Background: Intensive BP lowering in the Systolic Blood Pressure Intervention Trial (SPRINT) produced acute decreases in kidney function and higher risk for AKI. We evaluated the effect of intensive BP lowering on long-term changes in kidney function using trial and outpatient electronic health record (EHR) creatinine values.Methods: SPRINT data were linked with EHR data from 49 (of 102) study sites. The primary outcome was the total slope of decline in eGFR for the intervention phase and the post-trial slope of decline during the observation phase using trial and outpatient EHR values. Secondary outcomes included a >= 30% decline in eGFR to < 60 ml/min per 1.73 m 2 and a >= 50% decline in eGFR or kidney failure among participants with baseline eGFR >= 60 and < 60 ml/min per 1.73 m 2 , respectively.Results: EHR creatinine values were available for a median of 8.3 years for 3041 participants. The total slope of decline in eGFR during the intervention phase was -0.67 ml/min per 1.73 m 2 per year (95% confidence interval [CI], -0.79 to -0.56) in the standard treatment group and -0.96 ml/min per 1.73 m 2 per year (95% CI, -1.08 to -0.85) in the intensive treatment group ( P < 0.001). The slopes were not significantly different during the observation phase: -1.02 ml/min per 1.73 m 2 per year (95% CI, -1.24 to -0.81) in the standard group and -0.85 ml/min per 1.73 m 2 per year (95% CI, -1.07 to -0.64) in the intensive group. Among participants without CKD at baseline, intensive treatment was associated with higher risk of a >= 30% decline in eGFR during the intervention (hazard ratio, 3.27; 95% CI, 2.43 to 4.40), but not during the postintervention observation phase. In those with CKD at baseline, intensive treatment was associated with a higher hazard of eGFR decline only during the intervention phase (hazard ratio, 1.95; 95% CI, 1.03 to 3.70).Conclusions: Intensive BP lowering was associated with a steeper total slope of decline in eGFR and higher risk for kidney events during the intervention phase of the trial, but not during the postintervention observation phase.

INTRODUCTION Patients with end-stage renal disease (ESRD) on dialysis and COVID-19 infection have an increased risk of in-hospital mortality, but whether these patients have a higher long-term mortality risk is unknown. MATERIALS AND METHODS Retrospective chart review of 958 patients admitted with COVID-19 infection or those with ESRD admitted for any other reason between February 2020 and August 2020. We collected data on demographics, comorbidities, laboratory tests, and mortality. The primary outcome was all-cause 1-year mortality. The secondary outcome was in-hospital mortality. We used primarily logistic regression models to assess the mortality risk. RESULTS In total, 651 patients without ESRD with COVID-19 (COVID+ESRD-), 259 with ESRD without -COVID-19 (ESRD+COVID-), and 48 with ESRD with COVID-19 (COVID+ESRD+) were hospitalized between February 2020 and August 2020. Patients were followed after discharge until September 2021. The all-cause 1-year mortality rates were 24% in patients with COVID+ESRD-, 22% in ESRD+COVID- patients, and 40% in those with COVID+ESRD+ (p < 0.05). Compared to the COVID+ESRD- group, the unadjusted and adjusted odds ratio (OR) for all-cause 1-year mortality in the COVID+ESRD+ group was 2.13 (95% confidence interval (CI), 1.16 - 3.91) and 2.15 (95% CI,1.12 - 4.14), respectively. The unadjusted and adjusted OR for all-cause in-hospital mortality in the COVID+ESRD+ group was 1.79 (95% CI, 0.92 - 3.49); and 1.79 (95% CI, 0.88 - 3.65), respectively. We found no statistically significant difference between the COVID+ESRD- and ESRD+COVID- groups for both in-hospital and 1-year mortality (p > 0.05). CONCLUSION Patients with COVID+ESRD+ have significantly higher odds for all-cause 1-year mortality compared to COVID+ESRD- patients. Future studies should investigate the mechanisms of long-term mortality risk in ESRD patients with COVID-19 infection.

Background Intensive BP lowering in the Systolic Blood Pressure Intervention Trial (SPRINT) produced acute decreases in kidney function and higher risk for AKI. We evaluated the effect of intensive BP lowering on long-term changes in kidney function using trial and outpatient electronic health record (EHR) creatinine values. Methods SPRINT data were linked with EHR data from 49 (of 102) study sites. The primary outcome was the total slope of decline in eGFR for the intervention phase and the post-trial slope of decline during the observation phase using trial and outpatient EHR values. Secondary outcomes included a ≥30% decline in eGFR to <60 ml/min per 1.73 m 2 and a ≥50% decline in eGFR or kidney failure among participants with baseline eGFR ≥60 and <60 ml/min per 1.73 m 2 , respectively. Results EHR creatinine values were available for a median of 8.3 years for 3041 participants. The total slope of decline in eGFR during the intervention phase was −0.67 ml/min per 1.73 m 2 per year (95% confidence interval [CI], −0.79 to −0.56) in the standard treatment group and −0.96 ml/min per 1.73 m 2 per year (95% CI, −1.08 to −0.85) in the intensive treatment group ( P < 0.001). The slopes were not significantly different during the observation phase: −1.02 ml/min per 1.73 m 2 per year (95% CI, −1.24 to −0.81) in the standard group and −0.85 ml/min per 1.73 m 2 per year (95% CI, −1.07 to −0.64) in the intensive group. Among participants without CKD at baseline, intensive treatment was associated with higher risk of a ≥30% decline in eGFR during the intervention (hazard ratio, 3.27; 95% CI, 2.43 to 4.40), but not during the postintervention observation phase. In those with CKD at baseline, intensive treatment was associated with a higher hazard of eGFR decline only during the intervention phase (hazard ratio, 1.95; 95% CI, 1.03 to 3.70). Conclusions Intensive BP lowering was associated with a steeper total slope of decline in eGFR and higher risk for kidney events during the intervention phase of the trial, but not during the postintervention observation phase.

Key Points Identifying ways to prevent AKI may reduce mortality further in the setting of intensive BP control. Creatinine-based ascertainment of AKI, enabled by electronic health record data, may be more sensitive and less biased than traditional serious adverse event adjudication.Background Adjudication of inpatient AKI in the Systolic Blood Pressure Intervention Trial (SPRINT) was based on billing codes and admission and discharge notes. The purpose of this study was to evaluate the effect of intensive versus standard BP control on creatinine-based inpatient and outpatient AKI, and whether AKI was associated with cardiovascular disease (CVD) and mortality.Methods We linked electronic health record (EHR) data from 47 clinic sites with trial data to enable creatinine-based adjudication of AKI. Cox regression was used to evaluate the effect of intensive BP control on the incidence of AKI, and the relationship between incident AKI and CVD and all-cause mortality.Results A total of 3644 participants had linked EHR data. A greater number of inpatient AKI events were identified using EHR data (187 on intensive versus 155 on standard treatment) as compared with serious adverse event (SAE) adjudication in the trial (95 on intensive versus 61 on standard treatment). Intensive treatment increased risk for SPRINT-adjudicated inpatient AKI (HR, 1.51; 95% CI, 1.09 to 2.08) and for creatinine-based outpatient AKI (HR, 1.40; 95% CI, 1.15 to 1.70), but not for creatinine-based inpatient AKI (HR, 1.20; 95% CI, 0.97 to 1.48). Irrespective of the definition (SAE or creatinine based), AKI was associated with increased risk for all-cause mortality, but only creatinine-based inpatient AKI was associated with increased risk for CVD.Conclusions Creatinine-based ascertainment of AKI, enabled by EHR data, may be more sensitive and less biased than traditional SAE adjudication. Identifying ways to prevent AKI may reduce mortality further in the setting of intensive BP control.

As vaccines against COVID-19 became available for distribution, the University of Miami addressed several challenges to facilitate vaccine allocation to the highest risk employees, patients, and students. Advanced use of technology allowed for the automation of key processes in the mass vaccination effort, which expedited vaccine outreach and scheduling, while maintaining routine delivery of healthcare services. The University's employees were initially prioritized for vaccination; employees who opted in were stratified into 5 vaccine administration phases. A similar process was implemented for students. When the state of Florida mandated expansion of vaccine allocation to include individuals aged 65 and older, an algorithm for patients was designed, taking into account age, comorbidities, date of last visit, and presence of an activated patient portal account. Innovative use of technology allowed for 19 000 vaccines to be administered within the first 37 days, which comprised 100% vaccine allotment, without wasting a single vaccine dose.

When South Florida became a hot spot for COVID-19 disease in March 2020, we faced an urgent need to develop test capability to detect SARS-CoV-2 infection. We assembled a transdisciplinary team of knowledgeable and dedicated physicians, scientists, technologists, and administrators who rapidly built a multiplatform, polymerase chain reaction- and serology-based detection program, established drive-through facilities, and drafted and implemented guidelines that enabled efficient testing of our patients and employees. This process was extremely complex, due to the limited availability of needed reagents, but outreach to our research scientists and multiple diagnostic laboratory companies, and government officials enabled us to implement both Food and Drug Administration authorized and laboratory-developed testing-based testing protocols. We analyzed our workforce needs and created teams of appropriately skilled and certified workers to safely process patient samples and conduct SARS-CoV-2 testing and contact tracing. We initiated smart test ordering, interfaced all testing platforms with our electronic medical record, and went from zero testing capacity to testing hundreds of health care workers and patients daily, within 3 weeks. We believe our experience can inform the efforts of others when faced with a crisis situation.

Soorus, Shane; Gershengorn, Hayley; Warde, Prem; Pebanco, Gilbert; Suarez, Maritza; Ferreira, Tanira Author Information

OBJECTIVES: Cardiovascular disease (CVD) continues to disproportionately affect disadvantaged populations, leading to calls to address social determinants of health (SDOH) as a preventive strategy. Our aim is to create a weighed SDOH score and to test the impact of each SDOH factor on the Framingham risk score (FRS) and on individual traditional CVD risk factors. STUDY DESIGN: We conducted a retrospective cohort study. METHODS: We included patients seen at a primary care clinic at UHealth/University of Miami Health System who answered a SDOH survey between September 16, 2016, and September 10, 2017. The survey included SDOH domains recommended by the American Heart Association position statement and by the National Academy of Medicine. We selected the FRS as well as all traditional CVD risk factors as our outcome metrics. RESULTS: We included 2876 patients. The mean (SD) age of our cohort was 53.8 (15.8) years, 61% were female, 9% were Black, 38% were Hispanic, and 87% reported speaking English. The statistically significant beta coefficients in the FRS model corresponded to being born outside of the United States, being a racial minority, living alone, having a high social isolation score, and having a low geocoded median household income (P <.01). Increasing quartile of SDOH score was significantly associated with higher systolic blood pressure, FRS, glycated hemoglobin, and smoking pack-years (P <.05). It was also associated with fewer minutes spent exercising weekly (P <.01). CONCLUSIONS: The addition of self-reported SDOH data has a dose effect on CVD risk factors. Future studies should address how to intervene to address social factors.

Evidence-based medicine, or the practice of medical decision-making grounded on the results of widely accepted findings in scientific literature, rules patient care in well-functioning medical facilities. Unfortunately, evidence-based medicine is not always evidence-based—often, populations of patients represented in studies do not reflect the target populations for the treatments being administered. The ability to generalize conclusions and identify effective solutions relies on a patient sample that accurately represents the general population. Recruiting a diverse population of individuals to participate in clinical studies, however, has posed a challenge to many researchers. Maximizing participation in clinical research first involves understanding which patient populations are underrepresented. Despite vast research describing the low participation of racial, ethnic, and other demographic minorities, little is known about how provider characteristics, including specialty type, influence patient involvement in clinical research. While previous literature has identified primary care physicians (PCPs) as the providers who see more demographically diverse populations and as potential gatekeepers who can influence patient awareness of clinical trials, it is unknown if a referral gap to trial participation exists between PCPs and specialists. Because physician invitation to trial participation is a primary motivator for patient enrollment, it is essential to understand the type of provider to whom patients turn when considering clinical research involvement. Using electronic medical record (EMR) data from a culturally and ethnically diverse large academic medical center, we identify predictors of patient willingness to be contacted for future clinical trials. This knowledge could improve the effectiveness of interventions aimed at increasing patient participation in clinical research. Data from patients aged 18 years and older seen at a culturally and ethnically diverse large academic medical center between November 2016 and August 2018 were extracted from PatientAtlas, an EMR-relational database. Willingness to be contacted for future research of any kind was assessed within the mandatory check-in forms during the first clinic encounter, and the provider’s specialty (primary or non-primary care) denoted the type of care received. Primary care departments included internal medicine, family medicine, general pediatrics, psychiatry, obstetrics/gynecology, hospitalist services, and pharmacy clinics. The relationship between willingness to consent to be contacted for clinical trials and provider specialty was analyzed using a binary logistic linear regression model while controlling for age, gender, ethnicity, and race. Of 320,117 total patients, 68,543 were seen by PCPs and 251,594 were seen by specialists at the time of choosing their consent preference. Primary care patients gave consent at a significantly lower proportion than specialty patients—18% (12,037) of all primary care patients and 24% (61,419) of all specialty care patients consented to be contacted regarding future clinical studies (Figure 1). After controlling for potential confounders including age, gender, ethnicity, and race, consenting to be contacted for clinical trials was significantly associated with seeing a specialist (adjusted odds ratio = 1.56 (95% confidence interval = 1.52–1.59)). In addition, patients who were of Caucasian race, non-Hispanic ethnicity, and in the age range of 60–69 years consented to be contacted at a significantly higher proportion than other groups of patients. Patient participation in clinical studies is a necessary component of patient-centered care and assists individuals in making informed healthcare decisions. Our analysis shows that patients seen in primary care departments were significantly less likely to give consent to be contacted for research than patients seen in specialty care clinics at a large academic center. Specialty care patients have more complicated, expensive, and later-stage illnesses, and therefore may be more willing

Background: Management of chronic kidney disease (CKD) patients includes efforts directed toward modifying traditional cardiovascular risk factors. Such efforts include optimal management of hypertension together with the initiation of statin therapy. Methods: In this observational study, we determine the modifying effect of statins on the relationship of systolic blood pressure (SBP) goal with mortality and other outcomes in patients with CKD participating in a clinical trial. At baseline, 2,646 CKD patients (estimated glomerular filtration rate < 60 mL/min/1.73 m2) were randomized to an intensive SBP goal < 120 mm Hg or standard SBP goal <140 mm Hg. One thousand two hundred and seventy-three were not on statin, 1,354 were on a statin, and in 19 the use of statin was unknown. The 2 primary outcomes were all-cause mortality and cardiovascular disease (CVD) mortality. Results: The relationships of SBP goal with all-cause mortality (interaction p = 0.009) and cardiovascular (CV) mortality (interaction p = 0.021) were modified by the use of statin after adjusting for age, gender, race, CVD history, smoking, aspirin use, and blood pressure at baseline. In the statin group, targeting SBP to < 120 mm Hg compared to SBP < 140 mm Hg significantly reduced the risk of all-cause mortality (adjusted hazard ratio [aHR] 0.44 [0.28–0.71]; event rates 1.16 vs. 2.5 per 100 patient-years) and CV mortality (aHR 0.29 [0.12–0.74]; event rates 0.28 vs. 0.92 per 100 patient-years) after a median follow-up of 3.26 years. In the non-statin group, the risk of all-cause mortality (aHR 1.07 [0.69–1.66]; event rates 2.01 vs. 1.94 per 100 patient-years) and CV mortality (aHR 1.42 [0.56–3.59]; event rates 0.52 vs. 0.41 per 100 patient-years) were not significantly different in both SBP goal arms. Conclusion: The combination of statin therapy and intensive SBP management leads to improved survival in hypertensive patients with CKD.

Topic Signficance & Study Purpose/Background/RationaleWe initially conducted a quality review of patients treated with CAR-T therapies and noted inconsistencies in cytokine release syndrome (CRS) and neurotoxicity grading due to data located in various areas within our EMR and fragmented documentation between the EMR and paper record. Consistent documentation of CAR T-cell toxicity amongst providers is important for assesment and management of toxicities post-infusion in cellular therapy programs. EMR flowsheets facilitate tracking and communication within the healthcare team and ensuring proper clinical documentation.Method, Interventions, & AnalysisKey members from the Informatics and Adult Stem Cell Transplant Program collaborated to develop a flowsheet to capture all elements to grade, identify symptoms and toxicities. To enhace clinical documentation, we created a tool that brings in several flowsheet enteries to the notes and to guide providers for accruate grading of CRS and neurotoxicity related to CAR T-cell infusions for the Epic® EMR.Findings & InterpretationThese electronic tools were developed during the planning phase of the CAR T-cell program. Two flowsheets were used by nurses to document CRS (Figure 1) and neurotoxicity (Figure 2) based on practice guidelines established by program leaders. The flowsheet enteries were added to provider's daily documentation through “smart phrases” (Figure 3) that captured grading of toxicities and guided clinical decision making.Discussion and ImplicationsDevelopment of tools to standardize symptomology reporting and effectively capture data is imperative for CAR T-cell therapy programs. EMR tools can facilitate management and streamline data collection for reporting of toxicities. As more patients receive this novel immunotherapy, it will be important to have tools in place to assist with tracking patient outcomes. The future goal is to have grading auto-generated to enhance real time care delivery with alert based warnings for worsening CRS or neurotoxicity, and for these tools to reflect current standards of care, including revised consensus criteria for grading and reporting of CAR-T toxicities currently being developed by the ASBMT. We initially conducted a quality review of patients treated with CAR-T therapies and noted inconsistencies in cytokine release syndrome (CRS) and neurotoxicity grading due to data located in various areas within our EMR and fragmented documentation between the EMR and paper record. Consistent documentation of CAR T-cell toxicity amongst providers is important for assesment and management of toxicities post-infusion in cellular therapy programs. EMR flowsheets facilitate tracking and communication within the healthcare team and ensuring proper clinical documentation. Key members from the Informatics and Adult Stem Cell Transplant Program collaborated to develop a flowsheet to capture all elements to grade, identify symptoms and toxicities. To enhace clinical documentation, we created a tool that brings in several flowsheet enteries to the notes and to guide providers for accruate grading of CRS and neurotoxicity related to CAR T-cell infusions for the Epic® EMR. These electronic tools were developed during the planning phase of the CAR T-cell program. Two flowsheets were used by nurses to document CRS (Figure 1) and neurotoxicity (Figure 2) based on practice guidelines established by program leaders. The flowsheet enteries were added to provider's daily documentation through “smart phrases” (Figure 3) that captured grading of toxicities and guided clinical decision making. Development of tools to standardize symptomology reporting and effectively capture data is imperative for CAR T-cell therapy programs. EMR tools can facilitate management and streamline data collection for reporting of toxicities. As more patients receive this novel immunotherapy, it will be important to have tools in place to assist with tracking patient outcomes. The future goal is to have grading auto-generated to enhance real time care delivery with alert based warnings for worsening CRS or neurotoxicity, and for these tools to reflect current standards of care, including revised consensus criteria for grading and reporting of CAR-T toxicities currently being developed by the ASBMT. Figs. 1, 2 and 3.Figure 2View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 3View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Social determinants of health (SDH) impact health outcomes. Medical centers have begun to collect SDH data, urged by government and scientific entities. Provider perspectives on collecting SDH are unknown. The aim is to understand differences in views and preferences according to provider characteristics. A cross-sectional survey of University of Miami clinical faculty was conducted in late 2016. The survey contained 11 questions: 8 demographic and departmental responsibilities questions and 3 Likert scale questions to capture collection and use of SDH perspectives. The main outcome was whether providers thought the benefit of collecting SDH outweighs the burden and risks. In all, 240 faculty members were included. The majority were men (64%), with a mean age of 51 years. Among participants, 53.5% were non-Hispanic white, 32% were Hispanic, 5% were Black/African American, and 5% were Asian. The majority agreed that SDH are important predictors of health outcomes and quality of care (83%). When comparing minority to nonminority faculty, 25% believed that SDH should only be available to PCPs, compared to 8% of nonminorities (P < 0.01). In a multivariate model, belonging to a racial ethnic minority was the only characteristic associated with believing that benefits of collecting SDH outweigh the risks (odds ratio 1.87, 95% confidence interval 1.02- 3.5) after adjusting for age, sex, minority status, health care provider type, type of responsibilities, and department. This study reveals that although most providers of a health system believe social risks impact health outcomes and quality metrics, the buy-in to collect SDH varies according to the racial/ethnic composition of the faculty.

This paper considers the evidence documenting the linkage of EMRs and census when conducting clinical research. Our systematic review included 25 studies. They collected information on an average of 434,541 study participants and 72% of the studies focused on adult populations. The findings include that the most common diseases evaluated were obesity, cancer, and diabetes. The most commonly used census variables were location, income, and education. Twelve of the studies linked only the census and the EMR, while 13 studies linked the census, EMR, and additional research resources. This linkage was most prevalently used to describe a problem rather than for quality improvement purposes. Efforts should be channeled to increase the use of the census for health disparities and social determinants of health.

Population Health ManagementVol. 20, No. 6 CommentaryA Road Map to Integrate Social Determinants of Health into Electronic Health RecordsAna Palacio, Maritza Suarez, Leonardo Tamariz, and David SeoAna PalacioDepartment of Medicine, University of Miami Miller School of Medicine, Miami, Florida.Geriatric Research Education and Clinical Center, Veterans Affairs Medical Center, Miami, Florida.Search for more papers by this author, Maritza SuarezDepartment of Medicine, University of Miami Miller School of Medicine, Miami, Florida.Search for more papers by this author, Leonardo TamarizDepartment of Medicine, University of Miami Miller School of Medicine, Miami, Florida.Geriatric Research Education and Clinical Center, Veterans Affairs Medical Center, Miami, Florida.Search for more papers by this author, and David SeoDepartment of Medicine, University of Miami Miller School of Medicine, Miami, Florida.Division of Cardiology, University of Miami, Miami, Florida.Search for more papers by this authorPublished Online:1 Dec 2017https://doi.org/10.1089/pop.2017.0019AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byPromoting health equity in the health-care system: How can we identify potentially vulnerable patients?13 December 2021 | Scandinavian Journal of Public Health, Vol. 50, No. 7Automating data collection methods in electronic health record systems: a Social Determinant of Health (SDOH) viewpoint19 May 2022 | Health Systems, Vol. 111Toward Successful and Sustainable Statewide Screening for Social Determinants of Health: Testing the Interest of Hospitals Christina E. Freibott, Elizabeth Beaudin, Billie-Jo Frazier, Anthony Dias, and Mary Reich Cooper18 October 2021 | Population Health Management, Vol. 24, No. 5Social and Behavioral Variables in the Electronic Health Record: A Path Forward to Increase Data Quality and Utility16 March 2021 | Academic Medicine, Vol. 96, No. 7Precision Medicine and Health Disparities27 February 2021Social Determinants of Health Mediate COVID-19 Disparities in South Florida18 November 2020 | Journal of General Internal Medicine, Vol. 36, No. 2“Engaging stakeholders in integrating social determinants of health into electronic health records: a scoping review”12 July 2021 | International Journal of Circumpolar Health, Vol. 80, No. 1Provider Perspectives When Integrating Social Determinants of Health in Response to Schickedanz A, Hamity C, Rogers A, et al, Clinician Experiences and Attitudes Regarding Screening for Social Determinants of Health in a Large Integrated Health System1 November 2019 | Medical Care, Vol. 58, No. 2Integrating Social and Behavioral Determinants of Health into Population Health Analytics: A Conceptual Framework and Suggested Road Map Zachary Predmore, Elham Hatef, and Jonathan P. Weiner2 December 2019 | Population Health Management, Vol. 22, No. 6Screening for the Social and Behavioral Determinants of Health at a School-Based ClinicJournal of Pediatric Health Care, Vol. 33, No. 5Developing a Regional Distributed Data Network for Surveillance of Chronic Health Conditions: The Colorado Health Observation Regional Data ServiceJournal of Public Health Management and Practice, Vol. 25, No. 5Provider Perspectives on the Collection of Social Determinants of Health Ana Palacio, David Seo, Heidy Medina, Vivek Singh, Maritza Suarez, and Leonardo Tamariz28 November 2018 | Population Health Management, Vol. 21, No. 6Linking census data with electronic medical records for clinical research: A systematic reviewJournal of Economic and Social Measurement, Vol. 43, No. 1-2 Volume 20Issue 6Dec 2017 InformationCopyright 2017, Mary Ann Liebert, Inc.To cite this article:Ana Palacio, Maritza Suarez, Leonardo Tamariz, and David Seo.A Road Map to Integrate Social Determinants of Health into Electronic Health Records.Population Health Management.Dec 2017.424-426.http://doi.org/10.1089/pop.2017.0019Published in Volume: 20 Issue 6: December 1, 2017Online Ahead of Print:April 14, 2017PDF download

Background: Ideal management of CKD includes management of hypertension and statin therapy. It however remains unclear if there is a positive interaction between more aggressive blood pressure control and the use of statins. Methods: We conducted a post-hoc analysis focusing on patients with CKD from the SPRINT study. These patients had been originally randomized to either an intensive SBP goal 2 . Our goal was to evaluate the added value of statins on the relationship between SBP goal and total mortality using Cox models. Subgroup models were adjusted for age, gender, race, SBP and eGFR at baseline. Results: 2646 patients with CKD were included in our analysis; of these, 1,354 (51%) were not on statins and 1,273 (49%) patients were on a statins at baseline. CKD patients on statins were predominantly non-Hispanic white (71.6%) men (64.1%), former smokers (51.3%); were often on aspirin (68.8%) and at least one other antihypertensive drug (97%). In a Cox model, the effect of SBP goal on total mortality was modified by the use of statins at baseline (interaction p=0.014). For the CKD subgroup on statins, the total mortality risk was significantly lower in the intensive SBP goal group compared to the standard SBP goal group (1.16 vs. 2.50 deaths/100 patients-years; adjusted hazard ratio (aHR) 0.46 [95% confidence interval [CI] 0.28-0.73]). For the CKD subgroup not on statins, the mortality risk was similar between the two SBP goal groups (2.01 vs. 1.95 deaths/100 patients-years; aHR 1.06 [95% CI 0.69-1.65]) (fig. 1). Conclusion: Statin therapy modify the risk relationship between SBP goal and total mortality in the SPRINT trial. Patients randomized to intensive SBP goal using statins at baseline had the lowest mortality rate compared to other groups. Efforts should be directed towards the implementation of statin therapy altogether with optimal blood pressure control in individuals with CKD.