Predicting the outcome of death by CALL Score in COVID-19 patients

SUMMARY OBJECTIVE: The aim of this study was to assess the performance of the CALL Score tool in predicting the death outcome in COVID-19 patients. METHODS: A total of 897 patients were analyzed. Univariate and multivariate logistic regression analyses were conducted to determine the association between characteristics of the CALL Score and the occurrence of death. The relationship between CALL Score risk classification and the occurrence of death was also examined. Receiver operating characteristic curve analysis was performed to identify optimal cutoff points for the CALL Score and the outcome. RESULTS: The study revealed that age>60 years, DHL>500, and lymphocyte count ≤1000 emerged as independent predictors of death. Higher risk classifications of the CALL Score were associated with an increased likelihood of death. The optimal CALL Score cutoff point for predicting the death outcome was 9.5 (≥9.5), with a sensitivity of 70.4%, specificity of 80.3%, and accuracy of 80%. CONCLUSION: The CALL Score showed promising discriminatory ability for death outcomes in COVID-19 patients. Age, DHL level, and lymphocyte count were identified as independent predictors. Further validation and external evaluation are necessary to establish the robustness and generalizability of the CALL Score in diverse clinical settings.

An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience / Predição do influxo de atendimentos e casos de trauma em um serviço de urgência oftalmológico brasileiro realizado por um oftalmologista sem experiência em programação

ABSTRACT Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients' volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. Conclusions: The development of models was previously restricted to data scientists' experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx.

RESUMO Objetivo: Esse estudo tem como objetivo criar um modelo de Machine Learning por um oftalmologista sem experiência em programação utilizando auto Machine Learning predizendo influxo de pacientes em serviço de emergência e casos de trauma. Métodos: Um dataset de 366,610 visitas em Hospital Universitário da Universidade Federal de São Paulo de 01 de janeiro de 2014 até 31 de dezembro de 2019 foi incluído no treinamento do modelo, incluindo visitas/dia e código internacional de doenças. O treinamento e predição foram realizados com o Amazon Forecast por dois oftalmologistas sem experiência com programação. Resultados: O período de previsão estimou um volume de 206,37 pacientes/dia em p90, 180,75 em p50, 140,35 em p10 e média de 7,42 casos de trauma/dia em p90, 3,99 em p50 e 0,56 em p10. Janeiro de 2020 teve um total de 6.604 pacientes e média de 206,37 pacientes/dia, 13,5% menos do que a predição em p50. O período teve um total de 199 casos de trauma e média de 6,21 casos/dia, 55,77% mais casos do que a predição em p50. Conclusão: O desenvolvimento de modelos era restrito a cientistas de dados com experiencia em programação, porém a transferência de ensino com a tecnologia de auto Machine Learning permite o desenvolvimento de algoritmos por qualquer pessoa sem experiencia em programação. Esse estudo mostra um modelo com valores preditos próximos ao que ocorreram em janeiro de 2020. Fatores que podem ter influenciados no resultado foram feriados e tamanho do banco de dados. Esse é o primeiro estudo que aplicada auto Machine Learning em predição de visitas hospitalares com resultados próximos aos que ocorreram.

Exploring the effect of the structural model of active aging on the self-assessment of quality of life among older people: A cross-sectional and analytical study

ABSTRACT BACKGROUND: Although studies have examined the relationship between variables associated with active aging and quality of life (QoL), no studies have been identified to have investigated the effect of a structural model of active aging on QoL in a representative sample of older people in the community. OBJECTIVE: To measure the domains and facets of QoL in older people and identify the effect of the structural model of active aging on the self-assessment of QoL. DESIGN AND SETTING: This cross-sectional analytical study included 957 older people living in urban areas. Data were collected from households using validated instruments between March and June 2018. Descriptive, confirmatory factor, and structural equation modeling analyses were performed. RESULTS: Most older people self-rated their QoL as good (58.7%), and the highest mean scores were for the social relationships domain (70.12 ± 15.4) and the death and dying facet (75.43 ± 26.7). In contrast, the lowest mean scores were for the physical domains (64.41 ± 17.1) and social participation (67.20 ± 16.2) facets. It was found that active aging explained 50% of the variation in self-assessed QoL and directly and positively affected this outcome (λ = 0.70; P < 0.001). CONCLUSION: Active aging had a direct and positive effect on the self-assessment of QoL, indicating that the more individuals actively aged, the better the self-assessment of QoL.

Uso da modelagem de equações estruturais na compreensão da incapacidade funcional de idosos com catarata autorreferida / Use of structural equation modeling to understand the functional disability of elderly people with self-reported cataract

Objetivo: Verificar as associações diretas e indiretas entre variáveis demográficas, econômicas, biopsicossociais e comportamentais com a incapacidade funcional de idosos com catarata autorreferida. Método: Estudo transversal entre 260 idosos com catarata autorreferida e residentes na área urbana de uma microrregião de saúde de Minas Gerais. A coleta dos dados foi realizada nos domicílios mediante a aplicação de instrumentos validados no Brasil. Procederam-se as análises descritiva e de trajetórias (p<0,05). Resultados: O declínio funcional ocorreu de forma hierárquica. O pior desempenho físico associou-se diretamente à maior incapacidade funcional para as atividades básicas (p= 0,003), instrumentais (p<0,001) e avançadas (p= 0,003) da vida diária. A inatividade física esteve associada diretamente à maior incapacidade funcional para as atividades instrumentais (p<0,001) e avançadas (p<0,001). A menor escolaridade (p= 0,020), o maior número de sintomas depressivos (p<0,001) e o menor escore de apoio social (p<0,001) associaram-se diretamente à maior incapacidade funcional para as atividades avançadas, tal como a maior idade (p= 0,001) para as instrumentais. Observaram-se associações indiretas, mediadas pelo pior desempenho físico, entre o sexo feminino e o maior número de morbidades com a incapacidade funcional para as três atividades da vida diária. Conclusão: Idosos com catarata autorreferida apresentaram comprometimento da capacidade funcional relacionado à idade mais avançada, à baixa escolaridade, ao pior desempenho físico, à inatividade física, à presença de sintomas depressivos e ao menor nível de apoio social.

Objective: To verify the direct and indirect associations between demographic, economic, biopsychosocial and behavioral variables with the functional disability of the elderly with self-reported cataract. Method: Cross-sectional study among 260 elderly people with self- reported cataract and residents in the urban area of ​​a health micro-region in Minas Gerais. Data collection was carried out in the households through the application of instruments validated in Brazil. Descriptive and trajectory analyzes were carried out (p<0.05). Results: The functional decline occurred in a hierarchical manner. The worst physical performance was directly associated with greater functional incapacity for basic (p= 0.003), instrumental (p<0.001) and advanced (p= 0.003) activities of daily living. Physical inactivity was directly associated with greater functional disability for instrumental (p<0.001) and advanced (p<0.001) activities. Lower schooling (p= 0.020), higher number of depressive symptoms (p<0.001) and lower social support score (p<0.001) were directly associated with greater functional incapacity for advanced activities, such as older age (p= 0.001) for the instruments. Indirect associations, mediated by worse physical performance, were observed between females and the highest number of morbidities with functional incapacity for the three activities of daily living. Conclusion: Elderly people with self-reported cataract showed impairment of functional capacity related to older age, low education, worse physical performance, physical inactivity, presence of depressive symptoms and lower level of social support.

Quality of life in older adults according to race/color: a cross-sectional study

ABSTRACT BACKGROUND: Increased longevity is accompanied by new social and health demands, such as the race/color social construct, indicating the need to identify the specific needs of older adults to maintain and improve their quality of life. OBJECTIVE: We aimed to verify the direct and indirect associations of demographic, economic, and biopsychosocial characteristics with self-assessed quality of life in older adults according to race/color. DESIGN AND SETTING: This cross-sectional study included 941 older adults living in the urban area of a health microregion in Minas Gerais, Brazil. METHODS: Older adults were divided into three groups: white (n = 585), brown (n = 238), and black (n = 102) race/color. Descriptive and trajectory analyses were performed (P < 0.05). RESULTS: Among the three groups, worse self-assessed quality of life was directly associated with lower social support scores and greater numbers of depressive symptoms. Worse self-assessed quality of life was also directly associated with a higher number of functional disabilities in basic activities of daily living and the absence of a partner among older adults of brown and black race/color. Lower monthly income and higher numbers of morbidities and compromised components of the frailty phenotype were observed among participants of white race/color, as well as lower levels of education in the brown race/color group. CONCLUSION: Factors associated with poorer self-assessed quality of life among older adults in the study community differed according to race/color.

Use of structural models to elucidate the occurrence of falls among older adults according to abdominal obesity: a cross-sectional study

Abstract BACKGROUND: Obesity is a risk factor for falls in older adults, but the effects of body fat distribution and its interaction with other factors are not well established. OBJECTIVES: To verify the occurrence of falls among older adults with and without abdominal obesity and the effects of sociodemographic, health, and behavioral variables on this outcome. DESIGN AND SETTING: A cross-sectional study in an urban area of Alcobaça, Brazil. METHODS: Men and women older than 60 years with (270) and without (184) abdominal obesity were included. Sociodemographic, health, and behavioral data were collected using validated questionnaires in Brazil. Descriptive and path analyses were performed (P < 0.05). RESULTS: The occurrence of falls was high in participants with abdominal obesity (33.0%). In both groups, a higher number of morbidities (β = 0.25, P < 0.001; β = 0.26, P = 0.002) was directly associated with a higher occurrence of falls. Among participants without abdominal obesity, a lower number of medications (β = -0.16; P = 0.04), a higher number of depressive symptoms (β = 0.15; P = 0.04), worse performance on the agility and dynamic balance tests (β = 0.37; P < 0.001), and lower functional disability for basic activities of daily living (β = -0.21; P = 0.006) were directly associated with the occurrence of falls. CONCLUSION: Adults older than 60 years with abdominal obesity have a higher prevalence of falls. Different factors were associated with the occurrence of falls in both groups.

Current research status of prognostic models for transjugular intrahepatic portosystemic shunt / 临床肝胆病杂志

Transjugular intrahepatic portosystemic shunt (TIPS) is a safe and effective method for the treatment of portal hypertension complications in patients with decompensated liver cirrhosis. At present, there are many prognostic scoring tools for risk stratification of poor prognosis after TIPS. This article briefly introduces seven prognostic scoring tools commonly used for TIPS and summarizes the clinical research evidence of each scoring tool. The literature review shows that there is currently no sufficient research evidence to determine the optimal prognostic scoring tool after TIPS. Future clinical studies should comprehensively explore the advantages and disadvantages of different scoring tools in predicting short- and long-term adverse prognostic events after TIPS and develop new prognostic scoring tools in combination with new prognostic markers.

Development of acute－on－chronic liver failure in progressive chronic liver diseases： Risk prediction and stratified management / 临床肝胆病杂志

Patients with advanced chronic liver disease （ACLD） are hospitalized due to hepatitis， acute decompensation or liver failure and its complications， and they often require stratified management due to different severities. The patients with acute－on－chronic liver failure （ACLF） have the highest short－term mortality rate among ACLD patients and should be treated in tertiary hospitals. Although non－ACLF patients tend to have a relatively low mortality rate， they still have the risk of progression to ACLF， and there is a significant increase in mortality rate after progression to ACLF， which requires stratified management. The patients with extremely low progression rates often have favorable clinical outcomes and can be administrated in primary hospitals， while the high－risk population should be closely monitored and timely transferred in case of disease progression. However， currently there is still a lack of accurate predictive models for evaluating the risk of progression to ACLF， and further studies are needed to find new biomarkers or algorithms.

Risk factors analysis and prediction model establishment of anti-MDA5 antibody-positive dermatomyositis with rapid progression of interstitial lung disease / 中国医师杂志

Objective:To analyze the Risk factors for rapid progression of inpatients with anti-melanoma differentiation associated gene5 (MDA5) antibody-positive dermamyositis (DM) complicated with interstitial lung disease (ILD), and construct a clinical predictive model.Methods:A total of 63 hospitalized patients with anti MDA5 positive DM combined with ILD (MDA5+ DM-ILD) from January 1, 2016 to May 30, 2022 at the Second Affiliated Hospital of the Air Force Military Medical University were included in the study. They were divided into a control group (DM-ILD) and an observation group (DM-RPPILD) based on whether they had rapidly progressing interstitial lung disease (RPILD). Retrospective collection and organization of clinical case data from patients were conducted, and binary logistic regression was used to summarize the risk factors of DM-RPILD. R software was used to construct a clinical prediction model for RPILD occurrence using training set data, and validation set data was used to verify the predictive ability of the model.Results:The proportion of patients with SpO 2<90% at the initial diagnosis of ILD, the titers of anti MDA5 antibodies, immunoglobulin M (IgM), serum ferritin (FER) levels, and positive rates of anti Ro52 antibodies in the observation group were higher than those in the control group, the lymphocyte (LYM) count level was lower than that of the control group (all P<0.05). Binary logistic regression analysis showed SpO 2<90% at the initial diagnosis of ILD, FER level, LYM count, and anti Ro52 antibody were the influencing factors for the occurrence of RPILD (all P<0.05). The area under the curve (AUC) of the training set prediction model for predicting resistance to MDA5+ DM-RPILD was 0.922(95% CI: 0.887-0.957), with a sensitivity of 95.7% and a specificity of 72.5%; In the validation set, the prediction model predicted an AUC of 0.939(95% CI: 0.904-0.974) for resistance to MDA5+ DM-RPILD, with a sensitivity of 90.0% and a specificity of 88.9%; The calibration curves of the training and validation sets indicated that the predictive model had good calibration ability. Conclusions:SpO 2<90% at the initial diagnosis of ILD, FER levels increase, LYM count levels decrease, and anti Ro52 antibody positivity are risk factors for RPILD. The constructed clinical model has good predictive ability and has certain guiding significance for clinical work.

Building a diagnosis and prediction model for prostate cancer based on multimodal data / 中国医师杂志

Objective:To explore the diagnostic value of clinical, multi-parameter magnetic resonance imaging (MP-MRI) combined with transrectal ultrasound elasticity data for prostate cancer.Methods:A retrospective analysis was conducted on patient data from November 2021 to March 2023 when transrectal prostate two-dimensional ultrasound, real-time strain elastography of the prostate, MP-MRI examination of the prostate, and prostate biopsy were performed simultaneously at the Meizhou People′s Hospital. We collected patient age, height, weight, free serum prostate specific antigen (fPSA), total prostate specific antigen (tPSA), fPSA/tPSA, MRI prostate imaging report and data system (PI-RADS) scores, and ultrasound elasticity values. Four predictive models for prostate cancer diagnosis were constructed using multivariate logistic regression for comparison, and the optimal model was selected to construct a column chart. The diagnostic performance of different models was evaluated using receiver operating characteristic (ROC) curves, and the diagnostic performance of column charts was evaluated using calibration curves.Results:This study included a total of 117 patients with 117 prostate lesions, 47 benign prostate lesions, and 70 prostate cancer lesions. There were statistically significant differences in age, fPSA, tPSA, fPSA/tPSA, PI-RADS scores, and ultrasound elasticity values between benign and malignant lesions patients (all P<0.01). The area under the curve (AUC) of the clinical model (age+ tPSA+ fPSA+ fPSA/tPSA), MRI model (PI-RADS score), ultrasound elastic model, and clinical+ MRI+ ultrasound elastic combined model for diagnosing prostate cancer were 0.86, 0.86, 0.92, and 0.98, respectively. Conclusions:Compared with a single diagnostic model, the combination of age, tPSA, fPSA/tPSA, PI-RADS scores, and ultrasound elasticity value model can improve the diagnostic rate of prostate cancer.

Analysis of the risk factors of acute gastrointestinal injury above grade II in elderly patients with severe pneumonia / 中国医师杂志

Objective:To establish a prediction model of acute gastrointestinal injury (AGI) above grade II in elderly patients with severe pneumonia, and to evaluate and validate the model internally.Methods:A retrospective analysis was performed on 268 patients aged >65 years with severe pneumonia admitted to the Second People′s Hospital of Hefei from June 2019 to May 2022 (207 cases in the training set and 61 cases in the verification set). Sixteen indicators, including age, sex, underlying disease, pneumonia Severity index (PSI) score, dosage of sedative and analgesic drugs, and mechanical ventilation time of all patients were collected. After logistic regression analysis in the training set, a model was established to predict AGI above grade Ⅱ in elderly patients with severe pneumonia. Receiver operating characteristic (ROC) curve was drawed and correction curve was used to evaluate the reliability of the model. The model was internally validated by validation set data.Results:Among 207 patients with severe pneumonia in the training set, 50 patients developed AGI above grade Ⅱ during treatment. The prediction model was established by logistic regression analysis as follows: When L=Sequential Organ Failure Assessment (SOFA)×0.181+ PSI score×0.066+ propofol dosage×0.607+ reifentanil dosage×1.187, L>19.288, it can be considered that patients with severe pneumonia have a 93.24% chance of developing grade Ⅱ or above AGI. The ROC curve showed that the model was well differentiated, AUC=0.960. H-L test indicated (χ 2=7.39, P=0.496>0.05) the model fit was good. The sensitivity and specificity of the model were 82.00% and 96.82% respectively. AUC=94.58% (sensitivity 81.25%, specificity 93.33%), H-L test indicated ( χ 2=4.51, P=0.808>0.05) the prediction accuracy was 90.16%. Conclusions:The prediction model for AGI after severe pneumonia in elderly patients can be used clinically to help predict the occurrence of AGI in elderly patients with multiple injuries.

Value of a risk assessment model in predicting venous thromboembolism in patients with liver failure after artificial liver support therapy / 临床肝胆病杂志

Objective To investigate the value of a risk assessment model in predicting venous thromboembolism (VTE) in patients with liver failure after artificial liver support therapy. Methods A retrospective analysis was performed for the clinical data of 124 patients with liver failure who received artificial liver support therapy in Affiliated Drum Tower Hospital of Nanjing University Medical School from March 2019 to December 2021, among whom there were 41 patients with VTE (observation group) and 143 patients without VTE (control group). Related clinical data were compared between the two groups, and the Caprini risk assessment model was used for scoring and risk classification of the patients in both groups. The t -test was used for comparison of continuous data between two groups; the chi-square test was used for comparison of categorical data between two groups; the Mann-Whitney U rank sum test was used for comparison of ranked data between two groups. The logistic regression analysis was used to investigate the independent risk factors for VTE in patients with liver failure after artificial liver support therapy. The receiver operating characteristic (ROC) curve was used to investigate the value of Caprini score and the multivariate predictive model used alone or in combination in predicting VTE. Results The observation group had a significantly higher Caprini score than the control group (4.39±1.10 vs 3.12±1.04, t =6.805, P < 0.001). There was a significant difference between the two groups in risk classification based on Caprini scale ( P < 0.05), and the patients with high risk or extremely high risk accounted for a higher proportion among the patients with VTE. The univariate analysis showed that there were significant differences between the two groups in age ( t =6.400, P < 0.001), catheterization method ( χ 2 =14.413, P < 0.001), number of times of artificial liver support therapy ( Z =-4.720, P < 0.001), activity ( Z =-6.282, P < 0.001), infection ( χ 2 =33.071, P < 0.001), D-dimer ( t =8.746, P < 0.001), 28-day mortality rate ( χ 2 =5.524, P =0.022). The multivariate analysis showed that number of times of artificial liver support therapy (X 1 ) (odds ratio [ OR ]=0.251, 95% confidence interval [ CI ]: 0.111-0.566, P =0.001), activity (X 2 ) ( OR =0.122, 95% CI : 0.056-0.264, P < 0.001), D-dimer (X 3 ) ( OR =2.921, 95% CI : 1.114-7.662, P =0.029) were independent risk factors for VTE in patients with liver failure after artificial liver support therapy. The equation for individual predicted probability was P =1/[1+e -(7.425-1.384X 1 -2.103X 2 +1.072X 3 ) ]. The ROC curve analysis showed that Caprini score had an area under the ROC curve of 0.802 (95% CI : 0.721-0.882, P < 0.001), and the multivariate model had an area under the ROC curve of 0.768 (95% CI : 0.685-0.851, P < 0.001), while the combination of Caprini score and the multivariate model had an area under the ROC curve of 0.957 (95% CI : 0.930-0.984, P < 0.001). Conclusion The Caprini risk assessment model has a high predictive efficiency for the risk of VTE in patients with liver failure after artificial liver support therapy, and its combination with the multivariate predictive model can significantly improve the prediction of VTE.

Construction and validation of nomograms for predicting the prognosis of late-stage hepatocellular carcinoma / 国际外科学杂志

Objective:To construct and validate prognostic nomograms predicting overall survival (OS) and cancer-specific survival (CSS) of patients with late-stage hepatocellular carcinoma (HCC).Methods:A retrospective cohort study was used in this report. Screened 2382 late-stage HCC patients obtained from Surveillance, Epidemiology, and End Results (SEER) database (2010—2015), were randomly classified into the training cohort and the internal validation cohort by using the function in R software according to the ratio of 1∶1. Chi-square test was applied to verify the comparability of data between two groups. The external validation cohort ( n=62) were collected from the Affiliated Zhangjiagang Hospital of Soochow University. Based on univariate and multivariate COX regression analyses in the training cohort, this study constructed nomograms for 6- and 12- month OS and CSS. Concordance index (C-index), calibration plots, the receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were applied to measure the performance of nomograms in the training cohort and to validate nomograms in two validation cohorts. The clinical utility was measured by decision curve analysis (DCA). Results:Two nomograms were constructed. The identified risk factors included sex, Edmondson-Steiner grade, T stage, N stage, M stage, tumor size, bone metastasis, Alpha-fetoprotein (AFP), surgery of primary site, radiation and chemotherapy. The C-index for OS in the training and two validation cohorts was 0.729(95% CI: 0.711-0.747), 0.721(95% CI: 0.705-0.737) and 0.860(95 CI: 0.831-0.889), respectively. The C-index for CSS in the training and two validation cohorts was 0.732(95% CI: 0.714-0.750), 0.725(95% CI: 0.707-0.743) and 0.862(95% CI: 0.829-0.895), respectively. Afterwards, for nomograms in the training and two validation cohorts, C-index and calibration plots expressed great predictive accuracy and concordance. ROC curves and Kaplan-Meier survival curves demonstrated good prognostic ability. Furthermore, nomograms performed superior to other models. DCA showed substantial clinical utility. Conclusion:This study has developed and validated nomograms predicting 6- and 12- month OS and CSS of patients with late-stage HCC, which may be useful to develop the individualized treatment.

Ambient air pollutants relate to hospital admissions for chronic obstructive pulmonary disease in Ganzhou, China

ABSTRACT OBJECTIVE To evaluate the relationship between ambient air pollutants and chronic obstructive pulmonary disease in relatively low-polluted areas in China. METHODS Atmospheric pollutants levels and meteorological data were obtained from January 2016 to December 2020. The medical database including daily hospital admissions for chronic obstructive pulmonary disease (ICD10: J44) was derived from the First Affiliated Hospital of Gannan Medical University. The generalized additive model was used to analyze the percentage change with 95% confidence interval in daily hospital admissions for chronic obstructive pulmonary disease associated with a 10 µg/m3 increase in atmospheric pollutants levels. RESULTS In total, occurred 4,980 chronic obstructive pulmonary disease hospital admissions (not including emergency department visits) during 2016-2020. The mean concentrations of daily PM2.5, PM10, SO2, NO2, O3, and CO were 37.5 μg/m3, 60.1 μg/m3, 18.7 μg/m3, 23.5 μg/m3, 70.0 μg/m3, and 1.2 mg/m3 in Ganzhou. Each 10 µg/m3 increment of PM2.5, PM10, NO2, and O3 were significantly associated with 2.8% (95%CI: 1.0-4.7), 1.3% (95%CI: 0.3-2.4), 2.8% (95%CI: 0.4-5.4), and 1.5% (95%CI: 0.2-2.7) elevation in daily chronic obstructive pulmonary disease hospital admissions. The estimates of delayed effects of PM2.5, PM10, NO2, and O3 were observed at lag6, lag6, lag8, lag1, respectively. The health effects of particulate pollutants (PM2.5 and PM10) may be independent of other pollutants. The adverse effects of air pollutants were more evident in the warm season (May-Oct) than in the cold season (Nov-Apr). CONCLUSION Our study demonstrated that elevated concentrations of atmospheric pollutant (PM2.5, PM10, NO2, and O3), especially particulate pollutants, can be associated with increased daily count of hospital admissions for chronic obstructive pulmonary disease , which may promote further understanding of the potential hazards of relatively low levels of air pollution on chronic obstructive pulmonary disease and other respiratory disorders.

Social support among older adults understood through structural equation modeling / Apoyo social entre mujeres y hombres mayores entendido a través de modelos de ecuaciones estructurales / Apoio social entre mulheres e homens idosos compreendido por meio da modelagem de equações estruturais

ABSTRACT Objectives: to analyze factors associated, directly and indirectly, with lower social support of older adults, according to sex. Methods: a cross-sectional study, with 941 older adults from a health micro-region in Minas Gerais. Descriptive and trajectory analyzes were carried out (p<0.05). Results: in groups of women and men, direct and significant associations were observed between a smaller social network (p<0.001;p<0.001), single-person housing (p=0.046;p<0.001), greater number of depressive symptoms (p<0.001;p=0.010) and lower participation in advanced activities (p<0.001;p=0.005) with lower social support. In women, younger age was directly and significantly associated with outcome (p<0.001). In men, older age, mediated by lower participation in advanced activities, was indirectly associated with outcome. Conclusions: men and women showed less social support associated with social network, housing arrangement, depressive symptoms and participation in advanced activities. Understanding the context of social support among older adults makes it possible to establish more effective measures to improve care.

RESUMEN Objetivos: analizar los factores asociados, directa e indirectamente, al menor apoyo social de los ancianos, según sexo. Métodos: estudio transversal con 941 ancianos de una microrregión de salud de Minas Gerais. Se realizaron análisis descriptivos y de trayectoria (p<0,05). Resultados: en los grupos de mujeres y hombres se observaron asociaciones directas y significativas entre menor red social (p<0,001;p<0,001), vivienda unipersonal (p=0,046;p<0,001), mayor número de síntomas depresivos (p<0,001;p=0,010) y menor participación en actividades avanzadas (p<0,001;p=0,005) con menor apoyo social, respectivamente. En las mujeres, la menor edad se asoció directa y significativamente con el resultado (p<0,001). En los hombres, la mayor edad, mediada por una menor participación en actividades avanzadas, se asoció indirectamente con el resultado. Conclusiones: hombres y mujeres mostraron menor apoyo social asociado a la red social, arreglo de vivienda, síntomas depresivos y participación en actividades avanzadas. Comprender el contexto de apoyo social entre los ancianos permite establecer medidas más eficaces para mejorar la atención.

RESUMO Objetivos: analisar os fatores associados, direta e indiretamente, ao menor apoio social de idosos, segundo o sexo. Métodos: estudo transversal, com 941 idosos de uma microrregião de saúde de Minas Gerais. Realizaram-se análises descritiva e de trajetórias (p<0,05). Resultados: observaram-se, nos grupos de mulheres e homens, associações diretas e significativas entre menor rede social (p<0,001;p<0,001), moradia unipessoal (p=0,046;p<0,001), maior número de sintomas depressivos (p<0,001;p=0,010) e menor participação nas atividades avançadas (p<0,001;p=0,005) com menor apoio social, respectivamente. Nas mulheres, a menor idade se associou direta e significativamente ao desfecho (p<0,001). Nos homens, a maior idade, mediada pela menor participação nas atividades avançadas, associou-se indiretamente ao desfecho. Conclusões: os homens e mulheres apresentaram menor apoio social associado à rede social, arranjo de moradia, sintomatologia depressiva e participação nas atividades avançadas. A compreensão do contexto do apoio social entre idosos possibilita o estabelecimento de medidas mais eficazes na melhoria do cuidado.

Application of machine learning in the prediction of college students suicidal ideation / 中国学校卫生

Objective@#To explore the predictive effect of machine learning algorithms on college students suicidal ideation and to analyze the associated factors of college students suicidal ideation.@*Methods@#The mental health data of 21 224 undergraduates was selected from a university in 2021. The independent variables were 37 demographic and internal and external mental health factors. The dependent variable was whether college students had suicidal ideation. Support vector machine, random forest and LightGBM algorithm were used to establish prediction models. The model was used in test set to so as to evaluate the model s prediction effect by using detection rate, F1 score and accuracy rate. Based on the superior model, the highrisk factors of suicidal ideation in college students were analyzed.@*Results@#The detection rates of support vector machine, random forest, and LightGBM models were 61.0% ,64.0%, 69.0%; F1 scores were 0.63, 0.63, 0.64, and accuracy rates were 73.0%, 73.0%, 72.0%, respectively. Based on the superior LightGBM model, risk factors of suicidal ideation in college students included, depression, grade, gender, despair, place of origin, sense of meaning, attitude toward suicide, dependence, family economic situation, hallucinatory delusion symptoms, anxiety, internet addiction, and interpersonal distress.@*Conclusion@#The LightGBM model has a better prediction effect than the support vector machine and random forest models.

Application of machine learning in the prediction of college students suicidal ideation / 中国学校卫生

Objective@#To explore the predictive effect of machine learning algorithms on college students suicidal ideation and to analyze the associated factors of college students suicidal ideation.@*Methods@#The mental health data of 21 224 undergraduates was selected from a university in 2021. The independent variables were 37 demographic and internal and external mental health factors. The dependent variable was whether college students had suicidal ideation. Support vector machine, random forest and LightGBM algorithm were used to establish prediction models. The model was used in test set to so as to evaluate the model s prediction effect by using detection rate, F1 score and accuracy rate. Based on the superior model, the highrisk factors of suicidal ideation in college students were analyzed.@*Results@#The detection rates of support vector machine, random forest, and LightGBM models were 61.0% ,64.0%, 69.0%; F1 scores were 0.63, 0.63, 0.64, and accuracy rates were 73.0%, 73.0%, 72.0%, respectively. Based on the superior LightGBM model, risk factors of suicidal ideation in college students included, depression, grade, gender, despair, place of origin, sense of meaning, attitude toward suicide, dependence, family economic situation, hallucinatory delusion symptoms, anxiety, internet addiction, and interpersonal distress.@*Conclusion@#The LightGBM model has a better prediction effect than the support vector machine and random forest models.

Establishment and validation of a noninvasive diagnostic model for chronic hepatitis B liver fibrosis based on LASSO regression / 临床肝胆病杂志

Objective To establish a noninvasive diagnostic model for chronic hepatitis B (CHB) liver fibrosis based on LASSO regression using serological parameters, and to investigate the value of this model in the diagnosis of CHB liver fibrosis. Methods A total of 240 patients who were diagnosed with CHB in Changzhou Second People's Hospital, Nanjing Medical University, from September 2019 to September 2021 were enrolled as subjects, and according to the results of liver biopsy and pathology, they were divided into significant liver fibrosis (stage F2-F4) group with 175 patients and non-significant liver fibrosis (stage F0-F1) group with 65 patients. The two groups were compared in terms of sex, age, blood biochemical parameters, and liver stiffness measurement (LSM) measured by two-dimensional shear wave elastography, and LASSO regression and the multivariate logistic regression analysis were used screen out the risk factors for liver fibrosis. A nomogram model was established and then verified by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve. A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups, and the least significant difference t -test was used for further comparison between two groups; the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between groups; the chi-square test was used for comparison of categorical data between groups. Results There were significant differences between the patients with stage F3/F4 liver fibrosis and those with stage F2/F0-F1 liver fibrosis in age, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, gamma-glutamyl transpeptidase, total bilirubin, platelet count, procollagen type Ⅲ, type Ⅳ collagen, hyaluronic acid, and LSM (all P < 0.05). Five important variables were screened out by LASSO regression, and the logistic regression analysis showed that hyaluronic acid (odds ratio [ OR ]=1.432, P < 0.05), type Ⅳ collagen ( OR =1.243, P < 0.05), procollagen type Ⅲ( OR =1.146, P < 0.05), and LSM ( OR =1.656, P < 0.05) were the independent risk factors for liver fibrosis, while platelet count ( OR =0.567, P < 0.05) was a protective factor. Compared with the patients with stage F2/F0-F1 liver fibrosis, the patients with stage F3/F4 liver fibrosis had significantly higher score of the nomogram model, LSM, aspartate aminotransferase-to-platelet ratio index (APRI), King score, Forns index, and fibrosis-4 (FIB-4) index (all P < 0.05). The ROC curve was used to analyze the predictive value of the nomogram model, and the results showed an area under the ROC curve of 0.876, which was significantly higher than that of LSM, APRI, King score, Forns index, and FIB-4 (all P < 0.05). Calibration curve and decision curve showed that the nomogram model had acceptable consistency and benefit. Conclusion The noninvasive nomogram model based on LASSO regression is established by using serum parameters including hyaluronic acid, type Ⅳ collagen, procollagen type Ⅲ, platelet count, and LSM, and as a quantitative tool for the clinical diagnosis of CHB liver fibrosis, it has a high diagnostic efficiency and thus holds promise for clinical application.

Characteristics and trends of adolescents health risk behaviors in Shanghai from 2004 to 2019 / 中国学校卫生

Objective@#To analyze the trend of adolescent health risk behaviors in Shanghai, and to provide reference for the comprehensive intervention of middle school students health risk behaviors.@*Methods@#Based on the health risk behavior questionnaire of Chinese Center for Disease Control and Prevention, the questionnaire was adapted according to the actual monitoring needs. It was divided into junior high school version and senior high school version. From 2004 to 2019, using the method of multistage stratified cluster random sampling, 59 209 middle school students who completed the questionnaire in 6 surveys were selected for analysis. @*Results@#From 2004 to 2019, in the 7 days prior to the survey, 9.2%-50.6% of middle school students drank a glass of soda more than or equal to once a day and 54.2%-76.1% of middle school students reported eating dessert twice or more. Within 7 days, 48.3%-60.7% of middle school students had≥60 min of exercise per day for less than or equal to 3 days, and 16.1%- 35.2% of middle school students reported too much extracurricular activities, and the reporting rate increased year by year with the annual percent change ( APC ) of 5.15%( t =9.28, P <0.01). The reporting rate of long time online learning was 6.0%-13.6%, which showed an upward trend among high school students, with the APC of 5.35%( t =3.14, P <0.05). The reporting rate of middle school students pedestrian safety problems decreased from 69.1% in 2004 to 27.6% in 2019, with the APC of -6.28%( t =-8.18, P <0.01), but the reporting rate of cycling safety problems have increased in recent years. The reporting rate of intentional injury behaviors decreased by year such as fighting, bullying, etc. The reporting rate of initial smoking age ≤13 years old decreased, but attempted drinking behavior increased in junior middle school students, and the APC was 1.61%( t =3.48, P <0.05). A total of 1.6%-3.4% of middle school students had an Internet addiction behavior tendency. The detection rate of Internet addiction tendency was increasing in high school students and girls, and APC was 6.59% and 10.29% respectively( t =6.37, 8.62, P <0.01).@*Conclusion@#From 2004 to 2019, unintentional injury behavior, intentional injury behavior and substance addiction behavior of middle school students in Shanghai have improved. However, unhealthy diet and physical inactivity are still high. In the follow up. It need to focus on adverse diet and lack of physical activity behavior and take comprehensive intervention measures to control them.

Establishment and validation of a risk prediction model for early-stage complications after liver transplantation / 临床肝胆病杂志

Objective To investigate the risk factors for early-stage complications among liver transplant recipients, and to establish and validate a risk prediction model for early-stage complications after transplantation. Methods A retrospective analysis was performed for the clinical data of 234 patients who underwent orthotopic liver transplantation in Department of Liver Transplantation, Tianjin First Central Hospital, from January 2016 to December 2018. According to the presence or absence of Clavien-Dindo grade ≥Ⅲ complications after liver transplantation, the patients were divided into complication group with 97 patients and non-complication group with 137 patients. The two groups were compared in terms of the indices including age, sex, body mass index (BMI), blood type, psoas muscle thickness/height (PMTH), Controlling Nutritional Status (CONUT) score, Model for End-Stage Liver Disease (MELD) score, total serum bilirubin, serum creatinine, international normalized ratio of prothrombin time, blood urea nitrogen, hemoglobin, white blood cell count, platelet count, amount of intraoperative red blood cell transfusion, amount of frozen plasma transfusion, blood loss, anhepatic phase, time of operation, donor age, donor BMI, cold ischemia time of donor liver, and warm ischemia time of donor liver. The independent samples t -test was used for comparison of normally distributed continuous data between two groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups; the chi-square test was used for comparison of categorical data between two groups. Univariate analysis and the binary logistic regression analysis were used to investigate the risk factors for early-stage complications after liver transplantation, and a risk prediction model for complications after liver transplantation was established based on the method for establishing a scoring system using the logistic model provided by Framingham Research Center. Internal validation of the model was performed by C-index, receiver operating characteristic (ROC) curve, calibration curve, and the Hosmer-Lemeshow test, and the decision curve was used to evaluate the clinical applicability of the model. The Kaplan-Meier method was used to compare the incidence rate of early-stage complications after liver transplantation between the patients with different risk scores. Results Compared with the non-complication group, the complication group had significantly higher MELD score, proportion of patients with low PMTH, total serum bilirubin, serum creatinine, blood urea nitrogen, CONUT score, amount of intraoperative red blood cell transfusion, and amount of frozen plasma transfusion, as well as a significantly lower level of hemoglobin (all P < 0.1). The multivariate binary logistic regression analysis showed that MELD score (odds ratio [ OR ]=1.104, 95% confidence interval [ CI ]: 1.057-1.154, P < 0.05), PMTH ( OR =2.858, 95% CI : 1.451-5.626, P < 0.05), and CONUT score ( OR =1.481, 95% CI : 1.287-1.703, P < 0.05) were independent risk factors for grade ≥Ⅲ complications in the early stage after liver transplantation. MELD score, PMTH, and CONUT score were included in a predictive model, and this model had the highest score of 24 points, a C-index of 0.828, an area under the ROC curve of 0.812( P < 0.001), a sensitivity of 0.792, and a specificity of 0.751, suggesting that this predictive model had good discriminatory ability. The calibration curve of this model was close to the reference curve, and the Hosmer-Lemeshow test obtained a chi-square value of 8.528( P =0.382), suggesting that this predictive model had a high degree of fitting. The decision curve showed that most patients were able to benefit from the predictive model and achieved a high net benefit rate, suggesting that this predictive model had good clinical applicability. The score of 11 was selected as the cut-off value according to the optimal Youden index of 0.507, and the patients were divided into low-risk (< 8 points) group with 55 patients, moderate-risk (8-10 points) group with 63 patients, high-risk (11-14 points) group with 67 patients, and extremely high-risk (≥15 points) group with 49 patients. These four groups had a 90-day cumulative incidence rate of early-stage postoperative complications of 3.6%, 28.6%, 59.7%, and 75.5%, respectively, and the incidence rate of complications increased with the increase in risk score ( P < 0.001). Conclusion MELD score, PMTH, and CONUT score are independent risk factors for early-stage complications among liver transplant recipients, and the risk prediction model established based on these factors has a high predictive value in high-risk patients.