How accurate are physicians' clinical predictions of survival and the available prognostic tools in estimating survival times in terminally ill cancer patients? A systematic review

Multidisciplinary Prognostication Using the Palliative Prognostic Score in an Australian Cancer Center

CONTEXT

Accurate prognostication is important in oncology and palliative care. A multidisciplinary approach to prognostication provides a novel approach, but its accuracy and application is poorly researched. In this study, we describe and analyze our experience of multidisciplinary prognostication in palliative care patients with cancer.

OBJECTIVES

To assess our accuracy of prognostication using multidisciplinary team prediction of survival (MTPS) alone and within the Palliative Prognostic (PaP) Score.

METHODS

This retrospective study included all new patients referred to a palliative care consultation service in a tertiary cancer center between January 2010 and December 2011. Initial assessment data for 421 inpatients and 223 outpatients were analyzed according to inpatient and outpatient groups to evaluate the accuracy of prognostication using MTPS alone and within the PaP score (MTPS-PaP) and their correlation with overall survival.

RESULTS

Inpatients with MTPS-PaP group A, B, and C had a median survival of 10.9, 3.4, and 0.7 weeks, respectively, and a 30-day survival probability of 81%, 40%, and 10%, respectively. Outpatients with MTPS-PaP group A and B had a median survival of 17.3 and 5.1 weeks, respectively, and a 30-day survival probability of 94% and 50%, respectively. MTPS overestimated survival by a factor of 1.5 for inpatients and 1.2 for outpatients. The MTPS-PaP score correlated better than MTPS alone with overall survival.

CONCLUSION

This study suggests that a multidisciplinary team approach to prognostication within routine clinical practice is possible and may substitute for single clinician prediction of survival within the PaP score without detracting from its accuracy. Multidisciplinary team prognostication can assist treating teams to recognize and articulate prognosis, facilitate treatment decisions, and plan end-of-life care appropriately. PaP was less useful in the outpatient setting, given the longer survival interval of the outpatient palliative care patient group.

How to integrate predictions in outcomes in planning clinical care

BACKGROUND

The CKD population is becoming increasingly elderly with multiple comorbidities. For this reason, accurate predictive information related to the progression into ESRD, mortality, and functional decline is critical to allow for optimal shared decision making (SDM).

SUMMARY

This review will assess the current literature on the methodologies for the estimation of prognosis and prognostic tools developed for CKD. A practical clinical approach is discussed that involves the estimation of prognosis and integration of prognosis into SDM.

KEY MESSAGE

There are validated, easy-to-use prognostic tools that help clinicians engage in effective shared decision making with their CKD patients and family.

Patterns of health care utilization preceding a colorectal cancer diagnosis are strong predictors of dying quickly following diagnosis

Background

Understanding the predictors of a quick death following diagnosis may improve timely access to palliative care. The objective of this study was to explore whether factors in the 24 months prior to a colorectal cancer (CRC) diagnosis predict a quick death post-diagnosis.

Methods

Data were from a longitudinal study of all adult persons diagnosed with CRC in Nova Scotia, Canada, from 01Jan2001-31Dec2005. This study included all persons who died of any cause by 31Dec2010, except those who died within 30 days of CRC surgery (n = 1885 decedents). Classification and regression tree models were used to explore predictors of time from diagnosis to death for the following time intervals: 2, 4, 6, 8, 12, and 26 weeks from diagnosis to death. All models were performed with and without stage at diagnosis as a predictor variable. Clinico-demographic and health service utilization data in the 24 months pre-diagnosis were provided via linked administrative databases.

Results

The strongest, most consistent predictors of dying within 2, 4, 6, and 8 weeks of CRC diagnosis were related to health services utilization in the 24 months prior to diagnosis: i.e., number of specialist visits, number of days spent in hospital, and number of family physician visits. Stage at diagnosis was the strongest predictor of dying within 12 and 26 weeks of diagnosis.

Conclusions

Identifying potential predictors of a short timeframe between cancer diagnosis and death may aid in the development of strategies to facilitate timely and appropriate referral to palliative care upon a cancer diagnosis.

Longitudinal temporal and probabilistic prediction of survival in a cohort of patients with advanced cancer

CONTEXT

Survival prognostication is important during the end of life. The accuracy of clinician prediction of survival (CPS) over time has not been well characterized.

OBJECTIVES

The aims of the study were to examine changes in prognostication accuracy during the last 14 days of life in a cohort of patients with advanced cancer admitted to two acute palliative care units and to compare the accuracy between the temporal and probabilistic approaches.

METHODS

Physicians and nurses prognosticated survival daily for cancer patients in two hospitals until death/discharge using two prognostic approaches: temporal and probabilistic. We assessed accuracy for each method daily during the last 14 days of life comparing accuracy at Day -14 (baseline) with accuracy at each time point using a test of proportions.

RESULTS

A total of 6718 temporal and 6621 probabilistic estimations were provided by physicians and nurses for 311 patients, respectively. Median (interquartile range) survival was 8 days (4-20 days). Temporal CPS had low accuracy (10%-40%) and did not change over time. In contrast, probabilistic CPS was significantly more accurate (P < .05 at each time point) but decreased close to death.

CONCLUSION

Probabilistic CPS was consistently more accurate than temporal CPS over the last 14 days of life; however, its accuracy decreased as patients approached death. Our findings suggest that better tools to predict impending death are necessary.

Patient's Factors at Entering Hospice Affecting Length of Survival in a Hospice Center

PURPOSE

In order to provide effective hospice care, adequate length of survival (LOS) in hospice is necessary. However the reported average LOS is much shorter. Analysis of LOS in hospice has not been reported from Korea. We evaluated the duration of LOS and the factors associated with LOS at our hospice center.

MATERIALS AND METHODS

We retrospectively examined 446 patients who were admitted to our hospice unit between January 2010 and December 2012. We performed univariate and multivariate analysis for analysis of factors associated with LOS.

RESULTS

The median LOS was 9.5 days (range, 1 to 186 days). The LOS of 389 patients (86.8%) was< 1 month. At the time of admission to hospice, 112 patients (25.2%) were completely bedridden, 110 patients (24.8%) had mouth care only without intake, and 134 patients (30.1%) had decreased consciousness, from confusion to coma. The median time interval between the day of the last anticancer treatment and the day of hospice admission was 75 days. By analysis of the results of multivariate analysis, decreased intake and laboratory results showing increased total white blood cell (WBC), decreased platelet count, increased serum creatinine, increased aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH) level were poor prognostic factors for survival in hospice.

CONCLUSION

Before hospice admission, careful evaluation of the patient's performance, particularly the oral intake, and total WBC, platelet, creatinine, AST, ALT, and LDH level is essential, because these were strong predictors of shorter LOS. In the future, conduct of prospective controlled studies is warranted in order to confirm the relationship between potential prognostic factors and LOS in hospice.

Do unto others: doctors' personal end-of-life resuscitation preferences and their attitudes toward advance directives

OBJECTIVE

High-intensity interventions are provided to seriously-ill patients in the last months of life by medical sub-specialists. This study was undertaken to determine if doctors' age, ethnicity, medical sub-specialty and personal resuscitation and organ donation preferences influenced their attitudes toward Advance Directives (AD) and to compare a cohort of 2013 doctors to a 1989 (one year before the Patient Self Determination Act in 1990) cohort to determine any changes in attitudes towards AD in the past 23 years.

DESIGN

Doctors in two academic medical centers participated in an AD simulation and attitudes survey in 2013 and their responses were compared to a cohort of doctors in 1989.

OUTCOMES

Resuscitation and organ donation preferences (2013 cohort) and attitudes toward AD (1989 and 2013 cohorts).

RESULTS

In 2013, 1081 (94.2%) doctors of the 1147 approached participated. Compared to 1989, 2013 cohort did not feel that widespread acceptance of AD would result in less aggressive treatment even of patients who do not have an AD (p<0.001, AUC = 0.77); had greater confidence in their treatment decisions if guided by an AD (p<.001, AUC = 0.58) and were less worried about legal consequences of limiting treatment when following an AD (p<.001, AUC  = 0.57). The gender (p = 0.00172), ethnicity (χ2 14.68, DF = 3,p = .0021) and sub-specialty (χ2 28.92, p = .004, DF = 12) influenced their attitudes towards AD. 88.3% doctors chose do-not-resuscitate status and wanted to become organ donors. Those less supportive of AD were more likely to opt for "full code" even if terminally ill and were less supportive of organ donation.

CONCLUSIONS

Doctors' attitudes towards AD has not changed significantly in the past 23 years. Doctors' gender, ethnicity and sub-specialty influence their attitudes towards AD. Our study raises questions about why doctors continue to provide high-intensity care for terminally ill patients but personally forego such care for themselves at the end of life.

Predicting survival time in noncurative patients with advanced cancer: a prospective study in China

BACKGROUND

Accurate prediction of prognosis for cancer patients is important for good clinical decision making in therapeutic and care strategies. The application of prognostic tools and indicators could improve prediction accuracy.

OBJECTIVE

This study aimed to develop a new prognostic scale to predict survival time of advanced cancer patients in China.

METHODS

We prospectively collected items that we anticipated might influence survival time of advanced cancer patients. Participants were recruited from 12 hospitals in Shanghai, China. We collected data including demographic information, clinical symptoms and signs, and biochemical test results. Log-rank tests, Cox regression, and linear regression were performed to develop a prognostic scale.

RESULTS

Three hundred twenty patients with advanced cancer were recruited. Fourteen prognostic factors were included in the prognostic scale: Karnofsky Performance Scale (KPS) score, pain, ascites, hydrothorax, edema, delirium, cachexia, white blood cell (WBC) count, hemoglobin, sodium, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) values. The score was calculated by summing the partial scores, ranging from 0 to 30. When using the cutoff points of 7-day, 30-day, 90-day, and 180-day survival time, the scores were calculated as 12, 10, 8, and 6, respectively.

CONCLUSIONS

We propose a new prognostic scale including KPS, pain, ascites, hydrothorax, edema, delirium, cachexia, WBC count, hemoglobin, sodium, total bilirubin, direct bilirubin, AST, and ALP values, which may help guide physicians in predicting the likely survival time of cancer patients more accurately. More studies are needed to validate this scale in the future.

Prognostic accuracy of clinicians for back, neck and shoulder patients in routine practice

Background

Chronicity amongst musculoskeletal patients remains a considerable burden and predicting outcomes in these patients has proven difficult. Although a large number of studies have investigated a range of predictors of outcome few have looked at the practitioners’ ability to discern those that improve from those most likely to fail to improve. This study aimed to investigate the ability of chiropractors to predict patient outcomes.

Methods

Prediction and outcome data were collected from 440 consecutive patients with back, neck or shoulder pain accepted for chiropractic care within 5 linked private practices.

Predictions by chiropractors were compared to patient outcomes as measured by Bournemouth Questionnaire (BQ) scores, pain NRS scores and patient global impression of change (PGIC) collected at 4 and 12 weeks following the initial consultation.

Results

Overall, chiropractors appear unable to accurately predict poor outcomes in their patients particularly in the longer term. Although some conditions (neck) faired a little better in some cases with some trends in short term pain scores being associated with the clinicians prediction, this was marginal. Subgrouping by practitioners or duration did not improve the performance of these predictions

Conclusions

Chiropractors generally fail to reliably predict poor treatment outcome of patients at initial consultation.

How radiation oncologists evaluate and incorporate life expectancy estimates into the treatment of palliative cancer patients: a survey-based study

PURPOSE

We surveyed how radiation oncologists think about and incorporate a palliative cancer patient's life expectancy (LE) into their treatment recommendations.

METHODS AND MATERIALS

A 41-item survey was e-mailed to 113 radiation oncology attending physicians and residents at radiation oncology centers within the Boston area. Physicians estimated how frequently they assessed the LE of their palliative cancer patients and rated the importance of 18 factors in formulating LE estimates. For 3 common palliative case scenarios, physicians estimated LE and reported whether they had an LE threshold below which they would modify their treatment recommendation. LE estimates were considered accurate when within the 95% confidence interval of median survival estimates from an established prognostic model.

RESULTS

Among 92 respondents (81%), the majority were male (62%), from an academic practice (75%), and an attending physician (70%). Physicians reported assessing LE in 91% of their evaluations and most frequently rated performance status (92%), overall metastatic burden (90%), presence of central nervous system metastases (75%), and primary cancer site (73%) as "very important" in assessing LE. Across the 3 cases, most (88%-97%) had LE thresholds that would alter treatment recommendations. Overall, physicians' LE estimates were 22% accurate with 67% over the range predicted by the prognostic model.

CONCLUSIONS

Physicians often incorporate LE estimates into palliative cancer care and identify important prognostic factors. Most have LE thresholds that guide their treatment recommendations. However, physicians overestimated patient survival times in most cases. Future studies focused on improving LE assessment are needed.

Predicting life expectancy in patients with metastatic cancer receiving palliative radiotherapy: the TEACHH model

BACKGROUND

Predicting life expectancy (LE) in patients with metastatic cancer who are receiving palliative therapies is a difficult task. The purpose of the current study was to develop a LE prediction model among patients receiving palliative radiotherapy (RT) that identifies those patients with short (< 3 months) and long (> 1 year) LEs.

METHODS

The records of 862 patients with metastatic cancer receiving palliative RT at the Dana-Farber/Brigham and Women's Cancer Center between June 2008 and July 2011 were retrospectively reviewed. Cox proportional hazards models were used to evaluate established and potential clinical predictors of LE to construct a model predicting LE of < 3 months and > 1 year.

RESULTS

The median survival was 5.6 months. On multivariate analysis, factors found to be significantly associated with a shorter LE were cancer type (lung and other vs breast and prostate), older age (> 60 years vs ≤ 60 years), liver metastases, Eastern Cooperative Oncology Group performance status (2-4 vs 0-1), hospitalizations within 3 months before palliative RT (0 vs ≥ 1), and prior palliative chemotherapy courses (≥ 2 vs 0-1). Patients were divided into 3 groups with distinct median survivals: group A (those with 0-1 risk factors), 19.9 months (95% confidence interval [95% CI, 13.9 months-31.1 months]); group B (those with 2-4 risk factors), 5.0 months (95% CI, 4.3 months -5.6 months); and group C (those with 5-6 risk factors), 1.7 months (95% CI, 1.2 months-2.1 months).

CONCLUSIONS

The TEACHH model (type of cancer, Eastern Cooperative Oncology Group performance status, age, prior palliative chemotherapy, prior hospitalizations, and hepatic metastases) divides patients receiving palliative RT into 3 distinct LE groups at clinically informative extremes of the LE spectrum. It holds promise to assist radiation oncologists in tailoring palliative therapies to a patient's LE.

Can patients or clinicians predict the severity or duration of an acute upper respiratory infection?

PURPOSE

Acute upper respiratory infections (URI) are the second most common diagnosis in primary care offices. As treatments have limited effectiveness, patient counseling regarding expectations for the course of the URI is an important aspect of care. It is unknown how accurate patients, clinicians or questionnaires such as the Wisconsin Upper Respiratory Symptom Survey (WURSS) instrument are at predicting URI severity and duration, and whether these predictions should be used to counsel patients.

METHODS

Seven hundred and nineteen individuals with recent onset cold in community clinic settings participated. Participants and clinicians predicted the severity and duration of the URI and participants completed the WURSS instrument at initial visit. Subsequent URI global severity was calculated as area under the curve using an average of twice-daily WURSS-21 self-reports as the y-axis and illness duration as the x-axis. URI duration was determined by self-report of beginning and end of illness. Linear regression analysis was used to correlate baseline predictions with subsequent outcomes. Analyses by gender, age and income were also performed.

RESULTS

There was no significant association between participant and clinician predictions of severity or duration. Initial WURSS values explained 0.119 (95% CI: 0.074-0.163) of the variance in subsequent severity outcomes. There were no significant differences in associations by age, gender or income.

CONCLUSIONS

Clinicians should not use their predictive assessments or their patients' predictions when advising patients on the expected course of a URI. This study also suggests that the WURSS instrument could give some predictive information, but whether this is clinically useful is uncertain.

Predicting 7-day survival using heart rate variability in hospice patients with non-lung cancers

BACKGROUND

A simple and accurate survival prediction tool can facilitate decision making processes for hospice patients with advanced cancers. The objectives of this study were to explore the association of cardiac autonomic functions and survival in patients with advanced cancer and to evaluate the prognostic value of heart rate variability (HRV) in 7-day survival prediction.

METHODS

A prospective study was conducted on 138 patients with advanced cancer recruited from the hospice ward of a regional hospital in southern Taiwan. Information on functional status and symptom burden of the patients was recorded. Frequency-domain HRV was obtained for the evaluation of cardiac autonomic functions at admission. The end point of the study was defined as the survival status at day 7 after admission to the hospice ward. Multivariate logistic regression analyses were performed to evaluate the independent associations between HRV indices and survival of 7 days or less.

RESULTS

The median survival time of the patients was 20 days (95% CI, 17-28 days). Results from the multivariate logistic regression analysis indicated that the natural logarithm-transformed high-frequency power (lnHFP) of a value less than 2 (OR = 3.8, p = 0.008) and ECOG performance status of 3 or 4 (OR = 3.4, p = 0.023) were significantly associated with a higher risk of survival of 7 days or less. Receiver operating characteristic (ROC) curve analysis revealed that the area under the curve was 0.71 (95% CI, 0.61-0.81).

CONCLUSIONS

In hospice patients with non-lung cancers, an lnHPF value below 2 at hospice admission was significantly associated with survival of 7 days or less. HRV might be used as a non-invasive and objective tool to facilitate medical decision making by improving the accuracy in survival prediction.

Risk factors for survival of 106 surgically treated patients with symptomatic spinal epidural metastases

PURPOSE

Evaluation of risk factors for survival in patients surgically treated for symptomatic spinal epidural metastases (SEM).

METHODS

One hundred and six patients who were surgically treated for symptomatic SEM in a 10-year period in two cooperatively working hospitals were retrospectively studied for nine risk factors: age, gender, site of the primary tumor, location of the symptomatic spinal metastasis, functional and neurologic status, the presence of visceral metastases and the presence of other spinal and extraspinal bone metastases. Analysis was performed using the Kaplan-Meier method, univariate log-rank tests and Cox-regression models.

RESULTS

Overall median survival was 10.7 months (0.2-107.5 months). Overall 30-day complication rate was 33 %. Multivariate Cox-regression analysis showed that fast growing primary tumors (HR 3.1, 95 % CI 1.6-6.2, p = 0.001), the presence of visceral metastases (HR 1.7, 95 % CI 1.0-2.9, p = 0.033) and a low performance status (HR 2.7, 95 % CI 1.1-6.6, p = 0.025) negatively influenced the survival.

CONCLUSION

Primary tumor type, presence of visceral metastases and performance status are significant predictors for survival after surgery for symptomatic SEM and should be evaluated before deciding on the extent of treatment. More accurate prediction models are needed to select the best treatment option for the individual patient.

A flexible alternative to the Cox proportional hazards model for assessing the prognostic accuracy of hospice patient survival

Prognostic models are often used to estimate the length of patient survival. The Cox proportional hazards model has traditionally been applied to assess the accuracy of prognostic models. However, it may be suboptimal due to the inflexibility to model the baseline survival function and when the proportional hazards assumption is violated. The aim of this study was to use internal validation to compare the predictive power of a flexible Royston-Parmar family of survival functions with the Cox proportional hazards model. We applied the Palliative Performance Scale on a dataset of 590 hospice patients at the time of hospice admission. The retrospective data were obtained from the Lifepath Hospice and Palliative Care center in Hillsborough County, Florida, USA. The criteria used to evaluate and compare the models' predictive performance were the explained variation statistic R², scaled Brier score, and the discrimination slope. The explained variation statistic demonstrated that overall the Royston-Parmar family of survival functions provided a better fit (R² = 0.298; 95% CI: 0.236–0.358) than the Cox model (R² = 0.156; 95% CI: 0.111–0.203). The scaled Brier scores and discrimination slopes were consistently higher under the Royston-Parmar model. Researchers involved in prognosticating patient survival are encouraged to consider the Royston-Parmar model as an alternative to Cox.

A clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy

BACKGROUND

Palliative chemotherapy is often administered to terminally ill cancer patients to relieve symptoms. Yet, unnecessary use of chemotherapy can worsen patients' quality of life due to treatment-related toxicities. Thus, accurate prediction of survival in terminally ill patients can help clinicians decide on the most appropriate palliative care for these patients. However, studies have shown that clinicians often make imprecise predictions of survival in cancer patients. Hence, the purpose of this study was to create a clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy.

MATERIALS AND METHODS

Data were obtained from a retrospective study of 400 randomly selected terminally ill cancer patients in the National Cancer Centre Singapore (NCCS) from 2008 to 2009. After removing patients with missing data, there were 325 patients remaining for model development. Three classification algorithms, naive Bayes (NB), neural network (NN), and support vector machine (SVM) were used to create the models. A final model with the best prediction performance was then selected to develop the tool.

RESULTS

The NN model had the best prediction performance. The accuracy, specificity, sensitivity, and area under the curve (AUC) of this model were 78%, 82%, 74%, and 0.857, respectively. Five patient attributes (albumin level, alanine transaminase level (ATL), absolute neutrophil count, Eastern Cooperative Oncology Group (ECOG) status, and number of metastatic sites) were included in the model.

CONCLUSIONS

A decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy was created. With further validation, this tool coupled with the professional judgment of clinicians can help improve patient care.

Development of a prognostic model for six-month mortality in older adults with declining health

CONTEXT

Estimation of six-month prognosis is essential in hospice referral decisions, but accurate, evidence-based tools to assist in this task are lacking.

OBJECTIVES

To develop a new prognostic model, the Patient-Reported Outcome Mortality Prediction Tool (PROMPT), for six-month mortality in community-dwelling elderly patients.

METHODS

We used data from the Medicare Health Outcomes Survey linked to vital status information. Respondents were 65 years old or older, with self-reported declining health over the past year (n=21,870), identified from four Medicare Health Outcomes Survey cohorts (1998-2000, 1999-2001, 2000-2002, and 2001-2003). A logistic regression model was derived to predict six-month mortality, using sociodemographic characteristics, comorbidities, and health-related quality of life (HRQOL), ascertained by measures of activities of daily living and the Medical Outcomes Study Short Form-36 Health Survey; k-fold cross-validation was used to evaluate model performance, which was compared with existing prognostic tools.

RESULTS

The PROMPT incorporated 11 variables, including four HRQOL domains: general health perceptions, activities of daily living, social functioning, and energy/fatigue. The model demonstrated good discrimination (c-statistic=0.75) and calibration. Overall diagnostic accuracy was superior to existing tools. At cut points of 10%-70%, estimated six-month mortality risk sensitivity and specificity ranged from 0.8% to 83.4% and 51.1% to 99.9%, respectively, and positive likelihood ratios at all mortality risk cut points ≥40% exceeded 5.0. Corresponding positive and negative predictive values were 23.1%-64.1% and 85.3%-94.5%. Over 50% of patients with estimated six-month mortality risk ≥30% died within 12 months.

CONCLUSION

The PROMPT, a new prognostic model incorporating HRQOL, demonstrates promising performance and potential value for hospice referral decisions. More work is needed to evaluate the model.

Antibiotics and mortality in patients with lower respiratory infection and advanced dementia

OBJECTIVES

To describe long-term mortality rate and to assess associations between mortality rate and antibiotic treatment of lower respiratory infection in patients with advanced dementia; antibiotic treatment allocation was independent of mortality risk-leaving less room for biased associations than in previous multicenter observational studies.

DESIGN

Prospective study (2004-2009). Multilevel Cox proportional hazard analyses with adjustment for mortality risk were used to assess associations between antibiotics and mortality using time-dependent covariates.

SETTING

A US Department of Veterans Affairs nursing home.

PARTICIPANTS

Ninety-four residents with advanced dementia who developed 109 episodes.

MEASUREMENTS

Survival, treatment, mortality risk, illness severity, fluid intake, and several other patient characteristics.

RESULTS

Ten-day mortality was 48%, and 6-month mortality was 74%. Antibiotics were used in 77% of episodes. Overall, antibiotics were not associated with mortality rate (Hazard Ratio [HR] 0.70, Confidence Interval [CI] 0.38-1.30); however, antibiotics were associated with reduced 10-day mortality rate (HR 0.51, CI, 0.30-0.87; rate after 10 days: 1.5, CI 0.42-5.2). Benefit from antibiotics was less likely with inadequate fluid intake, and when experiencing the first episode.

CONCLUSION

In our sample of male nursing home residents with advanced dementia and lower respiratory infection, mortality was substantial despite antibiotic treatment. Antibiotics prolonged life but in many cases only for several days. Treatment decisions should take into account that antibiotics may delay death but may also prolong the dying process, indicating a need for accurate prediction of mortality and study of characteristics that may alter effectiveness of antibiotics.

Survival prediction for terminally ill cancer patients: revision of the palliative prognostic score with incorporation of delirium

PURPOSE

An existing and validated palliative prognostic (PaP) score predicts survival in terminally ill cancer patients based on dyspnea, anorexia, Karnofsky performance status score, clinical prediction of survival, total WBC, and lymphocyte percentage. The PaP score assigns patients to three different risk groups according to a 30-day survival probability--group A, >70%; group B, 30%-70%; group C, <30%. The impact of delirium is known but was not incorporated into the PaP score.

MATERIALS AND METHODS

Our aim was to incorporate information on delirium into the PaP score based on a retrospective series of 361 terminally ill cancer patients. We followed the approach of "validation by calibration," proposed by van Houwelingen and later adapted by Miceli for achieving score revision with inclusion of a new variable. The discriminating performance of the scores was estimated using the K statistic.

RESULTS

The prognostic contribution of delirium was confirmed as statistically significant (p < .001) and the variable was accordingly incorporated into the PaP score (D-PaP score). Following this revision, 30-day survival estimates in groups A, B, and C were 83%, 50%, and 9% for the D-PaP score and 87%, 51%, and 16% for the PaP score, respectively. The overall performance of the D-PaP score was better than that of the PaP score.

CONCLUSION

The revision of the PaP score was carried out by modifying the cutoff values used for prognostic grouping without, however, affecting the partial scores of the original tool. The performance of the D-PaP score was better than that of the PaP score and its key feature of simplicity was maintained.

The accuracy of probabilistic versus temporal clinician prediction of survival for patients with advanced cancer: a preliminary report

Clinicians have limited accuracy in the prediction of patient survival. We assessed the accuracy of probabilistic clinician prediction of survival (CPS) and temporal CPS for advanced cancer patients admitted to our acute palliative care unit, and identified factors associated with CPS accuracy. Eight physicians and 20 nurses provided their estimation of survival on admission by (a) the temporal approach, "What is the approximate survival for this patient (in days)?" and (b) the probabilistic approach, "What is the approximate probability that this patient will be alive (0%-100%)?" for ≥24 hours, 48 hours, 1 week, 2 weeks, 1 month, 3 months, and 6 months. We also collected patient and clinician demographics. Among 151 patients, the median age was 58 years, 95 (63%) were female, and 138 (81%) had solid tumors. The median overall survival time was 12 days. The median temporal CPS was 14 days for physicians and 20 days for nurses. Physicians were more accurate than nurses. A higher accuracy of temporal physician CPS was associated with older patient age. Probabilistic CPS was significantly more accurate than temporal CPS for both physicians and nurses, although this analysis was limited by the different criteria for determining accuracy. With the probabilistic approach, nurses were significantly more accurate at predicting survival at 24 hours and 48 hours, whereas physicians were significantly more accurate at predicting survival at 6 months. The probabilistic approach was associated with high accuracy and has practical implications.

How accurate are physicians in the prediction of patient survival in advanced lung cancer?

BACKGROUND

Because most cases of non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage with a poor prognosis, patient inclusion in clinical trials is critical. Most trials require an estimated life expectancy >3 months, based on clinician estimates of patient survival probability, without providing formal guidelines. The aim of this study was to assess the accuracy of clinicians' predictions of survival in NSCLC patients (stages IIIB, and IV) and the possible impact of patient quality of life on survival estimation.

METHODS

At diagnosis, clinical, biological, and quality of life data (QLQ-C30 questionnaire) were recorded, and doctors "forecast" each patient's estimated survival. Concordance between predicted and actual survival was assessed with the intraclass correlation coefficient.

RESULTS

Eighty-five patients with a mean age of 62.2 years, 81.1% male, were included (squamous cell carcinoma, 33; adenocarcinoma, 42; large cell carcinoma, 8; neuroendocrine carcinoma, 2). The mean follow-up was 40 months and median survival time was 11.7 (range, 0.4-143.7) weeks. All clinicians (residents, registrars, and consultants) overestimated patient survival time, with a moderate concordance between predicted and actual survival time. A worse global health status was associated with a lower discrepancy between estimated and actual patient survival, and a worse role functioning was associated with a larger difference between estimated and actual patient survival.

CONCLUSION

The absence of specific recommendations to estimate patient survival may introduce major selection in clinical studies. Further research should investigate whether the accuracy of patient survival estimates by clinicians would be improved by taking into account patient quality of life.

A computer-assisted model for predicting probability of dying within 7 days of hospice admission in patients with terminal cancer

OBJECTIVE

The aim of the present study is to compare the accuracy in using laboratory data or clinical factors, or both, in predicting probability of dying within 7 days of hospice admission in terminal cancer patients.

METHODS

We conducted a prospective cohort study of 727 patients with terminal cancer. Three models for predicting the probability of dying within 7 days of hospice admission were developed: (i) demographic data and laboratory data (Model 1); (ii) demographic data and clinical symptoms (Model 2); and (iii) combination of demographic data, laboratory data and clinical symptoms (Model 3). We compared the models by using the area under the receiver operator curve using stepwise multiple logistic regression.

RESULTS

We estimated the probability dying within 7 days of hospice admission using the logistic function, P = Exp(betax)/[1 + Exp(betax)]. The highest prediction accuracy was observed in Model 3 (82.3%), followed by Model 2 (77.8%) and Model 1 (75.5%). The log[probability of dying within 7 days/(1 - probability of dying within 7 days)] = -6.52 + 0.77 x (male = 1, female = 0) + 0.59 x (cancer, liver = 1, others = 0) + 0.82 x (ECOG score) + 0.59 x (jaundice, yes = 1, no = 0) + 0.54 x (Grade 3 edema = 1, others = 0) + 0.95 x (fever, yes = 1, no = 0) + 0.07 x (respiratory rate, as per minute) + 0.01 x (heart rate, as per minute) - 0.92 x (intervention tube = 1, no = 0) - 0.37 x (mean muscle power).

CONCLUSIONS

We proposed a computer-assisted estimated probability formula for predicting dying within 7 days of hospice admission in terminal cancer patients.

MRI correlates of protein deposition and disease severity in postmortem frontotemporal lobar degeneration

BACKGROUND

Frontotemporal lobar degeneration (FTLD) can be classified based on the presence of the microtubule-associated protein tau and the TAR DNA binding protein-43 (TDP-43). Future treatments will likely target these proteins, therefore it is important to identify biomarkers to help predict protein biochemistry.

OBJECTIVE

To determine whether there is an MRI signature pattern of tau or TDP-43 using a large cohort of FTLD subjects and to investigate how patterns of atrophy change according to disease severity using a large autopsy-confirmed cohort of FTLD subjects.

METHODS

Patterns of gray matter loss were assessed using voxel-based morphometry in 37 tau-positive and 44 TDP-43-positive subjects compared to 35 age and gender-matched controls, and compared to each other. Comparisons were also repeated in behavioral variant frontotemporal dementia (bvFTD) subjects (n = 15 tau-positive and n = 30 TDP-43-positive). Patterns of atrophy were also assessed according to performance on the Clinical Dementia Rating (CDR) scale and Mini-Mental State Examination (MMSE).

RESULTS

The tau-positive and TDP-43-positive groups showed patterns of frontotemporal gray matter loss compared to controls with no differences observed between the groups, for all subjects and for bvFTD subjects. Patterns of gray matter loss increased in a graded manner by CDR and MMSE with loss in the frontal lobes, insula and hippocampus in mild subjects, spreading to the temporal and parietal cortices and striatum in more advanced disease.

CONCLUSION

There is no signature pattern of atrophy for tau or TDP-43; however, patterns of atrophy in FTLD progress with measures of clinical disease severity.

A longitudinal study of the role of patient-reported outcomes on survival prediction of palliative cancer inpatients in Taiwan

GOALS OF WORK

This study explores the significance of patient-reported outcomes for predicting length of survival of palliative cancer patients.

PATIENTS AND METHODS

Patients were recruited upon admission to the inpatient palliative care unit. Weekly assessment of 180 terminal cancer patients was carried out throughout their survival time using the Medical Outcome Study 36-Item Short-Form Health Survey, the Taiwanese version of the M.D. Anderson Symptom Inventory (MDASI-T), the Karnofsky Performance Status (KPS), the Brief Pain Inventory, and the Brief Fatigue Inventory. Generalized estimating equations (GEE) were utilized to analyze whether the patient-reported outcomes predicted survival time.

MAIN RESULTS

Of all patients, 64 had one assessment, 51 had two, 25 had three, and 40 had four or more assessments, up to a maximum of eight. The univariate analysis showed that gender (P < 0.01), KPS (P < 0.01), the physical component summary score (P = 0.02), the MDASI-T total score (P < 0.01), composite fatigue severity (P < 0.01), and composite pain severity (P < 0.01) were significantly associated with length of survival. The multivariate analysis showed that gender (P < 0.01), KPS (P < 0.01), and the MDASI-T total score (P = 0.01) were significant predictors of survival time.

CONCLUSIONS

This is the first study to explore the significance of patient-related outcomes for predicting length of survival of palliative cancer patients using the GEE method. This study confirms that overall symptom severity is a significant factor in assessing the length of survival of palliative cancer patients.

[Diagnostic and treatment goals in elderly patients]

Improved quality of life and greater independence are becoming increasingly important as treatment goals in elderly patients, while merely extending life expectancy is only rarely the primary treatment goal. In elderly patients in particular, the patient's wishes are extremely important when deciding on the treatment goals. If patients are no longer able to express their wishes, the treating physician must establish what their presumed wishes are. Relatives and carers are particularly important in determining a patient's presumed wishes. A standardized geriatric assessment and interventions conducted on the basis of this assessment can give patients greater independence in everyday activities (e.g., walking, personal hygiene, eating) and can avoid them having to go into a nursing home or at least delay this move. In addition, the patient's prognosis is improved, which is manifested inter alia in a longer life-span. A basic geriatric assessment should therefore be conducted in all elderly patients. Standardized testing methods are used to examine the following areas: everyday activities, mobility/risk of falling, and cognition. Patients with the relevant risk combinations should also be screened for malnutrition. Comorbidities are a decisive factor influencing the prognosis in tumor patients. The comorbidities should be recorded using a structured method, e.g., the Charlson Comorbidity Index, and taken into account when deciding on treatment.

Prognostic factors in patients with recently diagnosed incurable cancer: a systematic review

GOALS OF WORK

To review the literature and develop a conceptual framework about prognostic factors for people presenting to medical oncologists with recently diagnosed incurable cancer.

MATERIALS AND METHODS

Medline was searched from January 2000 to October 2003 to identify articles testing associations between clinical or laboratory variables and survival time in adults with advanced solid tumours and median survival of 3 to 24 months. We recorded how frequently prognostic factors were significantly associated with survival in univariable and multivariable analyses.

RESULTS

There were 53 studies included. The factors associated with survival were organised into four categories related to attributes of the host the tumour, the treatment and the interactions between host, tumour and treatment (symptoms, quality of life, performance status and laboratory tests). Co-morbidity was consistently associated with shorter survival. Age and gender were not consistently associated with survival duration, except in lung cancer where females survived longer. Tumour-related factors associated with shorter survival included primary tumour (lung), metastatic site (liver, brain and visceral) and disease extent. Symptoms associated with shorter survival included those of the anorexia-cachexia syndrome, dyspnoea, pain and impaired physical well being. Performance status was strongly associated with survival in most studies. Laboratory tests associated with shorter survival included anaemia, thrombocytopenia, hypoalbuminaemia and elevated serum levels of both alkaline phosphatase and lactate dehydrogenase.

CONCLUSION

Prognostic factors in patients with advanced cancer can be conceptualised as attributes of the host, tumour, treatment and interactions between the three reflected in symptoms, quality of life performance status and laboratory tests.

Factors influencing death at home in terminally ill patients with cancer: systematic review

OBJECTIVES

To determine the relative influence of different factors on place of death in patients with cancer.

DATA SOURCES

Four electronic databases-Medline (1966-2004), PsycINFO (1972-2004), CINAHL (1982-2004), and ASSIA (1987-2004); previous contacts with key experts; hand search of six relevant journals.

REVIEW METHODS

We generated a conceptual model, against which studies were analysed. Included studies had original data on risk factors for place of death among patients, > 80% of whom had cancer. Strength of evidence was assigned according to the quantity and quality of studies and consistency of findings. Odds ratios for home death were plotted for factors with high strength evidence.

RESULTS

58 studies were included, with over 1.5 million patients from 13 countries. There was high strength evidence for the effect of 17 factors on place of death, of which six were strongly associated with home death: patients' low functional status (odds ratios range 2.29-11.1), their preferences (2.19-8.38), home care (1.37-5.1) and its intensity (1.06-8.65), living with relatives (1.78-7.85), and extended family support (2.28-5.47). The risk factors covered all groups of the model: related to illness, the individual, and the environment (healthcare input and social support), the latter found to be the most important.

CONCLUSIONS

The ne of factors that influence where patients with cancer die is complicated. Future policies and clinical practice should focus on ways of empowering families and public education, as well as intensifying home care, risk assessment, and training practitioners in end of life care.

[Clinical impressions of terminal cancer patients as an estimator of time of survival]

OBJECTIVE

To evaluate the relationship between the prognosis of the survival time of terminal cancer patients by doctors and nurses belonging to a Home Care Support Team (HCST) and the actual time of survival found. The first assessment and the last before death were examined.

DESIGN

Prospective, descriptive study.

SETTING

Area 7 of primary care, Madrid.

PARTICIPANTS

Were terminal cancer patients attended by the HCST between February 2001 and August 2002.

MAIN MEASUREMENTS

Age, sex, location of the tumour, presence of metastasis, number and location of metastases, real survival time and the time estimated by the doctor and nurse at the first assessment and at the last before death. The quotient of survival time found/survival time estimated was calculated. If it was between 0.67 and 1.33, the prediction was considered correct; < 0.67, optimistic, and >1.33, pessimistic.

RESULTS

121 patients were studied, 57% men, with an average age of 72 +/- 12.8 years. At the first assessment, 30% of doctors' predictions were correct, 40% optimistic and 30% pessimistic. Of nurses' predictions, 40% were correct, 30% optimistic and 30% pessimistic. The intra-class correlation coefficients (ICC) between real and estimated survival times were 0.64 for doctors and 0.54 for nurses. At the final assessment, doctors had 38% correct predictions, 44% optimistic and 18% pessimistic; and nurses had 44% correct, 32% optimistic and 24% pessimistic. The ICCs were 0.83 and 0.84.

CONCLUSIONS

The accuracy of professionals' clinical impression was only acceptable at the moment of quantifying the prognosis.

Setting up a palliative care clinic within a radiotherapy department: a model for developing countries

Nearly 50% of all newly diagnosed cancer patients in India (and other developing countries) are terminally ill with advanced disease. These patients are usually neglected or often receive futile anticancer treatment(s), whereas what they really need is maximum medical management in the form of palliative care and psychosocial support. Since advanced and incurable cancer cases are mostly referred for radiotherapy (RT), a palliative care (PC) clinic was started in the Department of Radiotherapy, PGIMER, Chandigarh. The PC clinic staff consisted of one specialist doctor, a nurse and volunteers. Previous disease and treatment records maintained by the RT colleagues were noted. Proforma-based assessments were done in the PC clinic and focused on patients' Karnofsky Performance Status, physical symptoms, drugs prescribed, and the doctor's or patient's/relative's response to/satisfaction with the treatment in each case. Prospective data on 100 patients (March to August 2001) revealed that various distressing physical symptoms (cachexia, dyspnoea, constipation) had not been routinely assessed earlier. Despite previous treatment, adequate pain management as per the WHO ladder was needed in 67 of 88 (76%) patients when they were seen by the PC team. On the regular follow-up visits to the PC clinic, 42% and 50% of the patients/relatives reported a response to and satisfaction with the treatment at their second and third visits. We believe two conclusions are justified. (1) Attention to palliative care needs could result in good treatment outcome and high level of patients' and doctors' satisfaction. (2) Since a specialist PC set-up is lacking in most medical institutions in India, the RT department is the best suited to delivery of palliative care for patients with advanced cancer.