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.

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

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.

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.

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.

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.

Development and validation of a risk prediction model for low Apgar scores in extremely premature infants / 中国新生儿科杂志

Objective:To establish a risk prediction model for the occurrence of low 1 min Apgar scores in extremely premature infants (EPIs).Methods:From January 2017 to December 2021, EPIs delivered at our hospital were retrospectively analyzed and randomly assigned into training set group and validation set group in a 7∶3 ratio. 17 clinical indicators were selected as predictive variables and low Apgar scores after birth as outcome variables. Lasso regression and multi-factor logistic regression were used within the training set group to select the final predictors for the final model, and the calibration, distinguishability and clinical decision making curves of the final model were evaluated in the validation set group.Results:A total of 169 EPIs were enrolled, including 117 in the training set group and 52 in the validation set group. 4 indicators including gender, fetal distress, assisted conception and delivery time were selected as the final predictors in the final model. Both the training set group and the validation set group had good calibration curves. The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.731, the sensitivity was 72.2%, the specificity was 60.5% and the AUC of the external validation curve was 0.704. The clinical decision making curve showed that the model had a greater benefit in predicting the occurrence of low Apgar score in EPIs within the threshold of 2% to 75%.Conclusions:The clinical prediction model established in this study has good distinguishability, calibration and clinical accessibility and can be used as a reference tool to predict low Apgar scores in EPIs.

Construction of recurrence prediction model after radical resection of middle and low rectal cancer based on magnetic resonance imaging measurement of perirectal fat content and its application value / 中华消化外科杂志

Objective:To investigate the influencing factors of recurrence after radical resection of middle and low rectal cancer, and to establish a prediction model based on magnetic resonance imaging (MRI) measurement of perirectal fat content and investigate its application value.Methods:The retrospective cohort study was constructed. The clinicopathological data of 254 patients with middle and low rectal cancer who were admitted to Tianjin Union Medical Center from December 2016 to December 2021 were collected. There were 188 males and 66 females, aged (61±9)years. All patients underwent radical resection of rectal cancer and routine pelvic MRI examina-tion. Observation indicators: (1) follow-up and quantitative measurement of perirectal fat content; (2) factors influencing tumor recurrence after radical resection of middle and low rectal cancer; (3) construction and evaluation of the nomogram prediction model of tumor recurrence after radical resection of middle and low rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M(rang) and M( Q1, Q2). Count data were described as absolute numbers. Univariate and multivariate analyses were conducted using the COX regression model. The rms software package (4.1.3 version) was used to construct the nomogram and calibration curve. The survival software package (4.1.3 version) was used to calculate the C-index. The ggDCA software package (4.1.3 version) was used for decision curve analysis. Results:(1) Follow-up and quantitative measurement of perirectal fat content. All 254 patients were followed up for 41.0(range, 1.0?59.0)months after surgery. During the follow-up period, there were 81 patients undergoing tumor recurrence with the time to tumor recurrence as 15.0(range, 1.0?43.0)months, and there were 173 patients without tumor recurrence. The preoperative rectal mesangial fascia envelope volume, preoperative rectal mesangial fat area, preoperative rectal posterior mesangial thickness were 159.1(68.6,266.5)cm3, 17.0(5.1,34.4)cm2, 1.2(0.4,3.2)cm in the 81 patients with tumor recurrence, and 178.5(100.1,310.1)cm3, 19.8(5.3,40.2)cm2 and 1.6(0.3,3.7)cm in the 173 patients without tumor recurrence. (2) Factors influencing tumor recurrence after radical resection of middle and low rectal cancer. Results of multivariate analysis showed that poorly differentiated tumor, tumor pathological N staging as N1?N2 stage, rectal posterior mesangial thickness ≤1.43 cm, magnetic resonance extra mural vascular invasion, tumor invasion surrounding structures were independent risk factors of tumor recurrence after radical resection of middle and low rectal cancer ( hazard ratio=1.64, 2.20, 3.19, 1.69, 4.20, 95% confidence interval as 1.03?2.61, 1.29?3.74, 1.78?5.71, 1.02?2.81, 2.05?8.63, P<0.05). (3) Construction and evaluation of the nomogram prediction model of tumor recurrence after radical resection of middle and low rectal cancer. Based on the results of multivariate analysis, the tumor differentiation, tumor pathological N staging, rectal posterior mesangial thickness, magnetic resonance extra mural vascular invasion, tumor invasion surrounding structures were included to construct the nomogram predic-tion model of tumor recurrence after radical resection of middle and low rectal cancer. The total score of these index in the nomogram prediction model corresponded to the probability of post-operative tumor recurrence. The C-index of the nomogram was 0.80, indicating that the prediction model with good prediction accuracy. Results of calibration curve showed that the nomogram prediction model with good prediction ability. Results of decision curve showed that the prediction probability threshold range was wide when the nomogram prediction model had obvious net benefit rate, and the model had good clinical practicability. Conclusions:Poorly differentiated tumor, tumor pathological N staging as N1?N2 stage, rectal posterior mesangial thickness ≤1.43 cm, magnetic resonance extra mural vascular invasion, tumor invasion surrounding structures are independent risk factors of tumor recurrence after radical resection of middle and low rectal cancer. Nomogram prediction model based on MRI measurement of perirectal fat content can effectively predict the probability of postoperative tumor recurrence.

Risk factors of acute biliopancreatic complications in patients of pregnancy combined with gallbladder stone and construction of prediction model / 中华消化外科杂志

Objective:To investigate the risk factors of acute biliopancreatic complica-tions in patients of pregnancy combined with gallbladder stone and construction of prediction model.Methods:The retrospective case-control study was constructed. The clinical data of 98 patients of pregnancy combined with gallbladder stone who were admitted to the First Hospital of Lanzhou University from September 2011 to October 2022 and 53 patients of pregnancy combined with gallbladder stone who were admitted to Gansu Provincial Hospital May 2014 to October 2021 were collected. The age of 151 patients was 29(25,32)years. Observation indicators: (1) situations of patients of pregnancy combined with gallbladder stone; (2) risk factors of acute biliopancreatic com-plications in patients of pregnancy combined with gallbladder stone; (3) construction of prediction model for acute biliopancreatic complications in patients of pregnancy combined with gallbladder stone. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and comparison between groups was conducted using the chi-square test. Comparison of ordinal data was conducted using the rank sum test. Univariate and multi-variate analyses were conducted using the Logistic regression model. Nomogram prediction model was conducted, and the receiver operating characteristic (ROC) curve was used to evaluate discri-mination of the nomogram predic-tion model. The calibration curve and clinical decision curve were used to evaluate calibration and net clinical benefit of the nomogram prediction model. Internal validation of the prediction model was performed by applying 10-fold cross-validation. Results:(1) Situations of patients of pregnancy combined with gallbladder stone. The total cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, prepregnancy body mass index (<18.5 kg/m 2, 18.5?24.0 kg/m 2, >24.0 kg/m 2), gesta-tional period (early, mid, late), primipara (positive, negative), stone type (solitary, non solitary), diameter of stone (≤10 mm, >10 mm), gallbladder wall thickness (≥4 mm, <4 mm) were (4.9±1.4)mmol/L, 1.88(1.22,2.93)mmol/L, 1.48(1.22,1.83)mmol/L, (2.8±0.9)mmol/L, 13, 75, 58, 37, 45, 69, 86, 65, 37, 114, 89, 62, 38, 113 in the 151 patients of pregnancy combined with gallbladder stone. Of the 151 patients, the age, prepregnancy body mass index (<18.5 kg/m 2, 18.5?24.0 kg/m 2, >24.0 kg/m 2), primipara (positive, negative), stone type (solitary, non solitary), diameter of stone (≤10 mm, >10 mm), gallbladder wall thickness (≥4 mm, <4 mm) were 31(28,37)years, 3, 30, 36, 29, 40, 32, 37, 26, 43, 4, 65 in 69 cases without symptom, versus 27(24,31)years, 10, 45, 22, 57, 25, 5, 77, 63, 19, 34, 48 in 82 cases combined with acute biliopancreatic complications, showing significant differences in the above indicators between them ( Z=?3.636, ?2.385, χ2=11.544, 32.862, 23.729, 25.310, P<0.05). Five of the 82 patients of pregnancy combined with gallbladder stone missed data of prepregnancy body mass index. Of the 82 patients, there were 42 patients of simple acute cholecystitis, 40 patients of common bile duct stone and/or acute biliary pancreatitis including 18 cases of common bile duct stone, 13 cases of acute biliary pancreatitis and 9 cases of common bile duct stone combined with acute biliary pancreatitis. (2) Risk factors of acute biliopancreatic complications in patients of pregnancy combined with gallbladder stone. Results of multivariate analysis showed that primipara, non solitary stone, diameter of stone ≤10 mm, gallbladder wall thickness ≥4 mm were independent risk factors of acute biliopancreatic complications in patients of pregnancy combined with gallbladder stone ( odds ratio=3.102, 6.305, 3.674, 6.686, 95% confidence interval as 1.280?7.519, 1.886?21.080, 1.457?9.265, 1.984?22.528, P<0.05). Results of multivariate analysis in further analysis showed that primipara, non solitary stone, gallbladder wall thickness ≥4 mm were independent risk factors of simple acute cholecystitis in patients of pregnancy combined with gallbladder stone ( odds ratio=3.671, 8.905, 7.137, 95% confidence interval as 1.386?9.723, 2.332?34.006, 1.902?26.773, P<0.05), and age, non solitary stone, diameter of stone ≤10 mm, gallbladder wall thickness ≥4 mm were independent risk factors of common bile duct stone and/or acute biliary pancreatitis in patients of pregnancy combined with gallbladder stone ( odds ratio=0.883, 5.361, 5.472, 8.895, 95% confidence interval as 0.789?0.988, 1.062?27.071, 1.590?18.827, 2.064?38.325, P<0.05). (3) Construction of prediction model for acute biliopancreatic complications in patients of pregnancy combined with gallbladder stone. The nomogram prediction model for acute biliopancreatic complications in patients of pregnancy combined with gallbladder stone was constructed based on the clinical factors of age, primipara, stone type, diameter of stone and gallbladder wall thickness. The area under the curve (AUC) of ROC curve of prediction model was 0.869 (95% confidence interval as 0.813?0.923), indicating that the prediction model with good predictive ability. Results of Hosmer-Lemeshow test showed a good fit ( χ2=5.680, P>0.05), indicating that the prediction model with good calibration. Results of decision curve analysis showed the prediction model with high net clinical benefit. Results of internal validation of the prediction model based on 10-fold cross-validation showed the AUC of ROC curve for the cross-validation sample was 0.833, indicating that the prediction model with good stability. Conclusions:Primigravida, non solitary stone, diameter of stone ≤10 mm, gallbladder wall thickness ≥4 mm are independent risk factors of acute biliopancreatic complications in patients of pregnancy combined with gallbladder stone. The prediction model for acute biliopancreatic complications has good predictive ability.

Clinical study of cytokine models to predict organ functional impairment after treatment with chimeric antigen receptor T cells in children with B-lineage lymphocytic leukemia / 中国小儿急救医学

Objective:To explore the predictive value of peripheral blood cytokine models on organ functional impairment after chimeric antigen receptor T(CAR-T) cell therapy in children with B-lineage lymphocytic leukemia.Methods:The clinical data of 44 children with acute B-lineage lymphoblastic leukemia who received CAR-T cell therapy at Children′s Hospital of Soochow University from September 2018 to October 2020 were retrospectively analyzed.Peripheral blood cytokines, including interleukin(IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor-α, interferon(IFN)-γ and IL-17A, were measured daily for 14 days after receiving CAR-T cell therapy.The trend of peripheral blood cytokine levels was analyzed at the endpoint of organ function recovery or death within 14 days after CAR-T cell treatment.Receiver operating characteristic curve was used to establish a mathematical prediction model to predict the occurrence of organ damage in the children.Results:Of the 44 children, 31 cases were boys and 13 cases were girls, with a median age of 7.96 (5.19, 11.48)years.Cytokine release syndrome(CRS) response occurred in 95.5% (42/44) children, with 88.1% (37/42) had a grade 1-3 CRS response, and 16.7% (7/42) had a severe grade 4-5 CRS response.Using IL-6>3 892.95 pg/mL as cut-off value, the area under the curve(AUC) for predicting acute respiratory failure was 0.818, with a sensitivity of 0.8 and a specificity of 0.735, while combining IFN-γ>414.4 pg/mL, IL-6>3 892.95 pg/mL and IL-2>27.05 pg/mL were the three cut-off values, with an AUC of 0.741, sensitivity of 0.6 and specificity of 0.912 for predicting acute respiratory failure. Using IFN-γ>1 699.5 pg/mL as cut-off value, the AUC for predicting shock was 0.908, with a sensitivity of 0.722 and a specificity of 1.With IL-6>4 607.3 pg/mL as cut-off value, the AUC for predicting liver injury was 0.964, with a sensitivity of 1 and a specificity of 0.906, while combining both IL-6>4 607.3 pg/mL and IFN-γ>1 446.2 pg/mL as cut-off values, the AUC for predicting liver injury was 0.977, with a sensitivity of 1 and a specificity of 0.906.Combining both IL-6>6 972.2 pg/mL and IFN-γ>3 981.5 pg/mL predicted a positive predictive value of 62.5% and a negative predictive value of 94.4% for grade 4-5 CRS response, with an AUC of 0.846, a predictive sensitivity of 0.714 and a specificity of 0.838, and all children had a combination of two or more organ function injuries.Conclusion:The combination of IL-6 and IFN-γ can effectively predict the incidence of liver injury and cytokine release syndrome.The combination of peripheral blood cytokines IFN-γ, IL-6 and IL-2 can be used to predict the incidence of acute respiratory failure after the treatment of CAR-T cells in children with acute B-lineage lymphoblastic leukaemia.IFN-γ single index can be used to predict the incidence of shock.The combination of IL-6 and IFN-γ can be used to predict the incidence of liver injury and the severity of CRS.

Clinical prediction model for complicated appendicitis in children under five years old / 中国小儿急救医学

Objective:To retrospectively analyze the independent risk factors of complicated appendicitis(CA)in children under five years old and establish a clinical prediction model, and to evaluate the clinical application of this model.Methods:A retrospective analysis was performed on children under five years old who underwent appendectomy at Children′s Hospital of Shanghai Jiao Tong University School of Medicine from January 2018 to December 2021.The children were divided into CA group and uncomplicated appendicitis group according to whether there was sign of perforation or gangrene in appendiceal tissue after operation.The differences in clinical features and preoperative laboratory test results between two groups were compared.The independent risk factors of CA were identified and a clinical prediction model was established.The clinical prediction model was verified by receiver operating characteristic curve.Results:A total of 140 children were enrolled in this study, including 84 cases in the CA group and 56 cases in uncomplicated appendicitis group.Univariate and binary Logistic regression analysis showed that the duration of symptoms>23.5 h( OR=6.650, 95% CI 2.469-17.912, P<0.05), abdominal muscle tension( OR=3.082, 95% CI 1.190-7.979, P<0.05) and C-reactive protein>41 mg/L ( OR=3.287, 95% CI 1.274-8.480, P<0.05) were independent risk factors for CA( P<0.05). The clinical prediction model of CA was constructed by the above mentioned three independent risk factors.The area under the receiver operating characteristic curve of the clinical prediction model was 0.881(95% CI 0.825-0.936), the sensitivity was 77.4%, the specificity was 87.5%, the positive predictive value was 91.3% and the negative predictive value was 70.0%. Conclusion:Acute appendicitis in children under five years old is more likely to progress to CA if the duration of symptoms>23.5 h, the level of C-reactive protein is increased, and the abdominal muscle tension is accompanied.The clinical prediction model of CA constructed by common clinical information in pediatric clinics has good prediction efficiency, which provides a simple and feasible reference method for clinicians to distinguish CA from uncomplicated appendicitis.

Construction and application of neonatal asphyxia risk prediction model in cesarean section / 中国实用护理杂志

Objective:To establish a risk prediction model for neonatal asphyxia in cesarean section and test its application effect.Methods:This was a retrospective study. We retrospectively analyzed the clinical data of 2 244 infants (modeling group) who were delivered by cesarean section in Affiliated Hospital of Weifang Medical University from April 2021 to December 2021. Newborns were divided into asphyxia group ( n=176) and non-asphyxia group ( n=2 068) according to the occurrence of neonatal asphyxia. Logistic regression was used to screen the risk factors of neonatal asphyxia in cesarean section and a line chart model was established to predict the risk. Another 683 neonates were selected as validation group for external validation of the model from January to March in 2022. Results:Five factors including preterm birth, fetal distress, fetal growth restriction, abnormal S/D value of umbilical artery and umbilical cord around the neck were included in the prediction model. The area under ROC curve of the modeling group was 0.902, the Youden index was 0.687, the sensitivity was 0.837, and the specificity was 0.850. Hosmer-lemeshow test showed that χ2=1.79, and P=0.877. In the validation group, the area under ROC curve was 0.823, the Youden index was 0.555, the sensitivity was 0.835, and the specificity was 0.720. It showed that the model had a good fitting effect and identification validity. Conclusions:The risk prediction model has a good clinical application value in the prediction of neonatal asphyxia in cesarean section, and provides reference for obstetricians to take preventive management measures of neonatal asphyxia in time.

Analysis on constructing a risk prediction model for premature ovarian function failure in patients with uterine fibroids complicated with hypertension after surgery based on decision tree method / 中华内分泌外科杂志

Objective:The decision tree Chi-square automatic interactive detection (CHAID) algorithm and binary Logistic regression analysis were used to construct the risk prediction model of premature ovarian failure (POF) in patients with uterine fibroids complicated with hypertension after surgery, and the results of the risk prediction model were compared and analyzed.Methods:Patients with uterine fibroids complicated with hypertension admitted to Taizhou Hospital of Zhejiang Province from Jan. 2019 to Sep. 2022 were retrospectively analyzed as the research objects. CHAID algorithm and Logistic regression analysis were used to establish risk prediction models, respectively. The area under the curve (AUC) of receiver operating characteristic curve (ROC) was used to compare and evaluate the prediction effects of the two models.Results:A total of 860 patients were collected, including 56 patients with premature ovarian function failure after operation, and the incidence of premature ovarian function failure was 6.51%. CHAID method and Logistic regression analysis showed that uterine myoma surgery, hypertension, smoking or passive smoking, family history of premature ovarian failure, sleep status, physical exercise and history of induced curettage were important influencing factors of premature ovarian failure. The accuracy of risk prediction of decision tree model was 88.2%, and the fitting effect of the model was good. The Logistic regression model Hosmer-Leme-show goodness of fit test showed that the model fit was good. The AUC of Logistic regression model was 0.893 (95% CI: 0.862-0.899), and the AUC of decision tree model was 0.882 (95% CI: 0.856-0.899). The predictive value of the two models was moderate, and there was no significant difference between them ( Z=0.254, P>0.05) . Conclusions:The combination of decision tree and Logistic regression model can find the influencing factors of premature ovarian function failure in patients with uterine fibroids complicated with hypertension after operation from different levels, and the relationship between the factors can be more fully understood. The establishment of a risk model for premature ovarian function failure in patients with uterine fibroids complicated with hypertension after surgery can provide a reference for postoperative intervention in patients with uterine fibroids complicated with hypertension, and more effectively help patients actively prevent and slow down the occurrence and development of POF.

Analysis of early risk factors and establishment of predictive model for prognosis of traumatic pancreatitis / 中华急诊医学杂志

Objective:To analyze the prognostic risk factors of patients with traumatic pancreatitis (TP) and establish an early combined prediction of multiple indicators model for TP.Methods:Patients admitted to the ICU of the First Affiliated Hospital of Zhengzhou University from June 2017 to June 2022 were collected retrospectively. Based on their prognosis, the patients were divided into two groups: the good prognosis group and the poor prognosis group. The general data such as sex, age, underlying diseases, Glasgow Coma Scale (GCS), acute physiology and chronic health evaluationⅡ (APACHEⅡ), injury severity score (ISS), bedside index for severity in acute pancreatitis (BISAP), and clinical test indices such as blood routine, blood coagulation, blood gas analysis, and liver and kidney function at admission were compared between the two groups. Univariate analysis and multivariate logistic regression analysis were used to screen the early independent predictors of poor prognosis of TP, and the prediction model of TP was established by combining all of the independent indicators. The receiver operating characteristic (ROC) curve of each independent predictor and prediction model was drawn, and the area under the curve (AUC), sensitivity, specificity, and optimal cut-off value were calculated to examine the diagnostic impact of each independent predictor and the combined prediction model.Results:There were statistically significant differences in the complication rate of mental disorders, GCS, APACHE II, combined craniocerebral injury, combined chest injury, activated partial thromboplastin time, fibrin(pro)degradation products, lactate, aspartate aminotransferase, glomerular filtration rate, amylase, lipase, NT-proBNP, myoglobin, procalcitonin, ISS, and BISAP between the good and poor prognosis groups (all P<0.05). Multivariate logistic regression analysis showed that lactate ( OR=1.636, 95% CI: 1.046-2.559), lipase ( OR=1.005, 95% CI: 1.001-1.008), and ISS ( OR=1.161, 95% CI: 1.064-1.266) were independent risk factors influencing the prognosis of patients with TP. Based on the risk factors listed above, a prediction model was created: Logit P=-9.260+0.492×lactate+0.005×lipase+0.149×ISS, and the ROC curve was plotted. The AUC curve of the prediction model was 0.96 (95% CI: 0.91-1.00). Conclusions:Lactate, lipase, and ISS are early independent risk factors associated with the prognosis of TP. Their combined multi-indicator prediction model has an excellent clinical prediction effect, which can provide a clinical reference for early prediction and treatment of TP.

Construction of blood consumption prediction model for emergency trauma patients based on machine learning algorithm / 中华急诊医学杂志

Objective:To establish a blood consumption prediction model for emergency trauma patients based on machine learning algorithm, so as to guide blood collection and blood supply institutions to prepare for the early blood demand of mass casualties in public emergencies.Methods:A retrospective analysis was conducted on trauma patients in the emergency system database of 12 hospitals in Zhejiang Province from January 2018 to December 2020. Patients with chronic medical history such as hematological diseases and tumors, and transferred from other hospitals after external treatment were excluded. The patients were divided into the transfusion group and non-transfusion group according to whether they received blood transfusion. The differences in demographic and clinical characteristics between the two groups were compared, and the computer learning algorithm (XGBoost) was used to build the blood consumption prediction model and blood consumption volume prediction model of emergency trauma patients.Results:Totally 2025 patients were included in this study, including 1146 patients in the transfusion group and 879 patients in the non-transfusion group. The blood demand of emergency trauma patients mainly occurred within 3 days of admission (60%). The main variables affecting the blood consumption prediction model of emergency trauma patients were shock index, hematocrit, systolic blood pressure, abdominal injury, pelvic injury, ascites and hemoglobin. Compared with the traditional prediction model, XGBoost model had the highest hit rate of 59.0%. The accuracy of blood consumption prediction model was the highest when seven levels of blood volume were adopted, and the deviation fluctuated between [0~1] U. According to the prediction model, the blood consumption prediction formula was∑ nw× c. Conclusions:The preliminarily constructed prediction model of blood transfusion and blood consumption for emergency trauma patients has better performance than the traditional prediction model of blood transfusion, which provides reference for optimizing the decision-making ability of blood demand assessment of hospitals and blood supply institutions under public emergencies.

Risk prediction of acute kidney injury in paraquat poisoning patients / 中华急诊医学杂志

Objective:To establish a risk prediction model of acute kidney injury in paraquat (PQ) poisoning patients.Methods:A retrospective observational cohort of adult patients with acute PQ poisoning between September 10, 2010 and January 16, 2020 from the Emergency Department of West China Hospital, Sichuan University were conducted. Data on demographics, clinical records, and laboratory results were collected from electronic medical record. The patients were divided into the AKI group and the non-AKI group according to whether AKI occurred during hospitalization. The patients were randomly divided into the training and validation groups (7:3). Multivariate logistic regression analysis was used to screen the independent risk factors of AKI and the nomogram was used to establish a prediction model. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the differentiation and calibration of the prediction model. Decision curve analysis (DCA) was used to evaluate the clinical validity of the prediction model.Results:A total of 718 patients were included in this study. AKI occurred in 323 (45%) patients in hospital and 378 (52.6%) patients died. The mortality rate of the AKI group was higher than that of the non-AKI group (72.8% vs. 36.2%, P < 0.05). Multivariate logistic regression analysis showed that the time from poisoning to treatment ( OR=1.018, 95% CI:1.006-1.030), white blood cell count ( OR=1.128, 95% CI: 1.084-1.173), aspartate aminotransferase ( OR=1.017, 95% CI:1.006-1.027), cystatin C ( OR=516.753, 95% CI: 99.337-2688.172), and PQ concentration ( OR=1.064, 95% CI:1.044-1.085) in blood on admission were independent risk factors of AKI in patients with PQ poisoning ( P<0.01). The area under the ROC curve was 0.943 (95% CI: 0.923-0.962) in the training cohort, and the sensitivity and specificity were 82.4% and 93.6%, respectively. The calibration curve showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that the nomogram conferred high clinical net benefit. Conclusions:The time from poisoning to treatment, white blood cell count, aspartate aminotransferase, cystatin C, and PQ concentration in blood on admission were independent risk factors of AKI. The predictive model based on the above indicators has high sensitivity and specificity in evaluating AKI after PQ poisoning. Whether this prediction model can be applied to other PQ poisoning patients needs to be further expanded for verification.