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Identifier 000416940
Title Έκβαση σοβαρών ανεπιθύμητων συμβαμάτων σε νοσηλευόμενους ασθενείς με βάση προειδοποιητικούς δείκτες
Alternative Title Severe adverse events outcome of patients treated in hospital general wards according to early warning system
Author Ζωγραφάκης-Σφακιανάκης, Μιχαήλ
Thesis advisor Αγγουριδάκης, Παναγιώτης
Reviewer Ασκητοπούλου, Ελένη
De Bree, Elcon
Γκίκας, Αχιλλέας
Ζώρας, Οδυσσέας
Χρήστος, Ιωάννου
Παπαίωάννου, Αλεξάνδρα
Abstract Patients’ health admitted to general hospital may deteriorate during their hospitalization in general wards or the severity of their clinical condition might be underestimated at the time of admission. Consequently, these patients may suffer a Severe Adverse Event (SAE), defined as cardiac arrest or life-threatening organ failure that could result in death or unplanned admission in the Intensive Care Unit (ICU). Furthermore, cardiac arrest cases and unplanned admissions to ICU are associated with increased ICU and in-hospital mortality rate, increasing the number of potentially avoidable deaths. The significance of this problem can be recognized in the results of several studies showing that a SAE is neither sudden nor an unpredictable situation, but rather a progressive process that remained unrecognized (or received suboptimal treatment) for up to 24 hours prior to a SAE. Many in-hospital arrests should be early identified by the warning signs and therefore an early intervention could be decided promoting better outcomes. A large observational study with hospitalized patients has shown that 9% had at least one clinically abnormal vital sign or adverse event in early stages of admission. Patients’ mortality rate is associated with signs missed during admission and hospitalization. Aim of the study: To determine the value of an algorithm such as the Modified Early Warning Score (MEWS) for general wards' patients and its potential use as an alarm tool for ward nurses. Methods: A combined prospective – retrospective observational study was conducted through 153 patients, in a university hospital. All patients were admitted in the Intensive Care Unit (ICU) from general wards. Parameters retrospectively studied were: five MEWS values in 4-hourly intervals, up to 20 hours before ICU admission. Parameters prospectively studied were: ICU Length of Stay, ICU mortality and mortality after ICU discharge. Results: Most frequent severe adverse events were acute respiratory failure (39.9%) and septic shock (20.3%). MEWS increased gradually during the last 20 hours and most 72 patients remained in the wards, above a cutoff point ≥7, 4 hours before admission. Significant associations between latest MEWS score and ICU mortality and ICU Length of Stay were found. MEWS ≥7, hours before admission, was highly associated with increased ICU and hospital mortality. Discussion: This study confirmed the existence of undetected critically ill patients in general wards and the progressive nature of their deterioration. It highlights the patients’ clinical deterioration during the last 20 hours and mostly the last 8 hours before their admission in the ICU. This was measured using MEWS system and gives evidence to support the use of MEWS system for nurses’ routinely observation, as an alarm tool for deterioration that can also be easily incorporated to the patient’s records. Using MEWS algorithms, nurses can talk a common language with doctors, regarding the patient’s assessment in emergency situations and this is a critical issue at the time of the incident. Usually, in some developed countries which run budget deficit and austerity measures, there is a lack of nursing and medical staff in hospitals. Conclusion: The implementation of algorithms such as MEWS could be a perfect tool and a strong predictor of the outcome, to avoid underestimation of patients’ clinical situation, which could help all understaffed hospital wards. Also, this could reflect to reduced ICU admission and mortality rates. Avoiding ICU admissions, is a cost containment measure, which is essential for all governmental health policies.
Language Greek, English
Subject Intensive care unit
Μονάδα εντατικής θεραπείας
Issue date 2018-07-18
Collection   Faculty/Department--School of Medicine--Department of Medicine--Doctoral theses
  Type of Work--Doctoral theses
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