Baseline Pulse Pressure, Acute Kidney Injury, and Mortality After Noncardiac Surgery

Oprea, Adriana D.; Lombard, Frederick W.; Liu, Wen Wei; White, William D.; Karhausen, Jörn; Li, Yi-Ju; Miller, Timothy E.; Aronson, Solomon; Gan, Tong J.; Fontes, Manuel L.; Kertai, Miklós D.

doi:10.1213/ane.0000000000001557

Cited by 19 publications

(8 citation statements)

References 53 publications

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“…The perioperative period contains significant risk, where clinical instability is the norm more than the exception 1,2 . Up to 43% of surgical patients may exhibit some kind of perioperative complication [3][4][5][6][7][8][9][10][11][12][13][14][15][16] , and short-term morbidities are associated with longer-term outcomes. A recently published manuscript has demonstrated that perioperative mortality is the third leading cause of death internationally 17 .…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set

et al. 2020

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During the perioperative period patients often suffer complications, including acute kidney injury (AKI), reintubation, and mortality. In order to effectively prevent these complications, high-risk patients must be readily identified. However, most current risk scores are designed to predict a single postoperative complication and often lack specificity on the patient level. In other fields, machine learning (ML) has been shown to successfully create models to predict multiple end points using a single input feature set. We hypothesized that ML can be used to create models to predict postoperative mortality, AKI, reintubation, and a combined outcome using a single set of features available at the end of surgery. A set of 46 features available at the end of surgery, including drug dosing, blood loss, vital signs, and others were extracted. Additionally, six additional features accounting for total intraoperative hypotension were extracted and trialed for different models. A total of 59,981 surgical procedures met inclusion criteria and the deep neural networks (DNN) were trained on 80% of the data, with 20% reserved for testing. The network performances were then compared to ASA Physical Status. In addition to creating separate models for each outcome, a multitask learning model was trialed that used information on all outcomes to predict the likelihood of each outcome individually. The overall rate of the examined complications in this data set was 0.79% for mortality, 22.3% (of 21,676 patients with creatinine values) for AKI, and 1.1% for reintubation. Overall, there was significant overlap between the various model types for each outcome, with no one modeling technique consistently performing the best. However, the best DNN models did beat the ASA score for all outcomes other than mortality. The highest area under the receiver operating characteristic curve (AUC) models were 0.792 (0.775-0.808) for AKI, 0.879 (0.851-0.905) for reintubation, 0.907 (0.872-0.938) for mortality, and 0.874 (0.864-0.866) for any outcome. The ASA score alone achieved AUCs of 0.652 (0.636-0.669) for AKI, 0.787 (0.757-0.818) for reintubation, 0.839 (0.804-0.875) for mortality, and 0.76 (0.748-0.773) for any outcome. Overall, the DNN architecture was able to create models that outperformed the ASA physical status to predict all outcomes based on a single feature set, consisting of objective data available at the end of surgery. No one model architecture consistently performed the best.

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Section: Introductionmentioning

confidence: 99%

Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set

et al. 2020

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“…In this study, we observed that many previously described risk factors were more prevalent in patients with postoperative AKI. Indeed, these observations provide further support for the concept of using a set of patient- and procedure-related risk factors--many of which are modifiable --to identify before surgery those high-risk noncardiac surgery patients who could benefit from risk management strategies aimed at mitigating the risk for postoperative AKI [ 42 ].…”

Section: Discussionmentioning

confidence: 98%

Limited clinical utility for GWAS or polygenic risk score for postoperative acute kidney injury in non-cardiac surgery in European-ancestry patients

et al. 2022

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Background Prior studies support a genetic basis for postoperative acute kidney injury (AKI). We conducted a genome-wide association study (GWAS), assessed the clinical utility of a polygenic risk score (PRS), and estimated the heritable component of AKI in patients who underwent noncardiac surgery. Methods We performed a retrospective large-scale genome-wide association study followed by a meta-analysis of patients who underwent noncardiac surgery at the Vanderbilt University Medical Center (“Vanderbilt” cohort) or Michigan Medicine, the academic medical center of the University of Michigan (“Michigan” cohort). In the Vanderbilt cohort, the relationship between polygenic risk score for estimated glomerular filtration rate and postoperative AKI was also tested to explore the predictive power of aggregating multiple common genetic variants associated with AKI risk. Similarly, in the Vanderbilt cohort genome-wide complex trait analysis was used to estimate the heritable component of AKI due to common genetic variants. Results The study population included 8248 adults in the Vanderbilt cohort (mean [SD] 58.05 [15.23] years, 50.2% men) and 5998 adults in Michigan cohort (56.24 [14.76] years, 49% men). Incident postoperative AKI events occurred in 959 patients (11.6%) and in 277 patients (4.6%), respectively. No loci met genome-wide significance in the GWAS and meta-analysis. PRS for estimated glomerular filtration rate explained a very small percentage of variance in rates of postoperative AKI and was not significantly associated with AKI (odds ratio 1.050 per 1 SD increase in polygenic risk score [95% CI, 0.971–1.134]). The estimated heritability among common variants for AKI was 4.5% (SE = 4.5%) suggesting low heritability. Conclusion The findings of this study indicate that common genetic variation minimally contributes to postoperative AKI after noncardiac surgery, and likely has little clinical utility for identifying high-risk patients.

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“…16 Elevated baseline PP also was found to be associated with higher odds for postoperative AKI with an adjusted odds ratio of 1.17 (95% CI 1.10-1.25, p < 0.0001) for every 20-mmHg increase in PP. 17 Results from peripheral vascular surgery appear on the contrary, however, with 2 papers describing no significant association between PP and adverse outcomes, including major adverse cardiovascular events 18 and short-and long-term mortality. 19 Non-association simply could be the result of methodological limitations, 20 or perhaps in this group of patients presenting with significant peripheral vascular disease, the severity of arterial vasculature stiffening regardless of PP may be such that there leaves little room for discriminating outcome differences between groups.…”

Section: To Our Knowledge the Multicentre Study Of Perioperativementioning

confidence: 99%

Hypertension in Post-Bypass Acute Kidney Injury: Not Just About Systolic and Diastolic Blood Pressures?

Koh

Hwang

2019

Journal of Cardiothoracic and Vascular Anesthesia

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Baseline Pulse Pressure, Acute Kidney Injury, and Mortality After Noncardiac Surgery

Cited by 19 publications

References 53 publications

Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set

Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set

Limited clinical utility for GWAS or polygenic risk score for postoperative acute kidney injury in non-cardiac surgery in European-ancestry patients

Hypertension in Post-Bypass Acute Kidney Injury: Not Just About Systolic and Diastolic Blood Pressures?

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