Clinical Characteristics and In-Hospital Mortality of Cardiac Arrest Survivors in Brazil: A Large Retrospective Multicenter Cohort Study.

OBJECTIVES: Data on cardiac arrest survivors from developing countries are scarce. This study investigated clinical characteristics associated with in-hospital mortality in resuscitated patients following cardiac arrest in Brazil. DESIGN: Retrospective analysis of prospectively collected data. SETTING: Ninety-two general ICUs from 55 hospitals in Brazil between 2014 and 2015. PATIENTS: Adult patients with cardiac arrest admitted to the ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed 2,296 patients (53% men; median 67 yr (interquartile range, 54-79 yr]). Eight-hundred patients (35%) had a primary admission diagnosis of cardiac arrest suggesting an out-of-hospital cardiac arrest; the remainder occurred after admission, comprising an in-hospital cardiac arrest cohort. Overall, in-hospital mortality was 83%, with only 6% undergoing withholding/withdrawal-of-life support. Random-effects multivariable Cox regression was used to assess associations with survival. After adjusting for age, sex, and severity scores, mortality was associated with shock (adjusted odds ratio, 1.25 [95% CI, 1.11-1.39]; p < 0.001), temperature dysregulation (adjusted odds ratio for normothermia, 0.85 [95% CI, 0.76-0.95]; p = 0.007), increased lactate levels above 4 mmol/L (adjusted odds ratio, 1.33 [95% CI, 1.1-1.6; p = 0.009), and surgical or cardiac cases (adjusted odds ratio, 0.72 [95% CI, 0.6-0.86]; p = 0.002). In addition, survival was better in patients with probable out-of-hospital cardiac arrest, unless ICU admission was delayed (adjusted odds ratio for interaction, 1.63 [95% CI, 1.21-2.21]; p = 004). CONCLUSIONS: In a large multicenter cardiac arrest cohort from Brazil, we found a high mortality rate and infrequent withholding/withdrawal of life support. We also identified patient profiles associated with worse survival, such as those with shock/hypoperfusion and arrest secondary to nonsurgical admission diagnoses. Our findings unveil opportunities to improve postarrest care in developing countries, such as prompt ICU admission, expansion of the use of targeted temperature management, and implementation of shock reversal strategies (i.e., early coronary angiography), according to modern guidelines recommendations.

Customization and external validation of the Simplified Mortality Score for the Intensive Care Unit (SMS-ICU) in Brazilian critically ill patients.

PURPOSE: To customize and externally validate the recently proposed Simplified Mortality Score for the ICU (SMS-ICU, a simple score for 90-day mortality that has no need for ancillary testing results) for in-hospital mortality and to compare its performance to SAPS 3. MATERIAL AND METHODS: We used data from two distinct large cohorts of adult Brazilian patients with unplanned ICU admissions to perform a first-level customization (43,017 patients admitted to 78 ICUs) of the original SMS-ICU score for in-hospital mortality and, sequentially, externally validate it (313,365 patients admitted to 99 ICUs). Performance of SMS-ICU was assessed through measurements of discrimination and calibration and compared with SAPS 3. RESULTS: In the validation cohort, median SMS-ICU was 13 (IQR 8-16) points and median SAPS 3 was 44 (IQR 36-51). Discrimination of SMS-ICU was good (AUC 0.817; 95% CI 0.814-0.819) but slightly lower than of SAPS 3 (AUC 0.845; 95% CI 0.843-0.848;). The customized SMS-ICU predictions were comparable to SAPS 3 in terms of calibration. CONCLUSION: In this external validation of the SMS-ICU in a large Brazilian cohort, we observed good discrimination of SMS-ICU and acceptable calibration after first-level customization. SMS-ICU can be used as a measure of illness severity for acutely admitted ICU patients in clinical studies.

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

BACKGROUND: The 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. METHODS: This 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). RESULTS: Mean 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. CONCLUSIONS: In a large contemporary database, the SAPS 3-SE was accurate in predicting outcomes, supporting its use for performance evaluation and benchmarking in Brazilian ICUs.