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.
ObjectivesTo explore a reliable and simple predictive tool for 30-day mortality of influenza A community-acquired pneumonia (CAP).MethodsA multicenter retrospective study was conducted on 178 patients hospitalized with influenza A CAP, including 144 alive patients and 34 dead patients. Receiver operating characteristic (ROC) curves were performed to verify the accuracy of severity scores as 30-day mortality predictors in the study patients.ResultsThe 30-day mortality of influenza A CAP was 19.1%. The actual mortality of PSI risk class Ⅰ-Ⅱ and CURB-65 score 0-1 were 14.5% and 15.7%, respectively, which were much higher than the predicted mortality. Logistic regression confirmed blood urea nitrogen >7 mmol/L (U), albumin <35 g/L (A) and peripheral blood lymphocyte count <0.7×10 9/L (L) were independent risk factors for 30-day mortality of influenza A CAP. The area under the ROC curve (AUC) of UAL (blood urea nitrogen >7 mmol/L+ albumin <35 g/L+ peripheral blood lymphocyte count <0.7×10 9/L) was 0.891, which was higher than CURB-65 score (AUC=0.777, P=0.008 3), CRB-65 score (AUC=0.590, P<0.000 1), and PSI risk class (AUC=0.568,P=0.000 1).ConclusionUAL is a reliable and simple predictive tool for 30-day mortality of influenza A CAP.
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 evaluate the predictors of generalized anxiety disorder (GAD) among teachers in 3 months after Lushan earthquake.
MethodsA prospective cohort study was conducted to diagnostically evaluate the psychological sequelae and GAD during 14-20 days and 85-95 days after the earthquake. The possible predictive factors of psychological sequelae were assessed by a self-made questionnaire and the GAD was assessed by the GAD symptom criterion of M.I.N.I. in 3 months. The univariate and multivariate logistic regression analysis (ULRA, MLRA) were applied to analyze the predictors of GAD after the two-staged assessments.
ResultsThere were a total of 319 teachers completed the two-staged assessments. The total response rate was 51.3%. Seventy teachers were diagnosed as GAD and the prevalence of GAD in 3 months was 21.9%. The predictive factors by ULRA included:male, older than 35 years old, having unlivable house, living in tents, sleeping difficulties, easy to feel sad, physical discomfort, loss of appetite, feeling short of social support, unable to calm down for working, feeling difficult for teaching, observing more inattention of students, and wanting to ask for a leave. The independent predictors by MLRA included:male, having unlivable house, feeling short of social support, and feeling difficult for teaching.
ConclusionThe teachers have a higher likelihood of GAD after earthquake. It is essential to pay more attention to those male teachers, who feel short of social support and don't have a livable house thus to prevent the GAD at the early stage of post-earthquake.
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.
ObjectiveThis study intends to analyze the changing disease burden of mood disorders in China from 1990 to 2021 and project the epidemiological trends in the next two decades. MethodsThis study uses data from the Global Burden of Disease (GBD) 2021 database on three mood disorders in China (bipolar disorder, major depressive disorder, and dysthymia) from 1990 to 2021. The indicators such as age-standardized number of diseases and disability-adjusted life years (DALYs) were used to explore the characteristics of time, gender, and age distribution of the disease burden of mental disorders. The BAPC model was used to predict the disease burden in the next two decades. ResultsIn 2021, the number of cases of dysthymia, MDD, and BD in China was 27.84 million, 26.0 million, and 2.85 million, with an increase of 73.24%, 38.33%, and 36.79% compared with 1990, respectively. In 2021, DALYs of dysthymic disorder, MDD and BD were 2.67 million, 5.2 million and 0.61 million person-years, which increased by 71.45%, 34.29% and 34.76% compared with 1990, respectively. The burden of mood disorders is heavier among women and the middle-aged and elderly population. In addition, it is expected that ASPR and ASDR of dysthymia will continue to increase after a brief decline, MDD will show a downward trend, while BD will show a slight upward trend in the next two decades. ConclusionThe disease burden of mood disorders in China remains substantial, with dysthymia and BD showing persistent upward tendency. More resources should be invested in mental health care.
ObjectiveTo develop a score system to predict the probability of failure of monotherapy in epilepsy patients with initial treatment, and then provide pillars for early use of polytherapy.MethodsThis is a retrospective analysis of the clinical data of 189 patients with epilepsy treated in Department of Neurology, the Third Xiangya Hospital of Central South University from January 2019 to July 2020. Patients were divided into monotherapy acceptable group and monotherapy poor effect group according to their drug treatment plan and drug efficacy. The influencing factors were screened out by single factor analysis and binary logistic regression analysis. And on the basis of this β value, a quantitative scoring table for predicting the unsatisfying treatment effect of monotherapy is developed. And the receiver operating curve (ROC curve) was used to evaluate the effectiveness of the scale.ResultsBased on a standard of 75% reduction in seizures during the observation period, 138 cases (73%) were effective with monotherapy plan, while 51 cases (23%) were unsatisfactory. Regression analysis showed that multiple forms of seizures, status epilepticus (t2), brain damage, and the number of seizures ≥ 7 times before treatment are independent risk factors for poor outcome of monotherapy. The resulting score sheet has a total score of 12 points; the area under the ROC curve is 0.779, and the critical score is 6 points (sensitivity: 0.314; specificity: 0.957). Patients with more than this score have a strong probability of poor response in monotherapy.ConclusionThis prediction model can effectively assess the risk of unsatisfactory therapeutic effect of monotherapy in epilepsy patients who are initially treated, and thus has reference function for the early selection of polytherapy.
ObjectiveTo construct and verify the nomogram prediction model of pregnant women's fear of childbirth. MethodsA convenient sampling method was used to select 675 pregnant women in tertiary hospital in Tangshan City, Hebei Province from July to September 2022 as the modeling group, and 290 pregnant women in secondary hospital in Tangshan City from October to December 2022 as the verification group. The risk factors were determined by logistic regression analysis, and the nomogram was drawn by R 4.1.2 software. ResultsSix predictors were entered into the model: prenatal education, education level, depression, pregnancy complications, anxiety and preference for delivery mode. The areas under the ROC curves of the modeling group and the verification group were 0.834 and 0.806, respectively. The optimal critical values were 0.113 and 0.200, respectively, with sensitivities of 67.2% and 77.1%, the specificities were 87.3% and 74.0%, and the Jordan indices were 0.545 and 0.511, respectively. The calibration charts of the modeling group and the verification group showed that the coincidence degree between the actual curve and the ideal curve was good. The results of Hosmer-Lemeshow goodness of fit test were χ2=6.541 (P=0.685) and χ2=5.797 (P=0.760), and Brier scores were 0.096 and 0.117, respectively. DCA in modeling group and verification group showed that when the threshold probability of fear of childbirth were 0.00 to 0.70 and 0.00 to 0.70, it had clinical practical value. ConclusionThe nomogram model has good discrimination, calibration and clinical applicability, which can effectively predict the risk of pregnant women's fear of childbirth and provide references for early clinical identification of high-risk pregnant women and targeted intervention.
ObjectiveConstructing a prediction model for seizures after stroke, and exploring the risk factors that lead to seizures after stroke. MethodsA retrospective analysis was conducted on 1 741 patients with stroke admitted to People's Hospital of Zhongjiang from July 2020 to September 2022 who met the inclusion and exclusion criteria. These patients were followed up for one year after the occurrence of stroke to observe whether they experienced seizures. Patient data such as gender, age, diagnosis, National Institute of Health Stroke Scale (NIHSS) score, Activity of daily living (ADL) score, laboratory tests, and imaging examination data were recorded. Taking the occurrence of seizures as the outcome, an analysis was conducted on the above data. The Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen predictive variables, and multivariate Logistic regression analysis was performed. Subsequently, the data were randomly divided into a training set and a validation set in a 7:3 ratio. Construct prediction model, calculate the C-index, draw nomogram, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the model's performance and clinical application value. ResultsThrough LASSO regression, nine non-zero coefficient predictive variables were identified: NIHSS score, homocysteine (Hcy), aspartate aminotransferase (AST), platelet count, hyperuricemia, hyponatremia, frontal lobe lesions, temporal lobe lesions, and pons lesions. Multivariate logistic regression analysis revealed that NIHSS score, Hcy, hyperuricemia, hyponatremia, and pons lesions were positively correlated with seizures after stroke, while AST and platelet count were negatively correlated with seizures after stroke. A nomogram for predicting seizures after stroke was established. The C-index of the training set and validation set were 0.854 [95%CI (0.841, 0.947)] and 0.838 [95%CI (0.800, 0.988)], respectively. The areas under the ROC curves were 0.842 [95%CI (0.777, 0.899)] and 0.829 [95%CI (0.694, 0.936)] respectively. Conclusion These nine variables can be used to predict seizures after stroke, and they provide new insights into its risk factors.
Objective To evaluate the predicted value of APACHEⅡ score at admission for deep fungal infection（DFI） in patients with severe acute pancreatitis （SAP）.Methods The clinical data of 132 patients with SAP from January 2006 to June 2011 in our hospital were analyzed retrospectively. The receiver operating characteristic curve （ROC） was used for evaluating the predicted value.Results Thirty-nine patients with SAP infected DFI （29.5%）,of which 36 patients （92.3%） infected with Candida albicans,2 patients （5.1%） with Candida tropicalis,1 patient （2.6%） with pearl bacteria.And,among these 39 patients,27 patients （69.2%） infected at single site,12 patients （30.8%） infected at multi-site. The APACHEⅡ score in 39 patients with DFI was higher than that of 93 patients without DFI （17.1±3.8 versus 9.7±2.1, t=14.316,P=0.000）.The ROC for APACHEⅡ score predicting DFI was 0.745（P=0.000）, 95%CI was 0.641－0.849.When the cut off point was 15,it showed the best forecast performance,with specificity 0.81, sensitivity 0.72,Youden index 0.53. Conclusions The APACHEⅡ score at admission can preferably predict DFI in patients with SAP； when the APACHEⅡ score is greater than 15,it prompts highly possible of DFI,so preventive anti-fungal treatment may be necessary.
