 0 (-0 to 0) <0.001 0.4 (0.3 - 0.5) 0.3 (0.3 - 0.4) 0.2 (0.1 - 0.2) SBP ≥220 mmHg 0 [0 - 0] 0 (0 - 0.1) 0 [0 - 0] 0 (0 - 0.1) 0 [0 - 0] 0 (0 - 0.1) 0 (-0 to 0) 0.9 0 (-0 to 0) 0.82 Number of specific alerts per patient per day without any filtering and following artefact removal and application of clinical criteria filters based on deviation severity and duration. Values are presented as medians [IQR] and means (95% CI). Differences are reported as mean differences (95% CI). A p-value <0.05 is considered significant. bpm: beats/min, brpm: breaths/min, HR: heart rate, RR: respiratory rate, SBP: systolic blood pressure, SpO 2 : peripheral oxygen saturation Additional Declarations Competing interest reported. EKA and CSM: Founders of a spin-out company, WARD24/7 ApS, with the aim of pursuing the regulatory and commercial activities of the WARD-project (Wireless Assessment of Respiratory and circulatory Distress, a project developing a clinical support system for continuous wireless monitoring of vital signs). WARD24/7 ApS has obtained license agreement for any WARD-project software and patents. One patent has been filed: “Wireless Assessment of Respiratory and circulatory Distress (WARD), EP 21184712.4 and EP 21205557.8”. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 02 Mar, 2026 Editor assigned by journal 21 Feb, 2026 Submission checks completed at journal 21 Feb, 2026 First submitted to journal 20 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8924964","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600025670,"identity":"d083960a-9cf8-47d0-a20c-062020c0d1d6","order_by":0,"name":"Emilie Sigvardt","email":"data:image/png;base64,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","orcid":"","institution":"Copenhagen University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Emilie","middleName":"","lastName":"Sigvardt","suffix":""},{"id":600025671,"identity":"de5a83d0-7de7-4602-81e4-cc3405b13c97","order_by":1,"name":"Jesper Mølgaard","email":"","orcid":"","institution":"Copenhagen University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jesper","middleName":"","lastName":"Mølgaard","suffix":""},{"id":600025672,"identity":"53b77443-91b2-4b64-8489-ba54ac205854","order_by":2,"name":"Hanne Nygaard","email":"","orcid":"","institution":"Copenhagen University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hanne","middleName":"","lastName":"Nygaard","suffix":""},{"id":600025673,"identity":"67cb3b02-c28d-4bc8-b385-c737501f8f8c","order_by":3,"name":"Christian S. Meyhoff","email":"","orcid":"","institution":"Copenhagen University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"S.","lastName":"Meyhoff","suffix":""},{"id":600025674,"identity":"b3c24ee9-273e-4395-a7f8-60707808005c","order_by":4,"name":"Eske K. Aasvang","email":"","orcid":"","institution":"Copenhagen University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Eske","middleName":"K.","lastName":"Aasvang","suffix":""}],"badges":[],"createdAt":"2026-02-20 10:58:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8924964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8924964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104182081,"identity":"006c57d2-0d9e-44e5-a380-b5e6962b9e33","added_by":"auto","created_at":"2026-03-08 17:33:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134526,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of any alert between three filtering methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoxplot illustrating the median number of alerts per patient per day without any filtering and following artefact removal and WARD-CSS filters (clinical criteria filters based on deviation duration and severity).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8924964/v1/555669da0e39b11b3e5a46d9.png"},{"id":104182083,"identity":"d8b1e184-b55f-4817-b072-b39b10d0c1ef","added_by":"auto","created_at":"2026-03-08 17:33:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific vital sign alerts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: Boxplots illustrating the median number of alerts triggered by deviating vital signs for each vital sign parameter threshold. Bpm, beats per minute. Brpm, breaths per minute, SpO\u003csub\u003e2, \u003c/sub\u003eperipheral oxygen saturation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8924964/v1/ad5b23056d472d6184623d62.png"},{"id":104182082,"identity":"5cdd619a-c168-4777-b5ee-848200959778","added_by":"auto","created_at":"2026-03-08 17:33:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal distribution of vital sign alerts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: Distribution of alerts triggered by deviating vital signs during a 24-h period. SpO\u003csub\u003e2\u003c/sub\u003e: peripheral oxygen saturation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8924964/v1/a2c6d4df392d6e20c29bbfc9.png"},{"id":104403584,"identity":"0e4e6679-7511-44b7-8d89-522df5138f87","added_by":"auto","created_at":"2026-03-11 12:18:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1491114,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8924964/v1/160b8393-0d99-4500-9dd3-3ce0ef7c574a.pdf"}],"financialInterests":"Competing interest reported. EKA and CSM: Founders of a spin-out company, WARD24/7 ApS, with the aim of pursuing the regulatory and commercial activities of the WARD-project (Wireless Assessment of Respiratory and circulatory Distress, a project developing a clinical support system for continuous wireless monitoring of vital signs). WARD24/7 ApS has obtained license agreement for any WARD-project software and patents. One patent has been filed: “Wireless Assessment of Respiratory and circulatory Distress (WARD), EP 21184712.4 and EP 21205557.8”.","formattedTitle":"Alert burden when monitoring patients’ vital signs continuously at home","fulltext":[{"header":"Introduction","content":"\u003cp\u003eContinuous monitoring of vital signs in hospitalized patients outperforms manual intermittent monitoring regarding the detection of vital sign deviations, and a significant reduction in adverse events and healthcare costs has been documented [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Home monitoring of recently discharged patients is emerging as a relevant extension of hospital care, offering the possibility of similar timely recognition of post-discharge deterioration and avoidance of preventable readmissions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContinuous vital sign monitoring with wearable devices provides clinicians with real-time data on patients\u0026rsquo; physiological status and can trigger alerts in response to abnormal vital signs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, alarm fatigue is a well-recognized barrier to the effective use of continuous monitoring systems [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Studies from intensive care units have shown that up to 80\u0026ndash;90% of alarms are non-actionable, leading to desensitization among clinical staff and delayed responses to true deterioration [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This highlights the importance of designing alarm systems that detect true deterioration without generating excessive noise.\u003c/p\u003e \u003cp\u003eMonitoring in home settings is technically feasible [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] but differs fundamentally from the hospital environment: patients are typically more mobile, interact less with healthcare personnel, demonstrate greater physiological variability due to daily activities, and have different adherence to wearing the devices [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These factors may contribute to an increased likelihood of data artefacts and false alerts when patients\u0026rsquo; vital signs are monitored remotely [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Without appropriate filtering, continuous vital sign monitoring at home carries an inherent risk of generating large volumes of non-actionable alarms, placing unnecessary burden on the attending healthcare provider [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious strategies - such as artefact removal, adaptive thresholds, and delay algorithms - have been developed to reduce alarm burden, but these have primarily been designed and validated in in-hospital settings [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and there is limited evidence to support their applicability in patients recovering at home. Existing studies are based on data from healthy volunteers or simulated scenarios [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; hence, it cannot be assumed that alarm dynamics effective in hospitals will perform similarly in the home. This underscores the need to investigate such strategies specifically in the home setting.\u003c/p\u003e \u003cp\u003eThe aim of this study was to assess the impact of evidence-based augmented algorithms on alert frequency and the ability to detect complications. We hypothesized that application of these algorithms would reduce alert frequency compared with both unfiltered data and data with artefact removal.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was an explorative cohort study analysing data from two prospective studies (NCT05223504 and NCT05536206). Both studies were part of the Wireless Assessment of Respiratory and circulatory Distress at Home (WARD-HOME) project. All eligible patients provided written informed consent before enrolment. The studies were approved by the regional ethics committee (H-20009132, including subsequent amendment 91724) before data collection and analysis. This sub-analysis was registered at ClinicalTrials.gov (NCT07096648) before data extraction.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSetting\u003c/h3\u003e\n\u003cp\u003ePatient recruitment was conducted between August 2021 and July 2023 at Bispebjerg Hospital, Copenhagen, Denmark. Overall inclusion criteria were adult patients (\u0026ge;\u0026thinsp;18 years) admitted with an acute medical disease and scheduled for discharge within five days from inclusion. Exclusion criteria were allergy to plaster/silicone, pacemaker or implantable cardioverter defibrillator device, inability to give informed consent, or deemed not able to open the front door when visited by the investigator.\u003c/p\u003e \u003cp\u003ePatients were enrolled in the studies during the final days of hospitalization, and the study period was defined as starting when patients entered their home environment. All patients in our study adhered to standard discharge criteria and were not discharged earlier due to study participation\u003c/p\u003e \u003cp\u003eUp to three wearable and wireless monitoring devices were applied to the patient either before discharge or in the patient\u0026rsquo;s home shortly thereafter, with the intent of a maximum of eight days of monitoring. Patients were taught how to replace the devices following personal hygiene and how to change batteries after two days of monitoring. Daily follow-up, either in-person or by telephone, was conducted to ensure correct device placement, assess signal quality, and confirm patient well-being. Patients were included in the analyses if monitoring lasted for a minimum of six hours.\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003eDemographic data were obtained from the electronic medical record and included height, weight, former and current diagnoses, and smoking history. Continuous monitoring of vital signs was performed using up to three CE- and FDA-approved devices measuring heart rate (HR), respiratory rate (RR), systolic and diastolic blood pressure (SBP and DBP), and peripheral oxygen saturation (SpO₂). All devices were connected via Bluetooth to a gateway tablet (Isansys Lifecare, Oxfordshire, United Kingdom), which transmitted data through an encrypted 4G connection to the hospital\u0026rsquo;s secure internal server. HR and RR were measured using a single-lead electrocardiogram (ECG) patch (Lifetouch, Isansys, Oxfordshire, United Kingdom), which recorded ECG signals for detection of QRS complexes and R peaks. The ECG was only transmitted every 10. second to reduce battery consumption of the devices. SpO₂ was measured using a wrist-worn pulse oximeter (WristOx 3150, Nonin Medical Inc., Minnesota, USA), capturing SpO₂ values with each pulse beat. SBP and DBP were measured using an automated cuff-based device (TM-2441, A\u0026amp;D Medical, Tokyo, Japan) at 60-minute intervals. Patient outcomes were assessed from the electronic medical record 30 days after discharge to identify any serious adverse events (SAEs) occurring in the period following hospitalization. All adverse events were classified according to a predefined outcome manual that specified a total of 35 outcomes, grouped into six different categories: neurologic, respiratory, cardiovascular, infectious, other complications, mortality, ICU-admission, and readmissions.\u003c/p\u003e\n\u003ch3\u003eExposure variables\u003c/h3\u003e\n\u003cp\u003eThe exposure variables were vital sign signal processing according to these three approaches:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRaw signals: No artefact removal and no signals considered of bad quality. The signal with raw N-N intervals was discretized for each minute, and mean values were calculated. Alerts were considered present every time a vital sign crossed the predefined thresholds for more than 1 minute of duration.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eApplication of artefact removal: We used data generated with a frequency of 1 minute from the Isansys data. Alerts were generated and reissued every 10 minutes a deviation lasted.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eApplication of artefact removal and WARD-Clinical Support System (WARD-CSS) filters: Clinical duration and severity criteria added to the previously described artefact removal procedure, followed by a two-dimensional alert rule combining the magnitude of a deviation with its duration (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Alerts are only reissued if they are triggered after a 10-minute cooldown period.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the total number of vital sign alerts per patient per day.\u003c/p\u003e \u003cp\u003eSecondary outcomes were the number of alerts per specific vital sign parameter per patient per day, frequency of any vital sign alert per hour, including the hour of day with the highest frequency of alerts (peak alert time), and the number of alerts occurring in the 24 hours preceding an SAE (for patients having an SAE within 30 days after discharge).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eOutcome data are described using non-parametric statistics and are presented as medians with interquartile ranges (IQR) and means with standard deviations (SD). Alert differences are presented as mean differences and relative reductions with percentages and 95% confidence intervals (CI). No formal sample size calculation was performed as the study was exploratory with a fixed patient cohort. Differences in alert counts between groups were assessed using the Wilcoxon rank-sum test.\u003c/p\u003e \u003cp\u003eA p-value\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05\u003c/em\u003e was considered statistically significant. Data were analysed using R version 4.2.2 (R Core Team, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 110 enrolled patients, ten patients were monitored for less than six hours, and two patients withdrew consent before monitoring was initiated. Thus, 98 were monitored at home for at least six hours between August 2021 and July 2023. The total monitoring time was 8,372 hours, corresponding to a median of 71 [IQR 55\u0026ndash;124] hours per patient. Median age was 66 years, 48% were women, and 52% had chronic obstructive pulmonary disease (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePrimary outcome\u003c/h3\u003e\n\u003cp\u003eThe frequency of any vital sign alerts was 74 [IQR 36\u0026ndash;125] alerts/patient/day before any filtering. The frequency of any vital sign alerts was a median of 67 [IQR 33\u0026ndash;103] alerts/patient/day after artefact removal, which was reduced to 5 [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] alerts/patient/day following application of the clinical criteria filters (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This was equivalent to a relative reduction by 84% (95% CI: 81\u0026ndash;87) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eSecondary outcomes and specific vital sign alerts\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eFor several variables \u0026ndash; including bradycardia, hypotension, and severe desaturation \u0026ndash; the median number of alerts was 0 both before and after artefact removal and clinical criteria filters (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The total number of alerts had a peak around 19:00 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRespiration and peripheral oxygen saturation\u003c/h2\u003e \u003cp\u003eAlert frequency for tachypnoea decreased from 7 [IQR 1\u0026ndash;23] to 0 [IQR 0\u0026ndash;1], corresponding to a mean difference of -15 (95% CI: -18 to -12) alerts per patient per day (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All four SpO\u003csub\u003e2\u003c/sub\u003e alert thresholds had fewer alerts following application of clinical criteria filters, most pronounced for SpO\u003csub\u003e2\u003c/sub\u003e \u0026lt;92%, where the number of alerts was reduced from median 16 [IQR 3\u0026ndash;38] to 0 [IQR 0\u0026ndash;1] alerts per patient per day, corresponding to a mean difference of -23 (95% CI: -25 to -20) alerts per patient per day after application of artefact removal and clinical criteria filters. When both clinical and artefact filters were applied, alert frequency was significantly reduced across all saturation thresholds (SpO\u003csub\u003e2\u003c/sub\u003e \u0026lt;88%, SpO\u003csub\u003e2\u003c/sub\u003e \u0026lt;85%, and SpO\u003csub\u003e2\u003c/sub\u003e \u0026lt;80%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHeart rate\u003c/h2\u003e \u003cp\u003eMedian alert frequency for both sinus tachycardia and sinus bradycardia was 0. Alert counts were significantly reduced across all threshold categories following application of both artefact removal and clinical criteria filters. The median number of alerts for sinus tachycardia\u0026thinsp;\u0026ge;\u0026thinsp;110 bpm decreased with a mean difference of -9 (95% CI: -11 to -7) alerts per patient per day (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBlood pressure\u003c/h2\u003e \u003cp\u003eAlerts for systolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;90 mmHg and \u0026gt;\u0026thinsp;180 mmHg were also reduced when artefact removal and clinical criteria filters were applied. However, these events had no significant difference in median frequency between the different filtering approaches (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComplications vs alerts\u003c/h2\u003e \u003cp\u003eFourteen patients suffered an SAE within 30 days after initiation of monitoring, of whom three patients had their SAE (one unspecified and two respiratory) during monitoring or up to 24 hours after the end of monitoring. The absolute number of alerts in the 24 hours preceding the SAE was 17, 22, and 34 before any filtering. After application of the WARD-CSS filters, these counts were reduced to 0, 3, and 8 alerts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this explorative study, we found application of evidence-based augmented algorithms to result in statistically significant alert reduction compared with both unfiltered signals and artefact removal alone.\u003c/p\u003e \u003cp\u003eThe frequency of clinically non-actionable alerts generated during home monitoring was reduced by 84% from 74 alerts to 5 alerts per patient per day when applying filters based on a 2-dimensional combination of clinically relevant thresholds and event duration. This finding is consistent with previous studies demonstrating that algorithmic filtering can effectively reduce alert burden without substantially compromising sensitivity. Kj\u0026aelig;rgaard et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] performed a similar study on 716 in-hospital patients admitted for severe medical disease or major surgery and reported that the addition of the WARD-CSS filtering reduced the frequency of false alarms by 86%. The alert frequencies in our study in the home setting were generally lower, illustrated by an overall median frequency of 5 alerts/patient/day. It is therefore reasonable to assume that our cohort was less affected by illness, demonstrated higher activity levels, and was closer to their habitual physiological state. In a randomized controlled trial by Alcoceba-Herrero et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], 95% of more than 60,000 alerts for deviating vital signs generated by 100 continuously monitored patients at home were technical and automatically resolved without clinician involvement, while the remaining 5% clinically relevant alerts were managed by healthcare professionals. Notably, none of these led to unnecessary escalation, underscoring the potential of algorithmic systems to maintain patient safety while reducing alert burden.\u003c/p\u003e \u003cp\u003eIn our study, the median alert frequencies for many vital sign deviations were low, often 0, indicating that only a minority of patients generated alerts, while a few patients contributed largely to the total alert burden. This is as expected, as the majority of discharged patients should be stable with improving physiology, and the asymmetric distribution indicates that the generation of alerts is driven by individual factors such as comorbidity or patient mobility rather than by identical physiological variation across the population. Such heterogeneity supports the concept of individualized monitoring and adaptive alert strategies. Schmid et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] introduced adaptive time delays and demonstrated a reduction of clinically irrelevant alerts by 73% while improving the positive predictive value of true alerts. In a cohort of 60 surgical patients, Van Rossum et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] demonstrated that adaptive threshold strategies, including patient-specific baselines with individual physiological variability, can reduce false alerts while maintaining sensitivity to true clinical deterioration. However, individualized alarm thresholds are not without challenges, as their implementation may be influenced by caregiver bias, require continuous recalibration, and demand transparency in how thresholds are adjusted [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Application of clinical filtering requires a strong scientific documentation \u0026ndash; otherwise, important, serious complications may be overlooked.\u003c/p\u003e \u003cp\u003eThe concept of reducing alert frequency is primarily motivated by the need to prevent alert fatigue and to avoid unnecessary escalation triggered by non-actionable alerts [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Alert fatigue refers to the desensitization that occurs when caregivers are repeatedly exposed to frequent or clinically irrelevant alerts, which may lead to delayed or missed responses to true deterioration [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This phenomenon is well described in hospital settings, where studies have shown that most monitor alarms are either false or non-actionable, leading to staff inattention and decreased trust in monitoring systems [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similar concerns may arise in remote monitoring environments, where clinical decisions must be made at a distance and without physical examination of the patient. In such contexts, excessive alert load not only increases workload but also risks the lack of confidence in system reliability and long-term adherence to remote monitoring systems [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe primary strength of this study was the real-world setting, reflecting true conditions of home monitoring after hospital discharge. All continuous data were collected in patient homes and included variations in patient mobility, adherence, and signal quality that are typically excluded from simulated scenarios. This enhances the external validity and clinical relevance of the findings. While the overall number of alerts in our study decreased after application of artefact removal and clinical filters, some parameters demonstrated an increase in alert frequency after artefact removal alone. This finding may reflect improved signal quality, allowing detection of true physiological deviations that were previously masked by signal noise. Thus, an increase in alerts after filtering does not necessarily indicate poorer performance but rather enhanced sensitivity to physiological events. As with all studies, ours has limitations. More than half of the study population were older patients with chronic respiratory disease, and findings may not be directly generalizable to a younger or postoperative population. As participants were clinically stable at inclusion and adhered to standard discharge criteria, the monitoring system was not challenged by early discharge, which may have resulted in lower alert frequencies. Finally, low adherence to blood pressure monitoring substantially limits the interpretability of blood pressure-related alerts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eArtefact removal and the application of filters based on clinically relevant thresholds and event duration significantly reduced the frequency of alerts in patients continuously monitored for vital signs at home after hospital discharge. These findings provide a promising path towards clinically feasible continuous monitoring at home that may improve the ability to detect complications after hospital discharge to allow for earlier interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest statement:\u003c/h2\u003e\n\u003cp\u003eEKA and CSM: Founders of a spin-out company, WARD24/7 ApS, with the aim of pursuing the regulatory and commercial activities of the WARD-project (Wireless Assessment of Respiratory and circulatory Distress, a project developing a clinical support system for continuous wireless monitoring of vital signs). WARD24/7 ApS has obtained license agreement for any WARD-project software and patents. One patent has been filed: “Wireless Assessment of Respiratory and circulatory Distress (WARD), EP 21184712.4 and EP 21205557.8”.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement:\u003c/strong\u003e The was performed in line with the principles of the Declaration of Helsinki. The study was approved by the Danish Research Ethics Committees (case no. 20009132/91724, 09/2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEKA and CSM: Founders of a spin-out company, WARD24/7 ApS, with the aim of pursuing the regulatory and commercial activities of the WARD-project (Wireless Assessment of Respiratory and circulatory Distress, a project developing a clinical support system for continuous wireless monitoring of vital signs). WARD24/7 ApS has obtained license agreement for any WARD-project software and patents. One patent has been filed: “Wireless Assessment of Respiratory and circulatory Distress (WARD), EP 21184712.4 and EP 21205557.8”.\u003c/p\u003e\n\u003ch2\u003eFunding statement:\u003c/h2\u003e\n\u003cp\u003eThis study was funded by The Agency for Digital Government, Denmark. The funding source did not have any role in the study design, in the collection, analysis and interpretation of data, or in the writing and decision to submit this manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Project administration, methodology, data collection and analysis were performed by E.S. Data analysis, review and editing were performed by J.M. H.N were the site lead and performed review and editing. Funding acquisition, methodology and supervision were performed by C.S.M and E.K.A. The original draft of the manuscript was written by ES, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData is available upon request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLarsen AT, Sopina L, Aasvang EK, Meyhoff CS, Kristensen SR, Kjellberg J. Estimation of the maximum potential cost saving from reducing serious adverse events in hospitalized patients. Acta Anaesthesiol Scand. 2024;68:1471\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/aas.14525\u003c/span\u003e\u003cspan address=\"10.1111/aas.14525\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026oslash;lgaard J, Gr\u0026oslash;nb\u0026aelig;k KK, Rasmussen SS, et al. 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JMIR Med Inf. 2025;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/66066\u003c/span\u003e\u003cspan address=\"10.2196/66066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoyal College of Physicians London. Natl Early Warning Score (NEWS) 2. 2012.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 \u0026ndash; Overview of alerts used in the WARD-CSS system\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlert\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.2119%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThreshold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration before alert\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesaturation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.2119%;\"\u003e\n \u003cp\u003eSpO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e\u0026le; 92%\u003c/p\u003e\n \u003cp\u003eSpO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e\u0026le; 88%\u003c/p\u003e\n \u003cp\u003eSpO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e\u0026le; 85%\u003c/p\u003e\n \u003cp\u003eSpO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e\u0026le; 80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003e\u0026ge;60 min.\u003c/p\u003e\n \u003cp\u003e\u0026ge;10 min.\u003c/p\u003e\n \u003cp\u003e\u0026ge;5 min\u003c/p\u003e\n \u003cp\u003e\u0026ge;1 min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTachypnoea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.2119%;\"\u003e\n \u003cp\u003eRespiratory rate \u0026ge;24 brpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003e\u0026ge;5 min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBradypnea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.2119%;\"\u003e\n \u003cp\u003eRespiratory rate \u0026le;5 brpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003e\u0026ge;1 min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSinus tachycardia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.2119%;\"\u003e\n \u003cp\u003eHeart rate \u0026ge; 111 bpm\u003c/p\u003e\n \u003cp\u003eHeart rate \u0026gt; 130 bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003e\u0026ge;60 min\u003c/p\u003e\n \u003cp\u003e\u0026ge;30 min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBradycardia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.2119%;\"\u003e\n \u003cp\u003eHeart rate \u0026lt; 30 bpm\u003c/p\u003e\n \u003cp\u003eHeart rate = 30-40 bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003e\u0026ge;1 min\u003c/p\u003e\n \u003cp\u003e\u0026ge;5 min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypotension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.2119%;\"\u003e\n \u003cp\u003eSystolic blood pressure \u0026lt;70 mmHg\u003c/p\u003e\n \u003cp\u003eSystolic blood pressure \u0026lt;90 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003e\u0026ge;1 measurement\u003c/p\u003e\n \u003cp\u003e\u0026ge;30 min.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.2119%;\"\u003e\n \u003cp\u003eSystolic blood pressure \u0026ge;180 mmHg\u003c/p\u003e\n \u003cp\u003eSystolic blood pressure \u0026ge;220 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003e\u0026ge;60 min.\u003c/p\u003e\n \u003cp\u003e\u0026ge;1 measurement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBased on the NEWS threshold and the hypothesis of increasing harm with longer deviations (duration-severity criteria).\u003cbr\u003eBpm, beats per minute. Brpm, breaths per minute. SpO\u003csub\u003e2,\u0026nbsp;\u003c/sub\u003eperipheral oxygen saturation. NEWS: National Early Warning Score [29].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u0026ndash; Baseline characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003eN=98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e65.7 [20.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e47 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 64.5051%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight, cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e173 [9.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 64.5051%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight, kg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e77 [18]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e43 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e25 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e37 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e51 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eCongestive heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eMyocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e4 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e14 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReason for admission\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eAnemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e6 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eAsthma in exacerbation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eCovid-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e7 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eAECOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e24 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e15 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eUrinary tract infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e2 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.5154%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e39 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData is presented as n (%) and means [SD]. COPD: chronic obstructive pulmonary disease. AECOPD: acute exacerbation of chronic obstructive pulmonary disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Frequency of any alert after filtration by the three criteria\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"767\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13.4289%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1.69492%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4302%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9518%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2555%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.558%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eRelative reduction\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.601%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30117%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77836%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13.4289%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1.69492%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4302%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo artifact removal (1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9518%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlerts after artefact\u0026nbsp;\u003cbr\u003e\u0026nbsp;removal (2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2555%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlerts with artefact removal and clinical filters (3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.558%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFilter 2 compared to filter 1*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.601%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFilter 3 compared to filter 1**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30117%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; P*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77836%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.65059%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77836%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll alerts/pt/day\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1.69492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4302%;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003cp\u003e[36 - 125]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9518%;\"\u003e\n \u003cp\u003e67\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[33 - 103]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2555%;\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1 - 13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.558%;\"\u003e\n \u003cp\u003e-18% (-27% to -9.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.601%;\"\u003e\n \u003cp\u003e-84% (-87% to -81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30117%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77836%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMedian [IQR] number of alerts per patient per day without any filtering and following artefact removal and application of clinical criteria filters based on deviation severity and duration. Relative values are presented as % (95% CI).\u0026nbsp;\u003cbr\u003e\u0026nbsp;A p-value \u0026lt;0.05 is considered statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 \u0026ndash; Number of specific alerts per 24 hours in the three filtering groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"117%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith no artefact\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eremoval (1)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith artefact removal (2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith artefact\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eremoval and\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eclinical filters (3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFilter 2 compared to filter 1*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e*P\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFilter 3 compared\u0026nbsp;\u003cbr\u003e\u0026nbsp;to filter 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e**P\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesaturation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSpO2 \u0026le; 92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 [3 - 38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15 [3 \u0026ndash; 30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0 [0 - 1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-6 (- 9 to -3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-23 (-25 to -20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e24 (21 - 26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e19 (17 - 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e1 (0.9 - 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eSpO2 \u0026le; 88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e2 [0 - 9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e4 [0 - 13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e1 (-1 to 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e-7 (-8 to -5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e7.8 (6.3 - 9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e9.1 (7.8 - 10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e1.4 (1.1 - 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eSpO2 \u0026le; 85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e2 [0 - 6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e1 (-1 to 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e-3.0 (-4to -2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e4.2 (3.1 - 5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e5.1 (4.3 - 6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e1.3 (1 - 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eSpO2 \u0026le; 80%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 1]\u003c/p\u003e\n \u003cp\u003e1.6 (0.9 - 2.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 2]\u003c/p\u003e\n \u003cp\u003e1.8 (1.2 - 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 2]\u003c/p\u003e\n \u003cp\u003e1.8 (1.2 - 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e-0 (-1 to 1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e-0 (-1 to 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBradypnea\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRR \u0026lt; 5 brpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003cp\u003e0.7 (0.4 - 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003cp\u003e0.7 (0.4 - 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003cp\u003e0.7 (0.4 - 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTachypnea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eRR \u0026ge; 25 brpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e7 [1- 23]\u003c/p\u003e\n \u003cp\u003e17 (14 - 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e6 [1 - 17]\u003c/p\u003e\n \u003cp\u003e12 (10 - 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 1]\u003c/p\u003e\n \u003cp\u003e2.1 (1.5 - 2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e-6 (-9 to -3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e-15 (-18 to -12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSinus bradycardia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eHR \u0026lt; 30 bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e-1 (-1 to -1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e-1 (-1 to -1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e1.2 (0.9 - 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0.1 (0 - 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 (0 - 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eHR= 30-40 bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e-0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e-0 (-0 to -0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0.2 (0.1 - 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0.2 (0.1 - 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 (0 - 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSinus tachycardia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eHR \u0026ge;111 bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e1 [0 - 10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e3 [0 - 10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e-2 (-4 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e-9 (-11 to -7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e9.1 (7.3 - 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e7.1 (6.2 - 8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0.1 (0.0 - 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eHR \u0026gt;130 bpm\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypotension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 1]\u003c/p\u003e\n \u003cp\u003e2 (1.4 - 2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 1]\u003c/p\u003e\n \u003cp\u003e1.6 (1.2 - 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003cp\u003e0 (0 - 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e-0 (-1 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e-2.0 (-3 to -1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eSBP \u0026lt;70 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 (0 - 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 (0 - 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 (0 - 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eSBP \u0026lt;90 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003cp\u003e0.5 (0.3 - 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003cp\u003e0.5 (0.4 - 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003cp\u003e0.2 (0.2 - 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eSBP \u0026ge;180 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0.4 (0.3 - 0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0.3 (0.3 - 0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0.2 (0.1 - 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003eSBP \u0026ge;220 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.15789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003cp\u003e0 (0 - 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003cp\u003e0 (0 - 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e0 [0 - 0]\u003c/p\u003e\n \u003cp\u003e0 (0 - 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6842%;\"\u003e\n \u003cp\u003e0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.36842%;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6316%;\"\u003e\n \u003cp\u003e0 (-0 to 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.21053%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNumber of specific alerts per patient per day without any filtering and following artefact removal and application of clinical criteria filters based on deviation severity and duration. Values are presented as medians [IQR] and means (95% CI). Differences are reported as mean differences (95% CI). A p-value \u0026lt;0.05 is considered significant. bpm: beats/min, brpm: breaths/min, HR: heart rate, RR: respiratory rate, SBP: systolic blood pressure, SpO\u003csub\u003e2\u003c/sub\u003e:\u003csub\u003e\u0026nbsp;\u003c/sub\u003eperipheral oxygen saturation\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-clinical-monitoring-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Clinical Monitoring and Computing](https://www.springer.com/journal/10877)","snPcode":"10877","submissionUrl":"https://submission.nature.com/new-submission/10877/3","title":"Journal of Clinical Monitoring and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Vital signs, Continuous monitoring, remote monitoring, vital signs monitoring","lastPublishedDoi":"10.21203/rs.3.rs-8924964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8924964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eContinuous monitoring of vital signs after hospital discharge aims to detect clinical deterioration. However, the utility may be challenged by high alert frequencies. The current study aimed to assess the impact of evidence-based augmented algorithms on alert frequency and the ability to detect complications.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAdult patients (\u0026ge;\u0026thinsp;18 years) discharged after acute medical admission were monitored continuously using wearable devices that measured heart rate, respiratory rate, blood pressure, and oxygen saturation. The primary outcome was the number of alerts per patient per day. We compared outcomes across three filtering strategies: (1) no filtering, (2) artefact removal, and (3) filtering with artefact removal and clinical criteria based upon severity and duration.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNinety-eight patients were enrolled; the total vital sign alert frequency was reduced from a median of 67 [IQR 33\u0026ndash;103] alerts/patient/day after artefact removal to 5 [IQR 1\u0026ndash;13] alerts/patient/day following application of the clinical criteria filters (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The number of any vital sign alert following the three filtering approaches was 74 [IQR 36\u0026ndash;125], 67 [IQR 33\u0026ndash;103], and 5 [IQR 1\u0026ndash;13] alerts/patient/day, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eArtefact removal and the application of filters based on severity and event duration significantly reduced the frequency of alerts by 84% in patients continuously monitored at home after hospital discharge.\u003c/p\u003e","manuscriptTitle":"Alert burden when monitoring patients’ vital signs continuously at home","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 17:33:44","doi":"10.21203/rs.3.rs-8924964/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T19:50:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T09:54:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318283316316522899598632510196498232221","date":"2026-03-26T15:40:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T16:47:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130255396518734647789651726085041650223","date":"2026-03-03T13:48:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-02T20:32:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-21T14:34:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-21T14:32:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Clinical Monitoring and Computing","date":"2026-02-20T10:27:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-clinical-monitoring-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Clinical Monitoring and Computing](https://www.springer.com/journal/10877)","snPcode":"10877","submissionUrl":"https://submission.nature.com/new-submission/10877/3","title":"Journal of Clinical Monitoring and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a2e099c9-d378-4ba0-aa8d-465c3c9a8a25","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T19:54:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 17:33:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8924964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8924964","identity":"rs-8924964","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}