Simplified acute physiology score III is excellent for predicting in-hospital mortality in coronary care unit patients with acute myocardial infarction: A retrospective study.

Background: Coronary care unit (CCU) patients with acute myocardial infarction (AMI) lack effective predictors of in-hospital mortality. This study aimed to investigate the performance of four scoring systems in predicting in-hospital mortality in CCU patients with AMI. Methods: The baseline data, the logistic organ dysfunction system (LODS), the Oxford acute severity of illness score (OASIS), the simplified acute physiology score II (SAPS II), and the simplified acute physiology score III (SAPS III) scores of the patients were extracted from the fourth edition of the Medical Information Mart for Critical Care (MIMIC-IV) database. Independent risk factors for in-hospital mortality were identified by regression analysis. We performed receiver operating characteristic (ROC) curves and compared the area under the curve (AUC) to clarify the predictive value of the four scoring systems. Meanwhile, Kaplan-Meier curves and decision curve analysis (DCA) were performed to determine the optimal scoring system for predicting in-hospital mortality. Results: A total of 1,098 patients were included. The SAPS III was an independent risk factor for predicting in-hospital mortality in CCU patients with AMI before and after the propensity score matching (PSM) analysis. The discrimination of in-hospital mortality by SAPS III was superior to that of LODS, OASIS, and SAPS II. The AUC of the SAPS III scoring system was the highest among the four scoring systems, at 0.901 (before PSM) and 0.736 (after PSM). Survival analysis showed that significantly more in-hospital mortality occurred in the high-score SAPS III group compared to the low-score SAPS III group before PSM (HR 7.636, P < 0.001) and after PSM (HR 2.077, P = 0.005). The DCA curve of SAPS III had the greatest benefit score across the largest threshold range compared to the other three scoring systems. Conclusion: The SAPS III was an independent risk factor for predicting in-hospital mortality in CCU patients with AMI. The predictive value for in-hospital mortality with SAPS III is superior to that of LODS, OASIS, and SAPS II. The results of the DCA analysis suggest that SAPS III may provide a better clinical benefit for patients. We demonstrated that SAPS III is an excellent scoring system for predicting in-hospital mortality for CCU patients with AMI.

Prediction of in-hospital mortality: An adaptive severity-of-illness score for a tertiary ICU in South Africa.

Background: A scoring system based on physiological conditions was developed in 1984 to assess the severity of illness. This version, and subsequent versions, were labelled Simplified Acute Physiology Scores (SAPS). Each extension addressed limitations in the earlier version, with the SAPS III model using a data-driven approach. However, the SAPS III model did not include data collected from the African continent, thereby limiting the generalisation of the results. Objectives: To propose a scoring system for assessing severity of illness at intensive care unit (ICU) admission and a model for prediction of in-hospital mortality, based on the severity of illness score. Methods: This is a prospective cohort study which included patients who were admitted to an ICU in a South African tertiary hospital in 2017. Logistic regression modelling was used to develop the proposed scoring system, and the proposed mortality prediction model. Results: The study included 829 patients. Less than a quarter of patients (21.35%; n=177) died during the study period. The proposed model exhibited good calibration and excellent discrimination. Conclusion: The proposed scoring system is able to assess severity of illness at ICU admission, while the proposed statistical model may be used in the prediction of in-hospital mortality. Contributions of the study: This study is the first to develop a model similar to the SAPS III model, based on data collected in South Africa. In addition, this study provides a potential starting point for the development of a model that can be used nationally.

In-hospital mortality and severe outcomes after hospital discharge due to COVID-19: A prospective multicenter study from Brazil.

Background: We evaluated in-hospital mortality and outcomes incidence after hospital discharge due to COVID-19 in a Brazilian multicenter cohort. Methods: This prospective multicenter study (RECOVER-SUS, NCT04807699) included COVID-19 patients hospitalized in public tertiary hospitals in Brazil from June 2020 to March 2021. Clinical assessment and blood samples were performed at hospital admission, with post-hospital discharge remote visits. Hospitalized participants were followed-up until March 31, 2021. The outcomes were in-hospital mortality and incidence of rehospitalization or death after hospital discharge. Kaplan-Meier curves and Cox proportional-hazard models were performed. Findings: 1589 participants [54.5% male, age=62 (IQR 50-70) years; BMI=28.4 (IQR,24.9-32.9) Kg/m² and 51.9% with diabetes] were included. A total of 429 individuals [27.0% (95%CI,24.8-29.2)] died during hospitalization (median time 14 (IQR,9-24) days). Older age [vs<40 years; age=60-69 years-aHR=1.89 (95%CI,1.08-3.32); age=70-79 years-aHR=2.52 (95%CI,1.42-4.45); age≥80-aHR=2.90 (95%CI 1.54-5.47)]; noninvasive or mechanical ventilation at admission [vs facial-mask or none; aHR=1.69 (95%CI 1.30-2.19)]; SAPS-III score≥57 [vs<57; aHR=1.47 (95%CI 1.13-1.92)] and SOFA score≥10 [vs <10; aHR=1.51 (95%CI 1.08-2.10)] were independently associated with in-hospital mortality. A total of 65 individuals [6.7% (95%CI 5.3-8.4)] had a rehospitalization or death [rate=323 (95%CI 250-417) per 1000 person-years] in a median time of 52 (range 1-280) days post-hospital discharge. Age ≥ 60 years [vs <60, aHR=2.13 (95%CI 1.15-3.94)] and SAPS-III ≥57 at admission [vs <57, aHR=2.37 (95%CI 1.22-4.59)] were independently associated with rehospitalization or death after hospital discharge. Interpretation: High in-hospital mortality rates due to COVID-19 were observed and elderly people remained at high risk of rehospitalization and death after hospital discharge. Funding: Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Programa INOVA-FIOCRUZ.

SAPS III is superior to SOFA for predicting 28-day mortality in sepsis patients based on Sepsis 3.0 criteria.

INTRODUCTION: The discrimination and calibration accuracy of prediction models tends to become poor over time. The performance of predictive models should be reevaluated periodically. The aim of this study was to reassess the discrimination of the six commonly used models for predicting 28-day mortality in patients with sepsis based on the Sepsis 3.0 criteria. METHODS: Patient data were extracted from the fourth edition of the Medical Information Mart for Critical Care (MIMIC IV) database. The systemic inflammatory response syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS), Logistic Organ Dysfunction System (LODS), and Simplified Acute Physiology Score II (SAPS II) and III (SAPS III) scores were calculated and collected. The area under the receiver operating characteristic curve (AUROC) was used to compare the discrimination abilities of the models using non-parametric Wilcoxon statistics. The Delong method was used to perform pairwise comparisons of the AUROCs of the models. Multiple subgroup analyses for age, body mass index, and sex were performed with regard to the 28-day mortality prediction of the models. RESULTS: A total of 12 691 patients were included. The mean age of the patients was 65.97 ± 15.77 years; 7673 patients (60.50%) were male. The mean SIRS, SOFA, OASIS, SAPS II, LODS, and SAPS III scores were higher in the non-survivor group than in the survivor group. The discrimination for 28-day mortality with the SAPS III (AUROC 0.812, 95% confidence interval (CI) 0.802-0.822) and LODS (AUROC 0.804, 95% CI 0.743-0.765) models was superior to that of the SIRS (AUROC 0.575, 95% CI 0.562-0.589), SOFA (AUROC 0.612, 95% CI 0.598-0.626), OASIS (AUROC 0.753, 95% CI 0.742-0.764), and SAPS II (AUROC 0.754, 95% CI 0.743-0.765) models. The Youden index of the SAPS III model was 0.484, which was the highest among the models. Subgroup analyses showed similar results to the overall results. CONCLUSIONS: The discrimination for 28-day mortality with the SAPS III and LODS models was superior to that of the SIRS, SOFA, OASIS, and SAPS II models. The SAPS III model showed the best discrimination capacity for 28-day mortality compared with the other models.

Experiencia en el cuadro clínico, diagnóstico y tratamiento de COVID-19 / Experience in the clinical picture, diagnosis and treatment of COVID-19 / Experiência no quadro clínico, diagnóstico e tratamento da COVID-19

Resumen: Introducción: La infección por SARS-CoV-2 en Wuhan, China, ocasionó una pandemia de tal magnitud que ha provocado la muerte por neumonía a causa de enfermedad infecciosa por coronavirus 19 (COVID-19) de millones de personas. Nos dimos a la tarea de recolectar todas las características de los pacientes que estuvieron hospitalizados por esta enfermedad en nuestra UCI adultos. Material y métodos: Se realizó un estudio de tipo analítico, descriptivo, observacional y retrospectivo en pacientes con diagnóstico de COVID-19 ingresados en la Unidad de Cuidados Intensivos (UCI) del Hospital Ángeles Clínica Londres en la Ciudad de México, evaluados en el periodo del 23 de marzo de 2020 al 10 de mayo de 2020. Se revisaron los expedientes y se tomaron los datos de los mismos, se describieron variables de tipo demográfico, factores de riesgo, signos y síntomas, tratamiento médico y atención respiratoria. Se revisaron escalas de mortalidad SAPS III, APACHE II, SOFA y CALL-score. Se formaron dos grupos con y sin mortalidad realizándose análisis bivariado y multivariado de las variables significativas. El análisis estadístico se efectuó con el programa SPSS 25. Resultados: En el periodo considerado, 25 expedientes cumplieron con los criterios de inclusión, de ellos la demografía y factores de riesgo, 18 (72%) correspondieron a hombres y siete (38%) a mujeres con una mortalidad de 10 (40%). Los factores de riesgo más frecuentes fueron diabetes mellitus (DM) en siete (38%) pacientes, hipertensión arterial (HAS) en seis (24%), obesidad en cuatro (16%), enfermedad pulmonar obstructiva crónica (EPOC) en uno (4%), tabaquismo en 11 (44%) y alcoholismo en siete (28%). Se encontraron diferencias estadísticamente significativas en los grupos sin mortalidad y con mortalidad, 15 y 10 pacientes, respectivamente, observando las siguientes significancias: glucosa 105 mg/dL (percentil [PE 88]) versus 171 mg/dL (PE 125) p = 0.012, urea 33 mg/dL (PE 22) versus 95 mg/dL (PE 57) p = 0.03, BUN 15.3 mg/dL (PE 11) versus 44.2 mg/dL (PE 26.28) p = 0.04, TGO 32 U/L (PE 24.4) versus 58 U/L (PE 43.8) p = 0.010, DHL 239 U/L (PE 198) 454 U/L (PE 260) p = 0.003, triglicéridos 148 mg/dL (PE 120) versus 187.5 mg/dL (PE 165) p = 0.002, CPK 70 U/L (PE 35) versus 81 U/L (PE 78.25) p = 0.003, ferritina 446 mg/L (PE 238) versus 1,030 mg/L (PE 665) p = 0.007. Se realizó un análisis bivariado con regresión logística binaria, con la variable mortalidad dicotómica, no resultando significativa con esta prueba. Conclusiones: Se entiende que ninguna variable es predominantemente importante para explicar la mortalidad y que se recurre a muchos factores que se conjugan para explicar este desenlace, uno de éstos es la severidad misma del problema respiratorio en que se encuentre el paciente.

Abstract: Introduction: The SARS-CoV-2 infection in Wuhan, China caused a pandemic of such magnitude that it has caused the death of millions of people from pneumonia due to infectious disease caused by coronavirus 19 (COVID-19). We took on the task of collecting all the characteristics of the patients who were hospitalized for this disease in our Adult Intensive Care Unit. Material and methods: An analytical, descriptive, observational and retrospective study was carried out in patients with a diagnosis of COVID-19 admitted to the Intensive Care Unit (ICU) of the Hospital Ángeles Clínica Londres in Mexico City, evaluated in the period of March 23 from 2020 to May 10, 2020. The files were reviewed and the data taken from them, demographic variables, risk factors, signs and symptoms, medical treatment and respiratory care were described. SAPS III, APACHE II, SOFA and CALL-score mortality scales were reviewed. Two groups were formed with and without mortality, performing bivariate and multivariate analyzes of the significant variables. Statistical analysis was performed with the SPSS 25 program. Results: In the period considered, 25 files met the inclusion criteria for them: demographics and risk factors were 18 (72%) corresponding to men and seven (38%) to women. With a mortality of 10 (40%). The most frequent risk factors are diabetes mellitus (DM) in seven (38%), arterial hypertension (SAH) six (24%), obesity four (16%), chronic obstructive pulmonary disease (COPD) one (4%), smoking 11 (44%) and alcoholism seven (28%). Statistically significant differences were found in the groups without mortality and with mortality 15 and 10 patients respectively, observing the following significance: glucose 105 mg/dL (percentil [PE] 88) versus 171 mg/dL (PE 125) p = 0.012, urea 33 mg/dL (PE 22) versus 95 mg/dL (PE 57) p = 0.03, BUN 15.3 mg/dL (PE 11) versus 44.2 mg/dL (PE 26.28) p = 0.04, TGO 32 U/L (PE 24.4) versus 58 U/L (PE 43.8) p = 0.010, DHL 239 U/L (PE 198) 454 U/L (PE 260) p = 0.003, triglycerides 148 mg/dL (PE 120) versus 187.5 mg/dL (PE 165) p = 0.002, CPK 70 U/L (PE 35) versus 81 U/L (PE 78.25) p = 0.003, ferritin 446 mg/L (PE 238) versus 1,030 mg/L (PE 665) p = 0.007. A bivariate analysis with binary logistic regression was performed, with the dichotomous mortality variable, not resulting in this significant test. Conclusions: It is understood that no variable is predominantly important to explain mortality and that many factors are involved that are combined to explain this outcome, one of these being the same severity of the respiratory problem in which the patient is.

Resumo: Introdução: A infecção por SARS-CoV-2 em Wuhan China causou uma pandemia de tal magnitude que causou a morte de milhões de pessoas por pneumonia devido a doença infecciosa causada pelo coronavírus 19 (COVID-19). Assumimos a tarefa de coletar todas as características dos pacientes internados por essa doença em nossa unidade de terapia intensiva adulto. Material e métodos: Realizou-se um estudo analítico, descritivo, observacional e retrospectivo em pacientes com diagnóstico de COVID-19 internados na Unidade de Terapia Intensiva (UTI) do Hospital Ángeles Clínica Londres na Cidade do México, validado para o período de 23 de março de 2020 a 10 de maio de 2020. Os prontuários médicos foram revisados e seus dados coletados, as variáveis do tipo demográficas foram descritas, fatores de risco, sinais e sintomas, tratamento médico e cuidados respiratórios. Foram revisadas as escalas de mortalidade SAPS III, APACHE II, SOFA e CALL-score. Dois grupos foram formados com e sem mortalidade, realizando análises bivariadas e multivariadas das variáveis significativas. A análise estatística foi realizada com o programa SPSS 25. Resultados: No período considerado, 25 prontuários atenderam aos critérios de inclusão para os mesmos: dados demográficos e fatores de risco foram 18 (72%) correspondentes a homens e 7 (38%) a mulheres. Com mortalidade de 10 (40%). Os fatores de risco mais frequentes são diabetes mellitus (DM) em 7 (38%), hipertensão arterial (HAS) 6 (24%), obesidade 4 (16%), doença pulmonar obstrutiva crônica (DPOC) 1 (4%), tabagismo 11 (44%) e alcoolismo 7 (28%). Encontrou-se diferenças estatisticamente significativas nos grupos sem mortalidade e com mortalidade de 15 e 10 pacientes respectivamente, observando a seguinte significância: glicose 105 mg/dL (percentil [PE] 88) versus 171 mg/dL (PE 125) p = 0.012, uréia 33 mg/L (PE 22) versus 95 mg/L (PE 57) p = 0.03, BUN 15.3 mg/L (PE 11) versus 44.2 mg/L (PE 26.28) p = 0.04, TGO 32 U/L (PE 24.4) versus 58 U/L (PE 43.8) p = 0.010, DHL 239 U/L (PE 198) 454 (PE 260) p = 0.003, triglicerídeos 148 mg/dL (PE 120) versus 187.5 mg/dL (PE 165) p = 0.002, CPK 70 U/L (PE 35) versus 81 U/L (PE 78.25) p = 0.003, ferritina 446 mg/L (PE 238) versus 1030 mg/L (PE 665) p = 0.007. Realizou-se análise bivariada com regressão logística binária, com a variável mortalidade dicotômica, não resultando em teste significativo. Conclusões: Entende-se que nenhuma variável é predominantemente importante para explicar a mortalidade e que usamos muitos fatores que se conjugam para explicar esse desfecho, sendo um deles a mesma gravidade do problema respiratório em que o paciente se encontra.

The predictive ability of SAPS II, APACHE II, SAPS III, and APACHE IV to assess outcome and duration of mechanical ventilation in respiratory intensive care unit.

OBJECTIVES: The objective is to determine utility of SAPS II, APACHE II, SAPS III, and APACHE IV scoring system in assessing outcome in mechanically ventilated patients in respiratory intensive care unit and to predict duration of mechanical ventilation (MV). MATERIALS AND METHODS: A prospective observational study where 83 mechanically ventilated patients were grouped into Group 1 (n1 = 40, NIV) and Group 2 (n2 = 43, Invasive ventilation) was conducted. SAPS II, APACHE II, SAPS III, and APACHE IV scores based predicted mortality (PM) were collected at day 1, and day 3. Outcomes (on day 7) were grouped into negative and positive. (NIV-negative outcome = Home NIV, intubation or death; positive outcome = NIV free. Invasive group-positive outcome = Extubation; negative outcome = Death). Binary logistic regression was applied to predict duration of MV (> or < 5 days). RESULTS: The data were analyzed using SPSS version 17.0 trials comparisons of PM on day 1 with SAPS II (P < 0.05) and APACHE IV (P < 0.007) were significant predictors of clinical outcomes in Group 1 where as in Group 2, none of the system could predict significantly. On day 3, Group 1 analysis revealed SAPS II (P < 0.002), SAPS III (P < 0.03), and APACHE IV (P < 0.004) based PM as significant predictors of outcome. APACHE II (P < 0.05) and APACHE IV (P < 0.02) PM were significant in Group 2. On day 3, APACHE IV could significantly predict (P < 0.05) duration of MV (>5 or < 5) while A-a gradient (P < 0.09) predicted poorly in Group 1. In Group 2, APACHE IV was a poor predictor (P < 0.09). Two full logistic regression models were also formulated for both the groups. CONCLUSION: Study concludes that day 3 severity scores are more significant predictors of outcome and duration. APACHE IV scoring system was found more effective than other systems, not only significantly differentiating outcomes of MV but also predicting duration of NIV.

Zinc and selenium status in critically ill patients according to severity stratification.

OBJECTIVE: The aim of this study was to evaluate the concentrations of zinc and selenium in different biological materials and to associate them with the clinical severity score according to the Simplified Acute Physiology Score (SAPS) III. METHODS: The study was conducted in a 10-bed general intensive care unit of the Américo Brasiliense State Hospital/SP, with 95 patients stratified by the SAPS III score cutoff points (63.5 points) as less or more severe and by the diagnosis of sepsis. Analyses of zinc and selenium concentrations in plasma, erythrocytes, and urine were conducted. RESULTS: Plasma concentrations were found to be lower than the reference values for both micronutrients (8.4 ± 4 and 0.18 ± 0.06 µmol/L, respectively, for zinc and selenium), and urinary zinc concentration was higher than the reference (38.6 ± 35.8 µmol/24 h). The mean selenium plasma concentration was significantly lower in patients with greater severity, which was not observed for zinc (P > 0.05). The mean selenium plasma and erythrocyte concentrations were significantly different between the groups diagnosed with sepsis, which was not observed in the analysis of zinc. Albumin levels (r = -0.26; P = 0.01) and C-reactive protein (r = 0.40; P < 0.001) correlated with the SAPS III severity score. CONCLUSION: Plasma concentrations of zinc and selenium are low in critically ill patients upon admission to the intensive care unit and may make these patients more susceptible to oxidative stress. The low concentration of erythrocyte selenium may represent an inadequate intake by this population. Additional studies using new biomarkers should be performed with the objective of identifying values for the local population.

Performance of the simplified acute physiology score III in acute organophosphate poisoning: A retrospective observational study.

The performances of acute physiology and chronic health evaluation (APACHE) II and simplified acute physiology score (SAPS) II have previously been evaluated in acute organophosphate poisoning. We aimed to compare the performance of the SAPS III with those of the APACHE II and SAPS II, as well as to identify the best tool for predicting case fatality using the standardized mortality ratios (SMRs) in acute organophosphate poisoning. A retrospective analysis of organophosphate poisoning was conducted. The APACHE II, SAPS II, and SAPS III were calculated within 24 h of admission. Discrimination was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). The SMRs were calculated as 95% confidence intervals (CIs). In total, 100 cases of organophosphate poisoning were included. The in-hospital case fatality was 19%. The median scores of the APACHE II, SAPS II, and SAPS III were 20.0 (10.0-27.0), 41.0 (28.0-54.8), and 53.0 (36.3-68.8), respectively. The AUROCs were not significantly different among the APACHE II (0.815; 95% CI, 0.712-0.919), SAPS II (0.820; 95% CI, 0.719-0.912), and SAPS III (0.850; 95% CI, 0.763-0.936). Based on these scores and in-hospital case fatality, the SMRs for the APACHE II, SAPS II, and SAPS III were 1.01 (95% CI, 0.50-2.72), 1.01 (95% CI, 0.54 -2.78), and 0.98 (95% CI, 0.33-1.99), respectively. The SAPS III provided a good discrimination and satisfactory calibration in acute organophosphate poisoning. It was therefore a useful tool in predicting case fatality in acute organophosphate poisoning, similar to the APACHE II and SAPS II.

Assessment of Euroscore and SAPS III as hospital mortality predicted in cardiac surgery. / Valoración del Euroscore y SAPS III como predictor de mortalidad hospitalaria en el postoperatorio de cirugía cardiaca.

OBJECTIVES: To perform an external validation of Euroscore I, Euroscore II and SAPS III. PATIENTS AND METHOD: Retrospective cohort study over three years on all adult patients who underwent cardiac surgery. We reviewed the clinical data, following the patient until outcome or discharge from hospital (dead, alive). We computed the predicted mortality by Euroscore I (EI), II (EII) and SAPS III. The model validation was assessed by discrimination: area under curve ROC; and calibration (Hosmer-Lemeshow test). RESULTS: 866 patients were included. 62.5% of them male, with a median age of 69 years, 6.1% died during hospitalization. Predicted mortality: E I 7.94%, E II 3.54, SAPS III 12.1%. Area under curve (95% IC): E I 0.862 (0.812-0.912); E II 0.861 (0.806-0.915); SAPS III 0.692 (0.601-0.784). Hosmer-Lemeshow test: E I 14.0046 (P=.08164); E II 33.67 (P=.00004660); SAPS III 11.57 (P=.171). CONCLUSIONS: EII had good discrimination, but the calibration was not good with predicted mortality lower than the real mortality. E I showed the best discrimination with good calibration and a tendency to overestimate the mortality. SAPS III showed poor discrimination with good calibration and a tendency to greatly overestimate the predicted mortality. We saw no improvement in the predictive performance of EII over I and we reject the use of SAPS III in this kind of patient.

Effectiveness of SAPS III to predict hospital mortality for post-cardiac arrest patients.

PURPOSE: The mortality for patients admitted to intensive care unit (ICU) after cardiac arrest (CA) remains high despite advances in resuscitation and post-resuscitation care. The Simplified Acute Physiology Score (SAPS) III is the only score that can predict hospital mortality within an hour of admission to ICU. The objective was to evaluate the performance of SAPS III to predict mortality for post-CA patients. METHODS: This retrospective single-center observational study included all patients admitted to ICU after CA between August 2010 and March 2013. The calibration (standardized mortality ratio [SMR]) and the discrimination of SAPS III (area under the curve [AUC] for receiver operating characteristic [ROC]) were measured. Univariate logistic regression tested the relationship between death and scores for SAPS III, SAPS II, Sequential Organ Failure Assessment (SOFA) Score and Out-of-Hospital Cardiac Arrests (OHCA) score. Independent factors associated with mortality were determined. RESULTS: One-hundred twenty-four patients including 97 out-of-hospital CA were included. In-hospital mortality was 69%. The SAPS III was unable to predict mortality (SMRSAPS III: 1.26) and was less discriminating than other scores (AUCSAPSIII: 0.62 [0.51, 0.73] vs. AUCSAPSII: 0.75 [0.66, 0.84], AUCSOFA: 0.72 [0.63, 0.81], AUCOHCA: 0.84 [0.77, 0.91]). An early return of spontaneous circulation, early resuscitation care and initial ventricular arrhythmia were associated with a better prognosis. CONCLUSIONS: The SAPS III did not predict mortality in patients admitted to ICU after CA. The amount of time before specialized CPR, the low-flow interval and the absence of an initial ventricular arrhythmia appeared to be independently associated with mortality and these factors should be used to predict mortality for these patients.