Assessment of percent positive agreement between fluorescent marker and ATPase for environmental cleaning monitoring during sequential application in an intensive care unit.
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ABSTRACT:
SUBMITTER: Burnham JP
PROVIDER: S-EPMC7127980 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development. OBJECTIVE:This study focused on the satisfaction of ICU staff with current patient monitoring and their suggestions for future improvements. We aimed to identify aspects of monitoring that interrupt patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff members are willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further aimed to identify differences in the responses of different professional groups. METHODS:This survey study was performed with ICU staff from 4 ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire, by analyzing a preceding qualitative interview study with ICU staff, about the clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and chi-square tests to compare the distributions of item responses of the professional groups. RESULTS:In total, 86 of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated they felt confident using the patient monitoring equipment, but that high rates of false-positive alarms and the many sensor cables interrupted patient care. Regarding future improvements, respondents asked for wireless sensors, a reduction in the number of false-positive alarms, and hospital standard operating procedures for alarm management. Responses to the display devices proposed for remote patient monitoring were divided. Most respondents indicated it would be useful for earlier alerting or when they were responsible for multiple wards. AI for ICUs would be useful for early detection of complications and an increased risk of mortality; in addition, the AI could propose guidelines for therapy and diagnostics. Transparency, interoperability, usability, and staff training were essential to promote the use of AI. The majority wanted to learn more about new technologies for the ICU and required more time for learning. Physicians had fewer reservations than nurses about AI-based intelligent alarm management and using mobile phones for remote monitoring. CONCLUSIONS:This survey study of ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm standard operating procedures, introducing wireless sensors, preparing for the use of AI, and enhancing the digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine. TRIAL REGISTRATION:ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173.
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Project description:Background:Analgosedation is a cornerstone therapy for mechanically ventilated patients in intensive care units (ICU). To avoid inadequate sedation and its complications, monitoring of analgosedation is of great importance. The aim of this study was to investigate whether monitoring of analgosedative drug concentrations (midazolam and sufentanil) might be beneficial to optimize analgosedation and whether drug serum concentrations correlate with the results of subjective (Richmond Agitation-Sedation Scale [RASS]/Ramsay Sedation Scale) and objective (bispectral (BIS) index) monitoring procedures. Methods:Forty-nine intubated, ventilated, and analgosedated critically ill patients treated in ICU were clinically evaluated concerning the depth of sedation using RASS Score, Ramsay Score, and BIS index twice a day. Serum concentrations of midazolam and sufentanil were determined in blood samples drawn at the same time. Clinical and laboratory data were statistically analyzed for correlations using the Spearman's rank correlation coefficient rho (?). Results:Average age of the population was 57.8?±?16.0 years, 61% of the patients were males. Most frequent causes for ICU treatments were sepsis (22%), pneumonia (22%), or a combination of both (25%). Serum concentrations of midazolam correlated weakly with RASS (??=?-?0.467) and Ramsay Scores (??=?0.476). Serum concentrations of sufentanil correlated weakly with RASS (??=?-?0.312) and Ramsay Scores (??=?0.295). Correlations between BIS index and serum concentrations of midazolam (??=?-?0.252) and sufentanil (??=?-?0.166) were low. Conclusion:Correlations between drug serum concentrations and clinical or neurophysiological monitoring procedures were weak. This might be due to intersubject variability, polypharmacy with drug-drug interactions, and complex metabolism, which can be altered in critically ill patients. Therapeutic drug monitoring is not beneficial to determine depth of sedation in ICU patients.
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Project description:ObjectiveMultitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer - impaired learning if tasks are not appropriately selected. We introduce a sequential subnetwork routing (SeqSNR) architecture that uses soft parameter sharing to find related tasks and encourage cross-learning between them.Materials and methodsUsing the MIMIC-III (Medical Information Mart for Intensive Care-III) dataset, we train deep neural network models to predict the onset of 6 endpoints including specific organ dysfunctions and general clinical outcomes: acute kidney injury, continuous renal replacement therapy, mechanical ventilation, vasoactive medications, mortality, and length of stay. We compare single-task (ST) models with naive multitask and SeqSNR in terms of discriminative performance and label efficiency.ResultsSeqSNR showed a modest yet statistically significant performance boost across 4 of 6 tasks compared with ST and naive multitasking. When the size of the training dataset was reduced for a given task (label efficiency), SeqSNR outperformed ST for all cases showing an average area under the precision-recall curve boost of 2.1%, 2.9%, and 2.1% for tasks using 1%, 5%, and 10% of labels, respectively.ConclusionsThe SeqSNR architecture shows superior label efficiency compared with ST and naive multitasking, suggesting utility in scenarios in which endpoint labels are difficult to ascertain.
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