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1.
J. pediatr. (Rio J.) ; 100(3): 305-310, May-June 2024. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1558317

ABSTRACT

Abstract Objective: To build a model based on cardiometabolic indicators that allow the identification of overweight adolescents at higher risk of subclinical atherosclerotic disease (SAD). Methods: Cross-sectional study involving 161 adolescents with a body mass index ≥ + 1 z-Score, aged 10 to 19 years. Carotid intima-media complex thickness (IMT) was evaluated using ultrasound to assess subclinical atherosclerotic disease. Cardiometabolic indicators evaluated included nutritional status, central adiposity, blood pressure, lipidic profile, glycemic profile, as well as age and sex. Data was presented using measures of central tendency and dispersion, as well as absolute and relative frequency. The relationship between IMT measurement (outcome variable) and other variables (independent variables) was assessed using Pearson or Spearman correlation, followed by multiple regression modeling with Gamma distribution to analyze predictors of IMT. Statistical analysis was performed using SPSS and R software, considering a significance level of 5 %. Results: It was observed that 23.7 % had Carotid thickening, and the prevalence of abnormal fasting glucose was the lowest. Age and fasting glucose were identified as predictors of IMT increase, with IMT decreasing with age by approximately 1 % per year and increasing with glucose by around 0.24 % per mg/dL. Conclusion: The adolescent at higher risk is younger with higher fasting glycemia levels.

2.
J. pediatr. (Rio J.) ; 100(3): 327-334, May-June 2024. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1558325

ABSTRACT

Abstract Objective: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. Method: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. Results: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. Conclusions: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.

3.
J. pediatr. (Rio J.) ; 100(3): 318-326, May-June 2024. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1558326

ABSTRACT

Abstract Objective: Reliably prediction models for coronary artery abnormalities (CAA) in children aged > 5 years with Kawasaki disease (KD) are still lacking. This study aimed to develop a nomogram model for predicting CAA at 4 to 8 weeks of illness in children with KD older than 5 years. Methods: A total of 644 eligible children were randomly assigned to a training cohort (n = 450) and a validation cohort (n = 194). The least absolute shrinkage and selection operator (LASSO) analysis was used for optimal predictors selection, and multivariate logistic regression was used to develop a nomogram model based on the selected predictors. Area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, Brier score, and decision curve analysis (DCA) were used to assess model performance. Results: Neutrophil to lymphocyte ratio, intravenous immunoglobulin resistance, and maximum baseline z-score ≥ 2.5 were identified by LASSO as significant predictors. The model incorporating these variables showed good discrimination and calibration capacities in both training and validation cohorts. The AUC of the training cohort and validation cohort were 0.854 and 0.850, respectively. The DCA confirmed the clinical usefulness of the nomogram model. Conclusions: A novel nomogram model was established to accurately assess the risk of CAA at 4-8 weeks of onset among KD children older than 5 years, which may aid clinical decisionmaking.

4.
Clinics ; 79: 100318, 2024. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1528429

ABSTRACT

Abstract Objective: This study aimed to develop and internally validate a prediction model for estimating the risk of spontaneous abortion in early pregnancy. Methods: This prospective cohort study included 9,895 pregnant women who received prenatal care at a maternal health facility in China from January 2021 to December 2022. Data on demographics, medical history, lifestyle factors, and mental health were collected. A multivariable logistic regression analysis was performed to develop the prediction model with spontaneous abortion as the outcome. The model was internally validated using bootstrapping techniques, and its discrimination and calibration were assessed. Results: The spontaneous abortion rate was 5.95% (589/9,895) 1. The final prediction model included nine variables: maternal age, history of embryonic arrest, thyroid dysfunction, polycystic ovary syndrome, assisted reproduction, exposure to pollution, recent home renovation, depression score, and stress score 1. The model showed good discrimination with a C-statistic of 0.88 (95% CI 0.87‒0.90) 1, and its calibration was adequate based on the Hosmer-Lemeshow test (p = 0.27). Conclusions: The prediction model demonstrated good performance in estimating spontaneous abortion risk in early pregnancy based on demographic, clinical, and psychosocial factors. Further external validation is recommended before clinical application.

5.
Rev. bras. cir. cardiovasc ; 39(2): e20230212, 2024. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1535540

ABSTRACT

ABSTRACT Introduction: Blood transfusion is a common practice in cardiac surgery, despite its well-known negative effects. To mitigate blood transfusion-associated risks, identifying patients who are at higher risk of needing this procedure is crucial. Widely used risk scores to predict the need for blood transfusions have yielded unsatisfactory results when validated for the Brazilian population. Methods: In this retrospective study, machine learning (ML) algorithms were compared to predict the need for blood transfusions in a cohort of 495 cardiac surgery patients treated at a Brazilian reference service between 2019 and 2021. The performance of the models was evaluated using various metrics, including the area under the curve (AUC), and compared to the commonly used Transfusion Risk and Clinical Knowledge (TRACK) and Transfusion Risk Understanding Scoring Tool (TRUST) scoring systems. Results: The study found that the model had the highest performance, achieving an AUC of 0.7350 (confidence interval [CI]: 0.7203 to 0.7497). Importantly, all ML algorithms performed significantly better than the commonly used TRACK and TRUST scoring systems. TRACK had an AUC of 0.6757 (CI: 0.6609 to 0.6906), while TRUST had an AUC of 0.6622 (CI: 0.6473 to 0.6906). Conclusion: The findings of this study suggest that ML algorithms may offer a more accurate prediction of the need for blood transfusions than the traditional scoring systems and could enhance the accuracy of predicting blood transfusion requirements in cardiac surgery patients. Further research could focus on optimizing and refining ML algorithms to improve their accuracy and make them more suitable for clinical use.

6.
Braz. j. infect. dis ; 28(1): 103721, 2024. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1550136

ABSTRACT

Abstract Introduction COVID-19 remains an important threat to global health and maintains the challenge of COVID-19 hospital care. To assist decision making regarding COVID-19 hospital care many instruments to predict COVID-19 progression to critical condition were developed and validated. Objective To validate eleven COVID-19 progression prediction scores for critically ill hospitalized patients in a Brazilian population. Methodology Observational study with retrospective follow-up, including 301 adults confirmed for COVID-19 sequentially. Participants were admitted to non-critical units for treatment of the disease, between January and April 2021 and between September 2021 and February 2022. Eleven prognostic scores were applied using demographic, clinical, laboratory and imaging data collected in the first 48 of the hospital admission. The outcomes of greatest interest were as originally defined for each score. The analysis plan was to apply the instruments, estimate the outcome probability reproducing the original development/validation of each score, then to estimate performance measures (discrimination and calibration) and decision thresholds for risk classification. Results The overall outcome prevalence was 41.8 % on 301 participants. There was a greater risk of the occurrence of the outcomes in older and male patients, and a linear trend with increasing comorbidities. Most of the patients studied were not immunized against COVID-19. Presence of concomitant bacterial infection and consolidation on imaging increased the risk of outcomes. College of London COVID-19 severity score and the 4C Mortality Score were the only with reasonable discrimination (ROC AUC 0.647 and 0.798 respectively) and calibration. The risk groups (low, intermediate and high) for 4C score were updated with the following thresholds: 0.239 and 0.318 (https://pedrobrasil.shinyapps.io/INDWELL/). Conclusion The 4C score showed the best discrimination and calibration performance among the tested instruments. We suggest different limits for risk groups. 4C score use could improve decision making and early therapeutic management at hospital care.

7.
Medicina (B.Aires) ; 84(1): 1-10, 2024. graf
Article in Spanish | LILACS-Express | LILACS | ID: biblio-1558445

ABSTRACT

Resumen Introducción : Es fundamental poner en práctica ac ciones preventivas y de diagnóstico poblacional precoz para detectar a las personas en riesgo de desarrollar Diabetes tipo 2 (DT2). El objetivo del trabajo fue evaluar el desempeño del score FINDRISC como método de cri bado para detectar prediabetes y DT2 sin diagnostico en trabajadores municipales. Métodos : Estudio epidemiológico, descriptivo de corte transversal desde 10/21 al 3/22. Ingresaron voluntarios mayores a 18 años sin diagnóstico previo de DT2, se excluyó quienes padecían una enfermedad aguda, emba razadas o que realizaban tratamiento con medicamentos que modifiquen la glucemia. Los participantes comple taron el FINDRISC y realizaron una Prueba Oral de Tole rancia a la Glucosa (POTG). El desempeño se determinó mediante el cálculo de la sensibilidad (S), especificidad (E), y el área bajo la curva (AUC-ROC). Se utilizó un índice de Youden para definir el punto de corte óptimo. Resultados : Ingresaron 148 personas, entre 18-67 años, con media de edad 42.9 ± 11.8 años, el 68.9% de sexo masculino. La frecuencia de DT2 sin diagnóstico fue del 3.3% (n = 5) y de prediabetes del 12.2% (n = 18). El promedio de puntos de FINDRISC fue de 10.0 ± 4.8. El punto de corte optimo fue ≥ 13 (S = 65.2% y E = 74.4%) y el AUC-ROC 0.76 (IC95%: 0.66-0.86). Conclusión : El FINDRISC demostró ser un método eficaz para identificar personas con DT2 y prediabetes con punto de corte 13 en la población, lugar y periodo de estudio.


Abstract Introduction : It is fundamental to put into practice preventive and early population diagnosis actions to detect people at risk for developing Type 2 diabetes (T2D). The aim of this study was to evaluate the FINDRISC score performance as screening method to detect prediabetes and unknown T2D in municipal workers. Methods : descriptive epidemiological and cross-sectional study from 10/21 to 03/22. People suffering from a severe illness, pregnant or were already receiv ing drugs that modify blood glucose, were excluded. Participants completed the FINDRISC and performed an oral glucose tolerance test (OGTT). The performance of the FINDRISC was determined by calculating sensitiv ity, specificity, and area under the curve (AUC-ROC). The Youden's J statistic index was used to define the optimal cutoff point. Results : 148 subjects between the ages of 18-65 were admitted, with a mean age of 42,9 ± 11,8, the 69% being males. The frequency of unknown T2D was of 3.3% (n = 5) and frequency of prediabetes was of 12.2% (n = 18). The mean of FINDRISC score was of 10.0 ± 4.8. The optimal cutoff point was ≥ 13 (sensitiv ity = 65.2%, Specificity = 74.4%) and the AUC-ROC 0.76 (IC95%: 0.66-0.86). Conclusion : The FINDRISC proved to be an effective method for identifying people with undiagnosed T2D and prediabetes with a cut-off point of 13 in the popula tion, place, and study period.

8.
Article in English | LILACS-Express | LILACS | ID: biblio-1559111

ABSTRACT

ABSTRACT Despite good hepatitis B virus (HBV) inhibition by nucleoside analogs (NAs), cases of hepatocellular carcinoma (HCC) still occur. This study proposed a non-invasive predictive model to assess HCC risk in patients with chronic hepatitis B (CHB) receiving NAs treatment. Data were obtained from a hospital-based retrospective cohort registered on the Platform of Medical Data Science Academy of Chongqing Medical University, from 2013 to 2019. A total of 501 patients under NAs treatment had their FIB-4 index updated semiannually by recalculation based on laboratory values. Patients were divided into three groups based on FIB-4 index values: < 1.45, 1.45-3.25, and ≥ 3.25. Subsequently, HCC incidence was reassessed every six months using Kaplan-Meier curves based on the updated FIB-4 index. The median follow-up time of CHB patients after receiving NAs treatment was 2.5 years. HCC incidences with FIB-4 index < 1.45, 1.45-3.25, and ≥ 3.25 were 1.18%, 1.32%, and 9.09%, respectively. Dynamic assessment showed that the percentage of patients with FIB-4 index < 1.45 significantly increased semiannually (P < 0.001), and of patients with FIB-4 index ≥ 3.25 significantly decreased (P < 0.001). HCC incidence was the highest among patients with FIB-4 index ≥ 3.25. The FIB-4 index effectively predicted HCC incidence, and its dynamic assessment could be used for regular surveillance to implement early intervention and reduce HCC risk.

9.
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery ; (12): 51-58, 2024.
Article in Chinese | WPRIM | ID: wpr-1006510

ABSTRACT

@#Objective     To explore the correlation between the quantitative and qualitative features of CT images and the invasiveness of pulmonary ground-glass nodules, providing reference value for preoperative planning of patients with ground-glass nodules. Methods    The patients with ground-glass nodules who underwent surgical treatment and were diagnosed with pulmonary adenocarcinoma from September 2020 to July 2022 at the Third Affiliated Hospital of Kunming Medical University were collected. Based on the pathological diagnosis results, they were divided into two groups: a non-invasive adenocarcinoma group with in situ and minimally invasive adenocarcinoma, and an invasive adenocarcinoma group. Imaging features were collected, and a univariate logistic regression analysis was conducted on the clinical and imaging data of the patients. Variables with statistical difference were selected for multivariate logistic regression analysis to establish a predictive model of invasive adenocarcinoma based on independent risk factors. Finally, the sensitivity and specificity were calculated based on the Youden index. Results     A total of 555 patients were collected. The were 310 patients in the non-invasive adenocarcinoma group, including 235 females and 75 males, with a meadian age of 49 (43, 58) years, and 245 patients in the invasive adenocarcinoma group, including 163 females and 82 males, with a meadian age of 53 (46, 61) years. The binary logistic regression analysis showed that the maximum diameter (OR=4.707, 95%CI 2.060 to 10.758), consolidation/tumor ratio (CTR, OR=1.027, 95%CI 1.011 to 1.043), maximum CT value (OR=1.025, 95%CI 1.004 to 1.047), mean CT value (OR=1.035, 95%CI 1.008 to 1.063), spiculation sign (OR=2.055, 95%CI 1.148 to 3.679), and vascular convergence sign (OR=2.508, 95%CI 1.345 to 4.676) were independent risk factors for the occurrence of invasive adenocarcinoma (P<0.05). Based on the independent predictive factors, a predictive model of invasive adenocarcinoma was constructed. The formula for the model prediction was: Logit(P)=–1.293+1.549×maximum diameter of lesion+0.026×CTR+0.025×maximum CT value+0.034×mean CT value+0.72×spiculation sign+0.919×vascular convergence sign. The area under the receiver operating characteristic curve of the model was 0.910 (95%CI 0.885 to 0.934), indicating that the model had good discrimination ability. The calibration curve showed that the predictive model had good calibration, and the decision analysis curve showed that the model had good clinical utility. Conclusion     The predictive model combining quantitative and qualitative features of CT has a good predictive ability for the invasiveness of ground-glass nodules. Its predictive performance is higher than any single indicator.

10.
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery ; (12): 35-43, 2024.
Article in Chinese | WPRIM | ID: wpr-1006507

ABSTRACT

@#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.

11.
Journal of Public Health and Preventive Medicine ; (6): 113-115, 2024.
Article in Chinese | WPRIM | ID: wpr-1005919

ABSTRACT

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.

12.
Acta Pharmaceutica Sinica ; (12): 76-83, 2024.
Article in Chinese | WPRIM | ID: wpr-1005439

ABSTRACT

Most chemical medicines have polymorphs. The difference of medicine polymorphs in physicochemical properties directly affects the stability, efficacy, and safety of solid medicine products. Polymorphs is incomparably important to pharmaceutical chemistry, manufacturing, and control. Meantime polymorphs is a key factor for the quality of high-end drug and formulations. Polymorph prediction technology can effectively guide screening of trial experiments, and reduce the risk of missing stable crystal form in the traditional experiment. Polymorph prediction technology was firstly based on theoretical calculations such as quantum mechanics and computational chemistry, and then was developed by the key technology of machine learning using the artificial intelligence. Nowadays, the popular trend is to combine the advantages of theoretical calculation and machine learning to jointly predict crystal structure. Recently, predicting medicine polymorphs has still been a challenging problem. It is expected to learn from and integrate existing technologies to predict medicine polymorphs more accurately and efficiently.

13.
International Eye Science ; (12): 284-288, 2024.
Article in Chinese | WPRIM | ID: wpr-1005396

ABSTRACT

AIM: To analyze the recurrence factors of patients with retinal vein occlusion(RVO)induced macular edema(ME)and construct a nomogram model.METHODS: Retrospective study. A total of 306 patients with RVO induced ME admitted to our hospital from January 2019 to June 2022 were included as study objects, and they were divided into modeling group with 214 cases(214 eyes)and 92 cases(92 eyes)in the verification group by 7:3. All patients were followed up for 1 a after receiving anti-vascular endothelial growth factor(VEGF)treatment, and patients in the modeling group were separated into a recurrence group(n=66)and a non recurrence group(n=148)based on whether they had recurrence. Clinical data were collected and multivariate Logistic regression was applied to analyze and determine the factors affecting recurrence in patients with RVO induced ME; R3.6.3 software was applied to construct a nomogram model for predicting the recurrence risk of patients with RVO induced ME; ROC curve and calibration curve were applied to evaluate the discrimination and consistency of nomogram model in predicting the recurrence risk of patients with RVO induced ME.RESULTS: There were statistically significant differences in central retinal thickness(CRT), course of disease, hyperreflective foci(HF), disorder of retinal inner layer structure, and injection frequency between the non recurrence group and the recurrence group before treatment(all P&#x0026;#x003C;0.05). The multivariate Logistic regression analysis showed that pre-treatment CRT(OR=1.011), course of disease(OR=1.104), HF(OR=5.074), retinal inner layer structural disorder(OR=4.640), and injection frequency(OR=4.036)were influencing factors for recurrence in patients with RVO induced ME(all P&#x0026;#x003C;0.01). The area under the ROC curve of the modeling group was 0.924(95%CI: 0.882-0.966), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed that χ2=11.817, P=0.160; the area under the ROC curve of the verification group was 0.939(95%CI: 0.892-0.985), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed χ2=6.082, P=0.638.CONCLUSION: Pre-treatment CRT, course of disease, HF, disorder of retinal inner layer structure, and injection frequency are independent risk factors for recurrence in patients with RVO induced ME. The nomogram model constructed based on this has a high discrimination and consistency in predicting the recurrence risk of patients with RVO induced ME.

14.
Organ Transplantation ; (6): 102-111, 2024.
Article in Chinese | WPRIM | ID: wpr-1005239

ABSTRACT

Objective To explore the public attitude towards kidney xenotransplantation in China by constructing and validating the prediction model based on xenotransplantation questionnaire. Methods A convenient sampling survey was conducted among the public in China with the platform of Wenjuanxing to analyze public acceptance of kidney xenotransplantation and influencing factors. Using random distribution method, all included questionnaires (n=2 280) were divided into the training and validation sets according to a ratio of 7:3. A prediction model was constructed and validated. Results A total of 2 280 questionnaires were included. The public acceptance rate of xenotransplantation was 71.3%. Multivariate analysis showed that gender, marital status, resident area, medical insurance coverage, religious belief, vegetarianism, awareness of kidney xenotransplantation and whether on the waiting list for kidney transplantation were the independent influencing factors for public acceptance of kidney xenotransplantation (all P<0.05). The area under the curve (AUC) of receiver operating characteristic (ROC) of the prediction model in the training set was 0.773, and 0.785 in the validation set. The calibration curves in the training and validation sets indicated that the prediction models yielded good prediction value. Decision curve analysis (DCA) suggested that the prediction efficiency of the model was high. Conclusions In China, public acceptance of kidney xenotransplantation is relatively high, whereas it remains to be significantly enhanced. The prediction model based on questionnaire survey has favorable prediction efficiency, which provides reference for subsequent research.

15.
China Pharmacy ; (12): 75-79, 2024.
Article in Chinese | WPRIM | ID: wpr-1005217

ABSTRACT

OBJECTIVE To construct a risk prediction model for bloodstream infection (BSI) induced by carbapenem-resistant Klebsiella pneumoniae (CRKP). METHODS Retrospective analysis was conducted for clinical data from 253 patients with BSI induced by K. pneumoniae in the First Hospital of Qinhuangdao from January 2019 to June 2022. Patients admitted from January 2019 to December 2021 were selected as the model group (n=223), and patients admitted from January 2022 to June 2022 were selected as the validation group (n=30). The model group was divided into the CRKP subgroup (n=56) and the carbapenem- sensitive K. pneumoniae (CSKP) subgroup (n=167) based on whether CRKP was detected or not. The univariate and multivariate Logistic analyses were performed on basic information such as gender, age and comorbid underlying diseases in two subgroups of patients; independent risk factors were screened for CRKP-induced BSI, and a risk prediction model was constructed. The established model was verified with patients in the validation group as the target. RESULTS Admissioning to intensive care unit (ICU), use of immunosuppressants, empirical use of carbapenems and empirical use of antibiotics against Gram-positive coccus were independent risk factors of CRKP-induced BSI (ORs were 3.749, 3.074, 2.909, 9.419, 95%CIs were 1.639-8.572, 1.292- 7.312, 1.180-7.717, 2.877-30.840, P<0.05). Based on this, a risk prediction model was established with a P value of 0.365. The AUC of the receiver operating characteristic (ROC) curve of the model was 0.848 [95%CI (0.779, 0.916), P<0.001], and the critical score was 6.5. In the validation group, the overall accuracy of the prediction under the model was 86.67%, and the AUC of ROC curve was 0.926 [95%CI (0.809, 1.000], P<0.001]. CONCLUSIONS Admission to ICU, use of immunosuppressants, empirical use of carbapenems and empirical use of antibiotics against Gram-positive coccus are independent risk factors of CRKP- induced BSI. The CRKP-induced BSI risk prediction model based on the above factors has good prediction accuracy.

16.
China Pharmacy ; (12): 980-985, 2024.
Article in Chinese | WPRIM | ID: wpr-1016722

ABSTRACT

OBJECTIVE To explore the predictive factors of cefoperazone/sulbactam-induced thrombocytopenia in adult inpatients, and to establish and validate the nomogram prediction model. METHODS Data of adult inpatients treated with cefoperazone/sulbactam in Xi’an Central Hospital from Jun. 30th, 2021 to Jun. 30th, 2023 were retrospectively collected. The training set and internal validation set were randomly constructed in a 7∶3 ratio. Singler factor and multifactor Logistic regression analysis were used to screen the independent predictors of cefoperazone/sulbactam-induced thrombocytopenia. The nomogram was drawn by using “RMS” of R 4.0.3 software, and the predictive performance of the model was evaluated by the receiver operating characteristic curve and C-index curve. Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration degree of the model. Using the same standard, the clinical data of hospitalized patients receiving cefoperazone/sulbactam in Xi’an First Hospital in the same period were collected for external validation of the nomogram prediction model. RESULTS A total of 1 045 patients in Xi’an Central Hospital were included in this study, among which 67 patients suffered from cefoperazone/sulbactam-induced thrombocytopenia, with an incidence of 6.41%. After the false positive patients were excluded, 473 patients were included finally, including 331 in the training set and 142 in theinternal validation set. Multifactor Logistic regression analysis showed that age [OR=1.043, 95%CI (1.017, 1.070)], estimated glomerular filtration rate (eGFR) [OR=0.988,95%CI(0.977, 0.998)], baseline platelet (PLT) [OR=0.989, 95%CI(0.982, 0.996)], nutritional risk [OR=3.863, 95%CI(1.884, 7.921)] and cumulative defined daily doses (DDDs) [OR=1.082, 95%CI(1.020, 1.147)] were independent predictors for cefoperazone/sulbactam-induced thrombocytopenia (P<0.05). The C-index values of the training set and the internal validation set were 0.824 [95%CI (0.759, 0.890)] and 0.828 [95%CI (0.749, 0.933)], respectively. The results of the Hosmer-Lemeshow test showed that χ 2 values were 0.441 (P=0.802) and 1.804 (P=0.406). In the external validation set, the C-index value was 0.808 [95%CI (0.672, 0.945)], the χ 2 value of the Hosmer-Lemeshow test was 0.899 (P=0.638). CONCLUSIONS The independent predictors of cefoperazone/sulbactam-induced thrombocytopenia include age, baseline PLT, eGFR, nutritional risk and cumulative DDDs. The model has good predictive efficacy and extrapolation ability, which can help clinic identify the potential risk of cefoperazone/sulbactam-induced thrombocytopenia quickly and accurately.

17.
International Eye Science ; (12): 671-676, 2024.
Article in Chinese | WPRIM | ID: wpr-1016576

ABSTRACT

AIM:To establish a nomogram model to predict the effect of serum ferritin on diabetic retinopathy and evaluate the model.METHODS:A total of 21 variables, including ferritin, were screened by univariate and multivariate regression analysis to determine the risk factors of diabetic retinopathy. A nomogram prediction model was established for evaluation and calibration.RESULTS:Ferritin, duration of diabetes, hemoglobin, urine microalbumin, regularity of medication and body mass index were included in the nomogram model. The consistency index of the prediction model with serum ferritin was 0.813(95%CI: 0.748-0.879). The calibration curves of internal and external verification showed good performance, and the probability of the threshold suggested by the decision curve was in the range 10% to 90%. The model had a high net profit value.CONCLUSIONS:Serum ferritin is an important risk factor for diabetic retinopathy. A new nomogram model, which includes body mass index, duration of diabetes, ferritin, hemoglobin, urine microalbumin and regularity of medication, has a high predictive accuracy and could provide early prediction for clinicians.

18.
Journal of Sun Yat-sen University(Medical Sciences) ; (6): 253-260, 2024.
Article in Chinese | WPRIM | ID: wpr-1016446

ABSTRACT

ObjectiveTo construct and validate a clinical prediction model for diabetic kidney disease (DKD) based on optical coherence tomography angiography (OCTA). MethodsThis study enrolled 567 diabetes patients. The random forest algorithm as well as logistic regression analysis were applied to construct the prediction model. The model discrimination and clinical usefulness were evaluated by receiver operating characteristic curve (ROC) and decision curve analysis (DCA), respectively. ResultsThe clinical prediction model for DKD based on OCTA was constructed with area under the curve (AUC) of 0.878 and Brier score of 0.11. ConclusionsThrough multidimensional verification, the clinical prediction nomogram model based on OCTA allowed for early warning and advanced intervention of DKD.

19.
Journal of Public Health and Preventive Medicine ; (6): 39-43, 2024.
Article in Chinese | WPRIM | ID: wpr-1016409

ABSTRACT

Objective To analyze the epidemic characteristics of varicella in Chongqing from 2014 to 2020, and to provide evidence for the development of scientific and effective varicella control strategies. Methods Data on the outbreak of varicella and vaccination in Chongqing from 2014 to 2020 were collected through the China Disease Prevention and Control Information System, and descriptive epidemiological methods were used for statistical analysis. Results A total of 181 551 cases of varicella were reported in Chongqing from 2014 to 2020, with an average annual incidence rate of 83.79 per 100 000. The incidence rate of varicella increased from 39.95 per 100 000 in 2014 to 81.88 per 100 000 in 2020 (P < 0.001). The incidence of varicella was seasonal, with the peak periods occurring from May to June and from October to December each year. The average annual incidence rate in municipal districts was 88.90/100 000, higher than 67.42/100 000 in counties and 82.50/100 000 in autonomous counties. The average annual incidence rate of varicella in males (87.13/100 000) was higher than that in females (80.38/100 000). The incidence of varicella was mainly distributed in people under 15 years old, with 143 508 cases (79.10%) reported, and the highest incidence age was 5-9 years old (37.00%). Among the affected occupations , 133 733 cases (62.6%) were students , 39 274 cases (18.40%) were children in nursery care, and 17 963 cases (8.4%) were scattered children. The actual number of doses of varicella vaccine from 2014 to 2020 was 2 302 522 doses, with the coverage rates of one-dose and two-dose vaccines being 75.56% and 32.17%, respectively. ARIMA predicted that there would be 2 604, 811, 756, 1 226, 2 405, 3 904, 2 410, 1 211, 2 034, 6 878, 10 887, and 8 955 cases of varicella from January to December 2021. Conclusion The incidence of varicella in Chongqing is on the rise, with obvious seasonal, regional and population distribution characteristics. It is necessary to strengthen the prevention and control of varicella epidemic, strengthen the prevention and control measures of key groups and key institutions in the high incidence season, strengthen the publicity of varicella vaccine, and improve the vaccination rate of two-doses of varicella vaccine for eligible children.

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Acta Anatomica Sinica ; (6): 98-104, 2024.
Article in Chinese | WPRIM | ID: wpr-1015157

ABSTRACT

Objective To investigate the risk factors for re-fracture after percutaneous kyphoplasty (PKP) in elderly patients with osteoporotic thoracolumbar compression fractures and to construct a line graph prediction model. Methods One hundred and eighty-two elderly patients with osteoporotic thoracolumbar compression fractures treated with PKP from January 2016 to November 2019 were selected for the study‚ and the patients were continuously followed up for 3 years after surgery. Clinical data were collected from both groups; Receiver operating characteristic (ROC) curve analysis was performed on the measures; Logistic regression analysis was performed to determine the independent risk factors affecting postoperative re-fracture in PKP; the R language software 4. 0 “rms” package was used to construct a predictive model for the line graph‚ and the calibration and decision curves were used to internally validate the predictive model for the line graph and for clinical evaluation of predictive performance. Results The differences between the two groups were statistically significant (P0. 22‚ which could provide a net clinical benefit‚ and the net clinical benefit was higher than the independent predictors. Conclusion BMD‚ number of injured vertebrae‚ single-segment cement injection‚ cement leakage‚ pre-and post-PKP vertebral height difference‚ and posterior convexity angle change are independent risk factors affecting the recurrent fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fracture‚ and this study constructs a column line graph model to predict the recurrent fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fracture as a predictor for clinical. This study provides an important reference for clinical prevention and treatment‚ and has clinical application value.

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