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.

Construction of a Prognostic Prediction Model of Patients with Pathologic N0 in Resected Invasive Mucinous Adenocarcinoma of the Lung / 中国肺癌杂志

BACKGROUND@#Invasive mucinous adenocarcinoma (IMA) was a rare and specific type of lung adenocarcinoma, which was often characterized by fewer lymphatic metastases. Therefore, it was difficult to evaluate the prognosis of these tumors based on the existing tumor-node-metastasis (TNM) staging. So, this study aimed to develop Nomograms to predict outcomes of patients with pathologic N0 in resected IMA.@*METHODS@#According to the inclusion criteria and exclusion criteria, IMA patients with pathologic N0 in The Affiliated Lihuili Hospital of Ningbo University (training cohort, n=78) and Ningbo No.2 Hospital (validation cohort, n=66) were reviewed between July 2012 and May 2017. The prognostic value of the clinicopathological features in the training cohort was analyzed and prognostic prediction models were established, and the performances of models were evaluated. Finally, the validation cohort data was put in for external validation.@*RESULTS@#Univariate analysis showed that pneumonic type, larger tumor size, mixed mucinous/non-mucinous component, and higher overall stage were significant influence factors of 5-year progression-free survival (PFS) and overall survival (OS). Multivariate analysis further indicated that type of imaging, tumor size, mucinous component were the independent prognostic factors for poor 5-year PFS and OS. Moreover, the 5-year PFS and OS rates were 62.82% and 75.64%, respectively. In subgroups, the survival analysis also showed that the pneumonic type and mixed mucinous/non-mucinous patients had significantly poorer 5-year PFS and OS compared with solitary type and pure mucinous patients, respectively. The C-index of Nomograms with 5-year PFS and OS were 0.815 (95%CI: 0.741-0.889) and 0.767 (95%CI: 0.669-0.865). The calibration curve and decision curve analysis (DCA) of both models showed good predictive performances in both cohorts.@*CONCLUSIONS@#The Nomograms based on clinicopathological characteristics in a certain extent, can be used as an effective prognostic tool for patients with pathologic N0 after IMA resection.

Establishment of a Predictive Model for Chronic Cough after Pulmonary Resection / 中国肺癌杂志

BACKGROUND@#Chronic cough after pulmonary resection is one of the most common complications, which seriously affects the quality of life of patients after surgery. Therefore, the aim of this study is to explore the risk factors of chronic cough after pulmonary resection and construct a prediction model.@*METHODS@#The clinical data and postoperative cough of 499 patients who underwent pneumonectomy or pulmonary resection in The First Affiliated Hospital of University of Science and Technology of China from January 2021 to June 2023 were retrospectively analyzed. The patients were randomly divided into training set (n=348) and validation set (n=151) according to the principle of 7:3 randomization. According to whether the patients in the training set had chronic cough after surgery, they were divided into cough group and non-cough group. The Mandarin Chinese version of Leicester cough questionnare (LCQ-MC) was used to assess the severity of cough and its impact on patients' quality of life before and after surgery. The visual analog scale (VAS) and the self-designed numerical rating scale (NRS) were used to evaluate the postoperative chronic cough. Univariate and multivariate Logistic regression analysis were used to analyze the independent risk factors and construct a model. Receiver operator characteristic (ROC) curve was used to evaluate the discrimination of the model, and calibration curve was used to evaluate the consistency of the model. The clinical application value of the model was evaluated by decision curve analysis (DCA).@*RESULTS@#Multivariate Logistic analysis screened out that preoperative forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), surgical procedure, upper mediastinal lymph node dissection, subcarinal lymph node dissection, and postoperative closed thoracic drainage time were independent risk factors for postoperative chronic cough. Based on the results of multivariate analysis, a Nomogram prediction model was constructed. The area under the ROC curve was 0.954 (95%CI: 0.930-0.978), and the cut-off value corresponding to the maximum Youden index was 0.171, with a sensitivity of 94.7% and a specificity of 86.6%. With a Bootstrap sample of 1000 times, the predicted risk of chronic cough after pulmonary resection by the calibration curve was highly consistent with the actual risk. DCA showed that when the preprobability of the prediction model probability was between 0.1 and 0.9, patients showed a positive net benefit.@*CONCLUSIONS@#Chronic cough after pulmonary resection seriously affects the quality of life of patients. The visual presentation form of the Nomogram is helpful to accurately predict chronic cough after pulmonary resection and provide support for clinical decision-making.

Establishment and validation of nomogram prediction model of cefoperazone/sulbactam-induced thrombocytopenia / 中国药房

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

Clinical Prediction Model for Diabetic Kidney Disease Based on Optical Coherence Tomography Angiography / 中山大学学报(医学科学版)

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

Risk analysis of re⁃fracture after percutaneous kyphoplasty in elderly patients with osteoporotic thoracolumbar compression fractures and construction of a columnar graph prediction model / 解剖学报

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

Exploration of CT imaging features of cystic pulmonary nodules and establishment of a prediction model for benign and malignant pulmonary nodules / 中国胸心血管外科临床杂志

@#Objective To explore the CT imaging features and independent risk factors for cystic pulmonary nodules and establish a malignant probability prediction model. Methods The patients with cystic pulmonary nodules admitted to the Department of Thoracic Surgery of the First People's Hospital of Neijiang from January 2017 to February 2022 were retrospectively enrolled. They were divided into a malignant group and a benign group according to the pathological results. The clinical data and preoperative chest CT imaging features of the two groups were collected, and the independent risk factors for malignant cystic pulmonary nodules were screened out by logistic regression analysis, so as to establish a prediction model for benign and malignant cystic pulmonary nodules. Results A total of 107 patients were enrolled. There were 76 patients in the malignant group, including 36 males and 40 females, with an average age of 59.65±11.74 years. There were 31 patients in the benign group, including 16 males and 15 females, with an average age of 58.96±13.91 years. Multivariate logistic analysis showed that the special CT imaging features such as cystic wall nodules [OR=3.538, 95%CI (1.231, 10.164), P=0.019], short burrs [OR=4.106, 95%CI (1.454, 11.598), P=0.008], cystic wall morphology [OR=6.978, 95%CI (2.374, 20.505), P<0.001], and the number of cysts [OR=4.179, 95%CI (1.438, 12.146), P=0.009] were independent risk factors for cystic lung cancer. A prediction model was established: P=ex/(1+ex), X=–2.453+1.264×cystic wall nodules+1.412×short burrs+1.943×cystic wall morphology+1.430×the number of cysts. The area under the receiver operating charateristic curve was 0.830, the sensitivity was 82.9%, and the specificity was 74.2%. Conclusion Cystic wall nodules, short burrs, cystic wall morphology, and the number of cysts are the independent risk factors for cystic lung cancer, and the established prediction model can be used as a screening method for cystic pulmonary nodules.

Blood concentration monitoring of tacrolimus in patients with nephrotic syndrome and establishment of MLP prediction model / 中国药房

OBJECTIVE To investigate the monitoring of tacrolimus blood concentration in patients with nephrotic syndrome （NS），and to establish a prediction model for tacrolimus blood concentration. METHODS Data from 509 concentration monitoring sessions of 166 NS patients using tacrolimus were collected from January 1， 2020 to August 31， 2023 in Zhongshan Hospital Affiliated to Xiamen University. The relationship of efficacy and adverse drug reaction（ADR） with blood concentration was analyzed. A multilayer perceptron （MLP） prediction model was established by using the blood concentration monitoring data of 302 times from 109 NS patients with genetic information， and then verified. RESULTS In terms of efficacy， the median blood concentration of tacrolimus in the non-remission group was 2.20 ng/mL， which was significantly lower than that in the partial remission group （4.00 ng/mL， P＜0.001） and the complete remission group （3.60 ng/mL， P＝0.002）. In terms of ADR， the median blood concentration of tacrolimus in the ADR group was 5.01 ng/mL， which was significantly higher than that in the non-ADR group （3.37 ng/mL） （P＝0.001）. According to the subgroup analysis of the receiver operating characteristic curve， when the blood concentration of tacrolimus was ≥6.65 ng/mL， patients were more likely to develop elevated blood creatinine [area under the curve （AUC） was 0.764， P＜0.001）； when the blood concentration of tacrolimus was ≥6.55 ng/mL， patients were more likely to develop blood glucose （AUC＝0.615， P＝ 0.005）. The established MLP prediction model has a loss function of 0.9， with an average absolute error of 0.279 5 ng/mL between the predicted and measured values. The determination coefficient of the validation scatter plot was 0.984， indicating an excellent predictive performance of the model. CONCLUSION Tacrolimus blood concentration has an impact on both efficacy and ADR in NS patients. The use of the MLP model for predicting blood concentration exhibits high accuracy with minimal error between predicted and measured values. The model can be used as an important tool in clinical individualized medication regimens.

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.

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.

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.

Early prediction of acute respiratory distress syndrome complicated by acute pancreatitis based on four machine learning models

Abstract Background Acute Respiratory Distress syndrome (ARDS) is a common complication of Acute Pancreatitis (AP) and is associated with high mortality. This study used Machine Learning (ML) to predict ARDS in patients with AP at admission. Methods The authors retrospectively analyzed the data from patients with AP from January 2017 to August 2022. Clinical and laboratory parameters with significant differences between patients with and without ARDS were screened by univariate analysis. Then, Support Vector Machine (SVM), Ensembles of Decision Trees (EDTs), Bayesian Classifier (BC), and nomogram models were constructed and optimized after feature screening based on these parameters. Five-fold cross-validation was used to train each model. A test set was used to evaluate the predictive performance of the four models. Results A total of 83 (18.04%) of 460 patients with AP developed ARDS. Thirty-one features with significant differences between the groups with and without ARDS in the training set were used for modeling. The Partial Pressure of Oxygen (PaO2), C-reactive protein, procalcitonin, lactic acid, Ca2+, the neutrophil:lymphocyte ratio, white blood cell count, and amylase were identified as the optimal subset of features. The BC algorithm had the best predictive performance with the highest AUC value (0.891) than SVM (0.870), EDTs (0.813), and the nomogram (0.874) in the test set. The EDT algorithm achieved the highest accuracy (0.891), precision (0.800), and F1 score (0.615), but the lowest FDR (0.200) and the second-highest NPV (0.902). Conclusions A predictive model of ARDS complicated by AP was successfully developed based on ML. Predictive performance was evaluated by a test set, for which BC showed superior predictive performance and EDTs could be a more promising prediction tool for larger samples.

Risk factors for neonatal asphyxia and establishment of a nomogram model for predicting neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture: a multicenter study / 中国当代儿科杂志

OBJECTIVES@#To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia.@*METHODS@#A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively.@*RESULTS@#Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia.@*CONCLUSIONS@#The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.

Epidemiological characteristics and risk prediction of pulmonary infection in elderly patients with chronic obstructive pulmonary disease / 公共卫生与预防医学

Objective To explore the epidemiological characteristics of pulmonary infection in elderly patients with chronic obstructive pulmonary disease (COPD), and to construct a risk prediction model. Methods Among of 125 elderly patients with COPD from May 2020 to June 2022 were selected as the research subjects. The epidemiological characteristics of infected patients were counted, and the risk factors of pulmonary infection in patients were analyzed and a prediction model was constructed. Results A total of the 125 elderly patients with COPD, there were 46 cases of pulmonary infection, with the infection rate of 36.80%. The detection rate of Gram-negative bacteria was higher than that of Gram-positive bacteria or fungi (64.44% vs 33.33% or 2.22%, P2=0.812 and P=0.295. ROC curve analysis revealed that the AUC value of the prediction model on predicting the pulmonary infection in elderly patients with COPD was 0.802. Conclusion The pathogenic bacteria of elderly patients with COPD complicated with pulmonary infection are mainly Gram-negative bacteria. The prediction model constructed according to the risk factors of pulmonary infection in patients has predictive value on pulmonary infection in patients.

Risk factors and risk model construction of coronary heart disease in patients with type 2 diabetes mellitus in Suining area / 公共卫生与预防医学

Objective To analyze the risk factors of coronary heart disease (CHD) in patients with type 2 diabetes mellitus (T2M) in Suining area, and build a risk prediction model to provide theoretical basis for the prevention and treatment of CHD in PATIENTS with T2M. Methods A total of 476 T2M patients treated in our hospital from January 2021 to December 2021 were selected and divided into experimental group (n=79) and control group (n=397) according to whether they had coronary heart disease. The angiographic characteristics of coronary artery lesions in patients with T2M combined with coronary heart disease were observed. Age, sex, body mass index (BMI), smoking, alcohol consumption , T2M course, FBG, FINS, HOMA, TC, LDL-C, SBP, DBP and UA levels of all patients were analyzed. Univariate analysis and Logistic regression were used to analyze the influencing factors of coronary heart disease and establish a risk prediction model. ROC curve was used to predict the efficiency of the model. Results A total of 79 cases (16.60%) of patients with T2M complicated with coronary heart disease, including 64 cases (81.01%) of patients with T2M complicated with coronary artery disease. Mild stenosis in 5 cases (6.33%), moderate stenosis in 20 cases (25.32%) and severe stenosis in 54 cases (68.35%); The mean age, smoking proportion, BMI, T2M course and the levels of FBG, FINS, HOMA-IR, TC, LDL-C, SBP, DBP and UA in experimental group were significantly higher than those in control group (P-(-0.513+0.919×(old age)+1.129×(increased SBP)+ 1.724×(increased FBG)+ 1.529×(increased LDL-C)]. ROC curve was used to analyze the predictive performance of the regression model. The results showed that the AUC of the risk prediction model for coronary heart disease in T2M patients was 0.728, 95% CI (0.651-0.829). Conclusions T2M patients in Suining have a high risk of coronary heart disease. For elderly patients with elevated SBP, LDL-C and FBG, the risk of coronary heart disease can be assessed by predictive model and targeted intervention measures can reduce the risk of coronary heart disease in T2M patients.