A combination of routine laboratory findings and vital signs can predict survival of advanced cancer patients without physician evaluation: a fractional polynomial model

Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis

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.

Optimal Machine Learning Models for Developing Prognostic Predictions in Patients With Advanced Cancer

CONTEXT

Accurate prognosis prediction for cancer patients in palliative care is critical for clinical decision-making and personalized care. Traditional statistical models have been complemented by machine learning approaches; however, their comparative effectiveness remains underexplored.

OBJECTIVES

To assess the prognostic accuracy of statistical and machine learning models in predicting 30-day survival in patients with advanced cancer using objective data, such as the result of the blood test.

METHODS

A secondary analysis of the Japan-Prognostic Assessment Tools Validation (J-ProVal) study was performed from September 2012 to April 2014. We used data from 58 palliative care services in Japan and enrolled 915 patients. Four models, fractional polynomial (FP) regression, Kernel Fisher discriminant analysis (KFDA), Kernel support vector machine (KSVM), and XGBoost, were compared using 17 objective clinical characteristics. Models were evaluated with the area under the receiver operating characteristic curve (AUC) as the primary metric.

RESULTS

The KSVM model demonstrated the highest predictive accuracy (AUC: 0.834), outperforming the FP model (AUC: 0.799). XGBoost showed comparatively lower performance; however, it was likely limited by the size of the dataset.

CONCLUSIONS

Machine learning, particularly KSVM, has high predictive accuracy in palliative care when sufficient data are available. However, our findings suggest that traditional statistical models offer advantages in stability and interpretability, underscoring the importance of tailored model selection based on data characteristics.

Is a Combination of Six Clinical Tests Useful as a Measure to Predict Short-Term Prognosis in Terminal Cancer Patients? A Prospective Observational Study in a Japanese Palliative Care Unit

Background

To address the need for short-term prognostic methods using objective measures, we developed a method to predict a 2- or 3-week prognosis using only six clinical tests (known as the WPCBAL score). However, the method has not yet been directly compared with globally accepted prognostic methods.

Objectives

This study aimed to clarify the usefulness of the WPCBAL score by comparing it with other prediction methods.

Setting/Subjects

A prospective observational study was conducted with patients admitted to the palliative care unit of a Municipal Hospital in Japan between November 2017 and May 2021.

Measurements

The primary endpoint was each prediction method's accuracy-the WPCBAL score, Glasgow Prognostic Score (GPS), Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP), Delirium-Palliative Prognostic Score (D-PaP), and Prognosis in Palliative Care Study predictor models (PiPS-A, PiPS-B)-in predicting a prognosis at 2 or 3 weeks. The secondary endpoints were sensitivity, specificity, positive and negative predictive values, area under the receiver operating characteristic curve, and each prediction method's feasibility rate.

Results

In total, 181 patients were included in this study. For the 3-week prognosis, the PaP had the highest accuracy (0.746), followed by the D-PaP (0.735), WPCBAL (0.696), PPI (0.652), and GPS (0.575). For the 2-week prognosis, the PiPS-B had the highest accuracy (0.702), followed by the WPCBAL (0.696) and PiPS-A (0.641).

Conclusions

The WPCBAL score's accuracy in predicting a 2- or 3-week prognosis was comparable to that of commonly used prognostic methods, thus suggesting its usefulness as a short-term prognostic method.

Palliative prognostic scores for survival prediction of cancer patients: a systematic review and meta-analysis

BACKGROUND

The palliative prognostic score is the most widely validated prognostic tool for cancer survival prediction, with modified versions available. A systematic evaluation of palliative prognostic score tools is lacking. This systematic review and meta-analysis aimed to evaluate the performance and prognostic utility of palliative prognostic score, delirium-palliative prognostic score, and palliative prognostic score without clinician prediction in predicting 30-day survival of cancer patients and to compare their performance.

METHODS

Six databases were searched for peer-reviewed studies and grey literature published from inception to June 2, 2023. English studies must assess palliative prognostic score, delirium-palliative prognostic score, or palliative prognostic score without clinician-predicted survival for 30-day survival in adults aged 18 years and older with any stage or type of cancer. Outcomes were pooled using the random effects model or summarized narratively when meta-analysis was not possible.

RESULTS

A total of 39 studies (n = 10 617 patients) were included. Palliative prognostic score is an accurate prognostic tool (pooled area under the curve [AUC] = 0.82, 95% confidence interval [CI] = 0.79 to 0.84) and outperforms palliative prognostic score without clinician-predicted survival (pooled AUC = 0.74, 95% CI = 0.71 to 0.78), suggesting that the original palliative prognostic score should be preferred. The meta-analysis found palliative prognostic score and delirium-palliative prognostic score performance to be comparable. Most studies reported survival probabilities corresponding to the palliative prognostic score risk groups, and higher risk groups were statistically significantly associated with shorter survival.

CONCLUSIONS

Palliative prognostic score is a validated prognostic tool for cancer patients that can enhance clinicians' confidence and accuracy in predicting survival. Future studies should investigate if accuracy differs depending on clinician characteristics. Reporting of validation studies must be improved, as most studies were at high risk of bias, primarily because calibration was not assessed.

Association between ICW/TBW ratio and cancer prognosis: Subanalysis of a population-based retrospective multicenter study

BACKGROUND & AIMS

Disease burden is known to alter cellular integrity and water balance. Therefore, the intracellular water/total body water (ICW/TBW) ratio is used as an adjunctive indicator to predict disease severity and prognosis. The ICW/TBW ratio of patients with cancer, who typically present with low muscle mass, poor nutritional status, and high inflammatory response, reportedly differs from that of the healthy population. Herein, we aimed to evaluate the effect of the ICW/TBW ratio on the prognosis of different subgroups of patients with cancer.

METHODS

This multicenter cohort study included 2787 patients with malignancies between June 2014 and December 2018. The association between covariates and overall survival (OS) was assessed using restricted cubic spline models. The multivariate Cox regression model included variables demonstrating a statistical significance in the univariate Cox regression analysis (P < 0.05) without multicollinearity. The generated nomogram used the C-index and calibration curves to validate the predictive accuracy of the scoring system.

RESULTS

The optimal cut-off value for the ICW/TBW ratio was 0.61. The ICW/TBW ratio was an independent prognostic factor (hazard ratio [HR]: 0.621; 95 % confidence interval [CI]: 0.537-0.719, P < 0.001). Moreover, the ICW/TBW ratio had a greater impact on the prognosis of patients receiving chemoradiotherapy than on those receiving chemotherapy alone (chemoradiotherapy: HR = 0.495, P = 0.005 vs. chemotherapy: HR = 0.646, P < 0.001). Multivariate Cox regression analysis showed that sex, age, tumor stage, body mass index, neutrophil-to-lymphocyte ratio (NLR), and ICW/TBW ratio were associated with OS. Subsequently, a nomogram was developed incorporating these variables and yielded a C-index of 0.743.

CONCLUSIONS

The ICW/TBW ratio was associated with muscle mass, nutritional status, and inflammation. A low ICW/TBW ratio is an independent risk factor for poor prognosis in patients with cancer, especially when they are female, have advanced cancer stage, have sarcopenia, and are receiving radiotherapy.

Vital Signs in Palliative Care: A Scoping Review

Vital signs are routinely measured in secondary healthcare settings and can be used to detect clinical problems, guide treatment, and monitor response to treatment. Vital signs are less frequently measured in palliative care settings. Reasons for this are unclear. This scoping review aimed to assess the generic use of vital signs in palliative care, and its role in prognostication for adult patients with cancer receiving palliative care. Medline, Embase, and CINAHL were searched for articles involving adult patients with advanced cancer receiving palliative care who had their vital signs measured. Twenty-six articles were identified in which one or a combination of vital signs, with or without other clinical parameters, was used to prognosticate for patients. An additional three articles investigated the generic use of vital signs in patients with advanced cancer. There was significant heterogeneity between identified studies, with some indication that changes in vital signs may indicate that a patient is close to death. However, other studies suggested that patients may maintain normal vital signs until the time of death. Further studies are needed to explore whether abnormal vital signs may be used as a prognostic indicator for patients with cancer receiving palliative care.

Validation of Modified Models of Objective Prognostic Score in Patients With Advanced Cancer

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.

Long-Term Effects of a Ketogenic Diet for Cancer

A ketogenic diet has been proposed as a potential supportive therapy for cancer patients, although its long-term influence on survival rates remain controversial. In our previous report, we presented promising results for 37 of 55 patients with advanced cancer enrolled between 2013 and 2018 who remained on a ketogenic diet for at least 3 months. We followed all 55 patients until March 2023 and analyzed the data up to March 2022. For the 37 patients with previously reported promising results, the median follow-up period was 25 (range of 3-104) months and 28 patients died. The median overall survival (OS) in this subset of 37 patients was 25.1 months and the 5-year survival rate was 23.9%. We also evaluated the association between the duration of the ketogenic diet and outcome in all 55 patients, except for 2 patients with insufficient data. The patients were divided into two groups: those who followed the diet for ≥12 months (n = 21) and those who followed it for <12 months (n = 32). The median duration of the ketogenic diet was 37 (range of 12-99) months for the ≥12 months group and 3 (range of 0-11) months for the <12 months group. During the follow-up period, 41 patients died (10/21 in the ≥12 months group and 31/32 in the <12 months group). The median OS was 19.9 months (55.1 months in the ≥12 months group and 12 months in the <12 months group). Following the inverse probability of treatment weighting to align the background factors of the two groups and make them comparable, the adjusted log-rank test showed a significantly better OS rate in the group that continued the ketogenic diet for a longer period (p < 0.001, adjusted log-rank test). These results indicate that a longer continuation of the ketogenic diet improved the prognosis of advanced cancer patients.

Risk factors of sudden unexpected death in patients with advanced cancer near the end of life

BACKGROUND

The definition of sudden unexpected death (SUD) in patients with advanced cancer near the end of life (EOL) was unclear.

METHODS

This study was conducted as a single-center retrospective analysis. We analyzed 1,282 patients who died of advanced cancer from August 2011 to August 2019 retrospectively. We divided into patients who died within 24 h after the acute change of general condition or others and analyzed risk factors by a multiple logistics method. The reason for SUD was found, the reason is detected by using an electronic medical record retrospectively. The risk factors in SUD were analyzed using age, sex, EOL symptom and treatment, the primary site of cancer, metastatic site of cancer, comorbidly, chemotherapy, and Eastern Cooperative Oncology Group Performance Status. The primary endpoint was to identify the frequency and risk factors of SUD in patients with advanced cancer near the EOL.

RESULTS

As a background, the median age is 73 years old, 690 males, 592 females, 227 gastroesophageal cancers, 250 biliary pancreatic cancers, 54 hepatocellular carcinomas, 189 colorectal cancer, 251 lung cancers, 71 breast cancers, 58 urological malignancies, 60 gynecological malignancies, 47 head and neck cancer, 31 hematological malignancies, and 22 sarcomas. The number of patients who died suddenly was 93 (7.2%) at EOL. In a multivariate analysis, Age (ORs 0.619), sex (ORs 1.700), patients with EOL delirium (ORs 0.483), nausea and vomiting (ORs 2.263), 1L or more infusion (ORs 3.479), EOL opioids (ORs 0.465), EOL sedations (ORs 0.339), and with cardiac comorbidity (ORs 0.345) were independent risk factors.

CONCLUSIONS

The frequency of patients who died suddenly was 7.2% (n = 93) at EOL. Age, sex, EOL symptom, EOL treatment, and cardiac comorbidity were independent risk factors in patients with advanced cancer near the EOL. Information on these risk factors is useful to explaining their EOL in advance.

Dynamic Risk Prediction of 30-Day Mortality in Patients With Advanced Lung Cancer: Comparing Five Machine Learning Approaches

PURPOSE

Administering systemic anticancer treatment (SACT) to patients near death can negatively affect their health-related quality of life. Late SACT administrations should be avoided in these cases. Machine learning techniques could be used to build decision support tools leveraging registry data for clinicians to limit late SACT administration.

MATERIALS AND METHODS

Patients with advanced lung cancer who were treated at the Department of Oncology, Aalborg University Hospital and died between 2010 and 2019 were included (N = 2,368). Diagnoses, treatments, biochemical data, and histopathologic results were used to train predictive models of 30-day mortality using logistic regression with elastic net penalty, random forest, gradient tree boosting, multilayer perceptron, and long short-term memory network. The importance of the variables and the clinical utility of the models were evaluated.

RESULTS

The random forest and gradient tree boosting models outperformed other models, whereas the artificial neural network–based models underperformed. Adding summary variables had a modest effect on performance with an increase in average precision from 0.500 to 0.505 and from 0.498 to 0.509 for the gradient tree boosting and random forest models, respectively. Biochemical results alone contained most of the information with a limited degradation of the performances when fitting models with only these variables. The utility analysis showed that by applying a simple threshold to the predicted risk of 30-day mortality, 40% of late SACT administrations could have been prevented at the cost of 2% of patients stopping their treatment 90 days before death.

CONCLUSION

This study demonstrates the potential of a decision support tool to limit late SACT administration in patients with cancer. Further work is warranted to refine the model, build an easy-to-use prototype, and conduct a prospective validation study.

Correlation between the 72-hour fatality ratios and out-of-hospital cardiac arrest ratios in patients with extremely high outlier values of 57 laboratory test items: A single-center retrospective inception cohort study

The association between extremely high outlier values (EHOV) of laboratory test items (LTIs) and short-term prognosis or out-of-hospital cardiac arrest (OHCA) remains unclear. This retrospective study investigated the correlation between 72-hour fatality ratios and OHCA ratios in patients with the top 100 EHOV of 57 LTIs without focusing on the disease group and which test items were predictors of 72-hour fatality. This single-center retrospective inception cohort study enrolled patients aged ≥ 18 years who underwent any combination of laboratory tests at the Saitama Medical Center, Japan between January 1, 2008, and December 31, 2013. The primary outcome was the correlation between the 72-hour fatality ratios and OHCA ratios in patients with the top 100 EHOV for 57 LTIs without focusing on the disease group. The LTIs included hematology, blood chemistry, erythrocyte sedimentation, blood coagulation, and arterial blood gas test results. The secondary outcome was which of the 57 LTIs with the top 100 EHOV were more likely to associate with the 72-hour fatality. We evaluated the correlation between the 72-hour fatality ratios and the OHCA ratios for each laboratory test item using the Passing-Bablok regression method. The 72-hour fatality ratios for the top 100 EHOV of 57 LTIs were significantly positively correlated with the OHCA ratios. The regression coefficient of the regression line was 0.394, and the correlation coefficient (95% confidence interval) was 0.644 (0.458-0.775, P < .001). These 72-hour fatality ratios tended to be lower than the OHCA ratios. The top 100 EHOV of 13 LTIs including total bilirubin, direct bilirubin, C-reactive protein, base excess, bicarbonate ion, creatine kinase, uric acid, partial pressure of oxygen, sodium, chloride, blood urea nitrogen, aspartate aminotransferase, and lactate dehydrogenase had 72-hour fatality ratios that were above the upper limit of the linear confidence region of the regression line, with higher 72-hour fatality ratios than the OHCA ratios. The 72-hour fatality ratios for the top 100 EHOV of 57 LTIs tended to be lower than the OHCA ratios. The top 100 EHOV of these 13 LTIs were found to be more likely to associate with 72-hour fatality than OHCA.

Prognostication of the last days of life: Review article

Accurate prediction of impending death (i.e., last few days of life) is essential for terminally-ill cancer patients and their families. International guidelines state that clinicians should identify patients with impending death, communicate the prognosis with patients and families, help them with their end-of-life decision-making, and provide sufficient symptom palliation. Over the past decade, several national and international studies have been conducted that systematically investigated signs and symptoms of impending death as well as how to communicate such a prognosis effectively with patients and families. In this article, we summarize the current evidence on prognostication and communication regarding the last days of life of patients with cancer, and future directions of clinical research.

A prognostic model to identify short survival expectancy of medical oncology patients at the time of hospital discharge

Background

Hospitalization of cancer patients is associated with poor overall survival, but prognostic misclassification may lead to suboptimal therapeutic decisions and transitions of care. No model is currently available for stratifying the heterogeneous population of oncological patients after a hospital admission to a general Medical Oncology ward. We developed a multivariable prognostic model based on readily available and objective clinical data to estimate survival in oncological patients after hospital discharge.

Methods

A multivariable model and nomogram for overall survival after hospital discharge was developed in a retrospective training cohort and prospectively validated in an independent set of adult patients with solid tumors and a first admission to a unit of medical oncology. Performance of the model was assessed by C-index and Kaplan–Meier survival curves stratified by risk categories.

Results

From a population of 1089 patients with a first hospitalization, 757 patients were included in the training group [median survival, 43 weeks; 95% confidence interval (CI), 37-51 weeks] and 200 patients in the validation cohort (median survival, 44 weeks; 95% CI, 34 weeks-not reached). An accelerated failure time log-normal model was built, including five variables (primary tumor, stage, cause of admission, active treatment, and age). The C-index was 0.71 (95% CI, 0.69-0.73), with a good calibration, and adequate validation in the prospective cohort (C-index: 0.69; 95% CI, 0.65-0.74). Median survival in three predefined model-based risk groups was 10.7 weeks (high), 27.0 weeks (intermediate), and 3 years (low) in the training cohort, with comparable values in the validation cohort.

Conclusions

In oncological patients, individualized predictions of survival after hospitalization were provided by a simple and validated model. Further evaluation of the model might determine whether its use improves shared decision making at discharge.

•

Hospitalization of poor prognosis oncology patients is a frequently missed opportunity for transition to palliative care.

•

We developed and validated a prognostic index for cancer patients at hospital discharge based on five objective variables.

•

Adequate prognostic stratification at discharge may facilitate transitions of care and shared decision making.

Effect of continuous deep sedation on survival in the last days of life of cancer patients: A multicenter prospective cohort study

BACKGROUND

Continuous deep sedation is ethically controversial with respect to whether it shortens a patient's life.

AIM

To examine whether continuous deep sedation shortens patient survival from the day of Palliative Performance Scale decline to 20 (PPS20).

DESIGN

A part of a multicenter prospective cohort study (EASED study).

SETTING/PARTICIPANTS

We recruited consecutive adult patients with advanced cancer admitted to 23 participating palliative care units in 2017 in Japan. We compared survival from PPS20 between those who did and did not receive continuous deep sedation. Continuous deep sedation was defined as the continuous administration of sedative medication with the intention to keep a patient continuously unconscious to alleviate otherwise uncontrollable symptoms, but the dose of sedatives was adjusted to achieve adequate symptom relief for each patient. The propensity score-weighting method was used to control for potential confounders, and five sensitivity analyses were performed.

RESULTS

A total of 1926 patients were enrolled. Patients discharged alive were excluded, and we analyzed 1625 patients of whom 156 (9.6%) received continuous deep sedation. Median survival from PPS20 of 1625 patients was 81 h (95% CI: 77-88). The RASS scores decreased to ⩽-4 was 66% at 24 h. Continuous deep sedation was not associated with a significant survival risk (adjusted hazard ratio: 1.06, 95% CI: 0.85-1.33). All sensitivity analyses, including continuous deep sedation defined as the RASS score was ⩽-4 achieved the essentially the same results.

CONCLUSIONS

Continuous deep sedation with careful dose adjustment was not associated with shorter survival in the last days of life in patients with advanced cancer.

Teleoncology for Veterans: High Patient Satisfaction Coupled With Positive Financial and Environmental Impacts

PURPOSE

There was rapid adoption of teleoncology care in the Veterans Health Administration during the COVID-19 pandemic. One third of 9 million Veterans Health Administration enrolled Veterans live in rural areas. Although digital solutions can expand capacity, enhance care access, and reduce financial burden, they may also exacerbate rural-urban health disparities. Careful evaluation of patients' perceptions and policy tradeoffs are necessary to optimize teleoncology postpandemic.

METHODS

Patients with ≥ 1 teleoncology visit with medical, surgical, or radiation oncology between March 2020 and June 2020 were identified retrospectively. Validated, Likert-type survey assessing patient satisfaction was developed. Follow-up survey was conducted on patients with ≥ 1 teleoncology visit from August 2020 to January 2021. Travel distance, time, cost, and carbon dioxide emissions were calculated based on zip codes.

RESULTS

A hundred surveys were completed (response rate, 62%). Patients overall were satisfied with teleoncology (83% Agree or Strongly Agree) but felt less satisfied than in-person visits (47% Agree or Strongly Agree). Audiovisual component improved patient perception of involvement in care, ability to self-manage health or medical needs, and comparability to in-person visits. Follow-up survey demonstrated similar satisfaction. Total travel-related savings are as follows: 86,470 miles, 84,374 minutes, $49,720 US dollars, and 35.5 metric tons of carbon dioxide.

CONCLUSION

Veterans are broadly satisfied with teleoncology. Audiovisual capabilities are critical to satisfaction. This is challenging for rural populations with lack of technology access. Patients experienced financial and time savings, and society benefitted from reduced carbon emissions. Continued optimization is needed to enhance patient experience and address secondary effects.

Association between respiratory and heart rate fluctuations and death occurrence in dying cancer patients: continuous measurement with a non-wearable monitor

BACKGROUND

The present study aimed to explore the association between impending death and continual changes in respiratory and heart rates measured using a non-wearable monitor every minute for the final 2 weeks of life in dying cancer patients.

METHODS

In this longitudinal study, we enrolled patients in a palliative care unit and continuously measured their respiratory and heart rates via a monitor and additionally captured their other vital signs and clinical status from medical records.

RESULT

A dataset was created comprising every 24-h data collected from every-minute raw data, including information from 240 days prior to death from 24 patients (345,600 data); each patient's data were measured for 3-14 days until death. After confirming the associations between the respiratory and heat rate values on the day of death (n = 24) or other days (2-14 days before death, n = 216) and the mean, maximum, minimum, and variance of respiratory and heart rates every 24 h by univariate analyses, we conducted a repeated-measures logistic regression analysis using a generalized estimating equation. Finally, the maximum respiratory rate and mean heart rate were significantly associated with death occurring within the following 3 days (0-24 h, 0-48 h, and 0-72 h), except for the maximum respiratory rate that occurs within 0-24 h.

CONCLUSION

The maximum respiratory rate and mean heart rate measured every minute using a monitor can warn family caregivers and care staff, with the support of palliative care professionals, of imminent death among dying patients at home or other facilities.

Accuracy of the Palliative Prognostic Score With or Without Clinicians' Prediction of Survival in Patients With Far Advanced Cancer

CONTEXT

Previous studies suggest that clinicians' prediction of survival (CPS) may have reduced the accuracy of objective indicators for prognostication in palliative care.

OBJECTIVES

We aimed to examine the accuracy of CPS alone, compared to the original Palliative Prognostic Score (PaP), and five clinical/laboratory variables of the PaP in patients with far advanced cancer.

METHODS

We compared the discriminative accuracy of three prediction models (the PaP-CPS [the score of the categorical CPS of PaP], PaP without CPS [sum of the scores of only the objective variables of PaP], and PaP total score) across 3 settings: inpatient palliative care consultation team, palliative care unit, and home palliative care. We computed the area under receiver operating characteristic curve (AUROC) for 30-day survival and concordance index (C-index) to compare the discriminative accuracy of these three models.

RESULTS

We included a total of 1534 subjects with median survival of 34.0 days. The AUROC and C-index in the three settings were 0.816-0.896 and 0.732-0.799 for the PaP total score, 0.808-0.884 and 0.713-0.782 for the PaP-CPS, and 0.726-0.815 and 0.672-0.728 for the PaP without CPS, respectively. The PaP total score and PaP-CPS showed similar AUROCs and C-indices across the three settings. The PaP total score had significantly higher AUROCs and C-indices than the PaP without CPS across the three settings.

CONCLUSION

Overall, the PaP total score, PaP-CPS, and PaP without CPS showed good discriminative performances. However, the PaP total score and PaP-CPS were significantly more accurate than the PaP without CPS.

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

BACKGROUND

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

OBJECTIVES

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

DESIGN

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

SETTING

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

PARTICIPANTS

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

MAIN OUTCOME MEASURES

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

RESULTS

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

LIMITATIONS

The results may not be generalisable to other populations.

CONCLUSIONS

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

FUTURE WORK

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

TRIAL REGISTRATION

Current Controlled Trials ISRCTN13688211.

FUNDING

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

Oral dryness and moisture degree at the lingual but not buccal mucosa predict prognosis in end-of-life cancer patients

PURPOSE

The aim of this study is to investigate the association of oral dryness with overall survival and determine the threshold points of moisture degree for predicting 7-day survival in palliative care patients.

METHODS

A total of 147 consecutive palliative care patients were included between January 2017 and November 2018. Oral dryness at the lingual and buccal mucosa was measured using an oral moisture-checking device. Overall survival was compared between patients with and without oral dryness using Kaplan-Meier curves with a log-rank test. Prediction accuracy was evaluated by the receiver operating characteristic (ROC) curve and the area under the curve (AUC).

RESULTS

Median survival (95% confidence interval) in patients with oral dryness at the lingual mucosa was shorter than that in patients without oral dryness (17 [11-24] days vs. 28 [22-37] days, log-rank test, p <0.001), but not at the buccal mucosa. Time-dependent ROC revealed that the AUCs for 7-, 14-, 21-, and 28-day survival predictions were 0.72, 0.68, 0.61, and 0.59 with a cutoff value of 19.2%, respectively. The prevalence of performance status (PS) 4 and oxygen administration in the 7-day death group were higher than those in the non-7-day death group. A stratified analysis indicated that moisture degree <19.2% showed fair predictive performance with an AUC of 0.74 and 0.74, in the case of PS ≤3 or without oxygen administration.

CONCLUSION

Oral dryness was associated with increased risk of mortality in palliative care patients. Moisture degree <19.2% at the lingual mucosa predicted less than 7-day survival.

Feasibility and Clinical Usefulness of a Novel Nonwearable Sheet-Type Monitor (Nemuri SCAN): Prognostic Value of Increased Respiratory Rate in Actively Dying Patients

Objective: The objective of this study was to explore the feasibility of monitoring actively dying patients hospitalized in a palliative care unit using a nonwearable sheet-type monitor that measured the state of sleep and vital signs per minute. In addition, we aimed to clarify the incidence of increased respiratory rate and its relationship with survival time. Design and Measurement: This study was conducted at a 51-bed palliative care unit in Japan from April 2018 through October 2019. Actively dying patients hospitalized in the palliative care unit were eligible to participate. Increased respiratory rate was measured by Nemuri SCAN, and patient's information was extracted from their medical records. Results: In this study, 23 patients were monitored until death; 19 patients with an observational period of 7 days or longer (163 patient days in total) were included in this analysis. There were no adverse events due to use of the nonwearable device. The cumulative incidence of increased respiratory rate (defined as more than 30 respiratory rate per minute) was 63.16% during the observational period, and the mean time between appearance of increased respiratory rate and death was 4.17 ± 4.04 days. Conclusion: This study clearly shows that hospitalized actively dying patients can be monitored using a nonwearable sheet-type monitor that measures sleeping state and vital signs per minute. Further studies are needed to utilize these noninvasive continuous monitoring devices in daily clinical practice.

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

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'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/ ).

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.

Palliative Care for Idiopathic Pulmonary Fibrosis Patients: Pulmonary Physicians' View

CONTEXT

Although idiopathic pulmonary fibrosis (IPF) has worse outcomes compared with most malignancies, patients with IPF receive poor access to optimal palliative care.

OBJECTIVES

This study aimed to characterize the practice of pulmonologists regarding palliative care and end-of-life communication for patients with IPF and identify perceived difficulties and barriers thereto.

METHODS

Self-administered questionnaires were sent by mail to representative pulmonologists from Shizuoka prefecture, Japan. Physician-reported practice, difficulties, timing of end-of-life communication, and barriers related to palliative care were investigated.

RESULTS

Among the 135 participants, 130 (96%) completed the questionnaire. Most of the participants reported that patients with IPF complained of dyspnea and cough. However, less morphine was prescribed for IPF than for lung cancer. The participants experienced greater difficulty in providing palliative care for IPF than for lung cancer. Moreover, actual end-of-life discussions in patients with IPF were conducted later than the physician-perceived ideal timing. Among the barriers identified, few established treatment and difficulty in predicting prognosis (odds ratio [OR] 2.0; P = 0.04), discrepancies in understanding and care goals among patients, family, and medical staff (OR 2.2; P = 0.03), and inadequate communication about goal of care (OR 2.3; P = 0.003) were significantly associated with the physician-perceived difficulties in providing palliative care for patients with IPF.

CONCLUSION

Pulmonologists experienced greater difficulty in providing palliative care to patients with IPF than to those with lung cancer. Clinical studies on the optimal palliative care for patients with IPF are urgently required.

Collapse of Fluid Balance and Association with Survival in Patients with Advanced Cancer Admitted to a Palliative Care Unit: Preliminary Findings

Background: Few studies have investigated water balance as a predictor of survival in cancer patients in the last days of life. Objective: To examine the association between extracellular water (ECW), intracellular water (ICW), ratio of ECW to ICW (ECW/ICW), and survival in nonedematous and edematous patients with advanced cancer admitted to a palliative care unit. Design: A prospective observational study. Setting/Subjects: Patients with advanced cancer admitted to a palliative care unit. Measurements: Upon enrollment, bioelectrical impedance analysis was used to assess ECW, ICW, and body composition. We conducted time-to-event analyses using the Kaplan-Meier method, log-rank test, and univariate and multivariate Cox regression analyses. Results: A total of 190 of 204 patients who participated in this study had ICW and ECW measures available for analysis. The median survival was 10 days (95% confidence interval [CI] 9-12) days. The median ECW, ICW, and ECW/ICW were 18.9 L, 19.1 L, and 1.0 for 72 nonedematous patients, and 21.9 L, 20.1 L, and 1.1 for 118 edematous patients, respectively. In univariate Cox regression analysis, ICW ≤20 L was associated with a shorter survival in the nonedematous patients (hazard ratio [HR] 1.92, 95% CI 1.10-3.34, p = 0.02) and a longer survival in the edematous patients (HR 0.61, 95% CI 0.41-0.90, p = 0.01). In multivariable analysis, ICW (≤20 L vs. >20 L) remained an independent prognostic factor in edematous patients (HR 0.64, 95% CI 0.43-0.95, p = 0.03). Conclusions: Greater ICW was an independent predictor of poorer survival in cancer patients with edema in the last days of life.