Big-data analysis: A clinical pathway on endoscopic retrograde cholangiopancreatography for common bile duct stones

BACKGROUND A clinical pathway(CP)is a standardized approach for disease management.However,big data-based evidence is rarely involved in CP for related common bile duct(CBD)stones,let alone outcome comparisons before and after CP implementation.AIM To investigate the value of CP implementation in patients with CBD stones undergoing endoscopic retrograde cholangiopancreatography(ERCP).METHODS This retrospective study was conducted at Nanjing Drum Tower Hospital in patients with CBD stones undergoing ERCP from January 2007 to December 2017.The data and outcomes were compared by using univariate and multivariable regression/linear models between the patients who received conventional care(non-pathway group,n=467)and CP care(pathway group,n=2196).RESULTS At baseline,the main differences observed between the two groups were the percentage of patients with multiple stones(P<0.001)and incidence of cholangitis complication(P<0.05).The percentage of antibiotic use and complications in the CP group were significantly less than those in the nonpathway group[adjusted odds ratio(OR)=0.72,95%confidence interval(CI):0.55-0.93,P=0.012,adjusted OR=0.44,95%CI:0.33-0.59,P<0.001,respectively].Patients spent lower costs on hospitalization,operation,nursing,medication,and medical consumable materials(P<0.001 for all),and even experienced shorter length of hospital stay(LOHS)(P<0.001)after the CP implementation.No significant differences in clinical outcomes,readmission rate,or secondary surgery rate were presented between the patients in the non-pathway and CP groups.CONCLUSION Implementing a CP for patients with CBD stones is a safe mode to reduce the LOHS,hospital costs,antibiotic use,and complication rate.

Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation

Taking the Chinese city of Xiamen as an example,simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019(COVID-19)and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures.A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios.The comparison was conducted between simulated and real cases in Xiamen.A web interface with adjustable parameters,including choice of intervention measures,intervention weights,vaccination,and viral variants,was designed for users to run the simulation.The total case number was set as the outcome.The cumulative number was 4,614,641 without restrictions and 78 under the strictest intervention set.Simulation with the parameters closest to the real situation of the Xiamen outbreak was performed to verify the accuracy and reliability of the model.The simulation model generated a duration of 52 days before the daily cases dropped to zero and the final cumulative case number of 200,which were 25 more days and 36 fewer cases than the real situation,respectively.Targeted interventions could benefit the prevention and control of COVID-19 outbreak while safeguarding public health and mitigating impacts on people’s livelihood.

Application of big data and artificial intelligence in epidemic surveillance and containment

Faced with the current time-sensitive COVID-19 pandemic,the overburdened healthcare systems have resulted in a strong demand to develop newer methods to control the spread of the pandemic.Big data and artificial intelligence(AI)have been leveraged amid the COVID-19 pandemic;however,little is known about their use for supporting public health efforts.In epidemic surveillance and containment,efforts are needed to treat critical patients,track and manage the health status of residents,isolate suspected cases,and develop vaccines and antiviral drugs.The applications of emerging practices of artificial intelligence and big data have become powerful"weapons"to fight against the pandemic and provide strong support in pandemic prevention and control,such as early warning,analysis and judgment,interruption and intervention of epidemic,to achieve goals of early detection,early report,early diagnosis,early isolation and early treatment.These are the decisive factors to control the spread of the epidemic and reduce the mortality.This paper systematically summarized the application of big data and AI in epidemic,and describes practical cases and challenges with emphasis on epidemic prevention and control.The included studies showed that big data and AI have the potential strength to fight against COVID-19.However,many of the proposed methods are not yet widely accepted.Thus,the most rewarding research would be on methods that promise value beyond COVID-19.More efforts are needed for developing standardized reporting protocols or guidelines for practice.

Weekly Assessment of the COVID-19 Pandemic and Risk of Importation--China, March 25, 2020

Along with the announcement of COVID-19 as a global pandemic by the World Health Organization(WHO)on March 12,2020,COVID-19 appeared to be spreading rapidly around the world.By 10:00 CET on March 25,2020,a total of 331,619 confirmed cases and 15,146 deaths were reported from 195 foreign countries and regions on 6 continents plus the Diamond Princess international cruise ship,and among them,124 countries and regions had local transmission.Cumulatively,the WHO website reported 15,918 confirmed COVID-19 cases from 16 countries and regions in the Western Pacific excluding China,220,516 cases from 60 countries and regions in Europe,2,344 cases from 10 countries and regions in South-East Asia,29,631 cases from 21 countries and regions in the Eastern Mediterranean,60,834 cases from 48 countries and regions in the Americas,and 1,664 cases from 39 countries and regions in Africa(1).

Serum semaphorin 4C as a diagnostic biomarker in breast cancer:A multicenter retrospective study

Background:To date,there is no approved blood-based biomarker for breast cancer detection.Herein,we aimed to assess semaphorin 4C(SEMA4C),a pivotal protein involved in breast cancer progression,as a serum diagnostic biomarker.Methods:We included 6,213 consecutive inpatients from Tongji Hospital,Qilu Hospital,and Hubei Cancer Hospital.Training cohort and two validation cohorts were introduced for diagnostic exploration and validation.A pan-cancer cohort was used to independently explore the diagnostic potential of SEMA4C among solid tumors.Breast cancer patients who underwent mass excision prior to modified radical mastectomy were also analyzed.We hypothesized that increased pretreatment serum SEMA4C levels,measured using optimized in-house enzymelinked immunosorbent assay kits,could detect breast cancer.The endpoints were diagnostic performance,including area under the receiver operating characteristic curve(AUC),sensitivity,and specificity.Post-surgery pathological diagnosis was the reference standard and breast cancer staging followed the TNM classification.There was no restriction on disease stage for eligibilities.Results:We included 2667 inpatients with breast lesions,2378 patients with other solid tumors,and 1168 healthy participants.Specifically,118 patients with breast cancer were diagnosed with stage 0(5.71%),620 with stage I(30.00%),966 with stage II(46.73%),217 with stage III(10.50%),and 8 with stage IV(0.39%).Patients with breast cancer had significantly higher serum SEMA4C levels than benign breast tumor patients and normal controls(P<0.001).Elevated serum SEMA4C levels had AUC of 0.920(95%confidence interval[CI]:0.900–0.941)and 0.932(95%CI:0.911–0.953)for breast cancer detection in the two validation cohorts.The AUCs for detecting early-stage breast cancer(n=366)and ductal carcinoma in situ(n=85)were 0.931(95%CI:0.916–0.946)and 0.879(95%CI:0.832–0.925),respectively.Serum SEMA4C levels significantly decreased after surgery,and the reduction was more striking after modified radical mastectomy,compared with mass excision(P<0.001).The positive rate of enhanced serum SEMA4C levels was 84.77%for breast cancer and below 20.75%for the other 14 solid tumors.Conclusions:Serum SEMA4C demonstrated promising potential as a candidate biomarker for breast cancer diagnosis.However,validation in prospective settings and by other study groups is warranted.