Predictors of Mortality in Patients with Advanced Cancer-A Systematic Review and Meta-Analysis

The Association Between Timely Documentation of Advance Care Planning, Hospital Care Consumption and Place of Death: A Retrospective Cohort Study

Objectives: (1) To describe ACPT implementation frequency in practice. (2) To assess associations of ACPT documentation with a) hospital care consumption, including systemic anti-tumor treatment in the last month(s) of life, and b) match between preferred and actual place of death, among oncology patients. Methods: A retrospective cohort study was performed. Data concerning ACPT documentation, hospital care consumption, and preferred and actual place of death were extracted from electronic patient records. Patients with completely documented ACPT (cACPT) and no ACPT were compared using multivariable logistic regression analyses. Results: ACPT was implemented in 64.5% (n = 793) of all deceased patients (n = 1230). In 17.6% (n = 216), preferred place of care or death was documented at least three months before death (cACPT). A cACPT was not associated with systemic anti-tumor treatment (Adjusted OR (AOR): 0.976; 95% CI: 0.642-1.483), but patients with cACPT had fewer diagnostic tests (AOR: 0.518; CI: 0.298-0.903) and less contacts with hospital disciplines (AOR: 0.545; CI: 0.338-0.877). In patients with cACPT, a match between preferred and actual place of death was found for 83% of the patients for whom the relevant information was available (n = 117/n = 141). In patients without ACPT, this information was mostly missing. Conclusion: Although the ACPT was implemented in two thirds of patients, timely documentation of preferred place of care or death is often missing. Yet, timely documentation of these preferences may promote out-hospital-death and save hospital care consumption.

Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements

Assessing objective physical function in patients with cancer is crucial for evaluating their ability to tolerate invasive treatments. Current assessment methods, such as the timed up and go (TUG) test and the short physical performance battery, tend to require additional resources and time, limiting their practicality in routine clinical practice. To address these challenges, we developed a system to assess physical function based on movements observed during clinical consultations and aimed to explore relevant features from inertial measurement unit data collected during those movements. As for the flow of the research, we first collected inertial measurement unit data from 61 patients with cancer while they replicated a series of movements in a consultation room. We then conducted correlation analyses to identify keypoints of focus and developed machine learning models to predict the TUG test outcomes using the extracted features. Regarding results, pelvic velocity variability (PVV) was identified using Lasso regression. A linear regression model using PVV as the input variable achieved a mean absolute error of 1.322 s and a correlation of 0.713 with the measured TUG results during five-fold cross-validation. Higher PVV correlated with shorter TUG test results. These findings provide a foundation for the development of an artificial intelligence-based physical function assessment system that operates without the need for additional resources.

Home Hospitalization in Palliative Care for Advanced Cancer and Dementia: A Systematic Review

CONTEXT

Home hospitalization (HHOSP) is an alternative care model aimed at alleviating pressure on healthcare systems and catering to the increasing patient population. It aligns with the preference for home-based palliative care (PALC) and end-of-life care.

OBJECTIVES

This study systematically reviewed the literature to evaluate HHOSP's role in PALC, focusing on hospital readmissions, length of stay, patient and caregiver safety and satisfaction, place of death, and overall survival.

METHODS

A systematic search was conducted in PubMed, Scopus, and Web of Science databases from 2013 to 2023.

PARTICIPANTS

patients of any age or gender diagnosed with advanced, metastatic, or incurable cancer or advanced dementia.

INTERVENTION

HHOSP.

COMPARATOR

usual care.

OUTCOMES

hospital readmissions, length of stay, patient and caregiver safety and satisfaction, place of death, and overall survival. The risk of bias was assessed using Cochrane tools.

RESULTS

Six studies with 843 participants from Denmark, France, Spain, and Israel were included. The overall risk of bias was moderate to high. HHOSP reduced hospital readmissions, with 42.2%-91% of patients avoiding further hospitalizations. Caregivers reported feeling safe and satisfied with HHOSP, experiencing reduced burden. Most patients died at home (52.2%-75%). Median overall survival ranged from 28 days-11.2 months.

CONCLUSION

The findings highlight HHOSP's potential as an alternative for delivering PALC, reducing hospital readmissions, and improving patient and caregiver satisfaction. Despite heterogeneity in study designs and outcomes, HHOSP aligns with patient and caregiver preferences, enhancing the quality of end-of-life care. Further standardized research is needed to optimize HHOSP implementation.

Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patients With Cancer

PURPOSE

Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patients with cancer and may help distinguish who benefits the most from palliative care support. We aim to explore the importance of several objective and subjective self-reported variables. Subjective variables were collected through electronic psycho-oncologic and palliative care self-assessment screenings. We used these variables to predict 1-year mortality.

MATERIALS AND METHODS

Between April 1, 2020, and March 31, 2021, a total of 265 patients with advanced cancer completed a patient-reported outcome tool. We documented objective and subjective variables collected from electronic health records, self-reported subjective variables, and all clinical variables combined. We used logistic regression (LR), 20-fold cross-validation, decision trees, and random forests to predict 1-year mortality. We analyzed the receiver operating characteristic (ROC) curve-AUC, the precision-recall curve-AUC (PR-AUC)-and the feature importance of the ML models.

RESULTS

The performance of clinical nonpatient variables in predictions (LR reaches 0.81 [ROC-AUC] and 0.72 [F1 score]) are much more predictive than that of subjective patient-reported variables (LR reaches 0.55 [ROC-AUC] and 0.52 [F1 score]).

CONCLUSION

The results show that objective variables used in this study are much more predictive than subjective patient-reported variables, which measure subjective burden. These findings indicate that subjective burden cannot be reliably used to predict survival. Further research is needed to clarify the role of self-reported patient burden and mortality prediction using ML.

Estimating the Prognostic Value of the NTRK Fusion Biomarker for Comparative Effectiveness Research in The Netherlands

OBJECTIVES

We evaluated the prognostic value of the neurotrophic tyrosine receptor kinase (NTRK) gene fusions by comparing the survival of patients with NTRK+ tumours with patients without NTRK+ tumours.

METHODS

We used genomic and clinical registry data from the Center for Personalized Cancer Treatment (CPCT-02) study containing a cohort of cancer patients who were treated in Dutch clinical practice between 2012 and 2020. We performed a propensity score matching analysis, where NTRK+ patients were matched to NTRK- patients in a 1:4 ratio. We subsequently analysed the survival of the matched sample of NTRK+ and NTRK- patients using the Kaplan-Meier method and Cox regression, and performed an analysis of credibility to evaluate the plausibility of our result.

RESULTS

Among 3556 patients from the CPCT-02 study with known tumour location, 24 NTRK+ patients were identified. NTRK+ patients were distributed across nine different tumour types: bone/soft tissue, breast, colorectal, head and neck, lung, pancreas, prostate, skin and urinary tract. NTRK fusions involving the NTRK3 gene (46%) and NTRK1 gene (33%) were most common. The survival analysis rendered a hazard ratio (HR) of 1.44 (95% CI 0.81-2.55) for NTRK+ patients. Using the point estimates of three prior studies on the prognostic value of NTRK fusions, our finding that the HR is > 1 was deemed plausible.

CONCLUSIONS

NTRK+ patients may have an increased risk of death compared with NTRK- patients. When using historic control data to assess the comparative effectiveness of TRK inhibitors, the prognostic value of the NTRK fusion biomarker should therefore be accounted for.

Identifying cancer patients who received palliative care using the SPICT-LIS in medical records: a rule-based algorithm and text-mining technique

BACKGROUND

Due to limited numbers of palliative care specialists and/or resources, accessing palliative care remains limited in many low and middle-income countries. Data science methods, such as rule-based algorithms and text mining, have potential to improve palliative care by facilitating analysis of electronic healthcare records. This study aimed to develop and evaluate a rule-based algorithm for identifying cancer patients who may benefit from palliative care based on the Thai version of the Supportive and Palliative Care Indicators for a Low-Income Setting (SPICT-LIS) criteria.

METHODS

The medical records of 14,363 cancer patients aged 18 years and older, diagnosed between 2016 and 2020 at Songklanagarind Hospital, were analyzed. Two rule-based algorithms, strict and relaxed, were designed to identify key SPICT-LIS indicators in the electronic medical records using tokenization and sentiment analysis. The inter-rater reliability between these two algorithms and palliative care physicians was assessed using percentage agreement and Cohen's kappa coefficient. Additionally, factors associated with patients might be given palliative care as they will benefit from it were examined.

RESULTS

The strict rule-based algorithm demonstrated a high degree of accuracy, with 95% agreement and Cohen's kappa coefficient of 0.83. In contrast, the relaxed rule-based algorithm demonstrated a lower agreement (71% agreement and Cohen's kappa of 0.16). Advanced-stage cancer with symptoms such as pain, dyspnea, edema, delirium, xerostomia, and anorexia were identified as significant predictors of potentially benefiting from palliative care.

CONCLUSION

The integration of rule-based algorithms with electronic medical records offers a promising method for enhancing the timely and accurate identification of patients with cancer might benefit from palliative care.

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.

Advances and Challenges in Nanomaterial-Based Electrochemical Immunosensors for Small Cell Lung Cancer Biomarker Neuron-Specific Enolase

Early and rapid detection of neuron-specific enolase (NSE) is highly significant, as it is putative biomarker for small-cell lung cancer as well as COVID-19. Electrochemical techniques have attracted substantial attention for the early detection of cancer biomarkers due to the important properties of simplicity, high sensitivity, specificity, low cost, and point-of-care detection. This work reviews the clinically relevant labeled and label-free electrochemical immunosensors developed so far for the analysis of NSE. The prevailing role of nanostructured materials as electrode matrices is thoroughly discussed. Subsequently, the key performances of various immunoassays are critically evaluated in terms of limit of detection, linear ranges, and incubation time for clinical translation. Electrochemical techniques coupled with screen-printed electrodes developing market level commercialization of NSE sensors is also discussed. Finally, the review concludes with the current challenges associated with available methods and provides a future outlook toward commercialization opportunities for easy detection of NSE.

Low Alanine Aminotransferase as a Marker for Sarcopenia and Frailty, Is Associated with Decreased Survival of Bladder Cancer Patients and Survivors-A Retrospective Data Analysis of 3075 Patients

BACKGROUND

Sarcopenia is characterized by the loss of muscle mass and function and is associated with frailty, a syndrome linked to an increased likelihood of falls, fractures, and physical disability. Both frailty and sarcopenia are recognized as markers for shortened survival in a number of medical conditions and in cancer patient populations. Low alanine aminotransferase (ALT) values, representing low muscle mass (sarcopenia), may be associated with increased frailty and subsequently shortened survival in cancer patients. In the current study, we aimed to assess the potential relationship between low ALT and shorter survival in bladder cancer patients and survivors.

PATIENTS AND METHODS

This was a retrospective analysis of bladder cancer patients and survivors, both in and outpatients. We defined patients with sarcopenia as those presenting with ALT < 17 IU/L.

RESULTS

A total of 5769 bladder cancer patients' records were identified. After the exclusion of patients with no available ALT values or ALT levels above the upper normal limit, the final study cohort included 3075 patients (mean age 73.2 ± 12 years), of whom 80% were men and 1362 (53% had ALT ≤ 17 IU/L. The mean ALT value of patients within the low ALT group was 11.44 IU/L, while the mean value in the higher ALT level group was 24.32 IU/L (p < 0.001). Patients in the lower ALT group were older (74.7 vs. 71.4 years; p < 0.001), had lower BMI (25.8 vs. 27; p < 0.001), and their hemoglobin values were lower (11.7 vs. 12.6 g/dL; p < 0.001). In a univariate analysis, low ALT levels were associated with a 45% increase in mortality (95% CI 1.31-1.60, p < 0.001). In a multivariate model controlling for age, kidney function, and hemoglobin, low ALT levels were still associated with 22% increased mortality.

CONCLUSIONS

Low ALT values, indicative of sarcopenia and frailty, are associated with decreased survival of bladder cancer patients and survivors and could potentially be applied for optimizing individual treatment decisions.

Decreased risk of radiation pneumonitis with concurrent use of renin-angiotensin system inhibitors in thoracic radiation therapy of lung cancer

Background

Radiation pneumonitis (RP) is the primary dose-limiting toxicity associated with radiotherapy. This study aimed to observe the effects of renin-angiotensin system inhibitors in Chinese patients with lung cancer who received thoracic radiation.

Methods

Patients with lung cancer who received thoracic radiation at a total dose of ≥45 Gray between October 2017 and December 2022 were enrolled in this study. We retrospectively evaluated the factors influencing grade 2 or higher RP.

Results

A total of 320 patients were enrolled in this study; 62 patients were identified as angiotensin receptor blockers or angiotensin-converting enzyme inhibitor users. Additionally, 99 patients (30.9%) had grade 2 or higher RP, and the incidence in the renin-angiotensin system inhibitor group was 17.7% (11 out of 62 patients). Patients in the renin-angiotensin system inhibitors (RASi) group were older and had a higher percentage of males, lower percentage of ECOG score 0, higher percentage of hypertension, and higher percentage of adenocarcinoma than those in the non-RASi group. ECOG score [hazard ratio (HR) = 1.69, p = 0.009], history of smoking (HR = 1.76, p = 0.049), mean dose (HR = 3.63, p = 0.01), and RASi (HR = 0.3, p = 0.003) were independent predictive factors for RP. All subgroups benefited from RASi.

Conclusion

This study showed that oral RASi administration has the potential to mitigate the incidence of grade 2 or higher RP in patients with lung cancer undergoing thoracic radiotherapy. To validate and further substantiate these findings, additional prospective research is warranted.

Impact of Waiting Times on Mortality in Advanced Stage Non-Small Cell Lung Cancer: A 10-Year Retrospective Cohort Study in Thailand

OBJECTIVE

This study investigated the relationship between mortality and waiting times from diagnosis to first treatment while also considering other important risk factors associated with mortality.

METHODS

This is a cohort study including 497 patients diagnosed with advanced stage non-small cell lung cancer (NSCLC) between 1st January 2012 and 31st December 2021. The risk factors and waiting periods were analysed to determine their association with mortality. The waiting periods were recorded based on the timeline of patient visits, including the time between the 1st visit and imaging, the time between the 1st visit and tissue diagnosis, the time between the procedure and tissue diagnosis, the time between tissue diagnosis and treatment and the time from the 1st visit until treatment. The data were assessed using Cox regression with time-varying covariates.

RESULTS

Waiting time for tissue diagnosis had a modest effect on mortality, a waiting time of more than four weeks indicated poor prognosis both in univariate and multivariate analyses [HR 1.48 (95%CI 1.18-1.87), p = < 0.01), adjusted HR 1.007 (95%CI 1.002-1.010), p = 0.02]. Waiting time for other services was not shown to be associated with mortality. The mortality rate was 3 times higher in patients with poor ECOG performance status than good ECOG performance [adjusted HR 3.17(2.04-4.91)]. Patients with EGFR sensitizing mutation who were treated with EGFR TKI therapy had a lower risk of lung cancer death compared to those being treated with chemotherapy [adjusted HR 0.49 (0.33-0.72)].

CONCLUSION

Molecular testing for EGFR sensitizing mutation and the TKI treatment were fundamental changes that assisted in improving survival rates for patients diagnosed with advanced stage lung cancer over the 10-year period. However, poor ECOG performance status remained a strong risk factor for lung cancer death. Longer waiting time for tissue diagnosis might indicate a poor prognosis.

Active cancer as the main predictor of mortality for COVID-19 in oncology patients in a specialized center

Introduction: The role of the type, stage and status of cancer in the outcome of COVID-19 remains unclear. Moreover, the characteristic pathological changes of severe COVID-19 reveled by laboratory and radiological findings are similar to those due to the development of cancer itself and antineoplastic therapies. Objective: To identify potential predictors of mortality of COVID-19 in cancer patients. Materials and methods: A retrospective and cross-sectional study was carried out in patients with clinical suspicion of COVID-19 who were confirmed for COVID-19 diagnosis by RT-PCR testing at the National Institute of Neoplastic Diseases between April and December 2020. Demographic, clinical, laboratory and radiological data were analyzed. Statistical analyses included area under the curve and univariate and multivariate logistic regression analyses. Results: A total of 226 patients had clinical suspicion of COVID-19, the diagnosis was confirmed in 177 (78.3%), and 70/177 (39.5%) died. Age, active cancer, leukocyte count ≥12.8 × 109/L, urea ≥7.4 mmol/L, ferritin ≥1,640, lactate ≥2.0 mmol/L, and lung involvement ≥35% were found to be independent predictors of COVID-19 mortality. Conclusion: Active cancer represents the main prognosis factor of death, while the role of cancer stage and type is unclear. Chest CT is a useful tool in the prognosis of death from COVID-19 in cancer patients. It is a challenge to establish the prognostic utility of laboratory markers as their altered values it could have either oncological or pandemic origins.

Neuroimaging based biotypes for precision diagnosis and prognosis in cancer-related cognitive impairment

Cancer related cognitive impairment (CRCI) is commonly associated with cancer and its treatments, yet the present binary diagnostic approach fails to capture the full spectrum of this syndrome. Cognitive function is highly complex and exists on a continuum that is poorly characterized by dichotomous categories. Advanced statistical methodologies applied to symptom assessments have demonstrated that there are multiple subclasses of CRCI. However, studies suggest that relying on symptom assessments alone may fail to account for significant differences in the neural mechanisms that underlie a specific cognitive phenotype. Treatment plans that address the specific physiologic mechanisms involved in an individual patient's condition is the heart of precision medicine. In this narrative review, we discuss how biotyping, a precision medicine framework being utilized in other mental disorders, could be applied to CRCI. Specifically, we discuss how neuroimaging can be used to determine biotypes of CRCI, which allow for increased precision in prediction and diagnosis of CRCI via biologic mechanistic data. Biotypes may also provide more precise clinical endpoints for intervention trials. Biotyping could be made more feasible with proxy imaging technologies or liquid biomarkers. Large cross-sectional phenotyping studies are needed in addition to evaluation of longitudinal trajectories, and data sharing/pooling is highly feasible with currently available digital infrastructures.

Is the ECOG-PS similar to the sarcopenia status for predicting mortality in older adults with cancer? A prospective cohort study

PURPOSE

Sarcopenia is a muscle dysfunction that increases negative outcomes in patients with cancer. However, its diagnosis remains uncommon in clinical practice. The Eastern Cooperative Oncology Group Performance Status (ECOG-PS) is a questionnaire to assess the functional status, but it is unknown if is comparable with sarcopenia. We aimed at comparing ECOG-PS with sarcopenia to predict 12-month mortality in patients with cancer.

METHODS

Cohort study including older adult patients with cancer in treatment (any stage of the disease or treatment) at a reference hospital for oncological care. Socio-demographic, clinical, and anthropometric data, muscle mass, and physical function variables (handgrip strength [HGS] and gait speed [GS]) were collected. Skeletal muscle quantity and quality were assessed by computed tomography at the L3. Sarcopenia was diagnosed according to the EWGSP2. ECOG-PS and all-cause mortality were evaluated. The Cox proportional hazards model was calculated.

RESULTS

We evaluated 159 patients (69 years old, 55% males). Low performance (ECOG-PS ≥ 2) was found in 23.3%, 35.8% presented sarcopenia, and 22.0% severe sarcopenia. ECOG-PS ≥ 2 was not an independent predictor of mortality. Sarcopenia, severe sarcopenia, and probable sarcopenia has increased by 3.25 (confidence interval, CI 95% 1.55-6.80), 2.64 (CI 95% 1.23-5.67), and 2.81 (CI 95% 1.30-6.07) times the risk of mortality, respectively.

CONCLUSION

Sarcopenia, but not ECOG-PS, was a predictor of mortality. Therefore, ECOG-PS was not similar to sarcopenia to predict mortality in patients with cancer.

Advanced Adrenocortical Carcinoma: From Symptoms Control to Palliative Care

The prognosis of patients with advanced adrenocortical carcinoma (ACC) is often poor: in the case of metastatic disease, five-year survival is reduced. Advanced disease is not a non-curable disease and, in referral centers, the multidisciplinary approach is the standard of care: if a shared decision regarding several treatments is available, including the correct timing for the performance of each one, overall survival is increased. However, many patients with advanced ACC experience severe psychological and physical symptoms secondary to the disease and the cancer treatments. These symptoms, combined with existential issues, debase the quality of the remaining life. Recent strong evidence from cancer research supports the early integration of palliative care principles and skills into the advanced cancer patient's trajectory, even when asymptomatic. A patient with ACC risks quickly suffering from symptoms/effects alongside the disease; therefore, early palliative care, in some cases concurrent with oncological treatment (simultaneous care), is suggested. The aims of this paper are to review current, advanced ACC approaches, highlight appropriate forms of ACC symptom management and suggest when and how palliative care can be incorporated into the ACC standard of care.

Development of a Clinical Prediction Model for 1-Year Mortality in Patients With Advanced Cancer

IMPORTANCE

To optimize palliative care in patients with cancer who are in their last year of life, timely and accurate prognostication is needed. However, available instruments for prognostication, such as the surprise question ("Would I be surprised if this patient died in the next year?") and various prediction models using clinical variables, are not well validated or lack discriminative ability.

OBJECTIVE

To develop and validate a prediction model to calculate the 1-year risk of death among patients with advanced cancer.

DESIGN, SETTING, AND PARTICIPANTS

This multicenter prospective prognostic study was performed in the general oncology inpatient and outpatient clinics of 6 hospitals in the Netherlands. A total of 867 patients were enrolled between June 2 and November 22, 2017, and followed up for 1 year. The primary analyses were performed from October 9 to 25, 2019, with the most recent analyses performed from June 19 to 22, 2022. Cox proportional hazards regression analysis was used to develop a prediction model including 3 categories of candidate predictors: clinician responses to the surprise question, patient clinical characteristics, and patient laboratory values. Data on race and ethnicity were not collected because most patients were expected to be of White race and Dutch ethnicity, and race and ethnicity were not considered as prognostic factors. The models' discriminative ability was assessed using internal-external validation by study hospital and measured using the C statistic. Patients 18 years and older with locally advanced or metastatic cancer were eligible. Patients with hematologic cancer were excluded.

MAIN OUTCOMES AND MEASURES

The risk of death by 1 year.

RESULTS

Among 867 patients, the median age was 66 years (IQR, 56-72 years), and 411 individuals (47.4%) were male. The 1-year mortality rate was 41.6% (361 patients). Three prediction models with increasing complexity were developed: (1) a simple model including the surprise question, (2) a clinical model including the surprise question and clinical characteristics (age, cancer type prognosis, visceral metastases, brain metastases, Eastern Cooperative Oncology Group performance status, weight loss, pain, and dyspnea), and (3) an extended model including the surprise question, clinical characteristics, and laboratory values (hemoglobin, C-reactive protein, and serum albumin). The pooled C statistic was 0.69 (95% CI, 0.67-0.71) for the simple model, 0.76 (95% CI, 0.73-0.78) for the clinical model, and 0.78 (95% CI, 0.76-0.80) for the extended model. A nomogram and web-based calculator were developed to support clinicians in adequately caring for patients with advanced cancer.

CONCLUSIONS AND RELEVANCE

In this study, a prediction model including the surprise question, clinical characteristics, and laboratory values had better discriminative ability in predicting death among patients with advanced cancer than models including the surprise question, clinical characteristics, or laboratory values alone. The nomogram and web-based calculator developed for this study can be used by clinicians to identify patients who may benefit from palliative care and advance care planning. Further exploration of the feasibility and external validity of the model is needed.

Simultaneous care in oncology: Assessment of benefit in relation to symptoms, sex, and age in 753 patients

BACKGROUND

Early activation of palliative care for patients with advanced cancer is central in the treatment trajectory. At the Veneto Institute of Oncology, a simultaneous-care outpatient clinic (SCOC) has been active since 2014, where patients are evaluated by an oncologist together with a palliative care team. Recently, we reported on consecutive patients admitted at SCOC from 2018 to 2021 in terms of appropriateness, process, and outcome indicators. Here, we report further analysis in the same group of 753 patients, evaluating other parameters and the correlation between symptom intensity, gender, age, and survival.

METHODS

SCOC data were retrieved from a prospectively maintained database.

RESULTS

Among the patients, 42.2% were women, and the median age was 68 years, with 46.7% of patients aged ≥70 years. The most prevalent disease type was gastrointestinal cancer (75.2%), and 90.9% of the patients had metastatic disease. The median score for the distress thermometer was 4; the vast majority of the patients (98.6%) reported physical problems, and 69.4% presented emotional issues. Younger women demonstrated a significantly greater median distress than other patients (p=0.0018). Almost all symptoms had a higher prevalence on the 0-3 Edmonton Symptom Assessment Scale (ESAS) score, except for fatigue. About 43.8% of the patients received systemic anticancer treatment (SAT) in the last 60 days of life, 15.0% of whom received SAT in the last month and 3.1% in the last 2 weeks. For some symptoms, women frequently had more ESAS >3. Pain and nausea were significantly less reported by older patients compared with younger adults. Men had a lower risk of having MUST score ≥ 2 (p=0.0311). Men and older patients showed a lower prognosis awareness (p=0.0011 and p=0.0049, respectively). Older patients received less SAT within the last 30 days of life (p=0.0006) and had death risk decreased by 20.0%.

CONCLUSION

Our study identified two subgroups of patients with advanced cancer who require special attention and support due to important symptoms' burden detected by Patient Reported Outcome Measures tests: women and younger adults. These categories of patients require special attention and should be provided early access at SCOC. The role of an oncologist remains crucial to intercept all patients in need of early palliative care and balancing trade-offs of anticancer treatment in advanced metastatic disease.