A 2-week prognostic prediction model for terminal cancer patients in a palliative care unit at a Japanese general hospital

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.

Cost-saving prediction model of transfer to palliative care for terminal cancer patients in a Japanese general hospital

BACKGROUND

Although medical costs need to be controlled, there are no easily applicable cost prediction models of transfer to palliative care (PC) for terminal cancer patients.

OBJECTIVE

Construct a cost-saving prediction model based on terminal cancer patients' data at hospital admission.

STUDY DESIGN

Retrospective cohort study.

SETTING

A Japanese general hospital.

PATIENTS

A total of 139 stage IV cancer patients transferred to PC, who died during hospitalization from April 2014 to March 2019.

MAIN OUTCOME MEASURE

Patients were divided into higher (59) and lower (80) total medical costs per day after transfer to PC. We compared demographics, cancer type, medical history, and laboratory results between the groups. Stepwise logistic regression analysis was used for model development and area under the curve (AUC) calculation.

RESULTS

A cost-saving prediction model (AUC = 0.78, 95% CI: 0.70, 0.85) with a total score of 13 points was constructed as follows: 2 points each for age ≤ 74 years, creatinine ≥ 0.68 mg/dL, and lactate dehydrogenase ≤ 188 IU/L; 3 points for hemoglobin ≤ 8.8 g/dL; and 4 points for potassium ≤ 3.3 mEq/L.

CONCLUSION

Our model contains five predictors easily available in clinical settings and exhibited good predictive ability.

Survival Prediction in Home Hospice Care Patients with Lung Cancer Based on LASSO Algorithm

PURPOSE

The aim of the present study was to develop a nomogram for prognostic prediction of patients with lung cancer in hospice.

METHODS

The data was collected from 1106 lung cancer patients in hospice between January 2008 and December 2018. The data were split into a training set, which was used to identify the most important prognostic factors by the least absolute shrinkage and selection operator (LASSO) and to build the nomogram, while the testing set was used to validate the nomogram. The performance of the nomogram was assessed by c-index, calibration curve and the decision curve analysis (DCA).

RESULTS

A total of 1106 patients, including 835 (75%) from the training set and 271 (25%) from testing set, were retrospectively analyzed in this study. Using the LASSO regression, 5 most important prognostic predictors that included sex, Karnofsky Performance Scale (KPS), quality-of-life (QOL), edema and anorexia, were selected out of 28 variables. Validated c-indexes of training set at 15, 30, and 90 days were .778 [.737-.818], .776 [.743-.809], and .751 [.713-.790], respectively. Similarly, the validated c-indexes of testing set at 15, 30, and 90 days were .789 [.714-.864], .748 [.685-.811], and .757 [.691-.823], respectively. The nomogram-predicted survival was well calibrated, as the predicted probabilities were close to the expected probabilities. Moreover, the DCA curve showed that nomogram received superior standardized net benefit at a broad threshold.

CONCLUSIONS

The study built a non-lab nomogram with important predictor to analyze the clinical parameters using LASSO. It may be a useful tool to allow clinicians to easily estimate the prognosis of the patients with lung cancer in hospice.

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.

Patient-reported symptoms and problems at admission to specialized palliative care improved survival prediction in 30,969 cancer patients: A nationwide register-based study

BACKGROUND

Large, nationally representative studies of the association between quality of life and survival time in cancer patients in specialized palliative care are missing.

AIM

The aim of this study was to investigate whether symptoms/problems at admission to specialized palliative care were associated with survival and if the symptoms/problems may improve prediction of death within 1 week and 1 month, respectively.

SETTING/PARTICIPANTS

All cancer patients who had filled in the EORTC QLQ-C15-PAL at admission to specialized palliative care in Denmark in 2010-2017 were included through the Danish Palliative Care Database. Cox regression was used to identify clinical variables (gender, age, type of contact (inpatient vs outpatient), and cancer site) and symptoms/problems significantly associated with survival. To test whether symptoms/problems improved survival predictions, the overall accuracy (area under the receiver operating characteristic curve) for different prediction models was compared. The validity of the prediction models was tested with data on 5,508 patients admitted to palliative care in 2018.

RESULTS

The study included 30,969 patients with an average age of 68.9 years; 50% were women. Gender, age, type of contact, cancer site, and most symptoms/problems were significantly associated with survival time. The predictive value of symptoms/problems was trivial except for physical function, which clearly improved the overall accuracy for 1-week and 1-month predictions of death when added to models including only clinical variables.

CONCLUSION

Most symptoms/problems were significantly associated with survival and mainly physical function improved predictions of death. Interestingly, the predictive value of physical function was the same as all clinical variables combined (in hospice) or even higher (in palliative care teams).

Survival time after marked reduction in oral intake in terminally ill noncancer patients: A retrospective study

Background

Methods

Results

Conclusion

Validation of a Short-Term, Objective, Prognostic Predictive Method for Terminal Cancer Patients in a Palliative Care Unit Using a Combination of Six Laboratory Test Items

Background: There is no established method to objectively predict short-term prognosis. Recently, we proposed objective, short-term, prognostic predictive methods that are combinations of laboratory test items: WPCBAL score, derived from six values (white blood cell, platelet, C-reactive protein, blood urea nitrogen, aspartate aminotransferase, and lactate dehydrogenase). However, that study was conducted in an acute-phase hospital to identify the test items useful for prognostic prediction; thus, whether WPCBAL score could be applied to terminal cancer patients in a palliative care unit was unverified. Objective: To verify the usefulness of WPCBAL score for terminal cancer patients. Design: A retrospective study. Setting/Subjects: Patients admitted to the palliative care unit of Ashiya Municipal Hospital (N = 128) in Japan in 2016. Measurements: The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve (AUROC) were compared between WPCBAL score and the Glasgow prognostic score (GPS). Results: For predicting three-week prognosis, WPCBAL score showed higher AUROC compared with GPS (0.7540 and 0.6573, respectively). WPCBAL score predicting two-week prognosis showed greater AUROC than GPS predicting three-week prognosis (0.7491 and 0.6573, respectively). Conclusion: WPCBAL score was verified to objectively predict the two- or three-week prognosis for terminal cancer patients in a palliative care unit. WPCBAL score may be a new option for prognostic prediction for terminal 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.

A New Approach for Determining Short-Term, Objective Prognostic Predictive Methods for Terminal Cancer Patients Based on the Change Point of Laboratory Test Values

BACKGROUND

In terminal phase cancer, predicting a prognosis precisely plays an important role for patients and their families to live meaningful lives. However, there are no established short-term, objective prognostic predictive methods.

OBJECTIVE

To develop simple, short-term, objective prognostic predictive methods through detecting a change point for laboratory test values.

DESIGN

A retrospective chart review.

SETTING/SUBJECTS

Subjects were cancer patients aged ≥16 years and discharged dead from Osaka University Hospital in 2008.

MEASUREMENTS

Using different laboratory test values, new prognostic predictive methods were determined based on either six laboratory test values (white blood cell [WBC], platelet [PLT], C-reactive protein, blood urea nitrogen [BUN], aspartate aminotransferase [AST], and lactase dehydrogenase [LDH]): the WPCBAL score, or five test values (WBC, PLT, BUN, AST, and LDH): the WPBAL score. Their utility, including sensitivity and specificity, was compared with that of Glasgow prognostic scores (GPSs).

RESULTS

In total, 121 cancer patients were enrolled. WPCBAL and WPBAL scores showed higher sensitivity (0.88 and 0.91 vs. 0.68), specificity (0.79 and 0.70 vs. 0.53), negative predictive value (0.98 and 0.97 vs. 0.76), and a much larger relative risk (16.5 and 14.2 vs. 1.78) as prognostic predictors within two weeks of death than GPS as a prognostic predictor within three weeks of death.

CONCLUSION

This is the first study that suggests that the objective prognostic predictive methods, through detecting the change point of laboratory test values, are useful for predicting short-term prognosis. The WPCBAL score and WPBAL score could objectively predict the remaining lifetime within two weeks of mortality.

Pharmacokinetics of Morphine, Morphine-3-Glucuronide and Morphine-6-Glucuronide in Terminally Ill Adult Patients

Background and Objective

Morphine dosing can be challenging in terminally ill adult patients due to the heterogeneous nature of the population and the difficulty of accurately assessing pain during sedation. To determine the pharmacokinetics of morphine, morphine-3-glucuronide (M3G) and morphine-6-glucuronide (M6G) in this population, and to find clinically relevant parameters for dose individualisation, we performed a population pharmacokinetic analysis.

Methods

Blood samples were randomly collected from 47 terminally ill patients in both the pre-terminal and terminal phases. Nonlinear mixed-effects modelling (NONMEM) was used to develop a population pharmacokinetic model and perform covariate analysis.

Results

The data were accurately described by a two-compartment model for morphine with two one-compartment models for both its metabolites. Typical morphine clearance was 48 L/h and fell exponentially by more than 10 L/h in the last week before death. Decreased albumin levels and a decreased estimated glomerular filtration rate (eGFR) resulted in lower metabolite clearance. Between-subject variability in clearance was 52 % (morphine), 75 % (M3G) and 79 % (M6G), and changed to 53, 29 and 34 %, respectively, after inclusion of the covariates.

Conclusions

Our results show that morphine clearance decreased up to the time of death, falling by more than 10 L/h (26 %) in the last week before death, and that M3G and M6G accumulated due to decreased renal function. Further studies are warranted to determine whether dose adjustment of morphine is required in terminally ill patients.

Using Laboratory Test Results at Hospital Admission to Predict Short-term Survival in Critically Ill Patients With Metastatic or Advanced Cancer

CONTEXT

Accurately estimating the life expectancy of critically ill patients with metastatic or advanced cancer is a crucial step in planning appropriate palliative or supportive care.

OBJECTIVES

We evaluated the results of laboratory tests performed within two days of hospital admission to predict the likelihood of death within 14 days.

METHODS

We retrospectively selected patients 18 years or older with metastatic or advanced cancer who were admitted to intensive care units or palliative and supportive care services in our hospital. We evaluated whether the following are independent predictors in a logistic regression model: age, sex, comorbidities, and the results of seven commonly available laboratory tests. The end point was death within 14 days in or out of the hospital.

RESULTS

Of 901 patients in the development cohort and 45% died within 14 days. The risk of death within 14 days after admission increased with increasing age, lactate dehydrogenase levels, and white blood cell counts and decreasing albumin levels and platelet counts (P < 0.01). The model predictions were confirmed using a separate validation cohort. The areas under the receiver operating characteristic curves were 0.74 and 0.70 for the development and validation cohorts, respectively, indicating good discriminatory ability for the model.

CONCLUSIONS

Our results suggest that laboratory test results performed within two days of admission are valuable in predicting death within 14 days for patients with metastatic or advanced cancer. Such results may provide an objective assessment tool for physicians and help them initiate conversations with patients and families about end-of-life care.

Pharmacokinetic considerations and recommendations in palliative care, with focus on morphine, midazolam and haloperidol

INTRODUCTION

A variety of medications are used for symptom control in palliative care, such as morphine, midazolam and haloperidol. The pharmacokinetics of these drugs may be altered in these patients as a result of physiological changes that occur at the end stage of life.

AREAS COVERED

This review gives an overview of how the pharmacokinetics in terminally ill patients may differ from the average population and discusses the effect of terminal illness on each of the four pharmacokinetic processes absorption, distribution, metabolism, and elimination. Specific considerations are also given for three commonly prescribed drugs in palliative care: morphine, midazolam and haloperidol).

EXPERT OPINION

The pharmacokinetics of drugs in terminally ill patients can be complex and limited evidence exists on guided drug use in this population. To improve the quality of life of these patients, more knowledge and more pharmacokinetic/pharmacodynamics studies in terminally ill patients are needed to develop individualised dosing guidelines. Until then knowledge of pharmacokinetics and the physiological changes that occur in the final days of life can provide a base for dosing adjustments that will improve the quality of life of terminally ill patients. As the interaction of drugs with the physiology of dying is complex, pharmacological treatment is probably best assessed in a multi-disciplinary setting and the advice of a pharmacist, or clinical pharmacologist, is highly recommended.

Objective palliative prognostic score among patients with advanced cancer

CONTEXT

The accurate prediction of survival is one of the key factors in the decision-making process for patients with advanced illnesses.

OBJECTIVES

This study aimed to develop a short-term prognostic prediction method that included such objective factors as medical history, vital signs, and blood tests for use with patients with advanced cancer.

METHODS

Medical records gathered at the admission of patients with advanced cancer to the hospice palliative care unit at a tertiary hospital in Taiwan were reviewed retrospectively. The records included demographics, history of cancer treatments, performance status, vital signs, and biological parameters, Multivariate logistic regression analyses and receiver operating characteristic curves were used for model development.

RESULTS

The Objective Palliative Prognostic Score was determined by using six objective predictors identified by multivariate logistic regression analysis. The predictors include heart rate >120/min, white blood cells >11,000/mm(3), platelets <130,000/mm(3), serum creatinine level >1.3 mg/dL, serum potassium level >5 mg/dL, and no history of chemotherapy. The area under the receiver operating characteristic curve used to predict seven-day survival was 82.0% (95% confidence interval 75.2%-88.8%). If any three predictors of the six were reached, death within seven days was predicted with 68.8% sensitivity, 86.0% specificity, 55.9% positive predictive value, and 91.4% negative predictive value.

CONCLUSION

The Objective Palliative Prognostic Score consists of six objective predictors for the estimation of seven-day survival among patients with advanced cancer and showed a relatively high accuracy, specificity, and negative predictive value. Objective signs, such as vital signs and blood test results, may help clinicians make decisions at the end of life.

A simple 5-point scoring system, NaURSE (Na+, urea, respiratory rate and shock index in the elderly), predicts in-hospital mortality in oldest old

BACKGROUND

the mortality is high in acutely ill oldest old patients. Understanding the prognostic factors which influence mortality will help clinicians make appropriate management decisions.

METHODS

we analysed prospective mortality audit data (November 2008 to January 2009) to identify variables associated with in-patient mortality in oldest old. We selected those with P < 0.10 from univariate analysis and determined at which cut-point they served as the strongest predictor of mortality. Using these cut-off points, we constructed multivariate logistic regression models. A 5-point score was derived from cut-off points which were significantly associated with mortality tested in a smaller independent re-audit sample conducted in October 2011.

RESULTS

a total of 405 patients (mean 93.5 ± 2.7 years) were included in the study. The mean length of stay was 18.5 ± 42.4 days and 13.8% died as in-patients. Variables (cut-off values) found to be significantly associated with in-patient mortality were admission sodium (>145 mmol/l), urea (≥14 mmol/l), respiratory rate (>20/min) and shock index (>1.0): creating a 5-point score (NaURSE: NaURS in the Elderly). The crude mortality rates were 9.5, 19.9, 34.4, 66.7, and 100% for scores 0, 1, 2, 3 and 4, respectively. Using the cut-off point of ≥2, the NaURSE score has a specificity of 87% (83.1-90.3) and sensitivity of 39% (28.5-50.0), with an AUC value of 0.69 (0.63-0.76). An external independent validation study (n = 121) showed similar results.

CONCLUSIONS

the NaURSE score may be particularly useful in identifying oldest old who are likely to die in that admission to guide appropriate care.

Prognostic factors of survival in patients with advanced cancer admitted to home care

CONTEXT

Data regarding prognostication of life expectancy in patients with advanced cancer are of paramount importance to patients, families, and clinicians. However, data regarding patients followed at home are lacking.

OBJECTIVES

The aim of this study was to evaluate the correlation between various factors recorded at the beginning of home care assistance and survival.

METHODS

A sample of consecutive patients admitted to two home care programs was surveyed. A preliminary consensus was achieved as to the possible variables easy to be recorded at home. These included age at the time of home care admission, gender, residence, marital status, primary cancer diagnosis, Karnofsky Performance Status (KPS) score, measures of systolic blood pressure and heart rate, cyanosis, use of oxygen, and body temperature. The Edmonton Symptom Assessment System was used to record the intensity of each symptom. Patients were divided into two groups: patients with a survival of less than 10 days (short survival) and patients with a survival of 10 days or more (medium-long survival).

RESULTS

Three hundred seventy-four consecutive patients admitted to home care programs were surveyed, of which 187 were male. The mean±SD age was 72.1±12.7 years. The mean survival was 56.2±65 days. Mean survival was 71.5±67 days (287 patients) and 5.6±2.7 days (87 patients) in the short and medium-long survival groups, respectively. No association between type of tumor and survival was observed (P=0.162). Univariate logistic regression analysis revealed that male gender (P=0.020), older age (P=0.012), lower KPS scores (P<0.0005), systolic blood pressure less than 100 mmHg (P=0.003), heart rate greater than 100 beats per minute (P=0.0006), delirium (P=0.004), the use of oxygen (P=0.002), intensity of fatigue (P=0.006), drowsiness (P<0.0005), anorexia (P<0.0005), dyspnea (P<0.0005), poor sense of well-being (P<0.0005), and distress score (P<0.0005) were associated with a survival of less than 10 days. Marital status, residence, cognitive function, fever, pain, depression, and anxiety were not found to be significantly correlated with survival. In a multiple logistic regression model, low systolic blood pressure and high heart rate, gender, delirium, use of oxygen, KPS score, drowsiness, anorexia, and dyspnea were significantly correlated with a shorter survival.

CONCLUSION

Low systolic blood pressure and high heart rate, male gender, poor KPS score, anorexia, and dyspnea were correlated with a shorter survival. Moreover, patients with low systolic blood pressure and high heart rate, male gender, poor KPS score, and greater intensity of anorexia and dyspnea are more likely to die within one week. The combination of physical symptoms from the Edmonton Symptom Assessment System and other parameters included in this study, which are simple to assess and are repeatable at home, should be further explored in future studies to provide a simple tool for use with patients with advanced cancer admitted to a home care program.

Prospective comparison of prognostic scores in palliative care cancer populations

PURPOSE

Predicting prognosis in advanced cancer aids physicians in clinical decision making and can help patients and their families to prepare for the time ahead.

MATERIALS AND METHODS

This multicenter, observational, prospective, nonrandomized population-based study evaluated life span prediction of four prognostic scores used in palliative care: the original palliative prognostic score (PaP Score), a variant of PaP Score including delirium (D-PaP Score), the Palliative Performance Scale, and the Palliative Prognostic Index.

RESULTS

A total of 549 patients were enrolled onto the study. Median survival of the entire group was 22 days (95% confidence intervals [95% CI] = 19-24). All four prognostic models discriminated well between groups of patients with different survival probabilities. Log-rank tests were all highly significant (p < .0001). The PaP and D-PaP scores were the most accurate, with a C index of 0.72 (95% CI = 0.70-0.73) and 0.73 (95% CI = 0.71-0.74), respectively.

CONCLUSION

It can be confirmed that all four prognostic scores used in palliative care studies accurately identify classes of patients with different survival probabilities. The PaP Score has been extensively validated and shows high accuracy and reproducibility in different settings.

Factors influencing survival after discharge from an Australian palliative care unit to residential aged care facilities: a retrospective audit

BACKGROUND

Increasing demand for palliative care unit (PCU) beds has led to shorter inpatient stays and a requirement to transfer some patients from a PCU to a residential aged care facility (RACF). Concerns have been raised regarding this move with suggestion that patients often die shortly after transfer. Published data investigating this patient group are limited. The aim of the current study was to audit discharges from a PCU to RACFs specifically looking at predictive factors for survival following discharge.

METHODS

A retrospective audit was undertaken of all discharges from the Barwon Health PCU to RACFs between July 2007 and July 2010. Data on patient demographics, clinical and functional status, admission and discharge details, and survival times were examined. Factors influencing survival were evaluated by Cox proportional-hazards regression analysis.

RESULTS

Sixty-two discharges from a PCU to an RACF were included in the analysis. The mean age at discharge was 76 and the majority of patients had malignant disease. Mean and median survival times post-transfer were 106 and 42.5 days, respectively, and 16% of subjects survived more than 100 days. From univariate analyses age, PCU length of stay, admission Resource Utilization Groups-Activities of Daily Living (RUG-ADL) score, dependent mobility, having lung cancer or cancer of unknown primary, and living alone or in an RACF pre PCU admission affected survival. Multivariate analyses showed age, PCU length of stay, RUG-ADL score, and living situation prior to PCU admission together were associated with postdischarge survival times.

CONCLUSIONS

This study is one of the largest investigating this cohort and suggests a number of factors that may predict survival for patients after discharge from a PCU to an RACF.