Objective To investigate the epidemical status of influenza in Mianyang during 2010-2011, so as to provide evidence for formulating prevention and control strategies. Methods Surveillance data, ILI etiological results, outbreak and epidemic situation of the influenza-like illnesses (ILI) in Mianyang during 2010-2011 were collected for analysis. Results There were 12 100 ILI cases reported in 2010, accounted for 2.72% of the total outpatients. While 8 364 ILI cases accounted for 1.83% of the total outpatients were reported in 2011, reduced by 32.47% compared with 2010. The temporal distribution of doctor-visiting ratio in those two years was in an increased bimodal pattern. Most cases were children aged 0-5 years, accounted for 46.24%. Most ILI cases were treated in the department of fever, accounted for 88.56%. A total of 788 ILI specimens were collected for the detection of Real time RT-PCR, of which 34 specimens showed positive strains (4.31%) including 5 influenza A/H1N1 (0.63%), 8 influenza A (1.02%), 1 seasonal influenza A/H3 (0.13%) and 20 influenza B (2.54%). No outbreak and epidemic situation in Mianyang during 2010-2011. Conclusion The influenza activity is relatively stable without large-scale outbreak in Mianyang during 2010-2011. The reporting quality of surveillance hospitals should be improved and the lab of flu surveillance network should actively prepare to do the isolation and identification of influenza virus. It is necessary to enhance flu surveillance so as to prevent and control influenza prevalence．
ObjectiveTo analyze epidemic characteristics of multidrug-resistant organism (MDRO) in Neurosurgical Intensive Care Unit (NSICU), and to analyze the status of infection and colonization, in order to provide reference for constituting intervention measures. MethodsPatients who stayed in NSICU during January 2014 to April 2015 were actively monitored for the MDRO situation. ResultsA total of 218 MDRO pathogens were isolated from 159 patients, and 42 cases were healthcare-associated infections (HAI) among 159 patients. The Acinetobacter baumannii was the most common one in the isolated acinetobacter. Colonization rate was positively correlated with the incidence of HAI. From January to December, there was a significantly increase in the colonization rate, but not in the incidence of HAI. ConclusionThe main MDRO situation is colonization in NSICU. The obvious seasonal variation makes the HAI risk at different levels. So it is necessary that full-time and part-time HAI control staff be on alert, issue timely risk warning, and strengthen risk management. The Acinetobacter baumannii has become the number one target for HAI prevention and control in NSICU, so their apparent seasonal distribution is worthy of more attention, and strict implementation of HAI prevention and control measures should be carried out.
Objective Establishing Nomogram to predict the overall survival (OS) rate of patients with gastric adenocarcinoma by utilizing the database of the Surveillance, Epidemiology, and End Results (SEER) Program. Methods Obtained the data of 3 272 gastric adenocarcinoma patients who were diagnosed between 2004 and 2014 from the SEER database. These patients were randomly divided into training (n=2 182) and validation (n=1 090) cohorts. The Cox proportional hazards regression model was performed to evaluate the prognostic effects of multiple clinicopathologic factors on OS. Significant prognostic factors were combined to build Nomogram. The predictive performance of Nomogram was evaluated via internal (training cohort data) and external validation (validation cohort data) by calculating index of concordance (C-index) and plotting calibration curves. Results In the training cohort, the results of Cox proportional hazards regression model showed that, age at diagnosis, race, grade, 6th American Joint Committee on Cancer (AJCC) stage, histologic type, and surgery were significantly associated with the survival prognosis (P<0.05). These factors were used to establish Nomogram. The Nomograms showed good accuracy in predicting OS rate, with C-index of 0.751 ［95%CI was (0.738, 0.764)］ in internal validation and C-index of 0.753 ［95% CI was (0.734, 0.772)］ in external validation. All calibration curves showed excellent consistency between prediction by Nomogram and actual observation. Conclusion Novel Nomogram for patients with gastric adenocarcinoma was established to predict OS in our study has good prognostic significance, it can provide clinicians with more accurate and practical predictive tools which can quickly and accurately assess the patients’ survival prognosis individually, and can better guiding clinicians in the follow-up treatment of patients.
ObjectiveTo compare the clinical characteristics of inpatients with different influenza subtypes, so as to identify the subtypes at an early stage.MethodsA retrospective case study was conducted, using influenza surveillance data from January 1st, 2016 to December 31st, 2018 at a tertiary surveillance outpost hospital in Chengdu. Patients diagnosed with different subtypes of influenza by nucleic acid testing or virus isolation and culture were investigated, and their clinical characteristics, laboratory test results, and prognosis were analyzed and compared among the four subtypes including H1N1, H3N2, Victoria (BV), and Yamagata (BY).ResultsThere were 127 inpatients with laboratory-confirmed influenza. Among the confirmed influenza patients, 85.8% (109/127) had low or normal white blood cell counts, and 78.8% (89/113) had abnormally high procalcitonin levels. Among the patients with different subtypes, statistical differences existed in age (P<0.001), low or normal white blood cell count (P=0.041), positive bacteria/fungus/mycoplasma/chlamydia culture (P=0.001), kidney damage (P=0.013), outcome at discharge (P<0.001), and hospitalization expenses (P=0.016). However, there was no statistical difference in gender, clinical symptoms, liver damage, cardiac damage, or length of hospital stay (P>0.05).ConclusionThe infection of influenza can lead to severe clinical complications or even death. The outcomes of patients with influenza A may be more severe. An elevated procalcitonin level can be detected in quite a few patients with influenza.
ObjectivesTo compare the survival outcomes between hepatocellular carcinoma and hepatic angiosarcoma, and to develop and validate a nomogram predicting the outcome of hepatic angiosarcoma.MethodsThe Surveillance, Epidemiology and End Results (SEER) database was electronically searched to collect the data of hepatic angiosarcoma patients and hepatocellular carcinoma patients from 2004 to 2016. Propensity score matching (PSM) was used to match the two groups by the ratio of 1:3. Cox regression analysis was used to compare the survival outcomes between hepatic angiosarcoma and HCC. In the angiosarcoma group, population was divided into training set and validation set by 6:4. Nomograms were built for the prediction of half- and one- year survival, and validated by concordance index (C-index) and calibration plots.ResultsA total of 210 histologically confirmed hepatic angiosarcoma patients and 630 hepatocellular carcinoma patients were included. The overall survival of HCC was significantly longer than angiosarcoma (3-year survival: 18.4% vs. 6.7%, median survival: 5 months vs. 1 month, P<0.001), and the nomogram achieved good accuracy with an internal C-index of 0.751 and an external C-index of 0.737.ConclusionsThe overall survival of HCC is significantly longer than angiosarcoma. The proposed nomograms can assist to predict survival probability in patients with hepatic angiosarcoma. Due to limitation of the data of included patients, more high-quality studies are required to verify above conclusions.
This paper expounds the classification and characteristics of healthcare-associated infections (HAI) surveillance systems from the perspective of the informatization needs of HAI monitoring, explains the determination requirements of numerator and denominator in the surveillance statistical data, and introduces the regular verification for auditing the quality of HAI surveillance. The basic knowledge of machine learning and its achievements are introduced in processing surveillance data as well. Machine learning may become the mainstream algorithm of HAI automatic monitoring system in the future. Infection control professionals should learn relevant knowledge, cooperate with computer engineers and data analysts to establish more effective, reasonable and accurate monitoring systems, and improve the outcomes of HAI prevention and control in medical institutions.
ObjectiveTo establish a predictive model of surgical site infection (SSI) following colorectal surgery using machine learning.MethodsMachine learning algorithm was used to analyze and model with the colorectal data set from Duke Infection Control Outreach Network Surveillance Network. The whole data set was divided into two parts, with 80% as the training data set and 20% as the testing data set. In order to improve the training effect, the whole data set was divided into two parts again, with 90% as the training data set and 10% as the testing data set. The predictive result of the model was compared with the actual infected cases, and the sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated, the area under receiver operating characteristic (ROC) curve was used to evaluate the predictive capacity of the model, odds ratio (OR) was calculated to tested the validity of evaluation with a significance level of 0.05.ResultsThere were 7 285 patients in the whole data set registered from January 15th, 2015 to June 16th, 2016, among whom 234 were SSI cases, with an incidence of SSI of 3.21%. The predictive model was established by random forest algorithm, which was trained by 90% of the whole data set and tested by 10% of that. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 76.9%, 59.2%, 3.3%, and 99.3%, respectively, and the area under ROC curve was 0.767 [OR=4.84, 95% confidence interval (1.32, 17.74), P=0.02].ConclusionThe predictive model of SSI following colorectal surgery established by random forest algorithm has the potential to realize semi-automatic monitoring of SSIs, but more data training should be needed to improve the predictive capacity of the model before clinical application.
ObjectiveTo construct a model for predicting prognosis risk in patients with pancreatic malignancy (PM).MethodsThe clinicopathological data of 8 763 patients with PM undergone resection between 2010 and 2015 were collected and analyzed by SEER*Stat (v8.3.5) and R software, respectively. The univariate and multivariate Cox proportional hazard regression analysis were used to analyze the factors for predicting prognosis outcome risk and constructed the nomograms of patients with PM, respectively. Kaplan-Meier method was used to evaluate the survival of patients according to relevant factors and the high risk group and low risk group of patients with PM. The discriminative ability and calibration of the nomograms to predict overall survival were tested by using C-index, area under ROC curve (AUC) and calibration plots.ResultsThe multivariate Cox proportional hazard regression analysis showed that age, T staging, N staging, M staging, histological type, the differentiation, number of regional lymph node dissection, chemotherapy, and radiotherapy were independent factors for predicting the prognosis of patients with PM (P<0.05). Based on regression analysis of patients with PM, a nomograms model for predicting the risk of patients with PM was established, including age, T staging, N staging, M staging, histological type, the differentiation, tumor location, type of surgery, number of regional lymph node dissection, chemotherapy, and radiotherapy. The discriminative ability and calibration of the nomograms revealed good predictive ability as indicated by the C-index (0.747 for modeling group and 0.734 for verification group). The 3- and 5-year survival AUC values of the modeling group were 0.766 and 0.781, and the validation group were 0.758 and 0.783, respectively. The calibration plots showed that predictive value of the 3- and 5-year survival were close to the actual values in both modeling group and the verification group. ConclusionsIndependent predictors of survival risk after curative-intent surgery for PM were selected to create nomograms for predicting overall survival. The nomograms provide a basis for judging the prognosis of PM patients.
ObjectiveTo evaluate the high-level disinfection effect of flexible endoscopes in the Endoscopy Center of the First People’s Hospital of Longquanyi District of Chengdu, explore the key links of flexible endoscope cleaning and disinfection, and provide theoretical guarantee and technical support for the next step of the endoscope center work.MethodsWe sampled and monitored the lumens, water and air injection ports and biopsy ports of 19 flexible endoscopes after high-level disinfection in the Endoscopy Center of the First People’s Hospital of Longquanyi District of Chengdu. A total of 307 specimens were collected from 108 flexible endoscopes. We compared the disinfection effects of different flexible endoscopes and different sampling sites, and compared the microbial detection status of different flexible endoscopes.ResultsThe qualified rates of disinfection of gastroscopes, colonoscope and duodenoscopy were 79.22%, 86.21% and 100.00%, respectively, and the difference was not statistically significant (P=0.721). The qualified rates of disinfection of the endoscopic lumen, water and air injection port and biopsy port were 87.04%, 93.00% and 94.95%, respectively, and the difference was not statistically significant (χ2=4.585, P=0.101). The qualified rates of the lumen, water and air injection port and biopsy port of gastroscope, colonoscope and duodenoscope were 84.42%, 93.10%, 100.00%, 92.96%, 92.59%, 100.00%, 94.29%, 96.30%, 100.00%, respectively. There was no statistically significant difference in the disinfection effect of various parts of different flexible endoscopes (P>0.05). Bacteriological identification showed that of the 28 specimens with excess bacteriological standards, 16 gram-positive bacteria (57.1%), and 12 gram-negative bacteria (42.9%) were found.ConclusionThe cleaning and disinfection effect of flexible endoscopes has certain defect. Endoscope should be treated in strict accordance with the technical specifications for cleaning and disinfection of the flexible endoscope to further improve the disinfection effect of the flexible endoscope.
ObjectiveTo explore the influencing factors of cancer-specific survival of patients with large hepatocellular carcinoma, and draw a nomogram to predict the cancer-specific survival rate of large hepatocellular carcinoma patients.MethodsThe clinicopathological data of patients with large hepatocellular carcinoma during the period from 1975 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) database were searched and randomly divided into training group and validation group at 1∶1. Using the training data, the Cox proportional hazard regression model was used to explore the influencing factors of cancer-specific survival and construct the nomogram; finally, the receiver operating characteristic curve (ROC curve) and the calibration curve were drawn to verify the nomogram internally and externally.ResultsThe results of the multivariate Cox proportional hazard regression model showed that the degree of liver cirrhosis, tumor differentiation, tumor diameter, T stage, M stage, surgery, and chemotherapy were independent influencing factors that affect the specific survival of patients with large hepatocellular carcinoma (P<0.05), and then these factors were enrolled into the nomogram of the prediction model. The areas under the 1, 3, and 5-year curves of the training group were 0.800, 0.827, and 0.814, respectively; the areas under the 1, 3, and 5-year curves of the validation group were 0.800, 0.824, and 0.801, respectively. The C index of the training group was 0.779, and the verification group was 0.777. The calibration curve of the training group and the verification group was close to the ideal curve of the actual situation.ConclusionThe nomogram of the prediction model drawn in this study can be used to predict the specific survival of patients with large hepatocellular carcinoma in the clinic.