Hemoadsorption: Consensus report of the 30th Acute Disease Quality Initiative workgroup.

Adsorption-based extracorporeal therapies have been subject to technical developments and clinical application for close to five decades. More recently, new technological developments in membrane and sorbent manipulation have made it possible to deliver more biocompatible extracorporeal adsorption therapies to patients with a variety of conditions. There are several key rationales based on physicochemical principles and clinical considerations that justify the application and investigation of such therapies as evidenced by multiple ex-vivo, experimental, and clinical observations. Accordingly, unspecific adsorptive extracorporeal therapies have now been applied to the treatment of a wide array of conditions from poisoning to drug overdoses, to inflammatory states and sepsis, and acute or chronic liver and kidney failure. In response to the rapidly expanding knowledge base and increased clinical evidence, we convened an Acute Disease Quality Initiative (ADQI) consensus conference dedicated to such treatment. The data show that hemoadsorption has clinically acceptable short-term biocompatibility and safety, technical feasibility, and experimental demonstration of specified target molecule removal. Pilot studies demonstrate potentially beneficial effects on physiology and larger studies of endotoxin-based hemoadsorption have identified possible target phenotypes for larger randomized controlled trials (RCTs). Moreover, in a variety of endogenous and exogenous intoxications, removal of target molecules has been confirmed in vivo. However, some studies have raised concerns about harm or failed to deliver benefits. Thus, despite many achievements, modern hemoadsorption remains a novel and experimental intervention with limited data, and a large research agenda.

Advances in critical care nephrology through artificial intelligence.

PURPOSE OF REVIEW: This review explores the transformative advancement, potential application, and impact of artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), on critical care nephrology. RECENT FINDINGS: AI algorithms have demonstrated the ability to enhance early detection, improve risk prediction, personalize treatment strategies, and support clinical decision-making processes in acute kidney injury (AKI) management. ML models can predict AKI up to 24-48 h before changes in serum creatinine levels, and AI has the potential to identify AKI sub-phenotypes with distinct clinical characteristics and outcomes for targeted interventions. LLMs and generative AI offer opportunities for automated clinical note generation and provide valuable patient education materials, empowering patients to understand their condition and treatment options better. To fully capitalize on its potential in critical care nephrology, it is essential to confront the limitations and challenges of AI implementation, including issues of data quality, ethical considerations, and the necessity for rigorous validation. SUMMARY: The integration of AI in critical care nephrology has the potential to revolutionize the management of AKI and continuous renal replacement therapy. While AI holds immense promise for improving patient outcomes, its successful implementation requires ongoing training, education, and collaboration among nephrologists, intensivists, and AI experts.

The Acute Kidney Intervention and Pharmacotherapy (AKIP) List: Standardized List of Medications That Are Renally Eliminated and Nephrotoxic in the Acutely Ill.

The objective of this project was to develop a standardized list of renally eliminated and potentially nephrotoxic drugs that will help inform initiatives to improve medication safety. Several available lists of medications from the published literature including original research articles and reviews, and from regulatory agencies, tertiary references, and clinical decision support systems were compiled, consolidated, and compared. Only systemically administered medications were included. Medication combinations were included if at least 1 active ingredient was considered renally dosed or potentially nephrotoxic. The medication list was reviewed for completeness and clinical appropriateness by a multidisciplinary team of individuals with expertise in critical care, nephrology, and pharmacy. An initial list of renally dosed and nephrotoxic drugs was created. After reconciliation and consensus from clinical experts, a standardized list of 681 drugs is proposed. The proposed evidence-based standardized list of renally dosed and potentially nephrotoxic drugs will be useful to harmonize epidemiologic and medication quality improvement studies. In addition, the list can be used for clinical purposes with surveillance in nephrotoxin stewardship programs. We suggest an iterative re-evaluation of the list with emerging literature and new medications on an approximately annual basis.

Generative AI in Critical Care Nephrology: Applications and Future Prospects.

BACKGROUND: Generative artificial intelligence (AI) is rapidly transforming various aspects of healthcare, including critical care nephrology. Large language models (LLMs), a key technology in generative AI, show promise in enhancing patient care, streamlining workflows, and advancing research in this field. SUMMARY: This review analyzes the current applications and future prospects of generative AI in critical care nephrology. Recent studies demonstrate the capabilities of LLMs in diagnostic accuracy, clinical reasoning, and continuous renal replacement therapy (CRRT) alarm troubleshooting. As we enter an era of multiagent models and automation, the integration of generative AI into critical care nephrology holds promise for improving patient care, optimizing clinical processes, and accelerating research. However, careful consideration of ethical implications and continued refinement of these technologies are essential for their responsible implementation in clinical practice. This review explores the current and potential applications of generative AI in nephrology, focusing on clinical decision support, patient education, research, and medical education. Additionally, we examine the challenges and limitations of AI implementation, such as privacy concerns, potential bias, and the necessity for human oversight. KEY MESSAGES: (i) LLMs have shown potential in enhancing diagnostic accuracy, clinical reasoning, and CRRT alarm troubleshooting in critical care nephrology. (ii) Generative AI offers promising applications in patient education, literature review, and academic writing within the field of nephrology. (iii) The integration of AI into electronic health records and clinical workflows presents both opportunities and challenges for improving patient care and research. (iv) Addressing ethical concerns, ensuring data privacy, and maintaining human oversight are crucial for the responsible implementation of AI in critical care nephrology.

Evaluating ChatGPT's Accuracy in Responding to Patient Education Questions on Acute Kidney Injury and Continuous Renal Replacement Therapy.

INTRODUCTION: Acute kidney injury (AKI) and continuous renal replacement therapy (CRRT) are critical areas in nephrology. The effectiveness of ChatGPT in simpler, patient education-oriented questions has not been thoroughly assessed. This study evaluates the proficiency of ChatGPT 4.0 in responding to such questions, subjected to various linguistic alterations. METHODS: Eighty-nine questions were sourced from the Mayo Clinic Handbook for educating patients on AKI and CRRT. These questions were categorized as original, paraphrased with different interrogative adverbs, paraphrased resulting in incomplete sentences, and paraphrased containing misspelled words. Two nephrologists verified the questions for medical accuracy. A χ2 test was conducted to ascertain notable discrepancies in ChatGPT 4.0's performance across these formats. RESULTS: ChatGPT provided notable accuracy in handling a variety of question formats for patient education in AKI and CRRT. Across all question types, ChatGPT demonstrated an accuracy of 97% for both original and adverb-altered questions and 98% for questions with incomplete sentences or misspellings. Specifically for AKI-related questions, the accuracy was consistently maintained at 97% for all versions. In the subset of CRRT-related questions, the tool achieved a 96% accuracy for original and adverb-altered questions, and this increased to 98% for questions with incomplete sentences or misspellings. The statistical analysis revealed no significant difference in performance across these varied question types (p value: 1.00 for AKI and 1.00 for CRRT), and there was no notable disparity between the artificial intelligence (AI)'s responses to AKI and CRRT questions (p value: 0.71). CONCLUSION: ChatGPT 4.0 demonstrates consistent and high accuracy in interpreting and responding to queries related to AKI and CRRT, irrespective of linguistic modifications. These findings suggest that ChatGPT 4.0 has the potential to be a reliable support tool in the delivery of patient education, by accurately providing information across a range of question formats. Further research is needed to explore the direct impact of AI-generated responses on patient understanding and education outcomes.

Identification of kidney-related medications using AI from self-captured pill images.

INTRODUCTION: ChatGPT, a state-of-the-art large language model, has shown potential in analyzing images and providing accurate information. This study aimed to explore ChatGPT-4 as a tool for identifying commonly prescribed nephrology medications across different versions and testing dates. METHODS: 25 nephrology medications were obtained from an institutional pharmacy. High-quality images of each medication were captured using an iPhone 13 Pro Max and uploaded to ChatGPT-4 with the query, 'What is this medication?' The accuracy of ChatGPT-4's responses was assessed for medication name, dosage, and imprint. The process was repeated after 2 weeks to evaluate consistency across different versions, including GPT-4, GPT-4 Legacy, and GPT-4.Ø. RESULTS: ChatGPT-4 correctly identified 22 out of 25 (88%) medications across all versions. However, it misidentified Hydrochlorothiazide, Nifedipine, and Spironolactone due to misreading imprints. For instance, Nifedipine ER 90 mg was mistaken for Metformin Hydrochloride ER 500 mg because 'NF 06' was misread as 'NF 05'. Hydrochlorothiazide 50 mg was confused with the 25 mg version due to imprint errors, and Spironolactone 25 mg was misidentified as Naproxen Sodium or Diclofenac Sodium. Despite these errors, ChatGPT-4 showed 100% consistency when retested, correcting misidentifications after receiving feedback on the correct imprints. CONCLUSION: ChatGPT-4 shows strong potential in identifying nephrology medications from self-captured images, though challenges with difficult-to-read imprints remain. Providing feedback improved accuracy, suggesting ChatGPT-4 could be a valuable tool in digital health for medication identification. Future research should enhance the model's ability to distinguish similar imprints and explore broader integration into digital health platforms.

An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems.

BACKGROUND: We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. METHODS: This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. RESULTS: Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. CONCLUSION: The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.

Real-World Clinical Practice Among Patients With Bipolar Disorder and Chronic Kidney Disease on Long-term Lithium Therapy.

PURPOSE: Long-term lithium therapy (LTLT) has been associated with chronic kidney disease (CKD). We investigated changes in clinical characteristics, pharmacotherapeutic treatments for medical/psychiatric disorders, and outcomes among patients with bipolar disorder (BD) and CKD on LTLT in a 2-year mirror-image study design. METHODS: Adult BD patients on LTLT for ≥1 year who enrolled in the Mayo Clinic Bipolar Disorder Biobank and developed CKD (stage 3) were included, and our study was approved by the Mayo Clinic Institutional Review Board. The primary outcome was the time to the first mood episode after CKD diagnosis among the lithium (Li) continuers and discontinuers. Cox proportional hazards models were used to estimate the time to the first mood episode. We tested for differences in other medication changes between the Li continuers and discontinuers group using Mantel-Haenszel χ2 tests (linear associations). RESULTS: Of 38 BD patients who developed CKD, 18 (47%) discontinued Li, and the remainder continued (n = 20). The median age of the cohort was 56 years (interquartile range [IQR], 48-67 years), 63.2% were female, and 97.4% were White. As compared with continuers, discontinuers had more psychotropic medication trials (6 [IQR, 4-6] vs 3 [IQR, 2-5], P = 0.02), a higher rate of 1 or more mood episodes (61% vs 10%, P = 0.002), and a higher risk of a mood episode after CKD diagnoses (Hazard Ratio, 8.38; 95% confidence interval, 1.85-38.0 [log-rank P = 0.001]]. CONCLUSIONS: Bipolar disorder patients on LTLT who discontinued Li had a higher risk for relapse and a shorter time to the first mood episode, suggesting a need for more thorough discussion before Li discontinuation after the CKD diagnosis.

Digital health utilities in acute kidney injury management.

PURPOSE OF REVIEW: Acute kidney injury (AKI) is a highly prevalent clinical syndrome that substantially impacts patient outcomes. It is accepted by the clinical communities that the management of AKI is time-sensitive. Unfortunately, despite growing proof of its preventability, AKI management remains suboptimal in community, acute care, and postacute care settings. Digital health solutions comprise various tools and models to improve care processes and patient outcomes in multiple medical fields. AKI development, progression, recovery, or lack thereof, offers tremendous opportunities for developing, validating, and implementing digital health solutions in multiple settings. This article will review the definitions and components of digital health, the characteristics of AKI that allow digital health solutions to be considered, and the opportunities and threats in implementing these solutions. RECENT FINDINGS: Over the past two decades, the academic output related to the use of digital health solutions in AKI has exponentially grown. While this indicates the growing interest in the topic, most topics are primarily related to clinical decision support by detecting AKI within hospitals or using artificial intelligence or machine learning technologies to predict AKI within acute care settings. However, recently, projects to assess the impact of digital health solutions in more complex scenarios, for example, managing nephrotoxins among adults of pediatric patients who already have AKI, is increasing. Depending on the type of patients, chosen digital health solution intervention, comparator groups, and selected outcomes, some of these studies showed benefits, while some did not indicate additional gain in care processes or clinical outcomes. SUMMARY: Careful needs assessment, selection of the correct digital health solution, and appropriate clinical validation of the benefits while avoiding additional health disparities are moral, professional, and ethical obligations for all individuals using these healthcare tools, including clinicians, data scientists, and administrators.

Moving toward a contemporary classification of drug-induced kidney disease.

Drug-induced kidney disease (DIKD) accounts for about one-fourth of all cases of acute kidney injury (AKI) in hospitalized patients, especially in critically ill setting. There is no standard definition or classification system of DIKD. To address this, a phenotype definition of DIKD using expert consensus was introduced in 2015. Recently, a novel framework for DIKD classification was proposed that incorporated functional change and tissue damage biomarkers. Medications were stratified into four categories, including "dysfunction without damage," "damage without dysfunction," "both dysfunction and damage," and "neither dysfunction nor damage" using this novel framework along with predominant mechanism(s) of nephrotoxicity for drugs and drug classes. Here, we briefly describe mechanisms and provide examples of drugs/drug classes related to the categories in the proposed framework. In addition, the possible movement of a patient's kidney disease between certain categories in specific conditions is considered. Finally, opportunities and barriers to adoption of this framework for DIKD classification in real clinical practice are discussed. This new classification system allows congruencies for DIKD with the proposed categorization of AKI, offering clarity as well as consistency for clinicians and researchers.

Urine Output and Mortality in Patients Resuscitated from out of Hospital Cardiac Arrest.

BACKGROUND: Limited data exist regarding urine output (UO) as a prognostic marker in out-of-hospital-cardiac-arrest (OHCA) survivors undergoing targeted temperature management (TTM). METHODS: We included 247 comatose adult patients who underwent TTM after OHCA between 2007 and 2017, excluding patients with end-stage renal disease. Three groups were defined based on mean hourly UO during the first 24 h: Group 1 (<0.5 mL/kg/h, n = 73), Group 2 (0.5-1 mL/kg/h, n = 81) and Group 3 (>1 mL/kg/h, n = 93). Serum creatinine was used to classify acute kidney injury (AKI). The primary and secondary outcomes respectively were in-hospital mortality and favorable neurological outcome at hospital discharge (modified Rankin Scale [mRS]<3). RESULTS: In-hospital mortality decreased incrementally as UO increased (adjusted OR 0.9 per 0.1 mL/kg/h higher; p = 0.002). UO < 0.5 mL/kg/h was strongly associated with higher in-hospital mortality (adjusted OR 4.2 [1.6-10.8], p = 0.003) and less favorable neurological outcomes (adjusted OR 0.4 [0.2-0.8], p = 0.007). Even among patients without AKI, lower UO portended higher mortality (40% vs 15% vs 9% for UO groups 1, 2, and 3 respectively, p < 0.001). CONCLUSION: Higher UO is incrementally associated with lower in-hospital mortality and better neurological outcomes. Oliguria may be a more sensitive early prognostic marker than creatinine-based AKI after OHCA.

Proposal for a New Classification of Solutes of Interest in Uremia and Hemodialysis.

Uremic toxins contribute to clinical manifestations of kidney dysfunction. These toxins include organic and inorganic elements or compounds. While the kidney typically clears uremic toxins, gut dysbiosis, and tissue inflammation could lead to increased production of substances that can further the clinical manifestations of uremia. The uremic toxins are quantitatively measurable in biological fluids and have an established relationship with azotemia signs and symptoms. Their elimination is associated with mitigated uremic manifestations, while their administration to the uremic levels leads to uremic signs in animal or human models or in vitro studies. Besides, the uremic toxins have an established and plausible pathophysiologic relationship with uremic manifestations. The previous classification of uremic toxins was mainly focused on the physicochemical characteristics of these substances to divide them into three categories, (1) free water-soluble low-molecular-weight (<500 Da) solutes, (2) protein-bound, water-soluble, low molecular weight (<500 Da), (3) middle molecular weight (>500 Da and <12,000 Da), and (4) high molecular weight (>12,000 Da). Unfortunately, the classification named above was not centered around patient outcomes and quality of life among those with severe kidney failure. Therefore, a panel of experts convened virtually to provide additional insights into the current state and propose a new uremic toxin classification. This article describes the group's consensus recommendations regarding the new classification of uremic toxins into more clinically oriented categories.

Nomenclature of Extracorporeal Blood Purification Therapies for Acute Indications: The Nomenclature Standardization Conference.

The development of new extracorporeal blood purification (EBP) techniques has led to increased application in clinical practice but also inconsistencies in nomenclature and misunderstanding. In November 2022, an international consensus conference was held to establish consensus on the terminology of EBP therapies. It was agreed to define EBP therapies as techniques that use an extracorporeal circuit to remove and/or modulate circulating substances to achieve physiological homeostasis, including support of the function of specific organs and/or detoxification. Specific acute EBP techniques include renal replacement therapy, isolated ultrafiltration, hemoadsorption, and plasma therapies, all of which can be applied in isolation and combination. This paper summarizes the proposed nomenclature of EBP therapies and serves as a framework for clinical practice and future research.

Body temperature and infection in critically ill patients on continuous kidney replacement therapy.

PURPOSE: Continuous kidney replacement therapy (CKRT) is an increasingly common intervention for critically ill patients with kidney failure. Because CKRT affects body temperature, detecting infections in patients on CKRT is challenging. Understanding the relation between CKRT and body temperature may facilitate earlier detection of infection. METHODS: We retrospectively reviewed adult patients (≥ 18 years) admitted to the intensive care unit at Mayo Clinic in Rochester, Minnesota, from December 1, 2006, through November 31, 2015, who required CKRT. We summarized central body temperatures for these patients according to the presence or absence of infection. RESULTS: We identified 587 patients who underwent CKRT during the study period, of whom 365 had infections, and 222 did not have infections. We observed no statistically significant differences in minimum (P = .70), maximum (P = .22), or mean (P = .55) central body temperature for patients on CKRT with infection vs. those without infection. While not on CKRT (before CKRT initiation and after cessation), all three body temperature measurements were significantly higher in patients with infection than in those without infection (all P < .02). CONCLUSION: Body temperature is insufficient to indicate an infection in critically ill patients on CKRT. Clinicians should remain watchful for other signs, symptoms, and indications of infection in patients on CKRT because of expected high infection rates.

Forecasting imminent atrial fibrillation in long-term electrocardiogram recordings.

BACKGROUND: Despite the morbidity associated with acute atrial fibrillation (AF), no models currently exist to forecast its imminent onset. We sought to evaluate the ability of deep learning to forecast the imminent onset of AF with sufficient lead time, which has important implications for inpatient care. METHODS: We utilized the Physiobank Long-Term AF Database, which contains 24-h, labeled ECG recordings from patients with a history of AF. AF episodes were defined as ≥5 min of sustained AF. Three deep learning models incorporating convolutional and transformer layers were created for forecasting, with two models focusing on the predictive nature of sinus rhythm segments and AF epochs separately preceding an AF episode, and one model utilizing all preceding waveform as input. Cross-validated performance was evaluated using area under time-dependent receiver operating characteristic curves (AUC(t)) at 7.5-, 15-, 30-, and 60-min lead times, precision-recall curves, and imminent AF risk trajectories. RESULTS: There were 367 AF episodes from 84 ECG recordings. All models showed average risk trajectory divergence of those with an AF episode from those without ∼15 min before the episode. Highest AUC was associated with the sinus rhythm model [AUC = 0.74; 7.5-min lead time], though the model using all preceding waveform data had similar performance and higher AUCs at longer lead times. CONCLUSIONS: In this proof-of-concept study, we demonstrated the potential utility of neural networks to forecast the onset of AF in long-term ECG recordings with a clinically relevant lead time. External validation in larger cohorts is required before deploying these models clinically.

Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing.

BACKGROUND: Artificial intelligence (AI) tools are more effective if accepted by clinicians. We developed an AI-based clinical decision support system (CDSS) to facilitate vancomycin dosing. This qualitative study assesses clinicians' perceptions regarding CDSS implementation. METHODS: Thirteen semi-structured interviews were conducted with critical care pharmacists, at Mayo Clinic (Rochester, MN), from March through April 2020. Eight clinical cases were discussed with each pharmacist (N = 104). Following initial responses, we revealed the CDSS recommendations to assess participants' reactions and feedback. Interviews were audio-recorded, transcribed, and summarized. RESULTS: The participants reported considerable time and effort invested daily in individualizing vancomycin therapy for hospitalized patients. Most pharmacists agreed that such a CDSS could favorably affect (N = 8, 62%) or enhance (9, 69%) their ability to make vancomycin dosing decisions. In case-based evaluations, pharmacists' empiric doses differed from the CDSS recommendation in most cases (88/104, 85%). Following revealing the CDSS recommendations, we noted 78% (69/88) discrepant doses. In discrepant cases, pharmacists indicated they would not alter their recommendations. The reasons for declining the CDSS recommendation were general distrust of CDSS, lack of dynamic evaluation and in-depth analysis, inability to integrate all clinical data, and lack of a risk index. CONCLUSION: While pharmacists acknowledged enthusiasm about the advantages of AI-based models to improve drug dosing, they were reluctant to integrate the tool into clinical practice. Additional research is necessary to determine the optimal approach to implementing CDSS at the point of care acceptable to clinicians and effective at improving patient outcomes.

Pharmacist-provider collaborative visits after hospital discharge in a comprehensive acute kidney injury survivor model.

BACKGROUND: Postdischarge follow-up in primary care is an opportunity for pharmacists to re-evaluate medication use in acute kidney injury (AKI) survivors. Of the emerging AKI survivor care models described in literature, only one involved a pharmacist with limited detail about the direct impact. OBJECTIVE: This study aimed to describe pharmacist contributions to a comprehensive postdischarge AKI survivorship program in primary care (the AKI in Care Transitions [ACT] program). METHODS: The ACT program was piloted from May to December of 2021 at Mayo Clinic as a bundled care strategy for patients who survived an episode of AKI and were discharged home without the need for hemodialysis. Patients received education and care coordination from nurses before discharge and later completed postdischarge laboratory assessment and clinician follow-up in primary care. During the follow-up encounter, patients completed a 30-minute comprehensive medication management visit with a pharmacist focusing on AKI survivorship considerations. Medication therapy recommendations were communicated to a collaborating primary care provider (PCP) before a separate 30-minute visit with the patient. PCPs had access to clinical decision support with evidence-based post-AKI care recommendations. Medication-related issues were summarized descriptively. RESULTS: Pharmacists made 28 medication therapy recommendations (median 3 per patient, interquartile range 2-3) and identified 14 medication discrepancies for the 11 patients who completed the pilot program, and 86% of the medication therapy recommendations were acted on by the PCP within 7 days. Six recommendations were made to initiate renoprotective medications, and 5 were acted on (83%). CONCLUSION: During the pilot phase of a multifaceted transitional care program for AKI survivors, pharmacists' successfully identified and addressed multiple medication therapy problems, including for renally active drugs. These results demonstrate the potential for pharmacist-provider collaborative visits in primary care to improve safe and effective medication use in AKI survivors.

Treatment Effect of Percutaneous Coronary Intervention in Dialysis Patients With ST-Elevation Myocardial Infarction.

RATIONALE & OBJECTIVE: Patients receiving maintenance dialysis have higher mortality after primary percutaneous coronary intervention (pPCI) than patients not receiving dialysis. Whether pPCI confers a benefit to patients receiving dialysis that is similar to that which occurs in lower-risk groups remains unknown. We compared the effect of pPCI on in-hospital outcomes among patients hospitalized for ST-elevation myocardial infarction (STEMI) and receiving maintenance dialysis with the effect among patients hospitalized for STEMI but not receiving dialysis. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: We used the National Inpatient Sample (2016-2018) and included all adult hospitalizations with a primary diagnosis of STEMI. PREDICTORS: Primary exposure was PCI. Confounders included dialysis status, demographics, insurance, household income, comorbidities, and the elective nature of the admission. OUTCOME: In-hospital mortality, stroke, acute kidney injury, new dialysis requirement, vascular complications, gastrointestinal bleeding, blood transfusion, mechanical ventilation, palliative care, and discharge destination. ANALYTICAL APPROACH: The average treatment effect (ATE) of pPCI was estimated using propensity score matching independently within the group receiving dialysis and the group not receiving dialysis to explore whether the effect is modified by dialysis status. Additionally, the average marginal effect (AME) was calculated accounting for the clustering within hospitals. RESULTS: Among hospitalizations, 4,220 (1.07%) out of 413,500 were for patients receiving dialysis. The dialysis cohort was older (65.2 ± 12.2 vs 63.4 ± 13.1, P < 0.001), had a higher proportion of women (42.4% vs 30.6%, P < 0.001) and more comorbidities, and had a lower proportion of White patients (41.1% vs 71.7%, P < 0.001). Patients receiving dialysis were less likely to undergo angiography (73.1% vs 85.4%, P < 0.001) or pPCI (57.5% vs 79.8%, P < 0.001). Primary PCI was associated with lower mortality in patients receiving dialysis (15.7% vs 27.1%, P < 0.001) as well as in those who were not (5.0% vs 17.4%, P < 0.001). The ATE on mortality did not differ significantly (P interaction = 0.9) between patients receiving dialysis (-8.6% [95% CI, -15.6% to -1.6%], P = 0.02) and those who were not (-8.2% [95% CI, -8.8% to -7.5%], P < 0.001). The AME method showed similar results among patients receiving dialysis (-9.4% [95% CI, -14.8% to -4.0%], P < 0.001) and those who were not (-7.9% [95% CI, -8.5% to -7.4%], P < 0.001) (P interaction = 0.6). Both the ATE and AME were comparable for other in-hospital outcomes in both groups. LIMITATIONS: Administrative data, lack of pharmacotherapy and long-term outcome data, and residual confounding. CONCLUSIONS: Compared with conservative management, pPCI for STEMI was associated with comparable reductions in short-term mortality among patients irrespective of their receipt of maintenance dialysis.

Nephrotoxin Exposure in the 3 Years following Hospital Discharge Predicts Development or Worsening of Chronic Kidney Disease among Acute Kidney Injury Survivors.

INTRODUCTION: Survivors of acute kidney injury (AKI) are at high risk of progression to chronic kidney disease (CKD), for which drugs may be a modifiable risk factor. METHODS: We conducted a population-based cohort study of Olmsted County, MN residents who developed AKI while hospitalized between January 1, 2006, and December 31, 2014, using Rochester Epidemiology Project data. Adults with a hospitalization complicated by AKI who survived at least 90 days after AKI development were included. Medical records were queried for prescription of potentially nephrotoxic medications over the 3 years after discharge. The primary outcome was de novo or progressive CKD defined by either a new diagnosis code for CKD or ≥30% decline in estimated glomerular filtration rate from baseline. The composite of CKD, AKI readmission, or death was also evaluated. RESULTS: Among 2,461 AKI survivors, 2,140 (87%) received a potentially nephrotoxic medication during the 3 years following discharge. When nephrotoxic medication use was analyzed in a time-dependent fashion, those actively prescribed at least one of these drugs experienced a significantly higher risk of de novo or progressive CKD (HR 1.38: 95% CI: 1.24, 1.54). Similarly, active potentially nephrotoxic medication use predicted a greater risk of the composite endpoint of CKD, AKI readmission, or death within 3 years of discharge (HR 1.41: 95% CI: 1.28, 1.56). CONCLUSION: In this population-based cohort study, AKI survivors actively prescribed one or more potentially nephrotoxic medications were at significantly greater risk for de novo or progressive CKD. An opportunity exists to reassess nephrotoxin appropriateness following an AKI episode to improve patient outcomes.

Validation of cardiogenic shock phenotypes in a mixed cardiac intensive care unit population.

BACKGROUND: Proposed phenotypes have recently been identified in cardiogenic shock (CS) populations using unsupervised machine learning clustering methods. We sought to validate these phenotypes in a mixed cardiac intensive care unit (CICU) population of patients with CS. METHODS: We included Mayo Clinic CICU patients admitted from 2007 to 2018 with CS. Agnostic K means clustering was used to assign patients to three clusters based on admission values of estimated glomerular filtration rate, bicarbonate, alanine aminotransferase, lactate, platelets, and white blood cell count. In-hospital mortality and 1-year mortality were analyzed using logistic regression and Cox proportional-hazards models, respectively. RESULTS: We included 1498 CS patients with a mean age of 67.8 ± 13.9 years, and 37.1% were females. The acute coronary syndrome was present in 57.3%, and cardiac arrest was present in 34.0%. Patients were assigned to clusters as follows: Cluster 1 (noncongested), 603 (40.2%); Cluster 2 (cardiorenal), 452 (30.2%); and Cluster 3 (hemometabolic), 443 (29.6%). Clinical, laboratory, and echocardiographic characteristics differed across clusters, with the greatest illness severity in Cluster 3. Cluster assignment was associated with in-hospital mortality across subgroups. In-hospital mortality was higher in Cluster 3 (adjusted odds ratio [OR]: 2.6 vs. Cluster 1 and adjusted OR: 2.0 vs. Cluster 2, both p < 0.001). Adjusted 1-year mortality was incrementally higher in Cluster 3 versus Cluster 2 versus Cluster 1 (all p < 0.01). CONCLUSIONS: We observed similar phenotypes in CICU patients with CS as previously reported, identifying a gradient in both in-hospital and 1-year mortality by cluster. Identifying these clinical phenotypes can improve mortality risk stratification for CS patients beyond standard measures.