Machine learning for predicting mortality in adult critically ill patients with Sepsis: A systematic review.

INTRODUCTION: Various Machine Learning (ML) models have been used to predict sepsis-associated mortality. We conducted a systematic review to evaluate the methodologies employed in studies to predict mortality among patients with sepsis. METHODS: Following a pre-established protocol registered at the International Prospective Register of Systematic Reviews, we performed a comprehensive search of databases from inception to February 2024. We included peer-reviewed articles reporting predicting mortality in critically ill adult patients with sepsis. RESULTS: Among the 1822 articles, 31 were included, involving 1,477,200 adult patients with sepsis. Nineteen studies had a high risk of bias. Among the diverse ML models, Logistic regression and eXtreme Gradient Boosting were the most frequently used, in 22 and 16 studies, respectively. Nine studies performed internal and external validation. Compared with conventional scoring systems such as SOFA, the ML models showed slightly higher performance in predicting mortality (AUROC ranges: 0.62-0.90 vs. 0.47-0.86). CONCLUSIONS: ML models demonstrate a modest improvement in predicting sepsis-associated mortality. The certainty of these findings remains low due to the high risk of bias and significant heterogeneity. Studies should include comprehensive methodological details on calibration and hyperparameter selection, adopt a standardized definition of sepsis, and conduct multicenter prospective designs along with external validations.

Arterial hyperoxia and mortality in the cardiac intensive care unit.

BACKGROUND: Arterial hyperoxia (hyperoxemia), defined as a high arterial partial pressure of oxygen (PaO2), has been associated with adverse outcomes in critically ill populations, but has not been examined in the cardiac intensive care unit (CICU). We evaluated the association between exposure to hyperoxia on admission with in-hospital mortality in a mixed CICU cohort. METHODS: We included unique Mayo Clinic CICU patients admitted from 2007 to 2018 with admission PaO2 data (defined as the PaO2 value closest to CICU admission) and no hypoxia (PaO2 < 60mmHg). The admission PaO2 was evaluated as a continuous variable and categorized (60-100 mmHg, 101-150 mmHg, 151-200 mmHg, 201-300 mmHg, >300 mmHg). Logistic regression was used to evaluate predictors of in-hospital mortality before and after multivariable adjustment. RESULTS: We included 3,368 patients with a median age of 70.3 years; 70.3% received positive-pressure ventilation. The median PaO2 was 99 mmHg, with a distribution as follows: 60-100 mmHg, 51.9%; 101-150 mmHg, 28.6%; 151-200 mmHg, 10.6%; 201-300 mmHg, 6.4%; >300 mmHg, 2.5%. A J-shaped association between admission PaO2 and in-hospital mortality was observed, with a nadir around 100 mmHg. A higher PaO2 was associated with increased in-hospital mortality (adjusted OR 1.17 per 100 mmHg higher, 95% CI 1.01-1.34, p = 0.03). Patients with PaO2 >300 mmHg had higher in-hospital mortality versus PaO2 60-100 mmHg (adjusted OR 2.37, 95% CI 1.41-3.94, p < 0.001). CONCLUSIONS: Hyperoxia at the time of CICU admission is associated with higher in-hospital mortality, primarily in those with severely elevated PaO2 >300 mmHg.

Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.

Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.

Magnesium Derangement among Critically Ill Patients with Acute Kidney Injury: An Association with Acute Kidney Disease.

INTRODUCTION: The association between magnesium level and progression to acute kidney disease (AKD) in acute kidney injury (AKI) patients was not well studied. With AKI transition to AKD, the burden of the disease on mortality, morbidity, and healthcare costs increases. Serum magnesium disturbances are linked with a decline in renal function and increased risk of death in CKD and hemodialysis patients. This study aims to assess the significance of magnesium derangements as a risk factor for the progression of AKI to AKD in critically ill patients. METHODS: This study was conducted among patients with AKI admitted to the intensive care units at Mayo Clinic from 2007 to 2017. Serum magnesium at AKI onset was categorized into five groups of <1.7, 1.7-1.9, 1.9-2.1, 2.1-2.3, and ≥2.3 mg/dL, with 1.9-2.1 mg/dL as the reference group. AKD was defined as AKI that persisted >7 days following the AKI onset. Logistic regression was used to evaluate the association between magnesium and AKD. RESULTS: Among 20,198 critically ill patients with AKI, the mean age was 66 ± 16 years, and 57% were male. The mean serum magnesium at AKI onset was 1.9 ± 0.4 mg/dL. The overall incidence of AKD was 31.4%. The association between serum magnesium and AKD followed a U-shaped pattern. In multivariable analysis, serum magnesium levels were associated with increased risk of AKD with the odds ratio of 1.17 (95% CI: 1.07-1.29), 1.13 (95% CI: 1.01-1.26), and 1.65 (95% CI: 1.48-1.84) when magnesium levels were <1.7, 2.1-2.3, and ≥2.3 mg/dL, respectively. CONCLUSION: Among patients with AKI, magnesium level derangement was an independent risk for AKD in critically ill AKI patients. Monitoring serum magnesium and proper correction in critically ill patients with AKI should be considered an AKD preventive intervention in future trials.

Behind the scenes: Key lessons learned from the RELIEVE-AKI clinical trial.

Continuous kidney replacement therapy (CKRT) is commonly used to manage critically ill patients with severe acute kidney injury. While recent trials focused on the correct dosing and timing of CKRT, our understanding regarding the optimum dose of net ultrafiltration is limited to retrospective data. The Restrictive versus Liberal Rate of Extracorporeal Volume Removal Evaluation in Acute Kidney Injury (RELIEVE-AKI) trial has been conducted to assess the feasibility of a prospective randomized trial in determining the optimum net ultrafiltration rate. This paper outlines the relevant challenges and solutions in implementing this complex ICU-based trial. Several difficulties were encountered, starting with clinical issues related to conducting a trial on patients with rapidly changing hemodynamics, low patient recruitment rates, increased nursing workload, and the enormous volume of data generated by patients undergoing prolonged CKRT. Following several brainstorming sessions, several points were highlighted to be considered, including the need to streamline the intervention, add more flexibility in the trial protocols, ensure comprehensive a priori planning, particularly regarding nursing roles and their compensation, and enhance data management systems. These insights are critical for guiding future ICU-based dynamically titrated intervention trials, leading to more efficient trial management, improved data quality, and enhanced patient safety.

The role of biomarkers in early identification of acute kidney injury among non-critically ill patients.

BACKGROUND: Prediction and/or early identification of acute kidney injury (AKI) and individuals at greater risk remains of great interest in clinical medicine. Acute kidney injury continues to be a common complication among hospitalized patients, with an incidence ranging from 6 to 58%, depending on the setting. Aim of this study was to determine the performance of Insulin-like growth factor binding protein-7 (IGFBP7), tissue metallopeptidase inhibitor 2 (TIMP2), and urinary neutrophil gelatinase-associated lipocalin (uNGAL) in early detection of AKI among non-critically ill patients. METHODS: In this prospective observational study at Mayo Clinic Hospitals in Rochester, Minnesota, USA, non-critically ill patients admitted from the emergency department between October 31st, 2016 and May 1st, 2018, who had an acute kidney injury (AKI) probability of 5% or higher were included. Biomarkers were measured in residual urine samples collected in the emergency department. The primary outcome was biomarker performance in predicting AKI development within the first 72 h. RESULTS: Among 368 included patients, the mean age was 79 ± 12 years, and 160 (43%) were male. Acute kidney injury occurred in 62 (17%) patients; 11.5% stage 1, 2.5% stage 2, and 3% stage 3. Twelve patients (3%) died during hospitalization and 102 (28%) within nine months after admission. The median uNGAL and IGFBP7-TIMP2 were 57 [20-236 ng/ml], and 0.3 [0.1-0.8], respectively. The C-statistic of uNGAL and IGFBP7-TIMP2 of > 0.3 and > 2.0 for AKI prediction were 0.56, 0.54, and 0.53, respectively. In a model where one point is assigned to each marker of AKI (elevated serum creatinine, IGFBP7-TIMP2 > 0.3, and uNGAL), a higher score correlated with higher nine-month mortality [OR of 1.32 per point (95% CI 1.02-1.71)]. CONCLUSION: Among non-critically ill hospitalized patients, the performance of uNGAL and IGFBP7-TIMP2 for AKI prediction within 72 h of admission was modest. This suggests a limited role for these biomarkers in AKI risk stratification among non-critically ill patients. Key learning points What was known Acute kidney injury (AKI) is a common complication among hospitalized patients. It is associated with increased morbidity and mortality. Various clinical prediction models and biomarkers have been developed to identify patients in special populations (such as ICU and cardiac surgery) who are at risk of AKI and diagnose AKI early. This study adds The performance of the biomarkers uNGAL, TIMP-2, and IGFBP-7 in predicting AKI within 72 h of admission in non-critically ill patients was modest. However, these biomarkers were found to have a prognostic value for predicting 9-month mortality. One potential application of these biomarkers is identifying patients at higher AKI risk before exposing them to nephrotoxic agents. Potential impact This study provides evidence regarding the real-world performance of current FDA-approved biomarkers (uNGAL, TIMP-2, and IGFBP-7) for predicting acute kidney injury (AKI) within 72 h of hospital admission among noncritically ill patients. While the performance of these biomarkers for predicting short-term AKI was modest, they may have a prognostic value for predicting 9-month mortality.

Admission Total Leukocyte Count as a Predictor of Mortality in Cardiac Intensive Care Unit Patients.

Background: Inflammation is a sequela of cardiovascular critical illness and a risk factor for mortality. Objectives: This study aimed to evaluate the association between white blood cell count (WBC) and mortality in a broad population of patients admitted to the cardiac intensive care unit (CICU). Methods: This retrospective cohort study included patients admitted to the Mayo Clinic CICU between 2007 and 2018. We analyzed WBC as a continuous variable and then categorized WBC as low (<4.0 × 103/mL), normal (≥4.0 to <11.0 × 103/mL), high (≥11.0 to <22.0 × 103/mL), or very high (≥22.0 × 103/mL). The association between WBC and in-hospital mortality was evaluated using multivariable logistic regression and random forest models. Results: We included 11,699 patients with a median age of 69.3 years (37.6% females). Median WBC was 9.6 (IQR: 7.4-12.7). Mortality was higher in the low (10.5%), high (12.0%), and very high (33.3%) WBC groups relative to the normal WBC group (5.3%). A rising WBC was incrementally associated with higher in-hospital mortality after adjustment (AICc adjusted OR: 1.03 [95% CI: 1.02-1.04] per 1 × 103 increase in WBC). After adjustment, only the high (AICc adjusted OR: 1.37 [95% CI: 1.15-1.64]) and very high (AICc adjusted OR: 1.99 [1.47-2.71]) WBC groups remained associated with increased risk of in-hospital mortality. Conclusions: Leukocytosis is associated with an increased mortality risk in a diverse cohort of CICU patients. This readily available marker of systemic inflammation may be useful for risk stratification within the increasingly complex CICU patient population.

Kidney Replacement Therapies and Ultrafiltration in Cardiorenal Syndrome.

BACKGROUND: Some patients with cardiorenal syndrome 1 and congestion exhibit resistance to diuretics. This scenario complicates management and is associated with a worse prognosis. In some cases, rescue treatment may be considered by starting kidney replacement therapies or ultrafiltration. This decision is complex and necessitates a profound understanding of these techniques and the pathophysiology of this syndrome. These modalities are classified into continuous, intermittent, and ultrafiltration therapies, each with its own advantages and disadvantages that are pertinent in selecting the optimal treatment. SUMMARY: In patients with diuretic-resistant cardiorenal syndrome, extracorporeal ultrafiltration and kidney replacement therapies have the potential to relieve congestion, restore the neurohormonal system, and improve quality of life. KEY MESSAGES: (i) In cardiorenal syndrome, the resistance to diuretics is common. (ii) Extracorporeal ultrafiltration and renal replacement therapies are rescue options that may improve the management of these patients. (iii) Better understanding of these modalities will help the development of new devices which are friendlier, safer, and more affordable for patients in these clinical settings.

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.

Association between the shock index on admission and in-hospital mortality in the cardiac intensive care unit.

BACKGROUND: An elevated shock index (SI) predicts worse outcomes in multiple clinical arenas. We aimed to determine whether the SI can aid in mortality risk stratification in unselected cardiac intensive care unit patients. METHODS: We included admissions to the Mayo Clinic from 2007 to 2015 and stratified them based on admission SI. The primary outcome was in-hospital mortality, and predictors of in-hospital mortality were analyzed using multivariable logistic regression. RESULTS: We included 9,939 unique cardiac intensive care unit patients with available data for SI. Patients were grouped by SI as follows: < 0.6, 3,973 (40%); 0.6-0.99, 4,810 (48%); and ≥ 1.0, 1,156 (12%). After multivariable adjustment, both heart rate (adjusted OR 1.06 per 10 beats per minute higher; CI 1.02-1.10; p-value 0.005) and systolic blood pressure (adjusted OR 0.94 per 10 mmHg higher; CI 0.90-0.97; p-value < 0.001) remained associated with higher in-hospital mortality. As SI increased there was an incremental increase in in-hospital mortality (adjusted OR 1.07 per 0.1 beats per minute/mmHg higher, CI 1.04-1.10, p-Value < 0.001). A higher SI was associated with increased mortality across all examined admission diagnoses. CONCLUSION: The SI is a simple and universally available bedside marker that can be used at the time of admission to predict in-hospital mortality in cardiac intensive care unit patients.

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.

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.

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.

Regional Practice Variation and Outcomes in the Standard Versus Accelerated Initiation of Renal Replacement Therapy in Acute Kidney Injury (STARRT-AKI) Trial: A Post Hoc Secondary Analysis.

OBJECTIVES: Among patients with severe acute kidney injury (AKI) admitted to the ICU in high-income countries, regional practice variations for fluid balance (FB) management, timing, and choice of renal replacement therapy (RRT) modality may be significant. DESIGN: Secondary post hoc analysis of the STandard vs. Accelerated initiation of Renal Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial (ClinicalTrials.gov number NCT02568722). SETTING: One hundred-fifty-three ICUs in 13 countries. PATIENTS: Altogether 2693 critically ill patients with AKI, of whom 994 were North American, 1143 European, and 556 from Australia and New Zealand (ANZ). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Total mean FB to a maximum of 14 days was +7199 mL in North America, +5641 mL in Europe, and +2211 mL in ANZ (p < 0.001). The median time to RRT initiation among patients allocated to the standard strategy was longest in Europe compared with North America and ANZ (p < 0.001; p < 0.001). Continuous RRT was the initial RRT modality in 60.8% of patients in North America and 56.8% of patients in Europe, compared with 96.4% of patients in ANZ (p < 0.001). After adjustment for predefined baseline characteristics, compared with North American and European patients, those in ANZ were more likely to survive to ICU (p < 0.001) and hospital discharge (p < 0.001) and to 90 days (for ANZ vs. Europe: risk difference [RD], -11.3%; 95% CI, -17.7% to -4.8%; p < 0.001 and for ANZ vs. North America: RD, -10.3%; 95% CI, -17.5% to -3.1%; p = 0.007). CONCLUSIONS: Among STARRT-AKI trial centers, significant regional practice variation exists regarding FB, timing of initiation of RRT, and initial use of continuous RRT. After adjustment, such practice variation was associated with lower ICU and hospital stay and 90-day mortality among ANZ patients compared with other regions.

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.

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.

Acetazolamide for acute kidney injury in patients undergoing high dose methotrexate therapy: a systematic review and meta-analysis.

BACKGROUND: Urine alkalization is one of the standard treatments to prevent acute kidney injury in patients receiving high-dose methotrexate. Carbonic anhydrase inhibitors are promising adjuvants/substitutes with advantages such as faster urine alkalization time and prevention of fluid overload. However, there is limited and contradictory evidence on its efficacy and safety. We aimed to compare the efficacy and safety of carbonic anhydrase inhibitors to standard treatments in adult patients receiving high-dose methotrexate. METHODS: The protocol was registered at PROSPERO (CRD42022352802) in August 2021. We evaluated the use of carbonic anhydrase inhibitors in combination with standard treatment compared to standard treatment alone. We excluded articles irrelevant to the efficacy and safety of acetazolamide in patients receiving high dose methotrexate and/or did not provide sufficient data regarding doses, recruitment criteria, and follow-up period. Two authors performed the data extraction independently. RESULTS: Among 198 articles retrieved, six observational studies met all eligibility criteria. Four studies with five datasets (totaling 558 patients/cycles) had enough data to be included in the meta-analysis. We independently report the results from the two remaining studies. The results did not show a significant difference between acetazolamide versus standard treatment in acute kidney injury (AKI) rate (OR = 0.79, 95% CI 0.48-1.29, P = 0.34, I2 = 0%). Regarding the time to urine pH goal, there was no significant time difference between the two groups (Mean Difference = 0.07, 95% CI - 1.9 to 2.04, P = 0.95, I2 = 25%). Furthermore, our meta-analysis showed that acetazolamide did not reduce length of stay (Mean Difference = 0.75, 95% CI - 0.8 to 2.31, P = 0.34, I2 = 0%). In one study, the only reported side effect of acetazolamide was hypokalemia (nearly 50% in the acetazolamide group). CONCLUSIONS: This systematic review showed no significant difference between acetazolamide and standard care treatment regarding urine alkalinization time and AKI rate in adult patients receiving high dose methotrexate. We suggest performing a large blinded, randomized, controlled trial to evaluate the potential benefits of this low-cost medication.