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Deferred Admission to the Intensive Care Unit: Rationing Critical Care or Expediting Care Transitions?

2012· letter· en· W1983421604 on OpenAlexaboutno aff
Jason Wagner

Bibliographic record

VenueArchives of Internal Medicine · 2012
Typeletter
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteAgency for Healthcare Research and Quality
KeywordsExpeditingIntensive care unitRationingMedicineIntensive care medicineEconomicsHealth care

Abstract

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There are several reasons to doubt that we can expand the supply of high-quality critical care to meet the expected surge in demand brought on by an aging population. First, critical care expenditures already strain nations’ abilities to meet other socially desirable goals.1 Second, most critically ill patients are cared for by physicians who lack specific training in critical care medicine,2 a staffing model that has been associated with worse outcomes in most studies.3 Third, severe shortages are projected in critical care workforces.4,5 Therefore, if the capacity of critical care is relatively fixed, we must instead try to improve the efficiency of care.6 Although these observations have spawned conceptual analyses about how critical care ought to be allocated, relatively little empirical work has documented how critical care is allocated. Therefore, the cohort study reported by Stelfox et al7 in this issue of the Archives is a welcome addition. To examine the influence of intensive care unit (ICU) bed scarcity on processes of care and patient outcomes, the authors evaluated 3494 consecutive episodes of sudden clinical deterioration leading to the activation of a medical emergency team (MET) at all 3 hospitals in Calgary, Canada, between January 1, 2007, and December 31, 2009. After adjusting for patient- and hospital-level covariates, the authors found that when fewer ICU beds were available at the time of MET activation, patients were less likely to be admitted to the ICU (or were admitted later) and that transitions of care were more common from resuscitative to either medical (no ICU transfer) or comfort care. Despite relatively large differences in these intermediate outcomes, in-hospital mortality was not affected by ICU bed availability. What does this seeming paradox tell us about decisions to admit patients to ICUs? Decisions to admit patients to the ICU should ideally be based on their severity of illness or other objective markers. However, this study adds to an evidence base8,9 showing that bed availability and other nonpatient-centered factors affect triage. Indeed, the present study suggests that decision making is directly affected by “nonnormative” factors such as the time of day and day of week of MET activation and the training level of the MET provider who happens to be on call. The influence of these factors on decision making, without corresponding differences in mortality, suggests that many ICU admissions are unnecessary because the patient is either too well or too sick to benefit. Furthermore, it appears that clinicians can allocate beds efficiently by eliminating nonbeneficial admissions when scarcity forces them to do so. This conclusion differs from a multicenter European study suggesting that ICU admission confers a survival advantage when beds are scarce9 but agrees with a Seattle-based study that was published a quarter of a century ago showing that although bed scarcity commonly caused clinicians to discharge patients “prematurely,” survival was unaffected.10 Such uncertainty regarding whether ICU admission could benefit some patients from whom it is denied defies efforts to label such decisions as rationing (withholding of potentially beneficial services) vs elimination of waste. Regardless, the most provocative suggestion of the present study is that clinicians may reduce ICU admissions that are not overtly beneficial by addressing patients’ goals of care in a more timely and determinative manner when they exhibit physiologic deterioration on the floor. If this mechanistic explanation is true, then scarcity may in fact motivate more patient-centered care near life’s end. Nonetheless, before we conclude that we can get better care at lower cost by closing existing ICU beds, several limitations of the study merit consideration. First, Stelfox and colleagues7 show that ICU bed availability is correlated with delayed or deferred ICU admission and with changes in goals of care, but causality remains uncertain. It is plausible that bed scarcity could cause these outcomes, and the observed “dose-response” relationships between the degree of bed scarcity and the frequency of these outcomes lend further causal support. However, there are also several alternate explanations for the results. If bed availability were truly a random variable, then residual confounding would be unlikely. However, the relationships identified, for example, between ICU bed availability and the type of MET provider, suggest degrees of nonrandomness. Furthermore, although the authors suggest that floor teams were unaware of ICU bed availability, this suggestion is uncertain and unlikely to generalize to the many hospitals in which electronic patient flow software is available at every computer terminal. Might the threshold for MET activation have been influenced by ICU bed availability? If floor teams activated METs slightly less often during times of ICU bed surplus because they were able to transfer their sickest patients directly to the ICU, then a bias against showing a mortality difference might arise. Second, the authors report that more patients are transferred to another hospital or facility if they clinically deteriorate when ICU beds are plentiful. Presumably, patients who are transferred are both sufficiently stable for transfer and invested in curative goals of care, and their exclusion potentially biases the results toward higher mortality during times of bed availability. Third, more patients were discharged home with support services when they experienced sudden declines during times of bed scarcity. If home hospice were included among these services, even greater changes toward comfort goals of care might occur when beds are scarce. However, because the ensuing deaths at home were excluded from analyses, this too would bias the relationship between hospital mortality and bed availability toward the null. Much work remains to be done before we can fully appreciate whether strained ICU capacity6 ultimately leads to more or less appropriate use of ICU resources. The study by Stelfox and colleagues7 highlights a new potential benefit of ICU bed scarcity: that it may expedite transitions of care toward palliation among patients who are likely to die with or without ICU admission. Therefore, this study’s greatest legacy may be to provide the impetus that is needed for efforts to better understand the conditions that promote clinicians’ willingness to do the difficult but important work of considering and frankly discussing all therapeutic options that may be appropriate for critically ill patients who are decompensating before their eyes.

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)0.012
metaresearch head score (Gemma)0.058
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.363
Teacher spread0.313 · 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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Citations6
Published2012
Admission routes1
Has abstractyes

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