Kashani, Kianoush B

Background: Sepsis-associated acute kidney injury (AKI) is linked to increased mortality and prolonged hospital stays. The exact relationship between central venous pressure (CVP) and AKI remains unclear. We explored the correlation between CVP and AKI in septic shock patients. Methods: This retrospective study included adult patients with septic shock admitted to Mayo Clinic Rochester between 2006 and 2018. CVP levels were measured at 6, 12, 24, and 48 h after the diagnosis of sepsis, and patients were stratified into two groups based on CVP levels (CVP < 8 or ≥8 mmHg). Results: Of 5600 patients with septic shock, 3128 patients without AKI on admission are included. One-thousand-and-ninety-eight patients (35.1%) developed AKI within a median of 4.4 days. The median CVP levels and frequency of elevated CVP at 6, 12, 24, and 48 h are significantly higher in the AKI group. Elevated CVP (≥8 mmHg) at 6, 12, 24, and 48 h is associated with AKI incidence, even after adjusting for mean arterial pressure (MAP) levels. This association, after multivariable adjustments, only remains significant at 12 h with an odds ratio (OR) of 1.60 (95% CI, 1.26–2.05), p < 0.001 and 48 h with an OR of 1.60 (95% CI, 1.29–1.99), p < 0.001. Conclusions: Our findings indicate that CVP ≥ 8 mmHg is strongly associated with an increased risk of AKI, even after adjusting for MAP at the 12 and 48 h time points. These findings underscore a critical 12 or 48h window for interventions to lower CVP.

Background The digital transformation in medical education is reshaping how clinical skills, such as point-of-care ultrasound (POCUS), are taught. In nephrology fellowship programs, POCUS is essential for enhancing diagnostic accuracy, guiding procedures, and optimizing patient management. To address these evolving demands, we developed an artificial intelligence (AI)-driven POCUS curriculum using a multiagent approach that integrates human expertise with advanced AI models, thereby elevating educational standards and better preparing fellows for contemporary clinical practice. Methods In April 2024, the Mayo Clinic Minnesota Nephrology Fellowship Program initiated a novel AI-assisted process to design a comprehensive POCUS curriculum. This process integrated multiple advanced AI models—including GPT-4.0, Claude 3.0 Opus, Gemini Advanced, and Meta AI with Llama 3—to generate initial drafts and iteratively refine content. A panel of blinded nephrology POCUS experts subsequently reviewed and modified the AI-generated material to ensure both clinical relevance and educational rigor. Results The curriculum underwent 12 iterative revisions, incorporating feedback from 29 communications across AI models. Key features of the final curriculum included expanded core topics, diversified teaching methods, enhanced assessment tools, and integration into inpatient and outpatient nephrology rotations. The curriculum emphasized quality assurance, POCUS limitations, and essential clinical applications, such as fistula/graft evaluation and software integration. Alignment with certification standards further strengthened its utility. AI models contributed significantly to the curriculum's foundational structure, while human experts provided critical clinical insights. Conclusion This curriculum, enhanced through a multiagent approach that combines AI and human collaboration, exemplifies the transformative potential of digital tools in nephrology education. The innovative framework seamlessly integrates advanced AI models with expert clinical insights, providing a scalable model for medical curriculum development that is responsive to evolving educational demands. The synergy between technological innovation and human expertise holds promising implications for advancing fellowship training. Future studies should evaluate its impact on clinical competencies and patient outcomes across diverse practice environments.

Key Points The Standard versus Accelerated Initiation of Renal Replacement Therapy in AKI trial found that starting dialysis earlier for AKI resulted in a 74% higher risk of still receiving dialysis at 90 days. Among patients who likely would have started dialysis under usual practice, the risks of dialysis dependence were similar. This suggests much of the risk observed may have been due to starting dialysis (versus never starting) rather than starting slightly earlier. Background The Standard versus Accelerated Initiation of Renal Replacement Therapy in AKI Trial showed that among critically ill patients with AKI, an accelerated KRT initiation strategy resulted in more KRT dependence at 90 days, as compared with a standard strategy. However, it is unclear whether this difference exists among participants who likely would have been treated with KRT in usual practice. Methods Secondary analysis of the Standard versus Accelerated Initiation of Renal Replacement Therapy in AKI randomized controlled trial, analyzing participants who received more than the median number of days of KRT, which served as a proxy for high probability of requiring KRT under usual practice. Two sensitivity analyses redefined the cohort as ( 1 ) those with ≥24 hours of oliguria who received KRT and ( 2 ) those who received any KRT. Using the same statistical approach as the original trial, we calculated unadjusted relative risks with 95% confidence intervals (CIs) for the exposure of randomized KRT initiation strategy (accelerated versus standard) on the outcome of KRT dependence at 90 days among survivors. Results Among the 1184 participants who received ≥6 days of KRT (670 and 514 in the accelerated and standard arms, respectively), baseline characteristics remained balanced between treatment arms. The relative risk of KRT dependence at 90 days was attenuated and nonsignificant: 1.21 (95% CI, 0.84 to 1.78), compared with 1.74 (95% CI, 1.24 to 2.43) in the overall trial. The results were similar in sensitivity analyses. Conclusions Among patients who likely would have been treated with KRT in usual practice, there was no significant difference in 90-day KRT dependence among survivors between those randomized to the accelerated and standard KRT initiation strategies. These results suggest that the harm of long-term KRT dependence may be largely due to KRT initiation (versus never initiation) rather than earlier initiation. Clinical Trial registry name and registration number: NCT02568722.

The accurate interpretation of CRRT machine alarms is crucial in the intensive care setting. ChatGPT, with its advanced natural language processing capabilities, has emerged as a tool that is evolving and advancing in its ability to assist with healthcare information. This study is designed to evaluate the accuracy of the ChatGPT-3.5 and ChatGPT-4 models in addressing queries related to CRRT alarm troubleshooting. This study consisted of two rounds of ChatGPT-3.5 and ChatGPT-4 responses to address 50 CRRT machine alarm questions that were carefully selected by two nephrologists in intensive care. Accuracy was determined by comparing the model responses to predetermined answer keys provided by critical care nephrologists, and consistency was determined by comparing outcomes across the two rounds. The accuracy rate of ChatGPT-3.5 was 86% and 84%, while the accuracy rate of ChatGPT-4 was 90% and 94% in the first and second rounds, respectively. The agreement between the first and second rounds of ChatGPT-3.5 was 84% with a Kappa statistic of 0.78, while the agreement of ChatGPT-4 was 92% with a Kappa statistic of 0.88. Although ChatGPT-4 tended to provide more accurate and consistent responses than ChatGPT-3.5, there was no statistically significant difference between the accuracy and agreement rate between ChatGPT-3.5 and -4. ChatGPT-4 had higher accuracy and consistency but did not achieve statistical significance. While these findings are encouraging, there is still potential for further development to achieve even greater reliability. This advancement is essential for ensuring the highest-quality patient care and safety standards in managing CRRT machine-related issues.

Background One‐time assessment of the Society for Cardiovascular Angiography and Interventions (SCAI) shock classification robustly predicts mortality in the cardiac intensive care unit (CICU). We sought to determine whether serial SCAI shock classification could improve risk stratification. Methods and Results Unique admissions to a single academic level 1 CICU from 2015 to 2018 were included in this retrospective cohort study. Electronic health record data were used to assign the SCAI shock stage during 4‐hour blocks of the first 24 hours of CICU admission. Shock was defined as hypoperfusion (SCAI shock stage C, D, or E). In‐hospital death was evaluated using logistic regression. Among 2918 unique CICU patients, 1537 (52.7%) met criteria for shock during ≥1 block, and 266 (9.1%) died in the hospital. The SCAI shock stage on admission was: A, 37.6%; B, 31.5%; C, 25.9%; D, 1.8%; and E, 3.3%. Patients who met SCAI criteria for shock on admission (first 4 hours) and those with worsening SCAI shock stage after admission were at higher risk for in‐hospital death. Each higher admission (adjusted odds ratio, 1.36 [95% CI, 1.18–1.56]; area under the receiver operating characteristic curve, 0.70), maximum (adjusted odds ratio, 1.59 [95% CI, 1.37–1.85]; area under the receiver operating characteristic curve, 0.73) and mean (adjusted odds ratio, 2.42 [95% CI, 1.99–2.95]; area under the receiver operating characteristic curve, 0.78) SCAI shock stage was incrementally associated with a higher in‐hospital mortality rate. Discrimination was highest for the mean SCAI shock stage ( P <0.05). Each additional 4‐hour block meeting SCAI criteria for shock predicted a higher mortality rate (adjusted odds ratio, 1.15 [95% CI, 1.07–1.24]). Conclusions Dynamic assessment of shock using serial SCAI shock classification assignment can improve mortality risk stratification in CICU patients by quantifying the magnitude and duration of shock.

AbstractStudy ObjectiveTo develop and validate a model for predicting acute kidney injury (AKI) after high‐dose methotrexate (HDMTX) exposure.DesignRetrospective analysis.SettingMultisite integrated health system throughout Minnesota and Wisconsin.PatientsAdult patients with lymphoma who received HDMTX as a 4‐h infusion.Measurements and Main ResultsLASSO methodology was used to identify factors available at the outset of therapy that predicted incident AKI within 7 days following HDMTX. The model was then validated in an independent cohort. The incidence of AKI within 7 days following HDMTX was 21.6% (95% confidence interval (CI) 18.4%‐24.8%) in the derivation cohort (435 unique patients who received a total of 1642 doses of HDMTX) and 15.6% (95% CI 5.3%‐24.8%) in the validation cohort (55 unique patients who received a total of 247 doses of HDMTX). Factors significantly associated with AKI after HDMTX in the multivariable model included age ≥ 55 years, male sex, and lower HDMTX dose number. Other factors that were not found to be significantly associated with AKI on multivariable analysis, but were included in the final model, were body surface area, Charlson Comorbidity Index, and estimated glomerular filtration rate. The c‐statistic of the model was 0.72 (95% CI 0.69‐0.75) in the derivation cohort and 0.72 (95% CI 0.60‐0.84) in the validation cohort.ConclusionThis model utilizing identified sociodemographic and clinical factors is predictive of AKI following HDMTX administration in adult patients with lymphoma.