Meda Nicola, Zammarrelli Josephine, Sambataro Fabio, De Leo Diego
Department of Neuroscience, University of Padova, Padova, Italy.
De Leo Fund, Research Division, Padova, Italy.
Front Psychiatry. 2024 Sep 17;15:1455247. doi: 10.3389/fpsyt.2024.1455247. eCollection 2024.
People in late adulthood die by suicide at the highest rate worldwide. However, there are still no tools to help predict the risk of death from suicide in old age. Here, we leveraged the Survey of Health, Ageing, and Retirement in Europe (SHARE) prospective dataset to train and test a machine learning model to identify predictors for suicide in late life.
Of more than 16,000 deaths recorded, 74 were suicides. We matched 73 individuals who died by suicide with people who died by accident, according to sex (28.8% female in the total sample), age at death (67 ± 16.4 years), suicidal ideation (measured with the EURO-D scale), and the number of chronic illnesses. A random forest algorithm was trained on demographic data, physical health, depression, and cognitive functioning to extract essential variables for predicting death from suicide and then tested on the test set.
The random forest algorithm had an accuracy of 79% (95% CI 0.60-0.92, p = 0.002), a sensitivity of.80, and a specificity of.78. Among the variables contributing to the model performance, the three most important factors were how long the participant was ill before death, the frequency of contact with the next of kin and the number of offspring still alive.
Prospective clinical and social information can predict death from suicide with good accuracy in late adulthood. Most of the variables that surfaced as risk factors can be attributed to the construct of social connectedness, which has been shown to play a decisive role in suicide in late life.
在全球范围内，老年人群的自杀死亡率最高。然而，目前仍没有工具可用于帮助预测老年人自杀死亡的风险。在此，我们利用欧洲健康、老龄化与退休调查（SHARE）前瞻性数据集来训练和测试一个机器学习模型，以识别晚年自杀的预测因素。
在记录的16000多例死亡病例中，有74例为自杀。我们根据性别（总样本中女性占28.8%）、死亡年龄（67±16.4岁）、自杀意念（用EURO-D量表测量）和慢性病数量，将73例自杀死亡者与意外死亡者进行匹配。基于人口统计学数据、身体健康状况、抑郁情况和认知功能训练随机森林算法，以提取预测自杀死亡的关键变量，然后在测试集上进行测试。
随机森林算法的准确率为79%（95%CI为0.60 - 0.92，p = 0.002），灵敏度为0.80，特异度为0.78。在对模型性能有贡献的变量中，三个最重要的因素是参与者在死亡前患病的时长、与近亲的联系频率以及在世子女的数量。
前瞻性临床和社会信息能够较好地预测成年晚期的自杀死亡情况。大多数作为风险因素出现的变量可归因于社会联系这一概念，社会联系已被证明在晚年自杀中起决定性作用。
