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Record W4404375886 · doi:10.1097/ccm.0000000000006452

Get Up, Stand Up! Take This Step to Decrease ICU Readmissions*

2024· article· en· W4404375886 on OpenAlexaboutno aff
Nika Filatova, Christa Schorr

Bibliographic record

VenueCritical Care Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEmergency medicineRetrospective cohort studyCohortResource useCohort studyIntensive care medicineInternal medicine

Abstract

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In this issue of Critical Care Medicine, there are two articles reporting an association of the ICU patient’s inability to sit-to-stand at ICU discharge to outcomes of mortality (Siao et al [1]) and ICU readmission (Brosseau et al [2]). Here, we discuss the study by Brosseau et al (2) and link of failure of sit-to-stand with ICU readmissions. Hospital beds are a finite resource and none more so than beds in the ICU. There is a continuous cycling of admissions and discharges essential to provide patients with the right care in the right place. Unfortunately, the pressures of hospital throughput by “making room” can force premature ICU discharges and subsequent ICU readmissions. Rates vary from 5% to 16% of the total ICU discharges (3). ICU readmissions are associated with increased resource use and poor outcomes including increased patient morbidity and two- to ten-fold increase in mortality (4). Also, between 13% and 86% of readmissions are classified as potentially preventable (4). The most common predictive factors for ICU readmission are age, hypoxia, and Acute Physiology and Chronic Health Evaluation scores (5). Brosseau et al (2) conducted a retrospective, multicenter cohort study to evaluate the association between the inability to stand at the time of ICU discharge and the probability of ICU readmission. The study used the ICU mobility scale, which defines standing as “weight-bearing through the feet in standing position with or without assistance.” Readmission was defined by Brosseau et al (2) as a new transfer from the ward to the ICU 24 hours or more after initial ICU discharge. Data were obtained from the Toronto Intensive Care Observational Registry (iCORE) project from September 2014 to January 2020. The registry includes prospectively collected data from daily case report forms of critically ill patients in nine ICUs from seven different hospitals in the Greater Toronto area. A total of 8017 patients with a single inclusion criterion, mechanical ventilation for at least 4 hours were evaluated. Exclusion criteria were death during the first ICU stay, transfer to another institution not included in the iCORE registry at ICU discharge or an ICU stay of less than 2 days. All participating ICUs had physical therapists (PTs) and used established guidelines to mobilize patients. Patient characteristics include a median age of 62 with a male predominance (62%) and comparable comorbidities (2). There were several notable differences between the able to stand group (n = 3289) and the unable to stand group (n = 4728); elective postoperative admission (43.9% vs. 23.2%) and interventions on the day of ICU discharge including mechanical ventilation (15.6% vs. 47%) and vasopressors (4.5% vs. 13.4%). Additional data was provided for patients with missing mobility assessments (n = 1378), which were not included in further analyses. Brosseau et al (2) report that of the 291 patients (3.6%) who were readmitted, nearly 80% were unable to stand at the time of transfer out of the ICU. A multivariable logistic regression was used to determine the association between inability to stand at ICU discharge and ICU readmission. The association was significant (odds ratio, 1.85%; 95% CI, 1.32–2.62; p < 0.001), even after adjusting for confounders including age, sex, comorbidities, admission status, need for renal replacement therapy, greater than 7-day stay, and interventions on the day of discharge. Although patients with missing exposure were not included in the analyses, the readmission rate in this group was 8.1%. There are several strengths of the study by Brosseau et al (2), one is the large sample size gathered from a registry containing detailed clinical variables. Second, these data were from institutions that followed mobility guidelines with support from PTs. Limitations include the inherent observational retrospective nature of the study, the sit-to-stand test was not done by or observed by a researcher allowing for validation of the score provided. There was no measure reported as to how long the patient could stand nor how much assistance the patient required. Patients that were readmitted to the ICU within 24 hours were not counted as a readmission. The absence of the reason for readmission is valuable information, as the cause for readmission may or may not be related to musculoskeletal weakness. Finally, there was no assessment at baseline regarding the patients’ mobility status or ability to complete a sit-to-stand position. It is clear in the study by Brosseau et al (2) that assisting patients in completing a simple mobility task, such as standing, can impact their potential to avoid ICU readmission. However, this may not be so “simple” after all. While the ICU Mobility Scale gives a clear picture regarding what mobility task a patient has achieved, standing is much more nuanced than that measurement. This outcome measure leaves valuable information out—How much assistance did the patient require? How long did the patient stand? To capture some of this pertinent information, perhaps it would be worth determining if a similar correlation to ICU readmission exists with the use of a different outcome measure, such as the Activity Measure for Post-Acute Care, which quantifies the amount of assistance a patient needs to perform specific mobility tasks or the John Hopkins Highest Level of Mobility scale, which for most levels, quantifies mobility via time or distance. Regardless of how it is measured, the fact that the association between a patient’s mobility level and an outcome as crucial as ICU readmission should encourage providers to begin to have thorough discussions about the overall mobility program and status of their patients in the ICU. Just stating that “PT is seeing the patient” is not enough. Clinicians strive to organize care at the end of rounds with mnemonics tools such as “FASTHUG-BID” to help the team address details of care and next steps (6) Yet, most tools do not address mobility or sit-to-stand ability. While many institutions have acknowledged and adopted the importance of dedicated ICU PTs, the physical activity of the patient is rarely discussed in any detail on rounds. Meanwhile, we are aware that decreased mobility is known to increase the patients’ risk of pressure ulcers, infections, deep vein thromboses, atelectasis, and aspiration (7) and now readmission. Additionally, a structured method to assess patients for readiness to downgrade/transfer is lacking in practice. The transfer readiness assessment is often reduced to broad factors including hemodynamic stability and oxygenation requirements (8). While PTs are specifically trained to mobilize patients, they are not always a readily available resource. In fact, only 34% of ICUs have dedicated PTs (9). Depending on the size of an ICU, it may not be feasible for the PT to see every appropriate patient every day. Aiming for adequate PT support to provide early mobilization in the ICU will require additional resources and can net a significant cost savings associated with decreased length of stay (10). Even with ICU-dedicated PTs, patients will spend most of their days immobile if the nursing staff does not engage patients in mobility. It may be that nurses do not have time or are concerned about safety. There are inherent safety concerns with mobilization of the critically ill population, including falls, dislodgement of catheters and tubes, and hemodynamic stability (11). While mobility is shown to be safe for critically ill patients with a variety of factors of care (e.g., mechanical support devices, invasive monitoring devices, etc), there may be several potential barriers from the nursing perspective. Some of these barriers include staffing issues, time constraints, no formal protocol/program for mobility, and limited equipment (12). Another concern is that 25% of workers’ compensation claims are a result of patient handling related injuries (11). The development and implementation of a formal early ICU mobility program, whether PT- or nurse-driven will take time, education, and resources. Meanwhile, patients are at risk of negative outcomes every minute they are immobile lying in bed. Immobility can have negative effects on the patient’s psychological and emotional well-being too. Try to recall when your critically ill patient sat in a chair or walked for the first time after a long ICU stay. The patient and unit were excited! Although it required a lot of teamwork and time, our patients are worth this effort. To get to the next step, it may be beneficial to immediately begin discussions with the multidisciplinary team (i.e., during daily rounding) regarding the importance of using the current available tools, such as the “chair position” function of the hospital beds, to promote upright positioning to address cardiovascular and respiratory compromise. These small steps to get your patients up and move toward sit-to-stand can develop into a more active mobility program. Finally, standardizing a method to assess transfer readiness including the patient’s ability to sit-to-stand may lead to less ICU readmissions in your hospital. Although ICU readmission prediction models exist, none include the inability to sit-to stand. Perhaps adding this variable to these prediction models is warranted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.056
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0060.005
Open science0.0020.002
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0560.023

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.361
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2024
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