Comparison of Prognostic Abilities of Palliative Prognostic Index, Laboratory Prognostic Score, and Palliative Prognostic Score

CONTEXT

Few studies have compared the prognostic value of scoring systems based on physical and blood parameters in terminally ill patients with cancer.

OBJECTIVES

This study evaluated the prognostic abilities of Palliative Prognostic Index (PPI), Laboratory Prognostic Score (LPS), and Palliative Prognostic Score (PaP).

METHODS

We included 989 terminally ill patients with cancer who consulted for admission to our palliative care unit. We compared the discriminative abilities of PPI, LPS, and PaP for 7-, 14-, 30-, 60-, and 90-day mortality. Additionally, we compared the estimated median survival of PPI, LPS, and PaP with the actual survival (AS). The prediction accuracy was considered adequate if the ratio of estimated median survival in days to AS in days fell within the range of 0.66 to 1.33, optimistic when it exceeds 1.33, and pessimistic when it falls below 0.66.

RESULTS

The accuracies for 7-, 14-, 30-, 60-, and 90-day mortality were superior for PPI, LPS, LPS, PaP, and PaP (72%, 73%, 71%, 80%, and 82%), respectively, although the discriminative abilities for 7-, 14-, 30-, 60-, and 90-day mortality were similar among the three scoring systems. The prediction accuracy of survival (PAS) was similar among the three scoring systems with adequate, optimistic, and pessimistic rates of 36%-41%, 20%-46%, and 16%-38%, respectively. PAS was superior in actual survival for 14-59 days.

CONCLUSIONS

The prognostic abilities of PPI, LPS, and PaP were comparable. The most adequate estimation occurred for patients with AS for 14-59 days. A more accurate prognostic model is needed for patients with longer survival.

Palliative prognostic scores for survival prediction of cancer patients: a systematic review and meta-analysis

BACKGROUND

The palliative prognostic score is the most widely validated prognostic tool for cancer survival prediction, with modified versions available. A systematic evaluation of palliative prognostic score tools is lacking. This systematic review and meta-analysis aimed to evaluate the performance and prognostic utility of palliative prognostic score, delirium-palliative prognostic score, and palliative prognostic score without clinician prediction in predicting 30-day survival of cancer patients and to compare their performance.

METHODS

Six databases were searched for peer-reviewed studies and grey literature published from inception to June 2, 2023. English studies must assess palliative prognostic score, delirium-palliative prognostic score, or palliative prognostic score without clinician-predicted survival for 30-day survival in adults aged 18 years and older with any stage or type of cancer. Outcomes were pooled using the random effects model or summarized narratively when meta-analysis was not possible.

RESULTS

A total of 39 studies (n = 10 617 patients) were included. Palliative prognostic score is an accurate prognostic tool (pooled area under the curve [AUC] = 0.82, 95% confidence interval [CI] = 0.79 to 0.84) and outperforms palliative prognostic score without clinician-predicted survival (pooled AUC = 0.74, 95% CI = 0.71 to 0.78), suggesting that the original palliative prognostic score should be preferred. The meta-analysis found palliative prognostic score and delirium-palliative prognostic score performance to be comparable. Most studies reported survival probabilities corresponding to the palliative prognostic score risk groups, and higher risk groups were statistically significantly associated with shorter survival.

CONCLUSIONS

Palliative prognostic score is a validated prognostic tool for cancer patients that can enhance clinicians' confidence and accuracy in predicting survival. Future studies should investigate if accuracy differs depending on clinician characteristics. Reporting of validation studies must be improved, as most studies were at high risk of bias, primarily because calibration was not assessed.

Prognostic factors in patients in the terminal phase of haematological malignancies who are receiving home medical care

Although there are many prognostic models for patients in the terminal phase of solid tumours, a reliable prognostic scoring system in patients in the terminal phase of haematological malignancies (HM) has not been established. We retrospectively evaluated 180 patients in the terminal phase of HM who were receiving home medical care (HMC). Multivariate analyses revealed that clinician's estimate, consciousness, loss of appetite, dyspnoea, neutrophil count, lymphocyte count, and lactate dehydrogenase were associated with overall survival (OS). Based on this result, we developed a novel prognostic scoring system, the Japan palliative haematological oncology prognostic estimates, in which four risk groups were shown to clearly differ in survival (p < 0.001): a low-risk group (n = 41, median OS of 434 days), an intermediate-low-risk group (n = 80, median OS of 112 days), an intermediate-high-risk group (n = 38, median OS of 31.5 days), and a high-risk group (n = 21, median OS of 10 days). This is the first investigation of prognostic factors that influence the OS of patients in the terminal phase of HM who are receiving HMC. Providing patients with reliable information about their prognosis is important for them to consider how to spend their remaining life.

Prognostic Impact of Pleural Effusion in Patients with Malignancy: A Systematic Review and Meta-Analysis

The exact role of pleural effusion in the prognosis of cancer patients remains unclear. We aimed to systematically review the prognostic value of pleural effusion in patients with cancer. We performed a systematic review and meta‐analysis with a systematic literature search. All cohort studies with available overall survival (OS) and progression‐free survival (PFS) results for patients with cancer with or without pleural effusion were included. The Mantel–Haenszel method was used to calculate the pooled hazard ratios (HRs) and 95% confidence intervals (CIs). Heterogeneity and publication bias were examined. Subgroup analysis and sensitivity analysis were performed. A total of 47 studies with 146,117 patients were included in the analysis. For OS, pleural effusion was a prognostic factor associated with a poor prognosis for patients with cancer (HR, 1.58, 95% CI, 1.43–1.75; I² 94.8%). In the subgroup analysis, pleural effusion was a prognostic factor associated with poor survival for patients with lung cancer (HR, 1.44, 95% CI, 1.35–1.54; I² 60.8%), hematological cancer (HR, 2.79, 95% CI, 1.63–4.77; I² 29.4%) and other types of cancer (HR, 2.08, 95% CI, 1.43–3.01; I² 55.1%). For PFS, pleural effusion was a prognostic factor associated with a poor prognosis for patients with cancer (HR, 1.61, 95% CI, 1.28–2.03; I² 42.9%). We also observed that massive pleural effusion was a prognostic factor associated with a poorer prognosis compared to minimal pleural effusion. Pleural effusion had prognostic value in both OS and PFS of patients with cancer, except for patients with malignant pleural mesothelioma, regardless of whether the malignant effusion was confirmed histologically or cytologically. However, future evidence of other pleural effusion characteristics is still needed.

Development and internal validation of laboratory prognostic score to predict 14-day mortality in terminally ill patients with gastrointestinal malignancy

BACKGROUND

Few studies have developed an easy scoring system for the short-term survival of patients with gastrointestinal (GI) malignancy.

METHODS

A total of 816 terminally ill patients with GI malignancy were admitted to our palliative care unit. They were randomly divided into the investigation (n = 490) and validation (n = 326) groups. A total of 19 laboratory blood parameters were analyzed. Receiver-operating characteristic analysis was performed for each blood factor, and the area under the curve was calculated to determine the predictive value for 14-day survival after the blood test. Multivariable logistic regression analysis was performed to identify significant independent prognostic factors for 14-day mortality. To develop a scoring system for 14-day mortality, the laboratory prognostic score for gastrointestinal malignancy (GI-LPS) was calculated using the sum of indices of the independent prognostic factors.

RESULTS

Multivariable analysis showed that 5 of 19 indices, namely total bilirubin ≥ 2.1 mg/dL, blood urea nitrogen ≥ 28 mg/dL, eosinophil percentage ≤ 0.5%, neutrophil-to-lymphocyte ratio ≥ 9.2, and platelet count ≤ 194 × 10³/μL, were significant independent factors of 14-day survival. GI-LPS showed acceptable accuracy for 14-day mortality in the investigation and validation groups. GI-LPS 3 (including any three factors) predicted death within 14 days, with a sensitivity of 56-58%, a specificity of 82-87%, a positive predictive value of 48-50%, and a negative predictive value of 87-90%.

CONCLUSIONS

GI-LPS showed an acceptable ability to predict 14-day survival and can provide additional information to conventional prognostic scores.

Knowledge and use of prognostic scales by oncologists and palliative care physicians in adult patients with advanced cancer: A national survey (ONCOPRONO study)

BACKGROUND

Prognostic scales exist to estimate patient survival in advanced cancer. However, there are no studies evaluating their use and practice. The objective of this study was to evaluate in a nationwide study the proportion of oncologists and palliative care physicians who had knowledge of these scales.

METHODS

A descriptive, national, cross-sectional study was conducted via an online questionnaire to oncologists and palliative care physicians across France.

RESULTS

Palliative care physicians had better knowledge of the scales than oncologists (42.3% (n = 74) vs. 27.8% (n = 33), p = 0.015). The Palliative Performance Status (PPS) and Pronopall Scale were the best-known (51.4% (n = 55) and 65.4% (n = 70), respectively) and the most widely used (35% (n = 28) and 60% (n = 48), respectively). Improved training in the use of these scales was requested by 85.4% (n = 251) of participants, while 72.8% (n = 214) reported that they did not use them at all. Limited training and lack of consensus on which scale to use were cited as the main obstacles to use.

CONCLUSION

This is the first national study on the use of prognostic scales in advanced cancer. Our findings highlight a need to improve training in these scales and to reach a consensus on scale selection.

Simple prognostic markers for optimal treatment of patients with unresectable pancreatic cancer

Most patients with pancreatic cancer are ineligible for curative resection at diagnosis, resulting in poor prognosis. This study aimed to evaluate the prognostic factors in patients with unresectable pancreatic cancer.We retrospectively collected clinical data from 196 patients with unresectable pancreatic cancer who received palliative chemotherapy (N = 153) or palliative care alone (N = 43) from January 2011 to December 2013. Patients' background data and overall survival were analyzed using the Cox proportional hazard regression model.In patients receiving palliative chemotherapy (gemcitabine-based regimen, 88.2%) and palliative care alone, the median (range) ages were 68 (43-91) and 78 (53-90) years, and metastatic diseases were present in 80% (N = 123) and 86% (N = 37), respectively. Multivariate analysis in the palliative chemotherapy patients showed that liver metastasis (hazard ratio [HR] 2.25, 95% confidence interval [CI] 1.58-3.20, P < .001), neutrophil-to-lymphocyte ratio (>4.5 vs ≤4.5; HR 3.45, 95% CI 2.22-5.36, P < .001), and cancer antigen 19-9 (CA19-9) (≥900 vs <900 U/mL; HR 1.45, 95% CI 1.02-2.05, P = .036) were independent prognostic factors. In those receiving palliative care alone, lung (HR 3.27, 95% Cl 1.46-7.35, p = 0.004) and peritoneum (HR 2.50, 95% CI 1.20-5.18, P = .014) metastases and the C-reactive protein-to-albumin ratio (≥1.3 vs <1.3; HR 3.33, 95% Cl 1.51-7.35, P = .003) were independent prognostic factors. Furthermore, patients with multiple factors had worse prognosis in both groups. Median survival time of palliative chemotherapy patients with risk factors 0, 1, 2, and 3 were 13.1 (95% CI 8.0-16.9), 9.4 (95% CI 7.9-10.1), 6.6 (95% CI 4.9-7.8), and 2.5 (95% CI 1.7-4.0) months, respectively. Similarly, median survival time was 5.7 (95% CI 1.3 -8.0), 2.1 (95% CI 1.5-3.9), and 1.3 (95% CI 0.6-1.7) months, respectively, for palliative care alone patients with risk factor 0, 1, and 2 to 3.Prognostic markers for pancreatic cancer were neutrophil-to-lymphocyte ratio, liver metastasis, and CA19-9 in patients undergoing palliative chemotherapy and C-reactive protein-to-albumin ratio and lung/peritoneum metastases in patients undergoing palliative care alone. These simple markers should be considered when explaining the prognosis and therapeutic options to patients.

Clinical indicators useful in decision-making about palliative chemotherapy for end-of-life ovarian cancer patients

PURPOSE

Chemotherapy for end-of-life ovarian cancer patients is a complex and delicate problem. We evaluated whether active palliative chemotherapy is beneficial for such patients using inflammatory parameters, nutritional indicators, and the PPI (Palliative Prognostic Index), which predicts short-term prognosis.

METHODS

Thirty-six patients among 49 patients who died from ovarian cancer from 2014 to 2019 at our hospital were enrolled, whom clinical and laboratory data just before starting their final chemotherapy regimen could be obtained. Associations between the time from last chemotherapy to death and the following parameters were investigated: age, performance status, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio, Modified Glasgow Prognostic Score (mGPS), Prognostic Nutritional Index (PNI) score, and PPI score.

RESULTS

The median age was 57 (range 19-80) years. The median time from last chemotherapy to death was 45.5 (range 11-110) days. Eight patients (22%) died within 30 days of their last chemotherapy regimen. In univariate analysis, median survival time was significantly shorter in patients with higher NLR, mGPS 2, and higher PPI values; NLR (≥ median vs. < median): 32 (range 11-80) days vs. 54 (range 35-110) days, p = 0.008; mGPS (2 vs. 0-1): 42 (range 11-80) days vs. 96 (range 49-110) days, p = 0.012; and PPI score (≥ median vs. < median): 38 (range 11-74) days vs. 60 (range 18-110) days, p = 0.005. However, in multivariate analysis, no factors were identified as independent prognostic factors for survival.

CONCLUSION

Parameters, such as NLR, mGPS, and PPI score, may be indicators for discontinuation of palliative chemotherapy, and may be useful for maximizing end-of-life care for ovarian cancer patients.

Validation of the Simplified Palliative Prognostic Index to Predict Survival for Advanced Cancer Patients in Home Hospice Setting

Background

The simplified Palliative Prognostic Index (sPPI) substitutes a single item from the Communication Capacity Scale (CCS) for the delirium item of the original PPI. This study aimed to examine the validity of the sPPI for patients with advanced cancer in a home-based hospice care setting.

Methods

This study included 75 patients with advanced cancer who received home-based hospice care. We used medical records maintained by professional hospice nurses who had visited the patients in their homes. Based on their sPPI score, patients were divided into three groups—A (<4), B (≥4 and <6), and C (≥6)—to compare survival. Further, we investigated the sPPI’s accuracy using the area under the receiver operating characteristic curve (AUC) and sensitivity and specificity for 3- and 6-week survival. We used three sPPIs including different substitutions for the delirium item (two methods using the CCS and one using the Korean Nursing Delirium Screening Scale).

Results

The median survival was 60–61 days for group A, 27–30 days for group B, and 12–16 days for group C. The difference in survival was significant (P<0.05). The AUC was 0.814–0.867 for 3-week survival and 0.736–0.779 for 6-week survival. For 3- and 6-week survival, prognostic prediction showed sensitivities of 76.2%–90.9% and 76.3%–86.8%, and specificities of 64.2%–88.7% and 51.4%–70.3%, respectively.

Conclusion

The sPPI, which is measured by professional hospice nurses, has acceptable validity to predict survival for patients with advanced cancer in a home hospice setting in South Korea.

"The surprise questions" using variable time frames in hospitalized patients with advanced cancer

OBJECTIVE

Several studies supported the usefulness of "the surprise question" in terms of 1-year mortality of patients. "The surprise question" requires a "Yes" or "No" answer to the question "Would I be surprised if this patient died in [specific time frame]." However, the 1-year time frame is often too long for advanced cancer patients seen by palliative care personnel. "The surprise question" with shorter time frames is needed for decision making. We examined the accuracy of "the surprise question" for 7-day, 21-day, and 42-day survival in hospitalized patients admitted to palliative care units (PCUs).

METHOD

This was a prospective multicenter cohort study of 130 adult patients with advanced cancer admitted to 7 hospital-based PCUs in South Korea. The accuracy of "the surprise question" was compared with that of the temporal question for clinician's prediction of survival.

RESULTS

We analyzed 130 inpatients who died in PCUs during the study period. The median survival was 21.0 days. The sensitivity, specificity, and overall accuracy for the 7-day "the surprise question" were 46.7, 88.7, and 83.9%, respectively. The sensitivity, specificity, and overall accuracy for the 7-day temporal question were 6.7, 98.3, and 87.7%, respectively. The c-indices of the 7-day "the surprise question" and 7-day temporal question were 0.662 (95% CI: 0.539-0.785) and 0.521 (95% CI: 0.464-0.579), respectively. The c-indices of the 42-day "the surprise question" and 42-day temporal question were 0.554 (95% CI: 0.509-0.599) and 0.616 (95% CI: 0.569-0.663), respectively.

SIGNIFICANCE OF RESULTS

Surprisingly, "the surprise questions" and temporal questions had similar accuracies. The high specificities for the 7-day "the surprise question" and 7- and 21-day temporal question suggest they may be useful to rule in death if positive.

Prognostic tools for survival prediction in advanced cancer patients: A systematic review

Survival prediction for palliative cancer patients by physicians is often optimistic. Patients with a very short life expectancy (<4 weeks) may not benefit from radiation therapy (RT), as the time to maximal symptom relief after treatment can take 4-6 weeks. We aimed to identify a prognostic tool (or tools) to predict survival of less than 4 weeks and less than 3 months in patients with advanced cancer to guide the choice of radiation dose and fractionation. We searched Embase, Medline (EBSCOhost) and CINAHL (EBSCOhost) clinical databases for literature published between January 2008 and June 2018. Seventeen studies met the inclusion criteria and were included in the review. Prediction accuracy at less than 4 weeks and less than 3 months were compared across the prognostic tools. Reporting of prediction accuracy among the different studies was not consistent: the Palliative Prognostic Score (PaP), Palliative Prognostic Index (PPI) and Number of Risk Factors (NRF) best-predicted survival duration of less than 4 weeks. The PPI, performance status with Palliative Prognostic Index (PS-PPI), NRF and Survival Prediction Score (SPS) may predict 3-month survival. We recommend PPI and PaP tools to assess the likelihood of a patient surviving less than 4 weeks. If predicted to survive longer and RT is justified, the NRF tool could be used to determine survival probability less than 3 months which can then help clinicians select dose and fractionation. Future research is needed to verify the reliability of survival prediction using these prognostic tools in a radiation oncology setting.

Laboratory prognostic score for predicting 14-day mortality in terminally ill patients with gynecologic malignancy

BACKGROUND

There are few studies developing a scoring system for short-term survival of patients with gynecologic malignancy.

METHODS

Seventy-three terminally ill patients with gynecologic malignancy who were admitted to our palliative care unit (PCU) from June 2009 to February 2018 were included. We accumulated routine blood data within 3 months before PCU discharge. Receiver-operating characteristic analysis was performed on each blood factor, and area under the curve (AUC) was calculated to determine the predictive value for 14-day survival after the blood test. Multivariable logistic regression analysis was performed to identify significant independent prognostic factors of 14-day mortality. To develop a scoring system for 14-day mortality, laboratory prognostic score for gynecologic malignancy (G-LPS) was calculated using the sum of indices of the independent prognostic factors.

RESULTS

Multivariable analysis showed that 6 of 24 indices, namely, C-reactive protein ≥ 13.3 mg/dL, total bilirubin ≥ 1.1 mg/dL, sodium < 131 mEq/L, blood urea nitrogen ≥ 28 mg/dL, white blood cell count ≥ 17.7 × 103/μL, and eosinophil level < 0.2%, were significant independent factors of 14-day survival. G-LPS was obtained from the sum of the six indices. The AUC was 0.7977 at the optimal cut-off value of G-LPS 3. G-LPS 3 predicted death within 14 days with a sensitivity of 72% and a specificity of 79%.

CONCLUSIONS

Six of the 24 laboratory indices were identified as independent prognostic factors of 14-day mortality in terminally ill patients with gynecologic malignancy. G-LPS showed acceptable ability of predicting 14-day survival.

Validation of the Palliative Prognostic Index, Performance Status-Based Palliative Prognostic Index and Chinese Prognostic Scale in a home palliative care setting for patients with advanced cancer in China

BACKGROUND

The predictive value of the prognostic tool for patients with advanced cancer is uncertain in mainland China, especially in the home-based palliative care (HPC) setting. This study aimed to compare the accuracy of the Palliative Prognostic Index (PPI), the Performance Status-Based Palliative Prognostic Index (PS-PPI), and the Chinese Prognosis Scale (ChPS) for patients with advanced cancer in the HPC setting in mainland China.

METHODS

Patients with advanced cancer admitted to the hospice center of Yuebei People's Hospital between January 2014 and December 2018 were retrospectively calculated the scores according to the three prognostic tools. The Kaplan-Meier method was used to compare survival times among different risk groups. Receiver operating characteristic curve analysis was used to assess the predictive value. The accuracy of 21-, 42- and 90-day survival was compared among the three prognostic tools.

RESULTS

A total of 1863 patients were included. Survival time among the risk groups of all prognostic tools was significantly different from each other except for the PPI. The AUROC of the ChPS was significantly higher than that of the PPI and PS-PPI for 7-, 14, 21-, 42-, 90-, 120-, 150- and 180-day survival (P < 0.05). The AUROC of the PPI and PS-PPI were not significantly different from each other (P > 0.05).

CONCLUSIONS

The ChPS is more suitable than the PPI and PS-PPI for advanced cancer patients in the HPC setting. More researches are needed to verify the predictive value of the ChPS, PPI, and PS-PPI in the HPC setting in the future.

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.

PALLIA-10, a screening tool to identify patients needing palliative care referral in comprehensive cancer centers: A prospective multicentric study (PREPA-10)

Purpose

The identification and referral of patients in need of palliative care should be improved. The French society for palliative support and care recommended to use the PALLIA‐10 questionnaire and its score greater than 3 to refer patients to palliative care. We explored the use of the PALLIA‐10 questionnaire and its related score in a population of advanced cancer patients.

Methods

This prospective multicentric study is to be conducted in authorized French comprehensive cancer centers on hospitalized patients on a given day. We aimed to use the PALLIA‐10 score to determine the proportion of palliative patients with a score >3. Main secondary endpoints were to determine the proportion of patients already managed by palliative care teams at the study date or referred to palliative care in six following months, the prevalence of patients with a score greater than 5, and the overall survival using the predefined thresholds of 3 and 5.

Results

In 2015, eighteen French cancer centers enrolled 840 patients, including 687 (82%) palliative patients. 479 (69.5%) patients had a score >3, 230 (33.5%) had a score >5, 216 (31.4%) patients were already followed‐up by a palliative care team, 152 patients were finally referred to PC in the six subsequent months.

The PALLIA‐10 score appeared as a reliable predictive (adjusted OR_Ref≤3: 1.9 [1.17‐3.16] and 3.59 [2.18‐5.91]) and prognostic (adjusted HR_Ref≤3 = 1.58 [95%CI 1.20‐2.08] and 2.18 [95%CI 1.63‐2.92]) factor for patients scored 4‐5 and >5, respectively.

Conclusion

The PALLIA‐10 questionnaire is an easy‐to‐use tool to refer cancer inpatients to palliative care in current practice. However a score greater than 5 using the PALLIA‐10 questionnaire would be more appropriate for advanced cancer patients hospitalized in comprehensive cancer center.

Laboratory prognostic score for predicting 30-day mortality in terminally ill cancer patients

Conventional prognostic scores for terminally ill cancer patients may have less objectivity because they include subjective or categorical variables that do not consider intensity or severity. The aim of this study was to identify prognostic factors for 30-day mortality from routine blood examination of terminally ill cancer patients. A total of 1308 study patients in a hospice setting were divided into investigation (n=761) and validation (n=547) groups. Twenty laboratory blood parameters were analyzed. Multivariate analysis revealed that ten variables (C-reactive protein ≥5.4 mg/dL, serum albumin <2.8 g/dL, blood urea nitrogen ≥21 mg/dL, white blood cell count ≥8.600 × 103/μL, eosinophil percentage <0.8%, neutrophil-to-lymphocyte ratio ≥11.1, hemoglobin level ≥ 13.2 g/dL, mean corpuscular volume ≥ 93.7 fl, red cell distribution width ≥ 16, and platelet count < 159 × 103/μL) were significant independent prognostic factors for 30-day survival. The laboratory prognostic score (LPS) was calculated by the sum of blood indices among the ten variables. The LPS showed acceptable accuracy for 30-day mortality in the investigation and validation groups. LPS 5 (including any five factors) predicted death within 30 days, with a sensitivity of 85%, a specificity of 55%, a positive predictive value of 72%, and a negative predictive value of 74%. The predictive value of LPS was comparable to those of conventional prognostic scores, which include signs and symptoms. The LPS can provide additional information to conventional prognostic scores.

Physical Exercise: An Evaluation of a New Clinical Biomarker of Survival in Hospice Patients

PURPOSE

Survival analysis is an important issue in palliative care. However, there is a lack of quality clinical biomarkers for assessing survival, especially in bedridden patients. Recent research supports the benefit of physiotherapy in palliative care, as majority of hospice patients are able to perform physical therapy. We propose the hypothesis that the difference in activity during physical exercise can be used as a biomarker of survival in hospice care.

METHODS

We examined 536 consecutive patients who performed physical exercises in our hospice from March 2013 to July 2017. Univariate, multivariate, and Kaplan-Meier analysis were performed to explore the association between the level of physical exercise activity and survival.

RESULTS

Physical exercises were performed by almost 70% of our hospice patients. The patients who initially performed active exercises lived longer, on average, compared to patients who only managed passive exercises (15 days vs 6 days, hazard ratio 0.60, 0.49-0.74). Surprisingly, the difference in survival based on the level of physical activity remained consistent regardless of the patient performance score, emphasizing its usefulness as an independent survival biomarker in a hospice setting. This tool also gave us an option to recognize a significant proportion of bedridden patients performing active exercises (30%), previously unrecognized using standard performance scales, exhibiting longer survival compared to others with the same performance score.

CONCLUSION

Patients' level of activity during physical exercises has the potential to be a valuable new clinical biomarker in palliative care, whether used individually or combined with commonly used performance scales.

Prevalence and Implications of Abnormal Laboratory Results in Patients in the Terminal Phase of Life

BACKGROUND

Pathophysiological changes at the end of life may affect pharmacokinetics of drugs. However, caregivers typically do not extensively monitor patients' laboratory parameters at the end of life.

OBJECTIVE

Our aim was to describe laboratory parameters of hospice patients in the week before death.

METHODS

A cohort study was conducted on available laboratory results in the week before death, including clinical chemistry and hematology tests.

RESULTS

Laboratory data of 125 patients in a palliative care center were included, assessed at a median of 3 days before death. Eighty percent of patients had anemia and almost all had hypoalbuminemia (97%). Elevated levels of gamma-glutamyl transferase (gGT) were found in 75%, of alkaline phosphatase (ALP) in 60%, of aspartate aminotransferase (ASAT) in 60%, and of calcium (Ca) in 68%. Alanine aminotransferase (ALAT), bilirubin, sodium (Na), and potassium (K) were abnormal in from 8.8% to 36.0% of patients. A previous unknown poor kidney function was found in 60% of patients. Thirteen patients (22%) with a regular morphine prescription and one patient treated with a non-steroidal anti-inflammatory drug (NSAID) had severe kidney failure.

CONCLUSIONS

Abnormal laboratory results were expected due to the pathophysiological changes that occur during the last phase of life. Remarkably, however, electrolytes (Na and K) were balanced even shortly before death. Estimated glomerular filtration rate (eGFR), reflecting the kidney function, seems the most clinically relevant laboratory parameter, because it may guide drug choice and dosing.

Prognostic Tools in Patients With Advanced Cancer: A Systematic Review

PURPOSE

In 2005, the European Association for Palliative Care made recommendations for prognostic markers in advanced cancer. Since then, prognostic tools have been developed, evolved, and validated. The aim of this systematic review was to examine the progress in the development and validation of prognostic tools.

METHODS

Medline, Embase Classic and Embase were searched. Eligible studies met the following criteria: patients with incurable cancer, >18 years, original studies, population n ≥100, and published after 2003. Descriptive and quantitative statistical analyses were performed.

RESULTS

Forty-nine studies were eligible, assessing seven prognostic tools across different care settings, primary cancer types, and statistically assessed survival prediction. The Palliative Performance Scale was the most studied (n = 21,082), comprising six parameters (six subjective), was externally validated, and predicted survival. The Palliative Prognostic Score composed of six parameters (four subjective and two objective), the Palliative Prognostic Index composed of nine parameters (nine subjective), and the Glasgow Prognostic Score composed of two parameters (two objective) and were all externally validated in more than 2000 patients with advanced cancer and predicted survival.

CONCLUSION

Various prognostic tools have been validated but vary in their complexity, subjectivity, and therefore clinical utility. The Glasgow Prognostic Score would seem the most favorable as it uses only two parameters (both objective) and has prognostic value complementary to the gold standard measure, which is performance status. Further studies comparing all proved prognostic markers in a single cohort of patients with advanced cancer are needed to determine the optimal prognostic tool.

A systematically structured review of biomarkers of dying in cancer patients in the last months of life; An exploration of the biology of dying

BACKGROUND

The Neuberger review made a number of recommendations to improve end of life care, including research into the biology of dying. An important aspect of the biology of dying is the identification of biomarkers as indices of disease processes. Biomarkers have the potential to inform the current, limited understanding of the dying process and assist clinicians in recognising dying, in particular how to distinguish dying from reversible acute deterioration.

OBJECTIVES

To critically appraise the literature on biological factors that may be used as prognostic indicators in advanced cancer patients and to identify candidate biomarkers of the dying process that can be measured serially in cancer patients' bodily fluids.

METHODS

A systematically structured review was conducted using three electronic databases. A hand search of six peer-reviewed journals and conference abstracts was also conducted. Studies reporting prognostic biomarkers in cancer patients with a median survival of ≤90 days and post-mortem studies were included. Final levels of evidence and recommendations were made using the Evidence Based Medicine modified GRADE system.

RESULTS

30 articles were included. Seven prognostic biological factors demonstrated Grade A evidence (lymphocyte count, white blood cell count, serum C-reactive protein, albumin, sodium, urea and alkaline phosphatase). An additional eleven prognostic factors were identified with Grade B evidence (platelet count, international normalised ratio, serum vitamin B12, prealbumin, bilirubin, cholesterol, aspartate aminotransferase, alanine transaminase, lactate dehydrogenase, pseudocholinesterase and urate). A number of biomarkers were specifically identified in the last two weeks of life but limitations exist. No post-mortem studies met the inclusion criteria.

CONCLUSION

The biology of dying is an important area for future research, with the evidence focused on signs, symptoms and prognostic factors. This review identifies a number of common themes shared amongst advanced cancer patients and highlights candidate biomarkers which may be indicative of a common biological process to dying.

Prognostic value of parameters derived from white blood cell and differential counts in patients receiving palliative radiotherapy

The aim of the present study was to identify white blood cell (WBC) parameters with high prognostic value for the survival of patients receiving palliative radiotherapy. The prognostic value of seven parameters derived from WBC and differential counts was retrospectively evaluated in patients who underwent palliative radiotherapy between October, 2010 and June, 2013. The analyzed parameters were the total WBC count, the absolute and relative lymphocyte count, the absolute and relative neutrophil count, and the neutrophil-to-lymphocyte and lymphocyte-to-monocyte ratios. Following univariate analysis, multivariate Cox regression analysis was performed to adjust for gender, age, disease type, previous chemotherapy, previous radiotherapy and the levels of albumin and lactate dehydrogenase. A total of 220 patients with a median survival of 4.7 months were identified. All seven parameters were found to be statistically significant predictors of survival on univariate Cox regression analysis (P<0.05). Of these parameters, the low relative lymphocyte and high relative neutrophil counts were consistent predictors of poor survival in patients who received chemotherapy within 1 month prior to blood sampling (n=68) and in patients who received steroid treatment at the time of sampling (n=49). Multivariate Cox regression analysis revealed that the relative lymphocyte and neutrophil counts were independent predictors of survival in all 220 patients (P<0.05). In conclusion, relative lymphocyte and neutrophil counts were of high prognostic value for the survival of patients receiving palliative radiotherapy, even in those receiving medications that affect WBC and differential counts.

Rough set theory based prognostic classification models for hospice referral

BACKGROUND

This paper explores and evaluates the application of classical and dominance-based rough set theory (RST) for the development of data-driven prognostic classification models for hospice referral. In this work, rough set based models are compared with other data-driven methods with respect to two factors related to clinical credibility: accuracy and accessibility. Accessibility refers to the ability of the model to provide traceable, interpretable results and use data that is relevant and simple to collect.

METHODS

We utilize retrospective data from 9,103 terminally ill patients to demonstrate the design and implementation RST- based models to identify potential hospice candidates. The classical rough set approach (CRSA) provides methods for knowledge acquisition, founded on the relational indiscernibility of objects in a decision table, to describe required conditions for membership in a concept class. On the other hand, the dominance-based rough set approach (DRSA) analyzes information based on the monotonic relationships between condition attributes values and their assignment to the decision class. CRSA decision rules for six-month patient survival classification were induced using the MODLEM algorithm. Dominance-based decision rules were extracted using the VC-DomLEM rule induction algorithm.

RESULTS

The RST-based classifiers are compared with other predictive and rule based decision modeling techniques, namely logistic regression, support vector machines, random forests and C4.5. The RST-based classifiers demonstrate average AUC of 69.74 % with MODLEM and 71.73 % with VC-DomLEM, while the compared methods achieve average AUC of 74.21 % for logistic regression, 73.52 % for support vector machines, 74.59 % for random forests, and 70.88 % for C4.5.

CONCLUSIONS

This paper contributes to the growing body of research in RST-based prognostic models. RST and its extensions posses features that enhance the accessibility of clinical decision support models. While the non-rule-based methods-logistic regression, support vector machines and random forests-were found to achieve higher AUC, the performance differential may be outweighed by the benefits of the rule-based methods, particularly in the case of VC-DomLEM. Developing prognostic models for hospice referrals is a challenging problem resulting in substandard performance for all of the evaluated classification methods.

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.

Survival prediction for advanced cancer patients in the real world: A comparison of the Palliative Prognostic Score, Delirium-Palliative Prognostic Score, Palliative Prognostic Index and modified Prognosis in Palliative Care Study predictor model

PURPOSE

The aim of this study was to investigate the feasibility and accuracy of the Palliative Prognostic Score (PaP score), Delirium-Palliative Prognostic Score (D-PaP score), Palliative Prognostic Index (PPI) and modified Prognosis in Palliative Care Study predictor model (PiPS model).

PATIENTS AND METHODS

This multicentre prospective cohort study involved 58 palliative care services, including 19 hospital palliative care teams, 16 palliative care units and 23 home palliative care services, in Japan from September 2012 to April 2014. Analyses were performed involving four patient groups: those treated by palliative care teams, those in palliative care units, those at home and those receiving chemotherapy.

RESULTS

We recruited 2426 participants, and 2361 patients were finally analysed. Risk groups based on these instruments successfully identified patients with different survival profiles in all groups. The feasibility of PPI and modified PiPS-A was more than 90% in all groups, followed by PaP and D-PaP scores; modified PiPS-B had the lowest feasibility. The accuracy of prognostic scores was ⩾69% in all groups and the difference was within 13%, while c-statistics were significantly lower with the PPI than PaP and D-PaP scores.

CONCLUSION

The PaP score, D-PaP score, PPI and modified PiPS model provided distinct survival groups for patients in the three palliative care settings and those receiving chemotherapy. The PPI seems to be suitable for routine clinical use for situations where rough estimates of prognosis are sufficient and/or patients do not want invasive procedure. If clinicians can address more items, the modified PiPS-A would be a non-invasive alternative. In cases where blood samples are available or those requiring more accurate prediction, the PaP and D-PaP scores and modified PiPS-B would be more appropriate.

Combination of initial palliative prognostic index and score change provides a better prognostic value for terminally ill cancer patients: a six-year observational cohort study

CONTEXT

The Palliative Prognostic Index (PPI) is among the most popular scores used to predict life expectancy in terminally ill patients worldwide. PPI assessed on the first day of palliative care might be inappropriate because the contribution from subsequent changes in a patient's condition are not taken into account.

OBJECTIVES

The aim of this study is to determine the utility of sequential PPI assessments as a better prognostic tool for patients with terminal cancer.

METHODS

In total, 2392 terminally ill cancer patients with initial and one-week PPI assessments under the palliative care consultation service between January 2006 and December 2011 at a single medical center in Taiwan were selected. Patients were categorized into initial PPI, Week 1 PPI, score change (initial PPI - Week 1 PPI; Δscore), and combined initial PPI and Δscore subgroups for survival analysis.

RESULTS

Overall median survival was 32 days (range eight to 180 days), and 2183 patients (91.3%) died within 180 days of palliative care consultation service care. A significant difference in survival was observed among patient subgroups (P < 0.001). Subgroup survival analysis showed significant difference in patients with Δscores >0, 0, and <0 in each prognostic group categorized by initial PPI. The c-statistic for predicting life expectancy <30 days was significantly higher with the combined initial PPI and Δscore (c-statistic, 0.71; 95% CI, 0.694-0.731) than with the initial PPI (c-statistic, 0.63; 95% CI, 0.61-0.65), Week 1 PPI (c-statistic, 0.67; 95% CI, 0.652-0.690), or Δscore (c-statistic, 0.64; 95% CI, 0.62-0.66).

CONCLUSION

Combination of initial PPI and score change is more useful than initial PPI for identifying patients with poor outcomes in good prognostic groups and patients with better outcomes in poor prognostic groups.

Consciousness levels one week after admission to a palliative care unit improve survival prediction in advanced cancer patients

BACKGROUND

Consciousness is an important factor of survival prediction in advanced cancer patients. However, effects on survival of changes over time in consciousness in advanced cancer patients have not been fully explored.

OBJECTIVE

This study evaluated changes in consciousness after admission to a palliative care unit and their correlation with prognosis in terminal cancer patients.

METHODS

This is a prospective observational study. From a palliative care unit in Taiwan, 531 cancer patients (51.8% male) were recruited. Consciousness status was assessed at admission and one week afterwards and recorded as normal or impaired.

RESULTS

The mean age was 65.28±13.59 years, and the average survival time was 23.41±37.69 days. Patients with normal consciousness at admission (n=317) had better survival than those with impaired consciousness at admission (n=214): (17.0 days versus 6.0 days, p<0.001). In the analysis on survival within one week after admission, those with normal consciousness at admission had a higher percentage of survival than the impaired (78.9% versus 44.3%, p<0.001). Patients were further classified into four groups according to consciousness levels: (1) normal at admission and one week afterwards, (2) impaired at admission but normal one week afterwards, (3) normal at admission but impaired one week afterwards, and (4) impaired both at admission and one week afterwards. The former two groups had significantly better survival than the latter two groups: (median survival counted from day 7 after admission), 25.5, 27.0, 7.0, and 7.0 days, respectively.

CONCLUSION

Consciousness levels one week after admission should be integrated into survival prediction in advanced cancer patients.

Prediction of survival in terminally ill cancer patients at the time of terminal cancer diagnosis

PURPOSE

We aimed to investigate the prognostic factors that can predict terminal stage survival (TSS) at the time of terminal cancer diagnosis.

METHODS

We prospectively evaluated 141 patients immediately after the diagnosis of terminal cancer by their attending oncologists. A total of 32 factors, including performance status, clinical prediction of survival, time to terminal cancer (TTC), clinical symptoms, signs, and laboratory tests including the neutrophil-lymphocyte ratio (NLR), were analyzed. TSS was defined as the time from the diagnosis of terminal cancer to death.

RESULTS

The mean age of the 141 patients studied was 58.7 years, and 53 were female (38 %). The median TSS was 1.7 months (95 % confidence interval [CI] 1.43-1.97). In the univariate analyses, the TSS was significantly associated with 16 of the 32 factors tested. In the multivariate analysis, a lower Karnofsky performance status (KPS), a shorter TTC (<24 months), a high NLR (≥5), and a high C-reactive protein (CRP) level (≥10 mg/dL) were independently associated with a poorer prognosis. A scoring system (scale, 0-6) developed based on the multivariate analysis could be used to classify terminal cancer patients into better (0-2 points; TSS 3.9 months), intermediate (3-4 points; TSS 1.7 months), or worse (5-6 points; TSS 0.9 month, P < 0.001) prognosis.

CONCLUSION

The median TSS after the diagnosis of terminal cancer in advanced cancer patients was 1.7 months. The scoring system using KPS, TTC, NLR, and CRP could predict TSS in these patients.

Usefulness of the Palliative Prognostic Index in patients with lung cancer

The usefulness of the Palliative Prognostic Index (PPI) has been successfully validated in a variety of clinical settings. However, while lung cancer is the leading cause of death worldwide, patients with lung cancer accounted for only 6.9-25.8 % of the study populations in these previous studies. We conducted a retrospective study to evaluate the usefulness of the PPI for survival prediction in patients with lung cancer. Patients with lung cancer who were admitted to our hospital between 2009 and 2013 to receive palliative care were enrolled. The association between the Palliative Prognostic Index, determined based on the data recorded in the clinical charts at the last admission to our hospital, and survival was evaluated. The patient group with a PPI of >6 showed a significantly shorter survival time than the patient group with a PPI of ≤ 6 (P < 0.0001, log-rank test). The sensitivity and specificity of the PPI determined using the cutoff value of 6 for predicting less than 3 weeks of survival were 61.3 and 86.8 %, respectively. However, the sensitivity decreased to 50.0 % when the assessment was carried out in only patients with small cell lung carcinoma. Our findings suggest the existence of a close association between the PPI and survival in patients with lung cancer receiving palliative care. However, the sensitivity of the index for predicting less than 3 weeks of survival was relatively low in patients with small cell lung carcinoma.

Prospective clarification of the utility of the palliative prognostic index for patients with advanced cancer in the home care setting

AIMS

This study aimed to prospectively clarify the accuracy of the Palliative Prognostic Index (PPI) for advanced cancer patients in home care settings.

METHOD

The study included 66 advanced cancer patients who received home visiting services between April 2010 and June 2012, and who died at home or in the hospital. Using medical records from initial home visits, we prospectively calculated PPI scores along with sensitivity and specificity.

RESULTS

For 3- and 6-week survival, prognostic prediction showed respective sensitivities of 60% and 70.6%, and specificities of 87.0% and 71.9%.

CONCLUSION

The sensitivity of the PPI for advanced cancer patients in home care settings was lower than that reported for patients in palliative care units. Development of prognostic tools suitable for home care settings is needed.

An alternative view on the task of prognosis

Prognosis is central to clinical medical practice. In oncology, an accurate prognosis is a key requirement for making the right therapeutical decisions. Prognosis is even more important when medicine extends its predictive capacities using genetic data. Based on statistics and probability, indices, survival curves and prognostic scores are established. Intending to be an objective and neutral description of reality, this kind of prognosis and the corresponding practices of medical care however carry and imply a particular conception of human life and destiny. Medical Humanities can help to develop reflexivity regarding prognostic practices. This paper intends to clarify which particular assumptions are implied in the current way of doing prognosis in medicine, and highlights its advantages and limitations. Prognosis not only describes but also affects present and future patient experience. An alternative view on the task of prognosis is then developed with a broader understanding of the prognostic act. Based on a phenomenology of time, distinguishing between a quantitative chronological understanding of time (chronos) and an experienced qualitative time (kairos), the article contrasts a conception of the prognosis as forecast ('probabilistic prognosis') with a conception of prognosis as perspective ('hermeneutic prognosis'). In probabilistic prognosis, the future is seen as something that can be read from presently accessible signs, as something that we can anticipate within ranges of uncertainty. The patient's lifetime, which has been open toward the future, becomes closed through this kind of prognosis. In a hermeneutic conception of prognosis as a perspective on the future, which is thought of as an open space of possibilities, the future as not-yet is what can be envisioned without being known. A hermeneutic approach emphasizes the meanings of the experience of illness for the patient, helps to improve the practice of prognosis today, and offers caregivers and patients an opportunity for living better.

A predictive model to identify hospitalized cancer patients at risk for 30-day mortality based on admission criteria via the electronic medical record

BACKGROUND

This study sought to develop a predictive model for 30-day mortality in hospitalized cancer patients, by using admission information available through the electronic medical record.

METHODS

Observational cohort study of 3062 patients admitted to the oncology service from August 1, 2008, to July 31, 2009. Matched numbers of patients were in the derivation and validation cohorts (1531 patients). Data were obtained on day 1 of admission and included demographic information, vital signs, and laboratory data. Survival data were obtained from the Social Security Death Index.

RESULTS

The 30-day mortality rate of the derivation and validation samples were 9.5% and 9.7% respectively. Significant predictive variables in the multivariate analysis included age (P < .0001), assistance with activities of daily living (ADLs; P = .022), admission type (elective/emergency) (P = .059), oxygen use (P < .0001), and vital signs abnormalities including pulse oximetry (P = .0004), temperature (P = .017), and heart rate (P = .0002). A logistic regression model was developed to predict death within 30 days: Score = 18.2897 + 0.6013*(admit type) + 0.4518*(ADL) + 0.0325*(admit age) - 0.1458*(temperature) + 0.019*(heart rate) - 0.0983*(pulse oximetry) - 0.0123 (systolic blood pressure) + 0.8615*(O2 use). The largest sum of sensitivity (63%) and specificity (78%) was at -2.09 (area under the curve = -0.789). A total of 25.32% (100 of 395) of patients with a score above -2.09 died, whereas 4.31% (49 of 1136) of patients below -2.09 died. Sensitivity and positive predictive value in the derivation and validation samples compared favorably.

CONCLUSIONS

Clinical factors available via the electronic medical record within 24 hours of hospital admission can be used to identify cancer patients at risk for 30-day mortality. These patients would benefit from discussion of preferences for care at the end of life.

Development of a prognostic score using the complete blood cell count for survival prediction in unselected critically ill patients

Objective. The purpose of this study was to develop a new prognostic scoring system for critically ill patients using the simple complete blood cell count (CBC). Methods. CBC measurements in samples from 306 patients in an intensive care unit were conducted with automated analyzers, including levels of neutrophils, lymphocytes, erythrocytes, hemoglobin, and platelets. The time of sampling and the time of death were recorded. Z values were calculated according to the measured values, reference mean values, and standard deviations. The prognostic score was equivalent to the median of the Z value of each of the measured parameters. Results. There was a significant correlation between survival time and neutrophil, lymphocyte, and platelet levels (P < 0.05). Prognostic scores were calculated from the Z value of these three parameters. Survival times decreased as the prognostic score increased. Conclusions. This study suggests that a model that uses levels of neutrophils, lymphocytes, and platelets is potentially useful in the objective evaluation of survival time or disease severity in unselected critically ill patients.

Towards a classification model to identify hospice candidates in terminally ill patients

This paper presents a Rough Set Theory (RST) based classification model to identify hospice candidates within a group of terminally ill patients. Hospice care considerations are particularly valuable for terminally ill patients since they enable patients and their families to initiate end-of-life discussions and choose the most desired management strategy for the remainder of their lives. Unlike traditional data mining methodologies, our approach seeks to identify subgroups of patients possessing common characteristics that distinguish them from other subgroups in the dataset. Thus, heterogeneity in the data set is captured before the classification model is built. Object related reducts are used to obtain the minimum set of attributes that describe each subgroup existing in the dataset. As a result, a collection of decision rules is derived for classifying new patients based on the subgroup to which they belong. Results show improvements in the classification accuracy compared to a traditional RST methodology, in which patient diversity is not considered. We envision our work as a part of a comprehensive decision support system designed to facilitate end-of-life care decisions. Retrospective data from 9105 patients is used to demonstrate the design and implementation details of the classification model.

Usefulness of Palliative Prognostic Index for patient with advanced cancer in home care setting

AIMS

This study aimed to clarify the accuracy of the Palliative Prognostic Index (PPI) for advanced cancer patients in home care settings.

METHOD

The study included 65 advanced cancer patients who received home visiting services between April 2007 and June 2009, and who died at home or in the hospital. Using the medical records from initial home visits, we retrospectively calculated PPI scores along with sensitivity and specificity.

RESULTS

For 3- and 6-week survival, prognostic prediction demonstrated respective sensitivities of 55% and 63%, and specificities of 79% and 77%.

CONCLUSION

The sensitivity of the PPI for advanced cancer patients in home care settings was lower than reported for those in palliative care units. Development of prognostic tools suitable for home care settings is needed.

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.