A qualitative study documenting unmet needs in the management of diabetic kidney disease (DKD) in the primary care setting

Abstract

Background A majority of diabetic kidney disease (DKD) patients receive medical care in the primary care setting, making it an important opportunity to improve patient management. There is limited evidence evaluating whether primary care physicians (PCPs) are equipped to effectively manage these patients in routine clinical practice. The present study was undertaken to identify gaps in primary care and unmet needs in the diagnosis and monitoring of DKD in type 2 diabetes (T2D) patients among PCPs. Methods This was a qualitative analysis based on 30–45-min interviews with PCPs treating T2D patients. PCPs were recruited via email and were board-certified, in practice for more than 3 years, spent most of their time in direct clinical care, and provided care for more than three T2D patients in a week. Descriptive data analysis was conducted to identify and examine themes that were generated by interviews. Two reviewers evaluated interview data to identify themes and developed consensus on the priority themes identified. Results A total of 16 PCPs satisfying the inclusion criteria were recruited for qualitative interviews. Although the PCPs recognized kidney disease as an important comorbidity in T2D patients, testing for kidney disease was not consistently top of mind, with 56% reportedly performing kidney function testing in their T2D patients. PCPs most frequently reported using estimated glomerular filtration rate (eGFR) alone to monitor and stage DKD; only 25% PCPs reported testing for albuminuria. Most PCPs incorrectly believed that a majority of DKD patients are diagnosed in early stages. Also, early stages of DKD emerged as ambiguous areas of decision-making, wherein treatments prescribed greatly varied among PCPs. Lastly, early and accurate risk stratification of DKD patients emerged as the most important unmet need; which, if it could be overcome, was consistently identified by PCPs as a key to monitoring, appropriate nephrologist referrals, and intervening to improve outcomes in patients with DKD. Conclusions Our study highlights important unmet needs in T2D DKD testing, staging, and stratification in the PCP setting that limit effective patient care. Health systems and insurers in the U.S. should prioritize the review and approval of new strategies that can improve DKD staging and risk stratification.

The need for risk stratification in type 2 diabetes and chronic kidney disease: Proposed clinical value of KidneyIntelX

“…While the Kidney Disease: Improving Global Outcomes guidelines (KDIGO) incorporates eGFR and UACR to identify risk stratification through all stages of disease including early stages [12], the classification is limited by the physiologic variability of both eGFR and UACR [11,13,14] as well as confounding by hyperfiltration in early stages of DKD [14][15][16]. As a result, primary care physicians (PCPs) and other specialists are not able to appropriately risk stratify and counsel patients on the progressive nature of DKD [17][18][19][20][21][22].…”

Real World Evidence and Clinical Utility of KidneyIntelX on Patients With Early-Stage Diabetic Kidney Disease: Interim Results on Decision Impact and Outcomes

“… 11 , 13 , 14 As a result, PCPs and other specialists are not able to appropriately risk stratify and counsel patients on the progressive nature of DKD. 7 , 8 , 15 - 18 …”

“…11,13,14 As a result, PCPs and other specialists are not able to appropriately risk stratify and counsel patients on the progressive nature of DKD. 7,8,[15][16][17][18] Several plasma biomarkers have been investigated to aid in the prediction of kidney disease progression. Three of the most widely studied are soluble tumor necrosis factor receptors (TNFR) 1 and 2 as well as plasma kidney injury molecule-1 (KIM-1).…”

Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients

“…Given that the early and improved diagnosis of these conditions can improve outcomes 15 , a comprehensive treatment approach to control CKD and HF in patients with early stages T2DM is desirable 3,16 . However, current evidence suggests that CKD and HF are not optimally diagnosed in patients with T2DM , leading to higher rates of disease progression and poor prognosis [17][18][19][20] .Multiple prediction models using machine learning (ML) techniques have been built over the last few years for risk assessment accounting for diabetes severity, complications, hospitalizations, disease progression, and…”

“…Given that the early and improved diagnosis of these conditions can improve outcomes 15 , a comprehensive treatment approach to control CKD and HF in patients with early stages T2DM is desirable 3,16 . However, current evidence suggests that CKD and HF are not optimally diagnosed in patients with T2DM , leading to higher rates of disease progression and poor prognosis [17][18][19][20] .…”