Validation of the Acute Physiology and Chronic Health Evaluation (APACHE) II and IV Score in COVID-19 Patients

Vandenbrande, Jeroen; Verbrugge, Laurens; Bruckers, Liesbeth; Geebelen, Laurien; Geerts, Ester; Callebaut, Ina; Gruyters, Ine; Heremans, Liesbeth; Dubois, Jasperina; Stessel, Björn

doi:10.1155/2021/5443083

Cited by 23 publications

(22 citation statements)

References 23 publications

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“…In comparison, our non-survivors could be considered to have more severe COVID-19 disease. Similarly, the COVID-19 severity class along with the prognostic scores of APACHE II, SOFA, and MEWS score were all worse in the non-survivor group like various previous studies [ 13 , 15 - 16 ].…”

Section: Discussionsupporting

confidence: 84%

Epidemiology and Risk Factors of COVID-19-Related Mortality

Roy¹,

Gupta²,

Tomar³

et al. 2021

Cureus

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IntroductionDuring the coronavirus disease 2019 (COVID-19) pandemic in India, several characteristics of hospitalized COVID-19 patients, based on demographics, mortality predictors, and presence of comorbidities, were found to be associated with poor outcomes. The objective of this study was to identify such epidemiological and clinical characteristics among the patients admitted at a tertiary-care center in India that may have predisposed them to COVID-19-related mortality.

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Section: Discussionsupporting

confidence: 84%

Epidemiology and Risk Factors of COVID-19-Related Mortality

Roy¹,

Gupta²,

Tomar³

et al. 2021

Cureus

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“…People who have not been infected with SARS-CoV-2 or have not developed symptoms may not experience such biomarker changes or express symptoms of undiscovered morbidities. Only a few studies have evaluated a risk tool for use in COVID-19 risk prediction that employed a previously derived score,46 47 and in neither of these studies were the scores based primarily on laboratory tests nor did the studies evaluate subjects based on health status prior to COVID-19 diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Association of the Intermountain Risk Score with major adverse health events in patients positive for COVID-19: an observational evaluation of a US cohort

et al. 2022

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ObjectivesThe Intermountain Risk Score (IMRS), composed using published sex-specific weightings of parameters in the complete blood count (CBC) and basic metabolic profile (BMP), is a validated predictor of mortality. We hypothesised that IMRS calculated from prepandemic CBC and BMP predicts COVID-19 outcomes and that IMRS using laboratory results tested at COVID-19 diagnosis is also predictive.DesignProspective observational cohort study.SettingPrimary, secondary, urgent and emergent care, and drive-through testing locations across Utah and in sections of adjacent US states. Viral RNA testing for SARS-CoV-2 was conducted from 3 March to 2 November 2020.ParticipantsPatients aged ≥18 years were evaluated if they had CBC and BMP measured in 2019 and tested positive for COVID-19 in 2020.Primary and secondary outcome measuresThe primary outcome was a composite of hospitalisation or mortality, with secondary outcomes being hospitalisation and mortality separately.ResultsAmong 3883 patients, 8.2% were hospitalised and 1.6% died. Subjects with low, mild, moderate and high-risk IMRS had the composite endpoint in 3.5% (52/1502), 8.6% (108/1256), 15.5% (152/979) and 28.1% (41/146) of patients, respectively. Compared with low-risk, subjects in mild-risk, moderate-risk and high-risk groups had HR=2.33 (95% CI 1.67 to 3.24), HR=4.01 (95% CI 2.93 to 5.50) and HR=8.34 (95% CI 5.54 to 12.57), respectively. Subjects aged <60 years had HR=3.06 (95% CI 2.01 to 4.65) and HR=7.38 (95% CI 3.14 to 17.34) for moderate and high risks versus low risk, respectively; those ≥60 years had HR=1.95 (95% CI 0.99 to 3.86) and HR=3.40 (95% CI 1.63 to 7.07). In multivariable analyses, IMRS was independently predictive and was shown to capture substantial risk variation of comorbidities.ConclusionsIMRS, a simple risk score using very basic laboratory results, predicted COVID-19 hospitalisation and mortality. This included important abilities to identify risk in younger adults with few diagnosed comorbidities and to predict risk prior to SARS-CoV-2 infection.

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“…APACHE IV values were more accurate at predicting ICU mortality than SOFA scores. In addition, the APACHE IV intensity scoring system had the greatest calibration values in comparison to APACHE II and SOFA [ 32 ]. In Schoe et al’s study, only the SAPS II model exhibited adequate calibration for measuring the result of ICU mortality in patients who underwent cardiac surgery in the ICU.…”

Section: Discussionmentioning

confidence: 99%

Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches

Mirzakhani

Sadoughi

Hatami

et al. 2022

BMC Med Inform Decis Mak

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Background A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared with artificial intelligence predictive models (Artificial Neural Network and Decision Tree) in terms of the prediction of the survival rate of the patients admitted to the intensive care unit. Methods This retrospective cohort study was performed on the data of the patients admitted to the ICU of Ghaemshahr’s Razi Teaching Care Center from March 20th, 2017, to September 22nd, 2019. The required data for calculating conventional severity classification models (SOFA, SAPS II, APACHE II, and APACHE IV) were collected from the patients’ medical records. Subsequently, the score of each model was calculated. Artificial intelligence predictive models (Artificial Neural Network and Decision Tree) were developed in the next step. Lastly, the performance of each model in predicting the survival of the patients admitted to the intensive care unit was evaluated using the criteria of sensitivity, specificity, accuracy, F-measure, and area under the ROC curve. Also, each model was validated externally. The R program, version 4.1, was used to create the artificial intelligence models, and SPSS Statistics Software, version 21, was utilized to perform statistical analysis. Results The area under the ROC curve of SOFA, SAPS II, APACHE II, APACHE IV, multilayer perceptron artificial neural network, and CART decision tree were 76.0, 77.1, 80.3, 78.5, 84.1, and 80.0, respectively. Conclusion The results showed that although the APACHE II model had better results than other conventional models in predicting the survival rate of the patients admitted to the intensive care unit, the other conventional models provided acceptable results too. Moreover, the findings showed that the artificial neural network model had the best performance among all the studied models, indicating the discrimination power of this model in predicting patient survival compared to the other models.

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Validation of the Acute Physiology and Chronic Health Evaluation (APACHE) II and IV Score in COVID-19 Patients

Cited by 23 publications

References 23 publications

Epidemiology and Risk Factors of COVID-19-Related Mortality

Epidemiology and Risk Factors of COVID-19-Related Mortality

Association of the Intermountain Risk Score with major adverse health events in patients positive for COVID-19: an observational evaluation of a US cohort

Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches

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