Academic Journal
Predictive Risk Models to Identify Patients at High-Risk for Severe Clinical Outcomes With Chronic Kidney Disease and Type 2 Diabetes
| العنوان: | Predictive Risk Models to Identify Patients at High-Risk for Severe Clinical Outcomes With Chronic Kidney Disease and Type 2 Diabetes |
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| المؤلفون: | Richard Sheer, Radhika Nair, Margaret K. Pasquale, Thomas Evers, Meghan Cockrell, Alain Gay, Rakesh Singh, Niklas Schmedt |
| المصدر: | Journal of Primary Care & Community Health, Vol 13 (2022) |
| بيانات النشر: | SAGE Publishing, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Public aspects of medicine |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270 |
| الوصف: | Introduction/Objective: Predictive risk models identifying patients at high risk for specific outcomes may provide valuable insights to providers and payers regarding points of intervention and modifiable factors. The goal of our study was to build predictive risk models to identify patients with chronic kidney disease (CKD) and type 2 diabetes (T2D) at high risk for progression to end stage kidney disease (ESKD), mortality, and hospitalization for cardiovascular disease (CVD), cerebrovascular disease (CeVD), and heart failure (HF). Methods: This was a retrospective observational cohort study utilizing administrative claims data in patients with CKD (stage 3-4) and T2D aged 65 to 89 years enrolled in a Medicare Advantage Drug Prescription plan offered by Humana Inc. between 1/1/2012 and 12/31/2017. Patients were enrolled ≥1 year pre-index and followed for outcomes, including hospitalization for CVD, CeVD and HF, ESKD, and mortality, 2 years post-index. Pre-index characteristics comprising demographic, comorbidities, laboratory values, and treatment (T2D and cardiovascular) were evaluated and included in the models. LASSO technique was used to identify predictors to be retained in the final models followed by logistic regression to generate parameter estimates and model performance statistics. Inverse probability censoring weighting was used to account for varying follow-up time. Results: We identified 169 876 patients for inclusion. Declining estimated glomerular filtration rate (eGFR) increased the risk of hospitalization for CVD (38.6%-61.8%) and HF (2-3 times) for patients with eGFR 15 to 29 mL/min/1.73 m 2 compared to patients with eGFR 50 to 59 mL/min/1.73 m 2 . Patients with urine albumin-to-creatinine ratio (UACR) ≥300 mg/g had greater chance for hospitalization for CVD (2.0 times) and HF (4.9 times), progression to ESKD (2.9 times) and all-cause mortality (2.4 times) than patients with UACR |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2150-1327 21501319 |
| Relation: | https://doaj.org/toc/2150-1327 |
| DOI: | 10.1177/21501319211063726 |
| URL الوصول: | https://doaj.org/article/ad63ed6a0af04f59b28d4bd8bb1aa408 |
| رقم الانضمام: | edsdoj.63ed6a0af04f59b28d4bd8bb1aa408 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21501327 21501319 |
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| DOI: | 10.1177/21501319211063726 |