Predicting survival in patients with advanced disease

Palliative prognostic scores for survival prediction of cancer patients: a systematic review and meta-analysis

BACKGROUND

The palliative prognostic score is the most widely validated prognostic tool for cancer survival prediction, with modified versions available. A systematic evaluation of palliative prognostic score tools is lacking. This systematic review and meta-analysis aimed to evaluate the performance and prognostic utility of palliative prognostic score, delirium-palliative prognostic score, and palliative prognostic score without clinician prediction in predicting 30-day survival of cancer patients and to compare their performance.

METHODS

Six databases were searched for peer-reviewed studies and grey literature published from inception to June 2, 2023. English studies must assess palliative prognostic score, delirium-palliative prognostic score, or palliative prognostic score without clinician-predicted survival for 30-day survival in adults aged 18 years and older with any stage or type of cancer. Outcomes were pooled using the random effects model or summarized narratively when meta-analysis was not possible.

RESULTS

A total of 39 studies (n = 10 617 patients) were included. Palliative prognostic score is an accurate prognostic tool (pooled area under the curve [AUC] = 0.82, 95% confidence interval [CI] = 0.79 to 0.84) and outperforms palliative prognostic score without clinician-predicted survival (pooled AUC = 0.74, 95% CI = 0.71 to 0.78), suggesting that the original palliative prognostic score should be preferred. The meta-analysis found palliative prognostic score and delirium-palliative prognostic score performance to be comparable. Most studies reported survival probabilities corresponding to the palliative prognostic score risk groups, and higher risk groups were statistically significantly associated with shorter survival.

CONCLUSIONS

Palliative prognostic score is a validated prognostic tool for cancer patients that can enhance clinicians' confidence and accuracy in predicting survival. Future studies should investigate if accuracy differs depending on clinician characteristics. Reporting of validation studies must be improved, as most studies were at high risk of bias, primarily because calibration was not assessed.

A review of the utility of prognostic tools in predicting 6-month mortality in cancer patients, conducted in the context of voluntary assisted dying

BACKGROUND

Eligibility to access the Victorian voluntary assisted dying (VAD) legislation requires that people have a prognosis of 6 months or less (or 12 months or less in the setting of a neurodegenerative diagnosis). Yet prognostic determination is frequently inaccurate and prompts clinician discomfort. Based on functional capacity and clinical and biochemical markers, prognostic tools have been developed to increase the accuracy of life expectancy predictions.

AIMS

This review of prognostic tools explores their accuracy to determine 6-month mortality in adults when treated under palliative care with a primary diagnosis of cancer (the diagnosis of a large proportion of people who are requesting VAD).

METHODS

A systematic search of the literature was performed on electronic databases Medline, Embase and Cinahl.

RESULTS

Limitations of prognostication identified include the following: (i) prognostic tools still provide uncertain prognoses; (ii) prognostic tools have greater accuracy predicting shorter prognoses, such as weeks to months, rather than 6 months; and (iii) functionality was often weighted significantly when calculating prognoses. Challenges of prognostication identified include the following: (i) the area under the curve (a value that represents how well a model can distinguish between two outcomes) cannot be directly interpreted clinically and (ii) difficulties exist related to determining appropriate thresholds of accuracy in this context.

CONCLUSIONS

Prognostication is a significant aspect of VAD, and the utility of the currently available prognostic tools appears limited but may prompt discussions about prognosis and alternative means (other than prognostic estimates) to identify those eligible for VAD.

Multi-Domain Screening: Identification of Patient's Risk Profile Prior to Head-and-Neck Cancer Treatment

BACKGROUND

Head-and-neck cancer (HNC) can give rise to oropharyngeal dysphagia (OD), malnutrition, sarcopenia, and frailty. Early identification of these phenomena in newly diagnosed HNC patients is important to reduce the risk of complications and to improve treatment outcomes. The aim of this study was (1) to determine the prevalence of the risk of OD, malnutrition, sarcopenia, and frailty; and (2) to investigate the relation between these phenomena and patients' age, performance status, and cancer group staging.

METHODS

Patients (N = 128) underwent multi-domain screening consisting of the Eating Assessment Tool-10 for OD, Short Nutritional Assessment Questionnaire and BMI for malnutrition, Short Physical Performance Battery and Hand Grip Strength for sarcopenia, and Distress Thermometer and Maastricht Frailty Screening Tool for frailty.

RESULTS

26.2%, 31.0%, 73.0%, and 46.4% of the patients were at risk for OD, malnutrition, sarcopenia, or frailty, respectively. Patients with an advanced cancer stage had a significantly higher risk of OD and high levels of distress prior to cancer treatment.

CONCLUSIONS

This study identified the risk profile of newly diagnosed HNC patients using a standardized 'quick and easy' multi-domain screening prior to cancer treatment.

Response Rates to Methylnaltrexone in Hospitalized Cancer Patients

Context: Methylnaltrexone is a peripherally-acting mu-opioid receptor antagonist studied in both cancer and non-cancer patients with opioid-induced constipation (OIC), but mostly in the outpatient setting. For adult hospitalized cancer patients with OIC, its effectiveness is unknown. Objectives: Describe the efficacy of methylnaltrexone for OIC in the inpatient setting, defined as bowel movement (BM) within 24 hours of methylnaltrexone administration. Methods: We performed a single-center, retrospective chart review of all hospitalized, adult patients with a cancer diagnosis who received methylnaltrexone from the palliative care team between January 1st, 2012 and July 1st, 2019. Results: We identified 194 patients. The mean age was 59, 50.5% were male and 88% were white. 192 patients (98%) received the 8 mg dose subcutaneously. The median oral morphine equivalent (OME) was 135 mg (IQR 70-354 mg). 45% (95% confidence interval, 38-53%) had a BM within 24 hours. Higher OME was correlated with successful BM, with a response in 93% (86/92) of patients receiving ≥150 OME and 2% (2/102) of patients receiving <150 OME (P < .0001). Prior laxative use did not predict response at 24 hours whether these were osmotic laxatives (40.7% vs 47.1%, P = .52), stimulant laxatives (45.7% vs 45.2%, P > .99), or stool softeners (44.7% vs 46.1%, P = .89). Conclusion: Methylnaltrexone has a high response rate when used as treatment for OIC in hospitalized adult cancer patients, especially for patients taking ≥150 OME.

Performance of the Palliative Prognostic Index for cancer patients: A systematic review and meta-analysis

BACKGROUND

Clinician predicted survival for cancer patients is often inaccurate, and prognostic tools may be helpful, such as the Palliative Prognostic Index (PPI). The PPI development study reported that when PPI score is greater than 6, it predicted survival of less than 3 weeks with a sensitivity of 83% and specificity of 85%. When PPI score is greater than 4, it predicts survival of less than 6 weeks with a sensitivity of 79% and specificity of 77%. However, subsequent PPI validation studies have evaluated various thresholds and survival durations, and it is unclear which is most appropriate for use in clinical practice. With the development of numerous prognostic tools, it is also unclear which is most accurate and feasible for use in multiple care settings.

AIM

We evaluated PPI model performance in predicting survival of adult cancer patients based on different thresholds and survival durations and compared it to other prognostic tools.

DESIGN

This systematic review and meta-analysis was registered in PROSPERO (CRD42022302679). We calculated the pooled sensitivity and specificity of each threshold using bivariate random-effects meta-analysis and pooled diagnostic odds ratio of each survival duration using hierarchical summary receiver operating characteristic model. Meta-regression and subgroup analysis were used to compare PPI performance with clinician predicted survival and other prognostic tools. Findings which could not be included in meta-analyses were summarised narratively.

DATA SOURCES

PubMed, ScienceDirect, Web of Science, CINAHL, ProQuest and Google Scholar were searched for articles published from inception till 7 January 2022. Both retrospective and prospective observational studies evaluating PPI performance in predicting survival of adult cancer patients in any setting were included. The Prediction Model Risk of Bias Assessment Tool was used for quality appraisal.

RESULTS

Thirty-nine studies evaluating PPI performance in predicting survival of adult cancer patients were included (n = 19,714 patients). Across meta-analyses of 12 PPI score thresholds and survival durations, we found that PPI was most accurate for predicting survival of <3 weeks and <6 weeks. Survival prediction of <3 weeks was most accurate when PPI score>6 (pooled sensitivity = 0.68, 95% CI 0.60-0.75, specificity = 0.80, 95% CI 0.75-0.85). Survival prediction of <6 weeks was most accurate when PPI score>4 (pooled sensitivity = 0.72, 95% CI 0.65-0.78, specificity = 0.74, 95% CI 0.66-0.80). Comparative meta-analyses found that PPI performed similarly to Delirium-Palliative Prognostic Score and Palliative Prognostic Score in predicting <3-week survival, but less accurately in <30-day survival prediction. However, Delirium-Palliative Prognostic Score and Palliative Prognostic Score only provide <30-day survival probabilities, and it is uncertain how this would be helpful for patients and clinicians. PPI also performed similarly to clinician predicted survival in predicting <30-day survival. However, these findings should be interpreted with caution as limited studies were available for comparative meta-analyses. Risk of bias was high for all studies, mainly due to poor reporting of statistical analyses. while there were low applicability concerns for most (38/39) studies.

CONCLUSIONS

PPI score>6 should be used for <3-week survival prediction, and PPI score>4 for <6-week survival. PPI is easily scored and does not require invasive tests, and thus would be easily implemented in multiple care settings. Given the acceptable accuracy of PPI in predicting <3- and <6-week survival and its objective nature, it could be used to cross-check clinician predicted survival especially when clinicians have doubts about their own judgement, or when clinician estimates seem to be less reliable. Future studies should adhere to the reporting guidelines and provide comprehensive analyses of PPI model performance.

Development and external validation of a predictive multivariable model for last-weeks survival of advanced cancer patients in the palliative home care setting (PACS)

PURPOSE

Various prognostic indexes have been proposed to improve physicians' ability to predict survival time in advanced cancer patients, admitted to palliative care (PC) with a survival probably to a few weeks of life, but no optimal score has been identified. The study aims therefore to develop and externally validate a new multivariable predictive model in this setting.

METHODS

We developed a model to predict short-term overall survival in cancer patients on the basis of clinical factors collected at PC admission. The model was developed on 1020 cancer patients prospectively enrolled to home palliative care at VIDAS Milan, Italy, between May 2018 and February 2020 and followed-up to June 2020, and validated in two separate samples of 544 home care and 247 hospice patients.

RESULTS

Among 68 clinical factors considered, five predictors were included in the predictive model, i.e., rattle, heart rate, anorexia, liver failure, and the Karnofsky performance status. Patient's survival probability at 5, 15, 30 and 45 days was estimated. The predictive model showed a good calibration and moderate discrimination (area under the receiver operating characteristic curve between 0.72 and 0.79) in the home care validation set, but model calibration was suboptimal in hospice patients.

CONCLUSIONS

The new multivariable predictive model for palliative cancer patients' survival (PACS model) includes clinical parameters routinely at patient's admission to PC and can be easily used to facilitate immediate and appropriate short-term clinical decisions for PC cancer patients in the home setting.

Plasmatic MicroRNAs and Treatment Outcomes of Patients with Metastatic Castration-Resistant Prostate Cancer: A Hospital-Based Cohort Study and In Silico Analysis

Prostate cancer (PCa) is one of the most common malignancies among men worldwide. Inevitably, all advanced PCa patients develop metastatic castration-resistant prostate cancer (mCRPC), an aggressive phase of the disease. Treating mCRPC is challenging, and prognostic tools are needed for disease management. MicroRNA (miRNA) deregulation has been reported in PCa, constituting potential non-invasive prognostic biomarkers. As such, this study aimed to evaluate the prognostic potential of nine miRNAs in the liquid biopsies (plasma) of mCRPC patients treated with second-generation androgen receptor axis-targeted (ARAT) agents, abiraterone acetate (AbA) and enzalutamide (ENZ). Low expression levels of miR-16-5p and miR-145-5p in mCRPC patients treated with AbA were significantly associated with lower progression-free survival (PFS). The two miRNAs were the only predictors of the risk of disease progression in AbA-stratified analyses. Low miR-20a-5p levels in mCRPC patients with Gleason scores of <8 were associated with worse overall survival (OS). The transcript seems to predict the risk of death regardless of the ARAT agent. According to the in silico analyses, miR-16-5p, miR-145-5p, and miR-20a-5p seem to be implicated in several processes, namely, cell cycle, proliferation, migration, survival, metabolism, and angiogenesis, suggesting an epigenetic mechanism related to treatment outcome. These miRNAs may represent attractive prognostic tools to be used in mCRPC management, as well as a step further in the identification of new potential therapeutic targets, to use in combination with ARAT for an improved treatment outcome. Despite the promising results, real-world validation is necessary.

Deep learning-extracted CT imaging phenotypes predict response to total resection in colorectal cancer

BACKGROUND

Deep learning surpasses many traditional methods for many vision tasks, allowing the transformation of hierarchical features into more abstract, high-level features.

PURPOSE

To evaluate the prognostic value of preoperative computed tomography (CT) image texture features and deep learning self-learning high-throughput features (SHF) on postoperative overall survival in the treatment of patients with colorectal cancer (CRC).

MATERIAL AND METHODS

The dataset consisted of 810 enrolled patients with CRC confirmed from 10 November 2011 to 10 February 2018. In contrast, SHF extracted by deep learning with multi-task training mechanism and texture features were extracted from the CT with tumor volume region of interest, respectively, and combined with the Cox proportional hazard (CoxPH) model for initial validation to obtain a RAD score to classify patients into high- and low-risk groups. The SHF stability was further validated in combination with Neural Multi-Task Logistic Regression (N-MTLR) model. The overall recognition ability and accuracy of CoxPH and N-MTLR model were evaluated by C-index and Integrated Brier Score (IBS).

RESULTS

SHF had a more significant degree of differentiation than texture features. The result is (SHF vs. texture features: C-index: 0.884 vs. 0.611; IBS: 0.025 vs. 0.073) in the CoxPH model, and (SHF vs. texture features: C-index: 0.861 vs. 0.630; IBS: 0.024 vs. 0.065) in N-MTLR.

CONCLUSION

SHF is superior to texture features and has potential application for the preoperative prediction of the individualized treatment of CRC.

Prediction of conditional survival in esophageal cancer in a population-based cohort study

BACKGROUND

The authors aimed to produce a prediction model for survival at any given date after surgery for esophageal cancer (conditional survival), which has not been done previously.

MATERIALS AND METHODS

Using joint density functions, the authors developed and validated a prediction model for all-cause and disease-specific mortality after surgery with esophagectomy, for esophageal cancer, conditional on postsurgery survival time. The model performance was assessed by the area under the receiver operating characteristic curve (AUC) and risk calibration, with internal cross-validation. The derivation cohort was a nationwide Swedish population-based cohort of 1027 patients treated in 1987-2010, with follow-up throughout 2016. This validation cohort was another Swedish population-based cohort of 558 patients treated in 2011-2013, with follow-up throughout 2018.

RESULTS

The model predictors were age, sex, education, tumor histology, chemo(radio)therapy, tumor stage, resection margin status, and reoperation. The medians of AUC after internal cross-validation in the derivation cohort were 0.74 (95% CI: 0.69-0.78) for 3-year all-cause mortality, 0.76 (95% CI: 0.72-0.79) for 5-year all-cause mortality, 0.74 (95% CI: 0.70-0.78) for 3-year disease-specific mortality, and 0.75 (95% CI: 0.72-0.79) for 5-year disease-specific mortality. The corresponding AUC values in the validation cohort ranged from 0.71 to 0.73. The model showed good agreement between observed and predicted risks. Complete results for conditional survival any given date between 1 and 5 years of surgery are available from an interactive web-tool: https://sites.google.com/view/pcsec/home .

CONCLUSION

This novel prediction model provided accurate estimates of conditional survival any time after esophageal cancer surgery. The web-tool may help guide postoperative treatment and follow-up.

Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study

BACKGROUND

Gastric cancer is the most common malignant tumor worldwide and a leading cause of cancer deaths. This neoplasm has a poor prognosis and heterogeneous outcomes. Survivability prediction may help select the best treatment plan based on an individual's prognosis. Numerous clinical and pathological features are generally used in predicting gastric cancer survival, and their influence on the survival of this cancer has not been fully elucidated. Moreover, the five-year survivability prognosis performances of feature selection methods with machine learning (ML) classifiers for gastric cancer have not been fully benchmarked. Therefore, we adopted several well-known feature selection methods and ML classifiers together to determine the best-paired feature selection-classifier for this purpose.

METHODS

This was a retrospective study on a dataset of 974 patients diagnosed with gastric cancer in the Ayatollah Talleghani Hospital, Abadan, Iran. First, four feature selection algorithms, including Relief, Boruta, least absolute shrinkage and selection operator (LASSO), and minimum redundancy maximum relevance (mRMR) were used to select a set of relevant features that are very informative for five-year survival prediction in gastric cancer patients. Then, each feature set was fed to three classifiers: XG Boost (XGB), hist gradient boosting (HGB), and support vector machine (SVM) to develop predictive models. Finally, paired feature selection-classifier methods were evaluated to select the best-paired method using the area under the curve (AUC), accuracy, sensitivity, specificity, and f1-score metrics.

RESULTS

The LASSO feature selection algorithm combined with the XG Boost classifier achieved an accuracy of 89.10%, a specificity of 87.15%, a sensitivity of 89.42%, an AUC of 89.37%, and an f1-score of 90.8%. Tumor stage, history of other cancers, lymphatic invasion, tumor site, type of treatment, body weight, histological type, and addiction were identified as the most significant factors affecting gastric cancer survival.

CONCLUSIONS

This study proved the worth of the paired feature selection-classifier to identify the best path that could augment the five-year survival prediction in gastric cancer patients. Our results were better than those of previous studies, both in terms of the time required to form the models and the performance measurement criteria of the algorithms. These findings may be very promising and can, therefore, inform clinical decision-making and shed light on future studies.

Prediction Accuracy Between Terminally Ill Patients' Survival Length and the Estimations Made From Different Medical Staff, a Prospective Cohort Study

Background: Previous reports suggested the clinical predictions of survival (CPS) and prognostic scores had similar accuracy in patients with days to weeks of life. Objective: We aimed to evaluate and compare the accuracy of CPS by attending physicians, residents, and nurses in an acute palliative care unit at a medical center. Methods: This was a 1-year prospective cohort study. Survival prediction was made within 3 days after patients' admission and re-evaluated every week until patients' discharge or death. Associated factors of accurate survival predictions were also explored by multivariate logistic regression. Results: A total of 179 inpatients were recruited and 115 of them were included in this analysis. The mean age of participants was 72.9 years and the average length of actual survival was 11.5 ± 12.0 days. For patients with survival within 30 days, the medical staff tended to overestimate their life span. The predictions made by physicians and nurses showed much closer to actual survival length through repeated estimations. Patients with metastatic cancer (odds ratio: OR 2.77, 95% CI 1.23-6.22) or cognitive impairment (OR 2.39, 95% CI 1.12-5.11) had higher associations with accurate CPS. Poor performance status of ECOG (OR 1.82, 95% CI 1.09-3.02) and dysphagia (OR 2.01, 95% CI 1.07-3.77) were significant predictors for accurate CPS in patients with the survival of less than 2 weeks. Conclusions: The accuracy of CPS between different medical staff did not reveal significant differences in the study. The importance of re-evaluation for patients' survival length in clinical practice is worthy of attention.

Radiation Therapy at the End of-Life: Quality of Life and Financial Toxicity Considerations

In patients with advanced cancer, radiation therapy is considered at various time points in the patient's clinical course from diagnosis to death. As some patients are living longer with metastatic cancer on novel therapeutics, radiation oncologists are increasingly using radiation therapy as an ablative therapy in appropriately selected patients. However, most patients with metastatic cancer still eventually die of their disease. For those without effective targeted therapy options or those who are not candidates for immunotherapy, the time frame from diagnosis to death is still relatively short. Given this evolving landscape, prognostication has become increasingly challenging. Thus, radiation oncologists must be diligent about defining the goals of therapy and considering all treatment options from ablative radiation to medical management and hospice care. The risks and benefits of radiation therapy vary based on an individual patient's prognosis, goals of care, and the ability of radiation to help with their cancer symptoms without undue toxicity over the course of their expected lifetime. When considering recommending a course of radiation, physicians must broaden their understanding of risks and benefits to include not only physical symptoms, but also various psychosocial burdens. These include financial burdens to the patient, to their caregiver and to the healthcare system. The burden of time spent at the end-of-life receiving radiation therapy must also be considered. Thus, the consideration of radiation therapy at the end-of-life can be complex and requires careful attention to the whole patient and their goals of care.

CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy

BACKGROUND

Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy.

METHODS

We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used.

RESULTS

The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738-0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594-0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783-0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated.

CONCLUSIONS

Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.

End-of-life communication strategies for healthcare professionals: A scoping review

BACKGROUND

Timely and effective communication about end-of-life issues, including conversations about prognosis and goals of care, are extremely beneficial to terminally ill patients and their families. However, given the context, healthcare professionals may find it challenging to initiate and facilitate such conversations. Hence, it is critical to improving the available communication strategies to enhance end-of-life communication practices.

AIM

To summarise the end-of-life communication strategies recommended for healthcare professionals, identify research gaps and inform future research.

DESIGN

A scoping review performed in accordance with the Arksey and O'Malley framework.

DATA SOURCES

A literature search was conducted between January 1990 and January 2022 using PubMed, CINAHL, Embase, PsycINFO, Web of Science, Scopus, Cochrane Library and China National Knowledge Infrastructure databases and Google, Google Scholar and ProQuest Dissertations & Theses Global. Studies that described recommended end-of-life communication strategies for healthcare professionals were included.

RESULTS

Fifty-nine documents were included. Seven themes of communication strategies were found: (a) preparation; (b) exploration and assessment; (c) family involvement; (d) provision and tailoring of information; (e) empathic emotional responses; (f) reframing and revisiting the goals of care; and (g) conversation closure.

CONCLUSIONS

The themes of communication strategies found in this review provide a framework to integrally promote end-of-life communication. Our results will help inform healthcare professionals, thereby promoting the development of specialised training and education on end-of-life communication.

A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.

Comparison of Objective Prognostic Score and Palliative Prognostic Score performance in inpatients with advanced cancer in Japan and Korea

OBJECTIVE

Accurate prognostication is important for patients and their families to prepare for the end of life. Objective Prognostic Score (OPS) is an easy-to-use tool that does not require the clinicians' prediction of survival (CPS), whereas Palliative Prognostic Score (PaP) needs CPS. Thus, inexperienced clinicians may hesitate to use PaP. We aimed to evaluate the accuracy of OPS compared with PaP in inpatients in palliative care units (PCUs) in three East Asian countries.

METHOD

This study was a secondary analysis of a cross-cultural, multicenter cohort study. We enrolled inpatients with far-advanced cancer in PCUs in Japan, Korea, and Taiwan from 2017 to 2018. We calculated the area under the receiver operating characteristics (AUROC) curve to compare the accuracy of OPS and PaP.

RESULTS

A total of 1,628 inpatients in 33 PCUs in Japan and Korea were analyzed. OPS and PaP were calculated in 71.7% of the Japanese patients and 80.0% of the Korean patients. In Taiwan, PaP was calculated for 81.6% of the patients. The AUROC for 3-week survival was 0.74 for OPS in Japan, 0.68 for OPS in Korea, 0.80 for PaP in Japan, and 0.73 for PaP in Korea. The AUROC for 30-day survival was 0.70 for OPS in Japan, 0.71 for OPS in Korea, 0.79 for PaP in Japan, and 0.74 for PaP in Korea.

SIGNIFICANCE OF RESULTS

Both OPS and PaP showed good performance in Japan and Korea. Compared with PaP, OPS could be more useful for inexperienced physicians who hesitate to estimate CPS.

Families Addressing Cancer Together (FACT): feasibility and acceptability of a web-based psychosocial intervention for parents with cancer

PURPOSE

Although parents with cancer report that talking with their children about cancer and dying is distressing, accessible support is rare. We assessed the feasibility, acceptability, and preliminary effects of Families Addressing Cancer Together (FACT), a web-based, tailored psychosocial intervention to help parents talk about their cancer with their children.

METHODS

This pilot study used a pre-posttest design. Eligible participants were parents with new or metastatic solid tumors who had minor (ages 3-18) children. Participants who completed baseline assessments received online access to FACT. We assessed feasibility through enrollment and retention rates and reasons for study refusal. Acceptability was evaluated by satisfaction ratings. We examined participants' selection of intervention content and preliminary effects on communication self-efficacy and other psychosocial outcomes (depression and anxiety symptoms, health-related quality of life, family functioning) at 2- and 12-week post-intervention.

RESULTS

Of 68 parents we approached, 53 (78%) agreed to participate. Forty-six parents completed baseline assessments and received the FACT intervention. Of the 46 participants, 35 (76%) completed 2-week assessments, and 25 (54%) completed 12-week assessments. Parents reported that FACT was helpful (90%), relevant (95%), and easy to understand (100%). Parents' psychosocial outcomes did not significantly improve post-intervention, but parents endorsed less worry about talking with their child (46% vs. 37%) and reductions in the number of communication concerns (3.4 to 1.8).

CONCLUSION

The FACT intervention was feasible, acceptable, and has potential to address communication concerns of parents with cancer. A randomized trial is needed to test its efficacy in improving psychological and parenting outcomes.

TRIAL REGISTRATION

This study was IRB-approved and registered with clinicaltrials.gov (NCT04342871).

Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia

Introduction

Chronic myeloid leukemia (CML) is a myeloproliferative disorder resulting from the translocation of chromosomes 19 and 22. CML includes 15–20% of all cases of leukemia. Although bone marrow transplant and, more recently, tyrosine kinase inhibitors (TKIs) as a first-line treatment have significantly prolonged survival in CML patients, accurate prediction using available patient-level factors can be challenging. We intended to predict 5-year survival among CML patients via eight machine learning (ML) algorithms and compare their performance.

Methods

The data of 837 CML patients were retrospectively extracted and randomly split into training and test segments (70:30 ratio). The outcome variable was 5-year survival with potential values of alive or deceased. The dataset for the full features and important features selected by minimal redundancy maximal relevance (mRMR) feature selection were fed into eight ML techniques, including eXtreme gradient boosting (XGBoost), multilayer perceptron (MLP), pattern recognition network, k-nearest neighborhood (KNN), probabilistic neural network, support vector machine (SVM) (kernel = linear), SVM (kernel = RBF), and J-48. The scikit-learn library in Python was used to implement the models. Finally, the performance of the developed models was measured using some evaluation criteria with 95% confidence intervals (CI).

Results

Spleen palpable, age, and unexplained hemorrhage were identified as the top three effective features affecting CML 5-year survival. The performance of ML models using the selected-features was superior to that of the full-features dataset. Among the eight ML algorithms, SVM (kernel = RBF) had the best performance in tenfold cross-validation with an accuracy of 85.7%, specificity of 85%, sensitivity of 86%, F-measure of 87%, kappa statistic of 86.1%, and area under the curve (AUC) of 85% for the selected-features. Using the full-features dataset yielded an accuracy of 69.7%, specificity of 69.1%, sensitivity of 71.3%, F-measure of 72%, kappa statistic of 75.2%, and AUC of 70.1%.

Conclusions

Accurate prediction of the survival likelihood of CML patients can inform caregivers to promote patient prognostication and choose the best possible treatment path. While external validation is required, our developed models will offer customized treatment and may guide the prescription of personalized medicine for CML patients.

Development and Validation of the PaP Score Nomogram for Terminally Ill Cancer Patients

The validated Palliative Prognostic (PaP) score predicts survival in terminally ill cancer patients, assigning patients to three different risk groups according to a 30-day survival probability: group A, >70%; group B, 30−70%; and group C, <30%. We aimed to develop and validate a PaP nomogram to provide individualized prediction of survival at 15, 30 and 60 days. Three cohorts of consecutive terminally ill cancer patients were used: one (n = 519) for nomogram development and internal validation, and a second (n = 451) and third (n = 549) for external validation. Multivariate analyses included dyspnea, anorexia, Karnofsky performance status, clinical prediction of survival, total white blood count and lymphocyte percentage. The predictive accuracy of the nomogram was determined by Harrell’s concordance index (95% CI), and calibration plots were generated. The nomogram had a concordance index of 0.74 (0.72−0.75) and showed good calibration. The internal validation showed no departures from ideal prediction. The accuracy of the nomogram at 15, 30 and 60 days was 74% (70−77), 89% (85−92) and 72% (68−76) in the external validation cohorts, respectively. The PaP nomogram predicts the individualized estimate of survival and could greatly facilitate clinical care decision-making at the end of life.

Caring for depression in the dying is complex and challenging - survey of palliative physicians

BACKGROUND

Depression is prevalent in people with very poor prognoses (days to weeks). Clinical practices and perceptions of palliative physicians towards depression care have not been characterised in this setting. The objective of this study was to characterise current palliative clinicians' reported practices and perceptions in depression screening, assessment and management in the very poor prognosis setting.

METHODS

In this cross-sectional cohort study, 72 palliative physicians and 32 psychiatrists were recruited from Australian and New Zealand Society of Palliative Medicine and Royal Australian and New Zealand College of Psychiatrists between February and July 2020 using a 23-item anonymous online survey.

RESULTS

Only palliative physicians results were reported due to poor psychiatry representation. Palliative physicians perceived depression care in this setting to be complex and challenging. 40.0% reported screening for depression. All experienced uncertainty when assessing depression aetiology. Approaches to somatic symptom assessment varied. Physicians were generally less likely to intervene for depression than in the better prognosis setting. Most reported barriers to care included the perceived lack of rapidly effective therapeutic options (77.3%), concerns of patient burden and intolerance (71.2%), and the complexity in diagnostic differentiation (53.0%). 66.7% desired better collaboration between palliative care and psychiatry.

CONCLUSIONS

Palliative physicians perceived depression care in patients with very poor prognoses to be complex and challenging. The lack of screening, variations in assessment approaches, and the reduced likelihood of intervening in comparison to the better prognosis setting necessitate better collaboration between palliative care and psychiatry in service delivery, training and research.

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

Background:

Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients.

Methods:

A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model.

Results:

The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy, mean specificity, mean sensitivity, mean area under the curve, and mean F1-score of 88.37%, 86.24%, 89.72%, 88.11%, and 89.91%, respectively, gained the best performance.

Conclusions:

The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine.

PG-SGA SF in nutrition assessment and survival prediction for elderly patients with cancer

Background

This study was sought to report the prevalence of malnutrition in elderly patients with cancer. Validate the predictive value of the nutritional assessment tool (Patient-Generated Subjective Global Assessment Short Form, PG-SGA SF) for clinical outcomes and assist the therapeutic decision.

Methods

This is a secondary analysis of a multicentric, observational cohort study. Elderly patients with cancer older than 65 years were enrolled after the first admission. Nutritional status was identified using the PG-SGA SF.

Results

Of the 2724 elderly patients included in the analysis, 65.27% of patients were male (n = 1778); the mean age was 71.00 ± 5.36 years. 31.5% of patients were considered malnourished according to PG-SGA SF. In multivariate analysis, malnutrition(PG-SGA SF > 5) was significantly associated with worse OS (HR: 1.47,95%CI:1.29–1.68), affects the quality of life, and was related to more frequent nutrition impact symptoms. During a median follow-up of 4.5 years, 1176 death occurred. The mortality risk was 41.10% for malnutrition during the first 12 months and led to a rate of 323.98 events per-1000-patient-years. All nutritional assessment tools were correlated with each other (PG-SGA SF vs. PG-SGA: r = 0.98; PG-SGA SF vs. GLIM[Global Leadership Initiative on Malnutrition]: r = 0.48, all P < 0.05). PG-SGA SF and PG-SGA performed similarly to predict mortality but better than GLIM. PG-SGA SF improves the predictive ability of the TNM classification system for mortality in elderly patients with cancer, including distinguishing patients’ prognoses and directing immunotherapy.

Conclusions

The nutritional status as measured by PG-SGA SF which is a prognostic factor for OS in elderly cancer patients and could improve the prognostic model of TNM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02662-4.

Examining the Landscape of Prognostic Factors and Clinical Outcomes for Cancer Control

Prognostic factors have important utility in various aspects of cancer surveillance, including research, patient care, and cancer control programmes. Nevertheless, there is heterogeneity in the collection of prognostic factors and outcomes data globally. This study aimed to investigate perspectives on the utility and application of prognostic factors and clinical outcomes in cancer control programmes. A qualitative phenomenology approach using expert interviews was taken to derive a rich description of the current state and future outlook of cancer prognostic factors and clinical outcomes. Individuals with expertise in this work and from various regions and institutions were invited to take part in one-on-one semi-structured interviews. Four areas related to infrastructure and funding challenges were identified by participants, including (1) data collection and access; (2) variability in data reporting, coding, and definitions; (3) limited coordination among databases; and (4) conceptualization and prioritization of meaningful prognostic factors and outcomes. Two areas were identified regarding important future priorities for cancer control: (1) global investment and intention in cancer surveillance and (2) data governance and exchange globally. Participants emphasized the need for better global collection of prognostic factors and clinical outcomes data and support for standardized data collection and data exchange practices by cancer registries.

Prognosis palliative care study, palliative prognostic index, palliative prognostic score and objective prognostic score in advanced cancer: a prospective comparison

BACKGROUND

Predicting how long a patient with far advanced cancer has to live is a significant part of hospice and palliative care. Various prognostic models have been developed, but have not been fully compared in South Korea.

OBJECTIVES

We aimed to compare the accuracy of the Prognosis in Palliative Care Study (PiPS), Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP) and Objective Prognostic Score (OPS) for patients with far advanced cancer in a palliative care unit in South Korea.

METHODS

This prospective study included patients with far advanced cancer who were admitted to a single palliative care unit at the National University Hospital. Variables for calculating the prognostic models were recorded by a palliative care physician. The survival rate was estimated using the Kaplan-Meier method. The sensitivity, specificity, positive predictive value and negative predictive value of each model were calculated.

RESULTS

A total of 160 patients participated. There was a significant difference in survival rates across all groups, each categorised through the five prognostic models. The overall accuracy (OA) of the prognostic models ranged between 54.5% and 77.6%. The OA of clinicians' predictions of survival ranged between 61.9% and 81.3%.

CONCLUSION

The PiPS, PPI, PaP and OPS were successfully validated in a palliative care unit of South Korea. There was no difference in accuracy between the prognostic models, and OA tended to be lower than in previous studies.

Predicting the probability of death using proteomics

Predicting all-cause mortality risk is challenging and requires extensive medical data. Recently, large-scale proteomics datasets have proven useful for predicting health-related outcomes. Here, we use measurements of levels of 4,684 plasma proteins in 22,913 Icelanders to develop all-cause mortality predictors both for short- and long-term risk. The participants were 18-101 years old with a mean follow up of 13.7 (sd. 4.7) years. During the study period, 7,061 participants died. Our proposed predictor outperformed, in survival prediction, a predictor based on conventional mortality risk factors. We could identify the 5% at highest risk in a group of 60-80 years old, where 88% died within ten years and 5% at the lowest risk where only 1% died. Furthermore, the predicted risk of death correlates with measures of frailty in an independent dataset. Our results show that the plasma proteome can be used to assess general health and estimate the risk of death.

Eiriksdottir et al. use a temporal proteomic dataset from over 22,000 Icelandic individuals to identify predictors and predict all-cause mortality. Their findings suggest that the plasma proteome may be of value in general health screening for risk of death.

Disparities in access to palliative care facilities for patients with and without cancer: A retrospective review

BACKGROUND

To date, little is known about the characteristics of patients who are admitted to a palliative care bed for end-of-life care. Previous data suggest that there are disparities in access to palliative care services based on age, sex, diagnosis, and socioeconomic status, but it is unclear whether these differences impact access to a palliative care bed.

AIM

To better identify patient factors associated with the likelihood/rate of admission to a palliative care bed.

DESIGN

A retrospective chart review of all initiated palliative care bed applications through an electronic referral program was conducted over a 24-month period.

SETTING/PARTICIPANTS

Patients who apply and are admitted to a palliative care bed in a Canadian metropolitan city.

RESULTS

A total of 2743 patients made a total of 5202 bed applications to 9 hospice/palliative care units in 2015-2016. Referred and admitted cancer patients were younger, male, and more functional than compared to non-cancer patients (all p < 0.001). Referred and admitted patients without cancer were more advanced in their illness trajectory, with an anticipated prognosis <1 month and Palliative Performance Status of 10%-20% (all p < 0.001). On multivariate analysis, a diagnosis of cancer and a prognosis of <3 months were associated with increased likelihood and/or rate of admission to a bed, whereas the presence of care needs, a longer prognosis and a PPS of 30%-40% were associated with decreased rates and/or likelihood of admission.

CONCLUSION

Patients without cancer have reduced access to palliative care facilities at end-of-life compared to patients with cancer; at the time of their application and admission, they are "sicker" with very low performance status and poorer prognoses. Further studies investigating disease-specific clinical variables and support requirements may provide more insights into these observed disparities.

Prediction Model of Long-term Survival After Esophageal Cancer Surgery

OBJECTIVE

We aimed to develop prediction models for estimating the long-term survival in patients who have undergone surgery for esophageal cancer.

BACKGROUND

Few prediction models have been developed for the long-term survival in esophageal cancer patients.

METHODS

This nationwide Swedish population-based cohort study included 1542 patients who survived for ≥90 days after esophageal cancer surgery between 1987 and 2010, with follow-up until 2016. Risk prediction models for 1-, 3-, and 5-year all-cause mortality and 3- and 5-year disease-specific mortality were developed using logistic regression. Candidate predictors were established and readily identifiable prognostic factors. The performance of the models was assessed by the area under receiver-operating characteristic curve (AUC) with interquartile range (IQR) using bootstrap cross-validation and risk calibration.

RESULTS

Predictors included in all models were age, sex, pathological tumor stage, tumor histology, and resection margin status. The models also included various additional predictors depending on the outcome, that is, education level, neoadjuvant therapy, reoperation (within 30 d of primary surgery) and comorbidity (Charlson comorbidity index). The AUC statistics after cross-validation were 0.71 (IQR 0.69-0.74) for 1-year, 0.77 (IQR 0.75-0.80) for 3-year, and 0.78 (IQR 0.76-0.81) for 5-year all-cause mortality. The corresponding values were 0.76 (IQR 0.74-0.79) for 3-year and 0.77 (IQR 0.71-0.83) for 5-year disease-specific mortality. All models showed good agreement between the observed and predicted risks.

CONCLUSIONS

These models showed good performance for predicting long-term survival after esophageal cancer surgery and may thus be useful for patients in planning their lives and to guide the postoperative treatment and follow-up.

A novel model with nutrition-related parameters for predicting overall survival of cancer patients

BACKGROUND

Increasing evidence indicates that nutritional status could influence the survival of cancer patients. This study aims to develop and validate a nomogram with nutrition-related parameters for predicting the overall survival of cancer patients.

PATIENTS AND METHODS

A total of 8749 patients from the multicentre cohort study in China were included as the primary cohort to develop the nomogram, and 696 of these patients were recruited as a validation cohort. Patients' nutritional status were assessed using the PG-SGA. LASSO regression models and Cox regression analysis were used for factor selection and nomogram development. The nomogram was then evaluated for its effectiveness in discrimination, calibration, and clinical usefulness by the C-index, calibration curves, and decision curve analysis. Kaplan-Meier survival curves were used to compare the survival rate.

RESULTS

Seven independent prognostic factors were identified and integrated into the nomogram. The C-index was 0.73 (95% CI, 0.72 to 0.74) and 0.77 (95% CI, 0.74 to 0.81) for the primary cohort and validation cohort, which were both higher than 0.59 (95% CI, 0.58 to 0.61) of the TNM staging system. DCA demonstrated that the nomogram was higher than the TNM staging system and the TNM staging system combined with PG-SGA. Significantly median overall survival differences were found by stratifying patients into different risk groups (score < 18.5 and ≥ 18.5) for each TNM category (all Ps < 0.001).

CONCLUSION

Our study screened out seven independent prognostic factors and successfully generated an easy-to-use nomogram, and validated and shown a better predictive validity for the overall survival of cancer patients.

Prognostic tools for survival prediction in advanced cancer patients: A systematic review

Survival prediction for palliative cancer patients by physicians is often optimistic. Patients with a very short life expectancy (<4 weeks) may not benefit from radiation therapy (RT), as the time to maximal symptom relief after treatment can take 4-6 weeks. We aimed to identify a prognostic tool (or tools) to predict survival of less than 4 weeks and less than 3 months in patients with advanced cancer to guide the choice of radiation dose and fractionation. We searched Embase, Medline (EBSCOhost) and CINAHL (EBSCOhost) clinical databases for literature published between January 2008 and June 2018. Seventeen studies met the inclusion criteria and were included in the review. Prediction accuracy at less than 4 weeks and less than 3 months were compared across the prognostic tools. Reporting of prediction accuracy among the different studies was not consistent: the Palliative Prognostic Score (PaP), Palliative Prognostic Index (PPI) and Number of Risk Factors (NRF) best-predicted survival duration of less than 4 weeks. The PPI, performance status with Palliative Prognostic Index (PS-PPI), NRF and Survival Prediction Score (SPS) may predict 3-month survival. We recommend PPI and PaP tools to assess the likelihood of a patient surviving less than 4 weeks. If predicted to survive longer and RT is justified, the NRF tool could be used to determine survival probability less than 3 months which can then help clinicians select dose and fractionation. Future research is needed to verify the reliability of survival prediction using these prognostic tools in a radiation oncology setting.

Poor performance status and brain metastases treatment: who may benefit from the stereotactic radiotherapy?

BACKGROUND

Poor Performance Status (PS) of cancer patients, defined as PS score 2-3, is an impediment for many drug- and irradiation-based treatments, supported by the trials that exclude subjects with PS < 1. Reports on the benefits of stereotactic radiotherapy (SRT) for brain metastases (BMs) in poor PS patients are scarce. We sought to review the characteristics and survival outcomes of this cohort, to assess who may benefit most from SRT.

METHODS

We retrospectively evaluated 73 patients with PS 2 or 3 (63 and 10 cases) treated with SRT for 150 BMs from 2012 to 2018. Patients' characteristics and post-SRT survival, stratified by concomitant systemic treatment (CST) were assessed using the Kaplan-Meier method (p-value < 0.05).

RESULTS

Non-small cell lung cancer was the most frequent primary tumor. Extracranial metastases were present in 86.3% of patients. The median overall survival (mOS) after SRT was estimated as 6.0 months (range 0.2-37.7), with 6- and 12-month survival rates of 51.0% and 21.0%, respectively. CST was administrated to 59.7% of patients (immunotherapy, target therapy or chemotherapy). Patients treated with CST presented larger mOS (6.7 vs. 4.4 months for patients treated with SRT alone, p = 0.3), and better 6- and 12-month survival rates (59% and 24% vs. 37% and 18% in patients not treated with CST).

CONCLUSIONS

Survival rate after SRT for BMs in poor performance patients, especially with PS 2, can justify SRT, in particular if an effective systemic treatment is available. Both SRT and CST should be more accessible for these patients in clinical practice.

A-qCPR risk score screening model for predicting 1-year mortality associated with hospice and palliative care in the emergency department

BACKGROUND

Evaluating the need for palliative care and predicting its mortality play important roles in the emergency department.

AIM

We developed a screening model for predicting 1-year mortality.

DESIGN

A retrospective cohort study was conducted to identify risk factors associated with 1-year mortality. Our risk scores based on these significant risk factors were then developed. Its predictive validity performance was evaluated using area under receiving operating characteristic analysis and leave-one-out cross-validation.

SETTING AND PARTICIPANTS

Patients aged 15 years or older were enrolled from June 2015 to May 2016 in the emergency department.

RESULTS

We identified five independent risk factors, each of which was assigned a number of points proportional to its estimated regression coefficient: age (0.05 points per year), qSOFA ⩾ 2 (1), Cancer (4), Eastern Cooperative Oncology Group Performance Status score ⩾ 2 (2), and Do-Not-Resuscitate status (3). The sensitivity, specificity, positive predictive value, and negative predictive value of our screening tool given the cutoff larger than 3 points were 0.99 (0.98-0.99), 0.31 (0.29-0.32), 0.26 (0.24-0.27), and 0.99 (0.98-1.00), respectively. Those with screening scores larger than 9 points corresponding to 64.0% (60.0-67.9%) of 1-year mortality were prioritized for consultation and communication. The area under the receiving operating characteristic curves for the point system was 0.84 (0.83-0.85) for the cross-validation model.

CONCLUSIONS

A-qCPR risk scores provide a good screening tool for assessing patient prognosis. Routine screening for end-of-life using this tool plays an important role in early and efficient physician-patient communications regarding hospice and palliative needs in the emergency department.

Predicting the survival in patients with malignant pleural effusion undergoing indwelling pleural catheter insertion

CONTEXT:

Malignant pleural effusion (MPE) is a common comorbid condition in advanced malignancies with variable survival.

AIMS:

The aim of this study was to predict the survival in patients with MPE undergoing indwelling pleural catheter (IPC) insertion.

SETTINGS AND DESIGN:

This was a cross-sectional study conducted at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan.

METHODS:

One hundred and ten patients with MPE who underwent IPC insertion from January 2011 to December 2019 were reviewed. Kaplan–Meier method was used to determine the overall survival (OS) of the patient's cohort with respect to LENT score.

STATISTICAL ANALYSIS USED:

The IBM SPSS version 20 was used for statistical analysis.

RESULTS:

We retrospectively reviewed 110 patients who underwent IPC insertion for MPE, with a mean age of 49 ± 15 years. 76 (69.1%) patients were females, of which majority 59 (53.6%) had a primary diagnosis of breast cancer. The LENT score was used for risk stratification, and Kaplan–Meier survival curves were used to predict the OS. The proportion of patients with low-risk LENT score had 91%, 58%, and 29% survival, the moderate-risk group had 76%, 52%, and 14% survival, and in the high-risk group, 61%, 15%, and 0% patients survived at 1, 3, and 6 months, respectively. In addition, there was a statistically significant survival difference (P = 0.05) in patients who received chemotherapy pre- and post-IPC insertion.

CONCLUSIONS:

LENT score seems to be an easy and attainable tool, capable of predicting the survival of the patients with MPE quite accurately. It can be helpful in palliating the symptoms of patients with advanced malignancies by modifying the treatment strategies.

Clinically Significant Depressive Symptoms Are Prevalent in People With Extremely Short Prognoses-A Systematic Review

CONTEXT

Currently, systematic evidence of the prevalence of clinically significant depressive symptoms in people with extremely short prognoses is not available to inform its global burden, assessment, and management.

OBJECTIVES

To determine the prevalence of clinically significant depressive symptoms in people with advanced life-limiting illnesses and extremely short prognoses (range of days to weeks).

METHODS

A systematic review and meta-analysis (random-effects model) were performed (PROSPERO: CRD42019125119). MEDLINE, Embase, PsycINFO, CINAHL, and CareSearch were searched for studies (1994-2019). Data were screened for the prevalence of clinically significant depressive symptoms (assessed using validated depression-specific screening tools or diagnostic criteria) of adults with advanced life-limiting illnesses and extremely short prognoses (defined by survival or functional status). Quality assessment was performed using the Joanna Briggs Institute Systematic Reviews Checklist for Prevalence Studies for individual studies and Grading of Recommendations Assessment, Development and Evaluation (GRADE) across studies.

RESULTS

Thirteen studies were included. The overall pooled prevalence of clinically significant depressive symptoms in adults with extremely short prognoses (n = 10 studies; extremely short prognoses: N = 905) using depression-specific screening tools was 50% (95% CI: 29%-70%; I² = 97.6%). Prevalence of major and minor depression was 10% (95% CI: 4%-16%) and 5% (95% CI: 2%-8%), respectively. Major limitations included high heterogeneity, selection bias, and small sample sizes in individual studies.

CONCLUSIONS

Clinically, significant depressive symptoms were prevalent in people with advanced life-limiting illnesses and extremely short prognoses. Clinicians need to be proactive in the recognition and assessment of these symptoms to allow for timely intervention.

Validation of the Palliative Prognostic Index, Performance Status-Based Palliative Prognostic Index and Chinese Prognostic Scale in a home palliative care setting for patients with advanced cancer in China

BACKGROUND

The predictive value of the prognostic tool for patients with advanced cancer is uncertain in mainland China, especially in the home-based palliative care (HPC) setting. This study aimed to compare the accuracy of the Palliative Prognostic Index (PPI), the Performance Status-Based Palliative Prognostic Index (PS-PPI), and the Chinese Prognosis Scale (ChPS) for patients with advanced cancer in the HPC setting in mainland China.

METHODS

Patients with advanced cancer admitted to the hospice center of Yuebei People's Hospital between January 2014 and December 2018 were retrospectively calculated the scores according to the three prognostic tools. The Kaplan-Meier method was used to compare survival times among different risk groups. Receiver operating characteristic curve analysis was used to assess the predictive value. The accuracy of 21-, 42- and 90-day survival was compared among the three prognostic tools.

RESULTS

A total of 1863 patients were included. Survival time among the risk groups of all prognostic tools was significantly different from each other except for the PPI. The AUROC of the ChPS was significantly higher than that of the PPI and PS-PPI for 7-, 14, 21-, 42-, 90-, 120-, 150- and 180-day survival (P < 0.05). The AUROC of the PPI and PS-PPI were not significantly different from each other (P > 0.05).

CONCLUSIONS

The ChPS is more suitable than the PPI and PS-PPI for advanced cancer patients in the HPC setting. More researches are needed to verify the predictive value of the ChPS, PPI, and PS-PPI in the HPC setting in the future.

YB-1 variant and androgen receptor axis-targeted agents in metastatic castration-resistant prostate cancer patients

Aim: To evaluate the influence of YB-1 rs10493112 variant as a genetic marker for response to second-generation androgen receptor axis-target agents. Methods: A hospital-based cohort study of 78 patients with metastatic castration-resistant prostate cancer was conducted. Genotyping was performed by TaqMan^® allelic discrimination technology. Main results: In abiraterone-treated and high-risk patients, YB-1 rs10493112 AA genotype carriers showed lower progression-free survival than C allele genotype patients (4 vs 17 months; p = 0.009). For carriers of AA genotype, multivariate Cox regression analysis revealed a fivefold increased risk of progression (p = 0.035). Conclusion: The study findings suggest that, for metastatic and castration-resistant prostate cancer patients, this polymorphism might be a putative marker for the clinical outcome.

Design of a randomized placebo controlled trial of high dose intravenous thiamine for the prevention of delirium in allogeneic hematopoietic stem cell transplantation

BACKGROUND

Delirium is a highly prevalent and preventable neuropsychiatric condition with major health consequences. Thiamine deficiency is a well-established cause of delirium in those with chronic, severe alcoholism, but there remains an underappreciation of its significance in non-alcoholic populations, including patients with cancer. Treatment of suspected thiamine-related mental status changes with high dose intravenous (IV) thiamine has preliminary evidence for improving a variety of cognitive symptoms in oncology inpatient settings but has never been studied for the prevention of delirium in any population.

OBJECTIVES

The primary objective of this clinical trial is to determine if high dose IV thiamine can prevent delirium in patients receiving allogeneic hematopoietic stem cell transplantation (HSCT) for treatment of cancer. Secondary objectives are to determine if thiamine status is predictive of delirium onset and if high dose IV thiamine can attenuate the deleterious impact of delirium on health-related quality of life (HRQOL), functional status, and long-term neuropsychiatric outcomes.

METHODS

In this phase II study, we are recruiting 60 patients undergoing allogeneic HSCT, randomizing them to treatment with high dose IV thiamine (n = 30) versus placebo (n = 30), and systematically evaluating all participants for delirium and related comorbidities. We use the Delirium Rating Scale to measure the severity and duration of delirium during hospitalization for HSCT. We obtain thiamine levels weekly during the transplantation hospitalization. We assess HRQOL, functional status, depression, post-traumatic stress symptoms, and cognitive function prior to and at one, three, and six months after transplantation.

NECPAL tool prognostication in advanced chronic illness: a rapid review and expert consensus

OBJECTIVE

To develop a proposal for a 2-year mortality prognostic approach for patients with advanced chronic conditions based on the palliative care need (PCN) items of the NECesidades PALiativas (NECPAL) CCOMS-ICO V.3.1 2017 tool.

METHODS

A phase 1 study using three components based on the NECPAL items: (1) a rapid review of systematic reviews (SRs) on prognostic factors of mortality in patients with advanced chronic diseases and PCNs; (2) a clinician and statistician experts' consensus based on the Delphi technique on the selection of mortality prognostic factors; and (3) a panel meeting to discuss the findings of components (1) and (2).

RESULTS

Twenty SRs were included in a rapid review, and 50% were considered of moderate quality. Despite methodological issues, nutritional and functional decline, severe and refractory dyspnoea, multimorbidity, use of resources and specific disease indicators were found to be potentially prognostic variables for mortality across four clinical groups and end-of-life (EoL) trajectories: cancer, dementia and neurologic diseases, chronic organ failure and frailty. Experts' consensus added 'needs' identified by health professionals. However, clinicians were less able to discriminate which NECPAL items were more reliable for a 'general' model. A retrospective cohort study was designed to evaluate this proposal in phase 2.

CONCLUSIONS

We identified several parameters with prognostic value and linked them to the tool's utility to timely identify PCNs of patients with advanced chronic conditions in all settings of care. Initial results show this is a clinical and feasible tool, that will help with clinical pragmatic decision-making and to define services.

Prediction model for short-term mortality after palliative radiotherapy for patients having advanced cancer: a cohort study from routine electronic medical data

We developed a predictive score system for 30-day mortality after palliative radiotherapy by using predictors from routine electronic medical record. Patients with metastatic cancer receiving first course palliative radiotherapy from 1 July, 2007 to 31 December, 2017 were identified. 30-day mortality odds ratios and probabilities of the death predictive score were obtained using multivariable logistic regression model. Overall, 5,795 patients participated. Median follow-up was 39.6 months (range, 24.5-69.3) for all surviving patients. 5,290 patients died over a median 110 days, of whom 995 (17.2%) died within 30 days of radiotherapy commencement. The most important mortality predictors were primary lung cancer (odds ratio: 1.73, 95% confidence interval: 1.47-2.04) and log peripheral blood neutrophil lymphocyte ratio (odds ratio: 1.71, 95% confidence interval: 1.52-1.92). The developed predictive scoring system had 10 predictor variables and 20 points. The cross-validated area under curve was 0.81 (95% confidence interval: 0.79-0.82). The calibration suggested a reasonably good fit for the model (likelihood-ratio statistic: 2.81, P = 0.094), providing an accurate prediction for almost all 30-day mortality probabilities. The predictive scoring system accurately predicted 30-day mortality among patients with stage IV cancer. Oncologists may use this to tailor palliative therapy for patients.

Surprise Question and Performance Status Indicate Urgency of Palliative Care Needs in Patients with Advanced Cancer at the Emergency Department: An Observational Cohort Study

Background: The surprise question (SQ), "Would I be surprised if this patient died within one year?", is a simple instrument to identify patients with palliative care needs. The SQ-performance has not been evaluated in patients with advanced cancer visiting the emergency department (ED). Objective: To evaluate SQ's test characteristics and predictive value in patients with advanced cancer visiting the ED. Design: Observational cohort study. Setting: Patients >18 years with advanced cancer in the palliative phase visiting the ED of an academic medical center. Methods: Attending physicians answered the SQ (not surprised [NS] or surprised [S]) and estimated Eastern Cooperative Oncology Group (ECOG)-performance status. Disease, visit, and follow-up characteristics were retrospectively collected from charts. SQ's sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), and Harrell's c-index were calculated. Prognostic values of SQ and other variables were assessed by using Cox proportional hazards models. Results: Two-hundred-and-forty-five patients were included (203 NS [83%] and 42 S [17%]), median age 62 years, 48% male. Follow-up on overall survival was updated until February 2019. At ED entry, NS-patients had worse ECOG-performance and more symptoms. At study closure, 233 patients had died (95%). Median survival was three months for NS-patients (interquartile [IQ]-range: 1-8); nine months for S-patients (IQ-range: 3-28) (p < 0.0001). SQ-performance for one-year mortality: sensitivity 89%, specificity 40%, PPV 85%, NPV 50%, c-index 0.56, and hazard ratio 2.1 for approaching death. ECOG 3-4 predicted death in NS-patients; addition to the SQ improved c-index (0.65); sensitivity (40%), specificity (92%), PPV (95%), and NPV (29%). Conclusions: At the ED, the SQ plus ECOG 3-4 helps identifying patients with advanced cancer and a limited life expectancy. Its use supports initiating appropriate care related to urgency of palliative care needs.

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.

Psychosocial interventions for cancer survivors: A meta-analysis of effects on positive affect

PURPOSE

Positive affect has demonstrated unique benefits in the context of health-related stress and is emerging as an important target for psychosocial interventions. The primary objective of this meta-analysis was to determine whether psychosocial interventions increase positive affect in cancer survivors.

METHODS

We coded 28 randomized controlled trials of psychosocial interventions assessing 2082 cancer survivors from six electronic databases. We calculated 76 effect sizes for positive affect and conducted synthesis using random effects models with robust variance estimation. Tests for moderation included demographic, clinical, and intervention characteristics.

RESULTS

Interventions had a modest effect on positive affect (g = 0.35, 95% CI [0.16, 0.54]) with substantial heterogeneity of effects across studies ([Formula: see text]; I² = 78%). Three significant moderators were identified: in-person interventions outperformed remote interventions (P = .046), effects were larger when evaluated against standard of care or wait list control conditions versus attentional, educational, or component controls (P = .009), and trials with survivors of early-stage cancer diagnoses yielded larger effects than those with advanced-stage diagnoses (P = .046). We did not detect differential benefits of psychosocial interventions across samples varying in sex, age, on-treatment versus off-treatment status, or cancer type. Although no conclusive evidence suggested outcome reporting biases (P = .370), effects were smaller in studies with lower risk of bias.

CONCLUSIONS

In-person interventions with survivors of early-stage cancers hold promise for enhancing positive affect, but more methodological rigor is needed.

IMPLICATIONS FOR CANCER SURVIVORS

Positive affect strategies can be an explicit target in evidence-based medicine and have a role in patient-centered survivorship care, providing tools to uniquely mobilize human strengths.

Applied Informatics Decision Support Tool for Mortality Predictions in Patients With Cancer

PURPOSE

With rapidly evolving treatment options in cancer, the complexity in the clinical decision-making process for oncologists represents a growing challenge magnified by oncologists' disposition of intuition-based assessment of treatment risks and overall mortality. Given the unmet need for accurate prognostication with meaningful clinical rationale, we developed a highly interpretable prediction tool to identify patients with high mortality risk before the start of treatment regimens.

METHODS

We obtained electronic health record data between 2004 and 2014 from a large national cancer center and extracted 401 predictors, including demographics, diagnosis, gene mutations, treatment history, comorbidities, resource utilization, vital signs, and laboratory test results. We built an actionable tool using novel developments in modern machine learning to predict 60-, 90- and 180-day mortality from the start of an anticancer regimen. The model was validated in unseen data against benchmark models.

RESULTS

We identified 23,983 patients who initiated 46,646 anticancer treatment lines, with a median survival of 514 days. Our proposed prediction models achieved significantly higher estimation quality in unseen data (area under the curve, 0.83 to 0.86) compared with benchmark models. We identified key predictors of mortality, such as change in weight and albumin levels. The results are presented in an interactive and interpretable tool ( www.oncomortality.com ).

CONCLUSION

Our fully transparent prediction model was able to distinguish with high precision between highest- and lowest-risk patients. Given the rich data available in electronic health records and advances in machine learning methods, this tool can have significant implications for value-based shared decision making at the point of care and personalized goals-of-care management to catalyze practice reforms.

The (un)availability of prognostic information in the last days of life: a prospective observational study

OBJECTIVES

The aims of this study were (1) to document the clinical condition of patients considered to be in the last 2 weeks of life and (2) to compare patients who did or did not survive for 72 hours.

DESIGN

A prospective observational study.

SETTING

Two sites in London, UK (a hospice and a hospital palliative care team).

PARTICIPANTS

Any inpatient, over 18 years old, English speaking, who was identified by the palliative care team as at risk of dying within the next 2 weeks was eligible.

OUTCOME MEASURES

Prognostic signs and symptoms were documented at a one off assessment and patients were followed up 7 days later to determine whether or not they had died.

RESULTS

Fifty participants were recruited and 24/50 (48%) died within 72 hours of assessment. The most prevalent prognostic features observed were a decrease in oral food intake (60%) and a rapid decline of the participant's global health status (56%). Participants who died within 72 hours had a lower level of consciousness and had more care needs than those who lived longer. A large portion of data was unavailable, particularly that relating to the psychological and spiritual well-being of the patient, due to the decreased consciousness of the patient.

CONCLUSIONS

The prevalence of prognostic signs and symptoms in the final days of life has been documented between those predicted to die and those who did not. How doctors make decisions with missing information is an area for future research, in addition to understanding the best way to use the available information to make more accurate predictions.

Nomogram-based parameters to predict overall survival in a real-world advanced cancer population undergoing palliative care

Background

Although palliative care has been accepted throughout the cancer trajectory, accurate survival prediction for advanced cancer patients is still a challenge. The aim of this study is to identify pre-palliative care predictors and develop a prognostic nomogram for overall survival (OS) in mixed advanced cancer patients.

Methods

A total of 378 consecutive advanced cancer patients were retrospectively recruited from July 2013 to October 2015 in one palliative care unit in China. Twenty-three clinical and laboratory characters were collected for analysis. Prognostic factors were identified to construct a nomogram in a training cohort (n = 247) and validated in a testing cohort (n = 131) from the setting.

Results

The median survival time was 48.0 (95% CI: 38.1–57.9) days for the training cohort and 52.0 (95% CI: 34.6–69.3) days for the validation cohort. Among pre-palliative care factors, sex, age, tumor stage, Karnofsky performance status, neutrophil count, hemoglobin, lactate dehydrogenase, albumin, uric acid, and cystatin-C were identified as independent prognostic factors for OS. Based on the 10 factors, an easily obtained nomogram predicting 90-day probability of mortality was developed. The predictive nomogram had good discrimination and calibration, with a high C-index of 0.76 (95% CI: 0.73–0.80) in the development set. The strong discriminative ability was externally conformed in the validation cohort with a C-index of 0.75.

Conclusions

A validated prognostic nomogram has been developed to quantify the risk of mortality for advanced cancer patients undergoing palliative care. This tool may be useful in optimizing therapeutic approaches and preparing for clinical courses individually.

Electronic supplementary material

The online version of this article (10.1186/s12904-019-0432-7) contains supplementary material, which is available to authorized users.

Space for intuition - the 'Surprise'-Question in haemato-oncology: Qualitative analysis of experiences and perceptions of haemato-oncologists

BACKGROUND

Early integration of palliative care can improve outcomes for people with cancer and non-cancer diagnoses. However, prediction of survival for individuals is challenging, in particular in patients with haematological malignancies who are known to have limited access to palliative care. The 'Surprise'-Question can be used to facilitate referral to palliative care.

AIM

To explore experiences, views and perceptions of haemato-oncologists on the use of the 'Surprise'-Question in the haemato-oncology outpatients clinics of a university hospital in Germany.

DESIGN

A qualitative study using individual semi-structured interviews transcribed verbatim and analysed thematically based on the framework approach.

SETTING/PARTICIPANTS

The study took place at the haemato-oncology outpatient clinic and the bone marrow transplantation outpatient clinic of a university hospital. Nine haemato-oncologists participated in qualitative interviews.

RESULTS

Thematic analysis identified 4 themes and 11 subthemes: (1) meaning and relevance of the 'Surprise'-Question; (2) feasibility; (3) the concept of 'surprise' and (4) personal aspects of prognostication. A key function of the 'Surprise'-Question was to stimulate intuition and promote patient-centred goals of care by initiating a process of pause → reflection → change of perspective. It was easy and quick to use, but required time and communication skills to act on. Participants' training in palliative care enhanced their willingness to use the 'Surprise'-Question.

CONCLUSION

Irrespective of its use in prognostication, the 'Surprise'-Question is a valuable tool to facilitate consideration of patient-centred goals and promote holistic care in haemato-oncology. However, prognostic uncertainty, lack of time and communication skills are barriers for integration into daily practice. Further research should involve haematology patients to integrate their needs and preferences.

Protocol for the ORaClES study: an online randomised controlled trial to improve clinical estimates of survival using a training resource for medical students

INTRODUCTION

Clinicians often struggle to recognise when palliative care patients are imminently dying (last 72 hours of life). A previous study identified the factors that expert palliative care doctors (with demonstrated prognostic skills) had used, to form a judgement about which patients were imminently dying. This protocol describes a study to evaluate whether an online training resource showing how experts weighted the importance of various symptoms and signs can teach medical students to formulate survival estimates for palliative care patients that are more similar to the experts' estimates.

METHODS AND ANALYSIS

This online double-blind randomised controlled trial will recruit at least 128 students in the penultimate or final year of medical school in the UK. Participants are asked to review three series of vignettes describing patients referred to palliative care and provide an estimate about the probability (0%-100%) that each patient will die within 72 hours. After the first series, students randomised to the intervention arm are given access to an online training resource. All participants are asked to complete a second series of vignettes. After 2 weeks, all participants are asked to complete a third series. The primary outcome will be the probability of death estimates (0%-100%) provided by students in the intervention and control arms for the second series of vignettes. Secondary outcomes include the maintenance effect at 2-week follow-up, weighting of individual symptoms and signs, and level of expertise (discrimination and consistency).

ETHICS AND DISSEMINATION

Approval has been obtained from the UCL Research Ethics Committee (8675/002) and local approvals will be obtained as appropriate. Results will be published in peer-reviewed journals using an open access format and presented at academic conferences. We will also publicise our findings on the Marie Curie website.

TRIAL REGISTRATION NUMBER

NCT03360812; Pre-results.