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Objective:To compare the predictive performance of different machine learning models using pre-hospital data to predict adverse inhospital outcome in patients with severe trauma.Methods:A retrospective cohort study was conducted to analyze the clinical data of 100 135 patients with severe trauma from the National Trauma Data Bank (NTDB) from January 2017 to December 2018. There were 69 644 males and 30 480 females apart from 11 patients with missing gender information, with the range age of 16-89 years [(50.1±21.1)years]. Clinical characteristics included demographic information (sex and age), trauma type (blunt or penetrating trauma), pre-hospital time [emergency medical services (EMS) response time, EMS scene time, and EMS transport time], pre-hospital vital signs (systolic blood pressure, pulse rate, respiratory rate, and oxygen saturation), trauma score [Glasgow coma score (GCS) and injury severity score (ISS)]. The original data were divided into the training set (in the year 2017) and the testing set (in the year 2018) according to the year of admission, including 50 429 patients in the training set and 49 706 patients in the testing set. The patients were classified into non-adverse outcome group ( n=94 526) and adverse outcome group ( n=5 609), according to whether they had an adverse outcome or not. There were 2 808 patients with adverse outcome in the training set and 2 801 patients with adverse outcome in the testing set. All models were built based on the training set. Eight machine learning algorithms consisting of neural network (NNET), naive Bayes (NB), gradient boosting machine (GBM), adaptive boosting (Ada), random forest (RF), bagging tree (BT), categorical boosting (CatBoost) and extreme gradient boosting (XGB) were used to construct prediction models for clinical outcomes among patients with severe trauma based on their clinical features. Models were evaluated according to the sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC) and Hosmer-Lemeshow goodness-of-fit test. Results:Of the NNET, NB, GBM, Ada, RF, BT, CatBoost and XGB models in the testing set, the sensitivity was 0.84, 0.83, 0.27, 0.79, 0.83, 0.81, 0.62 and 0.78, respectively; the specificity was 0.79, 0.76, 0.81, 0.79, 0.79, 0.74, 0.83 and 0.79, respectively; the AUC was 0.89 (95% CI 0.88, 0.90), 0.86 (95% CI 0.85, 0.87), 0.54 (95% CI 0.53, 0.55), 0.86 (95% CI 0.85, 0.87), 0.88 (95% CI 0.88, 0.90), 0.83 (95% CI 0.82, 0.85), 0.77 (95% CI 0.76, 0.79) and 0.86 (95% CI 0.85, 0.87), respectively. The NNET model had the best differentiation. In terms of calibration degree, both NNET and NB showed good performance ( P>0.05 for Hosmer-Lemeshow goodness-of-fit test). Conclusion:The NNET model has a favorable predictive performance for adverse inhospital outcome in patients with severe trauma, which may provide a reference for the rapid prediction of prognosis in patients with severe trauma.
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In order to facilitate systematic reviews and meta-analyses by medical researchers， this paper provides a tutorial-style review of the relevant concepts， necessary steps， and some key considerations in conducting systematic reviews and meta-analyses. Specifically， it offers detailed explanations on literature search， evaluation of study bias， effect size selection， outcomes pooling， and testing and correction of publication bias. Additionally， the paper provides references to softwares and literature for implementing the methods， and concludes with a brief overview of the requirements for writing research papers.
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Aristolochic acids (AAs) have long been considered as a potent carcinogen due to its nephrotoxicity. Aristolochic acid I (AAI) reacts with DNA to form covalent aristolactam (AL)-DNA adducts, leading to subsequent A to T transversion mutation, commonly referred as AA mutational signature. Previous research inferred that AAs were widely implicated in liver cancer throughout Asia. In this study, we explored whether AAs exposure was the main cause of liver cancer in the context of HBV infection in mainland China. Totally 1256 liver cancer samples were randomly retrieved from 3 medical centers and a refined bioanalytical method was used to detect AAI-DNA adducts. 5.10% of these samples could be identified as AAI positive exposure. Whole genome sequencing suggested 8.41% of 107 liver cancer patients exhibited the dominant AA mutational signature, indicating a relatively low overall AAI exposure rate. In animal models, long-term administration of AAI barely increased liver tumorigenesis in adult mice, opposite from its tumor-inducing role when subjected to infant mice. Furthermore, AAI induced dose-dependent accumulation of AA-DNA adduct in target organs in adult mice, with the most detected in kidney instead of liver. Taken together, our data indicate that AA exposure was not the major threat of liver cancer in adulthood.
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Objective@#To compare the evaluation effects of abdominal enhanced computed tomography (CT) coronal imaging versus three-dimensional (3D) vascular reconstruction for critical blood vessels in right colon cancer.@*Methods@#The retrospective and descriptive study was conducted. The clinicopathological data of 50 patients with right colon cancer who were admitted to Changhai Hospital Affiliated to Naval Medical University from January to September in 2018 were collected. There were 33 males and 17 females, aged from 33 to 86 years, with an average age of 63 years. All the 50 patients underwent abdominal multi-slice CT examination on the same CT equipment. The CT examination data were analyzed by two-dimensional (2D) coronal imaging and 3D vascular reconstruction. Observation indicators: (1) anatomical type of Henle trunk; (2) the length of Henle trunk and surgical trunk; (3) the positional relationship between ileocolic vein (ICV) and ileocolic artery (ICA). Measurement data with normal distribution were represented as Mean±SD, and count data were represented as absolute numbers. Kappa coefficients were used to measure the consistency between anatomical types of Henle trunk on 2D coronal images and on 3D vascular reconstructed images. Pearson coefficients were used to evaluate the correlation between the length of Henle trunk and surgical trunk on 2D coronal images and on 3D vascular reconstructed images. Bland-Altman method was used to assess the consistency between the length of Henle trunk and surgical trunk on 2D coronal images and on 3D vascular reconstructed images.@*Results@#(1) Anatomical type of Henle trunk: on the 2D coronal images, 43 of 50 patients had the Henle trunk and 7 had no Henle trunk. On the 3D vascular reconstructed images, 44 of 50 patients had the Henle trunk and 6 had no Henle trunk. There were 2, 21, 17, 3 patients classified as type 0, Ⅰ, Ⅱ, Ⅲ of Henle trunk on the 2D coronal images of 43 patients. There were 6, 19, 16, 3 patients classified as type 0, Ⅰ, Ⅱ, Ⅲ of Henle trunk on the 3D vascular reconstructed images of 44 patients. Six patients with no Henle trunk, 2 in type 0, 18 in type Ⅰ, 15 in type Ⅱ, and 3 in type Ⅲ had the same anatomical type of Henle trunk on the 2D and 3D images. The consistency between anatomic types of Henle trunk on 2D coronal images and on 3D vascular reconstructed images was high (κ=0.830, 95% confidence interval: 0.705-0.956, P<0.05). (2) The length of Henle trunk and surgical trunk: on the 2D coronal images, 43 of 50 patients had the length of Henle trunk as (10±5)mm, and 42 of 50 patients had the length of surgical trunk as (34±12)mm. On the 3D vascular reconstructed images, 44 of 50 patients had the length of Henle trunk as (9±5)mm, and 43 of 50 patients had the length of surgical truck as (35±12)mm. The correlation between the length of Henle trunk and surgical trunk on 2D coronal images and on 3D vascular reconstructed images was positive (r=0.872, 0.979, P<0.05). Bland-Altman plot showed a high consistency between the length of Henle trunk and surgical trunk on 2D coronal images and on 3D vascular reconstructed images (P<0.05). (3) The positional relationship between ICV and ICA: on the 2D coronal images, 24 of 50 patients had anterior crossing between ICV and ICA, 26 had posterior crossing between ICV and ICA. On the 3D vascular reconstructed images, 24 of 50 patients had anterior crossing between ICV and ICA, 26 had posterior crossing between ICV and ICA. There was a complete consistency in the positional relationship between ICV and ICA on the 2D coronal images and on 3D vascular reconstructed images.@*Conclusion@#Abdominal enhanced CT coronal imaging and 3D vascular reconstruction have the similar evaluation effects for position of critical blood vessels in right colon cancer, with a good consistency.
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Objective To compare the evaluation effects of abdominal enhanced computed tomography (CT) coronal imaging versus three-dimensional (3D) vascular reconstruction for critical blood vessels in right colon cancer.Methods The retrospective and descriptive study was conducted.The clinicopathological data of 50 patients with right colon cancer who were admitted to Changhai Hospital Affiliated to Naval Medical University from January to September in 2018 were collected.There were 33 males and 17 females,aged from 33 to 86 years,with an average age of 63 years.All the 50 patients underwent abdominal multi-slice CT examination on the same CT equipment.The CT examination data were analyzed by two-dimensional (2D) coronal imaging and 3D vascular reconstruction.Observation indicators:(1) anatomical type of Henle trunk;(2) the length of Henle trunk and surgical trunk;(3) the positional relationship between ileocolic vein (ICV) and ileocolic artery (ICA).Measurement data with normal distribution were represented as Mean±SD,and count data were represented as absolute numbers.Kappa coefficients were used to measure the consistency between anatomical types of Henle trunk on 2D coronal images and on 3D vascular reconstructed images.Pearson coefficients were used to evaluate the correlation between the length of Henle trunk and surgical trunk on 2D coronal images and on 3D vascular reconstructed images.Bland-Ahman method was used to assess the consistency between the length of Henle trunk and surgical trunk on 2D coronal images and on 3D vascular reconstructed images.Results (1) Anatomical type of Henle trunk:on the 2D coronal images,43 of 50 patients had the Henle trunk and 7 had no Henle trunk.On the 3D vascular reconstructed images,44 of 50 patients had the Henle trunk and 6 had no Henle trunk.There were 2,21,17,3 patients classified as type 0,Ⅰ,Ⅱ,Ⅲ of Henle trunk on the 2D coronal images of 43 patients.There were 6,19,16,3 patients classified as type 0,Ⅰ,Ⅱ,Ⅲ of Henle trunk on the 3D vascular reconstructed images of 44 patients.Six patients with no Henle trunk,2 in type 0,18 in type Ⅰ,15 in type Ⅱ,and 3 in type Ⅲ had the same anatomical type of Henle trunk on the 2D and 3D images.The consistency between anatomic types of Henle trunk on 2D coronal images and on 3D vascular reconstructed images was high (κ =0.830,95％ confidence interval:0.705-0.956,P＜0.05).(2) The length of Henle trunk and surgical trunk:on the 2D coronal images,43 of 50 patients had the length of Henle trunk as (10±5)mm,and 42 of 50 patients had the length of surgical trunk as (34± 12)mm.On the 3D vascular reconstructed images,44 of 50 patients had the length of Henle trunk as (9±5)mm,and 43 of 50 patients had the length of surgical truck as (35± 12)mm.The correlation between the length of Henle trunk and surgical trunk on 2D coronal images and on 3D vascular reconstructed images was positive (r=0.872,0.979,P＜0.05).Bland-Altman plot showed a high consistency between the length of Henle trunk and surgical trunk on 2D coronal images and on 3D vascular reconstructed images (P＜0.05).(3) The positional relationship between ICV and ICA:on the 2D coronal images,24 of 50 patients had anterior crossing between ICV and ICA,26 had posterior crossing between ICV and ICA.On the 3D vascular reconstructed images,24 of 50 patients had anterior crossing between ICV and ICA,26 had posterior crossing between ICV and ICA.There was a complete consistency in the positional relationship between ICV and ICA on the 2D coronal images and on 3D vascular reconstructed images.Conclusion Abdominal enhanced CT coronal imaging and 3D vascular reconstruction have the similar evaluation effects for position of critical blood vessels in right colon cancer,with a good consistency.