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 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.
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.
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
Objective To observe the correlation between the level of advanced glycosylation end products (AGE) in skin and diabetic retinopathy (DR), and establish and preliminatively verify the nomogramolumbaric model for predicting the risk of DR. MethodsA clinical case-control study. A total of 346 patients with type 2 diabetes mellitus (T2DM) who were admitted to the Department of Endocrinology and Ophthalmology of the First Affiliated Hospital of Zhengzhou University from January 2023 to June 2024 were included in the study. Among them, 198 were males and 148 were females. The mean age was (54.77±10.92). According to whether the patients were accompanied by DR, the patients were divided into the non-DR group (NDR group) and the DR group (DR group), 174 and 172 cases, respectively. All patients underwent skin AGE detection using a noninvasive diabetes detector. Diabetes duration, hemoglobin A1c (HbA1c), fasting plasma glucose, Urea, creatinine (Crea), uric acid, total cholesterol, triglyceride, estimated glomerular filtration rate (eGFR), urinary albumin concentration (UALB), and body mass index (BMI) were collected in detail. Univariate analysis and multivariate logistic regression analysis were used to determine the independent risk factors for T2DM concurrent DR, and to construct a nomogram prediction model for DR risk. Receiver operating characteristic curve (ROC curve), calibration curve and decision curve (DCA) were used to evaluate the model. ResultsHypertension prevalence rate (χ2=3.892), Diabetes duration (Z=?7.708), BMI (Z=?2.627), HbA1c (Z=?4.484), Urea (Z=?4.620), Crea (Z=?3.526), UALB (Z=?6.999), AGE (Z=?8.097) in DR group were significantly higher than those in NDR group, with statistical significance (P＜0.05); eGFR was lower than that in NDR group, the difference was statistically significant (Z=?6.061, P＜0.05). Logistic regression analysis showed that AGE, diabetes duration, HbA1c, UALB and eGFR were independent risk factors for DR (P＜0.05). Based on the results of multi-factor regression analysis, a nomogram prediction model was constructed. The area under ROC curve of the model was 0.843, 95% confidence interval was 0.802-0.884, sensitivity and specificity were 79.1% and 75.9%, respectively. The calibration curve was basically consistent with the ideal curve. The results of DCA analysis showed that when the model predicted the risk threshold of patients with DR between 0.17 and 0.99, the clinical net benefit provided by the nomogram model was＞0. ConclusionsSkin AGE level is an independent risk factor for DR. The nomogram prediction model based on AGE, diabetes duration, HbA1c, eGFR and UALB can accurately predict the risk of DR, and has good clinical practicability.
ObjectiveTo investigate the predictive factors of portal vein thrombosis (PVT) before and after splenectomy and gastroesophageal devascularization for liver cirrhosis with portal hypertension. MethodsSixty-one cases of liver cirrhosis with portal hypertension who underwent splenectomy and gastroesophageal devascularization were enrolled retrospectively. The patients were divided into PVT group and non-PVT group based on the presence or absence of postoperative PVT on day 7. The clinical factors related with PVT were analyzed. ResultsThere were 25 cases in the DVT group and 36 cases in the non-DVT group. The results of univariate analysis showed that the preoperative platelet (P=0.006), activated partial thromboplastin time (P=0.048), prothrombin time (P=0.028), and international normalized ratio (P=0.029), postoperative fibrin degradation product (P=0.002) and D-dimer (P=0.014) on day 1, portal venous diameter (P=0.050) had significant differences between the DVT group and non-DVT group. The results of logistic multivariate regression analysis showed that the preoperative platelet (OR=0.966, 95% CI 0.934-1.000, P=0.048) and postoperative fibrin degradation product on day 1(OR=1.055, 95% CI 1.011-1.103, P=0.017) were correlated with the PVT. The PVT might happen when preoperative platelet was less than 34.5×109/L (sensitibity 80.6%, specificity 60.0%) or postoperative fibrin degradation product on day 1 was more than 64.75 mg/L (sensitibity 48.0%, specificity 91.7%). ConclusionPreoperative platelet and postoperative fibrin degradation product on day 1 might predict PVT after splenectomy and gastroesophageal devascularization for liver cirrhosis with portal hypertension.
Objective To investigate the differences between six parts of China in prediction models of lung function.Methods The predicted values of 360 healthy volunteers underwent pulmonary functiontest in east China were compared with that of north China, northeast China, northwest China, southwest China, south China and Asian American. Results In the male group, the prediction values of east China were as follows: VC ( 4. 19 ±0. 33) L, coefficient correlation( r) to the other five regions were 0. 803-0. 983,the differences to the other five regions were 1. 90% -4. 30% ; FVC ( 4. 06 ±0. 35) L, r to the others were 0. 912-0. 981, the differences to the other four regions were 0-2. 46% except for south China of 8. 10% , to Asian American ×0. 88 and Asian American ×0. 94 were 1. 97% and 4. 68% ; FEV1 ( 3. 34 ±0. 40) L, r to the others were 0. 963-0. 992, the differences to north China and east China were 0. 9% and 3. 59% , to southwest China and south China were gt;5% , to Asian American ×0. 88 and Asian American×0. 94 were 6. 89% and 0. 6% ; FEV1 /FVC ( 80. 87 ±3. 84) % , r to the others were 0. 989-0. 999, the differences to north China, northeast China and south China were 0. 42% -3. 04% , to the others were gt;5% . In the female group, the prediction values of east China were as follows: VC ( 3. 00 ±0. 33) L, r to the other five regions were 0. 899-0. 993, the differences to the other five regions were 0. 33% -3. 67% ; FVC( 2. 92 ±0. 34) L, r to the other five regions were 0. 929-0. 990, the differences to the other five regions were 1. 02% -2. 40%, to Asian American ×0. 88 and Asian American ×0. 94 were 4. 79% and 6. 16% ; FEV1 ( 2. 38 ±0. 39) L, r to the other five regions were 0. 958-0. 994, the differences to northeast China, southwest China, south China were 0. 84% -3. 36% , to north china was 7. 19% , to Asian American ×0. 88 and Asian American ×0. 94 were 2. 10% and 4. 62% ; FEV1 /FVC ( 82. 04 ±3. 94) % , r to the others were 0. 991-1. 000, the differences to the other four regions were 1. 34% -4. 55% except for southwest China was 7. 59% . Conclusions VC and FVC predicted values are coincident between six parts of China. FEV1 /FVC predicted values are coincident in the four parts of China except for westnorth and westsouth China. FEV1 predicted values are different. The oversea predicted values should be used with appropriate conversion factors.
ObjectiveTo systematically review the early clinical prediction value of machine learning (ML) for cardiac arrest (CA).MethodsPubMed, EMbase, WanFang Data and CNKI databases were electronically searched to retrieve all ML studies on predicting CA from January 2015 to February 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. The value of each model was evaluated based on the area under receiver operating characteristic curve (AUC) and accuracy.ResultsA total of 38 studies were included. In terms of data sources, 13 studies were based on public database, and other studies retrospectively collected clinical data, in which 21 directly predicted CA, 3 predicted CA-related arrhythmias, and 9 predicted sudden cardiac death. A total of 51 models had been adopted, among which the most popular ML methods included artificial neural network (n=11), followed by random forest (n=9) and support vector machine (n=5). The most frequently used input feature was electrocardiogram parameters (n=20), followed by age (n=12) and heart rate variability (n=10). Six studies compared the ML models with other traditional statistical models and the results showed that the AUC value of ML was generally higher than that in traditional statistical models.ConclusionsThe available evidence suggests that ML can accurately predict the occurrence of CA, and the performance is significantly superior to traditional statistical model in certain cases.