scholarly journals Prediction Model of Soleus Muscle Depth Based on Anthropometric Features: Potential Applications for Dry Needling

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 284 ◽  
Author(s):  
Juan Antonio Valera-Calero ◽  
Ladislao Laguna-Rastrojo ◽  
Fernando de-Jesús-Franco ◽  
Eduardo Cimadevilla-Fernández-Pola ◽  
Joshua A. Cleland ◽  
...  
Keyword(s):  
Prediction Model ◽  
Soleus Muscle ◽  
Diagnostic Study ◽  
Regression Analyses ◽  
Healthy Individuals ◽  
Dry Needling ◽  
Anthropometric Features ◽  
Potential Applications ◽  
Asymptomatic Individuals ◽  
Proper Length

This study was conducted to investigate if anthropometric features can predict the depth of the soleus muscle, as assessed with ultrasound imaging, in a sample of healthy individuals to assist clinicians in the application of dry needling. A diagnostic study to calculate the accuracy of a prediction model for soleus muscle depth, as assessed with ultrasonography, in the middle-third and distal-third of the calf, based on anthropometric features such as age, height, weight, body mass index (BMI), calf length, mid-third and distal-third calf girth, was conducted on 48 asymptomatic healthy subjects (75% male) involving a total of 96 calves. Multiple linear regression analyses were used to determine which variables contributed significantly to the variance in the soleus muscle depth at middle-third and distal-third of the calf by gender. Women were found to have a deeper soleus muscle than men (p < 0.001). Weight, height, BMI, and mid-third calf perimeter explained 69.9% of variance in men, whereas mid-third calf perimeter, calf length, height, and distal-third calf girth explained 73% of the variance in women of the distal-third soleus depth (p < 0.001). Additionally, mid-third calf girth and calf length explained 28.8% of variance in men, whereas mid-third calf perimeter, calf length, and weight explained 67.8% of variance in women of the mid-third soleus depth (p < 0.001). This study identified anthropometric features that predict soleus muscle depth, as assessed with ultrasound, in asymptomatic individuals, but these features are different in men and women. Our findings could assist clinicians in choosing the proper length of the needle to avoiding passing through the soleus during dry needling.

scholarly journals Prediction model of rhomboid major and pleura depth based on anthropometric features to decrease the risk of pneumothorax during dry needling

2020 ◽  
Author(s):  
Juan Antonio Valera-Calero ◽  
Enrique Cendra-Martín ◽  
Tomás Fernández-Rodríguez ◽  
César Fernández-de-las-Peñas ◽  
Gracia María Gallego-Sendarrubias ◽  
...  
Keyword(s):  
Adverse Events ◽  
Prediction Model ◽  
Healthy Subjects ◽  
Diagnostic Study ◽  
Dry Needling ◽  
Needle Length ◽  
Serious Adverse Events ◽  
Depth Limit ◽  
Anthropometric Features ◽  
Maximum Inspiration

Background: Although mostly common adverse events associated to dry needling can be considered minor, serious adverse events including induced pneumothorax cannot be excluded, and safety instructions for reducing the risk of pleura puncture are needed. Objective: To investigate if anthropometric features can predict the rhomboid major muscle and pleura depth in a sample of healthy subjects to avoid the risk of pneumothorax during dry needling. Methods: A diagnostic study was conducted on 59 healthy subjects (52.5 % male) involving a total of 236 measurements (both sides in maximum inspiration and expiration), to calculate the accuracy of a prediction model for both pleura and rhomboid depth, as assessed with ultrasound imaging, based on sex, age, height, weight, body mass index (BMI), breathing and chest circumference. A correlation matrix and a multiple linear regression analyses were used to detect those variables contributing significantly to the variance in both locations. Results: Men showed greater height, weight, BMI, thorax circumference and skin-to-rhomboid, rhomboid-to-pleura y skin-to-pleura distances (p<0.001). Sex, BMI, and thorax circumference explained 51.5% of the variance of the rhomboid (p<0.001) and 69.7% of pleura (p<0.001) depth limit. In general, inserting a maximum length of 19 mm is recommended to reach the deep limit of rhomboid major decreasing the risk of passing through the pleura. Conclusion: This study identified that gender, BMI and thorax circumference can predict both rhomboid and pleura depth, as assessed with ultrasonography, in healthy subjects. Our findings could assist clinicians in the needle length election for avoiding the risk of induced pneumothorax during dry needling.

scholarly journals Prediction model of rhomboid major and pleura depth based on anthropometric features to decrease the risk of pneumothorax during dry needling

2021 ◽  
Author(s):  
Juan Antonio Valera‐Calero ◽  
Enrique Cendra‐Martel ◽  
Tomás Fernández‐Rodríguez ◽  
César Fernández‐de‐las‐Peñas ◽  
Gracia María Gallego‐Sendarrubias ◽  
...  
Keyword(s):  
Prediction Model ◽  
Dry Needling ◽  
Anthropometric Features

scholarly journals Prediction of 11-year incidence of psychophysically dependent status or death among community-dwelling younger elderlies: from an age-specified community-based cohort study (the NISSIN project)

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Satoe Okabayashi ◽  
Takashi Kawamura ◽  
Hisashi Noma ◽  
Kenji Wakai ◽  
Masahiko Ando ◽  
...  
Keyword(s):  
Older Adults ◽  
Logistic Regression ◽  
Cohort Study ◽  
Prediction Model ◽  
Nursing Care ◽  
Family Members ◽  
Limited Resource ◽  
Community Dwelling ◽  
Older Individuals ◽  
Regression Analyses

Abstract Background Predicting adverse health events and implementing preventative measures are a necessary challenge. It is important for healthcare planners and policymakers to allocate the limited resource to high-risk persons. Prediction is also important for older individuals, their family members, and clinicians to prepare mentally and financially. The aim of this study is to develop a prediction model for within 11-year dependent status requiring long-term nursing care or death in older adults for each sex. Methods We carried out age-specified cohort study of community dwellers in Nisshin City, Japan. The older adults aged 64 years who underwent medical check-up between 1996 and 2005 were included in the study. The primary outcome was the incidence of the psychophysically dependent status or death or by the end of the year of age 75 years. Univariable logistic regression analyses were performed to assess the associations between candidate predictors and the outcome. Using the variables with p-values less than 0.1, multivariable logistic regression analyses were then performed with backward stepwise elimination to determine the final predictors for the model. Results Of the 1525 female participants at baseline, 105 had an incidence of the study outcome. The final prediction model consisted of 15 variables, and the c-statistics for predicting the outcome was 0.763 (95% confidence interval [CI] 0.714–0.813). Of the 1548 male participants at baseline, 211 had incidence of the study outcome. The final prediction model consisted of 16 variables, and the c-statistics for predicting the outcome was 0.735 (95% CI 0.699–0.771). Conclusions We developed a prediction model for older adults to forecast 11-year incidence of dependent status requiring nursing care or death in each sex. The predictability was fair, but we could not evaluate the external validity of this model. It could be of some help for healthcare planners, policy makers, clinicians, older individuals, and their family members to weigh the priority of support.

scholarly journals Pulmonary infection after cardiopulmonary bypass surgery in children: a risk estimation model in China

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Chunnian Ren ◽  
Chun Wu ◽  
Zhengxia Pan ◽  
Quan Wang ◽  
Yonggang Li
Keyword(s):  
Risk Factors ◽  
Cardiopulmonary Bypass ◽  
Prediction Model ◽  
Congenital Heart ◽  
Bypass Surgery ◽  
Pulmonary Infection ◽  
Regression Analyses ◽  
Cardiopulmonary Bypass Surgery ◽  
Independent Risk Factors ◽  
Individualized Prediction

Abstract Objectives The occurrence of pulmonary infection after congenital heart disease (CHD) surgery can lead to significant increases in intensive care in cardiac intensive care unit (CICU) retention time, medical expenses, and risk of death risk. We hypothesized that patients with a high risk of pulmonary infection could be screened out as early after surgery. Hence, we developed and validated the first risk prediction model to verify our hypothesis. Methods Patients who underwent CHD surgery from October 2012 to December 2017 in the Children’s Hospital of Chongqing Medical University were included in the development group, while patients who underwent CHD surgery from December 2017 to October 2018 were included in the validation group. The independent risk factors associated with pulmonary infection following CHD surgery were screened using univariable and multivariable logistic regression analyses. The corresponding nomogram prediction model was constructed according to the regression coefficients. Model discrimination was evaluated by the area under the receiver operating characteristic curve (ROC) (AUC), and model calibration was conducted with the Hosmer-Lemeshow test. Results The univariate and multivariate logistic regression analyses identified the following six independent risk factors of pulmonary infection after cardiac surgery: age, weight, preoperative hospital stay, risk-adjusted classification for congenital heart surgery (RACHS)-1 score, cardiopulmonary bypass time and intraoperative blood transfusion. We established an individualized prediction model of pulmonary infection following cardiopulmonary bypass surgery for CHD in children. The model displayed accuracy and reliability and was evaluated by discrimination and calibration analyses. The AUCs for the development and validation groups were 0.900 and 0.908, respectively, and the P-values of the calibration tests were 0.999 and 0.452 respectively. Therefore, the predicted probability of the model was consistent with the actual probability. Conclusions Identified the independent risk factors of pulmonary infection after cardiopulmonary bypass surgery. An individualized prediction model was developed to evaluate the pulmonary infection of patients after surgery. For high-risk patients, after surgery, targeted interventions can reduce the risk of pulmonary infection.

Beyond Fundamental Dimensions of Mood in Predicting Depressive Symptoms and Suicidal Ideation in Victims of Interpersonal Violence: Examining the Role of Dispositional Optimism in Chinese Females With and Without Experience of Victimization

2021 ◽  
pp. 107780122110309
Author(s):  
Yifeng Du ◽  
Olivia D. Chang ◽  
Mingqi Li ◽  
Misu Kwon
Keyword(s):  
Depressive Symptoms ◽  
Suicidal Ideation ◽  
Prediction Model ◽  
Interpersonal Violence ◽  
Suicide Risk ◽  
Negative Affectivity ◽  
Dispositional Optimism ◽  
Hierarchical Regression ◽  
Regression Analyses

The present study tested a prediction model involving affectivity and dispositional optimism as predictors of suicide risk (i.e., depressive symptoms and suicidal ideation) in young adult Chinese females with and without prior interpersonal violence (IPV) victimization (294 nonvictimized and 94 victimized females). Results of hierarchical regression analyses indicated that negative affectivity was a significant predictor of both depressive symptoms and suicidal ideation for Chinese females, regardless of IPV victimization. Beyond affectivity, dispositional optimism was found to further add to the prediction model of depressive symptoms in both groups, but only for suicidal ideation in the IPV victimized group.

scholarly journals Establishment of non–small-cell lung cancer risk prediction model based on prognosis-associated ADME genes

2021 ◽  
Author(s):  
Ke Han ◽  
Jukun Wang ◽  
Kun Qian ◽  
Teng Zhao ◽  
Yi Zhang
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Small Cell ◽  
Risk Prediction Model ◽  
Low Risk ◽  
Regression Analyses ◽  
Small Cell Lung ◽  
Immune Infiltration

Purpose: ADME genes are those involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs. In this study, a non–small-cell lung cancer (NSCLC) risk prediction model was established using prognosis-associated ADME genes, and the predictive performance of this model was evaluated and verified. In addition, multifaceted difference analysis was performed on groups with high and low risk scores. Methods: An NSCLC sample transcriptome and clinical data were obtained from public databases. The prognosis-associated ADME genes were obtained by univariate Cox and lasso regression analyses to build a risk model. Tumor samples were divided into high-risk and low-risk score groups according to the risk score. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of the differentially expressed genes and the differences in the immune infiltration, mutation, and medication reactions in the two groups were studied in detail. Results: A risk prediction model was established with seven prognosis-associated ADME genes. Its good predictive ability was confirmed by studies of the model’s effectiveness. Univariate and multivariate Cox regression analyses showed that the model’s risk score was an independent prognostic factor for patients with NSCLC. The study also showed that the risk score closely correlated with immune infiltration, mutations, and medication reactions. Conclusion: The risk prediction model established with seven ADME genes in this study can predict the prognosis of patients with NSCLC. In addition, significant differences in immune infiltration, mutations, and therapeutic efficacy exist between the high- and low-risk score groups.

Stromal Score-based Gene Signature: A Prognostic Prediction Model for Colon Cancer

2020 ◽  
Author(s):  
Jing Jia ◽  
Yuhan Dai ◽  
Qing Zhang ◽  
Peiyu Tang ◽  
Qiang Fu ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Overall Survival ◽  
Prediction Model ◽  
Differentially Expressed Genes ◽  
Gene Signature ◽  
Rank Test ◽  
Regression Analyses ◽  
Log Rank Test ◽  
Auc Value ◽  
Prognostic Prediction

Abstract BackgroundGrowing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.MethodStromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate and multivariate CoxPH regression analyses were used successively to select prognostic gene signature. An independent dataset from GEO was used as a validation cohort.ResultsLow stromal score was demonstrated to be a favorable factor to overall survival of colon cancer patients in TCGA cohort (log-rank test p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate and multivariate CoxPH regression analyses, a gene signature consisting of LEP, SYT3, NOG and IGSF11 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (log-rank test p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for 5-year overall survival of the model (AUC value = 0.736). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was verified to be of significant prognostic value for different subgroups in an independent colon cancer cohort from GEO database, which was demonstrated to be especially accurate for young patients (AUC value = 0.752). ConclusionThe well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

scholarly journals Platelet Serotonin Concentration Is Associated with Illness Duration in Schizophrenia and Chronological Age in Depression

2020 ◽  
Vol 17 (6) ◽  
pp. 579-586
Author(s):  
Vjekoslav Peitl ◽  
Biserka Getaldić-Švarc ◽  
Dalibor Karlović
Keyword(s):  
Platelet Count ◽  
Blood Platelets ◽  
Diagnostic Value ◽  
Healthy Controls ◽  
Regression Analyses ◽  
Healthy Individuals ◽  
Serotonin Concentration ◽  
Illness Duration ◽  
Peripheral Model ◽  
Platelet Serotonin

Objective Impaired serotonergic neurotransmission has been implicated in the pathogenesis of depression and schizophrenia. Blood platelets have been used for years as a peripheral model of neuronal serotonin dynamics. The objective was to investigate platelet count and serotonin concentration in patients with depression and schizophrenia, in an attempt to ascertain their clinical usefulness.Methods 953 participants were included in the study, 329 patients with depression, 339 patients with schizophrenia and 285 healthy controls. ELISA was used to assess platelet serotonin concentrations.Results There were no statistically significant differences among groups regarding age, total platelet count and serotonin concentration. Linear regression analyses revealed inverse correlations between platelet serotonin concentration and age of patients with depression and healthy individuals, as well as between platelet serotonin concentration and illness duration in patients with schizophrenia. In other words, longer illness duration in patients with schizophrenia, and higher age in patients with depression and healthy individuals was associated with lower platelet serotonin concentrations.Conclusion Platelet count and serotonin concentration did not prove to be of diagnostic value in differentiating patients and healthy individuals. However, illness duration in patients with schizophrenia may be associated with reduced concentrations of platelet serotonin.

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