Materiały źródłowe

• #1 The APPS: an outcome score for the acute respiratory distress syndrome

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5107525/

  It is estimated that about one million patients around the world develop ARDS each year; more than one third of them will not survive the syndrome (3). […] It has been demonstrated that lung injury severity 24 hours after ARDS onset is a major determinant of outcome (4). […] Current definitions of ARDS are incapable of properly identifying or stratifying ARDS patients in terms of severity and prognosis (4). […] The APPS model showed an excellence performance in a large cohort of consecutive patients with moderate and severe ARDS ventilated with a lung protective mechanical ventilation strategy. […] The use of APPS to guide risk stratification of patients with ARDS can be expected to be an important tool for guiding therapy, and for increasing the potential of success of future clinical trials. […] We do not exclude that in the future, incorporating markers of increased pulmonary vascular permeability and right ventricular dysfunction could improve the prognostic accuracy of the APPS.

• #2 Establishment and validation of predictive model of ARDS in critically ill patients | Journal of Translational Medicine | Full Text

  https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-024-06054-1

  Acute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. […] The final model included gender, Lung Injury Prediction Score (LIPS), Hepatic Disease, Shock, and combined Lung Contusion. […] Based on the AUC and DCA in the validation group, the logistic model demonstrated excellent performance, achieving an AUC of 0.836 (95% CI: 0.7620.910). […] Combining the LIPS score with other clinical parameters in a logistic regression model provides a more accurate, clinically applicable, and user-friendly ARDS prediction tool than the LIPS score alone.

• #3 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

  https://pmc.ncbi.nlm.nih.gov/articles/PMC7202606/

  Prognosis of ARDS-patients can be established 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. […] The best predictive capacities were found within the first two days after intubation. […] OI better predicts 28-days-mortality compared to Berlin-definition, AECC and Murray_mod. […] EVLWI is a strong and independent predictor of 28-days-mortality. […] The combination of EVLWI and OI further increases the predictive capacities of each parameter alone. OI+EVLWI provides larger ROC-AUCs than SOFA and APACHE-II on the first two days. […] EVLWI, OI and SOFA were independently associated with 28-days-mortality.

• #4 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 | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0232720

  Prognosis of ARDS-patients can be established 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. […] 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). […] The best predictive capacities were found within the first two days after intubation. […] EVLWI is a strong and independent predictor of 28-days-mortality. […] The combination of EVLWI and OI further increases the predictive capacities of each parameter alone. OI+EVLWI provides larger ROC-AUCs than SOFA and APACHE-II on the first two days.

• #5 Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach | Scientific Reports

  https://www.nature.com/articles/s41598-024-68653-8

  Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this study was to evaluate the predictive values of dynamic clinical indices by developing machine-learning (ML) models for early and accurate clinical assessment of the disease prognosis of ARDS. […] An RF model trained using clinical data collected at day 3 showed improved performance and prognostication efficacy (area under the curve [AUC]: 0.84, 95% CI: 0.780.89) compared to baseline with an AUC value of 0.72 (95% CI: 0.650.78). […] Thus, clinical features collected early (day 3) improved performance of integrative ML models with better prognostication for mortality. Among these, MAP represented the most important feature for ARDS patients early risk stratification.

• #6 Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach | Scientific Reports

  https://www.nature.com/articles/s41598-024-68653-8

  In this study, we found that MAP was the most significant clinical characteristic for predicting 60-day death, along with other crucial clinical features. […] Our results suggested seven clinical parameters (MAP, bicarbonate, age, platelet count, albumin, heart rate and glucose) were the most important features at day 3 for ARDS mortality risk in the FACTT trial. […] The RF classifier consistently demonstrated outstanding performance at both days, and obtained the highest AUC value of 0.84 at day 3. […] Given its emerging significance in ARDS prognostication, in the following section, we further evaluated the association between elevated MAP and organ dysfunction among non-survivors. […] The decision rules developed within the RF model at day 3 can predict the mortality rates of patients in advance with more than 80% accuracy.

• #7 Predictors of survival in critically ill patients with acute respiratory distress syndrome (ARDS): an observational study | BMC Anesthesiology | Full Text

  https://bmcanesthesiol.biomedcentral.com/articles/10.1186/s12871-016-0272-4

  Currently there is no ARDS definition or classification system that allows optimal prediction of mortality in ARDS patients. […] The oxygenation index is suggested to be the most suitable parameter to predict mortality in ARDS, preferably assessed on day 3 after admission to a specialized centre. […] The third day after ARDS criteria were met at our hospital was found to represent the best compromise between earliness and accuracy of prognosis of mortality regarding the time of assessment. […] In the group of patients with an OI of 15 or greater on day 3, mortality was higher; length of stay (both in the ICU and hospital) and duration of mechanical ventilation were longer. […] OI was found to be the most accurate parameter with respect to predictive validity. […] The OI has been proven to be a more suitable parameter to predict mortality of patients suffering from ARDS in national referral centres when compared to PaO2/FiO2 ratio, the AECC or Berlin definition. […] The OI appears to be especially helpful for prediction of mortality on day three after admission to a specialized referral centre, as a response to standardised ARDS treatment may be observed by that time.

• #8 Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning

  https://www.mdpi.com/2077-0383/10/17/3824

  Predicting MV duration after ARDS onset over time is complex and cannot be adequately performed by critical care physicians. […] Our findings showed that the ML-based early prediction of MV duration is more accurate when predictive models are based on the clinical features of ARDS patients in the second ICU day after ARDS onset. […] Supervised ML can make early and accurate predictions of MV duration in ARDS after onset over time across ICUs. […] An early predictive model for MV duration can optimize ICU-level resource utilization. […] Our findings could be used to facilitate optimal triage, more timely management, and ICU resource utilization. […] The prediction results of LightGBM models based on the data of the second ICU day are very close to those corresponding results of LightGBM models based on the data of the third ICU day. […] Developing early predictive models using ML could assist to implement policies for the reduction of high acute care costs in ARDS.

• #9 A mortality score for acute respiratory distress syndrome: predicting the future without a crystal ball – Santos – Journal of Thoracic Disease

  https://jtd.amegroups.org/article/view/8464/html

  Based on these inconclusive data, a combination of the LIPS prediction score with a biomarker that reflects ARDS pathogenesis seems to be a promising alternative. […] Prognostic scores, such as APACHE III, SAPS 3 and MPM III0, can provide estimates of the probability of death for individual patients in the ICU. […] Villar et al. designed an outcome score (APPS) that combines variables that are readily and routinely obtained at bedside 24 h after ARDS diagnosis (age, PaO2/FiO2, and plateau pressure). They reported that the APPS can be useful for predicting patients at high risk of fatal outcome, selecting patients with ARDS in clinical studies, and guiding ventilator management.

• #10 ARDS Mortality Prediction Model Using Evolving Clinical Data and Chest Radiograph Analysis

  https://www.mdpi.com/2227-9059/12/2/439

  In the evolving landscape of Acute Respiratory Distress Syndrome (ARDS) research, the utilization of machine learning techniques, particularly in conjunction with chest radiography, holds promise for providing valuable insights into the prediction of mortality and severity in patients with COVID-19-associated ARDS (C-ARDS). […] Our main message is that we proved that integrating deep learning image features in the logistic regression model exhibited superior predictive accuracy, providing valuable insights into mortality prediction in C-ARDS patients. […] The robust performance metrics, especially within the internal test group, underscore the potential clinical utility of the proposed model. […] This integrated approach is not merely confined to a technological juxtaposition but represents a fundamental shift in the paradigm of ARDS research.

• #11 ARDS Mortality Prediction Model Using Evolving Clinical Data and Chest Radiograph Analysis

  https://www.mdpi.com/2227-9059/12/2/439

  Our study delves into the predictive capacity of deep learning features extracted from chest radiographs, surpassing the predictive capability of the P/F ratio in terms of mortality. […] Given its high volume of images and cost-effectiveness, chest radiography emerges as an appealing modality for machine learning applications. […] In conclusion, our findings underscore the significance of integrating chest radiography and machine learning in early mortality prediction in C-ARDS.

• #12 Establishment and validation of predictive model of ARDS in critically ill patients | Journal of Translational Medicine | Full Text

  https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-024-06054-1

  The study utilized 400 patients from the PUTH as the derivation cohort, with 117 patients diagnosed with ARDS, yielding an incidence rate of 29.3% during hospitalization. […] In this study, we employed LASSO regression and multifactorial logistic regression analyses to identify five key variables (Gender, LIPS, shock, lung contusion, and Hepatic disease) out of 35 factors associated with the risk of ARDS in patients. […] Our model combines the LIPS score with other clinical data, and the final model had a sensitivity of 63.6%, a specificity of 83.6%, a positive predictive value of 41.9%, and a negative predictive value of 92.5% in the test set. […] In conclusion, our ML model, incorporating sex, LIPS, shock, pulmonary contusion, and liver disease as risk factors, effectively predicts ARDS in high-risk patients. Compared to the LIPS score, the model demonstrates a significantly higher positive predictive value (41.9% vs. 18%).

• #13 Predictive validity of the sequential organ failure assessment score for mortality in patients with acute respiratory distress syndrome in Vietnam | Scientific Reports

  https://www.nature.com/articles/s41598-025-92199-y

  Evaluating the prognosis of ARDS patients using grading systems can enhance treatment decisions. This retrospective observational study evaluated the predictive accuracy of the SOFA score, APACHE II score, SpO2/FiO2 ratio, and PaO2/FiO2 ratio for mortality in ARDS patients in Vietnam. […] The SOFA (AUROC: 0.651) and APACHE II scores (AUROC: 0.693) showed poor discriminatory ability for hospital mortality. […] In multivariable analyses, after adjusting for the same set of confounding variables, the APACHE II score (adjusted OR: 1.152), SpO2/FiO2 ratio (adjusted OR: 0.985), and PaO2/FiO2 ratio (adjusted OR: 0.989) were independently associated with hospital mortality. […] Although the SOFA score (adjusted OR: 1.132) indicated a potential association with hospital mortality, it did not reach statistical significance (p=0.081). However, a SOFA score of 10 emerged as an independent predictor (adjusted OR: 3.398) of hospital mortality.

• #14 Predictive validity of the sequential organ failure assessment score for mortality in patients with acute respiratory distress syndrome in Vietnam | Scientific Reports

  https://www.nature.com/articles/s41598-025-92199-y

  These findings emphasize the need for further studies to develop more accurate scoring systems for predicting outcomes in ARDS patients. […] In this study, over two-thirds of ARDS patients died in the hospital. […] Both the SOFA and APACHE II scores demonstrated poor ability to predict hospital mortality upon admission; a positive correlation was observed between these scores and hospital mortality. […] The multivariable logistic regression analyses revealed that, after adjusting for the same set of confounding variables, only the APACHE II score, SpO2/FiO2 ratio, and PaO2/FiO2 ratio were independently associated with hospital mortality. […] Notably, a SOFA score of 10 was identified as an independent predictor of hospital mortality. […] Overall, the study highlights the importance of the APACHE II score and the SpO2/FiO2 and PaO2/FiO2 ratios as independent predictors of hospital mortality. […] However, the ability of these grading systems to predict hospital mortality is somewhat limited.

• #15 A mortality score for acute respiratory distress syndrome: predicting the future without a crystal ball – Santos – Journal of Thoracic Disease

  https://jtd.amegroups.org/article/view/8464/html

  In a recent paper published in Critical Care Medicine, Villar et al. proposed a scoring system that could be easily calculated at the bedside to predict mortality in patients with acute respiratory distress syndrome (ARDS) 24 hours after diagnosis. […] To date, no validated scoring system has been available to predict mortality rates in ARDS. […] Prediction scores were developed to assess ARDS prognosis and risk of death. […] A multicenter prospective cohort study in the ICU setting that included 646 patients with ARDS showed that death after hospital discharge was more related to underlying comorbidity and age than to ARDS severity, and that comorbidities could better predict long-term ARDS outcome. […] The lung injury prediction score (LIPS) seems to be a promising tool for ARDS outcome prediction.

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