Management of endocrine conditions at the end of life

Bray, Anne de; Tomas, Jon; Gittoes, Neil; Hassan‐Smith, Zaki

doi:10.12968/hmed.2020.0096

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Of note, exploding 4P-MACE components, it appears that the RNN model with the true GLM sequence outperformed RNN models with the inverted or random sequence for discriminating occurrence of myocardial infarction, while a paradoxical worse performance was observed for all-cause death. Reasons for this latter unexpected finding may be found in the competing risk issue or in the de-prescription of GLMs that occurs in some patients with very short life expectancy, for whom diabetes management is no longer a priority [ 21 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

Time-resolved trajectory of glucose lowering medications and cardiovascular outcomes in type 2 diabetes: a recurrent neural network analysis

Longato

Camillo

Sparacino

et al. 2022

Cardiovasc Diabetol

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Aim Treatment algorithms define lines of glucose lowering medications (GLM) for the management of type 2 diabetes (T2D), but whether therapeutic trajectories are associated with major adverse cardiovascular events (MACE) is unclear. We explored whether the temporal resolution of GLM usage discriminates patients who experienced a 4P-MACE (heart failure, myocardial infarction, stroke, death for all causes). Methods We used an administrative database (Veneto region, North-East Italy, 2011–2018) and implemented recurrent neural networks (RNN) with outcome-specific attention maps. The model input included age, sex, diabetes duration, and a matrix of GLM pattern before the 4P-MACE or censoring. Model output was discrimination, reported as area under receiver characteristic curve (AUROC). Attention maps were produced to show medications whose time-resolved trajectories were the most important for discrimination. Results The analysis was conducted on 147,135 patients for training and model selection and on 10,000 patients for validation. Collected data spanned a period of ~ 6 years. The RNN model efficiently discriminated temporal patterns of GLM ending in a 4P-MACE vs. those ending in an event-free censoring with an AUROC of 0.911 (95% C.I. 0.904–0.919). This excellent performance was significantly better than that of other models not incorporating time-resolved GLM trajectories: (i) a logistic regression on the bag-of-words encoding all GLM ever taken by the patient (AUROC 0.754; 95% C.I. 0.743–0.765); (ii) a model including the sequence of GLM without temporal relationships (AUROC 0.749; 95% C.I. 0.737–0.761); (iii) a RNN model with the same construction rules but including a time-inverted or randomised order of GLM. Attention maps identified the time-resolved pattern of most common first-line (metformin), second-line (sulphonylureas) GLM, and insulin (glargine) as those determining discrimination capacity. Conclusions The time-resolved pattern of GLM use identified patients with subsequent cardiovascular events better than the mere list or sequence of prescribed GLM. Thus, a patient’s therapeutic trajectory could determine disease outcomes.

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Section: Discussionmentioning

confidence: 99%