Diagnostic and treatment delay, quality of life and satisfaction with care in colorectal cancer patients: a study protocol

The influence of diagnoses on patient satisfaction during inpatient stays: A prospective study

The aim of this study was to assess patient satisfaction relating to inpatient stays. In this prospective observational study, adult patients with oncological (oral cancer, medication-related osteonecrosis of the jaw) and non-oncological (mandibular fracture) diagnoses, and who had undergone surgery, were contacted 4 weeks after discharge. Two validated questionnaires were used: EORTC QLQ-C30 for quality of life and IN-PATSAT32 for patient satisfaction. For quality of life, the mandibular fracture group had a lower impairment of physical functioning (M = 83.59, SD = 24.44; p = 0.029) in comparison with both other groups (M = 68.84, SD = 26.24; M = 59.33, SD = 24.43, for oral cancer and osteonecrosis, respectively). Regarding patient satisfaction, patients with oral cancer were slightly more satisfied with doctors' availability (M = 48.91, SD = 24.11; p = 0.583) compared with the other groups (M = 36.54, SD = 19.11; M = 46.67, SD = 20.86, for mandibular fracture and ostenecrosis, respectively). Patients with an unplanned inpatient stay following an acute event tended to be less satisfied than patients with a planned inpatient stay. Within the limitations of the study it seems that knowledge of these influencing external factors and their effects can support physicians and nursing staff in providing improved patient care.

SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder

MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms.

Treatment delay of cancer patients in Indonesia: a reflection from a national referral hospital

BACKGROUND Cancer is a complex disease requiring a multidisciplinary approach in establishing prompt diagnosis and treatment. Treatment in a timely manner is crucial for the outcomes. Hence, this study aimed to provide information on treatment delay including patient and provider delays and its associated factors. METHODS Cancer patients were recruited conveniently in the outpatient clinic of Department of Radiation Oncology, Cipto Mangunkusumo Hospital, Indonesia between May and August 2015. All patients were asked to fill a questionnaire and interviewed in this cross-sectional study. Treatment delay was explored and categorized into patient delay and provider delay. Patient delay could be happened before (patient-delay-1) or after (patient-delay-2) the patient was diagnosed with cancer. Provider delay could be due to physician, system-diagnosis, and system-treatment delays. RESULTS Among 294 patients, 86% patient had treatment delay. Patient delay was observed in 153 patients, and 43% of them had a history of alternative treatment. An older age (p = 0.047), lower educational level (p = 0.047), and history of alternative treatment (p<0.001) were associated with patient delay. Meanwhile, 214 patients had provider delay, and 9%, 36%, and 80% of them experienced physician, system-diagnosis, and system-treatment delays, respectively. All types of provider delay were associated with patient delay (p<0.001). CONCLUSIONS Most of the patient had treatment delay caused by either patient or provider.

Characterisation of long-term cancer survivors and application of statistical cure models: a protocol for an observational follow-up study in patients with colorectal cancer

BACKGROUND

Improved colorectal cancer (CRC) survival rates have been reported over the last years, with more than half of these patients surviving more than 5 years after the initial diagnosis. Better understanding these so-called long-term survivors could be very useful to further improve their prognosis as well as to detect other problems that may cause a significant deterioration in their health-related quality of life (HRQoL). Cure models provide novel statistical tools to better estimate the long-term survival rate for cancer and to identify characteristics that are differentially associated with a short or long-term prognosis. The aim of this study will be to investigate the long-term prognosis of CRC patients, characterise long-term CRC survivors and their HRQoL, and demonstrate the utility of statistical cure models to analyse survival and other associated factors in these patients.

METHODS

This is a single-centre, ambispective, observational follow-up study in a cohort of n = 1945 patients with CRC diagnosed between 2006 and 2013. A HRQoL sub-study will be performed in the survivors of a subset of n = 485 CRC patients for which baseline HRQoL data from the time of their diagnosis is already available. Information obtained from interviews and the clinical records for each patient in the cohort is already available in a computerised database from previous studies. This data includes sociodemographic characteristics, family history of cancer, comorbidities, perceived symptoms, tumour characteristics at diagnosis, type of treatment, and diagnosis and treatment delay intervals. For the follow-up, information regarding local recurrences, development of metastases, new tumours, and mortality will be updated using hospital records. The HRQoL for long-term survivors will be assessed with the EORTC QLQ-C30 and QLQ-CR29 questionnaires. An analysis of global and specific survival (competitive risk models) will be performed. Relative survival will be estimated and mixture cure models will be applied. Finally, HRQoL will be analysed through multivariate regression models.

DISCUSSION

We expect the results from this study to help us to more accurately determine the long-term survival of CRC, identify the needs and clinical situation of long-term CRC survivors, and could be used to propose new models of care for the follow-up of CRC patients.

A graph auto-encoder model for miRNA-disease associations prediction

Emerging evidence indicates that the abnormal expression of miRNAs involves in the evolution and progression of various human complex diseases. Identifying disease-related miRNAs as new biomarkers can promote the development of disease pathology and clinical medicine. However, designing biological experiments to validate disease-related miRNAs is usually time-consuming and expensive. Therefore, it is urgent to design effective computational methods for predicting potential miRNA-disease associations. Inspired by the great progress of graph neural networks in link prediction, we propose a novel graph auto-encoder model, named GAEMDA, to identify the potential miRNA-disease associations in an end-to-end manner. More specifically, the GAEMDA model applies a graph neural networks-based encoder, which contains aggregator function and multi-layer perceptron for aggregating nodes' neighborhood information, to generate the low-dimensional embeddings of miRNA and disease nodes and realize the effective fusion of heterogeneous information. Then, the embeddings of miRNA and disease nodes are fed into a bilinear decoder to identify the potential links between miRNA and disease nodes. The experimental results indicate that GAEMDA achieves the average area under the curve of $93.56\pm 0.44\%$ under 5-fold cross-validation. Besides, we further carried out case studies on colon neoplasms, esophageal neoplasms and kidney neoplasms. As a result, 48 of the top 50 predicted miRNAs associated with these diseases are confirmed by the database of differentially expressed miRNAs in human cancers and microRNA deregulation in human disease database, respectively. The satisfactory prediction performance suggests that GAEMDA model could serve as a reliable tool to guide the following researches on the regulatory role of miRNAs. Besides, the source codes are available at https://github.com/chimianbuhetang/GAEMDA.

Waiting times for prostate cancer: A review

Prostate cancer is one of the most common diagnosed cancers in men and the waiting time has become an important issue not only for clinical reasons, but also mostly for the psychological implications on patients. The aim of our study was to review and analyze the literature on waiting times for prostate cancer. In February-March 2019 we performed a search for original peerreviewed papers in the electronic database PubMed (MEDLINE). The key search terms were "prostate cancer AND waiting list", "prostate cancer AND waiting times". We included in our narrative review articles in Italian, English or French, published in 2009-2019 containing original data about the waiting times for prostate cancer. The literature search yielded 680 publications. Finally, we identified 8 manuscripts eligible for the review. The articles were published between 2010 and 2019; the studies involved a minimum of 16 to a maximum of 95438 participants. Studies have been conducted in 6 countries. The waiting times from cancer suspicion to histopathological diagnosis and to treatment had an important reduction in the last years, and this constant decrease could lead to an increase of patients' satisfaction.

Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model

In recent years, accumulating evidences have shown that microRNA (miRNA) plays an important role in the exploration and treatment of diseases, so detection of the associations between miRNA and disease has been drawn more and more attentions. However, traditional experimental methods have the limitations of high cost and time- consuming, a computational method can help us more systematically and effectively predict the potential miRNA-disease associations. In this work, we proposed a novel network embedding-based heterogeneous information integration method to predict miRNA-disease associations. More specifically, a heterogeneous information network is constructed by combining the known associations among lncRNA, drug, protein, disease, and miRNA. After that, the network embedding method Learning Graph Representations with Global Structural Information (GraRep) is employed to learn embeddings of nodes in heterogeneous information network. In this way, the embedding representations of miRNA and disease are integrated with the attribute information of miRNA and disease (e.g. miRNA sequence information and disease semantic similarity) to represent miRNA-disease association pairs. Finally, the Random Forest (RF) classifier is used for predicting potential miRNA-disease associations. Under the 5-fold cross validation, our method obtained 85.11% prediction accuracy with 80.41% sensitivity at the AUC of 91.25%. In addition, in case studies of three major Human diseases, 45 (Colon Neoplasms), 42 (Breast Neoplasms) and 44 (Esophageal Neoplasms) of top-50 predicted miRNAs are respectively verified by other miRNA-disease association databases. In conclusion, the experimental results suggest that our method can be a powerful and useful tool for predicting potential miRNA-disease associations.

In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion

Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion-based Regularized Least Squares for MiRNA-Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA-disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave-one-out cross-validation (LOOCV) and 5-fold cross-validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5-fold cross-validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.

A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration

A lot of research studies have shown that many complex human diseases are associated not only with microRNAs (miRNAs) but also with long noncoding RNAs (lncRNAs). However, most of the current existing studies focus on the prediction of disease-related miRNAs or lncRNAs, and to our knowledge, until now, there are few literature studies reported to pay attention to the study of impact of miRNA-lncRNA pairs on diseases, although more and more studies have shown that both lncRNAs and miRNAs play important roles in cell proliferation and differentiation during the recent years. The identification of disease-related genes provides great insight into the underlying pathogenesis of diseases at a system level. In this study, a novel model called PADLMHOOI was proposed to predict potential associations between diseases and lncRNA-miRNA pairs based on the higher-order orthogonal iteration, and in order to evaluate its prediction performance, the global and local LOOCV were implemented, respectively, and simulation results demonstrated that PADLMHOOI could achieve reliable AUCs of 0.9545 and 0.8874 in global and local LOOCV separately. Moreover, case studies further demonstrated the effectiveness of PADLMHOOI to infer unknown disease-related lncRNA-miRNA pairs.

Health-Related Quality of Life and Satisfaction With Health Care: Relation to Clinical Stage in Mexican Patients With Multiple Myeloma

Multiple myeloma (MM) is characterized by bone pain, pathologic fractures, bone destruction, and secondary hypercalcemia, all these conditions impact on health-related quality of life of patients. The objective was to evaluate the global health state and health-related quality of life in a group of patients with MM who attended a tertiary health-care center of the Instituto Mexicano del Seguro Social in Mexico, through the questionnaires designed by European Organization for Research and Treatment of Cancer (EORTC) quality of life group. Exploratory cross-sectional study in patients with MM treated in a Department of Hematology in a High-Specialty Medical Unit was conducted. Patients older than 18 years of age, men and women, were selected, and their informed written consent was obtained. We included all consecutive cases treated from January 2012 to December 2014. Questionnaires EORTC QLQ-C30, EORTC QLQ-MY20, and EORTC IN-PATSAT-32 were used. We studied 37 patients, 19 (51%) men and 18 women. The mean age was 61.9 years. Twenty-two (59.46%) patients presented with clinical stage III. The mean time for diagnosis was 33.11 months. The most used first-line treatment schedule was melphalan/prednisone/thalidomide (15; 40%). The global health median was 66.67, and symptoms showed a median score of 22.22. Treatment side effects score was 16.67; for general satisfaction, the median score was 75. In conclusion, the patients showed an advanced clinical stage and poor prognosis but had scores higher than 50 in functional scales and lower than 50 for symptom scales. The scores for symptom scales were related to age, renal failure, and disease-free survival. Identification of quality of life and satisfaction of care markers allow for early therapeutic intervention and efficiency and enable a change in quality of life and perception of care in Health Services.

A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network

Motivation

Increasing studies have demonstrated that many human complex diseases are associated with not only microRNAs, but also long-noncoding RNAs (lncRNAs). LncRNAs and microRNA play significant roles in various biological processes. Therefore, developing effective computational models for predicting novel associations between diseases and lncRNA-miRNA pairs (LMPairs) will be beneficial to not only the understanding of disease mechanisms at lncRNA-miRNA level and the detection of disease biomarkers for disease diagnosis, treatment, prognosis, and prevention, but also the understanding of interactions between diseases and LMPairs at disease level.

Results

It is well known that genes with similar functions are often associated with similar diseases. In this article, a novel model named PADLMP for predicting associations between diseases and LMPairs is proposed. In this model, a Disease-LncRNA-miRNA (DLM) tripartite network was designed firstly by integrating the lncRNA-disease association network and miRNA-disease association network; then we constructed the disease-LMPairs bipartite association network based on the DLM network and lncRNA-miRNA association network; finally, we predicted potential associations between diseases and LMPairs based on the newly constructed disease-LMPair network. Simulation results show that PADLMP can achieve AUCs of 0.9318, 0.9090 ± 0.0264, and 0.8950 ± 0.0027 in the LOOCV, 2-fold, and 5-fold cross validation framework, respectively, which demonstrate the reliable prediction performance of PADLMP.

Health-related quality of life in colorectal cancer survivors: are there differences between sporadic and hereditary patients?

Purpose

To compare health-related quality of life (HRQoL) in colorectal cancer (CRC) survivors with sporadic CRC to those with hereditary cancer, specifically Lynch syndrome (LS).

Methods

Participants completed a mailed self-administered questionnaire that assessed, among other things, demographics, clinical characteristics, and health-related quality of life. Using a case-case design, CRC survivors with LS or sporadic cancer were matched on age, sex, race/ethnicity, cancer stage, geography, and time since diagnosis. Participants were recruited from patient registries at The University of Texas MD Anderson Cancer Center (MD Anderson) (n = 33 LS; n = 75 sporadic) and through social media (n = 42 LS). The final sample included 71 LS and 74 sporadic CRC survivors.

Results

For LS patients, the mean FACT-C HRQoL score was 84.8 (11.9) [Median = 86.0; Interquartile Range-17] compared to sporadic patients mean score of 85.8 (16.7) [Median = 92.0; Interquartile Range-21], which indicates high quality of life for both groups. LS patients and sporadic CRC patients had similar HRQoL mean scores across 7 different HRQoL metrics, with no significant differences between groups. Exploratory regression analyses indicate some differences in known predictors of HRQoL by group despite no bivariate differences.

Conclusions

HRQoL is an important component of survivorship in CRC patients. Given the clinical distinctions between LS and sporadic patients, we expected to find significant differences between these patients. However, the patients’ experiences/quality of life does not appear to illustrate such a clear dissimilarity within CRC survivors. Given the limited data in this area, larger studies, ideally with data obtained from multiple sites, is needed to better investigate the alignment between clinical determination and patient experience as well as to explore the relationship between HRQOL, treatment regimens, and health outcomes.

MCMDA: Matrix completion for MiRNA-disease association prediction

Nowadays, researchers have realized that microRNAs (miRNAs) are playing a significant role in many important biological processes and they are closely connected with various complex human diseases. However, since there are too many possible miRNA-disease associations to analyze, it remains difficult to predict the potential miRNAs related to human diseases without a systematic and effective method. In this study, we developed a Matrix Completion for MiRNA-Disease Association prediction model (MCMDA) based on the known miRNA-disease associations in HMDD database. MCMDA model utilized the matrix completion algorithm to update the adjacency matrix of known miRNA-disease associations and furthermore predict the potential associations. To evaluate the performance of MCMDA, we performed leave-one-out cross validation (LOOCV) and 5-fold cross validation to compare MCMDA with three previous classical computational models (RLSMDA, HDMP, and WBSMDA). As a result, MCMDA achieved AUCs of 0.8749 in global LOOCV, 0.7718 in local LOOCV and average AUC of 0.8767+/−0.0011 in 5-fold cross validation. Moreover, the prediction results associated with colon neoplasms, kidney neoplasms, lymphoma and prostate neoplasms were verified. As a consequence, 84%, 86%, 78% and 90% of the top 50 potential miRNAs for these four diseases were respectively confirmed by recent experimental discoveries. Therefore, MCMDA model is superior to the previous models in that it improves the prediction performance although it only depends on the known miRNA-disease associations.

Perceived diagnostic delay and cancer-related distress: a cross-sectional study of patients with colorectal cancer

OBJECTIVE

This study aimed to examine the effect of perceived diagnostic delay on cancer-related distress and determine whether fear of cancer-recurrence and quality of life mediate this relationship.

METHODS

Cross-sectional study in which 311 colorectal cancer (CRC) survivors in Scotland completed a survey, which included questions on cancer-related distress (IES-R), perceived diagnostic delay, quality of life (trial outcome index of the FACT-C: FACT-C TOI) and fear of cancer recurrence. Fifteen patients withheld consent to data matching with medical records, leaving a sample size of 296. Participants were an average of 69 years old (range 56 to 81) and between 3.5 and 12 years post-diagnosis. Multiple regressions were used to test predictors of distress and regression and bootstrapping to test for mediation.

RESULTS

Perceived diagnostic delay was correlated with higher cancer-related distress, while objective markers of diagnostic delay (disease stage at diagnosis and treatment received) were not. Some of the relationship between perceived diagnostic delay and cancer-related distress was mediated by quality of life, but not by fear of cancer recurrence.

CONCLUSIONS

Perceived diagnostic delay was associated with higher cancer-related distress among CRC survivors. While poorer quality of life partly explained such associations, fear of cancer recurrence, stage at diagnosis and treatment did not. The exact features of diagnostic delay that are associated with cancer-related distress remain unclear. Future research should examine the experiences patients go through prior to diagnosis that may increase distress, in an effort to improve our understanding of the factors affecting emotional wellbeing among CRC survivors. Copyright © 2016 John Wiley & Sons, Ltd.

Self-rated health supersedes patient satisfaction with service quality as a predictor of survival in prostate cancer

Background

We have previously reported that higher patient satisfaction (PS) with service quality is associated with favorable survival outcomes in a variety of cancers. However, we argued that patients with greater satisfaction might be the ones with better self-rated health (SRH), a recognized predictor of cancer survival. We therefore investigated whether SRH can supersede patient satisfaction as a predictor of survival in prostate cancer.

Methods

Nine hundred seventeen prostate cancer treated at four Cancer Treatment Centers of America® hospitals between July 2011 and March 2013. PS was measured on a 7-point scale ranging from “completely dissatisfied” to “completely satisfied”. SRH was measured on a 7-point scale ranging from “very poor” to “excellent”. Both were dichotomized into two categories: top box response (7) versus all others (1–6). Patient survival was the primary end point. Cox regression was used to evaluate the association between PS and survival controlling for covariates.

Results

The response rate for this study was 72 %. Majority of patients (n = 517) had stage II disease. Seven hundred eighty-seven (85.8 %) patients were “completely satisfied”. Three hundred nineteen (34.8 %) patients had “excellent” SRH. There was a weak but significant correlation between satisfaction and SRH (Kendall’s tau b = 0.18; p < 0.001). On univariate analysis, “completely satisfied” patients had a significantly lower risk of mortality (HR = 0.46; 95 % CI: 0.25-0.85; p = 0.01). Similarly, patients with “excellent” SRH had a significantly lower risk of mortality (HR = 0.25; 95 % CI: 0.11-0.58; p = 0.001). On multivariate analysis, SRH was found to be a significant predictor of survival (HR = 0.31; 95 % CI: 0.12-0.79; p = 0.01) while patient satisfaction was not (HR = 0.76; 95 % CI: 0.40-1.5; p = 0.40).

Conclusions

SRH supersedes patient satisfaction with service quality as a predictor of survival in prostate cancer. SRH should be used as a control variable in analyses involving patient satisfaction as a predictor of clinical cancer outcomes.

The Relationship between Patient Satisfaction with Service Quality and Survival in Non-Small Cell Lung Cancer - Is Self-Rated Health a Potential Confounder?

BACKGROUND/AIMS

Previously we reported that higher patient satisfaction (PS) with service quality is associated with favorable survival outcomes in a variety of cancers. However, we cautioned the readers that patients with greater satisfaction might be the ones with better self-rated health (SRH), a well-established prognosticator of cancer survival. In other words, SRH could potentially confound the PS and survival relationship. We investigated this hypothesis in non-small cell lung cancer (NSCLC).

METHODS

778 NSCLC patients (327 males and 451 females; mean age 58.8 years) treated at 4 Cancer Treatment Centers of America hospitals between July 2011 and March 2013. PS was measured on a 7-point scale ranging from "completely dissatisfied" to "completely satisfied". SRH was measured on a 7-point scale ranging from "very poor" to "excellent". Both were dichotomized into 2 categories: top box response (7) versus all others (1-6). Patient survival was the primary end point. Cox regression was used to evaluate the association between PS and survival controlling for covariates.

RESULTS

74, 70, 232 and 391 patients had stage I, II, III and IV disease respectively. 631 (81.1%) patients were "completely satisfied". 184 (23.7%) patients had "excellent" SRH. There was a weak but significant correlation between overall PS and SRH (Kendall's tau b = 0.19; p<0.001). On univariate analysis, "completely satisfied" patients had a significantly lower risk of mortality (HR = 0.75; 95% CI: 0.57 to 0.99; p = 0.04). Similarly, patients with "excellent" SRH had a significantly lower risk of mortality (HR = 0.61; 95% CI: 0.46 to 0.81; p = 0.001). On multivariate analysis controlling for stage at diagnosis, treatment history and gender, SRH was found to be a significant predictor of survival (HR = 0.67; 95% CI: 0.50 to 0.89; p = 0.007) while PS was not (HR = 0.86; 95% CI: 0.64 to 1.2; p = 0.32). Among the individual PS items, the only significant independent predictor of survival was "teams communicating with each other concerning your medical condition and treatment" (HR = 0.59; 95% CI: 0.36 to 0.94; p = 0.03).

CONCLUSION

SRH appears to confound the PS-survival relationship in NSCLC. SRH should be used as a control/stratification variable in analyses involving PS as a predictor of clinical cancer outcomes.