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10th Anniversary of the European Association for Predictive, Preventive and Personalised (3P) Medicine - EPMA World Congress Supplement 2020.

ABSTRACT: In 2019, the EPMA celebrated its 10th anniversary at the 5th World Congress in Pilsen, Czech Republic. The history of the International Professional Network dedicated to Predictive, Preventive and Personalised Medicine (PPPM / 3PM) is rich in achievements. Facing the coronavirus COVID-19 pandemic it is getting evident globally that the predictive approach, targeted prevention and personalisation of medical services is the optimal paradigm in healthcare demonstrating the high potential to save lives and to benefit the society as a whole. The EPMA World Congress Supplement 2020 highlights advances in 3P medicine.

SUBMITTER: Golubnitschaja O

PROVIDER: S-EPMC7435254 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSuboptimal health status (SHS) is an intermediate health status between health and illness, a syndrome characterised by the perception of health complaints, general weakness and low energy. This study aimed to investigate the prevalence of SHS and the correlation between SHS and psychological symptoms among Chinese college students and to identify the SHS-related risk factors from the perspective of predictive, preventive and personalised medicine (PPPM).MethodsA cross-sectional study was conducted among 4119 college students who were enrolled from Taishan Medical University and Baoji Vocational and Technical College in the eastern and western areas of China. SHS levels of the participants were measured by an established self-reporting Suboptimal Health Status Questionnaire-25 (SHSQ-25). Psychosomatic conditions were estimated by the self-rating Symptom Checklist-90 (SCL-90) scale. Spearman correlation analysis was applied to analyse the relationship between SHSQ-25 scores and SCL-90 estimates. Logistic regression analysis was applied for multivariate analysis.ResultsThe prevalence of SHS was 21.0% (864/4119), with 23.3% (701/3005) for female students and 14.6% (163/1114) for male students. The prevalence of general positive psychological symptom was 14.2% (586/4119), with 15.6% (470/3005) for female students and 10.4% (116/1114) for male students. A strong correlation was identified between SHS score and SCL-90 estimates, with the correlation coefficient (r) of 0.719. Logistic regression showed that variables significantly associated with SHS were somatisation (adjusted odds ratio (aOR) = 3.185, 95% confidence interval [CI] = 2.048-4.953), obsessive-compulsive (aOR = 3.518, 95% CI = 2.834-4.368), interpersonal sensitivity (aOR = 1.883, 95% CI = 1.439-2.463) and depression (aOR = 1.847, 95% CI = 1.335-2.554).ConclusionsOur findings confirm that there is a high prevalence of SHS among college students and there is a strong association between SHS and psychological symptoms among Chinese college students. High susceptibility of SHS occurs particularly in vulnerable groups: female students, sophomore students, medical students and students from rural area. Identification of SHS and prompt application of personalised psychological health-supporting activities will promote college students' health status.

Project description:BackgroundRecognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing.MethodsThis investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used to inform the models. Model performance was mainly evaluated by the area under the receiver operating characteristic (AUC) curve. Additional techniques-permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP)-were applied for explaining the black-box ML models.ResultsFifteen routine haematological and biochemical features were selected to establish ML-based models for the prediction of IS. The XGBoost-based model achieved the highest predictive performance, reaching AUCs of 0.91 (0.90-0.92) and 0.92 (0.91-0.93) in the internal and external datasets respectively. PFI globally revealed that demographic feature age, routine haematological parameters, haemoglobin and neutrophil count, and biochemical analytes total protein and high-density lipoprotein cholesterol were more influential on the model's prediction. LIME and SHAP showed similar local feature attribution explanations.ConclusionIn the context of PPPM/3PM, we used the selected predictors obtained from the results of common blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction. By incorporating the individualised patient profile, this prediction tool is simple and quick to administer. This is promising to support subjective decision making in resource-limited settings or primary care, thereby shortening the time window for the treatment, and improving outcomes after IS.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-022-00283-4.

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10th Anniversary of the European Association for Predictive, Preventive and Personalised (3P) Medicine - EPMA World Congress Supplement 2020.

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