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Objetivo: Determinar si es posible predecir la valoración del recién nacido según el estado nutricional materno a través de un modelo de árbol de decisión. Métodos: Estudio analítico transversal. Se revisaron 326 historias clínicas de gestantes de un hospital público peruano, 2021. Se valoró el recién nacido mediante el puntaje APGAR, edad gestacional al nacer, peso al nacer, peso y talla para la edad gestacional. El estado nutricional materno incluyó el índice de masa corporal pregestacional y la ganancia de peso gestacional. La predicción se realizó mediante un modelo de aprendizaje automático supervisado denominado "árbol de decisión". Resultados: No fue posible predecir mediante el estado nutricional materno, el puntaje APGAR al minuto y la talla para la edad gestacional. La probabilidad de tener edad gestacional a término al nacer es de 97,2 % cuando la ganancia de peso gestacional es > 5,4 Kg (p = 0,007). Las probabilidades más altas de peso adecuado al nacer fueron con ganancia de peso gestacional entre 4,5 Kg (p < 0,001) y 17 Kg (p < 0,001) y con índice de masa corporal pregestacional ≤ 36,523 Kg/m2 (p = 0,004). Finalmente, la mayor probabilidad de peso adecuado para la edad gestacional es cuando la ganancia de peso gestacional es ≤ 11,8 Kg (p < 0,001) y con un índice de masa corporal pregestacional ≤ 36,523 Kg/m2 (p = 0,005). Conclusiones: Es posible predecir la valoración del recién nacido a partir del estado nutricional materno mediante un aprendizaje automático(AU)
Objective: To determine whether it is possible to predict the assessment of the newborn according to maternal nutritional status through a decision tree model. Methods: Cross-sectional analytical study. A total of 326 medical records of pregnant women from a Peruvian public hospital were reviewed, in 2021. The newborn was assessed using the APGAR score, gestational age at birth, birth weight, weight and height for gestational age. Maternal nutritional status included pregestational body mass index and gestational weight gain. The prediction was made using a supervised machine learning model called a "decision tree." Results: The APGAR score at one minute and height for gestational age were not possible to predict by maternal nutritional status. The probability of having full-term gestational age at birth is 97.2% when gestational weight gain is > 5.4 kg (p = 0.007). The highest probabilities of adequate birth weight were with gestational weight gain between 4.5 kg (p < 0.001) and 17 kg (p < 0.001) and with pregestational body mass index ≤ 36.523 kg/m2 (p = 0.004). Finally, the highest probability of adequate weight for gestational age is when gestational weight gain is < 11.8 Kg (p < 0.001) and with a pregestational body mass index ≤ 36.523 Kg/m2 (p = 0.005). Conclusions: It is possible to predict the assessment of the newborn based on the mother's nutritional status using machine learning(AU)
الموضوعات
Humans , Female , Pregnancy , Adult , Infant, Newborn , Nutritional Status , Forecasting , Body Mass Index , Gestational Age , Overweight , Gestational Weight Gain , Obesityالملخص
Fundamento: el proyecto de vida profesional constituye una formación psicológica compleja que debe ser desarrollada y evaluada durante las acciones que se realizan en los procesos sustantivos universitarios: formación, investigación y extensión universitaria. Objetivo: validar la efectividad del procedimiento para la formación del proyecto de vida profesional en estudiantes de Medicina. Métodos: se realizó una investigación cuantitativa de tipo preexperimental en la Universidad de Ciencias Médicas de Holguín, Facultad de Ciencias Médicas Mariana Grajales Coello, desde enero de 2021 hasta diciembre del 2022. Se utilizaron como métodos teóricos el analítico sintético, inductivo-deductivo y la modelación. Los métodos empíricos aplicados fueron el cuestionario a expertos, la observación y el instrumento "Exploración del proyecto de vida profesional". La investigación se llevó a efecto en tres etapas para la presentación, la valoración y la evaluación del procedimiento. Resultados: se aportó un procedimiento para la formación del proyecto de vida profesional en estudiantes de la carrera Medicina a implementarse durante los procesos sustantivos universitarios. El criterio de expertos permitió evaluar el procedimiento como bastante adecuado. El preexperimento mostró su efectividad para alcanzar el objetivo. Conclusiones: existen insuficiencias en el tratamiento del proyecto de vida profesional durante la formación inicial. La validación del procedimiento fue aceptada por los expertos porque contribuye a la calidad del proceso pedagógico en la carrera Medicina.
Foundation: the professional life project constitutes a complex psychological training that must be developed and evaluated during the actions carried out in the substantive university processes: training, research and university extension. Objective: validate the formation procedure effectiveness for the professional life project in medical students. Methods: a pre-experimental quantitative research was carried out at the Holguín Medical Sciences University, Mariana Grajales Coello Medical Sciences Faculty, from January 2021 to December 2022. Synthetic analytical, inductive-deductive and analytical methods were used as theoretical methodsand modeling. The empirical methods applied were the expert questionnaire, observation and the Exploration of the professional life project instrument. The research was carried out in three stages for the presentation, assessment and evaluation of the procedure. Results: a procedure was provided for the professional life project formation in Medicine students to be implemented during the substantive university processes. Expert judgment allowed the procedure to be evaluated as quite adequate. The pre-experiment showed its effectiveness in achieving the objective. Conclusions: there are insufficiencies in the treatment of the professional life project during initial training. The validation of the procedure was accepted by the experts because it contributes to the pedagogical process quality in the Medicine career.
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The high intra- and inter-tumor heterogeneity of gastric cancer leads to a great difference in the immunotherapy efficacy and the prognosis among patients. Several biomarkers, including programmed death-ligand 1, human epidermal growth factor receptor 2, the features of tumor microenvironment, the peripheral blood inflammatory markers and Claudin18.2 have predictive value in the immunotherapy efficacy and the prognosis of gastric cancer patients, which might help the clinicians find the potential patients who will benefit from immunotherapy, and achieve the goal of precision medicine.
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Objective To systematically review the risk prediction models for intradialytic hypotension in maintenance hemodialysis patients,with a view to provide references for clinical practice.Methods PubMed,Embase,Web of Science,Cochrane Library,CINAHL,CNKI,VIP,Wanfang and CBM were searched from inception to May 29,2023.2 reviewers independently screened the literature,extracted information and assessed methodological quality using the Prediction Model Risk of Bias Assessment Tool.Results A total of 20 studies and 25 models were included with the sample size of 68～9 292 cases and the incidence of outcome events of 2.1～51％.Baseline systolic blood pressure,age,ultrafiltration rate,diabetes and dialysis duration were the top 5 predictors of repeated reporting of the models.20 models reported the area under the curve of ranging from 0.649 to 0.969,and 5 models reported calibration metrics.There were 9 internal validations and 4 combined internal and external validation models.The overall applicability of the 20 studies was good,but all had a high risk of bias,mainly in data analysis.Conclusion Research on risk prediction models for intradialytic hypotension in maintenance hemodialysis patients is still in the developmental stage.Future studies should improve the research design and reporting process,and validation studies of existing models should be carried out to further evaluate the effectiveness and feasibility in clinical practice.
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Objective:To evaluate the value of 18F-FDG PET metabolic parameters in predicting histopathological grade of soft tissue sarcoma (STS). Methods:From December 2012 to December 2021, 51 patients (26 males, 25 females, age range: 32-84 years) who underwent 18F-FDG PET/CT imaging before treatment and confirmed STS pathologically in the First Affiliated Hospital of Dalian Medical University were retrospectively collected. 18F-FDG PET metabolic parameters SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and intertumoral FDG uptake heterogeneity (IFH) were measured. Kruskal-Wallis rank sum test was used to analyze the differences in metabolic parameters among different groups and Spearman rank correlation analysis was used to analyze the correlation of each metabolic parameter and histological grade. Logistic regression was used to screen and construct the prediction model for high-grade STS. ROC curve was plotted and Delong test was used to analyze the differences among AUCs. Results:The metabolic parameters SUV max, MTV, TLG and IFH were significantly different among French Federation of Cancer Centers Sarcoma Group (FNCLCC)Ⅰ( n=8), Ⅱ( n=10) and Ⅲ ( n=33) grade groups ( H values: 16.24, 10.52, 19.29 and 16.99, all P<0.05), and each metabolic parameter was positively correlated with histological grade ( rs values: 0.58, 0.45, 0.52, and 0.62, all P<0.05). Multivariate logistic regression analysis showed that SUV max(odds ratio ( OR)=1.27, 95% CI: 1.06-1.51, P=0.009) and IFH ( OR=6.83, 95% CI: 1.44-32.27, P=0.015) were independent risk indicators for high-grade STS. The prediction model constructed by combining SUV max and IFH had better diagnostic efficacy for differentiating high-grade STS with the AUC of 0.93, and the sensitivity of 93.9%(31/33) and the specificity of 16/18, respectively. The AUC of prediction model was significant different from SUV max, MTV, TLG and IFH (AUCs: 0.81, 0.78, 0.86 and 0.85; z values: 2.69, 2.53, 1.94 and 1.97, all P<0.05). Conclusions:The metabolic parameters SUV max, MTV, TLG and IFH are valuable predictors for histological grade of STS. The combination of SUV max and IFH may be a more meaningful method than using each of the above metabolic parameters alone.
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Objective:To predict the short-term postoperative recurrence status of patients with refractory temporal lobe epilepsy (TLE) by analyzing preoperative 18F-FDG PET images and patients′ clinical characteristics based on deep residual neural network (ResNet). Methods:Retrospective analysis was conducted on preoperative 18F-FDG PET images and clinical data of 220 patients with refractory TLE (132 males and 88 females, age 23.0(20.0, 30.2) years)) in the First Affiliated Hospital of Jinan University between January 2014 and June 2020. ResNet was used to perform high-throughput feature extraction on preprocessed PET images and clinical features, and to perform a postoperative recurrence prediction task for differentiating patients with TLE. The predictive performance of ResNet model was evaluated by ROC curve analysis, and the AUC was compared with that of classical Cox proportional risk model using Delong test. Results:Based on PET images combined with clinical feature training, AUCs of the ResNet in predicting 12-, 24-, and 36-month postoperative recurrence were 0.895±0.073, 0.861±0.058 and 0.754±0.111, respectively, which were 0.717±0.093, 0.697±0.081 and 0.645±0.087 for Cox proportional hazards model respectively ( z values: -3.00, -2.98, -1.09, P values: 0.011, 0.018, 0.310). The ResNet showed best predictive effect for recurrence events within 12 months after surgery. Conclusion:The ResNet model is expected to be used in clinical practice for postoperative follow-up of patients with TLE, helping for risk stratification and individualized management of postoperative patients.
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Objective To construct a nomogram prediction model for major adverse cardiovascular events(MACE)within 1 year after percutaneous coronary intervention(PCI)in elderly patients with acute coronary syndrome(ACS).Methods A retrospective analysis was conducted on 551 patients with diagnosed ACS and undergoing PCI in Department of Cardiovascular Medicine of Air Force Medical Center from 1 January 2020 to 1 April 2022.According to the occurrence of MACE during 1 year of follow-up,they were classified into MACE group(n=176)and non-MACE group(n=375).Risk factors for the occurrence of MACE in elderly ACS patients within 1 year after PCI were analysed using univariate and multivariate logistic regression,a nomogram prediction model was constructed,and the predictive power of the model was assessed using the area under the ROC curve(AUC).Results The MACE group had significantly higher Gensini score,systemic immune-inflammation index,and GRACE score,but obviously lower prognostic nutritional index than the non-MACE group(P＜0.01).Multivariate logistic regression analysis showed that recent smoking(OR=2.222,95％CI:1.361-3.628,P=0.010),hyperlipidaemia(OR=1.881,95％CI:1.145-3.089,P=0.013),prognostic nutritional index(OR=4.645,95％CI:2.788-7.739,P=0.001),LVEF(OR=5.177,95％CI:3.160-8.483,P=0.001),systemic immune-inflammation index(OR=5.396,95％CI:3.179-9.159,P=0.001),and preoperative di-agnosis of non-STEMI(OR=2.829,95％CI:1.356-5.901,P=0.006)or STEMI(OR=3.451,95％CI:1.596-7.463,P=0.002)were independent influencing factors for occurrence of MACE after PCI in elderly ACS patients.ROC curve analysis showed that the AUC value of the nomo-gram model for predicting MACE within 1 year after PCI in elderly ACS patients was 0.888.Con-clusion Our developed nomogram model is simple and practical,and can effectively predict the occurrence of MACE within 1 year after PCI in elderly ACS patients.And external validation should be carried out to ensure its generality.
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Objective To investigate the predictive value of systemic immune-inflammation index(SII)and N-terminal pro-brain natriuretic peptide(NT-proBNP)level in elderly patients with acute ST-segment elevation myocardial infarction(STEMI)developing contrast-induced acute kidney injury(CIAKI)after PCI.Methods A total of 1085 elderly STEMI patients undergoing emergency PCI in the Affiliated Hospital of Xuzhou Medical University from January 2018 to March 2023 were consecutively recruited as a training set,and another 287 elderly STEMI pa-tients receiving emergency PCI in the East Branch of the Affiliated Hospital from January 2021 to June 2023 were included as a verification set.According to the diagnostic criteria of CIAKI,they were divided into CIAKI group(n=95)and non-CIAKI group(n=990).Based on the results of restricted cubic spline(RCS)analysis,the patients from the training set were assigned into low-risk subgroup(n=292),moderate-risk group(n=515)and high-risk group(n=278).Multivari-ate logistic regression analysis was used to analyze the independent risk factors of CIAKI in elder-ly STEMI patients after PCI,and ROC curve was plotted to analyze the predictive value of combi-nation of SII and NT-proBNP.The risk of CIAKI was compared among the patients at different risk grades.Results Age,SII,baseline serum creatinine,NT-proBNP,fasting blood glucose and use of diuretics were independent risk factors for CIAKI after primary PCI in elderly STEMI patients(P＜0.05,P＜0.01).The AUC value of SII combined with NT-proBNP in predicting CIAKI was 0.801(95％CI:0.761-0.842,P＜0.01),with a sensitivity of 83.2％and a specificity of 67.5％,both superior to that of SII or NT-proBNP alone.RCS analysis revealed an increased risk of CIAKI at SII ≥1084.97 × 109/L and NT-proBNP ≥296.12 ng/L.The incidence of CIAKI was increased with the increase of risk grades(1.71％vs 6.41％vs 20.50％).Conclusion SII and NT-proBNP are independent risk factors for CIAKI after emergency PCI in elderly STEMI pa-tients.And their combination has better predictive value for CIAKI.
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Objective To explore the correlation between serum beta 2-microglobulin(B2M)level and cerebral microbleeds(CMB)in the elderly.Methods A retrospective analysis of 636 elderly patients with chronic diseases admitted to the Department of Neurology of our hospital from Janu-ary 2020 to November 2022 was made.On the second day after admission,venous blood samples were collected to detect the serum B2M level,and brain magnetic resonance susceptibility weigh-ted imaging was performed.Then these patients were assigned into CMB group(82 cases)and CMB-free group(554 cases).Binary logistic regression analysis was employed to identify the inde-pendent risk factors for CMB.Results Binary logistic regression analysis showed that serum B2M level was an independent risk factor for CMB in elderly patients(Model 1:β=0.179,OR=1.196,95％CI:1.017-1.407,P=0.031;Model 2:β=0.215,OR=1.240,95％CI:1.048-1.468,P=0.012)after adjusting confounding factors.ROC curve analysis indicated that the optimal cutoff value of serum B2M level in diagnosing CMB was 1.805 mg/L,with a sensitivity of 70.7％and a specificity of 52.5％,and an AUC value of 0.657(95％CI:0.595-0.719,P＜0.01).Conclusion The increment of serum B2M level is closely related to CMB in the elderly population,so the pro-tein can be used as one of indicators for prediction of CMB in the population.
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Objective To construct a nomogram model for predicting the risk of in-hospital death in CHF patients by using noninvasive hemodynamic monitoring combined with age,DBP,CRP and renal insufficiency(serum creatinine≥ 442 μmol/L).Methods A total of 223 elderly patients with acute onset of CHF admitted in First,Second Medical Centre of Chinese PLA General Hos-pital from September 2022 to March 2023 were recruited in this study.According to their clinical outcomes,they were divided into survival group(196 cases)and death group(27 cases).Based on the in-hospital death and other related indicators,a nomogram model was constructed to predict the risk factors of in-hospital death in CHF.Results Noninvasive hemodynamic mornitoring indi-cated that the death group had significantly higher LVEF and LCWI values but lower LVEDV value than the survival group(P＜0.05,P＜0.01).Multivariate logistic regression analysis showed that age(OR=1.131,95％CI:1.052-1.213,P=0.001),DBP(OR=0.932,95％CI:0.882-0.982,P=0.011),CRP(OR=1.171,95％CI:1.021-1.352,P=0.024),LVEDV(OR=0.984,95％CI:0.962-0.992,P=0.011)and renal insufficiency(OR=5.863,95％CI:1.351-1.731,P=0.004)were independent risk factors for the short-term prognosis of the elderly CHF patients.The AUC value of the nomogram model was 0.902(95％CI:0.819-0.948,P＜0.05),and calibration curve analysis showed the C-index was 0.902,indicating accurate predictive perform-ance.Conclusion Age,DBP,LVEDV,CRP and renal insufficiency are independent risk factors for the short-term prognosis of the elderly CHF patients.
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Objective To investigate the serum expression level of miR-182-5p in patients with chronic heart failure(CHF),and analyze its correlation with left ventricular remodeling and prog-nosis.Methods A total of 138 CHF patients admitted to Liaocheng People's Hospital from Janu-ary 2019 to December 2021 were enrolled as CHF group,and another 120 healthy volunteers who took physical examinations at the same time served as the healthy group.The expression level of miR-182-5p in serum was detected in the two groups.Pearson analysis was used to analyze the correlation between its expression level and left ventricular remodeling.ROC curve was plotted to analyze the diagnostic value of miR-182-5p expression level.During 1 year of follow-up,their sur-vival status was collected and analyzed in the CHF patients.The prognostic value of miR-182-5p expression level was evaluated by Kaplan-Meier survival curve.Results The CHF patients had significantly lower LVEF value,but higher left ventricular remodeling index(LVRI)and miR-182-5p expression level than the healthy group(P＜0.05,P＜0.01).The expression level of miR-182-5p was negatively correlated with LVEF(r=-0.496,P=0.000)and positively with LVRI(r=0.460,P=0.000).The AUC value of miR-182-5p expression level in diagnosing CHF was 0.964,the cutoff value was 0.905,the sensitivity was 91.3％,and the specificity was 86.7％.Kaplan-Meier survival curve analysis showed that the high expression level of miR-182-5p could predict the overall survival of CHF patients(P=0.039).Conclusion The expression level of miR-182-5p is higher in CHF patients than healthy people,and the patients with higher level indi-cate more serious left ventricular remodeling.Detecting the expression level of miR-182-5p is help-ful for the diagnosis and poorgnosis prediction of CHF patients.
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Objective To analyze the correlation between plasma Pannexin-1(Panx-1)level and no-reflow after percutaneous coronary intervention(PCI)in patients with ST-segment elevation my-ocardial infarction(STEMI).Methods A prospective trial was performed on 218 STEMI patients who underwent PCI in our hospital from January 2019 to December 2021.According to the blood flow classification of myocardial infarction thrombolysis(TIMI)after PCI,they were divided into normal reflow group(110 cases),slow reflow group(69 cases)and no reflow group(39 cases).The plasma Panx-1 level was determined by ELISA,and the levels of P-selectin,activated glyco-protein Ⅱ b/Ⅲ a(aGP Ⅱ b/Ⅲ a)and platelet-leukocyte aggregates(PLA)were determined by flow cytometry.Results Older age,larger ratio of diabetes mellitus,longer time from symptom onset to PCI,higher platelet count and levels of LDL-C,D-dimer,P-selectin,GP Ⅱ b/Ⅲ a,PNA,PM A,PLyA and plasma Panx-1 were observed in the no-reflow group than the normal and slow reflow groups(P＜0.05).The plasma Panx-1 level in STEMI patients was positively correlated with P-selectin,GP Ⅱ b/Ⅲ a,PNA,PM A and PLyA(P＜0.05,P＜0.01).LDL-C ≥3.20 mmol/L and plasma Panx-1＞0.88 μg/mL were independent risk factors for no-reflow after PCI in STEMI pa-tients(OR=2.198,95％CI:1.252-3.858,P=0.006;OR=16.849,95％CI:4.481-63.357,P=0.000).The AUC value of Panx-1 was 0.826(95％CI:0.744-0.907,P＜0.01)in predicting no re-flux in STEMI patients after PCI.Conclusion The increase of plasma Panx-1 level is closely asso-ciated with the occurrence of no reflow in STEMI patients after PCI,and the protein can be used as a predictive biomarker for the phenomenon.
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Objective To investigate the changes in serum HP and ADMA levels in patients with ACI and the correlation of their levels with recanalization after venous thrombolysis and poor prognosis.Methods A total of 260 ACI patients undergoing venous thrombolysis in our hospital from January 2020 to March 2023 were retrospectively recruited,and were categorized into reper-fusion group(n=196)and non-reperfusion group(n=64)based on the efficacy of thrombolysis.After a 90-day follow-up,they were further divided into good prognosis group(n=159)and poor prognosis group(n=101)according to the results of a modified Rankin scale.Serum levels of HP and ADMA at admission were compared between the two groups.Logistic regression analysis was used to analyze the risk factors for non-reperfusion and poor prognosis in ACI patients.ROC curve analysis was performed to evaluate the predictive value of serum HP and ADMA levels for non-reperfusion and the diagnostic efficiency for poor prognosis in ACI patients.Results The non-reperfusion group exhibited notably elevated serum HP and ADMA levels than the reperfusion group(2.10±0.21 g/Lvs1.29±0.31 g/L,1.68±0.19 μmol/L vs 0.69±0.11 μmol/L,P＜0.01).HP and ADMA were identified as significant risk factors for uncanalization after treatment(P＜0.01).The AUC value of their combination in diagnosing uncanalization after venous thrombolys-is was 0.869(95％CI:0.830-0.908).Furthermore,significantly higher serum levels of HP and ADMA were observed in the poor prognosis group than the good prognosis group(2.27±0.19 g/L vs 1.15±0.34 g/L,1.72±0.21 μmol/L vs 0.64±0.10 μmol/L,P＜0.01).HP and ADMA were also recognized as influencing factors for poor prognosis in 90 d after treatment(P＜0.01).The AUC value was 0.816(95％CI:0.768-0.865)when their combination was used to predict poor prognosis in 90 d after treatment.Conclusion HP and ADMA are highly expressed in the se-rum of ACI patients with failed venous thrombolysis and poor prognosis.Their combined detec-tion can effectively predict both uncanalization and poor prognosis.
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Objective To investigate the clinical value of serum soluble Axl receptor tyrosine ki-nase(sAxlTK)in evaluating short-term prognosis in patients with acute decompensated heart failure(ADHF).Methods A total of 238 elderly ADHF patients admitted to Bozhou People's Hospital from January 1,2018 to October 1,2021 were recruited and divided into poor prognosis group(45 patients)and good prognosis group(193 patients)according to the occurrence of com-plex events within 90 d of follow-up.Based on the optimal cut-off value of serum sAxlTK level,they were also assigned into high level group(80 cases)and low level group(158 cases).Serum levels of sAxlTK,troponin Ⅰ(cTnⅠ)and N-terminal B-type natriuretic peptide precursor(NT-proBNP)were detected.Results Serum sAxlTK level was significantly higher in the poor prognosis group than the good prognosis group[43.89(33.95,51.44)μg/L vs 23.89(18.73,33.92)μg/L,P＜0.01].Multivariate logistic regression analysis showed that serum cTnⅠ and sAxlTK levels were independent risk factors for short-term poor prognosis in ADHF patients(OR=1.922,95％CI:1.035-3.568,P=0.039;OR=1.021,95％CI:1.008-1.034,P=0.001).ROC curve analysis indicated that the AUC value of combined serum sAxlTK,cTnⅠ and NT-proBNP levels to predict short-term poor prognosis was 0.836(95％CI:0.778-0.895).The incidence of complex events within 90 d was significantly higher in the high level group than the low level group(45.0％vs 5.7％,P＜0.05).Kaplan-Meier curve analysis revealed that the cumulative inci-dence of complex events was also higher in the high level group than the low level group(X2=66.991,Plog rank＜0.01).The high level group had significantly lower overall survival rate and worse survival prognosis than the low level group(X2=16.899,Plog rank＜0.01).Conclusion High serum sAxlTK level in elderly ADHF patients at admission is associated with a higher risk of 90-day short-term poor prognosis.Serum sAxlTK has the potential to become a useful tool for early prediction of short-term poor prognosis,and its combination with cTnⅠ and NT-proBNP can fur-ther improve the accuracy of prognosis prediction.
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Objective To detect the content of intestinal Escherichia coli(E.coli)in patients with acute ischemic stroke(AIS)and to analyze the relationship between the bacteria and short-term prognosis of patients with cerebral stroke.Methods A total of 75 elderly AIS patients admitted to our department from March to December 2022 were enrolled,and divided into good prognosis group(47 cases)and poor prognosis group(28 cases)according to the results of modified Rankin scale 3 months after discharge.Multivariate logistic regression analysis was used to analyze the factors affecting the prognosis of the patients,and ROC curve analysis was employed to analyze the predictive value of the intestinal bacterial content for the short-term prognosis of stroke pa-tients.Results The NIHSS score at admission and E.coli content were significantly higher in the poor prognosis group than the good prognosis group(P＜0.01).Multivariate logistic regression analysis showed that NIHSS score at admission(OR=1.302,95％CI:1.077-1.573,P=0.006)and E.coli content(OR=2.803,95％CI:1.454-5.404,P=0.002)were independent risk factors for short-term poor prognosis in the AIS patients.ROC curve analysis indicated that the AUC value was 0.758 for E.coli content,0.718 for NIHSS score,and 0.818 when the 2 indicators combined together.Conclusion Intestinal E.coli content and NIHSS score may be related to the poor prog-nosis of elderly AIS patients.The higher the content of E.coli is,the worse the recovery of neuro-logical function,which affects the short-term prognosis of the patients.
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Objective:To investigate the relationship between the age-adjusted Charlson comorbidity index(aCCI)and the risk of in-hospital death for people aged ≥ 90 years with community-acquired pneumonia(CAP), and to construct a novel scoring model for predicting in-hospital mortality.Methods:Basic personal and medical data about sex, age, hospitalization days, hospitalization expenses, in-hospital outcomes and discharge/admitting diagnosis of CAP patients aged ≥ 90 years hospitalized in Peking University Third Hospital between 2010 and 2019 were collected retrospectively.Multivariate Logistic regression analysis was conducted to examine the association between aCCI or other complications and in-hospital death.The receiver operating characteristic curve(ROC)was used to assess the value of aCCI and a new scoring model in predicting in-hospital death of CAP in people aged ≥ 90 years.Results:A total of 274 CAP patients aged ≥ 90 years were included in this study, of whom 85 died in hospital.Multivariate Logistic regression analysis showed that malnutrition( OR=2.21, 95% CI: 1.05-4.67, P<0.05), respiratory failure( OR=18.91, 95% CI: 9.34-38.25, P<0.001)and aCCI( OR=1.51, 95% CI: 1.23-1.85, P<0.001)were prognostic factors for in-hospital death in CAP patients aged ≥ 90 years.Based on the above results, a novel scoring model, MRC(malnutrition, respiratory failure, aCCI)was established.The area under the ROC curve of the aCCI score for predicting the risk of in-hospital death in CAP patients aged ≥ 90 years was 0.743(95% CI: 0.684-0.802). The area under the ROC curve of the MRC score was 0.891(95% CI: 0.848-0.933), indicating a higher predictive value than that of the aCCI score alone( Z=6.337, P<0.001). Conclusions:The MRC score model can be used to evaluate and predict the risk of in-hospital death in long-living CAP patients.
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Objective:To investigate the clinical application value of commonly used preoperative indicators of sarcopenia in predicting postoperative pneumonia in patients aged 70 years and above with esophageal cancer.Methods:A retrospective analysis was conducted on the clinical data of 398 elderly patients(≥70 years old)with esophageal squamous cell carcinoma who underwent thoracic laparoscopic radical resection of esophageal cancer in our hospital from January 2020 to December 2021.The study aimed to investigate the correlation between clinical pathological indicators and commonly used measurement indicators of sarcopenia and postoperative pneumonia.Statistical analysis was performed to analyze the data.Results:The study found that the proportion of postoperative pneumonia in esophageal squamous cell carcinoma patients aged 70 years and above was 27.9%(111 out of 398). The pneumonia group had significantly lower preoperative BMI and peak expiratory flow(PEF)measurements compared to the non-pneumonia group, with statistically significant differences( t=2.799, 2.674, both P<0.05). Logistic multivariate analysis revealed that low PEF, low psoas major muscle index(PMI), and low psoas muscle density(PMD)were the primary risk factors for postoperative pneumonia in esophageal cancer patients aged 70 years and above(Wald χ2 values were 7.577, 6.091, 6.845, all P<0.05). The risk of postoperative pneumonia in esophageal cancer patients aged 70 years and above with low PEF, low PMI, and low PMD was found to be 1.969 times higher(95% CI: 1.215-3.185, P=0.006), 1.912 times higher(95% CI: 1.143-3.205, P=0.014), and 1.832 times higher(95% CI: 1.164-2.882, P=0.009)respectively, compared to patients with high PEF, high PMI, and high PMD. Conclusions:Low PEF, low PMI, and low PMD are significant risk factors for postoperative pneumonia in esophageal cancer patients aged 70 years and older.Preoperative PEF, PMI, and PMD, which are commonly utilized measurement indicators for sarcopenia, can be utilized as early screening indicators for postoperative pneumonia.
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Objective:To investigate the predictive value of a radiomics model based on biparametric magnetic resonance imaging(bpMRI)for biochemical recurrence(BCR)after radical prostatectomy(RP)in elderly prostate cancer patients(≥60 years old).Methods:A retrospective analysis was conducted on data from 175 patients treated at Beijing Hospital from August 2017 to December 2021.Based on pathological results, image segmentation was performed on preoperative bpMRI T2, diffusion weighted imaging(DWI), and apparent diffusion coefficient(ADC)sequences.Pyradiomics was utilized to extract radiomic features, and Cox regression, Spearman correlation coefficient, and LASSO regression were employed for feature dimensionality reduction, leading to the construction of radiomic labels.Clinical models and image-clinical combined models were developed using multifactorial Cox regression analysis, and the performance of these models in predicting BCR was evaluated using the concordance index(C-index).Results:The 175 patients were randomly divided into a training set(122 cases)and a test set(53 cases)at a ratio of 7∶3, with 24 cases(19.7%, 24/122)and 11 cases(20.8%, 11/53)experiencing BCR, respectively.A total of 5 775 radiomic features were extracted from the three sequences, and after dimensionality reduction, 5 features were selected to construct the radiomic labels.The radiomics model exhibited C-index values of 0.764(95% CI: 0.655-0.872)and 0.769(95% CI: 0.632-0.906)in the training and test sets, respectively.Multifactorial Cox regression analysis revealed serum prostate-specific antigen(PSA)( HR=1.032, 95% CI: 1.010-1.054), postoperative pathology International Society of Urological Pathology(ISUP)grade grouping( HR=1.682, 95% CI: 1.039-2.722), and positive surgical margins( HR=2.513, 95% CI: 1.094-5.774)as independent predictors of BCR.The clinical model exhibited C-index values of 0.751(95% CI: 0.655-0.846)and 0.753(95% CI: 0.630-0.877)in the training and test sets, respectively.Following combined modeling of clinical factors and radiomic labels, the image-clinical combined model demonstrated the highest C-index values, namely 0.782(95% CI: 0.679-0.874)and 0.801(95% CI: 0.677-0.915)in the training and test sets, respectively. Conclusions:The radiomics model based on bpMRI can predict the occurrence of BCR after RP in elderly prostate cancer patients.Combined modeling of clinical factors and radiomic labels can enhance predictive efficiency.
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Objective:To develop a prediction model using machine learning to identify anxiety and depression in elderly individuals.Methods:This study collected data from 15079 elderly individuals in Shanxi Province, including their social demographic factors and disease status.Anxiety and depression were evaluated using GAD-7 and PHQ-9 scales to understand the characteristics of mental illness in the elderly.The evaluation indexes included accuracy, recall, precision, F1 score, Receiver Operating Characteristic Curve(ROC), and area under the curve(AUC), which were derived from the confusion matrix and several models.Results:The output of our study clearly demonstrates that the full feature prediction based on LightGBM is highly accurate, with an AUC of 0.805[95% CI: 0.794-0.811]. This outperforms the Random Forest model, which achieved an AUC of 0.730[95% CI: 0.702-0.741], and the XGboost model, which achieved an AUC of 0.802[95% CI: 0.780-0.807]. Therefore, LightGBM algorithm proves to be a strong prediction model.Our simplified model, based on eight selected features, also achieves a respectable AUC of approximately 0.75. Conclusions:The new prediction model for anxiety and depression specifically designed for the elderly can be effectively utilized in grassroots health surveys or for self-examinations to efficiently predict anxiety and depression levels among the elderly population in the community.
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Objective:To examine the risk factors and predictive value of depression following mild acute ischemic stroke in elderly individuals.The aim is to enhance early identification and intervention, ultimately leading to improved prognosis.Methods:A case-control study was conducted on 988 elderly patients with mild acute ischemic stroke.The study collected general population and social data, as well as clinical laboratory data such as blood glucose, blood lipids, and AD7C-NTP in urine.Additionally, the patients underwent assessments using the Montreal Cognitive Assessment Scale(MoCA), National Institutes of Health Stroke Scale(NHISS), Barthel index(BI), Hamilton Anxiety Scale(HAMA), and Hamilton Depression Scale(HAMD).Based on the HAMD depression scale score, the patients were divided into a nopost-stooke depression(NPSD)group and a post-stooke depression(PSD)group.The study then analyzed the related risk factors and predictive value of PSD.Results:A total of 988 patients were eligible for inclusion, with 132 being excluded and 856 being included.The NPSD and PSD groups showed significant differences in age, hypertension, smoking history, education level, and stroke history(all P<0.05).Regarding clinical data, there were statistically significant differences between the two groups in total cholesterol(TC), triacylglycerol(TG), HDL, urinary AD7C-NTP, MoCA, and HAMA scores(all P<0.05).The results of the multi-factor logistic regression analysis revealed that gender( OR=1.975, 95% CI: 1.223-3.190, P=0.005), stroke history( OR=1.352, 95% CI: 0.877-2.086, P=0.042), and HAMA score( OR=1.216, 95% CI: 0.932-1.526, P=0.043)were identified as independent risk factors for post-stroke depression in the elderly.Conversely, MoCA score( OR=0.873, 95% CI: 0.814-0.937, P<0.001)was found to be an independent protective factor.Furthermore, the ROC curve analysis demonstrated that the HAMA score(AUC=0.892, sensitivity: 0.721, specificity: 0.854, cut-off value: 9.5)exhibited significant predictive value, while the other indexes had limited predictive value. Conclusions:Gender, stroke history, and HAMA score have been identified as potential independent risk factors for post-stroke depression(PSD)in the elderly, while MoCA score may serve as an independent protective factor.Notably, HAMA score demonstrates a strong predictive ability for PSD.Early identification of these factors and timely intervention could significantly contribute to improving prognosis.