A modified acute respiratory distress syndrome prediction score: a multicenter cohort study in China

Abstract

Background: Early recognition of the risks of acute respiratory distress syndrome (ARDS) and prevention of the development of ARDS may be more effective in improving patient outcomes. We performed the present study to determine the ARDS risk factors in a Chinese population and validate a score to predict the development of ARDS.Methods: This was an observational multicenter cohort study performed in 13 tertiary hospitals in China. Patients admitted into participating intensive care units (ICUs) from January 1 to January 31, 2012, and from January 1 to January 10, 2013, were enrolled in a retrospective derivation cohort and a prospective validation cohort, respectively. In the derivation cohort, the potential risk factors of ARDS were collected.The confirmed risk factors were determined with univariate and multivariate logistic regression analyses, and then the modified ARDS prediction score (MAPS) was established. We prospectively enrolled patients to verify the accuracy of MAPS.Results: A total of 479 and 198 patients were enrolled into the retrospective derivation cohort and the prospective validation cohort, respectively. A total of 93 (19.4%) patients developed ARDS in the derivation cohort. Acute pancreatitis, pneumonia, hypoalbuminemia, acidosis, and high respiratory rate were the risk factors for ARDS. The MAPS discriminated patients who developed ARDS from those who did not, with an area under the curve (AUC) of 0.809 [95% confidence interval (CI), 0.758−0.859, P<0.001]. In the prospective validation cohort, performance of the MAPS was similar to the retrospective derivation cohort, with an AUC of 0.792 (95% CI, 0.717−0.867, P<0.001). The lung injury prediction score (LIPS) showed a predicted value of an AUC of 0.770 (95% CI, 0.728−0.812, P<0.001) in our patients, which was significantly lower than our score (P<0.046).

The role of artificial neural networks in prediction of severe acute pancreatitis associated acute respiratory distress syndrome: A retrospective study

“…Although the NPV was 97%, the PPV was only 18%, [17] which may have led to a considerable degree of misdiagnosis. Xie et al [18] developed a modified ARDS prediction score (MAPS) based on a multicenter cohort that consisted of 7 risk factors and achieved favorable discrimination (AUC = 0.792) in the prospective validation cohort. However, because the MAPS was derived from a Chinese population, its predictive performance in individuals of other races remains unclear.…”

Zukunft der Intensivmedizin – Künstliche Intelligenz

“…[13]. Auch andere Untersuchungen basierten auf einer Risikoabschätzung durch Entwicklung prädiktiver Scores unter Einbezug diverser klinischer Parameter wie Atemfrequenz, Sauerstoffkonzentration oder anderer Vitaldaten [14,15]. Zumeist wurden jedoch nur geringe, kleine Datensätze mit wenigen, ausgewählten Parametern untersucht.…”

Genomics and the Acute Respiratory Distress Syndrome: Current and Future Directions

“…Furthermore, the development of ARDS has significant long-term consequences among survivors, such as physical and cognitive impairment [4]. Current identification of ARDS lacks a simple diagnostic test and is based on the constellation of diverse clinical and imaging signs that are indicative of acute respiratory failure [1,5]. Depending on the severity of the lung failure, measured by the PaO 2 /FiO 2 ratio, ARDS is currently classified as mild (200 < PaO 2 /FiO 2 ≤300), moderate (100 < PaO 2 /FiO 2 ≤ 200) or severe (PaO 2 /FiO 2 ≤100) [1].…”

“…Given the complexity of ARDS, the need to establish or improve predictive and prognostic scores is clear [5]. One approach that has the promise to become key for complex traits is the analysis of polygenic risk scores (PRS) (Table 1), which predict complex traits based on combined risk variants using a single score of risk burden (Figure 2).…”