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Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO2max), or indirectly assessed using heart rate responses to standard exercise tests. However, such testing is costly and burdensome because it requires specialized equipment such as treadmills and oxygen masks, limiting its utility. Modern wearables capture dynamic real-world data which could improve fitness prediction. In this work, we design algorithms and models that convert raw wearable sensor data into cardiorespiratory fitness estimates. We validate these estimates' ability to capture fitness profiles in free-living conditions using the Fenland Study (N=11,059), along with its longitudinal cohort (N = 2675), and a third external cohort using the UK Biobank Validation Study (N = 181) who underwent maximal VO2max testing, the gold standard measurement of fitness. Our results show that the combination of wearables and other biomarkers as inputs to neural networks yields a strong correlation to ground truth in a holdout sample (r = 0.82, 95CI 0.80–0.83), outperforming other approaches and models and detects fitness change over time (e.g., after 7 years). We also show how the model's latent space can be used for fitness-aware patient subtyping paving the way to scalable interventions and personalized trial recruitment. These results demonstrate the value of wearables for fitness estimation that today can be measured only with laboratory tests. [ABSTRACT FROM AUTHOR]

Purpose: To test if two of the adverse event triggers proposed by the Institute of Healthcare Improvement can detect adverse drug events (ADEs) in a UK secondary care setting, using an electronic prescribing and health record system. Methods: In order to identify triggers for over-anticoagulation and potential opioid overdose and we undertook a retrospective review of electronic medical and prescription records from 54,244 hospital admissions over a 1-year period, alongside a review of medical incident reports. Once prescription data were linked to triggers and duplicates were removed, case note review eliminated the false positive ADEs. Additionally, we tested the use of an electronic algorithm for the International Normalized Ratio (INR) ≥6 trigger. Results: The INR ≥6 electronic trigger identified 46 potential ADEs and the naloxone electronic trigger identified 82 ADEs. Based on the available case note review, the INR ≥6 trigger had a positive predictive value (PPV) of 38 % (14/37) and the naloxone trigger had a PPV of 91 % (61/67). The electronic algorithm for the INR ≥6 trigger identified 12 ADEs, thus reducing the need of case note review. This was in comparison with one and two critical incidents reported in the trust medical incident reports system, which respectively related to over-coagulation with warfarin and over-sedation with opioid medication. Conclusions: We have integrated automated and manual methods of detecting ADEs using previously defined triggers. Incorporating electronic triggers in already established electronic health records with prescription and laboratory test data can improve the detection of ADEs, and potentially lead to methods to avert them. [ABSTRACT FROM AUTHOR]

Summary: We examined the effect of glucocorticoid dose on FRAX® derived fracture probabilities in a UK setting. A relatively simple adjustment of conventional FRAX estimates of probabilities of hip fracture and a major osteoporotic fracture can be applied to modulate the risk assessment with knowledge of the dose of glucocorticoids. Introduction: The WHO fracture risk assessment (FRAX) tool estimates 10-year probability of fracture based upon multiple clinical risk factors and an optional femoral neck BMD measurement. Ever (past and current) use of systemic glucocorticoids is a dichotomous risk factor (yes/no) and does not therefore take account of the dose of glucocorticoids. The aim of this work was to estimate the adjustment for fracture probability based upon the dose of glucocorticoids. Methods: Dose responses for fracture risk during exposure to glucocorticoids were taken from the General Practice Research Database and used to adjust the relative risks for glucocorticoids in FRAX. In addition to fracture risk, a dose response for the death hazard was estimated and both variables were used to populate the FRAX model for the UK. Results: The exposure to glucocorticoids was found to significantly affect fracture probability. The following rule was formulated. For low-dose exposure (<2.5 mg daily of prednisolone or equivalent), the probability of a major fracture is decreased by about 20% depending on age. For medium doses (2.5-7.5 mg daily), the unadjusted FRAX value can be used. For high doses (>7.5 mg daily), probabilities can be upward revised by about 15%. Conversion factors were also determined for the adjustment of hip fracture probability. Conclusion: A relatively simple adjustment of conventional FRAX estimates of probabilities of hip fracture and a major osteoporotic fracture can be applied to modulate the risk assessment with knowledge of the dose of glucocorticoids. [ABSTRACT FROM AUTHOR]