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Chen-Xu M, Kassam C, Cameron E, Ryba S, Yiu V. Impact of electronic AKI alert/care bundle on AKI inpatient outcomes: a retrospective single-center cohort study. Ren Fail 2024; 46:2313177. [PMID: 38345055 PMCID: PMC10863540 DOI: 10.1080/0886022x.2024.2313177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 01/27/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Outcomes among acute kidney injury (AKI) patients are poor in United Kingdom (UK) hospitals, and electronic alerts and care bundles may improve them. We implemented such a system at West Suffolk Hospital (WSH) called the 'AKI order set'. We aimed to assess its impact on all-cause mortality, length of stay (LOS) and renal function among AKI patients, and its utilization. METHODS Retrospective, single-center cohort study of patients ≥ 18 years old with AKI at WSH, a 430-bed general hospital serving a rural UK population of approximately 280,000. 7243 unique AKI events representing 5728 patients with full data were identified automatically from our electronic health record (EHR) between 02 September 2018 and 1 July 2021 (median age 78 years, 51% male). All-cause mortality, LOS and improvement in AKI stage, demographic and comorbidity data, medications and AKI order set use were automatically collected from the EHR. RESULTS The AKI order set was used in 9.8% of AKI events and was associated with 28% lower odds of all-cause mortality (multivariable odds ratio [OR] 0.72, 95% confidence interval [CI] 0.57-0.91). Median LOS was longer when the AKI order set was utilized than when not (11.8 versus 8.8 days, p < .001), but was independently associated with improvement in the AKI stage (28.9% versus 8.7%, p < .001; univariable OR 4.25, 95% CI 3.53-5.10, multivariable OR 4.27, 95% CI 3.54-5.14). CONCLUSIONS AKI order set use led to improvements in all-cause mortality and renal function, but longer LOS, among AKI patients at WSH.
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Affiliation(s)
- Michael Chen-Xu
- West Suffolk Hospital NHS Foundation Trust, Suffolk, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Christopher Kassam
- West Suffolk Hospital NHS Foundation Trust, Suffolk, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Emma Cameron
- West Suffolk Hospital NHS Foundation Trust, Suffolk, UK
| | - Szymon Ryba
- West Suffolk Hospital NHS Foundation Trust, Suffolk, UK
| | - Vivian Yiu
- West Suffolk Hospital NHS Foundation Trust, Suffolk, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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2
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Jiang Y, Zhang J, Ainiwaer A, Liu Y, Li J, Zhou L, Yan Y, Zhang H. Development and validation of a predictive model for acute kidney injury in patients with ureterolithiasis. Ren Fail 2024; 46:2394634. [PMID: 39177235 PMCID: PMC11346321 DOI: 10.1080/0886022x.2024.2394634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/08/2024] [Accepted: 08/15/2024] [Indexed: 08/24/2024] Open
Abstract
OBJECTIVES This study aims to identify risk factors for acute kidney injury (AKI) in patients with ureterolithiasis and to develop a predictive model for early AKI detection in this population. METHODS A retrospective analysis was conducted on data from 1,016 patients with ureterolithiasis who presented to our outpatient emergency department between January 2021 and December 2022. Using multifactorial logistic regression, we identified independent risk factors for AKI and constructed a nomogram to predict AKI risk. The predictive model's efficacy was assessed through the area under the ROC curve, calibration curves, Hosmer-Lemeshow (HL) test, and decision curve analysis (DCA). RESULTS AKI was diagnosed in 18.7% of the patients. Independent risk factors identified included age, fever, diabetes, hyperuricemia, bilateral calculi, functional solitary kidney, self-medication, and prehospital delay. The nomogram demonstrated excellent discriminatory capabilities, with AUCs of 0.818 (95% CI, 0.775-0.861) for the modeling set and 0.782 (95% CI, 0.708-0.856) for the validation set. Both calibration curve and HL test results confirmed strong concordance between the model's predictions and actual observations. DCA highlighted the model's significant clinical utility. CONCLUSIONS The predictive model developed in this study provides clinicians with a valuable tool for early identification and management of patients at high risk for AKI, thereby potentially enhancing patient outcomes.
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Affiliation(s)
- Yufeng Jiang
- School of Medicine, Tongji University, Shanghai, China
- Department of Urology, Chongming Branch, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | | | | | - Yuchao Liu
- School of Medicine, Tongji University, Shanghai, China
| | - Jing Li
- Department of Urology, Chongming Branch, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Liuliu Zhou
- Medical Department, Chongming Branch, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yang Yan
- Department of Urology, Chongming Branch, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
- Department of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Haimin Zhang
- Department of Urology, Chongming Branch, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
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3
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Rubulotta F, Bahrami S, Marshall DC, Komorowski M. Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction. Crit Care Med 2024:00003246-990000000-00361. [PMID: 39133071 DOI: 10.1097/ccm.0000000000006390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction are increasingly used. Therefore, understanding risks and benefits of such algorithms is relevant at the bedside. ARDS is a complex and severe lung condition that can be challenging to define precisely due to its multifactorial nature. It often arises as a response to various underlying medical conditions, such as pneumonia, sepsis, or trauma, leading to widespread inflammation in the lungs. ML has shown promising potential in supporting the recognition of ARDS in ICU patients. By analyzing a variety of clinical data, including vital signs, laboratory results, and imaging findings, ML models can identify patterns and risk factors associated with the development of ARDS. This detection and prediction could be crucial for timely interventions, diagnosis and treatment. In summary, leveraging ML for the early prediction and detection of ARDS in ICU patients holds great potential to enhance patient care, improve outcomes, and contribute to the evolving landscape of precision medicine in critical care settings. This article is a concise definitive review on artificial intelligence and ML tools for the prediction and detection of ARDS in critically ill patients.
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Affiliation(s)
- Francesca Rubulotta
- Department of Critical Care Medicine, McGill University, Montreal, QC, Canada
| | - Sahar Bahrami
- Department of Critical Care Medicine, McGill University, Montreal, QC, Canada
| | - Dominic C Marshall
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Matthieu Komorowski
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
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Chen JJ, Lee TH, Chan MJ, Tsai TY, Fan PC, Lee CC, Wu VC, Tu YK, Chang CH. Electronic Alert Systems for Patients With Acute Kidney Injury: A Systematic Review and Meta-Analysis. JAMA Netw Open 2024; 7:e2430401. [PMID: 39190304 DOI: 10.1001/jamanetworkopen.2024.30401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/28/2024] Open
Abstract
Importance The acute kidney injury (AKI) electronic alert (e-alert) system was hypothesized to improve the outcomes of AKI. However, its association with different patient outcomes and clinical practice patterns remains systematically unexplored. Objective To assess the association of AKI e-alerts with patient outcomes (mortality, AKI progression, dialysis, and kidney recovery) and clinical practice patterns. Data Sources A search of Embase and PubMed on March 18, 2024, and a search of the Cochrane Library on March 20, 2024, to identify all relevant studies. There were no limitations on language or article types. Study Selection Studies evaluating the specified outcomes in adult patients with AKI comparing AKI e-alerts with standard care or no e-alerts were included. Studies were excluded if they were duplicate cohorts, had insufficient outcome data, or had no control group. Data Extraction and Synthesis Two investigators independently extracted data and assessed bias. The systematic review and meta-analysis followed the PRISMA guidelines. Random-effects model meta-analysis, with predefined subgroup analysis and trial sequential analyses, were conducted. Main Outcomes and Measures Primary outcomes included mortality, AKI progression, dialysis, and kidney recovery. Secondary outcomes were nephrologist consultations, post-AKI exposure to nonsteroidal anti-inflammatory drugs (NSAID), post-AKI angiotensin-converting enzyme inhibitor and/or angiotensin receptor blocker (ACEI/ARB) prescription, hospital length of stay, costs, and AKI documentation. Results Thirteen unique studies with 41 837 unique patients were included (mean age range, 60.5-79.0 years]; 29.3%-48.5% female). The risk ratios (RRs) for the AKI e-alerts group compared with standard care were 0.96 for mortality (95% CI, 0.89-1.03), 0.91 for AKI stage progression (95% CI, 0.84-0.99), 1.16 for dialysis (95% CI, 1.05-1.28), and 1.13 for kidney recovery (95% CI, 0.86-1.49). The AKI e-alerts group had RRs of 1.45 (95% CI, 1.04-2.02) for nephrologist consultation, 0.75 (95% CI, 0.59-0.95) for post-AKI NSAID exposure. The pooled RR for post-AKI ACEI/ARB exposure in the AKI e-alerts group compared with the control group was 0.91 (95% CI, 0.78-1.06) and 1.28 (95% CI, 1.04-1.58) for AKI documentation. Use of AKI e-alerts was not associated with lower hospital length of stay (mean difference, -0.09 [95% CI, -0.47 to 0.30] days) or lower cost (mean difference, US $655.26 [95% CI, -$656.98 to $1967.5]) but was associated with greater AKI documentation (RR, 1.28 [95% CI, 1.04-1.58]). Trial sequential analysis confirmed true-positive results of AKI e-alerts on increased nephrologist consultations and reduced post-AKI NSAID exposure and its lack of association with mortality. Conclusions and Relevance In this systematic review and meta-analysis, AKI e-alerts were not associated with a lower risk for mortality but were associated with changes in clinical practices. They were associated with lower risk for AKI progression. Further research is needed to confirm these results and integrate early AKI markers or prediction models to improve outcomes.
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Affiliation(s)
- Jia-Jin Chen
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Tao-Han Lee
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Nephrology, Chansn Hospital, Taoyuan City, Taiwan
| | - Ming-Jen Chan
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
| | - Tsung-Yu Tsai
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Pei-Chun Fan
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Cheng-Chia Lee
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Vin-Cent Wu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- National Taiwan University Study Group on Acute Renal Failure, Taipei, Taiwan
| | - Yu-Kang Tu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University
| | - Chih-Hsiang Chang
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
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5
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Wilson FP. The Use of Kidney Disease Improving Global Outcomes AKI Definitions in AKI Research: COMMENTARY. KIDNEY360 2024; 5:948-949. [PMID: 38833314 PMCID: PMC11296550 DOI: 10.34067/kid.0000000000000457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 04/24/2024] [Indexed: 06/06/2024]
Affiliation(s)
- F Perry Wilson
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut
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6
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Bogale TN, Derseh L, Abraham L, Willems H, Metzger J, Abere B, Tilaye M, Hailegeberel T, Bekele TA. Effect of electronic records on mortality among patients in hospital and primary healthcare settings: a systematic review and meta-analyses. Front Digit Health 2024; 6:1377826. [PMID: 38988733 PMCID: PMC11233798 DOI: 10.3389/fdgth.2024.1377826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 06/13/2024] [Indexed: 07/12/2024] Open
Abstract
Background Electronic medical records or electronic health records, collectively called electronic records, have significantly transformed the healthcare system and service provision in our world. Despite a number of primary studies on the subject, reports are inconsistent and contradictory about the effects of electronic records on mortality. Therefore, this review examined the effect of electronic records on mortality. Methods The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guideline. Six databases: PubMed, EMBASE, Scopus, CINAHL, Cochrane Library, and Google Scholar, were searched from February 20 to October 25, 2023. Studies that assessed the effect of electronic records on mortality and were published between 1998 and 2022 were included. Joanna Briggs Institute quality appraisal tool was used to assess the methodological quality of the studies. Narrative synthesis was performed to identify patterns across studies. Meta-analysis was conducted using fixed effect and random-effects models to estimate the pooled effect of electronic records on mortality. Funnel plot and Egger's regression test were used to assess for publication bias. Results Fifty-four papers were found eligible for the systematic review, of which 42 were included in the meta-analyses. Of the 32 studies that assessed the effect of electronic health record on mortality, eight (25.00%) reported a statistically significant reduction in mortality, 22 (68.75%) did not show a statistically significant difference, and two (6.25%) studies reported an increased risk of mortality. Similarly, among the 22 studies that determined the effect of electronic medical record on mortality, 12 (54.55%) reported a statistically significant reduction in mortality, and ten (45.45%) studies didn't show a statistically significant difference. The fixed effect and random effects on mortality were OR = 0.95 (95% CI: 0.93-0.97) and OR = 0.94 (95% CI: 0.89-0.99), respectively. The associated I-squared was 61.5%. Statistical tests indicated that there was no significant publication bias among the studies included in the meta-analysis. Conclusion Despite some heterogeneity among the studies, the review indicated that the implementation of electronic records in inpatient, specialized and intensive care units, and primary healthcare facilities seems to result in a statistically significant reduction in mortality. Maturity level and specific features may have played important roles. Systematic Review Registration PROSPERO (CRD42023437257).
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Affiliation(s)
| | | | - Loko Abraham
- John Snow Research and Training Institute, Inc. (JSI), Addis Ababa, Ethiopia
| | - Herman Willems
- John Snow Research and Training Institute, Inc. (JSI), Boston, MA, United States
| | - Jonathan Metzger
- John Snow Research and Training Institute, Inc. (JSI), Washington, DC, United States
| | - Biruhtesfa Abere
- John Snow Research and Training Institute, Inc. (JSI), Addis Ababa, Ethiopia
| | - Mesfin Tilaye
- United State Agency for International Development, Addis Ababa, Ethiopia
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7
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Goren A, Santos HC, Davis TW, Lowe RB, Monfette M, Meyer MN, Chabris CF. Comparison of Clinical Decision Support Tools to Improve Pediatric Lipid Screening. J Pediatr 2024; 269:113973. [PMID: 38401785 DOI: 10.1016/j.jpeds.2024.113973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/12/2024] [Accepted: 02/16/2024] [Indexed: 02/26/2024]
Abstract
OBJECTIVE To test whether different clinical decision support tools increase clinician orders and patient completions relative to standard practice and each other. STUDY DESIGN A pragmatic, patient-randomized clinical trial in the electronic health record was conducted between October 2019 and April 2020 at Geisinger Health System in Pennsylvania, with 4 arms: care gap-a passive listing recommending screening; alert-a panel promoting and enabling lipid screen orders; both; and a standard practice-no guideline-based notification-control arm. Data were analyzed for 13 346 9- to 11-year-old patients seen within Geisinger primary care, cardiology, urgent care, or nutrition clinics, or who had an endocrinology visit. Principal outcomes were lipid screening orders by clinicians and completions by patients within 1 week of orders. RESULTS Active (care gap and/or alert) vs control arm patients were significantly more likely (P < .05) to have lipid screening tests ordered and completed, with ORs ranging from 1.67 (95% CI 1.28-2.19) to 5.73 (95% CI 4.46-7.36) for orders and 1.54 (95% CI 1.04-2.27) to 2.90 (95% CI 2.02-4.15) for completions. Alerts, with or without care gaps listed, outperformed care gaps alone on orders, with odds ratios ranging from 2.92 (95% CI 2.32-3.66) to 3.43 (95% CI 2.73-4.29). CONCLUSIONS Electronic alerts can increase lipid screening orders and completions, suggesting clinical decision support can improve guideline-concordant screening. The study also highlights electronic record-based patient randomization as a way to determine relative effectiveness of support tools. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT04118348.
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Affiliation(s)
- Amir Goren
- Behavioral Insights Team, Steele Institute for Health Innovation, Geisinger Health System, Danville, PA.
| | - Henri C Santos
- Behavioral Insights Team, Steele Institute for Health Innovation, Geisinger Health System, Danville, PA
| | - Thomas W Davis
- Department of Internal Medicine, Geisinger Health System, Danville, PA
| | - Robert B Lowe
- Department of Internal Medicine, Geisinger Health System, Danville, PA
| | - Mariya Monfette
- Clinical Informatics, Steele Institute for Health Innovation, Geisinger Health System, Danville, PA
| | - Michelle N Meyer
- Behavioral Insights Team, Steele Institute for Health Innovation, Geisinger Health System, Danville, PA; Department of Bioethics and Decision Sciences, Geisinger College of Health Sciences, Geisinger Health System, Danville, PA
| | - Christopher F Chabris
- Behavioral Insights Team, Steele Institute for Health Innovation, Geisinger Health System, Danville, PA; Department of Bioethics and Decision Sciences, Geisinger College of Health Sciences, Geisinger Health System, Danville, PA
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8
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Catling FJR, Nagendran M, Festor P, Bien Z, Harris S, Faisal AA, Gordon AC, Komorowski M. Can Machine Learning Personalize Cardiovascular Therapy in Sepsis? Crit Care Explor 2024; 6:e1087. [PMID: 38709088 DOI: 10.1097/cce.0000000000001087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024] Open
Abstract
Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and proven patient benefit. Systems may be vulnerable to confounding and poor generalization to new patient populations or contemporary patterns of care. Typical electronic health records do not capture rich enough data, at sufficient temporal resolution, to produce systems that make actionable treatment suggestions. To resolve these issues, we recommend a simultaneous focus on technical challenges and removing barriers to translation. This will involve improving data quality, adopting causally grounded models, prioritizing safety assessment and integration into healthcare workflows, conducting randomized clinical trials and aligning with regulatory requirements.
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Affiliation(s)
- Finneas J R Catling
- Institute of Healthcare Engineering, University College London, London, United Kingdom
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Imperial College London, London, United Kingdom
| | - Myura Nagendran
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Imperial College London, London, United Kingdom
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom
| | - Paul Festor
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom
- Department of Computing, Imperial College London, London, United Kingdom
| | - Zuzanna Bien
- School of Life Course & Population Sciences, King's College London, United Kingdom
| | - Steve Harris
- Department of Critical Care, University College London Hospital, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
| | - A Aldo Faisal
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom
- Department of Computing, Imperial College London, London, United Kingdom
- Institute of Artificial and Human Intelligence, Universität Bayreuth, Bayreuth, Germany
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Anthony C Gordon
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Imperial College London, London, United Kingdom
| | - Matthieu Komorowski
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Imperial College London, London, United Kingdom
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9
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Xie F, Xu M. SOX4 silencing alleviates renal injury in rats with acute renal failure by inhibiting the NF-κB signaling pathway and reducing apoptosis and oxidative stress. J Biochem Mol Toxicol 2024; 38:e23703. [PMID: 38605439 DOI: 10.1002/jbt.23703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 03/12/2024] [Accepted: 03/29/2024] [Indexed: 04/13/2024]
Abstract
Acute renal failure (ARF) is a huge threat to the lives of most patients in intensive care units, and there is currently no satisfactory treatment strategy. SRY-box transcription factor 4 (SOX4) plays a key role in the development of various diseases, but its effect on ARF is unknown. Therefore, this study aimed to explore the relationship between SOX4 and ARF. Blood samples were collected from 20 ARF patients and 20 healthy volunteers. We also established an ARF rat model by excising the right kidney and ligating the left renal artery, and SOX4 knockdown in ARF rats was achieved down by means of lentiviral infection. Subsequently, we used quantitative polymerase chain reaction and western bolt assays to detect the expression levels of SOX4 and nuclear factor-κB (NF-κB) signaling pathway-related proteins in human blood or rat renal tissue and hematoxylin and eosin and terminal deoxynucleotidyl transferase (TdT) 2'-deoxyuridine 5'-triphosphate (dUTP) nick-end labeling staining to observe the pathological changes and apoptosis of renal tissue. Enzyme-linked immunosorbent assay and biochemical kits were used to measure the levels of renal function-related indicators (blood urea nitrogen, creatinine, and neutrophil gelatinase-associated lipocalin) and inflammatory factors (interleukin [IL]-1β, IL-6, and tumor necrosis factor-alpha), as well as changes in oxidative stress-related indicators (malondialdehyde [MDA], superoxide dismutase [SOD], and reactive oxygen species [ROS]) in rat serum. SOX4 expression levels in blood samples from ARF patients and renal tissue from ARF rats were significantly higher compared with those in healthy volunteers and control rats, respectively. ARF model rats displayed the typical ARF phenotype, while SOX4 silencing significantly improved pathological injury and apoptosis of renal tissue in ARF rats. Moreover, SOX4 silencing significantly inhibited increased levels of renal function-related indicators and inflammatory factors and reduced the level of excessive oxidative stress (MDA and ROS were upregulated, and SOD was downregulated) in ARF rats. SOX4 also reduced the activity of the NF-κB signaling pathway in ARF samples. Thus, SOX4 knockdown may reduce oxidative stress, the inflammatory response, and apoptosis by reducing the activity of the NF-κB signaling pathway, thereby improving renal injury in ARF rats.
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Affiliation(s)
- Fengyan Xie
- Department of Nephrology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, China
- Department of Nephrology, The Wujin Clinical College of Xuzhou Medical University, Changzhou, China
| | - Min Xu
- Department of Nephrology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, China
- Department of Nephrology, The Wujin Clinical College of Xuzhou Medical University, Changzhou, China
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10
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Ryan DK, Maclean RH, Balston A, Scourfield A, Shah AD, Ross J. Artificial intelligence and machine learning for clinical pharmacology. Br J Clin Pharmacol 2024; 90:629-639. [PMID: 37845024 DOI: 10.1111/bcp.15930] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023] Open
Abstract
Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation in clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.
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Affiliation(s)
- David K Ryan
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Rory H Maclean
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Alfred Balston
- Department of Clinical Pharmacology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Andrew Scourfield
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anoop D Shah
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Jack Ross
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
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11
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Fuhrman DY, Stanski NL, Krawczeski CD, Greenberg JH, Arikan AAA, Basu RK, Goldstein SL, Gist KM. A proposed framework for advancing acute kidney injury risk stratification and diagnosis in children: a report from the 26th Acute Disease Quality Initiative (ADQI) conference. Pediatr Nephrol 2024; 39:929-939. [PMID: 37670082 PMCID: PMC10817991 DOI: 10.1007/s00467-023-06133-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 07/24/2023] [Accepted: 08/09/2023] [Indexed: 09/07/2023]
Abstract
Acute kidney injury (AKI) in children is associated with increased morbidity, reduced health-related quality of life, greater resource utilization, and higher mortality. Improvements in the timeliness and precision of AKI diagnosis in children are needed. In this report, we highlight existing, novel, and on-the-horizon diagnostic and risk-stratification tools for pediatric AKI, and outline opportunities for integration into clinical practice. We also summarize pediatric-specific high-risk diagnoses and exposures for AKI, as well as the potential role of real-time risk stratification and clinical decision support to improve outcomes. Lastly, the key characteristics of important pediatric AKI phenotypes will be outlined. Throughout, we identify key knowledge gaps, which represent prioritized areas of focus for future research that will facilitate a comprehensive, timely and personalized approach to pediatric AKI diagnosis and management.
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Affiliation(s)
- Dana Y Fuhrman
- Department of Critical Care Medicine, UPMC Children's Hospital of Pittsburgh, 4401 Penn Avenue, Suite 2000, Pittsburgh, PA, 15224, USA.
- Department of Pediatrics, Division of Nephrology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA.
| | - Natalja L Stanski
- Department of Pediatrics, Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Catherine D Krawczeski
- Department of Pediatrics, Division of Cardiology, Nationwide Children's Hospital, Ohio State University, Columbus, OH, USA
| | - Jason H Greenberg
- Department of Pediatrics, Division of Nephrology, Yale University Medical Center, New Haven, CT, USA
| | - A Ayse Akcan Arikan
- Department of Pediatrics, Division of Critical Care Medicine, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
- Department of Pediatrics, Division of Nephrology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
| | - Raj K Basu
- Department of Pediatrics, Division of Critical Care Medicine, Northwestern University Feinberg School of Medicine, Ann & Robert Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Stuart L Goldstein
- Department of Pediatrics, Division of Nephrology & Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Katja M Gist
- Department of Pediatrics, Division of Cardiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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12
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Nelson SD. Artificial intelligence and the future of pharmacy. Am J Health Syst Pharm 2024; 81:83-84. [PMID: 38141260 DOI: 10.1093/ajhp/zxad316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Indexed: 12/25/2023] Open
Affiliation(s)
- Scott D Nelson
- Department of Biomedical Informatics Vanderbilt University Medical Center, Nashville, TN, USA
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13
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Chen Y, Shah A, Jani Y, Higgins D, Saleem N, Chafer K, Sydes MR, Asselbergs FW, Lumbers RT. Rationale and design of the THIRST Alert feasibility study: a pragmatic, single-centre, parallel-group randomised controlled trial of an interruptive alert for oral fluid restriction in patients treated with intravenous furosemide. BMJ Open 2024; 14:e080410. [PMID: 38216198 PMCID: PMC10806795 DOI: 10.1136/bmjopen-2023-080410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/11/2023] [Indexed: 01/14/2024] Open
Abstract
INTRODUCTION Acute heart failure (HF) is a major cause of unplanned hospitalisation characterised by excess body water. A restriction in oral fluid intake is commonly imposed on patients as an adjunct to pharmacological therapy with loop diuretics, but there is a lack of evidence from traditional randomised controlled trials (RCTs) to support the safety and effectiveness of this intervention in the acute setting.This study aims to explore the feasibility of using computer alerts within the electronic health record (EHR) system to invite clinical care teams to enrol patients into a pragmatic RCT at the time of clinical decision-making. It will additionally assess the effectiveness of using an alert to help address the clinical research question of whether oral fluid restriction is a safe and effective adjunct to pharmacological therapy for patients admitted with fluid overload. METHODS AND ANALYSIS THIRST (Randomised Controlled Trial within the electronic Health record of an Interruptive alert displaying a fluid Restriction Suggestion in patients with the treatable Trait of congestion) Alert is a single-centre, parallel-group, open-label pragmatic RCT embedded in the EHR system that will be conducted as a feasibility study at an National Health Service (NHS) hospital in London. The clinical care team will be invited to enrol suitable patients in the study using a point-of-care alert with a target sample size of 50 patients. Enrolled patients will then be randomised to either restricted or unrestricted oral fluid intake. Two primary outcomes will be explored (1) the proportion of eligible patients enrolled in the study and (2) the mean difference in oral fluid intake between randomised groups. A series of secondary outcomes are specified to evaluate the effectiveness of the alert, adherence to the randomised treatment allocation and the quality of data generated from routine care, relevant to the outcomes of interest. ETHICS AND DISSEMINATION This study was approved by Riverside Research Ethics Committee (Ref: 22/LO/0889) and will be published on completion. TRIAL REGISTRATION NUMBER NCT05869656.
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Affiliation(s)
- Yang Chen
- Institute of Health Informatics, University College London, London, UK
- Clinical and Research Informatics Unit, NIHR UCLH Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anoop Shah
- Institute of Health Informatics, University College London, London, UK
- Clinical and Research Informatics Unit, NIHR UCLH Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - Yogini Jani
- Centre for Medicines Optimisation Research & Education - CMORE, University College London Hospitals NHS Foundation Trust, London, UK
| | - Daniel Higgins
- Clinical and Research Informatics Unit, NIHR UCLH Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - Nausheen Saleem
- Clinical and Research Informatics Unit, NIHR UCLH Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - Kris Chafer
- Clinical and Research Informatics Unit, NIHR UCLH Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - Matthew Robert Sydes
- Institute of Clinical Trials and Methodology, Medical Research Council Clinical Trials Unit at University College London, London, UK
- Health Data Research UK, London, UK
| | - Folkert W Asselbergs
- Institute of Health Informatics, University College London, London, UK
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, UK
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14
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Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts' consensus on the application of intensive care big data. Front Med (Lausanne) 2024; 10:1174429. [PMID: 38264049 PMCID: PMC10804886 DOI: 10.3389/fmed.2023.1174429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 11/09/2023] [Indexed: 01/25/2024] Open
Abstract
The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.
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Affiliation(s)
- Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shengjun Liu
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chaodong Chen
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Kai Chen
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Ming Chen
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yaolong Chen
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Yisong Cheng
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yating Cui
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qi Ding
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Renyu Ding
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tao Gao
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xiaohua Gu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongli He
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jiawei He
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bo Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Huang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaobo Huang
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Huizhen Jiang
- Department of Information Center, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Jiang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Yunping Lan
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jun Li
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Linfeng Li
- Medical Data Research Institute, Chongqing Medical University, Chongqing, China
| | - Lu Li
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wenxiong Li
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Yongzai Li
- Information Network Center, QiLu Hospital, ShanDong University, Jinan, China
| | - Jin Lin
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xufei Luo
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Feng Lyu
- Department of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - He Miao
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaopu Shang
- Department of Information Management, Beijing Jiaotong University, Beijing, China
| | - Xiuling Shang
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwen Shen
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Yinghuan Shi
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Qihang Sun
- British Chinese Society of Health Informatics, Beijing, China
| | - Weijun Sun
- Faculty of Automation, Guangdong University of Technology, Guangzhou, China
| | - Zhiyun Tang
- Department of Intensive Care Unit, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Emergency and Intensive Care Unit Center, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bo Wang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Haijun Wang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongliang Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Luhao Wang
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Sicong Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhanwen Wang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Zhong Wang
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Dong Wei
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Jianfeng Wu
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
| | - Qin Wu
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Xuezhong Xing
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jin Yang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Xianghong Yang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangquan Yu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenkui Yu
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yuan Yu
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Hao Yuan
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Qian Zhai
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Hao Zhang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lina Zhang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Meng Zhang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunguang Zhao
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Ruiqiang Zheng
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lei Zhong
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Weiguo Zhu
- Department of General Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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15
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Li T, Wu B, Li L, Bian A, Ni J, Liu K, Qin Z, Peng Y, Shen Y, Lv M, Lu X, Xing C, Mao H. Automated Electronic Alert for the Care and Outcomes of Adults With Acute Kidney Injury: A Randomized Clinical Trial. JAMA Netw Open 2024; 7:e2351710. [PMID: 38241047 PMCID: PMC10799260 DOI: 10.1001/jamanetworkopen.2023.51710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/03/2023] [Indexed: 01/22/2024] Open
Abstract
Importance Despite the expansion of published electronic alerts for acute kidney injury (AKI), there are still concerns regarding their effect on the clinical outcomes of patients. Objective To evaluate the effect of the AKI alert combined with a care bundle on the care and clinical outcomes of patients with hospital-acquired AKI. Design, Setting, and Participants This single-center, double-blind, parallel-group randomized clinical trial was conducted in a tertiary teaching hospital in Nanjing, China, from August 1, 2019, to December 31, 2021. The inclusion criteria were inpatient adults aged 18 years or older with AKI, which was defined using the Kidney Disease: Improving Global Outcomes creatinine criteria. Participants were randomized 1:1 to either the alert group or the usual care group, which were stratified by medical vs surgical ward and by intensive care unit (ICU) vs non-ICU setting. Analyses were conducted on the modified intention-to-treat population. Interventions A programmatic AKI alert system generated randomization automatically and sent messages to the mobile telephones of clinicians (alert group) or did not send messages (usual care group). A care bundle accompanied the AKI alert and consisted of general, nonindividualized, and nonmandatory AKI management measures. Main Outcomes and Measures The primary outcome was maximum change in estimated glomerular filtration rate (eGFR) within 7 days after randomization. Secondary patient-centered outcomes included death, dialysis, AKI progression, and AKI recovery. Care-centered outcomes included diagnostic and therapeutic interventions for AKI. Results A total of 2208 patients (median [IQR] age, 65 [54-72] years; 1560 males [70.7%]) were randomized to the alert group (n = 1123) or the usual care group (n = 1085) and analyzed. Within 7 days of randomization, median (IQR) maximum absolute changes in eGFR were 3.7 (-6.4 to 19.3) mL/min/1.73 m2 in the alert group and 2.9 (-9.2 to 16.9) mL/min/1.73 m2 in the usual care group (P = .24). This result was robust in all subgroups in an exploratory analysis. For care-centered outcomes, patients in the alert group had more intravenous fluids (927 [82.6%] vs 670 [61.8%]; P < .001), less exposure to nonsteroidal anti-inflammatory drugs (56 [5.0%] vs 119 [11.0%]; P < .001), and more AKI documentation at discharge (560 [49.9%] vs 296 [27.3%]; P < .001) than patients in the usual care group. No differences were observed in patient-centered secondary outcomes between the 2 groups. Conclusions and Relevance Results of this randomized clinical trial showed that the electronic AKI alert did not improve kidney function or other patient-centered outcomes but changed patient care behaviors. The findings warrant the use of a combination of high-quality interventions and AKI alert in future clinical practice. Trial Registration ClinicalTrials.gov Identifier: NCT03736304.
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Affiliation(s)
- Ting Li
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Buyun Wu
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Li Li
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ao Bian
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Juan Ni
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Kang Liu
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhongke Qin
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yudie Peng
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yining Shen
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Mengru Lv
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyi Lu
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Changying Xing
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Huijuan Mao
- Department of Nephrology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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16
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Hamidi S, Kim SJ, Auguste BL. Electronic Alerts in Acute Kidney Injury-More Questions Than Answers. JAMA Netw Open 2024; 7:e2351682. [PMID: 38241052 DOI: 10.1001/jamanetworkopen.2023.51682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2024] Open
Affiliation(s)
- Shabnam Hamidi
- Division of Nephrology, Humber River Hospital, Toronto, Ontario, Canada
| | - S Joseph Kim
- Division of Nephrology, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Centre for Quality Improvement and Patient Safety, Toronto, Ontario, Canada
| | - Bourne L Auguste
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Centre for Quality Improvement and Patient Safety, Toronto, Ontario, Canada
- Division of Nephrology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
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Alexiuk M, Elgubtan H, Tangri N. Clinical Decision Support Tools in the Electronic Medical Record. Kidney Int Rep 2024; 9:29-38. [PMID: 38312784 PMCID: PMC10831391 DOI: 10.1016/j.ekir.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 10/23/2023] [Indexed: 02/06/2024] Open
Abstract
The integration of clinical decision support (CDS) tools into electronic medical record (EMR) systems has become common. Although there are many benefits for both patients and providers from successful integration, barriers exist that prevent consistent and effective use of these tools. Such barriers include tool alert fatigue, lack of interoperability between tools and medical record systems, and poor acceptance of tools by care providers. However, successful integration of CDS tools into EMR systems have been reported; examples of these include the Statin Choice Decision Aid, and the Kidney Failure Risk Equation (KFRE). This article reviews the history of EMR systems and its integration with CDS tools, the barriers preventing successful integration, and the benefits reported from successful integration. This article also provides suggestions and strategies for improving successful integration, making these tools easier to use and more effective for care providers.
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Affiliation(s)
- Mackenzie Alexiuk
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Community Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Heba Elgubtan
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Community Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Navdeep Tangri
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Department of Internal Medicine, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
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Abou Alaiwi S, Samsky MD, Ahmad T. Is There a Role for Risk Scores in the Management of Heart Failure? JACC. HEART FAILURE 2024; 12:197-198. [PMID: 38069995 DOI: 10.1016/j.jchf.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 10/10/2023] [Indexed: 01/06/2024]
Affiliation(s)
- Sarah Abou Alaiwi
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marc D Samsky
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Tariq Ahmad
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
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Taylor DM, Nimmo AMS, Hole B, Melendez-Torres GJ. An introduction to realist evaluation and synthesis for kidney research. Kidney Int 2024; 105:46-53. [PMID: 37805129 DOI: 10.1016/j.kint.2023.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 10/09/2023]
Abstract
Realist research describes a methodological approach that aims to explore how and why interventions work, for whom, and under what circumstances. Rather than quantifying how well an intervention works under specific conditions, realist theory explores the function of interventions in detail and specifically considers how the contexts in which interventional components are delivered influence the mechanisms that lead to outcomes. Realist methods can be applied to primary data (realist evaluation) or secondary data (realist synthesis). Although realist techniques are increasingly being used in the evaluation of complex interventions, there are relatively few published studies in the field of kidney care. In this review, we outline the theory and principles behind realist methods through discussion of a published realist synthesis describing complex interventions promoting delivery of optimal chronic kidney disease care. We discuss other kidney studies that have used realist methodology and situations where realist techniques could be applied to advance our understanding of how to best deliver care to patients with kidney disease.
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Affiliation(s)
| | - Ailish M S Nimmo
- Renal Department, Royal Infirmary of Edinburgh, Edinburgh, UK; Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Barnaby Hole
- Renal Department, Southmead Hospital, Bristol, UK; Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK
| | - G J Melendez-Torres
- Peninsula Technology Assessment Group, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
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Barreto EF, Cerda J, Freshly B, Gewin L, Kwong YD, McCoy IE, Neyra JA, Ng JH, Silver SA, Vijayan A, Abdel-Rahman EM. Optimum Care of AKI Survivors Not Requiring Dialysis after Discharge: An AKINow Recovery Workgroup Report. KIDNEY360 2024; 5:124-132. [PMID: 37986185 PMCID: PMC10833609 DOI: 10.34067/kid.0000000000000309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 11/08/2023] [Indexed: 11/22/2023]
Abstract
AKI survivors experience gaps in care that contribute to worse outcomes, experience, and cost.Challenges to optimal care include issues with information transfer, education, collaborative care, and use of digital health tools.Research is needed to study these challenges and inform optimal use of diagnostic and therapeutic interventions to promote recovery AKI affects one in five hospitalized patients and is associated with poor short-term and long-term clinical and patient-centered outcomes. Among those who survive to discharge, significant gaps in documentation, education, communication, and follow-up have been observed. The American Society of Nephrology established the AKINow taskforce to address these gaps and improve AKI care. The AKINow Recovery workgroup convened two focus groups, one each focused on dialysis-independent and dialysis-requiring AKI, to summarize the key considerations, challenges, and opportunities in the care of AKI survivors. This article highlights the discussion surrounding care of AKI survivors discharged without the need for dialysis. On May 3, 2022, 48 patients and multidisciplinary clinicians from diverse settings were gathered virtually. The agenda included a patient testimonial, plenary sessions, facilitated small group discussions, and debriefing. Core challenges and opportunities for AKI care identified were in the domains of transitions of care, education, collaborative care delivery, diagnostic and therapeutic interventions, and digital health applications. Integrated multispecialty care delivery was identified as one of the greatest challenges to AKI survivor care. Adequate templates for communication and documentation; education of patients, care partners, and clinicians about AKI; and a well-coordinated multidisciplinary posthospital follow-up plan form the basis for a successful care transition at hospital discharge. The AKINow Recovery workgroup concluded that advancements in evidence-based, patient-centered care of AKI survivors are needed to improve health outcomes, care quality, and patient and provider experience. Tools are being developed by the AKINow Recovery workgroup for use at the hospital discharge to facilitate care continuity.
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Affiliation(s)
| | - Jorge Cerda
- Division of Nephrology, Department of Medicine, Albany Medical College, Albany, New York
| | | | - Leslie Gewin
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Y. Diana Kwong
- Division of Nephrology, Department of Medicine, University of California, San Francisco, California
| | - Ian E. McCoy
- Division of Nephrology, Department of Medicine, University of California, San Francisco, California
| | - Javier A. Neyra
- Division of Nephrology, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Jia H. Ng
- Division of Kidney Diseases and Hypertension, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Great Neck, New York
| | - Samuel A. Silver
- Division of Nephrology, Kingston Health Sciences Center, Queen's University, Kingston, Ontario, Canada
| | - Anitha Vijayan
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Emaad M. Abdel-Rahman
- Division of Nephrology, Department of Medicine, University of Virginia, Charlottesville, VA
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21
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Wan YKJ, Wright MC, McFarland MM, Dishman D, Nies MA, Rush A, Madaras-Kelly K, Jeppesen A, Del Fiol G. Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review. J Am Med Inform Assoc 2023; 31:256-273. [PMID: 37847664 PMCID: PMC10746326 DOI: 10.1093/jamia/ocad203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/12/2023] [Accepted: 10/02/2023] [Indexed: 10/19/2023] Open
Abstract
OBJECTIVE Surveillance algorithms that predict patient decompensation are increasingly integrated with clinical workflows to help identify patients at risk of in-hospital deterioration. This scoping review aimed to identify the design features of the information displays, the types of algorithm that drive the display, and the effect of these displays on process and patient outcomes. MATERIALS AND METHODS The scoping review followed Arksey and O'Malley's framework. Five databases were searched with dates between January 1, 2009 and January 26, 2022. Inclusion criteria were: participants-clinicians in inpatient settings; concepts-intervention as deterioration information displays that leveraged automated AI algorithms; comparison as usual care or alternative displays; outcomes as clinical, workflow process, and usability outcomes; and context as simulated or real-world in-hospital settings in any country. Screening, full-text review, and data extraction were reviewed independently by 2 researchers in each step. Display categories were identified inductively through consensus. RESULTS Of 14 575 articles, 64 were included in the review, describing 61 unique displays. Forty-one displays were designed for specific deteriorations (eg, sepsis), 24 provided simple alerts (ie, text-based prompts without relevant patient data), 48 leveraged well-accepted score-based algorithms, and 47 included nurses as the target users. Only 1 out of the 10 randomized controlled trials reported a significant effect on the primary outcome. CONCLUSIONS Despite significant advancements in surveillance algorithms, most information displays continue to leverage well-understood, well-accepted score-based algorithms. Users' trust, algorithmic transparency, and workflow integration are significant hurdles to adopting new algorithms into effective decision support tools.
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Affiliation(s)
- Yik-Ki Jacob Wan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States
| | - Melanie C Wright
- College of Pharmacy, Idaho State University, Meridian, ID 83642, United States
| | - Mary M McFarland
- Eccles Health Sciences Library, University of Utah, Salt Lake City, UT 84112, United States
| | - Deniz Dishman
- Cizik School of Nursing Department of Research, University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Mary A Nies
- College of Health, Idaho State University, Pocatello, ID 83209, United States
| | - Adriana Rush
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States
| | - Karl Madaras-Kelly
- College of Pharmacy, Idaho State University, Meridian, ID 83642, United States
| | - Amanda Jeppesen
- College of Pharmacy, Idaho State University, Meridian, ID 83642, United States
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States
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22
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Coca SG. Do Novel Biomarkers Have Utility in the Diagnosis and Prognosis of AKI? CON. KIDNEY360 2023; 4:1667-1669. [PMID: 37291706 PMCID: PMC10758505 DOI: 10.34067/kid.0000000000000188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 06/10/2023]
Affiliation(s)
- Steven G Coca
- Barbara T. Murphy Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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23
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Kashani KB, Awdishu L, Bagshaw SM, Barreto EF, Claure-Del Granado R, Evans BJ, Forni LG, Ghosh E, Goldstein SL, Kane-Gill SL, Koola J, Koyner JL, Liu M, Murugan R, Nadkarni GN, Neyra JA, Ninan J, Ostermann M, Pannu N, Rashidi P, Ronco C, Rosner MH, Selby NM, Shickel B, Singh K, Soranno DE, Sutherland SM, Bihorac A, Mehta RL. Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup. Nat Rev Nephrol 2023; 19:807-818. [PMID: 37580570 PMCID: PMC11285755 DOI: 10.1038/s41581-023-00744-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2023] [Indexed: 08/16/2023]
Abstract
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.
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Affiliation(s)
- Kianoush B Kashani
- Division of Nephrology and Hypertension, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Linda Awdishu
- Clinical Pharmacy, San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Sean M Bagshaw
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, Canada
| | | | - Rolando Claure-Del Granado
- Division of Nephrology, Hospital Obrero No 2 - CNS, Cochabamba, Bolivia
- Universidad Mayor de San Simon, School of Medicine, Cochabamba, Bolivia
| | - Barbara J Evans
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA
| | - Lui G Forni
- Department of Critical Care, Royal Surrey Hospital NHS Foundation Trust & Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
| | - Erina Ghosh
- Philips Research North America, Cambridge, MA, USA
| | - Stuart L Goldstein
- Center for Acute Care Nephrology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Sandra L Kane-Gill
- Biomedical Informatics and Clinical Translational Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jejo Koola
- UC San Diego Health Department of Biomedical Informatics, Department of Medicine, La Jolla, CA, USA
| | - Jay L Koyner
- Section of Nephrology, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Mei Liu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Raghavan Murugan
- The Program for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- The Clinical Research, Investigation, and Systems Modelling of Acute Illness Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Girish N Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai; Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Javier A Neyra
- Division of Nephrology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jacob Ninan
- Division of Pulmonary, Critical Care and Sleep Medicine, Mayo Clinic, Rochester, MN, USA
| | - Marlies Ostermann
- Department of Critical Care, King's College London, Guy's & St Thomas' Hospital, London, UK
| | - Neesh Pannu
- Division of Nephrology, University of Alberta, Edmonton, Canada
| | - Parisa Rashidi
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA
| | - Claudio Ronco
- Università di Padova; Scientific Director Foundation IRRIV; International Renal Research Institute; San Bortolo Hospital, Vicenza, Italy
| | - Mitchell H Rosner
- Department of Medicine, University of Virginia Health, Charlottesville, VA, USA
| | - Nicholas M Selby
- Centre for Kidney Research and Innovation, Academic Unit of Translational Medical Sciences, University of Nottingham, Nottingham, UK
- Department of Renal Medicine, Royal Derby Hospital, Derby, UK
| | - Benjamin Shickel
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA
| | - Karandeep Singh
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Danielle E Soranno
- Section of Nephrology, Department of Pediatrics, Indiana University, Riley Hospital for Children, Indianapolis, IN, USA
| | - Scott M Sutherland
- Division of Nephrology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Azra Bihorac
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA.
| | - Ravindra L Mehta
- Division of Nephrology-Hypertension, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
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24
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Yasrebi-de Kom IAR, Dongelmans DA, Abu-Hanna A, Schut MC, de Lange DW, van Roon EN, de Jonge E, Bouman CSC, de Keizer NF, Jager KJ, Klopotowska JE. Acute kidney injury associated with nephrotoxic drugs in critically ill patients: a multicenter cohort study using electronic health record data. Clin Kidney J 2023; 16:2549-2558. [PMID: 38045998 PMCID: PMC10689186 DOI: 10.1093/ckj/sfad160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Indexed: 12/05/2023] Open
Abstract
Background Nephrotoxic drugs frequently cause acute kidney injury (AKI) in adult intensive care unit (ICU) patients. However, there is a lack of large pharmaco-epidemiological studies investigating the associations between drugs and AKI. Importantly, AKI risk factors may also be indications or contraindications for drugs and thereby confound the associations. Here, we aimed to estimate the associations between commonly administered (potentially) nephrotoxic drug groups and AKI in adult ICU patients whilst adjusting for confounding. Methods In this multicenter retrospective observational study, we included adult ICU admissions to 13 Dutch ICUs. We measured exposure to 44 predefined (potentially) nephrotoxic drug groups. The outcome was AKI during ICU admission. The association between each drug group and AKI was estimated using etiological cause-specific Cox proportional hazard models and adjusted for confounding. To facilitate an (independent) informed assessment of residual confounding, we manually identified drug group-specific confounders using a large drug knowledge database and existing literature. Results We included 92 616 ICU admissions, of which 13 492 developed AKI (15%). We found 14 drug groups to be associated with a higher hazard of AKI after adjustment for confounding. These groups included established (e.g. aminoglycosides), less well established (e.g. opioids) and controversial (e.g. sympathomimetics with α- and β-effect) drugs. Conclusions The results confirm existing insights and provide new ones regarding drug associated AKI in adult ICU patients. These insights warrant caution and extra monitoring when prescribing nephrotoxic drugs in the ICU and indicate which drug groups require further investigation.
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Affiliation(s)
- Izak A R Yasrebi-de Kom
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam, The Netherlands
| | - Dave A Dongelmans
- Amsterdam Public Health, Amsterdam, The Netherlands
- Amsterdam UMC location University of Amsterdam, Department of Intensive Care Medicine, Amsterdam, The Netherlands
| | - Ameen Abu-Hanna
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam, The Netherlands
| | - Martijn C Schut
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam, The Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Laboratory Medicine, Amsterdam, The Netherlands
| | - Dylan W de Lange
- Department of Intensive Care and Dutch Poison Information Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Eric N van Roon
- Department of Clinical Pharmacy and Pharmacology, Medical Center Leeuwarden, Leeuwarden, The Netherlands
| | - Evert de Jonge
- Department of Intensive Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Catherine S C Bouman
- Amsterdam UMC location University of Amsterdam, Department of Intensive Care Medicine, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | - Nicolette F de Keizer
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam, The Netherlands
| | - Kitty J Jager
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam, The Netherlands
| | - Joanna E Klopotowska
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam, The Netherlands
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25
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Horne K, Noble R, Karelia S, Selby NM. Electronic alerts in acute kidney injury: why does evidence of benefit remain elusive? Curr Opin Nephrol Hypertens 2023; 32:522-527. [PMID: 37615506 DOI: 10.1097/mnh.0000000000000921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
PURPOSE OF REVIEW Acute kidney injury (AKI) is a common syndrome characterized by a sudden reduction in kidney function. It is strongly associated with high mortality and longer, more expensive hospital stays. As AKI often presents silently, a lack of recognition can prevent recommended standards of care. Over the last decade or more, electronic alerts (eAlerts) for AKI have been studied and implemented to address this. This review will summarize the major randomized trials in this area. RECENT FINDINGS A number of randomized trials now exist that study the effectiveness of AKI eAlerts in isolation or as part of more complex interventions. Varying results arise from differences in study design, healthcare system in which the eAlert is introduced, nature of alert, supporting interventions, implementation plan, stated aim (prevention or treatment of established AKI) and choice of outcome measures. SUMMARY Current randomized trial evidence does not show any benefit of eAlerts on mortality. However, variously reported reductions in AKI incidence, AKI progression and AKI duration support a conclusion that strategies incorporating eAlerts can meaningfully benefit delivery of AKI care. Future work should consider how best eAlerts can be utilised, targeted and implemented.
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Affiliation(s)
- Kerry Horne
- Centre for Kidney Research and Innovation, Academic Unit of Translational Medical Sciences, School of Medicine, University of Nottingham
- Department of Renal Medicine, University Hospitals of Derby and Burton NHS Foundation Trust, Derby, UK
| | - Rebecca Noble
- Centre for Kidney Research and Innovation, Academic Unit of Translational Medical Sciences, School of Medicine, University of Nottingham
- Department of Renal Medicine, University Hospitals of Derby and Burton NHS Foundation Trust, Derby, UK
| | - Shivaali Karelia
- Centre for Kidney Research and Innovation, Academic Unit of Translational Medical Sciences, School of Medicine, University of Nottingham
- Department of Renal Medicine, University Hospitals of Derby and Burton NHS Foundation Trust, Derby, UK
| | - Nicholas M Selby
- Centre for Kidney Research and Innovation, Academic Unit of Translational Medical Sciences, School of Medicine, University of Nottingham
- Department of Renal Medicine, University Hospitals of Derby and Burton NHS Foundation Trust, Derby, UK
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26
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Fragasso T, Raggi V, Passaro D, Tardella L, Lasinio GJ, Ricci Z. Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit. JOURNAL OF ANESTHESIA, ANALGESIA AND CRITICAL CARE 2023; 3:37. [PMID: 37853430 PMCID: PMC10583404 DOI: 10.1186/s44158-023-00125-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 10/09/2023] [Indexed: 10/20/2023]
Abstract
BACKGROUND Acute kidney injury (AKI) is among the most common complications following cardiac surgery in adult and pediatric patients, significantly affecting morbidity and mortality. Artificial Intelligence (AI) with Machine Learning (ML) can be used to predict outcomes. AKI diagnosis anticipation may be an ideal target of these methods. The scope of the study is building a Machine Learning (ML) train model with Random Forest (RF) algorithm, based on electronic health record (EHR) data, able to forecast AKI continuously after 48 h in post-cardiac surgery children, and to test its performance. Four hundred nineteen consecutive patients out of 1115 hospital admissions were enrolled in a single-center retrospective study. Patients were younger than 18 years and admitted from August 2018 to February 2020 in a pediatric cardiac intensive care unit (PCICU) undergoing cardiac surgery, invasive procedure (hemodynamic studies), and medical conditions with complete EHR records and discharged after 48 h or more. RESULTS Thirty-six variables were selected to build the algorithm according to commonly described cardiac surgery-associated AKI clinical predictors. We evaluated different models for different outcomes: binary AKI (no AKI vs. AKI), severe AKI (no-mild vs severe AKI), and multiclass classification (maximum AKI and the most frequent level of AKI, mode AKI). The algorithm performance was assessed with the area under the curve receiver operating characteristics (AUC ROC) for binary classification, with accuracy and K for multiclass classification. AUC ROC for binary AKI was 0.93 (95% CI 0.92-0.94), and for severe AKI was 0.99 (95% CI 0.98-1). Mode AKI accuracy was 0.95, and K was 0.80 (95% CI 0.94-0.96); maximum AKI accuracy was 0.92, and K was 0.71 (95% CI 0.91-0.93). The importance matrix plot demonstrated creatinine, basal creatinine, platelets count, adrenaline support, and lactate dehydrogenase for binary AKI with the addition of cardiopulmonary bypass duration for severe AKI as the most relevant variables of the model. CONCLUSIONS We validated a ML model to detect AKI occurring after 48 h in a retrospective observational study that could help clinicians in individuating patients at risk of AKI, in which a preventive strategy can be determinant to improve the occurrence of renal dysfunction.
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Affiliation(s)
- Tiziana Fragasso
- Pediatric Cardiac Intensive Care Unit, Bambino Gesù Children's Hospital, IRCCS, Piazza S.Onofrio 4, 00165, Rome, Italy.
| | - Valeria Raggi
- Pediatric Cardiac Intensive Care Unit, Bambino Gesù Children's Hospital, IRCCS, Piazza S.Onofrio 4, 00165, Rome, Italy
| | - Davide Passaro
- Department of Statistical Sciences, Sapienza - University of Rome, Rome, Italy
| | - Luca Tardella
- Department of Statistical Sciences, Sapienza - University of Rome, Rome, Italy
| | | | - Zaccaria Ricci
- Pediatric Intensive Care Unit, Department of Anesthesia and Critical Care, Meyer Children's University Hospital, IRCCS, Florence, Italy
- Department of Health Sciences, Section of Anesthesiology and Intensive Care, University of Florence, Florence, Italy
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27
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Baeseman L, Gunning S, Koyner JL. Biomarker Enrichment in Sepsis-Associated Acute Kidney Injury: Finding High-Risk Patients in the Intensive Care Unit. Am J Nephrol 2023; 55:72-85. [PMID: 37844555 PMCID: PMC10872813 DOI: 10.1159/000534608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/12/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND Sepsis-associated acute kidney injury (AKI) is a leading comorbidity in admissions to the intensive care unit. While a gold standard definition exists, it remains imperfect and does not allow for the timely identification of patients in the setting of critical illness. This review will discuss the use of biochemical and electronic biomarkers to allow for prognostic and predictive enrichment of patients with sepsis-associated AKI over and above the use of serum creatinine and urine output. SUMMARY Current data suggest that several biomarkers are capable of identifying patients with sepsis at risk for the development of severe AKI and other associated morbidity. This review discusses these data and these biomarkers in the setting of sub-phenotyping and endotyping sepsis-associated AKI. While not all these tests are widely available and some require further validation, in the near future we anticipate several new tools to help nephrologists and other providers better care for patients with sepsis-associated AKI. KEY MESSAGES Predictive and prognostic enrichment using both traditional biomarkers and novel biomarkers in the setting of sepsis can identify subsets of patients with either similar outcomes or similar pathophysiology, respectively. Novel biomarkers can identify kidney injury in patients without consensus definition AKI (e.g., changes in creatinine or urine output) and can predict other adverse outcomes (e.g., severe consensus definition AKI, inpatient mortality). Finally, emerging artificial intelligence and machine learning-derived risk models are able to predict sepsis-associated AKI in critically ill patients using advanced learning techniques and several laboratory and vital sign measurements.
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Affiliation(s)
- Louis Baeseman
- Section of Nephrology, Department of Medicine, University of Chicago, Chicago IL USA
| | - Samantha Gunning
- Section of Nephrology, Department of Medicine, University of Chicago, Chicago IL USA
| | - Jay L. Koyner
- Section of Nephrology, Department of Medicine, University of Chicago, Chicago IL USA
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28
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Park S, Yi J, Lee YJ, Kwon EJ, Yun G, Jeong JC, Chin HJ, Na KY, Kim S. Electronic alert outpatient protocol improves the quality of care for the risk of postcontrast acute kidney injury following computed tomography. Kidney Res Clin Pract 2023; 42:606-616. [PMID: 37813523 PMCID: PMC10565459 DOI: 10.23876/j.krcp.22.148] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 10/27/2022] [Accepted: 11/18/2022] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Prevention and diagnosis of postcontrast acute kidney injury (AKI) after contrast-enhanced computed tomography is burdensome in outpatient department. We investigated whether an electronic alert system could improve prevention and diagnosis of postcontrast AKI. METHODS In March 2018, we launched an electronic alert system that automatically identifies patients with a baseline estimated glomerular filtration rate of <45 mL/min/1.73 m2, provides a prescription of fluid regimen, and recommends a follow-up for serum creatinine measurement. Participants prescribed contrast-enhanced computed tomography at outpatient department before and after the launch of the system were categorized as historical and alert group, respectively. Propensity for the surveillance of postcontrast AKI was compared using logistic regression. Risks of AKI, admission, mortality, and renal replacement therapy were analyzed. RESULTS The historical and alert groups included 289 and 309 participants, respectively. The alert group was more likely to be men and take diuretics. The most frequent volume of prophylactic fluid in historical and alert group was 1,000 and 750 mL, respectively. Follow-up for AKI was more common in the alert group (adjusted odds ratio, 6.00; p < 0.001). Among them, incidence of postcontrast AKI was not statistically different. The two groups did not differ in risks of admission, mortality, or renal replacement therapy. CONCLUSION The electronic alert system could assist in the detection of high-risk patients, prevention with reduced fluid volume, and proper diagnosis of postcontrast AKI, while limiting the prescribing clinicians' burden. Whether the system can improve long-term outcomes remains unclear.
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Affiliation(s)
- Seokwoo Park
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jinyeong Yi
- Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Eun-Jeong Kwon
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Giae Yun
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jong Cheol Jeong
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Ho Jun Chin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Ki Young Na
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sejoong Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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29
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Jeon H, Jang HR. Electronic alerts based on clinical decision support system for post-contrast acute kidney injury. Kidney Res Clin Pract 2023; 42:541-545. [PMID: 37813522 PMCID: PMC10565452 DOI: 10.23876/j.krcp.23.186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 10/13/2023] Open
Affiliation(s)
- Hojin Jeon
- Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hye Ryoun Jang
- Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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30
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Wong A, Berenbrok LA, Snader L, Soh YH, Kumar VK, Javed MA, Bates DW, Sorce LR, Kane-Gill SL. Facilitators and Barriers to Interacting With Clinical Decision Support in the ICU: A Mixed-Methods Approach. Crit Care Explor 2023; 5:e0967. [PMID: 37644969 PMCID: PMC10461946 DOI: 10.1097/cce.0000000000000967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Abstract
OBJECTIVES Clinical decision support systems (CDSSs) are used in various aspects of healthcare to improve clinical decision-making, including in the ICU. However, there is growing evidence that CDSS are not used to their full potential, often resulting in alert fatigue which has been associated with patient harm. Clinicians in the ICU may be more vulnerable to desensitization of alerts than clinicians in less urgent parts of the hospital. We evaluated facilitators and barriers to appropriate CDSS interaction and provide methods to improve currently available CDSS in the ICU. DESIGN Sequential explanatory mixed-methods study design, using the BEhavior and Acceptance fRamework. SETTING International survey study. PATIENT/SUBJECTS Clinicians (pharmacists, physicians) identified via survey, with recent experience with clinical decision support. INTERVENTIONS An initial survey was developed to evaluate clinician perspectives on their interactions with CDSS. A subsequent in-depth interview was developed to further evaluate clinician (pharmacist, physician) beliefs and behaviors about CDSS. These interviews were then qualitatively analyzed to determine themes of facilitators and barriers with CDSS interactions. MEASUREMENTS AND MAIN RESULTS A total of 48 respondents completed the initial survey (estimated response rate 15.5%). The majority believed that responding to CDSS alerts was part of their job (75%) but felt they experienced alert fatigue (56.5%). In the qualitative analysis, a total of five facilitators (patient safety, ease of response, specificity, prioritization, and feedback) and four barriers (excess quantity, work environment, difficulty in response, and irrelevance) were identified from the in-depth interviews. CONCLUSIONS In this mixed-methods survey, we identified areas that institutions should focus on to improve appropriate clinician interactions with CDSS, specific to the ICU. Tailoring of CDSS to the ICU may lead to improvement in CDSS and subsequent improved patient safety outcomes.
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Affiliation(s)
- Adrian Wong
- Beth Israel Deaconess Medical Center, Department of Pharmacy, Boston, MA
| | | | - Lauren Snader
- University of Pittsburgh, School of Pharmacy, Pittsburgh, PA
| | - Yu Hyeon Soh
- University of Pittsburgh, School of Pharmacy, Pittsburgh, PA
| | | | | | - David W Bates
- Brigham and Women's Hospital, Division of General Internal Medicine and Primary Care, Boston, MA
- Harvard Medical School, School of Medicine, Boston, MA
| | - Lauren R Sorce
- Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
- Northwestern University Feinberg School of Medicine, Division of Pediatric Critical Care, Chicago, IL
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Sangal RB, Sharifi M, Rhodes D, Melnick ER. Clinical Decision Support: Moving Beyond Interruptive "Pop-up" Alerts. Mayo Clin Proc 2023; 98:1275-1279. [PMID: 37661138 PMCID: PMC10491420 DOI: 10.1016/j.mayocp.2023.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/22/2023] [Accepted: 05/30/2023] [Indexed: 09/05/2023]
Affiliation(s)
- Rohit B Sangal
- Department of Emergency Medicine, School of Medicine, Yale University, New Haven, CT.
| | - Mona Sharifi
- Section of General Pediatrics, Department of Pediatrics, School of Medicine, Yale University, New Haven, CT
| | - Deborah Rhodes
- Department of Medicine, Yale New Haven Hospital, New Haven, CT
| | - Edward R Melnick
- Department of Emergency Medicine, School of Medicine, Yale University, New Haven, CT
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Shao J, Liu F, Ji S, Song C, Ma Y, Shen M, Sun Y, Zhu S, Guo Y, Liu B, Wu Y, Qin H, Lai S, Fan Y. Development, External Validation, and Visualization of Machine Learning Models for Predicting Occurrence of Acute Kidney Injury after Cardiac Surgery. Rev Cardiovasc Med 2023; 24:229. [PMID: 39076716 PMCID: PMC11266781 DOI: 10.31083/j.rcm2408229] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/06/2023] [Accepted: 02/17/2023] [Indexed: 07/31/2024] Open
Abstract
Background Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in short- and long-term mortality among patients. Here, we adopted machine learning algorithms to build prediction models with the overarching goal of identifying patients who are at a high risk of such unfavorable kidney outcomes. Methods A total of 1686 patients (development cohort) and 422 patients (validation cohort), with 126 pre- and intra-operative variables, were recruited from the First Medical Centre and the Sixth Medical Centre of Chinese PLA General Hospital in Beijing, China, respectively. Analyses were performed using six machine learning techniques, namely K-nearest neighbor, logistic regression, decision tree, random forest (RF), support vector machine, and neural network, and the APPROACH score, a previously established risk score for CSA-AKI. For model tuning, optimal hyperparameter was achieved by using GridSearch with 5-fold cross-validation from the scikit-learn library. Model performance was externally assessed via the receiver operating characteristic (ROC) and decision curve analysis (DCA). Explainable machine learning was performed using the Python SHapley Additive exPlanation (SHAP) package and Seaborn library, which allow the calculation of marginal contributory SHAP value. Results 637 patients (30.2%) developed CSA-AKI within seven days after surgery. In the external validation, the RF classifier exhibited the best performance among the six machine learning techniques, as shown by the ROC curve and DCA, while the traditional APPROACH risk score showed a relatively poor performance. Further analysis found no specific causative factor contributing to the development of CSA-AKI; rather, the development of CSA-AKI appeared to be a complex process resulting from a complex interplay of multiple risk factors. The SHAP summary plot illustrated the positive or negative contribution of RF-top 20 variables and extrapolated risk of developing CSA-AKI at individual levels. The Seaborn library showed the effect of each single feature on the model output of the RF prediction. Conclusions Efficient machine learning approaches were successfully established to predict patients with a high probability of developing acute kidney injury after cardiac surgery. These findings are expected to help clinicians to optimize treatment strategies and minimize postoperative complications. Clinical Trial Registration The study protocol was registered at the ClinicalTrials Registration System (https://www.clinicaltrials.gov/, #NCT04966598) on July 26, 2021.
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Affiliation(s)
- Jiakang Shao
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Feng Liu
- Department of Vascular and Endovascular Surgery, The First Medical Center
of Chinese PLA General Hospital, 100853 Beijing, China
| | - Shuaifei Ji
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Chao Song
- Medical School of Chinese PLA, 100853 Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese
PLA General Hospital, 100853 Beijing, China
| | - Yan Ma
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Ming Shen
- Department of Cardiovascular Medicine, The First Hospital of Hebei Medical
University, 050000 Shijiazhuang, Hebei, China
| | - Yuntian Sun
- Medical School of Chinese PLA, 100853 Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese
PLA General Hospital, 100853 Beijing, China
| | - Siming Zhu
- Medical School of Chinese PLA, 100853 Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese
PLA General Hospital, 100853 Beijing, China
| | - Yilong Guo
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Bing Liu
- Department of Cardiovascular Surgery, the Sixth Medical Centre of Chinese
PLA General Hospital, 100048 Beijing, China
| | - Yuanbin Wu
- Medical School of Chinese PLA, 100853 Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese
PLA General Hospital, 100853 Beijing, China
| | - Handai Qin
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Shengwei Lai
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Yunlong Fan
- Medical School of Chinese PLA, 100853 Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese
PLA General Hospital, 100853 Beijing, China
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Mayerhofer E, Zaba NO, Parodi L, Ganbold AS, Biffi A, Rosand J, Yechoor N, Anderson CD. Disparities in brain health comorbidity management in intracerebral hemorrhage. Front Neurol 2023; 14:1194810. [PMID: 37360335 PMCID: PMC10285101 DOI: 10.3389/fneur.2023.1194810] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/24/2023] [Indexed: 06/28/2023] Open
Abstract
Background Intracerebral hemorrhage (ICH) disproportionally affects underserved populations, and coincides with risk factors for cardiovascular events and cognitive decline after ICH. We investigated associations between social determinants of health and management of blood pressure (BP), hyperlipidemia, diabetes, obstructive sleep apnea (OSA), and hearing impairment before and after ICH hospitalization. Methods Survivors of the Massachusetts General Hospital longitudinal ICH study between 2016 and 2019 who received healthcare at least 6 months after ICH were analyzed. Measurements of BP, LDL and HbA1c and their management in the year surrounding ICH and referrals for sleep studies and audiology up to 6 months after ICH were gathered from electronic health records. The US-wide area deprivation index (ADI) was used as proxy for social determinants of health. Results The study included 234 patients (mean 71 years, 42% female). BP measurements were performed in 109 (47%) before ICH, LDL measurements were performed in 165 (71%), and HbA1c measurements in 154 (66%) patients before or after ICH. 27/59 (46%) with off-target LDL and 3/12 (25%) with off-target HbA1c were managed appropriately. Of those without history of OSA or hearing impairment before ICH, 47/207 (23%) were referred for sleep studies and 16/212 (8%) to audiology. Higher ADI was associated with lower odds of BP, LDL, and HbA1c measurement prior to ICH [OR 0.94 (0.90-0.99), 0.96 (0.93-0.99), and 0.96 (0.93-0.99), respectively, per decile] but not with management during or after hospitalization. Conclusion Social determinants of health are associated with pre-ICH management of cerebrovascular risk factors. More than 25% of patients were not assessed for hyperlipidemia and diabetes in the year surrounding ICH hospitalization, and less than half of those with off-target values received treatment intensification. Few patients were evaluated for OSA and hearing impairment, both common among ICH survivors. Future trials should evaluate whether using the ICH hospitalization to systematically address co-morbidities can improve long-term outcomes.
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Affiliation(s)
- Ernst Mayerhofer
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Natalie O. Zaba
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Livia Parodi
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, United States
| | - Alena S. Ganbold
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Alessandro Biffi
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Jonathan Rosand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Nirupama Yechoor
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Christopher D. Anderson
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, United States
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Wilson FP, Yamamoto Y, Martin M, Coronel-Moreno C, Li F, Cheng C, Aklilu A, Ghazi L, Greenberg JH, Latham S, Melchinger H, Mansour SG, Moledina DG, Parikh CR, Partridge C, Testani JM, Ugwuowo U. A randomized clinical trial assessing the effect of automated medication-targeted alerts on acute kidney injury outcomes. Nat Commun 2023; 14:2826. [PMID: 37198160 PMCID: PMC10192367 DOI: 10.1038/s41467-023-38532-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 04/28/2023] [Indexed: 05/19/2023] Open
Abstract
Acute kidney injury is common among hospitalized individuals, particularly those exposed to certain medications, and is associated with substantial morbidity and mortality. In a pragmatic, open-label, National Institutes of Health-funded, parallel group randomized controlled trial (clinicaltrials.gov NCT02771977), we investigate whether an automated clinical decision support system affects discontinuation rates of potentially nephrotoxic medications and improves outcomes in patients with AKI. Participants included 5060 hospitalized adults with AKI and an active order for any of three classes of medications of interest: non-steroidal anti-inflammatory drugs, renin-angiotensin-aldosterone system inhibitors, or proton pump inhibitors. Within 24 hours of randomization, a medication of interest was discontinued in 61.1% of the alert group versus 55.9% of the usual care group (relative risk 1.08, 1.04 - 1.14, p = 0.0003). The primary outcome - a composite of progression of acute kidney injury, dialysis, or death within 14 days - occurred in 585 (23.1%) of individuals in the alert group and 639 (25.3%) of patients in the usual care group (RR 0.92, 0.83 - 1.01, p = 0.09). Trial Registration Clinicaltrials.gov NCT02771977.
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Affiliation(s)
- F Perry Wilson
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA.
| | - Yu Yamamoto
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Melissa Martin
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Claudia Coronel-Moreno
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Joint Data Analytics Team. Yale New Haven Health System, New Haven, CT, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Chao Cheng
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Abinet Aklilu
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Lama Ghazi
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Epidemiology, University of Alabama School of Public Health, Birmingham, Alabama, USA
| | - Jason H Greenberg
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Stephen Latham
- Interdisciplinary Center for Bioethics, Yale University, New Haven, CT, USA
| | - Hannah Melchinger
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Sherry G Mansour
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Dennis G Moledina
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Chirag R Parikh
- Division of Nephrology. Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Caitlin Partridge
- Joint Data Analytics Team. Yale New Haven Health System, New Haven, CT, USA
| | - Jeffrey M Testani
- Section of Cardiovascular Medicine. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Ugochukwu Ugwuowo
- Clinical and Translational Research Accelerator. Department of Medicine, Yale School of Medicine, New Haven, CT, USA
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Bajaj T, Koyner JL. Cautious Optimism: Artificial Intelligence and Acute Kidney Injury. Clin J Am Soc Nephrol 2023; 18:668-670. [PMID: 36795027 PMCID: PMC10278802 DOI: 10.2215/cjn.0000000000000088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Tushar Bajaj
- Section of Nephrology, Department of Medicine, The University of Chicago, Chicago, Illinois
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36
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Honeyford K, Nwosu AP, Lazzarino R, Kinderlerer A, Welch J, Brent AJ, Cooke G, Ghazal P, Patil S, Costelloe CE. Prevalence of electronic screening for sepsis in National Health Service acute hospitals in England. BMJ Health Care Inform 2023; 30:e100743. [PMID: 37169397 PMCID: PMC10186434 DOI: 10.1136/bmjhci-2023-100743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/12/2023] [Indexed: 05/13/2023] Open
Abstract
Sepsis is a worldwide public health problem. Rapid identification is associated with improved patient outcomes-if followed by timely appropriate treatment. OBJECTIVES Describe digital sepsis alerts (DSAs) in use in English National Health Service (NHS) acute hospitals. METHODS A Freedom of Information request surveyed acute NHS Trusts on their adoption of electronic patient records (EPRs) and DSAs. RESULTS Of the 99 Trusts that responded, 84 had an EPR. Over 20 different EPR system providers were identified as operational in England. The most common providers were Cerner (21%). System C, Dedalus and Allscripts Sunrise were also relatively common (13%, 10% and 7%, respectively). 70% of NHS Trusts with an EPR responded that they had a DSA; most of these use the National Early Warning Score (NEWS2). There was evidence that the EPR provider was related to the DSA algorithm. We found no evidence that Trusts were using EPRs to introduce data driven algorithms or DSAs able to include, for example, pre-existing conditions that may be known to increase risk.Not all Trusts were willing or able to provide details of their EPR or the underlying algorithm. DISCUSSION The majority of NHS Trusts use an EPR of some kind; many use a NEWS2-based DSA in keeping with national guidelines. CONCLUSION Many English NHS Trusts use DSAs; even those using similar triggers vary and many recreate paper systems. Despite the proliferation of machine learning algorithms being developed to support early detection of sepsis, there is little evidence that these are being used to improve personalised sepsis detection.
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Affiliation(s)
- Kate Honeyford
- Team Health Informatics, Institute of Cancer Research, London, UK
| | - Amen-Patrick Nwosu
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Runa Lazzarino
- Nuffield Department of Primary Care and Health Sciences, University of Oxford, Oxford, UK
| | | | - John Welch
- Critical Care Department, University College Hospital, London, UK
| | - Andrew J Brent
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Graham Cooke
- Imperial College Healthcare NHS Trust, London, UK
- Department of Infectious Disease, Imperial College, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Peter Ghazal
- Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK
| | - Shashank Patil
- Emergency Department, Chelsea and Westminster Healthcare NHS Trust, London, UK
| | - Ceire E Costelloe
- Team Health Informatics, Institute of Cancer Research, London, UK
- Health Informatics Team, Royal Marsden NHS Foundation Trust, London, UK
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Aklilu AM, O'Connor KD, Martin M, Yamamoto Y, Coronel-Moreno C, Shvets K, Jones C, Kadhim B, Corona-Villalobos CP, Baker ML, Tan J, Freeman N, Groener M, Menez S, Brown D, Culli SE, Lindsley J, Orias M, Parikh C, Smith A, Sundararajan A, Wilson FP. Personalised recommendations for hospitalised patients with Acute Kidney Injury using a Kidney Action Team (KAT-AKI): protocol and early data of a randomised controlled trial. BMJ Open 2023; 13:e071968. [PMID: 37068906 PMCID: PMC10111926 DOI: 10.1136/bmjopen-2023-071968] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/03/2023] [Indexed: 04/19/2023] Open
Abstract
INTRODUCTION Although studies have examined the utility of clinical decision support tools in improving acute kidney injury (AKI) outcomes, no study has evaluated the effect of real-time, personalised AKI recommendations. This study aims to assess the impact of individualised AKI-specific recommendations delivered by trained clinicians and pharmacists immediately after AKI detection in hospitalised patients. METHODS AND ANALYSIS KAT-AKI is a multicentre randomised investigator-blinded trial being conducted across eight hospitals at two major US hospital systems planning to enrol 4000 patients over 3 years (between 1 November 2021 and 1 November 2024). A real-time electronic AKI alert system informs a dedicated team composed of a physician and pharmacist who independently review the chart in real time, screen for eligibility and provide combined recommendations across the following domains: diagnostics, volume, potassium, acid-base and medications. Recommendations are delivered to the primary team in the alert arm or logged for future analysis in the usual care arm. The planned primary outcome is a composite of AKI progression, dialysis and mortality within 14 days from randomisation. A key secondary outcome is the percentage of recommendations implemented by the primary team within 24 hours from randomisation. The study has enrolled 500 individuals over 8.5 months. Two-thirds were on a medical floor at the time of the alert and 17.8% were in an intensive care unit. Virtually all participants were recommended for at least one diagnostic intervention. More than half (51.6%) had recommendations to discontinue or dose-adjust a medication. The median time from AKI alert to randomisation was 28 (IQR 15.8-51.5) min. ETHICS AND DISSEMINATION The study was approved by the ethics committee of each study site (Yale University and Johns Hopkins institutional review board (IRB) and a central IRB (BRANY, Biomedical Research Alliance of New York). We are committed to open dissemination of the data through clinicaltrials.gov and sharing of data on an open repository as well as publication in a peer-reviewed journal on completion. TRIAL REGISTRATION NUMBER NCT04040296.
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Affiliation(s)
- Abinet Mathias Aklilu
- Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, USA
- Section of Nephrology, Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Kyle D O'Connor
- Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, USA
| | - Melissa Martin
- Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, USA
| | - Yu Yamamoto
- Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, USA
| | - Claudia Coronel-Moreno
- Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kristina Shvets
- Department of Pharmacology, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Charles Jones
- Department of Pharmacology, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Bashar Kadhim
- Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Celia P Corona-Villalobos
- Division of Nephrology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Megan L Baker
- Section of Nephrology, Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Jiawei Tan
- Department of Internal Medicine, Bridgeport Hospital, Bridgeport, Connecticut, USA
| | - Natasha Freeman
- Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Marwin Groener
- Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Steven Menez
- Division of Nephrology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Dannielle Brown
- Department of Pharmacology, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Samuel E Culli
- Department of Pharmacy, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - John Lindsley
- Department of Pharmacy, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Marcelo Orias
- Section of Nephrology, Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Chirag Parikh
- Division of Nephrology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Abigail Smith
- Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, USA
| | - Anusha Sundararajan
- Section of Nephrology, Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Francis P Wilson
- Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, USA
- Section of Nephrology, Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut, USA
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Lazzareschi D, Mehta RL, Dember LM, Bernholz J, Turan A, Sharma A, Kheterpal S, Parikh CR, Ali O, Schulman IH, Ryan A, Feng J, Simon N, Pirracchio R, Rossignol P, Legrand M. Overcoming barriers in the design and implementation of clinical trials for acute kidney injury: a report from the 2020 Kidney Disease Clinical Trialists meeting. Nephrol Dial Transplant 2023; 38:834-844. [PMID: 35022767 PMCID: PMC10064977 DOI: 10.1093/ndt/gfac003] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Indexed: 12/15/2022] Open
Abstract
Acute kidney injury (AKI) is a growing epidemic and is independently associated with increased risk of death, chronic kidney disease (CKD) and cardiovascular events. Randomized-controlled trials (RCTs) in this domain are notoriously challenging and many clinical studies in AKI have yielded inconclusive findings. Underlying this conundrum is the inherent heterogeneity of AKI in its etiology, presentation and course. AKI is best understood as a syndrome and identification of AKI subphenotypes is needed to elucidate the disease's myriad etiologies and to tailor effective prevention and treatment strategies. Conventional RCTs are logistically cumbersome and often feature highly selected patient populations that limit external generalizability and thus alternative trial designs should be considered when appropriate. In this narrative review of recent developments in AKI trials based on the Kidney Disease Clinical Trialists (KDCT) 2020 meeting, we discuss barriers to and strategies for improved design and implementation of clinical trials for AKI patients, including predictive and prognostic enrichment techniques, the use of pragmatic trials and adaptive trials.
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Affiliation(s)
- Daniel Lazzareschi
- Department of Anesthesia & Perioperative Care, Division of Critical Care Medicine, University of California, San Francisco (UCSF), San Francisco, CA, USA
| | - Ravindra L Mehta
- Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Laura M Dember
- Renal-Electrolyte and Hypertension Division, Department of Medicine, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Pennsylvania, PA, USA
| | | | - Alparslan Turan
- Department of Anesthesiology, Lerner College of Medicine of Case Western University, Cleveland, OH, USA
- Department of Outcomes Research, Cleveland Clinic, Cleveland, OH, USA
| | | | - Sachin Kheterpal
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Chirag R Parikh
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Omar Ali
- Verpora Ltd, Nottingham, UK
- University of Portsmouth, UK
| | - Ivonne H Schulman
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Abigail Ryan
- Division of Chronic Care Management, Centers for Medicare & Medicaid Services, Woodlawn, MD, USA
| | - Jean Feng
- Department of Epidemiology and Biostatistics, University of California, San Francisco (UCSF), San Francisco, CA, USA
| | - Noah Simon
- Department of Biostatistics, University of Washington (UW), Seattle, WA, USA
| | - Romain Pirracchio
- Department of Anesthesia & Perioperative Care, Division of Critical Care Medicine, University of California, San Francisco (UCSF), San Francisco, CA, USA
| | - Patrick Rossignol
- INI-CRCT Network, Nancy, France
- University of Lorraine, Inserm 1433 CIC-P CHRU de Nancy, Inserm U1116, Nancy, France
| | - Matthieu Legrand
- Department of Anesthesia & Perioperative Care, Division of Critical Care Medicine, University of California, San Francisco (UCSF), San Francisco, CA, USA
- INI-CRCT Network, Nancy, France
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Seki T, Aki M, Furukawa TA, Kawashima H, Miki T, Sawaki Y, Ando T, Katsuragi K, Kawashima T, Ueno S, Miyagi T, Noma S, Tanaka S, Kawakami K. Electronic Health Record-Nested Reminders for Serum Lithium Level Monitoring in Patients With Mood Disorder: Randomized Controlled Trial. J Med Internet Res 2023; 25:e40595. [PMID: 36947138 PMCID: PMC10139684 DOI: 10.2196/40595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 01/12/2023] [Accepted: 02/21/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Clinical guidelines recommend regular serum lithium monitoring every 3 to 6 months. However, in the real world, only a minority of patients receive adequate monitoring. OBJECTIVE This study aims to examine whether the use of the electronic health record (EHR)-nested reminder system for serum lithium monitoring can help achieve serum lithium concentrations within the therapeutic range for patients on lithium maintenance therapy. METHODS We conducted an unblinded, single-center, EHR-nested, parallel-group, superiority randomized controlled trial comparing EHR-nested reminders with usual care in adult patients receiving lithium maintenance therapy for mood disorders. The primary outcome was the achievement of therapeutically appropriate serum lithium levels between 0.4 and 1.0 mEq/L at 18 months after enrollment. The key secondary outcomes are included as follows: the number of serum lithium level monitoring except for the first and final monitoring; exacerbation of the mood disorder during the study period, defined by hospitalization, increase in lithium dose, addition of antipsychotic drugs or mood stabilizers, or addition or increase of antidepressants; adherence defined by the proportion of days covered by lithium carbonate prescription during the study period. RESULTS A total of 111 patients were enrolled in this study. A total of 56 patients were assigned to the reminder group, and 55 patients were assigned to the usual care group. At the follow-up, 38 (69.1%) patients in the reminder group and 33 (60.0%) patients in the usual care group achieved the primary outcome (odds ratio 2.14, 95% CI 0.82-5.58, P=.12). The median number of serum lithium monitoring was 2 in the reminder group and 0 in the usual care group (rate ratio 3.62; 95% CI 2.47-5.29, P<.001). The exacerbation of mood disorders occurred in 17 (31.5%) patients in the reminder group and in 16 (34.8%) patients in the usual care group (odds ratio 0.97, 95% CI 0.42-2.28, P=.95). CONCLUSIONS We found insufficient evidence for an EHR-nested reminder to increase the achievement of therapeutic serum lithium concentrations. However, the number of monitoring increased with relatively simple and inexpensive intervention. The EHR-based reminders may be useful to improve quality of care for patients on lithium maintenance therapy, and they have potentials to be applied to other problems. TRIAL REGISTRATION University Hospital Medical Information Network Clinical Trials Registry UMIN000033633; https://tinyurl.com/5n7wtyav.
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Affiliation(s)
- Tomotsugu Seki
- Department of Pharmacoepidemiology, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Morio Aki
- Department of Psychiatry, Toyooka Hospital, Toyooka, Japan
- Department of Psychiatry, Kyoto University Hospital, Kyoto, Japan
| | - Toshi A Furukawa
- Department of Health Promotion and Human Behavior, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hirotsugu Kawashima
- Department of Psychiatry, Toyooka Hospital, Toyooka, Japan
- Department of Psychiatry, Kyoto University Hospital, Kyoto, Japan
| | - Tomotaka Miki
- Department of Psychiatry, Toyooka Hospital, Toyooka, Japan
- Department of Psychiatry, Kyoto University Hospital, Kyoto, Japan
| | - Yujin Sawaki
- Department of Psychiatry, Toyooka Hospital, Toyooka, Japan
- Department of Psychiatry, Kyoto University Hospital, Kyoto, Japan
- National Epilepsy Center, National Hospital Organization Shizuoka Institute of Epilepsy and Neurological Disorders, Shizuoka, Japan
| | - Takaaki Ando
- Department of Psychiatry, Toyooka Hospital, Toyooka, Japan
- Department of Psychiatry, Kyoto University Hospital, Kyoto, Japan
| | - Kentaro Katsuragi
- Department of Psychiatry, Toyooka Hospital, Toyooka, Japan
- Department of Psychiatry, Kyoto University Hospital, Kyoto, Japan
| | - Takahiko Kawashima
- Department of Psychiatry, Toyooka Hospital, Toyooka, Japan
- Department of Psychiatry, Kyoto University Hospital, Kyoto, Japan
| | - Senkei Ueno
- Department of Psychiatry, Toyooka Hospital, Toyooka, Japan
- Department of Psychiatry, Kyoto University Hospital, Kyoto, Japan
| | - Takashi Miyagi
- Department of Psychiatry, Toyooka Hospital, Toyooka, Japan
- Department of Psychiatry, Kyoto University Hospital, Kyoto, Japan
- Department of Psychiatry, Kyoto-Katsura Hospital, Kyoto, Japan
| | - Shun'ichi Noma
- Department of Psychiatry, Toyooka Hospital, Toyooka, Japan
- Department of Psychiatry, Kyoto University Hospital, Kyoto, Japan
- Noma-Kokoro Clinic, Kyoto, Japan
| | - Shiro Tanaka
- Department of Clinical Biostatistics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koji Kawakami
- Department of Pharmacoepidemiology, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Bunch DR, Durant TJ, Rudolf JW. Artificial Intelligence Applications in Clinical Chemistry. Clin Lab Med 2023; 43:47-69. [PMID: 36764808 DOI: 10.1016/j.cll.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promise of AI across all phases of testing including preanalytic, analytic, and postanalytic phases; this includes novel methods for detecting common specimen collection errors, predicting laboratory results and diagnoses, and enhancing autoverification workflows. Although AI applications pose several ethical and operational challenges, these technologies are expected to transform the practice of the clinical chemistry laboratory in the near future.
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Affiliation(s)
- Dustin R Bunch
- Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital, 700 Children's Drive, C1923, Columbus, OH 43205-2644, USA; Department of Pathology, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Thomas Js Durant
- Department of Laboratory Medicine, Yale School of Medicine, 55 Park Street, Room PS 502A, New Haven, CT 06510, USA
| | - Joseph W Rudolf
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA; ARUP Laboratories, 500 Chipeta Way, MC 115, Salt Lake City, UT 84108, USA.
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Kotwal S, Herath S, Erlich J, Boardman S, Qian J, Lawton P, Campbell C, Whatnall A, Teo S, Horvath AR, Endre ZH. Electronic alerts and a care bundle for acute kidney injury-an Australian cohort study. Nephrol Dial Transplant 2023; 38:610-617. [PMID: 35438795 DOI: 10.1093/ndt/gfac155] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Early recognition of hospital-acquired acute kidney injury (AKI) may improve patient management and outcomes. METHODS This multicentre study was conducted at three hospitals (H1-intervention; H2 and H3-controls) served by a single laboratory. The intervention bundle [an interruptive automated alerts (aAlerts) showing AKI stage and baseline creatinine in the eMR, a management guide and junior medical staff education] was implemented only at H1. Outcome variables included length-of-stay (LOS), all-cause in-hospital mortality and management quality. RESULTS Over 6 months, 639 patients developed AKI (265 at H1 and 374 at controls), with 94.7% in general wards; 537 (84%) patients developed Stage 1, 58 (9%) Stage 2 and 43 (7%) Stage 3 AKI. Median LOS was 9 days (IQR 4-17) and was not different between intervention and controls. However, patients with AKI stage 1 had shorter LOS at H1 [median 8 versus 10 days (P = 0.021)]. Serum creatinine had risen prior to admission in most patients. Documentation of AKI was better in H1 (94.8% versus 83.4%; P = 0.001), with higher rates of nephrology consultation (25% versus 19%; P = 0.04) and cessation of nephrotoxins (25.3 versus 18.8%; P = 0.045). There was no difference in mortality between H1 versus controls (11.7% versus 13.0%; P = 0.71). CONCLUSIONS Most hospitalized patients developed Stage 1 AKI and developed AKI in the community and remained outside the intensive care unit (ICU). The AKI eAlert bundle reduced LOS in most patients with AKI and increased AKI documentation, nephrology consultation rate and cessation of nephrotoxic medications.
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Affiliation(s)
- Sradha Kotwal
- Prince of Wales Hospital, Randwick, NSW, Australia.,University of New South Wales, Kensington, NSW, Australia.,The George Institute for Global Health, University of New South Wales, Newtown, NSW, Australia
| | - Sanjeeva Herath
- Prince of Wales Hospital, Randwick, NSW, Australia.,University of New South Wales, Kensington, NSW, Australia
| | - Jonathan Erlich
- Prince of Wales Hospital, Randwick, NSW, Australia.,University of New South Wales, Kensington, NSW, Australia
| | - Sally Boardman
- Prince of Wales Hospital, Randwick, NSW, Australia.,University of New South Wales, Kensington, NSW, Australia
| | - Jennifer Qian
- Prince of Wales Hospital, Randwick, NSW, Australia.,University of New South Wales, Kensington, NSW, Australia
| | - Paul Lawton
- Alfred Health, Melbourne, Victoria, Australia.,Monash University, Melbourne, Victoria, Australia.,Menzies School of Health Research, Darwin, NT, Australia
| | - Craig Campbell
- NSW Health Pathology, Department of Chemical Pathology, Prince of Wales Hospital, Randwick, NSW, Australia
| | | | - Su Teo
- Department of Renal Medicine, Singapore General Hospital, Outram Road, Singapore
| | - A Rita Horvath
- University of New South Wales, Kensington, NSW, Australia.,NSW Health Pathology, Department of Chemical Pathology, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Zoltán H Endre
- Prince of Wales Hospital, Randwick, NSW, Australia.,University of New South Wales, Kensington, NSW, Australia
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Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol 2023; 36:1101-1117. [PMID: 36786976 PMCID: PMC10227138 DOI: 10.1007/s40620-023-01573-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 01/01/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVES In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management. METHODS We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. RESULTS From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context. CONCLUSIONS Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.
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James MT, Sawhney S. System-Based Solutions to Minimizing Nephrotoxin-Induced Acute Kidney Injury. Clin J Am Soc Nephrol 2023; 18:157-159. [PMID: 36754003 PMCID: PMC10103378 DOI: 10.2215/cjn.0000000000000053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- Matthew T. James
- Departments of Medicine and Community Health Sciences, O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Simon Sawhney
- Aberdeen Centre for Health Data Science, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, NHS Grampian, Aberdeen, Scotland
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Atia J, Evison F, Gallier S, Hewins P, Ball S, Gavin J, Coleman J, Garrick M, Pankhurst T. Does acute kidney injury alerting improve patient outcomes? BMC Nephrol 2023; 24:14. [PMID: 36647011 PMCID: PMC9843843 DOI: 10.1186/s12882-022-03031-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 12/05/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Electronic alerts (e-alerts) for Acute Kidney Injury (AKI) have been implemented into a variety of different Electronic Health Records (EHR) systems worldwide in order to improve recognition and encourage early appropriate management of AKI. We were interested in the impact on patient safety, specialist referral and clinical management. METHODS All patients admitted to our institution with AKI were included in the study. We studied AKI progression, dialysis dependency, length of hospital stay, emergency readmission, ICU readmission, and death, before and after the introduction of electronic alerts. The impact on prescription of high risk drugs, fluid administration, and referral to renal services was also analysed. RESULTS After the introduction of the e-alert, progression to higher AKI stage, emergency readmission to hospital and death during admission were significantly reduced. More prescriptions were stopped for drugs that adversely affect renal function in AKI and there was a significant increase in the ICU admissions and in the number of patients having dialysis, especially in earlier stages. Longer term mortality, renal referrals, and fluid alteration did not change significantly after the AKI e-alert introduction. CONCLUSIONS AKI e-alerts can improve clinical outcomes in hospitalised patients.
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Affiliation(s)
- Jolene Atia
- grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK ,grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Institute of Translational Medicine, Edgbaston, Birmingham, UK
| | - Felicity Evison
- grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK ,grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Institute of Translational Medicine, Edgbaston, Birmingham, UK
| | - Suzy Gallier
- grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK ,grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Institute of Translational Medicine, Edgbaston, Birmingham, UK ,grid.6572.60000 0004 1936 7486PIONEER: HDR-UK Health Data Research Hub for Acute Care, University of Birmingham, Birmingham, UK
| | - Peter Hewins
- grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Simon Ball
- grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK ,grid.6572.60000 0004 1936 7486HDRUK Better Care Science Priority and Health Data Research UK Midlands, University of Birmingham, Birmingham, UK
| | - Joseph Gavin
- grid.415953.f0000 0004 0400 1537Lister Hospital, Stevenage, UK
| | - Jamie Coleman
- grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK ,grid.6572.60000 0004 1936 7486School of Medicine, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Mark Garrick
- grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Tanya Pankhurst
- grid.412563.70000 0004 0376 6589University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK ,grid.415490.d0000 0001 2177 007XQueen Elizabeth Hospital Birmingham, Mindelsohn Way, Birmingham, B15 2TH UK
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Li Y, Lin F, Li W, Chen G, Li S, Liu B, Li H, Song C, Lu R, Pan P. Comparison of clinical, laboratory and radiological characteristics between Chlamydia psittaci and adenovirus pneumonias: a multicenter retrospective study. Int J Infect Dis 2023; 126:114-124. [PMID: 36455811 DOI: 10.1016/j.ijid.2022.11.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/23/2022] [Accepted: 11/23/2022] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVES Pneumonia caused by Chlamydia psittaci is a significant global public health issue. Symptom onset and laboratory characteristics may be confused with those of other respiratory viral infections, including adenovirus pneumonia. We aimed to determine differences in clinical presentations and establish a simple nomogram to differentiate C. psittaci and adenovirus pneumonias. METHODS We conducted a multicenter retrospective study in 10 tertiary general hospitals to compare patients with either C. psittaci (n = 78) or adenovirus (n = 102) pneumonia. A multivariable logistic regression model was used to identify risk factors of C. psittaci pneumonia that were used to establish a nomogram. RESULTS C. psittaci and adenovirus pneumonia showed certain similar clinical symptoms, including fever, dyspnea, and fatigue, but differed in other characteristics. The multivariate logistic regression showed that age, sex, nervous system symptoms, lymphocyte count, C-reactive protein level, and bilateral lung lesions were risk factors for C. psittaci pneumonia. After incorporating these six factors, the established nomogram achieved a good concordance value (0.949 [95% CI 0.917-0.982]) in differentiating the types of pneumonia, with well-fitting calibration curves. CONCLUSION Despite having similar clinical features, the variables of age, sex, nervous system symptoms, lymphocytes, C-reactive protein levels, and bilateral lung lesions were combined into a clinically useful nomogram for the rapid and early differentiation of C. psittaci pneumonia from adenovirus pneumonia. This nomogram may help improve treatments and clinical outcomes.
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Affiliation(s)
- Yi Li
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China; Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
| | - Fengyu Lin
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China; Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
| | - Wen Li
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China; Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
| | - Gang Chen
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China; Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
| | - Sha Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Ben Liu
- Department of Emergency Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Haitao Li
- First Department of Thoracic Medicine, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Chao Song
- Nosocomial Infection Control Center, Xiangya Hospital, Central South University, Changsha, China
| | - Rongli Lu
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China; Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
| | - Pinhua Pan
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China; Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China.
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Impact of an Electronic Alert in Combination with a Care Bundle on the Outcomes of Acute Kidney Injury. Diagnostics (Basel) 2022; 12:diagnostics12123121. [PMID: 36553128 PMCID: PMC9777607 DOI: 10.3390/diagnostics12123121] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/29/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
Early diagnosis is essential for the appropriate management of acute kidney injury (AKI). We evaluated the impact of an electronic AKI alert together with a care bundle on the progression and mortality of AKI. This was a single-center prospective study that included AKI patients aged ≥ 18 years, whereas those in palliative care, nephrology, and transplantation departments were excluded. An AKI alert was issued in electronic medical records and a care bundle was suggested. A series of classes were administered to the multidisciplinary teams by nephrologists, and a clinical pharmacist audited prescriptions. Patients were categorized into pre-alert and post-alert groups. The baseline characteristics were comparable between the pre-alert (n = 1613) and post-alert (n = 1561) groups. The 30-day mortality rate was 33.6% in the entire cohort and was lower in the post-alert group (30.5% vs. 36.7%; p < 0.001). Age, pulmonary disease, malignancy, and ICU admission were associated with an increase in 30-day mortality. The electronic AKI alert together with a care bundle and a multidisciplinary education program was associated with a reduction in 30-day mortality in patients with AKI.
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Abstract
PURPOSE OF REVIEW The purpose of this review is to describe acute kidney injury (AKI) phenotypes in children. RECENT FINDINGS AKI is a heterogenous disease that imposes significant morbidity and mortality on critically ill and noncritically ill patients across the age spectrum. As our understanding of AKI and its association with outcomes has improved, it is becoming increasingly apparent that there are distinct AKI subphenotypes that vary by cause or associated conditions. We have also learned that severity, duration, and repeated episodes of AKI impact outcomes, and that integration of novel urinary biomarkers of tubular injury can also reveal unique subphenotypes of AKI that may not be otherwise readily apparent. SUMMARY Studies that further delineate these unique AKI subphenotypes are needed to better understand the impact of AKI in children. Further delineation of these phenotypes has both prognostic and therapeutic implications.
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Opportunities in digital health and electronic health records for acute kidney injury care. Curr Opin Crit Care 2022; 28:605-612. [PMID: 35942677 DOI: 10.1097/mcc.0000000000000971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PURPOSE OF REVIEW The field of digital health is evolving rapidly with applications relevant to the prediction, detection and management of acute kidney injury (AKI). This review will summarize recent publications in these areas. RECENT FINDINGS Machine learning (ML) approaches have been applied predominantly for AKI prediction, but also to identify patients with AKI at higher risk of adverse outcomes, and to discriminate different subgroups (subphenotypes) of AKI. There have been multiple publications in this area, but a smaller number of ML models have robust external validation or the ability to run in real-time in clinical systems. Recent studies of AKI alerting systems and clinical decision support systems continue to demonstrate variable results, which is likely to result from differences in local context and implementation strategies. In the design of AKI alerting systems, choice of baseline creatinine has a strong effect on performance of AKI detection algorithms. SUMMARY Further research is required to overcome barriers to the validation and implementation of ML models for AKI care. Simpler electronic systems within the electronic medical record can lead to improved care in some but not all settings, and careful consideration of local context and implementation strategy is recommended.
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Shi Y, Wang H, Bai L, Wu Y, Zhang L, Zheng X, Lv JH, Pei HH, Bai ZH. The rate of acute kidney injury (AKI) alert detection by the attending physicians was associated with the prognosis of patients with AKI. Front Public Health 2022; 10:1031529. [PMID: 36466503 PMCID: PMC9712962 DOI: 10.3389/fpubh.2022.1031529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction Early identification of AKI was always considered to improve patients' prognosis. Some studies found that AKI early warning tools didn't affect patients' prognosis. Therefore, additional studies were necessary to explore the reasons. Methods This study was a secondary analysis of a multicenter randomized controlled trial that found electronic health record warnings for AKI did not influence patients' prognoses. Univariate, multivariate, subgroup, curve fitting, and threshold effect analysis were used to explore the association between AKI warnings detected by attending physicians and the patient's prognosis. Results A total of 6,030 AKI patients were included in the study. The patients were classified into two groups based on the rate of AKI alerts detected by attending physicians: the partial group (n = 5,377), and the complete group (n = 653). In comparison to the partial group, the complete group significantly decreased 14-day AKI progression, 14-day dialysis, and 14-day mortality, with adjusted ORs of 0.48 (0.33, 0.70), 0.26 (0.09, 0.77), and 0.53 (0.33, 0.84) respectively, and the complete group significantly improve the discharge to home, with an OR value of 1.50 (1.21, 1.87). When the rate of AKI alerts detected by the attending physicians as a continuity variable, we found that the rate of alerts seen by attending physicians was associated with 14-day mortality and the discharge to home, with adjusted ORs of 1.76 (1.11, 2.81) and 1.42 (1.13, 1.80). The sensitivity analysis, curve-fitting analysis, and threshold effect analysis also showed that the rate of alert seen by the attending physician was correlated with the patient's prognosis. Conclusion The rate of AKI alert detection by attending physician were related to the patient's prognosis. The higher the rate of AKI alert detection by attending physicians, the better the prognosis of patients with AKI.
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Affiliation(s)
- Yu Shi
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hai Wang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ling Bai
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yuan Wu
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Li Zhang
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xin Zheng
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jun-hua Lv
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hong-hong Pei
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,*Correspondence: Hong-hong Pei
| | - Zheng-hai Bai
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Zheng-hai Bai
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Bajaj T, Koyner JL. Artificial Intelligence in Acute Kidney Injury Prediction. Adv Chronic Kidney Dis 2022; 29:450-460. [PMID: 36253028 PMCID: PMC10259199 DOI: 10.1053/j.ackd.2022.07.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/20/2022] [Accepted: 07/27/2022] [Indexed: 01/25/2023]
Abstract
The use of artificial intelligence (AI) in nephrology and its associated clinical research is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques. We discuss the relative improvement in AKI detection as well as the relative dearth of data around the clinical implementation and patient outcomes using these models. The use of AI for AKI detection and clinical care is in its infancy, and this review describes how we arrived at our current position and hints at the promise of the future.
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Affiliation(s)
- Tushar Bajaj
- Section of Nephrology, Department of Medicine University of Chicago, Chicago, IL, USA
| | - Jay L Koyner
- Section of Nephrology, Department of Medicine University of Chicago, Chicago, IL, USA.
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