Background Pancreatic 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. Objective The objective of this study was to evaluate the utility of wearable telemonitoring technologies to predict treatment outcomes using patient activity metrics and machine learning. Methods In 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. Results Between 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. Conclusions Machine 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.
Post-operative complications and hospital readmission are of great concern to surgical patients and health care providers. Wearable devices such as Fitbit wristbands enable long-term and non-intrusive monitoring of patients outside clinical environments. To build accurate predictive models based on wearable data, however, requires effective feature engineering to extract high-level features from time series data collected by the wearable sensors. This paper presents a pipeline for developing clinical predictive models based on wearable sensors. The core of the pipeline is a multi-level feature engineering framework for extracting high-level features from fine-grained time series data. The framework integrates a set of techniques tailored for noisy and incomplete wearable data collected in real-world clinical studies: (1) singular spectrum analysis for extracting high-level features from daily features over the course of the study; (2) a set of daily features that are resilient to missing data in wearable time series data; (3) a K-Nearest Neighbors (KNN) method for imputing short missing heart rate segments; (4) the integration of patients' clinical characteristics and wearable features. We evaluated the feature engineering approach and machine learning models in a clinical study involving 61 patients undergoing pancreatic surgery. Linear support vector machine (SVM) with integrated feature engineering achieved an AUROC of 0.8802 for predicting post-operative readmission or severe complications, which significantly outperformed the existing rule-based model used in clinical practice and other state-of-the-art feature engineering approaches.
Background Little is known about what factors predict better outcomes for patients who undergo minimally invasive pancreaticoduodenectomy (MIPD) versus open pancreaticoduodenectomy (OPD). We hypothesized that patients with dilated pancreatic ducts have improved postoperative outcomes with MIPD compared to OPD. Methods All patients undergoing pancreaticoduodenectomy were prospectively followed over a time period of 47 months, and perioperative and pathologic covariates and outcomes were compared. Ideal outcome after PD was defined as follows: (1) no complications, (2) postoperative length of stay < 7 days, and (3) negative (R0) margins on pathology. Patients with dilated pancreatic ducts (≥ 3 mm) who underwent MIPD were 1:3 propensity score-matched to patients with dilated ducts who underwent OPD and outcomes compared. Likewise, patients with non-dilated pancreatic ducts (< 3 mm) who underwent MIPD were 1:3 propensity score-matched to patients with non-dilated ducts who underwent OPD and outcomes were compared. Results 371 patients underwent PD—74 (19.9%) MIPD and 297 (80.1%) underwent OPD. Overall, patients who underwent MIPD had significantly less intraoperative blood loss. After 1:3 propensity score matching, patients with dilated pancreatic ducts who underwent MIPD ( n = 45) had significantly lower overall complication and 90-day readmission rates compared to matched OPD patients ( n = 135) with dilated ducts. Patients with dilated duct who underwent MIPD were more likely to have an ideal outcome than patients with OPD (29 vs 15%, p = 0.035). There were no significant differences in postoperative outcomes among propensity score-matched patients with non-dilated pancreatic ducts who underwent MIPD ( n = 29) compared to matched patients undergoing OPD ( n = 87) with non-dilated ducts. Conclusions MIPD is safe with comparable perioperative outcomes to OPD. Patients with pancreatic ducts ≥ 3 mm appear to derive the most benefit from MIPD in terms of fewer complications, lower readmission rates, and higher likelihood of ideal outcome. Supplementary Information The online version contains supplementary material available at 10.1007/s00464-021-08611-x.
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