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Prognostic model for patients with advanced cancer using a combination of routine blood test values

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Taeko Miyagi
Taeko Miyagi
Taeko Miyagi
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Satoshi Miyata
Satoshi Miyata
Satoshi Miyata
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Keita Tagami at National Cancer Center East, Japan, Kashiwa
Keita Tagami
  • National Cancer Center East, Japan, Kashiwa
Yusuke Hiratsuka
Yusuke Hiratsuka
Yusuke Hiratsuka
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Abstract and Figures

PurposeThe purpose of this study was to develop a simple prognostic model based on objective indicators alone, i.e., routine blood test data, without using any subjective variables such as patient’s symptoms and physician’s prediction.Methods The subjects of this retrospective study were patients at the palliative care unit of Tohoku University Hospital, Japan. Eligible patients were over 20 years old and had advanced cancer (n = 225). The model for predicting survival was developed based on Cox proportional hazards regression models for univariable and multivariable analyses of 20 items selected from routine blood test data. All the analyses were performed according to the TRIPOD statement (https://www.tripod-statement.org/).ResultsThe univariable and multivariable regression analyses identified total bilirubin, creatinine, urea/creatinine ratio, aspartate aminotransferase, albumin, total leukocyte count, differential lymphocyte count, and platelet/lymphocyte ratio as significant risk factors for mortality. Based on the hazard ratios, the area under the curve for the new risk model was 0.87 for accuracy, 0.83 for sensitivity, and 0.74 for specificity. Diagnostic accuracy was higher than provided by the Palliative Prognostic Score and the Palliative Prognostic Index. The Kaplan–Meier analysis demonstrated a survival significance of classifying patients according to their score into low-, medium-, and high-mortality risk groups having median survival times of 67 days, 34 days, and 11 days, respectively (p < 0.001).Conclusions We developed a simple and accurate prognostic model for predicting the survival of patients with advanced cancer based on routine blood test values alone that may be useful for appropriate advanced care planning in a palliative care setting.
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ORIGINAL ARTICLE
Prognostic model for patients with advanced cancer using
a combination of routine blood test values
Taeko Miyagi
1
&Satoshi Miyata
2
&Keita Tagami
1
&Yusuke Hiratsuka
1
&Mamiko Sato
1
&Ikuo Takeda
1
&
Katsura Kohata
1
&Noriaki Satake
1
&Hiroaki Shimokawa
2
&Akira Inoue
1
Received: 19 August 2020 / Accepted: 7 December 2020
#The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2020
Abstract
Purpose The purpose of this study was to develop a simple prognostic model based on objective indicators alone, i.e., routine
blood test data, without using any subjective variables such as patient’s symptoms and physician’sprediction.
Methods The subjects of this retrospective study were patients at the palliative care unit of Tohoku University Hospital, Japan.
Eligible patients were over 20 years old and had advanced cancer (n= 225). The model for predicting survival was developed
based on Cox proportional hazards regression models for univariable and multivariable analyses of20 items selected from routine
blood test data. All the analyses were performed according to the TRIPOD statement (https://www.tripod-statement.org/).
Results The univariable and multivariable regression analyses identified total bilirubin, creatinine, urea/creatinine ratio, aspartate
aminotransferase, albumin, total leukocyte count, differential lymphocyte count, and platelet/lymphocyte ratio as significant risk
factors for mortality. Based on the hazard ratios, the area under the curve for the new risk model was 0.87 for accuracy, 0.83 for
sensitivity, and 0.74 for specificity. Diagnostic accuracy was higher than provided by the Palliative Prognostic Score and the
Palliative Prognostic Index. The Kaplan–Meier analysis demonstrated a survival significance of classifying patients according to
their score into low-, medium-, and high-mortality risk groups having median survival times of 67 days, 34 days, and 11 days,
respectively (p<0.001).
Conclusions We developed a simple and accurate prognostic model for predicting the survival of patients with advanced cancer
based on routine blood test values alone that may be useful for appropriate advanced care planning in a palliative care setting.
Keywords Advanced cancer .Prognostic model .Palliative care .Blood tests .Cox regression analysis
Introduction
The availability of accurate prognostic information for pa-
tients with advanced cancer is of great importance for timely
and appropriate advance care planning (ACP), incorporat-
ing advanced healthcare decision-making [1–3]. Such infor-
mation would be useful for discussing their condition with
patients and their families and would be of help for ACP and
clinical decision-making. One of the major concerns in
clinical practice regarding patients with advanced cancers
is the need for accurate predictions of the probability of
short- and long-term survival, and such prediction is central
to optimal end-of-life decisions. It is important to devise a
simple and objective tool for predicting short- or long-term
survival that could be used by all medical personnel to pro-
vide patient care. Validated widely used prognostic tools
that have been developed thus far to predict the survival of
patients with advanced cancer include the Palliative
Prognostic Index (PPI) [4], Palliative Prognostic (PaP)
score [5], and Prognosis in Palliative Care study (PiPS)
score [6]. All of them provide acceptable sensitivity, spec-
ificity, and predictive accuracy, but have the limitation of
the survival predictions being based on several subjective
items, including the performance status, patient symptoms,
and physician judgments, in addition to biological parame-
ters. Most of them require the physicians to conduct a sub-
jective assessment of the patients’status and symptoms, a
*Taeko Miyagi
miyagi-ta@nifty.com
1
Department of Palliative Medicine, Tohoku University School of
Medicine, 2-1 Seiryomachi, Sendai, Miyagi 980-8575, Japan
2
Department of Cardiovascular Medicine, Tohoku University School
of Medicine, 2-1 Seiryomachi, Sendai, Miyagi 980-8575, Japan
https://doi.org/10.1007/s00520-020-05937-5
/ Published online: 14 January 2021
Supportive Care in Cancer (2021) 29:4431–4437
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Since the Working Group of the Research Network of the European Association for Palliative Care recommended the use of some indicators, including clinician prediction of survival (CPS), biochemical indicators, clinical signs, and psychosocial variables, to predict the survival of advanced patients (7), numerous prognostic models have been developed, evolved, and validated (8). However, most of them have incorporated subjective variables such as the patient's symptoms, performance status, and the CPS, which are greatly dependent on the evaluator's experience and competence (9). Recent research revealed that the accuracy of the palliative prognostic (PaP) score will be improved when the CPS, a well-known subjective measure, is excluded from the composite score (10). ...
... Some biological parameters like white blood cell, Score ≤88 Score >88 Score ≤88 Score >88 Score ≤88 Score ≤88 Score >88 Score >88 aminotransferase, and lactase dehydrogenase) and the WPBAL model (C-reactive protein is removed) to predict the mortality within two weeks with high specificity and sensibility. Miyagi et al. (9) formulated a tool solely based on routine blood test data, including total bilirubin, creatinine, urea/creatinine ratio, aspartate aminotransferase, albumin, total leukocyte count, differential lymphocyte count, and PLR, with accuracy of 0.87. It is generally accepted that deaths occurring within 30 days is an indicator of the quality of cancer care (4). ...
Development of a prognostic nomogram based on objective blood test data for patients with advanced cancer undergoing palliative care
Article
Full-text available
  • May 2023
Background: Accurate estimation of prognosis can help provide early palliative care to patients. However, few studies have developed nomograms that are totally based on objective blood test parameters. The current study constructed a simple and objective prognostic nomogram and validated the model using advanced cancer patients. Methods: A total of 245 patients were retrospectively analyzed (training sample, n=162; validation sample, n=54), from January 2020 to December 2021. Blood test and demographic data were collated. Cox proportional hazard regression was performed to identify the independent factors, which were built into a nomogram to visualize the probability of patient survival within 30 days. Calibration and discrimination of the model was assessed. The decision curve analysis (DCA) was developed to summarize the performance of the model in supporting decision making. Results: The median survival was 17.0 (8, 37) days and 21.0 (10, 46) days for the training set and the validation set, respectively. Serum calcium (>2.65 mmol/L), neutrophil count (<2 mmol/L and >7 mmol/L), urea (>7.6 nmol/L), and glutamic oxalacetic transaminase (>40 U/L) were identified and an easily obtained nomogram predicting the 30-day probability of mortality was developed. The nomogram model had adequate discrimination and calibration. The Harrell's concordance index (C-index) of the training set and validation set was 0.69 and 0.71, respectively, while the values of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve were 0.76 and 0.70, respectively. Conclusions: A simple and objective prognostic nomogram model for predicting the 30-day survival of patients with advanced cancer was developed and validated, with adequate calibration and discrimination. It is expected to guide practical prognosis evaluation in the clinical setting. Further validation is still required in a prospective, multicenter, and large sample study.
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... The PPI has been validated in various cancer settings, such as hospices Subramaniam et al. 2013), palliative care units (Gerber et al. 2021;Miyagi et al. 2021), 2 Si Qi Yoong et al. ...
Prognostic utility of Palliative Prognostic Index in advanced cancer: A systematic review and meta-analysis
Article
Full-text available
  • Mar 2025
  • Palliat Support Care
Objectives To evaluate the prognostic utility of Palliative Prognostic Index (PPI) scores in predicting the death of adults with advanced cancer. Methods A systematic review and meta-analysis were conducted. Six databases were searched for articles published from inception till 16 February 2024. Observational studies reporting time-to-event outcomes of PPI scores used in any setting, timing and score cutoffs were eligible. Participants were adults with advanced cancer residing in any setting. Random effects meta-analysis was used to pool hazard, risk, or odds ratios. Findings were narratively synthesized when meta-analysis was not possible. Results Twenty-three studies ( n = 11,235 patients) were included. All meta-analyses found that higher PPI scores or risk categories were significantly associated with death and, similarly, in most narratively synthesized studies. PPI > 6 vs PPI ≤ 4 (pooled adjusted HR = 5.42, 95% confidence intervals [CI] 2.01–14.59, p = 0.0009; pooled unadjusted HR = 5.05, 95% CI 4.10–6.17, p < 0.00001), 4 < PPI ≤ 6 vs PPI ≤ 4 (pooled adjusted HR = 2.04, 95% CI 1.30–3.21, p = 0.002), PPI ≥ 6 vs PPI < 6 (pooled adjusted HR = 2.52, 95% CI 1.39–4.58, p = 0.005), PPI ≤ 4 vs PPI > 6 for predicting inpatient death (unadjusted RR = 3.48, 95% CI 2.46–4.91, p < 0.00001), and PPI as a continuous variable (pooled unadjusted HR = 1.30, 95% CI 1.22–1.38, p < 0.00001) were significant predictors for mortality. Changes in PPI scores may also be useful as a prognostic factor. Significance of results A higher PPI score is likely an independent prognostic factor for an increased risk of death, but more research is needed to validate the risk groups as defined by the original development study. Meta-analysis results need to be interpreted cautiously, as only 2–4 studies were included in each analysis. Clinicians and researchers may find this useful for guiding decision-making regarding the suitability of curative and/or palliative treatments and clinical trial design.
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Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis
Article
Full-text available
  • Mar 2025
  • BMC Palliat Care
Background Prognostication of survival among patients with advanced cancer is essential for palliative care (PC) planning. The implementation of a clinical point-of-care prognostic model may inform clinicians and facilitate decision-making. While early PC referral yields better clinical outcomes, actual referral time differs by clinical contexts and accessible. To summarize the various prognostic models that may cater to these needs, we conducted a systematic review and meta-analysis. Methods A systematic literature search was conducted in Ovid Medline, Embase, CINAHL Ultimate, and Scopus to identify eligible studies focusing on incurable solid tumors, validation of prognostic models, and measurement of predictive performances. Model characteristics and performances were summarized in tables. Prediction model study Risk Of Bias Assessment Tool (PROBAST) was adopted for risk of bias assessment. Meta-analysis of individual models, where appropriate, was performed by pooling C-index. Results 35 studies covering 35 types of prognostic models were included. Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP), and Objective Prognostic Score (OPS) were most frequently identified models. The pooled C-statistic of PPI for 30-day survival prediction was 0.68 (95% CI: 0.62–0.73, n = 6). The pooled C-statistic of PaP for 30-day survival prediction was 0.76 (95% CI: 0.70–0.80, n = 11), while that for 21-day survival prediction was 0.80 (0.71–0.86, n = 4). The pooled C-statistic of OPS for 30-days survival prediction was 0.69 (95% CI: 0.65–0.72, n = 3). All included studies had high risk of bias. Conclusion PaP appears to perform better but further validation and implementation studies were needed for confirmation.
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Proteomic Profiling of Serum Extracellular Vesicles Identifies Diagnostic Signatures and Therapeutic Targets in Breast Cancer
Article
Full-text available
Analysis of extracellular vesicles (EV) is a promising noninvasive liquid biopsy approach for breast cancer detection, prognosis, and therapeutic monitoring. A comprehensive understanding of the characteristics and proteomic composition of breast cancer–specific EVs from human samples is required to realize the potential of this strategy. In this study, we applied a mass spectrometry–based, data-independent acquisition proteomic approach to characterize human serum EVs derived from patients with breast cancer (n = 126) and healthy donors (n = 70) in a discovery cohort and validated the findings in five independent cohorts. Examination of the EV proteomes enabled the construction of specific EV protein classifiers for diagnosing breast cancer and distinguishing patients with metastatic disease. Of note, TALDO1 was found to be an EV biomarker of distant metastasis of breast cancer. In vitro and in vivo analysis confirmed the role of TALDO1 in stimulating breast cancer invasion and metastasis. Finally, high-throughput molecular docking and virtual screening of a library consisting of 271,380 small molecules identified a potent TALDO1 allosteric inhibitor, AO-022, which could inhibit breast cancer migration in vitro and tumor progression in vivo. Together, this work elucidates the proteomic alterations in the serum EVs of breast cancer patients to guide the development of improved diagnosis, monitoring, and treatment strategies. Significance: Characterization of the proteomic composition of circulating extracellar vesicles in breast cancer patients identifies signatures for diagnosing primary and metastatic tumors and reveals tumor-promoting cargo that can be targeted to improve outcomes.
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Palliative prognostic scores for survival prediction of cancer patients: a systematic review and meta-analysis
Article
Full-text available
  • Feb 2024
  • J NATL CANCER I
Background The Palliative Prognostic Score (PaP) is the most widely validated prognostic tool for cancer survival prediction, with modified versions available. A systematic evaluation of PaP tools is lacking. This systematic review and meta-analysis aimed to evaluate the performance and prognostic utility of PaP, Delirium-PaP (D-PaP), and PaP without clinician prediction in predicting 30-day survival of cancer patients and compare their performance. Methods Six databases were searched for peer-reviewed studies and grey literature published from inception till 2/6/2023. English studies must assess PaP, D-PaP, or PaP without clinician predicted survival for 30-day survival in adults ≥18 years old with any stage or type of cancer. Outcomes were pooled using the random effects model or summarised narratively when meta-analysis was not possible. Results Thirty-nine studies (n = 10,617 patients) were included. PaP is an accurate prognostic tool (pooled AUC = 0.82, 95% CI 0.79-0.84) and outperforms PaP without clinician predicted survival (pooled AUC = 0.74, 95% CI 0.71-0.78), suggesting that the original PaP should be preferred. The meta-analysis found PaP and D-PaP performance to be comparable. Most studies reported survival probabilities corresponding to the PaP risk groups, and higher risk groups were significantly associated with shorter survival. Conclusions PaP 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.
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O uso de ferramenta prognóstica para estimar sobrevida e esta-belecer plano de cuidados no câncer avançado: revisão de litera-tura
Article
  • Sep 2023
Introdução: Determinar a sobrevida de pacientes com câncer avançado para orientar decisões clínicas e desejos do paciente é uma medida substancial no planejamento de cuidados avançados. Nesse contexto, as ferramentas de estimativa do prognóstico permitem avaliar o paciente de maneira abrangente, direcionando a tomada de decisões e o estabelecimento de um plano de cuidados. Objetivo: Identificar as ferramentas prognósticas utilizadas na estimativa da sobrevida de pacientes oncológicos e nos planos de cuidados. Métodos: Estudo de revisão integrativa da literatura. Três bases de dados de acesso online foram selecionadas para a pesquisa: Pubmed/Medline, Literatura Latino-Americana e do Caribe em Ciências da Saúde (LILACS) e Scientific Eletronic Library Online (SciELO). Com um vocabulário controlado na estratégia de busca em cada uma das bases de dados bibliográficas, os seguintes termos foram utilizados: “tool”, “predict”, “survival” e “advance cancer”. Resultados: Um total de 265 estudos foram identificados e 45 estudos foram incluídos. 37 ferramentas prognósticas foram identificadas, sendo frequentemente observado a Palliative Prognostic Index-PPI (13; 28,8%), Palliative Prognostic Score - PaP (9; 20%), Palliative Performance Scale - PPS (8; 17,7%), Prognostic in Palliative Care Study - PiPS (6; 13,3%), Glasgow Prognostic Score - GPS (4; 8,8%), Clinical Prediction of Survival - CPS (4; 8,8%) respectivamente. Quanto a acurácia, 23 estudos avaliaram este quesito nas ferramentas prognóstico. Para o plano de cuidados, sua aplicabilidade foi observada em 42 estudos, e dentre estes estabelecido os critérios relacionados a qualidade de vida, controle de sinais e sintomas, cuidados em fim-de-vida e tratamento na tomada de decisão. Conclusão: As ferramentas prognósticas variam quanto aos tipos e à acurácia. O seu uso apropriado e de forma individualizada para pacientes oncológicos demonstrou-se útil para estimar a sobrevida, direcionar a tomada de decisões e definir planos de cuidados multidimensionais.
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Performance of the Palliative Prognostic Index for cancer patients: A systematic review and meta-analysis
Article
Full-text available
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.
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Validation of Modified Models of Objective Prognostic Score in Patients With Advanced Cancer
Article
  • May 2023
  • J PALLIAT MED
Background: The objective prognostic score (OPS) needs to be modified to reflect practical palliative care circumstances. Objectives: We aimed to validate modified models of OPS with few or no laboratory tests for patients with advanced cancer. Design: An observational study was performed. Setting/Subjects: A secondary analysis of an international, multicenter cohort study of patients in East Asia was performed. The subjects were inpatients with advanced cancer in the palliative care unit. Measurements: We developed two modified OPS (mOPS) models to predict two-week survival: mOPS-A consisted of two symptoms, two objective signs, and three laboratory results, while mOPS-B consisted of three symptoms, two signs, and no laboratory data. We compared the accuracy of the prognostic models using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Calibration plots for two-week survival and net reclassification indices (NRIs) were compared for the two models. Survival differences between higher and lower score groups of each model were identified by the log-rank test. Results: We included a total of 1796 subjects having median survival of 19.0 days. We found that mOPS-A had higher specificity (0.805-0.836) and higher AUROCs (0.791-0.797). In contrast, mOPS-B showed higher sensitivity (0.721-0.725) and acceptable AUROCs (0.740-0.751) for prediction of two-week survival. Two mOPSs showed good concordance in calibration plots. Considering NRIs, replacing the original OPS with mOPSs improved overall reclassification (absolute NRI: 0.47-4.15%). Higher score groups of mOPS-A and mOPS-B showed poorer survival than those of lower score groups (p < 0.001). Conclusions: mOPSs used reduced laboratory data and had relatively good accuracy for predicting survival in advanced cancer patients receiving palliative care.
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Survival prediction in advanced cancer patients - a narrative review
Article
  • Apr 2023
  • Curr Opin Support Palliat Care
Purpose of review: The exploration for accurate ways to predict survival for advanced cancer patients continues to be a significant theme despite the advent of objective criteria and their combination with clinical criteria. The purpose of this article was to review some of the latest studies relating to prognostication and the capacity to predict survival during the terminal cancer stage. Recent findings: Recent studies show notable prognostication approaches using genetic tests and advanced computation methods such as machine learning, which we will summarize. Summary: Significant effort has been made to improve the accuracy of survival estimation for advanced cancer patients. The main goals are to optimize individualized patient management and uses of resources. Advanced techniques, including genetic markers and machine learning techniques, may improve the accuracy of prediction.
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Pretreatment C-reactive protein to albumin ratio for predicting overall survival in advanced pancreatic cancer patients
Article
Full-text available
  • Jun 2017
Although previous studies demonstrated that elevated C-reactive protein to albumin ratio (CAR) predicted poor prognosis in various solid tumors, little was known about the prognostic value of CAR in patients with advanced pancreatic cancer (APC). The aim of the present study was to assess CAR as one independent prognostic factor in predicting overall survival (OS) in APC patients who had received palliative chemotherapy. Data of 142 APC patients who received palliative chemotherapy between 2009 and 2014 were retrospectively documented. We classified the patients into two groups based on the optimal cutoff value of CAR identified by generating receiver operating characteristics (ROC) curve. The clinicopathological parameters were compared between two CAR groups. Pearson correlation test showed that the level of C-reactive protein (CRP) was inversely correlated with albumin (r = −0.387; P < 0.001). Kaplan-Meier analysis demonstrated overall survival (OS) was significantly longer in CAR < 0.156 group than CAR ≥ 0.156 group (11.2 vs 5.9 months, P < 0.001). CAR was an independent prognostic factor for OS in the Cox regression model (HR, 1.623; 95% CI, 1.093–2.410; P = 0.016). Furthermore, the discrimination ability of CAR (AUC = 0.648, P = 0.025) was slightly higher than that of other inflammation-based factors. Therefore, pretreatment CAR could be an independent prognostic biomarker for APC patients.
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A systematically structured review of biomarkers of dying in cancer patients in the last months of life; An exploration of the biology of dying
Article
Full-text available
Background The Neuberger review made a number of recommendations to improve end of life care, including research into the biology of dying. An important aspect of the biology of dying is the identification of biomarkers as indices of disease processes. Biomarkers have the potential to inform the current, limited understanding of the dying process and assist clinicians in recognising dying, in particular how to distinguish dying from reversible acute deterioration. Objectives To critically appraise the literature on biological factors that may be used as prognostic indicators in advanced cancer patients and to identify candidate biomarkers of the dying process that can be measured serially in cancer patients’ bodily fluids. Methods A systematically structured review was conducted using three electronic databases. A hand search of six peer-reviewed journals and conference abstracts was also conducted. Studies reporting prognostic biomarkers in cancer patients with a median survival of ≤90 days and post-mortem studies were included. Final levels of evidence and recommendations were made using the Evidence Based Medicine modified GRADE system. Results 30 articles were included. Seven prognostic biological factors demonstrated Grade A evidence (lymphocyte count, white blood cell count, serum C-reactive protein, albumin, sodium, urea and alkaline phosphatase). An additional eleven prognostic factors were identified with Grade B evidence (platelet count, international normalised ratio, serum vitamin B12, prealbumin, bilirubin, cholesterol, aspartate aminotransferase, alanine transaminase, lactate dehydrogenase, pseudocholinesterase and urate). A number of biomarkers were specifically identified in the last two weeks of life but limitations exist. No post-mortem studies met the inclusion criteria. Conclusion The biology of dying is an important area for future research, with the evidence focused on signs, symptoms and prognostic factors. This review identifies a number of common themes shared amongst advanced cancer patients and highlights candidate biomarkers which may be indicative of a common biological process to dying.
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C-reactive protein, symptoms and activity of daily living in patients with advanced cancer receiving palliative care: CRP, symptoms, and ADL in advanced cancer patients
Article
Full-text available
  • Mar 2017
Background: The association between C-reactive protein (CRP) level, symptoms, and activities of daily living (ADL) in advanced cancer patients is unclear. Methods: Secondary data analysis of a multicenter prospective cohort study consisted of 2426 advanced cancer patients referred to palliative care settings was conducted to examine the cross-sectional relationships between CRP level, symptoms, and ADL disabilities. Laboratory data, symptoms, ADL, and manual muscle testing (MMT) results were obtained at baseline. Participants were divided into four groups: low (CRP < 1 mg/dl), moderate (1 = < CRP <5 mg/dl), high (5 = < CRP < 10 mg/dl), and very high CRP (10 mg/dl = < CRP). The proportions of eight symptoms, five ADL disabilities, and three categories of MMT according to the CRP groups were tested by chi-square tests. Multiple-adjusted odd ratios (ORs) were calculated by using ordinal logistic regression after adjustment for age, gender, site of primary cancer, metastatic disease, performance status, chemotherapy, and setting of care. Results: A total of 1702 patients were analysed. Positive rates of symptoms and ADL disabilities increased with increasing CRP level. In the very high-CRP group, rates of positivity for anorexia, fatigue, and weight loss were 89.8%, 81.0%, and 79.2%, respectively, and over 70% of patients received assistance for bathing, dressing, going to the toilet, and transfer. The grade of MMT also deteriorated with increasing CRP level. Adjusted ORs for the accumulated symptoms significantly increased with increasing CRP level in the moderate-CRP, high-CRP, and very high-CRP groups [1.6 (95% confidence interval 1.2-2.0), P < 0.001; 2.5 (1.9-3.2), P < 0.001; 3.5 (2.7-4.6), P < 0.001, respectively]. Adjusted ORs for the accumulated ADL disabilities significantly increased in the very high-CRP groups [2.1 (1.5-2.9), P < 0.001]. Conclusions: Associations between CRP level, symptoms, and ADL were observed in advanced cancer patients receiving palliative care.
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The Prognostic Value of PLR in Lung Cancer, a Meta-analysis Based on Results from a Large Consecutive Cohort
Article
Full-text available
  • Oct 2016
Recently, many studies have been conducted to explore prognostic value of platelet to lymphocyte ratio (PLR) for patients with lung cancer, while the results remain controversial. We collected pretreatment, clinicopathological and follow-up data of 1388 lung cancer patients receiving surgery between 2006 and 2011 in our hospital, and reviewed relevant articles from Embase, Pubmed, Web of science databases, then performed a meta-analysis to clarify the relationship between PLR and prognosis of lung cancer patients. Finally, 11 articles with our study were included, results indicated elevated PLR was negatively related to overall survival (HR = 1.33, 95% CI: 1.10–1.62), but not related to progress-free survival (HR = 1.21, 95% CI: 0.97–1.49). Subgroup analysis suggested high PLR was correlated with poor survival in non-small cell lung cancer (HR = 1.43, 95% CI: 1.14–1.78), but not in small cell lung cancer (HR = 1.10, 95% CI: 0.76–1.58). Besides, for patients treated by chemotherapy or radiotherapy (HR = 1.66, 95% CI: 1.15–2.38) and patients in late stage (HR = 1.41, 95% CI: 1.19–1.68), PLR had significantly prognostic value. Additionally, the result was significant for patients when cut-off value of PLR was between 150 and 200 (HR = 1.47, 95% CI: 1.18–1.82). In Conclusion, this meta-analysis revealed that elevated PLR was associated with poor prognosis in lung cancer.
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Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?
Article
Full-text available
  • Jun 2016
Background: Clinician prediction of survival (CPS) has low accuracy in the advanced cancer setting, raising the need for prediction models such as the palliative prognostic (PaP) score that includes a transformed CPS (PaP-CPS) and five clinical/laboratory variables (PaP-without CPS). However, it is unclear if the PaP score is more accurate than PaP-CPS, and whether PaP-CPS helps to improve the accuracy of PaP score. We compared the accuracy among PaP-CPS, PaP-without CPS and PaP-total score in patients with advanced cancer. Patients and methods: In this prospective study, PaP score was documented in hospitalised patients seen by palliative care. We compared the discrimination of PaP-CPS versus PaP-total and PaP-without CPS versus PaP-total using four indices: concordance statistics, area under the receiver-operating characteristics curve (AUC), net reclassification index and integrated discrimination improvement for 30-day survival and 100-day survival. Results: A total of 216 patients were enrolled with a median survival of 109 d (95% confidence interval [CI] 71-133 d). The AUC for 30-day survival was 0.57 (95% CI 0.47-0.67) for PaP-CPS, 0.78 (95% CI 0.7-0.87) for PaP-without CPS, and 0.73 (95% CI 0.64-0.82) for PaP-total score. PaP-total was significantly more accurate than PaP-CPS according to all four indices for both 30-day and 100-day survival (P < 0.001). PaP-without CPS was significantly more accurate than PaP-total for 30-day survival (P < 0.05). Conclusion: We found that PaP score was more accurate than CPS, and the addition of CPS to the prognostic model reduced its accuracy. This study highlights the limitations of clinical gestalt and the need to use objective prognostic factors and models for survival prediction.
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Prognostic Significance of Systemic Inflammation-Based Lymphocyte- Monocyte Ratio in Patients with Lung Cancer: Based on a Large Cohort Study
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Increasing evidence indicates cancer-related inflammatory biomarkers show great promise for predicting the outcome of cancer patients. The lymphocyte- monocyte ratio (LMR) was demonstrated to be independent prognostic factor mainly in hematologic tumor. The aim of the present study was to investigate the prognostic value of LMR in operable lung cancer. We retrospectively enrolled a large cohort of patients with primary lung cancer who underwent complete resection at our institution from 2006 to 2011. Inflammatory biomarkers including lymphocyte count and monocyte count were collected from routinely performed preoperative blood tests and the LMR was calculated. Survival analyses were calculated for overall survival (OS) and disease-free survival (DFS). A total of 1453 patients were enrolled in the study. The LMR was significantly associated with OS and DFS in multivariate analyses of the whole cohort (HR = 1.522, 95% CI: 1.275-1.816 for OS, and HR = 1.338, 95% CI: 1.152-1.556 for DFS). Univariate subgroup analyses disclosed that the prognostic value was limited to patients with non-small-cell lung cancer (NSCLC) (HR: 1.824, 95% CI: 1.520-2.190), in contrast to patients with small cell lung cancer (HR: 1.718, 95% CI: 0.946-3.122). Multivariate analyses demonstrated that LMR was still an independent prognostic factor in NSCLC. LMR can be considered as a useful independent prognostic marker in patients with NSCLC after complete resection. This will provide a reliable and convenient biomarker to stratify high risk of death in patients with operable NSCLC.
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A combination of routine laboratory findings and vital signs can predict survival of advanced cancer patients without physician evaluation: a fractional polynomial model
Article
  • Dec 2018
Introduction: There have been no reports about predicting survival of patients with advanced cancer constructed entirely with objective variables. We aimed to develop a prognostic model based on laboratory findings and vital signs using a fractional polynomial (FP) model. Methods: A multicentre prospective cohort study was conducted at 58 specialist palliative care services in Japan from September 2012 to April 2014. Eligible patients were older than 20 years and had advanced cancer. We developed models for predicting 7-day, 14-day, 30-day, 56-day and 90-day survival by using the FP modelling method. Results: Data from 1039 patients were analysed to develop each prognostic model (Objective Prognostic Index for advanced cancer [OPI-AC]). All models included the heart rate, urea and albumin, while some models included the respiratory rate, creatinine, C-reactive protein, lymphocyte count, neutrophil count, total bilirubin, lactate dehydrogenase and platelet/lymphocyte ratio. The area under the curve was 0.77, 0.81, 0.90, 0.90 and 0.92 for the 7-day, 14-day, 30-day, 56-day and 90-day model, respectively. The accuracy of the OPI-AC predicting 30-day, 56-day and 90-day survival was significantly higher than that of the Palliative Prognostic Score or the Prognosis in Palliative Care Study model, which are based on a combination of symptoms and physician estimation. Conclusion: We developed highly accurate prognostic indexes for predicting the survival of patients with advanced cancer from objective variables alone, which may be useful for end-of-life management. The FP modelling method could be promising for developing other prognostic models in future research.
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A New Approach for Determining Short-Term, Objective Prognostic Predictive Methods for Terminal Cancer Patients Based on the Change Point of Laboratory Test Values
Article
  • Nov 2017
Background: In terminal phase cancer, predicting a prognosis precisely plays an important role for patients and their families to live meaningful lives. However, there are no established short-term, objective prognostic predictive methods. Objective: To develop simple, short-term, objective prognostic predictive methods through detecting a change point for laboratory test values. Design: A retrospective chart review. Setting/subjects: Subjects were cancer patients aged ≥16 years and discharged dead from Osaka University Hospital in 2008. Measurements: Using different laboratory test values, new prognostic predictive methods were determined based on either six laboratory test values (white blood cell [WBC], platelet [PLT], C-reactive protein, blood urea nitrogen [BUN], aspartate aminotransferase [AST], and lactase dehydrogenase [LDH]): the WPCBAL score, or five test values (WBC, PLT, BUN, AST, and LDH): the WPBAL score. Their utility, including sensitivity and specificity, was compared with that of Glasgow prognostic scores (GPSs). Results: In total, 121 cancer patients were enrolled. WPCBAL and WPBAL scores showed higher sensitivity (0.88 and 0.91 vs. 0.68), specificity (0.79 and 0.70 vs. 0.53), negative predictive value (0.98 and 0.97 vs. 0.76), and a much larger relative risk (16.5 and 14.2 vs. 1.78) as prognostic predictors within two weeks of death than GPS as a prognostic predictor within three weeks of death. Conclusion: This is the first study that suggests that the objective prognostic predictive methods, through detecting the change point of laboratory test values, are useful for predicting short-term prognosis. The WPCBAL score and WPBAL score could objectively predict the remaining lifetime within two weeks of mortality.
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Prognostic Tools in Patients With Advanced Cancer: A Systematic Review
Article
  • Jan 2017
Purpose: In 2005, the European Association for Palliative Care (EAPC) 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; 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 (PPS) Palliative Performance Scale was the most studied (n=21,082), composed of 6 parameters (6 subjective), was externally validated and predicted survival. The Palliative Prognostic Score (PaP) composed of 6 parameters (4 subjective, 2 objective), the Palliative Prognostic Index (PPI) composed of 9 parameters (9 subjective), and the Glasgow Prognostic Score (GPS) composed of 2 parameters (2 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 GPS would seem the most favourable 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 proven prognostic markers in a single cohort of patients with advanced cancer, are needed to determine the optimal prognostic tool.
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Objective Predictive Score as a Feasible Biomarker for Short-term Survival in TerminalIy Ill Patients with Cancer
Article
  • Jan 2017
Background: In palliative care, prediction of life expectancy is one of the most crucial issues for patients, family and medical staff, in order to provide appropriate end-of-life care. The aim of this study was to formulate a new objective score to predict life expectancy within 1 week for terminally ill patients with cancer. Patients and methods: Medical records were obtained from 187 terminally-ill patients with cancer who were admitted for palliative care. The biomarkers for a potential 'Objective Predictive Score' were assessed. Results: Profiling of blood parameters demonstrated that elevated levels of alanine aminotransferase (ALT), total bilirubin (T-bil), blood urea nitrogen (BUN), creatinine (Cr) and a decreased platelet count were significantly correlated with death within 1 week in a training cohort. Our formulated Objective Predictive Score was able to predict death within 1 week with high accuracy in a training and a validation cohort. Conclusion: Our scoring system might enable the assessment of prognostication with higher accuracy in a terminal care setting.
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