Risk of cardiovascular outcomes among women with endometriosis in the United Kingdom: a retrospective matched cohort study.

OBJECTIVE: To describe the prevalence and incidence of endometriosis and to estimate the risk of cardiovascular outcomes in women with endometriosis. DESIGN: Population-based cohort study using The Health Improvement Network database. SETTING: UK primary care. POPULATION: Women aged 16-50 years were followed from 1995 to 2018. METHODS: Adjusted hazard ratios (aHR) for cardiovascular outcomes comparing women with endometriosis with those without endometriosis were estimated using multivariable Cox regression models. Prevalence and incidence of endometriosis were estimated using annual (1998-2017) sequential cross-sectional and cohort studies, respectively. MAIN OUTCOME MEASURE: The primary outcome was composite cardiovascular disease (CVD) including, ischaemic heart disease (IHD), heart failure (HF) and cerebrovascular disease. Secondary outcomes were arrhythmia, hypertension and all-cause mortality. RESULTS: In all, 56 090 women with endometriosis and 223 669 matched controls without endometriosis were included in the analysis of cardiovascular risk. Compared with women without endometriosis, the aHR for cardiovascular outcomes among women with endometriosis were: composite CVD 1.24 (95% CI 1.13-1.37); IHD 1.40 (95% CI 1.22-1.61); cerebrovascular disease 1.19 (95% CI 1.04-1.36); HF 0.76 (95% CI 0.54-1.07); arrhythmia 1.26 (95% CI 1.11-1.43); hypertension 1.12 (95% CI 1.07-1.17) and all-cause mortality 0.66 (95% CI 0.59-0.74). The incidence of endometriosis was 12.3 per 10 000 person-years in 1998 and 11.5 per 10 000 person-years in 2017. The prevalence of endometriosis increased from 119.7 per 10 000 population in 1998 to 201.3 per 10 000 population in 2017. CONCLUSION: Endometriosis is associated with an increased risk of cardiovascular outcomes. Young women with endometriosis are a potential target for CVD risk assessment and prevention. TWEETABLE ABSTRACT: Endometriosis is associated with increased risk of cardiovascular outcomes: a UK retrospective matched cohort study.

Impact of the Diabetes Inpatient Care and Education (DICE) project on length of stay and mortality.

AIM: To determine whether the Diabetes Inpatient Care and Education (DICE) programme, a whole-systems approach to managing inpatient diabetes, reduces length of stay, in-hospital mortality and readmissions. RESEARCH DESIGN AND METHODS: Diabetes Inpatient Care and Education initiatives included identification of all diabetes admissions, a novel DICE care-pathway, an online system for prioritizing referrals, use of web-linked glucose meters, an enhanced diabetes team, and novel diabetes training for doctors. Patient administration system data were extracted for people admitted to Ipswich Hospital from January 2008 to June 2016. Logistic regression was used to compare binary outcomes (mortality, 30-day readmissions) 6 months before and after the intervention; generalized estimating equations were used to compare lengths of stay. Interrupted time series analysis was performed over the full 7.5-year period to account for secular trends. RESULTS: Before-and-after analysis revealed a significant reduction in lengths of stay for people with and without diabetes: relative ratios 0.89 (95% CI 0.83, 0.97) and 0.93 (95% CI 0.90, 0.96), respectively; however, in interrupted time series analysis the change in long-term trend for length of stay following the intervention was significant only for people with diabetes (P=0.017 vs P=0.48). Odds ratios for mortality were 0.63 (0.48, 0.82) and 0.81 (0.70, 0.93) in people with and without diabetes, respectively; however, the change in trend was not significant in people with diabetes, while there was an apparent increase in those without diabetes. There was no significant change in 30-day readmissions, but interrupted time series analysis showed a rising trend in both groups. CONCLUSION: The DICE programme was associated with a shorter length of stay in inpatients with diabetes beyond that observed in people without diabetes.

Temporal and external validation of a prediction model for adverse outcomes among inpatients with diabetes.

AIM: To temporally and externally validate our previously developed prediction model, which used data from University Hospitals Birmingham to identify inpatients with diabetes at high risk of adverse outcome (mortality or excessive length of stay), in order to demonstrate its applicability to other hospital populations within the UK. METHODS: Temporal validation was performed using data from University Hospitals Birmingham and external validation was performed using data from both the Heart of England NHS Foundation Trust and Ipswich Hospital. All adult inpatients with diabetes were included. Variables included in the model were age, gender, ethnicity, admission type, intensive therapy unit admission, insulin therapy, albumin, sodium, potassium, haemoglobin, C-reactive protein, estimated GFR and neutrophil count. Adverse outcome was defined as excessive length of stay or death. RESULTS: Model discrimination in the temporal and external validation datasets was good. In temporal validation using data from University Hospitals Birmingham, the area under the curve was 0.797 (95% CI 0.785-0.810), sensitivity was 70% (95% CI 67-72) and specificity was 75% (95% CI 74-76). In external validation using data from Heart of England NHS Foundation Trust, the area under the curve was 0.758 (95% CI 0.747-0.768), sensitivity was 73% (95% CI 71-74) and specificity was 66% (95% CI 65-67). In external validation using data from Ipswich, the area under the curve was 0.736 (95% CI 0.711-0.761), sensitivity was 63% (95% CI 59-68) and specificity was 69% (95% CI 67-72). These results were similar to those for the internally validated model derived from University Hospitals Birmingham. CONCLUSIONS: The prediction model to identify patients with diabetes at high risk of developing an adverse event while in hospital performed well in temporal and external validation. The externally validated prediction model is a novel tool that can be used to improve care pathways for inpatients with diabetes. Further research to assess clinical utility is needed.

Predicting inpatient hypoglycaemia in hospitalized patients with diabetes: a retrospective analysis of 9584 admissions with diabetes.

AIMS: To explore whether a quantitative approach to identifying hospitalized patients with diabetes at risk of hypoglycaemia would be feasible through incorporation of routine biochemical, haematological and prescription data. METHODS: A retrospective cross-sectional analysis of all diabetic admissions (n=9584) from 1 January 2014 to 31 December 2014 was performed. Hypoglycaemia was defined as a blood glucose level of <4 mmol/l. The prediction model was constructed using multivariable logistic regression, populated by clinically important variables and routine laboratory data. RESULTS: Using a prespecified variable selection strategy, it was shown that the occurrence of inpatient hypoglycaemia could be predicted by a combined model taking into account background medication (type of insulin, use of sulfonylureas), ethnicity (black and Asian), age (≥75 years), type of admission (emergency) and laboratory measurements (estimated GFR, C-reactive protein, sodium and albumin). Receiver-operating curve analysis showed that the area under the curve was 0.733 (95% CI 0.719 to 0.747). The threshold chosen to maximize both sensitivity and specificity was 0.15. The area under the curve obtained from internal validation did not differ from the primary model [0.731 (95% CI 0.717 to 0.746)]. CONCLUSIONS: The inclusion of routine biochemical data, available at the time of admission, can add prognostic value to demographic and medication history. The predictive performance of the constructed model indicates potential clinical utility for the identification of patients at risk of hypoglycaemia during their inpatient stay.