CoChat
Sign Up
Cited by 31Sepsis Diagnosis and TreatmentEmergency and Acute Care StudiesHospital Admissions and Outcomes

Analysis of Benefit of Intensive Care Unit Transfer for Deteriorating Ward Patients: A Patient-Centered Approach to Clinical Evaluation

Open access
Grieve, Richard, O’Neill, Stephen, Basu, Anirban, Keele, Luke, Rowan, Kathryn M., Harris, Steve·2019·JAMA Network Open
DOI 10.1001/jamanetworkopen.2018.7704PMC PMC6484590
Open this paper in CoChat — free accountOpen PDF

Abstract

Importance: It is unknown which deteriorating ward patients benefit from intensive care unit (ICU) transfer. Objectives: To use an instrumental variable (IV) method that assesses heterogeneity and to evaluate estimates of person-centered treatment effects of ICU transfer and 28-day hospital mortality by age and illness severity. Design, Setting, and Participants: An analysis of a prospective cohort study from November 1, 2010, to December 31, 2011. The dates of this analysis were June 1, 2017, to June 30, 2018. The setting was a multicenter study of 49 UK National Health Service hospitals. Participants were 9192 deteriorating ward patients assessed for ICU transfer (4596 matched pairs). The study matched on baseline characteristics to strengthen the IV and to balance observed confounders between the comparison groups. Exposures: Transfer to the ICU or continued care on general wards. Main Outcomes and Measures: Mortality at 28 days (primary outcome) and 90 days. To address unobserved confounding, ICU bed availability was the IV for whether or not a patient was transferred. The study used the IV approach to evaluate estimates of treatment effect of ICU transfer and mortality according to age and physiological severity alone and in combination. Results: Both comparison groups included 4596 patients. In the group assessed with "many" ICU beds available (median, 7), 52.8% were male, and the mean (SD) age was 65.2 (17.7) years; in the group assessed with "few" ICU beds available (median, 2), 53.3% were male, and the mean (SD) age was 65.0 (17.3) years. The overall 28-day mortality estimates were 23.2% (2090 predicted deaths) if all of the matched patients were transferred vs 28.1% (2534 predicted deaths) if none of the matched patients were transferred, an estimated risk difference of -4.9% (95% CI, -26.4% to 16.6%). The estimated effects of ICU transfer differed by age and by physiological severity according to the National Early Warning Score (NEWS): the absolute risk differences in 28-day mortality after ICU transfer ranged from 7.7% (95% CI, -5.5% to 21.0%) for ages 18 to 23 years to -5.0% (95% CI -26.5% to 16.6%) for age 78 to 83 years and ranged from 3.7% (95% CI, -12.1% to 19.5%) for NEWS of 0 to -25.4% (95% CI, -50.6% to -0.2%) for NEWS of 19. The absolute risk differences for elderly patients (≥75 years) were -11.6% (95% CI, -39.0% to 15.8%) for those with high NEWS (>6), -4.8% (95% CI, -30.5% to 20.9%) for those with moderate NEWS (5-6), and -1.0% (95% CI, -24.8% to 22.8%) for those with low NEWS (<5). The corresponding estimates for subgroups of younger patients (<75 years) were -8.4% (95% CI, -31.0% to 14.1%), -2.1% (95% CI, -21.1% to 16.9%), and 1.4% (95% CI, -14.5% to 17.4%). Conclusions and Relevance: This study using a this person-centered IV approach found that the benefits of ICU care may increase among patients at high levels of baseline physiological severity across different age groups, especially among elderly patients.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1001/jamanetworkopen.2018.7704

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Early ICU transfer is a window of critical opportunity for timely clinical interventions that alter the trajectory of a patient's clinical progression, prevent adverse outcomes and improve survival.

    “Early ICU transfer has been shown to be a ‘window of critical opportunity’ for timely clinical interventions that alter the trajectory of a patient’s clinical progression, prevent adverse outcomes and improve survival35–39.”

    From Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial (2025) · cites this paper as [39]

    The Results report an estimated reduction in 28-day mortality after ICU transfer, with greater benefit for some patient groups, but the overall estimate has a wide confidence interval. The Introduction explicitly says this study assessed ICU transfer itself, unlike an earlier study focused on transfer within four hours. It does not establish that early transfer provides a critical window for interventions that alter clinical progression or prevent adverse outcomes. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 41 · Cited by 31

Research works printed but not linked: 36

  1. [1]Flaatten H, de Lange DW, Artigas A, The status of intensive care medicine research and a future agenda for very old patients in the ICU. Intensive Care Med. 2017; 43(9):-. doi: 10.1007/s00134-017-4718-z 28238055 · DOI
  2. [2]Angus DC Admitting elderly patients to the intensive care unit: is it the right decision? JAMA. 2017; 318(15): 1443- 1444. doi: 10.1001/jama.2017.14535 28973429 · DOI
  3. [3]Halpern NA, Pastores SM Critical care medicine beds, use, occupancy and costs in the United States: a methodological review. Crit Care Med. 2015; 43(11): 2452- 2459. doi: 10.1097/CCM.0000000000001227 26308432 PMC5520980 · DOI
  4. [4]Gabler NB, Ratcliffe SJ, Wagner J, Mortality among patients admitted to strained intensive care units. Am J Respir Crit Care Med. 2013; 188(7): 800- 806. doi: 10.1164/rccm.201304-0622OC 23992449 PMC3826272 · DOI
  5. [5]Renaud B, Santin A, Coma E, Association between timing of intensive care unit admission and outcomes for emergency department patients with community-acquired pneumonia. Crit Care Med. 2009; 37(11): 2867- 2874. doi: 10.1097/CCM.0b013e3181b02dbb 19770748 · DOI
  6. [6]Simchen E, Sprung CL, Galai N, Survival of critically ill patients hospitalized in and out of intensive care. Crit Care Med. 2007; 35(2): 449- 457. doi: 10.1097/01.CCM.0000253407.89594.15 17167350 · DOI
  7. [7]Guidet B, Leblanc G, Simon T,; ICE-CUB 2 Study Network Effect of systematic intensive care unit triage on long-term mortality among critically ill elderly patients in France: a randomized clinical trial. JAMA. 2017; 318(15): 1450- 1459. doi: 10.1001/jama.2017.13889 28973065 PMC5710364 · DOI
  8. [8]Sprung CL, Artigas A, Kesecioglu J, The Eldicus prospective, observational study of triage decision making in European intensive care units, part II: intensive care benefit for the elderly. Crit Care Med. 2012; 40(1): 132- 138. doi: 10.1097/CCM.0b013e318232d6b0 22001580 · DOI
  9. [9]Sinuff T, Kahnamoui K, Cook DJ, Luce JM, Levy MM; Values Ethics and Rationing in Critical Care Task Force Rationing critical care beds: a systematic review. Crit Care Med. 2004; 32(7): 1588- 1597. doi: 10.1097/01.CCM.0000130175.38521.9F 15241106 · DOI
  10. [10]Guidet B, Flaatten H, Boumendil A,; VIP1 Study Group Withholding or withdrawing of life-sustaining therapy in older adults (≥ 80 years) admitted to the intensive care unit. Intensive Care Med. 2018; 44(7): 1027- 1038. doi: 10.1007/s00134-018-5196-7 29774388 · DOI
  11. [12]Lu YF, Goldstein DB, Angrist M, Cavalleri G Personalized medicine and human genetic diversity. Cold Spring Harb Perspect Med. 2014; 4(9): a008581. doi: 10.1101/cshperspect.a008581 25059740 PMC4143101 · DOI
  12. [13]Ashley EA The precision medicine initiative: a new national effort. JAMA. 2015; 313(21): 2119- 2120. doi: 10.1001/jama.2015.3595 25928209 · DOI
  13. [14]Seymour CW, Gomez H, Chang CH, Precision medicine for all? challenges and opportunities for a precision medicine approach to critical illness. Crit Care. 2017; 21(1): 257. doi: 10.1186/s13054-017-1836-5 29047353 PMC5648512 · DOI
  14. [15]Iwashyna TJ, Burke JF, Sussman JB, Prescott HC, Hayward RA, Angus DC Implications of heterogeneity of treatment effect for reporting and analysis of randomized trials in critical care. Am J Respir Crit Care Med. 2015; 192(9): 1045- 1051. doi: 10.1164/rccm.201411-2125CP 26177009 PMC4642199 · DOI
  15. [16]Basu A, Gore JL Are elderly patients with clinically localized prostate cancer overtreated? exploring heterogeneity in survival effects. Med Care. 2015; 53(1): 79- 86. doi: 10.1097/MLR.0000000000000260 25397964 PMC5845767 · DOI
  16. [17]Basu A Estimating person-centered treatment (PeT) effects using instrumental variables: an application to evaluating prostate cancer treatments. J Appl Econ (Chichester Engl). 2014; 29(4): 671- 691. doi: 10.1002/jae.2343 25620844 PMC4300990 · DOI
  17. [18]Basu A Person-centered treatment (PeT) effects: individualized treatment effects using instrumental variables. Stata J. 2015;15(2): 397-410. doi: 10.1177/1536867X1501500204 · DOI
  18. [19]Heckman JJ, Vytlacil E Structural equations, treatment effects, and econometric policy evaluation. Econometrica. 2005; 73(3): 669- 738. doi: 10.1111/j.1468-0262.2005.00594.x · DOI · Paper page
  19. [20]Harris S, Singer M, Sanderson C, Grieve R, Harrison D, Rowan K Impact on mortality of prompt admission to critical care for deteriorating ward patients: an instrumental variable analysis using critical care bed strain [published online May 7, 2018] [published correction appears in Intensive Care Med. 2018;44(5):701]. Intensive Care Med. doi: 10.1007/s00134-018-5148-2 29736785 PMC6006241 · DOI · Paper page
  20. [21]Valley TS, Sjoding MW, Ryan AM, Iwashyna TJ, Cooke CR Association of intensive care unit admission with mortality among older patients with pneumonia. JAMA. 2015; 314(12): 1272- 1279. doi: 10.1001/jama.2015.11068 26393850 PMC4758179 · DOI
  21. [22]Stukel TA, Fisher ES, Wennberg DE, Alter DA, Gottlieb DJ, Vermeulen MJ Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. JAMA. 2007; 297(3): 278- 285. doi: 10.1001/jama.297.3.278 17227979 PMC2170524 · DOI
  22. [23]von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med. 2007; 147(8): 573- 577. doi: 10.7326/0003-4819-147-8-200710160-00010 17938396 · DOI · Paper page
  23. [26]Kim SH, Chan CW, Olivares M, Escobar GJ Association among ICU congestion, ICU admission decision, and patient outcomes. Crit Care Med. 2016; 44(10): 1814- 1821. doi: 10.1097/CCM.0000000000001850 27332046 · DOI
  24. [28]Harrison DA, Parry GJ, Carpenter JR, Short A, Rowan K A new risk prediction model for critical care: the Intensive Care National Audit & Research Centre (ICNARC) model. Crit Care Med. 2007; 35(4): 1091- 1098. doi: 10.1097/01.CCM.0000259468.24532.44 17334248 · DOI
  25. [29]Vincent JL, Moreno R, Takala J, The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. Intensive Care Med. 1996; 22(7): 707- 710. doi: 10.1007/BF01709751 8844239 · DOI · Paper page
  26. [31]Keele LJ, Harris S, Grieve RD Stronger instruments and refined covariate balance in an observational study of the effectiveness of prompt admission to the ICU in the UK [published online December 24, 2018]. J R Stat Soc Ser A. doi: 10.1111/rssa.12437 · DOI
  27. [32]Lorch SA, Baiocchi M, Ahlberg CE, Small DS The differential impact of delivery hospital on the outcomes of premature infants. Pediatrics. 2012; 130(2): 270- 278. doi: 10.1542/peds.2011-2820 22778301 PMC4074612 · DOI
  28. [33]Neuman MD, Rosenbaum PR, Ludwig JM, Zubizarreta JR, Silber JH Anesthesia technique, mortality, and length of stay after hip fracture surgery. JAMA. 2014; 311(24): 2508- 2517. doi: 10.1001/jama.2014.6499 25058085 PMC4344128 · DOI
  29. [34]Baiocchi M, Small DS, Lorch S, Rosenbaum PR Building a stronger instrument in an observational study of perinatal care for premature infants. J Am Stat Assoc. 2010; 105(492): 1285- 1296. doi: 10.1198/jasa.2010.ap09490 · DOI
  30. [35]Baiocchi M, Small DS, Yang L, Polsky D, Groeneveld PW Near/far matching: a study design approach to instrumental variables. Health Serv Outcomes Res Methodol. 2012; 12(4): 237- 253. doi: 10.1007/s10742-012-0091-0 27087781 PMC4831129 · DOI
  31. [36]Stock J, Yogo M Testing for weak instruments in linear IV regression In: Andrews D, ed. Identification and Inference for Econometric Models. New York, NY: Cambridge University Press; 2005: 80- 108. doi: 10.1017/CBO9780511614491.006 · DOI · Paper page
  32. [37]Heckman JJ, Vytlacil EJ Local instrumental variables and latent variable models for identifying and bounding treatment effects. Proc Natl Acad Sci U S A. 1999; 96(8): 4730- 4734. doi: 10.1073/pnas.96.8.4730 10200330 PMC16400 · DOI
  33. [38]Heckman JJ, Vytlacil E Local instrumental variables In: Hsiao C, Morimue K, Powell J, eds. Nonlinear Statistical Modeling: Proceedings of the Thirteenth International Symposium in Economic Theory and Econometrics: Essays in Honor of Takeshi Amemiya. Vol 13. New York, NY: Cambridge University Press; 2001:1-46. doi: 10.1017/CBO9781139175203.003 · DOI
  34. [39]Rowan KM, Angus DC, Bailey M,; PRISM Investigators Early, goal-directed therapy for septic shock: a patient-level meta-analysis. N Engl J Med. 2017; 376(23): 2223- 2234. doi: 10.1056/NEJMoa1701380 28320242 · DOI · Paper page
  35. [40]Wooldridge JM Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: The MIT Press; 2010.
  36. [41]Ioannidis JPA The proposal to lower P value thresholds to.005. JAMA. 2018; 319(14): 1429- 1430. doi: 10.1001/jama.2018.1536 29566133 · DOI

Authors
Grieve, R., O’Neill, S., Basu, A., Keele, L., Rowan, K. M., & Harris, S.
Affiliations
  • London School of Hygiene & Tropical Medicine (GB)
  • Ollscoil na Gaillimhe – University of Galway (IE)
  • University of Washington (US)
  • University of Pennsylvania (US)
  • Intensive Care National Audit & Research Centre (ICNARC), London, United Kingdom
  • University College London (GB)
Corresponding author
Grieve, Richard
ORCID
Venue
JAMA Network Open
Publisher
American Medical Association (AMA)
Published
2019-02-15
Volume
2
Issue
2
Pages
e187704
ISSN
2574-3805 (ISSN-L)
DOI
10.1001/jamanetworkopen.2018.7704
PMC
PMC6484590
OpenAlex
W2911927281
Cited by
31
References
41
Citations by year
2026: 4 · 2025: 2 · 2024: 3 · 2023: 7 · 2022: 6 · 2021: 2 · 2020: 4 · 2019: 3
FWCI
2.85
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JAMA Network Open (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • Wellcome Trust
  • National Institute for Health and Care Research (SRF-2013-06-016)
Topics
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
  • Health Sciences › Medicine › Emergency Medicine › Hospital Admissions and Outcomes
Keywords
ICU transfer, 28-day mortality, 90-day mortality, ICU bed availability, National Early Warning Score, general ward care, instrumental variable estimation, unmeasured confounding, in-hospital mortality prediction, treatment effect heterogeneity, National Health Service hospitals
MeSH terms
Aged, Aged, 80 and over, Female, Humans, Intensive Care Units, Male, Middle Aged, Patient Transfer, Prospective Studies, Critical Illness, Patient-Centered Care, Sepsis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review