Clinical Risk Factors for Hip Fracture in Elderly Women: A Case–Control Study

2002 ◽  
Vol 16 (6) ◽  
pp. 379-385 ◽  
Author(s):  
P. Haentjens ◽  
Ph. Autier ◽  
S. Boonen
2019 ◽  
Vol 25 (10) ◽  
pp. 1-16
Author(s):  
Georgia Zacharopoulou ◽  
Vasiliki Zacharopoulou ◽  
Eleni Voudouri ◽  
Lili Leondiou ◽  
Zacharias Dermatis

Background/Aims The aim of the study was to investigate the socioeconomic and clinical risk factors for hip fracture among a community-dwelling elderly population in Greece. It also aimed to identify characteristics associated with reducing mobility. Methods A case-control study was conducted on 202 patients who had a hip fracture and on 202 other members of the elderly population who did not have a hip fracture as the control group. Results In the multivariate analysis, the variables related to an increased risk of hip fracture were: gender (odds ration [OR]=10.88; 95%confidence Interval [CI]=2.28–51.98), income (OR=32.50; 95%CI=2.96–356.43), income adequacy (OR=129,34; 95%CI=7,09–2360,88), inability to pay expenses/medication (OR=0.02; 95%CI=0.003–0.09), depression (OR=0.03; 95%CI=0.002–0.35), multimorbidity (OR=0.01; 95%CI=0.001–0.97), number of medication (OR=0.02; 95%CI=0.001–0.28) and history of falls (OR=0.08; 95%CI=0.01–0.40). Factors related to deterioration of mobility were: age (OR=28.43; 95%CI:5.45–148.32), dementia (OR=15.60; 95%CI:1.80–135.27), walking ability (OR=0.20; 95%CI:0.07–0.56), balance (OR=9.10; 95%CI:1.89–43.75), use of walking aid (OR=7.42; 95%CI:2.70–20.39), and length of hospitalisation (OR=3.01; 95%CI:1.27–7.14). Conclusions Socioeconomic and clinical factors that lead to an increased risk of hip fracture were identified, as well as factors affecting post-operative functional ability that could guide prevention programmes.


Neurology ◽  
1991 ◽  
Vol 41 (9) ◽  
pp. 1393-1393 ◽  
Author(s):  
E. Kokmen ◽  
C. M. Beard ◽  
V. Chandra ◽  
K. P. Offord ◽  
B. S. Schoenberg ◽  
...  

Addiction ◽  
2015 ◽  
Vol 111 (3) ◽  
pp. 499-510 ◽  
Author(s):  
Ingrid A. Binswanger ◽  
Marc F. Stern ◽  
Traci E. Yamashita ◽  
Shane R. Mueller ◽  
Travis P. Baggett ◽  
...  

BMJ Open ◽  
2012 ◽  
Vol 2 (4) ◽  
pp. e001036 ◽  
Author(s):  
Kumari Vinita ◽  
Sarangapani Sripriya ◽  
Krishnamurthy Prathiba ◽  
Kulothungan Vaitheeswaran ◽  
Ravichandran Sathyabaarathi ◽  
...  

2013 ◽  
Vol 41 (5) ◽  
Author(s):  
Maximilian Klar ◽  
Martina Laub ◽  
Juergen Schulte-Moenting ◽  
Heinrich Proempeler ◽  
Mirjam Kunze

Author(s):  
Marisa A. Ryan ◽  
Andrew F. Olshan ◽  
Mark A. Canfield ◽  
Adrienne T. Hoyt ◽  
Angela E. Scheuerle ◽  
...  

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Chavalit Chotruangnapa ◽  
Titima Tansakun ◽  
Weranuj Roubsanthisuk

Abstract Background Night-time BP, especially non-dipper, is a stronger predictor of adverse cardiovascular outcomes. Ambulatory blood pressure monitoring (ABPM) is a gold standard for the detection of non-dippers but it often is unavailable and expensive. This study aims to determine clinical risk factors that predict non-dipper. Methods An exploratory traditional case-control study, exclusive sampling of control was conducted from January 2013 to September 2018 to explore clinical risk factors associated with non-dippers in hypertensive patients. Subgroup analysis was performed in each treated and untreated hypertensive patient. The parsimonious predictive score for non-dippers was constructed. Results The study included 208 hypertensive patients receiving 24 h ABPM. There were 104 dippers and 104 non-dippers. Significant clinical risk factors associated with non-dippers were the age of > 65 years, average office diastolic blood pressure (DBP), and fasting plasma glucose of > 5.6 mmol/L. Results of subgroup analysis showed that dyslipidemia, history of coronary artery disease, use of angiotensin-converting enzyme inhibitors (ACEIs) and direct vasodilators, average office DBP, and serum uric acid were associated with non-dippers in treated hypertensive patients, however, there were no risk factors associated with non-dippers in the untreated group. The predictive score for non-dippers in treated group included average office DBP, dyslipidemia, serum uric acid, male, calcium channel blockers and ACEIs use. The area under Receiver Operating Characteristic (AuROC) was 0.723. A cut-off point which was > 0.0701 and prevalence of non-dippers of 46%, this score had a sensitivity of 77.4%, specificity of 65.6%, positive predictive value (PPV) of 66.1%, and negative predictive value (NPV) of 79.6%. For untreated group, age, hemoglobin and body mass index were included in the predictive model. AuROC was 0.74. There was a sensitivity of 51.9%, specificity of 91.2%, PPV of 82.4%, and NPV of 70.5% at the cut-off point of > 0.357, and prevalence of 44%. Conclusion There were several significant clinical risk factors associated with non-dippers in treated hypertensive patients. The predictive score might be useful for the detection of non-dippers; however, it cannot replace ABPM.


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