Development of a National Core Data Set for the Iranian Icu Patient Outcome Prediction (original) (raw)
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Journal of Innovation in Health Informatics
Objective: To define a core dataset for ICU Patients Outcome Prediction in Iran. This core data set will lead us to design ICU outcome prediction models with the most effective parameters.Methods: A combination of literature review, national survey and expert consensus meetings were used. First, a literature review was performed by a general search in PubMed to find the most appropriate models for intensive care mortality prediction and their parameters. Secondly, in a national survey, experts from a couple of medical centers in all parts of Iran were asked to comment on a list of items retrieved from the earlier literature review study. In the next step, a multi-disciplinary committee of experts was installed. In 4 meetings each data item was examined separately and included/excluded by committee consensus.Results: The combination of the literature review findings and experts’ consensus resulted in a draft dataset including 26 data items. 92% percent of data items in the draft dat...
Evaluation of Basic Parameters for Prediction of ICU Mortality
Journal of Critical and Intensive Care
Aim: The performance of common mortality prediction models in the intensive care units (ICU) are extensively validated, predominantly in high-income countries. Simple and fast models with region specific features are needed. Study design: Retrospective case-control study Methods: We reviewed the medical records of 1057 ICU-admitted patients within three years. Patient survival was defined as discharge before 28 days. Multivariate logistic regression modeling was applied, basic parameters were selected, and a simple model was tried using four of them (age, albumin, platelet, C-reactive protein); as Quick Prediction of Mortality (Qpm) score, and then tested. The Qpm score predictions were compared to calculated APACHE II predicted mortality (APM) score predictions. Both scores were then weighted by calculated standardized mortality ratios (SMR). Results: 933 patients were included into the analyses. The patients' overall observed mortality rate was 47%. APACHEII score prediction was 49% (p< 0.001, AUC= 0.810, r: 0.518). Qpm score prediction was 57% (p< 0.001, AUC= 0.699, r: 0.338). The SMR for Qpm was 0.82 in comparison to APM score SMR = 0.96. Conclusion: This simple prediction model has showed an acceptable performance in our ICU sample and needs to be prospectively evaluated for feasibility. In addition, further studies could be planned for external evaluations and validations in different settings.
Predicting the risk of death in patients in intensive care unit
Archives of Iranian medicine, 2007
The ability to identify critically ill patients who will not survive until hospital discharge may yield substantial cost savings. The aim of this study was to validate the mortality prediction model II (MPM II) in in-hospital mortality of critically ill patients for quality management and risk-adjusted monitoring. The data were collected prospectively from consecutive admissions to the Intensive Care Unit of Imam Hossein Medical Center in Tehran. A total of 274 admissions were analyzed using tests of discrimination and calibration of the logistic regression equation for mortality prediction model II at admission (MPM0 II) and at 24th hour (MPM24 II). The mortality prediction model II exhibited excellent discrimination (receiver operating characteristic curve area). Calibration curves and Hosmer-Lemeshow statistics demonstrated good calibration of both models on outcome. We recommend using mortality prediction model II in Iranian ICUs for routine audit requirements. Mortality predict...
F1000Research, 2017
Clinical assessment of disease severity is an important part of medical practice for prediction of mortality and morbidity in Intensive Care Unit (ICU). A disease severity scoring system can be used as guidance for clinicians for objective assessment of disease outcomes and estimation of the chance of recovery. This study aimed to evaluate the hypothesis that the mortality and length of stay in emergency ICUs predicted by APACHE-IV is different to the real rates of mortality and length of stay observed in our emergency ICU in Iran. This was a retrospective cohort study conducted on the data of 839 consecutive patients admitted to the emergency ICU of Nemazi Hospital, Shiraz, Iran, during 2012-2015. The relevant variables were used to calculate APACHE-IV. Length of stay and death or discharge, Glasgow coma score, and acute physiology score were also evaluated. Moreover, the accuracy of APACHE-IV for mortality was assessed using area under the Receiver Operator Characteristic (ROC) c...
Critical care (London, England), 2002
The purpose of this study is to assess the performance of Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, Mortality Probability Model MPM II0 and MPM II24 systems in a major tertiary care hospital in Riyadh, Saudi Arabia. The following data were collected prospectively on all consecutive patients admitted to the Intensive Care Unit between 1 March 1999 and 31 December 2000: demographics, APACHE II and SAPS II scores, MPM variables, ICU and hospital outcome. Predicted mortality was calculated using original regression formulas. Standardized mortality ratio (SMR) was computed with 95% confidence intervals (CI). Calibration was assessed by calculating Lemeshow-Hosmer goodness-of-fit C statistics. Discrimination was evaluated by calculating the Area Under the Receiver Operating Characteristic Curves (ROC AUC). Predicted mortality by all systems was not significantly different from actual mortality [SMR for MPM II0: 1.00 (0.91-1.10...
Outcome prediction in intensive care: results of a prospective, multicentre, Portuguese study
Intensive Care Medicine, 1997
in an independent database, using formal statistical assessment. Design: Analysis of the database of a multicentre, prospective study. Setting: 19 intensive care units (ICUs) in Portugal. Patients: Data for 1094 patients consecutively admitted to the ICUs were collected over a period of 4 months. Following the original SAPS II and APACHE II criteria, the analysis excluded patients younger than 18 years of age, readmissions, acute myocardial infarction, burns, patients in the post-operative period after coronary artery bypass surgery, and patients with a length of stay in the ICU of less than 24 h. The group analysed comprised 982 patients. Interventions: Collection of the first 24 h admission data necessary for the calculation of SAPS II, APA-CHE II, Therapeutic Intervention Scoring System (TISS), Simplified TISS, organ system failure and basic demographic statistics. Vital status at discharge from the hospital was registered. Measurements and results: In this cohort, discrimination was better for SAPS II than for APACHE II (SAPS II: area under the receiver operating characteristic curve 0.817, standard error 0.015; APACHE II: 0.787, 0.015; p < 0.001); however, both models presented a poor calibration, with significant differences between observed and predicted mortality (Hosmer-Lemeshow goodness-of-fit tests H and C, p < 0.001). In a stratified analysis, this study was unable to demonstrate any definite pattern of association between the poor performance of the models and specific subgroups of patients except for the most severely ill patients, where both models overestimated mortality. Conclusions: SAPS II performed better than APACHE II in this independent database, but the results do not allow its use, at least without being customised, to analyse quality of care or performance among ICUs in the target population.
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.
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.
International journal of critical illness and injury science, 2014
To assess the performance and utility of two mortality prediction models viz. Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) in a single Indian mixed tertiary intensive care unit (ICU). Secondary objectives were bench-marking and setting a base line for research. In this observational cohort, data needed for calculation of both scores were prospectively collected for all consecutive admissions to 28-bedded ICU in the year 2011. After excluding readmissions, discharges within 24 h and age <18 years, the records of 1543 patients were analyzed using appropriate statistical methods. Both models overpredicted mortality in this cohort [standardized mortality ratio (SMR) 0.88 ± 0.05 and 0.95 ± 0.06 using APACHE II and SAPS II respectively]. Patterns of predicted mortality had strong association with true mortality (R (2) = 0.98 for APACHE II and R (2) = 0.99 for SAPS II). Both models performed poorly in formal Hosmer-Leme...
BMJ Open, 2017
Introduction: Mortality prediction scores are widely used in intensive care units (ICUs) and in research, but their predictive value deteriorates as scores age. Existing mortality prediction scores are imprecise and complex, which increases the risk of missing data and decreases the applicability bedside in daily clinical practice. We propose the development and validation of a new, simple and updated clinical prediction rule: the Simplified Mortality Score for use in the Intensive Care Unit (SMS-ICU). Methods and analysis: During the first phase of the study, we will develop and internally validate a clinical prediction rule that predicts 90-day mortality on ICU admission. The development sample will comprise 4247 adult critically ill patients acutely admitted to the ICU, enrolled in 5 contemporary high-quality ICU studies/trials. The score will be developed using binary logistic regression analysis with backward stepwise elimination of candidate variables, and subsequently be converted into a point-based clinical prediction rule. The general performance, discrimination and calibration of the score will be evaluated, and the score will be internally validated using bootstrapping. During the second phase of the study, the score will be externally validated in a fully independent sample consisting of 3350 patients included in the ongoing Stress Ulcer Prophylaxis in the Intensive Care Unit trial. We will compare the performance of the SMS-ICU to that of existing scores. Ethics and dissemination: We will use data from patients enrolled in studies/trials already approved by the relevant ethical committees and this study requires no further permissions. The results will be reported in accordance with the Transparent Reporting of multivariate prediction models for Individual Prognosis Or Diagnosis (TRIPOD) statement, and submitted to a peer-reviewed journal.