Combination of initial palliative prognostic index and score change provides a better prognostic value for terminally ill cancer patients: a six-year observational cohort study

Changepoint Detection in Heart Rate Variability Indices in Older Patients Without Cancer at End of Life Using Ballistocardiography Signals: Preliminary Retrospective Study

BACKGROUND

In an aging society such as Japan, where the number of older people continues to increase, providing in-hospital end-of-life care for all deaths, and end-of-life care outside of hospitals, such as at home or in nursing homes, will be difficult. In end-of-life care, monitoring patients is important to understand their condition and predict survival time; this information gives family members and caregivers time to prepare for the end of life. However, with no clear indicators, health care providers must subjectively decide if an older patient is in the end-of-life stage, considering factors such as condition changes and decreased food intake. This complicates decisions for family members, especially during home-based care.

OBJECTIVE

The purpose of this preliminary retrospective study was to determine whether and how changes in heart rate variability (HRV) indices estimated from ballistocardiography (BCG) occur before the date of death in terminally ill older patients, and ultimately to predict the date of death from the changepoint.

METHODS

This retrospective pilot study assessed the medical records of 15 older patients admitted to a special nursing home between August 2019 and December 2021. Patient characteristics and time-domain HRV indices such as the average normal-to-normal (ANN) interval, SD of the normal-to-normal (SDNN) interval, and root mean square of successive differences (RMSSD) from at least 2 months before the date of death were collected. Overall trends of indices were examined by drawing a restricted cubic spline curve. A repeated measures ANOVA was performed to evaluate changes in the indices over the observation period. To explore more detailed changes in HRV, a piecewise regression analysis was conducted to estimate the changepoint of HRV indices.

RESULTS

The 15 patients included 8 men and 7 women with a median age of 93 (IQR 91-96) years. The cubic spline curve showed a gradual decline of indices from approximately 30 days before the patients' deaths. The repeated measures ANOVA showed that when compared with 8 weeks before death, the ratio of the geometric mean of ANN (0.90, 95% CI 0.84-0.98; P=.005) and RMSSD (0.83, 95% CI 0.70-0.99; P=.03) began to decrease 3 weeks before death. The piecewise regression analysis estimated the changepoints for ANN, SDNN, and RMSSD at -34.5 (95% CI -42.5 to -26.5; P<.001), -33.0 (95% CI -40.9 to -25.1; P<.001), and -35.0 (95% CI -42.3 to -27.7; P<.001) days, respectively, before death.

CONCLUSIONS

This preliminary study identified the changepoint of HRV indices before death in older patients at end of life. Although few data were examined, our findings indicated that HRV indices from BCG can be useful for monitoring and predicting survival time in older patients at end of life. The study and results suggest the potential for more objective and accurate prognostic tools in predicting end-of-life outcomes.

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.

Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study

BACKGROUND

An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life.

OBJECTIVE

This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings.

METHODS

This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance.

RESULTS

From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase.

CONCLUSIONS

We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.

Prognostic evaluation in patients with advanced cancer in the last months of life: ESMO Clinical Practice Guideline

•This ESMO Clinical Practice Guideline provides key recommendations for using prognostic estimates in advanced cancer. •The guideline covers recommendations for patients with cancer and an expected survival of months or less. •An algorithm for use of clinical predictions, prognostic factors and multivariable risk prediction models is presented. •The author group encompasses a multidisciplinary group of experts from different institutions in Europe, USA and Asia. •Recommendations are based on available scientific data and the authors’ collective expert opinion.

Knowledge and use of prognostic scales by oncologists and palliative care physicians in adult patients with advanced cancer: A national survey (ONCOPRONO study)

BACKGROUND

Prognostic scales exist to estimate patient survival in advanced cancer. However, there are no studies evaluating their use and practice. The objective of this study was to evaluate in a nationwide study the proportion of oncologists and palliative care physicians who had knowledge of these scales.

METHODS

A descriptive, national, cross-sectional study was conducted via an online questionnaire to oncologists and palliative care physicians across France.

RESULTS

Palliative care physicians had better knowledge of the scales than oncologists (42.3% (n = 74) vs. 27.8% (n = 33), p = 0.015). The Palliative Performance Status (PPS) and Pronopall Scale were the best-known (51.4% (n = 55) and 65.4% (n = 70), respectively) and the most widely used (35% (n = 28) and 60% (n = 48), respectively). Improved training in the use of these scales was requested by 85.4% (n = 251) of participants, while 72.8% (n = 214) reported that they did not use them at all. Limited training and lack of consensus on which scale to use were cited as the main obstacles to use.

CONCLUSION

This is the first national study on the use of prognostic scales in advanced cancer. Our findings highlight a need to improve training in these scales and to reach a consensus on scale selection.

Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients

Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant component in survival prognostication. While current performance status evaluation tools are limited to their subjective nature, the advent of wearable technology enables continual recordings of patients' activity and has the potential to measure performance status objectively. We hypothesize that wristband actigraphy monitoring devices can predict in-hospital death of end-stage cancer patients during the time of their hospital admissions. The objective of this study was to train and validate a long short-term memory (LSTM) deep-learning prediction model based on activity data of wearable actigraphy devices. The study recruited 60 end-stage cancer patients in a hospice care unit, with 28 deaths and 32 discharged in stable condition at the end of their hospital stay. The standard Karnofsky Performance Status score had an overall prognostic accuracy of 0.83. The LSTM prediction model based on patients' continual actigraphy monitoring had an overall prognostic accuracy of 0.83. Furthermore, the model performance improved with longer input data length up to 48 h. In conclusion, our research suggests the potential feasibility of wristband actigraphy to predict end-of-life admission outcomes in palliative care for end-stage cancer patients. Clinical Trial Registration: The study protocol was registered on ClinicalTrials.gov (ID: NCT04883879).

The Magnitude and Effects of Early Integration of Palliative Care Into Oncology Service Among Adult Advanced Cancer Patients at a Tertiary Care Hospital

Background

Palliative care (PC) has a positive effect on symptom burden, quality of life, psychosocial communication, prognostic understanding, mood, and quality of care at the end of life of patients with advanced cancer.

Objectives

To investigate the timing of the first palliative consultation and referral of advanced cancer patients to the palliative care service and their determinants at King Faisal Specialist Hospital and Research Center (KFSHRC), Jeddah, Saudi Arabia.

Subjects and methods

A retrospective cohort study was conducted at KFSHRC. It included advanced cancer patients who died between January 1, 2019 and Jun 30, 2020. The dependent variable of primary interest is the timing of PC consultation and the timing of PC referral. The independent variables included age, sex, marital status, nationality, date of death, types of cancer, Eastern Cooperative Oncology Group (ECOG), palliative performance status (PPS), palliative prognostic index (PPI), code status (do not resuscitate [DNR]), the severity of symptoms (assessed by the Edmonton Symptom Assessment System - Revised [ESAS-r]), referral to home health care (HHC), referral to long-term care (LTC), referral to interdisciplinary team (IDT), length of survival after the first PC consultation, length of survival after the referral to the PC service, length of hospital stay, frequency of emergency room (ER) visits and hospital admission in the last year before death, and involvement in bereavement with advanced care planning (ACP) services.

Results

Of the 210 advanced cancer patients, 109 (51.9%) were male, and their ages ranged between 18 and 90 years. More than half of patients (56.7%) had a history of PC consultation. Among them, PC consultation was described as late in 60.5% of patients. Concerning the timing of palliative care referral among advanced cancer patients, it was too late and much too late among 25.7% and 58.1% of them, respectively. Patients who visited ER more frequently (≥3 times) (p=0.014) and those who referred to HHC (p=0.005) were more likely to consult PC early compared to their counterparts. Length of survival was significantly higher among patients who reported early PC consultation compared to those without PC consultation and those with late PC consultation, p<0.001. Referral to PC for both transfer of care and symptom management was associated with earlier PC consultation, p=0.021. Patients who were admitted to the hospital three times or more were less likely to be much too late referred to PC services, p=0.046. Also, patients who were not referred to long-term care or home health care were more likely to be referred to PC services much too late, p<0.001. Among 28.8% of patients whose PPS ranged between 30% and 50% compared to 14.9% of those whose PPS ranged between 10% and 20% expressed too late referral time to PC, p=0.040.

Conclusion

In a considerable proportion of terminal cancer patients, palliative care was consulted late, and the timing of palliative care referral was too late/much too late among most of those consulted palliative care. Length of survival was higher among patients who reported early PC consultation and who with ideal referral time to PC services than others. Therefore, future considerations to facilitate early integration of palliative care in cancer patients are highly recommended through mainly improving staff education in communication skills and palliative care approach.

Palliative Prognostic Index accuracy of survival prediction in an inpatient palliative care service at a Brazilian tertiary hospital

PURPOSE

The Palliative Prognostic Index (PPI) was developed to improve survival prediction for advanced cancer patients. However, there is limited data about the PPI application in a real-world scenario. This study aimed to assess the accuracy of PPI > 6 in predicting survival of cancer inpatients.

METHODS

A prospective observational cohort in an inpatient palliative care service at a tertiary hospital in São Paulo-SP, Brazil, between May 2011 and December 2018.

RESULTS

We included 1,376 critically ill cancer inpatients. Patients were divided into three PPI subgroups: PPI ≤ 4, PPI 4-6, and PPI ≥ 6. Their respective medium overall survival values were 44 days (95% confidence interval [CI] 35.52-52.47), 20 days (95% CI 15.40-24.59), and 8 days (95% CI 7.02-8.98), (p < 0.001). PPI ≥ 6 predicted survival of <3 weeks with a positive predictive value (PPV) of 72% and an negative predictive value (NPV) of 68% (sensitivity 67%, specificity 72%). PPI > 4 predicted survival of <6 weeks with a PPV of 88% and an NPV of 36% (sensitivity 74%, specificity 59%). When PPI was <4, the mortality rate over 3 weeks was 39% with a relative risk (RR) of 0.15 (95% CI 0.11-0.20; p < 0.001), and the 6-week mortality rate was 63% with a RR of 0.18 (95% CI 0.13-0.25; p < 0.001) compared to PPI ≥ 4.

CONCLUSIONS

PPI was a good discriminator of survival among critically ill cancer inpatients and could assist in hospital discharge decision. PPI may help healthcare policymakers and professionals in offering high-quality palliative care to patients.

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 application of the palliative prognostic index in predicting the life expectancy of patients in palliative care: a systematic review and meta-analysis

BACKGROUND

The palliative prognostic index (PPI) is a commonly used tool to predict the life expectancy in palliative care patients. However, there is no universal cutoff, and the accuracy of different cutoffs varies. Therefore, we conducted this meta-analysis to explore the validity and accuracy of different PPI scores for different survival time in palliative care setting.

METHODS

PubMed, Embase, Cochrane, Scopus and Chinese CNKI databases were searched to identify studies using the PPI as a prognostic tool to predict survival time in palliative care. We calculated pooled hazard ratios (HRs) with corresponding 95% confidence intervals (CIs), and subgroup analyses were also conducted by different cutoffs. After extracting data, we estimated the pooled sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS

We identified 15 studies with 7455 assessments. Seven of these studies were synthesized for a combined HR. The pooled HR was 1.94 (95% CI 1.54-2.44) when cutoffs were 2 and 4, and 2.34 (95% CI 1.50-3.66) when cutoffs were 4 and 6. Of all the studies, 13 studies reported their accuracy, of which four studies were assessed by meta-analysis. The sensitivity of the PPI for 3-week survival ranged from 51 to 92% and specificity ranged from 60.0 to 94.0%, respectively. The sensitivity and specificity of the PPI for 6-week survival were from 46.0 to 89.1% and from 51.7 to 84.4%, respectively. The pooled sensitivity and specificity of the PPI for 3-week survival were 68% (6 as cutoff) and 76% (6 as cutoff), respectively. As for 6-week survival prediction, the pooled sensitivity and specificity were 68% (4 as cutoff) and 82% (4 as cutoff), respectively.

CONCLUSION

The PPI is a useful prognosticator of life expectancy of patients in palliative care, especially for patients with short survival time. However, there were no universal cutoff, and the predicted life span varies. Our data eliminated that using 4 and 6 as cutoffs can better predict the patients' survival time for 3 or 6 weeks. Due to small number of studies and poor qualities of them, result may alter as more studies with better quality are enrolled in the future.

Palliative care in gynecologic oncology

PURPOSE OF REVIEW

Patients with gynecologic malignancies face many difficult issues in the course of their diseases, ranging from physical symptoms to advance care planning in light of a poor prognosis. This review examines the evidence supporting integration of palliative care early in the course of disease and symptom management, and provides a framework for difficult conversations.

RECENT FINDINGS

Palliative care has been demonstrated to improve quality of life and promote survival if integrated early in the course of disease. An evidence-based approach should guide symptom management, such as pain and nausea. Advance care planning and goals of care discussions are enhanced by a framework guiding discussion and the incorporation of empathetic responses.

SUMMARY

Palliative care is a diverse multidisciplinary field that can provide significant benefit for patients with gynecologic malignancies.

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.

Palliative Performance Scale Score at 1 Week After Palliative Care Unit Admission is More Useful for Survival Prediction in Patients With Advanced Cancer in South Korea

Background:

The Palliative Performance Scale (PPS) is a useful prognostic index in palliative care. Changes in PPS score over time may add useful prognostic information beyond a single measurement.

Objective:

To investigate the usefulness of repeated PPS measurement to predict survival time of inpatients with advanced cancer admitted to a palliative care unit (PCU) in South Korea.

Design:

Prospective observational cohort study.

Setting/Patients:

138 patients with advanced cancer admitted to a PCU in a university hospital in South Korea from June 2015 to May 2016.

Measurements:

The PPS score was measured on enrollment and after 1 week. We used Cox regression analyses to calculate hazard ratios (HRs) to demonstrate the relationship between survival time and the groups categorized by PPS and changes in PPS score, after adjusting for clinical variables.

Results:

There were significant differences in survival time among 3 groups stratified by PPS (10-20, 30-50, and ≥60) after 1 week. A group with a PPS of 10 to 20 at 1 week had the highest risk (HR: 5.18 [95% confidence interval, 1.57-17.04]) for shortened survival. On the contrary, there were no significant differences among these groups by initial PPS alone. Similarly, change in PPS was prognostic; median survival was 13 (10.96-15.04) days for those whose PPS decreased after 1 week and 27 (10.18-43.82) days for those with stable or increased PPS ( P < .001).

Conclusions:

Measuring PPS over time can be very helpful for predicting survival in terminally ill patients with cancer, beyond a single PPS measure at PCU admission.

Prognosis prediction with two calculations of Palliative Prognostic Index: further prospective validation in hospice cancer patients with multicentre study

OBJECTIVES

In palliative care settings, predicting prognosis is important for patients and clinicians. The Palliative Prognostic Index (PPI), a prognostic tool calculated using clinical indices alone has been validated within cancer population. This study was to further test the discriminatory ability of the PPI (ie, its ability to determine whether a subject will live more or less than a certain amount of time) in a larger sample but with a palliative care context and to compare predictions at two different points in time.

METHODS

Multicentre, prospective, observational study in 10 inpatient hospices in the UK. The PPI score was calculated on the day of admission (PPI₁) and again once on days 3-5 of inpatient stay (PPI₂). Patients were followed up for 6 weeks or until death, whichever was earlier.

RESULTS

Of the 1164 patients included in the study, 962 had both scores available. The results from PPI₂ showed improved sensitivity, specificity, positive predictive value and negative predictive value compared with PPI₁. For PPI₁versus PPI₂, area under receiver operator character curve (ROC) for <21 days were 0.73 versus 0.82 and for ≥42 days prediction 0.72 versus 0.80. The median survival days for patients with PPI₁ ≤4, 4.5-6 and >6 were 38 (31 to 44), 17 (14 to 19) and 5 (4 to 7).

CONCLUSION

This study showed improved discriminatory ability using the PPI score calculated between day 3and day5 of admission compared with that calculated on admission. This study further validated PPI as a prognostic tool within a palliative care population and showed recording at two time points improved accuracy.

Prognostic Tools in Patients With Advanced Cancer: A Systematic Review

PURPOSE

In 2005, the European Association for Palliative Care made recommendations for prognostic markers in advanced cancer. Since then, prognostic tools have been developed, evolved, and validated. The aim of this systematic review was to examine the progress in the development and validation of prognostic tools.

METHODS

Medline, Embase Classic and Embase were searched. Eligible studies met the following criteria: patients with incurable cancer, >18 years, original studies, population n ≥100, and published after 2003. Descriptive and quantitative statistical analyses were performed.

RESULTS

Forty-nine studies were eligible, assessing seven prognostic tools across different care settings, primary cancer types, and statistically assessed survival prediction. The Palliative Performance Scale was the most studied (n = 21,082), comprising six parameters (six subjective), was externally validated, and predicted survival. The Palliative Prognostic Score composed of six parameters (four subjective and two objective), the Palliative Prognostic Index composed of nine parameters (nine subjective), and the Glasgow Prognostic Score composed of two parameters (two objective) and were all externally validated in more than 2000 patients with advanced cancer and predicted survival.

CONCLUSION

Various prognostic tools have been validated but vary in their complexity, subjectivity, and therefore clinical utility. The Glasgow Prognostic Score would seem the most favorable as it uses only two parameters (both objective) and has prognostic value complementary to the gold standard measure, which is performance status. Further studies comparing all proved prognostic markers in a single cohort of patients with advanced cancer are needed to determine the optimal prognostic tool.

Sequential Assessments of the Eastern Cooperative Oncology Group Performance Scale Enhance Prognostic Value in Patients With Terminally Ill Cancer Receiving Palliative Care

This study aimed to assess the utility of the Eastern Cooperative Oncology Group (ECOG) performance scale assessments on days 1 and 8 of palliative care, as well as scale change between these assessments, as prognostic tools for patients with terminally ill cancer. A total of 2392 patients with terminally ill cancer who received palliative care between January 2006 and December 2011 at a single medical center were analyzed. Our study showed that the ECOG scale is a useful prognostic tool to predict life expectancy in patients with terminally ill cancer. The ECOG scale assessments at different time points under palliative care were independent predictors for overall survival. The combined ECOG scale assessments on days 1 and 8 predicted survival more precisely than using day 1 ECOG scale assessment alone.

Prognostication of Survival in Patients With Advanced Cancer: Predicting the Unpredictable?

BACKGROUND

Prognosis is a key driver of clinical decision-making. However, available prognostication tools have limited accuracy and variable levels of validation.

METHODS

Principles of survival prediction and literature on clinician prediction of survival, prognostic factors, and prognostic models were reviewed, with a focus on patients with advanced cancer and a survival rate of a few months or less.

RESULTS

The 4 principles of survival prediction are (a) prognostication is a process instead of an event, (b) prognostic factors may evolve over the course of the disease, (c) prognostic accuracy for a given prognostic factor/ tool varies by the definition of accuracy, the patient population, and the time frame of prediction, and (d) the exact timing of death cannot be predicted with certainty. Clinician prediction of survival is the most commonly used approach to formulate prognosis. However, clinicians often overestimate survival rates with the temporal question. Other clinician prediction of survival approaches, such as surprise and probabilistic questions, have higher rates of accuracy. Established prognostic factors in the advanced cancer setting include decreased performance status, delirium, dysphagia, cancer anorexia-cachexia, dyspnea, inflammation, and malnutrition. Novel prognostic factors, such as phase angle, may improve rates of accuracy. Many prognostic models are available, including the Palliative Prognostic Score, the Palliative Prognostic Index, and the Glasgow Prognostic Score.

CONCLUSIONS

Despite the uncertainty in survival prediction, existing prognostic tools can facilitate clinical decision-making by providing approximated time frames (months, weeks, or days). Future research should focus on clarifying and comparing the rates of accuracy for existing prognostic tools, identifying and validating novel prognostic factors, and linking prognostication to decision-making.

The application of the Palliative Prognostic Index, charlson comorbidity index, and Glasgow Prognostic Score in predicting the life expectancy of patients with hematologic malignancies under palliative care

Background

The clinical course for hematologic malignancy varies widely and no prognostic tool is available for patients with a hematologic malignancy under palliative care. To assess the application of the Palliative Prognostic Index (PPI), Charlson Comorbidity Index (CCI), and Glasgow Prognostic Score (GPS) as prognostic tools in patients with hematologic malignancies under palliative care.

Methods

We included 217 patients with pathologically proven hematologic malignancies under palliative care consultation service (PCCS) between January 2006 and December 2012 at a single medical center in Taiwan. Patients were categorized into subgroups by PPI, CCI, and GPS for survival analysis.

Results

The median survival was 16 days (interquartile range, 4–47.5 days) for all patients and 204 patients (94%) died within 180 days after PCCS. There was a significant difference in survival among patients categorized using the PPI (median survival 49, 15, and 7 days in patients categorized into a good, intermittent, and poor prognostic group, respectively) and the GPS (median survival 66 and 13 days for GPS 0 and 1, respectively). There was no difference in survival between patients with a GPS score of 0 versus 2, or a CCI score of 0 versus ≥1. The survival time was significantly discriminated after stratifying patients with a good PPI score based on the CCI (median survival 102 and 41 days in patients with a CCI score of 0 and ≥1, respectively) from those with a poor PPI score by using the GPS (median survival 47 and 7 days in patients with GPS scores of 0 and 1–2, respectively).

Conclusions

PPI is a useful prognosticator of life expectancy in terminally ill patients under palliative care for a hematologic malignancy. Concurrent use of the GPS and CCI improved the accuracy of prognostication using the PPI.