医学研究杂志
醫學研究雜誌
의학연구잡지
JOURNAL OF MEDICAL RESEARCH
2009年
8期
47-49
,共3页
孟新科%赵志刚%石少权%吴光凤%魏刚%刘德红%郑晓英%苏顺庭
孟新科%趙誌剛%石少權%吳光鳳%魏剛%劉德紅%鄭曉英%囌順庭
맹신과%조지강%석소권%오광봉%위강%류덕홍%정효영%소순정
心肺复苏%自主循环恢复%昏迷%预后%评分
心肺複囌%自主循環恢複%昏迷%預後%評分
심폐복소%자주순배회복%혼미%예후%평분
Cardiopulmonary resuscitation%Restoration of spontaneous circulation%Comatose%Outcome%Score
目的 建立心肺复苏自主循环恢复(CPRROSC)昏迷病人预后的评价方法 ,提高该类病人预后的预测能力.方法 分析文献,找出心肺复苏自主循环恢复昏迷病人预后的相关因素,赋予每个因素一定分值,建立CPRROSC预后评分法.用该评分法回顾性评价115例CPRROSC住院病人的预后,比较不同预后病人CPRROSC预后评分的差异,计算其对两种严重不良预后(死亡或植物状态)与其他类型预后区别能力的ROC曲线下面积.结果 5种不同预后(正常、轻度神经功能障碍、重度神经作者单位:518035深圳市第二人民医院急诊科(盂新科、赵志刚、吴光风、魏刚、刘德红、郑晓英、苏顺庭);中山大学附属第五医院妇产科(石少权)功能障碍、植物状态和死亡)病人CPRROSC预后评分比较.总的差异有统计学意义(F=65.91,P=0.000).其中正常组与神经功能轻度异常组、死亡组与植物状态组之间差异无统计学意义(3.52±3.03 vs 4.88±3.52,P=0.318;15.47±3.31 vs 14.04±3.84,P=0.108);其他各组之间相互比较差异均有统计学意义(植物状态组vs重度神经功能异常组为14.04±3.84 vs 10.70±3.30,P=0.011;其他各组之间比较,均为P=0.000).CPRROSC预后评分在8分以下对预后良好(正常或神经功能轻度异常)的病人区别能力最强;13分以上对预后严重不良的病人区别能力最强.CPRROSC预后评分对严重不良预后预测的ROC曲线下面积为0.950.结论 CPRROSC预后评分对病人严重不良预后具有较高预测和区别能力,可以作为心肺复苏后昏迷病人最终预后预测的评价工具.
目的 建立心肺複囌自主循環恢複(CPRROSC)昏迷病人預後的評價方法 ,提高該類病人預後的預測能力.方法 分析文獻,找齣心肺複囌自主循環恢複昏迷病人預後的相關因素,賦予每箇因素一定分值,建立CPRROSC預後評分法.用該評分法迴顧性評價115例CPRROSC住院病人的預後,比較不同預後病人CPRROSC預後評分的差異,計算其對兩種嚴重不良預後(死亡或植物狀態)與其他類型預後區彆能力的ROC麯線下麵積.結果 5種不同預後(正常、輕度神經功能障礙、重度神經作者單位:518035深圳市第二人民醫院急診科(盂新科、趙誌剛、吳光風、魏剛、劉德紅、鄭曉英、囌順庭);中山大學附屬第五醫院婦產科(石少權)功能障礙、植物狀態和死亡)病人CPRROSC預後評分比較.總的差異有統計學意義(F=65.91,P=0.000).其中正常組與神經功能輕度異常組、死亡組與植物狀態組之間差異無統計學意義(3.52±3.03 vs 4.88±3.52,P=0.318;15.47±3.31 vs 14.04±3.84,P=0.108);其他各組之間相互比較差異均有統計學意義(植物狀態組vs重度神經功能異常組為14.04±3.84 vs 10.70±3.30,P=0.011;其他各組之間比較,均為P=0.000).CPRROSC預後評分在8分以下對預後良好(正常或神經功能輕度異常)的病人區彆能力最彊;13分以上對預後嚴重不良的病人區彆能力最彊.CPRROSC預後評分對嚴重不良預後預測的ROC麯線下麵積為0.950.結論 CPRROSC預後評分對病人嚴重不良預後具有較高預測和區彆能力,可以作為心肺複囌後昏迷病人最終預後預測的評價工具.
목적 건립심폐복소자주순배회복(CPRROSC)혼미병인예후적평개방법 ,제고해류병인예후적예측능력.방법 분석문헌,조출심폐복소자주순배회복혼미병인예후적상관인소,부여매개인소일정분치,건립CPRROSC예후평분법.용해평분법회고성평개115례CPRROSC주원병인적예후,비교불동예후병인CPRROSC예후평분적차이,계산기대량충엄중불량예후(사망혹식물상태)여기타류형예후구별능력적ROC곡선하면적.결과 5충불동예후(정상、경도신경공능장애、중도신경작자단위:518035심수시제이인민의원급진과(우신과、조지강、오광풍、위강、류덕홍、정효영、소순정);중산대학부속제오의원부산과(석소권)공능장애、식물상태화사망)병인CPRROSC예후평분비교.총적차이유통계학의의(F=65.91,P=0.000).기중정상조여신경공능경도이상조、사망조여식물상태조지간차이무통계학의의(3.52±3.03 vs 4.88±3.52,P=0.318;15.47±3.31 vs 14.04±3.84,P=0.108);기타각조지간상호비교차이균유통계학의의(식물상태조vs중도신경공능이상조위14.04±3.84 vs 10.70±3.30,P=0.011;기타각조지간비교,균위P=0.000).CPRROSC예후평분재8분이하대예후량호(정상혹신경공능경도이상)적병인구별능력최강;13분이상대예후엄중불량적병인구별능력최강.CPRROSC예후평분대엄중불량예후예측적ROC곡선하면적위0.950.결론 CPRROSC예후평분대병인엄중불량예후구유교고예측화구별능력,가이작위심폐복소후혼미병인최종예후예측적평개공구.
Objective To exactly predict the prognoses of comatose patients with restoration of spontaneous circulation (ROSC) af-ter eardiopulmonary resuscitation (CPR) attempts, we adopted a predictive model based on summation score of multiple prognostic fac-tons. Methods We screen prognostic factors associated with survival after a resuscitation attempt by systematically reviewing published literature. Each factor included in the predictive model was assigned to a value. The total score of factors' values for a comatose patient was used to predict his/her outcome, so as what we call "CPRROSC predictive score". We retrospectively analyzed outcomes of 115 CPR-ROSC patients in coma using the predictive model. Score of patients with different outcomes was compared. Its predictive power for two categories of patients with poor outcomes and other patients was evaluated by calculating areas under ROC Curve. Results There were differences among CPRROSC predictive score of patients with five different outcomes (Good cerebral performance, moderate cerebral disa-bility, severe cerebral disability, vegetative state, and death) (F = 65.91 ,P = 0.000). However, There was no statistical difference be-tween patients with good cerebral performance and moderate cerebral disability (3.52±3.03 vs 4.88±3.52, P = 0.318), so it was with death and vegetative state(15.47±3.31 vs 14.04±3.84 P = 0.108). There were significant differences among patients with other out-comes. (vegetative state vs severe cerebral disability: 14.04±3.84 vs 10.70±3.30, P = 0.011). CPRROSC predictive Score was the most powerful indicator to predict patients with good cerebral performance when it was under 8 and patients with poor outcomes (death or vegetative state) when it was over 13. The area under ROC Curve for CPRROSC predictive score to predict outcomes of the alleged two kinds of patients was 0.95. Conclusion CPRROSC predictive score is more capable of exactly predicting the prognosis of patients with poor outcomes. It is also a useful modality to predict the final prognosis of comatose survivors after cardiopulmonary resuscitation.