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Prediction of postoperative outcomes using intraoperative hemodynamic monitoring data.

ABSTRACT: Major surgeries can result in high rates of adverse postoperative events. Reliable prediction of which patient might be at risk for such events may help guide peri- and postoperative care. We show how archiving and mining of intraoperative hemodynamic data in orthotopic liver transplantation (OLT) can aid in the prediction of postoperative 180-day mortality and acute renal failure (ARF), improving upon predictions that rely on preoperative information only. From 101 patient records, we extracted 15 preoperative features from clinical records and 41 features from intraoperative hemodynamic signals. We used logistic regression with leave-one-out cross-validation to predict outcomes, and incorporated methods to limit potential model instabilities from feature multicollinearity. Using only preoperative features, mortality prediction achieved an area under the receiver operating characteristic curve (AUC) of 0.53 (95% CI: 0.44-0.78). By using intraoperative features, performance improved significantly to 0.82 (95% CI: 0.56-0.91, P?=?0.001). Similarly, including intraoperative features (AUC?=?0.82; 95% CI: 0.66-0.94) in ARF prediction improved performance over preoperative features (AUC?=?0.72; 95% CI: 0.50-0.85), though not significantly (P?=?0.32). We conclude that inclusion of intraoperative hemodynamic features significantly improves prediction of postoperative events in OLT. Features strongly associated with occurrence of both outcomes included greater intraoperative central venous pressure and greater transfusion volumes.

SUBMITTER: Prasad V

PROVIDER: S-EPMC5703992 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Prediction of postoperative outcomes using intraoperative hemodynamic monitoring data.

Prasad Varesh V Guerrisi Maria M Dauri Mario M Coniglione Filadelfo F Tisone Giuseppe G De Carolis Elisa E Cillis Annagrazia A Canichella Antonio A Toschi Nicola N Heldt Thomas T

Scientific reports 20171127 1

Major surgeries can result in high rates of adverse postoperative events. Reliable prediction of which patient might be at risk for such events may help guide peri- and postoperative care. We show how archiving and mining of intraoperative hemodynamic data in orthotopic liver transplantation (OLT) can aid in the prediction of postoperative 180-day mortality and acute renal failure (ARF), improving upon predictions that rely on preoperative information only. From 101 patient records, we extracted ...[more]

PMID: 29180647

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Project description:Importance:Acute kidney injury (AKI) is one of the most common complications after noncardiac surgery. Yet current postoperative AKI risk stratification models have substantial limitations, such as limited use of perioperative data. Objective:To examine whether adding preoperative and intraoperative data is associated with improved prediction of noncardiac postoperative AKI. Design, Setting, and Participants:A prognostic study using logistic regression with elastic net selection, gradient boosting machine (GBM), and random forest approaches was conducted at 4 tertiary academic hospitals in the United States. A total of 42?615 hospitalized adults with serum creatinine measurements who underwent major noncardiac surgery between January 1, 2014, and April 30, 2018, were included in the study. Serum creatinine measurements from 365 days before and 7 days after surgery were used in this study. Main Outcomes and Measures:Postoperative AKI (defined by the Kidney Disease Improving Global Outcomes within 7 days after surgery) was the primary outcome. The area under the receiver operating characteristic curve (AUC) was used to assess discrimination. Results:Among 42?615 patients who underwent noncardiac surgery, the mean (SD) age was 57.9 (15.7) years, 23?943 (56.2%) were women, 27?857 (65.4%) were white, and the most frequent surgery types were orthopedic (15?718 [36.9%]), general (8808 [20.7%]), and neurologic (6564 [15.4%]). The rate of postoperative AKI was 10.1% (n?=?4318). The progressive addition of clinical data improved model performance across all modeling approaches, with GBM providing the highest discrimination by AUC. In GBM models, the AUC increased from 0.712 (95% CI, 0.694-0.731) using prehospitalization variables to 0.804 (95% CI, 0.788-0.819) using preoperative variables (inclusive of prehospitalization variables) (P?<?.001 for AUC comparison). The AUC further increased to 0.817 (95% CI, 0.802-0.832) when adding intraoperative variables (P?<?.001 for comparison vs model using preoperative variables). However, the statistically significant improvements in discrimination did not appear to be clinically significant. In particular, the AKI rate among patients classified as high risk improved from 29.1% to 30.0%, a net of 15 patients were appropriately reclassified as high risk, and an additional 15 patients were appropriately reclassified as low risk. Conclusions and Relevance:The findings of the study suggest that electronic health record data may be used to accurately stratify patients at risk of perioperative AKI, but the modest improvements from adding intraoperative data should be weighed against challenges in using intraoperative data.

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Prediction of postoperative outcomes using intraoperative hemodynamic monitoring data.

Publications

Prediction of postoperative outcomes using intraoperative hemodynamic monitoring data.

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