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

Decision tree algorithm to predict mortality in incurable cancer: a new prognostic model

OBJECTIVES

To develop and validate a new prognostic model to predict 90-day mortality in patients with incurable cancer.

METHODS

In this prospective cohort study, patients with incurable cancer receiving palliative care (n = 1322) were randomly divided into two groups: development (n = 926, 70%) and validation (n = 396, 30%). A decision tree algorithm was used to develop a prognostic model with clinical variables. The accuracy and applicability of the proposed model were assessed by the C-statistic, calibration and receiver operating characteristic (ROC) curve.

RESULTS

Albumin (75.2%), C reactive protein (CRP) (47.7%) and Karnofsky Performance Status (KPS) ≥50% (26.5%) were the variables that most contributed to the classification power of the prognostic model, named Simple decision Tree algorithm for predicting mortality in patients with Incurable Cancer (acromion STIC). This was used to identify three groups of increasing risk of 90-day mortality: STIC-1 - low risk (probability of death: 0.30): albumin ≥3.6 g/dL, CRP <7.8 mg/dL and KPS ≥50%; STIC-2 - medium risk (probability of death: 0.66 to 0.69): albumin ≥3.6 g/dL, CRP <7.8 mg/dL and KPS <50%, or albumin ≥3.6 g/dL and CRP ≥7.8 mg/dL; STIC-3 - high risk (probability of death: 0.79): albumin <3.6 g/dL. In the validation dataset, good accuracy (C-statistic ≥0.71), Hosmer-Lemeshow p=0.12 and area under the ROC curve=0.707 were found.

CONCLUSIONS

STIC is a valid, practical tool for stratifying patients with incurable cancer into three risk groups for 90-day mortality.

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

BACKGROUND

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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.

Role of systemic immune-inflammation index in predicting mortality in cancer patients in palliative care units

Aim: In our study, we aimed to investigate whether the systemic immune-inflammation index (SII) can evaluate mortality in cancer patients treated in the palliative care unit (PCU). Material and Method: Cancer patients who received palliative care treatments in the PCU were screened retrospectively, and 309 patients were included in the study. The patients were divided into two groups; hospitalizations ending with discharge as Group 1 (n=154) and hospitalizations ending with exitus as Group 2 (n=155). SII values of the two groups were compared. SII was calculated with the formula of neutrophil count x platelet count / lymphocyte count. To determine the best cut-off value for the mortality distinction ability of the SII, a Receiver Operating Curve (ROC) analysis was used. Results: The mean age and distribution of genders of the two groups were similar (p=0.706, p=0.964). There was a statistically significant difference between the SII values of the two groups (p

A prognostic model using the neutrophil-albumin ratio and PG-SGA to predict overall survival in advanced palliative lung cancer

Objective

Inflammation and malnutrition are common in patients with advanced lung cancer undergoing palliative care, and their survival time is limited. In this study, we created a prognostic model using the Inflam-Nutri score to predict the survival of these patients.

Methods

A retrospective cohort study was conducted on 223 patients with advanced, histologically confirmed unresectable lung cancer treated between January 2017 and December 2018. The cutoff values of the neutrophil-albumin ratio (NAR) and Patient-Generated Subjective Global Assessment (PG-SGA) score were determined by the X-tile program. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analysis were performed to identify prognostic factors of overall survival (OS). We then established a nomogram model. The model was assessed by a validation cohort of 72 patients treated between January 2019 and December 2019. The predictive accuracy and discriminative ability were assessed by the concordance index (C-index), a plot of the calibration curve and risk group stratification. The clinical usefulness of the nomogram was measured by decision curve analysis (DCA).

Results

The nomogram incorporated stage, supportive care treatment, the NAR and the PG-SGA score. The calibration curve presented good performance in the validation cohorts. The model showed discriminability with a C-index of 0.76 in the training cohort and 0.77 in the validation cohort. DCA demonstrated that the nomogram provided a higher net benefit across a wide, reasonable range of threshold probabilities for predicting OS. The survival curves of different risk groups were clearly separated.

Conclusions

The NAR and PG-SGA scores were independently related to survival. Our prognostic model based on the Inflam-Nutri score could provide prognostic information for advanced palliative lung cancer patients and physicians.

Clinical Relevance and Prognostic Value of Inflammatory Biomarkers: A prospective Study in Terminal Cancer Patients Receiving Palliative Care

CONTEXT

Inflammatory biomarkers have prognostic value in cancer patients, but the feasibility of their use with terminal cancer patients and the related cutoff points are poorly explored.

OBJECTIVES

To describe the percentiles values of inflammatory biomarkers; to identify their cutoff points in relation to death; and to determine the prognostic value of C-reactive protein (CRP), leukocytes, neutrophils, neutrophil/lymphocyte ratio (NLR), CRP/albumin ratio (CAR), and modified Glasgow Prognostic Score for death within 90 days, in terminal cancer patients receiving palliative care.

METHODS

Prospective cohort study that included patients who received palliative care at the Palliative Care Unit of the National Cancer Institute (Brazil) between October 2019 and March 2020. Receiver operating characteristic curves were used to identify the optimal cutoff points of the inflammatory biomarkers for the prediction of death in 90 days. Kaplan-Meier curves and Cox regression were used to verify the prognostic value of these cutoff points and concordance statistic (C-statistic) was used to test their predictive accuracy.

RESULTS

A total 205 patients (mean age: 62.5 years; female: 59%) were included in the study. The optimal cutoff points were CRP ≥6.7mg/L, CAR ≥2.0, leukocytes ≥9300/μL, neutrophils ≥7426/μL and NLR ≥6.0. All biomarkers showed prognostic value and good predictive accuracy when their cutoff points were used, especially CAR, which presented excellent discrimination power (C-statistic: 0.80).

CONCLUSION

The inflammatory biomarkers analyzed are independent predictive factors for death within 90 days in terminal cancer patients. CAR appears to be the most useful parameter for predicting survival in these patients.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Utility of Palliative Prognostic Index in Predicting Survival Outcomes in Patients With Hematological Malignancies in the Acute Ward Setting

BACKGROUND

The palliative prognostic index (PPI) predicts the life expectancy of patients with terminally ill cancer in hospice settings. This study aimed to evaluate PPI as a prognostic tool for predicting the life expectancy of patients with hematological malignancies admitted to the acute ward.

METHODS

A total of 308 patients with hematological malignancies admitted to the hematological ward at a medical center between January 2016 and December 2017 were consecutively enrolled. PPI was scored within 24 h of admission. All patients were categorized into 3 groups by PPI for comparing survival and in-hospital mortality rates.

RESULTS

The median survival times were 38.4, 3.6, and 1.1 months for patients with good, intermediate, and poor prognostic group, respectively. The hazard ratio was 2.31 (95% CI 1.59-3.35, p < 0.001) when comparing the intermediate and good prognosis groups, and 3.90 (95% CI 2.52-6.03, p < 0.001) when comparing the poor and good prognosis groups. Forty-five (14.6%) patients died at discharge; in-hospital mortality rates among the good, intermediate, and poor prognostic groups were 9.0%, 23.4%, and 46.4%, respectively. The adjusted odds ratio for in-hospital mortality was 1.96 (95% CI, 0.80-4.82, p = 0.14) and 5.25 (95% CI, 2.01-13.7, p < 0.001) for patients in the intermediate and poor prognostic groups compared to those in the good prognostic group.

CONCLUSION

PPI is an accurate prognostic tool for predicting survival times and in-hospital mortality rates in patients with hematological malignancies in an acute ward setting. PPI could assist clinicians in discussing end-of-life issues and in referring patients with hematological malignancies to palliative care.

The Utility of Glasgow Prognostic Score and Palliative Prognostic Index in Patients With Head and Neck Squamous Cell Carcinoma Under Palliative Care

OBJECTIVES

Palliative care patients with head and neck squamous cell carcinoma (HNSCC) often experience dysphagia and airway trouble; thus, each patient requires a specific palliative care plan based on their prognostication. However, no established specific prognostic tool performed on the day of starting end-of-life care is available for such patients. We assessed the accuracy of Glasgow prognostic score (GPS) and palliative prognostic index (PPI) and their combination to establish a specified prognostic tool for patients with HNSCC in end-of-life setting.

METHODS

A retrospective clinical chart review was undertaken on patients with HNSCC in end-of life setting who were decided in Kagawa University Hospital and National Hospital Organization Shikoku Cancer Center between April 2011 and March 2019. The patients were divided into 2 categories according to GPS (0-1 and 2) and PPI (groups A-B and C). These were combined into 4 categories (PPI group A-B and GPS score 0-1: good; PPI group A-B and GPS score 2: intermediate; PPI group C and GPS score 2: poor; and PPI group C and GPS score 0-1: others). The survival curves were compared for the former 3 categories.

RESULTS

The median survival of the scores 0-1 and 2 on GPS were 114 (72-148) and 39 (25-52) days, respectively (P < .01). These of groups A-B and C on PPI were 79 (64-99) and 16 (9-29) days, respectively (P < .01). The median survival of the good, intermediate, and poor categories was 127 (73-149), 64 (44-80), and 15 (9-27) days, respectively (P < .01 among all categories).

CONCLUSIONS

In this study, the survival of terminally ill patients with HNSCC can be predicted by the GPS, PPI, and their combination with sufficient probability.