How accurate are physicians' clinical predictions of survival and the available prognostic tools in estimating survival times in terminally ill cancer patients? A systematic review

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.

External validation of GO-FAR 2 calculator for outcomes after in-hospital cardiac arrest with comparison to GO-FAR and trial of expanded applications

Aim

Externally validate the GO-FAR 2 tool for predicting survival with good neurologic function after in-hospital cardiac arrest with comparison to the original GO-FAR tool. Additionally, we collected qualitative descriptors and performed exploratory analyses with various levels of neurologic function and discharge destination.

Methods

Retrospective chart review of all patients who underwent in-hospital resuscitation after cardiac arrest during the calendar years 2016-2019 in our institution (n = 397). GO-FAR and GO-FAR 2 scores were calculated based on information available in the medical record at the time of hospital admission. Cerebral performance category (CPC) scores at the time of admission and discharge were assessed by chart review.

Results

The GO-FAR 2 score accurately predicted outcomes in our study population with a c-statistic of 0.625. The original GO-FAR score also had accurate calibration with a stronger c-statistic of 0.726. The GO-FAR score had decreased predictive value for lesser levels of neurologic function (c-statistic 0.56 for alive at discharge) and discharge destination (0.69). Descriptors of functional status by CPC score were collected.

Conclusion

Our findings support the validity of the GO-FAR and GO-FAR 2 tools as published, but the c-statistics suggest modest predictive discrimination. We include functional descriptors of CPC outcomes to aid clinicians in using these tools. We propose that information about expected outcomes could be valuable in shared decision-making conversations.

Long-Term Survival in Patients with Cancers: Surveillance, epidemiology and end results-based analysis

Objectives

This study aimed to explore real-world data on the long-term survival of cancer patients using historical records from the Surveillance, Epidemiology, and End Results (SEER) Programme. Long-term survival is an important endpoint in the management of different malignancies. It is rarely assessed due to the unfeasibility of follow-up for a long duration of time. Besides reporting the five-year relative survival, the 10- and 20-year survival rates for different types of cancers were analysed. Additionally, survival trends as a function of time, age and tumour type were reviewed and reported.

Methods

The study used SEER*Stat (Version 8.3.6.1) for data acquisition from the SEER 9 Regs (November 2019) database. Data from patients diagnosed with cancer between 1975 and 2014 were retrieved and included in the analysis.

Results

For patients diagnosed with any malignant disease (N = 4,412,024), there was a significant increase in median overall survival over time (P <0.001). The 20-, 10-, and 5-year survival rates were higher in solid tumours compared to haematological malignancies (50.8% versus 38%; 57% versus 47.4%; and 62.2% versus 57.4%, respectively). The highest 20-year relative survival rates were observed in thyroid cancer (95.2%), germ cell and trophoblastic neoplasms (90.3%), melanoma (86.8%), Wilms' tumour (86.2%) and prostate cancer (83.5%).

Conclusion

Long-term follow-up data were suggestive of high 20-year relative survival rates for most tumour types. Relative survival showed an improving trend over time, especially in solid tumours.

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.

Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time

The Cox proportional hazards model is commonly used in evaluating risk factors in cancer survival data. The model assumes an additive, linear relationship between the risk factors and the log hazard. However, this assumption may be too simplistic. Further, failure to take time-varying covariates into account, if present, may lower prediction accuracy. In this retrospective, population-based, prognostic study of data from patients diagnosed with cancer from 2008 to 2015 in Ontario, Canada, we applied machine learning-based time-to-event prediction methods and compared their predictive performance in two sets of analyses: (1) yearly-cohort-based time-invariant and (2) fully time-varying covariates analysis. Machine learning-based methods-gradient boosting model (gbm), random survival forest (rsf), elastic net (enet), lasso and ridge-were compared to the traditional Cox proportional hazards (coxph) model and the prior study which used the yearly-cohort-based time-invariant analysis. Using Harrell's C index as our primary measure, we found that using both machine learning techniques and incorporating time-dependent covariates can improve predictive performance. Gradient boosting machine showed the best performance on test data in both time-invariant and time-varying covariates analysis.

A Review of Clinical Signs and Symptoms of Imminent End-of-Life in Individuals With Advanced Illness

Background: World population is not only aging but suffering from serious chronic illnesses, requiring an increasing need for end-of-life care. However, studies show that many healthcare providers involved in the care of dying patients sometimes express challenges in knowing when to stop non-beneficial investigations and futile treatments that tend to prolong undue suffering for the dying person. Objective: To evaluate the clinical signs and symptoms that show end-of-life is imminent in individuals with advanced illness. Design: Narrative review. Methods: Computerized databases, including PubMed, Embase, Medline,CINAHL, PsycInfo, and Google Scholar were searched from 1992 to 2022 for relevant original papers written in or translated into English language that investigated clinical signs and symptoms of imminent death in individuals with advanced illness. Results: 185 articles identified were carefully reviewed and only those that met the inclusion criteria were included for review. Conclusion: While it is often difficult to predict the timing of death, the ability of healthcare providers to recognize the clinical signs and symptoms of imminent death in terminally-ill individuals may lead to earlier anticipation of care needs and better planning to provide care that is tailored to individual's needs, and ultimately results in better end-of-life care, as well as a better bereavement adjustment experience for the families.

Factors Influencing Physician Prognosis: A Scoping Review

Introduction. Prognosis is an essential component of informed consent for medical decision making. Research shows that physicians display discrepancies in their prognostication, leading to variable, inaccurate, optimistic, or pessimistic prognosis. Factors driving these discrepancies and the supporting evidence have not been reviewed systematically. Methods. We undertook a scoping review to explore the literature on the factors leading to discrepancies in medical prognosis. We searched Medline (Ovid) and Embase (Ovid) databases for peer-reviewed articles from 1970 to 2017. We included articles that discussed prognosis variation or discrepancy and where factors influencing prognosis were evaluated. We extracted data outlining the participants, methodology, and prognosis discrepancy information and measured factors influencing prognosis. Results. Of 4,723 articles, 73 were included in the final analysis. There was significant variability in research methodologies. Most articles showed that physicians were pessimistic regarding patient outcomes, particularly in early trainees and acute care specialties. Accuracy rates were similar across all time periods. Factors influencing prognosis were clustered in 4 categories: patient-related factors (such as age, gender, race, diagnosis), physician-related factors (such as age, race, gender, specialty, training and experience, attitudes and values), clinical situation-related factors (such as physician-patient relationship, patient location, and clinical context), and environmental-related factors (such as country or hospital size). Discussion. Obtaining accurate prognostic information is one of the highest priorities for seriously ill patients. The literature shows trends toward pessimism, especially in early trainees and acute care specialties. While some factors may prove difficult to change, the physician's personality and psychology influence prognosis accuracy and could be tackled using debiasing strategies. Exposure to long-term patient outcomes and a multidisciplinary practice setting are environmental debiasing strategies that may warrant further research.

Highlights

Literature on discrepancies in physician's prognostication is heterogeneous and sparse.Literature shows that physicians are mostly pessimistic regarding patient outcomes.Literature shows that a physician's personality and psychology influence prognostic accuracy and could be improved with evidence-based debiasing strategies.Medical specialty strongly influences prognosis, with specialties exposed to acutely ill patients being more pessimistic, whereas specialties following patients longitudinally being more optimistic.Physicians early in their training were more pessimist than more experienced physicians.

Spinal metastasis: narrative reviews of the current evidence and treatment modalities

The treatment for spinal metastasis has evolved significantly during the past decade. An advancement in systemic therapy has led to a prolonged overall survival in cancer patients, thus increasing the incidence of spinal metastasis. In addition, with the improved treatment armamentarium, the prediction of patient survival using traditional prognostic models may have limitations and these require the incorporation of some novel parameters to improve their prognostic accuracy. The development of minimally-invasive spinal procedures and minimal access surgical techniques have facilitated a quicker patient recovery and return to systemic treatment. These modern interventions help to alleviate pain and improve quality of life, even in candidates with a relatively short life expectancy. Radiotherapy may be considered in non-surgical candidates or as adjuvant therapy for improving local tumour control. Stereotactic radiosurgery has facilitated this even in radioresistant tumours and may even replace surgery in radiosensitive malignancies. This narrative review summarizes the current evidence leading to the paradigm shifts in the modern treatment of spinal metastasis.

The accuracy of clinician predictions of survival in the Prognosis in Palliative care Study II (PiPS2): A prospective observational study

BACKGROUND

Prognostic information is important for patients with cancer, their families, and clinicians. In practice, survival predictions are made by clinicians based on their experience, judgement, and intuition. Previous studies have reported that clinicians' survival predictions are often inaccurate. This study reports a secondary analysis of data from the Prognosis in Palliative care Study II (PiPS2) to assess the accuracy of survival estimates made by doctors and nurses.

METHODS AND FINDINGS

Adult patients (n = 1833) with incurable, locally advanced or metastatic cancer, recently referred to palliative care services (community teams, hospital teams, and inpatient palliative care units) were recruited. Doctors (n = 431) and nurses (n = 777) provided independent prognostic predictions and an agreed multi-professional prediction for each patient. Clinicians provided prognostic estimates in several formats including predictions about length of survival and probability of surviving to certain time points. There was a minimum follow up of three months or until death (whichever was sooner; maximum follow-up 783 days). Agreed multi-professional predictions about whether patients would survive for days, weeks or months+ were accurate on 61.9% of occasions. The positive predictive value of clinicians' predictions about imminent death (within one week) was 77% for doctors and 79% for nurses. The sensitivity of these predictions was low (37% and 35% respectively). Specific predictions about how many weeks patients would survive were not very accurate but showed good discrimination (patients estimated to survive for shorted periods had worse outcomes). The accuracy of clinicians' probabilistic predictions (assessed using Brier's scores) was consistently better than chance, improved with proximity to death and showed good discrimination between groups of patients with different survival outcomes.

CONCLUSIONS

Using a variety of different approaches, this study found that clinicians predictions of survival show good discrimination and accuracy, regardless of whether the predictions are about how long or how likely patients are to survive. Accuracy improves with proximity to death. Although the positive predictive value of estimates of imminent death are relatively high, the sensitivity of such predictions is relatively low. Despite limitations, the clinical prediction of survival should remain the benchmark against which any innovations in prognostication are judged.

STUDY REGISTRATION

ISRCTN13688211. http://www.isrctn.com/ISRCTN13688211.

How well do neurosurgeons predict survival in patients with high-grade glioma?

Due to the lack of reliable prognostic tools, prognostication and surgical decisions largely rely on the neurosurgeons’ clinical prediction skills. The aim of this study was to assess the accuracy of neurosurgeons’ prediction of survival in patients with high-grade glioma and explore factors possibly associated with accurate predictions. In a prospective single-center study, 199 patients who underwent surgery for high-grade glioma were included. After surgery, the operating surgeon predicted the patient’s survival using an ordinal prediction scale. A survival curve was used to visualize actual survival in groups based on this scale, and the accuracy of clinical prediction was assessed by comparing predicted and actual survival. To investigate factors possibly associated with accurate estimation, a binary logistic regression analysis was performed. The surgeons were able to differentiate between patients with different lengths of survival, and median survival fell within the predicted range in all groups with predicted survival < 24 months. In the group with predicted survival > 24 months, median survival was shorter than predicted. The overall accuracy of surgeons’ survival estimates was 41%, and over- and underestimations were done in 34% and 26%, respectively. Consultants were 3.4 times more likely to accurately predict survival compared to residents (p = 0.006). Our findings demonstrate that although especially experienced neurosurgeons have rather good predictive abilities when estimating survival in patients with high-grade glioma on the group level, they often miss on the individual level. Future prognostic tools should aim to beat the presented clinical prediction skills.

Communication about Prognosis during Patient-Initiated Second Opinion Consultations in Advanced Cancer Care: An Observational Qualitative Analysis

Prognostic communication is essential for patients with advanced cancer to enable informed medical decision-making and end-of-life planning. Discussing prognosis is challenging, and might be especially complex for oncologists conducting a second opinion (SO). Survival data are often lacking, and consulting oncologists need to consider previously conveyed information and patients’ relationship with the referring oncologist. We qualitatively investigated how advanced cancer patients and consulting oncologists discuss prognosis during audio-recorded SO consultations (N = 60), including prognostic information received from the referring oncologist. Our results show that patients regularly expressed implicit cues to discuss prognosis or posed explicit questions tentatively. Consulting oncologists were mostly unresponsive to patients’ cues and cautious to prognosticate. They also seemed cautious when patients brought up the referring oncologist. Consulting oncologists checked which prognostic information patients had received from the referring oncologist, before estimating prognosis. They agreed with the first opinion or rectified discrepancies carefully. Altogether, this study exposes missed opportunities for open prognostic discussions in SOs. Consulting oncologists could explicitly explore patients’ information preferences and perceptions of prognosis. If desired, they can provide tailored, independent information to optimise patients’ prognostic awareness and informed medical decision-making. They may additionally support patients in dealing with prognosis and the uncertainties associated with it.

A Nomogram for Predicting Risk of Thromboembolism in Gastric Cancer Patients Receiving Chemotherapy

Purpose: The aims of this study were to develop and validate a novel nomogram to predict thromboembolism (TE) in gastric cancer (GC) patients receiving chemotherapy and to test its predictive ability.

Methods: This retrospective study included 544 GC patients who received chemotherapy as the initial treatment at two medical centers. Among the 544 GC patients who received chemotherapy, 275 and 137 patients in the First Affiliated Hospital of Nanchang University from January 2014 to March 2019 were enrolled in the training cohort and the validation cohort, respectively. A total of 132 patients in the Beilun branch of the First Affiliated Hospital of Zhejiang University from January 2015 to August 2019 were enrolled in external validation cohorts. The nomogram was based on parameters determined by univariate and multivariate logistic analyses. The prediction performance of the nomogram was measured by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and decision curve analysis (DCA). The applicability of the nomogram was internally and independently validated.

Results: The predictors included the Eastern Cooperative Oncology Group Performance Status (ECOG), presence of an active cancer (AC), central venous catheter (CVC), and D-dimer levels. These risk factors are shown on the nomogram and verified. The nomogram demonstrated good discrimination and fine calibration with an AUROC of 0.875 (0.832 in internal validation and 0.807 in independent validation). The DCA revealed that the nomogram had a high clinical application value.

Conclusions: We propose the nomogram for predicting TE in patients with GC receiving chemotherapy, which can help in making timely personalized clinical decisions for different risk populations.

Improving patient selection for immuno-oncology phase 1 trials: External validation of six prognostic scores in a French Cancer Center

We compared the performance of six prognostic scores (Royal Marsden Hospital, MDACC: MD Anderson Clinical Center and MDACC + NLR: neutrophil-to-lymphocyte ratio, MD Anderson - immune checkpoint inhibitors (MDA-ICI), GRIm: Gustave Roussy Immune Score and LIPI: Lung Immune Prognostic Index) in predicting overall survival (OS) in phase I trial patients treated with immune checkpoint inhibitors (ICI). Medical records of patients with advanced solid tumors enrolled in ICI phase I trials between 2015 and 2018 at Institut Universitaire du Cancer de Toulouse-Oncopole were reviewed. The performance of prognostic scores on OS was compared using different criteria. A total of 259 patients were included. Median age was 63 years (range: 18-83). Main primary cancers were melanoma (19%), head and neck (16%), lung (13%) and bladder (10%). With a median follow-up of 15 months (95% confidence interval [CI] = [11.6;17.5]), median OS was 12.5 months (95% CI = [10.3;16.0]). All scores were associated with OS. The MDACC, LIPI and GRIm scores performed better than the others. Concordance of risk group assignment between the scoring systems was poor. According to our results, the MDACC, GRIm and LIPI scores better suited to ICI phase I settings. Adequate scoring would allow better patient selection in early ICI trials, especially during the critical period of dose escalation, and in proof-of-concept expansion cohorts.

Prognostic models of survival in patients with advanced incurable cancer: the PiPS2 observational study

BACKGROUND

The Prognosis in Palliative care Study (PiPS) prognostic survival models predict survival in patients with incurable cancer. PiPS-A (Prognosis in Palliative care Study - All), which involved clinical observations only, and PiPS-B (Prognosis in Palliative care Study - Blood), which additionally required blood test results, consist of 14- and 56-day models that combine to create survival risk categories: 'days', 'weeks' and 'months+'.

OBJECTIVES

The primary objectives were to compare PIPS-B risk categories against agreed multiprofessional estimates of survival and to validate PiPS-A and PiPS-B. The secondary objectives were to validate other prognostic models, to assess the acceptability of the models to patients, carers and health-care professionals and to identify barriers to and facilitators of clinical use.

DESIGN

This was a national, multicentre, prospective, observational, cohort study with a nested qualitative substudy using interviews with patients, carers and health-care professionals.

SETTING

Community, hospital and hospice palliative care services across England and Wales.

PARTICIPANTS

For the validation study, the participants were adults with incurable cancer, with or without capacity to consent, who had been recently referred to palliative care services and had sufficient English language. For the qualitative substudy, a subset of participants in the validation study took part, along with informal carers, patients who declined to participate in the main study and health-care professionals.

MAIN OUTCOME MEASURES

For the validation study, the primary outcomes were survival, clinical prediction of survival and PiPS-B risk category predictions. The secondary outcomes were predictions of PiPS-A and other prognostic models. For the qualitative substudy, the main outcomes were participants' views about prognostication and the use of prognostic models.

RESULTS

For the validation study, 1833 participants were recruited. PiPS-B risk categories were as accurate as agreed multiprofessional estimates of survival (61%; p = 0.851). Discrimination of the PiPS-B 14-day model (c-statistic 0.837, 95% confidence interval 0.810 to 0.863) and the PiPS-B 56-day model (c-statistic 0.810, 95% confidence interval 0.788 to 0.832) was excellent. The PiPS-B 14-day model showed some overfitting (calibration in the large -0.202, 95% confidence interval -0.364 to -0.039; calibration slope 0.840, 95% confidence interval 0.730 to 0.950). The PiPS-B 56-day model was well-calibrated (calibration in the large 0.152, 95% confidence interval 0.030 to 0.273; calibration slope 0.914, 95% confidence interval 0.808 to 1.02). PiPS-A risk categories were less accurate than agreed multiprofessional estimates of survival (p < 0.001). The PiPS-A 14-day model (c-statistic 0.825, 95% confidence interval 0.803 to 0.848; calibration in the large -0.037, 95% confidence interval -0.168 to 0.095; calibration slope 0.981, 95% confidence interval 0.872 to 1.09) and the PiPS-A 56-day model (c-statistic 0.776, 95% confidence interval 0.755 to 0.797; calibration in the large 0.109, 95% confidence interval 0.002 to 0.215; calibration slope 0.946, 95% confidence interval 0.842 to 1.05) had excellent or reasonably good discrimination and calibration. Other prognostic models were also validated. Where comparisons were possible, the other prognostic models performed less well than PiPS-B. For the qualitative substudy, 32 health-care professionals, 29 patients and 20 carers were interviewed. The majority of patients and carers expressed a desire for prognostic information and said that PiPS could be helpful. Health-care professionals said that PiPS was user friendly and may be helpful for decision-making and care-planning. The need for a blood test for PiPS-B was considered a limitation.

LIMITATIONS

The results may not be generalisable to other populations.

CONCLUSIONS

PiPS-B risk categories are as accurate as agreed multiprofessional estimates of survival. PiPS-A categories are less accurate. Patients, carers and health-care professionals regard PiPS as potentially helpful in clinical practice.

FUTURE WORK

A study to evaluate the impact of introducing PiPS into routine clinical practice is needed.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN13688211.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 28. See the NIHR Journals Library website for further project information.

Impact of Prognostic Nutritional Index on Terminal Cancer Patients

Background:

In terminal cancer patients (TCPs), one of the most important things is to define the survival to help the main responsible physicians, patients, and main caregivers make decisions, set goals, and work across the end-of-life strategies.

Patients and Methods:

We retrospectively reviewed the medical files of TCPs, who died during September 2011 and December 2017, to recognize the correlation between prognostic nutritional indices (PNIs) and survival in those subtypes of patients. The receiver operating characteristic (ROC) curve was used to identify the cutoff value of PNI.

Results:

A total of 858 TCPs were eligible and included, the median age was 62 years (range: 18–107). The most common primary cancer sites were colorectal cancer in 151 patients (17.6%), hepatobiliary in 129 (15%), lung cancer in 115 (13.4%), breast cancer in 114 (13.3%), and genitourinary in 80 (9.3%). The mean value of PNI for all cancer types was 32.9 ± 6.7. The values showed different levels across cancer types. For patients who lived >2 weeks, PNI was 36.7 compared with that who died within 2 weeks was 29.3, which was a statistically significant (P < 0.001). By the ROC curve, the cutoff value of PNI was 32.3 and area under the curve was 0.888. The sensitivity, specificity, positive predictive value, and negative predictive value were 91.28% (95% confidence interval [CI]: 88.2–93.8), 71.09% (95% CI: 66.5–75.4), 76.5% (95% CI: 73.7–79.2), and 88.8% (95% CI: 85.3–91.5), respectively.

Conclusion:

The PNI is an easy and an applicable biomarker to estimate life expectancy in TCPs.

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.

A non-lab nomogram of survival prediction in home hospice care patients with gastrointestinal cancer

BACKGROUND

Patients suffering from gastrointestinal cancer comprise a large group receiving home hospice care in China, however, little is known about the prediction of their survival time. This study aimed to develop a gastrointestinal cancer-specific non-lab nomogram predicting survival time in home-based hospice.

METHODS

We retrospectively studied the patients with gastrointestinal cancer from a home-based hospice between 2008 and 2018. General baseline characteristics, disease-related characteristics, and related assessment scale scores were collected from the case records. The data were randomly split into a training set (75%) for developing a predictive nomogram and a testing set (25%) for validation. A non-lab nomogram predicting the 30-day and 60-day survival probability was created using the least absolute shrinkage and selection operator (LASSO) Cox regression. We evaluated the performance of our predictive model by means of the area under receiver operating characteristic curve (AUC) and calibration curve.

RESULTS

A total of 1618 patients were included and divided into two sets: 1214 patients (110 censored) as training dataset and 404 patients (33 censored) as testing dataset. The median survival time for overall included patients was 35 days (IQR, 17-66). The 5 most significant prognostic variables were identified to construct the nomogram among all 28 initial variables, including Karnofsky Performance Status (KPS), abdominal distention, edema, quality of life (QOL), and duration of pain. In training dataset validation, the AUC at 30 days and 60 days were 0.723 (95% CI, 0.694-0.753) and 0.733 (95% CI, 0.702-0.763), respectively. Similarly, the AUC value was 0.724 (0.673-0.774) at 30 days and 0.725 (0.672-0.778) at 60 days in the testing dataset validation. Further, the calibration curves revealed good agreement between the nomogram predictions and actual observations in both the training and testing dataset.

CONCLUSION

This non-lab nomogram may be a useful clinical tool. It needs prospective multicenter validation as well as testing with Chinese clinicians in charge of hospice patients with gastrointestinal cancer to assess acceptability and usability.

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.

Dealing with prognostic uncertainty: the role of prognostic models and websites for patients with advanced cancer

PURPOSE OF REVIEW

To provide an updated overview of prognostic models in advanced cancer and highlight the role of prognostic calculators.

RECENT FINDINGS

In the advanced cancer setting, many important healthcare decisions are driven by a patient's prognosis. However, there is much uncertainty in formulating prognosis, particularly in the era of novel cancer therapeutics. Multiple prognostic models have been validated for patients seen by palliative care and have a life expectancy of a few months or less, such as the Palliative Performance Scale, Palliative Prognostic Score, Palliative Prognostic Index, Objective Prognostic Score, and Prognosis in Palliative Care Study Predictor. However, these models are seldom used in clinical practice because of challenges related to limited accuracy when applied individually and difficulties with model selection, computation, and interpretation. Online prognostic calculators emerge as tools to facilitate knowledge translation by overcoming the above challenges. For example, www.predictsurvival.com provides the output for seven prognostic indexes simultaneously based on 11 variables.

SUMMARY

Prognostic models and prognostic websites are currently available to augment prognostication in the advanced cancer setting. Further studies are needed to examine their impact on prognostic accuracy, confidence, and clinical outcomes.

The Accuracy of Imminent Death Diagnosis in a Palliative Care Setting

Background

Prognostication is important for patients and their family members as they need this information for the preparation and planning of their future. It is important for physicians as they desire to be accurate in their prognostication skills in order to plan and deliver better care to their patients; healthcare managers require it as they need this information for planning and distribution of hospital resources. We intended to study the accuracy of imminent death diagnosis (IDD) in a palliative care setting in all patients who died at the Palliative Care Unit (PCU) at King Fahad Medical City between December 2012 and December 2014.

Methods

We conducted a retrospective chart review of all consecutive patients who died in the PCU between 2012 and 2014. We studied the percentage of patients who were diagnosed with imminent death. We further looked at the accuracy of IDD by calculating the time between the diagnosis of imminent death and death. The primary outcomes were the percentage of patients who had an IDD and the proportion of those who died within 14 days of IDD. The secondary outcomes were the difference between patients who die after IDD and patients who die without imminent death diagnosis (NIDD) at the end of life interventions.

Results

During the period from December 2012 until December 2014, 48 patients died in the PCU. The majority of 28/48 (58%) died with IDD. However, 20/48 (42%) died NIDD. In the IDD group, 25/28 (89.3%) died within 14 days of diagnosis while 3/28 (10.3%) died after 14 days

Conclusions

IDD is a critical skill for palliative care physicians to make an advance care plan. Our study showed a high degree of accuracy of prediction of fourteen-day mortality in PCU patients. The median survival was two days. However, a large proportion of patients still died without a documented IDD. Multidisciplinary team input improves the accuracy of IDD. We recommend further studies be done to explore how IDD could improve care planning for dying patients and families.

Current concepts in the surgical treatment of skeletal metastases

Symptomatic metastatic bone disease affects a large proportion of patients with malignant tumours and significantly impairs patients’ quality of life. There are still controversies regarding both surgical indications and methods, mainly because of the relatively few high-quality studies in this field. Generally, prosthetic reconstruction has been shown to result in fewer implant failures and should be preferred in patients with a good prognosis. Survival estimation tools should be used as part of preoperative planning. Adjuvant treatment, which relies on radiotherapy and inhibition of osteoclast function may also offer symptomatic relief and prevent implant failure. In this review we discuss the epidemiology, indications for surgery, preoperative planning, surgical techniques and adjuvant treatment of metastatic bone disease.

Development and Validation of a Prognostic Survival Model With Patient-Reported Outcomes for Patients With Cancer

IMPORTANCE

Existing prognostic cancer tools include biological and laboratory variables. However, patients often do not know this information, preventing them from using the tools and understanding their prognosis.

OBJECTIVE

To develop and validate a prognostic survival model for all cancer types that incorporates information on symptoms and performance status over time.

DESIGN, SETTING, AND PARTICIPANTS

This is a retrospective, population-based, prognostic study of data from patients diagnosed with cancer from January 1, 2008, to December 31, 2015, in Ontario, Canada. Patients were randomly selected for model derivation (60%) and validation (40%). The derivation cohort was used to develop a multivariable Cox proportional hazards regression model with baseline characteristics under a backward stepwise variable selection process to predict the risk of mortality as a function of time. Covariates included demographic characteristics, clinical information, symptoms and performance status, and health care use. Model performance was assessed on the validation cohort by C statistics and calibration plots. Data analysis was performed from February 6, 2018, to November 6, 2019.

MAIN OUTCOMES AND MEASURES

Time to death from diagnosis (year 0) recalculated at each of 4 annual survivor marks after diagnosis (up to year 4).

RESULTS

A total of 255 494 patients diagnosed with cancer were identified (135 699 [53.1%] female; median age, 65 years [interquartile range, 55-73 years]). The cohort decreased to 217 055, 184 822, 143 649, and 109 569 patients for each of the 4 years after diagnosis. In the derivation cohort year 0, and the most common cancers were breast (30 855 [20.1%]), lung (19 111 [12.5%]), and prostate (18 404 [12.0%]). A total of 47 614 (31.1%) had stage III or IV disease. The mean (SD) time to death in year 0 was 567 (715) days. After backward stepwise selection in year 0, the following factors were associated with increased risk of death by more than 10%: being hospitalized; having congestive heart failure, chronic obstructive pulmonary disease, or dementia; having moderate to high pain; having worse well-being; having functional status in the transitional or end-of-life phase; having any problems with appetite; receiving end-of-life home care; and living in a nursing home. Model discrimination was high for all models (C statistic: 0.902 [year 0], 0.912 [year 1], 0.912 [year 2], 0.909 [year 3], and 0.908 [year 4]).

CONCLUSIONS AND RELEVANCE

The model accurately predicted changing cancer survival risk over time using clinical, symptom, and performance status data and appears to have the potential to be a useful prognostic tool that can be completed by patients. This knowledge may support earlier integration of palliative care.

High C-reactive protein to albumin ratio and the short-term survival prognosis within 30 days in terminal cancer patients receiving palliative care in a hospital setting: A retrospective analysis

Survival estimates are very important to patients with terminal cancer. The C-reactive protein (CRP)/albumin ratio is associated with cancer outcomes. However, few studies have investigated the dose-response association in terminal cancer patients. Therefore, we aimed to evaluate the association between the CRP/albumin ratio and mortality in terminal cancer patients using a longitudinal analysis. We retrospectively investigated the electronic medical records of 435 inpatients with terminal cancer admitted to the palliative care unit of Yeouido St. Mary's Hospital between October 8, 2015, and January 17, 2018. In total, 382 patients with terminal cancer were enrolled in the study. The serum CRP/albumin ratio measured at admission had a linear dose-response relationship with the risk of death among the terminal cancer patients (P for linearity = .011). The multivariate analyses showed that the CRP/albumin ratio was an independent prognostic factor (Model 1, CRP/albumin ratio >48.53 × 10: HR = 2.68, 95% CI = 1.82-3.93; Model 2, tertile 2: HR = 1.91, 95% CI = 1.31-2.82 and tertile 3: HR = 3.66, 95% CI = 2.24-5.97). The relationship between a high CRP/albumin ratio and poor survival was a flat L-shape for survival time with an inflection point at approximately 15 days, while the relationship was not significant in terminal cancer patients who survived beyond 30 days. This study demonstrated that high CRP/albumin ratios are significantly and independently associated with the short-term survival prognosis of terminal cancer patients within 30 days.

Comparing Physician and Nurse Eastern Cooperative Oncology Group Performance Status (ECOG-PS) Ratings as Predictors of Clinical Outcomes in Patients with Cancer

BACKGROUND

The Eastern Cooperative Oncology Group Performance Status (ECOG-PS) scale is commonly used by physicians and nurses in oncology, as it correlates with cancer morbidity, mortality, and complications from chemotherapy and can help direct clinical decisions and prognostication. This retrospective cohort study aimed to identify whether ECOG-PS scores rated by oncologist versus nurses differ in their ability to predict clinical outcomes.

MATERIALS AND METHODS

Over 19 months, 32 oncologists and 41 chemotherapy nurses from a single academic comprehensive cancer center independently scored ECOG-PS (range: 0-5) for a random sample of 311 patients with cancer receiving chemotherapy. Logistic regression models were fit to evaluate the ability of nurse and physician ECOG-PS scores, as well as the nurse-physician ECOG-PS score difference (nurse minus physician), to predict the occurrence of chemotherapy toxicity (CTCAE v4, grade ≥3) and hospitalizations within 1 month from ECOG-PS ratings, as well as 6-month mortality or hospice referrals.

RESULTS

Physician/nurse ECOG-PS agreement was 71% (Cohen's κ = 0.486, p < .0001). Nurse ECOG-PS scores had stronger odds ratio for 6-month mortality or hospice (odds ratio [OR], 3.29, p < .0001) than physician ECOG-PS scores (OR, 2.71, p = .001). Furthermore, ECOG-PS ratings by nurses, but not physicians, correlated with 1-month chemotherapy toxicity (OR, 1.44, p = .021) and 1-month hospitalizations (OR, 1.57, p = .041). Nurse-physician disagreement, but only when physicians gave "healthier" (lower) ratings, was also associated with worse outcomes (chemotherapy toxicity OR = 1.51, p = .045; 1-month hospitalization OR, 1.86, p = .037; 6-month mortality or hospice OR, 2.99, p < .0001).

CONCLUSION

Nurse ECOG-PS ratings seem more predictive of important outcomes than those of physicians, and physician-nurse disagreement in ECOG-PS ratings predicts worse outcomes; scoring by nurses may result in additional clinical benefit.

IMPLICATIONS FOR PRACTICE

Nurse-rated Eastern Cooperative Oncology Group Performance Status (ECOG-PS) scores, compared with those rated by oncologists, better predicted hospitalizations and severe chemotherapy toxicity within 1 month from ECOG-PS assessment, as well as mortality or hospice referrals within 6 months. Physician-nurse disagreement in ECOG-PS scoring was associated with worse hospitalization, chemotherapy toxicity, and mortality and hospice referral rates. Rating performance statuses of patients with cancer by nurses instead or in addition to oncologists can result in additional clinical benefits, such as improved prognostication, as well as better informed clinical decision making regarding whether or not to administer chemotherapy, the need for additional supportive care, and goals of care discussions.

Prognostic models for spinal metastatic disease: evolution of methodologies, limitations, and future opportunities

Advances in cancer biology and therapy have increased survival of metastatic disease patients and, in turn, the rates of metastatic epidural spinal cord compression (MESCC). Surgery can improve patient quality of life, but accurate estimation of postoperative survival is critical for appropriate patient selection, multidisciplinary management, and shared decision making. Survival estimation on the basis of clinician judgement alone has been shown to be inaccurate and unreliable. Numerous prognostic scoring systems have been developed to address this need but the inputs to these models, the modeling methodologies, and the model outputs have evolved significantly over time. Here we discuss the available scoring systems, existing limitations, and future opportunities.

Prediction of survival after surgical management of femoral metastatic bone disease - A comparison of prognostic models

Background

Operative fixation for femoral metastatic bone disease is based on the principles of reducing pain and restoring function. Recent literature has proposed a number of prognostic models for appendicular metastatic bone disease. The aim of this study was to compare the accuracy of proposed soring systems in the setting of femoral metastatic bone disease in order to provide surgeons with information to determine the most appropriate scoring system in this setting.

Methods

A retrospective cohort analysis of patients who underwent surgical management of femoral metastatic bone disease at a single institution were included. A pre-operative predicted survival for all 114 patients was retrospectively calculated utilising the revised Katagiri model, PathFx model, SSG score, Janssen nomogram, OPTModel and SPRING 13 nomogram. Univariate and multivariate Cox regression proportional hazard models were constructed to assess the role of prognostic variables in the patient group. Area under the receiver characteristics and Brier scores were calculated for each prognostic model from comparison of predicted survival and actual survival of patients to quantify the accuracy of each model.

Results

For the femoral metastatic bone disease patients treated with surgical fixation, multivariate analysis demonstrated a number of pre-operative factors associated with survival in femoral metastatic bone disease, consistent with established literature. The OPTIModel demonstrated the highest accuracy at predicting 12-month (Area Under the Curve [AUC] = 0.79) and 24-month (AUC = 0.77) survival after surgical management. PathFx model was the most accurate at predicting 3-month survival (AUC = 0.70) and 6-month (AUC = 0.70) survival. The PathFx model was successfully externally validated in the femoral patient dataset for all time periods.

Conclusions

Among six prognostic models assessed in the setting of femoral metastatic bone disease, the present study observed the most accurate model for 3-month, 6-month, 12-month and 24-month survival. The results of this study may be utilised by the treating surgical team to determine the most accurate model for the required time period and therefore improve decision-making in the care of patients with femoral metastatic bone disease.

Predicting the survival of patients with bone metastases treated with radiation therapy: a validation study of the Katagiri scoring system

Background

The selection of radiation therapy dose fractionation schedules for bone metastases is often based on the estimation of life expectancy. Therefore, accurate prognosis prediction is an important issue. It is reported that the Katagiri scoring system can be used to predict the survival of patients with bone metastases. We aimed to assess prognostic factors and validate the Katagiri scoring system in patients who were treated with radiation therapy for bone metastases.

Materials/Methods

We retrospectively reviewed data of all patients who were treated with radiation therapy for bone metastases between 2004 and 2013. Age, sex, Karnofsky performance status (KPS), Eastern Cooperative Oncology Group performance status (ECOG PS), primary site (lesions and characteristics), visceral metastases, laboratory data, previous chemotherapy, and multiple bone metastases were analyzed for associations with overall survival (OS). Katagiri scores were calculated for each patient and were used to compare OS.

Results

Out of the 616 patients included in this analysis, 574 had died and 42 remained alive. The median follow-up time for survivors was 42 months. Univariate analysis revealed that age (P = 0.604) and multiple bone metastases (P = 0.691) were not significantly associated with OS. Multivariate analysis revealed that sex, ECOG PS, KPS, primary characteristics, visceral metastases, laboratory data, and previous chemotherapy were significantly associated with OS. The survival rates at 3, 6, 12, and 24 months, categorized by Katagiri score, were as follows: score 0–3, 94.4, 77.8, and 61.1%, respectively; score 4–6, 67.7, 48.7, and 31.2%, respectively; and score 7–10, 39.1, 22.1, and 9.0%, respectively (P < 0.001).

Conclusion

Sex, ECOG PS, KPS, primary characteristics, visceral metastases, laboratory data, and previous chemotherapy were significant predictors of survival in patients with bone metastases. The Katagiri scoring system was significantly correlated with OS and can help us select the optimal dose-fractionation.

Electronic supplementary material

The online version of this article (10.1186/s13014-019-1218-z) contains supplementary material, which is available to authorized users.

Prognosticating in Palliative Care: A survey of Canadian Palliative Care Physicians

Objective: To determine how palliative care physicians view the accuracy and importance of prognostication, what information they consider, and what processes they use. Methods: A questionnaire was sent to members of the Canadian Society of Palliative Care Physicians (CSPCP). Respondents recorded their perceptions about prognostication and the factors they considered when predicting survival. A patient scenario was described in which a prognosis was requested by two different people: a patient's daughter and a palliative care admissions coordinator. Results: 90 responses were received from 219 CSPCP members (41.1 percent). There was moderate agreement be tween respondents’ perceptions of their own accuracy and that of other physicians (κ=0.549). Of all the respondents, 89.9 percent believed that prognosticating was somewhat or very important. They considered clinical factors most commonly when prognosticating. A range of predictions was given for the scenario; often, the same physician gave different answers to the two people requesting a prognosis. Conclusion: Palliative care physicians believe that prognostication is important and use clinical factors to estimate survival. They often give different estimates to different information recipients.

Evaluation of frequency of Clinical Symptoms and Signs within Six Months Prior to Death in Patients with Advanced Solid Cancers

This retrospective study documented the frequency of the clinical symptoms and signs that increase in advanced cancer patients as they move toward death in order to create a sum score and correlate it with survival. Of 572 adult patients who were treated in four selected hospitals and who died in 1998 and 1999, data at six, three, and one month(s) prior to death was available for 257. The results showed that the number of symptoms and certain clinical findings accelerated toward death, increasing the sum score. Younger patients obtained higher sum scores at one month prior to death than did elderly ones (p=0.014); this suggests that elderly patients die at a point where they show less worsening in their clinical condition than do younger patients. The score was independent of cancer type or gender. The results of this analysis provide data for further development of a clinical tool to predict long-term survival in palliative care settings.

Development and Validation of a Nomogram for Assessing Survival in Patients With Metastatic Lung Cancer Referred for Radiotherapy for Bone Metastases

Importance

A survival prediction model for patients with bone metastases arising from lung cancer would be highly valuable.

Objective

To develop and validate a nomogram for assessing the survival probability of patients with metastatic lung cancer receiving radiotherapy for osseous metastases.

Design, Setting, Participants

In this prognostic study, the putative prognostic indicators for constructing the nomogram were identified using multivariable Cox regression analysis with backward elimination and model selection based on the Akaike information criterion. The nomogram was subjected to internal (bootstrap) and external validation; its calibration and discriminative ability were evaluated with calibration plots and the Uno C statistic, respectively. The training and validation set cohorts were from a tertiary medical center in northern Taiwan and a tertiary institution in southern Taiwan, respectively. The training set comprised 477 patients with metastatic lung cancer who received radiotherapy for osseous metastases between January 2000 and December 2013. The validation set comprised 235 similar patients treated between January 2011 and December 2017. Data analysis was conducted May 2018 to July 2018.

Main Outcomes and Measures

The nomogram end points were death within 3, 6, and 12 months.

Results

Of 477 patients in the training set, 292 patients (61.2%) were male, and the mean (SD) age was 62.86 (11.66) years. Of 235 patients in the validating set, 113 patients (48.1%) were male, and the mean (SD) age was 62.65 (11.49) years. In the training set, 186 (39%), 291 (61%), and 359 (75%) patients died within 3, 6, and 12 months, respectively, and the median overall survival was 4.21 (95% CI, 3.68-4.90) months. In the validating set, 84 (36%), 120 (51%), and 144 (61%) patients died within 3, 6, and 12 months, respectively, and the median overall survival was 5.20 (95% CI, 4.07-7.17) months. Body mass index (18.5 to <25 vs ≥25: hazard ratio [HR], 1.42; 95% CI, 1.14-1.78 and <18.5 vs ≥25: HR, 2.31; 95% CI, 1.56-3.44), histology (non-small cell vs small cell lung cancer: HR, 0.59; 95% CI, 0.41-0.86), epidermal growth factor receptor mutation (positive vs unknown: HR, 0.66; 95% CI, 0.46-0.93 and negative vs unknown: HR, 0.98; 95% CI, 0.66-1.45), smoking status (ever smoker vs never smoker: HR, 1.50; 95% CI, 1.24-1.83), age, and neutrophil to lymphocyte ratio were incorporated. The HRs of age and neutrophil to lymphocyte ratio were modeled nonlinearly with restricted cubic splines (both P < .001). The nomogram's discriminative ability was good in the training set (C statistic, ≥0.77; P < .001) and was validated using both an internal bootstrap method (C statistic, ≥0.76; P < .001) and an external validating set (C statistic, ≥0.75; P < .001). The calibration plots for the end points showed optimal agreement between the nomogram's assessment and actual observations.

Conclusions and Relevance

The nomogram (with web-based tool) can be useful for assessing the probability of survival at 3, 6, and 12 months in patients with metastatic lung cancer referred for radiotherapy to treat bone metastases, and it may guide radiation oncologists in treatment decision making and engaging patients in end-of-life discussions and/or hospice referrals at appropriate times.

Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives

We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. In a single framework, we integrated semantic data mapping and neural embedding technique to produce a text processing method that extracts relevant information from heterogeneous types of clinical notes in an unsupervised manner, and we designed a recurrent neural network to model the temporal dependency of the patient visits. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset (1818 patients). Our method achieved an area under the ROC curve (AUC) of 0.89. To provide explain-ability, we developed an interactive graphical tool that may improve physician understanding of the basis for the model's predictions. The high accuracy and explain-ability of the PPES-Met model may enable our model to be used as a decision support tool to personalize metastatic cancer treatment and provide valuable assistance to the physicians.

Bone-targeted agent treatment patterns and the impact of bone metastases on patients with advanced breast cancer in real-world practice in six European countries

BACKGROUND

Bone metastases (BMs) are common in patients with breast cancer and can lead to skeletal-related events (SREs), which are associated with increased pain and reduced quality of life (QoL). Bone-targeted agents (BTAs), like zoledronic acid and denosumab, reduce the incidence of SREs and delay progression of bone pain.

MATERIALS AND METHODS

We evaluated the management of BMs and pain in six European countries (Belgium, France, Germany, Italy, Spain, and UK) using the Adelphi Breast Cancer Disease Specific Programme, which included a physician survey and patient-reported outcomes (PROs) to assess the impact of BMs on pain and QoL.

RESULTS

301 physicians completed patient record forms for 2984 patients with advanced breast cancer; 1408 with BMs and 1136 with metastases at sites other than bone (non-BMs). Most patients with BMs (88%) received a BTA, with 81% receiving treatment during 3 months following BM diagnosis. For those who did not receive a BTA, the main reasons given were: very recent BM diagnosis, perceived low risk of bone complications, and short life expectancy. Most patients with BMs (68%) were experiencing bone pain and, of these, 97% were taking analgesics (including 28% receiving strong opioids). Despite this, moderate to severe pain was reported in 20% of patients who were experiencing pain. PROs were assessed in 766 patients with advanced breast cancer (392 with BMs, 374 with non-BMs). Overall, patients with BMs reported worse pain and QoL outcomes than those with non-BMs, those not receiving a BTA reported worse pain.

CONCLUSION

Despite the large proportion of patients receiving BTAs in this study, some patients with BMs are still not receiving early treatment to prevent SREs or to manage pain. Improving physicians' understanding of the role of BTAs and the importance of early treatment following BM diagnosis has the potential to improve patient care.

Development and validation of a prognostic nomogram for ambulatory patients with advanced cancer

Predicting survival of advanced cancer patients (ACPs) is a difficult task. We aimed at developing and testing a new prognostic tool in ACPs when they were first referred to palliative care (PC). A total of 497 patients were analyzed in this study (development sample, n = 221; validation sample, n = 276). From 35 initial putative prognostic variables, 14 of them were selected for multivariable Cox regression analyses; the most accurate final model was identified by backward variable elimination. Parameters were built into a nomogram to estimate the probability of patient survival at 30, 90, and 180 days. Calibration and discrimination properties of the Barretos Prognostic Nomogram (BPN) were evaluated in the validation phase of the study. The BPN was composed of 5 parameters: sex, presence of distant metastasis, Karnofsky Performance Status (KPS), white blood cell (WBC) count, and serum albumin concentration. The C-index was 0.71. The values of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve were 0.84, 0.74, and 0.74 at 30, 90, and 180 days, respectively. There were good calibration results according to the Hosmer-Lemeshow test. The median survival times were 313, 129, and 37 days for the BPN scores <25th percentile (<125), 25th to 75th percentile (125-175), and >75th percentile (>175), respectively (P < .001). The BPN is a new prognostic tool with adequate calibration and discrimination properties. It is now available to assist oncologists and palliative care physicians in estimating the survival of adult patients with advanced solid tumors.

Life Expectancy Predictions for Older Diabetic Patients as Estimated by Physicians and a Prognostic Model

Background: Multiple medical organizations recommend using life expectancy (LE) to individualize diabetes care goals. We compare the performance of patient LE predictions made by physicians to LE predictions from a simulation model (the Chicago model) in a cohort of older diabetic patients. Design: Retrospective cohort study of a convenience sample (n = 447) of diabetes patients over 65 years and their physicians. Measurements: Physicians provided LE estimates for individual patients during a baseline survey (2000–2003). The prognostic model included a comprehensive geriatric type 2 diabetes simulation model (the Chicago model) and combinations of the physician estimate and the Chicago model (“And,” “Or,” and “Average” models). Observed survival was determined based on the National Death Index through 31 December 2010. The predictive accuracy of LE predictions was assessed using c-statistic for 5-year mortality; Harrell’s c-statistic, and Integrated Brier score for overall survival. Results: The patient cohort had a mean (SD) age of 73.4 (5.9) years. The majority were female (62.6%) and black (79.4%). At 5 years, 108 (24.2%) patients had died. The c-statistic for 5-year mortality was similar for physicians (0.69) and the Chicago model (0.68), while the average of estimates by physicians and Chicago model yielded the highest c-statistic of any method tested (0.73). The estimates of overall survival yielded a similar pattern of results. Limitations: Generalizability of patient cohort and lack of updated model parameters. Conclusions: Compared with individual methods, the average of LE estimates by physicians and the Chicago model had the best predictive performance. Prognostic models, such as the Chicago model, may complement and support physicians’ intuitions as they consider treatment decisions and goals for older patients with chronic conditions like diabetes.

A systematic review of prognostic factors at the end of life for people with a hematological malignancy

Background

Accurate prognosticating is needed when patients are nearing the end of life to ensure appropriate treatment decisions, and facilitate palliative care provision and transitioning to terminal care. People with a hematological malignancy characteristically experience a fluctuating illness trajectory leading to difficulties with prognosticating. The aim of this review was to identify current knowledge regarding ‘bedside’ prognostic factors in the final 3 months of life for people with a hematological malignancy associated with increased risk of mortality.

Methods

A systematic review of the literature was performed across: PubMed; CINAHL; PsycINFO; and Cochrane with set inclusion criteria: 1) prognostic cohort studies; 2) published 2004–2014; 3) sample ≥ 18 years; 4) >50% sample had a hematological malignancy; 5) reported ‘bedside’ prognostic factors; 6) median survival of <3 months; and 7) English language. Quality appraisal was performed using the Quality In Prognostic Studies (QUIPS) tool. Results are reported in line with PRISMA guidelines.

Results

The search returned 4860 studies of which 28 met inclusion criteria. Twenty-four studies were rated moderate quality, three were high quality and one study was deemed to be of low quality. Most studies were set in the ICU (n = 24/28) and were retrospective (n = 25/28). Forty ‘bedside’ prognostic factors were identified as associated with increased risk of mortality encompassing the following broad categories: 1) demographics; 2) physiological complications or conditions; 3) disease characteristics; 4) laboratory blood values; and 5) interventions.

Conclusions

The literature on prognosticating in the final months of life was predominantly focused on people who had experienced acute physiological deterioration and were being treated aggressively in the in-patient setting. A significant gap in the literature exists for people who are treated less aggressively or are on a palliative trajectory. Findings did not report on, or confirm the significance of, many of the key prognostic factors associated with increased risk of mortality at the end of life in the solid tumour population, demonstrating key differences in the two populations.

Trial registration

This systematic review was not registered.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3207-7) contains supplementary material, which is available to authorized users.

Symptomatic spinal metastasis: A systematic literature review of the preoperative prognostic factors for survival, neurological, functional and quality of life in surgically treated patients and methodological recommendations for prognostic studies

Purpose

While several clinical prediction rules (CPRs) of survival exist for patients with symptomatic spinal metastasis (SSM), these have variable prognostic ability and there is no recognized CPR for health related quality of life (HRQoL). We undertook a critical appraisal of the literature to identify key preoperative prognostic factors of clinical outcomes in patients with SSM who were treated surgically. The results of this study could be used to modify existing or develop new CPRs.

Methods

Seven electronic databases were searched (1990–2015), without language restriction, to identify studies that performed multivariate analysis of preoperative predictors of survival, neurological, functional and HRQoL outcomes in surgical patients with SSM. Individual studies were assessed for class of evidence. The strength of the overall body of evidence was evaluated using GRADE for each predictor.

Results

Among 4,818 unique citations, 17 were included; all were in English, rated Class III and focused on survival, revealing a total of 46 predictors. The strength of the overall body of evidence was very low for 39 and low for 7 predictors. Due to considerable heterogeneity in patient samples and prognostic factors investigated as well as several methodological issues, our results had a moderately high risk of bias and were difficult to interpret.

Conclusions

The quality of evidence for predictors of survival was, at best, low. We failed to identify studies that evaluated preoperative prognostic factors for neurological, functional, or HRQoL outcomes in surgical patients with SSM. We formulated methodological recommendations for prognostic studies to promote acquiring high-quality evidence to better estimate predictor effect sizes to improve patient education, surgical decision-making and development of CPRs.

Effectiveness of radiotherapy for metastatic spinal cord compression in patients with short life expectancy

AIM

To analyze the effect of radiotherapy (RT) in patients with metastatic spinal cord compression (MSCC) and poor prognosis in our center.

BACKGROUND

RT is an effective treatment for MSCC.

MATERIALS AND METHODS

Prospective evaluation on patients with MSCC and limited survival (according to Rades' scale), and treated with single-dose 8 Gy RT (February 2013-August 2014). Pain, ambulatory status and sphincter control were recorded. Pain relief was evaluated following the International Bone Metastases Consensus Working Party Guidelines. Ambulatory status was evaluated with Frankel's scale. Spinal fracture and instability were recorded. Health aspects were evaluated via a short survey and measuring the time spent on RT.

RESULTS

35 patients were included. 51% had unfavorable histologies; 60% bone fracture and 17% spinal instability. Median Karnofsky score was 60; 100% were on high doses of opioids. Median survival was 1.5 months. 49% had a partial pain response at 2 weeks post-radiation, and 47% at one month. Significant reductions in pain intensity were present at 2 weeks (Visual analog scale, VAS score, from 8 ± 1.5 to 5 ± 1.9). Negligible effects were observed on motor and bladder function, along with side effects. KPS score was maintained during follow-up. 80% of patients spent ≤5% of their remaining lifetime on RT. A survey comparison between clinical judgment and the results according to treatment decision consider that these patients merit treatment evaluation.

CONCLUSIONS

A moderate pain response tailored to life expectancy can be obtained in patients treated with radiation. 8-Gy single-dose is an option for patients with limited survival.

Prognostic Value of Heart Rate Variability in Patients With Cancer

INTRODUCTION

Previous studies have shown that autonomic dysfunction is associated with shorter survival in patients with advanced cancer. We examined the association between heart rate variability, a measure of autonomic function, and survival in a large cohort of patients with cancer.

METHODS

We retrospectively examined the records of 651 patients with cancer who had undergone ambulatory electrocardiogram monitoring for 20 to 24 hours. Time domain heart rate variability (SD of normal-to-normal beat interval [SDNN]) was calculated using power spectral analysis. Survival data were compared between patients with SDNN ≥ 70 milliseconds (Group 1, n = 520) and SDNN < 70 milliseconds (Group 2, n = 131).

RESULTS

Two groups were similar in most variables, except that patients in group 2 had a significantly higher percentage of male patients (P = 0.03), hematological malignancies (P = 0.04), and use of non-selective serotonin reuptake inhibitor antidepressants (P = 0.04). Patients in group 2 had a significantly shorter survival rate (25% of patients in group 2 died by 18.7 weeks vs. 78.9 weeks in group 1 patients; P < 0.0001). Multivariate analysis showed that SDNN < 70 milliseconds remained significant for survival (hazard ratio 1.9 [95% confidence interval: 1.4-2.5]) independent of age, cancer stage, and performance status.

CONCLUSION

The presence of cancer in combination with decreased heart rate variability (SDNN < 70 milliseconds) is associated with shorter survival time.

Accuracy of Oncologists' Life-Expectancy Estimates Recalled by Their Advanced Cancer Patients: Correlates and Outcomes

BACKGROUND

Oncologists are often reluctant to discuss life-expectancy estimates with their patients because of concerns about their inaccuracy and limited evidence regarding benefits.

OBJECTIVE

Determine oncologist accuracy in predicting their advanced cancer patients' life expectancy and correlates associated with accuracy.

DESIGN

Multicenter prospective, longitudinal study of patients with advanced cancer, assessed once at baseline and followed to death. At baseline, patients were asked whether their oncologist had provided them with a life-expectancy estimate.

SETTING/SUBJECTS

Eighty-five patients with advanced cancer recruited from outpatient cancer clinics.

MEASUREMENTS

Patients' baseline sociodemographic and time to death, and clinical characteristics were examined to determine their associations with the accuracy of the oncologists' life-expectancy estimates as recalled by their patients.

RESULTS

Seventy-four percent (63/85) of patients recalled that physician life-expectancy estimates were accurate to within a year; estimates were most accurate when patients had 9-12 months to live. Factors significantly (p < 0.05) positively associated with oncologists' greater accuracy to within a year were the patient's age, recruitment from a community-based oncology clinic, poor performance status, and quality-of-life at baseline. Oncologists' prognoses that were accurate to within a year were associated with greater likelihood of patients, at baseline, acknowledging that they were terminally ill (OR = 12.20, 95% CI = 2.24-66.59), engaging in an end-of-life discussion (OR = 4.22, 95% CI = 1.45-12.29), completing a do-not-resuscitate (DNR) order (OR = 2.94, 95% CI = 1.03-8.41), a lower likelihood of using palliative chemotherapy (OR = 0.30, 95% CI = 0.11-0.85), and clinical trial enrollment (OR = 0.09, 95% CI = 0.02-0.50).

CONCLUSIONS

Oncologists are able to estimate their patients' life expectancy to within a year. Accuracy to within a year is associated with higher rates of DNR order completion, advance care planning, and lower likelihood of chemotherapy use near death.

A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?

Background

Prognostic accuracy in palliative care is valued by patients, carers, and healthcare professionals. Previous reviews suggest clinicians are inaccurate at survival estimates, but have only reported the accuracy of estimates on patients with a cancer diagnosis.

Objectives

To examine the accuracy of clinicians’ estimates of survival and to determine if any clinical profession is better at doing so than another.

Data Sources

MEDLINE, Embase, CINAHL, and the Cochrane Database of Systematic Reviews and Trials. All databases were searched from the start of the database up to June 2015. Reference lists of eligible articles were also checked.

Eligibility Criteria

Inclusion criteria: patients over 18, palliative population and setting, quantifiable estimate based on real patients, full publication written in English. Exclusion criteria: if the estimate was following an intervention, such as surgery, or the patient was artificially ventilated or in intensive care.

Study Appraisal and Synthesis Methods

A quality assessment was completed with the QUIPS tool. Data on the reported accuracy of estimates and information about the clinicians were extracted. Studies were grouped by type of estimate: categorical (the clinician had a predetermined list of outcomes to choose from), continuous (open-ended estimate), or probabilistic (likelihood of surviving a particular time frame).

Results

4,642 records were identified; 42 studies fully met the review criteria. Wide variation was shown with categorical estimates (range 23% to 78%) and continuous estimates ranged between an underestimate of 86 days to an overestimate of 93 days. The four papers which used probabilistic estimates tended to show greater accuracy (c-statistics of 0.74–0.78). Information available about the clinicians providing the estimates was limited. Overall, there was no clear “expert” subgroup of clinicians identified.

Limitations

High heterogeneity limited the analyses possible and prevented an overall accuracy being reported. Data were extracted using a standardised tool, by one reviewer, which could have introduced bias. Devising search terms for prognostic studies is challenging. Every attempt was made to devise search terms that were sufficiently sensitive to detect all prognostic studies; however, it remains possible that some studies were not identified.

Conclusion

Studies of prognostic accuracy in palliative care are heterogeneous, but the evidence suggests that clinicians’ predictions are frequently inaccurate. No sub-group of clinicians was consistently shown to be more accurate than any other.

Implications of Key Findings

Further research is needed to understand how clinical predictions are formulated and how their accuracy can be improved.

ENT COBRA (Consortium for Brachytherapy Data Analysis): interdisciplinary standardized data collection system for head and neck patients treated with interventional radiotherapy (brachytherapy)

PURPOSE

Aim of the COBRA (Consortium for Brachytherapy Data Analysis) project is to create a multicenter group (consortium) and a web-based system for standardized data collection.

MATERIAL AND METHODS

GEC-ESTRO (Groupe Européen de Curiethérapie - European Society for Radiotherapy & Oncology) Head and Neck (H&N) Working Group participated in the project and in the implementation of the consortium agreement, the ontology (data-set) and the necessary COBRA software services as well as the peer reviewing of the general anatomic site-specific COBRA protocol. The ontology was defined by a multicenter task-group.

RESULTS

Eleven centers from 6 countries signed an agreement and the consortium approved the ontology. We identified 3 tiers for the data set: Registry (epidemiology analysis), Procedures (prediction models and DSS), and Research (radiomics). The COBRA-Storage System (C-SS) is not time-consuming as, thanks to the use of "brokers", data can be extracted directly from the single center's storage systems through a connection with "structured query language database" (SQL-DB), Microsoft Access(®), FileMaker Pro(®), or Microsoft Excel(®). The system is also structured to perform automatic archiving directly from the treatment planning system or afterloading machine. The architecture is based on the concept of "on-purpose data projection". The C-SS architecture is privacy protecting because it will never make visible data that could identify an individual patient. This C-SS can also benefit from the so called "distributed learning" approaches, in which data never leave the collecting institution, while learning algorithms and proposed predictive models are commonly shared.

CONCLUSIONS

Setting up a consortium is a feasible and practicable tool in the creation of an international and multi-system data sharing system. COBRA C-SS seems to be well accepted by all involved parties, primarily because it does not influence the center's own data storing technologies, procedures, and habits. Furthermore, the method preserves the privacy of all patients.

Overall survival after reirradiation of spinal metastases - independent validation of predictive models

BACKGROUND

It is unknown if survival prediction tools (SPTs) sufficiently predict survival in patients who undergo palliative reirradiation of spinal metastases. We therefore set out to clarify if SPTs can predict survival in this patient population.

METHODS

We retrospectively analyzed spinal reirradiations performed (n = 58, 52 patients, 44 included in analysis). SPTs for patients with spinal metastases were identified and compared to a general palliative score and to a dedicated SPT to estimate prognosis in palliative reirradiation independent of site (SPT-Nieder).

RESULTS

Consistently in all tests, SPT-Nieder showed best predictive performance as compared to other tools. Items associated with survival were general condition (KPS), liver metastases, and steroid use. Other factors like primary tumor site, pleural effusion, and bone metastases were not correlated with survival. We adapted an own score to the data which performed comparable to SPT-Nieder but avoids the pleural effusion item. Both scores showed good performance in identifying long-term survivors with late recurrences.

CONCLUSIONS

Survival prediction in case of spinal reirradiation is possible with sufficient predictive separation. Applying SPTs in case of reirradiation helps to identify patients with good life expectancy who might benefit from dose escalation or longer treatment courses.

Factors Associated with Short-Term Mortality After Surgical Oncologic Emergencies

BACKGROUND

The clinical outcome of patients with oncologic emergencies is often poor and mortality is high. It is important to determine which patients may benefit from invasive treatment, and for whom conservative treatment and/or palliative care would be appropriate. In this study, prognostic factors for clinical outcome are identified in order to facilitate the decision-making process for patients with surgical oncologic emergencies.

METHODS

This was a prospective registration study for patients over 18 years of age, who were consulted for surgical oncologic emergencies between November 2013 and April 2014. Multiple variables were registered upon emergency consultation, and the follow-up period was 90 days. Multivariate logistic regression analysis was performed to identify factors associated with 30- and 90-day mortality.

RESULTS

During the study period, 207 patients experienced surgical oncologic emergencies-101 (48.8 %) men and 106 (51.2 %) women, with a median age of 64 years (range 19-92). The 30-day mortality was 12.6 % and 90-day mortality was 21.7 %. Factors significantly associated with 30-day mortality were palliative intent of cancer treatment prior to emergency consultation (p = 0.006), Eastern Cooperative Oncology Group performance score (ECOG-PS) >0 (p for trend: p = 0.03), and raised lactate dehydrogenase (LDH) (p < 0.001). Additional factors associated with 90-day mortality were low handgrip strength (HGS) (p = 0.01) and low albumin (p = 0.002).

CONCLUSIONS

Defining the intent of prior cancer treatment and the ECOG-PS are of prognostic value when deciding on treatment for patients with surgical oncologic emergencies. Additional measurements of HGS, LDH, and albumin levels can serve as objective parameters to support the clinical assessment of individual prognosis.

Mortality Risk Prediction: Can Comorbidity Indices Be Improved With Psychosocial Data?

BACKGROUND

Predicting risk of premature death is one of the most basic tasks in medicine and public health, but has proven to be difficult over the long term even with the best prognostic models. One popular strategy has been to improve prognostic models with candidate genes and other novel biomarkers. However, the gains in predictive power have been modest and the costs have been high, leading to a demand for cost-effective alternatives. We conducted a proof-of-principle investigation to examine whether simple, cheap, and noninvasive paper-and-pencil measures of social class and personality phenotype could improve the performance of one of the most widely used prediction models for all-cause mortality, the Charlson Comorbidity Index (CCI).

METHODS

We used data from baseline and 25-year mortality follow-up of the UK Health and Lifestyle Study cohort. In a subset of the cohort, we first identified 5 psychosocial factors highly predictive of mortality: income, education, type A personality, communalism (preference for the company of others), and "lie" scale (a measure of denial, putatively associated with ill health). We then examined the predictive performance of the CCI with and without these measures in a validation subsample.

RESULTS

Across 5-, 10-, 15-, 20-, and 25-year time horizons, the psychosocially augmented CCI showed substantially better discrimination [area under the receiver-operating curves (95% confidence interval) from 0.83 (0.81-0.85) to 0.84 (0.83-0.86)] than the CCI [area under the receiver-operating curves from 0.74 (0.71-0.76) to 0.77 (0.76-0.79)]. These translated into net reclassification improvements from 27% (23%-31%) to 35% (32%-38%) of survivors and from 23% (17%-30%) to 34% (17%-30%) of decedents; and 23%-42% reductions in the Number Needed to Screen. Calibration improved at all time horizons except 25 years, where it was decreased.

CONCLUSION

Widespread attempts to improve prognostic models might consider not only novel biomarkers, but also psychosocial questionnaire measures.

Prediction of life expectancy in patients with spinal epidural metastasis

BACKGROUND

The treatment of spinal epidural metastasis is multidisciplinary and usually involves a team of medical oncologists, radiologists, radiotherapists, and spinal surgeons. Life expectancy is one of the factors considered when deciding whether surgery is warranted. Because expert estimates of life expectancy are generally not reliable, a prediction model is needed. Here, we temporally validated a model that was previously validated geographically.

METHODS

The records of 110 consecutive patients who were referred with a spinal epidural metastasis were collected prospectively from 2009 to 2013 in order to validate the model, which was published in 2011. The actual and estimated life expectancies were represented graphically, and calibration and discrimination were determined. The calibration slope, Harrell's c-index, D, and R2D were calculated. Hazard ratios in the derivation set of 2011 were compared with the validation set. Misspecification was determined using the joint test for β*.

RESULTS

The calibration slope was 0.64 ± 0.15 (95% CI: 0.34-0.94), Harrell's c-index was 0.72, D was 1.08, and R2D was 0.22, indicating slightly worse discrimination in the derivation set. The joint test for β* = 0 was statistically significant and indicated misspecification; however, this misspecification was attributed entirely to the surgical group.

CONCLUSIONS

We validated a prediction model for surgical decision making, showing that the model's overall performance is good. Based on these results, this model will help clinicians to decide whether to offer surgery to patients with spinal epidural metastasis.