Assessment of ICU readmission risk with the Stability and Workload Index for Transfer score

Oakes, Daiane Ferreira; Borges, Ingrid Nemitz Kras; Forgiarini, Luiz Alberto; Rieder, Marcelo de Mello

doi:10.1590/s1806-37132014000100011

Cited by 9 publications

(8 citation statements)

References 12 publications

(16 reference statements)

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“…Previous publications have shown promising results of using the SWIFT score to predict unplanned ICU readmission [ 14 , 23 , 24 ]. For example, Gagic et al [ 14 ] demonstrated the superiority of the SWIFT score, compared with APACHE III, in predicting unplanned ICU readmission (AUC 0.75 [95% CI, 0.70–0.80] versus AUC 0.62 [95% CI, 0.56–0.68]).…”

Section: Discussionmentioning

confidence: 99%

“…For example, Gagic et al [ 14 ] demonstrated the superiority of the SWIFT score, compared with APACHE III, in predicting unplanned ICU readmission (AUC 0.75 [95% CI, 0.70–0.80] versus AUC 0.62 [95% CI, 0.56–0.68]). Moreover, Oakes et al [ 23 ] found good predictive accuracy for SWIFT (AUC 0.76 [95% CI, 0.61–0.91]) in a small sample of ICU patients in southern Brazil. However, in our cohort, the SWIFT, a tool designed specifically to predict unplanned ICU readmission, was not superior to conventional scores of clinical severity (SOFA) or loading interventions (TISS-28) on the day of discharge from the ICU.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2015

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PurposeEarly 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.MethodsWe 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.ResultsA 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 values were 0.66, 0.65 and 0.74, respectively; P = 0.58).ConclusionsSWIFT, 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2015

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“…Bizim bulgularımızın aksine Oakes ve ark. çalısmasında SWIFT skorunun yeniden başvuruyu belirlemede (AUROC: 0,76) kullanılabileceği belirtilmiştir (6). ROC altında kalan alanı 0,458 (%95 CI;0,256-0,659) olarak hesapladığımız hastalarımızda, SWIFT skorunun yoğun bakımımıza yeniden başvuruyu belirlemede yeterli olmadığını gördük.…”

Section: Discussionunclassified

“…SWIFT skoru toplam 64 puan üzerinden taburculuğun planlandığı gün hesaplanır. Geçerliliği gösterilmiş olan bu skorun 15 puan üzerinde olması yeniden başvuru riskininin yüksek olduğunu gösteririr (4,5,6).…”

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Evaluation of Readmission to Intensive Care Unit Prediction Score in a Training Hospital

Ceylan¹,

Baltalı²,

A³

et al. 2019

tybd

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“…These include the Sabadell score which is a subjective measure by the ICU physician to predict overall prognosis following discharge (39) and multivariable algorithms described by Badawi and Breslow (40). The SWIFT score, which utilizes five variables including original source of ICU admission, ICU LOS, PaO 2 / FiO 2 , Glascow Coma Scale, and PaCO 2 to predict risk of readmission, has demonstrated mixed results predicting ICU readmission in adults (41,42). Attempts to apply the SWIFT score to pediatrics have not been successful (43).…”

Section: Prediction Toolsmentioning

confidence: 99%

Going back to the ward—transitioning care back to the ward team

Herbst¹,

Desai²,

Benscoter³

et al. 2018

Transl Pediatr

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Transition of care from the intensive care unit (ICU) to the ward is usually an indication of the patient's improving clinical status, but is also a time when patients are particularly vulnerable. The transition between care teams poses a higher risk of medical error, which can be mitigated by safe and complete patient handoff and medication reconciliation. ICU readmissions are associated with increased mortality as well as ICU and hospital length of stay (LOS); however tools to accurately predict ICU readmission risk are limited. While there are many mechanisms in place to carefully identify patients appropriate for transfer to the ward, the optimal timing of transfer can be affected by ICU strain, limited resources such as ICU beds, and overall hospital capacity and flow leading to suboptimal transfer times or delays in transfer. The patient and family perspectives should also be considered when planning for transfer from the ICU to the ward. During times of transition, families will meet a new care team, experience uncertainty of future care plans, and adjust to a different daily routine which can lead to increased stress and anxiety. Additionally, a subset of patients, such as those with new technology, require additional multidisciplinary support, education and care coordination which can contribute to longer hospital LOS if not addressed proactively early in the hospitalization while the patient remains in the ICU. In this review article, we describe key components of the transfer from ICU to the ward, discuss current strategies to optimize timing of patient transfers, explore strategies to partner with patients and families during the transfer process, highlight patient populations where additional considerations are needed, and identify future areas of exploration which could improve the care transition from the ICU to the ward.

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Assessment of ICU readmission risk with the Stability and Workload Index for Transfer score

Cited by 9 publications

References 12 publications

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

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

Evaluation of Readmission to Intensive Care Unit Prediction Score in a Training Hospital

Going back to the ward—transitioning care back to the ward team

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