Validation of APACHE II, APACHE III and SAPS II scores in short- and long-term mortality prediction in a mixed intensive care unit in Poland: a cohort study (original) (raw)
Related papers
International Journal of Research in Medical Sciences, 2019
Background: The prognostication of critically ill patients, in a systematic way, based on definite objective data is an integral part of the quality of care in Intensive Care Unit (ICU). Acute physiology and chronic health evaluation (APACHE) scoring systems provide an objective means of mortality prediction in Intensive Care Unit (ICU). The aims of this study were to compare the performance of APACHE II and APACHE IV in predicting mortality in our intensive care unit (ICU).Methods: A prospective observational study was conducted in a 13 bedded intensive care unit (ICU) of a tertiary level teaching hospital. All the patients above the age of 12 years, irrespective of diagnosis managed in ICU for >24hours were enrolled. APACHE II and APACHE IV scores were calculated based on the worst values in the first 24hours of admission. All enrolled patients were followed up, and outcome was recorded as survivors or non survivors. Observed mortality rates were compared with predicted mortali...
Asian Journal of Nursing Education and Research
Background: Prognostication of critically ill patients is an integral part of the quality of care in ICU. The use of scoring system such as Acute Physiology and Chronic Health Evaluation (APACHE) to predict risk of mortality and evaluating outcome in critically ill patients is important in modern evidence-based medicine. the aim of the study was to compare the APACHE II and APACHE IV in predicting the mortality of patients intensive care unit. Methods: A prospective descriptive was among 100 adult patients admitted irrespective of diagnosis and managed for >24hours in the 25 bedded multidisciplinary ICU of a tertiary care hospital. The APACHE II and APACHE IV scores were calculated using the online calculators, based on the worst values in the first 24hours of admission. All the study participants were followed up, to determine the observed mortality rates and length of stay of ICU which were compared with predicted mortality rates obtained from both the APACHE II and APACHE IV s...
Short- and long-term mortality prediction in critically ill subjects: a cohort study
2020
Background. There are several scoring systems used for in-hospital mortality prediction in critical illness. Their application in a local scenario requires validation to ensure appropriate diagnostic accuracy. Also, their use in assessing post-discharge mortality in the ICU survivors has not been extensively studied.Aim. To evaluate the ability of APACHE II, III and SAPS II to predict in-hospital and post-discharge mortality in adult ICU patients.Material and methods. APACHE II, APACHE III and SAPS II, with corresponding predicted mortality ratios, were calculated for 303 consecutive patients admitted to the 10-bed ICU in 2016. Long-term mortality was calculated based on information taken from PESEL database.Results. Median APACHE II, APACHE III and SAPS II scores were 19, 67 and 44 points, with corresponding in-hospital mortality ratios of 28.1, 18.5 and 34.8%. Observed in-hospital mortality was 35.6%. 12-month post-discharge mortality reached 17.4%. All systems predicted in-hospit...
2018
The Acute Physiology and Chronic Health Evaluation (APACHE) II is still commonly used as an index of illness severity in patients admitted to intensive care unit (ICU) and has been validated in many research and clinical audit purposes. The aim of this study is to investigate the diagnostic value of APACHE II score for predicting mortality rate of critically ill patients. This was a retrospective cross-sectional study of 200 patients admitted in the medical-surgical adult ICU. Demographic data, pre-existing comorbidities, and required variables for calculating APACHE II score were recorded. Receiver operating characteristic (ROC) curves were constructed and the area under the ROC curves was calculated to assess the predictive value of the APACHE II score of in-hospital mortality. Of the 200 patients with mean age of 55.27 ± 21.59 years enrolled in the study, 112 (54%) were admitted in the medical ICU, and 88 (46%) in the surgical ICU. Finally, 116 patients (58%) died and 84 patients (42%) survived. The overall actual and predicted hospital mortality were 58% and 25.16%, respectively. The mean APACHE II score was 16.31 in total patients, 17.78 in medical ICU, and 14.45 in surgical ICU, and the difference was statistically significant between the two groups (P= 0.003). Overall, the area under ROC curve was 0.88. APACHE II with a score of 15 gave the best diagnostic accuracy to predict mortality of patients with a sensitivity, specificity, positive and negative predictive values of 85.3%, 77.4%, 83.9%, and 73.9%, respectively. Despite significant progress has been made in recent decades in terms of technology and equipment, therapeutics and process of care and identifies in the ICU setting, these scientific efforts have not directly led to a further reduction in mortality for patients hospitalized in the ICU.
Romanian Journal of Anaesthesia and Intensive Care, 2019
Background and Aims: In this study, we aimed to evaluate whether the age or the APACHE-II score was a better predictor of mortality in each group. The secondary objective was to investigate the factors affecting the mortality in each individual age group. Methods: We designed this retrospective study between 2016-2017. Age groups were classified into 3 classes: Patients < 60 years were Group 1, patients between 60-70 years were Group 2, and patients > 70 years were Group 3. We recorded patients’ age, ICU indication, demographic data, APACHE-II, ASA, length of hospital stays and mortality. Results: We analysed 150 patients and reported mortality for 58 patients (38.7%). We did not detect any association between age and mortality for all groups. ASA, length of ICU stays and predicted mortality rate, were significantly higher for exitus patients (p < 0.001). The ROC curve for the APACHE-II score, with a cut-off point of 23, demonstrated 74.14% sensitivity, 60.87% specificity, ...
Annals of Intensive Care, 2019
Background: The early postoperative period is critical for surgical patients. SOFA, SAPS 3 and APACHE II are prognostic scores widely used to predict mortality in ICU patients. This study aimed to evaluate these index tests for their prognostic accuracy for intra-ICU and in-hospital mortalities as target conditions in patients admitted to ICU after urgent or elective surgeries and to test whether they aid in decision-making. The process comprised the assessment of discrimination through analysis of the areas under the receiver operating characteristic curves and calibration of the prognostic models for the target conditions. After, the clinical relevance of applying them was evaluated through the measurement of the net benefit of their use in the clinical decision. Results: Index tests were found to discriminate regular for both target conditions with a poor calibration (C statistics-intra-ICU mortality AUROCs: APACHE II 0.808, SAPS 3 0.821 and SOFA 0.797/in-hospital mortality AUROCs: APACHE II 0.772, SAPS 3 0.790 and SOFA 0.742). Calibration assessment revealed a weak correlation between the observed and expected number of cases in several thresholds of risk, calculated by each model, for both tested outcomes. The net benefit analysis showed that all score's aggregate value in the clinical decision when the calculated probabilities of death ranged between 10 and 40%. Conclusions: In this study, we observed that the tested ICU prognostic scores are fair tools for intra-ICU and in-hospital mortality prediction in a cohort of postoperative surgical patients. Also, they may have some potential to be used as ancillary data to support decision-making by physicians and families regarding the level of therapeutic investment and palliative care.
Acta Anaesthesiologica Scandinavica, 2017
Background: Intensive care unit (ICU) mortality prediction scores deteriorate over time, and their complexity decreases clinical applicability and commonly causes problems with missing data. We aimed to develop and internally validate a new and simple score that predicts 90-day mortality in adults upon acute admission to the ICU: the Simplified Mortality Score for the Intensive Care Unit (SMS-ICU). Methods: We used data from an international cohort of 2139 patients acutely admitted to the ICU and 1947 ICU patients with severe sepsis/septic shock from 2009 to 2016. We performed multiple imputations for missing data and used binary logistic regression analysis with variable selection by backward elimination, followed by conversion to a simple point-based score. We assessed the apparent performance and validated the score internally using bootstrapping to present optimism-corrected performance estimates. Results: The SMS-ICU comprises seven variables available in 99.5% of the patients: two numeric variables: age and lowest systolic blood pressure, and five dichotomous variables: haematologic malignancy/metastatic cancer, acute surgical admission and use of vasopressors/inotropes, respiratory support and renal replacement therapy. Discrimination (area under the receiver operating characteristic curve) was 0.72 (95% CI: 0.71-0.74), overall performance (Nagelkerke's R 2) was 0.19 and calibration (intercept and slope) was 0.00 and 0.99, respectively. Optimism-corrected performance was similar to apparent performance. Conclusions: The SMS-ICU predicted 90-day mortality with reasonable and stable performance. If performance remains adequate after external validation, the SMS-ICU could prove a valuable tool for ICU clinicians and researchers because of its simplicity and expected very low number of missing values. Editorial comment Predicting mortality or survival for critically ill patients is challenging. In this paper, a relatively simple and new model using seven variables is presented based on analysis of a large international cohort. This model next needs to be tested and validated on a different large and reliable database before its predictive value can be appreciated.
Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine, 2018
Mortality prediction in the Intensive Care Unit (ICU) setting is complex, and there are several scoring systems utilized for this process. The Acute Physiology and Chronic Health Evaluation (APACHE) II has been the most widely used scoring system; although, the more recent APACHE IV is considered an updated and advanced prediction model. However, these two systems may not give similar mortality predictions. The aim of this study is to compare the mortality prediction ability of APACHE II and APACHE IV scoring systems among patients admitted to a tertiary care ICU. In this prospective longitudinal observational study, APACHE II and APACHE IV scores of ICU patients were computed using an online calculator. The outcome of the ICU admissions for all the patients was collected as discharged or deceased. The data were analyzed to compare the discrimination and calibration of the mortality prediction ability of the two scores. Out of the 1670 patients' data analyzed, the area under the...
Intensive Care Medicine, 2005
Objective To develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data. Design Prospective multicentre, multinational cohort study. Patients and setting A total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002. Measurements and results ICU admission data (recorded within ±1 h) were used, describing: prior chronic conditions and diseases; circumstances related to and physiologic derangement at ICU admission. Selection of variables for inclusion into the model used different complementary strategies. For cross-validation, the model-building procedure was run five times, using randomly selected four fifths of the sample as a development- and the remaining fifth as validation-set. Logistic regression methods were then used to reduce complexity of the model. Final estimates of regression coefficients were determined by use of multilevel logistic regression. Variables selection and weighting were further checked by bootstraping (at patient level and at ICU level). Twenty variables were selected for the final model, which exhibited good discrimination (aROC curve 0.848), without major differences across patient typologies. Calibration was also satisfactory (Hosmer-Lemeshow goodness-of-fit test Ĥ=10.56, p=0.39, Ĉ=14.29, p=0.16). Customised equations for major areas of the world were computed and demonstrate a good overall goodness-of-fit. Conclusions The SAPS 3 admission score is able to predict vital status at hospital discharge with use of data recorded at ICU admission. Furthermore, SAPS 3 conceptually dissociates evaluation of the individual patient from evaluation of the ICU and thus allows them to be assessed at their respective reference levels.