Risk Factors Analysis and Risk Prediction Model Construction of Depression in Inpatients of Geriatrics Department of a Hospital in Yunnan
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摘要:
目的 研究老年住院患者抑郁状态的危险因素,并构建风险预测模型。 方法 选取2017年9月至2020年7月云南省第一人民医院老年医学科老年住院患者(≥60岁)525例为研究对象,采用云南省第一人民医院老年医学科自主研发的“老年综合评估系统软件”对患者进行老年综合评估,并利用其中的SDS量表对住院患者进行抑郁状态的评估。采用二元Logistic回归分析探讨老年抑郁的影响因素,建立风险预测模型,采用ROC曲线下面积及Hosmer-Lemeshow检验评估模型预测效果。 结果 在525例新入院患者中,非抑郁组418例(79.6%),抑郁组107例(20.4%);2组间的年龄、性别、慢病数量、MNA-SF、SAS、MMSE、BADL、ALS、Morse、RomaⅢ量表,差异均有统计学意义(P < 0.05);且慢病数量(OR = 1.473,95%CI:1.050~2.067,P = 0.025)、失能(OR = 1.450,95%CI:1.073~1.958,P = 0.015)、焦虑(OR = 2.442,95%CI:1.829~3.259,P < 0.001)、认知功能障碍(OR = 1.488,95%,CI:1.072~2.066,P = 0.018)、失眠(OR = 1.620,95%CI:1.147~2.288,P = 0.006) 、社会支持障碍(OR = 2.544,95%,CI:1.165~5.555,P = 0.019)是老年住院患者抑郁状态的独立危险因素;另外,通过将模型预测概率作为新变量绘制ROC曲线,结果显示AUC为0.859,临界值为0.595(CI:0.822~0.897,P < 0.001),预测模型的灵敏度为0.818,特异度为0.782。 结论 老年抑郁状态与多种危险因素密切相关,基于该危险因素所建立的风险预测模型亦具有较高的准确性。 Abstract:Objective To study the risk factors of depression in elderly inpatients and build a risk prediction model. Methods From September 2017 to July 2020, 525 elderly patients (≥60 years old) in the Department of Geriatrics of the First People’s Hospital of Yunnan Province were selected as the research objects. The comprehensive geriatric assessment system software independently developed by the geriatric medical department of the First People’s Hospital of Yunnan province was used for the comprehensive geriatric assessment, and the SDS scale in it was used to evaluate the depressive state of hospitalized patients. Binary Logistic regression analysis was used to explore the influencing factors of depression in the elderly, and a risk prediction model was established. The area under the ROC curve and the Hosmer-Lemeshow test were used to evaluate the prediction effect of the model. Results Among 525 newly admitted patients, there were 418 cases (79.6%) in the non-depressed group and 107 cases (20.4%) in the depressed group. There were statistically significant differences in age, gender, number of chronic diseases, MNA-SF, SAS, MMSE, BADL, ALS, MORSE and RomaⅢ scales between the two groups (P < 0.05). The number of chronic diseases (OR = 1.473, 95%CI: 1.050 ~ 2.067, P = 0.025), daily dysfunction (OR = 1.450, 95%CI: 1.073 ~ 1.958, P = 0.015), anxiety (OR = 2.442, 95%CI: 1.829 ~ 3.259, P < 0.001), cognitive dysfunction (OR = 1.488, 95%CI: 1.072~2.066, P = 0.018), insomnia (OR = 1.620, 95%CI: 1.147~2.288, P = 0.006), social support disorder (OR = 2.544, 95%, CI: 1.165~5.555, P = 0.019) were independent risk factors for depression in elderly inpatients. In addition, the ROC curve was drawn by using the prediction probability of the model as a new variable, the results showed that the AUC was 0.859, with a critical value of 0.595 (CI: 0.822~0.897, P < 0.001), and the sensitivity and specificity of the prediction model were 0.818 and 0.782. Conclusion The state of senile depression is closely related to a variety of risk factors, and the risk prediction model based on the risk factors also has high accuracy. -
Key words:
- Geriatric symptoms /
- Depression /
- Prediction model /
- Inpatient
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表 1 一般资料及老年综合征的比较[n(%)]
Table 1. Comparison of general information and geriatric syndromes [n(%)]
变量 非抑郁组(n = 418) 抑郁组
(n = 107)χ2 P 性别 15.573 < 0.001* 男 302(72.2) 56(52.3) 女 116(27.8) 56(47.7) 年龄 7.147 0.028* 65~74岁 136(32.5) 22(20.6) 75~84岁 132(31.6) 42(31.8) 84岁以上 150(35.9) 51(47.7) BMI(kg/m2) 0.780 0.854 体重过轻 41(9.8) 9(8.4) 体重正常 241(57.7) 59(55.1) 体重过重 108(25.8) 30(28.0) 肥胖 28(6.7) 9(8.4) 失能情况 53.728 < 0.001* 日常功能良好 196(46.9) 23(21.5) 轻度功能障碍 160(38.3) 35(32.7) 中度功能障碍 28(6.7) 18(16.8) 重度功能障碍 21(5.0) 19(17.8) 完全残疾 13(3.1) 12(11.2) 焦虑情况 123.511 < 0.001* 无焦虑 212(50.7) 11(10.3) 可疑焦虑 161(38.5) 37(36.4) 中度焦虑 28(6.7) 29(27.1) 明显焦虑 21(5.0) 19(17.8) 严重焦虑 2(0.5) 7(6.5) 失眠情况 46.598 < 0.001* 无失眠 179(42.8) 15(14.0) 可疑失眠 失眠 认知功能 38.866 < 0.001* 正常认知功能 223(53.3) 26(24.3) 轻度认知障碍 123(29.4) 37(34.6) 中度认知障碍 60(14.4) 34(31.8) 重度认知障碍 12(2.9) 10(9.3) 营养状况 23.594 < 0.001* 营养良好 171(40.9) 22(20.6) 潜在营养不良 198(47.4) 56(52.3) 营养不良 49(11.7) 29(27.1) 1 a内尿失禁情况 无尿失禁 387(92.6) 94(87.9) 有尿失禁 31(7.4) 13(12.1) 便秘情况 9.258 0.002* 无便秘 281(67.2) 55(51.4) 有便秘 137(32.8) 52(48.6) 有无疼痛 0.026 0.871 无疼痛 335(80.1) 85(79.4) 有疼痛 83(19.9) 22(20.6) 跌倒风险 32.926 < 0.001* 低度跌倒风险 226(54.1) 30(28.0) 中度跌倒风险 111(26.6) 30(28.0) 高度跌倒风险 81(19.4) 47(43.9) 谵妄 10.064 0.002* 无谵妄 370(88.5) 82(76.6) 有谵妄 48(11.5) 25(23.4) 慢病数量 10.074 0.006* 0~5种 189(45.2) 33(28.0) 6~10种 159(38.0) 44(41.1) 10种以上 70(16.7) 25(018.0) 社会支持情况 30.242 < 0.001* 社会支持良好 320(76.6) 53(49.5) 社会支持障碍 98(23.4) 54(50.5) *P < 0.05。 表 2 Logistic回归分析各变量的赋值
Table 2. Logistic regression analysis of the assignment of variables
变量 赋值 因变量 抑郁 0 = 无抑郁,1 = 有抑郁 自变量 失能 0 = 无失能,1 = 轻度失能,2=中度失能,3=重度失能,4=完全残疾 焦虑 0 = 无焦虑状态,1 = 焦虑状态 认知功能 0 = 无认知障碍,1 = 轻度障碍,2=中度障碍,3=重度障碍 营养状况 0 = 营养良好,1 = 潜在营养不良,2=营养不良 失眠情况 0 = 睡眠良好,1 = 潜在失眠,2=失眠 跌倒风险 0 = 低度风险,1 = 中度风险,2=高度风险 社会支持 0 = 社会支持良好,1 = 社会支持障碍 谵妄 0 = 无谵妄,1 = 有谵妄 便秘情况 0 = 无便秘,1 = 有便秘 慢病数量 0 = 0~5种,1 = 6~10种,2=10种以上 表 3 抑郁相关因素的Logistic回归分析
Table 3. Logistic regression analysis of depression-related factors
因素 β SE Wald Sig EXP(β) EXP(β)的95%Cl 上限 下限 慢病数量 0.387 0.173 5.029 0.025 1.473 1.050 2.067 失能 0.371 0.153 5.863 0.015 1.450 1.073 1.958 焦虑状态 0.893 0.147 36.715 < 0.001 2.442 1.829 3.259 认知功能障碍 0.397 0.167 5.631 0.018 1.488 1.072 2.066 失眠 0.483 0.176 7.512 0.006 1.620 1.147 2.288 社会支持障碍 0.934 0.398 5.493 0.019 2.544 1.165 5.555 -
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