Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study

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.

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.

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.

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.

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.

Dexamethasone inhibits NF‑кBp65 and HMGB1 expression in the pancreas of rats with severe acute pancreatitis

Severe acute pancreatitis (SAP) starts as a local inflammation of pancreatic tissue that induces the development of multiple extra-pancreatic organ dysfunction; however, the underlying mechanisms remain unclear. The present study was designed to evaluate the effect of dexamethasone (DXM) on pancreatic damage and to investigate the role of high-mobility group box-1 (HMGB1) and nuclear factor-κB (NF-κBp65) in the development of SAP in animal and cell models. For the in vivo experiment, 35 Sprague-Dawley rats were randomly assigned to three groups: The sham-operation control group, the SAP group and the DXM treatment group. Histological analysis revealed that, when DXM was infused into SAP rats, edema formation and structural alterations with necrosis were reduced, and the number of apoptotic cells was markedly reduced. In addition, compared with the SAP group, the expression level of HMGB1 was significantly decreased in the nucleus and the expression level of NF-κBp65 was significantly decreased in the cytoplasm from rats treated with DXM. In vitro, DXM was able to suppress the apoptosis and cell death induced by caerulein (CAE), and DXM could suppress the expression of NF-κBp65 and HMGB1 induced by CAE, as demonstrated by western blotting and immunofluorescence analysis. Therefore, these results provide an experimental basis for investigating the underlying therapeutic mechanisms of DXM treatment for SAP.

The Relationship Between Tumor Glucose Metabolism and Host Systemic Inflammatory Responses in Patients with Cancer: A Systematic Review

One of the most important and long recognized characteristics of tumor cells is their dysregulated cellular energetics with anaerobic driven glucose uptake. In patients with cancer, the prognostic value of the systemic inflammatory response has been well established, and the recent combination of PET and CT scanning combines the assessment of tumor physiologic activity with detailed anatomic localization. The aim of this study was to perform a systematic review of the assessment of the relationship between both the tumor and the host inflammatory responses using PET/CT. Methods: An extensive literature review using targeted subject headings was performed in the U.S. National Library of Medicine, the Excerpta Medica database, and the Cochrane Database of Systematic Reviews on March 31, 2018. On completion of the online search, the title and abstracts of each identified study were examined for relevance. Studies with duplicate datasets, not available in English, and that did not have full text availability were excluded. Full texts of relevant articles were obtained and were then examined to identify any further relevant articles. Results: Twelve studies containing 2,588 patients were included in the final analysis. All of the included studies used the ¹⁸F-FDG tracer in PET/CT imaging and had biochemical assessment of the systemic inflammatory response. Most studies showed a direct relationship between the tumor and bone marrow glucose uptake and host systemic inflammatory responses as measured by C-reactive protein (n = 2), albumin (n = 2), white cell count (n = 3), neutrophils (n = 2), and platelets (n = 2). Most of the studies (n = 8) also showed a direct relationship between tumor and bone marrow glucose uptake and poor outcomes. Conclusion: This review suggests a direct relationship between the tumor and bone marrow glucose uptake and host systemic inflammation. This may suggest new approaches for more optimal therapeutic targeting and monitoring strategies in patients with cancer.