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Background/Aims  Polymyalgia rheumatica (PMR) is a chronic inflammatory condition that commonly affects the elderly and may be treated with long-term steroids. In these patients, fracture risk is higher than the general population and can lead to increased morbidity and mortality. This retrospective observational cohort study aimed to identify factors which help predict fracture risk in PMR patients. Methods  Data were collected from June 2004 to October 2010 on patients with PMR at a District General Hospital. This included dexa scan data: bone mineral density (BMD), Z score, T score, fat mass, lean mass, percentage body fat, BMI and average tissue thickness. Demographic data, steroid use, alcohol consumption, smoking, secondary osteoporosis and presence of fracture was also recorded for each patient. Fracture risk was predicted by a series of binomial logistic regression models, which were adjusted for age and sex. Odds ratios with 95% confidence intervals and area under ROC curve (AUC) were calculated. Results  714 patients with PMR were studied of whom 532 were female, the mean age was 70.5. Steroid use, secondary osteoporosis, lean mass, fat mass, BMI, average tissue thickness, average percentage fat and alcohol consumption were not significant predictors of fracture in regression models. BMD, T score and Z score predicted fracture risk. AUC of BMD was lower than that of T and Z score for each level. The AUC for L2 models were higher than other levels in BMD, T and Z score. Odds ratios, 95% confidence intervals and AUC of the significant predictors of fracture are shown in the table. P101 Table 1:Odds ratios, 95% confidence intervals and AUC valuesLevelBone Mineral DensityBMD AUCT ScoreT score AUCZ ScoreZ Score AUCLeft Hip0.098 (0.023,0.412)0.68240.728 (0.607,0.873)0.69380.677 (0.552,0.831)0.6938Right Hip0.062 (0.014,0.285)0.69170.713 (0.593,0.858)0.69180.662 (0.538,0.815)0.6934Left Femoral Neck0.104 (0.022,0.492)0.67270.738 (0.600,0.908)0.68330.703 (0.560,0.881)0.6848Right Femoral Neck0.087 (0.014,0.430)0.68370.734 (0.597,0.902)0.68360.694 (0.553,0.871)0.6836L10.192 (0.066,0.560)0.67890.820 (0.716,0.940)0.69160.798 (0.688,0.924)0.6907L20.138 (0.053,0.358)0.69770.787 (0.697,0.888)0.70950.763 (0.669,0.871)0.7108L30.192 (0.079,0.463)0.68810.823 (0.735,0.921)0.69600.805 (0.713,0.908)0.6963L40.243 (0.108,0.544)0.68370.852 (0.768,0.945)0.69110.837 (0.749,0.934)0.6914 Conclusion  These data suggest that BMD, T and Z score help predict fracture in PMR patients. Lifestyle factors and other body composition data from dexa scans do not predict fracture risk. Strongest predictor models were at the level of L2. FRAX could therefore underestimate the fracture risk as it uses femoral measurings. Limitations of the study are that it was retrospective and only studied patients who underwent DEXA scans. Steroid data were binary, not reflecting dose and duration of use. The study may have been underpowered to detect the impact of some factors predicting fracture risk. Disclosure  R. Ark: None. M. Bukhari: None.

Background:Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease and increases the risk of developing osteoporosis. Incidence of fracture is higher in this group of patients compared to the general population and can lead to increased morbidity (1). Bone strength of the proximal femur is not only linked to bone mineral density; it also depends on the geometric properties of the bone mass (2). Hip structural analysis (HSA) is a technique used to assess hip bone structure that takes geometric measurements of the femur from dual-energy X-ray absorptiometry (DEXA) images (3).Objectives:To determine whether HSA measurements help predict fracture in patients with RA.Methods:Data were collected from June 2004 to August 2017 from RA patients who underwent a DEXA scan at a District General Hospital. This included hip axis length (HAL), cross-sectional area (CSA), cross-sectional moment of inertia (CSMI), distance from centre of femoral head to centre of femoral neck (D1) and to inter-trochanteric line (D2), mean femoral neck diameter (D3), shaft angle (A) neck/shaft angle (Θ) and proximal femur strength index (SI) and distance from centre of mass of femoral neck to superior neck margin (Y). Fracture was predicted by a series of binomial logistic regression models, adjusted for sex, age and bone mineral density (BMD). Odds ratios with 95% confidence intervals and area under the receiver operating characteristic curve (AUC) were calculated.Results:2077 patients with RA were identified, 1632 were female and the mean age was 66.7. HAL, D1, D2, D3, A, Θ and Y were not significant predictors of fracture in regression models; odds ratios are included in table 1. CSA, CSMI and SI predicted fracture risk. The AUC for CSA, CSMI and SI regression models were 0.632, 0.609 and 0.625 respectively.Table 1.Odds ratios of fracture for different HSA parameters in RA patientsHSA ParameterOdds Ratio (95% Confidence Interval)HAL1.01410 (0.99958 - 1.02883)CSMI0.99994 (0.99990 - 0.99998)CSA0.98523 (0.98065 - 0.98982)D11.01683 (0.98925 - 1.04518)D21.01286 (0.99886 - 1.02705)D31.00664 (0.96958 - 1.04511)Y1.04580 (0.98633 - 1.10886)A1.00898 (0.98878 - 1.02959)Θ1.00276 (0.98672 - 1.01906)SI0.56769 (0.43400 - 0.74258)Figure 1.Receiver operating characteristic curves for CSA (red), CSMI (green) and SI (blue). AUC for CSA was 0.632, CSMI-0.609 and SI-0.625.Conclusion:These data suggest that CSA, CSMI and SI help predict the fracture risk in patients with RA. HAL, D1, D2, D3, A, Θ and Y do not predict risk of fracture. The CSA regression model was the strongest predictor of fracture. HSA measurements can therefore help predict risk of fracture in conjunction with other factors. Limitations of the study are that it was retrospective and only studied patients who had a DEXA scan.References:[1]Xue A, Wu S, Jiang L, Feng A, Guo H, Zhao P. Bone fracture risk in patients with rheumatoid arthritis: A meta-analysis. Medicine. 2017; 96 (36): e6983. Available from: doi: 10.1097/MD.0000000000006983.[2]Faulkner KG, Wacker WK, Barden HS, Simonelli C, Burke PK, Ragi S, Del Rio L. Femur strength index predicts hip fracture independent of bone density and hip axis length. Osteoporos Int. 2006;17(4):593-9. doi: 10.1007/s00198-005-0019-4.[3]Kaptoge S, Beck TJ, Reeve J, Stone KL, Hillier TA, Cauley JA, et al. Prediction of Incident Hip Fracture Risk by Femur Geometry Variables Measured by Hip Structural Analysis in the Study of Osteoporotic Fractures. Journal of Bone and Mineral Research. 2008; 23 (12): 1892-1904. Available from: doi: https://doi.org/10.1359/jbmr.080802.Disclosure of Interests:None declared

Background:Polymyalgia Rheumatica (PMR) is an inflammatory condition which commonly affects the elderly. Risk of fracture is higher in this group of patients compared to the general population and can lead to increased morbidity and mortality (1). Hip structural analysis (HSA) is a technique that uses dual-energy X-ray absorptiometry (DEXA) images to assess hip bone structure (2).Objectives:To identify whether HSA measurements help predict fracture in patients with PMR.Methods:Data were collected from June 2004 to October 2010 from PMR patients who had a DEXA scan at a District General Hospital. This included hip axis length (HAL), cross-sectional area (CSA), cross-sectional moment of inertia (CSMI), distance from centre of femoral head to centre of femoral neck (D1) and to inter-trochanteric line (D2), mean femoral neck diameter (D3), shaft angle (A) neck/shaft angle (Θ) and proximal femur strength index (SI) and distance from centre of mass of femoral neck to superior neck margin (Y). Fracture was predicted by a series of binomial logistic regression models, adjusted for age and sex. Odds ratios with 95% confidence intervals and area under the receiver operating characteristic curve (AUC) were calculated.Results:714 patients with PMR were identified, 182 were male and the mean age was 70.5. HAL, CSMI, D1, D2, D3, A, Θ, SI and Y were not significant predictors of fracture in regression models; odds ratios are included in Table 1. CSA predicted fracture risk; odds ratio was 0.988 with a 95% confidence interval of 0.980-0.997. The AUC for the CSA regression model was 0.6739.Table 1.Odds ratios of fracture for different HSA parametersHSA ParameterOdds Ratio (95% confidence interval)HAL1.008 (0.982 - 1.035)CSMI1.000 (0.999 - 1.000)CSA0.988 (0.980 - 0.997)D11.029 (0.972 - 1.089)D21.010 (0.981 - 1.040)D31.033 (0.962 - 1.109)Y1.087 (0.966 - 1.223)A0.983 (0.940 - 1.029)Θ1.007 (0.975 - 1.039)SI0.683 (0.406 - 1.150)Conclusion:These data suggest that CSA helps predict the risk of fracture in patients with PMR. HAL, CSMI, D1, D2, D3, A, Θ, SI and Y do not predict fracture risk. Limitations of the study are that it was retrospective and only studied patients who underwent DEXA scans. The study may have been underpowered to detect the impact of some HSA measurements on fracture risk.References:[1]Chatzigeorgiou C, Mackie SL. Comorbidity in polymyalgia rheumatica. Reumatismo. 2018; 70 1:35-43. Available from: http://eprints.whiterose.ac.uk/132109/\\.[2]Kaptoge S, Beck TJ, Reeve J, Stone KL, Hillier TA, Cauley JA, et al. Prediction of Incident Hip Fracture Risk by Femur Geometry Variables Measured by Hip Structural Analysis in the Study of Osteoporotic Fractures. Journal of Bone and Mineral Research. 2008; 23 (12): 1892-1904. Available from: doi: https://doi.org/10.1359/jbmr.080802Disclosure of Interests:None declared.

AimsAn accurate and complete history is a key component of a medical consultation. Evidence suggests that up to 80% of diagnosis may be made entirely off the patient history. The aim of this closed loop audit was to examine the effects of a clerking pro forma on the quality of doctors clerking histories of new patients admitted to an acute psychiatric inpatient unit, against standards suggested in the New Oxford Textbook of Psychiatry.MethodData for this audit were gathered by finding the initial clerking history for inpatients at The Orchard on ECR and RIO. The clerking histories of the 18 inpatients present on 12.10.20 were initially audited. These standards recommend in the in the New Oxford Textbook of Psychiatry include; Patient Identification (ID), Presenting Complaint (PC), History of Presenting Complaint (HPC), Psychiatric history, Medical history, Family history, Forensic history, Social history, Personal history, Premorbid personality, Mental state exam (MSE). After analysis of the results of the first loop, a clerking pro forma was created and distributed to junior doctors to implement. The clerking histories for the subsequent 18 patients to be admitted were then audited and compared.ResultThe results of the first audit cycle were poor. Only patient identification and presenting complaint were present in 100% of clerked histories. Concerningly, only 72% of the histories included the patients’ medical histories, forensic histories were included 44% of the time, and social history just 39% of the time.The implementation of a clerking history proforma showed improvements in all areas of clerking. Patient ID, PC, HPC, psychiatric history and MSE were now present in 100% of clerked histories. Forensic history showed a statistically significant improvement from 44% to 73% [X2(1) = 5.9; p = 0.015]. Social history showed a statistically significant improvement from 39% to 78% [X2(1) = 5.6; p = 0.018]. Premorbid personality showed a statistically significant improvement from 44% to 89% [X2(1) = 8.0; p = 0.005]. Personal history showed a non-statistically significant improvement from 39% to 56%, as did medical history from 72% to 94%, and family history from 39% to 61%.ConclusionIn conclusion, the implementation of a clerking history pro-forma has significantly improved the quality and completeness of clerking histories gathered by doctors at The Orchard. This is hopefully increase diagnostic accuracy and improve the quality of care of patients in the hospital.