1.The clinical effect of shuxuening treatment on organophosphorus pesticide poisoning with toxic myocarditis
Lingxiao HAO ; Shuai CHENG ; Yalin LI ; Tong WEI
Chinese Journal of Primary Medicine and Pharmacy 2016;23(5):701-704
Objective To investigate the clinical effect of shuxuening treatment on organophosphorus pesti-cide poisoning with toxic myocarditis.Methods 60 patients with organophosphorus toxic myocarditis were selected in our hospital as the research subjects,and 60 cases were divided into two groups:observation group(n =30) and control group(n =30).Control group was given conventional treatment,treatment group was given shuxuening injec-tion 14 days on the basis of conventional treatment.After treatment,creatine kinase isoenzyme (CKMB),troponin (TNI),interleukin 6 (IL -6) and cholinesterase(ChE) were compared,and the changes of clinical symptoms were observed at the same time.Results There was no significant difference between the observation group and the control group(χ2 =0.630,P =0.730).The TNI,IL -6,CKMB could reflect the severity of myocardial injury in patients with different degrees of organic phosphorus poisoning,TNI,CK -MB,IL -6 increased with the degree of poisoning,the differences were statistically significant(F =11.863,4.512,3.774;P =0.000,0.015,0.029).After treatment for 4, 9,14 days,TnI,CK,CK -MB,levels of IL -6 in the two groups were recovered,but the recovery levels of TnI,CK -MB and IL -6 of the observation group significantly better than the control group,the differences were statistically sig-nificant(Fourth days,t =8.125,5.128,10.461;P =0.000,0.001,0.000;Ninth days,t =5.464,4.674,9.510;P =0.001,0.002,0.000,t =6.162,8.248,5.523;P =0.000,0.000,0.001).Conclusion Conventional treatment combined with shuxuening in the treatment of organophosphorus pesticide poisoning with toxic myocarditis has better therapeutic effect,it is worthy of promotion.
2.Establishment and evaluation of Cox proportional-hazards prediction model for secondary intracranial hypertension in perioperative period in patients with acute subdural hematoma
Lingxiao TONG ; Hu QIN ; Baofeng YAN
Journal of Clinical Medicine in Practice 2024;28(13):36-40,57
Objective To construct a Cox proportional hazards prediction model for secondary intracranial hypertension in patients with acute subdural hematoma(ASDH)during the perioperative period and validate its effectiveness.Methods Clinical data of 78 patients with ASDH were retro-spectively collected and divided into secondary group(25 cases with secondary intracranial hyperten-sion during perioperation)and control group(53 cases without secondary intracranial hypertension during perioperation).Differences in demographic indicators,comorbidities,clinical biochemical indi-cators,and imaging data between the two groups were compared.The Cox proportional hazards model was used to perform a multivariate analysis of independent risk factors that may affect secondary in-tracranial hypertension in ASDH patients during the perioperative period.A prediction model for sec-ondary intracranial hypertension in ASDH patients during the perioperative period was established,and Harrell's C index was calculated to assess the predictive accuracy of the model.The degree of agreement between the model prediction and actual risks was evaluated through a nomogram and calibration curve.Results The six-month follow-up rate was 89.74%(70/78).Age,smoking history,hyperten-sion,diabetes,preoperative Glasgow Coma Scale(GCS)score,Glasgow Outcome Scale(GOS)score,complex hematoma,intracranial hematoma volume,mean arterial pressure,glycated hemo-globin(HbA1c),international normalized ratio(INR),interleukin-6(IL-6),and procalcitonin(PCT)in the secondary group showed statistically significant differences compared with the control group(P<0.05).Age(OR=2.895;95%CI,1.264 to 6.633;P=0.022),smoking history(OR=2.146;95%CI,1.029 to 4.475;P=0.036),GOS score(OR=0.288;95%CI,0.112 to 0.741;P=0.015),HbA1c(OR=3.325;95%CI,1.243 to 8.894;P=0.028),INR(OR=2.746;95%CI,1.203 to6.267;P=0.027),and PCT(OR=3.426;95%CI,1.335 to 8.795;P=0.019)were independent influencing factors for secondary intracranial hypertension in ASDH patients during the perioperative period.Harrell's C index was 0.812(95%CI,0.789 to 0.872).The nomogram and calibration curve showed good consistency between the actual risk and the model prediction.Conclusion Cox proportional hazards model for patients with acute subdural hematoma has high accuracy in predicting the risk of secondary intracranial hypertension during the periopera-tive period and is suitable for clinical promotion.
3.Establishment and evaluation of Cox proportional-hazards prediction model for secondary intracranial hypertension in perioperative period in patients with acute subdural hematoma
Lingxiao TONG ; Hu QIN ; Baofeng YAN
Journal of Clinical Medicine in Practice 2024;28(13):36-40,57
Objective To construct a Cox proportional hazards prediction model for secondary intracranial hypertension in patients with acute subdural hematoma(ASDH)during the perioperative period and validate its effectiveness.Methods Clinical data of 78 patients with ASDH were retro-spectively collected and divided into secondary group(25 cases with secondary intracranial hyperten-sion during perioperation)and control group(53 cases without secondary intracranial hypertension during perioperation).Differences in demographic indicators,comorbidities,clinical biochemical indi-cators,and imaging data between the two groups were compared.The Cox proportional hazards model was used to perform a multivariate analysis of independent risk factors that may affect secondary in-tracranial hypertension in ASDH patients during the perioperative period.A prediction model for sec-ondary intracranial hypertension in ASDH patients during the perioperative period was established,and Harrell's C index was calculated to assess the predictive accuracy of the model.The degree of agreement between the model prediction and actual risks was evaluated through a nomogram and calibration curve.Results The six-month follow-up rate was 89.74%(70/78).Age,smoking history,hyperten-sion,diabetes,preoperative Glasgow Coma Scale(GCS)score,Glasgow Outcome Scale(GOS)score,complex hematoma,intracranial hematoma volume,mean arterial pressure,glycated hemo-globin(HbA1c),international normalized ratio(INR),interleukin-6(IL-6),and procalcitonin(PCT)in the secondary group showed statistically significant differences compared with the control group(P<0.05).Age(OR=2.895;95%CI,1.264 to 6.633;P=0.022),smoking history(OR=2.146;95%CI,1.029 to 4.475;P=0.036),GOS score(OR=0.288;95%CI,0.112 to 0.741;P=0.015),HbA1c(OR=3.325;95%CI,1.243 to 8.894;P=0.028),INR(OR=2.746;95%CI,1.203 to6.267;P=0.027),and PCT(OR=3.426;95%CI,1.335 to 8.795;P=0.019)were independent influencing factors for secondary intracranial hypertension in ASDH patients during the perioperative period.Harrell's C index was 0.812(95%CI,0.789 to 0.872).The nomogram and calibration curve showed good consistency between the actual risk and the model prediction.Conclusion Cox proportional hazards model for patients with acute subdural hematoma has high accuracy in predicting the risk of secondary intracranial hypertension during the periopera-tive period and is suitable for clinical promotion.
4.Prediction of seizures in sleep based on power spectrum.
Weinan LIU ; Yan LIU ; Baotong TONG ; Lingxiao ZHAO ; Yingxue YANG ; Yuping WANG ; Yakang DAI
Journal of Biomedical Engineering 2018;35(3):329-336
Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.