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ABSTRACT Introduction: The aim of this study was to assess the impact of aortic angulation (AA) on periprocedural and in-hospital complications as well as mortality of patients undergoing Evolut™ R valve implantation. Methods: A retrospective study was conducted on 264 patients who underwent transfemoral-approach transcatheter aortic valve replacement with self-expandable valve at our hospital between August 2015 and August 2022. These patients underwent multislice computer tomography scans to evaluate AA. Transcatheter aortic valve replacement endpoints, device success, and clinical events were assessed according to the definitions provided by the Valve Academic Research Consortium-3. Cumulative events included paravalvular leak, permanent pacemaker implantation, new-onset stroke, and in-hospital mortality. Patients were divided into two groups, AA ≤ 48° and AA > 48°, based on the mean AA measurement (48.3±8.8) on multislice computer tomography. Results: Multivariable logistic regression analysis was performed to identify predictors of cumulative events, utilizing variables with a P-value < 0.2 obtained from univariable logistic regression analysis, including AA, age, hypertension, chronic renal failure, and heart failure. AA (odds ratio [OR]: 1.73, 95% confidence interval [CI]: 0.89-3.38, P=0.104), age (OR: 1.04, 95% CI: 0.99-1.10, P=0.099), hypertension (OR: 1.66, 95% CI: 0.82-3.33, P=0.155), chronic renal failure (OR: 1.82, 95% CI: 0.92-3.61, P=0.084), and heart failure (OR: 0.57, 95% CI: 0.27-1.21, P=0.145) were not found to be significantly associated with cumulative events in the multivariable logistic regression analysis. Conclusion: This study demonstrated that increased AA does not have a significant impact on intraprocedural and periprocedural complications of patients with new generation self-expandable valves implanted.
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@#Objective To evaluate the risk factors for postoperative in-hospital mortality in elderly patients receiving cardiac valvular surgery, and develop a new prediction models using the least absolute shrinkage and selection operator (LASSO)-logistic regression. Methods The patients≥65 years who underwent cardiac valvular surgery from 2016 to 2018 were collected from the Chinese Cardiac Surgery Registry (CCSR). The patients who received the surgery from January 2016 to June 2018 were allocated to a training set, and the patients who received the surgery from July to December 2018 were allocated to a testing set. The risk factors for postoperative mortality were analyzed and a LASSO-logistic regression prediction model was developed and compared with the EuroSCOREⅡ. Results A total of 7 163 patients were collected in this study, including 3 939 males and 3 224 females, with a mean age of 69.8±4.5 years. There were 5 774 patients in the training set and 1 389 patients in the testing set. Overall, the in-hospital mortality was 4.0% (290/7 163). The final LASSO-logistic regression model included 7 risk factors: age, preoperative left ventricular ejection fraction, combined coronary artery bypass grafting, creatinine clearance rate, cardiopulmonary bypass time, New York Heart Association cardiac classification. LASSO-logistic regression had a satisfying discrimination and calibration in both training [area under the curve (AUC)=0.785, 0.627] and testing cohorts (AUC=0.739, 0.642), which was superior to EuroSCOREⅡ. Conclusion The mortality rate for elderly patients undergoing cardiac valvular surgery is relatively high. LASSO-logistic regression model can predict the risk of in-hospital mortality in elderly patients receiving cardiac valvular surgery.
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Objective To assess the risk of nosocomial infection in patients with multiple myeloma during their first hospitalization. Methods Totally 480 patients with multiple myeloma who were hospitalized for the first time in department of hematology of West China Hospital, Sichuan University from August 2021 to August 2022 were included, and the nosocomial infection during treatment was statistically analyzed. The patients were divided into infected group and uninfected group. The independent influencing factors of nosocomial infection were analyzed and a prediction model was established. The reliability of the prediction model was analyzed by receiver operating characteristic curve (ROC). Results The incidence rate of nosocomial infection was 31.2% among 480 patients hospitalized for the first time. There were statistically significant differences in age, ISS staging, controlling nutritional status (CONUT) score, agranulocytosis, hemoglobin, and albumin between the infected group and the uninfected group (P<0.05). Logistic multivariate regression analysis showed that age, ISS staging, CONUT score, agranulocytosis, hemoglobin level, and albumin level were all independent correlated factors of nosocomial infection in patients with multiple myeloma hospitalized for the first time (P<0.05). The area under the ROC curve (AUC), sensitivity and specificity of multivariate logistic regression prediction model were 0.88 (95%CI: 0.840-0.920), 85.00% and 76.36%, respectively. Conclusion The incidence rate of nosocomial infection is high among patients with multiple myeloma in the first hospitalization. The prediction model established according to independent correlated factors of nosocomial infection has high predictive value on the occurrence of nosocomial infection.
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A lot of research is available on the effectiveness of search as an advertising channel. Most of these studies tend to treat a click on a search ad as a binary event. All of them study the events leading to the click. This paper goes beyond this to study the post click actions taken by a user subsequent to clicking on a search ad, and refers to those actions as depth of interaction, and testing the variables that have an effect on the nal outcome. We use a prescriptive research design employing binary logistic regression analysis. Results indicate that the duration of time spent, device used, and recency of visit have a very high positive effect on the nal outcome.
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Estimar a prevalência de diabetes mellitus e os fatores associados em adultos. Trata-se de um inquérito realizado com 1.637 indivíduos nas zonas urbana e rural do município de Rio Branco, Acre. Diabetes foi definido pela presença de glicemia no plasma em jejum ≥ 126 mg/dl ou utilização de hipoglicemiante oral ou insulina. Medidas de associação foram estimadas por regressão logística hierarquizada. A prevalência de diabetes foi de 6,5% (n = 202). Após análise, a chance de ser diabético esteve independente e positivamente associada a idade ≥ 60 anos (OR: 6,67; IC95%: 1,83-24,30); história familiar de diabetes mellitus (OR: 2,88; IC95%: 1,43-5,81); circunferência da cintura aumentada (OR: 1,83; IC95%:1,01-3,33); dislipidemia (OR: 2,95; IC95%: 1,34-6,49); anemia (OR: 3,15; IC95%: 1,30-7,60); e doença renal crônica (DRC) (OR: 4,00; IC95%: 1,70-9,33). Foi detectada uma prevalência de 6,5%, estando o diabetes associado com idade, história familiar, anemia e DRC. Indica-se a necessidade do adequado rastreio de comorbidades nesses pacientes.
To estimate the prevalence of diabetes mellitus and associated factors in adults.Survey carried out with 1,637 individuals in urban and rural areas of the municipality of Rio Branco, state of Acre. Diabetes was defined by the presence of fasting plasma glucose ≥ 126 mg/dl or the use of oral hypoglycemic agents or insulin. Association measures were estimated by hierarchical logistic regression.The prevalence of diabetes was 6.5% (n = 202). After analysis, the chance of being diabetic was independently and positively associated with age ≥ 60 years (OR: 6.67; 95%CI: 1.83-24.30); family history of diabetes mellitus (OR: 2.88; 95%CI: 1.43-5.81); increased waist circumference (OR: 1.83; 95%CI: 1.01-3.33); dyslipidemia (OR: 2.95; 95%CI: 1.34-6.49); anemia (OR: 3.15; 95%CI: 1.30-7.60); and chronic kidney disease (CKD) (OR: 4.00; 95%CI: 1.70-9.33). A prevalence of 6.5% was detected, with diabetes associated with age, family history, anemia, and CKD. The need for adequate screening of comorbidities in these patients is indicated.
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Introducción: Un aneurisma intracraneal roto provoca una hemorragia subaracnoidea. La enfermedad presenta una alta mortalidad y morbilidad. Sin embargo, no todos se rompen. Mejorar la predicción de rotura permitirá un tratamiento quirúrgico preventivo en un grupo de pacientes y evitará una intervención quirúrgica con riesgos en otro grupo de enfermos. Es necesario identificar factores predictivos para mejorar la estratificación del riesgo de rotura y optimizar el tratamiento de los aneurismas intracraneales incidentales. Objetivo: Identificar factores predictivos de rotura de aneurismas intracraneales. Métodos: En una muestra de 152 pacientes espirituanos con aneurismas intracraneales saculares rotos (n = 138) y no rotos (n = 22) y 160 imágenes de angiografía por tomografía computarizada, se realizaron mensuraciones de los índices o factores morfológicos, los cuales se combinaron mediante análisis de regresión logística con variables demográficas y clínicas. Resultados: El grupo de edad con mayor frecuencia de presentación de aneurismas fue el de mayor de 65 años. La muestra estuvo representada, en su gran mayoría, por el sexo femenino. Se identificaron tres factores clínicos y cuatro factores morfológicos estadísticamente significativos, asociados con la rotura. El índice de no esfericidad (p = 0,002 y el sexo femenino (p = 0,02) fueron los de mayor significación estadística. Conclusiones: Se detectaron siete factores predictivos de rotura de aneurismas intracraneales estadísticamente significativos, de los cuales el índice de no esfericidad resultó el de mayor significación(AU)
Introduction: A ruptured intracranial aneurysm causes a subarachnoid hemorrhage. The disease has high mortality and morbidity. However, not all of them break. Improving the rupture prediction will allow preventive surgical treatment in a group of patients and it will avoid risky surgical intervention in another group of patients. It is necessary to identify predictive factors to improve rupture risk stratification and to optimize treatment of incidental intracranial aneurysms. Objective: To identify rupture predictive factors for intracranial aneurysms. Methods: Measurements of the morphological indices or factors were performed in a sample of 152 patients from Sancti Spiritus with ruptured (n = 138) and unruptured (n = 22) saccular intracranial aneurysms and 160 computed tomography angiography images. They were combined using logistic regression analysis with demographic and clinical variables. Results: The age group with the highest frequency of aneurysm presentation was older than 65. The sample was represented, in its vast majority, by the female sex. Three clinical factors and four statistically significant morphological factors associated with rupture were identified. The non-sphericity index (p = 0.002) and the female sex (p = 0.02) were the most statistically significant. Conclusions: Seven statistically significant predictors of intracranial aneurysm rupture were detected, the non-sphericity index being the most significant(AU)
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Humanos , Masculino , Femenino , Modelos Logísticos , Aneurisma Intracraneal/diagnóstico por imagen , Predicción/métodosRESUMEN
Background: Care giving of children with leukemia involves considerable stress and anxiety on the part of family caregivers. Although caregivers’ burden is a crucial predictor of the health of both the child and the caregiver, it is often overlooked. Aim and Objectives: The present study aimed to assess the burden faced by caregivers of pediatric leukemia patients attending a tertiary care hospital in West Bengal, to elicit their sociodemographic characteristics and patients’ profile, and to find out relationship among these, if any. Materials and Methods: The study was descriptive observational type with cross-sectional design. It was conducted among caregivers of pediatric leukemia patients. Data were collected from 38 caregivers using predesigned, pretested, semi-structured schedule, and patients’ records. Burden was measured using Zarit Burden Interview, which is a 22 item 5-point Likert scale. Data were compiled and analyzed in Microsoft Excel and Statistical Software for the Social Sciences 20.0 for statistical analysis. Sociodemographic and clinical variables were expressed as number, percentages, mean, and standard deviations. To find out the association between different factors and caregiver burden, a logistic regression model was used. P < 0.05 was considered as statistically significant. Results: Majority of the caregivers were the mothers of the patients (68.42%), and most of the families of caregivers belonged to lower middle class according to modified BG Prasad Scale. Half of the caregivers (50%) experienced moderate–to-severe burden according to Zarit Burden Interview. Association was found between burden experienced and duration of disease and treatment. However, socioeconomic status was found to be the most significant determinant of burden as per multiple logistic regression by ENTER method. Conclusions: Majority of the caregivers were having moderate to severe and severe burden, which was significantly more among people coming from lower socioeconomic status. Prolonged disease duration and treatment were also found to be associated with increased burden of the caregivers.
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Objective:To construct a prediction model for difficult tracheal intubation in the patients with obstructive sleep apnea-hypopnea syndrome (OSAHS).Methods:A total of 324 patients with OSAHS undergoing surgery with general anesthesia, admitted to our hospital from June 2019 to June 2021, were included in model group, and 175 patients with OSAHS undergoing surgery with general anesthesia, admitted from July 2021 to July 2022, were selected and served as validation group. The patients in model group were divided into occurrence group and non-occurrence group according to whether difficult tracheal intubation occurred. Logistic regression was used to construct the prediction model, and R4.2.1 software was used to draw the risk nomogram and calibration curve. The predictive accuracy of the model was evaluated by the area under the receiver operating characteristic curve.Results:Body mass index (BMI), sagittal minimum cross-sectional area, horizontal minimum cross-sectional area and mandibular distance were risk factors for difficult tracheal intubation in OSAHS patients ( P<0.05). A prediction model was developed using the above factors: Logit P=33.726+ 1.411×BMI score-0.014×sagittal airway minimum cross-sectional area-0.013×airway horizontal minimum cross-sectional area-0.312× mandibular distance. The area under the receiver operating characteristic curve was 0.846, Youden index 0.585, sensitivity 0.831, specificity 0.755, and the accuracy 0.889 (Hosmer-Lemeshow test χ2=9.24, P=0.322) in model group. The area under the external validation curve was 0.802, Youden index 0.545, sensitivity 0.636, specificity 0.908, and the accuracy 0.893 (Hosmer-Lemeshow test χ2=10.24, P=0.287) in validation group. Conclusions:The prediction model based on BMI, sagittal minimum cross-sectional area of airway, horizontal minimum cross-sectional area of airway and mandibular distance has a high value in predicting the risk of difficult tracheal intubation in patients with OSAHS.
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Objective:To investigate the current status of health checkups and the management willingness of post-test abnormal values in residents of mega communities, and to explore the factors affecting the demand for health management of abnormal values after examination.Methods:A cross-sectional study. The residents of Huaguoyuan Community in Guiyang City were enrolled as the objects of this surveywith stratified systematic sampling method and questionnaire survey. The binary logistic regression was used to analyze the influencing factors of health management demand for abnormal values after examination.Results:There were 404 residents participating in this survey, and 182 cases were male (45.05%) and 222 cases were female (54.95%); the mean age was (39.64±15.03) years. There were 179 (44.31%) urban residents and 225 (55.69%) rural residents. There were 162 (40.10%) floating population and 242 (59.90%) non-floating population. Of the residents, 34.2% participated in the physical examination independently due to physical reasons. The age ( χ 2=16.227), household registration ( χ 2=16.117) and occupation ( χ 2=36.454) had statistically significant differences in residents′ participation in physical examination; the household registration ( χ 2=18.726, P<0.001) and occupation ( χ 2=18.094, P=0.034) had significant differences in the handling methods of abnormal values of physical examination. The age ( OR=7.00, P=0.032) and income ( OR=0.33, P=0.047) were the influencing factors of health management needs of abnormal values after health checkup. Conclusion:The awareness of active physical examination of residents in mega community is weak, and it is recommended to strengthen health education and health promotion; residents have a high willingness to the management of abnormal values after health checkup, it can be an important supplement to improve the service quality of physical examination related institutions.
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Objective:To investigate the clinical outcome after surgery and first 131I treatment in patients with moderate-risk papillary thyroid cancer (PTC), and analyze the relevant factors that affect the therapeutic effect. Methods:From January 2018 to April 2019, 135 patients (48 males, 87 females; age (42.7±11.1) years) with moderate-risk PTC in the Second Affiliated Hospital of Chongqing Medical University were retrospectively analyzed. According to the 2015 American Thyroid Association (ATA) guidelines, patients were divided into excellent response (ER) group, inderteriminate response (IDR) group, biochemical incomplete response (BIR) group and structural incomplete response (SIR) group, of which IDR, BIR, SIR were collectively referred to as the non-ER group. χ2 test and Mann-Whitney U test were used to compare the general clinical features between the ER and non-ER groups, and then multivariate logistic regression analysis was performed. The predicted value of pre-ablation stimulated thyroglobulin (ps-Tg) to ER was assessed by ROC curve analysis. Results:The treatment responses of 94 patients were ER, and those of 41 were non-ER. The differences in tumor size (0.80(0.50, 1.10) vs 1.00(0.55, 1.50) cm; U=1 491.50, P=0.036), the number of metastatic lymph nodes (3(2, 5) vs 4(2, 12); U=1 422.00, P=0.015), metastatic lymph node size (0.50(0.30, 0.65) vs 0.50(0.30, 1.45) cm; U=1 396.50, P=0.013), metastatic lymph node involvement rate (50%(30%, 70%) vs 60%(50%, 85%); U=1 441.50, P=0.024), metastatic lymph node location (central/lateral: 76/18 vs 24/17; χ2=7.40, P=0.007) and ps-Tg level (2.1(0.8, 5.3) vs 14.0(3.2, 35.2) μg/L; U=680.00, P<0.001) were statistically significant between the ER and non-ER groups. Multivariate logistic regression analysis showed that ps-Tg (odds ratio ( OR)=1.200, 95% CI: 1.107-1.302, P<0.001) was an independent factor influencing ER. The cut-off value of ps-Tg for predicting ER was 7.38 μg/L, with the sensitivity and specificity of 68.3%(28/41) and 87.2%(82/94) respectively. Conclusion:Moderate-risk PTC patients with smaller tumor size, fewer metastatic lymph nodes, lower metastatic lymph node involvement rate, metastatic lymph nodes in central area, smaller metastatic lymph node size, and ps-Tg<7.38 μg/L have better therapeutic effect after initial 131I treatment.
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Objective:To analyze the relationship between plasma uranium concentration and renal injury.Methods:A case-control study was conducted in Hunan province, involving 102 renal injury cases and 102 matched controls. The association between plasma uranium concentration and renal injury was analyzed using conditional logistic regression models, and the dose-response relationship was analyzed through restricted cubic spline regression. The linear regression model and Spearman correlation were used to analyze the association between plasma uranium concentration and renal injury indicators.Results:The median of plasma uranium concentration was 8.94 ng/L in all subjects and 10.19 ng/L in the case group. The plasma uranium may be a risk factor for renal injury, with a dose-response relationship between the both representing nonlinear association ( χ2=5.15, P<0.05). The risk of renal injury was 4.21 times higher in the group exposed to highest uranium concentration than that in the group exposed to lowest uranium concentration. Plasma uranium concentration was closely related to glomerular filtration rate, serum creatinine and β 2-microglobulin ( r=0.211, -0.142, 0.195, P<0.05). Conclusions:The plasma uranium concentration is significantly associated with the renal injury, which may provide epidemiology evidence for the prevention of renal injury.
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Objective:To investigate the risk factors of non-valvular paroxysmal atrial fibrillation (NVPAF) with cerebral ischemic stroke(CIS) and analyze NVPAF by using left atrial automatic imaging (AFILA). Logistic regression model was established for left atrial(LA) function parameters.Methods:A total of 205 patients with NVPAF were included in the study and divided into the NVPAF group without ischemic stroke (154 patients) and the CIS group (51 patients). The clinical baseline data, blood biochemical results and AFILA ultrasound data of all patients were collected. Univariate analysis was performed to compare the above data between the two groups of patients. The independent risk factors were obtained by multivariate logistic regression analysis. Logistic regression model was compared with CHA2DS2-VASc scoring system in terms of area under ROC curve, sensitivity and specificity.Results:There were significant differences in age, CHA2DS2-VASc score, taking anticoagulant drugs, history of hypertension, diabetes and coronary heart disease, LAEF, S_R, S_CT, WBC, NEUT, HCY, UREA, NDD, NT-proBNP, Fibrinogen(Fib), Cardiac troponin I(cTnI) and NLR between the two groups (all P<0.05). The results of multifactor analysis showed that: age, hypertension, S_ CT, UREA, NLR, Fib and cTnI were independent risk factors associated with CIS in patients with paroxysmal atrial fibrillation[ OR value: 1.608 ( P=0.003), 3.821 ( P=0.019), 1.259 ( P=0.001), 1.326( P=0.001), 1.352 ( P=0.011), 1.502 ( P=0.042), 7.651( P=0.001)]. After adjusting for the age, sex and history of hypertension included in CHA2DS2-VASc score, S_CT significantly led to NVPAF complicated with stroke[ OR value 1.259 (1.095-1.447), P=0.001]. The diagnostic efficacy of Logistic regression model is better than that of CHA2DS2-VASc scoring (AUC of 0.931 vs 0.717, 95% CI: 0.896-0.967 vs 0.634-0.799, sensitivity of 0.883 vs 0.755, specificity of 0.849 vs 0.713, all P<0.001). Conclusions:Age, hypertension, S_CT, UREA, NLR, fibrinogen, cTnI are independently associated risk factors for patients with combined CIS; The diagnostic efficacy of Logistic regression model is better than that of CHA2DS2-VASc scoring model.And the sensitivity and specificity are high.
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Objective:To analyze risk factors for prognosis of adult patients with traumatic brain injury (TBI), construct the prognostic model of TBI and evaluate its predictive value.Methods:A case-control study was used to analyze the clinical data of 522 patients with TBI admitted to Xijing Hospital of Air Force Medical University from March 2011 to September 2019, including 438 males and 84 females; aged 18-75 years [(44.9±15.0)years]. According to the Glasgow outcome score (GOS) at discharge, the patients were divided into good prognosis group (GOS 4-5 points, n=165) and poor prognosis group (GOS 1-3 points, n=357). The two groups were compared with regards to qualitative data such as sex, underlying diseases, causes of injury, multiple injuries, open injuries, intracranial foreign bodies, cerebral herniation, consciousness status on admission and at discharge, surgery, lung infection on admission, tracheostomy, ventilator-assisted ventilation, hospital-acquired pneumonia/pathogenic bacteria and intracranial infection, and quantitative data such as Glasgow coma score (GCS) on admission and at discharge, age, measurements on admission [systolic blood pressure, diastolic blood pressure, mean arterial pressure, temperature, heart rate, creatinine, urea nitrogen, blood sodium, blood potassium, blood glucose, prothrombin time (PT), activated partial thromboplastin time (APTT), platelets, international normalized ratio (INR), pupil size of both eyes] and length of hospital stay. Univariate analysis and Lasso regression analysis were used to screen the risk factors affecting the prognosis of TBI patients, and the selected influencing factors were included in multivariate Logistic regression analysis to identify independent risk factors and construct regression equations. R was used to draw a visual nomogram based on regression equation for predicting the prognosis of TBI patients. The prognostic predictive value of the nomogram was evaluated by using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC), Youden index, sensitivity, specificity and consistency index (C index) were calculated. Results:Univariate analysis showed that there were significant differences between the two groups in underlying diseases, open injuries, cerebral herniation, consciousness status on admission and at discharge, lung infection on admission, tracheostomy, ventilator-assisted ventilation, hospital-acquired pneumonia/pathogenic bacteria, GCS on admission and at discharge, age, and measurements on admission (systolic blood pressure, mean arterial pressure, body temperature, heart rate, creatinine, urea nitrogen, blood potassium, blood glucose, PT, INR, pupil size of right eye) (all P<0.05 or 0.01). There were no significant differences between the two groups in gender, causes of injury, multiple injuries, intracranial foreign bodies, surgery, intracranial infection, measurements on admission (diastolic blood pressure, blood sodium, APTT, platelets, pupil size of left eye) and length of hospital stay (all P>0.05). After screening by Lasso regression model, the results of multivariate Logistic regression analysis showed that GCS on admission ( OR=0.67, 95% CI 0.62, 0.73, P<0.01), age ( OR=1.03, 95% CI 1.01, 1.04, P<0.01), blood glucose on admission ( OR=1.17, 95% CI 1.06, 1.30, P<0.01) and INR on admission ( OR=17.08, 95% CI 2.12, 137.89, P<0.01) could be used as the main risk factors to construct the prediction model, and the regression equation was constructed: Logit [ P/(1- P)]=-0.398× "GCS on admission"+0.024× "age"+0.158×"blood glucose on admission"+2.838×"INR on admission"-1.693. The AUC for the prognosis prediction in adult patients with TBI using R based on a visual nomogram model was 0.87 (95% CI 0.83, 0.89, P<0.01). The Youden index for the predicted probability was 0.60 (sensitivity of 85.2% and specificity of 75.2%), with the C index of 0.87. Conclusion:Age, GCS on admission, blood glucose on admission and INR on admission are the main risk factors affecting the prognosis of TBI in adults, and the nomogram drawn by these parameters can better predict their clinical outcome.
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Objective:To investigate the role of a simple Nomogram model in evaluating the severity of mycoplasma pneumoniae pneumonia (MPP) in adults.Methods:The clinical data of 162 patients with MPP who received treatment in Wenzhou Central Hospital from March 2015 to October 2022 were retrospectively analyzed. These patients were divided into a severe group ( n = 67) and a common group ( n = 95) according to whether they were diagnosed with severe MPP. The clinical data of patients were recorded. Fourteen clinical variables were screened, including age, sex, onset season, fever, heat peak, fever duration, cough duration, white blood cell count, percentage of neutrophils, percentage of lymphocytes, hemoglobin, platelet count, C-reactive protein, and procalcitonin. Multivariate logistic regression analysis of statistically significant variables in univariate analysis was performed. The Nomogram model was constructed with the R language software package (version 3.6.2). The model was verified with a calibration curve and receiver operating characteristic curve. Results:Univariate analysis results showed that in the severe group, the fever peak ( Z = 5.03, P < 0.001) was higher, fever duration ( χ2 = 27.55, P < 0.001), and cough duration ( χ2 = 28.72, P < 0.001) were longer, white cell count ( t = 2.93, P = 0.004), percentage of neutrophils ( t = 9.08, P < 0.001), C-reactive protein ( t = 35.05, P < 0.001), and procalcitonin level ( t = 15.09, P < 0.001) were greater compared with the common group. The percentage of lymphocytes ( t = 1.16, P < 0.001), hemoglobin level ( t = 1.22, P < 0.001), and platelet count ( t = 2.82, P < 0.001) in the severe group were significantly lower than those in the common group. Multivariate logistic regression analysis results showed that heat peak, cough duration, and C-reactive protein were positively correlated with the severity of MPP (all P < 0.05). The percentage of lymphocytes, hemoglobin concentration, and platelet count were negatively correlated with the severity of MPP (all P < 0.05). The establishment and validation results of the Nomogram model showed that the accuracy of the model was good, with a sensitivity of 88.73%, a specificity of 77.61%, and a C-index of 0.904. Conclusion:Heat peak, cough duration, percentage of lymphocytes, platelet count, and C-reactive protein are closely related to the severity of early MPP. A simple Nomogram model can be one of the tools for early assessment of the severity of MPP.
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Objective:To investigate the risk factors of moderate to severe pain in patients with non-small cell lung cancer within 3 days after lobectomy.Methods:The clinical data of 297 patients with non-small cell lung cancer who underwent lobectomy in the Department of Thoracic Surgery, Sun Yat-sen University Cancer Center from December 2020 to June 2021 were retrospectively analyzed. A numerical rating scale was used to score the most severe pain within 3 days after surgery. Pain score ≥ 4 was defined as moderate to severe pain. The risk factors for moderate to severe pain were analyzed by binary Logistic regression. General linear model repeated measures and linear mixed models were used to analyze the trend of risk factors influencing postoperative pain with time.Results:The incidence of moderate to severe pain was 34.2% (102/297), 59.8% (178/297), 66.4% (198/297), and 28.2% (84/297) on days 0, 1, 2, and 3 after surgery respectively. The risk for moderate to severe pain was significantly higher in patients undergoing thoracotomy than patients undergoing thoracoscopic surgery on days 1 ( OR = 1.99, P = 0.009), 2 ( OR = 3.08, P < 0.001), and 3 ( OR = 3.88, P < 0.001) after surgery. However, the risk for moderate to severe pain in patients undergoing thoracotomy was slightly, but not significantly, higher than that in patients undergoing thoracoscopic surgery ( OR = 1.53, P = 0.087). The risk for moderate to severe pain was higher in female patients than male patients on day 2 ( OR = 1.62, P = 0.077), and in particular on day 3 after surgery ( OR = 2.39, P = 0.002). Prophylactic use of parecoxib significantly reduced the risk of moderate to severe pain on day 0 ( OR = 0.32, P = 0.004), 1 ( OR = 0.20, P < 0.001), 2 ( OR = 0.36, P < 0.001) and 3 ( OR = 0.56, P = 0.047). Conclusion:The incidence of moderate to severe pain on days 1 and 2 after lobectomy was relatively high in patients with non-small cell lung cancer. Patients undergoing thoracotomy have a higher risk of moderate to severe pain than those who underwent thoracoscopic surgery. Female patients have a higher risk for moderate to severe pain on days 2 and 3 after surgery than male patients. Prophylactic use of parecoxib can decrease the risk for moderate to severe pain in patients with non-small cell lung cancer.
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Objective:To explore the construction of a Logistic prediction model and countermeasures for type 2 diabetic nephropathy based on clinical data.Methods:The patients with type 2 diabetic nephropathy admitted to Shijiazhuang Second Hospital from September 2019 to September 2021 (study group) were selected and the patients were selected according to a 1∶1 ratio using individual matching (control group), each group with 200 patients. Single and multiple factors analysis were used to analyze the factors influencing type 2 diabetic nephropathy, and Logistic regression equation models were developed to verify their predictive value.Results:Logistic regression equation model showed that the course of type 2 diabetes, glycosylated hemoglobin (HbA 1c), fasting plasma glucose (FPG), homocysteine (Hcy), urinary microalbumin, and serum creatinine (Scr) were high risk factors for type 2 diabetic nephropathy ( P<0.05). The results of Logistic regression model evaluation showed that the model was established with statistical significance, and the coefficients of the regression equations had statistically significant differences. The Hosmer-Lemeshow goodness-of-fit test showed that the model fitting effect was good. Logistic regression model was used to statistically analyzed the data set, and the receiver operating characteristic (ROC) curve of type 2 diabetic nephropathy was drawn, the area under the curve was 0.949(95% CI 0.922 - 0.968), the prediction sensitivity was 81.50%, the specificity was 95.50%, the calibration curve showed that the predicted results was in good agreement with the observed results. Conclusions:The independent predictors of type 2 diabetic nephropathy involve HbA 1c, FPG, Hcy, urinary microalbumin. The Logistic prediction model based on these predictors has reliable predictive value and can help guide clinical diagnosis and treatment.
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Objective:To analyze the influencing factors of delayed nausea and vomiting in patients with primary liver cancer after transarterial chemoembolization based on Logistic regression model and decision tree model.Methods:This was a cross-sectional study. A total of 236 patients with primary liver cancer after transarterial chemoembolization in The Second Affiliated Hospital of Air Force Military Medical University from March 2021 to June 2022 were conveniently selected as the research subjects. The factors related to delayed nausea and vomiting were collected, and Logistic regression and decision tree models were established, respectively, and the differences between the two models were compared.Results:The incidence of delayed nausea and vomiting of patients with primary liver cancer after transarterial chemoembolization was 45.34% (107/236). Logistic regression model showed that age, anxiety, sleep disorder, emetic risk level of chemotherapeutic drugs, embolic agent type, and pain 24 hours after surgery were the influencing factors of delayed nausea and vomiting in patients with primary liver cancer after transarterial chemoembolization(all P<0.05). Decision tree model showed that age, sleep disorder, emetic risk level of chemotherapeutic drugs, embolic agent type, and pain 24 hours after surgery were the influencing factors of delayed nausea and vomiting in patients with primary liver cancer after transarterial chemoembolization (all P<0.05). The classification accuracy rates of Logistic regression, decision tree model and combined diagnosis of two models were 72.9%, 71.2% and 72.0% respectively; the areas under the ROC curve were 0.778, 0.781 and 0.806 respectively, with no significant difference (all P>0.05). Conclusions:The analysis results of Logistic regression and decision tree model on the influencing factors of delayed nausea and vomiting in patients with primary liver cancer after transarterial chemoembolization are highly consistent, which can be combined to provide a more comprehensive reference for the evaluation and intervention of medical staff.
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Objective:To construct a simple model of arteriovenous fistula classification,and to achieve the classification of arteriovenous fistula in hemodialysis patients.Methods:The study was a retrospective analysis, a total of 304 hemodialysis patients with internal fistula in People′s Hospital of Deyang City from January 2016 to January 2021 were selected by convenience sampling method,depending on whether the internal fistula was dysfunctional, patients were divided into 64 in the internal fistula failure group and 240 in the internal fistula patency group. Independent influence factors and their regression coefficient were obtained by single-factor analysis and logistic regression analysis, The risk score formula was established based on the regression coefficient to form a simple model of internal fistula classification.The model was evaluated by receiver operating characteristic curve and the scoring criteria for internal fistula classification was determined.Results:Logistic regression analysis showed that diabetes mellitus, hypotension, age≥60 years old, compression time≥30 min, blood phosphorus>1.78 mmol/L, triglyceride>1.71 mmol/L and fibrinogen>4 g/L were independent influencing factors of internal fistula failure (all P<0.05).The area under the receiver operating characteristic curve was 0.858(95% CI 0.789-0.928, P<0.01), and the best critical value of the internal fistula classification was 7.5, the sensitivity was 80.4% and the specificity was 84.8%. Conclusions:By obtaining the predictors of internal fistula failure, conducted the risk score, and constructed a simple model of internal fistula classification, which can effectively predicted the risk of internal fistula failure. It is conducive to the implementation of internal fistula classification management and the puncture of corresponding grade, to ensure the pathway safety of patients.
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Objective:To explore risk factors for clinical onset in children with uncontrolled self-limited epilepsy with centrotemporal spikes (SeLECTS) managed by 2 anti-seizure medications (ASMs).Methods:A total of 112 children with SeLECTS who were diagnosed at the Department of Pediatric Neurology of the Third Affiliated Hospital of Zhengzhou University from January 2018 to May 2021 were retrospectively reviewed.All of them were treated with conventional ASMs, and regularly followed up for 1-2 years.Types of therapeutic drugs, clinical seizure control status, presence of new seizure forms, electroencephalogram (EEG) were reviewed at follow-up visits.According to whether the seizures were controlled after the use of no more than 2 ASMs, patients were divided into poor response group (43 cases) and good response group (69 cases), and their clinical data and EEG characteristics were compared.Multivariate Logistic regression analysis was used to explore the risk factors for seizures that were uncontrolled by 2 ASMs. Results:There were significant differences in the age of onset ( χ2=8.919, P=0.003), seizure form ( χ2=4.218, P=0.040), seizure frequency ( Z=-7.664, P<0.001), EEG background slowing ( χ2=10.284, P=0.001), emergence of electrical status epilepticus during slow-wave sleep (ESES)( χ2=11.921, P=0.001), discharge generalization ( χ2=25.377, P<0.001), and presence of epileptic encephalopathy with spike-and-wave activation in sleep (EE-SWAS)( χ2=54.334, P<0.001) between groups.Multivariate Logistic regression analysis showed that seizure frequency ( P<0.001, OR=0.086, 95% CI: 0.022-0.329), discharge generalization ( P=0.006, OR=9.942, 95% CI: 1.918-51.527) and EEG background slowing ( P=0.041, OR=6.648, 95% CI: 1.077-41.038) were the 3 main risk factors associated with poor response to short-term medications of ASMs. Conclusions:Seizures are easily controlled in most SeLECTS patients medicated with ASMs with a favorable prognosis.Seizure frequency, discharge generalization and EEG background slowing are risk factors for the poor response to short-term pharmacotherapy in children with SeLECTS.
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Objective:To explore the risk factors affecting endometrial lesions after breast cancer surgery, and build a nomogram prediction model.Methods:From Oct. 2019 to Nov. 2021, 103 patients with abnormal bleeding after breast cancer surgery were selected, the clinical data of the patients were collected, and they were divided into the non-lesion group and the lesion group according to whether the endometrial lesion occurred. A Logistic risk regression model was established to analyze the risk factors affecting endometrial lesions in postoperative patients with breast cancer, a nomogram prediction model was constructed and verified, and receiver operating characteristic curve (ROC) analysis was performed to analyze the nomogram model for predicting sensitivityof endometrial lesions.Results:Childbirth history ( OR=37.100, 95% CI: 3.777-527.7, P=0.004), endometrial thickness ( OR=2.489, 95% CI: 1.699-4.007, P<0.001), menopause ( OR=0.099, 95% CI: 0.015-0.499, P=0.009), abnormal bleeding time ( OR=6.922, 95% CI: 2.221-24.800, P=0.002), and types of treatment drugs ( OR=3.738, 95% CI: 1.187-13.200, P=0.030) had statistical significance in predicting endometrial lesions in postoperative patients with breast cancer. Using the above five variables to construct a nomogram model, the consistency of the nomogram in predicting endometrial lesions in postoperative patients with breast cancer was 0.739, and the discrimination was good. The calibration curve showed that the average absolute error between the predicted probability and the actual probability was 0.041,and ROC curve showed that the AUC value of the nomogram model for predicting endometrial lesions was 0.800. Conclusion:Establishing a nomogram model for predicting the risk of endometrial lesions in postoperative patients with breast cancer has good accuracy and high clinical value.