The “new” scores: what problems have been fixed, and what remain?

Moreno, Rui P.; Matos, Ricardo

doi:10.1097/00075198-200006000-00003

Cited by 21 publications

(11 citation statements)

References 82 publications

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“…During the past few years a series of studies dealing with the application of outcome prediction models in general critically ill patients demonstrated a similar pattern – good discrimination with poor calibration. This pattern has been observed in different settings and with different instruments [27]. Information regarding the usefulness of these general scores in cancer patients requiring ICU care is still restricted and most reports are limited by relatively small sample sizes and/or the statistical analyses used in the assessment of models' performance [28-32].…”

Section: Discussionmentioning

confidence: 98%

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et al. 2004

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Introduction The aim of this study was to evaluate the performance of five general severity-of-illness scores (Acute Physiology and Chronic Health Evaluation II and III-J, the Simplified Acute Physiology Score II, and the Mortality Probability Models at admission and at 24 hours of intensive care unit [ICU] stay), and to validate a specific score -the ICU Cancer Mortality Model (CMM) -in cancer patients requiring admission to the ICU. Methods A prospective observational cohort study was performed in an oncological medical/surgical ICU in a Brazilian cancer centre. Data were collected over the first 24 hours of ICU stay. Discrimination was assessed by area under the receiver operating characteristic curves and calibration was done using Hosmer-Lemeshow goodness-of-fit H-tests. Results A total of 1257 consecutive patients were included over a 39-month period, and 715 (56.9%) were scheduled surgical patients. The observed hospital mortality was 28.6%. Two performance analyses were carried out: in the first analysis all patients were studied; and in the second, scheduled surgical patients were excluded in order to better compare CMM and general prognostic scores. The results of the two analyses were similar. Discrimination was good for all of the six studied models and best for Simplified Acute Physiology Score II and Acute Physiology and Chronic Health Evaluation III-J. However, calibration was uniformly insufficient (P < 0.001). General scores significantly underestimated mortality (in comparison with the observed mortality); this was in contrast to the CMM, which tended to overestimate mortality. Conclusion None of the model scores accurately predicted outcome in the present group of critically ill cancer patients. In addition, there was no advantage of CMM over the other general models.

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Section: Discussionmentioning

confidence: 98%

Untitled

et al. 2004

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“…16 It is higher compared with those reported in Germany (9%), Australia (16%), and Saudi Arabia (31.6%). [17][18][19] The high mortality can be attributed to shortage of ICU beds in our centre (ICU bed: hospital bed ratio is 0.006). It has been documented that physicians tend to be more selective in their ICU admissions during times of bed shortages, with patients having higher severity of illness being admitted.…”

Section: Discussionmentioning

confidence: 99%

Assessment and comparison of 3 mortality prediction models SAPS II, APACHE II and SOFA for prediction of mortality in patients of sepsis

et al. 2017

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Background: Little is known about outcomes of patients admitted to the ICU with severe sepsis and septic shock, despite the seriousness of sepsis as a public health problem in developing countries. Understanding sepsis outcome studies is hampered by lack of an agreed severity of illness scoring system for sepsis patients. The objective of the present study is to assess and compare the validity of 3 mortality prediction models SAPS 2, APACHE II and SOFA for prediction of mortality in patients of sepsis.Methods: One hundred patients of Sepsis were selected after applying the inclusion and exclusion criteria. Informed consent was taken from the patients or their relatives A careful and detailed history was recorded to assess the onset and duration of clinical events and the probable risk factors for the same; a detailed general physical examination was performed. Blood sampling for CBC, RFT, LFT and arterial blood gas analysis was done. SAPS 2, APACHE II and SOFA scores were calculated on the day of admission.Results: The ROC analysis shows that the best discrimination was provided by SAPS 2 score (AUROC=0.981), followed by APACHE II (AUROC=0.978) and SOFA (AUROC=0.911).Conclusions: SAPS 2 score was superior to the APACHE II and SOFA scores for predicting survival in patients with septic shock but a combination of factors must be taken in consideration to estimate the prognosis in the ICU.

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“…However, many experts have argued that this methodology is flawed and that risk adjustment should be based on random effects models. 17,19,[37][38][39] However, using random-effects modeling and shrinkage estimators, provider effects are "shrunken" back toward the overall mean for the cohort. Providers with low volumes or whose performance deviates more from the mean are "shrunken" more than high-volume providers or providers whose performance deviates less from the mean.…”

Section: Discussionmentioning

confidence: 99%

Impact of Changing the Statistical Methodology on Hospital and Surgeon Ranking

Glance

Dick²,

Osler³

et al. 2006

Medical Care

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Shrinkage estimators based on random-effects models are slightly more conservative in identifying quality outliers compared with the traditional approach based on fixed-effects modeling and standard regression. Explicitly modeling surgeon provider effect (fixed-effects and random-effects models) did not significantly alter the distribution of quality outliers when compared with standard logistic regression (which does not model provider effect). Compared with the standard parametric approach, the use of a bootstrap approach to construct 95% confidence interval around the O/E ratio resulted in more providers being identified as quality outliers.

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The “new” scores: what problems have been fixed, and what remain?

Cited by 21 publications

References 82 publications

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Assessment and comparison of 3 mortality prediction models SAPS II, APACHE II and SOFA for prediction of mortality in patients of sepsis

Impact of Changing the Statistical Methodology on Hospital and Surgeon Ranking

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