Sex Bias in Prediction and Diagnosis of Cardiac Surgery Associated Acute Kidney Injury.

Background: Female sex has been recognized as a risk factor for cardiac surgery associated acute kidney injury (CS-AKI). The current study sought to evaluate whether female sex is a risk factor for CS-AKI, or modifies the association of peri-operative change in serum creatinine with CS-AKI. Methods: Observational study of adult patients undergoing cardiac surgery between 2000 and 2019 in a single U.S. center. The main variable of interest was registered patient sex, identified from electronic medical records. The main outcome was CS-AKI within 2 weeks of surgery. Results: Of 58526 patients, 19353 (33%) were female; 12934 (22%) incurred AKI based on ≥ 0.3 mg/dL or ≥ 50% rise in serum creatinine (any AKI), 3320 (5.7%) had moderate to severe AKI, and 1018 (1.7%) required dialysis within 2 weeks of surgery. Female sex was associated with higher risk for AKI in models that were based on preoperative serum creatinine (OR, 1.35; 95% CI, 1.29-1.42), and lower risk with the use of estimated glomerular filtration, (OR, 0.90; 95% CI, 0.86-0.95). The risk for moderate to severe CS-AKI for a given immediate peri-operative change in serum creatinine was higher in female compared to male patients (p < .0001 and p < .0001 for non-linearity), and the association was modified by pre-operative kidney function (p < .0001 for interaction). Conclusions: The association of patient sex with CS-AKI and its direction was dependent on the operational definition of pre-operative kidney function, and differential outcome misclassification due to AKI defined by absolute change in serum creatinine.

Hemodynamic Determinants of Cardiac Surgery-Associated Acute Kidney Injury.

OBJECTIVES: Examine the: 1) relative role of hemodynamic determinants of acute kidney injury (AKI) obtained in the immediate postcardiac surgery setting compared with established risk factors, 2) their predictive value, and 3) extent mediation via central venous pressure (CVP) and mean arterial pressure (MAP). DESIGN: Retrospective observational study. The main outcome of the study was moderate to severe AKI, per kidney disease: improving global outcomes, within 14 days of surgery. SETTING: U.S. academic medical center. PATIENTS: Adult patients undergoing cardiac surgery between January 2000 and December 2019 (n = 40,426) in a single U.S.-based medical center. Pulmonary artery catheter measurements were performed at a median of 102 minutes (11, 132) following cardiopulmonary bypass discontinuation. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The median age of the cohort was 67 years (58, 75), and 33% were female; 70% had chronic hypertension, 29% had congestive heart failure, and 3% had chronic kidney disease. In a multivariable model, which included comorbidities and traditional intraoperative risk factors, CVP (p < 0.0001), heart rate (p < 0.0001), cardiac index (p < 0.0001), and MAP (p < 0.0001), were strong predictors of AKI, and superseded factors such as surgery type and cardiopulmonary bypass duration. The cardiac index had a significant interaction with heart rate (p = 0.026); a faster heart rate had a differentiating effect on the relationship of cardiac index with AKI, where a higher heart rate heightened the risk of AKI primarily in patients with low cardiac output. There was also significant interaction observed between CVP and MAP (p = 0.009); where the combination of elevated CVP and low MAP had a synergistic effect on AKI incidence. CONCLUSIONS: Hemodynamic factors measured within a few hours of surgery showed a strong association with AKI. Furthermore, determinants of kidney perfusion, namely CVP and arterial pressure are interdependent; as are constituents of stroke volume, that is, cardiac output and heart rate.

Rationale and design of the Nephrotic Syndrome Study Network (NEPTUNE) Match in glomerular diseases: designing the right trial for the right patient, today.

Glomerular diseases are classified using a descriptive taxonomy that is not reflective of the heterogeneous underlying molecular drivers. This limits not only diagnostic and therapeutic patient management, but also impacts clinical trials evaluating targeted interventions. The Nephrotic Syndrome Study Network (NEPTUNE) is poised to address these challenges. The study has enrolled >850 pediatric and adult patients with proteinuric glomerular diseases who have contributed to deep clinical, histologic, genetic, and molecular profiles linked to long-term outcomes. The NEPTUNE Knowledge Network, comprising combined, multiscalar data sets, captures each participant's molecular disease processes at the time of kidney biopsy. In this editorial, we describe the design and implementation of NEPTUNE Match, which bridges a basic science discovery pipeline with targeted clinical trials. Noninvasive biomarkers have been developed for real-time pathway analyses. A Molecular Nephrology Board reviews the pathway maps together with clinical, laboratory, and histopathologic data assembled for each patient to compile a Match report that estimates the fit between the specific molecular disease pathway(s) identified in an individual patient and proposed clinical trials. The NEPTUNE Match report is communicated using established protocols to the patient and the attending nephrologist for use in their selection of available clinical trials. NEPTUNE Match represents the first application of precision medicine in nephrology with the aim of developing targeted therapies and providing the right medication for each patient with primary glomerular disease.

Black Patients Equally Benefit From Renal Genetics Evaluation but Substantial Barriers in Access Exist.

Introduction: Genetic testing is increasingly accessible to patients with kidney diseases. Racial disparities in renal genetics evaluations have not been investigated. Methods: A cohort of patients evaluated by the Cleveland Clinic Renal Genetics Clinic (RGC) from January 2019 to March 2022 was analyzed. Results: Forty-eight Black patients, including 27 (56.3%) males, median age 34 (22-49) years and 232 White patients, including 76 (32.8%) males, median age 35 (21-53) years, were evaluated. Black patients were more likely to have end-stage kidney disease (ESKD) at the time of referral compared with White patients (23% vs. 7.3%, P = 0.004), more likely to be covered by Medicaid (46% vs. 15%, P < 0.001), and less likely to be covered by private insurance (35% vs. 66%, P < 0.001). Black patients were more likely to "no show" to scheduled appointment(s) or not submit specimens for genetic testing compared with White patients (24.1% vs. 6.7%, P = 0.0005). Genetic testing was completed in 35 Black patients. Of these, 37% had a positive result with 9 unique monogenic disorders and 1 chromosomal disorder diagnosed. Sixty-nine percent of Black patients with positive results received a new diagnosis or a change in diagnosis. Of these, 44% received a significant change in disease management. No differences in diagnostic yield and implications of management were noted between Black and White patients. Conclusion: Black patients equally benefit from renal genetics evaluation, but barriers to access exist. Steps must be taken to ensure equitable and early access for all patients. Further studies investigating specific interventions to improve access are needed.

Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images.

Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, clustering-based spatial analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations. 107,471 histopathology images obtained from 172 biopsy cores were used in the clustering and in the deep learning model. To incorporate spatial information over the clustered image patterns on the biopsy sample, we spatially encoded clustered patterns with colors and performed spatial analysis through graph neural network. A random forest classifier with various groups of features were used to predict CKD. For predicting eGFR at the biopsy, we achieved a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. AUC was 0.96. For predicting eGFR changes in one-year, we achieved a sensitivity of 0.83, specificity of 0.85, and accuracy of 0.84. AUC was 0.85. This study presents the first spatial analysis based on unsupervised machine learning algorithms. Without expert annotation, CluSA framework can not only accurately classify and predict the degree of kidney function at the biopsy and in one year, but also identify novel predictors of kidney function and renal prognosis.

COVID-19 Vaccine Antibody Response in a Single-Center Urban Hemodialysis Unit.

BACKGROUND: The longitudinal response to the COVID-19 vaccines among patients on hemodialysis with and without prior SARS-CoV-2 infection has not been well characterized. METHODS: To guide vaccination strategies in patients on hemodialysis, it is critical to characterize the longevity and efficacy of the vaccine; therefore, we conducted a prospective single-center monthly antibody surveillance study between March 2021 and March 2022 to investigate the dynamic humoral response to a series of COVID-19 mRNA vaccines in patients on hemodialysis with and without prior SARS-CoV-2 infection. Monthly quantitative antibody testing was performed using the Beckman Coulter Access SARS-CoV-2 IgG Antibody Test©, which detects IgG antibodies targeting the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. RESULTS: This cohort of 30 participants (mean age: 61 ± 3 years) predominantly self-identified as African American (97%) and male (53%). Eight participants (27%) had recovered from COVID-19 (recovered) before the vaccine initiation. All participants received two vaccine doses, and 86.6% received a 6-month booster dose. Among patients naïve to COVID-19, the antibody positivity rate (APR) was 55% post-first-dose, 91% post-second-dose, 50% pre-booster at 6 months, 100% post-booster, and 89% at 6 months post-booster. Recovered patients sustained a consistent 100% APR throughout the year. The naïve patients demonstrated lower peak antibody levels post-second-dose than the recovered patients (17.9 ± 3.2 vs. 44.7 ± 5.6, p < 0.001). The peak antibody levels post-booster showed no significant difference between both groups (27.1 ± 3.9 vs. 37.9 ± 8.2, p = 0.20). Two naïve patients contracted COVID-19 during the follow-up period. CONCLUSIONS: The patients naïve to COVID-19 exhibited an attenuated and foreshortened antibody response following two doses of the mRNA vaccines compared with the recovered patients, who maintained 100% APR before the booster dose. The 6-month booster dose counteracted declining immunity and stimulated antibody responses in the naïve patients, even in previously non-responsive patients. This observation implies that different booster vaccination strategies might be required for COVID-19-naïve and -recovered patients. Post-vaccination antibody testing may serve as a valuable tool for guiding vaccination strategies.

Dialysis Patients' Social Networks and Living Donation Offers.

Rationale & Objective: Most living kidney donors are members of a hemodialysis patient's social network. Network members are divided into core members, those strongly connected to the patient and other members; and peripheral members, those weakly connected to the patient and other members. We identify how many hemodialysis patients' network members offered to become kidney donors, whether these offers were from core or peripheral network members, and whose offers the patients accepted. Study Design: A cross-sectional interviewer-administered hemodialysis patient social network survey. Setting & Participants: Prevalent hemodialysis patients in 2 facilities. Predictors: Network size and constraint, a donation from a peripheral network member. Outcomes: Number of living donor offers, accepting an offer. Analytical Approach: We performed egocentric network analyses for all participants. Poisson regression models evaluated associations between network measures and number of offers. Logistic regression models determined the associations between network factors and accepting a donation offer. Results: The mean age of the 106 participants was 60 years. Forty-five percent were female, and 75% self-identified as Black. Fifty-two percent of participants received at least one living donor offer (range 1-6); 42% of the offers were from peripheral members. Participants with larger networks received more offers (incident rate ratio [IRR], 1.26; 95% CI, 1.12-1.42; P = 0.001), including networks with more peripheral members (constraint, IRR, 0.97; 95% CI, 0.96-0.98; P < 0.001). Participants who received a peripheral member offer had 3.6 times greater odds of accepting an offer (OR, 3.56; 95% CI, 1.15-10.8; P = 0.02) than those who did not receive a peripheral member offer. Limitations: A small sample of only hemodialysis patients. Conclusions: Most participants received at least one living donor offer, often from peripheral network members. Future living donor interventions should focus on both core and peripheral network members.

Addressing kidney health disparities with new national policy: the time is now.

End-stage kidney disease (ESKD) affects over 780,000 Americans and is associated with excess morbidity and premature death. Kidney disease health disparities are well-recognized, manifesting as ESKD overburden among racial and ethnic minority populations. Specifically, Black and Hispanic individuals have a 3.4-fold and 1.3-fold greater life risk of developing ESKD than their white counterparts. There is compelling evidence that communities of color have less opportunity to benefit from kidney-specific care throughout the course of their disease, from pre-ESKD, to ESKD home therapies and kidney transplantation. These healthcare inequities have the combined devastating impact of worse outcomes and quality of life for patients and families at a significant financial cost on the healthcare system. In the last three years, across two presidential administrations, bold, broad initiatives have been outlined that, together could lead to significant transformation in kidney health. The Advancing American Kidney Health (AAKH) initiative was established as a national framework to revolutionize kidney care but did not address health equity. More recently, the Advancing Racial Equity executive order was announced, outlining initiatives to promote equity for historically underserved communities. Building from these presidential directives, we outline strategies to address the complex issue of kidney health disparities, focusing on patient awareness, care delivery, scientific advancement, and workforce initiatives. An equity-focused framework will guide policy advancements to reduce the kidney disease burden in susceptible populations and positively impact the health and well-being of all Americans.

Renal Genetics Clinic: 3-Year Experience in the Cleveland Clinic.

Rationale & Objective: There has been an increasing demand for the expertise provided by a renal genetics clinic. Such programs are limited in the United States and typically operate in a genomics research setting. Here we report a 3-year, real-world, single-center renal genetics clinic experience. Study Design: Retrospective cohort. Setting & Participants: Outpatient cases referred to the renal genetics clinic of the Cleveland Clinic between January 2019 and March 2022 were reviewed. Analytical Approach: Clinical and laboratory characteristics were analyzed. All genetic testing was performed in clinical labs. Results: 309 new patients referred from 15 specialties were evaluated, including 118 males and 191 females aged 35.1 ± 20.3 years. Glomerular diseases were the leading presentation followed by cystic kidney diseases, electrolyte disorders, congenital anomalies of kidneys and urinary tract, nephrolithiasis, and tubulointerstitial kidney diseases. Dysmorphic features were noted in 27 (8.7%) patients. Genetic testing was recommended in 292 (94.5%) patients including chromosomal microarray (8.9%), single-gene tests (19.5%), multigene panels (77.3%), and exome sequencing (17.5%). 80.5% of patients received insurance coverage for genetic testing. 45% (115/256) of patients had positive results, 25% (64/256) had variants of unknown significance, and 22.3% (57/256) had negative results. 43 distinct monogenic disorders were diagnosed. Family history of kidney disease was present in 52.8% of patients and associated with positive genetic findings (OR, 2.28; 95% CI, 1.40-3.74). 69% of patients with positive results received a new diagnosis and/or a change in the diagnosis. Among these, 39.7% (31/78) of patients received a significant change in disease management. Limitations: Retrospective and single-center study. Conclusions: The renal genetics clinic plays important roles in the diagnosis and management of patients with genetic kidney diseases. Multigene panels are the most frequently used testing modality with a high diagnostic yield. Family history of kidney disease is a strong indication for renal genetics clinic referral.

Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis.

The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single-nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing.

Apixaban for Patients With Atrial Fibrillation on Hemodialysis: A Multicenter Randomized Controlled Trial.

BACKGROUND: There are no randomized data evaluating the safety or efficacy of apixaban for stroke prevention in patients with end-stage kidney disease on hemodialysis and with atrial fibrillation (AF). METHODS: The RENAL-AF trial (Renal Hemodialysis Patients Allocated Apixaban Versus Warfarin in Atrial Fibrillation) was a prospective, randomized, open-label, blinded-outcome evaluation (PROBE) of apixaban versus warfarin in patients receiving hemodialysis with AF and a CHA2DS2-VASc score ≥2. Patients were randomly assigned 1:1 to 5 mg of apixaban twice daily (2.5 mg twice daily for patients ≥80 years of age, weight ≤60 kg, or both) or dose-adjusted warfarin. The primary outcome was time to major or clinically relevant nonmajor bleeding. Secondary outcomes included stroke, mortality, and apixaban pharmacokinetics. Pharmacokinetic sampling was day 1, day 3, and month 1. RESULTS: From January 2017 through January 2019, 154 patients were randomly assigned to apixaban (n=82) or warfarin (n=72). The trial stopped prematurely because of enrollment challenges. Time in therapeutic range (international normalized ratio, 2.0-3.0) for warfarin-treated patients was 44% (interquartile range, 23%-59%). The 1-year rates for major or clinically relevant nonmajor bleeding were 32% and 26% in apixaban and warfarin groups, respectively (hazard ratio, 1.20 [95% CI, 0.63-2.30]), whereas 1-year rates for stroke or systemic embolism were 3.0% and 3.3% in apixaban and warfarin groups, respectively. Death was the most common major event in the apixaban (21 patients [26%]) and warfarin (13 patients [18%]) arms. The pharmacokinetic substudy enrolled the target 50 patients. Median steady-state 12-hour area under the curve was 2475 ng/mL×h (10th to 90th percentiles, 1342-3285) for 5 mg of apixaban twice daily and 1269 ng/mL×h (10th to 90th percentiles, 615-1946) for 2.5 mg of apixaban twice daily. There was substantial overlap between minimum apixaban blood concentration, 12-hour area under the curve, and maximum apixaban blood concentration for patients with and without a major or clinically relevant nonmajor bleeding event. CONCLUSIONS: There was inadequate power to draw any conclusion regarding rates of major or clinically relevant nonmajor bleeding comparing apixaban and warfarin in patients with AF and end-stage kidney disease on hemodialysis. Clinically relevant bleeding events were ≈10-fold more frequent than stroke or systemic embolism among this population on anticoagulation, highlighting the need for future randomized studies evaluating the risks versus benefits of anticoagulation among patients with AF and end-stage kidney disease on hemodialysis. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT02942407.

The Michigan O'Brien Kidney Research Center: transforming translational kidney research through systems biology.

Research on kidney diseases is being transformed by the rapid expansion and innovations in omics technologies. The analysis, integration, and interpretation of big data, however, have been an impediment to the growing interest in applying these technologies to understand kidney function and failure. Targeting this urgent need, the University of Michigan O'Brien Kidney Translational Core Center (MKTC) and its Administrative Core established the Applied Systems Biology Core. The Core provides need-based support for the global kidney community centered on enabling incorporation of systems biology approaches by creating web-based, user-friendly analytic and visualization tools, like Nephroseq and Nephrocell, guiding with experimental design, and processing, analysis, and integration of large data sets. The enrichment core supports systems biology education and dissemination through workshops, seminars, and individualized training sessions. Meanwhile, the Pilot and Feasibility Program of the MKTC provides pilot funding to both early-career and established investigators new to the field, to integrate a systems biology approach into their research projects. The relevance and value of the portfolio of training and services offered by MKTC are reflected in the expanding community of young investigators, collaborators, and users accessing resources and engaging in systems biology-based kidney research, thereby motivating MKTC to persevere in its mission to serve the kidney research community by enabling access to state-of-the-art data sets, tools, technologies, expertise, and learning opportunities for transformative basic, translational, and clinical studies that will usher in solutions to improve the lives of people impacted by kidney disease.

Predictive Accuracy of a Perioperative Laboratory Test-Based Prediction Model for Moderate to Severe Acute Kidney Injury After Cardiac Surgery.

Importance: Effective treatment of acute kidney injury (AKI) is predicated on timely diagnosis; however, the lag in the increase in serum creatinine levels after kidney injury may delay therapy initiation. Objective: To determine the derivation and validation of predictive models for AKI after cardiac surgery. Design, Setting, and Participants: Multivariable prediction models were derived based on a retrospective observational cohort of adult patients undergoing cardiac surgery between January 2000 and December 2019 from a US academic medical center (n = 58 526) and subsequently validated on an external cohort from 3 US community hospitals (n = 4734). The date of final follow-up was January 15, 2020. Exposures: Perioperative change in serum creatinine and postoperative blood urea nitrogen, serum sodium, potassium, bicarbonate, and albumin from the first metabolic panel after cardiac surgery. Main Outcomes and Measures: Area under the receiver-operating characteristic curve (AUC) and calibration measures for moderate to severe AKI, per Kidney Disease: Improving Global Outcomes (KDIGO), and AKI requiring dialysis prediction models within 72 hours and 14 days following surgery. Results: In a derivation cohort of 58 526 patients (median [IQR] age, 66 [56-74] years; 39 173 [67%] men; 51 503 [91%] White participants), the rates of moderate to severe AKI and AKIrequiring dialysis were 2674 (4.6%) and 868 (1.48%) within 72 hours and 3156 (5.4%) and 1018 (1.74%) within 14 days after surgery. The median (IQR) interval to first metabolic panel from conclusion of the surgical procedure was 10 (7-12) hours. In the derivation cohort, the metabolic panel-based models had excellent predictive discrimination for moderate to severe AKI within 72 hours (AUC, 0.876 [95% CI, 0.869-0.883]) and 14 days (AUC, 0.854 [95% CI, 0.850-0.861]) after the surgical procedure and for AKI requiring dialysis within 72 hours (AUC, 0.916 [95% CI, 0.907-0.926]) and 14 days (AUC, 0.900 [95% CI, 0.889-0.909]) after the surgical procedure. In the validation cohort of 4734 patients (median [IQR] age, 67 (60-74) years; 3361 [71%] men; 3977 [87%] White participants), the models for moderate to severe AKI after the surgical procedure showed AUCs of 0.860 (95% CI, 0.838-0.882) within 72 hours and 0.842 (95% CI, 0.820-0.865) within 14 days and the models for AKI requiring dialysis and 14 days had an AUC of 0.879 (95% CI, 0.840-0.918) within 72 hours and 0.873 (95% CI, 0.836-0.910) within 14 days after the surgical procedure. Calibration assessed by Spiegelhalter z test showed P >.05 indicating adequate calibration for both validation and derivation models. Conclusions and Relevance: Among patients undergoing cardiac surgery, a prediction model based on perioperative basic metabolic panel laboratory values demonstrated good predictive accuracy for moderate to severe acute kidney injury within 72 hours and 14 days after the surgical procedure. Further research is needed to determine whether use of the risk prediction tool improves clinical outcomes.

Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease.

Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver variability, we developed an unsupervised bag-of-words model. Our study applied to the C-PROBE cohort of patients with chronic kidney disease (CKD). 107,471 histopathology images were obtained from 161 biopsy cores and identified important morphological features in biopsy tissue that are highly predictive of the presence of CKD both at the time of biopsy and in one year. To evaluate the performance of our model, we estimated the AUC and its 95% confidence interval. We show that this method is reliable and reproducible and can achieve 0.93 AUC at predicting glomerular filtration rate at the time of biopsy as well as predicting a loss of function at one year. Additionally, with this method, we ranked the identified morphological features according to their importance as diagnostic markers for chronic kidney disease. In this study, we have demonstrated the feasibility of using an unsupervised machine learning method without human input in order to predict the level of kidney function in CKD. The results from our study indicate that the visual dictionary, or visual image pattern, obtained from unsupervised machine learning can predict outcomes using machine-derived values that correspond to both known and unknown clinically relevant features.

Changes in Antihypertensive Medication Following Bariatric Surgery.

Obesity is a leading cause of hypertension (i.e., high blood pressure [BP]). While hypertension can be managed with antihypertensive medication, substantial weight loss can also lower BP, reducing the need for antihypertensive medication. Articles in this review (n = 60) presented data on antihypertensive medication use among adults pre- and postoperatively. Roux-en-Y gastric bypass was the most studied surgical approach followed by Laparoscopic Sleeve Gastrectomy. Antihypertensive medication was discontinued in a large proportion of patients after surgery, and the mean number of antihypertensive medications decreased by approximately one. In almost a third of the studies, over 75% of participants experienced hypertension remission. All articles aside from two reported a decrease in systolic BP, with about 40% reporting a decrease of ≥ 10 mm Hg.