Recovery After ICU Discharge: Post–Intensive Care Syndrome

Makic, Mary Beth Flynn

doi:10.1016/j.jopan.2015.12.006

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…Patients in the ICU are exposed to various interventions, but these interventions are necessary for survival. They include lifesaving practices such as mechanical ventilation, endotracheal intubation and chest tube insertion, central venous and arterial catheterization, and intensive drug administration (Bennett, 2015;Makic, 2016). Cognitive and psychological stressors often originate from sleep withdrawal, sedation, pain, anxiety, fear, delusions, and delirium.…”

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confidence: 99%

The Post-Intensive Care Syndrome in Children

Ekim

2018

Comprehensive Child and Adolescent Nursing

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Improvements in devices and techniques used to provide life support for patients at intensive care units have reduced patient mortality. Increases in the number of survivors from a critical illness have brought long-term complications experienced during the post-intensive care period into question. The term postintensive care syndrome (PICS) is defined as a new and deteriorating disorder in the cognitive, mental, and physical health status experienced by the survivor after intensive care unit discharge that might continue for months or even years. Opioid and sedation exposure, the severity of illnesses and injuries, dense life support interventions, length of stay in the intensive care unit, and social isolation constitute risk factors for PICS in children. These factors cause the child to experience deterioration in physical, cognitive, and psychological health domains. Such deteriorations occur on various levels and have negative effects on quality of life. The purpose of this article is to raise awareness and help pediatric nurses to develop an understanding of the condition. Increasing awareness by pediatric nurses about the magnitude and effects of complications after discharge from the intensive care unit will be the first step to protect survivors from new problems, to provide assistance for ongoing problems, and to develop follow-up strategies. PICS-related morbidities affect the majority of children discharged from PICUs. We need to understand the scope of those morbidities and develop efficient nursing interventions accordingly. It is time to expand our goal for critical and noncritical care from life-saving into improvement of functional health status and quality of life.

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confidence: 99%

The Post-Intensive Care Syndrome in Children

Ekim

2018

Comprehensive Child and Adolescent Nursing

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“…2,4,36 Continuous intravenous sedation typically necessitates an ICU environment and prolonged use of sedative medications can contribute to both short-term cognitive impairment that requires a higher level of care and long-term disability. 37,38 In this multi-center study at large tertiary referral centers, we were able to describe a significant reduction in fentanyl, midazolam, and propofol within 48-h of the tracheostomy procedure. Further, we describe a significantly higher dose of intravenous fentanyl in patients undergoing late-tracheostomy in comparison to the early-tracheostomy cohort.…”

Section: Discussionmentioning

confidence: 88%

Sedation and Analgesia in Patients Undergoing Tracheostomy in COVID-19, a Multi-Center Registry

Kapp

Latifi

Feller‐Kopman

et al. 2021

J Intensive Care Med

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Introduction Patients with COVID-19 ARDS require significant amounts of sedation and analgesic medications which can lead to longer hospital/ICU length of stay, delirium, and has been associated with increased mortality. Tracheostomy has been shown to decrease the amount of sedative, anxiolytic and analgesic medications given to patients. The goal of this study was to assess whether tracheostomy decreased sedation and analgesic medication usage, improved markers of activity level and cognitive function, and clinical outcomes in patients with COVID-19 ARDS. Study Design and Methods A retrospective registry of patients with COVID-19 ARDS who underwent tracheostomy creation at the University of Pennsylvania Health System or the Johns Hopkins Hospital from 3/2020 to 12/2020. Patients were grouped into the early (≤14 days, n = 31) or late (15 + days, n = 97) tracheostomy groups and outcome data collected. Results 128 patients had tracheostomies performed at a mean of 19.4 days, with 66% performed percutaneously at bedside. Mean hourly dose of fentanyl, midazolam, and propofol were all significantly reduced 48-h after tracheostomy: fentanyl (48-h pre-tracheostomy: 94.0 mcg/h, 48-h post-tracheostomy: 64.9 mcg/h, P = .000), midazolam (1.9 mg/h pre vs. 1.2 mg/h post, P = .0012), and propofol (23.3 mcg/kg/h pre vs. 8.4 mcg/kg/h post, P = .0121). There was a significant improvement in mobility score and Glasgow Coma Scale in the 48-h pre- and post-tracheostomy. Comparing the early and late groups, the mean fentanyl dose in the 48-h pre-tracheostomy was significantly higher in the late group than the early group (116.1 mcg/h vs. 35.6 mcg/h, P = .03). ICU length of stay was also shorter in the early group (37.0 vs. 46.2 days, P = .012). Interpretation This data supports a reduction in sedative and analgesic medications administered and improvement in cognitive and physical activity in the 48-h period post-tracheostomy in COVID-19 ARDS. Further, early tracheostomy may lead to significant reductions in intravenous opiate medication administration, and ICU LOS.

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“…Annually, over 75,000 patients are admitted to Dutch intensive care units (ICUs). Due to advances in critical care medicine, more patients survive their critical illness 1 . However, it is estimated that 25%–75% of ICU survivors experience long‐term physical, psychological, or cognitive complaints, as well as problems related to daily functioning 2,3 .…”

Section: Introductionmentioning

confidence: 99%

Optimizing an existing prediction model for quality of life one‐year post‐intensive care unit: An exploratory analysis

Jonge¹,

Wubben²,

Kaam³

et al. 2022

Acta Anaesthesiol Scand

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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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Recovery After ICU Discharge: Post–Intensive Care Syndrome

Cited by 8 publications

References 8 publications

The Post-Intensive Care Syndrome in Children

The Post-Intensive Care Syndrome in Children

Sedation and Analgesia in Patients Undergoing Tracheostomy in COVID-19, a Multi-Center Registry

Optimizing an existing prediction model for quality of life one‐year post‐intensive care unit: An exploratory analysis

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