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OBJECTIVE： To establish antibiotics use rationality evaluation model in type Ⅰ incision surgery patients， and to provide reference for prescription review of clinical pharmacists. METHODS： Totally 432 inpatients underwent type Ⅰ surgical incision in a hospital from Jan. 1st- Dec. 31st， 2017 were selected as the research objects. The information of diagnosis and treatment including age， nosocomial infection， the number of kinds of antibiotics used were extracted. Based on the results of clinical pharmacists’ comments on the antibiotics use rationality in patients’ prevention and treatment， non-conditional Logistic regression and support vector machine （SVM） in machine learning method were used to convert clinical pharmacists’ comments into objective index that can be recognized by the machine learning model， using categories of antibiotics （preventive or therapeutic use） as dependent variables and the patient’s diagnosis and treatment information as independent variables. Classification and identification model was established for antibiotics use rationality in type Ⅰ incision surgery patients. Using sensitivity， specificity and Youden index as indexes， established mode was validated on the other 61 samples of type Ⅰ incision surgery patients. The rationality of antibiotics prescriptions in type Ⅰ incision surgery patients before （by manual review， Jan.-Dec. 2017） and after （Jan.-Oct. 2018） using the model were collected， and the effects of the model were evaluated. RESULTS： The sensitivity， specificity and Youden index of non-conditional Logistic regression model were 65.63％， 75.00％ and 40.63％， respectively. Main parameters of the model established by SVM included gamma 0.01， cost 10， sensitivity 92.19％， specificity 87.50％， Youden index 79.69％. The model established by SVM was better than non-conditional Logistic regression. SVM was used to validate established mode， and sensitivity， specificity and Youden index were 100％， 88.57％ and 88.57％， respectively. Compared with before using the model， the evaluation ratio increased from 69.44％ to 100％， the rate of prophylactic use of antibiotics decreased from 23.84％ to 16.43％， the rate of rational drug type selection increased from 37.86％ to 54.39％， and treatment course shortened from 5.01 days to 3.26 days after using the model. CONCLUSIONS： Established antibiotics use rationality evaluation model in typeⅠincision surgery patients by SVM in machine learning method fully covers all the patients， promotes rational use of antibiotics in typeⅠincision surgery patients， and provides a new idea for pharmacist prescription comment.
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Recently , with the extensive use of endoscopic technology , polymer materials and other medical equipments ,great progress and constant perfection have been achieved in the operating treatment of primary cannalicu -lits.Many new treatment methods are applied in clinical practice ,such as canalicular incision ,canalicular incision and silicon tube implants ,resection of anterior canalicular wall and lacrimal stent intubation ,and operating treatment with lacrimal endoscope .In this article , the surgical techniques , clinical practices , advantages and disadvantages of the therapies are summarized,and the existing problems are pointed out .
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Objective:To standardize the process of abandonment management of large medical equipment in hospital, and to achieve the reasonable reuse after large medical equipment were abandoned.Methods: To discuss the conditions, process and reuse assessment when large medical equipments were abandoned, and the concrete operation methods about the abandonment disposition and reused process of large medical equipments.Results:This research has achieved safety and efficient abandonment for large medical equipments under ensuring maximizing benefits of economic efficient.Conclusion: This method reduces the cost of medical device in hospital and create higher social benefits under ensuring maximizing benefits of economic efficiency by standardizing abandonment process and reuse assessment and the safety application for large medical equipment.
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Objective To investigate the diagnostic value and clinical significance of phosphorylated signal transducer and activa-tor of transcription 3(pSTAT3)in malignant pleural effusion(MPE)caused by lung adenocarcinoma.Methods 47 pleural effusion samples were collected and detected by using liquid-based cytologic test (LCT).Furthermore,the expression of pSTAT3 was detec-ted by using the method of immunocytochemistry in all of the specimens.Results Among the 47 cases of pleural effusion,40 cases of MPE were caused by lung adenocarcinoma and 7 cases were benign pleural effusion,which were confirmed by clinical manifesta-tion,biopsy and imaging data.The positive expression rate of pSTAT3 was 80.0%(32/40)in MPE caused by lung adenocarcino-ma,significantly higher than benign group(P <0.05).13 cases of MPE caused by lung adenocarcinoma and 7 cases of benign pleural effusion were diagnosed with liquid-based cytologic test alone.The other 27 cases were undetermined and needed to be differentiate between adenocarcinoma and atypical mesothelial cells.However,32 cases of lung adenocarcinoma and 7 cases could be clearly diag-nosed if liquid-based cytologic test was used combined with immunocytochemistry detection of pSTAT3 expression.Only 8 cases were undetermined.Diagnosis of grey area was significantly narrowed.There was significant statistical significance between the two methods(P <0.05).Conclusion High expression of pSTAT3 is related to the MPE caused by lung adenocarcinoma.Detection of pSTAT3 expression can be used as a new method in the diagnosis of MPE caused by lung adenocarcinoma.pSTAT3 may play an important role in the development of the MPE.
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Objective To investigate the related risk factors for stroke among residents aged over 26 years old in communities in Chongqing,to set up a quantitative criteria for determining risk for stroke,and to evaluate and predict the risk of the stroke for individuals.Methods 1 034 cases with 1:2 matched controls were chosen from five communities in Chongqing,which were interviewed with a uniform questionnaire.The risk factors of stroke were analyzed with Logistic regression,collected the odds ratios of risk factors for stroke,and different levels of risk exposure factors be converted into a risk scores by using statistical models.The related risk scores were added to obtain a combined risk score to predict the individual's risk of stoke in the future.Results An individual health risk appraisal model of stroke applicable to individuals of different sex,age,health behavior,disease and family history was established.For men,11 risk factors,including hypertension(5.728),coronary heart disease(5.404),family history of stroke(4.599),and so on,were entered the main effect model;for women,hypertension(5.270),family history of stroke(4.866),hyperlipidemia(4.346),and other eight risk factors entered the main effect model.Conclusion Personal diseases,including hypertension,CHD and hyperlipidemia,etc.,are very important risk factors for stoke.The stroke prevalence can be effectively reduced by changing the unhealthy lifestyle and curing the related individual diseases.The evaluation model of the risk score for stroke based on the main risk factors could be useful in the future health education and community health service on stroke prevention.