Sarcopenia diagnosed using masseter muscle area predictive of early mortality following severe traumatic brain injury

Traumatic brain injury（TBI）represents a global pandemic and is currently a leading cause of injury related death worldwide.Unfortunately,those who survive initial injury often suffer devastating functional,social,and economic consequences.

A Mortality Risk Assessment Approach on ICU Patients Clinical Medication Events Using Deep Learning

ICU patients are vulnerable to medications,especially infusion medications,and the rate and dosage of infusion drugs may worsen the condition.The mortality prediction model can monitor the real-time response of patients to drug treatment,evaluate doctors’treatment plans to avoid severe situations such as inverse Drug-Drug Interactions(DDI),and facilitate the timely intervention and adjustment of doctor’s treatment plan.The treatment process of patients usually has a time-sequence relation(which usually has the missing data problem)in patients’treatment history.The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network(RNN).However,sometimes,patients’treatment can last for a long period of time,which RNN may not fit for modelling long time sequence data.Therefore,we propose to use the heterogeneous medication events driven LSTM to predict the outcome of the patient,and the Natural Language Processing and Gaussian Process(GP),which can handle noisy,incomplete,sparse,heterogeneous and unevenly sampled patients’medication records.In our work,we emphasize the semantic meaning of each medication event and the sequence of the medication events on patients,while also handling the missing value problem using kernel-based Gaussian process.We compare the performance of LSTM and Phased-LSTM on modelling the outcome of patients’treatment and data imputation using kernel-based Gaussian process and conduct an empirical study on different data imputation approaches.

THE PREDICTION OF CANCER MORTALITY IN SHANDONG PROVINCE (1991-2000) AND CANCER CONTROL STRATEGY

Shandong Province, with a population of 84 million and located in the east coastline of China, is rich in natural resources and ranks middle in economic develpment of the whole nation. Around 90000 people are dead of cancer each year. In the recent twenty years, trends in malignant neoplasm

The prediction of early mortality in off-pump coronary artery bypass surgery:SinoSCORE versus EuroSCORE

Objective To compare the validation of the Sino System for Coronary Operative Risk Evaluation (SinoSCORE) with the European system for cardiac operative risk evaluation (EuroSCORE) in patients undergoing off-pump coronary artery bypass (OPCAB) surgery in China. Methods Data of patients who underwent OPCAB between 2004 and 2005 in

Predictive value of SinoSCORE on in-hospital mortality and postoperative complications after coronary artery bypass surgery

Objective To evaluate the performance of the Sino System for Coronary Operative Risk Evaluation (SinoSCORE) on in hospital mortality and postoperative complications in patients undergoing coronary artery bypass grafting (CABG) in a single heart center. Methods From January 2007 to December 2008,clinical information of 201 consecutive patients undergoing isolated CABG in our hospital was collected. The SinoSCORE was used to

A good prognostic predictor for liver transplantation recipients with benign end-stage liver cirrhosis

BACKGROUND： Post-transplant model for predicting mortality（PMPM, calculated as-5.359＋1.988×ln（serum creatinine [mg/d L]）＋1.089×ln（total bilirubin [mg/d L]）） score has been proved to be a simple and accurate model for predicting the prognosis after liver transplantation（LT） in a single center study. Here we aim to verify this model in a large cohort of patients.METHODS： A total of 2727 patients undergoing LT with endstage liver cirrhosis from January 2003 to December 2010 were included in this retrospective study. Data were collected from the China Liver Transplant Registry（CLTR）. PMPM score was calculated at 24-h and 7-d following LT. According to the PMPM score at 24-h, all patients were divided into the low-risk group（PMPM score ≤-1.4, n=2509） and the high-risk group（PMPM score 〉-1.4, n=218）. The area under receiver operator characteristic curve（AUROC） was calculated for evaluating the prognostic accuracy.RESULTS： The 1-, 3-, and 5-year patient survival rates in the low-risk group were significantly higher than those in the high-risk group（90.23%, 88.01%, and 86.03% vs 63.16%, 59.62%, and 56.43%, respectively, P〈0.001）. In the high-risk group, 131 patients had a decreased PMPM score（≤-1.4） at 7-d, and their cumulative survival rate was significantly higher than the other 87 patients with sustained high PMPM score（〉-1.4）（P〈0.001）. For predicting 3-month mortality, PMPM score showed a much higher AUROC than post-transplant MELD score（P〈0.05）.CONCLUSION： PMPM score is a simple and effective tool to predict short-term mortality after liver transplantation in patients with benign liver diseases, and an indicator for prompt salvaging treatment as well.

Clinical characteristics and mortality risk prediction model in children with acute myocarditis

Background Acute myocarditis(AMC)can cause poor outcomes or even death in children.We aimed to identify AMC risk factors and create a mortality prediction model for AMC in children at hospital admission.Methods This was a single-center retrospective cohort study of AMC children hospitalized between January 2016 and January 2020.The demographics,clinical examinations,types of AMC,and laboratory results were collected at hospital admission.In-hospital survival or death was documented.Clinical characteristics associated with death were evaluated.Results Among 67 children,51 survived,and 16 died.The most common symptom was digestive disorder(67.2%).Based on the Bayesian model averaging and Hosmer–Lemeshow test,we created a final best mortality prediction model(acute myocarditis death risk score,AMCDRS)that included ten variables(male sex,fever,congestive heart failure,left-ventricular ejection fraction<50%,pulmonary edema,ventricular tachycardia,lactic acid value>4,fulminant myocarditis,abnormal creatine kinase-MB,and hypotension).Despite differences in the characteristics of the validation cohort,the model discrimination was only marginally lower,with an AUC of 0.781(95%confidence interval=0.675–0.852)compared with the derivation cohort.Model calibration likewise indicated acceptable fit(Hosmer‒Lemeshow goodness-of-fit,P¼=0.10).Conclusions Multiple factors were associated with increased mortality in children with AMC.The prediction model AMCDRS might be used at hospital admission to accurately identify AMC in children who are at an increased risk of death.

Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients:A Multi-Center Retrospective Study in China

Coronavirus disease 2019(COVID-19)has become a worldwide pandemic.Hospitalized patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients.Here,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death.The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all cohorts.Moreover,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of 0.9551.In summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.

Prognostic Nomogram for Patients with Hepatitis E Virus-related Acute Liver Failure:A Multicenter Study in China

Background and Aims:Timely and effective assessment scoring systems for predicting the mortality of patients with hepatitis E virus-related acute liver failure(HEV-ALF)are urgently needed.The present study aimed to establish an effective nomogram for predicting the mortality of HEV-ALF patients.Methods:The nomogram was based on a cross-sectional set of 404 HEV-ALF patients who were identified and enrolled from a cohort of 650 patients with liver failure.To compare the performance with that of the model for end-stage liver disease(MELD)scoring and CLIF-Consortiumacute-on-chronic liver failure score(CLIF-C-ACLFs)models,we assessed the predictive accuracy of the nomogram using the concordance index(C-index),and its discriminative ability using time-dependent receiver operating characteristics(td-ROC)analysis,respectively.Results:Multivariate logistic regression analysis of the development set carried out to predict mortality revealed that γ-glutamyl transpeptidase,albumin,total bilirubin,urea nitrogen,creatinine,international normalized ratio,and neutrophil-to-lymphocyte ratio were independent factors,all of which were incorporated into the new nomogram to predict the mortality of HEV-ALF patients.The area under the curve of this nomogram for mortality prediction was 0.671(95%confidence interval:0.602-0.740),which was higher than that of the MELD and CLIF-C-ACLFs models.Moreover,the td-ROC and decision curves analysis showed that both discriminative ability and threshold probabilities of the nomogram were superior to those of the MELD and CLIF-C-ACLFs models.A similar trend was observed in the validation set.Conclusions:The novel nomogram is an accurate and efficient mortality prediction method for HEV-ALF patients.

Performance of the PRISM I,PIM2,PELOD-2 and PRISM IV scoring systems in western China:a multicenter prospective study

Background The aim of this study was to evaluate the performance of the four scoring tools in predicting mortality in pediatric intensive care units(PICUs)in western China.Methods This was a multicenter,prospective,cohort study conducted in six PICUs in western China.The performances of the scoring systems were evaluated based on both discrimination and calibration.Discrimination was assessed by calculating the area under the receiver operating characteristic curve(AUC)for each model.Calibration was measured across defined groups based on mortality risk using the Hosmer-Lemeshow goodness-of-fit test.Results A total of 2034 patients were included in this study,of whom 127(6.2%)died.For the entire cohort,AUCs for Pediatric Risk of Mortality Score(PRISM)I,Pediatric Index of Mortality 2(PIM2),Pediatric Logistic Organ Dysfunction Score-2(PELOD-2)and PRISM IV were 0.88[95%confidence interval(CI)0.85–0.92],0.84(95%CI 0.80–0.88),0.80(95%CI 0.75–0.85),and 0.91(95%CI 0.88–0.94),respectively.The Hosmer-Lemeshow goodness-of-fit Chi-square value was 12.71(P=0.12)for PRISM I,4.70(P=0.79)for PIM2,205.98(P<0.001)for PELOD-2,and 7.50(P=0.48)for PRISM IV[degree of freedom(df)=8].The standardized mortality ratios obtained with the PRISM I,PIM2,PELOD-2,and PRISM IV models were 0.87(95%CI,0.75–1.01),0.97(95%CI,0.85–1.12),1.74(95%CI,1.58–1.92),and 1.05(95%CI,0.92–1.21),respectively.Conclusions PRISM IV performed best and can be used as a prediction tool in PICUs in Western China.However,PRISM IV needs to be further validated in NICUs.

The predictive value of monocyte to HDL cholesterol ratio on clinical outcomes in type 2 diabetes mellitus patients undergoing elective percutaneous coronary intervention

Background Monocyte to high density lipoprotein ratio（MHR） has been considered as a novel parameter related with adverse renal and cardiovascular outcomes.In this study we investigated the association of MHR with major adverse clinical events（MACEs） in patients with type 2 diabetes mellitus（T2DM） undergoing elective percutaneous coronary intervention（PCI）.Methods Consecutive T2 DM patients treated with elective PCI were prospectively recruited between July 2008-January 2016 in Department of Cardiology of Panyu Central Hospital.Subjects were categorized into two groups：as patients who developed MACEs（MACEs＋） and patients who did not develop MACEs（MACEs-） during hospitalization.MACEs were defined as the composite end points,including all-cause mortality,or acute heart failure,or target vessel revascularization,or stroke or recurrent angina.Results A total of 418 patients were included in the study.64 patients developed MACEs（15.3%）.In the MACEs（＋） patients,monocytes were higher（1.12 [0.78-1.42] vs.0.72 [0.68-0.92] 109/L,P 〈 0.01） and HDL cholesterol levels were lower（0.87 [0.72-1.21] vs.0.96 [0.81-1.11] mmol/L,P 〈 0.01）.In addition,MHR was significantly higher in the MACEs（＋） group（1.12 [0.91-2.09] vs.0.73[0.54-0.93] 109 mmol/L,P 〈 0.01）.The cutoff value of MHR for predicting MACEs was 22,with a sensitivity of 81% and a specificity of 75.1%（area under the curve0.79,P 〈 0.001）.In multivariate logistic regression analysis,MHR remained an independent factor correlated with MACEs（OR = 3.97,95%CI = 1.38-11.5,P 〈 0.01）.Conclusion Higher MHR levels may predict MACEsdevelopment after elective PCI in T2 DM patients.