scholarly journals Predicting the risk of sarcopenia in elderly patients with patellar fracture: Development and assessment of a new predictive nomogram

2019 ◽  
Author(s):  
Chen Yisheng ◽  
Tao Jie
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Elderly Patients ◽  
Prediction Model ◽  
Predictive Model ◽  
Body Mass ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Low Body Mass Index ◽  
Patellar Fractures

AbstractPurposeThis study was aimed at developing a risk prediction model for postoperative dysplasia in elderly patients with patellar fractures in China.Patients and methodsWe conducted a community survey of patients aged ≥55 years who underwent surgery for patellar fractures between January 2013 and October 2018, through telephone interviews, community visits, and outpatient follow-up. We established a predictive model for assessing the risk of sarcopenia after patellar fractures. We developed the prediction model by combining multivariate logistic regression analysis with the least absolute shrinkage model and selection operator regression (Lasso analysis). The predictive quality and clinical utility of the predictive model were determined using C-index, calibration plots, and decision curve analysis. We conducted internal sampling methods for qualitative assessment.ResultWe recruited 61 participants (males: 20, mean age: 68.1 years). Various risk factors were assessed, and low body mass index and diabetes mellitus were identified as the most important risk factors (P<0.05). The model showed a good prediction rate (C-index: 0.909; 95% confidence interval: 0.81–1.00) and good correction effect. The C-index remained high (0.828) even after internal sample verification. Decision curve analysis showed that the risk of sarcopenia was 8.3–80.0%, suggesting good clinical practicability.ConclusionOur prediction model shows promise as a cost-effective tool for predicting the risk of postoperative sarcopenia in elderly patients based on the following: advanced age, low body mass index, diabetes, longer postoperative hospital stay, no higher education, no postoperative rehabilitation, removal of internal fixation, and less outdoor exercise.

scholarly journals Predicting the risk of sarcopenia in elderly patients with patellar fracture: development and assessment of a new predictive nomogram

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8793
Author(s):  
Yi-sheng Chen ◽  
Yan-xian Cai ◽  
Xue-ran Kang ◽  
Zi-hui Zhou ◽  
Xin Qi ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Elderly Patients ◽  
Prediction Model ◽  
Predictive Model ◽  
Body Mass ◽  
Curve Analysis ◽  
Advanced Age ◽  
Decision Curve Analysis ◽  
Low Body Mass Index ◽  
Patellar Fractures

Purpose To develop a risk prediction model for postoperative sarcopenia in elderly patients with patellar fractures in China. Patients and methods We conducted a community survey of patients aged ≥55 years who underwent surgery for patellar fractures between January 2013 and October 2018, through telephone interviews, community visits, and outpatient follow-up. We established a predictive model for assessing the risk of sarcopenia after patellar fractures. We developed the prediction model by combining multivariate logistic regression analysis with the least absolute shrinkage model and selection operator regression (lasso analysis) as well as the Support Vector Machine (SVM) algorithm. The predictive quality and clinical utility of the predictive model were determined using C-index, calibration plots, and decision curve analysis. We also conducted internal sampling methods for qualitative assessment. Result We recruited 137 participants (53 male; mean age, 65.7 years). Various risk factors were assessed, and low body mass index and advanced age were identified as the most important risk factor (P < 0.05). The prediction rate of the model was good (C-index: 0.88; 95% CI [0.80552–0.95448]), with a satisfactory correction effect. The C index is 0.97 in the validation queue and 0.894 in the entire cohort. Decision curve analysis suggested good clinical practicability. Conclusion Our prediction model shows promise as a cost-effective tool for predicting the risk of postoperative sarcopenia in elderly patients based on the following: advanced age, low body mass index, diabetes, less outdoor exercise, no postoperative rehabilitation, different surgical methods, diabetes, open fracture, and removal of internal fixation.

Risk Factors for Low Bone Density in Inflammatory Bowel Disease: Use of Glucocorticoids, Low Body Mass Index, and Smoking

2019 ◽  
Vol 37 (4) ◽  
pp. 284-290 ◽  
Author(s):  
Razi Even Dar ◽  
Yoav Mazor ◽  
Amir Karban ◽  
Sofia Ish-Shalom ◽  
Elena Segal
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Inflammatory Bowel Disease ◽  
Bone Density ◽  
Body Mass ◽  
Bowel Disease ◽  
Mineral Density ◽  
Low Bone Density ◽  
Low Body Mass Index ◽  
Inflammatory Bowel

Background: Inflammatory bowel disease (IBD) patients are reported to have lower bone density compared to healthy controls. There is limited consensus regarding factors affecting bone density among these patients. Our aim, therefore, was to determine clinical and genetic variables that contribute to lower bone mineral density (BMD) in IBD patients. Methods: A cross-sectional study of IBD patients treated in a tertiary referral center was performed. Epidemiological and clinical data were collected, and genetic testing for the common mutations in Nucleotide-binding Oligomerization Domain-containing protein (NOD)2 was performed. We examined correlations between the different variables and BMD in the total hip, femoral neck, and lumbar spine. Results: Eighty-nine patients (49% males, 67 Crohn’s disease [CD]) participated in the study. 42Forty-two (63%) of the CD and 13 (59%) of the ulcerative colitis patients met the criteria for osteoporosis/osteopenia. Factors associated with lower Z scores were low body mass index (BMI; r = –0.307, p = 0.005), use of glucocorticoids (likelihood ratio [LR] 5.1, p = 0.028), and a trend for male gender (LR = 3.4, p = 0.079). Among CD patients, low bone density showed borderline significance for association with gastrointestinal surgery (LR = 4.1, p = 0.07) and smoking (LR = 3.58, p = 0.06). Low levels of 25OHD were not associated with low BMD, nor were mutations in NOD2. No increased rate of fractures was seen among patients with osteopenia or osteoporosis. Conclusion: In addition to the generally accepted risk factors for osteoporosis (glucocorticoids, low BMI, smoking), male IBD patients had a trend toward lower BMD. Carrying a mutaticon in NOD2 did not confer a risk for bone loss.

Low Body Mass Index and Low Intelligence Quotient Are Infection Risk Factors in Vagus Nerve Stimulation

2019 ◽  
Vol 130 ◽  
pp. e839-e845 ◽  
Author(s):  
Naoto Kuroda ◽  
Ayataka Fujimoto ◽  
Tohru Okanishi ◽  
Keishiro Sato ◽  
Shinji Itamura ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Vagus Nerve ◽  
Body Mass ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Intelligence Quotient ◽  
Infection Risk ◽  
Infection Risk Factors ◽  
Low Body Mass Index

scholarly journals A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9890
Author(s):  
Xue-ran Kang ◽  
Bin Chen ◽  
Yi-sheng Chen ◽  
Bin Yi ◽  
Xiaojun Yan ◽  
...  
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Surgical Outcome ◽  
Middle Turbinate ◽  
Multivariate Logistic Regression Analysis ◽  
Preoperative Assessment ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Nasal Septoplasty ◽  
Snot 22

Background To create a nomogram prediction model for the efficacy of endoscopic nasal septoplasty, and the likelihood of patient benefiting from the operation. Methods A retrospective analysis of 155 patients with nasal septum deviation (NSD) was performed to develop a predictive model for the efficacy of endoscopic nasal septoplasty. Quality of life (QoL) data was collected before and after surgery using Sinonasal Outcome Test-22 (SNOT-22) scores to evaluate the surgical outcome. An effective surgical outcome was defined as a SNOT-22 score change ≥ 9 points after surgery. Multivariate logistic regression analysis was then used to establish a predictive model for the NSD treatment. The predictive quality and clinical utility of the predictive model were assessed by C-index, calibration plots, and decision curve analysis. Results The identified risk factors for inclusion in the predictive model were included. The model had a good predictive power, with a AUC of 0.920 in the training group and a C index of 0.911 in the overall sample. Decision curve analysis revealed that the prediction model had a good clinical applicability. Conclusions Our prediction model is efficient in predicting the efficacy of endoscopic surgery for NSD through evaluation of factors including: history of nasal surgery, preoperative SNOT-22 score, sinusitis, middle turbinate plasty, BMI, smoking, follow-up time, seasonal allergies, and advanced age. Therefore, it can be cost-effective for individualized preoperative assessment.

Lifelong risk factors for osteoporosis and fractures in elderly women with low body mass index—A population-based study

Bone ◽  
2006 ◽  
Vol 39 (2) ◽  
pp. 385-391 ◽  
Author(s):  
R. Korpelainen ◽  
J. Korpelainen ◽  
J. Heikkinen ◽  
K. Väänänen ◽  
S. Keinänen-Kiukaanniemi
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Body Mass ◽  
Elderly Women ◽  
Population Based ◽  
Population Based Study ◽  
Low Body Mass Index

Prognostic impact and risk factors of low body mass index in patients undergoing liver transplantation

2017 ◽  
Vol 31 (9) ◽  
pp. e13048
Author(s):  
Jin-Chiao Lee ◽  
Wael M. A. Doush ◽  
Yu-Chao Wang ◽  
Chih-Hsien Cheng ◽  
Tsung-Han Wu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Liver Transplantation ◽  
Body Mass ◽  
Prognostic Impact ◽  
Low Body Mass Index

scholarly journals Underweight increases the risk of pulmonary tuberculosis in adult

2017 ◽  
Vol 36 (1) ◽  
pp. 4
Author(s):  
Galuh Chandra Irawan ◽  
Ani Margawati ◽  
Ali Rosidi
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Logistic Regression ◽  
Risk Factor ◽  
Pulmonary Tuberculosis ◽  
Body Mass ◽  
Low Income ◽  
Smoking Behavior ◽  
Significant Risk ◽  
Low Body Mass Index

<p>Background<br />Tuberculosis (TB) is a leading cause of morbidity and mortality, especially in middle- and low-income countries. The risk of developing TB may be related to nutritional status. Socioeconomic and behavioral factors are also shown to increase the susceptibility to TB infection. The objective of this study was to determine nutritional factors as risk factors of pulmonary TB in adult.</p><p>Methods<br />This was an observational study of case control design. The study subjects were community members consisting of 19 adult cases of pulmonary tuberculosis and 38 controls. Data on nutritional intakes were obtained by semiquantitative food frequency questionnaire (FFQ), while smoking behavior, history of DM, body mass index, education, and income were obtained by structured interviews. The data were analyzed by independent t-test and logistic regression for calculation of the odds ratio (OR).</p><p>Results<br />The bivariate test showed that the adequacy levels for energy (OR=6.8; 95% CI: 1.51-30.54), protein (OR=5.1; 95% CI: 1.52-17.14), vitamin A (OR=4.2; 5% CI: 1.31-13.54), vitamin C (OR=3.8;95% CI: 1.21-12.36), selenium (OR=4.2; 95% CI: 1.34-13.58), body mass index (OR=4.4; 95% CI: 1.32-14.35) and smoking behavior (OR=3.7; 95% CI: 1.15-11.9), were significant risk factors for pulmonary tuberculosis. Multiple logistic regression test showed that low body mass index (&lt;18.5 kg/m2) (OR=6.0; 95% CI: 1.32-27.18) was a the most influential risk factor of pulmonary tuberculosis.</p><p>Conclusion<br />Low body mass index is the most influential risk factor for pulmonary tuberculosis incidence in adult. Nutrition profile in adult is an important determinant of TB incidence.</p>

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