ArticleJournal of medical Internet research2021
Predicting Outcomes in Patients Undergoing Pancreatectomy Using Wearable Technology and Machine Learning: Prospective Cohort Study.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 4 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- The "Textbook Outcome" Isn't Textbook: Significant Variation in "Textbook Outcome" Definitions following a Systematic Review of the Surgical Oncology Literature.Annals of surgical oncology · 2026Pooled it
- The Use of Wearable Devices in Oncology Patients: A Systematic Review.The oncologist · 2024Pooled it
- A Smarter Health through the Internet of Surgical Things.Sensors (Basel, Switzerland) · 2022Pooled it
- The role of artificial intelligence in pancreatic surgery: a systematic review.Updates in surgery · 2022Pooled it
- Perioperative tislelizumab plus lenvatinib treatment for resectable hepatocellular carcinoma at high risk of recurrence: single-arm phase II trial.Nature communications · 2026Trial
- Translational Methods for Wearable Heart Rate Variability Monitoring in Older Adults: Preoperative Risk Stratification and Postoperative Monitoring.Journal of cardiovascular translational research · 2026Article
- Article
- Artificial intelligence-integrated wearable biomedical devices for cancer management.Journal of the National Cancer Center · 2025Review
- Machine Learning-Based Prediction of Clinical Outcomes in Patients With Cancer Receiving Systemic Treatment Using Step Count Data Measured With Smartphones.JCO clinical cancer informatics · 2025Observational
- Article
- The exposome as a target for primary prevention and a tool for early detection of pancreatic cancer.Best practice & research. Clinical gastroenterology · 2025Review
- Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.JMIR mHealth and uHealth · 2024Article
- Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.Online journal of public health informatics · 2024Article
- Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology.Cancer medicine · 2024Review
- Wearable Sensors as a Preoperative Assessment Tool: A Review.Sensors (Basel, Switzerland) · 2024Review
- Understanding the factors of wearable devices among the patients with thyroid cancer: A modified UTAUT2 model.PloS one · 2024Article
- Preoperative levels of physical activity can be increased in pancreatectomy patients via a remotely monitored, telephone-based intervention: A randomized trial.Surgery in practice and science · 2023Article
- Review
- Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundPancreatic cancer is the third leading cause of cancer-related deaths, and although pancreatectomy is currently the only curative treatment, it is associated with significant morbidity.
objectiveThe objective of this study was to evaluate the utility of wearable telemonitoring technologies to predict treatment outcomes using patient activity metrics and machine learning.
methodsIn this prospective, single-center, single-cohort study, patients scheduled for pancreatectomy were provided with a wearable telemonitoring device to be worn prior to surgery. Patient clinical data were collected and all patients were evaluated using the American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator (ACS-NSQIP SRC). Machine learning models were developed to predict whether patients would have a textbook outcome and compared with the ACS-NSQIP SRC using area under the receiver operating characteristic (AUROC) curves.
resultsBetween February 2019 and February 2020, 48 patients completed the study. Patient activity metrics were collected over an average of 27.8 days before surgery. Patients took an average of 4162.1 (SD 4052.6) steps per day and had an average heart rate of 75.6 (SD 14.8) beats per minute. Twenty-eight (58%) patients had a textbook outcome after pancreatectomy. The group of 20 (42%) patients who did not have a textbook outcome included 14 patients with severe complications and 11 patients requiring readmission. The ACS-NSQIP SRC had an AUROC curve of 0.6333 to predict failure to achieve a textbook outcome, while our model combining patient clinical characteristics and patient activity data achieved the highest performance with an AUROC curve of 0.7875.
conclusionsMachine learning models outperformed ACS-NSQIP SRC estimates in predicting textbook outcomes after pancreatectomy. The highest performance was observed when machine learning models incorporated patient clinical characteristics and activity metrics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.