High-Risk Nonsteroidal Anti-inflammatory Drug Prescribing: Variation by Documentation of Chronic Kidney Disease Status

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Epling, Michelle S. Rockwell This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7792064/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Chronic kidney disease (CKD) affects over 10% of people globally. Despite its prevalence, CKD is persistently under-recognized and under-documented in healthcare settings. Previous studies have shown that patients who meet laboratory-based criteria for CKD but have no formal CKD diagnosis in their medical record are less likely to receive guideline-directed medical care for CKD. However, it is unclear whether CKD documentation status influences receipt of medications that may be high-risk for patients with CKD, including nonsteroidal anti-inflammatory drugs (NSAIDs). Objective We evaluated variation in NSAID prescribing based on CKD documentation status over a 10-year period, hypothesizing that patients who met diagnostic criteria for CKD but had no corresponding ICD diagnosis on record were more likely to receive long-term prescription NSAIDs. Methods This retrospective cohort study used electronic health record (EHR) data from the TriNetX Research Network representing 67 U.S. healthcare organizations (2013–2023). Adult patients with CKD were classified by CKD documentation status (diagnosis on record vs. criteria for CKD but no diagnosis on record). We applied logistic regression to assess the relationship between CKD documentation status and receipt of long-term NSAIDs, adjusting for demographics, comorbidities, and year. Results Among 1,777,336 patients, 52.9% met diagnostic criteria for CKD but had no CKD diagnosis on record. Overall, 9.0% of patients were prescribed long-term NSAIDs (6.3% of patients with a CKD diagnosis on record and 11.5% of those without). Patients with no CKD diagnosis on record were at 1.76 [95% CI: 1.74–1.79] times greater odds of receiving long-term NSAIDs compared to patients with a diagnosis on record. Female sex and Black/African American race were independently associated with higher odds of receiving long-term NSAIDs (1.47 [95% CI:1.46–1.49] and 1.37 [95% CI:1.30–1.44], respectively). Conclusions Across 67 US healthcare organizations, more than half of patients with laboratory-based evidence of CKD did not have a CKD diagnosis on record in the EHR. Absence of CKD documentation was associated with increased likelihood of receiving long-term NSAIDs. These findings suggest improved EHR documentation as a target for reducing potentially harmful prescribing in patients with CKD. NSAID quality medication safety low-value care CKD Figures Figure 1 Background Chronic kidney disease (CKD) is a progressive condition associated with substantial morbidity and mortality. 1 , 2 As one of the costliest health conditions in the U.S., CKD spending exceeds $ 200 billion annually. 3 , 4 Progression of CKD to more advanced stages can be delayed or prevented through the provision of guideline-directed care that combines lifestyle modification, pharmacologic therapy, risk factor management, and avoidance of nephrotoxic exposures. 5 An important barrier to receipt of guideline-directed CKD care is recognition of the condition. Generally asymptomatic at early stages, many patients are unaware that they have CKD. 3 , 6 In addition, CKD is commonly under-documented in medical records. Multiple studies have identified patients with laboratory evidence of CKD but no corresponding diagnosis documented in the medical record. 7 – 9 Patients who meet diagnostic criteria for CKD but lack corresponding clinical documentation may be less likely to receive guideline-directed care and more likely to suffer disease complications compared with patients with appropriate documentation. 10 – 14 For example, Frigaard et al. 12 showed that patients with evidence of CKD based on estimated glomerular filtration rate (GFR) but no diagnosis assigned in the medical record were less likely to receive guideline-directed laboratory testing and statin prescriptions. Healthcare spending has also been shown to be greater for patients who lack CKD documentation. 11 , 15 , 16 Less is known about the influence of CKD documentation on likelihood of exposure to inappropriate or low-value care. In patients with CKD, regular use of nonsteroidal anti-inflammatory drugs (NSAIDs) can precipitate acute kidney injury, accelerate decline in kidney function, increase fluid retention and blood pressure, and raise cardiovascular risk. 17 , 18 As such, the National Kidney Foundation’s Kidney Disease: Improving Global Outcomes (KDIGO) guidelines recommend limited use of NSAIDs in all patients with CKD and avoidance of NSAIDs with estimated glomerular filtration rate (GFR) < 30 mL/min/1.73m. 2,5 Despite these guidelines, inappropriate NSAID prescribing persists. 19 – 21 Up to 33% of CKD patients are prescribed NSAIDs in the U.S. primary care setting. 19 Understanding the influence of CKD documentation status on receipt of high-risk medications such as NSAIDs can provide important insights into gaps in quality of care and highlight opportunities to improve healthcare delivery for patients with CKD. In the present study, we evaluated variation in NSAID prescribing based on CKD documentation status in a large U.S. cohort over 10 years (2013–2023), hypothesizing that patients who met diagnostic criteria for CKD but had no corresponding diagnosis on record were more likely to receive long-term prescription NSAIDs. Methods This retrospective cohort study used data from the TriNetX U.S. Research Network, which included electronic health record (EHR) data from 67 U.S. healthcare organizations. This study was approved by the Institutional Review Board (IRB) of Carilion Clinic ( IRB-23-1842). Participants’ informed consent was waived based on the use of aggregated, de-identified data. We used STROBE guidelines to guide the study and prepare this report. 22 Participants A cohort of adult ( > 18 years of age) patients with CKD who had been seen for at least 2 ambulatory visits during any 12-month period between January 1, 2013 and December 31, 2023 was established. Patients included in the cohort had 2 or more CKD ICD codes (Appendix Table 1) documented in the encounter or problem list or >2 consecutive GFR results <60 mL/min/1.73m 2 separated by at least 90 days during a single 365-day period. In effort to represent the GFR result likely considered by clinicians, we analyzed race-adjusted GFR for Black or African American patients whenever it was reported. Procedures We determined the proportion of patients with a CKD diagnosis (ICD-9 or -10) documented in the EHR overall and stratified by sex, ethnicity, race, U.S. census region, and comorbidities (hypertension, type 2 diabetes, and gastrointestinal conditions). We identified all oral NSAIDs (Appendix Table 2) prescribed during the study period. Long-term use was defined as 3 or more prescriptions separated by at least 21 days within a single 365-day period. The primary outcome variable was dichotomous – prescribed long-term NSAIDs vs. not prescribed long-term NSAIDs. Statistical Analysis We used logistic regression to evaluate the relationship between CKD documentation and long-term NSAID prescribing for patients with Stage 3-5 CKD, controlling for demographics, comorbidities, and year. All statistical analyses were performed using R, version 4.3.1. Results Our cohort included 1,777,336 CKD patients (age 66.7 (SD: 12.5) years, 50.4% female, 67.7% White and 14.8% Black or African American) representing all U.S. census regions (Table 1 ). The majority of patients (77.0%) had a diagnosis of hypertension on record, while 40.4% had a type 2 diabetes diagnosis on record. Within the cohort, 832,554 patients (47.1%) had a CKD diagnosis (ICD-9 or − 10) on record, while 944,782 (52.9%) met diagnostic criteria for CKD but had no diagnosis on record. A greater proportion of males than females and Black/African American patients than those representing other races had a CKD diagnosis on record (55.2% vs. 39.3% and 64.2% vs. 43.8%, respectively) (Table 1 ). Documentation of CKD status improved as CKD stage progressed. Among patients with Stage 3 CKD, 41.2% had a CKD diagnosis on record, while 74.1% of patients with Stage 4 or 5 CKD had a CKD diagnosis on record. Across the cohort, 159,070 patients (9.0%) received long-term NSAIDs (6.3% of patients with a CKD diagnosis on record and 11.5% of those without) (Fig. 1 ). The odds of receiving long-term NSAIDs declined by 13.3% per year during 2013–2023 (Table 2 ). Throughout the study period, the odds of receiving long-term NSAIDs was 1.76 times higher [95% CI: 1.74–1.79] in patients without vs. with a CKD diagnosis on record (Table 2 ). The influence of CKD documentation status on odds of long-term NSAIDs receipt varied by stage: Stage 3, 4, and 5 CKD patients without a diagnosis on record experienced 1.48 [95% CI: 1.46–1.50], 1.93 [95% CI: 1.86-2.00], and 1.39 [95% CI: 1.32–1.45] times greater odds of receiving long-term NSAIDs than those with documented CKD (Table 2 ). Some demographic and clinical factors independently influenced the odds of receiving long-term NSAIDs. Females experienced 1.47 [95% CI:1.46–1.49] times greater odds of receipt than males and Black/African American patients experienced 1.37 [95% CI:1.30–1.44] times greater odds than patients identifying with other races (Table 2 ). Having diagnoses of hypertension or gastrointestinal conditions on record increased the odds of receiving long-term NSAIDs by 1.37 [95% CI: 1.355–1.394] and 1.87 [95% CI: 1.848–1.889] times compared with patients without these diagnoses. Discussion Among a cohort of nearly 1.8 million U.S. patients, those with a CKD diagnosis documented in the EHR had 43% lower odds of being prescribed long-term NSAIDs than those who met diagnostic criteria for CKD but lacked corresponding diagnosis. Although some, 10–14 but not all, 8 prior studies have identified a positive relationship between CKD documentation status and the delivery of guideline-directed CKD care (e.g., albuminuria testing, nephroprotective drug prescribing, nephrologist referral), the present study is among the first to describe an influence on delivery of inappropriate or low-value care. These findings suggest that improved EHR documentation may be a target for improving medication safety and health outcomes for patients with CKD. Use of NSAIDs can precipitate acute kidney injury, accelerate progression of CKD, and exacerbate hypertension, edema, and heart failure, particularly among patients with stage 4 CKD or worse. 5 , 17 , 23 – 25 Prior studies have documented persistent NSAID use among CKD patients. 19 – 21 The present study identified utilization of long-term NSAIDs by 9% of CKD patients in our large national cohort. While it is encouraging that long-term NSAID prescribing rates declined throughout the 2013 to 2023 study period, continued de-implementation efforts are needed to reduce the avoidable risks associated with long-term NSAID use by patients with CKD. Initiatives to improve EHR documentation are a potential approach. These initiatives may be combined with clinician and patient education, clinical decision support, and audit and feedback, which may also contribute to the de-implementation of high-risk NSAIDs. 26 Improving access to alternate pain management for patients with CKD, who experience high rates of chronic pain, 27,28 may also be effective. There are numerous barriers to improved EHR documentation of CKD status. Stewart et al 29 identified a number of health system, primary care, and patient-level barriers identified in the literature. Examples include lack of EHR interoperability or decision support, limited reimbursement for comprehensive care for patients with CKD, limited time for visits, primary care clinicians’ lack of knowledge or comfort related to CKD management, and clinician’s lack of perceived agency for improving CKD outcomes. Clinicians’ concerns about overwhelming patients with a formal CKD diagnosis or deterring focus from other health conditions were also highlighted. 29 Facilitators to the improvement of EHR CKD documentation have also been reported, with supportive technology and coordination among the care team highlighted as key enablers. 30 We observed demographic disparities in long-term NSAID prescribing. For example, women with CKD were significantly more likely than men with CKD to receive long-term NSAIDs. Similarly, Black/African American patients experienced over 35% greater likelihood of receiving long-term NSAIDs than White patients. In the U.S., Black and African American patients receive poorer quality CKD care than patients representing other races. 31 – 34 These disparities contribute to inequitable outcomes and have been reinforced by practices such as race-adjusted GFR equations. Further research is needed to identify drivers of these disparities. The finding that patients with documented hypertension or gastrointestinal conditions were at greater odds of receiving long-term NSAIDs was surprising given professional recommendations to limit or avoid NSAIDs in patients with these conditions. 25 ,35 This finding warrants further investigation. Limitations This study has some limitations. First, use of EHR data from multiple U.S. health systems aggregated through the TriNetX Research Network may have introduced variation in coding. To mitigate this, we used standardized TriNetX data harmonization procedures and required at least two ambulatory encounters and two CKD indicators (ICD codes or GFR results) to strengthen diagnostic specificity and minimize misclassification. Second, like most aggregated EHR/real world datasets, lack of unstructured data from clinical notes and loss of information during the harmonization process across different health systems is a possibility. 36 Third, the TriNetX dataset does not contain comprehensive data about prescription dosage and days prescribed, which precluded nuanced assessment of changes in NSAID utilization. Fourth, we acknowledge that information related to clinician and patients’ decision to use NSAIDs despite the risks is not included in the dataset, nor is over-the-counter NSAID use. As such, our results may underestimate overall NSAIDs exposure. We reduced this bias by defining long-term use as > 3 prescriptions over a 12-month period, which likely reflects sustained, clinician-directed therapy, and interpreted our estimates conservatively as a lower bound of true exposure. Despite these limitations, this large cohort of CKD patients representing all U.S. census regions and diversity in age and race provides robust, generalizable insights into patterns of NSAID prescribing and the impact of CKD documentation on receipt of high-risk medications. Conclusions Less than half of patients in our cohort of nearly 1.8 million patients from 67 U.S. healthcare organizations had documentation of CKD in their medical record despite clinical evidence of CKD. Patients without EHR documentation on record were at 1.76 greater odds of being prescribed long-term NSAIDs, which are recognized as high-risk for most patients with CKD. These findings build upon previous research showing that omission of clinical documentation decreases the likelihood that CKD patients receive guideline-directed care, such as nephroprotective medications and referral to nephrologist. Efforts to improve CKD documentation, which may include EHR-based clinical decision support, education, and incentives, may contribute to improved quality of care for patients with CKD through the de-implementation of high-risk medication prescribing. Declarations Author Contribution IY prepared the study design, data analysis, interpretation, and prepared the first draft of the manuscript. MR helped contribute to the study design, interpretation, manuscript revision, and supervision. JE contributed to interpretation and manuscript revision. MA contributed to the study design, data analysis, manuscript revision, and supervision. YW contributed to data extraction and analysis, and manuscript revision. All authors approved the final version of the manuscript Ethics Approval and Consent to Participate: This research was approved by the Institutional Review Board (IRB) of Carilion Clinic ( IRB-23-1842) in compliance with the Declaration of Helsinki. Participants’ informed consent was waived based on the use of aggregated, de-identified EHR data in accordance with the Common Rule for waiver of informed consent (45 CFR 46.116(f)). All authors have reviewed, accepted, and acknowledged that this work adheres to the International Committee of Medical Journal Editors ( ICMJE) guidelines. Consent for Publication: Not applicable. Availability of Data and Materials: We are unable to make the data used for this study publicly available due to an institutional agreement with TriNetX. To obtain access to the data, contact TriNetX at 125 Cambridgepark Drive, Suite 500, Cambridge, MA, 02140, United States, (857) 285-6037, https://www.trinetx.com. Competing Interests: The authors declare that they have no conflicting and competing interests. Funding Declaration: This project was funded by the Carilion Clinic Research Acceleration Program. Acknowledgements: The authors thank Hunter Sharp (Carilion Clinic) for analytical support and Temrak Orchid Tucker and Sonia Warrior (Carilion Clinic) for assistance with manuscript preparation. References Francis A, Harhay MN, Ong ACM, Tummalapalli SL, Ortiz A, Fogo AB, Fliser D, Roy-Chaudhury P, Fontana M, Nangaku M, Wanner C, Malik C, Hradsky A, Adu D, Bavanandan S, Cusumano A, Sola L, Ulasi I, Jha V. Chronic kidney disease and the global public health agenda: an international consensus. Nat Rev Nephrol . 2024;20(7):473-485. doi:10.1038/s41581-024-00820-6 Liu P, Sawhney S, Heide-Jørgensen U, Quinn RR, Jensen SK, Mclean A, Christiansen CF, Gerds TA, Ravani P. 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Published online August 12, 2025. doi:10.1007/s11606-025-09808-9 Tables Table 1. Demographics and Clinical Characteristics of Chronic Kidney Disease Patients with and without a Diagnosis (ICD) on Record Variable Level Overall CKD Diagnosis on Record Meets CKD Diagnostic Criteria but no Diagnosis on Record n 1,777,336 832,554 944,782 Age mean (SD) 66.74 (12.5) 64.75 (13.6) 68.49 (11.1) Sex n (%) M 819,378 (46.1) 452,297 (54.3) 367,081 (38.9) F 896,065 (50.4) 352,538 (42.3) 54,327 (57.5) Other 61,898 (3.5) 27,719 (3.3) 34,179 (3.7) Race n (%) White 1,203,282 (67.7) 503,198 (60.4) 700,084 (74.1) Black/African American 262,574 (14.8) 168,631 (20.3) 93,943 (9.9) Asian 54,603 (3.1) 33,481 (4.0) 21,122 (2.2) Other 256,877 (14.5) 127,244 (15.3) 129,633 (13.7) Ethnicity n (%) Hispanic or Latino 98,259 (5.5) 62,337 (7.5) 35,922 (3.8) Not Hispano or Latino 1371,891 (77.2) 630,418 (75.7) 741,473 (78.5) Unknown 307,186 (17.3) 139,799 (16.8) 167,387 (17.7) Region n (%) Northeast 594,112 (33.4) 243,367 (29.2) 350,745 (37.1) Midwest 329,752 (18.6) 144,740 (17.4) 185,012 (19.6) West 224,931 (12.7) 127,325 (15.3) 97,606 (10.3) South 612,471 (34.5) 306,363 (36.8) 306,108 (32.4) Unknown 16,070 (0.9) 10,759 (1.3) 5,311 (0.6) Hypertension n (%) 1,368,891 (77.0) 701,698 (84.3) 667,193 (70.6) Type 2 Diabetes n (%) 718,725 (40.4) 403,127 (48.4) 315,598 (33.4) Gastrointestinal Conditions n (%) 599,334 (33.9) 267,904 (32.2) 331,430 (35.5) % refers to proportion of overall cohort Table 2. Likelihood of Receiving Long-Term NSAID Prescriptions Based on CKD Stage Overall OR [95% CI] CKD Stage 3 OR [95% CI] CKD Stage 4 OR [95% CI] CKD Stage 5 OR [95% CI] No CKD diagnosis on record ^ 1.76 [1.738-1.787] 1.48 [1.46-1.50] 1.93 [1.86-2.00] 1.39 [1.32-1.45] Female 1.47 [1.456-1.491] 1.47 [1.46-1.49] 1.52 [1.47-1.457] 1.26 [1.21-1.31] Race (Asian) ^^ 0.580 [0.522-0.650] 0.73 [0.70-0.76] 0.53 [0.47-0.60] 0.58 [0.51-0.65] Race (Black) ^^ 1.370 [1.301-1.437] 1.17 [1.15-1.19] 1.21 [1.17-1.27] 1.36 [1.30-1.42] Race (Other) ^^ 0.870 [0.811-0.932] 1.05 [1.03-1.07] 0.97 [0.91-1.03] 0.87 [0.82-0.92] Age 1.02 [1.020-1.021] 0.98 [0.98-0.98] 0.99 [0.99-0.99] 0.99 [0.98-0.99] Region (Northeast) ^^^ 0.451 [0.444-0.458] 0.47 [0.46-0.48] 0.41 [0.39-0.43] 0.34 [0.32-0.37] Region (South) ^^^ 0.574 [0.566-0.582] 0.60 [0.59-0.61] 0.54 [0.52-0.57] 0.48 [0.46-0.51] Region (West) ^^^ 0.728 [0.715-0.742] 0.74 [0.73-0.76] 0.70 [0.65-0.74] 0.73 [0.68-0.77] Hypertension 1.374 [1.355-1.394] 1.36 [1.34-1.38] 1.57 [1.47-1.66] 1.39 [1.31-1.48] Type 2 Diabetes 0.991 [0.979-1.002] 0.97 [0.96-0.98] 0.98 [0.95-1.02] 1.18 [1.14-1.23] Gastrointestinal Conditions 1.868 [1.848-1.889] 1.81 [1.79-1.83] 1.86 [1.80-1.92] 1.90 [1.82-1.98] Study Year 0.867 [0.865-0.868] 0.87 [0.87-0.87] 0.90 [0.89-0.90] 0.87 [0.87-0.88] CKD= Chronic Kidney Disease; GFR= Glomerular Filtration Rate; NSAID= Nonsteroidal Anti-inflammatory Drug; OR= Odds Ratio; CI= Confidence Interval ^ Patient met diagnostic criteria for CKD based on 2 consecutive GFR results 90 days during a 365-day period. We used race-adjusted GFR for Black/African American patients whenever it was reported to reflect values most likely used by clinicians. ^^ reference group= White ^^^ reference group= Midwest Additional Declarations No competing interests reported. Supplementary Files AppendixNSAIDsVariationbyCKDDocumentation.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 Dec, 2025 Reviewers invited by journal 25 Nov, 2025 Editor assigned by journal 18 Nov, 2025 Editor invited by journal 27 Oct, 2025 Submission checks completed at journal 26 Oct, 2025 First submitted to journal 26 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7792064","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":552916932,"identity":"9a2ac9d0-ba43-4111-931f-8119d4279693","order_by":0,"name":"Isaiah Yim","email":"","orcid":"","institution":"Virginia Tech Carilion School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Isaiah","middleName":"","lastName":"Yim","suffix":""},{"id":552916935,"identity":"53c72a53-658d-479f-b9f1-679cfbc3f277","order_by":1,"name":"Monica Ahrens","email":"","orcid":"","institution":"Virginia Tech","correspondingAuthor":false,"prefix":"","firstName":"Monica","middleName":"","lastName":"Ahrens","suffix":""},{"id":552916939,"identity":"4d4f249c-06de-48e7-9c12-03a42d471b5b","order_by":2,"name":"YingXing Wu","email":"","orcid":"","institution":"Carilion Clinic","correspondingAuthor":false,"prefix":"","firstName":"YingXing","middleName":"","lastName":"Wu","suffix":""},{"id":552916940,"identity":"810ffb47-938a-456c-ad13-07a85f98be2f","order_by":3,"name":"John W. Epling","email":"","orcid":"","institution":"Virginia Tech Carilion School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"W.","lastName":"Epling","suffix":""},{"id":552916942,"identity":"fcbc0d18-7c16-4d2a-be5e-81f9613d5222","order_by":4,"name":"Michelle S. Rockwell","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAmUlEQVRIiWNgGAWjYNCCAzZgSoIULWkMPKRqOUyCFt3+sw8f/DhzPnE/A/PB2zzEaDG7kW5s2HPjdmIPA1uyNZFa2NikGT7cNuZh4DGTJk7L+WMgLeeAWvi/EanlQBpQy40DckBb2IjUciON2bDnTLIcz2E2Y8s5RDqM8cGPY3Y87O3ND2+8IUYLAjCTpnwUjIJRMApGAT4AAPQRLGNmF80xAAAAAElFTkSuQmCC","orcid":"","institution":"Virginia Tech Carilion School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Michelle","middleName":"S.","lastName":"Rockwell","suffix":""}],"badges":[],"createdAt":"2025-10-06 14:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7792064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7792064/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97250050,"identity":"5bc06cfe-b06a-46e1-8993-f3ab74d97280","added_by":"auto","created_at":"2025-12-02 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10:37:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27861,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixNSAIDsVariationbyCKDDocumentation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7792064/v1/92ac879598888faaf0d639b0.docx"},{"id":97150836,"identity":"28c031c6-1e04-4d5a-afea-8ce7c020c063","added_by":"auto","created_at":"2025-12-01 10:37:20","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113971,"visible":true,"origin":"","legend":"","description":"","filename":"fa2784256edb48cab2ac072c8ca877c41enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7792064/v1/5c77ac32835114b403fc6ce7.xml"},{"id":97150834,"identity":"3b05ef5b-19a4-4acc-97ec-53716fe85420","added_by":"auto","created_at":"2025-12-01 10:37:20","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110868,"visible":true,"origin":"","legend":"","description":"","filename":"fa2784256edb48cab2ac072c8ca877c41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7792064/v1/1d05af8f19eecbadb7ac5bf2.xml"},{"id":97150835,"identity":"88e6c969-cc26-4b06-b869-e58a4d674219","added_by":"auto","created_at":"2025-12-01 10:37:20","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122149,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7792064/v1/2626fc093d09939897c5e6ba.html"},{"id":97150829,"identity":"f8aff459-e410-4f7e-8dd4-7293b862c4a0","added_by":"auto","created_at":"2025-12-01 10:37:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLong-Term NSAID Use Among CKD Patients With and Without a CKD Diagnosis on Record\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCKD= Chronic Kidney Disease, NSAIDs= nonsteroidal anti-inflammatory drugs\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7792064/v1/c157db0d96699263e01b8a43.png"},{"id":97252471,"identity":"0ca8a8ef-ca11-4368-a666-8b5b22fd6dcc","added_by":"auto","created_at":"2025-12-02 13:21:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":941489,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7792064/v1/fa06c9e9-2b44-44fa-ac30-778bd580561c.pdf"},{"id":97150832,"identity":"159e713c-2e52-42ad-814c-f3d49df24f00","added_by":"auto","created_at":"2025-12-01 10:37:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27861,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixNSAIDsVariationbyCKDDocumentation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7792064/v1/607db715c2b74711d6e13cb7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHigh-Risk Nonsteroidal Anti-inflammatory Drug Prescribing: Variation by Documentation of Chronic Kidney Disease Status\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic kidney disease (CKD) is a progressive condition associated with substantial morbidity and mortality.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eAs one of the costliest health conditions in the U.S., CKD spending exceeds \u003cspan\u003e$\u003c/span\u003e200\u0026nbsp;billion annually.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Progression of CKD to more advanced stages can be delayed or prevented through the provision of guideline-directed care that combines lifestyle modification, pharmacologic therapy, risk factor management, and avoidance of nephrotoxic exposures.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e An important barrier to receipt of guideline-directed CKD care is recognition of the condition. Generally asymptomatic at early stages, many patients are unaware that they have CKD.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In addition, CKD is commonly under-documented in medical records. Multiple studies have identified patients with laboratory evidence of CKD but no corresponding diagnosis documented in the medical record.\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePatients who meet diagnostic criteria for CKD but lack corresponding clinical documentation may be less likely to receive guideline-directed care and more likely to suffer disease complications compared with patients with appropriate documentation.\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e For example, Frigaard et al.\u003csup\u003e12\u003c/sup\u003e showed that patients with evidence of CKD based on estimated glomerular filtration rate (GFR) but no diagnosis assigned in the medical record were less likely to receive guideline-directed laboratory testing and statin prescriptions. Healthcare spending has also been shown to be greater for patients who lack CKD documentation.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Less is known about the influence of CKD documentation on likelihood of exposure to inappropriate or low-value care.\u003c/p\u003e\u003cp\u003eIn patients with CKD, regular use of nonsteroidal anti-inflammatory drugs (NSAIDs) can precipitate acute kidney injury, accelerate decline in kidney function, increase fluid retention and blood pressure, and raise cardiovascular risk.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e As such, the National Kidney Foundation\u0026rsquo;s Kidney Disease: Improving Global Outcomes (KDIGO) guidelines recommend limited use of NSAIDs in all patients with CKD and avoidance of NSAIDs with estimated glomerular filtration rate (GFR)\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73m.\u003csup\u003e2,5\u003c/sup\u003e Despite these guidelines, inappropriate NSAID prescribing persists.\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Up to 33% of CKD patients are prescribed NSAIDs in the U.S. primary care setting.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Understanding the influence of CKD documentation status on receipt of high-risk medications such as NSAIDs can provide important insights into gaps in quality of care and highlight opportunities to improve healthcare delivery for patients with CKD.\u003c/p\u003e\u003cp\u003eIn the present study, we evaluated variation in NSAID prescribing based on CKD documentation status in a large U.S. cohort over 10 years (2013\u0026ndash;2023), hypothesizing that patients who met diagnostic criteria for CKD but had no corresponding diagnosis on record were more likely to receive long-term prescription NSAIDs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective cohort study used data from the TriNetX U.S. Research Network, which included electronic health record (EHR) data from 67 U.S. healthcare organizations. This study was approved by the Institutional Review Board (IRB) of Carilion Clinic\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003eIRB-23-1842). Participants\u0026rsquo; informed consent was waived based on the use of aggregated, de-identified data.\u003c/p\u003e\n\u003cp\u003eWe used STROBE guidelines to guide the study and prepare this report.\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA cohort of adult (\u003cu\u003e\u0026gt;\u003c/u\u003e 18 years of age) patients with CKD who had been seen for at least 2 ambulatory visits during any 12-month period between January 1, 2013 and December 31, 2023 was established. Patients included in the cohort had 2 or more CKD ICD codes \u003cstrong\u003e(Appendix Table 1)\u003c/strong\u003e documented in the encounter or problem list or \u0026gt;2 consecutive GFR results \u0026lt;60 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e separated by at least 90 days during a single 365-day period. In effort to represent the GFR result likely considered by clinicians, we analyzed race-adjusted GFR for Black or African American patients whenever it was reported.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProcedures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe determined the proportion of patients with a CKD diagnosis (ICD-9 or -10) documented in the EHR overall and stratified by sex, ethnicity, race, U.S. census region, and comorbidities (hypertension, type 2 diabetes, and gastrointestinal conditions).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe identified all oral NSAIDs \u003cstrong\u003e(Appendix Table 2)\u003c/strong\u003e prescribed during the study period. Long-term use was defined as 3 or more prescriptions separated by at least 21 days within a single 365-day period. The primary outcome variable was dichotomous \u0026ndash; prescribed long-term NSAIDs vs. not prescribed long-term NSAIDs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used logistic regression to evaluate the relationship between CKD documentation and long-term NSAID prescribing for patients with Stage 3-5 CKD, controlling for demographics, comorbidities, and year. All statistical analyses were performed using R, version 4.3.1.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOur cohort included 1,777,336 CKD patients (age 66.7 (SD: 12.5) years, 50.4% female, 67.7% White and 14.8% Black or African American) representing all U.S. census regions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The majority of patients (77.0%) had a diagnosis of hypertension on record, while 40.4% had a type 2 diabetes diagnosis on record.\u003c/p\u003e\u003cp\u003eWithin the cohort, 832,554 patients (47.1%) had a CKD diagnosis (ICD-9 or \u0026minus;\u0026thinsp;10) on record, while 944,782 (52.9%) met diagnostic criteria for CKD but had no diagnosis on record. A greater proportion of males than females and Black/African American patients than those representing other races had a CKD diagnosis on record (55.2% vs. 39.3% and 64.2% vs. 43.8%, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Documentation of CKD status improved as CKD stage progressed. Among patients with Stage 3 CKD, 41.2% had a CKD diagnosis on record, while 74.1% of patients with Stage 4 or 5 CKD had a CKD diagnosis on record.\u003c/p\u003e\u003cp\u003eAcross the cohort, 159,070 patients (9.0%) received long-term NSAIDs (6.3% of patients with a CKD diagnosis on record and 11.5% of those without) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The odds of receiving long-term NSAIDs declined by 13.3% per year during 2013\u0026ndash;2023 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThroughout the study period, the odds of receiving long-term NSAIDs was 1.76 times higher [95% CI: 1.74\u0026ndash;1.79] in patients without vs. with a CKD diagnosis on record (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The influence of CKD documentation status on odds of long-term NSAIDs receipt varied by stage: Stage 3, 4, and 5 CKD patients without a diagnosis on record experienced 1.48 [95% CI: 1.46\u0026ndash;1.50], 1.93 [95% CI: 1.86-2.00], and 1.39 [95% CI: 1.32\u0026ndash;1.45] times greater odds of receiving long-term NSAIDs than those with documented CKD (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSome demographic and clinical factors independently influenced the odds of receiving long-term NSAIDs. Females experienced 1.47 [95% CI:1.46\u0026ndash;1.49] times greater odds of receipt than males and Black/African American patients experienced 1.37 [95% CI:1.30\u0026ndash;1.44] times greater odds than patients identifying with other races (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Having diagnoses of hypertension or gastrointestinal conditions on record increased the odds of receiving long-term NSAIDs by 1.37 [95% CI: 1.355\u0026ndash;1.394] and 1.87 [95% CI: 1.848\u0026ndash;1.889] times compared with patients without these diagnoses.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAmong a cohort of nearly 1.8\u0026nbsp;million U.S. patients, those with a CKD diagnosis documented in the EHR had 43% lower odds of being prescribed long-term NSAIDs than those who met diagnostic criteria for CKD but lacked corresponding diagnosis. Although some,\u003csup\u003e10\u0026ndash;14\u003c/sup\u003e but not all,\u003csup\u003e8\u003c/sup\u003e prior studies have identified a positive relationship between CKD documentation status and the delivery of guideline-directed CKD care (e.g., albuminuria testing, nephroprotective drug prescribing, nephrologist referral), the present study is among the first to describe an influence on delivery of inappropriate or low-value care. These findings suggest that improved EHR documentation may be a target for improving medication safety and health outcomes for patients with CKD.\u003c/p\u003e\u003cp\u003eUse of NSAIDs can precipitate acute kidney injury, accelerate progression of CKD, and exacerbate hypertension, edema, and heart failure, particularly among patients with stage 4 CKD or worse.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Prior studies have documented persistent NSAID use among CKD patients.\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e The present study identified utilization of long-term NSAIDs by 9% of CKD patients in our large national cohort. While it is encouraging that long-term NSAID prescribing rates declined throughout the 2013 to 2023 study period, continued de-implementation efforts are needed to reduce the avoidable risks associated with long-term NSAID use by patients with CKD. Initiatives to improve EHR documentation are a potential approach. These initiatives may be combined with clinician and patient education, clinical decision support, and audit and feedback, which may also contribute to the de-implementation of high-risk NSAIDs.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Improving access to alternate pain management for patients with CKD, who experience high rates of chronic pain,\u003csup\u003e27,28\u003c/sup\u003e may also be effective.\u003c/p\u003e\u003cp\u003eThere are numerous barriers to improved EHR documentation of CKD status. Stewart et al\u003csup\u003e29\u003c/sup\u003e identified a number of health system, primary care, and patient-level barriers identified in the literature. Examples include lack of EHR interoperability or decision support, limited reimbursement for comprehensive care for patients with CKD, limited time for visits, primary care clinicians\u0026rsquo; lack of knowledge or comfort related to CKD management, and clinician\u0026rsquo;s lack of perceived agency for improving CKD outcomes. Clinicians\u0026rsquo; concerns about overwhelming patients with a formal CKD diagnosis or deterring focus from other health conditions were also highlighted.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Facilitators to the improvement of EHR CKD documentation have also been reported, with supportive technology and coordination among the care team highlighted as key enablers.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWe observed demographic disparities in long-term NSAID prescribing. For example, women with CKD were significantly more likely than men with CKD to receive long-term NSAIDs. Similarly, Black/African American patients experienced over 35% greater likelihood of receiving long-term NSAIDs than White patients. In the U.S., Black and African American patients receive poorer quality CKD care than patients representing other races.\u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e These disparities contribute to inequitable outcomes and have been reinforced by practices such as race-adjusted GFR equations. Further research is needed to identify drivers of these disparities.\u003c/p\u003e\u003cp\u003eThe finding that patients with documented hypertension or gastrointestinal conditions were at greater odds of receiving long-term NSAIDs was surprising given professional recommendations to limit or avoid NSAIDs in patients with these conditions.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,35\u003c/sup\u003e This finding warrants further investigation.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has some limitations. First, use of EHR data from multiple U.S. health systems aggregated through the TriNetX Research Network may have introduced variation in coding. To mitigate this, we used standardized TriNetX data harmonization procedures and required at least two ambulatory encounters and two CKD indicators (ICD codes or GFR results) to strengthen diagnostic specificity and minimize misclassification. Second, like most aggregated EHR/real world datasets, lack of unstructured data from clinical notes and loss of information during the harmonization process across different health systems is a possibility.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Third, the TriNetX dataset does not contain comprehensive data about prescription dosage and days prescribed, which precluded nuanced assessment of changes in NSAID utilization. Fourth, we acknowledge that information related to clinician and patients\u0026rsquo; decision to use NSAIDs despite the risks is not included in the dataset, nor is over-the-counter NSAID use. As such, our results may underestimate overall NSAIDs exposure. We reduced this bias by defining long-term use as \u0026gt;\u0026thinsp;3 prescriptions over a 12-month period, which likely reflects sustained, clinician-directed therapy, and interpreted our estimates conservatively as a lower bound of true exposure. Despite these limitations, this large cohort of CKD patients representing all U.S. census regions and diversity in age and race provides robust, generalizable insights into patterns of NSAID prescribing and the impact of CKD documentation on receipt of high-risk medications.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eLess than half of patients in our cohort of nearly 1.8\u0026nbsp;million patients from 67 U.S. healthcare organizations had documentation of CKD in their medical record despite clinical evidence of CKD. Patients without EHR documentation on record were at 1.76 greater odds of being prescribed long-term NSAIDs, which are recognized as high-risk for most patients with CKD. These findings build upon previous research showing that omission of clinical documentation decreases the likelihood that CKD patients receive guideline-directed care, such as nephroprotective medications and referral to nephrologist. Efforts to improve CKD documentation, which may include EHR-based clinical decision support, education, and incentives, may contribute to improved quality of care for patients with CKD through the de-implementation of high-risk medication prescribing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eIY prepared the study design, data analysis, interpretation, and prepared the first draft of the manuscript. MR helped contribute to the study design, interpretation, manuscript revision, and supervision. JE contributed to interpretation and manuscript revision. MA contributed to the study design, data analysis, manuscript revision, and supervision. YW contributed to data extraction and analysis, and manuscript revision. All authors approved the final version of the manuscript\u003c/p\u003e\u003cp\u003eEthics Approval and Consent to Participate: This research was approved by the Institutional Review Board (IRB) of Carilion Clinic\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003eIRB-23-1842) in compliance with the Declaration of Helsinki. Participants\u0026rsquo; informed consent was waived based on the use of aggregated, de-identified EHR data in accordance with the Common Rule for waiver of informed consent (45 CFR 46.116(f)).\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed, accepted, and acknowledged that this work adheres to the International Committee of Medical Journal Editors (\u003cem\u003eICMJE)\u0026nbsp;\u003c/em\u003eguidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for Publication: Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWe are unable to make the data used for this study publicly available due to an institutional agreement with TriNetX. To obtain access to the data, contact TriNetX at 125 Cambridgepark Drive, Suite 500, Cambridge, MA, 02140, United States, (857) 285-6037, https://www.trinetx.com.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting Interests:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflicting and competing interests.\u003c/p\u003e\n\u003cp\u003eFunding Declaration: This project was funded by the Carilion Clinic Research Acceleration Program.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements: The authors thank Hunter Sharp (Carilion Clinic) for analytical support and Temrak Orchid Tucker and Sonia Warrior (Carilion Clinic) for assistance with manuscript preparation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFrancis A, Harhay MN, Ong ACM, Tummalapalli SL, Ortiz A, Fogo AB, Fliser D, Roy-Chaudhury P, Fontana M, Nangaku M, Wanner C, Malik C, Hradsky A, Adu D, Bavanandan S, Cusumano A, Sola L, Ulasi I, Jha V. 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Preprint posted online January 7, 2025:2025.01.03.25319965. doi:10.1101/2025.01.03.25319965\u003c/li\u003e\n\u003cli\u003eStrengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration | Annals of Internal Medicine. Accessed February 25, 2024. https://www.acpjournals.org/doi/full/10.7326/0003-4819-147-8-200710160-00010-w1\u003c/li\u003e\n\u003cli\u003eBaker M, Perazella MA. NSAIDs in CKD: Are They Safe? \u003cem\u003eAm J Kidney Dis\u003c/em\u003e. 2020;76(4):546-557. doi:10.1053/j.ajkd.2020.03.023\u003c/li\u003e\n\u003cli\u003eGuthrie B. Can NSAIDs Be Used Safely for Analgesia in Patients with CKD?: CON. \u003cem\u003eKidney360\u003c/em\u003e. 2020;1(11):1189-1191. doi:10.34067/KID.0005112020\u003c/li\u003e\n\u003cli\u003eAmerican Geriatrics Society Beers Criteria\u0026reg; Update Expert Panel. American Geriatrics Society 2023 updated AGS Beers Criteria\u0026reg; for potentially inappropriate medication use in older adults. \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e. 2023;71(7):2052-2081. doi:10.1111/jgs.18372\u003c/li\u003e\n\u003cli\u003eRockwell MS, Oyese EG, Singh E, Vinson M, Yim I, Turner JK, Epling JW. Scoping review of interventions to de-implement potentially harmful non-steroidal anti-inflammatory drugs (NSAIDs) in healthcare settings. \u003cem\u003eBMJ Open\u003c/em\u003e. 2024;14(4):e078808. doi:10.1136/bmjopen-2023-078808\u003c/li\u003e\n\u003cli\u003eLambourg E, Colvin L, Guthrie G, Murugan K, Lim M, Walker H, Boon G, Bell S. The prevalence of pain among patients with chronic kidney disease using systematic review and meta-analysis. \u003cem\u003eKidney Int\u003c/em\u003e. 2021;100(3):636-649. doi:10.1016/j.kint.2021.03.041\u003c/li\u003e\n\u003cli\u003eDavison SN, Rathwell S, Ghosh S, George C, Pfister T, Dennett L. The Prevalence and Severity of Chronic Pain in Patients With Chronic Kidney Disease: A Systematic Review and Meta-Analysis. \u003cem\u003eCan J Kidney Health Dis\u003c/em\u003e. 2021;8. doi:10.1177/2054358121993995\u003c/li\u003e\n\u003cli\u003eStewart S, Kalra PA, Blakeman T, Kontopantelis E, Cranmer-Gordon H, Sinha S. Chronic kidney disease: detect, diagnose, disclose\u0026mdash;a UK primary care perspective of barriers and enablers to effective kidney care. \u003cem\u003eBMC Med\u003c/em\u003e. 2024;22:331. doi:10.1186/s12916-024-03555-0\u003c/li\u003e\n\u003cli\u003eNeale EP, Middleton J, Lambert K. Barriers and enablers to detection and management of chronic kidney disease in primary healthcare: a systematic review. \u003cem\u003eBMC Nephrol\u003c/em\u003e. 2020;21:83. doi:10.1186/s12882-020-01731-x\u003c/li\u003e\n\u003cli\u003eChu CD, Powe NR, McCulloch CE, Crews DC, Han Y, Bragg-Gresham JL, Saran R, Koyama A, Burrows NR, Tuot DS, Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team. Trends in Chronic Kidney Disease Care in the US by Race and Ethnicity, 2012-2019. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. 2021;4(9):e2127014. doi:10.1001/jamanetworkopen.2021.27014\u003c/li\u003e\n\u003cli\u003eBenjamins MR, Lorenz P, Saiyed NS, Silva A, Mattix-Kramer HJ, Pys P, Schulz A. Black-White Inequities in Kidney Disease Mortality Across the 30 Most Populous US Cities. \u003cem\u003eJ Gen Intern Med\u003c/em\u003e. 2022;37(6):1351-1358. doi:10.1007/s11606-022-07444-1\u003c/li\u003e\n\u003cli\u003eSuarez J, Cohen JB, Potluri V, Yang W, Kaplan DE, Serper M, Shah SP, Reese PP. Racial Disparities in Nephrology Consultation and Disease Progression among Veterans with CKD: An Observational Cohort Study. \u003cem\u003eJ Am Soc Nephrol JASN\u003c/em\u003e. 2018;29(10):2563-2573. doi:10.1681/ASN.2018040344\u003c/li\u003e\n\u003cli\u003eZhan M, Peter WLS, Doerfler RM, Woods CM, Blumenthal JB, Diamantidis CJ, Hsu C yuan, Lash JP, Lustigova E, Mahone EB, Ojo AO, Slaven A, Strauss L, Taliercio JJ, Winkelmayer WC, Xie D, Fink JC, Investigators the CRIC (CRIC) S. Patterns of NSAIDs Use and Their Association with Other Analgesic Use in CKD. \u003cem\u003eClin J Am Soc Nephrol CJASN\u003c/em\u003e. 2017;12(11):1778. doi:10.2215/CJN.12311216\u003c/li\u003e\n\u003cli\u003eGoldstein ND, Olivieri-Mui B, Burstyn I. Are Aggregated Electronic Health Record Datasets Good for Research? \u003cem\u003eJ Gen Intern Med\u003c/em\u003e. Published online August 12, 2025. doi:10.1007/s11606-025-09808-9\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographics and Clinical Characteristics of Chronic Kidney Disease Patients with and without a Diagnosis (ICD) on Record\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"658\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD Diagnosis on Record\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeets CKD \u0026nbsp; \u0026nbsp; Diagnostic Criteria but no Diagnosis on Record\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1,777,336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e832,554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e944,782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e66.74 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e64.75 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e68.49 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 18.3891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e819,378 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e452,297 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e367,081 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e896,065 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e352,538 (42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e54,327 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e61,898 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e27,719 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e34,179 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 18.3891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1,203,282 (67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e503,198 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e700,084 (74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlack/African American\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e262,574 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e168,631 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e93,943 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e54,603 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e33,481 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e21,122 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e256,877 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e127,244 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e129,633 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 18.3891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Ethnicity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHispanic or Latino\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e98,259 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e62,337 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e35,922 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot Hispano or Latino\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1371,891 (77.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e630,418 (75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e741,473 (78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e307,186 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e139,799 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e167,387 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 18.3891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNortheast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e594,112 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e243,367 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e350,745 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMidwest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e329,752 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e144,740 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e185,012 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e224,931 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e127,325 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e97,606 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e612,471 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e306,363 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e306,108 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e16,070 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e10,759 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e5,311 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1,368,891 (77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e701,698 (84.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e667,193 (70.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType 2 Diabetes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e718,725 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e403,127 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e315,598 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3891%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGastrointestinal Conditions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e599,334 (33.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e267,904 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e331,430 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e% refers to proportion of overall cohort\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Likelihood of Receiving Long-Term NSAID Prescriptions Based on CKD Stage\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"893\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD Stage 3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOR [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD Stage 4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOR [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD Stage 5\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOR [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eNo CKD diagnosis on record\u003csup\u003e^\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.76\u003c/strong\u003e [1.738-1.787]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.48\u0026nbsp;\u003c/strong\u003e[1.46-1.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.93\u003c/strong\u003e [1.86-2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.39\u003c/strong\u003e [1.32-1.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.47\u003c/strong\u003e [1.456-1.491]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.47\u003c/strong\u003e [1.46-1.49]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.52\u003c/strong\u003e [1.47-1.457]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.26\u003c/strong\u003e [1.21-1.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eRace (Asian)\u003cstrong\u003e\u003csup\u003e^^\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.580\u003c/strong\u003e [0.522-0.650]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.73\u003c/strong\u003e [0.70-0.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.53\u003c/strong\u003e [0.47-0.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.58\u003c/strong\u003e [0.51-0.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eRace (Black)\u003cstrong\u003e\u003csup\u003e^^\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.370\u003c/strong\u003e [1.301-1.437]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.17\u003c/strong\u003e [1.15-1.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.21\u003c/strong\u003e [1.17-1.27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.36\u003c/strong\u003e [1.30-1.42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eRace (Other)\u003cstrong\u003e\u003csup\u003e^^\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.870\u003c/strong\u003e [0.811-0.932]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.05\u003c/strong\u003e [1.03-1.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.97\u003c/strong\u003e [0.91-1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87\u003c/strong\u003e [0.82-0.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.02\u003c/strong\u003e [1.020-1.021]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.98\u003c/strong\u003e [0.98-0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.99\u003c/strong\u003e [0.99-0.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.99\u003c/strong\u003e [0.98-0.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eRegion (Northeast)\u003cstrong\u003e\u003csup\u003e^^^\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.451\u003c/strong\u003e [0.444-0.458]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.47\u003c/strong\u003e [0.46-0.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.41\u003c/strong\u003e [0.39-0.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.34\u003c/strong\u003e [0.32-0.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eRegion (South)\u003cstrong\u003e\u003csup\u003e^^^\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.574\u003c/strong\u003e [0.566-0.582]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.60\u003c/strong\u003e [0.59-0.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.54\u003c/strong\u003e [0.52-0.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.48\u003c/strong\u003e [0.46-0.51]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eRegion (West)\u003cstrong\u003e\u003csup\u003e^^^\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.728\u003c/strong\u003e [0.715-0.742]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.74\u003c/strong\u003e [0.73-0.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.70\u003c/strong\u003e [0.65-0.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.73\u003c/strong\u003e [0.68-0.77]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.374\u003c/strong\u003e [1.355-1.394]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.36\u003c/strong\u003e [1.34-1.38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.57\u003c/strong\u003e [1.47-1.66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.39\u003c/strong\u003e [1.31-1.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eType 2 Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.991\u003c/strong\u003e [0.979-1.002]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.97\u003c/strong\u003e [0.96-0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.98\u003c/strong\u003e [0.95-1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.18\u003c/strong\u003e [1.14-1.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eGastrointestinal Conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.868\u003c/strong\u003e [1.848-1.889]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.81\u003c/strong\u003e [1.79-1.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.86\u003c/strong\u003e [1.80-1.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.90\u003c/strong\u003e [1.82-1.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 209px;\"\u003e\n \u003cp\u003eStudy Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.867\u003c/strong\u003e [0.865-0.868]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87\u003c/strong\u003e [0.87-0.87]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.90\u003c/strong\u003e [0.89-0.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87\u003c/strong\u003e [0.87-0.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCKD= Chronic Kidney Disease; GFR= Glomerular Filtration Rate; NSAID= Nonsteroidal Anti-inflammatory Drug; OR= Odds Ratio; CI= Confidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e^\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003ePatient met diagnostic criteria for CKD based on 2 consecutive GFR results \u0026lt;60 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e separated by \u0026gt;90 days during a 365-day period. We used race-adjusted GFR for Black/African American patients whenever it was reported to reflect values most likely used by clinicians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e^^\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003ereference group= White\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e^^^\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003ereference group= Midwest\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NSAID, quality, medication safety, low-value care, CKD","lastPublishedDoi":"10.21203/rs.3.rs-7792064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7792064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eChronic kidney disease (CKD) affects over 10% of people globally. Despite its prevalence, CKD is persistently under-recognized and under-documented in healthcare settings. Previous studies have shown that patients who meet laboratory-based criteria for CKD but have no formal CKD diagnosis in their medical record are less likely to receive guideline-directed medical care for CKD. However, it is unclear whether CKD documentation status influences receipt of medications that may be high-risk for patients with CKD, including nonsteroidal anti-inflammatory drugs (NSAIDs).\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eWe evaluated variation in NSAID prescribing based on CKD documentation status over a 10-year period, hypothesizing that patients who met diagnostic criteria for CKD but had no corresponding ICD diagnosis on record were more likely to receive long-term prescription NSAIDs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study used electronic health record (EHR) data from the TriNetX Research Network representing 67 U.S. healthcare organizations (2013\u0026ndash;2023). Adult patients with CKD were classified by CKD documentation status (diagnosis on record vs. criteria for CKD but no diagnosis on record). We applied logistic regression to assess the relationship between CKD documentation status and receipt of long-term NSAIDs, adjusting for demographics, comorbidities, and year.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 1,777,336 patients, 52.9% met diagnostic criteria for CKD but had no CKD diagnosis on record. Overall, 9.0% of patients were prescribed long-term NSAIDs (6.3% of patients with a CKD diagnosis on record and 11.5% of those without). Patients with no CKD diagnosis on record were at 1.76 [95% CI: 1.74\u0026ndash;1.79] times greater odds of receiving long-term NSAIDs compared to patients with a diagnosis on record. Female sex and Black/African American race were independently associated with higher odds of receiving long-term NSAIDs (1.47 [95% CI:1.46\u0026ndash;1.49] and 1.37 [95% CI:1.30\u0026ndash;1.44], respectively).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAcross 67 US healthcare organizations, more than half of patients with laboratory-based evidence of CKD did not have a CKD diagnosis on record in the EHR. Absence of CKD documentation was associated with increased likelihood of receiving long-term NSAIDs. These findings suggest improved EHR documentation as a target for reducing potentially harmful prescribing in patients with CKD.\u003c/p\u003e","manuscriptTitle":"High-Risk Nonsteroidal Anti-inflammatory Drug Prescribing: Variation by Documentation of Chronic Kidney Disease Status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 10:37:15","doi":"10.21203/rs.3.rs-7792064/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"217307205321842679338579730607355720586","date":"2025-12-06T07:05:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T10:43:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T10:41:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-27T07:33:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-26T16:46:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-10-26T16:43:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f989a2ef-a894-4bcc-bc90-cb45d92196f6","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T10:37:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 10:37:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7792064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7792064","identity":"rs-7792064","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}