Objective To explore the change of serum levels of neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinases-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP-7) in the early stage of multiple trauma, and their predictive efficacy for acute kidney injury (AKI). Methods The multiple trauma patients admitted between February 2020 and July 2021 were prospectively selected, and they were divided into AKI group and non-AKI group according to whether they developed AKI within 72 h after injury. The serum levels of NGAL, TIMP-2, and IGFBP-7 measured at admission and 12, 24, and 48 h after injury, the Acute Pathophysiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ) score, intensive care unit duration, rate of renal replacement therapy, and 28-day mortality rate were compared between the two groups. Results A total of 51 patients were included, including 20 in the AKI group and 31 in the non-AKI group. The APACHE Ⅱ at admission (20.60±3.57 vs. 11.61±3.44), intensive care unit duration [(16.75±2.71) vs. (11.13±3.41) d], rate of renal replacement therapy (35.0% vs. 0.0%), and 28-day mortality rate (25.0% vs. 3.2%) in the AKI group were higher than those in the non-AKI group (P<0.05). The serum levels of NGAL and IGFBP-7 at admission and 12, 24, and 48 h after injury in the AKI group were all higher than those in the non-AKI group (P<0.05). For the prediction of AKI, the areas under receiver operating characteristic curves and 95% confidence intervals of serum NGAL, TIMP-2 and IGFBP-7 12 h after injury were 0.98 (0.96, 1.00), 0.92 (0.83, 1.00), and 0.87 (0.78, 0.97), respectively. Conclusion Serum NGAL, TIMP-2, and IGFBP-7 have high predictive efficacy for AKI secondary to multiple trauma, and continuous monitoring of serum NGAL can be used for early prediction of AKI secondary to multiple trauma.
ObjectiveTo evaluate the predictive value of the diaphragm ultrasound for weaning from mechanical ventilation.MethodsThe patients who received mechanical ventilation in Fujian Provincial Hospital between February 2016 to December 2017 and met the criteria for a T-tube spontaneous breathing trial were included in the study. Then right diaphragmatic displacement (DD) and diaphragmatic thickening fraction (DTF) were evaluated using M-mode ultrasonography as well as the rapid shallow breathing index (RSBI, the ratio of respiratory rate to tidal volume). A new index was named as the diaphragmatic-RSBI (D-RSBI, the ratio of respiratory rate to DD). The patients were classified into a success group or a failure group according to the weaning outcomes. The receiver operating characteristic (ROC) curves were calculated to evaluate the predictive performance of each index.ResultsFifty-nine patients were weaned successfully and failure of weaning was found in 29 patients. There were no statistically significant differences in pre-weaning parameters including age, sex, systolic blood pressure, diastolic blood pressure, blood lipid index (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglyceride), or fast blood glucose between the weaning success group and the weaning failure group (P>0.05), but there were statistically significant differences in body mass index and acute physiology and chronic health condition Ⅱ score between two groups (P<0.05). DD [(13.44±3.23)mm vs. (10.28±2.82)mm, DTF [(32.43±12.35)% vs. (27.64±5.77)%, P<0.05] and D-RSBI [(1.49±0.47) breaths·min–1·mm–1 vs. (2.55±0.87) breaths·min–1·mm–1, P<0.05] differed significantly between the weaning success group and the weaning failure group. A cutoff of DTF≥27.9% yielded a sensitivity of 98.3%, a specificity of 62.1%, and an area under the ROC curve (AUC) of 0.873. A cutoff of D-RSBI≤1.73 breaths·min–1·mm–1 yielded a sensitivity of 76.3%, a specificity of 93.1%, and an AUC of 0.887. By comparison, when RSBI was ≤50.9 breaths·min–1·mm–1, there was a sensitivity of 91.5%, a specificity of 86.2%, and an AUC of 0.927. There was no statistically significant difference in AUC between D-RSBI and RSBI (P>0.05).ConclusionsDiaphragm ultrasound is feasible to predict the outcome of weaning. DTF and D-RSBI are as same accurate as the traditional RSBI in predicting the weaning outcome, but more objective and suitable for clinical application.
ObjectiveTo investigate the value of plasma microRNA-216 (miR-216) in patients with acute pancreatitis as a clinical biomarker to early identify severe acute pancreatitis (SAP).MethodsPatients with acute pancreatitis who admitted to the hospital within 48 hours after the onset of disease between September and November 2014 were enrolled in this study. Plasam and clinical data of all the patients were collected. MiR-216 in the plasma was detected using quantitative real time-polymerase chain reaction.ResultsA total of 25 patients were enrolled. The Ct value of plasma miR-216 in SAP patients (32.40±1.43) was significantly upregulated than mild acute pancreatitis (MAP) (35.85±1.91, P<0.05) and moderately severe acute pancreatitis (MSAP) patients (35.90±2.44,P<0.05), respectively. The area under receiver operating characteristic curve for plasmamiR-216 in predicting SAP was 0.792 (P<0.05), which did not differ much from other conventional parameters such as C-reactive protein, urinary nitrogen, and cytokines (P>0.05).ConclusionPlasma miR-216 is significantly upregulated in SAP patients compared with MAP and MSAP, but it shows no inferior efficiency than the investigated conventional predictors in predicting SAP.
ObjectiveTo predict the total hospitalization expenses of bronchopneumonia inpatients in a tertiay hospital of Sichuan Province through BP neural network and support vector machine models, and analyze the influencing factors.MethodsThe home page information of 749 cases of bronchopneumonia discharged from a tertiay hospital of Sichuan Province in 2017 was collected and compiled. The BP neural network model and the support vector machine model were simulated by SPSS 20.0 and Clementine softwares respectively to predict the total hospitalization expenses and analyze the influencing factors.ResultsThe accuracy rate of the BP neural network model in predicting the total hospitalization expenses was 81.2%, and the top three influencing factors and their importances were length of hospital stay (0.477), age (0.154), and discharge department (0.083). The accuracy rate of the support vector machine model in predicting the total hospitalization expenses was 93.4%, and the top three influencing factors and their importances were length of hospital stay (0.215), age (0.196), and marital status (0.172), but after stratified analysis by Mantel-Haenszel method, the correlation between marital status and total hospitalization expenses was not statistically significant (χ2=0.137, P=0.711).ConclusionsThe BP neural network model and the support vector machine model can be applied to predicting the total hospitalization expenses and analyzing the influencing factors of patients with bronchopneumonia. In this study, the prediction effect of the support vector machine is better than that of the BP neural network model. Length of hospital stay is an important influencing factor of total hospitalization expenses of bronchopneumonia patients, so shortening the length of hospital stay can significantly lighten the economic burden of these patients.
ObjectiveTo systematically review the risk prediction models for the occurrence of low anterior resection syndrome in patients with rectal cancer after surgery. MethodsThe PubMed, Web of Science, Embase, Cochrane Library, Scopus, CINHAL, CNKI, CBM, WanFang Data and VIP databases were electronically searched to collect studies related to the objectives from inception to June 13, 2023. Two reviewers independently screened the literature, extracted data using the critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) checklist, and assessed quality of the included studies using prediction model risk of bias assessment tool (PROBAST). ResultsA total of 14 studies were included, all studies reported model discrimination, and 10 studies reported calibration. The models were internally validated in 8 studies, externally validated in 5 studies. The most common predictors included in the models were tumour distance from the anal verge, neoadjuvant therapy, anastomotic leak and BMI. Only 5 studies had good overall applicability, and all studies had a high risk of bias, with the risk of bias mainly stemming from the field of participants, outcomes and analysis. ConclusionThere are still many shortcomings in the risk prediction models for the occurrence of low anterior resection syndrome in patients with rectal cancer after surgery. Future studies may consider external validation and recalibration of existing models. New prediction models should be built and validated according to methodological guidelines.
ObjectivesTo estimate the latest burden of disability adjusted life years (DALYs) for liver cancer in China and the long-term trend, and to make future prediction.MethodsBased on the visualization platform of Global Burden of Disease 2016, data on the DALYs for liver cancer in China was extracted. The very recent status in 2016 and the previous trend from 1990 to 2016 were described, using annualized rate of change (ARC). The burden from 2017 to 2050 was further predicted by combining the ARC and the Chinese population data projected by the United Nation.ResultsIn 2016, the total DALYs for liver cancer in China was estimated as 11 539 000 person years (accounting for 54.6% of the global burden), and years of life lost (YLLs) and years lived with disability (YLDs) contributed 98.9% and 1.1%, respectively. The age-standardized DALY rate was 844.1 per 100 000 (3.0 times of the global average) and the male-to-female ratio was 3.4. The DALY rate continuously increased from 1990–2016 (ARC=0.57%), particularly in recent 5 years (ARC=1.75%). Among the DALYs for all cancers, liver cancer contributed approximately 20% and constantly remained as the top 2 (ranking as the number one before year 2005). There were inverse trends in gender, with increasing in males and decreasing in females (ARC was 0.77% and –0.11%, respectively). Hepatitis B infection continually kept the leading cause of DALYs for liver cancer (accounting for nearly 57%), and the DALY rate was gradually increasing (ARC=0.43%). Although the peak age of DALY rate was stable at 65to 69 years, the peak age of the DALYs changed from 55 to 59 years in 1990 to 60 ~ 64 years in 2016. In 2050, the estimated DALYs for liver cancer in China will reach 14.37 million person years, 20.0% more than that in 2017.ConclusionsThe DALYs caused by liver cancer in China exceeds the overall burden of all other countries in the world, and accounts for 1/5 of DALYs for all cancers in local population. The burden in males has been continuously rising, and the leading cause remained unchanged as hepatitis B infection. With population aging, the DALYs for liver cancer in China will be incessant to increase, suggesting the necessity to implement continuous effort in risk factors prevention (e.g. hepatitis B infection), and efficient management in high risk population of liver cancer.
Objective To systematically review the performance of postpartum hemorrhage risk prediction models, and to provide references for the future construction and application of effective prediction models. Methods The CNKI, WanFang Data, VIP, CBM, PubMed, EMbase, The Cochrane Library, Web of Science, and CINAHL databases were electronically searched to identify studies reporting risk prediction models for postpartum hemorrhage from database inception to March 20th, 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies. Results A total of 39 studies containing 58 postpartum hemorrhage risk prediction models were enrolled. The area under the curve of 49 models was over 0.7. All but one of the models had a high risk of bias. Conclusion Models for predicting postpartum hemorrhage risk have good predictive performance. Given the lack of internal and external validation, and the differences in study subjects and outcome indicators, the clinical value of the models needs to be further verified. Prospective cohort studies should be conducted using uniform predictor assessment methods and outcome indicators to develop effective prediction models that can be applied to a wider range of populations.
Clinical prediction models typically utilize a combination of multiple variables to predict individual health outcomes. However, multiple prediction models for the same outcome often exist, making it challenging to determine the suitable model for guiding clinical practice. In recent years, an increasing number of studies have evaluated and summarized prediction models using the systematic review/meta-analysis method. However, they often report poorly on critical information. To enhance the reporting quality of systematic reviews/meta-analyses of prediction models, foreign scholars published the TRIPOD-SRMA reporting guideline in BMJ in March 2023. As the number of such systematic reviews/meta-analyses is increasing rapidly domestically, this paper interprets the reporting guideline with a published example. This study aims to assist domestic scholars in better understanding and applying this reporting guideline, ultimately improving the overall quality of relevant research.
ObjectiveTo systematically review mortality risk prediction models for acute type A aortic dissection (AAAD). MethodsPubMed, EMbase, Web of Science, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect studies of mortality risk prediction models for AAAD from inception to July 31th, 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Systematic review was then performed. ResultsA total of 19 studies were included, of which 15 developed prediction models. The performance of prediction models varied substantially (AUC were 0.56 to 0.92). Only 6 studies reported calibration statistics, and all models had high risk of bias. ConclusionsCurrent prediction models for mortality and prognosis of AAAD patients are suboptimal, and the performance of the models varies significantly. It is still essential to establish novel prediction models based on more comprehensive and accurate statistical methods, and to conduct internal and a large number of external validations.
Objective
To explore the predictive factors for long-term adverse prognosis in patients with tuberculosis meningitis.
Methods
We retrospectively analyzed the clinical data (general clinical data, laboratory test results, and imaging findings) of hospitalized cases of tuberculosis meningitis admitted to West China Hospital of Sichuan University from 00:00:00 on August 1st, 2011 to 23:59:59 on July 31st, 2012. We collected data of prognosis results after 6 years of illness by telephone follow-up, and quantified outcome measures by modified Rankin Scale (mRS) score (0–6 points). According to the mRS score, the cases obtaining 0 points≤mRS<3 points were divided into the good prognosis group and the cases obtaining 3≤mRS≤6 points were divided into the poor prognosis group, logistic regression analysis was executed to find the independent risk factors affecting long-term poor prognosis.
Results
A total of 119 cases were included, including 63 males and 56 females; the average age was (35±17) years. Among them, 53 patients had poor prognosis and 66 patients had good prognosis. After univariate analysis, the age (t=–3.812, P<0.001), systolic blood pressure at admission (t=–2.009, P=0.049), Glasgow Coma Scale score (t=3.987, P<0.001), Medical Research Council (MRC) staging system (Z=–4.854, P<0.001), headache (χ2=4.101, P=0.043), alter consciousness (χ2=10.621, P=0.001), cognitive dysfunction (χ2=4.075, P=0.044), cranial nerve palsy (χ2=5.853, P=0.016), peripheral nerve dysfunction (χ2=14.925, P<0.001), meningeal irritation (χ2=7.174, P=0.007), serum potassium (t=3.080, P=0.003), cerebrospinal fluid protein content (Z=–2.568, P=0.010), cerebrospinal fluid chlorine (t=2.543, P=0.012), hydrocephalus (χ2=11.766, P=0.001), and cerebral infarction (χ2=6.539, P=0.012) were associated with long-term poor prognosis of tuberculosis meningitis. Multivariate analysis showed that age [odds ratio (OR)=1.061, 95% confidence interval (CI) (1.027, 1.096), P<0.001], peripheral nerve dysfunction [OR=3.537, 95%CI (1.070, 11.697), P=0.038], MRC Stage Ⅱ[OR=9.317, 95%CI (1.692, 51.303), P=0.010], MRC Stage Ⅲ [OR=43.953, 95%CI (3.996, 483.398), P=0.002] were the independent risk factors for long-term poor prognosis of tuberculosis meningitis. Hydrocephalus [OR=2.826, 95%CI (0.999, 8.200), P=0.050] might be an independent risk factor for long-term poor prognosis of tuberculosis meningitis.
Conclusions
Age, MRC staging system (Stage Ⅱ, Stage Ⅲ) and peripheral neurological dysfunction are chronic poor-prognostic independent risk factors for tuberculosis meningitis. Hydrocephalus may be associated with long-term adverse prognosis of tuberculosis meningitis
