Wireless continuous single-lead ST-segment monitoring to detect new-onset myocardial injury at the general ward-An exploratory subanalysis.

Patients admitted for acute medical conditions and major noncardiac surgery are at risk of myocardial injury. This is frequently asymptomatic, especially in the context of concomitant pain and analgesics, and detection thus relies on cardiac biomarkers. Continuous single-lead ST-segment monitoring from wireless electrocardiogram (ECG) may enable more timely intervention, but criteria for alerts need to be defined to reduce false alerts. This study aimed to determine optimal ST-deviation thresholds from wireless single-lead ECG for detection of myocardial injury following major abdominal cancer surgery and during acute exacerbation of chronic obstructive pulmonary disease. Patients were monitored with a wireless single-lead ECG patch for up to 4 days and had daily troponin measurements. Single-lead ST-segment deviations of <0.255 mV and/or >0.245 mV (based on previous study comparison with 0.1 mV 12-lead ECG and variation in single-lead ECG) were analyzed for relation to myocardial injury defined as hsTnT elevation of 20-64 ng/L with an absolute change of ≥5 ng/L, or a hsTnT level ≥ 65 ng/L. In total, 528 patients were included for analysis, of which 15.5% had myocardial injury. For corrected ST-thresholds lasting ≥10 and ≥ 20 min, we found specificities of 91% and 94% and sensitivities of 17% and 13% with odds ratios of 2.0 (95% CI: 1.1; 3.9) and 2.4 (95% CI: 1.1; 5.1) for myocardial injury. In conclusion, wireless single-lead ECG monitoring with corrected ST thresholds detected patients developing myocardial injury with specificities >90% and sensitivities <20%, suggesting increased focus on sensitivity improvement.

Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

BACKGROUND: Gabaldi et al. utilized telemedicine data, web search trends, hospitalized patient characteristics, and resource usage data to estimate bed occupancy during the COVID-19 pandemic. The results showcase the potential of data-driven strategies to enhance resource allocation decisions for an effective pandemic response. OBJECTIVE: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. METHODS: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. RESULTS: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. CONCLUSION: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources. BACKGROUND: Developed models to forecast bed occupancy for up to 14 days and monitored errors for 365 days. BACKGROUND: Telemedicine calls from COVID-19 patients correlated with the number of patients hospitalized in the next 8 days.

Effectiveness of Implementing Modified Early Warning System and Rapid Response Team for General Ward Inpatients.

This retrospective study assessed the effectiveness and impact of implementing a Modified Early Warning System (MEWS) and Rapid Response Team (RRT) for inpatients admitted to the general ward (GW) of a medical center. This study included all inpatients who stayed in GWs from Jan. 2017 to Feb. 2022. We divided inpatients into GWnon-MEWS and GWMEWS groups according to MEWS and RRT implementation in Aug. 2019. The primary outcome, unexpected deterioration, was defined by unplanned admission to intensive care units. We defined the detection performance and effectiveness of MEWS according to if a warning occurred within 24 h before the unplanned ICU admission. There were 129,039 inpatients included in this study, comprising 58,106 GWnon-MEWS and 71,023 GWMEWS. The numbers of inpatients who underwent an unplanned ICU admission in GWnon-MEWS and GWMEWS were 488 (.84%) and 468 (.66%), respectively, indicating that the implementation significantly reduced unexpected deterioration (p < .0001). Besides, 1,551,525 times MEWS assessments were executed for the GWMEWS. The sensitivity, specificity, positive predicted value, and negative predicted value of the MEWS were 29.9%, 98.7%, 7.09%, and 99.76%, respectively. A total of 1,568 warning signs accurately occurred within the 24 h before an unplanned ICU admission. Among them, 428 (27.3%) met the criteria for automatically calling RRT, and 1,140 signs necessitated the nursing staff to decide if they needed to call RRT. Implementing MEWS and RRT increases nursing staff's monitoring and interventions and reduces unplanned ICU admissions.

Safer and more efficient vital signs monitoring protocols to identify the deteriorating patients in the general hospital ward: an observational study.

Background: The frequency at which patients should have their vital signs (e.g. blood pressure, pulse, oxygen saturation) measured on hospital wards is currently unknown. Current National Health Service monitoring protocols are based on expert opinion but supported by little empirical evidence. The challenge is finding the balance between insufficient monitoring (risking missing early signs of deterioration and delays in treatment) and over-observation of stable patients (wasting resources needed in other aspects of care). Objective: Provide an evidence-based approach to creating monitoring protocols based on a patient's risk of deterioration and link these to nursing workload and economic impact. Design: Our study consisted of two parts: (1) an observational study of nursing staff to ascertain the time to perform vital sign observations; and (2) a retrospective study of historic data on patient admissions exploring the relationships between National Early Warning Score and risk of outcome over time. These were underpinned by opinions and experiences from stakeholders. Setting and participants: Observational study: observed nursing staff on 16 randomly selected adult general wards at four acute National Health Service hospitals. Retrospective study: extracted, linked and analysed routinely collected data from two large National Health Service acute trusts; data from over 400,000 patient admissions and 9,000,000 vital sign observations. Results: Observational study found a variety of practices, with two hospitals having registered nurses take the majority of vital sign observations and two favouring healthcare assistants or student nurses. However, whoever took the observations spent roughly the same length of time. The average was 5:01 minutes per observation over a 'round', including time to locate and prepare the equipment and travel to the patient area. Retrospective study created survival models predicting the risk of outcomes over time since the patient was last observed. For low-risk patients, there was little difference in risk between 4 hours and 24 hours post observation. Conclusions: We explored several different scenarios with our stakeholders (clinicians and patients), based on how 'risk' could be managed in different ways. Vital sign observations are often done more frequently than necessary from a bald assessment of the patient's risk, and we show that a maximum threshold of risk could theoretically be achieved with less resource. Existing resources could therefore be redeployed within a changed protocol to achieve better outcomes for some patients without compromising the safety of the rest. Our work supports the approach of the current monitoring protocol, whereby patients' National Early Warning Score 2 guides observation frequency. Existing practice is to observe higher-risk patients more frequently and our findings have shown that this is objectively justified. It is worth noting that important nurse-patient interactions take place during vital sign monitoring and should not be eliminated under new monitoring processes. Our study contributes to the existing evidence on how vital sign observations should be scheduled. However, ultimately, it is for the relevant professionals to decide how our work should be used. Study registration: This study is registered as ISRCTN10863045. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 17/05/03) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 6. See the NIHR Funding and Awards website for further award information.

Patient recovery in hospital is tracked by measuring heart rate, blood pressure and other 'vital signs' and converting them into a score. These are 'observed' regularly by nursing staff so that deterioration can be spotted early. However, taking observations can disturb patients, and taking them too often causes extra work for staff. More frequent monitoring is recommended for higher scores, but evidence is lacking. To work out how often patients should be monitored, we needed to know how likely it is for patients to become more unwell between observations. We analysed over 400,000 patient records from two hospitals to understand how scores change with time. We looked at three of the most serious risks for patients in hospital. These risks are dying, needing intensive care or having a cardiac arrest. We also looked at the risk that a patient's condition would deteriorate significantly before their measurements were taken again. We identified early signs of deterioration and how changes in vital signs affected the risk of a patient's condition becoming worse. From this we calculated a maximum risk of deterioration. We then calculated different monitoring schedules that keep individual patients below this risk level. Some of those would consume less staff time than current National Health Service guidelines suggest. We also watched staff record patients' vital signs. We learnt it takes about 5 minutes to take these measurements from each patient. This information helped us calculate how costs would change if patients' vital signs were taken more or less often. We found that patients with a low overall score could have their vital signs monitored less often without being in danger of serious harm. This frees up nursing time so that patients with a higher score can be monitored more often. Importantly, this can be achieved without employing more staff.

External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study.

OBJECTIVES: The limitations of current early warning scores have prompted the development of deep learning-based systems, such as deep learning-based cardiac arrest risk management systems (DeepCARS). Unfortunately, in South Korea, only two institutions operate 24-hour Rapid Response System (RRS), whereas most hospitals have part-time or no RRS coverage at all. This study validated the predictive performance of DeepCARS during RRS operation and nonoperation periods and explored its potential beyond RRS operating hours. DESIGN: Retrospective cohort study. SETTING: In this 1-year retrospective study conducted at Yonsei University Health System Severance Hospital in South Korea, DeepCARS was compared with conventional early warning systems for predicting in-hospital cardiac arrest (IHCA). The study focused on adult patients admitted to the general ward, with the primary outcome being IHCA-prediction performance within 24 hours of the alarm. PATIENTS: We analyzed the data records of adult patients admitted to a general ward from September 1, 2019, to August 31, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Performance evaluation was conducted separately for the operational and nonoperational periods of the RRS, using the area under the receiver operating characteristic curve (AUROC) as the metric. DeepCARS demonstrated a superior AUROC as compared with the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS), both during RRS operating and nonoperating hours. Although the MEWS and NEWS exhibited varying performance across the two periods, DeepCARS showed consistent performance. CONCLUSIONS: The accuracy and efficiency for predicting IHCA of DeepCARS were superior to that of conventional methods, regardless of whether the RRS was in operation. These findings emphasize that DeepCARS is an effective screening tool suitable for hospitals with full-time RRS, part-time RRS, and even those without any RRS.

Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards.

Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools are often complicated and time-consuming, rendering them impractical. To overcome this problem, we designed this study to create a deep learning prediction model that predicts critical events with simplified variables. This retrospective observational study included patients under the age of 18 who were admitted to the general ward of a tertiary children's hospital between 2020 and 2022. A critical event was defined as cardiopulmonary resuscitation, unplanned transfer to the intensive care unit, or mortality. The vital signs measured during hospitalization, their measurement intervals, sex, and age were used to train a critical event prediction model. Age-specific z-scores were used to normalize the variability of the normal range by age. The entire dataset was classified into a training dataset and a test dataset at an 8:2 ratio, and model learning and testing were performed on each dataset. The predictive performance of the developed model showed excellent results, with an area under the receiver operating characteristics curve of 0.986 and an area under the precision-recall curve of 0.896. We developed a deep learning model with outstanding predictive power using simplified variables to effectively predict critical events while reducing the workload of medical staff. Nevertheless, because this was a single-center trial, no external validation was carried out, prompting further investigation.

[Hypercalcaemia: intensive care or general ward]. / Die Hyperkalzämie ­ Intensivmedizin oder Normalstation.

Hypercalcaemia is a life-threatening electrolyte imbalance, which not only occurs in the context of an endocrinological disease but is also frequently associated with a tumour. Its severity is determined by the level of deviation from normal, acuity of occurrence, and severity of the symptoms. These are unspecific, can affect any organ system and ultimately result in a life-threatening hypercalcaemic crisis characterised by cardiac arrhythmia, metabolic acidosis, exsiccosis, fever, psychotic states and, ultimately, coma. Endocrinological disorders, drugs such as vitamin D3, vitamin A, checkpoint inhibitors, but also malignancies can be causative for the development of hypercalcaemia. Up to 30% of tumour patients are affected by hypercalcaemia. It is associated with a poor prognosis and a high tumour burden. Malignant hypercalcaemia is mainly caused by PTHrP (parathormone-related peptide), which is secreted by the tumour cells. In oncological patients, serum calcium (ionised calcium and non-ionised calcium) should be evaluated regularly. As the level of serum calcium depends on the albumin concentration, the latter should also be evaluated. Treatment consists of increasing the intravasal volume, increasing calcium excretion and inhibiting calcium reabsorption.

Window Treatment-Bringing Art and Joy to Hospital Rooms.

This Arts and Medicine feature coauthored by a patient and her hospital clinicians describes the use of hand-drawn window art in hospital rooms as a way to bring color and creativity into inpatient settings and build community among hospital staff and patients.

[Changes in Patient Safety Culture Attitudes Among Emergency, Intensive Care, and General Ward Nurses During the COVID-19 Pandemic].

BACKGROUND: Patient safety culture is an indicator of healthcare quality and a topic of global importance in medical care. PURPOSE: In this study, the attitudes towards patient safety culture of nursing staff working in the emergency, intensive care, and general wards are compared before and during the COVID-19 pandemic. METHODS: A retrospective research design was utilized and an anonymous questionnaire survey conducted on the Taiwan Patient Safety Culture Survey web-based platform system was used to collect the data. The survey was administered in a regional hospital in northern Taiwan between 2018 and 2020. The 1,540 nursing personnel who participated in this study worked in the emergency, intensive care units, or general adult ward. The analysis focused on assessing participant attitudes towards patient safety culture in terms of both the overall score and sub-dimensions. RESULTS: The participants were mostly female and between 21 and 30 years old. A majority had completed a diploma or university education. The two analyses revealed the highest and lowest average scores were earned, respectively, in the "teamwork" and "resilience" dimensions of patient safety culture. In 2020, the average scores for all dimensions were lower than in 2018, and the average scores for the emergency and critical care group were lower than those for the general adult ward group. Sub-dimension analysis showed that the general adult ward group earned significantly higher scores in "teamwork" across all three sub-dimensions compared to the emergency and critical care groups. The general ward group exhibited the most significant score decline between the two surveys. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: Overall scores were found to have decreased during the COVID-19 pandemic period (2020). Notably, emergency and intensive care nurses earned consistently lower scores, likely due to the severity of patient conditions and increased pandemic-related workloads and stress. "Resilience" scores were the lowest among all nursing staff, with the most significant drop seen in general ward nurses. Enhancing nursing staff education and training as well as addressing their psychological well-being will be crucial to improving patient safety culture attitudes. Managers should provide infection control, resilience training, and psychological counseling to help nurses manage the challenges posed by infectious diseases effectively and enhance patient safety culture.

[The structural and spatial design of German intensive care units from the point of view of infection control measures : Survey among ICU-KISS participants]. / Die bauliche Struktur der deutschen Intensivstationen aus dem Blickwinkel der Infektionsprävention : Eine Umfrage unter den ITS-KISS-Teilnehmern.

INTRODUCTION: Intensive care unit (ICU) structural and spatial design may play a role in infection prevention and control. METHODS: Between 09/2021 and 11/2021 we performed an online survey among ICUs in Germany, Austria and Switzerland. RESULTS: A total of 597 (40%) of the invited ICUs answered the survey; 20% of the ICUs were built before 1990. The median number of single rooms with interquartile range is 4 (IQR 2-6). The median total room number is 8 (IQR 6-12). The median room size is 19 (IQR 16-22) m2 for single rooms and 31 (26-37.5) m2 for multiple bed rooms. Furthermore, 80% of ICUs have sinks and 86.4% have heating, ventilation, air conditioning (HVAC) systems in patient rooms. 54.6% of ICUs must store materials outside of storage rooms due to lack of space and only 33.5% have a room dedicated to disinfection and cleaning of used medical devices. Comparing ICUs built before 1990 and after 2011 we could show a slightly increase of single rooms (3 [IQR 2-5] before 1990 vs. 5 [IQR 2-8] after 2011; p < 0.001). DISCUSSION: A large proportion of German ICUs do not meet the requirements of German professional societies regarding the number of single rooms and size of the patient rooms. Many ICUs lack storage space and other functional rooms. CONCLUSION: There is an urgent need to support the construction and renovation of intensive care units in Germany with adequate funding.

Risk factors for thromboembolic events in patients hospitalized for COVID-19 pneumonia in a general ward and requiring treatment with oxygen.

PURPOSE: To assess risk factors for arterial and venous thromboses (AVT) in patients hospitalized in general wards for COVID-19 pneumonia and requiring oxygen therapy. METHODS: Our study was based on three randomized studies conducted as part of the CORIMUNO-19 platform in France between 27 March and 26 April 2020. Adult inpatients with COVID-19 pneumonia requiring at least 3 l/min of oxygen but not ventilation were randomized to receive standard care alone or standard care plus biologics. Patients were followed up for 3 months, and adverse events were documented. Risk factor for AVT and bleeding was identified by analyzing clinical, laboratory, and treatment data at baseline among the 315 patients with complete datasets. A Fine and Gray model was used to take account of competing events. RESULTS: During the 3-month follow-up period, 39 AVT occurred in 38 (10%) of the 388 patients: 26 deep vein thromboses and/or pulmonary embolisms in 25 (6%) patients, and 14 arterial thrombotic events in 13 (3%) patients. A history of diabetes at inclusion [sHR (95% CI) = 2.65 (1.19-5.91), P = .017] and the C-reactive protein (CRP) level (sHR = 1 [1-1.01], P = .049) were significantly associated with an elevated risk of thrombosis. Obesity was not associated with a higher risk of thrombosis (sHR = 1.01 [0.4-2.57], P = .98). The CRP level and diabetes were not risk factors for hemorrhage. CONCLUSION: Among patients hospitalized in general wards for COVID-19 pneumonia during the first wave of the epidemic, diabetes (but not obesity) and a high CRP level were risk factors for AVT. The use of higher doses of anticoagulant in these high-risk patients could be considered.

A unit's experience with hybrid NICU design: description of care model and implications for patients, families, and professionals.

As the first extra-uterine setting for hospitalized infants, the neonatal intensive care unit (NICU) environment can make a lasting impact on their long-term neurodevelopment. This impact is likely mediated through both specific characteristics of the physical design of the care environment, as well as the experiences that occur within this environment. Recent studies document many established benefits of single-family rooms (SFRs). However, there is concern that infants who spend a prolonged time in SFRs without their parents being intimately involved in their care have reduced opportunities for meaningful experiences, with possible adverse consequences. The purpose of this report is to share an example of an application of the family-centered developmental care model through a hybrid NICU design, inclusive of both SFRs and semi-private bays. In this paper, we empirically describe the physical and operational considerations of a hybrid model, outline the strengths and challenges of this approach, and discuss implications for patients, families, and professionals.

The effect of technical filtering and clinical criteria on alert rates from continuous vital sign monitoring in the general ward.

OBJECTIVES: Continuous vital sign monitoring at the general hospital ward has major potential advantages over intermittent monitoring but generates many alerts with risk of alert fatigue. We hypothesized that the number of alerts would decrease using different filters. METHODS: This study was an exploratory analysis of the alert reducing effect from adding two different filters to continuously collected vital sign data (peripheral oxygen saturation, blood pressure, heart rate, and respiratory rate) in patients admitted after major surgery or severe medical disease. Filtered data were compared to data without artifact removal. Filter one consists of artifact removal, filter two consists of artifact removal plus duration criteria adjusted for severity of vital sign deviation. Alert thresholds were based on the National Early Warning Score (NEWS) threshold. RESULTS: A population of 716 patients admitted for severe medical disease or major surgery with continuous wireless vital sign monitoring at the general ward with a mean monitoring time of 75.8 h, were included for the analysis. Without artifact removal, we found a median of 137 [IQR: 87-188] alerts per patient/day, artifact removal resulted in a median of 101 [IQR: 56-160] alerts per patient/day and with artifact removal combined with a duration-severity criterion, we found a median of 19 [IQR: 9-34] alerts per patient/day. Reduction of alerts was 86.4% (p < 0.001) for values without artifact removal (137 alerts) vs. the duration criteria and a reduction (19 alerts) of 81.5% (p < 0.001) for the criteria with artifact removal (101 alerts) vs. the duration criteria (19 alerts). CONCLUSION: We conclude that a combination of artifact removal and duration-severity criteria approach substantially reduces alerts generated by continuous vital sign monitoring.

Delirium risk screening and assessment among older patients in general wards and the emergency department: a best practice implementation project.

OBJECTIVES: The aim of this project was to improve compliance with evidence-based criteria regarding risk of delirium and the assessment of delirium among older patients in the general hospitalization wards and the emergency department. INTRODUCTION: More than 50% of older hospitalized patients experience delirium. Some studies have highlighted the need to implement an orientation protocol in the emergency department and to continue this in the general wards, with the aim of decreasing the delirium rate among older patients admitted to hospital. METHODS: The project followed the JBI evidence implementation framework. We conducted a baseline audit, a half-way audit, and final audit of 50 patients at risk of delirium admitted to the emergency department and the general wards, respectively. The audits measured compliance with eight criteria informed by the available evidence. RESULTS: In the final audit, three of the eight criteria achieved more than 50% compliance in the general wards: pressure injury screening (96%); monitoring changes (74%); and performing interventions (76%). In the emergency department, worse results were reported because of the service conditions. The exception was the criterion on the training of nurses on the topic, with 98%. The integration of a tool to screen for delirium in older patients in the hospital's electronic clinical history records increased the percentage of compliance with audit criteria regarding the use of the scale and delirium detection (rising from 0% to 32% in the final audit in the general wards). CONCLUSION: Through the implementation of this project, validated and evidence-based evaluation will ensure that nurses are supported through appropriate measures to reduce patient confusion and aggression resulting from delirium.

..úNo touch..Ñ methods for health care room disinfection: Focus on clinical trials.

BACKGROUND: Hospital patient room surfaces are frequently contaminated with multidrug-resistant organisms. Since studies have demonstrated that inadequate terminal room disinfection commonly occurs, ..úno touch..Ñ methods of terminal room disinfection have been developed such as ultraviolet light (UV) devices and hydrogen peroxide (HP) systems. METHODS: This paper reviews published clinical trials of ..úno touch..Ñ methods and ..úself-disinfecting..Ñ surfaces. RESULTS: Multiple papers were identified including clinical trials of UV room disinfection devices (N.ß=.ß20), HP room disinfection systems (N.ß=.ß8), handheld UV devices (N.ß=.ß1), and copper-impregnated or coated surfaces (N.ß=.ß5). Most but not all clinical trials of UV devices and HP systems for terminal disinfection demonstrated a reduction of colonization/infection in patients subsequently housed in the room. Copper-coated surfaces were the only ..úself-disinfecting..Ñ technology evaluated by clinical trials. Results of these clinical trials were mixed. DISCUSSION: Almost all clinical trials reviewed used a ..úweak..Ñ design (eg, before-after) and failed to assess potential confounders (eg, compliance with hand hygiene and environmental cleaning). CONCLUSIONS: The evidence is strong enough to recommend the use of a ..úno-touch..Ñ method as an adjunct for outbreak control, mitigation strategy for high-consequence pathogens (eg, Candida auris or Ebola), or when there are an excessive endemic rates of multidrug-resistant organisms.

The future of postoperative vital sign monitoring in general wards: improving patient safety through continuous artificial intelligence-enabled alert formation and reduction.

PURPOSE: Monitoring of vital signs at the general ward with continuous assessments aided by artificial intelligence (AI) is increasingly being explored in the clinical setting. This review aims to describe current evidence for continuous vital sign monitoring (CVSM) with AI-based alerts - from sensor technology, through alert reduction, impact on complications, and to user-experience during implementation. RECENT FINDINGS: CVSM identifies significantly more vital sign deviations than manual intermittent monitoring. This results in high alert generation without AI-evaluation, both in patients with and without complications. Current AI is at the rule-based level, and this potentially reduces irrelevant alerts and identifies patients at need. AI-aided CVSM identifies complications earlier with reduced staff workload and a potential reduction of severe complications. SUMMARY: The current evidence for AI-aided CSVM suggest a significant role for the technology in reducing the constant 10-30% in-hospital risk of severe postoperative complications. However, large, randomized trials documenting the benefit for patient improvements are still sparse. And the clinical uptake of explainable AI to improve implementation needs investigation.

Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards.

BACKGROUND: Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice. METHODS: This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea. All adult patients admitted to general wards during the 3-month study period were included. The primary outcome was predictive accuracy for the occurrence of IHCA or UIT within 24 h of the alarm being triggered. Area under the receiver operating characteristic curve (AUROC) values were used to compare the DeepCARS™ with the modified early warning score (MEWS), national early warning Score (NEWS), and single-parameter track-and-trigger systems. RESULTS: Among 55,083 patients, the incidence rates of IHCA and UIT were 0.90 and 6.44 per 1,000 admissions, respectively. In terms of the composite outcome, the AUROC for the DeepCARS™ was superior to those for the MEWS and NEWS (0.869 vs. 0.756/0.767). At the same sensitivity level of the cutoff values, the mean alarm counts per day per 1,000 beds were significantly reduced for the DeepCARS™, and the rate of appropriate alarms was higher when using the DeepCARS™ than when using conventional systems. CONCLUSION: The DeepCARS™ predicts IHCA and UIT more accurately and efficiently than conventional methods. Thus, the DeepCARS™ may be an effective screening tool for detecting clinical deterioration in real-world clinical practice. Trial registration This study was registered at ClinicalTrials.gov ( NCT04951973 ) on June 30, 2021.

A model study for the classification of high-risk groups for cardiac arrest in general ward patients using simulation techniques.

Currently, many hospitals use vital signs-based criteria such as modified early warning score (MEWS) and national early warning score (NEWS) to classify high-risk patients for cardiac arrest, but there are limitations in selecting high-risk patients with a possibility of cardiac arrest. The purpose of this study is to develop a cardiac arrest classification model to identify patients at high risk of cardiac arrest based on the patient family and past history, and blood test results after hospitalization, rather than vital signs. This study used electronic medical record (EMR) data from A university hospital, and patients in the high-risk group for cardiac arrest were defined as those who underwent cardio-pulmonary resuscitation (CPR) after cardiac arrest. Considering the use of the rapid response team of A university hospital, patients hospitalized in intensive care units (ICU), emergency medicine departments, psychiatric departments, pediatric departments, cardiology departments, and palliative care wards were excluded. This study included 325,534 patients, of which 3291 low-risk and 382 high-risk patients were selected for study. Data were split into training and validation data sets and univariate analysis was performed for 13 candidate risk factors. Then, multivariate analysis was performed using a bivariate logistic regression model, and an optimal model was selected using simulation analysis. In the training data set, it was calculated as sensitivity 75.25%, precision 21.59%, specificity 66.89%, accuracy 67.79%, F1 score 33.56, area under curve (AUC) 71.1 (95% confidence interval [CI] = 68.9-73.1 P value=<.001). In the validation data set, sensitivity 73.37%, precision 25.81%, specificity 75.03%, accuracy 74.86%, F1 score 38.19, AUC 74.2 (95% CI = 72.1-76.2, P value=<.001) were calculated. A model for classifying the high-risk group of cardiac arrest should be developed from various perspectives. In the future, in order to classify patients with high risk of cardiac arrest, a prospective study on the combined use of the model developed by this study and NEWS or MEWS should be conducted.

Risk factors for device-related pressure injuries in general ward inpatients of a tertiary general hospital: A case-control study.

AIMS: To identify the characteristics of device-related pressure injuries (DRPI) in general ward inpatients, and to confirm the DRPI risk factors by examining differences between a DRPI and non-DRPI group. METHODS: This study is a retrospective case-control study. High-risk adult patients for pressure injuries (rated at 16 points or less on the Braden scale) who were admitted to a general ward of a tertiary general hospital in South Korea from January 1 to September 30, 2021 were enrolled in this study. Among them, participants were selected by matching the patients with DRPI (n = 50) to the non-DRPI patient group (n = 100) in a ratio of 1:2. RESULTS: As for risk factors, longer hospitalization periods and the presence of oedema increased DRPI risk. In blood tests, higher glucose levels increased the risk by 1.03 times, and lower albumin levels increased the risk by 0.08 times. Furthermore, the risk of developing DRPI was 7.89 times higher when sedatives were administered. CONCLUSIONS: Based on the DRPI risk factors identified in this study, patients who have oedema, who have long hospital stays, use sedatives and devices, have a low albumin level, and whose blood glucose is not well controlled should be recognized as having a high risk of developing DRPI. In order to prevent the development of DRPI, it is necessary to recognize risk factors at an early stage, increase actively preventive interventions. The results of this study contribute to recognizing the risk of DRPI in patients and evaluating risk factors for DRPI prevention.

Relating clustered noise data to hospital patient satisfaction.

Hospital noise can be problematic for both patients and staff and consistently is rated poorly on national patient satisfaction surveys. A surge of research in the last two decades highlights the challenges of healthcare acoustic environments. However, existing research commonly relies on conventional noise metrics such as equivalent sound pressure level, which may be insufficient to fully characterize the fluctuating and complex nature of the hospital acoustic environments experienced by occupants. In this study, unsupervised machine learning clustering techniques were used to extract patterns of activity in noise and the relationship to patient perception. Specifically, nine patient rooms in three adult inpatient hospital units were acoustically measured for 24 h and unsupervised machine learning clustering techniques were applied to provide a more detailed statistical analysis of the acoustic environment. Validation results of five different clustering models found two clusters, labeled active and non-active, using k-means. Additional insight from this analysis includes the ability to calculate how often a room is active or non-active during the measurement period. While conventional LAeq was not significantly related to patient perception, novel metrics calculated from clustered data were significant. Specifically, lower patient satisfaction was correlated with higher Active Sound Levels, higher Total Percent Active, and lower Percent Quiet at Night metrics. Overall, applying statistical clustering to the hospital acoustic environment offers new insights into how patterns of background noise over time are relevant to occupant perception.