Feasibility of transitioning from APACHE II to SAPS III as prognostic model in a Brazilian general intensive care unit. A retrospective study

Neto, Ary Serpa; Assunção, Murillo Santucci César de; Pardini, Andréia; Silva, Eliézer

doi:10.1590/1516-3180.2013.8120014

Cited by 15 publications

(6 citation statements)

References 25 publications

(20 reference statements)

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“…Compared to recent studies conducted in other countries, patients in our study had higher disease severity, as evidenced by the higher mean APACHE II score (24.5 versus 17–21). 14,16–21 Similar to recent studies, the APACHE II in our study also had good discriminative validity, that is, 0.756 versus 0.729 to 0.805, 14,16–21 and poor calibration accuracy. 14,16–19 It is established that the latter will have a negative impact on the statistical risk adjustment in research studies and the SMR used in clinical audits.…”

Section: Discussionsupporting

confidence: 87%

Performance of the Acute Physiology and Chronic Health Evaluation II (APACHE II) in the prediction of hospital mortality in a mixed ICU in Singapore

Lew

Wong

Tan

et al. 2018

Proceedings of Singapore Healthcare

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Background: The Acute Physiology and Chronic Health Evaluation II (APACHE II) is used to quantify disease severity and hospital mortality risk in critically ill patients. It is widely used in intensive care units (ICUs) in Singapore, but its prognostic validity remains questionable as it has not been thoroughly assessed by established statistical methods. Objectives: This study aimed to: (a) evaluate the discrimination and calibration accuracy of the APACHE II in the prediction of hospital mortality in a mixed ICU, and (b) customise the APACHE II in an effort to maximise its prognostic performance. Methods: A prospective cohort study was conducted and all adult patients with >24 h of ICU admission in a tertiary care institution in Singapore were included. The outcome measure was hospital mortality, and all patients were followed-up until hospital discharge or death for up to one year after ICU admission. Results: There were 503 patients, and their mean (SD) age and APACHE II score were 61.2 (15.8) years and 24.5 (8.2), respectively. Hospital mortality was 31%, and no patients were lost to follow-up. The APACHE II has good discrimination (receiver operating characteristic: 0.76) but poor calibration (Hosmer–Lemeshow C test: <0.001). Customisation did not significantly improve calibration accuracy. Conclusions: The APACHE II and its customised version should not be used in the local setting as they both have poor calibration. There is an urgent need for larger studies to perform second-level customisation or to develop a new prognostic model tailored to the Singapore critical care setting.

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Section: Discussionsupporting

confidence: 87%

Performance of the Acute Physiology and Chronic Health Evaluation II (APACHE II) in the prediction of hospital mortality in a mixed ICU in Singapore

Lew

Wong

Tan

et al. 2018

Proceedings of Singapore Healthcare

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“…In terms of the severity of patients in the sample, the risk of death calculated by the SAPS 3 (34·1 ± 27·9) was superior to that identified by the LODS (20·9 ± 18·3), and the LODS value was closer to the mortality rate observed in the ICU (19·9%). Researchers have shown that the SAPS 3 score provides good discrimination of mortality rates in ICU patients (Serpa Neto et al, ). According to the current data, two assumptions can be considered: the LODS index better reflects the severity of patients in the sample or the excellent quality of care provided by the nursing unit resulted in lower mortality compared with that estimated by the SAPS 3.…”

Section: Discussionmentioning

confidence: 99%

Type of admission and nursing workload of critical patients: a cross‐sectional study

Romano

Garcia

Silva

et al. 2019

Nursing in Critical Care

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Background: According to the perception of nurses in the intensive care unit (ICU), surgical patients need more nursing care, thus requiring higher nursing workloads for these patients than those admitted as clinical patients. However, some study results on the relationship between the type of admission and the nursing workload are considered contradictory. Aims and objectives: To identify if the type of admission (clinical, emergency surgery or elective surgery) is a predictive factor of the nursing workload required by patients on the first day or throughout their stay in the ICU. Design: This was a quantitative cross-sectional study comprised of a retrospective analysis of clinical records of critical patients. Methods: Data were collected from 1 May 2015 to 30 September 2015 in a hospital located in São Paulo, Brazil. Nursing workload was measured using the Nursing Activities Score. The type of admission and the demographic and clinical variables of the patients were investigated. Multiple linear regression was used to identify nursing workload predictive factors, with 5% significance level. Results: In the analysed sample (n = 211; mean age of 60⋅3 ± 18⋅7 years), there was a prevalence of male gender (56⋅9%). A statistically significant difference (p = 0⋅025) was found between the type of admission and the nursing workload required for patients on the first ICU day. The Simplified Acute Physiologic Score (p = 0⋅009) was a predictor of nursing workload on the first day in the ICU, and the Logistic Organ Dysfunction System (p = 0⋅026) and mortality (p < 0⋅001) were predictors throughout the ICU stay. Conclusions: The type of admission was not a predictive factor of the nursing workload required by critical patients. Relevance to clinical practice: Identifying the predictive factors of nursing workload favours the appropriate staffing of the critical unit by nurses. However, nurses should not consider the type of admission in predicting the nursing workload required by patients in the ICU.

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“…However, to our knowledge, validation studies reported conflicting results and were mostly single centered, involving specific patient populations [7–13] and with relatively small sample sizes [14–16]. Moreover, as the calibration of severity-of-illness scores is expected to deteriorate overtime, the performance of such instruments should be reassessed on a regular basis [17].…”

Section: Introductionmentioning

confidence: 99%

“…In 2009, the Brazilian Association of Intensive Care (Associação de Medicina Intensiva Brasileira, AMIB) chose the SAPS 3 score as the severity-of-illness score recommended for performance evaluation and benchmarking in Brazilian ICUs [ 6 ]. However, to our knowledge, validation studies reported conflicting results and were mostly single centered, involving specific patient populations [ 7 – 13 ] and with relatively small sample sizes [ 14 – 16 ]. Moreover, as the calibration of severity-of-illness scores is expected to deteriorate overtime, the performance of such instruments should be reassessed on a regular basis [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

External validation of SAPS 3 and MPM0-III scores in 48,816 patients from 72 Brazilian ICUs

Moralez

Rabello

Lisboa³

et al. 2017

Ann. Intensive Care

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BackgroundThe performance of severity-of-illness scores varies in different scenarios and must be validated prior of being used in a specific settings and geographic regions. Moreover, models’ calibration may deteriorate overtime and performance of such instruments should be reassessed regularly. Therefore, we aimed at to validate the SAPS 3 in a large contemporary cohort of patients admitted to Brazilian ICUs. In addition, we also compared the performance of the SAPS 3 with the MPM0-III.MethodsThis is a retrospective cohort study in which 48,816 (medical admissions = 67.9%) adult patients are admitted to 72 Brazilian ICUs during 2013. We evaluated models’ discrimination using the area under the receiver operating characteristic curve (AUROC). We applied the calibration belt to evaluate the agreement between observed and expected mortality rates (calibration).ResultsMean SAPS 3 score was 44.3 ± 15.4 points. ICU and hospital mortality rates were 11.0 and 16.5%. We estimated predicted mortality using both standard (SE) and Central and South American (CSA) customized equations. Predicted mortality rates were 16.4 ± 19.3% (SAPS 3-SE), 21.7 ± 23.2% (SAPS 3-CSA) and 14.3 ± 14.0% (MPM0-III). Standardized mortality ratios (SMR) obtained for each model were: 1.00 (95% CI, 0.98–0.102) for the SAPS 3-SE, 0.75 (0.74–0.77) for the SAPS 3-CSA and 1.15 (1.13–1.18) for the MPM0-III. Discrimination was better for SAPS 3 models (AUROC = 0.85) than for MPM0-III (AUROC = 0.80) (p < 0.001). We applied the calibration belt to evaluate the agreement between observed and expected mortality rates (calibration): the SAPS 3-CSA overestimated mortality throughout all risk classes while the MPM0-III underestimated it uniformly. The SAPS 3-SE did not show relevant deviations from ideal calibration.ConclusionsIn a large contemporary database, the SAPS 3-SE was accurate in predicting outcomes, supporting its use for performance evaluation and benchmarking in Brazilian ICUs.Electronic supplementary materialThe online version of this article (doi:10.1186/s13613-017-0276-3) contains supplementary material, which is available to authorized users.

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Feasibility of transitioning from APACHE II to SAPS III as prognostic model in a Brazilian general intensive care unit. A retrospective study

Cited by 15 publications

References 25 publications

Performance of the Acute Physiology and Chronic Health Evaluation II (APACHE II) in the prediction of hospital mortality in a mixed ICU in Singapore

Performance of the Acute Physiology and Chronic Health Evaluation II (APACHE II) in the prediction of hospital mortality in a mixed ICU in Singapore

Type of admission and nursing workload of critical patients: a cross‐sectional study

External validation of SAPS 3 and MPM0-III scores in 48,816 patients from 72 Brazilian ICUs

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