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| DOI： |
| 中文关键词: 烧伤 预测模型 抑郁 心理健康 危险因素 |
| 英文关键词:Burn injury, Predictive modeling, Depression, Mental health, Risk factors |
| 基金项目: |
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| 摘要点击次数: 165 |
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| 中文摘要: |
| [摘要] 目的 分析中度以上烧伤患者发生抑郁症状的危险因素，并构建一种简单实用的预测模型，以帮助筛查中度以上烧伤后抑郁高危患者并针对性地进行出院随访。方法 选取2020年5月至2023年8月江苏省人民医院宿迁医院收治的89例中度以上烧伤患者作为研究对象，并采用汉密尔顿抑郁量表（HAMD）来衡量他们出院当天的情绪和躯体症状，如果总分大于等于7分，则定义为存在抑郁症状。收集可能影响患者抑郁症状发生的各因素，包括性别，年龄，婚姻，文化水平，烧伤面积等共9个因素，应用LASSO回归降低数据维度并筛选预测因子。使用多元logistic回归模型分析影响中度以上烧伤患者出院后抑郁症状发生的独立危险因素。一致性指数（C-index），受试者工作特征（ROC）曲线和校准曲线用于检查模型的鉴别和校准。决策分析曲线（DCA）用于评估该模型的临床适用性，并使用bootstrap验证评估内部验证。结果 89例中度以上烧伤患者中47例出现抑郁症状（52.8%），9个影响因子经LASSO回归分析筛选后均被保留，多元logistic回归分析结果显示，性别，婚姻状况，接受的教育程度，烧伤面积，烧伤原因，烧伤是否累及面颈部与患者抑郁症状的发生独立相关（95%CI为0.004-9.195、0.927-1.036、0.007-0.313、1.109-1.390、0.008-0.491、1.938-64.209，P=0.042、P＜0.001、P=0.003、P＜0.001、P=0.014、P=0.009）。预测模型预测中度以上烧伤患者出院后抑郁症状的C指数为0.919（95% CI：0.864–0.974），在区间验证中仍能达到 0.890的较高C指数值,校准曲线显示观察值和预测值之间具有良好的一致性，DCA 曲线表明该模型可以使患者受益。结论 该模型确定了影响中度以上烧伤患者出院后发生抑郁症状的相关因素，有助于早期识别和针对性干预中度以上烧伤后发生抑郁症状的高危患者。 |
| 英文摘要: |
| [Abstract] Objective To investigate the potential causes of depressive symptoms in those with moderate or higher burn, and to create a straightforward and practicable prognostication model to identify those with elevated risk of depression after moderate or higher burn and carry out targeted follow-up. Methods A total of 89 individuals suffering from moderate to severe burn who had been admitted to Suqian Hospital of Jiangsu Provincial People's Hospital between May 2020 and August 2023 were chosen as the study subjects, and the Hamilton Depression Scale (HAMD) was used to measure their emotional and somatic symptoms on the day of discharge, and if the total score was greater than or equal to 7, it was defined as the presence of depressive symptoms. Additionally, 9 factors that could potentially influence the emergence of depressive symptoms were evaluated, such as gender, age, marriage, educational attainment, burn site, and so on.Employing LASSO regression, the data dimension was reduced and predictors were screened. Subsequently, a Multiple Logistic Regression Model was employed to evaluate the independent risk factors that could lead to depression symptoms in those with moderate or above burn after discharge. To verify the identification and calibration of models, the C-index, receiver operating characteristic (ROC) curve and calibration curve are employed. The decision analysis curve (DCA) was then utilized to evaluate the clinical applicability of the model, while bootstrap validation was employed for internal validation. RESULTS Depressive symptoms were present in 47 of 89 patients with more than moderate burns (52.8%), and all nine influencing factors were retained after screening by LASSO regression analysis.Multiple logistic regression analysis showed that gender, marital status, education received, burn area, cause of burn, and whether the burn involved the face and neck were independently associated with the occurrence of patients' depressive symptoms (95% CIs of 0.004-9.195, 0.927-1.036, 0.007-0.313, 1.109-1.390, 0.008-0.491, 1.938-64.209, P=0.042, P<0.001, P=0.003, P<0.001, P=0.014, P=0.009). The predictive model predicted a C-index of 0.919 (95% CI: 0.864-0.974) for post-discharge depressive symptoms in patients with more than moderate burns, and still managed to achieve a high C-index value of 0.890 in interval validation, and the calibration curves demonstrated good agreement between observed and predicted values, and the DCA curves indicated that the model could benefit the patients. Conclusion The model identifies the factors affecting the development of depressive symptoms in patients with burns of more than moderate severity after discharge from the hospital, which can help early identification and targeted intervention for patients at high risk of developing depressive symptoms after burns of more than moderate severity. |
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