O’Hagan, Emma

Key Points Pooled discrimination metrics were acceptable (area under the receiver operating characteristic curve >0.70) for all AKI risk classification categories in both internal and external validation. Better performance was observed in most recently published studies and those with a low or unclear risk of bias. Significant heterogeneity in patient populations, definitions, clinical predictors, and methods limit implementation in real-world clinical scenarios. Background Artificial intelligence through machine learning models seems to provide accurate and precise AKI risk classification in some clinical settings, but their performance and implementation in real-world settings has not been established. Methods PubMed, Excerpta Medica (EMBASE) database, Web of Science, and Scopus were searched until August 2023. Articles reporting on externally validated models for prediction of AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric patients were searched using text words related to AKI, artificial intelligence, and machine learning. Two independent reviewers screened article titles, abstracts, and full texts. Areas under the receiver operating characteristic curves (AUCs) were used to compare model discrimination and pooled using a random-effects model. Results Of the 4816 articles initially identified and screened, 95 were included, representing 3.8 million admissions. The Kidney Disease Improving Global Outcomes (KDIGO)-AKI criteria were most frequently used to define AKI (72%). We identified 302 models, with the most common being logistic regression (37%), neural networks (10%), random forest (9%), and eXtreme gradient boosting (9%). The most frequently reported predictors of hospitalized incident AKI were age, sex, diabetes, serum creatinine, and hemoglobin. The pooled AUCs for AKI onset were 0.82 (95% confidence interval, 0.80 to 0.84) and 0.78 (95% confidence interval, 0.76 to 0.80) for internal and external validation, respectively. Pooled AUCs across multiple clinical settings, AKI severities, and post-AKI complications ranged from 0.78 to 0.87 for internal validation and 0.73 to 0.84 for external validation. Although data were limited, results in the pediatric population aligned with those observed in adults. Between-study heterogeneity was high for all outcomes (I 2 >90%), and most studies presented high risk of bias (86%) according to the Prediction Model Risk of Bias Assessment Tool. Conclusions Most externally validated models performed well in predicting AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric populations. However, heterogeneity in clinical settings, study populations, and predictors limits their generalizability and implementation at the bedside.

Objective: The objective was to explore and describe the role of pharmacists in providing postdischarge care to patients with kidney disease. Data Sources: PubMed, Embase (Elsevier), CINAHL (Ebscohost), Web of Science Core Collection, and Scopus were searched on January 30, 2023. Publication date limits were not included. Search terms were identified based on 3 concepts: kidney disease, pharmacy services, and patient discharge. Experimental, quasi-experimental, observational, and qualitative studies, or study protocols, describing the pharmacist’s role in providing postdischarge care for patients with kidney disease, excluding kidney transplant recipients, were eligible. Study Selection and Data Extraction: Six unique interventions were described in 10 studies meeting inclusion criteria. Data Synthesis: Four interventions targeted patients with acute kidney injury (AKI) during hospitalization and 2 evaluated patients with pre-existing chronic kidney disease. Pharmacists were a multidisciplinary care team (MDCT) member in 5 interventions and were the sole provider in 1. Roles commonly identified include medication review, medication reconciliation, medication action plan formation, kidney function assessment, drug dose adjustments, and disease education. Some studies showed improvements in diagnostic coding, laboratory monitoring, medication therapy problem (MTP) resolution, and patient education; prevention of hospital readmission was inconsistent. Limitations include lack of standardized reporting of kidney disease, transitions of care processes, and differences in outcomes evaluated. Relevance to Patient Care and Clinical Practice: This review identifies potential roles of a pharmacist as part of a postdischarge MDCT for patients with varying degrees of kidney disease. Conclusions: The pharmacist’s role in providing postdischarge care to patients with kidney disease is inconsistent. Multidisciplinary care teams including a pharmacist provided consistent identification and resolution of MTPs, improved patient education, and increased self-awareness of diagnosis.