Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study
Open accessAbstract
Background: Acute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care clinical information system (CCIS) on patient outcomes. Specifically, we hypothesize that integration of AKI guidelines into CCIS will decrease the proportion of patients with Stage 1 AKI deteriorating into higher stages of AKI. Methods: The study was conducted in two intensive care units (ICUs) at University Hospitals Bristol, UK, in a before (control) and after (intervention) format. The intervention consisted of the AKIN guidelines and AKI care bundle which included guidance for medication usage, AKI advisory and dashboard with AKI score. Clinical data and patient outcomes were collected from all patients admitted to the units. AKI stage was calculated using the Acute Kidney Injury Network (AKIN) guidelines. Maximum AKI stage per admission, change in AKI stage and other metrics were calculated for the cohort. Adherence to eGFR-based enoxaparin dosing guidelines was evaluated as a proxy for clinician awareness of AKI. Results: Each phase of the study lasted a year, and a total of 5044 admissions were included for analysis with equal numbers of patients for the control and intervention stages. The proportion of patients worsening from Stage 1 AKI decreased from 42% (control) to 33.5% (intervention), p = 0.002. The proportion of incorrect enoxaparin doses decreased from 1.72% (control) to 0.6% (intervention), p < 0.001. The prevalence of any AKI decreased from 43.1% (control) to 37.5% (intervention), p < 0.05. Conclusions: This observational study demonstrated a significant reduction in AKI progression from Stage 1 and a reduction in overall development of AKI. In addition, a reduction in incorrect enoxaparin dosing was also observed, indicating increased clinical awareness. This study demonstrates that AKI guidelines coupled with a newly designed AKI care bundle integrated into CCIS can impact patient outcomes positively.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Non-predictive real-time detection of guideline-based AKI reduces the rate of AKI occurrence.
“Studies have shown that non-predictive real-time detection of guideline-based AKI reduces the rate of AKI occurrence [18]”
From Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care (2021) · cites this paper as [18]
The Results (Secondary objectives; page unavailable) report fewer ICU patients with AKI during the intervention phase. But the Discussion says the alert appeared only after Stage 1 AKI developed, so the lower overall incidence could not be attributed to the detection tool itself. The before–after study also could not establish which part of the broader intervention caused the improvement. (LLM, conf=0.90)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
KDIGO Clinical Practice Guidelines for Acute Kidney Injury
Khwaja, A. (2012) · Nephron Clinical Practice · doi:10.1159/000339789 · Cited by 8,039 · see citing works
Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury
Mehta, R. L., Kellum, J. A., Shah, S. V., Molitoris, B. A., Ronco, C., Warnock, D. G., Levin, A., & the Acute Kidney Injury Network (2007) · Critical Care · doi:10.1186/cc5713 · Cited by 7,046 · see citing works
Levey, A. S., Coresh, J., Greene, T., Stevens, L. A., Zhang, Y., Hendriksen, S. M., Kusek, J. W., & Lente, F. V. (2006) · Annals of Internal Medicine · doi:10.7326/0003-4819-145-4-200608150-00004 · Cited by 5,726 · see citing works
Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study
Hoste, E. A. J., Bagshaw, S. M., Bellomo, R., Cely, C. M., Colman, R., Cruz, D. N., Edipidis, K., Forni, L. G., Gomersall, C. D., Govil, D., et al. (2015) · Intensive Care Medicine · doi:10.1007/s00134-015-3934-7 · Cited by 3,107 · see citing works
Bellomo, R., Kellum, J. A., & Ronco, C. (2012) · The Lancet · doi:10.1016/s0140-6736(11)61454-2 · Cited by 2,607 · see citing works
Acute Kidney Injury Associated with Cardiac Surgery
Rosner, M. H., & Okusa, M. D. (2005) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.00240605 · Cited by 1,061 · see citing works
Thakar, C. V., Christianson, A. L., Freyberg, R. W., et al. (2009) · Critical Care Medicine · doi:10.1097/ccm.0b013e3181a5906f · Cited by 446 · see citing works
Clinical Decision Support for In-Hospital AKI
Al‐Jaghbeer, M., DeAlmeida, D. R., Bilderback, A. L., Ambrosino, R., & Kellum, J. A. (2017) · Journal of the American Society of Nephrology · doi:10.1681/asn.2017070765 · Cited by 410 · see citing works
Levey, A. S., Coresh, J., Greene, T., Stevens, L. A., Zhang, Y., & Hendriksen, S. M. (2008) · Annals of Internal Medicine · Cited by 394 · see citing works
Use of Electronic Results Reporting to Diagnose and Monitor AKI in Hospitalized Patients
Selby, N. M., Crowley, L., Fluck, R., McIntyre, C. W., Monaghan, J., Lawson, N., & Kolhe, N. V. (2012) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.08970911 · Cited by 253 · see citing works
Colpaert, K. E., Hoste, E. A. J., Steurbaut, K., Benoît, D., Hoecke, S. V., Turck, F. D., & Decruyenaere, J. M. A. (2011) · Critical Care Medicine · doi:10.1097/ccm.0b013e3182387a6b · Cited by 246 · see citing works
Selby, N. M., Casula, A. M., Lamming, L., Stoves, J. L., Samarasinghe, Y. P., Lewington, A. J. P., Roberts, R., Shah, N. R., Johnson, M., Jackson, N. A., et al. (2019) · Journal of the American Society of Nephrology · doi:10.1681/asn.2018090886 · Cited by 172 · see citing works
Kolhe, N. V., Staples, D., Reilly, T., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0132279 · Cited by 146 · see citing works
Acute kidney injury: outcomes and quality of care
Aitken, E. L., Carruthers, C., Gall, L., et al. (2013) · QJM · doi:10.1093/qjmed/hcs237 · Cited by 122 · see citing works
Field, T. S., Rochon, P. A., Lee, M. H., Gavendo, L., Baril, J. L., & Gurwitz, J. H. (2009) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2981 · Cited by 119 · see citing works
Terrell, K. M., Perkins, A. J., Hui, S. L., et al. (2010) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2010.03.025 · Cited by 74 · see citing works
Ahmed, A., Vairavan, S., Akhoundi, A., Wilson, G., Chiofolo, C., Chbat, N., Cartin-Ceba, R., Li, G., & Kashani, K. (2015) · Journal of Critical Care · doi:10.1016/j.jcrc.2015.05.007 · Cited by 73 · see citing works
Bourdeaux, C., Thomas, M. J. C., Gould, T. H., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010129 · Cited by 70 · see citing works
Self-citationHodgson, L. E., Roderick, P. J., Venn, R. M., Yao, G., Dimitrov, B. D., & Forni, L. G. (2018) · PLoS ONE · doi:10.1371/journal.pone.0200584 · Cited by 70 · see citing works
Cox, Z. L., Neal, E. B., Waitman, L. R., Peterson, N. B., Bhave, G., Siew, E. D., Danciu, I., Lewis, J. B., Peterson, J. F., & McCoy, A. B. (2012) · Applied Clinical Informatics · doi:10.4338/aci-2012-03-ra-0009 · Cited by 53 · see citing works
The Role of Risk Prediction Models in Prevention and Management of AKI
Hodgson, L. E., Selby, N. M., Huang, T., & Forni, L. G. (2019) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2019.06.002 · Cited by 41 · see citing works
Feehally, J., Gilmore, I. C., Barasi, S., Bosomworth, M. P., Christie, B., Davies, A., Dhesi, J. K., Dowdle, R., Gibbins, C., Gonzalez, I., et al. (2013) · The Journal of the Royal College of Physicians of Edinburgh · doi:10.4997/jrcpe.2013.109 · Cited by 33 · see citing works
Acute Kidney Injury in Real Time: Prediction, Alerts, and Clinical Decision Support
Wilson, F. P., & Greenberg, J. H. (2018) · The Nephron journals/Nephron journals · doi:10.1159/000492064 · Cited by 30 · see citing works
Recent early clinical drug development for acute kidney injury
Gallagher, K., O’Neill, S., Harrison, E. M., et al. (2016) · Expert Opinion on Investigational Drugs · doi:10.1080/13543784.2017.1274730 · Cited by 28 · see citing works
Pachucki, M., Ghosh, E., Eshelman, L. J., et al. (2018) · Journal of the Intensive Care Society · doi:10.1177/1751143718771261 · Cited by 6 · see citing works
Self-citationUsing Clinical Decision Support Systems for Acute Kidney Injury Pragmatic Trials
KASHANI, K. B., Dalili, N., Carter, R. E., Kellum, J. A., & Mehta, R. L. (2019) · Journal of Translational Critical Care Medicine · doi:10.4103/jtccm.jtccm_6_18 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care
Dong, J., Feng, T., Thapa-Chhetry, B., Cho, B. G., Shum, T., Inwald, D. P., Newth, C. J. L., & Vaidya, V. U. (2021) · Critical Care · doi:10.1186/s13054-021-03724-0 · Cited by 233 · see citing works
“Studies have shown that non-predictive real-time detection of guideline-based AKI reduces the rate of AKI occurrence [18], and we plan to conduct a similar prospective study of prediction-based AKI monitoring to further reduce AKI.”
MethodWan, Y.-K. J., Wright, M. C., McFarland, M. M., Dishman, D., Nies, M. A., Rush, A., Madaras-Kelly, K., Jeppesen, A., & Del Fiol, G. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad203 · Cited by 11 · see citing works
- Authors
- Bourdeaux, C., Ghosh, E., Atallah, L., Palanisamy, K., Patel, P., Thomas, M., Gould, T., Warburton, J., Rivers, J., & Hadfield, J.
- Affiliations
- Bristol Royal Infirmary (GB)
- Corresponding author
- Ghosh, Erina
- Venue
- Critical Care
- Published
- 2020-11-23
- Volume
- 24
- Issue
- 1
- Pages
- 656-656
- ISSN
- 1364-8535 (ISSN-L)
- PMC
- PMC7684927
- OpenAlex
- W3109794688
- Cited by
- 28
- References
- 26
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 8 · 2022: 3 · 2021: 10
- FWCI
- 3.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Critical Care (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- acute kidney injury, decision support system, AKI progression, guideline adherence, intensive care unit
- MeSH terms
- Adult, Aged, Aged, 80 and over, Female, United Kingdom, Humans, Intensive Care Units, Male, Medical Informatics, Middle Aged, Prospective Studies, Risk Factors, Cohort Studies, Prevalence, Disease Progression, Guideline Adherence, Decision Support Systems, Clinical, Kaplan-Meier Estimate, Acute Kidney Injury
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
