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1.
Crit Care Nurs Q ; 46(2): 227-238, 2023.
Article in English | MEDLINE | ID: covidwho-2270420

ABSTRACT

This study aims to develop and validate a checklist of discharge readiness criteria for COVID-19 patients from the intensive care unit (ICU). We conducted a Delphi design study. The degree of agreement among 7 experts had been evaluated using the content validity index (CVI) through a 4-point Likert scale. The instrument was validated with 17 items. All the experts rated all items as very relevant which scored the item-CVI 1, which validates all checklist items. Using the mean of all items, the scale-CVI was calculated, and it was 1. This meant validation of the checklist as a whole. With regard to the overall checklist evaluation, the mean expert proportion of the instrument was 1, and the S-CVI/UA was 1. This discharge criteria checklist improves transition of care for COVID-19 patients and can help nurses, doctors, and academics to discharge COVID-19 patients from the ICU safely.


Subject(s)
COVID-19 , Checklist , Humans , Patient Discharge , Intensive Care Units , Reproducibility of Results
2.
Crit Care Nurs Q ; 46(2): 217-226, 2023.
Article in English | MEDLINE | ID: covidwho-2270419

ABSTRACT

We aimed to develop and validate a model for the criteria for admission of COVID-19 patients to the intensive care unit (ICU). A Delphi design study was conducted. The content validity index (CVI) was used to determine the degree of agreement among the experts to validate the content of the admission criteria tool. Eleven experts determined the validity. The evaluation was conducted using a 4-point rating scale. The accepted CVI value was 0.50 and more. The model was validated with 31 items in the 5 dimensions, with the item-CVI of 1, a face validity index of 1, and a scale-level content validity index (S-CVI) value of 1. We have developed and validated a red flag prediction model for ICU admission of COVID-19 patients. The accurate implementation of this model could improve the outcomes of those patients and possibly decrease mortality.


Subject(s)
COVID-19 , Humans , Surveys and Questionnaires , Intensive Care Units , Hospitalization , Reproducibility of Results
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