Acute kidney disease and renal recovery : consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup

Consensus definitions have been reached for both acute kidney injury (AKI) and chronic kidney disease (CKD) and these definitions are now routinely used in research and clinical practice. The KDIGO guideline defines AKI as an abrupt decrease in kidney function occurring over 7 days or less, whereas CKD is defined by the persistence of kidney disease for a period of > 90 days. AKI and CKD are increasingly recognized as related entities and in some instances probably represent a continuum of the disease process. For patients in whom pathophysiologic processes are ongoing, the term acute kidney disease (AKD) has been proposed to define the course of disease after AKI; however, definitions of AKD and strategies for the management of patients with AKD are not currently available. In this consensus statement, the Acute Disease Quality Initiative (ADQI) proposes definitions, staging criteria for AKD, and strategies for the management of affected patients. We also make recommendations for areas of future research, which aim to improve understanding of the underlying processes and improve outcomes for patients with AKD

Epidemiology, Trajectories and Outcomes of Acute Kidney Injury Among Hospitalized Patients: A Retrospective Multicenter Large Cohort Study

Background: Acute kidney injury (AKI) is a clinical syndrome affecting almost one fifth of hospitalized patients, as well as more than half of the patients who are admitted to the intensive care unit (ICU). Stratifying AKI patients into groups based on severity and duration would facilitate more targeted efforts for treating AKI. Methods: In a retrospective, multicenter and longitudinal cohort study of 935,679 patients who were admitted between 2012 and 2020 to health centers included in OneFlorida+ Network, we analyzed the impact of AKI trajectories which are rapidly reversed AKI, persistent AKI with renal recovery, and persistent AKI without renal recovery on patients' clinical outcomes, including hospital, 30-day, 1-year, and 3-year mortality, kidney replacement therapy, new chronic kidney disease (CKD) within 90 days or 1-year of discharge, CKD progression within 1-year of discharge, resource utilization, hospital disposition, and major complications during hospitalization. As analytical approaches, Kaplan-Meier estimators and survival curves, Cox proportional-hazards regression model, logistic regression model, Kruskal-Wallis test, analysis of variance, chi-square, Fisher's exact test were used. Results: Among 2,187,254 encounters, 14% had AKI, of which 63%, 21%, and 16% had Stage 1, 2, and 3, respectively, as the worst AKI stage. Fraction of patients with persistent AKI was 31%. Patients with AKI had worse clinical outcomes and increased resource utilization compared to patients without the condition. One-year mortality was 5 times greater for patients with persistent AKI compared to those without AKI. Conclusions: Persistent AKI was associated with prolonged hospitalization, increased ICU admission and mortality compared to the other groups. This may emphasize the critical need for devising strategies targeting effective management of AKI and prevention of persisting AKI.Comment: 61 pages, 2 tables, 3 figures, 13 supplemental tables, 3 supplemental figure

Global Contrastive Training for Multimodal Electronic Health Records with Language Supervision

Modern electronic health records (EHRs) hold immense promise in tracking personalized patient health trajectories through sequential deep learning, owing to their extensive breadth, scale, and temporal granularity. Nonetheless, how to effectively leverage multiple modalities from EHRs poses significant challenges, given its complex characteristics such as high dimensionality, multimodality, sparsity, varied recording frequencies, and temporal irregularities. To this end, this paper introduces a novel multimodal contrastive learning framework, specifically focusing on medical time series and clinical notes. To tackle the challenge of sparsity and irregular time intervals in medical time series, the framework integrates temporal cross-attention transformers with a dynamic embedding and tokenization scheme for learning multimodal feature representations. To harness the interconnected relationships between medical time series and clinical notes, the framework equips a global contrastive loss, aligning a patient's multimodal feature representations with the corresponding discharge summaries. Since discharge summaries uniquely pertain to individual patients and represent a holistic view of the patient's hospital stay, machine learning models are led to learn discriminative multimodal features via global contrasting. Extensive experiments with a real-world EHR dataset demonstrated that our framework outperformed state-of-the-art approaches on the exemplar task of predicting the occurrence of nine postoperative complications for more than 120,000 major inpatient surgeries using multimodal data from UF health system split among three hospitals (UF Health Gainesville, UF Health Jacksonville, and UF Health Jacksonville-North).Comment: 12 pages, 3 figures. arXiv admin note: text overlap with arXiv:2403.0401

Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study

Background: Acute kidney injury (AKI), the decline of kidney excretory function, occurs in up to 18% of hospitalized admissions. Progression of AKI may lead to irreversible kidney damage. Methods: This retrospective cohort study includes adult patients admitted to a non-intensive care unit at the University of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of Florida Health (UFH) (n = 127,202). We developed and compared deep learning and conventional machine learning models to predict progression to Stage 2 or higher AKI within the next 48 hours. We trained local models for each site (UFH Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a development cohort of patients from both sites (UFH-UPMC Model). We internally and externally validated the models on each site and performed subgroup analyses across sex and race. Results: Stage 2 or higher AKI occurred in 3% (n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under the receiver operating curve values (AUROC) for the UFH test cohort ranged between 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged between 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort. UFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80, 0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area under the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06]) for UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen remained the top three features with the highest influence across the models and health centers. Conclusion: Locally developed models displayed marginally reduced discrimination when tested on another institution, while the top set of influencing features remained the same across the models and sites

Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor

BackgroundThe algorithmic patient preference predictor (PPP) has been proposed to aid in decision making for incapacitated patients in the absence of advanced directives. Ethical and legal challenges aside, multiple practical barriers exist for building a personalized PPP. Here, we examine previous work using machine learning to predict patient reported outcome measures (PROMs) for capacitated patients undergoing diverse procedures, therapies, and life events. Demonstrating robust performance in predicting PROMs for capacitated patients could suggest opportunities for developing a model tailored to incapacitated ones.MethodsWe performed a scoping review of PubMed, Embase, and Scopus using the PRISMA-ScR guidelines to capture studies using machine learning to predict PROMs following a medical event alongside qualitative studies exploring a theoretical PPP.ResultsSixty-eight studies used machine learning to evaluate PROMs; an additional 20 studies focused on a theoretical PPP. For PROMs, orthopedic surgeries (n = 33) and spinal surgeries (n = 12) were the most common medical event. Studies used demographic (n = 30), pre-event PROMs (n = 52), comorbidities (n = 29), social determinants of health (n = 30), and intraoperative variables (n = 124) as predictors. Thirty-four different PROMs were used as the target outcome. Evaluation metrics varied by task, but performance was overall poor to moderate for the best reported scores. In models that used feature importance, pre-event PROMs were the most predictive of post-event PROMs. Fairness assessments were rare (n = 6). These findings reinforce the necessity of the integrating patient values and preferences, beyond demographic factors, to improve the development of personalized PPP models for incapacitated patients.ConclusionThe primary objective of a PPP is to estimate patient-reported quality of life following an intervention. Use of machine learning to predict PROMs for capacitated patients introduces challenges and opportunities for building a personalized PPP for incapacitated patients without advanced directives

The Potential Influence of Common Viral Infections Diagnosed during Hospitalization among Critically Ill Patients in the United States

Viruses are the most common source of infection among immunocompetent individuals, yet they are not considered a clinically meaningful risk factor among the critically ill. This work examines the association of viral infections diagnosed during the hospital stay or not documented as present on admission to the outcomes of ICU patients with no evidence of immunosuppression on admission. This is a population-based retrospective cohort study of University HealthSystem Consortium (UHC) academic centers in the U.S. from the years 2006 to 2009. The UHC is an alliance of over 90% of the non-profit academic medical centers in the U.S. A total of 209,695 critically ill patients were used in this analysis. Eight hospital complications were examined. Patients were grouped into four cohorts: absence of infection, bacterial infection only, viral infection only, and bacterial and viral infection during same hospital admission. Viral infections diagnosed during hospitalization significantly increased the risk of all complications. There was also a seasonal pattern for viral infections. Specific viruses associated with poor outcomes included influenza, RSV, CMV, and HSV. Patients who had both viral and bacterial infections during the same hospitalization had the greatest risk of mortality RR 6.58, 95% CI (5.47, 7.91); multi-organ failure RR 8.25, 95% CI (7.50, 9.07); and septic shock RR 271.2, 95% CI (188.0, 391.3). Viral infections may play a significant yet unrecognized role in the outcomes of ICU patients. They may serve as biological markers or play an active role in the development of certain adverse complications by interacting with coincident bacterial infection

Validation of cell-cycle arrest biomarkers for acute kidney injury using clinical adjudication.

RationaleWe recently reported two novel biomarkers for acute kidney injury (AKI), tissue inhibitor of metalloproteinases (TIMP)-2 and insulin-like growth factor binding protein 7 (IGFBP7), both related to G1 cell cycle arrest.ObjectivesWe now validate a clinical test for urinary [TIMP-2]·[IGFBP7] at a high-sensitivity cutoff greater than 0.3 for AKI risk stratification in a diverse population of critically ill patients.MethodsWe conducted a prospective multicenter study of 420 critically ill patients. The primary analysis was the ability of urinary [TIMP-2]·[IGFBP7] to predict moderate to severe AKI within 12 hours. AKI was adjudicated by a committee of three independent expert nephrologists who were masked to the results of the test.Measurements and main resultsUrinary TIMP-2 and IGFBP7 were measured using a clinical immunoassay platform. The primary endpoint was reached in 17% of patients. For a single urinary [TIMP-2]·[IGFBP7] test, sensitivity at the prespecified high-sensitivity cutoff of 0.3 (ng/ml)(2)/1,000 was 92% (95% confidence interval [CI], 85-98%) with a negative likelihood ratio of 0.18 (95% CI, 0.06-0.33). Critically ill patients with urinary [TIMP-2]·[IGFBP7] greater than 0.3 had seven times the risk for AKI (95% CI, 4-22) compared with critically ill patients with a test result below 0.3. In a multivariate model including clinical information, urinary [TIMP-2]·[IGFBP7] remained statistically significant and a strong predictor of AKI (area under the curve, 0.70, 95% CI, 0.63-0.76 for clinical variables alone, vs. area under the curve, 0.86, 95% CI, 0.80-0.90 for clinical variables plus [TIMP-2]·[IGFBP7]).ConclusionsUrinary [TIMP-2]·[IGFBP7] greater than 0.3 (ng/ml)(2)/1,000 identifies patients at risk for imminent AKI. Clinical trial registered with www.clinicaltrials.gov (NCT 01573962)