Frequency and factors associated with unexpected death in an acute palliative care unit: expect the unexpected

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.

Factors associated with good death of patients with advanced cancer: a prospective study in Japan

PURPOSE

It is important for palliative care providers to identify what factors are associated with a "good death" for patients with advanced cancer. We aimed to identify factors associated with a "good death" evaluated by the Good Death Scale (GDS) score among inpatients with advanced cancer in palliative care units (PCUs) in Japan.

METHODS

The study is a sub-analysis of a multicenter prospective cohort study conducted in Japan. All variables were recorded on a structured data collecting sheet designed for the study. We classified each patient into better GDS group or worse GDS group, and examined factors associated with better GDS using multivariate analysis.

RESULTS

Between January and December 2017, 1896 patients were enrolled across 22 PCUs in Japan. Among them, a total of 1157 patients were evaluated. Five variables were significantly associated with a better GDS score in multivariate analysis: preferred place of death at PCU (odds ratio [OR] 2.85; 95% confidence interval [CI] 1.72-4.71; p < 0.01), longer survival time (OR 1.02; 95% CI 1.00-1.03; p < 0.01), not sudden death (OR 1.96; 95% CI 1.27-3.04; p < 0.01), better spiritual well-being in the last 3 days in life (OR 0.53; 95% CI 0.42-0.68; p < 0.01), and better communication between patient and family (OR 0.81; 95% CI 0.66-0.98; p = 0.03).

CONCLUSIONS

We identified factors associated with a "good death" using GDS among advanced cancer patients in Japanese PCUs. Recognition of factors associated with GDS could help to improve the quality of end-of-life care.

Are Prognostic Scores Better Than Clinician Judgment? A Prospective Study Using Three Models

CONTEXT

Several prognostic models such as the Palliative Performance Scale (PPS), Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP) have been developed to complement clinician's prediction of survival (CPS). However, few studies with large scales have been conducted to show which prognostic tool had better performance than CPS in patients with weeks of survival.

OBJECTIVES

We aimed to compare the prognostic performance of the PPS, PPI, PaP, and CPS in inpatients admitted to palliative care units (PCUs).

METHODS

This study was part of a multi-center prospective observational study involving patients admitted to PCUs in Japan. We computed their prognostic performance using the area under the receiver operating characteristics curve (AUROC) and calibration plots for seven, 14-, 30- and 60-day survival.

RESULTS

We included 1896 patients with a median overall survival of 19 days. The AUROC was 73% to 84% for 60-day and 30-day survival, 75% to 84% for 14-day survival, and 80% to 87% for seven-day survival. The calibration plot demonstrated satisfactory agreement between the observational and predictive probability for the four indices in all timeframes. Therefore, all four prognostic indices showed good performance. CPS and PaP consistently had significantly better performance than the PPS and PPI from one-week to two-month timeframes.

CONCLUSION

The PPS, PPI, PaP, and CPS had relatively good performance in patients admitted to PCUs with weeks of survival. CPS and PaP had significantly better performance than the PPS and PPI. CPS may be sufficient for experienced clinicians while PPS may help to improve prognostic confidence for inexperienced clinicians.

Prevalence and predictors for 72-h mortality after transfer to acute palliative care unit

Purpose

Accurate prediction of survival is important to facilitate clinical decision-making and improve quality of care at the end of life. While it is well documented that survival prediction poses a challenge for treating physicians, the need for clinically valuable predictive factors has not been met. This study aims to quantify the prevalence of patient transfer 72 h before death onto the acute palliative care unit in a tertiary care center in Switzerland, and to identify factors predictive of 72-h mortality.

Methods

All patients hospitalized between January and December 2020 on the acute palliative care unit of the Competence Center Palliative Care of the Department of Radiation Oncology at the University Hospital Zurich were assessed. Variables were retrieved from the electronic medical records. Univariable and multivariable logistic regressions were used to identify predictors of mortality.

Results

A total of 398 patients were screened, of which 188 were assessed. Every fifth patient spent less than 72 h on the acute palliative care unit before death. In multivariable logistic regression analysis, predictors for 72-h mortality after transfer were no prior palliative care consult (p = 0.011), no advance care directive (p = 0.044), lower performance status (p = 0.035), lower self-care index (p = 0.003), and lower blood albumin level (p = 0.026).

Conclusion

Late transfer to the acute palliative care unit is not uncommon, which can cause additional distress to patients and caretakers. Though clinically practical short-term survival predictors remain largely unidentified, early integration of palliative care should be practiced more regularly in patients with life-limiting illness.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00520-022-07075-6.

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.

Assessment of Changes in Symptoms Is Feasible and Prognostic in the Last Weeks of Life: An International Multicenter Cohort Study

Background: Symptoms are not typically part of established various prognostic factors and scoring systems but are among the most frequently assessed issues in patient care. Objectives: To evaluate that, changes in symptoms can provide additional useful prognostic information. Design: A secondary analysis of an international cohort study in Japan, Korea, and Taiwan. Setting/Subjects: Subjects were adult patients with advanced cancer (n = 2074) who were admitted to 37 palliative care units (PCUs) in 3 countries from January 2017 to September 2018. Measurements: Symptoms (dyspnea, fatigue, dry mouth, and drowsiness) were assessed at admission and one-week later. Dyspnea was assessed by the presence of resting and exertional dyspnea, whereas other symptoms were assessed using the Integrated Palliative care Outcome Scales (IPOS) (range 0-4). For analysis, we grouped patients by symptom change, as either Improved, Stable, or Worsened (by having at least a one increment decrease, no change, or at least a one increment increase, respectively). Results: Worsened groups had the shortest survival (median survival 15-21 days) compared with those with Improved (median survival 23-31 days) and Stable symptoms (median survival 27-29 days) across all four symptoms (dyspnea, fatigue, dry mouth, and drowsiness). Survival differences were statistically significantly different across all three groups for all symptoms (all p < 0.001). Interestingly, Improved symptoms were associated with similar survival compared with Stable groups, with no statistical differences. Conclusions: Worsened symptoms at one week after admission were useful predictors of survival for patients with advanced cancer in PCUs during the final weeks of life. Longitudinal assessments are needed to reflect passage of time as well as impact of treatments.

Dying Alone during a Pandemic

Background: The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented experience that has had profound impact and consequences for health care providers, visitation policies, and procedures. Hospitals and health care facilities were forced to implement changes to visitation policies, in an effort, to minimize transmission of the virus, which unfortunately had negative impact on patients' and family members' well-being as well as moral distress for the staff. Objectives: We present here a case illustration of the impacts of such a response to the pandemic situation at our institution, including challenges for uniformly implementing such a change along with suggestions to support patients during these difficult times. Conclusion: Health care facilities should make efforts to maintain balance between safety precautions and minimizing potential negative impacts on patients, families, and staff by implementing innovative measures to support ongoing communication and access to family support.

Incidence and associated factors of sudden unexpected death in advanced cancer patients: A multicenter prospective cohort study

Purpose

A sudden unexpected death has significant negative impacts on patients, family caregivers, and medical staff in hospice/palliative care. This study aimed to clarify the incidence and associated factors of sudden unexpected death according to four definitions in advanced cancer patients.

Methods

We performed a prospective cohort study in 23 inpatient hospices/palliative care units in Japan. Advanced cancer patients aged ≥18 years who were admitted to inpatient hospices/palliative care units were included. The incidence and associated factors of sudden unexpected death were evaluated in all enrolled patients according to four definitions: (a) rapid decline death, defined as a sudden death preceded by functional decline over 1–2 days; (b) surprise death, defined if the primary responsible palliative care physician answered “yes” to the question, “Were you surprised by the timing of the death?”; (c) unexpected death, defined as a death that occurred earlier than the physicians had anticipated; and (d) performance status (PS)‐defined sudden death, defined as a death that occurred within 1 week of functional status assessment with an Australia‐modified Karnofsky PS ≥50.

Results

Among 1896 patients, the incidence of rapid decline death was the highest (30‐day cumulative incidence: 16.8%, 95% CI: 14.8–19.0%), followed by surprise death (9.6%, 8.1–11.4%), unexpected death (9.0%, 7.5–10.8%), and PS‐defined sudden death (6.4%, 5.2–8.0%). Male sex, liver metastasis, dyspnea, malignant skin lesion, and fluid retention were significantly associated with the occurrence of sudden unexpected death.

Conclusion

Sudden unexpected death is not uncommon even in inpatient hospices/palliative care units, with range of 6.4–16.8% according to the different definitions.

Patient with respiratory distress, facial oedema and refractory hypokalaemia

Small cell lung carcinoma, when associated with co-occurrence of complications such as paraneoplastic syndrome and superior vena cava syndrome, poses a greater management challenge to the clinical team. We report a 56-year-old man who was eventually diagnosed with stage III small cell lung carcinoma, presenting with respiratory distress, facial and upper body oedema, proximal muscle weakness, hypokalaemia, new-onset hypertension and hyperglycaemia. His medical management was complicated by associated superior vena cava syndrome and Cushing’s syndrome leading to refractory hypokalemia, immunosuppression and depression. Although the patient improved clinically and biochemically with the chemotherapy and other treatments, the development of neutropenic pneumonia led to his demise. This case highlights the importance of a multidisciplinary approach to achieve better patient care and the need for good clinical vigilance to identify possible humoral manifestations of aggressive malignancies such as small cell carcinoma of the lung to assist their early detection.

Self-care while undertaking qualitative nursing research

BACKGROUND

Nursing research is usually carried out by registered nurses with some experience of practice. However, nurses may find that the role of the nurse researcher is very different from that of the practising clinical nurse and has its own challenges.

AIM

To explore the differences between the two roles and offer some guidance to nurses carrying out qualitative research, particularly research into sensitive topics.

DISCUSSION

Nurses need to be aware of various issues that could affect their ability to carry out nursing research and be emotionally traumatic.

CONCLUSION

There is a general need for greater awareness about the potential risks of carrying out qualitative research into sensitive topics.

IMPLICATIONS FOR PRACTICE

Nurse researchers must consider self-care when planning a research project, assessing their role, personality, background, personal experiences and stress triggers, and ensuring peer and supervisory support, reflection and a healthy lifestyle.

Dealing with prognostic uncertainty: the role of prognostic models and websites for patients with advanced cancer

PURPOSE OF REVIEW

To provide an updated overview of prognostic models in advanced cancer and highlight the role of prognostic calculators.

RECENT FINDINGS

In the advanced cancer setting, many important healthcare decisions are driven by a patient's prognosis. However, there is much uncertainty in formulating prognosis, particularly in the era of novel cancer therapeutics. Multiple prognostic models have been validated for patients seen by palliative care and have a life expectancy of a few months or less, such as the Palliative Performance Scale, Palliative Prognostic Score, Palliative Prognostic Index, Objective Prognostic Score, and Prognosis in Palliative Care Study Predictor. However, these models are seldom used in clinical practice because of challenges related to limited accuracy when applied individually and difficulties with model selection, computation, and interpretation. Online prognostic calculators emerge as tools to facilitate knowledge translation by overcoming the above challenges. For example, www.predictsurvival.com provides the output for seven prognostic indexes simultaneously based on 11 variables.

SUMMARY

Prognostic models and prognostic websites are currently available to augment prognostication in the advanced cancer setting. Further studies are needed to examine their impact on prognostic accuracy, confidence, and clinical outcomes.

Acute palliative care unit-initiated interventions for advanced cancer patients at the end of life: prediction of impending death based on Glasgow Prognostic Score

PURPOSE

Unexpected death occurred in an unexpectedly high proportion of advanced cancer patients in the acute palliative care unit (APCU) setting and associated with fewer signs of impending death. Recognition of patients at high risk of approaching death, especially immediately after admitting APCU among clinicians, can improve the end-of-life trajectory. Our objective was accurate prognostication within a few days of admission.

METHODS

Patients admitted to an APCU of the NTT Medical Center Tokyo, Tokyo, Japan, between April 2009 and December 2016 were retrospectively examined. The Glasgow Prognostic Score (GPS) was optimized with concomitant neutrophilia, lymphocytopenia, thrombocytopenia, anemia, and monocytosis. Kaplan-Meier survival curves were estimated, and independent predictors for 3-day mortality were identified using univariate and multivariate analyses. The sensitivity, specificity, and likelihood ratios (LRs) associated with imminent death were also assessed.

RESULTS

Nine hundred ninety-one patients were included; 52.9% was male. The median age was 72 years. The median survival was 13 days (IQ range 6 to 26), and 11.7% died within 3 days of admission. Significant difference in survival with a GPS of 2 was observed in GPS optimized with concomitant thrombocytopenia, and it was the only significant predictor associated with 3-day mortality (p = 0.004), which had high specificity (> 95%) and high positive LR (> 5).

CONCLUSION

The prognostic value of the GPS was enhanced by adding thrombocytopenia. The concurrent use of the GPS and platelet count improved the prognostication of limited time of survival and could assist in the personal and clinical decisions for advanced cancer patients.

Factors associated with discharge disposition on an acute palliative care unit

PURPOSE

Acute palliative care units (APCUs) admit patients with cancer for symptom control, transition to community palliative care units or hospice (CPCU/H), or end-of-life care. Prognostication early in the course of admission is crucial for decision-making. We retrospectively evaluated factors associated with patients' discharge disposition on an APCU in a cancer center.

METHODS

We evaluated demographic, administrative, and clinical data for all patients admitted to the APCU in 2015. Clinical data included cancer diagnosis, delirium screening, and Edmonton Symptom Assessment System (ESAS) symptoms. An ESAS sub-score composed of fatigue, drowsiness, shortness of breath, and appetite (FDSA) was also investigated. Factors associated with patients' discharge disposition (home, CPCU/H, died on APCU) were identified using three-level multinomial logistic regression.

RESULTS

Among 280 patients, the median age was 65.5 and median length of stay was 10 days; 155 (55.4%) were admitted for symptom control, 65 (23.2%) for transition to CPCU/H, and 60 (21.4%) for terminal care. Discharge dispositions were as follows: 156 (55.7%) died, 63 (22.5%) returned home, and 61 (21.8%) were transferred to CPCU/H. On multivariable analysis, patients who died were less likely to be older (OR 0.97, p = 0.01), or to be admitted for symptom control (OR 0.06, p < 0.0001), and more likely to have a higher FDSA score 21-40 (OR 3.02, p = 0.004). Patients discharged to CPCU/H were less likely to have been admitted for symptom control (OR 0.06, p < 0.0001).

CONCLUSION

Age, reason for admission, and the FDSA symptom cluster on admission are variables that can inform clinicians about probable discharge disposition on an APCU.

Unexpected death in palliative care: what to expect when you are not expecting

PURPOSE OF REVIEW

Death is a certainty in life. Yet, the timing of death is often uncertain. When death occurs suddenly and earlier than anticipated, it is considered as an unexpected death. In this article, we shall discuss when is death expected and unexpected, and review the frequency, impact, causes, and approach to unexpected death in the palliative care setting.

RECENT FINDINGS

Even in the palliative care setting in which death is relatively common, up to 5% of deaths in hospice and 10% of deaths in palliative care units were considered to be unexpected. Unexpected death has significant impact on care, including unrealized dreams and unfinished business among patients, a sense of uneasiness and complicated bereavement among caregivers, and uncertainty in decision making among healthcare providers. Clinicians may minimize the impact of unexpected events by improving their accuracy of prognostication, communicating the uncertainty with patients and families, and helping them to expect the unexpected by actively planning ahead. Furthermore, because of the emotional impact of unexpected death on bereaved caregivers, clinicians should provide close monitoring and offer prompt treatment for complicated grief.

SUMMARY

Further research is needed to understand how we can better predict and address unexpected events.

Prognostication of Survival in Patients With Advanced Cancer: Predicting the Unpredictable?

BACKGROUND

Prognosis is a key driver of clinical decision-making. However, available prognostication tools have limited accuracy and variable levels of validation.

METHODS

Principles of survival prediction and literature on clinician prediction of survival, prognostic factors, and prognostic models were reviewed, with a focus on patients with advanced cancer and a survival rate of a few months or less.

RESULTS

The 4 principles of survival prediction are (a) prognostication is a process instead of an event, (b) prognostic factors may evolve over the course of the disease, (c) prognostic accuracy for a given prognostic factor/ tool varies by the definition of accuracy, the patient population, and the time frame of prediction, and (d) the exact timing of death cannot be predicted with certainty. Clinician prediction of survival is the most commonly used approach to formulate prognosis. However, clinicians often overestimate survival rates with the temporal question. Other clinician prediction of survival approaches, such as surprise and probabilistic questions, have higher rates of accuracy. Established prognostic factors in the advanced cancer setting include decreased performance status, delirium, dysphagia, cancer anorexia-cachexia, dyspnea, inflammation, and malnutrition. Novel prognostic factors, such as phase angle, may improve rates of accuracy. Many prognostic models are available, including the Palliative Prognostic Score, the Palliative Prognostic Index, and the Glasgow Prognostic Score.

CONCLUSIONS

Despite the uncertainty in survival prediction, existing prognostic tools can facilitate clinical decision-making by providing approximated time frames (months, weeks, or days). Future research should focus on clarifying and comparing the rates of accuracy for existing prognostic tools, identifying and validating novel prognostic factors, and linking prognostication to decision-making.