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

Moralez, G. M.; Rabello, Lígia Sarmet Cunha Farah; Lisboa, Thiago; Lima, Mariza da Fonte Andrade; Hatum, R; Marco, Fernando Vinicius Cesar De; Alves, Alessandra; Pinto, José Roberto Aparecido dos Santos; Araujo, Helia B.; Ramos, Grazielle Viana; Silva, Aline Reis; Fernandes, Guilherme Côrtes; Faria, Guilherme Brenande Alves; Mendes, Ciro Leite; Filho, Roberto Álvaro Ramos; Souza, Valdênia Pereira de; Brasil, Pedro Emmanuel Alvarenga Americano do; Bozza, Fernando A.; Salluh, Jorge I.; Soares, Márcio

doi:10.1186/s13613-017-0276-3

Cited by 25 publications

(37 citation statements)

References 27 publications

(45 reference statements)

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“…The general SAPS 3 equation provides a better calibration for the database and is thus used in the system for benchmarking purposes. ( 21 ) Some additional scores are also available in the system, including the aforementioned Charlson Comorbidity Index, ( 17 ) the SAPS II, the Acute Physiology and Chronic Health Evaluation (APACHE) II ( 22 ) and IV ( 8 ) and the SOFA. ( 23 ) The SOFA score can be calculated daily using the dynamic datasheets or the mobile applications.…”

Section: Introductionmentioning

confidence: 99%

The Epimed Monitor ICU Database®: a cloud-based national registry for adult intensive care unit patients in Brazil

Zampieri¹,

Soares²,

Borges³

et al. 2017

Revista Brasileira De Terapia Intensiva

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106

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ObjectiveTo describe the Epimed Monitor Database®, a Brazilian intensive care unit quality improvement database.MethodsWe described the Epimed Monitor® Database, including its structure and core data. We presented aggregated informative data from intensive care unit admissions from 2010 to 2016 using descriptive statistics. We also described the expansion and growth of the database along with the geographical distribution of participating units in Brazil.ResultsThe core data from the database includes demographic, administrative and physiological parameters, as well as specific report forms used to gather detailed data regarding the use of intensive care unit resources, infectious episodes, adverse events and checklists for adherence to best clinical practices. As of the end of 2016, 598 adult intensive care units in 318 hospitals totaling 8,160 intensive care unit beds were participating in the database. Most units were located at private hospitals in the southeastern region of the country. The number of yearly admissions rose during this period and included a predominance of medical admissions. The proportion of admissions due to cardiovascular disease declined, while admissions due to sepsis or infections became more common. Illness severity (Simplified Acute Physiology Score - SAPS 3 - 62 points), patient age (mean = 62 years) and hospital mortality (approximately 17%) remained reasonably stable during this time period.ConclusionA large private database of critically ill patients is feasible and may provide relevant nationwide epidemiological data for quality improvement and benchmarking purposes among the participating intensive care units. This database is useful not only for administrative reasons but also for the improvement of daily care by facilitating the adoption of best practices and use for clinical research.

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Section: Introductionmentioning

confidence: 99%

The Epimed Monitor ICU Database®: a cloud-based national registry for adult intensive care unit patients in Brazil

Zampieri¹,

Soares²,

Borges³

et al. 2017

Revista Brasileira De Terapia Intensiva

Self Cite

106

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“…Validation studies of prognostic models in LMIC settings are becoming more common; 16 of the 50 studies included were published in 2015, 2016 or 2017 and additional studies, for example Moralez et al in Brazil [ 54 ] and Haniffa et al [ 9 ] in Sri Lanka, have been published/awaiting publication subsequent to the literature search for this review. Consequently it is important for investigators to adhere to reporting standards, such as CHARMS—especially with regard to performance measures, outcomes and missing values— to enable better interpretation.…”

Section: Discussionmentioning

confidence: 99%

Performance of critical care prognostic scoring systems in low and middle-income countries: a systematic review

Haniffa

Isaam²,

Silva³

et al. 2018

Crit Care

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BackgroundPrognostic models—used in critical care medicine for mortality predictions, for benchmarking and for illness stratification in clinical trials—have been validated predominantly in high-income countries. These results may not be reproducible in low or middle-income countries (LMICs), not only because of different case-mix characteristics but also because of missing predictor variables. The study objective was to systematically review literature on the use of critical care prognostic models in LMICs and assess their ability to discriminate between survivors and non-survivors at hospital discharge of those admitted to intensive care units (ICUs), their calibration, their accuracy, and the manner in which missing values were handled.MethodsThe PubMed database was searched in March 2017 to identify research articles reporting the use and performance of prognostic models in the evaluation of mortality in ICUs in LMICs. Studies carried out in ICUs in high-income countries or paediatric ICUs and studies that evaluated disease-specific scoring systems, were limited to a specific disease or single prognostic factor, were published only as abstracts, editorials, letters and systematic and narrative reviews or were not in English were excluded.ResultsOf the 2233 studies retrieved, 473 were searched and 50 articles reporting 119 models were included. Five articles described the development and evaluation of new models, whereas 114 articles externally validated Acute Physiology and Chronic Health Evaluation, the Simplified Acute Physiology Score and Mortality Probability Models or versions thereof. Missing values were only described in 34% of studies; exclusion and or imputation by normal values were used. Discrimination, calibration and accuracy were reported in 94.0%, 72.4% and 25% respectively. Good discrimination and calibration were reported in 88.9% and 58.3% respectively. However, only 10 evaluations that reported excellent discrimination also reported good calibration. Generalisability of the findings was limited by variability of inclusion and exclusion criteria, unavailability of post-ICU outcomes and missing value handling.ConclusionsRobust interpretations regarding the applicability of prognostic models are currently hampered by poor adherence to reporting guidelines, especially when reporting missing value handling. Performance of mortality risk prediction models in LMIC ICUs is at best moderate, especially with limitations in calibration. This necessitates continued efforts to develop and validate LMIC models with readily available prognostic variables, perhaps aided by medical registries.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-017-1930-8) contains supplementary material, which is available to authorized users.

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“…Em 2009, a Associação de Medicina Intensiva Brasileira (AMIB) escolheu o índice prognóstico SAPS 3 como o escore de gravidade de doença recomendado para avaliação de performance e para comparação entre as UTIs brasileiras (4) . O desempenho do modelo deve ser avaliado por meio da discriminação (habilidade de um modelo em discriminar os pacientes que sobrevivem e os que evoluem para óbito, medida através da área sob a curva ROC receiver operating characteristic (AUROC)) e calibração (correlação entre a mortalidade esperada e a mortalidade real na população estudada) (1,4,6) .…”

Section: Dsicusãounclassified

“…Escores prognósticos são ferramentas úteis na avaliação da eficácia de tratamentos, risco de mortalidade e qualidade de serviços, possibilitando comparação entre diferentes UTIs, além da implementação de melhorias de protocolos de tratamento e políticas de saúde pública (2) . É essencial o conhecimento do nível de gravidade dos pacientes admitidos em uma UTI, de modo a guiar as decisões clínicas, monitorar e avaliar novas terapias, prognosticar mortalidade, avaliar a qualidade do serviço prestado, comparando os resultados com os de outros centros em que existam pacientes de gravidade semelhante e analisar o custo-benefício com a utilização de recursos, possibilitando melhorias (3,4) .…”

Section: Introductionunclassified

“…Porém, no Brasil os estudos de validação foram realizados em subgrupos específicos de pacientes ou em centros únicos, sendo que os resultados em geral, fornecem uma boa discriminação. Todavia, os resultados da calibração variaram entre os diferentes estudos (4) . O objetivo deste estudo consiste em avaliar o poder discriminatório do escore fisiológico agudo (SAPS 3) na população de pacientes internados em uma UTI geral de um hospital terciário de Teresina (PI) com relação à mortalidade hospitalar.…”

Section: Introductionunclassified

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Validação do índice prognóstico SAPS 3 em pacientes internados na UTI de um hospital terciário de Teresina (PI)

Leão

Marques

Mello

2019

Rev. Hosp. Univ. UFPI

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INTRODUÇÃO: A avaliação do nível de gravidade dos pacientes admitidos em UTI pode ser realizada através de sistemas de escore prognóstico,como o SAPS3,que prediz a mortalidade hospitalar, permitindo avaliar a qualidade do serviço prestado e comparar resultados com os de outros centros com pacientes de gravidade semelhante. OBJETIVOS:Avaliar o poder discriminatório do escore SAPS3 em pacientes internados em uma UTI geral de um hospital terciário de Teresina(PI) com relação à mortalidade hospitalar. METODOLOGIA:Estudo de coorte retrospectiva, observacional, de análise de prontuários dos pacientes internados no período de setembro a novembro de 2017. Os dados referentes à primeira hora de admissão na UTI foram utilizados para o cálculo do SAPS3.A validação foi calculada pelo método Hosmer-Lemeshow e a discriminação obtida através da curva ROC. Estudo aprovado pelo comitê de ética em pesquisa da instituição. RESULTADOS:Foram incluídos no estudo 107 pacientes, com idade média 60±17 anos. Admissões cirúrgicas foram responsáveis por 49% das internações. O grupo dos não-sobreviventes apresentou maiores tempos de internação hospitalar antes da admissão(p=0,017) e de internação na UTI (0,023). A calibração mostrou c2=20,34 (p=0,0024). O menor valor do SAPS3 foi 29 e o maior 121(mediana 58). O valor do SAPS3 com maior poder discriminatório foi 58 (S80%, E86%; AUROC 0,8936). A mortalidade observada foi 34%, menor que a estimada pelo SAPS3 (49±32%). A razão de mortalidade padronizada (SMR) foi 0,68 (IC95% 0,90-0,46). CONCLUSÃO:O índice prognóstico SAPS3 apresentou bom poder discriminatório, porém superestimou a mortalidade, quando utilizada a equação adaptada para a América do Sul.

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

Cited by 25 publications

References 27 publications

The Epimed Monitor ICU Database®: a cloud-based national registry for adult intensive care unit patients in Brazil

The Epimed Monitor ICU Database®: a cloud-based national registry for adult intensive care unit patients in Brazil

Performance of critical care prognostic scoring systems in low and middle-income countries: a systematic review

Validação do índice prognóstico SAPS 3 em pacientes internados na UTI de um hospital terciário de Teresina (PI)

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