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.
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.
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.
Objective To evaluate the prognostic value of surgical treatment in gallbladder squamous cell carcinoma (GSCC) by using real-world data with a large sample in the Surveillance, Epidemiology and End Results (SEER) database. Methods The clinical data of patients with pathologically diagnosed GSCC from 2000 to 2019 were extracted from the SEER database. According to the inclusion and exclusion criteria, a total of 257 patients were included after strict screening. The patients were divided into operation group and non-operation group according to whether they underwent surgery. The cancer-specific survival (CSS) and the overall survival (OS) between the two groups were compared, and the influencing factors for the CSS and the OS were analyzed by using Cox proportional hazard model. Results Of 257 patients, 127 (49.4%) were in the operation group, and 130 (50.6%) in the non-operation group. The average follow-up ranged from 0 to 220 months, with the median follow-up time of 3 months. Of the 127 patients in the operation group, 105 died (82.7%), including 88 tumor-related deaths (69.3%). Of the 130 patients in the non-operation group, 124 died (95.4%), including 115 tumor-related deaths (88.5%). The median survival time for OS in the operation group and the non-operation group were 6 months and 3 months, respectively, and that for CSS were 7 months and 3 months, respectively. The estimated 1-year OS of the operation group and the non-operation group were 30.1% and 4.6% respectively; the estimated 1-year CSS were 35.1% and 5.8%, respectively. There were significant differences between the two groups on OS and CSS (χ2=41.400, P<0.001; χ2=42.750, P<0.001). That the OS [HR=0.44, 95%CI (0.25, 0.77), P=0.004] and the CSS [HR=0.46, 95%CI (0.25, 0.84), P=0.011] in GSCC patients were significantly improved by surgical treatment, showed by the results of multivariate prognostic analysis via Cox proportional hazard mode. Conclusions Surgical treatment was an independent factor affecting the prognosis of GSCC, and it could improve the OS and the CSS. As for the modus operandi, R0 resection should be recommended.
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 analyze the factors affecting the prognosis of patients with primary tracheal malignancy, and establish a nomogram model for prediction its prognosis.MethodsA total of 557 patients diagnosed with primary tracheal malignancy from 1975 to 2016 in the Surveillance, Epidemiology, and End Results Data were collected. The factors affecting the overall survival rate of primary tracheal malignancy were screened and modeled by univariate and multivariate Cox regression analysis. The nomogram prediction model was performed by R 3.6.2 software. Using the C-index, calibration curves and receiver operating characteristic (ROC) curve to evaluate the consistency and predictive ability of the nomogram prediction model.ResultsThe median survival time of 557 patients with primary tracheal malignancy was 21 months, and overall survival rates of the 1-year, 3-year and 5-year were 59.1%±2.1%, 42.5%±2.1%, and 35.4%±2.2%. Univariate and multivariate Cox regression analysis showed that age, histology, surgery, radiotherapy, tumor size, tumor extension and the range of lymph node involvement were independent risk factors affecting the prognosis of patients with primary tracheal malignancy (P<0.05). Based on the above 7 risk factors to establish the nomogram prediction model, the C-index was 0.775 (95%CI 0.751-0.799). The calibration curve showed that the prediction model established in this study had a good agreement with the actual survival rate of the 1 year, 3 year and 5 years. The area under curve of 1-year, 3-year and 5-year predicting overall survival rates was 0.837, 0.827 and 0.836, which showed that the model had a high predictive power.ConclusionThe nomogram prediction model established in this study has a good predictive ability, high discrimination and accuracy, and high clinical value. It is useful for the screening of high-risk groups and the formulation of personalized diagnosis and treatment plans, and can be used as an evaluation tool for prognostic monitoring of patients with primary tracheal malignancy.
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 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.
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.