Modelo predictivo de enfermedad cardiovascular basado en inteligencia artificial en la atención primaria de salud / A Predictive Model for Cardiovascular Disease based on Artificial Intelligence in Primary Health Care

Introducción: En Cuba y en el resto del mundo, las enfermedades cardiovasculares son reconocidas como un problema de salud pública mayúsculo y creciente, que provoca una alta mortalidad. Objetivo: Diseñar un modelo predictivo para estimar el riesgo de enfermedad cardiovascular basado en técnicas de inteligencia artificial. Métodos: La fuente de datos fue una cohorte prospectiva que incluyó 1633 pacientes, seguidos durante 10 años, fue utilizada la herramienta de minería de datos Weka, se emplearon técnicas de selección de atributos para obtener un subconjunto más reducido de variables significativas, para generar los modelos fueron aplicados: el algoritmo de reglas JRip y el meta algoritmo Attribute Selected Classifier, usando como clasificadores el J48 y el Multilayer Perceptron. Se compararon los modelos obtenidos y se aplicaron las métricas más usadas para clases desbalanceadas. Resultados: El atributo más significativo fue el antecedente de hipertensión arterial, seguido por el colesterol de lipoproteínas de alta densidad y de baja densidad, la proteína c reactiva de alta sensibilidad y la tensión arterial sistólica, de estos atributos se derivaron todas las reglas de predicción, los algoritmos fueron efectivos para generar el modelo, el mejor desempeño fue con el Multilayer Perceptron, con una tasa de verdaderos positivos del 95,2 por ciento un área bajo la curva ROC de 0,987 en la validación cruzada. Conclusiones: Fue diseñado un modelo predictivo mediante técnicas de inteligencia artificial, lo que constituye un valioso recurso orientado a la prevención de las enfermedades cardiovasculares en la atención primaria de salud(AU)

Introduction: In Cuba and in the rest of the world, cardiovascular diseases are recognized as a major and growing public health problem, which causes high mortality. Objective: To design a predictive model to estimate the risk of cardiovascular disease based on artificial intelligence techniques. Methods: The data source was a prospective cohort including 1633 patients, followed for 10 years. The data mining tool Weka was used and attribute selection techniques were employed to obtain a smaller subset of significant variables. To generate the models, the rule algorithm JRip and the meta-algorithm Attribute Selected Classifier were applied, using J48 and Multilayer Perceptron as classifiers. The obtained models were compared and the most used metrics for unbalanced classes were applied. Results: The most significant attribute was history of arterial hypertension, followed by high and low density lipoprotein cholesterol, high sensitivity c-reactive protein and systolic blood pressure; all the prediction rules were derived from these attributes. The algorithms were effective to generate the model. The best performance was obtained using the Multilayer Perceptron, with a true positive rate of 95.2percent and an area under the ROC curve of 0.987 in the cross validation. Conclusions: A predictive model was designed using artificial intelligence techniques; it is a valuable resource oriented to the prevention of cardiovascular diseases in primary health care(AU)

Prognostic predictive model for PONV in cesarean delivery

Introduction: Apfel simplified risk score for postoperative nausea and vomiting (PONV) has shown to be useful in anesthesia; however, since it has not been calibrated in regional anesthesia or in pregnant patients, its use in cesarean section is limited. Objective: To develop a prognostic predictive model for postoperative nausea and vomiting in pregnant patients undergoing cesarean section under spinal anesthesia. Methods: In a cohort of 703 term pregnant patients scheduled of cesarean section, 15 variables were prospectively assessed, to design a prognostic predictive model for the development of postoperative nausea and vomiting. A logistic regression analysis was used to construct the model and its calibration and discrimination were based on the Hosmer-Lemeshow test, the calibration curves, and C statistic. Additionally, the internal calibration was performed with the Bootstrap resampling method. Results: Postoperative nausea and vomiting were experienced by 27% of the patients during the first six hours after surgery. The model included as prognostic variables the development of intraoperative nausea and vomiting, age under 28 years, a history of PONV, the mother's BMI and the weight of the newborn baby. The model showed an adequate calibration (x2: 4.65 p: 0.5888), though a low discrimination (Statistic C = 0.68). Conclusions: A prognostic predictive model was created for the development of PONV in cesarean section. This model was used to build a prognostic scale for the classification of patients into risk groups.

Introducción: La escala de riesgo simplificada de Apfel para náuseas y vómito posoperatorio (NVPO) ha mostrado utilidad en anestesia; sin embargo, al no haber sido calibrada en anestesia regional o en pacientes embarazadas, su utilidad en cesárea es limitado. Objetivo: Desarrollar un modelo de predicción pronóstica para náuseas y vómito posoperatorios en pacientes embarazadas, llevadas a cesárea bajo anestesia espinal. Métodos: En una cohorte de 703 pacientes con embarazo a término programadas para cesárea, se evaluaron 15 variables de forma prospectiva para construir un modelo de predicción pronóstica para el desarrollo de náuseas y vómito posoperatorio. Se utilizó el análisis de regresión logística para la construcción del modelo y se calculó su calibración y discriminación con la prueba de Hosmer-Lemeshow, las curvas de calibración y el estadístico C. Además, se realizó la calibración interna con el método de remuestreo Bootstrap. Resultados: Las náuseas y vómito posoperatorio se presentaron en el 27% de las pacientes durante las primeras seis horas después de la cirugía. El modelo incluyó como variables pro-nósticas el desarrollo de náuseas y vómito en el intraoperatorio, edad menor de 28 años, antecedentes de NVPO, índice de masa corporal (IMC) de la madre y el peso del recién nacido. El modelo mostró una adecuada calibración (x2: 4,65 p: 0,5888), aunque una baja discriminación (Estadístico C = 0,68). Conclusiones: Se construyó un modelo de predicción pronóstica para el desarrollo de NVPO en cirugía cesárea, y con este se construyó una escala pronóstica que permite clasificar a las pacientes por grupos de riesgo.

Aprendizaje automático aplicado a la predicción de diabetes mellitus, utilizando información socioeconómica y ambiental de usuarios del sistema de salud

Objetivo: Se propuso aplicar modelos basados en técnicas de aprendizaje automático como apoyo para el diagnóstico temprano de la diabetes mellitus, utilizando variables de datos ambientales, sociales, económicos y sanitarios, sin la dependencia de la toma de muestras clínicas. Metodología: Se utilizaron datos de 10 889 usuarios afiliados al régimen subsidiado de salud de la zona suroccidental en Colombia, diagnosticados con hipertensión y agrupados en usuarios sin (74,3 %) y con (25,7 %) diabetes mellitus. Se entrenaron modelos supervisados utilizando k vecinos más cercanos, árboles de decisión y bosques aleatorios, así como modelos basados en ensambles, aplicados a la base de datos antes y después de balancear el número de casos en cada grupo de diagnóstico. Se evalúo el rendimiento de los algoritmos mediante la división de la base de datos en datos de entreno y de prueba (70/30, respectivamente), y se utilizaron métricas de exactitud, sensibilidad, especificidad y área bajo la curva. Resultados: Los valores de sensibilidad aumentaron considerablemente al utilizar datos balanceados, pasando de valores máximos del 17,1 % (datos sin balancear) a valores de hasta 57,4 % (datos balanceados). El valor más alto de área bajo la curva (0,61) fue obtenido con los modelos de ensambles, al aplicar un balance en el número de datos por cada grupo y al codificar las variables categóricas. Las variables de mayor peso estuvieron asociadas con aspectos hereditarios (24,65 %) y con el grupo étnico (5.59 %), además de la dificultad visual, el bajo consumo de agua, una dieta baja en frutas y verduras, y el consumo de sal y azúcar. Conclusiones: Aunque los modelos predictivos, utilizando información socioeconómica y ambiental de las personas, surgen como una herramienta para el diagnóstico temprano de la diabetes mellitus, estos aún deben ser mejorados en su capacidad predictiva.

Objective: The objective was to apply models based on machine learning techniques to support the early diagnosis of diabetes mellitus, using environmental, social, economic and health data variables, without dependence on clinical sample collection. Methodology: Data from 10,889 users affiliated with the subsidized health system in the southwestern area of Colombia, diagnosed with hypertension and grouped into users without (74.3%) and with (25.7%) diabetes mellitus, were used. Supervised models were trained using k-nearest neighbors, decision trees, and random forests, as well as ensemble-based models, applied to the database before and after balancing the number of cases in each diagnostic group. The performance of the algorithms was evaluated by dividing the database into training and test data (70/30, respectively), and metrics of accuracy, sensitivity, specificity, and area under the curve were used. Results: Sensitivity values increased significantly when using balanced data, going from maximum values of 17.1% (unbalanced data) to values as high as 57.4% (balanced data). The highest value of area under the curve (0.61) was obtained with the ensemble models, by applying a balance in the amount of data for each group and by coding the categorical variables. The variables with the greatest weight were associated with hereditary aspects (24.65%) and with the ethnic group (5.59%), in addition to visual difficulty, low water consumption, a diet low in fruits and vegetables, and the consumption of salt and sugar. Conclusions: Although predictive models, using people's socioeconomic and environmental information, emerge as a tool for the early diagnosis of diabetes mellitus, their predictive capacity still needs to be improved.

Objetivo: Propôs-se aplicar modelos baseados em técnicas de aprendizagem automática como apoio para o diagnóstico precoce da diabetes mellitus, utilizando variáveis de dados ambientais, sociais, econômicos e sanitários, sem a dependência da coleta de amostras clínicas. Metodologia: Usaram-se dados de 10.889 usuários filiados ao regime subsidiado de saúde da zona sudoeste da Colômbia, diagnosticados com hipertensão e agrupados em usuários sem (74,3%) e com (25,7%) diabetes mellitus. Foram treinados modelos supervisionados utilizando k vizinhos mais próximos, árvores de decisão e florestas aleatórias, assim como modelos baseados em montagens, aplicados à base de dados antes de depois de equilibrar o número de casos em cada grupo de diagnóstico. Avaliou-se o desempenho dos algoritmos por meio da divisão da base de dados de treino e teste (70/30, respectivamente), e utilizaram-se métricas de exatidão, sensibilidade, especificidade e área sob a curva. Resultados: Os valores de sensibilidade aumentaram de maneira significativa ao utilizar dados equilibrados, passando de valores máximos de 17,1% (dados sem equilibrar) a valores de até 57,4% (dados equilibrados). O valor mais elevado de área sob a curva (0,61) foi obtido com os modelos de montagens, ao aplicar um balanço no número de dados por cada grupo e codificar as variáveis categóricas. As variáveis de maior peso estiveram associadas com fatores hereditários (24,65%) e com o grupo étnico (5,59%), além da dificuldade visual, o baixo consumo de água, um regime baixo em frutas e vegetais e o consumo de sal e açúcar. Conclusões: Embora os modelos preditivos, utilizando informação socioeconômica e ambiental das pessoas, surgem como uma ferramenta para o diagnóstico precoce da diabetes mellitus, ainda devem ser melhorados em sua capacidade preditiva.

Applicability of the acute leukemia (AL) - EBMT score as a prognostic model for allogeneic hematopoietic stem cell transplantation: a single-center, prospective, cohort study at a reference transplant center in Brazil

Abstract Introduction The Acute Leukemia-European Society for Blood and Marrow Transplantation (AL-EBMT) risk score was recently developed and validated by Shouval et al. Objective To assess the ability of this score in predicting the 2-year overall survival (OS-2), leukemia-free survival (LFS-2) and transplant-related mortality (TRM) in acute leukemia (AL) adult patients undergoing a first allogeneic hematopoietic stem cell transplant (HSCT) at a transplant center in Brazil. Methods In this prospective, cohort study, we used the formula published by Shouval et al. to calculate the AL-EBMT score and stratify patients into three risk categories. Results A total of 79 patients transplanted between 2008 and 2018 were analyzed. The median age was 38 years. Acute myeloid leukemia was the most common diagnosis (68%). Almost a quarter of the cases were at an advanced stage. All hematopoietic stem cell transplantations (HSCTs) were human leukocyte antigen-matched (HLA-matched) and the majority used familial donors (77%). Myeloablative conditioning was used in 92% of the cases. Stratification according to the AL-EBMT score into low-, intermediate- and high-risk groups yielded the following results: 40%, 12% and 47% of the cases, respectively. The high scoring group was associated with a hazard ratio of 2.1 (p= 0.007), 2.1 (p= 0.009) and 2.47 (p= 0.01) for the 2-year OS, LFS and TRM, respectively. Conclusion This study supports the ability of the AL-EBMT score to reasonably predict the 2-year post-transplant OS, LFS and TRM and to discriminate between risk categories in adult patients with AL, thus confirming its usefulness in clinical decision-making in this setting. Larger, multicenter studies may further help confirm these findings.

Construction of predictive model for pregnancy-related anxiety risk in pregnant women based on LASSO regression / 环境与职业医学

Background Pregnancy-related anxiety has a negative impact on the physical and mental health of pregnant women and the normal growth and development of the fetus. Establishing prediction models for pregnancy-related anxiety to screen associated predictive factors may provide important opportunities for prenatal intervention. Objective To establish a prediction model of pregnancy-related anxiety risk of pregnant women. Methods From January to July 2021, a questionnaire survey on pregnancy-related anxiety and predictors was conducted among pregnant women having routine prenatal check-ups provided by an obstetrics clinic of a tertiary grade A hospital in Ningxia. The socio-demographic characteristics of the subjects were collected, and the pregnant women were evaluated by the Life Event Scale (LES), Perceived Social Support Scale (PSSS), Family APGAR Index (APGAR), and Pregnancy-related Anxiety Questionnaire (PAQ). R 4.2.0 software was used to fit all selected variables by least absolute shrinkage and selection operator (LASSO) regression to identify predictors of pregnancy-related anxiety in the second and third trimesters. On the basis of logistic regression analysis, prediction models of pregnancy-related anxiety in the second and third trimesters were constructed, and the model nomogram and receiver operating characteristic curve (ROC) were drawn. The prediction effect of the model was evaluated by area under the curve (AUC). A calibration chart was drawn to evaluate the calibration of the model. Results A total of 1500 questionnaires were distributed, and 1448 valid questionnaires were recovered, with an effective rate of 96.53%. Among the 1448 pregnant women, the overall positive rate of pregnancy-related anxiety was 28.80% (417/1448), and the positive rates in the second and third trimesters were 29.21% (276/935) and 27.49% (141/513), respectively. The predictors entering the the second trimester model were age of marriage, family care, social support, family expectations for the fetus, physical condition during pregnancy, and whether experiencing life stressful events during pregnancy. The predictors entering the the third trimester model were pregnancy intention, physical discomfort, and whether experiencing life stress during pregnancy. A risk prediction model of pregnancy-related anxiety for the second trimester was established: risk of pregnancy-related anxiety=−0.07× marriage age +0.12× family care −0.03× social support −0.65× family expectation of fetal sex +0.42× physical condition during pregnancy +0.47× whether experiencing life stressful events during pregnancy. A risk prediction model of pregnancy-related anxiety for the third trimester was established: risk of pregnancy-related anxiety=−5.69+0.82× pregnancy intention +1.06× physical discomfort +0.94× whether experiencing life stressful events during pregnancy. The ROC curves of the two models were drawn. The AUC of the second trimester model was 0.71, and the AUC of related validation model was 0.68. The AUC of the third trimester model was 0.72, and the AUC of related validation model was 0.66. Conclusion The risk prediction models of pregnancy-related anxiety constructed based on LASSO regression and logistic regression have good prediction ability, and they suggest that pregnant women in the second trimester with short marriage age, high family care, low social support, family expectations for fetal sex, average physical condition, and experiencing life stress during pregnancy, and pregnant women in the third trimester with spontaneous pregnant intention, unintended pregnancy, physical discomfort, and experiencing life stress during pregnancy are high-risk groups for pregnancy-related anxiety.

Factors influencing pulmonary infection in elderly neurocritical patients and their predictive values: a data analysis for consecutive four-year / 中华危重病急救医学

Objective:To analyze the factors influencing pulmonary infections in elderly neurocritical patients in the intensive care unit (ICU) and to explore the predictive value of risk factors for pulmonary infections.Methods:The clinical data of 713 elderly neurocritical patients [age ≥ 65 years, Glasgow coma score (GCS) ≤ 12 points] admitted to the department of critical care medicine of the Affiliated Hospital of Guizhou Medical University from 1 January 2016 to 31 December 2019 were retrospectively analyzed. According to whether or not they had HAP, the elderly neurocritical patients were divided into hospital-acquired pneumonia (HAP) group and non-HAP group. The differences in baseline data, medication and treatment, and outcome indicators between the two groups were compared. Logistic regression analysis was used to analyze the factors influencing the occurrence of pulmonary infection.The receiver operator characteristic curve (ROC curve) was plotted for risk factors and a predictive model was constructed to evaluate the predictive value for pulmonary infection.Results:A total of 341 patients were enrolled in the analysis, including 164 non-HAP patients and 177 HAP patients. The incidence of HAP was 51.91%. According to univariate analysis, compared with the non-HAP group, mechanical ventilation time, the length of ICU stay and total hospitalization in the HAP group were significantly longer [mechanical ventilation time (hours): 171.00 (95.00, 273.00) vs. 60.17 (24.50, 120.75), the length of ICU stay (hours): 263.50 (160.00, 409.00) vs. 114.00 (77.05, 187.50), total hospitalization (days): 29.00 (13.50, 39.50) vs. 27.00 (11.00, 29.50), all P < 0.01], the proportion of open airway, diabetes, proton pump inhibitor (PPI), sedative, blood transfusion, glucocorticoids, and GCS ≤ 8 points were significantly increased than those in HAP group [open airway: 95.5% vs. 71.3%, diabetes: 42.9% vs. 21.3%, PPI: 76.3% vs. 63.4%, sedative: 93.8% vs. 78.7%, blood transfusion: 57.1% vs. 29.9%, glucocorticoids: 19.2% vs. 4.3%, GCS ≤ 8 points: 83.6% vs. 57.9%, all P < 0.05], prealbumin (PA) and lymphocyte count (LYM) decreased significantly [PA (g/L): 125.28±47.46 vs. 158.57±54.12, LYM (×10 9/L): 0.79 (0.52, 1.23) vs. 1.05 (0.66, 1.57), both P < 0.01]. Logistic regression analysis showed that open airway, diabetes, blood transfusion, glucocorticoids and GCS ≤ 8 points were independent risk factors for pulmonary infection in elderly neurocritical patients [open airway: odds ratio ( OR) = 6.522, 95% confidence interval (95% CI) was 2.369-17.961; diabetes: OR = 3.917, 95% CI was 2.099-7.309; blood transfusion: OR = 2.730, 95% CI was 1.526-4.883; glucocorticoids: OR = 6.609, 95% CI was 2.273-19.215; GCS ≤ 8 points: OR = 4.191, 95% CI was 2.198-7.991, all P < 0.01], and LYM, PA were the protective factors for pulmonary infection in elderly neurocritical patients (LYM: OR = 0.508, 95% CI was 0.345-0.748; PA: OR = 0.988, 95% CI was 0.982-0.994, both P < 0.01). ROC curve analysis showed that the area under the ROC curve (AUC) for predicting HAP using the above risk factors was 0.812 (95% CI was 0.767-0.857, P < 0.001), with a sensitivity of 72.3% and a specificity of 78.7%. Conclusions:Open airway, diabetes, glucocorticoids, blood transfusion, GCS ≤ 8 points are independent risk factors for pulmonary infection in elderly neurocritical patients. The prediction model constructed by the above mentioned risk factors has certain predictive value for the occurrence of pulmonary infection in elderly neurocritical patients.

Establishment and validation of a model for predicting recurrent wheezing in children with bronchiolitis / 中国医师进修杂志

Objective:To analyze the risk factors of recurrent wheezing in children with bronchiolitis and to construct a predictive model.Methods:Prospective research methods was used. One hundred and eighty children with bronchiolitis who were treated in Hefei Eighth People's Hospital from February 2017 to February 2019 were selected as the study subjects, and the included children were separated into a modeling group (126 cases) and a validation group (54 cases) according to 7∶3. The children were followed up for 3 years, and then the modeling group was divided into wheezing group (48 cases) and no wheezing group (78 cases) according to whether the children had recurrent wheezing. The Hosmer-Lemeshow fitting curve and receiver operating characteristic (ROC) curve were drawn to evaluate the validity and accuracy of the constructed prediction model.Results:Multivariate Logistic regression analysis showed that artificial feeding ( OR = 8.838, 95% CI 2.601 to 30.027), family history of allergies ( OR = 6.709, 95% CI 1.825 to 24.665), underlying diseases ( OR = 8.114, 95% CI 1.638 to 40.184), and higher IgE level ( OR = 1.020, 95% CI 1.012 to 1.029) were the independent risk factors for recurrent wheezing in children with bronchiolitis ( P<0.05). The area under the curve of the modeling group was 0.917 (95% CI 0.855 to 0.959), and the sensitivity and specificity were 83.33% and 85.90%, respectively; the area under the curve of the validation group was 0.911 (95% CI 0.847 to 0.954), and the sensitivity and specificity were 89.58% and 79.49%, respectively. Conclusions:Artificial feeding, family history of allergies, underlying diseases, and higher IgE level are the independent risk factors for recurrent wheezing in children with bronchiolitis. The constructed prediction model has good accuracy and validity, and can be used as an effective tool for clinical prediction of recurrent wheezing in children with bronchiolitis.

Construction and internal validation of a nomogram for predicting the risk of positive prostate biopsy in MRI-negative patients / 现代泌尿外科杂志

【Objective】 To establish a nomogram model for predicting the risk of positive prostate biopsy in MRI-negative patients, and to perform the internal validation. 【Methods】 We retrospectively analyzed the clinical data of 197 MRI-negative patients who underwent prostate biopsy at our hospital, analyzed the independent predictors of positive prostate biopsy with univariate and multivariate logistic regression analysis, constructed the nomogram model and conducted internal validation. 【Results】 Multivariate logistic regression analysis showed age (P=0.003), digital rectal examination (DRE)(P=0.005), total prostate-specific antigen (tPSA) (P=0.001) and prostate volume (PV)(P<0.001) were independent risk factors of MRI-negative but prostate biopsy-positive results. The nomogram model based on all variables was established. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.862, which was greater than that of tPSA (AUC=0.739), PV(AUC=0.711) and DRE(AUC=0.666) (all P<0.05). The average absolute error of the model was 1.1% after 500 internal resampling, indicating that the prediction of positive prostate biopsy was consistent with the actual situation. 【Conclusion】 The age, DRE, tPSA and PV were independent predictors of positive prostate biopsy in MRI-negative patients. The nomogram model has a good prediction performance.

The predictive value of CT pulmonary angiography combined with echocardiography for the degree of pulmonary hypertension / 西安交通大学学报(医学版)

【Objective】 To compare the value of CT pulmonary angiography (CTPA) and echocardiography in predicting the degree pulmonary hypertension (PH). 【Methods】 Fifty-four patients in our hospital who underwent right heart catheterization, CTPA and echocardiography for suspected or confirmed PH from November 2013 to April 2021 were retrospectively recruited. Pulmonary artery systolic pressure (PASP) and mean pulmonary artery pressure (PAMP) were measured by right heart catheterization. According to PAMP, the patients were divided into non-PH group and mild, moderate, and severe PH groups. The three-dimensional model of the pulmonary artery was obtained by computer-aided segmentation, and the best fitting circle diameter (Dfit), inscribed circle diameter (Dmin), circumscribed circle diameter (Dmax), hydraulic diameter (Dh), cross-sectional area (Area), circumference (Scf), and the largest area and largest short diameter of the right ventricle were measured. Echocardiography was used to estimate PASP. The differences in the above parameters between different PH groups were compared, the correlations of the above parameters with PASP and PAMP were analyzed, and statistically significant indicators were included to establish three predictive models of PAMP (Model 1: CTPA pulmonary artery; Model 2: CTPA pulmonary artery+CTPA right ventricle; Model 3: CTPA pulmonary artery+CTPA right ventricle+echocardiography), and receiver operating characteristic (ROC) curves were used to compare the diagnostic performance of the three models. 【Results】 Some parameters of main pulmonary artery (Dfit, Dmin, Dmax, Dh, Area, Scf), right pulmonary artery (Dmax, Dh, Area, Scf), left pulmonary artery (Dfit), and right ventricular short diameter increased with the increase of PAMP (P<0.05). Dfit, Dmin, Dh, and area of main pulmonary artery, right pulmonary artery and left pulmonary artery were positively correlated with PASP and PAMP (P<0.05). Right ventricular short diameter and right ventricular maximum area were positively correlated with PASP (P<0.05), and right ventricular short diameter was positively correlated with PAMP (P<0.05). The estimated value of pulmonary artery systolic blood pressure in echocardiography was positively correlated with PASP and PAMP (P<0.05). Model 1, Model 2 and Model 3 could all be used to identify mild PH and moderate PH, among which Model 3 had the best performance in identifying non-PH and mild PH, moderate PH and severe PH. 【Conclusion】 CTPA and echocardiography are helpful in assessing the degree of PH, and the combination of the two has better accuracy in distinguishing non-PH from mild PH, moderate and severe PH.

Development and validation of predictive model for cognitive impairment after stroke / 西安交通大学学报(医学版)

【Objective】 To construct and validate a risk prediction model for cognitive impairment after stroke based on demographic, clinical, and neuroimaging characteristics. 【Methods】 Through the medical record system, we collected all data of the patients. We finished cognitive function testing three months after the indexed stroke. The Mini-Mental State Examination Scale score≤26 was defined as cognitive dysfunction. Optimal subset regression analysis was used to screen variables, Logistic regression analysis was used to construct a predictive model for cognitive impairment, and C-index, calibration chart and clinical decision curve analyses were used to evaluate the discrimination, consistency, and clinical availability of the model. And nomograms were used to express the performance of the model. 【Results】 Seven variables were selected: cognitive function before stroke, age, years of education, National Institutes of Health Stroke Scale score at admission, history of ischemic heart disease, the number of old lacunar infarct lesions, and medial temporal lobe atrophy scale. The prediction model had a C-index of 0.845 (95% CI: 0.805-0.885). The clinical decision curve showed that the model had a positive net benefit when the threshold probability was 9.0%-90.0%. 【Conclusion】 The predictive model of cognitive impairment in stroke patients has good predictive efficiency and provides an effective assessment tool for screening high-risk cases of cognitive impairment in patients with stroke of various subtypes.

Construction and validation of an early predictive model for intraoperative massive transfusion of red blood cells in patients with Stanford type A aortic coarctation / 中国输血杂志

【Objective】 To analyze the risk factors for intraoperative massive red blood cell (RBC) transfusion in patients with Stanford type A aortic dissection (TAAD), in order to develop a risk-prediction model and validate its predictive effect. 【Methods】 The clinical data of 233 patients with TAAD admitted to our hospital from July 2018 to June 2021 (modeling set) were retrospectively analyzed. They were divided into routine transfusion group (n=128, RBC≤8 U) and massive transfusion group (n=105, RBC>8 U). Risk factors for intraoperative massive RBC transfusion in TAAD patients were analyzed by multivariate logistic regression and a risk prediction model was developed. Calibration curve and receiver operating characteristic (ROC) curve were used to assess the accuracy and discrimination of the model. In addition, 61 TAAD patients admitted to our hospital from July 2021 to May 2022 (validation set) were used for external validation. 【Results】 The rate of intraoperative massive RBC transfusion in 233 TAAD patients was 45.06% (95% CI: 38.59%-51.69%). Logistic analysis showed that women, age >50 years, preoperative Hb≤131.50 g/L, intraoperative bleeding >720 mL, and CPB time >155 min were independent risk factors for massive intraoperative RBC transfusion (P<0.05). The intraoperative risk prediction model formula for massive RBC infusion was: -4.427+ 0.925×gender+ 1.461×age+ 2.081×preoperative Hb+ 1.573×bleeding volume+ 2.823×CPB time. The area under the ROC curve of the modeling set and validation set were 0.904 (95% CI: 0.865-0.943) vs 0.868 (95%CI: 0.779-0.958), and the slopes of the calibration curves all converged to 1, indicating that the model predicted the risk of intraoperative massive RBC infusion in TAAD patients in good consistency with the actual risk of massive infusion. The decision curve shows that the model exhibits a positive net benefit with a threshold probability of 0.15-0.67 and has a high clinical application value. 【Conclusion】 The prediction model constructed based on the risk factors of intraoperative massive RBC infusion in TAAD patients can effectively predict the risk of intraoperative massive RBC infusion with high clinical predictive efficacy.

Establishment and evaluation of predictive models of platelet transfusion effect in multi-center tumor patients / 中国输血杂志

【Objective】 To study the platelet transfusion predictive models in tumor patients and evaluate its application effect. 【Methods】 A retrospective study was conducted on 944 tumor patients, including 533 males and 411 females who received platelet transfusion in the Affiliated Hospital of Traditional Chinese Medicine of Xinjiang Medical University, the Affiliated Cancer Hospital of Xinjiang Medical University and Kailuan General Hospital from August 2022 to January 2023. Multivariate Logistic regression analysis was used to establish the platelet transfusion predictive models, and Medcalc15.8 software was used to draw the receiver operating curve (ROC) to evaluate the application effect of the prediction model. The actual application effect of models was verified through 162 female clinical cases and 172 male clinical cases. 【Results】 The incidence of platelet transfusion refractoriness in tumor patients was 28.9% (273/944), with 33.2% (177/533) in males, significantly higher than that in females [23.4% (96/411)] (P<0.05). Platelet transfusion predictive models: Y1 (female) =-8.546+ (0.581×number of pregnancies) + (0.964×number of inpatient transfusion bags) + number of previous platelet transfusion bags (5-9 bags: 1.259, ≥20 bags: 1.959) + clinical diagnosis (lymphoma: 2.562, leukemia: 3.214); Y2 (male) =-7.600+ (1.150×inpatient transfusion bags) + previous platelet transfusion bags (10-19 bags: 1.015, ≥20 bags: 0.979) + clinical diagnosis (lymphoma: 1.81, leukemia: 3.208, liver cancer: 1.714). Application effect evaluation: The AUC (area under the curve), cut-off point, corresponding sensitivity and specificity of female and male platelet transfusion effect prediction models were 0.868, -0.354, 68.75%, 89.84% and 0.854, -0.942, 81.36%, 77.53%, respectively. Actual application results showed that the sensitivity, specificity, and accuracy of female and male model were 89.47%, 92.74%, 91.98% and 83.72%, 91.47%, 89.53%, respectively. 【Conclusion】 There is high incidence of platelet transfusion refractoriness in tumor patients, and the predictive model has good prediction effect on platelet transfusion refractoriness in tumor patients, which can provide reliable basis for accurate platelet transfusion in tumor patients.

Development and Validation of a Predictive Risk Model for Vision-threatening Diabetic Retinopathy in Patients with Type 2 Diabetes / 中山大学学报(医学科学版)

ObjectiveTo develop and validate a predictive risk model for vision-threatening diabetic retinopathy in patients with type 2 diabetes using readily accessible clinical data， which may provide a convenient and effective prediction tool for early identification and referral of at-risk populations. MethodsA nomogram model was developed using a dataset obtained from patients with T2DM who participated in the Guangzhou Diabetic Eye Study from November 2017 to December 2020. Logistic regression was used to construct the model， and model performance was evaluated using receiver operating characteristic curve， Hosmer-Lemeshow test， calibration curve and decision curve analysis. The model underwent internal validation through the mean AUC of k-fold cross-validation method， and further external validation was conducted in the Dongguan Eye Study. ResultsA total of 2 161 individuals were included in the model development dataset， of whom 135 （6.25%） people were diagnosed with VTDR. Age （P＜0.001，OR=0.927，95%CI：0.898~0.957） and body mass index （P＜0.001，OR =0.845，95%CI：0.821~0.932） were found to be negatively correlated with VTDR， whereas diabetes duration （P＜0.001，OR=1.064，95%CI：1.035~1.094）， insulin use （P =0.045，OR =1.534，95%CI：1.010~2.332）， systolic blood pressure （P＜0.001，OR =1.019，95%CI：1.008~1.029）， glycated hemoglobin （P＜0.001，OR =1.484，95%CI：1.341~1.643）， and serum creatinine （P＜0.001，OR =1.017，95%CI：1.010~1.023） were positively correlated with VTDR. All these variables were included in the model as predictors. The model showed strong discrimination in the development dataset with an area under the receiver operating characteristic curve （AUC） of 0.797 and in the external validation dataset （AUC 0.762）. The Hosmer-Lemeshow test（P＞0.05）and the calibration curve displayed good agreement. Decision curve analysis showed that the nomogram produced net benefit in the two datasets. ConclusionsIndependent factors influencing VTDR include age， duration of diabetes mellitus， insulin use， body mass index， systolic blood pressure， glycosylated hemoglobin， and serum creatinine. The nomogram constructed using these variables demonstrates a high degree of predictive validity. The model can serve as a valuable tool for early detection and referral of VTDR in primary care clinics. Therefore， its application and promotion are highly recommended.

Construction and Validation of A Predictive Model Including TCM Pathogenic Syndrome for Short-term Efficacy of PD-1 Inhibitors in Advanced Non-small Cell Lung Cancer / 肿瘤防治研究

Objective To evaluate predictive factors affecting the short-term efficacy of PD-1 inhibitors in non-small cell lung cancer (NSCLC) and to construct a prediction model. Methods From October 2019 to November 2021, 221 patients with advanced NSCLC who met the inclusion criteria and were treated with PD-1 inhibitors were prospectively enrolled. Patients who were enrolled before May 1st, 2021 were included inthe modeling group (n=149), whereas those who enrolled thereafter were included in the validation group (n=72). The general clinical data of patients, information of the four TCM diagnoses were collected, and TCM syndrome elements were identified. R software version 4.0.4 was used in constructing a nomogram clinical prediction model of objective response rate. The predictive ability and discrimination of the model were evaluated and externally validated by using a validation group. Results After two to four cycles of PD-1 inhibitor therapy in 221 patients, the overall objective response rate was 44.80%. Multivariate logistic regression analysis of the modeling group showed that the TPS score (OR=0.261, P=0.001), number of treatment lines (OR=3.749, P=0.002), treatment mode (OR=2.796, P=0.019), qi deficiency disease syndrome elements (OR=2.296, P=0.043), and syndrome elements of yin deficiency disease (OR=3.228, P=0.005) were the independent predictors of the short-term efficacy of PD-1 inhibitors. Based on the above five independent predictors, a nomogram prediction model for the short-term efficacy of PD-1 inhibitors was constructed. The AUC values of the modeling and validation groups were 0.8317 and 0.7535, respectively. The calibration curves of the two groups showed good agreement between the predicted and true values. The mean absolute errors were 0.053 and 0.039, indicating that the model has good predictive performance. Conclusion The nomogram model constructed on the basis of the syndrome elements of Qi-deficiency disease and Yin-deficiency syndrome of TCM, as well as TPS score, number of treatment lines and treatment mode, is a stable and effective tool for predicting the short-term efficacy of PD-1 inhibitors in advanced non-small cell lung cancer.

Establishment and validation of a risk prediction model for portal vein thrombosis in liver cirrhosis by nomogram / 中华消化内镜杂志

Objective:To explore the independent risk factors of portal vein thrombosis (PVT) in liver cirrhosis, and to establish and evaluate a risk prediction model for PVT in patients with cirrhosis.Methods:A total of 295 cases of cirrhosis hospitalized in Renmin Hospital of Wuhan University from December 2019 to October 2021 were divided into a modeling set ( n=207) and an internal validation set ( n=88) by the random number table. In addition, patients with cirrhosis hospitalized in Yichang Central People's Hospital, Wuhan Puren Hospital, No.2 People's Hospital of Fuyang City and People's Hospital of China Three Gorges University during the same period were collected as an external validation set ( n=92). The modeling set was divided into PVT group ( n=56) and non-PVT group ( n=151). Univariate analysis was used to preliminarily screen the related indicators of PVT, and then multivariate logistic regression analysis with forward stepwise regression was used to determine independent risk factors for PVT. A nomogram prediction model was constructed based on the independent risk factors obtained. The internal and external validation set were used to verify the predictive ability of the model. Distinction degree was used to evaluate the ability of the model to distinguish patients with or without PVT. Hosmer-Lemeshow goodness-of-fit test was used to evaluate the consistency between predicted risk and the actual risk of the model. Results:Univariate analysis showed that smoking, history of splenectomy, trans-jugular intrahepatic portosystemic shunt (TIPS), gastrointestinal bleeding and endoscopic variceal treatment, and levels of hemoglobin, alanine aminotransferase, aspartate aminotransferase and D-dimer were significantly different between the PVT group and the non-PVT group ( P<0.05). Multivariate logistic regression analysis found that smoking ( P=0.020, OR=31.21, 95% CI: 1.71-569.40), levels of D-dimer ( P=0.003, OR=1.12, 95% CI: 1.04-1.20) and hemoglobin ( P=0.039, OR=0.99, 95% CI: 0.97-1.00), history of TIPS ( P=0.011, OR=18.04, 95% CI: 1.92-169.90) and endoscopic variceal treatment ( P=0.001, OR=3.21, 95% CI: 1.59-6.50) were independent risk factors for PVT in patients with liver cirrhosis. Receiver operator characteristic (ROC) curve analysis showed that the area under the ROC curve (AUC) for the internal validation set was 0.802 (95% CI: 0.709-0.895) ( P<0.001), and the AUC for the external validation set was 0.811 (95% CI: 0.722-0.900) ( P<0.001). Both AUC were larger than 0.75. The calibration curve of Hosmer-Lemeshow goodness-of-fit test showed that the P values of both internal validation set ( χ2=3.602, P=0.891) and the external validation set ( χ2=11.025, P=0.200) were larger than 0.05. Conclusion:Smoking, history of TIPS or endoscopic variceal treatment, levels of D-dimer and hemoglobin are independent risk factors for PVT in patients with liver cirrhosis. The prediction nomogram model based on the above factors has strong predictive ability.

Research progress in antigenic evolution of influenza A (H3N2) virus / 中华微生物学和免疫学杂志

Influenza viruses are common pathogens causing respiratory infections in humans. Among the four seasonal influenza viruses, influenza A virus H3N2 has become the leading cause of seasonal influenza illness and death, posing a great threat to public health and the economy. Since it first emerged and caused a pandemic in 1968, H3N2 has been circulating repeatedly in human beings and continually evades host immune attack by antigenic drift, resulting in a decrease in vaccine efficacy. In this paper, the antigenic evolution of influenza A virus H3N2, the impact of antigenic evolution on the selection of vaccine strains and some models for predicting the evolution of influenza viruses were analyzed and reviewed, which paved the road for understanding the antigenic evolution of influenza virus and vaccine development.

Construction of a new model for evaluating insulin resistance in newly diagnosed type 2 diabetic patients using anthropometry parameters / 中华内分泌代谢杂志

Objective:To construct a new model for assessing insulin resistance(IR) in newly diagnosed type 2 diabetic patients by combining anthropometry parameters and biochemical parameters.Methods:A total of 677 newly diagnosed type 2 diabetic patients were included in this study. Clinical data, biochemical indicators, and body composition measurements were collected, and a predictive model was constructed using logistic regression analysis.Results:The IR prediction model was constructed based on five indicators: triglycerides(TG), fasting plasma glucose(FPG), visceral fat area(VFA), alanine aminotransferase(ALT), and uric acid(UA). The formula for the new predictive model was as follows: y=-17.765+ 1.389×ln VFA+ 1.045×ln UA+ 0.91×ln ALT+ 2.167×ln FPG+ 0.805×ln TG. The receiver operating characteristic curve(ROC) area under the curve(AUC) for the model was 0.82, with an optimal cutoff value of 1.67, sensitivity of 0.80, and specificity of 0.71. The AUC values for the triglyceride glucose(TyG) index, lipid accumulation product(LAP), and triglyceride/high-density lipoprotein cholesterol ratio(THR) were 0.75, 0.75, and 0.70, respectively. The corresponding sensitivities were 0.66, 0.84, and 0.71, and the specificities were 0.71, 0.59, and 0.60. The optimal cutoff values were 1.81, 30.31, and 1.14, respectively. Conclusion:The new model constructed using TG, FPG, VFA, ALT, and UA as indicators showed high predictive value and can serve as a new model for assessing IR in newly diagnosed type 2 diabetic patients.

Risk factors for postoperative cognitive dysfunction and the predictive model in elderly patients undergoing lumbar surgery under general anesthesia / 中华麻醉学杂志

Objective:To identify the risk factors for postoperative cognitive dysfunction (POCD) and develop the prediction model in elderly patients undergoing lumbar surgery under general anesthesia.Methods:The elderly patients undergoing elective lumbar surgery under general anesthesia in our hospital from July 2021 to July 2022 were enrolled. Cognitive function was assessed at 7 days after surgery using Mini-Mental State Examination and Montreal Cognitive Assessment. When the decrease in both scales≥ 1 standard deviation, the patients were considered as having POCD. The patients were divided into POCD group and non-POCD group according to whether POCD developed. The propensity score matching was used to balance the confounding bias between two groups. The multivariate logistic regression analysis was used to identify the risk factors for POCD. The prediction model was constructed, and a nomogram was drawn for visualization of the model. The receiver operating characteristic curve, calibration plot and decision curve analysis (DCA) were drawn to evaluate the differentiation, consistency and clinical validity of the model, respectively.Results:A total of 159 patients were enrolled in this study, and the incidence of POCD was 31.4%. There were statistically significant differences in the ratio of intraoperative blood transfusion, cumulative time of hypotension, total infusion volume and operation time between two groups ( n=32 each) after propensity score matching ( P<0.05). The results of multivariate logistic regression showed that age, educational levels, diabetes mellitus, previous two or more operations under general anesthesia, APTT and cumulative time of hypotension were independent risk factors for POCD in elderly patients undergoing lumbar surgery under general anesthesia ( P<0.05). A model was developed based on the risk factors mentioned above: LogitP=-15.878+ 0.263 × Age (years) - 0.122 × Educational Level (years)+ 1.601 × Diabetes Mellitus+ 1.468 × History of General Anesthesia for 2 or more times+ 0.608 × Cumulative Time of Hypotension(min) - 0.140 × APTT (s). The area under the receiver operating characteristic curve was 0.930 (95% CI 0.887-0.973), the sensitivity was 0.920, specificity was 0.798 and Youden index was 0.718. After visualizing the model via nomogram, the model was verified by Hosmer-Lemeshow test, P=0.403, C index was 0.930, and corrected C index was 0.914. Conclusions:Age, educational levels, diabetes mellitus, previous multiple operations under general anesthesia, APTT and cumulative time of hypotension are independent risk factors for POCD in elderly patients undergoing lumbar surgery under general anesthesia, and the established risk prediction model can effectively predict the occurrence of POCD in elderly patients undergoing lumbar surgery under general anesthesia.

An acute kidney injury risk prediction model for elderly patients with sepsis based on the intensive care medicine information database-Ⅳ / 中华老年医学杂志

Objective:To explore the risk factors of acute kidney injury(stage 3)developed within 48 hours in elderly patients with sepsis, and to use them to develop a risk prediction model and then evaluate and externally validate the model.Methods:Clinical data of all elderly patients(age≥ 60 years)with sepsis in the intensive care medicine information database(MIMIC-Ⅳ v1.0)were extracted.Independent risk factors were determined by multivariate logistic regression analysis.A risk prediction model was constructed, a nomogram was drawn, and the receiver operating characteristic curve(ROC)and the Hosmer-Lemeshow(H-L)test were used to evaluate the model's prediction accuracy and R-squared.Clinical data of elderly patients(age≥ 60 years)with sepsis admitted to the Department of Critical Care Medicine of the Second People's Hospital of Hefei from May 2019 to October 2021 were retrospectively collected and fed into the prediction model to conduct external validation.Results:A total of 1 977 elderly patients with sepsis were screened out from the MIMIC-IV database and included in the training set, of whom, 544 developed AKI-stage 3 within 48 hours.Univariate analysis was performed for factors that might be associated with acute kidney injury in elderly patients with sepsis.Compared with the normal group that did not progress to AKI stage 3, there were statistically significant differences in 28 indicators, such as the duration of ICU stay, intravenous fluid intake in 24 hours, and use of vasoactive drugs[5(3, 9)d vs.7(4, 12)d; 2.05(1.17, 3.27)ml·kg -1·h -1vs.2.37(1.47, 4.10)ml·kg -1·h -1; 761(53.11%) vs.375(68.93%), P<0.001]. Based on the results of multivariate logistic regression analysis, a prediction model was finally constructed with 9 variables: albumin( OR=0.983, 95% CI: 0.966-0.999, P=0.040), aspartate transaminase( OR=1.000, 95% CI: 1.000-1.000, P<0.001), APTT( OR=1.005, 95% CI: 1.001-1.009, P=0.028), total bilirubin( OR=1.003, 95% CI: 1.001-1.004, P=0.001), serum creatinine( OR=1.005, 95% CI: 1.004~1.007, P<0.001), Charlson score( OR=1.117, 95% CI: 1.061-1.177, P<0.001), intravenous fluid intake in 24 hours( OR=1.101, 95% CI: 1.034-1.173, P=0.003), weight( OR=1.023, 95% CI: 1.018-1.029, P<0.001), and mechanical ventilation( OR=2.412, 95% CI: 1.843-3.157, P<0.001). Then a nomogram was generated.The area under the ROC curve(AUC)of the prediction model was 0.755(95% CI: 0.731-0.780), and the H-L test was conducted( χ2=10.89, P=0.208>0.05), indicating a good fit.Data from 102 elderly patients were included in the validation set, with 27 cases that had developed AKI-stage3 within 48 hours, and were fed into the prediction model, with an AUC of 0.778(95% CI: 0.676-0.880)and χ2=3.72 and P=0.882>0.05 from the H-L test, consistent with the results of the training set. Conclusions:The model has some predictive value for acute kidney injury in elderly patients with sepsis.

Risk factors of carbapenem-resistant Klebsiella pneumoniae infection in ICU patients and construction of a prediction model for infection / 中华临床感染病杂志

Objective:To analyze the risk factors of carbapenem-resistant Klebsiella pneumoniae (CRKP) infection in intensive care unit (ICU) patients and to construct a prediction model for infection. Methods:The clinical data of 204 patients with Klebsiella pneumoniae infection admitted in ICU of Jining First Hospital during January 2020 to December 2022 were retrospectively analyzed. Patients admitted during January 2020 to December 2021 were selected as model set ( n=150), and patients admitted during January to December 2022 were selected as validation set ( n=54). In model set, there were 59 cases infected with CRKP (CRKP group) and 91 cases infected with carbapenem-sensitive Klebsiella pneumonia (CSKP group). The risk factor of CRKP infection in ICU patients were analyzed with multivariate Logistic regression, based on which an infection prediction model was constructed. The predictive value of the model was evaluated by ROC, and verified in the validation group. Results:Multivariate Logistic regression analysis showed that empirical use of beta-lactam antibiotics( OR=6.985, 95 % CI 1.658-29.423, P=0.008), central vein catheterization( OR=7.486, 95 % CI 2.776-20.186, P<0.001)and tracheal intubation/incision( OR=10.695, 95 % CI 2.701-42.351, P=0.001)were risk factors for CRKP infection in ICU patients. The regression equation for predicting the risk of infection was -4.851+ empirical use of beta-lactam antibiotics×1.944+ central vein catheterization×2.013+ tracheal intubation/incision×2.370. The area under the ROC curve (AUC) of the model for predicting infection in the model group was 0.905, with sensitivity and specificity of 79.7% and 90.1%, respectively. The AUC of the model for predicting infection in validation group was 0.881, with sensitivity and specificity of 84.2% and 85.7%, respectively. Conclusion:The constructed infection prediction model in the study can effectively predict CRKP infection in ICU patients.