Prediction model for critically ill patients with acute respiratory distress syndrome

The relationship between red cell distribution width, serum calcium ratio, and in-hospital mortality among patients with acute respiratory failure: A retrospective cohort study of the MIMIC-IV database

To investigate the impact of RDW/CA (the ratio of red cell distribution width to calcium) on in-hospital mortality in patients with acute respiratory failure (ARF). This retrospective cohort study analyzed the data of 6981 ARF patients from the Medical Information Mart for Intensive Care (MIMIC-IV) database 2.0. Critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Center in Boston. The primary outcome of interest was in-hospital mortality. A Cox proportional hazards regression model was used to determine whether the RDW/CA ratio independently correlated with in-hospital mortality. The Kaplan-Meier method was used to plot the survival curves of the RDW/CA. Subgroup analyses were performed to measure the mortality across various subgroups. After adjusting for potential covariates, we found that a higher RDW/CA was associated with an increased risk of in-hospital mortality (HR = 1.17, 95% CI: 1.01-1.35, P = .0365) in ARF patients. A nonlinear relationship was observed between RDW/CA and in-hospital mortality, with an inflection point of 1.97. When RDW/CA ≥ 1.97 was positively correlated with in-hospital mortality in patients with ARF (HR = 1.554, 95% CI: 1.183-2.042, P = .0015). The Kaplan-Meier curve indicated the higher survival rates for RDW/CA < 1.97 and the lower for RDW/CA ≥ 1.97 after adjustment for age, gender, body mass index, and ethnicity. RDW/CA is an independent predictor of in-hospital mortality in patients with ARF. Furthermore, a nonlinear relationship was observed between RDW/CA and in-hospital mortality in patients with ARF.

Impact of obesity on outcomes of patients with acute respiratory distress syndrome: a retrospective analysis of a large clinical database

OBJECTIVE

To evaluate the link between obesity and mortality in patients with acute respiratory distress syndrome (ARDS).

METHODS

We performed a retrospective cohort study of a large clinical database. A Cox proportional hazards regression model was used to calculate the hazard ratio (HR) and 95% confidence interval (CI) for the relationship between body mass index (BMI) and mortality. The primary endpoint was 30-day death rate and the secondary endpoints were 90-day and 1‑year mortality.

RESULTS

Overall, 418 patients with ARDS were enrolled in the study, including 185 women and 233 men (age: 70.7 ± 44.1 years; BMI: 28.7 ± 8.1 kg/m2). Compared with patients with normal weight, obese patients were younger (60.1 ± 13.7, p = 0.003) and a higher percentage of these patients were women (51.3% vs. 49.0%, p = 0.001). The HRs (95% CI) of 30-day mortality in the underweight, overweight, and obese populations were 1.82 (0.85, 3.90), 0.59 (0.29, 1.20), and 3.85 (1.73, 8.57), respectively, after adjustment for other confounding factors. A similar pattern was also seen for death after 90 days and after 1 year. A U-shaped association between BMI and 30-day mortality was discovered by curve fitting.

CONCLUSION

Obesity had a significant impact on the short- and long-term mortality in patients with ARDS. There was a U-shaped relationship between BMI and mortality, while a higher BMI was associated with an increased risk of death in patients with ARDS.

Association Between Temperature During Intensive Care Unit and Mortality in Patients With Acute Respiratory Distress Syndrome

The relationship between body temperature changes and prognosis in patients with acute respiratory distress syndrome (ARDS) remains inconclusive. Our study aimed to investigate the clinical value of body temperature in the management of ARDS. Data from the Medical Information Mart for Intensive Care III database were collected. Adult patients with ARDS were enrolled and further grouped based on their temperature values in the intensive care unit. Both the maximum (temperaturemax) and minimum (temperaturemin) temperatures were used. The primary outcome was 28-day mortality rate. Polynomial regression, subgroup analysis, and logistic regression analysis were performed in the final analysis. A total of 3922 patients with ARDS were enrolled. There was a U-shaped relationship between 28-day mortality and body temperature. For patients with infection, the elevated temperaturemax (≥37.0°C) was associated with decreased mortality, with an odds ratio ranging from 0.39 to 0.49, using temperaturemax from 36.5°C to 36.9°C as reference. For patients without infection, a similar tendency was observed, but the protective effect was lost at extremely high temperatures (≥38.0°C, p < 0.05). Elevated temperaturemin (≥37.0°C) and decreased temperaturemin (<35.0°C) were associated with increased mortality, using the temperaturemin from 36.0°C to 36.9°C as a reference. Hypothermia was associated with increased mortality in patients with ARDS, while the effect of hyperthermia (≥37.0°C) on the mortality of patients with ARDS was not fully consistent in the infection and noninfection subgroups. Short-term and transient temperatures above 37.0°C would be beneficial to patients with ARDS, but extreme hyperthermia and persistent temperatures above 37.0°C should be avoided.

Prediction model for in-hospital mortality in patients at high altitudes with ARDS due to COVID-19

INTRODUCTION

The diagnosis of acute respiratory distress syndrome (ARDS) includes the ratio of pressure arterial oxygen and inspired oxygen fraction (P/F) ≤ 300, which is often adjusted in locations more than 1,000 meters above sea level (masl) due to hypobaric hypoxemia. The main objective of this study was to develop a prediction model for in-hospital mortality among patients with ARDS due to coronavirus disease 2019 (COVID-19) (C-ARDS) at 2,600 masl with easily available variables at patient admission and to compare its discrimination capacity with a second model using the P/F adjusted for this high altitude.

METHODS

This study was an analysis of data from patients with C-ARDS treated between March 2020 and July 2021 in a university hospital located in the city of Bogotá, Colombia, at 2,600 masl. Demographic and laboratory data were extracted from electronic records. For the prediction model, univariate analyses were performed to screen variables with p <0.25. Then, these variables were automatically selected with a backward stepwise approach with a significance level of 0.1. The interaction terms and fractional polynomials were also examined in the final model. Multiple imputation procedures and bootstraps were used to obtain the coefficients with the best external validation. In addition, total adjustment of the model and logistic regression diagnostics were performed. The same methodology was used to develop a second model with the P/F adjusted for altitude. Finally, the areas under the curve (AUCs) of the receiver operating characteristic (ROC) curves of the two models were compared.

RESULTS

A total of 2,210 subjects were included in the final analysis. The final model included 11 variables without interaction terms or nonlinear functions. The coefficients are presented excluding influential observations. The final equation for the model fit was g(x) = age(0.04819)+weight(0.00653)+height(-0.01856)+haemoglobin(-0.0916)+platelet count(-0.003614)+ creatinine(0.0958)+lactate dehydrogenase(0.001589)+sodium(-0.02298)+potassium(0.1574)+systolic pressure(-0.00308)+if moderate ARDS(0.628)+if severe ARDS(1.379), and the probability of in-hospital death was p (x) = e g (x)/(1+ e g (x)). The AUC of the ROC curve was 0.7601 (95% confidence interval (CI) 0.74-0, 78). The second model with the adjusted P/F presented an AUC of 0.754 (95% CI 0.73-0.77). No statistically significant difference was found between the AUC curves (p value = 0.6795).

CONCLUSION

This study presents a prediction model for patients with C-ARDS at 2,600 masl with easily available admission variables for early stratification of in-hospital mortality risk. Adjusting the P/F for 2,600 masl did not improve the predictive capacity of the model. We do not recommend adjusting the P/F for altitude.

Preventative Management of Sepsis-Induced Acute Respiratory Distress Syndrome in the Geriatric Population

Sepsis and its treatment are the most common etiologies of acute respiratory distress syndrome (ARDS), which has a disturbing mortality rate. Sepsis management relies heavily on the introduction of resuscitative fluids. However, when fluids are paired with the circulating inflammatory mediators of sepsis, patients are prone to lung damage. Survivors of sepsis-induced ARDS become plagued with functional and/or psychological sequelae such as impaired memory, difficulty in concentrating, and decreased mental processing speed. Specific techniques can be implemented when diagnosing and treating elderly patients with sepsis to prevent the onset of ARDS, including bed elevation and early antibiotics. Additionally, albumin infusion may be beneficial; however, more research must be conducted. Finally, inflammatory mediators, including serum mannose biomarkers and extracellular histone therapy, show a promising avenue for future treatment. Although there is limited research on osteopathic manipulative medicine (OMT) on ARDS or sepsis-induced ARDS, OMT that focuses on alleviating rib and thoracic somatic dysfunctions has been used as an adjunct therapy to treat other respiratory diseases, such as pneumonia and chronic obstructive pulmonary disease (COPD). The results of these studies may garner interest in whether the use of OMT as an adjunct therapy may be beneficial for patients with ARDS or sepsis-induced ARDS. This paper is intended to review the current guidelines for sepsis and ARDS management in elderly patients to identify measures to prevent sepsis-induced ARDS.

A Nomogram for Predicting the Mortality of Patients with Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is an acute lung injury associated with high morbidity and mortality. This study aimed to establish an accurate prediction model for mortality risk in ARDS. 70% of patients from the Medical Information Mart for Intensive Care Database (MIMIC-III) were selected as the training group, and the remaining 30% as the testing group. Patients from a Chinese hospital were used for external validation. Univariate and multivariate regressions were used to screen the independent predictors. The receiver operating characteristic curve (ROC) analysis, the Hosmer–Lemeshow test, and the calibration curve were used for evaluating the performance of the model. Age, hemoglobin, heart failure, renal failure, Simplified Acute Physiology Score II (SAPS II), immune function impairment, total bilirubin (TBIL), and PaO₂/FiO₂ were identified as independent predictors. The algorithm of the prediction model was: ln (Pr/(1 + Pr)) = −3.147 + 0.037 ∗ age − 0.068 ∗ hemoglobin + 0.522 ∗ heart failure (yes) + 0.487 ∗ renal failure (yes) + 0.029 ∗ SAPS II + 0.697 ∗ immune function impairment (yes) + 0.280 ∗ TBIL (abnormal) − 0.006 ∗ PaO₂/FiO₂ (Pr represents the probability of death occurring). The AUC of the model was 0.791 (0.766–0.816), and the internal and the external validations both confirmed the good performance of the model. A nomogram for predicting the risk of death in ARDS patients was developed and validated. It may help clinicians early identify ARDS patients with high risk of death and thereby help reduce the mortality and improve the survival of ARDS.

Monocyte-to-lymphocyte ratio is associated with 28-day mortality in patients with acute respiratory distress syndrome: a retrospective study

Background

Systemic inflammation relates to the initiation and progression of acute respiratory distress syndrome (ARDS). Neutrophil-to-lymphocyte ratio (NLR) and red blood cell distribution width (RDW)/albumin ratio have been reported to be predictive prognostic biomarkers in ARDS patients. However, the role of monocyte-to-lymphocyte ratio (MLR) as a prognostic inflammatory biomarker in a variety of diseases is rarely mentioned in ARDS. In this study, we explored the relationship between MLR and disease severity in ARDS patients and compared it with other indicators associated with 28-day mortality in patients with ARDS.

Methods

We retrospectively included 268 patients who fulfilled the Berlin definition of ARDS and were admitted to a single institute from 2016 to 2020. Clinical characteristics and experimental test data were collected from medical records within 24 h after the ARDS diagnosis. MLR, NLR, and RDW/albumin ratio levels were calculated. The primary clinical outcome was 28-day mortality. Logistic regression analysis was used to illustrate the relationship between indicators and 28-day mortality. Receiver operating characteristic (ROC) curve was used to evaluate the area under the curve (AUC), and propensity score matching (PSM) was employed to validate our findings.

Results

The median MLR values were higher for non-survivors than for survivors before and after matching (P<0.001, P=0.001, respectively). MLR values were significantly associated with 28-day mortality (OR 2.956; 95% CI 1.873–4.665; P<0.001). MLR and NLR indicators were combined for predictive efficacy analysis, and its AUC reached 0.750. There was a significant increase in 28-day mortality depending on the increasing MLR level: low MLR group 38 (20.4%), high MLR group 47 (57.3%) (P<0.001).

Conclusions

Higher MLR values were associated with 28-day mortality in patients with ARDS. Further investigation is required to verify this relationship with prospectively collected data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40560-021-00564-6.

Impact of obesity on outcomes in patients with acute respiratory syndrome

Objectives

We assessed the relationship between obesity and all-cause mortality in patients with acute respiratory distress syndrome (ARDS).

Methods

In this retrospective cohort study, patient data were extracted from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care Database III. Body mass index (BMI) was grouped according to World Health Organization classifications: underweight, normal weight, overweight, obese. Cox regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause mortality related to obesity.

Results

Participants included 185 women and 233 men, mean age 70.7 ± 44.1 years and mean BMI 28.7 ± 8.1 kg/m². Compared with normal weight patients, obese patients tended to be younger (60.1 ± 13.7 years) and included more women (51.3% vs. 49.0%). In the unadjusted model, HRs (95% CIs) of 30-day mortality for underweight, overweight, and obesity were 1.57 (0.76, 3.27), 0.64 (0.39, 1.08), and 4.83 (2.25, 10.35), respectively, compared with those for normal weight. After adjustment, HRs (95% CIs) of 30-day mortality for underweight, overweight, and obesity were 1.82 (0.85, 3.90), 0.59 (0.29, 1.20), and 3.85 (1.73, 8.57), respectively, compared with the reference group; 90-day and 1-year all-cause mortalities showed similar trends.

Conclusions

Obesity was associated with increased all-cause mortality in patients with ARDS.

Platelets as a prognostic marker for sepsis: A cohort study from the MIMIC-III database

During sepsis, platelets dysfunction contributes to organ dysfunction. Studies on platelets dysfunction in the long-term prognosis of sepsis are lacking. The aim of this study was to assess the role of platelets in the long-term prognosis of sepsis patients.A total of 4576 sepsis patients were extracted from MIMIC III Database. Survival was analyzed by the Kaplan-Meier method. Univariate and multivariate cox analyses were performed to identify prognostic factors. Significant prognostic factors were combined to build a nomogram to predict 1 year overall survival (OS). The discriminative ability and predictive accuracy of the nomogram were evaluated using the receiver operating characteristic curve (ROC) analysis and calibration curves used for sepsis.The more abnormal the platelet level, the worse prognosis of patients. After final regression analysis, age, blood urea nitrogen, platelets, international normalized ratio, partial thromboplastin time, potassium, hemoglobin, white blood cell count, organ failures were found to be independent predictors of 1 year OS of sepsis patient and were entered into a nomogram. The nomogram showed a robust discrimination, with an area under the receiver operating characteristic curve of 0.752. The calibration curves for the probability of the prognosis of sepsis patients showed optimal agreement between the probability as predicted by the nomogram and the actual probability.Platelet was an independent prognostic predictor of 1 year OS for patients with sepsis. Platelet-related nomogram that can predict the 1 year OS of sepsis patients. It revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility.

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.

Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study

BACKGROUND

To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters.

METHODS

A secondary analysis of a multi-centre prospective observational cohort study from five hospitals in Beijing, China, was conducted from January 1, 2011, to August 31, 2014. A total of 296 patients at risk for developing ARDS admitted to medical intensive care units (ICUs) were included. We applied a random forest approach to identify the best set of predictors out of 42 variables measured on day 1 of admission.

RESULTS

All patients were randomly divided into training (80%) and testing (20%) sets. Additionally, these patients were followed daily and assessed according to the Berlin definition. The model obtained an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 and yielded a predictive accuracy of 83%. For the first time, four new biomarkers were included in the model: decreased minimum haematocrit, glucose, and sodium and increased minimum white blood cell (WBC) count.

CONCLUSIONS

This newly established machine learning-based model shows good predictive ability in Chinese patients with ARDS. External validation studies are necessary to confirm the generalisability of our approach across populations and treatment practices.

Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model

BACKGROUND

Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. The study aimed to develop a neural network model by using a genetic algorithm (GA) for the prediction of mortality in patients with ARDS.

METHODS

This was a secondary analysis of two multicenter randomized controlled trials conducted in forty-four hospitals that are members of the National Heart, Lung, and Blood Institute, founded to create an acute respiratory distress syndrome Clinical Trials Network. Model training and validation were performed using the SAILS and OMEGA studies, respectively. A GA was employed to screen variables in order to predict 90-day mortality, and a neural network model was trained for the prediction. This machine learning model was compared to the logistic regression model and APACHE III score in the validation cohort.

RESULTS

A total number of 1,071 ARDS patients were included for analysis. The GA search identified seven important variables, which were age, AIDS, leukemia, metastatic tumor, hepatic failure, lowest albumin, and FiO₂. A representative neural network model was constructed using the forward selection procedure. The area under the curve (AUC) of the neural network model evaluated with the validation cohort was 0.821 (95% CI [0.753-0.888]), which was greater than the APACHE III score (0.665; 95% CI [0.590-0.739]; p = 0.002 by Delong's test) and logistic regression model, albeit not statistically significant (0.743; 95% CI [0.669-0.817], p = 0.130 by Delong's test).

CONCLUSIONS

The study developed a neural network model using a GA, which outperformed conventional scoring systems for the prediction of mortality in ARDS patients.

Laboratory epidemiology of respiratory viruses in a large children's hospital: A STROBE-compliant article

Acute respiratory tract infection (ARTI) is the most common causes of outpatient visit and hospital admission for children. The study aimed to report epidemiological data on respiratory viruses in a university-affiliated children's hospital.The study was a retrospective study conducted in a university affiliated children's hospital from 2016 May to 2017 April. The results of all nasopharyngeal swab and sputum samples sent for the test for respiratory viruses (adenovirus, influenza A, influenza B, and respiratory syncytial virus) were extracted from the electronic healthcare records. Clinical characteristics were compared between groups with positive versus negative results for respiratory viruses. Multivariable regression models were employed by including age, gender, type of sample (swab vs sputum), source (emergency department vs others), and season to explore the independent factors associated with positive results for respiratory viruses.A total of 34,961 samples were identified during the study period. A total of 3102 (8.9%) samples were positive for adenovirus, 2811 (8.0%) were positive for influenza A, 3460 (9.9%) were positive for influenza B, and 4527 (13.0%) were positive for respiratory syncytial virus. The positive rate of adenovirus was highest in April (50.8%), and lowest in November (3%). The absolute number of positive samples for adenovirus was highest in June (n = 587) and April (n = 544). For the test of influenza A, age was independently associated with positive result. With 1 year increase in age, the odds of positive result increased by 12% (odds ratio [OR]: 1.12; 95% confidence interval [CI]: 1.11-1.13; P < .001). As compared with the autumn, the summer showed significantly lower rate of positive for RSV (OR: 0.49; 95% CI: 0.38-0.62; P < .001), whereas the winter had higher risk of positive result (OR: 3.88; 95% CI: 3.37-4.50; P < .001).The study reported epidemiological data on the prevalence of respiratory viruses in a large tertiary care children's hospital. Age, gender, type of sample, source, and season were associated with the positive rates for respiratory viruses.

Neutrophil-to-lymphocyte ratio as a prognostic marker in acute respiratory distress syndrome patients: a retrospective study

Background

Acute respiratory distress syndrome (ARDS) is the leading cause of high mortality in intensive care units (ICUs) worldwide. An effective marker for prognosis in ARDS is particularly important given the absence of effective treatment strategies aside from small tidal volume ventilation. Previous studies identified an association between the neutrophil-to-lymphocyte ratio (NLR) and prognosis in critical patients. In this study, we explored the prognostic and predictive value of the NLR in ARDS patients.

Methods

We retrospectively included 275 ARDS patients treated at a single institute from 2008 to 2015. After excluding patients with chronic lung disease, acute myocardial infarction and missing data, 247 patients were ultimately included in the analysis. Clinical characteristics and experimental test data, including the NLR, were collected from medical records at 24 hours after the ARDS diagnosis. Independent prognostic factors were determined by multivariate Cox regression analysis. Subgroup stratification was performed according to different factors, and the continuous factors were divided according to the median values.

Results

The NLR in survivors was significantly lower than that in non-survivors (P<0.001). We took the median NLR value as the cut-off point and further divided all patients into a high NLR group (NLR >14) and a low NLR group (NLR ≤14). We found that an NLR >14 was associated with a shorter overall survival (OS) (P=0.005). In the multivariate Cox regression model, we further identified an NLR >14 as an independent prognostic factor for OS [hazard ratio (HR) 1.532, (95% CI, 1.095-2.143), P=0.013]. Subgroup analysis showed that the prognostic value of the NLR was higher in hypertensive patients (P=0.009) and in patients with low red blood cell specific volume (P=0.013), high sodium (P=0.002) and high creatinine levels (P=0.017).

Conclusions

The NLR is potentially a predictive prognostic biomarker in ARDS patients.

Protective effect of baicalin against severe burn‑induced remote acute lung injury in rats

Baicalin exhibits antibacterial, anti‑viral, anti‑oxidative, antipyretic, analgesic, anti‑inflammatory and anti‑tumor properties. The chemical scavenges oxygen free radicals, protects the cardiovascular system and neurons, protects the liver, and has been used for the prevention and treatment of diabetes‑associated complications. The present study investigated the effect of baicalin on severe burn‑induced remote acute lung injury (ALI). The present study demonstrated that baicalin significantly decreased the lung wet‑to‑dry weight ratio, improved pulmonary histological alterations and reduced the expression of high mobility group protein B1 in the rat model of ALI. In addition, treatment with baicalin decreased tumor necrosis factor‑α, interleukin (IL)‑8, IL‑1β and IL‑18 concentrations in the serum, reduced myeloperoxidase activity and malondialdehyde content, and increased the level of superoxide dismutase in the serum in treated model rats with ALI. As a result, baicalin significantly suppressed nucleotide‑binding oligomerization, NACHT, LRR and PYD domains‑containing protein 3 (NLRP3), caspase‑1, nuclear factor‑κB and matrix metalloproteinase‑9 protein expression in the rat model of ALI. The results of the present study suggested that baicalin may serve a protective role against ALI in rats through the NLRP3 signaling pathway.

APACHE III Outcome Prediction in Patients Admitted to the Intensive Care Unit with Sepsis Associated Acute Lung Injury

BACKGROUND AND OBJECTIVE

Acute Physiology and Chronic Health Evaluation (APACHE) III score has been widely used for prediction of clinical outcomes in mixed critically ill patients. However, it has not been validated in patients with sepsis-associated acute lung injury (ALI). The aim of the study was to explore the calibration and predictive value of APACHE III in patients with sepsis-associated ALI.

METHOD

The study was a secondary analysis of a prospective randomized controlled trial investigating the efficacy of rosuvastatin in sepsis-associated ALI (Statins for Acutely Injured Lungs from Sepsis, SAILS). The study population was sepsis-related ALI patients. The primary outcome of the current study was the same as in the original trial, 60-day in-hospital mortality, defined as death before hospital discharge, censored 60 days after enrollment. Discrimination of APACHE III was assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC) with its 95% CI. Hosmer-Lemeshow goodness-of-fit statistic was used to assess the calibration of APACHE III. The Brier score was reported to represent the overall performance of APACHE III in predicting outcome.

MAIN RESULTS

A total of 745 patients were included in the study, including 540 survivors and 205 non-survivors. Non-survivors were significantly older than survivors (59.71 ± 16.17 vs 52.00 ± 15.92 years, p < 0.001). The primary causes of ALI were also different between survivors and non-survivors (p = 0.017). Survivors were more likely to have the cause of sepsis than non-survivors (21.2% vs. 15.1%). APACHE III score was higher in non-survivors than in survivors (106.72 ± 27.30 vs. 88.42 ± 26.86; p < 0.001). Discrimination of APACHE III to predict mortality in ALI patients was moderate with an AUC of 0.68 (95% confidence interval: 0.64-0.73).

CONCLUSION

this study for the first time validated the discrimination of APACHE III in sepsis associated ALI patients. The result shows that APACHE III score has moderate predictive value for in-hospital mortality among adults with sepsis-associated acute lung injury.