Figure - available from: Supportive Care in Cancer
This content is subject to copyright. Terms and conditions apply.
Predictive value of the final model according to the receiver operating characteristic curve analysis. The analysis yielded an area under the curve (AUC) values of 0.87 for accuracy, 0.83 for sensitivity, and 0.74 for specificity, in comparison with AUC values of 0.76 and 0.86 for accuracy with PPI and PaP, respectively
Source publication
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 Hos...
Citations
... 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. ...
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.
... 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). ...
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.
... There remains a vast unmet need of accessible blood diagnostics, particularly for routine testing which is important to the efficacy and safety of delicate cancer treatments [8][9][10][11][12][13][14]. Amino acid levels can be important clinical indicators to assess and guide cancer treatment [15][16][17]. ...
Diagnostic blood tests can guide the administration of healthcare to save and improve lives. Most clinical biosensing blood tests require a trained technician and specialized equipment to process samples and interpret results, which greatly limits test accessibility. Colorimetric paper-based diagnostics have an equipment-free readout, but raw blood obscures a colorimetric response which has motivated diverse efforts to develop blood sample processing techniques. This work uses inexpensive readily-available materials to engineer user-friendly dilution and filtration methods for blood sample collection and processing to enable a proof-of-concept colorimetric biosensor that is responsive to glutamine in 50 µL blood drop samples in less than 30 min. Paper-based user-friendly blood sample collection and processing combined with CFPS biosensing technology represents important progress towards the development of at-home biosensors that could be broadly applicable to personalized healthcare.
... CRP is a classic indicator of infection. Previous studies have shown that CRP can also be used as a prognostic indicator for hospitalised patients [39,40]. In addition, this study shows that increased leukocyte counts indicate increased mortality in hospitalised patients with infection. ...
Background
Invasive candidal infection combined with bacterial bloodstream infection is one of the common nosocomial infections that is also the main cause of morbidity and mortality. The incidence of invasive Candidal infection with bacterial bloodstream infection is increasing year by year worldwide, but data on China is still limited.
Methods
We included 246 hospitalised patients who had invasive candidal infection combined with a bacterial bloodstream infection from January 2013 to January 2018; we collected and analysed the relevant epidemiological information and used machine learning methods to find prognostic factors related to death (training set and test set were randomly allocated at a ratio of 7:3).
Results
Of the 246 patients with invasive candidal infection complicated with a bacterial bloodstream infection, the median age was 63 years (53.25–74), of which 159 (64.6%) were male, 109 (44.3%) were elderly patients (> 65 years), 238 (96.7%) were hospitalised for more than 10 days, 168 (68.3%) were admitted to ICU during hospitalisation, and most patients had records of multiple admissions within 2 years (167/246, 67.9%). The most common blood index was hypoproteinemia (169/246, 68.7%), and the most common inducement was urinary catheter use (210/246, 85.4%). Moreover, the most frequently infected fungi and bacteria were Candida parapsilosis and Acinetobacter baumannii , respectively. The main predictors of death prognosis by machine learning method are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, C-Reactive protein (CRP), leukocyte count, neutrophil count, Procalcitonin (PCT), and total bilirubin level.
Conclusion
Our results showed that the most common candida and bacteria infections were caused by Candida parapsilosis and Acinetobacter baumannii , respectively. The main predictors of death prognosis are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, CRP, leukocyte count, neutrophil count, PCT and total bilirubin level.
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.
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.
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.
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.
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.
