Martin D, Hübner M, Moulin E, Pache B, Clerc D, Hahnloser D, et al. Omadacycline for acute bacterial skin and skin structure infections. doi: 10.1056/NEJMoa1801467.Ībrahamian FM, Sakoulas G, Tzanis E, Manley A, Steenbergen J, Das AF, et al. Once-daily plazomicin for complicated urinary tract infections. Wagenlehner FME, Cloutier DJ, Komirenko AS, Cebrik DS, Krause KM, Keepers TR, et al. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.Īrtificial intelligence Deep learning Elderly patients Machine learning Postoperative infections. Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 (95% CI 0.681-0.844) with the sensitivity of 63.2% (95% CI 46-78.2) and specificity of 80.5% (95% CI 76.6-84).

The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545-0.737), and sensitivity and specificity were 34.2% (95% CI 19.6-51.4) and 88.8% (95% CI 85.6-91.6), respectively. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models.

We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. Elderly patients are susceptible to postoperative infections with increased mortality.

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