scholarly journals On the predictability of postoperative complications for cancer patients: a Portuguese cohort study

2021 ◽  
Author(s):  
Daniel Mateus Goncalves ◽  
Rui Henriques ◽  
Lucio Santos ◽  
Rafael S Costa
Keyword(s):  
Postoperative Complications ◽  
Cancer Patients ◽  
Predictive Models ◽  
Characteristic Curve ◽  
Postoperative Mortality ◽  
Absolute Error ◽  
Risk Scores ◽  
Prediction Errors ◽  
The Mean ◽  
The Creation

Postoperative complications following cancer surgeries are still hard to predict despite the historical efforts towards the creation of standard clinical risk scores. The differences among score calculators, contribute for the creation of highly specialized tools, with poor reusability in foreign contexts, resulting in larger prediction errors in clinical practice. This work aims to predict postoperative complications risk for cancer patients, offering two major contributions. First, to develop and evaluate a machine learning-based risk score, specific for the Portuguese population using a retrospective cohort of 847 cancer patients undergoing surgery between 2016 and 2018, predicting 4 outcomes of interest: i) existence of postoperative complications, ii) severity level of complications, iii) number of days in the Intermediate Care Unit (ICU), and iv) postoperative mortality within 1 year. An additional cohort of 137 cancer patients was used to validate the models. Second, to support the study with relevant findings and improve the interpretability of predictive models. In order to achieve these objectives, a robust methodology for the learning of risk predictors is proposed, offering new perspectives and insights into the clinical decision process. For postoperative complication's the mean Receiver Operating Characteristic Curve (AUC) was 0.69, for complications severity mean AUC was 0.65, for the days in the ICU the Mean Absolute Error (MAE) was 1.07 days, and for one-year postoperative mortality the mean AUC was 0.74, calculated on the development cohort. In this study, risk predictive models which may help guide physicians at estimating cancer patient's risk of developing surgical complications were developed. Additionally, a web-based decision support system is further provided to this end.

scholarly journals On the predictability of postoperative complications for cancer patients: a Portuguese cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniel Gonçalves ◽  
Rui Henriques ◽  
Lúcio Lara Santos ◽  
Rafael S. Costa
Keyword(s):  
Postoperative Complications ◽  
Cancer Patients ◽  
Predictive Models ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Postoperative Mortality ◽  
Clinical Decision ◽  
Risk Scores ◽  
Support Tool ◽  
The Creation

AbstractPostoperative complications are still hard to predict despite the efforts towards the creation of clinical risk scores. The published scores contribute for the creation of specialized tools, but with limited predictive performance and reusability for implementation in the oncological context. This work aims to predict postoperative complications risk for cancer patients, offering two major contributions. First, to develop and evaluate a machine learning-based risk score, specific for the Portuguese population using a retrospective cohort of 847 cancer patients undergoing surgery between 2016 and 2018, for 4 outcomes of interest: (1) existence of postoperative complications, (2) severity level of complications, (3) number of days in the Intermediate Care Unit (ICU), and (4) postoperative mortality within 1 year. An additional cohort of 137 cancer patients from the same center was used for validation. Second, to improve the interpretability of the predictive models. In order to achieve these objectives, we propose an approach for the learning of risk predictors, offering new perspectives and insights into the clinical decision process. For postoperative complications the Receiver Operating Characteristic Curve (AUC) was 0.69, for complications’ severity AUC was 0.65, for the days in the ICU the mean absolute error was 1.07 days, and for 1-year postoperative mortality the AUC was 0.74, calculated on the development cohort. In this study, predictive models which could help to guide physicians at organizational and clinical decision making were developed. Additionally, a web-based decision support tool is further provided to this end.

scholarly journals The outcomes of isolated ureteral resection and reconstruction in non-urologic cancer patients who underwent cytoreductive surgery (CRC) and hyperthermic intraperitoneal chemotherapy (HIPEC)

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Şevket Barış Morkavuk ◽  
Murat Güner ◽  
Mesut Tez ◽  
Ali Ekrem Ünal
Keyword(s):  
Postoperative Complications ◽  
Hospital Stay ◽  
Intraperitoneal Chemotherapy ◽  
Cytoreductive Surgery ◽  
Postoperative Mortality ◽  
Surgical Oncology ◽  
Ureter Reconstruction ◽  
Management Of Complications ◽  
Significant Difference ◽  
The Mean

Abstract Background Urinary system resections are performed during the cytoreductive surgery with hypertermic intraperitoneal chemotherapy (CRS-HIPEC). However, isolated ureter resection and reconstruction results are uncertain. The aim of this study was to evaluate the postoperative outcomes of isolated ureteral resection and reconstructions in patients who underwent CRC and HIPEC procedure. Methods A total of 257 patients that underwent CRC and HIPEC between 2015 and 2017 in the Department of Surgical Oncology, Faculty of Medicine, Ankara University, were retrospectively analyzed. Twenty patients that had undergone isolated ureteral resection and reconstruction were included in the study. Predisposing factors were investigated in patients who developed postoperative complications. Results The mean age of the patients was 55.1 years. The mean follow-up time of all the patients was 11.6 months. Postoperative mortality occurred in two patients. The mean PCI score was 13.9. Postoperative urologic complications were observed in eight patients after ureter reconstruction. There was no statistically significant difference between the groups in terms of reconstruction techniques and postoperative complications (P = 302). There was no correlation between age (P = 0.571) and gender (P = 0.161) with complications. CRS-HIPEC was performed mostly due to gynecologic malignancy. However, there was no correlation between the primary cancer diagnosis and the development of complications (P = 0.514). The hospital stay duration was higher in the group with complications (16.3 vs 8.8 days, P = 0.208). Conclusions Ureteral resections and reconstructions can be performed for R0/1 resections in CRS-HIPEC operations. It leads to an increase in hospital stay. But there is no significant difference in the development of complications. In the management of complications, conservative approach was sufficient.

scholarly journals Predicting Postoperative Complications in Cancer Patients: A Survey Bridging Classical and Machine Learning Contributions to Postsurgical Risk Analysis

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3217
Author(s):  
Daniel M. Gonçalves ◽  
Rui Henriques ◽  
Rafael S. Costa
Keyword(s):  
Machine Learning ◽  
Postoperative Complications ◽  
Risk Analysis ◽  
Cancer Patients ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Risk Scores ◽  
Learning Approaches ◽  
Qualitative Comparison ◽  
Postsurgical Complications

Postoperative complications can impose a significant burden, increasing morbidity, mortality, and the in-hospital length of stay. Today, the number of studies available on the prognostication of postsurgical complications in cancer patients is growing and has already created a considerable set of dispersed contributions. This work provides a comprehensive survey on postoperative risk analysis, integrating principles from classic risk scores and machine-learning approaches within a coherent frame. A qualitative comparison is offered, taking into consideration the available cohort data and the targeted postsurgical outcomes of morbidity (such as the occurrence, nature or severity of postsurgical complications and hospitalization needs) and mortality. This work further establishes a taxonomy to assess the adequacy of cohort studies and guide the development and assessment of new learning approaches for the study and prediction of postoperative complications.

Correlation of AGR2 expression with the incidence of metastasis in luminal breast cancer

2021 ◽  
pp. 1-5
Author(s):  
David Samuel Kereh ◽  
John Pieter ◽  
William Hamdani ◽  
Haryasena Haryasena ◽  
Daniel Sampepajung ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Mean Value ◽  
Rank Test ◽  
Luminal Breast Cancer ◽  
P Value ◽  
Strong Positive Correlation ◽  
Breast Cancer Patients ◽  
The Mean ◽  
Spearman Test

BACKGROUND: AGR2 expression is associated with luminal breast cancer. Overexpression of AGR2 is a predictor of poor prognosis. Several studies have found correlations between AGR2 in disseminated tumor cells (DTCs) in breast cancer patients. OBJECTIVE: This study aims to determine the correlation between anterior Gradient2 (AGR2) expression with the incidence of distant metastases in luminal breast cancer. METHODS: This study was an observational study using a cross-sectional method and was conducted at Wahidin Sudirohusodo Hospital and the network. ELISA methods examine AGR2 expression from blood serum of breast cancer patients. To compare the AGR2 expression in metastatic patients and the non-metastatic patient was tested with Mann Whitney test. The correlation of AGR2 expression and metastasis was tested with the Rank Spearman test. RESULTS: The mean value of AGR2 antibody expression on ELISA in this study was 2.90 ± 1.82 ng/dl, and its cut-off point was 2.1 ng/dl. Based on this cut-off point value, 14 subjects (66.7%) had overexpression of AGR2 serum ELISA, and 7 subjects (33.3%) had not. The mean value AGR2 was significantly higher in metastatic than not metastatic, 3.77 versus 1.76 (p < 0.01). The Spearman rank test obtained a p-value for the 2 tail test of 0.003 (p < 0.05), which showed a significant correlation of both, while the correlation coefficient of 0.612 showed a strong positive correlation of AGR2 overexpression and metastasis. CONCLUSIONS: AGR2 expression is correlated with metastasis in Luminal breast cancer.

scholarly journals Prenatal Sociodemographic Factors Predicting Maltreatment of Children up to 3 Years Old: A Prospective Cohort Study Using Administrative Data in Japan

2021 ◽  
Vol 18 (5) ◽  
pp. 2505
Author(s):  
Aya Isumi ◽  
Kunihiko Takahashi ◽  
Takeo Fujiwara
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Child Maltreatment ◽  
Survey Data ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Characteristic Curve ◽  
Risk Scores ◽  
Prenatal Risk ◽  
Prenatal Risk Factors

Identifying risk factors from pregnancy is essential for preventing child maltreatment. However, few studies have explored prenatal risk factors assessed at pregnancy registration. This study aimed to identify prenatal risk factors for child maltreatment during the first three years of life using population-level survey data from pregnancy notification forms. This prospective cohort study targeted all mothers and their infants enrolled for a 3- to 4-month-old health check between October 2013 and February 2014 in five municipalities in Aichi Prefecture, Japan, and followed them until the child turned 3 years old. Administrative records of registration with Regional Councils for Children Requiring Care (RCCRC), which is suggestive of child maltreatment cases, were linked with survey data from pregnancy notification forms registered at municipalities (n = 893). Exact logistic regression was used for analysis. A total of 11 children (1.2%) were registered with RCCRC by 3 years of age. Unmarried marital status, history of artificial abortion, and smoking during pregnancy were significantly associated with child maltreatment. Prenatal risk scores calculated as the sum of these prenatal risk factors, ranging from 0 to 7, showed high predictive power (area under receiver operating characteristic curve 0.805; 95% confidence interval (CI), 0.660–0.950) at a cut-off score of 2 (sensitivity = 72.7%, specificity = 83.2%). These findings suggest that variables from pregnancy notification forms may be predictors of the risk for child maltreatment by the age of three.

scholarly journals Implantation depth and its influence on complications after TAVI with self-expanding valves

2021 ◽  
Author(s):  
Philipp Breitbart ◽  
Jan Minners ◽  
Manuel Hein ◽  
Holger Schröfel ◽  
Franz-Josef Neumann ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
Fusion Imaging ◽  
Paravalvular Leakage ◽  
Conduction Disturbances ◽  
Transcatheter Aortic Valve ◽  
Transcatheter Heart Valve ◽  
Implantation Depth ◽  
The Mean ◽  
Valve Implantation ◽  
To Receive

AbstractPrior studies in patients with transcatheter aortic valve implantation (TAVI) demonstrated an influence of transcatheter heart valve (THV) position on the occurrence of new conductions disturbances (CD) and paravalvular leakage (PVL) post TAVI in balloon-expandable valves (BEV). Purpose of this study was to investigate the THV implantation depth and its influence on the occurrence of CD and PVL in self-expanding valves (SEV). We performed fusion imaging of pre- and post-procedural computed tomography angiography in 104 TAVI-patients (all with Evolut R) to receive a 3-D reconstruction of the THV within the native annulus region. The THV length below the native annulus was measured for assessment of implantation depth. Electrocardiograms pre-discharge were assessed for conduction disturbances (CD), PVL was determined in transthoracic echocardiography. The mean implantation depth of the THV in the whole cohort was 4.3 ± 3.0 mm. Using the best cut-off of ≥ 4 mm in receiver operating characteristic curve analysis (sensitivity 83.3%, specificity 60.0%) patients with lower THV position developed more new CD after TAVI (68.2 vs. 23.7%, P < 0.001). A deep THV position was identified as the only predictor for new CD after TAVI (odds ratio [CI] 1.312[1.119–1.539], P = 0.001). The implantation depth showed no influence on the grade of PVL (r = 0.052, P = 0.598). In patients with TAVI using the Evolut R SEV, a lower THV positioning (≥ 4 mm length below annulus) was a predictor for new conduction disturbances. In contrast, implantation depth was not associated with the extent of PVL. Graphic abstract Prostheses positions of self-expanding valves and their influence on the occurrence of new conduction disturbances and the grade of paravalvular leakage after TAVI.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

scholarly journals Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 68
Author(s):  
Jiwei Fan ◽  
Xiaogang Yang ◽  
Ruitao Lu ◽  
Xueli Xie ◽  
Weipeng Li
Keyword(s):  
Deep Learning ◽  
Autonomous Navigation ◽  
Detection Method ◽  
Active Role ◽  
Absolute Error ◽  
Face Mask ◽  
Learning Technology ◽  
Flight System ◽  
Crowd Density ◽  
The Mean

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

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