四川医学
四川醫學
사천의학
SICHUAN MEDICAL JOURNAL
2015年
4期
518-521
,共4页
王秋华%张国力%焦玉梅%郝立
王鞦華%張國力%焦玉梅%郝立
왕추화%장국력%초옥매%학립
脑卒中%健康管理%高危人群%预测模型
腦卒中%健康管理%高危人群%預測模型
뇌졸중%건강관리%고위인군%예측모형
cerebral stroke%health management%high risk population%forecasting model
目的:探讨社区接受健康管理的脑卒中高危人群不同健康行为对发病结果的影响。方法采用回顾性分析,纳入209例接受至少3年健康干预的脑卒中患者,按照1:2的比例纳入年龄、性别一致且同期接受健康管理的脑卒中高危居民。采用Logistic回归筛选出发病的危险行为因素,然后通过受试者工作特征曲线确定截断值,最后对样本进行代入并与实际发病结果进行对比,以评估模型的诊断价值和预测准确度。结果筛选出4个对脑卒中发病有统计学意义的行为变量,分别为生理指标控制情况、体重指数、锻炼习惯、参加健康讲座的次数,综合预测模型的灵敏度为81.34%,特异度为85.17%,诊断符合率为83.89%,Younden指数为0.6651,曲线下面积为0.87,95%CI为0.76~0.96。结论生理指标控制情况、体重指数、锻炼习惯、参加健康讲座的次数组成的综合预测模型有较好的预测价值。采用个体化指导措施提高指标控制血压、血脂、血糖达标率,鼓励高危人群尽可能多的参加健康宣讲活动及有规律的长期锻炼有助于降低高危人群的脑卒中发病风险,对体重超重的高危居民更应该注意加强健康管理。
目的:探討社區接受健康管理的腦卒中高危人群不同健康行為對髮病結果的影響。方法採用迴顧性分析,納入209例接受至少3年健康榦預的腦卒中患者,按照1:2的比例納入年齡、性彆一緻且同期接受健康管理的腦卒中高危居民。採用Logistic迴歸篩選齣髮病的危險行為因素,然後通過受試者工作特徵麯線確定截斷值,最後對樣本進行代入併與實際髮病結果進行對比,以評估模型的診斷價值和預測準確度。結果篩選齣4箇對腦卒中髮病有統計學意義的行為變量,分彆為生理指標控製情況、體重指數、鍛煉習慣、參加健康講座的次數,綜閤預測模型的靈敏度為81.34%,特異度為85.17%,診斷符閤率為83.89%,Younden指數為0.6651,麯線下麵積為0.87,95%CI為0.76~0.96。結論生理指標控製情況、體重指數、鍛煉習慣、參加健康講座的次數組成的綜閤預測模型有較好的預測價值。採用箇體化指導措施提高指標控製血壓、血脂、血糖達標率,鼓勵高危人群儘可能多的參加健康宣講活動及有規律的長期鍛煉有助于降低高危人群的腦卒中髮病風險,對體重超重的高危居民更應該註意加彊健康管理。
목적:탐토사구접수건강관리적뇌졸중고위인군불동건강행위대발병결과적영향。방법채용회고성분석,납입209례접수지소3년건강간예적뇌졸중환자,안조1:2적비례납입년령、성별일치차동기접수건강관리적뇌졸중고위거민。채용Logistic회귀사선출발병적위험행위인소,연후통과수시자공작특정곡선학정절단치,최후대양본진행대입병여실제발병결과진행대비,이평고모형적진단개치화예측준학도。결과사선출4개대뇌졸중발병유통계학의의적행위변량,분별위생리지표공제정황、체중지수、단련습관、삼가건강강좌적차수,종합예측모형적령민도위81.34%,특이도위85.17%,진단부합솔위83.89%,Younden지수위0.6651,곡선하면적위0.87,95%CI위0.76~0.96。결론생리지표공제정황、체중지수、단련습관、삼가건강강좌적차수조성적종합예측모형유교호적예측개치。채용개체화지도조시제고지표공제혈압、혈지、혈당체표솔,고려고위인군진가능다적삼가건강선강활동급유규률적장기단련유조우강저고위인군적뇌졸중발병풍험,대체중초중적고위거민경응해주의가강건강관리。
Objective To explore influential factors for different incidence of the high risk population of cerebral stroke that received health management in community. Methods Retrospectively analyze 209 cerebral stroke patients who received health Interventions at least for 3 years, including the high risk residents with same age and gander who received synchronous health management in proportion of 1:2. The risk factors were screened by Logistic regression analysis,then the cutoff value was detemined from working curve of subjects, finally we assessed the value of diagnosis and prediction accuracy by comparison of Sample prediction and the real diagnosis result. Results Physiological control, body mass index, exercise habit and the number of attending health seminars were significant factors. The sensitivity of integrated forecasting model was 81. 34%, and the specificity was 85. 17%. Younden index was 0. 6651, while the area under the curve was 0. 87 with 95% CI:0. 76, 0. 96. Conclusion The integrated forecasting model consisted of physiological control, body mass index, exercise habit and the number of attending health seminars was of good predictive value. Using individualized guidance measures to improve successful control rate of blood pressure, blood lipid and blood glucose, encouraging high risk groups to participate in the Health propaganda activities as much as possible and exercising rgularly over a long period of time can help reduce the risk of stroke. We should pay more attention to strengthening health management for high risk residents of overweight.