Wearable mobile health device for monitoring postoperative ambulation among patients with colorectal cancer undergoing minimally invasive surgery: A prospective comparison study.

BACKGROUND: This study aimed to evaluate the feasibility of using mHealth devices for monitoring postoperative ambulation among patients with colorectal cancer undergoing minimally invasive surgery (MIS). METHODS: Patients with colorectal cancer undergoing MIS were prospectively recruited to wear mHealth devices for recording postoperative ambulation between October 2018 and January 2021. The primary outcome was the compliance by evaluating the weekly submission rate of step counts. The secondary outcome was the association of weekly step counts and postoperative length of stay. RESULTS: Of 107 eligible patients, 53 patients wore mHealth devices, whereas 54 patients did not. The average weekly submission rate was 72.6% for the first month after surgery. The total step counts <4000 or >10 000 in the postoperative week one were negatively associated with postoperative length of stay (ß = -2.874, p = 0.038). CONCLUSIONS: mHealth devices provide an objective assessment of postoperative ambulation among patients with colorectal cancer undergoing MIS. CLINICAL TRIAL REGISTRATION: NCT03277235.

Prognostic Value of Pace Variability, a Novel 6MWT-Derived Feature, in Patients with Chronic Obstructive Pulmonary Disease.

Purpose: The 6-minute walk test (6MWT) is often used to evaluate chronic obstructive pulmonary disease (COPD) patients' functional capacity, with 6-minute walk distance (6MWD) and related measures being linked to mortality and hospitalizations. This study investigates the prognostic value of pace variability, a significant indicator in sports medicine, during the 6MWT for COPD patients. Patients and Methods: We retrospectively screened consecutive COPD patients who had been prospectively enrolled in a pay-for-performance program from January 2019 to May 2020 to determine their eligibility. Patient characteristics, including demographics, exacerbation history, and 6MWT data, were analyzed to investigate their potential associations with prognosis. The primary outcome was a composite of adverse events, including overall mortality or hospitalizations due to exacerbations during a 1-year follow-up period. To analyze the 6MWT data, we divided it into three 2-minute epochs and calculated the average walk speed for each epoch. We defined pace variability as the difference between the maximum and minimum average speed in a single 2-minute epoch, divided by the average speed for the entire 6-minute walk test. Results: A total of 163 patients with COPD were included in the study, and 19 of them (12%) experienced the composite adverse outcome. Multivariable logistic regression analyses revealed that two predictors were independently associated with the composite outcome: % predicted 6MWD <72 (adjusted odds ratio [aOR] 7.080; 95% confidence interval [CI] 1.481-33.847) and pace variability ≥0.39 (aOR 9.444; 95% CI 2.689-33.170). Patients with either of these adverse prognostic features had significantly worse composite outcome-free survival, with both log-rank P values less than 0.005. Notably, COPD patients with both adverse features experienced an especially poor outcome after 1 year. Conclusion: Patients with COPD who exhibited greater pace variability during the 6MWT had a significantly higher risk of overall mortality and COPD-related hospitalizations, indicating a worse prognosis.

Application of multiple deep learning models for automatic burn wound assessment.

PURPOSE: Accurate assessment of the percentage of total body surface area (%TBSA) burned is crucial in managing burn injuries. It is difficult to estimate the size of an irregular shape by inspection. Many articles reported the discrepancy of estimating %TBSA burned by different doctors. We set up a system with multiple deep learning (DL) models for %TBSA estimation, as well as the segmentation of possibly poor-perfused deep burn regions from the entire wound. METHODS: We proposed boundary-based labeling for datasets of total burn wound and palm, whereas region-based labeling for the dataset of deep burn wound. Several powerful DL models (U-Net, PSPNet, DeeplabV3+, Mask R-CNN) with encoders ResNet101 had been trained and tested from the above datasets. With the subject distances, the %TBSA burned could be calculated by the segmentation of total burn wound area with respect to the palm size. The percentage of deep burn area could be obtained from the segmentation of deep burn area from the entire wound. RESULTS: A total of 4991 images of early burn wounds and 1050 images of palms were boundary-based labeled. 1565 out of 4994 images with deep burn were preprocessed with superpixel segmentation into small regions before labeling. DeeplabV3+ had slightly better performance in three tasks with precision: 0.90767, recall: 0.90065 for total burn wound segmentation; precision: 0.98987, recall: 0.99036 for palm segmentation; and precision: 0.90152, recall: 0.90219 for deep burn segmentation. CONCLUSION: Combining the segmentation results and clinical data, %TBSA burned, the volume of fluid for resuscitation, and the percentage of deep burn area can be automatically diagnosed by DL models with a pixel-to-pixel method. Artificial intelligence provides consistent, accurate and rapid assessments of burn wounds.

A pressure ulcers assessment system for diagnosis and decision making using convolutional neural networks.

BACKGROUND/PURPOSE: Pressure ulcers are a common problem in hospital care and long-term care. Pressure ulcers are caused by prolonged compression of soft tissues, which can cause local tissue damage and even lead to serious infections. This study uses a deep learning algorithm to construct a system that diagnoses pressure ulcers and assists in making treatment decisions, thus providing additional reference for first-line caregivers. METHODS: We performed a retrospective research of medical records to find photos of patients with pressure ulcers at National Taiwan University Hospital from 2016 to 2020. We used photos from 2016 to 2019 for training and after removing the photos which were vague, underexposed, or overexposed, 327 photos were obtained. The photos were then labeled as "erythema" or "non-erythema" for the first classification task and "extensive necrosis", "moderate necrosis" or "limited necrosis" for the second, by consensus of three recruited physicians. An Inception-ResNet-v2 model, a kind of Convolutional Neural Network (CNN), was applied for training these two classification tasks to construct an assessment system. Finally, we tested the model with the photos of pressure ulcers taken from 2019 to 2020 to verify its accuracy. RESULTS: For the task of classification of erythema and non-erythema wounds, our CNN model achieved an accuracy of about 98.5%. For the task of classification of necrotic tissue, our model achieved accuracy of about 97%. CONCLUSION: Our CNN model, which was based on Inception-ResNet-v2, achieved high accuracy when classifying different types of pressure ulcers, making it applicable in clinical circumstances.

Web-Based Dashboard for the Interactive Visualization and Analysis of National Risk-Standardized Mortality Rates of Sepsis in the US.

Sepsis mortality is heavily influenced by the quality of care in hospitals. Comparing risk-standardized mortality rate (RSMR) of sepsis patients in different states in the United States has potentially important clinical and policy implications. In the current study, we aimed to compare national sepsis RSMR using an interactive web-based dashboard. We analyzed sepsis mortality using the National Inpatient Sample Database of the US. The RSMR was calculated by the hierarchical logistic regression model. We wrote the interactive web-based dashboard using the Shiny framework, an R package that integrates R-based statistics computation and graphics generation. Visual summarizations (e.g., heat map, and time series chart), and interactive tools (e.g., year selection, automatic year play, map zoom, copy or print data, ranking data by name or value, and data search) were implemented to enhance user experience. The web-based dashboard (https://sepsismap.shinyapps.io/index2/) is cross-platform and publicly available to anyone with interest in sepsis outcomes, health inequality, and administration of state/federal healthcare. After extrapolation to the national level, approximately 35 million hospitalizations were analyzed for sepsis mortality each year. Eight years of sepsis mortality data were summarized into four easy to understand dimensions: Sepsis Identification Criteria; Sepsis Mortality Predictors; RSMR Map; RSMR Trend. Substantial variation in RSMR was observed for different states in the US. This web-based dashboard allows anyone to visualize the substantial variation in RSMR across the whole US. Our work has the potential to support healthcare transparency, information diffusion, health decision-making, and the formulation of new public policies.

Chronic wound assessment and infection detection method.

BACKGROUND: Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As a result, with the help of the hand-held mobile devices, there is high demand for the development of a series of algorithms and related methods for wound infection early detection and wound self monitoring. METHODS: This research proposed an automated way to perform (1) wound image segmentation and (2) wound infection assessment after surgical operations. The first part describes an edge-based self-adaptive threshold detection image segmentation method to exclude nonwounded areas from the original images. The second part describes a wound infection assessment method based on machine learning approach. In this method, the extraction of feature points from the suture area and an optimal clustering method based on unimodal Rosin threshold algorithm that divides feature points into clusters are introduced. These clusters are then merged into several regions of interest (ROIs), each of which is regarded as a suture site. Notably, a support vector machine (SVM) can automatically interpret infections on these detected suture site. RESULTS: For (1) wound image segmentation, boundary-based evaluation were applied on 100 images with gold standard set up by three physicians. Overall, it achieves 76.44% true positive rate and 89.04% accuracy value. For (2) wound infection assessment, the results from a retrospective study using confirmed wound pictures from three physicians for the following four symptoms are presented: (1) Swelling, (2) Granulation, (3) Infection, and (4) Tissue Necrosis. Through cross-validation of 134 wound images, for anomaly detection, our classifiers achieved 87.31% accuracy value; for symptom assessment, our classifiers achieved 83.58% accuracy value. CONCLUSIONS: This augmentation mechanism has been demonstrated reliable enough to reduce the need for face-to-face diagnoses. To facilitate the use of this method and analytical framework, an automatic wound interpretation app and an accompanying website were developed. TRIAL REGISTRATION: 201505164RIND , 201803108RSB .

Wearable-Based Mobile Health App in Gastric Cancer Patients for Postoperative Physical Activity Monitoring: Focus Group Study.

BACKGROUND: Surgical cancer patients often have deteriorated physical activity (PA), which in turn, contributes to poor outcomes and early recurrence of cancer. Mobile health (mHealth) platforms are progressively used for monitoring clinical conditions in medical subjects. Despite prevalent enthusiasm for the use of mHealth, limited studies have applied these platforms to surgical patients who are in much need of care because of acutely significant loss of physical function during the postoperative period. OBJECTIVE: The aim of our study was to determine the feasibility and clinical value of using 1 wearable device connected with the mHealth platform to record PA among patients with gastric cancer (GC) who had undergone gastrectomy. METHODS: We enrolled surgical GC patients during their inpatient stay and trained them to use the app and wearable device, enabling them to automatically monitor their walking steps. The patients continued to transmit data until postoperative day 28. The primary aim of this study was to validate the feasibility of this system, which was defined as the proportion of participants using each element of the system (wearing the device and uploading step counts) for at least 70% of the 28-day study. "Definitely feasible," "possibly feasible," and "not feasible" were defined as ≥70%, 50%-69%, and <50% of participants meeting the criteria, respectively. Moreover, the secondary aim was to evaluate the clinical value of measuring walking steps by examining whether they were associated with early discharge (length of hospital stay <9 days). RESULTS: We enrolled 43 GC inpatients for the analysis. The weekly submission rate at the first, second, third, and fourth week was 100%, 93%, 91%, and 86%, respectively. The overall daily submission rate was 95.5% (1150 days, with 43 subjects submitting data for 28 days). These data showed that this system met the definition of "definitely feasible." Of the 54 missed transmission days, 6 occurred in week 2, 12 occurred in week 3, and 36 occurred in week 4. The primary reason for not sending data was that patients or caregivers forgot to charge the wearable devices (>90%). Furthermore, we used a multivariable-adjusted model to predict early discharge, which demonstrated that every 1000-step increment of walking on postoperative day 5 was associated with early discharge (odds ratio 2.72, 95% CI 1.17-6.32; P=.02). CONCLUSIONS: Incorporating the use of mobile phone apps with wearable devices to record PA in patients of postoperative GC was feasible in patients undergoing gastrectomy in this study. With the support of the mHealth platform, this app offers seamless tracing of patients' recovery with a little extra burden and turns subjective PA into an objective, measurable parameter.

Validity of ICD9-CM codes to diagnose chronic obstructive pulmonary disease from National Health Insurance claim data in Taiwan.

PURPOSE: Claim data from Taiwan's National Health Insurance (NHI) database have previously been utilized in the study of COPD. However, there are limited data on the positive predictive value of claim data for COPD diagnosis. Therefore, this study aimed to characterize and validate the COPD cohort identified from the NHI research database. METHODS: This cross-sectional study compared records from claim data with those from a medical center. From 2007 to 2014, a COPD cohort was constructed from claim data using ICD9-CM codes for COPD. The diagnostic positive predictive value of these data was assessed with reference to physician-verified COPD. In addition, a multivariate logistic regression model was built to identify independent factors associated with the positive predictive value of COPD diagnosis by claim data. RESULTS: During the 8-year study period, a total of 12,127 subjects met the criterion of having two or more outpatient codes in 1 year or one or more inpatient COPD codes in their claim data. Of this total, the diagnosis of COPD was verified by physicians in 7,701 (63.5%) subjects. Applying a more stringent criterion - three or more outpatient codes or two or more inpatient codes - improved the diagnostic positive predictive value to 72.2%. Age ≥65 years and a claim for spirometry were the two most important factors associated with the positive predictive value of claim-data-defined COPD. Adding spirometry testing to diagnostic ICD9-CM codes for COPD increased the positive predictive value to 84.6%. CONCLUSION: This study emphasizes the importance of validation of disease-specific diagnosis prior to applying an administrative database in clinical studies. It also indicates the limitation of ICD9-CM codes alone in recognizing COPD patients within the NHI research database.

A cross-hospital cost and quality assessment system by extracting frequent physician order set from a nationwide Health Insurance Research Database.

PURPOSE: Clinical pathways fall under the process perspective of health care quality. For care providers, clinical pathways can be compared to improve health care quality. The objective of this study was to design a convenient physician order set comparison system based on claim records from the National Health Insurance Research Database (NHIRD) of Taiwan. METHODS: Data were retrieved from the NHIRD for the period of 2003-2007 for frequent physician order sets found in hospital surgical hernia repair inpatient claim records. The derived frequent physician order sets were divided into five frequency thresholds: 80%, 85%, 90%, 95% and 100%. A consistency index was defined and calculated to understand each care providers' adherence to clinical pathways. In addition, the average count of physician orders, average amount of cost, Charlson comorbidity index, and recurrence rate were calculated; these variables were considered in frequent physician order sets comparison. RESULTS: Records for 3262 patients from 257 hospitals were retrieved. The frequent physician order sets of various frequency thresholds, Charlson comorbidities, and recurrence rates were extracted and computed for comparison among hospitals. A recurrence rate threshold of 2% was established to separate low and high quality of herniorrhaphy at each hospital. Univariable analysis showed that low recurrence rate was associated with high consistency index (70.99±23.88 vs. 52.60±20.30; P<.001), few surgeons at each hospital (3.50±4.41 vs. 7.09±6.57; P<.001), and non-medical center facility type (P=.042). A multivariable Cox regression analysis indicated an association of low recurrence rates with consistency index only (one percentage increased: OR=0.973; CI: 0.957-0.990; P=.002). CONCLUSIONS: The proposed system leveraged the claim records to generate frequent physician order sets at hospitals, thus solving the difficulty in obtaining clinical pathway data. This allows medical professionals and management to conveniently and effectively compare and query similarities and differences in clinical pathways among hospitals.