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Abstract Hip fractures (HFx) are associated with a higher morbidity and mortality rates, leading to a significant reduction in life quality and in limitation of patient´s mobility. The present study aimed to obtain real-world evidence on the clinical characteristics of patients with an initial and a second hip fracture (HFx) and develop a predictive model for second HFx using artificial intelligence. Electronic health records from one hospital centre in Spain from January 2011 to December 2019 were analysed using EHRead® technology, based on natural language processing and machine learning. A total of 1,960 patients with HFx were finally included during the study period after meeting all inclusion and exclusion criteria. From this total, 1835 (93.6%) patients were included in the HFx subgroup, while 124 (6.4%) were admitted to the second HFx (2HFx) subgroup. The mean age of the participants was 84 years and 75.5% were female. Most of comorbidities were more frequently identified in the HFx group, including hypertension (72.0% vs. 67.2%), cognitive impairment (33.0% vs. 31.2%), diabetes mellitus (28.7% vs. 24.8%), heart failure (27.6% vs. 22.4%) and chronic kidney disease (26.9% vs. 16.0%). Based on clinical criteria, 26 features were selected as potential prediction factors. From there, 16 demographics and clinical characteristics such as comorbidities, medications, measures of disabilities for ambulation and type of refracture were selected for development of a competitive risk model. Specifically, those predictors with different associated risk ratios, sorted from higher to lower risk relevance were visual deficit, malnutrition, walking assistance, hypothyroidism, female sex, osteoporosis treatment, pertrochanteric fracture, dementia, age at index, osteoporosis, renal failure, stroke, COPD, heart disease, anaemia, and asthma. This model showed good performance (dependent AUC: 0.69; apparent performance: 0.75) and could help the identification of patients with higher risk of developing a second HFx, allowing preventive measures. This study expands the current available information of HFx patients in Spain and identifies factors that exhibit potential in predicting a second HFx among older patients.

Abstract One of the hallmarks of SARS-CoV-2 infection is an induced immune dysregulation, in some cases resulting in cytokine storm syndrome and acute respiratory distress syndrome (ARDS). Several physiological parameters are altered as a result of infection and cytokine storm. Among them, microRNAs (miRNAs) might reflect this poor condition since they play a significant role in immune cellular performance including inflammatory responses. Circulating miRNAs in patients who underwent ARDS and needed mechanical ventilation (MV+; n = 15) were analyzed by next generation sequencing in comparison with patients who had COVID-19 poor symptoms but without intensive care unit requirement (MV−; n = 13). A comprehensive in silico analysis by integration with public gene expression dataset and pathway enrichment was performed. Whole miRNA sequencing identified 170 differentially expressed miRNAs between patient groups. After the validation step by qPCR in an independent sample set (MV+ = 10 vs. MV− = 10), the miR-369-3p was found significantly decreased in MV+ patients (Fold change − 2.7). After integrating with gene expression results from COVID-19 patients, the most significant GO enriched pathways were acute inflammatory response, regulation of transmembrane receptor protein Ser/Thr, fat cell differentiation, and regulation of biomineralization and ossification. In conclusion, miR-369-3p was altered in patients with mechanical ventilation requirement in comparison with COVID-19 patients without this requirement. This miRNA is involved in inflammatory response which it can be considered as a prognosis factor for ARDS in COVID-19 patients.