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Background: The ability to predict a long duration of mechanical ventilation (MV) by clinicians is very limited. We assessed the value of machine learning (ML) for early prediction of the duration of MV > 14 days in patients with moderate-to-severe acute respiratory distress syndrome (ARDS). Methods: This is a development, testing, and external validation study using data from 1173 patients on MV ≥ 3 days with moderate-to-severe ARDS. We first developed and tested prediction models in 920 ARDS patients using relevant features captured at the time of moderate/severe ARDS diagnosis, at 24 h and 72 h after diagnosis with logistic regression, and Multilayer Perceptron, Support Vector Machine, and Random Forest ML techniques. For external validation, we used an independent cohort of 253 patients on MV ≥ 3 days with moderate/severe ARDS. Results: A total of 441 patients (48%) from the derivation cohort (n = 920) and 100 patients (40%) from the validation cohort (n = 253) were mechanically ventilated for >14 days [median 14 days (IQR 8-25) vs. 13 days (IQR 7-21), respectively]. The best early prediction model was obtained with data collected at 72 h after moderate/severe ARDS diagnosis. Multilayer Perceptron risk modeling identified major prognostic factors for the duration of MV > 14 days, including PaO2/FiO2, PaCO2, pH, and positive end-expiratory pressure. Predictions of the duration of MV > 14 days showed modest discrimination [AUC 0.71 (95%CI 0.65-0.76)]. Conclusions: Prolonged MV duration in moderate/severe ARDS patients remains difficult to predict early even with ML techniques such as Multilayer Perceptron and using data at 72 h of diagnosis. More research is needed to identify markers for predicting the length of MV. This study was registered on 14 August 2023 at ClinicalTrials.gov (NCT NCT05993377).
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OBJECTIVES: To establish the epidemiological characteristics, ventilator management, and outcomes in patients with acute hypoxemic respiratory failure (AHRF), with or without acute respiratory distress syndrome (ARDS), in the era of lung-protective mechanical ventilation (MV). DESIGN: A 6-month prospective, epidemiological, observational study. SETTING: A network of 22 multidisciplinary ICUs in Spain. PATIENTS: Consecutive mechanically ventilated patients with AHRF (defined as Pao2/Fio2 ≤ 300 mm Hg on positive end-expiratory pressure [PEEP] ≥ 5 cm H2O and Fio2 ≥ 0.3) and followed-up until hospital discharge. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Primary outcomes were prevalence of AHRF and ICU mortality. Secondary outcomes included prevalence of ARDS, ventilatory management, and use of adjunctive therapies. During the study period, 9,803 patients were admitted: 4,456 (45.5%) received MV, 1,271 (13%) met AHRF criteria (1,241 were included into the study: 333 [26.8%] met Berlin ARDS criteria and 908 [73.2%] did not). At baseline, tidal volume was 6.9 ± 1.1 mL/kg predicted body weight, PEEP 8.4 ± 3.1 cm H2O, Fio2 0.63 ± 0.22, and plateau pressure 21.5 ± 5.4 cm H2O. ARDS patients received higher Fio2 and PEEP than non-ARDS (0.75 ± 0.22 vs 0.59 ± 0.20 cm H2O and 10.3 ± 3.4 vs 7.7 ± 2.6 cm H2O, respectively [p < 0.0001]). Adjunctive therapies were rarely used in non-ARDS patients. Patients without ARDS had higher ventilator-free days than ARDS (12.2 ± 11.6 vs 9.3 ± 9.7 d; p < 0.001). All-cause ICU mortality was similar in AHRF with or without ARDS (34.8% [95% CI, 29.7-40.2] vs 35.5% [95% CI, 32.3-38.7]; p = 0.837). CONCLUSIONS: AHRF without ARDS is a very common syndrome in the ICU with a high mortality that requires specific studies into its epidemiology and ventilatory management. We found that the prevalence of ARDS was much lower than reported in recent observational studies.
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OBJECTIVES: To develop a scoring model for stratifying patients with acute respiratory distress syndrome into risk categories (Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score) for early prediction of death in the ICU, independent of the underlying disease and cause of death. DESIGN: A development and validation study using clinical data from four prospective, multicenter, observational cohorts. SETTING: A network of multidisciplinary ICUs. PATIENTS: One-thousand three-hundred one patients with moderate-to-severe acute respiratory distress syndrome managed with lung-protective ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The study followed Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines for prediction models. We performed logistic regression analysis, bootstrapping, and internal-external validation of prediction models with variables collected within 24 hours of acute respiratory distress syndrome diagnosis in 1,000 patients for model development. Primary outcome was ICU death. The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score was based on patient's age, number of extrapulmonary organ failures, values of end-inspiratory plateau pressure, and ratio of Pao2 to Fio2 assessed at 24 hours of acute respiratory distress syndrome diagnosis. The pooled area under the receiver operating characteristic curve across internal-external validations was 0.860 (95% CI, 0.831-0.890). External validation in a new cohort of 301 acute respiratory distress syndrome patients confirmed the accuracy and robustness of the scoring model (area under the receiver operating characteristic curve = 0.870; 95% CI, 0.829-0.911). The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score stratified patients in three distinct prognostic classes and achieved better prediction of ICU death than ratio of Pao2 to Fio2 at acute respiratory distress syndrome onset or at 24 hours, Acute Physiology and Chronic Health Evaluation II score, or Sequential Organ Failure Assessment scale. CONCLUSIONS: The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score represents a novel strategy for early stratification of acute respiratory distress syndrome patients into prognostic categories and for selecting patients for therapeutic trials.
