Abstract:Purpose
To validate prescription registry data as a measurement of adherence to statins through a direct method using assays for selected statins in serial blood samples collected from two prospective cohorts of Danish colorectal cancer patients.
Methods
We linked information on statin prescriptions from the Aarhus University Prescription Database with the cancer cohorts from Aalborg University Hospital. For statin‐prescribed patients, we calculated a prescription window covering the anticipated duration of th… Show more
“…The major strengths of our study are its unselected Danish source population (which virtually eliminates the possibility of selection bias) 31 and its use of high-quality registry data on simvastatin exposure, 19,32 vital status, 16,17 and breast tumor characteristics, treatment, and recurrence. 33,34 We expect little to no residual confounding, as germline genetic status is encoded at conception, and thus cannot be acted upon causally by demographic, behavioral, or clinical factors that may influence recurrence risk.…”
“…The major strengths of our study are its unselected Danish source population (which virtually eliminates the possibility of selection bias) 31 and its use of high-quality registry data on simvastatin exposure, 19,32 vital status, 16,17 and breast tumor characteristics, treatment, and recurrence. 33,34 We expect little to no residual confounding, as germline genetic status is encoded at conception, and thus cannot be acted upon causally by demographic, behavioral, or clinical factors that may influence recurrence risk.…”
“…However, a study in the Danish setting has previously found high adherence to statins. 36 Logistic regression models were not suitable for comparisons with XGBoost when the models' complexity increased. This was mainly due to 2 reasons.…”
Background:
The increasing aging population and limited health care resources have placed new demands on the healthcare sector. Reducing the number of hospitalizations has become a political priority in many countries, and special focus has been directed at potentially preventable hospitalizations.
Objectives:
We aimed to develop an artificial intelligence (AI) prediction model for potentially preventable hospitalizations in the coming year, and to apply explainable AI to identify predictors of hospitalization and their interaction.
Methods:
We used the Danish CROSS-TRACKS cohort and included citizens in 2016-2017. We predicted potentially preventable hospitalizations within the following year using the citizens’ sociodemographic characteristics, clinical characteristics, and health care utilization as predictors. Extreme gradient boosting was used to predict potentially preventable hospitalizations with Shapley additive explanations values serving to explain the impact of each predictor. We reported the area under the receiver operating characteristic curve, the area under the precision-recall curve, and 95% confidence intervals (CI) based on five-fold cross-validation.
Results:
The best performing prediction model showed an area under the receiver operating characteristic curve of 0.789 (CI: 0.782–0.795) and an area under the precision-recall curve of 0.232 (CI: 0.219–0.246). The predictors with the highest impact on the prediction model were age, prescription drugs for obstructive airway diseases, antibiotics, and use of municipality services. We found an interaction between age and use of municipality services, suggesting that citizens aged 75+ years receiving municipality services had a lower risk of potentially preventable hospitalization.
Conclusion:
AI is suitable for predicting potentially preventable hospitalizations. The municipality-based health services seem to have a preventive effect on potentially preventable hospitalizations.
“…The laboratory test will also have to have demonstrated accuracy for identifying the measure of interest. One group validated the use of the Danish prescription registry as a measurement of adherence to statins via serially collected blood samples to detect statins and their metabolites and found that the electronic algorithm performed very well, implying such an approach will be appropriate for most studies that examine statin use 15 Exposure: To identify individuals treated with intravenous immunoglobulin in the inpatient setting, we use inpatient medical record data that documented the administration of the product of interest to the person of interest.Outcome: To identify intussusception as an outcome among US children who received rotavirus vaccine, we identify potential cases via an electronic algorithm based on diagnosis or procedure codes captured in claims data and only include in the analysis those cases that are confirmed by manual record review with clinician adjudication 16 .…”
A fundamental question in using real‐world data for clinical and regulatory decision making is: How certain must we be that the algorithm used to capture an exposure, outcome, cohort‐defining characteristic, or confounder is what we intend it to be? We provide a practical framework to help researchers and regulators assess and classify the fit‐for‐purposefulness of real‐world data by study variable for a range of data sources. The three levels of certainty (optimal, sufficient, and probable) must be considered in the context of each study variable, the specific question being studied, the study design, and the decision at hand.
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