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Improved ICU mortality prediction based on SOFA scores and gastrointestinal parameters.

ABSTRACT: BACKGROUND:The Sequential Organ Failure Assessment (SOFA) score is commonly used in ICUs around the world, designed to assess the severity of the patient's clinical state based on function/dysfunction of six major organ systems. The goal of this work is to build a computational model to predict mortality based on a series of SOFA scores. In addition, we examined the possibility of improving the prediction by incorporating a new component designed to measure the performance of the gastrointestinal system, added to the other six components. METHODS:In this retrospective study, we used patients' three latest SOFA scores recorded during an individual ICU stay as input to different machine learning models and ensemble learning models. We added three validated parameters representing gastrointestinal failure. Among others, we used classification models such as Support Vector Machines (SVMs), Neural Networks, Logistic Regression and a penalty function used to increase model robustness in regard to certain extreme cases, which may be found in ICU population. We used the Area under Curve (AUC) performance metric to examine performance. RESULTS:We found an ensemble model of linear and logistic regression achieves a higher AUC compared related works in past years. After incorporating the gastrointestinal failure score along with the penalty function, our best performing ensemble model resulted in an additional improvement in terms of AUC metrics. We implemented and compared 36 different models that were built using both the information from the SOFA score as well as that of the gastrointestinal system. All compared models have approximately similar and relatively large AUC (between 0.8645 and 0.9146) with the best results are achieved by incorporating the gastrointestinal parameters into the prediction models. CONCLUSIONS:Our findings indicate that gastrointestinal parameters carry significant information as a mortality predictor in addition to the conventional SOFA score. This information improves the predictive power of machine learning models by extending the SOFA to include information related to gastrointestinal organ system. The described method improves mortality prediction by considering the dynamics of the extended SOFA score. Although tested on a limited data set, the results' stability across different models suggests robustness in real-time use.

SUBMITTER: Aperstein Y

PROVIDER: S-EPMC6768479 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Improved ICU mortality prediction based on SOFA scores and gastrointestinal parameters.

Aperstein Yehudit Y Cohen Lidor L Bendavid Itai I Cohen Jonathan J Grozovsky Elad E Rotem Tammy T Singer Pierre P

PloS one 20190930 9

<h4>Background</h4>The Sequential Organ Failure Assessment (SOFA) score is commonly used in ICUs around the world, designed to assess the severity of the patient's clinical state based on function/dysfunction of six major organ systems. The goal of this work is to build a computational model to predict mortality based on a series of SOFA scores. In addition, we examined the possibility of improving the prediction by incorporating a new component designed to measure the performance of the gastroi ...[more]

PMID: 31568512

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Project description:INTRODUCTION: To systematically review studies evaluating the performance of Sequential Organ Failure Assessment (SOFA)-based models for predicting mortality in patients in the intensive care unit (ICU). METHODS: Medline, EMBASE and other databases were searched for English-language articles with the major objective of evaluating the prognostic performance of SOFA-based models in predicting mortality in surgical and/or medical ICU admissions. The quality of each study was assessed based on a quality framework for prognostic models. RESULTS: Eighteen articles met all inclusion criteria. The studies differed widely in the SOFA derivatives used and in their methods of evaluation. Ten studies reported about developing a probabilistic prognostic model, only five of which used an independent validation data set. The other studies used the SOFA-based score directly to discriminate between survivors and non-survivors without fitting a probabilistic model. In five of the six studies, admission-based models (Acute Physiology and Chronic Health Evaluation (APACHE) II/III) were reported to have a slightly better discrimination ability than SOFA-based models at admission (the receiver operating characteristic curve (AUC) of SOFA-based models ranged between 0.61 and 0.88), and in one study a SOFA model had higher AUC than the Simplified Acute Physiology Score (SAPS) II model. Four of these studies used the Hosmer-Lemeshow tests for calibration, none of which reported a lack of fit for the SOFA models. Models based on sequential SOFA scores were described in 11 studies including maximum SOFA scores and maximum sum of individual components of the SOFA score (AUC range: 0.69 to 0.92) and delta SOFA (AUC range: 0.51 to 0.83). Studies comparing SOFA with other organ failure scores did not consistently show superiority of one scoring system to another. Four studies combined SOFA-based derivatives with admission severity of illness scores, and they all reported on improved predictions for the combination. Quality of studies ranged from 11.5 to 19.5 points on a 20-point scale. CONCLUSIONS: Models based on SOFA scores at admission had only slightly worse performance than APACHE II/III and were competitive with SAPS II models in predicting mortality in patients in the general medical and/or surgical ICU. Models with sequential SOFA scores seem to have a comparable performance with other organ failure scores. The combination of sequential SOFA derivatives with APACHE II/III and SAPS II models clearly improved prognostic performance of either model alone. Due to the heterogeneity of the studies, it is impossible to draw general conclusions on the optimal mathematical model and optimal derivatives of SOFA scores. Future studies should use a standard evaluation methodology with a standard set of outcome measures covering discrimination, calibration and accuracy.

Project description:BackgroundOutcomes in well-resourced, intensive care units (ICUs) in Kenya are thought to be comparable to those in high-income countries (HICs) but risk-adjusted mortality data is unavailable. We undertook an evaluation of the Aga Khan University Hospital, Nairobi ICU to analyze patient clinical-demographic characteristics, compare the performance of Sequential Organ Failure Assessment (SOFA), delta-SOFA at 48 hours and Mortality Prediction Model-III (MPM-III) mortality prediction systems, and identify factors associated with increased risk of mortality.MethodsA retrospective cohort study was conducted of adult patients admitted to the ICU between January 2015 and September 2017. SOFA and MPM-III scores were determined at admission and SOFA repeated at 48 hours.ResultsApproximately 33% of patients did not meet ICU admission criteria. Mortality among the population of critically ill patients in the ICU was 31.7%, most of whom were male (61.4%) with a median age of 53.4 years. High adjusted odds of mortality were found among critically ill patients with leukemia (aOR 6.32, p<0.01), tuberculosis (aOR 3.96, p<0.01), post-cardiac arrest (aOR 3.57, p<0.01), admissions from the step-down unit (aOR 3.13, p<0.001), acute kidney injury (aOR 2.97, p<0.001) and metastatic cancer (aOR 2.45, p = 0.04). The area under the receiver-operating characteristic (ROC) curve of admission SOFA was 0.77 (95% CI, 0.73-0.81), MPM-III 0.74 (95% CI, 0.69-0.79), delta-SOFA 0.69 (95% CI, 0.63-0.75) and 48-hour SOFA 0.83 (95% CI, 0.79-0.87). The difference between SOFA at 48 hours and admission SOFA, MPM-III and delta-SOFA was statistically significant (chi2 = 17.1, 24.2 and 26.5 respectively; p<0.001). Admission SOFA, MPM-III and 48-hour SOFA were well calibrated (p >0.05) while delta-SOFA was borderline (p = 0.05).ConclusionMortality among the critically ill was higher than expected in this well-resourced ICU. 48-hour SOFA performed better than admission SOFA, MPM-III and delta-SOFA in our cohort. While a large proportion of patients did not meet admission criteria but were boarded in the ICU, critically ill patients stepped-up from the step-down unit were unlikely to survive. Patients admitted following a cardiac arrest, and those with advanced disease such as leukemia, stage-4 HIV and metastatic cancer, had particularly poor outcomes. Policies for fair allocation of beds, protocol-driven admission criteria and appropriate case selection could contribute to lowering the risk of mortality among the critically ill to a level on par with HICs.

Project description:BackgroundThe Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change.ObjectivesTo assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions.PopulationThere were 8,399 pediatric ICU admissions with 312 (3.7%) deaths from January 1, 2018 to February 29, 2020. We randomly selected 75% of patients for training, 13% for validation, and 12% for testing.ModelA neural network was trained to predict hospital survival or death during or following an ICU admission. Variables included age, gender, laboratory tests, vital signs, medications categories, and mechanical ventilation variables. The neural network was calibrated to mortality risk using nonparametric logistic regression.ResultsDiscrimination assessed across all time periods found an AUROC of 0.851 (0.841-0.862) and an AUPRC was 0.443 (0.417-0.467). When assessed for performance every 3 h, the AUROCs had a minimum value of 0.778 (0.689-0.867) and a maximum value of 0.885 (0.841,0.862); the AUPRCs had a minimum value 0.148 (0.058-0.328) and a maximum value of 0.499 (0.229-0.769). The calibration plot had an intercept of 0.011, a slope of 0.956, and the R2 was 0.814. Comparison of observed vs. expected proportion of deaths revealed that 95.8% of the 543 risk intervals were not statistically significantly different. Construct validity assessed by death and survivor risk trajectories analyzed by mortality risk quartiles and 7 high and low risk diseases confirmed a priori clinical expectations about the trajectories of death and survivors.ConclusionsThe Criticality Index-Mortality computing mortality risk every 3 h for pediatric ICU patients has model performance that could enhance the clinical assessment of severity of illness. The overall Criticality Index-Mortality framework was effectively applied to develop an institutionally specific, and clinically relevant model for dynamic risk assessment of pediatric ICU patients.

Project description:Background Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered models. Conclusions Our study provides a useful and reliable tool, through a machine learning approach, for predicting ICU mortality in COVID-19 patients. In all the estimated models, age was the variable showing the most important impact on mortality. Supplementary Information The online version contains supplementary material available at 10.1186/s44158-021-00002-x.

Project description:To investigate the possible predictive role of routinely used glycemic parameters for a first venous thromboembolism (VTE) episode in gastrointestinal (GI) cancer ambulatory patients - with or without clinically diagnosed type 2 diabetes (T2D) or obesity - treated with chemotherapy.Pre-treatment fasting blood glucose, insulin, glycated hemoglobin (HbA1c) and homeostasis model of risk assessment (HOMA) were retrospectively evaluated in a cohort study of 342 GI cancer patients. Surgery was performed in 142 (42%) patients with primary cancer, 30 (21%) and 112 (79%) of whom received neoadjuvant and adjuvant therapies, respectively. First-line chemotherapy was administered in 200 (58%) patients with metastatic disease. The study outcome was defined as the occurrence of a first symptomatic or asymptomatic VTE episode during active treatment.Impaired glucose tolerance (IGT) or T2D were diagnosed in 30% of GI cancer patients, while overweight/obesity had an incidence of 41%. VTE occurred in 9.4% of patients (7% of non-diabetic non-obese), especially in those with a high ECOG score (P = 0.025). No significant association was found between VTE incidence and T2D, obesity, different tumor types, metastatic disease, Khorana class of risk, or different anti-cancer drugs, although VTE rates were substantially higher in patients receiving bevacizumab (17% vs 8%, P = 0.044). Conversely, all glucose metabolic indexes were associated with increased VTE risk at ROC analysis. Multivariate Cox proportional analyses confirmed that HOMA index (HR = 4.13, 95%CI: 1.63-10.5) or fasting blood glucose (HR = 3.56, 95%CI: 1.51-8.39) were independent predictors of VTE occurrence during chemotherapy.The results here reported demonstrate that evaluating glucose metabolic asset may allow for VTE risk stratification in GI cancer, helping to identify chemotherapy-treated patients who might benefit from thromboprophylaxis. Further multicenter prospective studies involving a larger number of patients are presently needed.

Dataset Information

Improved ICU mortality prediction based on SOFA scores and gastrointestinal parameters.

Publications

Improved ICU mortality prediction based on SOFA scores and gastrointestinal parameters.

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