Introducing an AKI predictive tool for patients undergoing orthopaedic surgery

Baird, David; Rae, Fraser; Beecroft, Christina; Gallagher, Katherine; Sim, Stephanie; Vaessen, Robert; Wright, Emily; Bell, Samira

doi:10.1136/bmjoq-2017-000306

Cited by 7 publications

(14 citation statements)

References 12 publications

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“…The studies comprised 12 studies categorised as quasi-experimental,33 40–43 45–47 49–51 54 3 cohort studies32 37 44 and 8 prevalence studies (cross-sectional studies) 34–36 38 39 48 52 53. All were appraised using the JBI tools for assessing quasi-experimental and prevalence studies.…”

Section: Resultsmentioning

confidence: 99%

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Quality of fluid balance charting and interventions to improve it: a systematic review

Leinum,

Krogsgaard,

Tantholdt-Hansen

et al. 2023

BMJ Open Qual

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IntroductionFluid balance monitoring is pivotal to patients’ health. Thus, fluid balance charting is an essential part of clinical nursing documentation. This systematic review aimed to investigate and describe the quality of fluid balance monitoring in medical, surgical and intensive care units, with an emphasis on the completeness of charting data, calculation errors and accuracy, and to evaluate methods used to improve fluid balance charting.Materials and methodsQuantitative studies involving adult patients and reporting data on fluid balance monitoring were included in the review. We searched MEDLINE, Embase, CINAHL and the Cochrane Library. The risk of bias in the included studies was assessed using tools developed by the Joanna Briggs Institute.ResultsWe included a total of 23 studies, which involved 6649 participants. The studies were quasi-experimental, cohort or prevalence studies, and every third study was of low quality. Definitions of ‘completeness’ varied, as well as patient categories and time of evaluation. Eighteen studies reported the prevalence of patients with complete fluid balance charts; of those, 10 reported that not more than 50% of fluid balance charts were complete. Studies addressing calculation errors found them in 25%–35% of charts, including omissions of, for example, intravenous medications. The reported interventions consisted of various components such as policies, education, equipment, visual aids, surveillance and dissemination of results. Among studies evaluating interventions, only 38% (5 of 13) achieved compliance with at least 75% of complete fluid balance charts. Due to the heterogeneity of the studies, a meta-analysis was not possible.ConclusionThe quality of fluid balance charting is inadequate in most studies, and calculation errors influence quality. Interventions included several components, and the impact on the completion of fluid balance charts varied.

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Section: Resultsmentioning

confidence: 99%

“…Only 3 studies were of high quality,37 48 53 12 of moderate quality32 34–36 38 39 43–46 50 52 and 8 of low quality 33 40–42 47 49 51 54. Details of the quality appraisal can be found in table 2.…”

Section: Resultsmentioning

confidence: 99%

Quality of fluid balance charting and interventions to improve it: a systematic review

Leinum,

Krogsgaard,

Tantholdt-Hansen

et al. 2023

BMJ Open Qual

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“…Compared with studies attempting to predict AKI risk, 42–46 our work exhibits several strengths. Foremost, few prior pieces of research have concentrated on patients suffer from clinically low-risk AKI.…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning Algorithms to Predict Acute Kidney Injury in Elderly Orthopedic Postoperative Patients

Chen

Zhang

et al. 2022

CIA

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Objective There has been a worldwide increment in acute kidney injury (AKI) incidence among elderly orthopedic operative patients. The AKI prediction model provides patients’ early detection a possibility at risk of AKI; most of the AKI prediction models derive, however, from the cardiothoracic operation. The purpose of this study is to predict the risk of AKI in elderly patients after orthopedic surgery based on machine learning algorithm models. Methods We organized a retrospective study being comprised of 1000 patients with postoperative AKI undergoing orthopedic surgery from September 2016, to June, 2021. They were divided into training (80%;n=799) and test (20%;n=201) sets.We utilized nine machine learning (ML) algorithms and used intraoperative information and preoperative clinical features to acquire models to predict AKI. The performance of the model was evaluated according to the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Select the optimal model and establish the nomogram to make the prediction model visualization. The concordance statistic (C-statistic) and calibration curve were used to discriminate and calibrate the nomogram respectively. Results In predicting AKI, nine ML algorithms posted AUC of 0.656–1.000 in the training cohort, with the randomforest standing out and AUC of 0.674–0.821 in the test cohort, with the logistic regression model standing out. Thus, we applied the logistic regression model to establish nomogram. The nomogram was comprised of ten variables: age, body mass index, American Society of Anesthesiologists, hypoproteinemia, hypertension, diabetes, anemia, duration of low mean arterial pressure, mean arterial pressure, transfusion.The calibration curves showed good agreement between prediction and observation in both the training and test sets. Conclusion By including intraoperative and preoperative risk factors, ML algorithm can predict AKI and logistic regression model performing the best. Our prediction model and nomogram that are based on this ML algorithm can help lead decision-making for strategies to inhibit AKI over the perioperative duration.

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“…2,9,[12][13][14] As such, quality improvement (QI) efforts to reduce the incidence and severity of AKI have focused on risk assessment, timely diagnosis, optimization of hemodynamics, and avoidance of nephrotoxic agents. [15][16][17][18][19][20][21][22][23] Given its impact on renal health and patient outcomes, our team developed a multi-pronged QI clinical pathway to reduce AKI incidence after HT that included perioperative aminophylline use, optimization of postoperative renal perfusion pressure, and avoidance of nephrotoxic medications. We hypothesized that implementation of a standardized clinical pathway would result in reduced incidence and severity of AKI in patients undergoing HT.…”

Section: Introductionmentioning

confidence: 99%

“…Susceptibility to perioperative AKI is multifactorial, with previously reported risk factors of younger age, pre‐existing renal insufficiency, duration of cardiopulmonary bypass (CPB), pre‐ and postoperative low cardiac output and fluid overload, and exposure to nephrotoxic agents 2,9,12–14 . As such, quality improvement (QI) efforts to reduce the incidence and severity of AKI have focused on risk assessment, timely diagnosis, optimization of hemodynamics, and avoidance of nephrotoxic agents 15–23 …”

Section: Introductionmentioning

confidence: 99%

Impact of a clinical pathway on acute kidney injury in patients undergoing heart transplant

Algaze

Margetson

Sutherland

et al. 2021

Pediatric Transplantation

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Background To evaluate the impact of a clinical pathway on the incidence and severity of acute kidney injury in patients undergoing heart transplant. Methods This was a 2.5‐year retrospective evaluation using 3 years of historical controls within a cardiac intensive care unit in an academic children's hospital. Patients undergoing heart transplant between May 27, 2014, and April 5, 2017 (pre‐pathway) and May 1, 2017, and November 30, 2019 (pathway) were included. The clinical pathway focused on supporting renal perfusion through hemodynamic management, avoiding or delaying nephrotoxic medications, and providing pharmacoprophylaxis against AKI. Results There were 57 consecutive patients included. There was an unadjusted 20% reduction in incidence of any acute kidney injury (p = .05) and a 17% reduction in Stage 2/3 acute kidney injury (p = .09). In multivariable adjusted analysis, avoidance of Stage 2/3 acute kidney injury was independently associated with the clinical pathway era (AOR −1.3 [95% CI −2.5 to −0.2]; p = .03), achieving a central venous pressure of or less than 12 mmHg (AOR −1.3 [95% CI −2.4 to −0.2]; p = .03) and mean arterial pressure above 60 mmHg (AOR −1.6 [95% CI −3.1 to −0.01]; p = .05) in the first 48 h post‐transplant, and older age at transplant (AOR ‐ 0.2 [95% CI −0.2 to −0.06]; p = .002). Conclusions This report describes a renal protection clinical pathway associated with a reduction in perioperative acute kidney injury in patients undergoing heart transplant and highlights the importance of normalizing perioperative central venous pressure and mean arterial blood pressure to support optimal renal perfusion.

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Introducing an AKI predictive tool for patients undergoing orthopaedic surgery

Cited by 7 publications

References 12 publications

Quality of fluid balance charting and interventions to improve it: a systematic review

Quality of fluid balance charting and interventions to improve it: a systematic review

Application of Machine Learning Algorithms to Predict Acute Kidney Injury in Elderly Orthopedic Postoperative Patients

Impact of a clinical pathway on acute kidney injury in patients undergoing heart transplant

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