Personalized Medicine Transformed: ChatGPT's Contribution to Continuous Renal Replacement Therapy Alarm Management in Intensive Care Units.

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.

Determining steady-state trough range in vancomycin drug dosing using machine learning.

BACKGROUND: Vancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow therapeutic window, widely used in intensive care units (ICU). We aimed to predict the risk of inappropriate vancomycin trough levels and appropriate dosing for each ICU patient. METHODS: Observed vancomycin trough levels were categorized into sub-therapeutic, therapeutic, and supra-therapeutic levels to train and compare different classification models. We included adult ICU patients (≥ 18 years) with at least one vancomycin concentration measurement during hospitalization at Mayo Clinic, Rochester, MN, from January 2007 to December 2017. RESULT: The final cohort consisted of 5337 vancomycin courses. The XGBoost models outperformed other machine learning models with the AUC-ROC of 0.85 and 0.83, specificity of 53% and 47%, and sensitivity of 94% and 94% for sub- and supra-therapeutic categories, respectively. Kinetic estimated glomerular filtration rate and other creatinine-based measurements, vancomycin regimen (dose and interval), comorbidities, body mass index, age, sex, and blood pressure were among the most important variables in the models. CONCLUSION: We developed models to assess the risk of sub- and supra-therapeutic vancomycin trough levels to improve the accuracy of drug dosing in critically ill patients.

Temporal Relationship and Clinical Outcomes of Acute Kidney Injury Following Acute Respiratory Distress Syndrome: A Systematic Review and Meta-Analysis.

OBJECTIVES: Conduct a systematic review and meta-analysis to assess prevalence and timing of acute kidney injury (AKI) development after acute respiratory distress syndrome (ARDS) and its association with mortality. DATA SOURCES: Ovid MEDLINE(R), Ovid Embase, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Ovid PsycINFO database, Scopus, and Web of Science thought April 2023. STUDY SELECTION: Titles and abstracts were screened independently and in duplicate to identify eligible studies. Randomized controlled trials and prospective or retrospective cohort studies reporting the development of AKI following ARDS were included. DATA EXTRACTION: Two reviewers independently extracted data using a pre piloted abstraction form. We used Review Manager 5.4 software (Cochrane Library, Oxford, United Kingdom) and Open Meta software (Brown University, Providence, RI) for statistical analyses. DATA SYNTHESIS: Among the 3646 studies identified and screened, 17 studies comprising 9359 ARDS patients met the eligibility criteria and were included in the meta-analysis. AKI developed in 3287 patients (40%) after the diagnosis of ARDS. The incidence of AKI at least 48 hours after ARDS diagnosis was 20% (95% CI, 0.18-0.21%). The pooled risk ratio (RR) for the hospital (or 30-d) mortality among ARDS patients who developed AKI was 1.93 (95% CI, 1.71-2.18). AKI development after ARDS was identified as an independent risk factor for mortality in ARDS patients, with a pooled odds ratio from multivariable analysis of 3.69 (95% CI, 2.24-6.09). Furthermore, two studies comparing mortality between patients with late vs. early AKI initiation after ARDS revealed higher mortality in late AKI patients with RR of 1.46 (95% CI, 1.19-1.8). However, the certainty of evidence for most outcomes was low to very low. CONCLUSIONS: While our findings highlight a significant association between ARDS and subsequent development of AKI, the low to very low certainty of evidence underscores the need for cautious interpretation. This systematic review identified a significant knowledge gap, necessitating further research to establish a more definitive understanding of this relationship and its clinical implications.

Artificial Intelligence in Heart Failure and Acute Kidney Injury: Emerging Concepts and Controversial Dimensions.

BACKGROUND: The growing complexity of patient data and the intricate relationship between heart failure (HF) and acute kidney injury (AKI) underscore the potential benefits of integrating artificial intelligence (AI) and machine learning into healthcare. These advanced analytical tools aim to improve the understanding of the pathophysiological relationship between kidney and heart, provide optimized, individualized, and timely care, and improve outcomes of HF with AKI patients. SUMMARY: This comprehensive review article examines the transformative potential of AI and machine-learning solutions in addressing the challenges within this domain. The article explores a range of methodologies, including supervised and unsupervised learning, reinforcement learning, and AI-driven tools like chatbots and large language models. We highlight how these technologies can be tailored to tackle the complex issues prevalent among HF patients with AKI. The potential applications identified span predictive modeling, personalized interventions, real-time monitoring, and collaborative treatment planning. Additionally, we emphasize the necessity of thorough validation, the importance of collaborative efforts between cardiologists and nephrologists, and the consideration of ethical aspects. These factors are critical for the effective application of AI in this area. KEY MESSAGES: As the healthcare field evolves, the synergy of advanced analytical tools and clinical expertise holds significant promise to enhance the care and outcomes of individuals who deal with the combined challenges of HF and AKI.

Acute kidney injury in patients before and after extracorporeal membrane oxygenation (ECMO) - Retrospective longitudinal analysis of the hospital outcomes.

PURPOSE: Acute Kidney Injury (AKI) occurs in up to 85% of patients managed by ECMO support. Limited data are available comparing the outcomes among patients who develop AKI before and after ECMO initiation. METHODS: A retrospective longitudinal observational study was performed on all adult patients placed on ECMO from January 2000 to December 2015 at our institution. Longitudinal multivariate logistic regressional analysis was performed to identify the variables that are associated with the outcome measures (post-ECMO AKI and in-hospital mortality). RESULTS: A total of 329 patients were included in our analysis in which AKI occurred in 176 (53%) and 137 (42%) patients before and after ECMO, respectively. In the multivariate analysis, increasing age, pre-existing chronic kidney disease (CKD), increasing bilirubin, decreasing fibrinogen, and use of LVAD had significant association with post-ECMO AKI. In-hospital mortality was seen in 128 out of 176 (73%) patients in the pre-ECMO AKI group and 32 out of 137 (42%) in the post-ECMO AKI group. In the multivariate analysis, age, interstitial lung disease, pre-ECMO AKI, and post-ECMO RRT requirement were independently associated with mortality. CONCLUSION: AKI before ECMO initiation and the need for RRT post-ECMO are independently associated with poor patient survival.

Development and validation of a model to predict acute kidney injury following high-dose methotrexate in patients with lymphoma.

STUDY OBJECTIVE: To develop and validate a model for predicting acute kidney injury (AKI) after high-dose methotrexate (HDMTX) exposure. DESIGN: Retrospective analysis. SETTING: Multisite integrated health system throughout Minnesota and Wisconsin. PATIENTS: Adult patients with lymphoma who received HDMTX as a 4-h infusion. MEASUREMENTS AND MAIN RESULTS: LASSO 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. CONCLUSION: This model utilizing identified sociodemographic and clinical factors is predictive of AKI following HDMTX administration in adult patients with lymphoma.

PROGNOSTIC PERFORMANCE OF SERIAL DETERMINATION OF THE SOCIETY FOR CARDIOVASCULAR ANGIOGRAPHY AND INTERVENTIONS SHOCK CLASSIFICATION IN ADULTS WITH CRITICAL ILLNESS.

ABSTRACT: Purpose: The aim of the study is to evaluate whether serial assessment of shock severity can improve prognostication in intensive care unit (ICU) patients. Materials and Methods: This is a retrospective cohort of 21,461 ICU patient admissions from 2014 to 2018. We assigned the Society for Cardiovascular Angiography and Interventions (SCAI) Shock Stage in each 4-h block during the first 24 h of ICU admission; shock was defined as SCAI Shock stage C, D, or E. In-hospital mortality was evaluated using logistic regression. Results: The admission SCAI Shock stages were as follows: A, 39.0%; B, 27.0%; C, 28.9%; D, 2.6%; and E, 2.5%. The SCAI Shock stage subsequently increased in 30.6%, and late-onset shock developed in 30.4%. In-hospital mortality was higher in patients who had shock on admission (11.9%) or late-onset shock (7.3%) versus no shock (4.3%). Persistence of shock predicted higher mortality (adjusted OR = 1.09; 95% CI = 1.06-1.13, for each ICU block with shock). The mean SCAI Shock stage had higher discrimination for in-hospital mortality than the admission or maximum SCAI Shock stage. Dynamic modeling of the SCAI Shock classification improved discrimination for in-hospital mortality (C-statistic = 0.64-0.71). Conclusions: Serial application of the SCAI Shock classification provides improved mortality risk stratification compared with a single assessment on admission, facilitating dynamic prognostication.

Posthospital Multidisciplinary Care for AKI Survivors: A Feasibility Pilot.

Rationale & Objective: Innovative models are needed to address significant gaps in kidney care follow-up for acute kidney injury (AKI) survivors. Study Design: This quasi-experimental pilot study reports the feasibility of the AKI in Care Transitions (ACT) program, a multidisciplinary approach to AKI survivor care based in the primary care setting. Setting & Participants: The study included consenting adults with stage 3 AKI discharged home without dialysis. Interventions: The ACT intervention included predischarge education from nurses and coordinated postdischarge follow-up with a primary care provider and pharmacist within 14 days. ACT was implemented in phases (Usual Care, Education, ACT). Outcomes: The primary outcome was feasibility. Secondary outcomes included process and clinical outcomes. Results: In total, 46 of 110 eligible adults were enrolled. Education occurred in 18/18 and 14/15 participants in the Education and ACT groups, respectively. 30-day urine protein evaluation occurred in 15%, 28%, and 87% of the Usual Care, Education, and ACT groups, respectively (P < 0.001). Cumulative incidence of provider (primary care or nephrologist) and laboratory follow-up at 14 and 30 days was different across groups (14 days: Usual care 0%, Education 11%, ACT 73% [P < 0.01]; 30 days: 0%, 22%, and 73% [P < 0.01]). 30-day readmission rates were 23%, 44%, and 13% in the Usual Care, Education, and ACT groups, respectively (P = 0.13). Limitations: Patients were not randomly assigned to treatment groups. The sample size limited the ability to detect some differences or perform multivariable analysis. Conclusions: This study demonstrated the feasibility of multidisciplinary AKI survivor follow-up beginning in primary care. We observed a higher cumulative incidence of laboratory and provider follow-up in ACT participants. Trial Registration: ClinicalTrials.gov (NCT04505891). Plain-Language Summary: Abrupt loss of kidney function in hospitalized patients, acute kidney injury (AKI), increases the chances of long-term kidney disease and a worse health care experience for patients. One out of 3 people who experience AKI do not get the follow-up kidney care they need. We performed a pilot study to test whether a program that facilitates structured AKI follow-up in primary care called the AKI in Care Transitions (ACT) program was possible. ACT brings together the unique expertise of nurses, doctors, and pharmacists to look at the patient's kidney health plan from all angles. The study found that the ACT program was possible and led to more complete kidney care follow-up after discharge than the normal approach to care.

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.

Dose-related effects of norepinephrine on early-stage endotoxemic shock in a swine model.

Background: The benefits of early use of norepinephrine in endotoxemic shock remain unknown. We aimed to elucidate the effects of different doses of norepinephrine in early-stage endotoxemic shock using a clinically relevant large animal model. Methods: Vasodilatory shock was induced by endotoxin bolus in 30 Bama suckling pigs. Treatment included fluid resuscitation and administration of different doses of norepinephrine, to induce return to baseline mean arterial pressure (MAP). Fluid management, hemodynamic, microcirculation, inflammation, and organ function variables were monitored. All animals were supported for 6 h after endotoxemic shock. Results: Infused fluid volume decreased with increasing norepinephrine dose. Return to baseline MAP was achieved more frequently with doses of 0.8 µg/kg/min and 1.6 µg/kg/min (P <0.01). At the end of the shock resuscitation period, cardiac index was higher in pigs treated with 0.8 µg/kg/min norepinephrine (P <0.01), while systemic vascular resistance was higher in those receiving 0.4 µg/kg/min (P <0.01). Extravascular lung water level and degree of organ edema were higher in animals administered no or 0.2 µg/kg/min norepinephrine (P <0.01), while the percentage of perfused small vessel density (PSVD) was higher in those receiving 0.8 µg/kg/min (P <0.05) and serum lactate was higher in the groups administered no and 1.6 µg/kg/min norepinephrine (P <0.01). Conclusions: The impact of norepinephrine on the macro- and micro-circulation in early-stage endotoxemic shock is dose-dependent, with very low and very high doses resulting in detrimental effects. Only an appropriate norepinephrine dose was associated with improved tissue perfusion and organ function.

Serial Assessment of Shock Severity in Cardiac Intensive Care Unit Patients.

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.

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.

Shock Severity Classification and Mortality in Adults With Cardiac, Medical, Surgical, and Neurological Critical Illness.

OBJECTIVE: To evaluate whether the Society for Cardiovascular Angiography and Interventions (SCAI) Shock Classification could perform risk stratification in a mixed cohort of intensive care unit (ICU) patients, similar to its validation in patients with acute cardiac disease. METHODS: We included 21,461 adult Mayo Clinic ICU patient admissions from December 1, 2014, to February 28, 2018, including cardiac ICU (16.7%), medical ICU (37.4%), neurosciences ICU (27.7%), and surgical ICU (18.2%). The SCAI Shock Classification (a 5-stage classification from no shock [A] to refractory shock [E]) was assigned in each 4-hour period during the first 24 hours of ICU admission. RESULTS: The median age was 65 years, and 43.2% were female. In-hospital mortality occurred in 1611 (7.5%) patients, with a stepwise increase in in-hospital mortality in each higher maximum SCAI Shock stage overall: A, 4.0%; B, 4.6%; C, 7.0%; D, 13.9%; and E, 40.2%. The SCAI Shock Classification provided incremental mortality risk stratification in each ICU, with the best performance in the cardiac ICU and the worse performance in the neurosciences ICU. The SCAI Shock Classification was associated with higher adjusted in-hospital mortality (adjusted odds ratio, 1.32 per each stage; 95% CI, 1.24 to 1.41; P<.001); this association was not observed in the neurosciences ICU when considered separately. CONCLUSION: The SCAI Shock Classification provided incremental mortality risk stratification beyond established prognostic markers across the spectrum of medical and surgical critical illness, proving utility outside its original intent.

Acute Kidney Injury Management Strategies Peri-Cardiovascular Interventions.

In many countries, the aging population and the higher incidence of comorbid conditions have resulted in an ever-growing need for cardiac interventions. Acute kidney injury (AKI) is a common complication of these interventions, associated with higher mortalities, chronic or end-stage kidney disease, readmission rates, and hospital and post-discharge costs. The AKI pathophysiology includes contrast-associated AKI, hemodynamic changes, cardiorenal syndrome, and atheroembolism. Preventive measures include limiting contrast media dose, optimizing hemodynamic conditions, and limiting exposure to other nephrotoxins. This review article outlines the current state-of-art knowledge regarding AKI pathophysiology, risk factors, preventive measures, and management strategies in the peri-interventional period.

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.

Extracorporeal membrane oxygenation and acute kidney injury: a single-center retrospective cohort.

To assess the relationship between acute kidney injury (AKI) with outcomes among patients requiring extracorporeal membrane oxygenation (ECMO). This is a single-center, retrospective cohort study of adult patients admitted to intensive care units (ICU) at a tertiary referral hospital requiring ECMO from July 1, 2015, to August 30, 2019. We assessed the temporal relationship of AKI and renal replacement therapy with ECMO type (VV vs. VA). The primary outcome was in-hospital mortality rates. We used Kruskal-Wallis or chi-square tests for pairwise comparisons, cause-specific Cox proportional hazards models were utilized for the association between AKI prevalence and in-hospital mortality, and a time-dependent Cox model was used to describe the association between AKI incidence and mortality. After the screening, 190 patients met eligibility criteria [133 (70%) AKI, 81 (43%) required RRT]. The median age was 61 years, and 61% were males. Among AKI patients, 48 (36%) and 85 (64%) patients developed AKI before and after ECMO, respectively. The SOFA Day 1, baseline creatinine, respiratory rate (RR), use of vasopressin, vancomycin, proton pump inhibitor, antibiotics, duration of mechanical ventilation and ECMO, and ICU length of stay were higher in AKI patients compared with those without AKI (P < 0.01). While ICU and in-hospital mortality rates were 46% and 50%, respectively, there were no differences based on the AKI status. The type and characteristics of ECMO support were not associated with AKI risk. Among AKI patients, 77 (58%) were oliguric, and 46 (60%) of them received diuretics. Urine output in the diuretic group was only higher on the first day than in those who did not receive diuretics (P = 0.03). Among ECMO patients, AKI was not associated with increased mortality but was associated with prolonged duration of mechanical ventilation and ICU length of stay.

Changes in Doppler-Derived Kidney Venous Flow and Adverse Cardiorenal Outcomes in Patients With Heart Failure.

Background The impact of changes in Doppler-derived kidney venous flow in heart failure (HF) is not well studied. We aimed to investigate the association of Doppler-derived kidney venous stasis index (KVSI) and intrakidney venous-flow (IKVF) patterns with adverse cardiorenal outcomes in patients with HF. Methods and Results In this observational cohort study, consecutive inpatients with HF referred to a nephrologist because of a history of diuretic resistance and abnormal kidney function (n=216) underwent spectral kidney assessments after admission (Doppler 1) and 25 to 35 days later (Doppler 2) to identify IKVF patterns (continuous/pulsatile/biphasic/monophasic) and KVSI levels. Cox proportional hazard regression models were used to evaluate the associations between KVSI/IKVF patterns at Doppler 1 as well as changes from Doppler 1 to Doppler 2 and risk of cardiorenal events up to 18 months after admission. Worsening HF or death occurred in 126 patients. Both baseline KVSI (hazard ratio [HR], 1.49 [95% CI, 1.37-1.61] per 0.1-unit increase) and baseline IKVF pattern (HR, 2.47 [95% CI, 2.01-3.04] per 1 pattern severity increase) were significantly associated with worsening HF/death. Increases in both KVSI and IKVF pattern severity from Doppler 1 to 2 were also associated with an increased risk of worsening HF/death (HR, 3.00 [95% CI, 2.08-4.32] per 0.1-unit increase change; and HR, 6.73 [95% CI, 3.27-13.86] per 1 pattern increase in severity change, respectively). Similar results were observed for kidney outcomes. Conclusions Baseline kidney venous flow predicted adverse cardiorenal events, and inclusion of serial kidney venous flow in cardiorenal risk stratification could facilitate clinical decision-making for patients with HF. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03039959.

Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.

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.

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.