Objective To establish a prediction model for futile recanalization after mechanical thrombectomy(MT)in acute anterior circulation large vessel occlusion(ACLVO)stroke patients based on multimodal imaging features,and to evaluate its predictive performance.Methods Retrospectively enrolled consecutive ACLVO patients who underwent MT with successful recanalization(modified thrombolysis in cerebral infarction[mTICI]grade≥2b)at the Department of Neurology of Fuyang People's Hospital between June 2023 and December 2024.Demographic and clinical data were collected,including age,gender,hypertension,diabetes mellitus,atrial fibrillation,smoking history,alcohol consumption history,National Institutes of Health stroke scale(NIHSS)score upon admission,intravenous thrombolysis,wake-up stroke,onset-to-puncture time(OPT),puncture-to-recanalization time(PRT),occlusion vessel(internal carotid artery,middle cerebral artery),treatment method(suction thrombectomy,stent thrombectomy,suction+stent thrombectomy).All patients underwent pre-procedural CT perfusion(CTP)+CT angiography(CTA)of the head and neck.Imaging parameters included hypoperfusion(defined as time to peak＞6 s)volume(HPV),core infarct(defined as cerebral blood flow＜30%)volume(CIV),mismatch ratio(MMR;HPV/CIV),and Tan collateral score(poor collaterals:0-1 score,good collaterals:2-3 score).Patients were followed up at 90 days post-procedure via outpatient clinic or re-admission.Patients with a modified Rankin scale(mRS)score≤2 were classified into the effective recanalization group,while those with mRS score≥3 were classified into the futile recanalization group.Imaging variables with statistically significant differences between the futile recanalization and effective recanalization groups were included in multivariate Logistic regression analysis to identify independent predictors of futile recanalization and construct a nomogram model.The predictive value of the model was assessed using the receiver operating characteristic(ROC)curve.Model calibration was evaluated using the Hosmer-Lemeshow test(goodness-of-fit defined as P＞0.50).Results(1)A total of 105 ACLVO patients with successful MT recanalization were included(65 males,40 females,mean age[66±11]years,ranged 31～87 years).There were 60 patients in the effective recanalization group and 45 in the futile recanalization group.Compared to the effective recanalization group,the futile recanalization group had significantly higher age([69±11]years vs.[63±11]years,P=0.012),higher proportion of diabetes mellitus(33.33%[15/45]vs.16.67%[10/60],P=0.047),higher pre-treatment NIHSS score([15.51±2.73]vs.[13.25±2.71],P＜0.01),longer OPT([516.40±192.48]min vs.[322.98±171.22]min,P＜0.01)and PRT([94.96±17.37]min vs.[87.58±15.99]min,P=0.026),larger CIV([74.00±12.76]ml vs.[24.28±14.72]ml,P＜0.01)and HPV([121.43±22.21]ml vs.[91.62±11.34]ml,P＜0.01),smaller MMR([1.65±0.15]vs.[9.42±1.91],P＜0.01),higher 90-day mRS score([3.60±0.54]score vs.[1.22±0.83]score,P＜0.01),and a significantly different distribution of Tan collateral scores(P＜0.01).(2)Multivariate Logistic regression analysis was performed with futile recanalization as the dependent variable,identified the following independent predictors of futile recanalization:HPV(OR,2.042,95%CI 1.296-3.218,P=0.002),CIV(OR,2.373,95%CI 1.315-4.280,P=0.004),MMR(OR,1.758,95%CI 1.135-2.721,P=0.011),and Tan collateral score(OR,5.166,95%CI 2.100-12.651,P＜0.01).(3)A nomogram prediction model for futile recanalization after MT in ACLVO stroke was constructed based on the four imaging parameters as aforementioned.ROC curve analysis demonstrated that the area under the curve for this model in predicting futile recanalization after MT was 0.846(95%CI 0.739-0.912),with a sensitivity of 0.844 and a specificity of 0.817.The calibration curve and the Hosmer-Lemeshow test indicated the goodness-of-fit was high(P=0.617),and the overall stability of the model was good.Conclusion The predictive model for futile recanalization after MT for acute ACLVO constructed base on HPV,CIV,MMR and Tan collateral score facilitates the identification patients with high-risk of futile recanalization.

Objective To establish a prediction model for futile recanalization after mechanical thrombectomy(MT)in acute anterior circulation large vessel occlusion(ACLVO)stroke patients based on multimodal imaging features,and to evaluate its predictive performance.Methods Retrospectively enrolled consecutive ACLVO patients who underwent MT with successful recanalization(modified thrombolysis in cerebral infarction[mTICI]grade≥2b)at the Department of Neurology of Fuyang People's Hospital between June 2023 and December 2024.Demographic and clinical data were collected,including age,gender,hypertension,diabetes mellitus,atrial fibrillation,smoking history,alcohol consumption history,National Institutes of Health stroke scale(NIHSS)score upon admission,intravenous thrombolysis,wake-up stroke,onset-to-puncture time(OPT),puncture-to-recanalization time(PRT),occlusion vessel(internal carotid artery,middle cerebral artery),treatment method(suction thrombectomy,stent thrombectomy,suction+stent thrombectomy).All patients underwent pre-procedural CT perfusion(CTP)+CT angiography(CTA)of the head and neck.Imaging parameters included hypoperfusion(defined as time to peak＞6 s)volume(HPV),core infarct(defined as cerebral blood flow＜30%)volume(CIV),mismatch ratio(MMR;HPV/CIV),and Tan collateral score(poor collaterals:0-1 score,good collaterals:2-3 score).Patients were followed up at 90 days post-procedure via outpatient clinic or re-admission.Patients with a modified Rankin scale(mRS)score≤2 were classified into the effective recanalization group,while those with mRS score≥3 were classified into the futile recanalization group.Imaging variables with statistically significant differences between the futile recanalization and effective recanalization groups were included in multivariate Logistic regression analysis to identify independent predictors of futile recanalization and construct a nomogram model.The predictive value of the model was assessed using the receiver operating characteristic(ROC)curve.Model calibration was evaluated using the Hosmer-Lemeshow test(goodness-of-fit defined as P＞0.50).Results(1)A total of 105 ACLVO patients with successful MT recanalization were included(65 males,40 females,mean age[66±11]years,ranged 31～87 years).There were 60 patients in the effective recanalization group and 45 in the futile recanalization group.Compared to the effective recanalization group,the futile recanalization group had significantly higher age([69±11]years vs.[63±11]years,P=0.012),higher proportion of diabetes mellitus(33.33%[15/45]vs.16.67%[10/60],P=0.047),higher pre-treatment NIHSS score([15.51±2.73]vs.[13.25±2.71],P＜0.01),longer OPT([516.40±192.48]min vs.[322.98±171.22]min,P＜0.01)and PRT([94.96±17.37]min vs.[87.58±15.99]min,P=0.026),larger CIV([74.00±12.76]ml vs.[24.28±14.72]ml,P＜0.01)and HPV([121.43±22.21]ml vs.[91.62±11.34]ml,P＜0.01),smaller MMR([1.65±0.15]vs.[9.42±1.91],P＜0.01),higher 90-day mRS score([3.60±0.54]score vs.[1.22±0.83]score,P＜0.01),and a significantly different distribution of Tan collateral scores(P＜0.01).(2)Multivariate Logistic regression analysis was performed with futile recanalization as the dependent variable,identified the following independent predictors of futile recanalization:HPV(OR,2.042,95%CI 1.296-3.218,P=0.002),CIV(OR,2.373,95%CI 1.315-4.280,P=0.004),MMR(OR,1.758,95%CI 1.135-2.721,P=0.011),and Tan collateral score(OR,5.166,95%CI 2.100-12.651,P＜0.01).(3)A nomogram prediction model for futile recanalization after MT in ACLVO stroke was constructed based on the four imaging parameters as aforementioned.ROC curve analysis demonstrated that the area under the curve for this model in predicting futile recanalization after MT was 0.846(95%CI 0.739-0.912),with a sensitivity of 0.844 and a specificity of 0.817.The calibration curve and the Hosmer-Lemeshow test indicated the goodness-of-fit was high(P=0.617),and the overall stability of the model was good.Conclusion The predictive model for futile recanalization after MT for acute ACLVO constructed base on HPV,CIV,MMR and Tan collateral score facilitates the identification patients with high-risk of futile recanalization.

Objective To analyze the correlation of inflammatory markers C-reactive proten(CRP),serum amyloid protein A(SAA),interleukin-6(IL-6)levels and subjective sleep characteristics in patients with first-episode acute cerebral infarction.Methods A total of 113 patients with first-episode cerebral infarction admitted to the Department of Neurology,the People's Hospital of Fuyang from March 2022 to April 2023 were prospectively and continuously selected as subjects.According to the Pittsburgh sleep quality index(PSQI),they were divided into insomnia group(PSQI＞7 points)and non-insomnia group(PSQI ≤7 points).General demo-graphic data and differences in CRP,SAA,IL-6 levels,Hamilton anxiety scale(HAMA)and Hamilton depression scale(HAMD)scores were compared between the two groups.Partial correlation analysis was used to analyze the correlation between three serum markers and PSQI effect factors.Results There were no significant differences in age,gender,baseline NIHSS score and mRS score between the two groups(P＞0.05).HAMD scores(z=-3.993,P＜0.001),HAMA scores(z=-3.806,P＜0.001),CRP,IL-6,SAA(P＜0.001)in insomnia group were significantly higher than those in non-insomnia group.The history of hyperlipidemia between the two groups was statistically significant(z=5.913,P=0.015).Multivariate Logisitic regression analysis showed that CRP(OR=1.55,P＜0.01),HAMD scores,HAMA scores and hyperlipidemia were independent risk factors for chronic insomnia in patients with first-episode cerebral infarction,and HAMD scores had a greater effect than HAMA scores(OR:1.10 vs 1.04).Partial correlation analysis showed that IL-6 and CRP levels were significantly correlated with the total score of PSQI(P＜0.05),while SAA was not significantly correla-ted with the total score of PSQI(P＞0.05).IL-6 level was positively correlated with sleep quality(r=0.231)sleep efficiency(r=0.322)and sleep duration(r=0.221).SAA level was positively correlated with sleep efficiency(r=0.242),while CRP level was posi-tively correlated with sleep latency(r=0.194),sleep duration(r=0.247)and sleep efficiency(r=0.225).Conclusion The inflam-matory markers CRP,IL-6 and SAA levels were elevated in the patients with first-episode cerebral infarction accompanied by insomnia,which were correlated with the severity of insomnia.The correlation between CRP and IL-6 levels and sleep characteristics was consistent with each other.

To provide comprehensive, scientific, and precise big data supports for national pediatric cancer prevention and control, the National Center for Pediatric Cancer Surveillance constructed the Surveillance Platform in 2019. Based on stratified and service-oriented design concepts, and a microservices architecture, the platform constructed five layers: document storage, data storage, service, application, and visualization. The platform supported three data reporting methods: automatic collection, file import, and manual entry. It ensured data quality through both rule-based and process-based quality control measures. Additionally, strict data security measures had been established in areas such as security domains, permission management, and data de-identification to ensure the safety and reliability of the monitoring data. As to November 2024, the platform had covered 1 750 surveillance sites(hospitals) and collected information about 6 million pediatric cancer cases, achieving positive results. This practice had laid a solid foundation for the smooth implementation of national pediatric cancer surveillance work and provided scientific evidences for pediatric cancer prevention and control in China. In the future, the platform′s performance needs to be continuously optimized and upgraded. It should further integrate relevant datasets in this field and actively explore and expand new application scenarios with the help of cutting-edge technologies such as big language models.

The enterprise master patient index is an important tool for identifying the diagnosis and treatment records of the same patient in heterogeneous medical data from multiple sources. From June to December 2021, the National Children′s Cancer Monitoring Center screened and determined the enterprise master patient index index system and its recognition logic by literature analysis and expert consultation. Based on the National Children′s Cancer surveillance Platform (hereinafter referred to as the surveillance platform), a corresponding intelligent recognition algorithm system was developed. After multiple rounds of real data verification and adjustment, a enterprise master patient index suitable for multi-source heterogeneous medical data was constructed. From January 2022 to March 2024, the intelligent recognition algorithm system had completed the recognition of 2.46 million pediatric tumor case report cards, established 0.33 million primary indexes and their unique identification codes for malignant tumor patients, and improved the data management and application efficiency of the surveillance platform. The enterprise master patient index based on surveillance platforms had played an important role in the registration and follow-up of pediatric cancer cases and related medical research, which could provide references for the construction of master indexes on other medical big data platforms in China.

The national pediatric cancer surveillance data known as the pediatric cancer case report card(report card), had the characteristics of wide sources, diverse collection methods and a large amount of information. Based on the characteristics of the surveillance data, the National Center for Pediatric Cancer Surveillance (surveillance center) established quality control program for surveillance data according to the relevant norms and standards from China and other countries. The program defined the variables, requirements and rules for the quality control of surveillance data. The surveillance center designed different quality control processes according to the way of data reporting including manual filling/file import and port docking, and formulated a series of supporting measures to achieve the completeness, accuracy and standardization of surveillance data. By analyzing the report cards of patients discharged from hospital from 2021 to 2023, the surveillance center found that the number of problem report cards decreased from 40.6% (202 185 cards / 497 538 cards) before feedback to 31.1% (157 725 cards / 506 817 cards) after feedback. The data quality control program not only improved the quality of surveillance data, but also provided references for the establishment of the data quality control program of other registration systems of medical field.

Building a nationwide representative sibling information database of pediatric cancer is of great significance for the research of pediatric cancer. In October 2022, based on the national pediatric cancer surveillance platform, the National Center for Pediatric Cancer Surveillance(NCPCS) identified and integrated the information of pediatric cancer cases using the patient master index, and then determined and retrieved the diagnosis and treatment information of pediatric cancer siblings through the sibling pair matching algorithm system, to establish the sibling information database. The information database was stored in the sibling database module of the surveillance platform, which realized the dynamic update, retrieval, download, and analysis of sibling information. The database provided data and technical support for the further childhood cancer research among siblings, as well as provided a reference for the construction of research-oriented databases for other disease surveillance systems. As of March 2024, this database had included 2 980 childhood cancer patients, collecting nearly 30 000 related medical records. In the future, NCPCS should further improve the sensitivity of sibling decision logic and expand the functionality of the sibling information database, so as to better meet the diverse needs of clinical and scientific research.

In-depth understanding of the clinical diagnosis and treatment practices of various health workers is of great significance for optimizing the allocation of health workforce. In 2023, based on the surveillance platform of National Center for Pediatric Cancer Surveillance(NCPCS), the NCPCS effectively integrated human resources data with clinical diagnosis and treatment data. By clarifying the conceptual and logical structures of the database, a clinical health workforce database was constructed using a distributed relational database. This database adhered to the data quality control principles of uniqueness, integrity, logic, and validity, and implemented scientific and effective data security protection strategies throughout the entire data life cycle. In practical applications, statistical analyses could be conducted on this database from two dimensions: health workforce and diagnosis-treatment processes, assisting relevant departments and hospitals in the refined management of health workforce allocation and promoting discipline construction. As of May 2024, the database had incorporated 931 hospitals, with the number of various health workers exceeding 640 000. The clinical health workforce database provided references for health administrative departments and hospitals at all levels to grasp the clinical practices of various health workers, and to achieve a clinical-demand-oriented allocation of health workforce.