摘要
Objective: This study examines factors predicting self-reported voice symptoms in call center workers. Methods: Multivariate analysis and predictive modeling assess personal, work-related, acoustic, and behavioral factors. Generalized Linear Models (GLMs) and Receiver Operating Characteristic (ROC) curves are employed. Results: Age and sleep patterns impacted voice quality and effort, while workplace factors influenced symptom perception. Unhealthy vocal behaviors related to tense voice and increased effort, while hydration was protective. Voice acoustics showed diagnostic potential, supported by ROC data. These findings emphasize voice symptom complexity in call center professionals, necessitating comprehensive assessment. Limitations: This study recognizes its limitations, including a moderate-sized convenience sample and reliance on PROM metrics. Future research should incorporate more objective measures in addition to self-reports and acoustic analysis. Value: This research provides novel insights into the interplay of personal, occupational, and voice-related factors in developing voice symptoms among call center workers. Predictive modeling enhances risk assessment and understanding of individual susceptibility to voice disorders. Conclusion: Results show associations between various factors and self-reported voice symptoms. Protective factors include sleeping more than six hours and consistent hydration, whereas risk factors include working conditions, such as location and behaviors like smoking. Diagnostic models indicate good accuracy for some voice symptom PROMs, emphasizing the need for comprehensive models considering work factors, vocal behaviors, and acoustic parameters to understand voice issues complexity.
Objetivo: Este estudio examina los factores que predicen los síntomas de voz en los trabajadores de call centers. Métodos: Se utilizan análisis multivariados y modelos predictivos para evaluar factores personales, laborales, acústicos y de comportamiento. Se emplean Modelos Lineales Generalizados (GLM) y curvas ROC. Resultados: La edad y los patrones de sueño afectaron la calidad vocal y el esfuerzo, mientras que los factores laborales influyeron en la percepción de síntomas. Los comportamientos vocales no saludables se relacionaron con voz tensa y mayor esfuerzo, mientras que la hidratación fue protectora. Los parámetros acústicos de voz mostraron potencial diagnóstico respaldado por datos de ROC. Los hallazgos subrayan complejidad de síntomas vocales en profesionales de centros de llamadas, requiriendo una evaluación integral. Limitaciones: Este estudio reconoce sus limitaciones, que incluyen una muestra de conveniencia de tamaño moderado y la dependencia de medidas PROMs. Futuras investigaciones deberían incorporar medidas objetivas, además de los autorreportes y análisis acústico. Importancia: Esta investigación aporta nuevos conocimientos sobre factores personales, laborales y síntomas de voz en trabajadores de call centers. El modelado predictivo mejora la evaluación de riesgos y la comprensión de la susceptibilidad individual a trastornos de la voz. Conclusión: Los resultados muestran asociaciones entre diversos factores y los síntomas vocales reportados. Los factores de protección incluyen dormir más de seis horas y una hidratación constante; los factores de riesgo incluyen las condiciones de trabajo, como la ubicación y comportamientos como fumar. Los modelos de diagnóstico indican una buena precisión para algunas PROMs de síntomas de la voz, lo que subraya la necesidad de modelos integrales que tengan en cuenta los factores laborales, los comportamientos vocales y los parámetros acústicos para comprender la complejidad de los problemas de la voz.
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Abstract We aimed to develop a prognostic model for primary pontine hemorrhage (PPH) patients and validate the predictive value of the model for a good prognosis at 90 days. A total of 254 PPH patients were included for screening of the independent predictors of prognosis, and data were analyzed by univariate and multivariable logistic regression tests. The cases were then divided into training cohort (n=219) and validation cohort (n=35) based on the two centers. A nomogram was developed using independent predictors from the training cohort to predict the 90-day good outcome and was validated from the validation cohort. Glasgow Coma Scale score, normalized pixels (used to describe bleeding volume), and mechanical ventilation were significant predictors of a good outcome of PPH at 90 days in the training cohort (all P<0.05). The U test showed no statistical difference (P=0.892) between the training cohort and the validation cohort, suggesting the model fitted well. The new model showed good discrimination (area under the curve=0.833). The decision curve analysis of the nomogram of the training cohort indicated a great net benefit. The PPH nomogram comprising the Glasgow Coma Scale score, normalized pixels, and mechanical ventilation may facilitate predicting a 90-day good outcome.
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Resumo Fundamento Fibrilação atrial nova (FAN) ocorre em pacientes internados por COVID-19. Há controvérsias quanto ao valor preditivo de dados clínicos e laboratoriais à admissão hospitalar para ocorrência de FAN. Objetivos Analisar, à admissão hospitalar, variáveis com potencial preditivo para ocorrência de FAN em pacientes com pneumonia por COVID-19. Método Estudo observacional, retrospectivo, caso-controle. Foram avaliados prontuários eletrônicos de pacientes consecutivos ≥ 60 anos, hospitalizados com pneumonia por COVID-19 entre 1º de março e 15 de julho de 2020. Comparações feitas pelos testes `t' de Student ou qui-quadrado. Foi empregado modelo de risco proporcional de Cox para identificação de preditores de FAN. Considerou-se o valor de p < 0,05 como estatisticamente significativo. Resultados Entre 667 pacientes internados por COVID-19, 201 (30,1%) foram incluídos. FAN foi documentada em 29 pacientes (14,4%) (grupo 1). Grupo 2 foi composto por 162 pacientes que não apresentaram FAN. Dez pacientes excluídos por estarem em FA na admissão hospitalar. Houve diferenças entre os grupos 1 e 2, respectivamente, no tempo de permanência em UTI (11,1±10,5 dias vs. 4,9±7,5 dias; p=0,004), necessidade de ventilação invasiva (82,9% e 32,7%; p<0,001) e mortalidade hospitalar (75,9% vs. 32,1%; p<0,001). No modelo de Cox, idade > 71 anos (hazard ratio [HR]=6,8; p<0,001), leucometria ≤ 7.720 cels.μL-1 (HR=6,6; p<0,001), natremia ≤ 137 mEq.L-1 (HR=5,0; p=0,001), escore SAPS3 > 55 (HR=5,6; p=0,002) e desorientação (HR=2,5; p=0,04) foram preditores independentes de FAN. Conclusões FAN é uma arritmia comum em idosos hospitalizados com pneumonia por COVID-19. Parâmetros clínicos e laboratoriais avaliados na admissão são preditores de FAN durante internação.
Abstract Background New-onset atrial fibrillation (NOAF) occurs in patients hospitalized due to COVID-19. It is still unknown whether clinical and laboratory data assessed upon hospital admission have predictive value for NOAF. Objectives To analyze, upon hospital admission, variables with predictive potential for the occurrence of NOAF in patients with COVID-19 pneumonia. Methods Observational, retrospective, case-control study. Electronic medical reports of consecutive patients, 60 years of age or older, hospitalized due to COVID-19 pneumonia between March 1st and July 15th, 2020, were reviewed. Non-paired Student or chi-squared tests compared variables. A Cox proportional hazard model was employed to identify independent predictors of NOAF. P value < 0.05 was considered statistically significant. Results Among 667 patients hospitalized due to COVID-19, 201 (30.1%) fulfilled the inclusion criteria. NOAF was documented in 29 patients (14.4%), composing group 1. Group 2 was composed of 162 patients without NOAF. Ten patients were excluded due to the AF rhythm upon hospital admission. In groups 1 and 2, there were differences in overall in-hospital survival rate (24.1 % vs. 67.9%; p<0.001), length of stay in ICU (11.1 ± 10.5 days vs. 4.9 ± 7.5 days; p=0.004) and need for mechanical ventilation rate (82.9% vs. 32.7%; p<0.001). In the Cox model, age > 71 y/o (HR=6.8; p<0.001), total leukocyte count ≤ 7,720 cels.μL-¹ (HR=6.6; p<0.001), serum [Na+] ≤ 137 mEq.L-¹ (HR=5.0; p=0.001), SAPS3 score > 55 (HR=5.6; p=0.002), and disorientation (HR=2.5; p=0.04) on admission were independent predictors of NOAF. Conclusion NOAF is a common arrhythmia in elderly hospitalized patients with COVID-19 pneumonia. Clinical and laboratory parameters evaluated on admission have a predictive value for the occurrence of NOAF during hospitalization.
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Objective To investigate the risk factors of postoperative fecal contamination in children pa-tients with Hirschsprung's disease(HSCR),and to construct and evaluate the risk predictive model.Methods The clinical data in 377 children patients with HSCR in 3 class 3A hospitals in Guangxi from Janu-ary 2016 to June 2021were retrospectively analyzed by adopting the convenience sampling method.The pa-tients were divided into the modeling group(n=264)and testing model group(n=113)with a ratio of 7∶3.The risk factors of postoperative fecal soiling were analyzed by the single factor and multiple factors,and the risk predictive model was constructed.The receiver operating characteristic(ROC)curve was used to detect the discriminative ability of the model and the H-L test was used to determine the goodness of fit of the mod-el.The model was prospectively validated in 21 children patients with HSCR from August to December 2021.Results Among 377 children patients with HSCR,the fecal soiling occurred in 131 cases with a incidence rate of 34.75％.The constructed predictive model of fecal contamination risk after HSCR operation:logit(P)=-2.385+1.697 × special type of megacolon+0.929 × Soave+0.105 × length of bowel resection+2.065 × il-literate caregivers+0.808 × caregivers'implementation of postoperative diet+0.867 × postoperative defecation training by caregivers.The area under the curve(AUC)in the modeling group was 0.849,the Yoden index was 0.53,the optimal critical value of the model was 0.32,the sensitivity was 76.00％,and the specificity was 77.00％.The H-L test,X2=6.649,P=0.575.AUC of the testing model group was 0.736,the sensitivity was 81.25％,and the specificity was 78.46％.The prospective validation results showed that the sensitivity and specificity of the model were 66.67％and 100％respectively.Conclusion The constructed model has good i-dentification and predictive ability.
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Objective To survey the current situation of fertility intention to have a third child among childbearing age women in Wanzhou District of Chongqing Municipality under the background of the new fer-tility policy,and to analyze its influencing factors.Methods The convenience sampling method was adopted to conduct the survey on the third child fertility intention by the self-designed questionnaire and the Question-naire Star platform among the childbearing women with one child or two children in Wanzhou District of Chongqing Municipality from September to October 2022.The multiple linear regression analysis and two-step cluster analysis were used to explore the influencing factors.Results A total of 632 questionnaires were re-ceived,32 invalid questionnaires were excluded,and the effective recovery rate was 94.9％.The score of fertili-ty intention to have a third child in the respondents was(12.81±2.83)points.The multiple linear regression analysis showed that the age,marital status,place of residence,spouse cultural degree,family rearing child model,whether the family can provide support and previous vaginal delivery history were the influencing fac-tors of fertility intention to have a third child among the childbearing age women in this place(P＜0.05).The two step cluster analysis showed that"obstetrician/midwife's help"was the most important predictive factor for the childbearing age women to choose to have a third child.Conclusion The current situation of childbear-ing age women's fertility intention to have a third child in Wanzhou District of Chongqing Municipality is not optimistic.It is necessary to create the positive factors that encourage childbearing age women to make deci-sion about having a third child.
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Objective To explore the risk factors of complicating urogenic sepsis after percutaneous nephrolithotripsy(PCNL)and construct a nomogram prediction model.Methods The data of 291 patients with stage 1 PCNL in 940 Hospital of Joint Logistics Support Force from October 2016 to October 2021 were retrospectively analyzed.The patients were divided into the sepsis group and non-sepsis group according to whether complicating urogenic sepsis after operation.The general data,stone-related data,operation-related data and laboratory detection related data were included.The independent risk factors were screened by the univariate and multivariate logistic regression analysis,and the nomogram prediction model was constructed.Results The results of univariate and multivariate logistic regression analysis showed that age≥60 years old(OR=6.438,95％CI:1.548-26.769),urinary leukocyte 3+(OR=5.651,95％CI:1.614-31.766),urinary nitrite positive(OR=7.117,95％CI:1.190-42.561),operation time≥90 min(OR=4.626,95％CI:1.137-18.817)and perfusion volume 30 L(OR=3.312,95％CI:1.090-10.061)were the independent risk factors of postoperative complicating urogenic sepsis.C-index of the constructed nomogram prediction model in the modeling samples was 0.937,the calibrated C-index was 0.914,and the model predictive efficien-cy was good.Conclusion Age ≥60 years old,urinary leukocyte 3+,urinary nitrite positive,operation time 90 min and perfusion volume ≥30 L are the independent risk factors for complicating urogenic sepsis after PCNL;the constructed nomogram prediction model has a good predictive efficiency for the occurrence of post-operative urogenic sepsis.
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Objective To establish a nomogram prediction model of hyperuricemia(HUA)onset risk in overweight and obese children and adolescents in order to provide reference for the prevention and treatment of HUA in this population.Methods The clinical data of 1 410 overweight and obese children and adolescents aged 6-17 years old visiting in this hospital from September 2021 to August 2022 were retrospectively analyzed.A total of 987 overweight and obese children and adolescents were randomly extracted according to a ratio of 7:3 to establish the model,and the remaining 423 cases were validated internally.Referring to the definition of high uric acid in"Zhu-futang Practical Pediatrics",the subjects were divided into high uric acid group and non-high uric acid group.The logis-tic regression analysis was used to analyze the influencing factors of HUA in overweight and obese children and adoles-cents.The nomogram model was constructed by using the R language.The area under the receiver operating character-istic(ROC)curve(AUC),decision analysis curve(DIC),clinical impact curve(CIC)and C-index were used to evalu-ate the predictive ability of the model,and the Bootstrap repeated sampling method(taking samples for 1000 times)was used for internal validation of the model.Results The results of multivariate analysis showed that the age(OR=2.324,95％CI:1.155-4.672,P=0.018),gender(OR=0.456,95％CI:0.256-0.810,P=0.007),triglycerides(OR=3.775,95％CI:2.321-6.138,P＜0.001),blood calcium(OR=26.986,95％CI:3.186-228.589,P=0.003)and blood creatinine(OR=1.047,95％CI:1.026-1.070,P＜0.001)were the influen-cing factors of HUA in overweight and obese children and adolescents.AUC of the ROC curve of the model was 0.840,the sensitivity was 0.786,the specificity was 0.762,the Youden index was 0.548,and the C-index was 0.840.The risk probability of DC A was 0.1-0.8,the net benefit rate of both models was＞0,AUC of ROC curve in the internal verification was 0.871.Conclusion The constructed nomogram in this study has a good predictive efficiency for the onset risk of HUA in overweight and obese children and adolescents,and may provide reference for the early diagnosis and treatment of this population.
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Objective To investigate the clinical features of patients with recurrent acute pancreatitis(RAP)complicated with metabolic syndrome(MS)and the influencing factors of severe disease.Methods The clini-cal data of 382 RAP patients admitted to the hospital from June 2012 to June 2022 were retrospectively analyzed,and they were divided into the MS group(n=142)and the non-MS group(n=240)according to whether they were combined with MS,and into the severe group(n=29)and the non-severe group(n=353)according to the severity.The general data,serological parameters[triglyceride(TG),total cholesterol(TC),white blood cell count(WBC),neutrophil to lymphocyte ratio(NLR),blood calcium,D-dimer(D-D),lactate dehydrogen-ase(LDH),ALT,AST]and ICU occupancy rate and total length of stay were compared among all groups.Bi-nary logistic regression was used to analyze the independent influencing factors of RAP development into se-vere disease,and receiver operating characteristic(ROC)curve was used to analyze the predictive value of each indicator for RAP development.Results Hyperlipidemia was the most common cause of RAP in MS group(66.2％),and biliary origin was the most common cause of RAP in non-MS group(44.6％).There was sig-nificant difference among different causes was statistically significant(P＜0.05).There were significant differences in age,gender,proportion of hypertension,diabetes,MS,length of stay,LDH,blood calcium,D-D and NLR levels between the critical and non-critical groups(P＜0.05).The area under the curve(AUC)of blood calcium,D-D,LDH,NLR and combined diagnosis were 0.759,0.777,0.710,0.621,and 0.841,respec-tively.The AUC of single diagnosis had a certain predictive value,but combined diagnosis had a higher predic-tive value.Conclusion The most common cause in the MS group was hyperlipidemia,and the most common cause in the non-MS group was biliary.Blood calcium.D-D,LDH,NLR are reliable indicators to predict the development of RAP into severe disease,and the accuracy of combined diagnosis is higher.
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Objective To investigate the predictive value of the expression levels of YY1 transcription fac-tor(YY1)and microRNA(miR)-181a-5p in peripheral blood mononuclear cell for adverse pregnancy out-comes in gestational diabetes mellitus(GDM).Methods A total of 200 patients with GDM were enrolled as the GDM group.100 healthy pregnant women who underwent prenatal examinations during the same period were selected as the control group.The expressions levels of YY1 and miR-181a-5p in peripheral blood mono-nuclear cell were detected by fluorescent quantitative PCR.Receiver operating characteristic(ROC)curve was drawn to analyze the predictive value of YY1 and miR-181a-5p for adverse pregnancy outcomes in GDM pa-tients.Results Compared with the control group,the expression levels of YY1 and miR-181a-5p in peripheral blood mononuclear cell of GDM group were obviously decreased(P＜0.05),and the incidence rates of post-partum hemorrhage,macrosomia and neonatal hypoglycemia in GDM group were obviously higher(P＜0.05).Multivariate Logistic regression analysis showed that age and poor blood glucose control were inde-pendent risk factors for adverse pregnancy outcomes in GDM patients(P＜0.05),and the expression levels of peripheral blood mononuclear cell YY1 and miR-181a-5p were independent protective factors for adverse preg-nancy outcomes in GDM patients(P＜0.05).ROC curve results showed that the area under the curve(AUC)of the expression levels of YY1 and miR-181a-5p in peripheral blood alone and in combination in predicting ad-verse pregnancy outcomes in GDM patients was 0.717,0.751 and 0.832,respectively,and the AUC of their combination was obviously higher than that of the two alone(P＜0.05).Conclusion The decreased expres-sion levels of YY1 and miR-181a-5p in peripheral blood mononuclear cell of GDM patients could increase the risk of adverse pregnancy outcomes,YY1 and miR-181a-5p are closely related to adverse pregnancy outcomes in GDM patients,and both could be used as predictors of adverse pregnancy outcomes in GDM patients.
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Objective To investigate the relationship between serum microRNA(miR)-137 and miR-21 ex-pression and hypertensive retinopathy(HR).Methods A total of 123 hypertensive patients admitted to the hospital from March 2020 to August 2022 were selected as the hypertensive group and divided into HR and non-HR groups according to whether they were concomitant with HR,and another 58 healthy people who un-derwent the physical examination during the same period were selected as the control group.Clinical data of HR patients were collected and serum miR-137 and miR-21 expression was detected by real-time fluorescent quantitative PCR.The influencing factors of concurrent HR in hypertensive patients were analyzed,and receiv-er operating characteristic(ROC)curve was used for analzing predictive value of serum miR-137 and miR-21 expression in hypertensive patients with concurrent HR.Results Serum miR-137 and miR-21 relative expres-sion levels were higher in the hypertensive group than those in the control group(P<0.05).The incidence rate of HR in 123 hypertensive patients was 25.20%(31/123).Multivariate Logistic regression analysis showed that prolonged duration of hypertension and elevated systolic pressure,diastolic pressure,miR-137 rel-ative expression level,and miR-21 relative expression level were independent risk factors for HR in hyperten-sive patients(P<0.05).ROC curve analysis showed that the area under the curve(AUC)of serum miR-137 combined with miR-21 in predicting HR in hypertensive patients was 0.840,which was greater than 0.735 and 0.732 of the single detection of miR-137 and miR-21(P<0.05).Conclusion High expression of serum miR-137 and miR-21 is closely associated with hypertension complicating HR and may be an auxiliary predictor of HR.
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Objective To investigate the influencing factors of adverse pregnancy outcomes in patients with preeclampsia and the predictive value of serum trace elements in the second trimester.Methods A total of 98 patients with preeclampsia admitted to Qujing First People's Hospital from January 2019 to June 2022 were enrolled in the study.Patients were divided into poor outcome group and good outcome group according to whether they had adverse pregnancy outcomes.The clinical data of all patients enrolled in the study were col-lected and the serum levels of trace elements calcium,copper,zinc and iron were detected in the second trimes-ter.Univariate analysis and multivariate Logistic regression were used to analyze the influencing factors of ad-verse pregnancy outcomes in patients with preeclampsia.The levels of serum trace elements in the second tri-mester of pregnancy were compared between the poor outcome group and the good outcome group.The re-ceiver operating characteristic(ROC)curve was used to evaluate the predictive value of serum trace elements calcium,copper,zinc and iron for adverse pregnancy outcomes in patients with preeclampsia.Results Univari-ate analysis showed that compared with the good outcome group,the poor outcome group had significantly higher systolic blood pressure,24 h urinary protein quantitation,and D-dimer level(P＜0.05)and significantly less gestational age and platelet count at admission(P＜0.05).Multivariate Logistic regression analysis showed that 24 h urinary protein quantification,D-dimer and platelet count were the influencing factors of ad-verse pregnancy outcomes in patients with preeclampsia(P＜0.05).The levels of serum trace elements calci-um,copper,and zinc in the poor outcome group were significantly lower than those in the good outcome group(P＜0.05),and the level of iron was significantly higher than that in the good outcome group(P＜0.05).ROC curve analysis showed that the areas under the curves(AUCs)of serum calcium,copper,zinc,and iron in the second trimester of pregnancy for predicting adverse pregnancy outcomes in preeclampsia patients were 0.830(95％CI:0.780-0.880),0.855(95％CI:0.805-0.905),0.847(0.797-0.897)and 0.861(95％CI:0.811-0.911),respectively.Conclusion Adverse pregnancy outcomes in patients with preeclampsia are re-lated to 24 h urine protein,D-dimer and platelet count.The levels of serum trace elements calcium,copper,zinc and iron in the second trimester of pregnancy change significantly in patients with adverse pregnancy out-comes,which may become predictive markers of adverse pregnancy outcomes.
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Objective To investigate the predictive value of tissue plasminogen activator(t-PA),chro-mogranin A(CgA),and lipoprotein related phospholipase A2(LP-PLA2)in serum for major adverse cardio-vascular event(MACE)after percutaneous coronary intervention(PCI).Methods A total of 120 patients with coronary heart disease who underwent PCI in the hospital from August 2020 to August 2022 were en-rolled in the study.According to whether MACE occurred within 1 year after PCI,the patients were divided into MACE group(33 cases)and non-MACE group(87 cases).The levels of serum t-PA,CgA,LP-PLA2 and clinical data were compared between the MACE group and the non-MACE group.Multivariate Logistic regres-sion was used to analyze the risk factors of MACE after PCI.Receiver operating characteristic(ROC)curve was used to analyze the predictive value of t-PA,CgA and LP-PLA2 alone or in combination for MACE after PCI.Results The proportion of patients with smoking history,NT-proBNP,CgA,LP-PLA2,and the propor-tion of patients with hypertension in the MACE group were higher than those in the non-MACE group(P＜0.05),while left ventricular ejection fraction(LVEF)and t-PA were lower than those in the non-MACE group(P＜0.05).There were no significant differences in age,gender composition,serum creatinine,triglyc-erides(TG),total cholesterol(TC),low-density lipoprotein cholesterol(LDL-C),high-density lipoprotein cholesterol(HDL-C),drinking history,NYHA cardiac function classification,combined diabetes,application of aspirin,and application of clopidogrel between the two groups(P＞0.05).Multivariate Logistic regression analysis showed that smoking history,increased NT-proBNP,hypertension,decreased LVEF,decreased t-PA,increased CgA and increased LP-PLA2 were risk factors for MACE after PCI(P＜0.05).ROC curve analysis showed that the areas under the curves(AUCs)of serum t-PA,CgA and LP-PLA2 alone or in combination for predicting MACE after PCI were 0.785(95％CI:0.693-0.877),0.678(95％CI:0.564-0.791),0.730(95％CI:0.636-0.824),0.888(95％CI:0.830-0.946),respectively.The efficacy of combined prediction was bet-ter than those of single detections(P＜0.05).Conclusion The lower the serum t-PA level is and the higher the CgA and LP-PLA2 levels are,the greater the risk of MACE after PCI is.The combination of the three in-dicators has higher value in predicting MACE after PCI.
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Objective:To investigate associations between cardiac biomarkers with stroke severity and short-term outcome in patients with acute ischemic stroke (AIS).Methods:Patients with AIS admitted to the Affiliated Hospital of Qingdao University from June 2018 to February 2024 whose etiological classification was large artery atherosclerosis (LAA), small vessel occlusion (SVO) or cardioembolism (CE) were included retrospectively. According to the National Institutes of Health Stroke Scale score at admission, patients were divided into mild stroke group (≤8) and moderate to severe stroke group (>8). According to the modified Rankin Scale score at discharge, patients were divided into good outcome group (≤2) and poor outcome group (>2). Multivariate logistic regression analysis was used to determine the independent correlation between cardiac biomarkers and short-term outcome. The predictive value of cardiac biomarkers for poor outcome in patients with AIS and different stroke etiology subtypes were evaluated using receiver operating characteristic (ROC) curves. Results:A total of 2 151 patients with AIS were enrolled, including 1 256 males (58.4%), aged 67.40±11.34 years. 1 079 patents were LAA type (50.2%), 679 were SVO type (31.6%), and 393 were CE type (18.3%); 1 223 were mild stroke (56.86%) and 928 (43.14%) were moderate to severe stroke; 1 357 patients (63.09%) had good short-term outcome, and 794 (36.91%) had poor short-term outcome. Multivariate logistic regression analysis showed that N-terminal pro-B type natriuretic peptide (NT-proBNP), NT-proBNP/creatine kinase (CK) isoenzyme MB (CK-MB) ratio, and CK-MB/CK ratio were independent risk factors for poor short-term outcome. ROC curve analysis shows that the CK-MB/CK ratio had a higher predictive value for short-term poor outcome in patients with AIS (the area under the curve, 0.859, 95% confidence interval 0.839-0.879). Various cardiac biomarkers had a higher predictive value for short-term outcome of CE type and LAA type, but the predictive value for short-term outcome of SVO type was lower. Conclusions:Cardiac biomarkers are associated with the severity and poor outcome of AIS. NT-proBNP/CK-MB and CK-MB/CK ratios have higher predictive value for short-term poor outcome of AIS, especially in patients with CE type.
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Mechanical thrombectomy can improve the clinical outcome of patients with acute anterior circulation larger vessel occlusive stroke.However,a remarkable proportion of patients,even they have achieved a successful recanalization,still develop adverse outcomes,such as futile recanalization(FR).According to relevant literature reports,there are many factors that can affect futile recanalization.In this paper,a series of factors such as age,recanalization time,infarct volume,baseline severity,blood pressure that may affect futile recanalization of mechanical thrombectomy in patients with inanterior circulation large vessel occlusion will be comprehensively described and analyzed.(J Intervent Radiol,2024,33:321-324)
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Objective:The predictive model of cardiac arrest in the emergency room was constructed and validated based on Logistic regression.Methods:This study was a retrospective cohort study. Patients admitted to the emergency room of the First Affiliated Hospital of Xinjiang Medical University from January 2020 to July 2021 were included. The general information, vital signs, clinical symptoms, and laboratory examination results of the patients were collected, and the outcome was cardiac arrest within 24 hours. The patients were randomly divided into modeling and validation group at a ratio of 7:3. LASSO regression and multivariable logistic regression were used to select predictive factors and construct a prediction model for cardiac arrest in the emergency room. The value of the prediction model was evaluated using the area under the receiver operator characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).Results:A total of 784 emergency room patients were included in the study, 384 patients occurred cardiac arrest. The 10 variables were ultimately selected to construct a risk prediction model for cardiac arrest: Logit( P)= -4.503+2.159×modified early warning score (MEWS score)+2.095×chest pain+1.670×abdominal pain+ 2.021×hematemesis+2.015×cold extremities+5.521×endotracheal intubation+0.388×venous blood lactate-0.100×albumin+0.768×K ++0.001×D-dimer. The AUC of the model group was 0.984 (95% CI: 0.976-0.993) and that of the validation group was 0.972 (95% CI: 0.951-0.993). This prediction model demonstrates good calibration, discrimination, and clinical applicability. Conclusions:Based on the MEWS score, chest pain, abdominal pain, hematemesis, cold extremities, tracheal intubation, venous blood lactate, albumin, K +, and D-dimer, a predictive model for cardiac arrest in the in-hospital emergency room was constructed to predict the probability of cardiac arrest in emergency room patients and adjust the treatment strategy in time.
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Objective To build the early predictive model for chronic kidney disease(CKD)in hypertension and diabetes patients in the community.Methods The CKD patients were recruited from 4 health care centers in 4 urban areas in Kunming.The control group was residents without hypertension and diabetes(n = 1267).The disease group was residents with hypertension and/or diabetes(n = 566).The questionnaire survey,physical examination,laboratory testing,and 5 SNPs gene types in the PVT1 gene.The risk factors,which were filtered with logistics regression,were used to build predictive models.Four machine learning algorithms were built:support vector machine(SVM),random forest(RF),Na?ve Bayes(NB),and artificial neural network(ANN)models.Results Thirteen indicators included in the final diagnostic model:age,disease type,ethnicity,blood urea nitrogen,creatinine,eGFR from MDRD,ACR,eGFR from EPI2009,PAM13 score,sleep quality survey,staying-up late,PVT1 SNP rs11993333 and rs2720659.The accuracy,specificity,Kappa value,AUC of ROC,and PRC of ANN are greater than those of the other 3 models.The sensitivity of RF is the highest among 4 types of machine learning.Conclusions The ANN predictive model has a good ability of efficiency and classification to predict CKD with hypertension and/or diabetes patients in the community.
摘要
Objective·To establish a multifactorial discriminative model for predicting the degree of infiltration in patients with non-small cell lung adenocarcinoma based on clinically accessible laboratory indicators,such as tumor markers,coagulation function indicators,routine blood count indicators,and biochemical indicators.Methods·A retrospective study was conducted on 202 patients with lung adenocarcinoma admitted to Shanghai Chest Hospital in 2022.Multifactorial Logistic regression analysis was applied to screen independent factors that influenced the predictive infiltration degree of lung adenocarcinoma and to establish a regression model.In addition,machine learning was used to construct a discriminative model,and the area under the receiver operating characteristic curve(AUC)was used to evaluate the discriminative ability of the model to discriminate the degree of infiltration in lung adenocarcinoma patients.Results·A total of 202 patients with lung adenocarcinoma were included in the study,and divided into pre-invasive lesion group(n=59)and invasive lesion group(n=143).Multifactorial Logistic regression analysis revealed that urea,percentage of basophilic granulocytes,and albumin were independent factors for predicting the degree of infiltration of lung adenocarcinoma(all P<0.05).The predictive model expression was P = eX/(1 + eX),where X =(0.534×urea)+(1.527×percentage of basophilic granulocytes)-(1.916×albumin)+ 6.373.Machine learning results showed that the model performed best when urea,fibrinogen,albumin,percentage of basophilic granulocytes,prealbumin and carcino embryonic antigen(CEA)were included.After comparing the performance of 8 machine learning algorithms(based on ridge regression,least absolute shrinkage and selection operator,neural network,random forest,k-nearest neighbors,support vector machine,decision tree,and adaptive boosting algorithms)using the DeLong test,the ridge regression algorithm with the highest AUC was selected.The AUC of the predictive model was calculated to be 0.744(95%CI 0.656-0.832),with a sensitivity of 70.8%and a specificity of 70.2%.Conclusion·A comprehensive differentiation model constructed by urea,fibrinogen,albumin,percentage of basophilic granulocytes,prealbumin and CEA can effectively predict the infiltration degree of the enrolled lung adenocarcinoma patients,holding the potential to provide more precise guidance for the clinical grading and adjunctive treatment of lung adenocarcinoma.
摘要
Lung cancer is the malignant tumor with the highest incidence and mortality among the Chinese.Tumor node metastasis(TNM)staging established by the American Joint Committee on Cancer(AJCC)and International Union Against Cancer(UICC)is a commonly used criterion,but it still has limitations in judging the prognosis of non-small cell lung cancer(NSCLC)patients.With the advantages of real-time and convenient sampling,the immune score based on peripheral blood biomarkers have the ability to predict prognosis and efficacy of NSCLC patients,which have been developed and validated in clinical studies.However,clinical impleruentation of peripheral immune scores is still not widely in NSCLC patients.Therefore,this study introduces and evaluates the 6 peripheral immune scores and reviews the reseach progress of them in the treatment of NSCLC.
摘要
Objective To explore the value of serum stearoyl sphingosine(C18∶1-Cer)and 1-stearoyl-sn-glycero-3-phospho-choline(LPC 18∶0)levels in pregnant women's serum samples during pregnancy in predicting gestational diabetes mellitus(GDM).Methods The clinical data and laboratory indicators of 126 pregnant women were retrospectively analyzed.The sub-jects were divided into GDM group(n=66)and control group(n=60)according to the GDM diagnosis results.Mass spec-trometry was used to detect the serum C18∶1-Cer and LPC18∶0 levels of the subjects in early and mid pregnancy.Logistic re-gression analysis was used to screen out the risk factors for GDM.Receiver operating characteristic(ROC)curve was used to evaluate the predictive value of C18∶1-Cer,LPC18∶0 and their combination for GDM.Results Compared with the control group,the serum C18∶1-Cer and LPC18∶0 levels of the subjects in the GDM group were significantly increased in early(18.92±2.77ng/ml vs 23.47±4.18ng/ml,41.32±17.55ng/ml vs 88.08±16.02ng/ml)and mid pregnancy(23.14±4.10ng/ml vs 18.76±4.05ng/ml,84.60±14.53ng/ml vs 40.50±17.79ng/ml),and the differences were statistically significant(t=7.127,15.637;-5.984,2.174,all P＜0.05)C18∶1-Cer was positively correlated with fasting plasma glucose(FPG),fasting plasma insulin(FPI),homeostasis model assessment of insulin resistance(HOMA-IR),glycated hemoglobin(HbA1c)and triglyceride(TG)(r=0.458,0.209,0.317,0.223,0.219,all P<0.05).LPC18.0 was positively correlated with FPG,FPI,HOMA-IR,HbA1c,total cholesterol(TC)and TG(r= 0.715,0.426,0.580,0.465,0.232,0.372,all P<0.05).Logistic regression analysis results showed that C18∶1-Cer[OR(95%CI):1.522(1.136～2.039),P<0.05]and LPC18:0[OR(95%CI):1.198(1.102～1.302),P<0.001]were independent risk factors for GDM.ROC curve analysis results showed that the area under the curve(AUC)of serum C18∶1-Cer,LPC18∶0 and the combination of the two indicators were 0.819,0.971 and 0.986,respectively.The predictive performance of the combination of the two indicators was better than that of the single detection.Conclusion Serum C18∶1-Cer and LPC18∶0 in early pregnancy were closely related to the occurrence of GDM.C18∶1-Cer combined with LPC 18∶0 has a certain predictive value for the early diagnosis of GDM.
摘要
Objective:To construct a Chinese neonatal model of early-onset sepsis (EOS) using the Kaiser Permanente sepsis risk calculator and laboratory indicators and validate its clinical prediction potential.Methods:Newborns with a gestational age of ≥34 weeks, who were hospitalized in the Department of Neonatology, the First Affiliated Hospital of Zhengzhou University from January 2020 to June 2022 were retrospectively recruited.Their clinical data were collected.Predictors were screened via the multivariate regression analysis, and the Nomogram model was constructed using R software and RStudio software.Hosmer-Lemeshow test, receiver operating characteristic curve, the decision curve analysis (DCA) were used to evaluate the prediction potential of the Nomogram.Results:A total of 769 patients were enrolled, including 107 patients in the EOS group (5 culture-confirmed cases and 102 clinically diagnosed cases), and 662 cases in the non-EOS group.Ten variables were screened and introduced into the Nomogram, including the gestational age, birth weight, body temperature, white blood cell count, C-reactive protein, procalcitonin, premature rupture of membranes≥18 h, infection of Group B Streptococcus, ventilator application, and prenatal antibiotics.The predictive model showed good discrimination and consistency, with the area under the curve of 0.834 (95% CI: 0.771-0.896). The DCA of the prediction model showed that it was effective in clinical application within the effective threshold of 6%-95%, with a net benefit following the application of corresponding treatment measures. Conclusions:A Chinese neonatal model of EOS was created by using the Kaiser Permanente sepsis risk calculator and laboratory indicators, which has been validated effective.It provides references for clinical management and the guidance for the use of antibiotics.