ObjectiveTo explore the independent factors related to clinical severe events in community acquired pneumonia patients and to find out a simple, effective and more accurate prediction method.MethodsConsecutive patients admitted to our hospital from August 2018 to July 2019 were enrolled in this retrospective study. The endpoint was the occurrence of severe events defined as a condition as follows intensive care unit admission, the need for mechanical ventilation or vasoactive drugs, or 30-day mortality during hospitalization. The patients were divided into severe event group and non-severe event group, and general clinical data were compared between two groups. Multivariate logistic regression analysis was performed to identify the independent predictors of adverse outcomes. Receiver operating characteristic (ROC) curve was constructed to calculate and compare the area under curve (AUC) of different prediction methods.ResultsA total of 410 patients were enrolled, 96 (23.4%) of whom experienced clinical severe events. Age (OR: 1.035, 95%CI: 1.012 - 1.059, P=0.003), high-density lipoprotein (OR: 0.266, 95%CI: 0.088 - 0.802, P=0.019) and lactate dehydrogenase (OR: 1.006, 95%CI: 1.004 - 1.059, P<0.001) levels on admission were independent factors associated with clinical severe events in CAP patients. The AUCs in the prediction of clinical severe events were 0.744 (95%CI: 0.699 - 0.785, P=0.028) and 0.814 (95%CI: 0.772 - 0.850, P=0.025) for CURB65 and PSI respectively. CURB65-LH, combining CURB65, HDL and LDH simultaneously, had the largest AUC of 0.843 (95%CI: 0.804 - 0.876, P=0.022) among these prediction methods and its sensitivity (69.8%) and specificity (81.5%) were higher than that of CURB65 (61.5% and 76.1%) respectively.ConclusionCURB65-LH is a simple, effective and more accurate prediction method of clinical severe events in CAP patients, which not only has higher sensitivity and specificity, but also significantly improves the predictive value when compared with CURB65.
ObjectiveTo verify the existing domestic and foreign formulas of normal predictive value indicator for adult pulmonary diffusion capacity’s applicability at current stage in Kunming.MethodsBased on the pulmonary diffusion capacity parameters determination of diffusion capacity for carbon monoxide of the lung (DLCO) collected from one-breath breathing test completed by 680 adults with healthy lung function and without any disease which may cause pulmonary diffusion dysfunctions in Kunming, the regression equation of adult DLCO normal predicted value in Kunming was initially established; the fitting degree of DLCO predicted value and measured value was verified; and the correlation between European adults (instrument-inherent ECCS93) and the normal predicted values of adult DLCO in Shanghai, Chongqing and Lhasa were calculated and contrasted.ResultsThe regression equation of adult DLCO normal predicted value in Kunming was initially established: for male, 0.483+0.063×height (cm)+0.041×weight (kg)–0.071×age (years); for female, 1.679+0.055×height (cm)+0.018×weight (kg)–0.060×age (years). The data collected from the one-breath breathing test were similar to the predicted values obtained from the normal adult male and female DLCO prediction formulas in Kunming, the difference was not statistically significant (tM=–0.167, tF=–0.436, both P>0.05), suggesting that the formula for predicting the value established in this study was valid and well fitted. The predicted value of adult DLCO in Kunming area was statistically significant compared with the adult DLCO estimates of European adults and Lhasa, Chongqing and Shanghai in China (FM=713.4, FF=1 442.2, both P<0.001). Lhasa had the highest value; Kunming was the second highest; instrument-inherent European area and Chongqing came to third and fourth; and Shanghai had the lowest predicated adult DLCO value (all P<0.001).ConclusionThe current predictive formulas for adult pulmonary diffusion capacity indicators in China and worldwide are not suitable for the populations in Kunming.
ObjectiveTo observe the relationship between ventilator-associated pneumonia (VAP) and changes in bronchial mucosa and sputum in critically ill patients. A prediction model for SEH score was developed according to the abnormal degrees of airway sputum , mucosal edema and mucosal hyperemia , as well as to analyze the diagnostic value of the SEH scores for VAP during bronchoscopy. MethodsA collection of general data and initial bronchoscopy results was conducted for patients admitted to the department of intensive care unit at West China Hospital from March 1, 2024, to July 1, 2024. Patients were divided into infection group (n=138) and non-infection group (n=227) according to diagnostic criteria for VAP based on the date of their first bronchoscopy. T-tests were used to compare baseline data between groups, while analysis of variance was employed to assess differences in airway mucosal and sputum lesions. A binary logistic regression model was constructed using the SEH scores for predicting VAP risk, with receiver operating characteristic curve area under the curve (AUC) utilized to evaluate model accuracy. ResultsA total of 365 patients were included in this study, among which 138 cases (37.8%) were diagnosed with VAP. The AUC for using SEH scores in diagnosing VAP was found to be 0.81 [95% confidence interval (CI) 0.76-0.85], with an optimal cutoff value set at 6.5. The sensitivity and specificity of SEH scores for diagnosing VAP were determined as 79.7% (95% CI: 72.2%-85.6%) and 73.1% (95% CI:67.0%-78.5%). Patients with SEH scores over 6.5 exhibited a significantly higher rate of VAP infection (64.3% vs.14.4%, P<0.0001), elevated white blood cell count levels (WBC) [(13.3±7.5 vs.1.8±6.2), P=0.04], as well as increased hospital mortality rates (39.8 % vs.24.2 %, P=0.002). ConclusionsThe SEH scores has a certain efficacy in the diagnosis of VAP in patients with mechanical ventilation. Compared with the traditional VAP diagnostic criteria, SEH scores is easier to obtain in clinical practice, and has certain clinical application value.
ObjectiveTo review individual treatment effect (ITE) models developed from randomized controlled trials, with the aim of systematically summarizing the current state of model development and assessing the risk of bias. MethodsPubMed and Embase databases were searched for studies published between 1990 and 14 June 2024. Data were extracted using the CHARMS inventory, and the PROBAST risk of bias tool was used to assess model quality. ResultsA total of 11 publications were included, containing 19 ITE models. The ITE modelling methods were regression models with interaction terms (n=8, 42.1%), dual-range models (n=5, 26.3%) and machine learning (n=6, 31.6%). The ITE models had a reporting rate of 78.9%, 73.2% and 10.5% for differentiation, calibration and clinical validity, respectively. Fourteen models were assessed as having a high risk of bias (73.7%), particularly in the area of statistical analysis, due to inappropriate handling of missing data (n=15, 78.9%), inappropriate consideration of model fit issues (n=5, 26.3%), etc. ConclusionCommon approaches to ITE model development include constructing interaction terms, dual procedure theory, and machine learning, but suffer from a low number of model developments, more complex modeling methods, and non-standardized reporting. In the future, emphasis should be placed on further exploration of ITE models, promoting diversified modeling methods and standardized reporting to improve the clinical promotion and practical application value of the models.
ObjectiveTo analyze the burden of digestive diseases attributed to smoking in China from 1990 to 2019 and forecast its change in the next 10 years. MethodsThe Global Burden of Disease database 2019 was used to analyze the burden of digestive diseases attributed to smoking in China from 1990 to 2019. Joinpoint regression model was used to analyze the time variation trend. A time series model was used to predict the burden of digestive diseases attributable to smoking over the next 10 years. ResultsIn 2019, there were 12 900 deaths from digestive diseases attributed to smoking in China, with a DALY of 398 600 years, a crude death rate of 0.91/100 000 and a crude DALY rate of 28.02/100 000. The attributed standardized mortality rate was 0.69 per 100 000, and the standardized DALY rate was 19.79 per 100 000, which was higher than the global level. In 2019, the standardized mortality rate and DALY rate of males were higher than those of females (1.48/ 100 000 vs. 0.11/ 100 000, 38.42/ 100 000 vs. 293/100 000), and the standardized rates of males and females showed a downward trend over time. In 2019, both mortality and DALY rates from digestive diseases attributed to smoking increased with age. ARIMA predicts that over the next 10 years, the burden of disease in the digestive system caused by smoking will decrease significantly. ConclusionFrom 1990 to 2019, the burden of digestive diseases attributed to smoking showed a decreasing trend in China, and the problem of disease burden is more serious in men and the elderly population. A series of effective measures should be taken to reduce the smoking rate in key groups. The burden of digestive diseases caused by smoking will be significantly reduced in the next 10 years.
ObjectiveTo systematically evaluate the risk prediction model of knee osteoarthritis (KOA). MethodsThe CNKI, WanFang Data, VIP, PubMed, Embase, Web of Science and Cochrane Library databases were electronically searched to collect relevant studies on KOA’s risk prediction model from inception to April, 2024. After study screening and data extraction by two independent researchers, the PROBAST bias risk assessment tool was used to evaluate the bias risk and applicability of the risk prediction model. ResultsA total of 12 studies involving 21 risk prediction models for KOA were included. The number of predictors ranged from 3 to 12, and the most common predictors were age, sex, and BMI. The range of modeling AUC included in the model was 0.554-0.948, and the range of testing AUC was 0.6-0.94. The overall predictive performance of the models was mediocre and the risk of overall bias was high, and more than half of the models were not externally verified. ConclusionAt present, the overall quality and applicability of the KOA morbidity risk prediction model still have great room for improvement. Future modeling should follow the CHARMS and PROBAST to reduce the risk of bias, explore the combination of multiple modeling methods, and strengthen the external verification of the model.
ObjectiveTo establish a predictive model of surgical site infection (SSI) following colorectal surgery using machine learning.MethodsMachine learning algorithm was used to analyze and model with the colorectal data set from Duke Infection Control Outreach Network Surveillance Network. The whole data set was divided into two parts, with 80% as the training data set and 20% as the testing data set. In order to improve the training effect, the whole data set was divided into two parts again, with 90% as the training data set and 10% as the testing data set. The predictive result of the model was compared with the actual infected cases, and the sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated, the area under receiver operating characteristic (ROC) curve was used to evaluate the predictive capacity of the model, odds ratio (OR) was calculated to tested the validity of evaluation with a significance level of 0.05.ResultsThere were 7 285 patients in the whole data set registered from January 15th, 2015 to June 16th, 2016, among whom 234 were SSI cases, with an incidence of SSI of 3.21%. The predictive model was established by random forest algorithm, which was trained by 90% of the whole data set and tested by 10% of that. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 76.9%, 59.2%, 3.3%, and 99.3%, respectively, and the area under ROC curve was 0.767 [OR=4.84, 95% confidence interval (1.32, 17.74), P=0.02].ConclusionThe predictive model of SSI following colorectal surgery established by random forest algorithm has the potential to realize semi-automatic monitoring of SSIs, but more data training should be needed to improve the predictive capacity of the model before clinical application.
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.
ObjectiveTo establish a forecasting model for inpatient cases of pediatric limb fractures and predict the trend of its variation.MethodsAccording to inpatient cases of pediatric limb fractures from January 2013 to December 2018, this paper analyzed its characteristics and established the seasonal auto-regressive integrated moving average (SARIMA) model to make a short-term quantitative forecast.ResultsA total of 4 451 patients, involving 2 861 males and 1 590 females were included. The ratio of males to females was 1.8 to 1, and the average age was 5.655. There was a significant difference in age distribution between males and females (χ2=44.363, P<0.001). The inpatient cases of pediatric limb fractures were recorded monthly, with predominant peak annually, from April to June and September to October, respectively. Using the data of the training set from January 2013 to May 2018, a SARIMA model of SARIMA (0,1,1)(0,1,1)12 model (white noise test, P>0.05) was identified to make short-term forecast for the prediction set from June 2018 to November 2018, with RMSE=8.110, MAPE=9.386, and the relative error between the predicted value and the actual value ranged from 1.61% to 8.06%.ConclusionsCompared with the actual cases, the SARIMA model fits well with good short-term prediction accuracy, and it can help provide reliable data support for a scientific forecast for the inpatient cases of pediatric limb fractures.
Diffuse choroidal retinal atrophy (DCA) is a type of myopic macular disease that presents with yellowish-white atrophic changes at the posterior pole of the eyeball. DCA is an important critical feature in the diagnosis of pathological myopia. Early intervention and treatment of this disease are of great significance in delaying the progression of pathological myopia and reducing the impairment of visual function. Ophthalmic imaging data can be used to diagnose the disease, and color fundus photography is the most simple and intuitive. Choroidal thickness is also a key indicator in the diagnosis of DCA, but the diagnostic critical value of choroidal thickness has not been clearly defined. With the development and popularization of artificial intelligence technology, the analysis of lesion imaging data is more objective and accurate. In the future, it is expected to actively establish a standard quantitative evaluation system for DCA by means of artificial intelligence to achieve early detection, early diagnosis and early treatment of pathological myopia.