scholarly journals A novel model to predict mental distress among medical graduate students in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Guo ◽  
Min Yi ◽  
Li Sun ◽  
Ting Luo ◽  
Ruili Han ◽  
...  
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Graduate Students ◽  
Prediction Model ◽  
Learning Style ◽  
Mental Distress ◽  
Characteristic Curve ◽  
Training Group ◽  
Medical Graduate ◽  
Validation Group

Abstract Background Poor mental health was reported among medical graduate students in some studies. Identification of risk factors for predicting the mental health is capable of reducing psychological distress among medical graduate students. Therefore, the aim of the study was to identify potential risk factors relating to mental health and further create a novel prediction model to calculate the risk of mental distress among medical graduate students. Methods This study collected and analyzed 1079 medical graduate students via an online questionnaire. Included participants were randomly classified into a training group and a validation group. A model was developed in the training group and validation of the model was performed in the validation group. The predictive performance of the model was assessed using the discrimination and calibration. Results One thousand and fifteen participants were enrolled and then randomly divided into the training group (n = 508) and the validation group (n = 507). The prevalence of severe mental distress was 14.96% in the training group, and 16.77% in the validation group. The model was developed using the six variables, including the year of study, type of student, daily research time, monthly income, scientific learning style, and feeling of time stress. The area under the receiver operating characteristic curve (AUROC) and calibration slope for the model were 0.70 and 0.90 (95% CI: 0.65 ~ 1.15) in the training group, respectively, and 0.66 and 0.80 (95% CI, 0.51 ~ 1.09) in the validation group, respectively. Conclusions The study identified six risk factors for predicting anxiety and depression and successfully created a prediction model. The model may be a useful tool that can identify the mental status among medical graduate students. Trial registration No.ChiCTR2000039574, prospectively registered on 1 November 2020.

scholarly journals A novel model to predict mental distress among medical graduate students in China.

2021 ◽  
Author(s):  
Fei Guo ◽  
Min Yi ◽  
Li Sun ◽  
Ting Luo ◽  
Ruili Han ◽  
...  
Keyword(s):  
Graduate Students ◽  
Prediction Model ◽  
Learning Style ◽  
Psychological Health ◽  
Mental Distress ◽  
Negative Impact ◽  
Characteristic Curve ◽  
Training Group ◽  
Medical Graduate ◽  
Validation Group

Abstract Background: Several studies have reported serious mental status among medical graduate students, which triggered a negative impact on their physical and psychological health. This study aimed to develop a novel prediction model to calculate the risk of mental distress among medical graduate students. Methods: This study analyzed 1079 graduate students via an online questionnaire. Included subjects were randomly divided into the training group and validation group. In the training group, a formula was developed, and validation of the formula was performed in the validation group. The discrimination and calibration ability were assessed for the predictive performance of the formula. Results: One thousand and fifteen subjects were enrolled and randomly divided into the training group (n=508) and the validation group (n=507). The prevalence of severe mental distress was 14.96% in the training group, and 16.77% in the validation group. The formula included six variables, including year of study, type of student, daily research time, monthly income, scientific learning style, and feeling of time stress. The area under the receiver operating characteristic curve (AUROC) and calibration slope for the formula were 0.70 and 0.90 (95% CI: 0.65~1.15) in the training group, respectively; and 0.66 and 0.80 (95% CI: 0.51~1.09) in the validation group, respectively. Conclusion: Six risk factors for anxiety and depression were identified and a prediction model was created. The formula may be a useful model that can identify a high risk of mental distress among medical students.

scholarly journals Customization of Diet May Promote Exercise and Improve Mental Wellbeing in Mature Adults: The Role of Exercise as a Mediator

2021 ◽  
Vol 11 (5) ◽  
pp. 435
Author(s):  
Lina Begdache ◽  
Cara M. Patrissy
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Mental Distress ◽  
Rank Order ◽  
Principal Component ◽  
Dietary Factors ◽  
Mental Wellbeing ◽  
Modifiable Risk Factors ◽  
Food Groups ◽  
Dietary Practices

Diet, dietary practices and exercise are modifiable risk factors for individuals living with mental distress. However, these relationships are intricate and multilayered in such a way that individual factors may influence mental health differently when combined within a pattern. Additionally, two important factors that need to be considered are gender and level of brain maturity. Therefore, it is essential to assess these modifiable risk factors based on gender and age group. The purpose of the study was to explore the combined and individual relationships between food groups, dietary practices and exercise to appreciate their association with mental distress in mature men and women. Adults 30 years and older were invited to complete the food–mood questionnaire. The anonymous questionnaire link was circulated on several social media platforms. A multi-analyses approach was used. A combination of data mining techniques, namely, a mediation regression analysis, the K-means clustering and principal component analysis as well as Spearman’s rank–order correlation were used to explore these research questions. The results suggest that women’s mental health has a higher association with dietary factors than men. Mental distress and exercise frequency were associated with different dietary and lifestyle patterns, which support the concept of customizing diet and lifestyle factors to improve mental wellbeing.

P10.03 Establishment and validation of clinical prediction model in WHO grade II glioma

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii28-ii28
Author(s):  
X Xue ◽  
Q Gao
Keyword(s):  
Prediction Model ◽  
Training Group ◽  
Clinical Prediction ◽  
Validation Group ◽  
Who Grade ◽  
Clinical Prediction Model ◽  
Cox Regression Analysis ◽  
Who Grade Ii Glioma ◽  
Grade Ii ◽  
Grade Ii Glioma

Abstract OBJECTIVE WHO grade II glioma has the characteristics of heterogeneity, and this disease progresses rapidly in some patients, in whom the malignant degree is equivalent to that of high-grade glioma. In order to accurately predict the prognosis of patients, an effective clinical prediction model based on relevant risk factors is needed which could provide a theoretical basis for optimization of clinical individualized treatment. METHODS According to the inclusion and exclusion criteria, eligible patients from January 2010 to December 2018 in our hospital were selected, and those who met the criteria were randomly assigned 4:1 to the training group and the validation group, respectively. The predictors were screened by univariate and multivariate Cox regression analysis, the prediction model was established, and the model was verified and evaluated. RESULTS A total of 258 patients with WHO grade II glioma were recruited, including 208 patients as the training group and 50 patients as the validation group. Six independent risk factors, including patient age, preoperative Karnofsky performance status (KPS) score, preoperative seizure symptoms, surgical resection range, tumor size and IDH status, were selected and included into the prediction model by univariate and multivariate Cox regression analysis, and were visualized in the form of Nomogram. The concordance index (C index) was used to evaluate the predictive ability of the model. Results showed that the C-index was 0.832 in the training group and 0.853 in the validation group, respectively, indicating good performance for the prediction model. The calibration charts were drawn in both groups respectively, which showed that the calibration lines were in good agreement with the standard lines, indicating good consistency between the two groups. CONCLUSIONS In this study, a clinical prediction model for WHO grade II glioma was established, and it was verified that the model has good predictive ability, which may be beneficial for clinical work.

scholarly journals Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making

2021 ◽  
Vol 11 ◽  
Author(s):  
Jin-feng Pan ◽  
Rui Su ◽  
Jian-zhou Cao ◽  
Zhen-ya Zhao ◽  
Da-wei Ren ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Prostate Biopsy ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Elevated Serum ◽  
Training Group ◽  
Validation Group ◽  
Clinical Indicators

PurposeThe purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.MethodsWe retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.ResultsIn the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.ConclusionThe prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

Risk Factor Analysis and Predictive Model of Human Albumin Infusion during the Perioperative Period of Posterior Lumbar Interbody Fusion: A Study Based on 2015-2020 Data from a Local Hospital

2021 ◽  
Author(s):  
Bo Liu ◽  
Junpeng Pan ◽  
Hui Zong ◽  
Zhijie Wang
Keyword(s):  
Risk Factors ◽  
Interbody Fusion ◽  
Posterior Lumbar Interbody Fusion ◽  
Perioperative Period ◽  
Training Group ◽  
Human Albumin ◽  
Validation Group ◽  
Multivariate Logistic Regression ◽  
Albumin Infusion ◽  
Independent Risk Factors

Abstract BackgroundPerioperative hypoalbuminemia of the Posterior Lumbar Interbody Fusion (PLIF) can increase the risk of infection of the incision site, and it is challenging to accurately predict perioperative hypoproteinemia. The objective of this study was to create a clinical predictive nomogram and validate its accuracy by finding the independent risk factors for perioperative hypoalbuminemia of PLIF.MethodsThe patients who underwent PLIF at The Affiliated Hospital of Qingdao University between January 2015 and December 2020 were selected in this study. Besides, variables such as age, gender, BMI, current and past medical history, indications for surgery, surgery-related information, and results of preoperative blood routine tests were also collected from each patient. These patients were divided into injection group and non-injection group according to whether they were injected with human albumin. And they were also divided into training group and validation group, with the ratio of 4:1. Univariate and multivariate logistic regression analyses were performed in the training group to find the independent risk factors. The nomogram was developed based on these independent predictors. In addition, the area under the curve (AUC), the calibration curve and the decision curve analysis (DCA) were drawn in the training and validation groups to evaluate the prediction, calibration and clinical validity of the model. Finally, the nomograms in the training and validation groups and the receiver operating characteristic (ROC) curves of each independent risk factor were drawn to analyze the performance of this model.ResultsA total of 2,482 patients who met our criteria were recruited in this study and 256 (10.31%) patients were injected with human albumin perioperatively. There were 1,985 people in the training group and 497 in the validation group. Multivariate logistic regression analysis revealed 5 independent risk factors, including old age, accompanying T2DM, level of preoperative albumin, amount of intraoperative blood loss and fusion stage. We drew nomograms. The AUC of the nomograms in the training group and the validation group were 0.807, 95%CI = 0.774-0.840 and 0.859, 95%CI=0.797-0.920, respectively. The calibration curve shows consistency between the prediction and observation results. DCA showed a high net benefit from using nomograms to predict the risk of perioperative injection of human albumin. The AUCs of nomograms in the training and the validation groups were significantly higher than those of five independent risk factors mentioned above (P< 0.001), suggesting that the model is strongly predictive. ConclusionPreoperative low protein, operative stage ≥3, a relatively large amount of intraoperative blood loss, old age and history of diabetes were independent predictors of albumin infusion after PLIF. A predictive model for the risk of albumin injection during the perioperative period of PLIF was created using the above 5 predictors, and then validated. The model can be used to assess the risk of albumin injection in patients during the perioperative period of PLIF. The model is highly predictive, so it can be clinically applied to reduce the incidence of perioperative hypoalbuminemia.

scholarly journals Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
O. Karasch ◽  
M. Schmitz-Buhl ◽  
R. Mennicken ◽  
J. Zielasek ◽  
E. Gouzoulis-Mayfrank
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Prediction Model ◽  
Psychiatric Hospitalization ◽  
Previous Analysis ◽  
Psychiatric Hospitals ◽  
Data Set ◽  
Interaction Detection ◽  
Socioeconomic Data

Abstract Background The purpose of this study was to identify factors associated with a high risk of involuntary psychiatric in-patient hospitalization both on the individual level and on the level of mental health services and the socioeconomic environment that patients live in. Methods The present study expands on a previous analysis of the health records of 5764 cases admitted as in-patients in the four psychiatric hospitals of the Metropolitan City of Cologne, Germany, in the year 2011 (1773 cases treated under the Mental Health Act and 3991 cases treated voluntarily). Our previous analysis had included medical, sociodemographic and socioeconomic data of every case and used a machine learning-based prediction model employing chi-squared automatic interaction detection (CHAID). Our current analysis attempts to improve the previous one through (1) optimizing the machine learning procedures (use of a different type of decision-tree prediction model (Classification and Regression Trees (CART) and application of hyperparameter tuning (HT)), and (2) the addition of patients’ environmental socioeconomic data (ESED) to the data set. Results Compared to our previous analysis, model fit was improved. Main diagnoses of an organic mental or a psychotic disorder (ICD-10 groups F0 and F2), suicidal behavior upon admission, admission outside of regular service hours and absence of outpatient treatment prior to admission were confirmed as powerful predictors of detention. Particularly high risks were shown for (1) patients with an organic mental disorder, specifically if they were retired, admitted outside of regular service hours and lived in assisted housing, (2) patients with suicidal tendencies upon admission who did not suffer from an affective disorder, specifically if it was unclear whether there had been previous suicide attempts, or if the affected person lived in areas with high unemployment rates, and (3) patients with psychosis, specifically those who lived in densely built areas with a large proportion of small or one-person households. Conclusions Certain psychiatric diagnoses and suicidal tendencies are major risk factors for involuntary psychiatric hospitalization. In addition, service-related and environmental socioeconomic factors contribute to the risk for detention. Identifying modifiable risk factors and particularly vulnerable risk groups should help to develop suitable preventive measures.

scholarly journals Emergency Surgery Mortality (ESM) Score to Predict Mortality and Improve Patient Care in Emergency Surgery

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Sirirat Tribuddharat ◽  
Thepakorn Sathitkarnmanee ◽  
Pavit Sappayanon
Keyword(s):  
Risk Factors ◽  
Multivariate Analysis ◽  
Patient Care ◽  
Likelihood Ratio ◽  
Emergency Surgery ◽  
Predictive Value ◽  
Postoperative Mortality ◽  
Training Group ◽  
Validation Group ◽  
Surgery Mortality

Background. Emergency surgery has poor outcomes with high mortality. Numerous studies have reported the risk factors for postoperative death in order to stratify risk and improve perioperative care; nevertheless, a predictive model based upon these risk factors is lacking. Objective. We aimed to identify the risk factors of postoperative mortality and to construct a new model for predicting mortality and improving patient care. Methods. We included adult patients undergoing emergency surgery at Srinagarind Hospital between January 2012 and December 2014. The patients were randomized: 80% to the Training group for model construction and 20% to the Validation group. Patient data were extracted from medical records and then analyzed using univariate and multivariate logistic regression. Results. We recruited 758 patients, and the mortality rate was 14.5%. The Training group comprised 596 patients, and the Validation group comprised 162. Based upon a multivariate analysis in the Training group, we constructed a model to predict postoperative mortality—an Emergency Surgery Mortality (ESM) score based on the coefficient of each risk factor from the multivariate analysis. The ESM score comprised 7 risk factors, i.e., coagulopathy, ASA class 5, bicarbonate <15 mEq/L, heart rate >100/min, systolic blood pressure <90 mmHg, renal comorbidity, and general surgery, for a total score of 11. An ESM score ≥4 was predictive of postoperative mortality with an AUC of 0.83. The respective sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value, and accuracy for an ESM score ≥4 predictive of postoperative mortality was 70.2%, 94.9%, 13.8, 0.3, 69.4%, 95.1%, and 91.4%. The performance of the ESM score in the Validation group was comparable. Conclusions. An ESM score comprises 7 risk factors for a total score of 11. An ESM score ≥4 is predictive of postoperative mortality with a high AUC (0.83), sensitivity (70.2%), and specificity (94.9%). Four risk factors are preoperatively manageable for decreasing the probability of postoperative mortality and improving quality of patient care.

scholarly journals A clinical prediction model for estimating the risk of developing uveitis in patients with juvenile idiopathic arthritis

Rheumatology ◽  
2020 ◽  
Author(s):  
Joeri W van Straalen ◽  
Gabriella Giancane ◽  
Yasmine Amazrhar ◽  
Nikolay Tzaribachev ◽  
Calin Lazar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Protective Factor ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Information Criterion ◽  
Logistic Regression Models ◽  
Adjusted Risk ◽  
Independent Risk Factors

Abstract Objective To build a prediction model for uveitis in children with JIA for use in current clinical practice. Methods Data from the international observational Pharmachild registry were used. Adjusted risk factors as well as predictors for JIA-associated uveitis (JIA-U) were determined using multivariable logistic regression models. The prediction model was selected based on the Akaike information criterion. Bootstrap resampling was used to adjust the final prediction model for optimism. Results JIA-U occurred in 1102 of 5529 JIA patients (19.9%). The majority of patients that developed JIA-U were female (74.1%), ANA positive (66.0%) and had oligoarthritis (59.9%). JIA-U was rarely seen in patients with systemic arthritis (0.5%) and RF positive polyarthritis (0.2%). Independent risk factors for JIA-U were ANA positivity [odds ratio (OR): 1.88 (95% CI: 1.54, 2.30)] and HLA-B27 positivity [OR: 1.48 (95% CI: 1.12, 1.95)] while older age at JIA onset was an independent protective factor [OR: 0.84 (9%% CI: 0.81, 0.87)]. On multivariable analysis, the combination of age at JIA onset [OR: 0.84 (95% CI: 0.82, 0.86)], JIA category and ANA positivity [OR: 2.02 (95% CI: 1.73, 2.36)] had the highest discriminative power among the prediction models considered (optimism-adjusted area under the receiver operating characteristic curve = 0.75). Conclusion We developed an easy to read model for individual patients with JIA to inform patients/parents on the probability of developing uveitis.

scholarly journals Establishment of the Radiologic Tumor Invasion Index Based on Radiomics Splenic Features and Clinical Factors to Predict Serous Invasion of Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Bujian Pan ◽  
Weiteng Zhang ◽  
Wenjing Chen ◽  
Jingwei Zheng ◽  
Xinxin Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Prediction Model ◽  
Tumor Invasion ◽  
Training Group ◽  
P Value ◽  
Serosal Invasion ◽  
Invasion Risk ◽  
Training Cohort ◽  
Invasion Index

BackgroundCurrently, there are shortcomings in diagnosing gastric cancer with or without serous invasion, making it difficult for patients to receive appropriate treatment. Therefore, we aimed to develop a radiomic nomogram for preoperative identification of serosal invasion.MethodsWe selected 315 patients with gastric cancer, confirmed by pathology, and randomly divided them into two groups: the training group (189 patients) and the verification group (126 patients). We obtained patient splenic imaging data for the training group. A p-value of &lt;0.05 was considered significant for features that were selected for lasso regression. Eight features were chosen to construct a serous invasion prediction model. Patients were divided into high- and low-risk groups according to the radiologic tumor invasion risk score. Subsequently, univariate and multivariate regression analyses were performed with other invasion-related factors to establish a visual combined prediction model.ResultsThe diagnostic accuracy of the radiologic tumor invasion score was consistent in the training and verification groups (p&lt;0.001 and p=0.009, respectively). Univariate and multivariate analyses of invasion risk factors revealed that the radiologic tumor invasion index (p=0.002), preoperative hemoglobin &lt;100 (p=0.042), and the platelet and lymphocyte ratio &lt;92.8 (p=0.031) were independent risk factors for serosal invasion in the training cohort. The prediction model based on the three indexes accurately predicted the serosal invasion risk with an area under the curve of 0.884 in the training cohort and 0.837 in the testing cohort.ConclusionsRadiological tumor invasion index based on splenic imaging combined with other factors accurately predicts serosal invasion of gastric cancer, increases diagnostic precision for the most effective treatment, and is time-efficient.

scholarly journals Identification of risk factors for involuntary psychiatric hospitalization: Using environmental socioeconomic data and methods of machine learning to improve prediction

2020 ◽  
Author(s):  
Olaf Karasch ◽  
Mario Schmitz-Buhl ◽  
R Roman Mennicken ◽  
Jürgen Zielasek ◽  
Euphrosyne Gouzoulis-Mayfrank
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Prediction Model ◽  
Suicide Attempts ◽  
Psychiatric Hospitalization ◽  
Previous Analysis ◽  
Data Set ◽  
Interaction Detection ◽  
Socioeconomic Data

Abstract Background: The purpose of this study was to identify factors associated with a high risk of involuntary psychiatric in-patient hospitalization both on the individual level and on the level of mental health services and the socioeconomic environment that patients live in. Methods: The present study expands on a previous analysis of the health records of 5764 cases admitted as in-patients in the four psy­chiat­ric hospitals of the Metropolitan City of Cologne, Germany, in the year 2011 (1773 cases treated under the Mental Health Act and 3991 cases trea­ted voluntarily). Our previous analysis had included medical, socio­demographic and socioeconomic data of every case and used a machine learning-based prediction model employing chi-squared automatic interaction detection (CHAID). Our current analysis attempts to improve the previous one through (1) optimizing the machine learning procedures (use of a different type of decision-tree prediction model (CART) and application of hyperparameter tuning), and (2) the addition of socioeconomic data on the patients’ environment to the data set. Results: Compared to our previous analysis, model fit was improved. Main diagnoses of an organic mental or a psychotic disorder (ICD-10 groups F0 and F2), suicidal behavior upon admission, admission outside of regular service hours and absence of outpatient treatment prior to admission were confirmed as powerful predictors of detention. Particularly high risks were shown for (1) patients with an organic mental disorder, specifically if they were retired, admitted outside of regular service hours and lived in assisted housing, (2) patients with suicidal tendencies upon admission who did not suffer from an affective disorder, specifically if it was unclear whether there had been previous suicide attempts, or if the affected person lived in areas with high unemployment rates, and (3) patients with psychosis, specifically those who lived in densely built areas with a large proportion of small or one-person households. Conclusions: Certain psychiatric diagnoses and suicidal tendencies are major risk factors for involuntary psychiatric hospitalization. In addition, service-related and environmental socioeconomic factors contribute to the risk for detention. Identifying modifiable risk factors and particularly vulnerable risk groups should help to develop suitable preventive measures.

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