CoChat
Sign Up
Cited by 56Sepsis Diagnosis and TreatmentHospital Admissions and OutcomesEmergency and Acute Care Studies

Comparison of Unplanned Intensive Care Unit Readmission Scores: A Prospective Cohort Study

Open access
Rosa, Regis Goulart, Roehrig, Cintia, Oliveira, Roselaine Pinheiro de, Maccari, Juçara Gasparetto, Antônio, Ana Carolina Peçanha, Castro, Priscylla de Souza, Neto, Felippe Leopoldo Dexheimer, Balzano, Patrícia de Campos +1 more·2015·PLOS ONE
DOI 10.1371/journal.pone.0143127PMC PMC4658118
Open this paper in CoChat — free accountOpen PDF

Abstract

PURPOSE: Early discharge from the intensive care unit (ICU) may constitute a strategy of resource consumption optimization; however, unplanned readmission of hospitalized patients to an ICU is associated with a worse outcome. We aimed to compare the effectiveness of the Stability and Workload Index for Transfer score (SWIFT), Sequential Organ Failure Assessment score (SOFA) and simplified Therapeutic Intervention Scoring System (TISS-28) in predicting unplanned ICU readmission or unexpected death in the first 48 hours after discharge from the ICU. METHODS: We conducted a prospective cohort study in a single tertiary hospital in southern Brazil. All adult patients admitted to the ICU for more than 24 hours from January 2008 to December 2009 were evaluated. SWIFT, SOFA and TISS-28 scores were calculated on the day of discharge from the ICU. A stepwise logistic regression was conducted to evaluate the effectiveness of these scores in predicting unplanned ICU readmission or unexpected death in the first 48 hours after discharge from the ICU. Moreover, we conducted a direct accuracy comparison among SWIFT, SOFA and TISS-28 scores. RESULTS: A total of 1,277 patients were discharged from the ICU during the study period. The rate of unplanned ICU readmission or unexpected death in the first 48 hours after discharge from the ICU was 15% (192 patients). In the multivariate analysis, age (P = 0.001), length of ICU stay (P = 0.01), cirrhosis (P = 0.03), SWIFT (P = 0.001), SOFA (P = 0.01) and TISS-28 (P<0.001) constituted predictors of unplanned ICU readmission or unexpected death. The SWIFT, SOFA and TISS-28 scores showed similar predictive accuracy (AUC valueswere 0.66, 0.65 and 0.67, respectively; P = 0.58) [corrected]. CONCLUSIONS: SWIFT, SOFA and TISS-28 on the day of discharge from the ICU have only moderate accuracy in predicting ICU readmission or death. The present study did not find any differences in accuracy among the three scores.

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 · 10.1371/journal.pone.0143127

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?

  • The SWIFT score often exhibits low discriminative power, yielding an AUROC of only 0.58 in prospective cohorts.

    “As demonstrated in the study by Rosa et al. [11], the SWIFT score often exhibits low discriminative power, yielding an AUROC of only 0.58 in prospective cohorts.”

    From AI-Driven Predictions of Readmission and Mortality for Improved Discharge Decisions in Critical Care: A Retrospective Study (2026) · cites this paper as Rosa et al. [11]

    The Results (Table 7) report a SWIFT AUC of 0.65 in Rosa et al.’s prospective cohort, not 0.58. The Discussion attributes the 0.58 AUC to a separate retrospective study by Kastrup et al.; Rosa et al. describe their own score’s discrimination as moderate. (LLM, conf=0.90)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • The SWIFT score performs poorly and inconsistently upon implementation.

    “Nevertheless, although the SWIFT criterion is the only validated risk score for ICU discharge, it performs poorly and inconsistently upon implementation [10,11].”

    From AI-Driven Predictions of Readmission and Mortality for Improved Discharge Decisions in Critical Care: A Retrospective Study (2026) · cites this paper as [11]

    The Results report only moderate discrimination for SWIFT in this cohort (AUC 0.65), and the Discussion contrasts promising results in earlier studies with poorer results in another, supporting variability. Calling its performance uniformly poor overstates the paper, which concludes that SWIFT can predict readmission or death with moderate accuracy. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • The SWIFT score is the only validated risk score for ICU discharge.

    “Nevertheless, although the SWIFT criterion is the only validated risk score for ICU discharge, it performs poorly and inconsistently upon implementation [10,11].”

    From AI-Driven Predictions of Readmission and Mortality for Improved Discharge Decisions in Critical Care: A Retrospective Study (2026) · cites this paper as [11]

    The Introduction describes SWIFT as a score developed to predict readmission or death after ICU discharge, and the Discussion reports studies of its predictive performance. Neither section says it is the only validated risk score for ICU discharge; the paper also evaluates other scores without establishing their validation status. (LLM, conf=0.85)

    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.

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 30 · Cited by 56

Research works printed but not linked: 29

  1. [1]Center for Medicare and Medicaid Services. Medicare Program; Changes to the Hospital Inpatient Prospective Payment Systems and Fiscal Year 2009 Rates; Payments for Graduate Medical Education in Certain Emergency Situations; Changes to Disclosure of Physician Ownership in Hospitals and Physician Self-Referral Rules; Updates to the Long-Term Care Prospective Payment System; Updates to Certain IPPS-Excluded Hospitals; and Collection of Information Regarding Financial Relationships Between Hospitals. Final rules. Fed Regist. 2008; 73(161): 48433– 9084. Available: http://www.cms.gov/AcuteInpatientPPS/downloads/CMS-1390-F.pdf. 18956499
  2. [2]Angus DC. Grappling with intensive care unit quality—does the readmission rate tell us anything? Crit Care Med. 1998; 26: 1779– 1780. 9824061
  3. [3]Timmers TK, Hulstaert PF, Leenen LP. Patient outcomes can be associated with organizational changes: a quality improvement case study. Crit Care Nurs Q. 2014; 37: 125– 134. 10.1097/CNQ.0000000000000011 24309466 · DOI
  4. [4]Capuzzo M, Moreno RP, Alvis R. Admission and discharge of critically ill patients. Curr Opin Crit Care. 2010; 16: 499– 504. 10.1097/MCC.0b013e32833cb874 20644469 · DOI
  5. [5]Rosenberg AL, Watts C. Patients readmitted to ICUs: A systematic review of risk factors and outcomes. Chest. 2000; 118: 492– 502. 10936146
  6. [6]Kramer AA, Higgins TL, Zimmerman JE. Intensive care unit readmissions in U.S. hospitals: Patient characteristics, risk factors, and outcomes. Crit Care Med. 2012; 40: 3– 10. 10.1097/CCM.0b013e31822d751e 21926603 · DOI
  7. [7]Timmers TK, Verhofstad MHJ, Moons KGM, Leenen LPH. Intensive care performance: How should we monitor performance in the future? World J Crit Care Med. 2014; 3(4): 74– 79. 10.5492/wjccm.v3.i4.74 25374803 PMC4220140 · DOI
  8. [8]Campbell AJ, Cook JA, Adey G, Cuthbertson BH. Predicting death and readmission after intensive care discharge. Br J Anaesth. 2008; 100: 656– 662. 10.1093/bja/aen069 18385264 · DOI
  9. [9]Brown SES, Ratcliffe SJ, Kahn JM, Halpern SD. The epidemiology of intensive care unit readmissions in the United States. Am J Respir Crit Care Med. 2012; 185(9): 955– 964. 10.1164/rccm.201109-1720OC 22281829 PMC3359937 · DOI
  10. [10]Chen LM, Martin CM, Keenan SP, Sibbald WJ. Patients readmitted to the intensive care unit during the same hospitalization: Clinical features and outcomes. Crit Care Med. 1998; 26: 1834– 1841. 9824076
  11. [11]Rosenberg AL, Hofer TP, Hayward RA, Strachan C, Watts CM. Who bounces back? Physiologic and other predictors of intensive care unit readmission. Crit Care Med. 2001; 29: 511– 518. 11373413
  12. [12]Daly K, Beale R, Chang RW. Reduction in mortality after inappropriate early discharge from intensive care unit: Logistic regression triage model. BMJ. 2001; 322: 1274– 1276. 11375229 PMC31921
  13. [13]Chandra S, Agarwal D, Hanson A, Farmer JC, Pickering BW, Gajic O, et al The use of an electronic medical record based automatic calculation tool to quantify risk of unplanned readmission to the intensive care unit: A validation study. J Crit Care. 2011; 26: 634.e9– 634.e15. PMC3184347 21715140
  14. [14]Gajic O, Malinchoc M, Comfere TB, Harris MR, Achouiti A, Yilmaz M, et al The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: Initial development and validation. Crit Care Med. 2008; 36: 676– 682. 10.1097/CCM.0B013E318164E3B0 18431260 · DOI
  15. [15]Chen LM, Martin CM, Keenan SP, Sibbald WJ. Patients readmitted to the intensive care unit during the same hospitalization: Clinical features and outcomes. Crit Care Med. 1998; 26: 1834– 1841. 9824076
  16. [16]Zimmerman JE, Wagner DP, Draper EA, Knaus WA. Improving intensive care unit discharge decisions: supplementing physician judgment with predictions of next day risk for life support. Crit Care Med. 1994; 22: 1373– 1384. 8062558
  17. [17]Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure: On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996; 22: 707– 710. 8844239
  18. [18]Vincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM, et al Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on “sepsis-related problems” of the European Society of Intensive Care Medicine. Crit Care Med. 1998; 26: 1793– 1800. 9824069
  19. [19]Moreno R, Miranda DR, Matos R, Fevereiro T. Mortality after discharge from intensive care: the impact of organ system failure and nursing workload use at discharge. Intensive Care Med. 2001; 27: 999– 1004. 11497159
  20. [20]Smith L, Orts CM, O’Neil I, Batchelor AM, Gascoigne AD, Baudouin SV. TISS and mortality after discharge from intensive care. Intensive Care Med. 1999; 25: 1061– 1065. 10551959
  21. [21]Beck DH, McQuillan P, Smith GB. Waiting for the break of dawn? The effects of discharge time, discharge TISS scores and discharge facility on hospital mortality after intensive care. Intensive Care Med. 2002; 28: 1287– 1293. 12209279
  22. [22]Moreno R, Morais P. Validation of the simplified therapeutic intervention scoring system on an independent database. Intensive Care Med. 1997; 23: 640– 644. 9255643
  23. [23]Oakes DF, Borges IN, Forgiarini Junior LA, Rieder Mde M. Assessment of ICU readmission risk with the Stability and Workload Index for Transfer score. J Bras Pneumol. 2014; 40(1): 73– 76. 10.1590/S1806-37132014000100011 24626273 PMC4075915 · DOI
  24. [24]Kastrup M, Powollik R, Balzer F, Röber S, Ahlborn, Von Dossow-Hanfstingl V, et al Predictive ability of the stability and workload index for transfer score to predict unplanned readmissions after ICU discharge. Crit Care Med. 2013; 41(7): 1608– 1615. 10.1097/CCM.0b013e31828a217b 23660731 · DOI
  25. [25]Hosein FS, Bobrovitz N, Berthelot S, Zygun D, Ghali WA, Stelfox HT. A systematic review of tools for predicting severe adverse events following patient discharge from intensive care units. Crit Care. 2013; 17(3): R102 10.1186/cc12747 23718698 PMC4056089 · DOI
  26. [26]Vincent JL, Rubenfeld GD. Does intermediate care improve patient outcomes or reduce costs? Crit Care. 2015; 19(1): 89. 25774925 PMC4346102
  27. [27]Campos RA, Tena SA, Muncharaz AB, Font SM, Sellés AF, Campos LM, et al Study of post-ICU mortality during 4 years (2006–2009): Analysis of the factors related to death in the ward after discharge from the ICU. Med Intensiva. 2011; 35(3): 150– 156. 10.1016/j.medin.2010.12.012 21356566 · DOI
  28. [28]Giakoumidakis K, Eltheni R, Patelarou A, Patris V, Kuduvalli M, Brokalaki H. Incidence and predictors of readmission to the cardiac surgery intensive care unit: A retrospective cohort study in Greece. Ann Thorac Med. 2014; 9(1): 8– 13. 10.4103/1817-1737.124412 24551011 PMC3912693 · DOI
  29. [29]Badawi O, Breslow M. Readmissions and death after ICU discharge: development and validation of two predictive models. PLoS One. 2012; 7(11): e48758 10.1371/journal.pone.0048758 23144958 PMC3492441 · DOI

Authors
Rosa, R. G., Roehrig, C., Oliveira, R. P. D., Maccari, J. G., Antônio, A. C. P., Castro, P. D. S., Neto, F. L. D., Balzano, P. D. C., & Teixeira, C.
Affiliations
  • Universidade Federal de Ciências da Saúde de Porto Alegre (BR)
  • Hospital Moinhos de Vento (BR)
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2015-11-23
Volume
10
Issue
11
Pages
e0143127
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0143127
PMC
PMC4658118
OpenAlex
W2174393341
Cited by
56
References
30
Citations by year
2026: 4 · 2025: 7 · 2024: 5 · 2023: 5 · 2022: 4 · 2021: 4 · 2020: 7 · 2019: 9 · 2018: 5 · 2017: 3 · 2016: 3
FWCI
2.88
Type
journal article
Language
en
Update notices
correction (2016-02-05)
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • Health Sciences › Medicine › Emergency Medicine › Hospital Admissions and Outcomes
  • Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
Keywords
unplanned ICU readmission, SOFA score, TISS-28, risk score, mortality prediction, intensive care unit, post-discharge outcomes, prediction accuracy
MeSH terms
Adult, Aged, Death, Female, Humans, Intensive Care Units, Male, Patient Readmission, Prospective Studies, ROC Curve, Sensitivity and Specificity, Multivariate Analysis, Logistic Models, Workload, Organ Dysfunction Scores

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