scholarly journals The Effect of Individual Musculoskeletal Conditions on Depression: Updated Insights From an Irish Longitudinal Study on Aging

2021 ◽  
Vol 8 ◽  
Author(s):  
Wenyi Jin ◽  
Zilin Liu ◽  
Yubiao Zhang ◽  
Zhifei Che ◽  
Mingyong Gao
Keyword(s):  
Machine Learning ◽  
Depressive Symptoms ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Machine Learning Algorithms ◽  
Mineral Density ◽  
Classification Approach ◽  
Musculoskeletal Conditions ◽  
Increased Risk ◽  
Mediation Models

Few longitudinal studies have systematically investigated whether or how individual musculoskeletal conditions (IMCs) convey risks for negative psychological health outcomes, and approaches to assess such risk in the older population are lacking. In this Irish nationally representative longitudinal prospective study of 6,715 individuals aged 50 and above, machine learning algorithms and various models, including mediation models, were employed to elaborate the underlying mechanisms of IMCs leading to depression and to develop an IMC-induced negative psychological risk (IMCPR) classification approach. Resultantly, arthritis [odds ratio (95% confidence interval): 2.233 (1.700–2.927)], osteoporosis [1.681 (1.133–2.421)], and musculoskeletal chronic pain [MCP, 2.404 (1.838–3.151)] were found to increase the risk of depression after 2 years, while fracture and joint replacement did not. Interestingly, mediation models further demonstrated that arthritis per se did not increase the risk of depression; such risk was augmented only when arthritis-induced restrictions of activities (ARA) existed [proportion of mediation: 316.3% (ARA of usual), 213.3% (ARA of social and leisure), and 251.3% (ARA of sleep)]. The random forest algorithm attested that osteoarthritis, not rheumatoid arthritis, contributed the most to depressive symptoms. Moreover, bone mineral density was negatively associated with depressive symptoms. Systemic pain contributed the most to the increased risk of depression, followed by back, knee, hip, and foot pain (mean Gini-Index: 3.778, 2.442, 1.980, 1.438, and 0.879, respectively). Based on the aforementioned findings, the IMCPR classification approach was developed using an interpretable machine learning model, which stratifies participants into three grades. Among the IMCPR grades, patients with a grade of “severe” had higher odds of depression than those with a “mild” [odds ratio (95% confidence interval): 4.055 (2.907–5.498)] or “moderate” [3.584 (2.101–5.883)] grade. Females with a “severe” grade had higher odds of depression by 334.0% relative to those with a “mild” grade, while males had a relative risk of 258.4%. In conclusion, the present data provide systematic insights into the IMC-induced depression risk and updated the related clinical knowledge. Furthermore, the IMCPR classification approach could be used as an effective tool to evaluate this risk.

scholarly journals Thyroid Function Within the Normal Range and the Risk of Depression: A Population-Based Cohort Study

2014 ◽  
Vol 99 (4) ◽  
pp. 1213-1219 ◽  
Author(s):  
Marco Medici ◽  
Nese Direk ◽  
W. Edward Visser ◽  
Tim I. M. Korevaar ◽  
Albert Hofman ◽  
...  
Keyword(s):  
Cohort Study ◽  
Depressive Symptoms ◽  
Confidence Interval ◽  
Thyroid Function ◽  
Odds Ratio ◽  
Elderly Persons ◽  
Normal Range ◽  
Baseline Serum ◽  
Increased Risk ◽  
Depressive Syndromes

Context: Overt hypo- and hyperthyroidism are associated with an increased risk of depression. Little is known about the effects of variation in thyroid function within the normal range on the risk of depression. Objective: The objective of the study was to examine the association between normal-range thyroid function and the risk of depression. Design, Setting, and Participants: This was a cohort study in 1503 Dutch men and women, aged 70.6 (7.3) (mean [SD]) years. At baseline, serum TSH, thyroperoxidase antibody levels, and depressive symptoms [Center for Epidemiologic Studies Depression Scale (CES-D)] were assessed. A CES-D of 16 or greater is indicative of a depressive disorder. During follow-up (mean 8.0 y), participants were continuously monitored for the occurrence of incident depressive syndromes (n = 156). Results: Cross-sectionally, persons in the lowest TSH tertile (0.3–1.0 mU/L) had more depressive symptoms [CES-D score (mean): 7.95 vs 6.63, P = .014] as well as an increased risk of a CES-D of 16 or greater [10.7% vs 5.0%, odds ratio (95% confidence interval) 2.22 (1.18–4.17)], compared with persons in the highest normal range TSH tertile (1.6–4.0 mU/L). In the prospective analyses, persons in the lowest TSH tertile who were depression free at baseline had a higher risk of incident depressive syndromes [12.3% vs 7.6%, odds ratio (95% confidence interval) 1.85 (1.10–3.11)]. Thyroid autoimmunity (thyroperoxidase antibody positivity) was not associated with CES-D scores or incident depressive syndromes. Conclusions: Elderly persons with low-normal TSH levels have more concurrent depressive symptoms as well as a substantially increased risk of developing a depressive syndrome in the subsequent years. This study identifies low-normal TSH as an important risk factor for depression in the elderly.

scholarly journals A Hybrid Classification and Prediction Methodology for the Diagonosis of Osteoporosis

2019 ◽  
Vol 8 (10) ◽  
pp. 4648-4653
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Bone Disease ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mineral Density ◽  
Increased Risk ◽  
Textual Data ◽  
Prediction Approach

With the advent of technology medical science is growing very fast . Disease diagonosis using machine learning technique is quite cumbersome. But efforts are made by the innovative minds to develope an optimal and efficient prediction model for the prediction of the disease viz, bone disease . Bone disease prediction is also a broad area of research where machine learning techniques can be used. Better prediction and analysis can be helpful to cure such disease . Osteoporosis is an osteo- metabolic disease characterized by low bone mineral density (BMD) and deterioration of the micro- architecture of the bone tissues, causing an increase in bone fragility and consequently leading to an increased risk of fractures.. Machine learning algorithms play important role for predicting and analyzing such disease using available algorithms and by modifying them . There are many algorithms such as SVM (Support vector machine), Genetic Algorithm, Naive bayes classifier and other tree based classifiers which are proposed in traditional scenario for prediction of expected data from an image. The goal of this paper is to discuss about a proposed hybrid analysis and prediction approach for diagonosing osteoporosis. This paper also discuss about various prediction models viz, svm prediction model as the efficient one while processing the textual data for symptoms analysis. Here symptoms are called predictors . A comparison using the Accuracy and training time is performed. The approach shows the efficiency of proposed model over textual data as well as graphical image data analysis.

Do Children Who Experience Laryngospasm Have an Increased Risk of Upper Respiratory Tract Infection?

1996 ◽  
Vol 85 (3) ◽  
pp. 475-480. ◽  
Author(s):  
Mark S. Schreiner ◽  
Irene O'Hara ◽  
Dorothea A. Markakis ◽  
George D. Politis
Keyword(s):  
Confidence Interval ◽  
Respiratory Infection ◽  
Odds Ratio ◽  
Signs And Symptoms ◽  
Control Group ◽  
Upper Respiratory Tract ◽  
Upper Respiratory Infection ◽  
Airway Surgery ◽  
Increased Risk ◽  
Upper Respiratory

Background Laryngospasm is the most frequently reported respiratory complication associated with upper respiratory infection and general anesthesia in retrospective studies, but prospective studies have failed to demonstrate any increase in risk. Methods A case-control study was performed to examine whether children with laryngospasm were more likely to have an upper respiratory infection on the day of surgery. The parents of all patients (N = 15,183) who were admitted through the day surgery unit were asked if their child had an active or recent (within 2 weeks of surgery) upper respiratory infection and were questioned about specific signs and symptoms to determine if the child met Tait and Knight's definition of an upper respiratory infection. Control subjects were randomly selected from patients whose surgery had occurred within 1 day of the laryngospasm event. Results Patients who developed laryngospasm (N = 123) were 2.05 times (95% confidence interval 1.21-3.45) more likely to have an active upper respiratory infection as defined by their parents than the 492 patients in the control group (P < or = 0.01). The development of laryngospasm was not related to Tait and Knight's definition for an upper respiratory infection or to recent upper respiratory infection. Children with laryngospasm were more likely to be younger (odds ratio = 0.92, 95% confidence interval 0.87-0.99), to be scheduled for airway surgery (odds ratio = 2.08, 95% confidence interval 1.21-3.59), and to have their anesthesia supervised by a less experienced anesthesiologist (odds ratio = 1.69, 95% confidence interval 1.04-2.7) than children in the control group. Conclusion Laryngospasm was more likely to occur in children with an active upper respiratory infection, children who were younger, children who were undergoing airway surgery, and children whose anesthesia were supervised by less experienced anesthesiologists. Understanding the risk factors and the magnitude of the likely risk should help clinicians make the decision as to whether to anesthetize children with upper respiratory infection.

scholarly journals Validity of the LACE index for identifying frequent early readmissions after hospital discharge in children

2021 ◽  
Author(s):  
Thang S Han ◽  
David Fluck ◽  
Christopher H Fry
Keyword(s):  
At Risk ◽  
Confidence Interval ◽  
Hospital Discharge ◽  
Odds Ratio ◽  
Hospital Readmissions ◽  
Index Score ◽  
Children At Risk ◽  
Increased Risk ◽  
Scoring Tool ◽  
Event Rates

AbstractThe LACE index scoring tool has been designed to predict hospital readmissions in adults. We aimed to evaluate the ability of the LACE index to identify children at risk of frequent readmissions. We analysed data from alive-discharge episodes (1 April 2017 to 31 March 2019) for 6546 males and 5875 females from birth to 18 years. The LACE index predicted frequent all-cause readmissions within 28 days of hospital discharge with high accuracy: the area under the curve = 86.9% (95% confidence interval = 84.3–89.5%, p < 0.001). Two-graph receiver operating characteristic curve analysis revealed the LACE index cutoff to be 4.3, where sensitivity equals specificity, to predict frequent readmissions. Compared with those with a LACE index score = 0–4 (event rates, 0.3%), those with a score > 4 (event rates, 3.7%) were at increased risk of frequent readmissions: age- and sex-adjusted odds ratio = 12.4 (95% confidence interval = 8.0–19.2, p < 0.001) and death within 30 days of discharge: OR = 5.0 (95% CI = 1.5–16.7). The ORs for frequent readmissions were between 6 and 14 for children of different age categories (neonate, infant, young child and adolescent), except for patients in the child category (6–12 years) where odds ratio was 2.8.Conclusion: The LACE index can be used in healthcare services to identify children at risk of frequent readmissions. Focus should be directed at individuals with a LACE index score above 4 to help reduce risk of readmissions. What is Known:• The LACE index scoring tool has been widely used to predict hospital readmissions in adults. What is New:• Compared with children with a LACE index score of 0–4 (event rates, 0.3%), those with a score > 4 are at increased risk of frequent readmissions by 14-fold.• The cutoff of a LACE index of 4 may be a useful level to identify children at increased risk of frequent readmissions.

Second-Generation Consequences of Small-for-Dates Birth

PEDIATRICS ◽  
1989 ◽  
Vol 84 (2) ◽  
pp. 343-347
Author(s):  
Mark A. Klebanoff ◽  
Olav Meirik ◽  
Heinz W. Berendes
Keyword(s):  
Confidence Interval ◽  
Birth Outcomes ◽  
Odds Ratio ◽  
Intrauterine Growth Retardation ◽  
Long Term Effects ◽  
Intrauterine Growth ◽  
Increased Risk ◽  
Reported Study ◽  
Increase In Risk

This is the first reported study of birth outcomes of a group of women whose own birth weights and gestational ages had been previously recorded. Births occurring from 1972 to 1983 among 1154 Swedish women, born from 1955 to 1965, were studied. Women who were themselves small for gestational age (SGA) at birth were at increased risk of giving birth to a SGA infant (odds ratio = 2.21, 95% confidence interval = 1.41, 3.48). Women who had been SGA had an even greater increase in risk of giving birth to a preterm infant (odds ratio = 2.96, 95% confidence interval = 1.47, 5.94). Women who were preterm at birth were not at increased risk of giving birth to either preterm (odds ratio = 0.65, 95% confidence interval = 0.15, 2.74) or SGA (odds ratio 1.21, 95% confidence interval = 0.62, 2.38) infants. It is concluded that the long-term effects of intrauterine growth retardation may extend to the next generation; women who had been SGA should be considered at increased risk to give birth to both growth-retarded and preterm infants.

scholarly journals Identifying those at risk of reattendance at discharge from emergency departments using explainable machine learning

2020 ◽  
Author(s):  
F. P. Chmiel ◽  
M. Azor ◽  
F. Borca ◽  
M. J. Boniface ◽  
D. K. Burns ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Emergency Departments ◽  
Risk Groups ◽  
Machine Learning Algorithms ◽  
Care Indicator ◽  
Number Of Patients ◽  
Increased Risk ◽  
Single Centre Study ◽  
Patients At Risk

ABSTRACTShort-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance can help reduce the number of patients with missed or undertreated illness or injury, and could support appropriate discharges with focused interventions. In this manuscript we present a retrospective, single-centre study where we create and evaluate a machine-learnt classifier trained to identify patients at risk of reattendance within 72 hours of discharge from an emergency department. On a patient hold-out test set, our highest performing classifier obtained an AUROC of 0.748 and an average precision of 0.250; demonstrating that machine-learning algorithms can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. In parallel to our predictive model we train an explanation model, capable of explaining predictions at an attendance level, which can be used to help inform the design of interventional strategies.

Siblings and Birth Order—Are They Important for the Occurrence of ADHD?

2018 ◽  
Vol 25 (1) ◽  
pp. 81-90 ◽  
Author(s):  
Charlotte Reimelt ◽  
Nicole Wolff ◽  
Heike Hölling ◽  
Sabine Mogwitz ◽  
Stefan Ehrlich ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Birth Order ◽  
Odds Ratio ◽  
Epidemiological Data ◽  
Developmental Period ◽  
Order Number ◽  
Diagnostic Bias ◽  
German Health ◽  
Kiggs Study ◽  
Increased Risk

Objective: The associations of birth order, number of siblings, and ADHD was examined. Method: The analysis based on representative, epidemiological data from the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) study ( N = 13,488). Results: An increased risk for ADHD in firstborn versus youngest born children (odds ratio [OR] = 1.31, 95% confidence interval [CI] [1.09, 1.58]) and also versus children with no sibling (OR = 1.31, 95% CI [1.03, 1.68]) was revealed, while number of siblings was not associated with ADHD. Results remained stable after controlling for confounders. Conclusion: Firstborn children may receive simultaneously less parental resources and more responsibilities if younger siblings are born. This happens during the vulnerable developmental period of ADHD. In addition, due to higher levels of insecurity, parents are assumed to focus more on potential physical or psychological abnormities in their firstborn children. This may result in a diagnostic bias in firstborn children.

scholarly journals Association of cleft lip and palate on mother-to-infant bonding: a cross-sectional study in the Japan Environment and Children’s Study (JECS)

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Shinobu Tsuchiya ◽  
◽  
Masahiro Tsuchiya ◽  
Haruki Momma ◽  
Takeyoshi Koseki ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Maternal Depression ◽  
Odds Ratio ◽  
Maternal Age ◽  
Cleft Lip ◽  
Cross Sectional Study ◽  
Advanced Age ◽  
Sectional Study ◽  
Cross Sectional ◽  
Increased Risk

Abstract Background Cleft lip and/or palate is among the most prevalent congenital birth defects, and negatively affects maternal psychological status and may consequently result in higher prevalence of child maltreatment. However, the association of childbirths of infants with cleft lip and/or palate with maternal emotional involvement still remains unclear. We examined the association between childbirths of infants with cleft lip and/or palate and mother-to-infant bonding, using data from the Japan Environment and Children’s Study, a nationwide birth cohort study. Methods A cross-sectional study using the jecs-an-20,180,131 dataset was performed. A total 104,065 fetuses in 15 regional centres in Japan were enrolled after obtaining informed written consent. The Mother-to-Infant Bonding Scale, a self-report scale consisting of 10 items, was used to evaluate maternal bonding at one year after childbirth. Finally, the participants consisted of 79,140 mother-infant pairs, of which 211 mothers of infants with cleft lip and/or palate were included in our analyses. Multivariable logistic regression analysis using multiple imputation for missing data was performed to calculate the odds ratio and 95% confidence interval in the estimation of the association between bonding disorders and childbirths with cleft lip and/or palate. Results No increased risk of bonding disorders was observed among all the mothers of infants with cleft lip and/or palate (odds ratio [95% confidence interval]; 0.97 [0.63–1.48], p = 0.880), however, advanced maternal age or multiple parity may adversely affect the associations between bonding disorders and cleft lip and/or palate, respectively. After stratification with a combination of maternal age and parity, a significant association of cleft lip and/or palate with bonding disorders was found only among advanced-age multiparae (odds ratio [95% confidence interval] = 2.51 [1.17–5.37], p = 0.018), but it was weakened after additional adjustment for maternal depression. Conclusions Childbirths of infants with cleft lip and/or palate may increase the risk of bonding disorders among advanced-age multiparae, possibly through maternal depression. This finding provides valuable information for the provision of multidisciplinary cleft care.

scholarly journals The association of gout with an increased risk of hypertension and diabetes mellitus among stroke survivors in New Zealand: A cross-sectional study using routinely collected electronic health data

2019 ◽  
Vol 8 ◽  
pp. 204800401986323 ◽  
Author(s):  
Dina Eufemia D San Gabriel ◽  
Julia Slark
Keyword(s):  
Diabetes Mellitus ◽  
New Zealand ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Adjusted Odds Ratio ◽  
Cross Sectional Study ◽  
Stroke Survivors ◽  
Sectional Study ◽  
Cross Sectional ◽  
Increased Risk

Background There is a paucity of data relating to the association of gout with the occurrence of hypertension and diabetes mellitus in patients with stroke. This study aimed to determine the association of gout with the risk of hypertension and diabetes mellitus in a cohort of stroke patients from Auckland, Aotearoa New Zealand. Methods A cross-sectional study was conducted among stroke survivors in South and East Auckland, New Zealand from the years 2010 to 2014. Electronic health record data were collected and analysed using Statistical Package for Social Science version 23. Multivariate logistic regression modelling adjusted for age, gender, and ethnicity was conducted to determine the association of gout with the risk of hypertension and diabetes mellitus in patients discharged with a diagnosis of stroke. Results The age-, gender-, and ethnicity-adjusted odds ratio for having hypertension and diabetes mellitus among stroke survivors with gout history were 3.25 (95% confidence interval 1.32–8.03) and 1.94 (95% confidence interval 1.12–3.36), respectively. Māori stroke survivors with gout history had the highest risk of having diabetes mellitus with age- and gender-adjusted odds ratio of 5.10 (95% confidence interval 1.90–18.93). Conclusion The findings from this study suggest gout may be independently associated with an increased risk of hypertension and diabetes mellitus in patients with stroke. Māori who are the indigenous population of New Zealand show a greater risk of diabetes mellitus associated with a gout diagnosis compared to other populations. This finding highlights the importance of the need for further research with Māori stroke survivors and other indigenous populations.

scholarly journals Incidence and risk factors of transfusion reactions in postpartum blood transfusions

2019 ◽  
Vol 3 (15) ◽  
pp. 2298-2306 ◽  
Author(s):  
Lars Thurn ◽  
Agneta Wikman ◽  
Magnus Westgren ◽  
Pelle G. Lindqvist
Keyword(s):  
Blood Transfusion ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Adverse Reactions ◽  
Study Cohort ◽  
Blood Transfusions ◽  
Birth Registry ◽  
High Resource ◽  
Increased Risk ◽  
Transfusion Reactions

Abstract Postpartum hemorrhages with blood transfusions are increasing in many high-resource countries. Currently, up to 3% of all women receive blood transfusion postpartum. Most blood transfusions are safe and, in many cases, are lifesaving, but there are significant concerns about adverse reactions. Pregnancy is associated with higher levels of leukocyte antibodies and has a modulating effect on the immune system. Our objective was to investigate whether blood transfusions postpartum are accompanied by an increased risk for transfusion reactions (TRs) compared with transfusions given to nonpregnant women. We included all women who gave birth in Stockholm County, Sweden between 1990 and 2011. Data from the Swedish National Birth Registry were linked to the Stockholm Transfusion Database and included information on blood components administered and whether a TR occurred in women who received blood transfusions postpartum. Background controls were nonpregnant women who received blood transfusions during the study period. The study cohort consisted of 517 854 women. Of these, 12 183 (2.4%) received a blood transfusion. We identified 96 events involving a TR postpartum, giving a prevalence of 79 per 10 000 compared with 40 per 10 000 among nonpregnant women (odds ratio, 2.0; 95% confidence interval, 1.6-2.5). Preeclampsia was the single most important risk factor for TRs (odds ratio, 2.1; 95% confidence interval, 1.7-2.6). We conclude that special care should be taken when women with preeclampsia are considered for blood transfusion postpartum, because our findings indicate that pregnancy is associated with an increased risk for TRs.

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