MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery

Bïhorac, Azra; Ozrazgat‐Baslanti, Tezcan; Ebadi, Ashkan; Motaei, Amir; Madkour, Mohcine; Pardalos, P. M.; Lipori, Gloria; Hogan, William R.; Efron, Philip A.; Moore, Frederick A.; Moldawer, Lyle L.; Wang, Daisy Zhe; Hobson, Charles; Rashidi, Parisa; Li, Xin; Momčilović, Petar

doi:10.1097/sla.0000000000002706

Cited by 213 publications

(209 citation statements)

References 54 publications

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“…1) [14]. Left-aligned models predict the onset of sepsis following a fixed point in time, with varying time points such as on admission [15] or preoperatively [16,17]. Right-aligned models continuously predict whether sepsis will occur after a distinct period of time and are also known as real-time or continuous prediction models.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

et al. 2020

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Purpose: Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods: A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. Results: After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. Conclusion: This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside.

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Section: Introductionmentioning

confidence: 99%

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

et al. 2020

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“…For some tasks, like predicting mortality among heart failure patients, logistic regression can perform as well or better than certain machine learning methods like regression tree analysis (Austin et al, 2010). For complex tasks like predicting several postoperative complications, artificial intelligence models outperform regression-based techniques and clinician judgement (Bertsimas et al, 2018;Bihorac et al, 2018;Brennan et al, 2019). Bertsimas et al (2018) developed an Optimal Classification Trees machine learning model to predict mortality and 18 complications following emergency surgery, demonstrating superior accuracy compared with the ACS NSQIP calculator (AUROC 0.92 vs. 0.90).…”

Section: Advantages For Artificial Intelligence In Predictive Analytimentioning

confidence: 99%

“…Manual data entry requires more time and input from providers than an automated model, but obviates requirements for data security and encryption of protected health information from electronic health records (EHR). Bihorac et al (2018) developed and validated the MySurgeryRisk platform with automated EHR data linked to US Census data regarding neighborhood characteristics, using 285 variables to predict eight postoperative complications with AUROC 0.82-0.94 (Bihorac et al, 2018). EHR data feeds the algorithm automatically, obviating manual data search and entry, and overcoming a major obstacle to clinical adoption.…”

Section: Advantages For Artificial Intelligence In Predictive Analytimentioning

confidence: 99%

Mysteries, Epistemological Modesty, and Artificial Intelligence in Surgery

Loftus

Upchurch

Delitto

et al. 2020

Front. Artif. Intell.

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“…Preoperative identification of patients with increased surgical risk [3] should be followed by strategies to prevent potential postoperative complications by reducing their incidence and/or severity, and thus minimizing their clinical and economic impact. Therefore, the implementation of effective preventive interventions is an important milestone to achieve due to the current economic context.…”

Section: Introductionmentioning

confidence: 99%

Cost-effectiveness of a technology-supported multimodal prehabilitation program in moderate-to-high risk patients undergoing lung cancer resection: randomized controlled trial protocol

Barberán-García

Navarro-Ripoll

Sánchez-Lorente

et al. 2020

BMC Health Serv Res

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Background: Multimodal prehabilitation is a preoperative intervention with the objective to enhance cancer patients' functional status which has been showed to reduce both postoperative morbidity and hospital length of stay in digestive oncologic surgery. However, in lung cancer surgery patients further studies with higher methodological quality are needed to clarify the benefits of prehabilitation. The main aim of the current protocol is to evaluate the cost-effectiveness of a multimodal prehabilitation program supported by information and communication technologies in moderate-to-high risk lung cancer patients undergoing thoracic surgery. Methods: A Quadruple Aim approach will be adopted, assessing the prehabilitation program at the following levels: i) Patients' and professionals' experience outcomes (by means of standardized questionnaires, focus groups and structured interviews); ii) Population health-based outcomes (e.g. hospital length of stay, number and severity of postoperative complications, peak oxygen uptake and levels of systemic inflammation); and, iii) Healthcare costs. Discussion: This study protocol should contribute not only to increase the scientific basis on prehabilitation but also to detect the main factors modulating service adoption.

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MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery

Cited by 213 publications

References 54 publications

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Mysteries, Epistemological Modesty, and Artificial Intelligence in Surgery

Cost-effectiveness of a technology-supported multimodal prehabilitation program in moderate-to-high risk patients undergoing lung cancer resection: randomized controlled trial protocol

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