ObjectiveTo identify views, experiences and needs for shared decision-making (SDM) in the intensive care unit (ICU) according to ICU physicians, ICU nurses and former ICU patients and their close family members.DesignQualitative study.SettingTwo Dutch tertiary centres.Participants19 interviews were held with 29 participants: seven with ICU physicians from two tertiary centres, five with ICU nurses from one tertiary centre and nine with former ICU patients, of whom seven brought one or two of their close family members who had been involved in the ICU stay.ResultsThree themes, encompassing a total of 16 categories, were identified pertaining to struggles of ICU physicians, needs of former ICU patients and their family members and the preferred role of ICU nurses. The main struggles ICU physicians encountered with SDM include uncertainty about long-term health outcomes, time constraints, feeling pressure because of having final responsibility and a fear of losing control. Former patients and family members mainly expressed aspects they missed, such as not feeling included in ICU treatment decisions and a lack of information about long-term outcomes and recovery. ICU nurses reported mainly opportunities to strengthen their role in incorporating non-medical information in the ICU decision-making process and as liaison between physicians and patients and family.ConclusionsInterviewed stakeholders reported struggles, needs and an elucidation of their current and preferred role in the SDM process in the ICU. This study signals an essential need for more long-term outcome information, a more informal inclusion of patients and their family members in decision-making processes and a more substantial role for ICU nurses to integrate patients’ values and needs in the decision-making process.
Nonsurvivors in the PICU with a low predicted mortality risk have recognizable risk factors including complex chronic condition and unplanned admissions.
Background
This study aimed to improve the PREPARE model, an existing linear regression prediction model for long‐term quality of life (QoL) of intensive care unit (ICU) survivors by incorporating additional ICU data from patients' electronic health record (EHR) and bedside monitors.
Methods
The 1308 adult ICU patients, aged ≥16, admitted between July 2016 and January 2019 were included. Several regression‐based machine learning models were fitted on a combination of patient‐reported data and expert‐selected EHR variables and bedside monitor data to predict change in QoL 1 year after ICU admission. Predictive performance was compared to a five‐feature linear regression prediction model using only 24‐hour data (R2 = 0.54, mean square error (MSE) = 0.031, mean absolute error (MAE) = 0.128).
Results
The 67.9% of the included ICU survivors was male and the median age was 65.0 [IQR: 57.0–71.0]. Median length of stay (LOS) was 1 day [IQR 1.0–2.0]. The incorporation of the additional data pertaining to the entire ICU stay did not improve the predictive performance of the original linear regression model. The best performing machine learning model used seven features (R2 = 0.52, MSE = 0.032, MAE = 0.125). Pre‐ICU QoL, the presence of a cerebro vascular accident (CVA) upon admission and the highest temperature measured during the ICU stay were the most important contributors to predictive performance. Pre‐ICU QoL's contribution to predictive performance far exceeded that of the other predictors.
Conclusion
Pre‐ICU QoL was by far the most important predictor for change in QoL 1 year after ICU admission. The incorporation of the numerous additional features pertaining to the entire ICU stay did not improve predictive performance although the patients' LOS was relatively short.
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