Personalized prediction of adverse heart and kidney events using baseline and longitudinal data from SPRINT and ACCORD

Dinstag, Gal; Amar, David; Ingelsson, Erik; Ashley, Euan A.; Shamir, Ron

doi:10.1371/journal.pone.0219728

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…For the composite endpoint, we built a generalized linear model predicting the probabilities for the composite endpoint by using the distinct benefit scores for stroke and major bleeding as 2 independent variables. 33 A decision threshold for these probabilities is needed to decide whether a recommendation for either apixaban or rivaroxaban is to be made. We determined this decision threshold in the training data by dividing probabilities into deciles and chose the threshold maximizing the emulated benefit as if the model were implemented into regular care (see the ''Model Evaluation'' section).…”

Section: Discussionmentioning

confidence: 99%

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

et al. 2021

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Background: Decision making for the “best” treatment is particularly challenging in situations in which individual patient response to drugs can largely differ from average treatment effects. By estimating individual treatment effects (ITEs), we aimed to demonstrate how strokes, major bleeding events, and a composite of both could be reduced by model-assisted recommendations for a particular direct oral anticoagulant (DOAC). Methods: In German claims data for the calendar years 2014–2018, we selected 29 901 new users of the DOACs rivaroxaban and apixaban. Random forests considered binary events within 1 y to estimate ITEs under each DOAC according to the X-learner algorithm with 29 potential effect modifiers; treatment recommendations were based on these estimated ITEs. Model performance was evaluated by the c-for-benefit statistics, absolute risk reduction (ARR), and absolute risk difference (ARD) by trial emulation. Results: A significant proportion of patients would be recommended a different treatment option than they actually received. The stroke model significantly discriminated patients for higher benefit and thus indicated improved decisions by reduced outcomes (c-for-benefit: 0.56; 95% confidence interval [0.52; 0.60]). In the group with apixaban recommendation, the model also improved the composite endpoint (ARR: 1.69 % [0.39; 2.97]). In trial emulations, model-assisted recommendations significantly reduced the composite event rate (ARD: −0.78 % [−1.40; −0.03]). Conclusions: If prescribers are undecided about the potential benefits of different treatment options, ITEs can support decision making, especially if evidence is inconclusive, risk-benefit profiles of therapeutic alternatives differ significantly, and the patients’ complexity deviates from “typical” study populations. In the exemplary case for DOACs and potentially in other situations, the significant impact could also become practically relevant if recommendations were available in an automated way as part of decision making. Highlights It was possible to calculate individual treatment effects (ITEs) from routine claims data for rivaroxaban and apixaban, and the characteristics between the groups with recommendation for one or the other option differed significantly. ITEs resulted in recommendations that were significantly superior to usual (observed) treatment allocations in terms of absolute risk reduction, both separately for stroke and in the composite endpoint of stroke and major bleeding. When similar patients from routine data were selected (precision cohorts) for patients with a strong recommendation for one option or the other, those similar patients under the respective recommendation showed a significantly better prognosis compared with the alternative option. Many steps may still be needed on the way to clinical practice, but the principle of decision support developed from routine data may point the way toward future decision-making processes.

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Section: Discussionmentioning

confidence: 99%

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

et al. 2021

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Nomogram to predict risk of incident chronic kidney disease in high-risk population of cardiovascular disease in China: community-based cohort study

Zhang

Zhang²,

et al. 2021

BMJ Open

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AimsTo develop a nomogram for incident chronic kidney disease (CKD) risk evaluation among community residents with high cardiovascular disease (CVD) risk.MethodsIn this retrospective cohort study, 5730 non-CKD residents with high CVD risk participating the National Basic Public Health Service between January 2015 and December 2020 in Guangzhou were included. Endpoint was incident CKD defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m2 during the follow-up period. The entire cohorts were randomly (2:1) assigned to a development cohort and a validation cohort. Predictors of incident CKD were selected by multivariable Cox regression and stepwise approach. A nomogram based on these predictors was developed and evaluated with concordance index (C-index) and area under curve (AUC).ResultsDuring the median follow-up period of 4.22 years, the incidence of CKD was 19.09% (n=1094) in the entire cohort, 19.03% (727 patients) in the development cohort and 19.21% (367 patients) in the validation cohort. Age, body mass index, eGFR 60–89 mL/min/1.73 m2, diabetes and hypertension were selected as predictors. The nomogram demonstrated a good discriminative power with C-index of 0.778 and 0.785 in the development and validation cohort. The 3-year, 4-year and 5-year AUCs were 0.817, 0.814 and 0.834 in the development cohort, and 0.830, 0.847 and 0.839 in the validation cohort.ConclusionOur nomogram based on five readily available predictors is a reliable tool to identify high-CVD risk patients at risk of incident CKD. This prediction model may help improving the healthcare strategies in primary care.

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<p>Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data</p>

Meid¹,

Ruff²,

Wirbka³

et al. 2020

CLEP

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When healthcare professionals have the choice between several drug treatments for their patients, they often experience considerable decision uncertainty because many decisions simply have no single “best” choice. The challenges are manifold and include that guideline recommendations focus on randomized controlled trials whose populations do not necessarily correspond to specific patients in everyday treatment. Further reasons may be insufficient evidence on outcomes, lack of direct comparison of distinct options, and the need to individually balance benefits and risks. All these situations will occur in routine care, its outcomes will be mirrored in routine data, and could thus be used to guide decisions. We propose a concept to facilitate decision-making by exploiting this wealth of information. Our working example for illustration assumes that the response to a particular (drug) treatment can substantially differ between individual patients depending on their characteristics (heterogeneous treatment effects, HTE), and that decisions will be more precise if they are based on real-world evidence of HTE considering this information. However, such methods must account for confounding by indication and effect measure modification, eg, by adequately using machine learning methods or parametric regressions to estimate individual responses to pharmacological treatments. The better a model assesses the underlying HTE, the more accurate are predicted probabilities of treatment response. After probabilities for treatment-related benefit and harm have been calculated, decision rules can be applied and patient preferences can be considered to provide individual recommendations. Emulated trials in observational data are a straightforward technique to predict the effects of such decision rules when applied in routine care. Prediction-based decision rules from routine data have the potential to efficiently supplement clinical guidelines and support healthcare professionals in creating personalized treatment plans using decision support tools.

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Personalized prediction of adverse heart and kidney events using baseline and longitudinal data from SPRINT and ACCORD

Cited by 4 publications

References 18 publications

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

Nomogram to predict risk of incident chronic kidney disease in high-risk population of cardiovascular disease in China: community-based cohort study

<p>Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data</p>

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