scholarly journals SAPS-3 performance for hospital mortality prediction in 30,571 patients with COVID-19 admitted to ICUs in Brazil

2021 ◽  
Author(s):  
Pedro Kurtz ◽  
Leonardo S. L. Bastos ◽  
Jorge I. F. Salluh ◽  
Fernando A. Bozza ◽  
Marcio Soares
Keyword(s):  
Hospital Mortality ◽  
Mortality Prediction ◽  
Saps 3

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals. Methods Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865–0.868) and van Walraven’s weights (0.863, 95% CI, 0.862–0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI, 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights. Conclusions All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

Development and internal validation of the HIV In‐hospital Mortality Prediction (HIV‐IMP) risk score

HIV Medicine ◽  
2021 ◽  
Author(s):  
Abdullah E. Laher ◽  
Fathima Paruk ◽  
Willem D. F. Venter ◽  
Oluwatosin A. Ayeni ◽  
Feroza Motara ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Risk Score ◽  
Mortality Prediction ◽  
Internal Validation

Abstract W P256: Influence of Withdrawal of Care on Spontaneous Intracerebral Hemorrhage Predictive Scales

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Santiago Ortega Gutierrez ◽  
maria angeles aranda calleja ◽  
Pankhil Shah ◽  
Sergio Amaro Delgado ◽  
sachin agarwal ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Medical Records ◽  
Scoring Systems ◽  
Mortality Prediction ◽  
Youden Index ◽  
Major Influence ◽  
Term Outcome ◽  
Long Term Outcome ◽  
Radiological Criteria ◽  
Ich Score

Background: Various scoring systems combining different predictors have been developed to more accurately predict the short and long-term outcome after ICH. However, these different scoring systems do not take into account the major influence of the primary cause of mortality in ICH, namely the withdrawal of care (WC). We aim to compare the in-hospital mortality prediction performance after accounting for WC of three widely used scoring systems, the original ICH score (oICH), the ICH Grading scale (ICH-GS), and the simplified ICH score (sICH), in a cohort of ICH patients prior to the development of the aforementioned scales. Methods: Retrospective observational single center cohort study of adult patients presenting a confirmed diagnosis of ICH. Admission clinical and radiological criteria were obtained through review of medical records and CT at admission. In-hospital mortality was selected as a primary outcome and obtained from the medical records. In the event of death, groups weredivided into: ICH-direct cause of death (cardiac arrest or brain death) andneurological devastation leading to WC. Scoring systems were calculated in each individual patient. Receiver operating characteristic (ROC) analysis was used to assess the ability of each score to predict in-hospital mortality and the maximum Youden Index was identified to denote each score’s optimal predictive cutoff point for each scale. The area under the curve (AUC) between groups was compared by using the Delong et al method. P< 0.05 was set as statistically significant. Conclusion: Performance of ICH scoring systems accurately predicted in-hospital mortalityeven when WC care is taken into account.

scholarly journals The prognostic accuracy evaluation of SAPS 3, SOFA and APACHE II scores for mortality prediction in the surgical ICU: an external validation study and decision-making analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Antônio Luis Eiras Falcão ◽  
Alexandre Guimarães de Almeida Barros ◽  
Angela Alcântara Magnani Bezerra ◽  
Natália Lopes Ferreira ◽  
Claudinéia Muterle Logato ◽  
...  
Keyword(s):  
Decision Making ◽  
Validation Study ◽  
External Validation ◽  
Mortality Prediction ◽  
Apache Ii ◽  
Accuracy Evaluation ◽  
Saps 3 ◽  
Prognostic Accuracy ◽  
External Validation Study ◽  
Surgical Icu

scholarly journals Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China

2020 ◽  
Vol 71 (16) ◽  
pp. 2079-2088 ◽  
Author(s):  
Kun Wang ◽  
Peiyuan Zuo ◽  
Yuwei Liu ◽  
Meng Zhang ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Laboratory Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Extreme Gradient Boosting ◽  
Mortality Prediction Models

Abstract Background This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). Methods The training cohort included consecutive COVID-19 patients at the First People’s Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. Results A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80–.95); threshold, −2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92–.99); threshold, −2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68–.93) and 0.88 (.75–.96) for the clinical model and laboratory model, respectively. Conclusions We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

scholarly journals Addition of admission lactate levels to Baux score improves mortality prediction in severe burns

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ingrid Steinvall ◽  
Moustafa Elmasry ◽  
Islam Abdelrahman ◽  
Ahmed El-Serafi ◽  
Folke Sjöberg
Keyword(s):  
Prediction Models ◽  
Total Body Surface Area ◽  
Simplified Acute Physiology Score ◽  
Mortality Prediction ◽  
Saps 3 ◽  
Severe Burns ◽  
Crude Mortality ◽  
Mortality Prediction Models ◽  
Total Body ◽  
Lactate Levels

AbstractRisk adjustment and mortality prediction models are central in optimising care and for benchmarking purposes. In the burn setting, the Baux score and its derivatives have been the mainstay for predictions of mortality from burns. Other well-known measures to predict mortality stem from the ICU setting, where, for example, the Simplified Acute Physiology Score (SAPS 3) models have been found to be instrumental. Other attempts to further improve the prediction of outcome have been based on the following variables at admission: Sequential Organ Failure Assessment (aSOFA) score, determinations of aLactate or Neutrophil to Lymphocyte Ratio (aNLR). The aim of the present study was to examine if estimated mortality rate (EMR, SAPS 3), aSOFA, aLactate, and aNLR can, either alone or in conjunction with the others, improve the mortality prediction beyond that of the effects of age and percentage total body surface area (TBSA%) burned among patients with severe burns who need critical care. This is a retrospective, explorative, single centre, registry study based on prospectively gathered data. The study included 222 patients with median (25th–75th centiles) age of 55.0 (38.0 to 69.0) years, TBSA% burned was 24.5 (13.0 to 37.2) and crude mortality was 17%. As anticipated highest predicting power was obtained with age and TBSA% with an AUC at 0.906 (95% CI 0.857 to 0.955) as compared with EMR, aSOFA, aLactate and aNLR. The largest effect was seen thereafter by adding aLactate to the model, increasing AUC to 0.938 (0.898 to 0.979) (p < 0.001). Whereafter, adding EMR, aSOFA, and aNLR, separately or in combinations, only marginally improved the prediction power. This study shows that the prediction model with age and TBSA% may be improved by adding aLactate, despite the fact that aLactate levels were only moderately increased. Thereafter, adding EMR, aSOFA or aNLR only marginally affected the mortality prediction.

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