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Prediction of outcome in patients with ARDS: A prospective cohort study comparing ARDS-definitions and other ARDS-associated parameters, ratios and scores at intubation and over time.

ABSTRACT: BACKGROUND:Early recognition of high-risk-patients with acute respiratory distress syndrome (ARDS) might improve their outcome by less protracted allocation to intensified therapy including extracorporeal membrane oxygenation (ECMO). Among numerous predictors and classifications, the American European Consensus Conferenece (AECC)- and Berlin-definitions as well as the oxygenation index (OI) and the Murray-/Lung Injury Score are the most common. Most studies compared the prediction of mortality by these parameters on the day of intubation and/or diagnosis of ARDS. However, only few studies investigated prediction over time, in particular for more than three days. OBJECTIVE:Therefore, our study aimed at characterization of the best predictor and the best day(s) to predict 28-days-mortality within four days after intubation of patients with ARDS. METHODS:In 100 consecutive patients with ARDS severity according to OI (mean airway pressure*FiO2/paO2), modified Murray-score without radiological points (Murray_mod), AECC- and Berlin-definition, were daily documented for four days after intubation. In the subgroup of 49 patients with transpulmonary thermodilution (TPTD) monitoring (PiCCO), extravascular lung water index (EVLWI) was measured daily. PRIMARY ENDPOINT:Prediction of 28-days-mortality (Area under the receiver-operating-characteristic curve (ROC-AUC)); IBM SPSS 26. RESULTS:In the totality of patients the best prediction of 28-days-mortality was found on day-1 and day-2 (mean ROC-AUCs for all predictors/scores: 0.632 and 0.620). OI was the best predictor among the ARDS-scores (AUC=0.689 on day-1; 4-day-mean AUC = 0.625). AECC and Murray_mod had 4-day-means AUCs below 0.6. Among the 49 patients with TPTD, EVLWI (4-day-mean AUC=0.696) and OI (4-day-mean AUC=0.695) were the best predictors. AUCs were 0.789 for OI on day-1, and 0.786 for EVLWI on day-2. In binary regression analysis of patients with TPTD, EVLWI (B=-0.105; Wald=7.294; p=0.007) and OI (B=0.124; Wald=7.435; p=0.006) were independently associated with 28-days-mortality. Combining of EVLWI and OI provided ROC-AUCs of 0.801 (day-1) and 0.824 (day-2). Among the totality of patients, the use of TPTD-monitoring "per se" and a lower SOFA-score were independently associated with a lower 28-days-mortality. CONCLUSIONS:Prognosis of ARDS-patients can be estblished within two days after intubation. The best predictors were EVLWI and OI and their combination. TPTD-monitoring "per se" was independently associated with reduced mortality.

SUBMITTER: Huber W

PROVIDER: S-EPMC7202606 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prediction of outcome in patients with ARDS: A prospective cohort study comparing ARDS-definitions and other ARDS-associated parameters, ratios and scores at intubation and over time.

Huber Wolfgang W Findeisen Michael M Lahmer Tobias T Herner Alexander A Rasch Sebastian S Mayr Ulrich U Hoppmann Petra P Jaitner Juliane J Okrojek Rainer R Brettner Franz F Schmid Roland R Schmidle Paul P

PloS one 20200506 5

<h4>Background</h4>Early recognition of high-risk-patients with acute respiratory distress syndrome (ARDS) might improve their outcome by less protracted allocation to intensified therapy including extracorporeal membrane oxygenation (ECMO). Among numerous predictors and classifications, the American European Consensus Conferenece (AECC)- and Berlin-definitions as well as the oxygenation index (OI) and the Murray-/Lung Injury Score are the most common. Most studies compared the prediction of mor ...[more]

PMID: 32374755

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Project description: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.

Project description:BACKGROUND:Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission. METHODS AND FINDINGS:This study uses ML to assess the predictability of ICU mortality as a function of time. Logistic regression against physiological data alone outperformed APACHE-II and demonstrated several important interactions including between lactate & noradrenaline dose, between lactate & MAP, and between age & MAP consistent with the current sepsis definitions. ML models consistently outperformed logistic regression with Deep Learning giving the best results. Predictive power was maximal on the second day and was further improved by incorporating trend data. Using a limited range of physiological and demographic variables, the best machine learning model on the first day showed an area under the receiver-operator characteristic curve (AUC) of 0.883 (σ = 0.008), compared to 0.846 (σ = 0.010) for a logistic regression from the same predictors and 0.836 (σ = 0.007) for a logistic regression based on the APACHE-II score. Adding information gathered on the second day of admission improved the maximum AUC to 0.895 (σ = 0.008). Beyond the second day, predictive ability declined. CONCLUSION:This has implications for decision making in intensive care and provides a justification for time-limited trials of ICU therapy; the assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes. New ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools.

Project description:(1) Background: Respiratory insufficiency with acute respiratory distress syndrome (ARDS) and multi-organ dysfunction leads to high mortality in COVID-19 patients. In times of limited intensive care unit (ICU) resources, chest CTs became an important tool for the assessment of lung involvement and for patient triage despite uncertainties about the predictive diagnostic value. This study evaluated chest CT-based imaging parameters for their potential to predict in-hospital mortality compared to clinical scores. (2) Methods: 89 COVID-19 ICU ARDS patients requiring mechanical ventilation or continuous positive airway pressure mask ventilation were included in this single center retrospective study. AI-based lung injury assessment and measurements indicating pulmonary hypertension (PA-to-AA ratio) on admission CT, oxygenation indices, lung compliance and sequential organ failure assessment (SOFA) scores on ICU admission were assessed for their diagnostic performance to predict in-hospital mortality. (3) Results: CT severity scores and PA-to-AA ratios were not significantly associated with in-hospital mortality, whereas the SOFA score showed a significant association (p < 0.001). In ROC analysis, the SOFA score resulted in an area under the curve (AUC) for in-hospital mortality of 0.74 (95%-CI 0.63-0.85), whereas CT severity scores (0.53, 95%-CI 0.40-0.67) and PA-to-AA ratios (0.46, 95%-CI 0.34-0.58) did not yield sufficient AUCs. These results were consistent for the subgroup of more critically ill patients with moderate and severe ARDS on admission (oxygenation index <200, n = 53) with an AUC for SOFA score of 0.77 (95%-CI 0.64-0.89), compared to 0.55 (95%-CI 0.39-0.72) for CT severity scores and 0.51 (95%-CI 0.35-0.67) for PA-to-AA ratios. (4) Conclusions: Severe COVID-19 disease is not limited to lung (vessel) injury but leads to a multi-organ involvement. The findings of this study suggest that risk stratification should not solely be based on chest CT parameters but needs to include multi-organ failure assessment for COVID-19 ICU ARDS patients for optimized future patient management and resource allocation.

Project description:BackgroundEvery two years, long-term care organizations for the elderly are obliged to evaluate and publish the experiences of residents, representatives of psychogeriatric patients, and/or assisted-living clients with regard to quality of care. Our hypotheses are that publication of this quality information leads to improved performance, and that organizations with substandard performance will improve more than those whose performance is relatively good.MethodsThe analyses included organizational units that measured experiences twice between 2007 (t(0)) and 2009 (t(1)). Experiences with quality of care were measured with Consumer Quality Index (CQI) questionnaires. Besides descriptive analyses (i.e. mean, 5(th) and 95(th) percentile, and 90% central range) of the 19 CQI indicators and change scores of these indicators were calculated. Differences across five performance groups (ranging from 'worst' to 'best') were tested using an ANOVA test and effect sizes were measured with omega squared (?(2)).ResultsAt t0 experiences of residents, representatives, and assisted-living clients were positive on all indicators. Nevertheless, most CQI indicators had improved scores (up to 0.37 change score) at t(1). Only three indicators showed a minor decline (up to -0.08 change score). Change scores varied between indicators and questionnaires, e.g. they were more profound for the face-to-face interview questionnaire for residents in nursing homes than for the other two mail questionnaires (0.15 vs. 0.05 and 0.04, respectively), possibly due to more variation between nursing homes on the first measurement, perhaps indicating more potential for improvement. A negative relationship was found between prior performance and change, particularly with respect to the experiences of residents (?(2) = 0.16) and assisted-living clients (?(2) = 0.15). However, the relation between prior performance and improvement could also be demonstrated with respect to the experiences reported by representatives of psychogeriatric patients and by assisted-living clients. For representatives of psychogeriatric patients, the performance groups 1 and 2 ([much] below average) improved significantly more than the other three groups (?(2) = 0.05).ConclusionsBoth hypotheses were confirmed: almost all indicator scores improved over time and long-term care organizations for the elderly with substandard performance improved more than those with a performance which was already relatively good.

Project description:ObjectivesThis study compared risk discrimination for the prediction of coronary heart disease (CHD) and cardiovascular disease (CVD) deaths for the Pooled Cohort Equations (PCE), the MESA (Multi-Ethnic Study of Atherosclerosis) Risk Score (with and without coronary artery calcium [CAC]), and of simple addition of CAC to the PCE.BackgroundThe PCE predict 10-year risk of atherosclerotic CVD events, and the MESA Risk Score predicts risk of CHD. Their comparative performance for the prediction of fatal events is poorly understood.MethodsWe evaluated 53,487 patients ages 45 to 79 years from the CAC Consortium, a retrospective cohort study of asymptomatic individuals referred for clinical CAC scoring. Risk discrimination was measured using C-statistics.ResultsMean age was 57 years, 35% were women, and 39% had CAC of 0. There were 421 CHD and 775 CVD deaths over a mean 12-year follow-up. In the overall study population, discrimination with the MESA Risk Score with CAC and the PCE was almost identical for both outcomes (C-statistics: 0.80 and 0.79 for CHD death, 0.77 and 0.78 for CVD death, respectively). Addition of CAC to the PCE improved risk discrimination, yielding the largest C-statistics. The MESA Risk Score with CAC and the PCE plus CAC showed the best discrimination among the 45% of patients with 5% to 20% estimated risk. Secondary analyses by estimated CVD risk strata showed modestly improved risk discrimination with CAC also among low- and high-estimated risk groups.ConclusionsOur findings support the current guideline recommendation to use, among available risk scores, the PCE for initial risk assessment and to use CAC for further risk assessment in a broad borderline and intermediate risk group. Also, in select individuals at low or high estimated risk, CAC modestly improved discrimination. Studies in unselected populations will lead to further understanding of the potential value of tools combining risk scores and CAC for optimal risk assessment.

Project description:BackgroundPeptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction.Methodology/principal findingsA training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity.Conclusions/significanceMALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS.

Dataset Information

Prediction of outcome in patients with ARDS: A prospective cohort study comparing ARDS-definitions and other ARDS-associated parameters, ratios and scores at intubation and over time.

Publications

Prediction of outcome in patients with ARDS: A prospective cohort study comparing ARDS-definitions and other ARDS-associated parameters, ratios and scores at intubation and over time.

Similar Datasets

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