Development and internal validation of a clinical prediction model for spontaneous abortion risk in early pregnancy

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.

A disease predictive model based on epidemiological factors for the management of bacterial leaf blight of rice / Um modelo preditivo de doenças baseado em fatores epidemiológicos para o manejo da queima bacteriana das folhas do arroz

Rice is a widely consumed staple food for a large part of the world's human population. Approximately 90% of the world's rice is grown in Asian continent and constitutes a staple food for 2.7 billion people worldwide. Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is one of the devastating diseases of rice. A field experiment was conducted during the year 2016 and 2017 to investigate the influence of different meteorological parameters on BLB development as well as the computation of a predictive model to forecast the disease well ahead of its appearance in the field. The seasonal dataset of disease incidence and environmental factors was used to assess five rice varieties/ cultivars (Basmati-2000, KSK-434, KSK-133, Super Basmati, and IRRI-9). The accumulated effect of two year environmental data; maximum and minimum temperature, relative humidity, wind speed, and rainfall, was studied and correlated with disease incidence. Average temperature (maximum & minimum) showed a negative significant correlation with BLB disease and all other variables; relative humidity, rainfall, and wind speed had a positive correlation with BLB disease development on individual varieties. Stepwise regression analysis was performed to indicate potentially useful predictor variables and to rule out incompetent parameters. Environmental data from the growing seasons of July to October 2016 and 2017 revealed that, with the exception of the lowest temperature, all environmental factors contributed to disease development throughout the cropping season. A disease prediction multiple regression model was developed based on two-year data (Y = 214.3-3.691 Max T-0.508 Min T + 0.767 RH + 2.521 RF + 5.740 WS), which explained 95% variability. This disease prediction model will not only help farmers in early detection and timely management of bacterial leaf blight disease of rice but may also help reduce input costs and improve product quality and quantity. The model will be both farmer and environmentally friendly.

O arroz é um alimento básico amplamente consumido por grande parte da população humana mundial. Aproximadamente 90% do arroz do mundo é cultivado no continente asiático e constitui um alimento básico para 2,7 bilhões de pessoas em todo o mundo. O crestamento bacteriano das folhas (BLB) causado por Xanthomonas oryzae pv. oryzae é uma das doenças devastadoras do arroz. Um experimento de campo foi realizado durante os anos de 2016 e 2017 para investigar a influência de diferentes parâmetros meteorológicos no desenvolvimento do BLB, bem como o cálculo de um modelo preditivo para prever a doença bem antes de seu aparecimento em campo. O conjunto de dados sazonais de incidência de doenças e fatores ambientais foi usado para avaliar cinco variedades/cultivares de arroz (Basmati-2000, KSK-434, KSK-133, Super Basmati e IRRI-9). O efeito acumulado de dados ambientais de dois anos; temperatura máxima e mínima, umidade relativa do ar, velocidade do vento e precipitação pluviométrica foram estudados e correlacionados com a incidência da doença. A temperatura média (máxima e mínima) apresentou correlação significativa negativa com a doença BLB e todas as outras variáveis; umidade relativa, precipitação e velocidade do vento tiveram uma correlação positiva com o desenvolvimento da doença BLB em variedades individuais. A análise de regressão stepwise foi realizada para indicar variáveis preditoras potencialmente úteis e para descartar parâmetros incompetentes. Os dados ambientais das safras de julho a outubro de 2016 e 2017 revelaram que, com exceção da temperatura mais baixa, todos os fatores ambientais contribuíram para o desenvolvimento da doença ao longo da safra. Um modelo de regressão múltipla de previsão de doença foi desenvolvido com base em dados de dois anos (Y = 214,3-3,691 Max T-0,508 Min T + 0,767 RH + 2,521 RF + 5,740 WS), que explicou 95% de variabilidade. Este modelo de previsão de doenças não só ajudará os agricultores na detecção precoce e gestão atempada da doença bacteriana das folhas do arroz, mas também pode ajudar a reduzir os custos de insumos e melhorar a qualidade e a quantidade do produto. O modelo será agricultor e ambientalmente amigável.

Invasiveness assessment by CT quantitative and qualitative features of lung cancers manifesting ground-glass nodules in 555 patients: A retrospective cohort study / 中国胸心血管外科临床杂志

@#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.

A postoperative in-hospital mortality risk model for elderly patients undergoing cardiac valvular surgery based on LASSO-logistic regression / 中国胸心血管外科临床杂志

@#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.

Risk assessment of nosocomial infection in patients with multiple myeloma in the first hospitalization / 公共卫生与预防医学

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.

Construction and validation of a prediction model for public acceptance of kidney xenotransplantation in China / 器官移植

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.

Construction of a risk prediction model for bloodstream infection induced by carbapenem-resistant Klebsiella pneumoniae / 中国药房

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.

A disease predictive model based on epidemiological factors for the management of bacterial leaf blight of rice

Abstract Rice is a widely consumed staple food for a large part of the worlds human population. Approximately 90% of the worlds rice is grown in Asian continent and constitutes a staple food for 2.7 billion people worldwide. Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is one of the devastating diseases of rice. A field experiment was conducted during the year 2016 and 2017 to investigate the influence of different meteorological parameters on BLB development as well as the computation of a predictive model to forecast the disease well ahead of its appearance in the field. The seasonal dataset of disease incidence and environmental factors was used to assess five rice varieties/ cultivars (Basmati-2000, KSK-434, KSK-133, Super Basmati, and IRRI-9). The accumulated effect of two year environmental data; maximum and minimum temperature, relative humidity, wind speed, and rainfall, was studied and correlated with disease incidence. Average temperature (maximum & minimum) showed a negative significant correlation with BLB disease and all other variables; relative humidity, rainfall, and wind speed had a positive correlation with BLB disease development on individual varieties. Stepwise regression analysis was performed to indicate potentially useful predictor variables and to rule out incompetent parameters. Environmental data from the growing seasons of July to October 2016 and 2017 revealed that, with the exception of the lowest temperature, all environmental factors contributed to disease development throughout the cropping season. A disease prediction multiple regression model was developed based on two-year data (Y = 214.3-3.691 Max T-0.508 Min T + 0.767 RH + 2.521 RF + 5.740 WS), which explained 95% variability. This disease prediction model will not only help farmers in early detection and timely management of bacterial leaf blight disease of rice but may also help reduce input costs and improve product quality and quantity. The model will be both farmer and environmentally friendly.

Resumo O arroz é um alimento básico amplamente consumido por grande parte da população humana mundial. Aproximadamente 90% do arroz do mundo é cultivado no continente asiático e constitui um alimento básico para 2,7 bilhões de pessoas em todo o mundo. O crestamento bacteriano das folhas (BLB) causado por Xanthomonas oryzae pv. oryzae é uma das doenças devastadoras do arroz. Um experimento de campo foi realizado durante os anos de 2016 e 2017 para investigar a influência de diferentes parâmetros meteorológicos no desenvolvimento do BLB, bem como o cálculo de um modelo preditivo para prever a doença bem antes de seu aparecimento em campo. O conjunto de dados sazonais de incidência de doenças e fatores ambientais foi usado para avaliar cinco variedades/cultivares de arroz (Basmati-2000, KSK-434, KSK-133, Super Basmati e IRRI-9). O efeito acumulado de dados ambientais de dois anos; temperatura máxima e mínima, umidade relativa do ar, velocidade do vento e precipitação pluviométrica foram estudados e correlacionados com a incidência da doença. A temperatura média (máxima e mínima) apresentou correlação significativa negativa com a doença BLB e todas as outras variáveis; umidade relativa, precipitação e velocidade do vento tiveram uma correlação positiva com o desenvolvimento da doença BLB em variedades individuais. A análise de regressão stepwise foi realizada para indicar variáveis preditoras potencialmente úteis e para descartar parâmetros incompetentes. Os dados ambientais das safras de julho a outubro de 2016 e 2017 revelaram que, com exceção da temperatura mais baixa, todos os fatores ambientais contribuíram para o desenvolvimento da doença ao longo da safra. Um modelo de regressão múltipla de previsão de doença foi desenvolvido com base em dados de dois anos (Y = 214,3-3,691 Max T-0,508 Min T + 0,767 RH + 2,521 RF + 5,740 WS), que explicou 95% de variabilidade. Este modelo de previsão de doenças não só ajudará os agricultores na detecção precoce e gestão atempada da doença bacteriana das folhas do arroz, mas também pode ajudar a reduzir os custos de insumos e melhorar a qualidade e a quantidade do produto. O modelo será agricultor e ambientalmente amigável.

Diseño y validación de una escala de riesgo de parto pretérmino con enfoque periodontal / Design and validation of a preterm birth risk scale with a periodontal approach i: en

Introducción: La escala de riesgo diseñada para estimar la probabilidad de parto pretérmino con enfoque periodontal debe ser validada antes de su implementación en la práctica clínica. Objetivo: Diseñar y validar una escala de riesgo de parto pretérmino con enfoque periodontal. Métodos: Se realizó un estudio analítico, de casos y controles, de 1152 puérperas ingresadas en los hospitales maternos de la provincia de Santiago de Cuba en el período 2011-2022, para lo cual fueron seleccionadas 2 muestras: una de construcción del modelo (n=750) y otra de validación de la escala (n=402). Se determinaron los posibles predictores a través del análisis univariado y el cálculo del odds ratio, con un nivel de significación de p≤0,05; asimismo, se elaboró un modelo de regresión logística binaria multivariada y se obtuvo la escala de riesgo que fue validada por diferentes métodos. Resultados: La escala se obtuvo con 7 predictores y 2 estratos de riesgo. Esta alcanzó buena discriminación (80 %), así como buen nivel de ajuste y validez de constructo (p=0,72). Igualmente, aseguró una predicción correcta de más de 50 % de los partos pretérmino, valores de sensibilidad y especificidad aceptables (79,20 y 70,20 %, respectivamente), así como validez de contenido, validez interna y confiabilidad adecuadas. Conclusiones: La escala de riesgo para estratificar el riesgo de parto pretérmino incluye predictores de gravedad de la enfermedad periodontal, con buenos parámetros de validación para ser usada en la toma de decisiones para prevenir este tipo de parto.

Introduction: The risk scale designed to estimate the probability of preterm birth with periodontal approach should be validated before its implementation in the clinical practice. Objective: To design and validate a risk scale of preterm birth with periodontal approach. Methods: A cases and controls analytic study of 1152 newly-delivered women admitted to maternal hospitals in Santiago de Cuba province was carried out in the period 2011 - 2022, and 2 samples were selected: one of pattern construction (n=750) and another of scale validation(n=402). The possible predictors were determined through the single varied analysis and odds ratio calculation, with a significance level of p≤0.05; also, a multivariate binary logistical regression model was elaborated and the risk scale was obtained, which was validated by different methods. Results: The scale was obtained with 7 predictors and 2 risk stratum. It reached a good discrimination (80%), as well as a good adjustment level and construction validity (p=0.72). Likewise, it assured a correct prediction of more than 50% of preterm births, acceptable sensibility and specificity values (79.20 and 70.20%, respectively), as well as adequate content validity, internal validity and reliability. Conclusions: The risk scale to stratify the risk of preterm birth includes predictors of periodontal disease severity, with good validation parameters to be used in the decisions making to prevent this type of childbirth.

Development and validation of prognostic nomogram for malignant pleural mesothelioma / 中华肿瘤杂志

Objective: To development the prognostic nomogram for malignant pleural mesothelioma (MPM). Methods: Two hundred and ten patients pathologically confirmed as MPM were enrolled in this retrospective study from 2007 to 2020 in the People's Hospital of Chuxiong Yi Autonomous Prefecture, the First and Third Affiliated Hospital of Kunming Medical University, and divided into training (n=112) and test (n=98) sets according to the admission time. The observation factors included demography, symptoms, history, clinical score and stage, blood cell and biochemistry, tumor markers, pathology and treatment. The Cox proportional risk model was used to analyze the prognostic factors of 112 patients in the training set. According to the results of multivariate Cox regression analysis, the prognostic prediction nomogram was established. C-Index and calibration curve were used to evaluate the model's discrimination and consistency in raining and test sets, respectively. Patients were stratified according to the median risk score of nomogram in the training set. Log rank test was performed to compare the survival differences between the high and low risk groups in the two sets. Results: The median overall survival (OS) of 210 MPM patients was 384 days (IQR=472 days), and the 6-month, 1-year, 2-year, and 3-year survival rates were 75.7%, 52.6%, 19.7%, and 13.0%, respectively. Cox multivariate regression analysis showed that residence (HR=2.127, 95% CI: 1.154-3.920), serum albumin (HR=1.583, 95% CI: 1.017-2.464), clinical stage (stage Ⅳ: HR=3.073, 95% CI: 1.366-6.910) and the chemotherapy (HR=0.476, 95% CI: 0.292-0.777) were independent prognostic factors for MPM patients. The C-index of the nomogram established based on the results of Cox multivariate regression analysis in the training and test sets were 0.662 and 0.613, respectively. Calibration curves for both the training and test sets showed moderate consistency between the predicted and actual survival probabilities of MPM patients at 6 months, 1 year, and 2 years. The low-risk group had better outcomes than the high-risk group in both training (P=0.001) and test (P=0.003) sets. Conclusion: The survival prediction nomogram established based on routine clinical indicators of MPM patients provides a reliable tool for prognostic prediction and risk stratification.

Prediction model of radiation pneumonitis after chemoradiotherapy for esophageal cancer based on dosiomics / 中华放射肿瘤学杂志

Objective:To study the risk factors and prediction model of radiation pneumonitis (RP) after radical chemoradiotherapy for locally advanced esophageal cancer based on dosiomics.Methods:Clinical data of 105 patients with esophageal cancer undergoing radical chemoradiotherapy at Zhejiang Cancer Hospital between January 2020 and August 2021 were retrospectively analyzed. RP was scored using the National Cancer Institute's Common Terminology Criteria for Adverse Events version 5.0 (CTCAE 5.0). Clinical factors, traditional dosimetric features and dosiomics features were collected, respectively. The features for predicting PR were analyzed by limma package. Support vector machine, k-nearest neighbor, decision tree, random forest and extreme gradient boosting were used to establish the prediction model, and the ten-fold cross-validation method was employed to evaluate the performance of the model. The differences of this model when different features were chosen were analyzed by delong test.Results:The incidence of RP in the whole group was 21.9%. One clinical factor, 6 traditional dosimetric features and 42 dosiomics features were significantly correlated with the occurrence of RP (all P<0.05). Support vector machine using linear kernel function yielded the optimal prediction performance, and the area under the receiver operating characteristic (ROC) without and with dosiomics features was 0.72 and 0.75, respectively. The models established by support vector machine, random forest and extreme gradient boosting were significantly different with and without dosiomics features (all P<0.05). Conclusion:The addition of dosiomics features can effectively improve the performance of the prediction model of RP after radiotherapy for esophageal cancer.

Risk factors analysis and risk prediction model construction and validation of cognitive dysfunction after brain trauma / 中华行为医学与脑科学杂志

Objective:To analyze the independent risk factors for the occurrence of post-traumatic cognitive dysfunction, construct a prediction model for the risk factors of post-traumatic cognitive dysfunction, and verify the effectiveness of the risk prediction model, so as to provide a clinical tool for early prediction of the risk of post-traumatic cognitive impairment.Methods:Part I: patients with brain trauma (training set with 556 subjects) who were hospitalized in 21 tertiary and secondary hospitals from Tangshan, Cangzhou and Chengde cities of Hebei province were retrospectively collected from February to May 2021 for Montreal cognitive assessment, and 33 influencing factors (general data, symptoms and signs, laboratory and imaging parameters) were obtained obtained through literature research.The patients were divided into case group and control group according to whether they had cognitive impairment or not, and univariate and multivariate analysis were used to screen independent risk factors.Part Ⅱ: a binary Logistic regression equation was used to construct a cognitive impairment prediction model, the visualization model of line graph is presented.Part Ⅲ: brain trauma patients (260 subjects of the validation set) hospitalized in the aforementioned 21 hospitals from August to October 2021 were collected as a prospective validation population for the prediction model of cognitive impairment, and the grouping basis of case group and control group was the same as before.And the risk factors between the two groups were compared.The receiver operating characteristic curve(ROC), calibration curve and clinical applicability of the model were drawn to evaluate the effectiveness of the model for internal and external verification of the model.Results:Binary Logistic regression analysis showed that the risk factors for post-traumatic cognitive dysfunction were basal ganglia injury, severe injury, amnesia experience after injury, frequent headache after injury, upper limb dysfunction after injury, age ≥ 60 years, and education level of elementary school or below.Visual nomograms showed that the experience of amnesia after injury, frequent headache after injury, upper limb dysfunction, and degree of injury among the symptom factors were the factors that contributed greatly to the risk of traumatic brain injury cognitive impairment in this model.Predictive model discrimination using area under curve(AUC) values of the area under the ROC curve showed that internal validation and external validation were 0.868 and 0.885 for R language analysis and 0.868 and 0.901 for SPSS analysis, respectively.The curve after model calibration almost coincided with the reference line, Hosmer-Lemeshow test P>0.05.The two decision curve analysis (DCA) curves drawn by the clinical applicability of the model were higher than the two extreme curves, predicting that traumatic brain injury patients with cognitive impairment could benefit from the predictive model, and there was a net benefit rate in the range of Pt about 0.1-0.8, when Pt reached about 0.1 until the approximate 1.0 composite evaluation model. Conclusion:Risk factors such as experience of amnesia after injury, frequent headache after injury, upper limb dysfunction, and degree of injury are predicting factors contributed to the risk of cognitive impairment in traumatic brain injury, and their prediction models have good predictive effect, high predictive accuracy and good clinical applicability, which can be applied in clinical diagnosis.

Establishment and evaluation of early in-hospital death prediction model for patients with acute pancreatitis in intensive care unit / 中华危重病急救医学

Objective:To investigate the death risk prediction factors of acute pancreatitis (AP) patients in intensive care unit (ICU), and to establish a death prediction model and evaluate its efficacy.Methods:A retrospective cohort study was conducted using the data in the Medical Information Mart for Intensive Care-Ⅲ (MIMIC-Ⅲ). The clinical data of 285 AP patients admitted to the ICU in the database were collected, including age, gender, blood routine and blood biochemical indicators, comorbidities, simplified acute physiology score Ⅲ (SAPS Ⅲ) and hospital prognosis. By using univariate analysis, the differences in the clinical data of the patients were compared between the two groups. Binary multivariate Logistic regression analysis was used to screen out independent predictors of in-hospital death in AP patients. A death prediction model was established, and the nomogram was drawn. The receiver operator characteristic curve (ROC curve) was plotted, and the area under the ROC curve (AUC) was used to test the discrimination of the prediction model. In addition, the prediction model was compared with the SAPSⅢ score in predicting in-hospital death. The calibration ability of the prediction model was evaluated by the Hosmer-Lemeshow goodness of fit test, and a calibration map was drawn to show the calibration degree of the prediction model.Results:Among 285 patients with AP, 29 patients died in the hospital and 256 patients survived. Univariate analysis showed that the patients in the death group were older than those in the survival group (years old: 70±17 vs. 58±16), and had higher white blood cell count (WBC), total bilirubin (TBil), serum creatinine (SCr), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), proportion of congestive heart failure and SAPSⅢ score [WBC (×10 9/L): 18.5 (13.9, 24.3) vs. 13.2 (9.3, 17.9), TBil (μmol/L): 29.1 (15.4, 66.7) vs. 16.2 (10.3, 29.1), SCr (μmol/L): 114.9 (88.4, 300.6) vs. 79.6 (53.0, 114.9), BUN (mmol/L): 13.9 (9.3, 17.8) vs. 6.1 (3.7, 9.6), RDW: 0.152 (0.141, 0.165) vs. 0.141 (0.134, 0.150), congestive heart failure: 34.5% vs. 14.8%, SAPSⅢ score: 66 (52, 90) vs. 39 (30, 48), all P < 0.05]. Multivariate Logistic regression analysis showed that age [odds ratio ( OR) = 1.038, 95% confidence interval (95% CI) was 1.005-1.073], WBC ( OR = 1.103, 95% CI was 1.038-1.172), TBil ( OR = 1.247, 95% CI was 1.066-1.459), BUN ( OR = 1.034, 95% CI was 1.014-1.055) and RDW ( OR = 1.344, 95% CI was 1.024-1.764) were independent risk predictors of in-hospital death in patients with AP. Logistic regression model was established: Logit ( P) = 0.037×age+0.098×WBC+0.221×TBil+0.033×BUN+0.296×RDW-12.133. ROC curve analysis showed that the AUC of the Logistic regression model for predicting the in-hospital death of patients with AP was 0.870 (95% CI was 0.794-0.946), the sensitivity was 86.2%, and the specificity was 78.5%, indicating that the model had good predictive performance, and it was superior to the SAPSⅢ score [AUC was 0.831 (95% CI was 0.754-0.907), the sensitivity was 82.8%, and the specificity was 75.4%]. A nomogram model was established based on the result of multivariate Logistic regression analysis. The calibration map showed that the calibration curve of the nomogram model was very close to the standard curve, with the goodness of fit test: χ 2 = 6.986, P = 0.538, indicating that the consistency between the predicted death risk of the nomogram model and the actual occurrence risk was relatively high. Conclusions:The older the AP patient is, the higher the WBC, TBil, BUN, and RDW, and the greater the risk of hospital death. The death prediction Logistic regression model and nomogram model constructed based on the above indicators have good discrimination ability and high accuracy for high-risk patients with hospital death, which can accurately predict the probability of death in AP patients and provide a basis for prognosis judgment and clinical treatment of AP patients.

Risk factors of in-hospital death in severe pneumonia patients receiving enteral nutrition support / 中华临床营养杂志

Objective:The decline in nutritional status in patients with severe pneumonia may contribute to an increase in in-hospital mortality. Enteral nutrition support can improve the nutritional status of patients, and is relatively easy to manage, with low cost and fewer serious complications. On the other hand, adverse reactions such as gastric retention and gastric microbiota translocation may increase the incidence of nosocomial pneumonia and increase the uncertainty of patient prognosis. There is no predictive model for in-hospital death in severe pneumonia patients receiving enteral nutrition support. The objective of this study was to investigate the risk factors of in-hospital death in patients with severe pneumonia receiving enteral nutrition support and to establish a prognostic model for such patients.Methods:This was a single-center retrospective study. Patients with severe pneumonia who were hospitalized in Peking Union Medical College Hospital and received enteral nutrition support were included from January 1, 2015 to December 31, 2020. The primary endpoints were in-hospital mortality rate and unordered discharge rate. The independent risk factors were determined using univariate and multifactorial logistic regression analysis, the nomogram scoring model was constructed, and the decision curve analysis (DCA) was performed.Results:A total of 632 severe pneumonia patients who received enteral nutrition support were included. Patients were divided into death and survival groups according to the presence or absence of in-hospital death, and 24 parameters were found with significant differences between groups. Nine parameters were independent predictors of mortality, namely the duration of ventilator use, the presence of malignant hyperplasia diseases, the maximal levels of platelet and prothrombin during hospitalization, and the nadir levels of alanine aminotransferase, serum albumin, sodium, potassium, and blood glucose. Based on these variables, a risk prediction scoring model was established (ROC = 0.782; 95% CI: 0.744 to 0.819, concordance index: 0.772). Calibration curves, DCA, and clinical impact curve were plotted to evaluate the goodness of function, accuracy, and applicability of the predictive nomogram, using the training and test sets. Conclusion:This study summarized the clinical characteristics of patients with severe pneumonia receiving enteral nutrition support and developed a scoring model to identify risk factors and establish prognostic models.

Establishment of prediction model for severe acute pancreatitis complicated with abdominal hypertension / 中华胰腺病杂志

Objective:To construct the prediction model of SAP complicated with intra-abdominal hypertension (IAH), and evaluate the prediction efficiency of the model.Methods:The clinical data of 322 SAP patients admitted to the emergency department of Cangzhou Hospital of Integrated Chinese and Western Medicine in Hebei Province from January 2017 to December 2021 were retrospectively analyzed. They were divided into IAH group ( n=153) and control group ( n=169) according to whether they had IAH complications or not. The clinical characteristics and laboratory test results of the two groups were compared. Multifactor logistic step-up regression was used to analyze the risk factors of SAP patients complicated with IAH. A nomogram model for predicting SAP complicated with IAH was established by using R software. The receiver operating characteristic curve (ROC) of the model was plotted, and the area under the curve (AUC) was calculated to evaluate its prediction efficiency. Calibration chart, Hosmer-Lemesshow test and decision curve analysis were used to evaluate the prediction accuracy and clinical application value of the model. The Bootstrap method was applied to verify the model internally. Results:In IAH group, cases with body mass index, CRP, procalcitonin (PCT), WBC, acute physiological and chronic health assessmentⅡ (APACHEⅡ) score, modified CT Severity Index score (MCTSI), incidence of complications (abdominal effusion, abdominal infection, gastrointestinal dysfunction, shock, multiple organ dysfunction syndrome), mechanical ventilation, the number of high-volume fluid reactivation (24 h≥4 L) were more than those in control group; serum albumin and serum calcium in IAH group were lower than those in control group, and the differences were statistically significant (all P value <0.05). Multivariate logistic regression analysis showed that serum albumin ( OR=0.815, 95% CI 0.710-0.937), CRP ( OR=1.005, 95% CI 1.002-1.008), MCTSI ( OR=2.043, 95% CI 1.695-2.463), complication of gastrointestinal dysfunction ( OR=4.179, 95% CI 2.170-8.049), and high-volume fluid resuscitation ( OR=4.265, 95% CI 2.269-8.015) were independent risk factors for IAH in SAP.The Nomogram prediction model was established using the five factors above as parameters, and the AUC value for predicting IAH complication was 0.886. The Hosmer-Lemesshow test showed a high consistency between the prediction results and the actual clinical observation results ( P=0.189). The results of decision curve analysis showed that the prediction probability of the model was between 10% and 85%, which could bring more benefits to patients. Conclusions:The early prediction model of SAP with concurrent IAH is successfully established, which can better predict the risk of SAP with concurrent IAH.

LASSO regression based risk prediction model establishment for Klebsiella pneumonia infection in patients with severe acute pancreatitis / 中华胰腺病杂志

Objective:To construct a risk prediction model for infection with Klebsiella pneumonia (KP) for patients with severe acute pancreatitis (SAP).Methods:Retrospective analysis was done on the clinical data of 109 SAP patients who were admitted to Shanghai General Hospital, between March 2016 and December 2021. Patients were classified into infection group ( n=25) and non-infection group ( n=84) based on the presence or absence of KP infection, and the clinical characteristics of the two groups were compared. The least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimension of the variables with statistical significance in univariate analysis. A nomogram prediction model was created by incorporating the optimized features from the LASSO regression model into the multivariate logistic regression analysis. Receiver operating characteristic curve (ROC) was drawn and the area under curve (AUC) was calculated; and consistency index (C-index) were used to assess the prediction model's diagnostic ability. Results:A total of 25 strains of KP were isolated from 109 patients with SAP, of which 21(84.0%) had multi-drug resistance. 20 risk factors (SOFA score, APACHEⅡ score, Ranson score, MCTSI score, mechanical ventilation time, fasting time, duration of indwelling of the peritoneal drainage tube, duration of deep vein indwelling, number of invasive procedures, without or with surgical intervention, without or with endoscopic retrograde cholangiopancreatography (ERCP), types of high-level antibiotics used, digestion disorders, abnormalities in blood coagulation, metabolic acidosis, pancreatic necrosis, intra-abdominal hemorrhage, intra-abdominal hypertension, length of ICU stay and total length of hospital stay) were found to be associated with KP infection in SAP patients by univariate analysis. The four variables (APACHEⅡ score, duration of indwelling of the peritoneal drainage tube, types of high-level antibiotics used, and total length of hospital stay) were extracted after reduced by LASSO regression. These four variables were found to be risk factors for KP infection in SAP patients by multiple logistic regression analysis (all P value <0.05). Nomogram prediction model for KP infection in SAP was established based on the four variables above. The verification results of the model showed that the C-index of the model was 0.939, and the AUC was 0.939 (95% CI 0.888-0.991), indicating that the nomogram model had relatively accurate prediction ability. Conclusions:This prediction model establishes integrated the basic clinical data of patients, which could facilitate the risk prediction for KP infection in patients with SAP and thus help to formulate better therapeutic plans for patients.

Establishment and internal validation of a risk prediction model for urinary incontinence after transurethral holmium laser enucleation of the prostate / 现代泌尿外科杂志

【Objective】 To establish a model for predicting the risk of urinary incontinence after holmium laser enucleation of the prostate (HoLEP). 【Methods】 The clinical data of 258 patients with benign prostatic hyperplasia (BPH) who underwent HoLEP in our hospital during Jan.2019 and Feb.2022 were retrospectively analyzed. According to the occurrence of urinary incontinence after surgery, they were divided into the urinary incontinence group (n=84) and non-urinary incontinence group (n=174). Lasso regression was used to screen the predictors of urinary incontinence after HoLEP. Logistic regression was used to establish a suitable model, and a nomogram of urinary incontinence after HoLEP was drawn. Bootstrap was used to verify and draw the calibration curve of the model, calculate the C index, and draw the clinical decision curve to further verify the accuracy and identification ability of the model. 【Results】 Predictors including International Prostate Symptom Score (IPSS), Quality of Life Score (QoL), body mass index (BMI), diabetes, prostate volume (PV), and prostate-specific antigen (PSA) were selected, based on which a prediction model was constructed. The area under the receiver operating characteristic (ROC) curve of the prediction model was 0.766 0, and the 95% confidence interval was 0.704-0.828. Bootstrap internal validation showed a C-index of 0.766 2, and the calibration model curve coincided well with the actual model curve. The clinical decision curve analysis showed that the model had high accuracy, and net benefit in the probability of urinary incontinence was within 10% to 82%. 【Conclusion】 IPSS, QoL, diabetes, prostate volume, and PSA are predictors that can affect the occurrence of urinary incontinence after HoLEP. The model has high accuracy, identification ability and net benefit.

Development and validation of a nomogram for predicting survival outcomes in prostate cancer patients based on SEER database / 现代泌尿外科杂志

【Objective】 To establish and verify a nomogram model of overall survival (OS) of prostate cancer patients based on the SEER data. 【Methods】 A total of 12 642 patients diagnosed with prostate cancer during 2010 and 2015 were extracted from the SEER database. Patients were randomly divided into the model group (n=8 850) and validation group (n=3 792). The independent risk factors for OS were analyzed with univariate Cox proportional risk regression, lasso regression and multivariate Cox proportional risk regression. A nomogram was constructed to predict the 1-year, 3-year and 5-year OS. The prediction potential of the model was evaluated with the consistency index (C-index), calibration curve and receiver operating characteristic (ROC) curve. 【Results】 Multivariate Cox regression analysis showed that age, T stage, N stage, M stage, bone metastasis, liver metastasis and regional lymphadenectomy were independent risk factors for OS (P<0.05). The seven factors were used to construct an OS nomogram model. The C-index of the modeling set was 0.750, and the area under the ROC curve (AUC) at 1, 3 and 5 years were 0.77, 0.77 and 0.76, respectively;the C-index of the validation set was 0.765, and the AUC at 1, 3 and 5 years were 0.83, 0.79 and 0.76, respectively. The calibration curves of the modelling set and validation set showed a good agreement with the actual survival prediction rate. Risk stratification of patients based on the nomogram model showed that the OS of patients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). 【Conclusion】 The nomogram can be used to predict the prognosis of prostate cancer patients, and is important for individualized treatment plans.

Effects of preoperative lipid metabolism indexes on the prognosis of patients with non-muscular invasive bladder cancer / 现代泌尿外科杂志

【Objective】 To investigate the effects of preoperative lipid metabolism level on the postoperative prognosis of non-muscular invasive bladder cancer (NMIBC). 【Methods】 Clinical data of NMIBC patients who underwent surgical treatment in our hospital during Mar.2014 and May 2021 were retrospectively analyzed. Based on receiver operating characteristic (ROC) curve, the optimal cutoff values of all lipid metabolism indicators were determined and patients were classified accordingly. The independent risk factors for postoperative recurrence were identified with Cox regression model. The survival was analyzed with Kaplan-Meier, and recurrence-free survival (RFS) was compared using log-rank tests. A recurrence risk prediction model was established based on the high-density lipoprotein (HDL) and other clinic pathological factors and the accuracy of prediction was evaluated with the area under the ROC curve (AUC). 【Results】 Cox multivariate analysis showed HDL, tumor number, tumor size and histological grade were independent risk factors for recurrence (P<0.05). Kaplan-Meier analysis showed that RFS was significantly longer in the high-HDL group than in the low-HDL group (P<0.001). Incorporating HDL, tumor number, tumor size, histological grade, and tumor stage into the recurrence risk model, the AUC was 0.706, and internal cross validation showed the AUC was 0.711. 【Conclusion】 Preoperative HDL is an independent risk factor affecting the RFS of patients with NMIBC, and combining it with clinic pathological factors will improve the prediction of tumor recurrence.

Establishment and evaluation of a risk prediction model for severe obstructive sleep apnea / 西安交通大学学报(医学版)

【Objective】 To construct a prediction model of severe obstructive sleep apnea (OSA) risk in the general population by using nomogram in order to explore the independent risk factors of severe OSA and guide the early diagnosis and treatment. 【Methods】 We retrospectively enrolled patients who had been diagnosed by polysomnography and divided them into training and validation sets at the ratio of 7∶3. Patients were divided into severe OSA group and non-severe OSA group according to apnea hypopnea index (AHI)>30. Variables entering the model were identified by least absolute shrinkage and selection operator regression model (Lasso), and logistic regression (LR) method. Then, multivariable logistic regression analysis was used to establish the nomogram, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative properties of the nomogram model. Finally, we conducted decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire and Berlin questionnaire to assess clinical utility. 【Results】 Through single factor and multiple factor logistic regression analyses, the independent risk factors for severe OSA were screened out, including moderate and severe sleepiness, family history of hypertension, history of smoking, drinking, snoring, history of suffocation, sedentary lifestyle, male, age, body mass index (BMI), waist and neck circumference. Lasso logistic regression identified smoke, suffocation time, snoring time, waistline, Epworth sleepiness scale (ESS) and BMI as predictive factors for inclusion in the nomogram. The AUC of the model was 0.795 [95% confidence interval (CI): 0.769-0.820] . Hosmer-Lemeshow test indicated that the model was well calibrated (χ2=3.942, P=0.862). The DCA results on the visual basis confirmed that the nomogram had superior overall net benefits within a wide, practical threshold probability range which displayed the nomogram was higher than that of STOP-Bang questionnaire and Berlin questionnaire, which is clinically useful. The Clinical Impact Curve (CIC) analysis showed the clinical effectiveness of the prediction model when the threshold probability was greater than 82% of the predicted score probability value. The prediction model determined that the high-risk population with severe OSA was highly matched with the actual population with severe OSA, which confirmed the high clinical effectiveness of the prediction model. 【Conclusion】 The model performed better than STOP-Bang questionnaire and Berlin questionnaire in predicting severe OSA and can be applied to screening. And it can be helpful to the early diagnosis and treatment of OSA in order to reduce social burden.