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Background: Healthcare quality for systemic lupus erythematosus (SLE) is a modifiable target for improving patient outcomes. We aimed to assess the quality of care processes in different clinic settings, comparing a subspecialty lupus clinic with hospital-based and private general rheumatology clinics. Method(s): Patients with SLE (n = 258) were recruited in 2016 from a subspecialty lupus clinic (n = 147), two hospital general rheumatology clinics (n = 56) and two private rheumatology clinics (n = 55). Data were collected from medical records and patient questionnaires. Quality of care was assessed using 31 validated SLE quality indicators (QI) encompassing diagnostic work-up, disease and comorbidity assessments, drug monitoring, preventative care and reproductive health. Per-QI performance was measured as a percentage of patients that met the QI relative to the number of patients eligible. Per-patient QI performance was calculated as a percentage of QIs met relative to the number of eligible QIs for each patient. Per-QI and per-patient QI performance were compared between the three clinic settings, and multiple regression performed to adjust for sociodemographic, disease and healthcare factors. Result(s): Per-QI performance was generally high across all clinic settings for diagnostic work-up, comorbidity assessment, lupus nephritis, drug monitoring, prednisolone taper, osteoporosis and pregnancy care. Median [IQR] per-patient performance on eligible QIs was higher in the subspeciality lupus clinic (66.7% [57.1-74.1]) than the hospital general rheumatology (52.7% [47.5-58.1]) and private rheumatology (50.0% [42.9-60.9]) clinics (p <0.001) and the difference remained significant after multivariable adjustment. The subspecialty lupus clinic recorded higher per-QI performance for documentation of disease activity, disease damage, cardiovascular risk factor and drug toxicity assessments, pre-immunosuppression hepatitis and tuberculosis screening, new medication counsell

INTRODUCTION: The COVID-19 pandemic is driving unprecedented changes in healthcare services worldwide. This study aimed to quantify the impact of the first wave of the COVID-19 pandemic on diagnostic imaging services in Australia using an interrupted time series model. METHODS: Monthly data were extracted from the Australian Medicare Benefits Schedule for all diagnostic imaging services performed between January 2016 and December 2019. Holt-Winters forecasting models were developed for total imaging services as well as for each imaging modality. The models were used to predict monthly data between January 2020 and June 2020 with a 95% confidence interval (P < 0.05). Absolute and percentage residual differences (RD) between observed and predicted services for this time period were calculated. RESULTS: There were statistically significant reductions in total imaging services performed in March 2020 (RD: -332260, -13.1%, 95% CI: -17.5% to -8.4%), April 2020 (RD: -716957, -32.4%, 95% CI: -36.2% to -28.1%) and May 2020 (RD: -571634, -21.4%, 95% CI: -25.1% to -17.3%). Nuclear medicine and CT services were relatively less impacted than general radiography, ultrasound, and MRI services. There was also a statistically significant increase in nuclear medicine and CT services performed in June 2020 compared to predicted values. CONCLUSIONS: During the first wave of COVID-19 in Australia, there was a significant reduction in total diagnostic imaging services, with variable impacts on different imaging modalities. These findings may have significant public health implications and can be used to inform evidence-based strategies in the recovery phase of the pandemic.

BACKGROUND: Healthcare quality for systemic lupus erythematosus (SLE) is a modifiable target for improving patient outcomes. We aimed to assess the quality of care processes in different clinic settings, comparing a subspecialty lupus clinic with hospital-based and private general rheumatology clinics. METHODS: Patients with SLE (n = 258) were recruited in 2016 from a subspecialty lupus clinic (n = 147), two hospital general rheumatology clinics (n = 56) and two private rheumatology clinics (n = 55). Data were collected from medical records and patient questionnaires. Quality of care was assessed using 31 validated SLE quality indicators (QI) encompassing diagnostic work-up, disease and comorbidity assessments, drug monitoring, preventative care and reproductive health. Per-QI performance was measured as a percentage of patients that met the QI relative to the number of patients eligible. Per-patient QI performance was calculated as a percentage of QIs met relative to the number of eligible QIs for each patient. Per-QI and per-patient QI performance were compared between the three clinic settings, and multiple regression performed to adjust for sociodemographic, disease and healthcare factors. RESULTS: Per-QI performance was generally high across all clinic settings for diagnostic work-up, comorbidity assessment, lupus nephritis, drug monitoring, prednisolone taper, osteoporosis and pregnancy care. Median [IQR] per-patient performance on eligible QIs was higher in the subspeciality lupus clinic (66.7% [57.1-74.1]) than the hospital general rheumatology (52.7% [47.5-58.1]) and private rheumatology (50.0% [42.9-60.9]) clinics (p <0.001) and the difference remained significant after multivariable adjustment. The subspecialty lupus clinic recorded higher per-QI performance for documentation of disease activity, disease damage, cardiovascular risk factor and drug toxicity assessments, pre-immunosuppression hepatitis and tuberculosis screening, new medication counselling

Background: Island flaps are more mobile than local flaps in-continuity and rely on the laxity of the adjacent tissues. We present a new island flap design, called the inverted love-heart flap, used to reconstruct cutaneous defects of the limbs. Method(s): A retrospective chart review of patients who underwent inverted love-heart flap reconstruction post excision of a cutaneous malignancy during July 2017 to July 2019 was performed. Result(s): Seventeen patients underwent 18 inverted love-heart flap reconstructions postexcision of a cutaneous malignancies during the study period. There were no reported cases of partial or total flap necrosis. Conclusion(s): The inverted love-heart flap offers a reliable reconstruction for cutaneous defects. It has the advantage of a primary donor site closure, minimal patient morbidity and avoids the need for skin grafting and postoperative immobilization.

Background: Island flaps are more mobile than local flaps in-continuity and rely on the laxity of the adjacent tissues. We present a new island flap design, called the inverted love-heart flap, used to reconstruct cutaneous defects of the limbs. Method(s): A retrospective chart review of patients who underwent inverted love-heart flap reconstruction post excision of a cutaneous malignancy during July 2017 to July 2019 was performed. Result(s): Seventeen patients underwent 18 inverted love-heart flap reconstructions postexcision of a cutaneous malignancies during the study period. There were no reported cases of partial or total flap necrosis. Conclusion(s): The inverted love-heart flap offers a reliable reconstruction for cutaneous defects. It has the advantage of a primary donor site closure, minimal patient morbidity and avoids the need for skin grafting and postoperative immobilization.

BACKGROUND: Previous conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology. OBJECTIVE: We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017. METHODS: We collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model. RESULTS: Overall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in individuals with obesity (men: 17.78% [17.05%-18.43%]; women: 14.59% [13.99%-15.17%]) compared with that of nonobese individuals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (P<.001). CONCLUSIONS: A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can sig

Background: Healthcare quality for systemic lupus erythematosus (SLE) is a modifiable target for improving patient outcomes. Disease-specific subspecialty clinics offer experienced healthcare professionals, collaborative multidisciplinary teams and streamlined care processes. A single centre study in the USA has suggested superior performance of the subspecialty lupus clinic in the provision of quality care (1), but this has not been examined outside the USA where access to care may be influential. Objective(s): To assess the quality of SLE care provided in a subspecialty lupus clinic compared with hospital general rheumatology and private rheumatology clinics in a non-US, universal healthcare setting. Method(s): Lupus patients (n = 258) were recruited in 2016 from various clinic settings in Australia, including a subspecialty lupus clinic (n = 147), two hospital general rheumatology clinics (n = 56) and two private clinics (n = 55). Quality of care was assessed using 31 validated SLE quality indicators (QI) encompassing diagnostic work-up, disease and comorbidities assessment, drug monitoring, preventative care and reproductive health (2,3). Data were collected from medical records and patient questionnaires. Overall and individual QI performance was calculated and compared between the three clinic settings, and multivariable regression was performed to adjust for sociodemographic, disease and healthcare factors. Result(s): Median [IQR] overall performance on eligible QIs was higher in the lupus clinic (66.7% [16.9]) than the hospital general rheumatology (52.7% [10.6]) and private rheumatology (50.00% [18.0]) clinics (p <0.01), and remained significant with multivariable adjustment. This trend was still observed when the overall performance was reassessed to include patient self-report (73.1% [14.8] vs 68.1% [11.5] vs 63.2% [13.4], p <0.01). This difference may be due to consistent formal assessments of disease activity (100% vs 0% vs 0%, p <0.01) and disease dama

Aims: To assess the quality of SLE care provided in a subspecialty lupus clinic compared with public general rheumatology and private rheumatology clinics in a non-US, universal healthcare setting. Method(s): Lupus patients (n = 258) were recruited in 2016 from various clinic settings in Melbourne, Australia, including a subspecialty lupus clinic (n = 147), two public general rheumatology clinics (n = 56) and two private rheumatology clinics (n = 55). Quality of care was assessed using 31 validated SLE quality indicators (QI) encompassing diagnostic work-up, disease and comorbidities assessment, drug monitoring, preventative care and reproductive health. Data were collected from medical records and patient questionnaires. Overall and individual QI performance was calculated and compared between the three clinic settings, and multivariable regression was performed to adjust for sociodemographic, disease and healthcare factors. Result(s): Median [IQR] overall performance on eligible QIs was significantly higher in the lupus clinic (66.7% [16.9]) than the public general rheumatology (52.7% [10.6]) and private rheumatology (50.0% [18.0]) clinics (p <0.01), and remained significant with multivariable adjustment. This trend was still observed when the overall performance was reassessed to include patient self-report (73.1% [14.8] vs 68.1% [11.5] vs 63.2% [13.4], p <0.01). Performance was high across all clinic settings for diagnostic work-up, comorbidity assessment, drug monitoring, prednisolone taper, osteoporosis management, lupus nephritis and pregnancy quality indicators. The lupus clinic significantly outperformed the other clinic settings on eligible quality indicators for disease activity and disease damage assessment, new medication counselling, pre-immunosuppression hepatitis and tuberculosis screening, drug toxicity assessment, sun avoidance education, vaccinations, cardiovascular risk factor assessment and contraception counselling. Conclusion(s): SLE patients

Background: Healthcare quality for systemic lupus erythematosus (SLE) is a modifiable target for improving patient outcomes. Disease-specific subspecialty clinics offer experienced healthcare professionals, collaborative multidisciplinary teams and streamlined care processes. A single centre study in the USA has suggested superior performance of the subspecialty lupus clinic in the provision of quality care (1), but this has not been examined outside the USA where access to care may be influential. Objective(s): To assess the quality of SLE care provided in a subspecialty lupus clinic compared with hospital general rheumatology and private rheumatology clinics in a non-US, universal healthcare setting. Method(s): Lupus patients (n = 258) were recruited in 2016 from various clinic settings in Australia, including a subspecialty lupus clinic (n = 147), two hospital general rheumatology clinics (n = 56) and two private clinics (n = 55). Quality of care was assessed using 31 validated SLE quality indicators (QI) encompassing diagnostic work-up, disease and comorbidities assessment, drug monitoring, preventative care and reproductive health (2,3). Data were collected from medical records and patient questionnaires. Overall and individual QI performance was calculated and compared between the three clinic settings, and multivariable regression was performed to adjust for sociodemographic, disease and healthcare factors. Result(s): Median [IQR] overall performance on eligible QIs was higher in the lupus clinic (66.7% [16.9]) than the hospital general rheumatology (52.7% [10.6]) and private rheumatology (50.00% [18.0]) clinics (p <0.01), and remained significant with multivariable adjustment. This trend was still observed when the overall performance was reassessed to include patient self-report (73.1% [14.8] vs 68.1% [11.5] vs 63.2% [13.4], p <0.01). This difference may be due to consistent formal assessments of disease activity (100% vs 0% vs 0%, p <0.01) and disease dama

Aims: To assess the quality of SLE care provided in a subspecialty lupus clinic compared with public general rheumatology and private rheumatology clinics in a non-US, universal healthcare setting. Method(s): Lupus patients (n = 258) were recruited in 2016 from various clinic settings in Melbourne, Australia, including a subspecialty lupus clinic (n = 147), two public general rheumatology clinics (n = 56) and two private rheumatology clinics (n = 55). Quality of care was assessed using 31 validated SLE quality indicators (QI) encompassing diagnostic work-up, disease and comorbidities assessment, drug monitoring, preventative care and reproductive health. Data were collected from medical records and patient questionnaires. Overall and individual QI performance was calculated and compared between the three clinic settings, and multivariable regression was performed to adjust for sociodemographic, disease and healthcare factors. Result(s): Median [IQR] overall performance on eligible QIs was significantly higher in the lupus clinic (66.7% [16.9]) than the public general rheumatology (52.7% [10.6]) and private rheumatology (50.0% [18.0]) clinics (p <0.01), and remained significant with multivariable adjustment. This trend was still observed when the overall performance was reassessed to include patient self-report (73.1% [14.8] vs 68.1% [11.5] vs 63.2% [13.4], p <0.01). Performance was high across all clinic settings for diagnostic work-up, comorbidity assessment, drug monitoring, prednisolone taper, osteoporosis management, lupus nephritis and pregnancy quality indicators. The lupus clinic significantly outperformed the other clinic settings on eligible quality indicators for disease activity and disease damage assessment, new medication counselling, pre-immunosuppression hepatitis and tuberculosis screening, drug toxicity assessment, sun avoidance education, vaccinations, cardiovascular risk factor assessment and contraception counselling. Conclusion(s): SLE patients