Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial

Impact of electronic AKI alert/care bundle on AKI inpatient outcomes: a retrospective single-center cohort study

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.

Development and validation of a predictive model for acute kidney injury in patients with ureterolithiasis

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.

Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction

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.

Electronic Alert Systems for Patients With Acute Kidney Injury: A Systematic Review and Meta-Analysis

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.

Effect of electronic records on mortality among patients in hospital and primary healthcare settings: a systematic review and meta-analyses

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).

Comparison of Clinical Decision Support Tools to Improve Pediatric Lipid Screening

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.

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

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.

SOX4 silencing alleviates renal injury in rats with acute renal failure by inhibiting the NF-κB signaling pathway and reducing apoptosis and oxidative stress

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.

Artificial intelligence and machine learning for clinical pharmacology

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.

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

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.

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

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.

Chinese experts' consensus on the application of intensive care big data

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.

Automated Electronic Alert for the Care and Outcomes of Adults With Acute Kidney Injury: A Randomized Clinical Trial

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.

Clinical Decision Support Tools in the Electronic Medical Record

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.

An introduction to realist evaluation and synthesis for kidney research

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.

Optimum Care of AKI Survivors Not Requiring Dialysis after Discharge: An AKINow Recovery Workgroup Report

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.

Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review

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.

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

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

Acute kidney injury associated with nephrotoxic drugs in critically ill patients: a multicenter cohort study using electronic health record data

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.

Electronic alerts in acute kidney injury: why does evidence of benefit remain elusive?

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.

Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit

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.

Biomarker Enrichment in Sepsis-Associated Acute Kidney Injury: Finding High-Risk Patients in the Intensive Care Unit

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.

Electronic alert outpatient protocol improves the quality of care for the risk of postcontrast acute kidney injury following computed tomography

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.

Facilitators and Barriers to Interacting With Clinical Decision Support in the ICU: A Mixed-Methods Approach

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.

Development, External Validation, and Visualization of Machine Learning Models for Predicting Occurrence of Acute Kidney Injury after Cardiac Surgery

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.

Disparities in brain health comorbidity management in intracerebral hemorrhage

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.

A randomized clinical trial assessing the effect of automated medication-targeted alerts on acute kidney injury outcomes

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.

Prevalence of electronic screening for sepsis in National Health Service acute hospitals in England

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.

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

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.

Overcoming barriers in the design and implementation of clinical trials for acute kidney injury: a report from the 2020 Kidney Disease Clinical Trialists meeting

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.

Electronic Health Record-Nested Reminders for Serum Lithium Level Monitoring in Patients With Mood Disorder: Randomized Controlled Trial

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.

Artificial Intelligence Applications in Clinical Chemistry

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.

Electronic alerts and a care bundle for acute kidney injury-an Australian cohort study

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.

Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review

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.

Does acute kidney injury alerting improve patient outcomes?

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.

Comparison of clinical, laboratory and radiological characteristics between Chlamydia psittaci and adenovirus pneumonias: a multicenter retrospective study

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.

Impact of an Electronic Alert in Combination with a Care Bundle on the Outcomes of Acute Kidney Injury

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.

Subphenotypes of acute kidney injury in children

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.

Opportunities in digital health and electronic health records for acute kidney injury care

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.

The rate of acute kidney injury (AKI) alert detection by the attending physicians was associated with the prognosis of patients with AKI

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.

Artificial Intelligence in Acute Kidney Injury Prediction

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.