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Abstract Background This study aimed to investigate and analyze the risk factors for non-etiology-specific infantile spasms (IS) and unrelieved clinical symptoms after treatment. Methods Eighty-eight children with IS who were treated at our hospital from March 2018 to December 2021 were included in the study. The children were divided into etiology-specific (n = 46) and nonetiology-specific (n = 42) groups, based on the diagnostic results, and remission (n = 45) and nonremission (n = 43) groups, based on clinical outcomes after treatment. The clinical data from patients in the etiology-specific and nonetiology-specific groups and the remission and nonremission groups were compared. Risk factors for non-etiology-specific IS were identified using logistic regression analysis. Results Gender, family history, birth status, and metabolic abnormalities were significantly different between the etiology-specific and non-etiology-specific groups. Gender and metabolic abnormalities were risk factors for nonetiology-specific IS. Family history, birth status, metabolic abnormalities, and brain magnetic resonance imaging were significantly different between the remission and nonremission groups, and different etiologies were risk factors for unrelieved symptoms after treatment. Conclusion The occurrence of nonetiology-specific IS is associated with gender and metabolic abnormalities in children. After medication, unrelieved IS symptoms are associated with etiologies.

Abstract Background Several studies have investigated the correlation between physiological parameters and the risk of acute respiratory distress syndrome (ARDS), in addition, etiology-associated heterogeneity in ARDS has become an emerging topic quite recently; however, the intersection between the two, which is early prediction of target conditions in etiology-specific ARDS, has not been well-studied. We aimed to develop and validate a machine-learning model for the early prediction of moderate-to-severe condition of inhalation-induced ARDS. Methods Clinical expertise was applied with data-driven analysis. Using data from electronic intensive care units (retrospective derivation cohort) and the three most accessible vital signs (i.e. heart rate, temperature, and respiratory rate) together with feature engineering, we applied a random forest approach during the time window of 90 h that ended 6 h prior to the onset of moderate-to-severe respiratory failure (the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen ≤ 200 mmHg). Results The trained random forest classifier was validated using two independent validation cohorts, with an area under the curve of 0.9127 (95% confidence interval 0.8713–0.9542) and 0.9026 (95% confidence interval 0.8075–1), respectively. A Stable and Interpretable RUle Set (SIRUS) was used to extract rules from the RF to provide guidelines for clinicians. We identified several predictive factors, including resp_96h_6h_min 100, that could be used for predicting inhalation-induced ARDS (moderate-to-severe condition) 6 h prior to onset in critical care units. (‘xxx_96h_6h_min/mean/max’: the minimum/mean/maximum values of the xxx vital sign collected during a 90 h time window beginning 96 h prior to the onset of ARDS and ending 6 h prior to the onset from every recorded blood gas test). Conclusions This newly established random forest‑based interpretable model shows good predictive ability for moderate-to-severe inhalation-induced ARDS and may assist clinicians in decision-making, as well as facilitate the enrolment of patients in prevention programmes to improve their outcomes.