PubMed چکیده/رکورد

Intelligent Lung Support in the Intensive Care Unit (IntelliLung): study protocol for an international observational, prospective, multicentre study.

استودیوی صوتی مقاله

پخش حرفه‌ای فارسی و انگلیسی

در حال بررسی نسخه‌های صوتی ذخیره‌شده…

صوت تولیدشده با هوش مصنوعی است. برای کاربرد علمی یا درمانی، متن و منبع اصلی را بررسی کنید.
خواندن هوشمند فارسی و انگلیسی در حال آماده‌سازی صداهای مرورگر…
تنظیم صدای طبیعی و سرعت

صداهایی که در نامشان «Natural»، «Neural» یا «Online» دیده می‌شود معمولاً طبیعی‌ترند. انتخاب صدا به صداهای نصب‌شده در ویندوز و مرورگر شما بستگی دارد.

چکیده اصلی

INTRODUCTION: Mechanical ventilation (MV) is lifesaving in the intensive care unit (ICU) but can cause complications if not individualised according to the patient's needs. Artificial intelligence (AI)-driven decision support systems (AI-DSS) may theoretically optimise MV settings. This international observational, prospective, multicentre study aims to validate the IntelliLung AI-DSS in real clinical environments. METHODS AND ANALYSIS: In this study, patients aged ≥18 years requiring invasive MV for >24 hours are included. The primary objective is to evaluate the agreement between IntelliLung AI-DSS MV recommendations and the ventilator settings implemented by healthcare providers. The IntelliLung AI-DSS continuously analyses patient-specific data, including respiratory mechanics and gas exchange, to recommend optimal MV parameters. The primary endpoints are the relative time of matching ventilator settings for each (1) positive end-expiratory pressure, (2) fraction of inspired oxygen, (3) respiratory rate and (4) tidal volume during volume-controlled ventilation or inspiratory pressure (Pinsp) during pressure-controlled ventilation. Secondary endpoints include assessments of ventilator-free days and clinical decision-making practices. Patient-centred outcomes, such as quality of life and psychological stress, are also evaluated. Data collection spans ICU stay and follow-up at 30 and 180 days after enrolment. This trial is the first to validate the IntelliLung AI-DSS in a prospective, real-world clinical setting by comparing recommendations given by the IntelliLung AI-DSS to local standards of care. The results of the trial will serve as a foundation for future interventional studies to assess the IntelliLung AI-DSS impact on patient outcomes and ICU workflows. The study addresses a critical gap in the application of AI to intensive care, advancing personalised and evidence-based MV management. ETHICS AND DISSEMINATION: The TUD Medical Faculty Ethical Committee for clinical research approved the study on 4 November 2024 (File number Mono-EK-27907202). Additionally, the institutional review board at Sabadell, Madrid and Warsaw approved the study. IntelliLung is designed in accordance with the principles of the Declaration of Helsinki. The final main results will be published in a highly ranked, peer-reviewed scientific journal taking into account the recommendations of the International Committee of Medical Journal Editors. TRIAL REGISTRATION NUMBER: NCT06595602.

متن کامل اصلی

متن در JumpToDate ذخیره نشده است.

برای بررسی دسترسی کتابخانه‌ای یا خرید، رکورد اصلی را باز کنید.

رفتن به منبع اصلی

کلیدواژه‌ها

Adult intensive & critical careArtificial IntelligenceIntensive Care Units
در همین زیرشاخه

مقاله‌های مرتبط

PubMed2026

Bridging the Awareness-Action Gap in Disaster Preparedness: A Qualitative Study of Immersive Virtual Reality Learning Among Nursing Students.

BACKGROUND: Disasters increasingly challenge healthcare systems, highlighting the need to strengthen preparedness among future healthcare professionals. Although disaster risk awareness is important, it does not consistently translate into preparedness behaviours, reflecting the awareness-action gap. Immersive virtual reality (VR) may enhance experiential disaster learning, but how it influences preparedness processes remains insuffici…

PubMed2026

Comparison of the Validity and Reliability of Five Pressure Injury Risk Scales in Intensive Care.

BACKGROUND: The lack of consensus on the best pressure injury (PI) risk assessment tools demonstrates the need for further comparative research to identify the most effective options for intensive care unit (ICU) populations. AIM: This study compared the predictive validity and reliability of five PI risk assessment tools in adult ICU patients. STUDY DESIGN: A prospective cohort study was conducted with patients aged ≥ 18 years, admitt…

PubMed2026

Cross-Sectional Survey of Critical Care Provision in Ministry of Health Hospitals in Zambia.

BACKGROUND: Critical care is an essential component of universal health care; however, its provision in low- and middle-income countries remains poorly understood. Although Zambia has expanded critical care services over the last 15 years, national evidence regarding workforce capacity, infrastructure and service provision remains limited. AIMS: To evaluate critical care workforce, service provision and patient case mix across Ministry…

PubMed2026

Electrical Impedance Tomography-Guided Precision Nursing in an Older Patient With Septic Shock and ARDS: A Case Report.

Septic shock complicated by acute respiratory distress syndrome (ARDS) and multiple organ dysfunction syndrome (MODS) presents a fundamental therapeutic conflict: shock demands aggressive fluid resuscitation, whereas lung protection requires fluid restriction and a negative fluid balance. We report the precision nursing management of a 79-year-old patient with septic shock, ARDS and MODS who received invasive mechanical ventilation and…