ACS-NSQIP risk calculator predicts cohort but not individual risk of complication following colorectal resection

Hyde, Laura Z.; Valizadeh, Neda; Al-Mazrou, Ahmed M.; Kiran, Ravi P.

doi:10.1016/j.amjsurg.2018.11.017

Cited by 27 publications

(26 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Online risk calculators like the National Surgical Quality Improvement Program Surgical Risk Calculator can reduce variability and increase the likelihood that patients will engage in prehabilitation, but they have time-consuming manual data acquisition and entry requirements, which hinders their clinical adoption. [13][14][15][16][17][18] Emerging technologies can circumvent this problem. The MySurgeryRisk platform autonomously draws data from multiple input sources and uses machine learning techniques to predict postoperative complications and mortality.…”

Section: Discussionmentioning

confidence: 99%

“…Online risk calculators like the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator can reduce variability and increase the likelihood that patients will engage in prehabilitation, but they have time-consuming manual data acquisition and entry requirements, which hinders their clinical adoption [17][18][19][20][21][22] . Emerging technologies can circumvent this problem.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Added Value of Intraoperative Data for Predicting Postoperative Complications: The MySurgeryRisk PostOp Extension

Datta

Loftus

Ruppert

et al. 2020

Journal of Surgical Research

View full text Add to dashboard Cite

Background: Models that predict postoperative complications often ignore important intraoperative events and physiological changes. This study tested the hypothesis that accuracy, discrimination, and precision in predicting postoperative complications would improve when using both preoperative and intraoperative data input data compared with preoperative data alone. Methods: This retrospective cohort analysis included 43,943 adults undergoing 52,529 inpatient surgeries at a single institution during a 5-y period. Random forest machine learning models in the validated MySurgeryRisk platform made patient-level predictions for seven postoperative complications and mortality occurring during hospital admission using electronic health record data and patient neighborhood characteristics. For each outcome, one model trained with preoperative data alone; one model trained with both preoperative and intraoperative data. Models were compared by accuracy, discrimination (expressed as area under the receiver operating characteristic curve), precision (expressed as area under the precision-recall curve), and reclassification indices. Results: Machine learning models incorporating both preoperative and intraoperative data had greater accuracy, discrimination, and precision than models using preoperative data alone for predicting all seven postoperative complications (intensive care unit length of stay >48 h, mechanical ventilation >48 h, neurologic complications including delirium, cardiovascular complications, acute kidney injury, venous thromboembolism, and wound complications), and in-hospital mortality (accuracy: 88% versus 77%; area under the receiver operating characteristic curve: 0.93 versus 0.87; area under the precision-recall

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Added Value of Intraoperative Data for Predicting Postoperative Complications: The MySurgeryRisk PostOp Extension

Datta

Loftus

Ruppert

et al. 2020

Journal of Surgical Research

View full text Add to dashboard Cite

show abstract

“…3 However, input variables must be entered manually, and its predictive accuracy is suboptimal, especially for nonelective operations, representing opportunities for improvement. [4][5][6][7]…”

Section: Challenges In Surgical Decision-makingmentioning

confidence: 99%

Artificial Intelligence and Surgical Decision-making

et al. 2020

View full text Add to dashboard Cite

IMPORTANCE-Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.OBSERVATIONS-Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.

show abstract

“…It is constructed from massive volumes of patient-level input variables—including procedure type, demographics, and physiology—and conveys risks through regression cutoff values. Yet, the ability of regression coefficients to represent complex, non-linear associations among correlated, interacting, and nested input variables is questionable, especially when applied to atypical patient presentations and non-elective operations 63–66 . In such instances, it may be advantageous to leverage analytic techniques that learn from data rather than conforming to rules and static variable thresholds.…”

Section: Discussionmentioning

confidence: 99%

Aligning Patient Acuity With Resource Intensity After Major Surgery

et al. 2021

View full text Add to dashboard Cite

Objective: Develop unifying definitions and paradigms for data-driven methods to augment postoperative resource intensity decisions. Summary Background Data: Postoperative level-of-care assignments and frequency of vital sign and laboratory measurements (ie, resource intensity) should align with patient acuity. Effective, data-driven decision-support platforms could improve value of care for millions of patients annually, but their development is hindered by the lack of salient definitions and paradigms. Methods: Embase, PubMed, and Web of Science were searched for articles describing patient acuity and resource intensity after inpatient surgery. Study quality was assessed using validated tools. Thirty-five studies were included and assimilated according to PRISMA guidelines. Results: Perioperative patient acuity is accurately represented by combinations of demographic, physiologic, and hospital-system variables as input features in models that capture complex, non-linear relationships. Intraoperative physiologic data enriche these representations. Triaging high-acuity patients to low-intensity care is associated with increased risk for mortality; triaging low-acuity patients to intensive care units (ICUs) has low value and imparts harm when other, valid requests for ICU admission are denied due to resource limitations, increasing their risk for unrecognized decompensation and failure-to-rescue. Providing high-intensity care for low-acuity patients may also confer harm through unnecessary testing and subsequent treatment of incidental findings, but there is insufficient evidence to evaluate this hypothesis. Compared with data-driven models, clinicians exhibit volatile performance in predicting complications and making postoperative resource intensity decisions. Conclusion: To optimize value, postoperative resource intensity decisions should align with precise, data-driven patient acuity assessments augmented by models that accurately represent complex, non-linear relationships among risk factors.

show abstract

ACS-NSQIP risk calculator predicts cohort but not individual risk of complication following colorectal resection

Cited by 27 publications

References 33 publications

Added Value of Intraoperative Data for Predicting Postoperative Complications: The MySurgeryRisk PostOp Extension

Added Value of Intraoperative Data for Predicting Postoperative Complications: The MySurgeryRisk PostOp Extension

Artificial Intelligence and Surgical Decision-making

Aligning Patient Acuity With Resource Intensity After Major Surgery

Contact Info

Product

Resources

About