Lactate dehydrogenase as a prognostic factor for survival time of terminally ill cancer patients: A preliminary study

The relationship between LDH and GLIM criteria for cancer cachexia: Systematic review and meta-analysis

INTRODUCTION

Cancer cachexia is a clinical condition characterized by recognizable "sickness behaviors" accompanied by loss of lean body tissue. The Global Leadership on Malnutrition (GLIM) has proposed phenotypic (unintentional weight loss, low body mass index and low muscle mass) and aetiologic (reduced food intake and inflammation or disease burden) diagnostic criteria. Recent work has suggested serum lactate dehydrogenase (LDH) might represent a 3rd aetiologic criteria. Little is known of its relationship with GLIM. A systematic review and meta-analysis of their comparative prognostic value and association was performed.

METHODS

A search of electronic databases (PubMed, Medline, Ovid, Cochrane) up to February 2023 was used to identify studies that compared the prognostic value of LDH and components of the GLIM criteria in cancer. An analysis of the relationship between LDH and the components of GLIM was undertaken where this data was available. RevMan 5.4.1 was used to perform a meta-analysis for each diagnostic criteria that had 3 or more studies which reported hazard ratios with a 95 per cent confidence interval for overall survival (OS).

RESULTS

A total of 119 studies were reviewed. Advanced lung cancer was the most studied population. Included in the meta-analysis were 6 studies (n=2165) on LDH and weight loss, 17 studies (n=7540) on LDH and low BMI, 5 studies (n=758) on LDH and low muscle mass, 0 studies on LDH and food intake and 93 studies (n=32,190) on LDH and inflammation. There was a significant association between elevated serum LDH and each of low BMI (OR 1.39, 1.09 - 1.77; p=0.008), elevated NLR (OR 2.04, 1.57 - 2.65; p<0.00001) and elevated CRP (OR 2.58, 1.81 - 3.67; p<0.00001). There was no association between elevated serum LDH and low muscle mass. Only one study presented data on the association between LDH and unintentional weight loss. Elevated LDH showed a comparative OS (HR 1.86, 1.57 - 2.07; p<0.00001) to unintentional weight loss (HR 1.57, 1.23 - 1.99; p=0.0002) and had a similar OS (HR 2.00, 1.70 - 2.34; p<0.00001) to low BMI (HR 1.57, 1.29-2.90; p<0.0001). LDH also showed an OS (HR 2.25, 1.76 - 2.87; p<0.00001) congruous with low muscle mass (HR 1.93, 1.14 - 3.27; p=0.01) and again, LDH conferred as poor an OS (HR 1.77, 1.64-1.90; p<0.00001) as elevated NLR (HR 1.61, 1.48 - 1.77; p<0.00001) or CRP (HR 1.55, 1.43 - 1.69; p<0.00001).

CONCLUSION

Current literature suggests elevated serum LDH is associated with inflammation in cancer (an aetiologic GLIM criterion), however more work is required to establish the relationship between LDH and the phenotypic components of GLIM. Additionally, elevated serum LDH appears to be a comparative prognosticator of overall survival in cancer when compared to the GLIM criteria.

Validation of Modified Models of Objective Prognostic Score in Patients With Advanced Cancer

Background: The objective prognostic score (OPS) needs to be modified to reflect practical palliative care circumstances. Objectives: We aimed to validate modified models of OPS with few or no laboratory tests for patients with advanced cancer. Design: An observational study was performed. Setting/Subjects: A secondary analysis of an international, multicenter cohort study of patients in East Asia was performed. The subjects were inpatients with advanced cancer in the palliative care unit. Measurements: We developed two modified OPS (mOPS) models to predict two-week survival: mOPS-A consisted of two symptoms, two objective signs, and three laboratory results, while mOPS-B consisted of three symptoms, two signs, and no laboratory data. We compared the accuracy of the prognostic models using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Calibration plots for two-week survival and net reclassification indices (NRIs) were compared for the two models. Survival differences between higher and lower score groups of each model were identified by the log-rank test. Results: We included a total of 1796 subjects having median survival of 19.0 days. We found that mOPS-A had higher specificity (0.805-0.836) and higher AUROCs (0.791-0.797). In contrast, mOPS-B showed higher sensitivity (0.721-0.725) and acceptable AUROCs (0.740-0.751) for prediction of two-week survival. Two mOPSs showed good concordance in calibration plots. Considering NRIs, replacing the original OPS with mOPSs improved overall reclassification (absolute NRI: 0.47-4.15%). Higher score groups of mOPS-A and mOPS-B showed poorer survival than those of lower score groups (p < 0.001). Conclusions: mOPSs used reduced laboratory data and had relatively good accuracy for predicting survival in advanced cancer patients receiving palliative care.

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008-2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76-0.99]) vs. 0.81 (95% CI: [0.65-0.94]) and 0.81 (95% CI: [0.72-0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.

Palliative care outpatients in a German comprehensive cancer center-identifying indicators for early and late referral

PURPOSE

Despite that early integration of palliative care is recommended in advanced cancer patients, referrals to outpatient specialised palliative care (SPC) frequently occur late. Well-defined referral criteria are still missing. We analysed indicators associated with early (ER) and late referral (LR) to SPC of an high volume outpatient unit of a comprehensive cancer center.

METHODS

Characteristics, laboratory parameters and symptom burden of 281 patients at first SPC referral were analysed. Timing of referral was categorized as early, intermediate and late (> 12, 3-12 and < 3 months before death). Ordinal logistic regression analysis was used to identify factors related to referral timing. Kruskal-Wallis test was used to determine symptom severity and laboratory parameter in each referral category.

RESULTS

LRs (50.7%) had worse scores of weakness, loss of appetite, drowsiness, assistance of daily living (all p < 0.001) and organisation of care (p < 0.01) in contrast to ERs. The mean symptom sum score was significantly higher in LRs than ERs (13.03 vs. 16.08; p < 0.01). Parameters indicative of poor prognosis, such as elevated LDH, CRP and neutrophil-to-lymphocyte ratio (NLR) (p < 0.01) as well as the presence of ascites (p < 0.05), were significantly higher (all p < 0.001) in LRs. In univariable analyses, psychological distress (p < 0.05) and female gender (p < 0.05) were independently associated with an ER.

CONCLUSION

A symptom sum score and parameters of poor prognosis like NLR or LDH might be useful to integrate into palliative care screening tools.

Improved Survival Prediction by Combining Radiological Imaging and S-100B Levels Into a Multivariate Model in Metastatic Melanoma Patients Treated With Immune Checkpoint Inhibition

Purpose

We explored imaging and blood bio-markers for survival prediction in a cohort of patients with metastatic melanoma treated with immune checkpoint inhibition.

Materials and Methods

94 consecutive metastatic melanoma patients treated with immune checkpoint inhibition were included into this study. PET/CT imaging was available at baseline (Tp0), 3 months (Tp1) and 6 months (Tp2) after start of immunotherapy. Radiological response at Tp2 was evaluated using iRECIST. Total tumor burden (TB) at each time-point was measured and relative change of TB compared to baseline was calculated. LDH, CRP and S-100B were also analyzed. Cox proportional hazards model and logistic regression were used for survival analysis.

Results

iRECIST at Tp2 was significantly associated with overall survival (OS) with C-index=0.68. TB at baseline was not associated with OS, whereas TB at Tp1 and Tp2 provided similar predictive power with C-index of 0.67 and 0.71, respectively. Appearance of new metastatic lesions during follow-up was an independent prognostic factor (C-index=0.73). Elevated LDH and S-100B ratios at Tp2 were significantly associated with worse OS: C-index=0.73 for LDH and 0.73 for S-100B. Correlation of LDH with TB was weak (r=0.34). A multivariate model including TB change, S-100B, and appearance of new lesions showed the best predictive performance with C-index=0.83.

Conclusion

Our analysis shows only a weak correlation between LDH and TB. Additionally, baseline TB was not a prognostic factor in our cohort. A multivariate model combining early blood and imaging biomarkers achieved the best predictive power with regard to survival, outperforming iRECIST.

Radiomics signature from [18F]FDG PET images for prognosis predication of primary gastrointestinal diffuse large B cell lymphoma

OBJECTIVES

To investigate the prognostic value of PET radiomics feature in the prognosis of patients with primary gastrointestinal diffuse large B cell lymphoma (PGI-DLBCL) treated with R-CHOP-like regimen.

METHODS

A total of 140 PGI-DLBCL patients who underwent pre-therapy [¹⁸F] FDG PET/CT were enrolled in this retrospective analysis. PET radiomics features obtained from patients in the training cohort were subjected to three machine learning methods and Pearson's correlation test for feature selection. Support vector machine (SVM) was used to build a radiomics signature classifier associated with progression-free survival (PFS) and overall survival (OS). A multivariate Cox proportional hazards regression model was established to predict survival outcomes.

RESULTS

A total of 1421 PET radiomics features were extracted and reduced to 5 features to build a radiomics signature which was significantly associated with PFS and OS (p < 0.05). The combined model incorporating radiomics signatures, metabolic metrics, and clinical risk factors showed high C-indices in both the training (PFS: 0.825, OS: 0.834) and validation sets (PFS: 0.831, OS: 0.877). Decision curve analysis (DCA) demonstrated that the combined models achieved the most net benefit across a wider reasonable range of threshold probabilities for predicting PFS and OS.

CONCLUSION

The newly developed radiomics signatures obtained by the ensemble strategy were independent predictors of PFS and OS for PGI-DLBCL patients. Moreover, the combined model with clinical and metabolic factors was able to predict patient prognosis and may enable personalized treatment decision-making.

KEY POINTS

• Radiomics signatures generated from the optimal radiomics feature set from the [¹⁸F]FDG PET images can predict the survival of PGI-DLBCL patients. • The optimal radiomics feature set is constructed by integrating the feature selection outputs of LASSO, RF, Xgboost, and PC methods. • Combined models incorporating radiomics signatures from¹⁸F-FDG PET images, metabolic parameters, and clinical factors outperformed clinical models, and NCCN-IPI.

Prognostic Significance of LDH Ratio in Serum/Cerebral Spinal Fluid of Patients with Primary Testicular Diffuse Large B-Cell Lymphoma

Objective

Primary testicular diffuse large B-cell lymphoma (PT-DLBCL) is relatively rare, and risk factors of this disease are still not well understood. This study aims to identify clinical features and prognostic factors of PT-DLBCL patients.

Methods

Thirty-two patients were included in this retrospective study who were diagnosed as PT-DLBCL and treated in Fudan University Shanghai Cancer Center between November 2010 and May 2018. The demographic details, clinico-pathological characteristics of the patients were summarized, and the impact on progression-free survival (PFS) and overall survival (OS) was analyzed.

Results

The median age of the patients was 57 (range 36-76) years old. All patients received rituximab in combination with cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) for 4-6 cycles and central nervous system (CNS) prophylaxis, with a CR rate 87.5% and an ORR 96.9%. Nineteen patients continued prophylactic contralateral testis radiation therapy (PCTRT) in our hospital. The 3-year PFS and OS rates were 79% and 92%, respectively. None of the 19 patients who received PCTRT experienced local recurrence. All three patients who suffered from CNS relapse were germinal center B-cell subtype. Kaplan-Meier analyses showed that PT-DLBCL patients with late-stage (Stage IV) (P =0.022), higher IPI score (IPI≥ 2) (P =0.017), B symptoms (P =0.004), and elevated LDH level (P =0.03) had a shorter PFS. More importantly, we found that patients with the ratio of the LDH level in serum to that in CSF ≥ 6.5 suffered from a worse PFS (P =0.028).

Conclusion

Our work revealed that staging IV, IPI score ≥2, having B symptoms and elevated LDH level were risk factors for PT-DLBCL patients. Significantly, the PT-DLBCL patients with a high ratio of LDH level in serum to that in CSF were indicated to have a worse PFS.

The effect of earthworm extract on mice S180 tumor growth and apoptosis

Great efforts have been made to explore the potential treatment for cancers, and the most common therapies include surgery, chemotherapy and radiotherapy. As an alternative medication, earthworms have drawn increased attention considering its abundance in resource, easy access and minor side effects compared to traditional therapies. However, few studies had focused on the antitumor effect of earthworm-derived components. The purpose of this study is to investigate whether earthworm extract has an effect on tumor cell apoptosis and growth. Earthworm extract (EE) was purified through multiple steps of centrifugation and chromatography. Mice were inoculated with ascitic fluid derived from mice inoculated with S180 sarcoma tumor cells and fed orally with different amounts of EE for 25 days. Tumor samples were analyzed for size and cell apoptosis. And we found that the weight and sizes of tumor decreased gradually as the amount of EE administered increased. More apoptotic cells and lowered level of lactate dehydrogenase (LDH), a biomarker of tumor invasiveness, was detected in EE-treated group than the untreated group. Our results suggested that EE could dramatically promote tumor apoptosis and reduce tumor size in vivo, suggesting a novel alternative therapy for cancer patients.

Replacement, Refinement, and Reduction in Animal Studies With Biohazardous Agents

Animal models are critical to the advancement of our knowledge of infectious disease pathogenesis, diagnostics, therapeutics, and prevention strategies. The use of animal models requires thoughtful consideration for their well-being, as infections can significantly impact the general health of an animal and impair their welfare. Application of the 3Rs—replacement, refinement, and reduction—to animal models using biohazardous agents can improve the scientific merit and animal welfare. Replacement of animal models can use in vitro techniques such as cell culture systems, mathematical models, and engineered tissues or invertebrate animal hosts such as amoeba, worms, fruit flies, and cockroaches. Refinements can use a variety of techniques to more closely monitor the course of disease. These include the use of biomarkers, body temperature, behavioral observations, and clinical scoring systems. Reduction is possible using advanced technologies such as in vivo telemetry and imaging, allowing longitudinal assessment of animals during the course of disease. While there is no single method to universally replace, refine, or reduce animal models, the alternatives and techniques discussed are broadly applicable and they should be considered when infectious disease animal models are developed.

Patient inflammatory status and CD4+/CD8+ intraepithelial tumor lymphocyte infiltration are predictors of outcomes in high-grade serous ovarian cancer

BACKGROUND

High-grade serous ovarian cancer (HGSOC) is the most prevalent and aggressive histologic type of ovarian cancer. To date, there are no reliable biomarkers to effectively predict patient prognosis. Studies have demonstrated inflammation and tumor infiltrating lymphocytes (TILs) correlate with a bad and good prognosis, respectively. Here, we sought to evaluate systemic inflammation and TILs as early prognostic markers of survival.

METHODS

Neutrophil-to-lymphocyte ratio (NLR) and serum Lactate Dehydrogenase (LDH) were used as indicators of systemic inflammation. NLR, serum LDH, tumor infiltrating lymphocytes (TILs), PDL1 and quality of debulking surgery were evaluated as determinants of progression-free survival (PFS) and overall survival (OS) in a cohort of 128 HGSOC patients.

RESULTS

Initial univariate analysis showed that systemic inflammation measures (NLR and serum LDH), debulking surgery, and intra-epithelial TILs have a significant impact on both PFS and OS. After adjustment for several variables, multivariate analyses confirmed intraepithelial CD4+ T-cells, systemic inflammation measures, PDL1 and debulking surgery as determinants of better OS and PFS.

CONCLUSIONS

Systemic inflammation and TILs are early determinants of OS in HGSOC. Other variables such as the quality of debulking surgery and PDL1 also improve survival of patients. Regarding TIL sub-populations, intraepithelial CD4+ cells are associated to an increase in both PFS and OS. We also confirmed previous reports that demonstrate intraepithelial CD8+ cells correlate with an increase on PFS in ovarian cancer. A combined score using systemic inflammation and TILs may be of prognostic value for HGSOC patients.

The Cytoreductive Effect of Radiotherapy for Small Cell Ovarian Carcinoma of the Pulmonary Type: A Case Report and Review of the Literature

Small cell ovarian carcinoma of the pulmonary type is a rare and highly aggressive tumor for which a suitable treatment strategy has not been established. A 45-year-old woman presented with abdominal swelling, and primary ovarian carcinoma was suspected. The postoperative pathological diagnosis was small cell ovarian carcinoma of the pulmonary type. She also had complicated grade 1 endometrioid carcinoma of the uterine corpus. Three courses of cisplatin and etoposide therapy were administered as adjuvant chemotherapy. Because the tumor was chemotherapy resistant, she underwent palliative abdominal irradiation at a dose of 26 Gy in 13 fractions, which induced cytoreduction and provided symptomatic relief. She died 4 months after surgery. Lactate dehydrogenase was a useful tumor marker during treatment. Here, we present an extremely rare case of a patient with small cell ovarian carcinoma of the pulmonary type treated with radiotherapy after surgery and chemotherapy.

Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study

Background

We aimed to develop an adaptable prognosis prediction model that could be applied at any time point during the treatment course for patients with cancer receiving chemotherapy, by applying time-series real-world big data.

Methods

Between April 2004 and September 2014, 4,997 patients with cancer who had received systemic chemotherapy were registered in a prospective cohort database at the Kyoto University Hospital. Of these, 2,693 patients with a death record were eligible for inclusion and divided into training (n = 1,341) and test (n = 1,352) cohorts. In total, 3,471,521 laboratory data at 115,738 time points, representing 40 laboratory items [e.g., white blood cell counts and albumin (Alb) levels] that were monitored for 1 year before the death event were applied for constructing prognosis prediction models. All possible prediction models comprising three different items from 40 laboratory items (₄₀C₃ = 9,880) were generated in the training cohort, and the model selection was performed in the test cohort. The fitness of the selected models was externally validated in the validation cohort from three independent settings.

Results

A prognosis prediction model utilizing Alb, lactate dehydrogenase, and neutrophils was selected based on a strong ability to predict death events within 1–6 months and a set of six prediction models corresponding to 1,2, 3, 4, 5, and 6 months was developed. The area under the curve (AUC) ranged from 0.852 for the 1 month model to 0.713 for the 6 month model. External validation supported the performance of these models.

Conclusion

By applying time-series real-world big data, we successfully developed a set of six adaptable prognosis prediction models for patients with cancer receiving chemotherapy.

Tumor LDH-A expression and serum LDH status are two metabolic predictors for triple negative breast cancer brain metastasis

There are limited therapeutic methods for triple negative breast cancer in the clinic, which is easy to progress into the brain to form metastatic lesions and evolve into the terminal stage. Because both the primary cancer and the brain metastasis have high glycolysis, we hypothesize that lactate dehydrogenase (LDH), which catalyzes the final step of glycolysis, may be a predictor, as well as a treatment target, for breast cancer brain metastasis. Therefore, the expression of LDH-A was detected on 119 triple negative breast cancer tissues with immunohistochemistry, and the serum LDH levels were also measured. Our results showed that the LDH-A expression inside the tumor was significantly higher than the matched normal tissues. Tumor LDH-A expression, serum LDH status, and the slope of serum LDH status were closely associated with triple negative breast cancer brain metastasis and brain metastasis free survival. This study indicates that tumor LDH and serum LDH status are two predictors for triple negative breast cancer brain metastasis.

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.

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

CONTEXT

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Pre-treatment serum lactate dehydrogenase as a biomarker in small cell lung cancer

BACKGROUND

Small cell lung cancer is a rapidly progressive disease with high fatality. No sensitive and specific biomarker to assist in managing this disease exists currently.

AIM

Role of pretreatment serum lactate dehydrogenase as a biomarker in small cell lung cancer.

METHODS

A hospital-based cancer registry was used to identify eligible patients from 1999 to 2009. Demographic data, lactate dehydrogenase level and clinical outcome of patients were collected for analysis.

RESULTS

One hundred and sixty-eight patients were identified: 61% (n = 103) males and 39% (n = 65) females. Majority had extensive stage (67%). High lactate dehydrogenase (≥230 U/L) was present in 60.4% (n = 75); mean reading 260 U/L (range 148-898 U/L) in limited stage and 470 U/L (range 116-5462 U/L) in extensive stage. Extensive stage patients with high lactate dehydrogenase had lower treatment response rate compared to those with normal lactate dehydrogenase (39% vs 79%, P = 0.002); no difference in treatment response was seen among patients with limited stage. High lactate dehydrogenase conferred a worse survival; mean overall survivals in limited and extensive stage were 8.0 and 5.2 months, respectively, in patients with elevated lactate dehydrogenase. Those with normal lactate dehydrogenase had an overall survival of 16.5 and 8.2 months, respectively. The association remained significant after adjustment for age, sex and treatment (HR 1.8, 95% CI 1.16-2.80, P = 0.009).

CONCLUSION

High pretreatment lactate dehydrogenase is a prognostic marker of survival in both stages of small cell lung cancer. It is also a predictive marker of response to therapy in extensive stage. Larger prospective studies to validate our findings would be beneficial.

Retrospective study of unplanned hospital admission for metastatic cancer patients visiting the emergency department

PURPOSE

The purpose of the study was to identify factors that predict unplanned admission for metastatic cancer patients visiting the emergency department (ED).

METHODS

Patients visiting the ED of a general hospital from April 2012 to March 2013 were investigated retrospectively. Data including demographics, vital signs, and laboratory measurements were collected from a chart review for each patient. Factors related to emergency admission were identified by univariate and multivariate analyses.

RESULTS

A total of 15,716 individuals visiting the ED during the study period included 1244 (7.9%) patients with cancer. Among the 491 cancer patients with metastasis, univariate analysis revealed that emergency admission was significantly associated with an age of ≥76 years; an altered mental status; fever (≥38 °C); a blood oxygen saturation of <90%; a white blood cell (WBC) count of ≤2000 or ≥10,000/μL; hypoalbuminemia (≤2.5 g/dL); and elevated levels of aspartate aminotransferase (≥100 IU/L), blood urea nitrogen (≥25 mg/dL), and C-reactive protein (CRP, ≥10 mg/dL). Multivariate analysis identified age, an altered mental status, hypoxemia, an abnormal WBC count, and elevated CRP as putative independent predictive factors for emergency admission. The number of these five factors present was also correlated with 30-day mortality (c-statistic = 0.72).

CONCLUSIONS

Age, unconsciousness, hypoxemia, an abnormal WBC count, and elevated CRP were found to be associated with emergency admission and 30-day mortality for metastatic cancer patients. Prospective validation of a predictive scoring system based on these findings is warranted.

Inverse Association between Platelet-Lymphocyte Ratio and Prognosis in Terminally Ill Cancer Patients: A Preliminary Study

BACKGROUND

An elevated platelet-lymphocyte ratio (PLR) is an indicator for worse outcomes in cancer, but its significance at the end of life remains unclear.

OBJECTIVE

This study aimed to investigate the value of PLR as an independent prognostic factor in terminally ill cancer patients.

METHODS

This retrospective cohort study included 312 terminal cancer patients and was conducted in a palliative care unit of a tertiary cancer center. Patient demographic data, clinical information, and laboratory values, including complete blood cell count, were obtained. Survival was analyzed using the Kaplan-Meier method and log-rank test. The Cox proportional hazards model was used to identify independent prognostic factors for survival.

RESULTS

Median survival was 16 days in patients with PLR ≥200 and 9 days in patients with PLR <200 (p = 0.008). Results of multivariate analysis showed that the following factors predicted worse survival: poor performance status (adjusted hazard ratio [aHR], 2.16; 95% confidence interval [CI], 1.50-3.09), azotemia (aHR, 1.43; 95% CI, 1.01-2.02), hypoalbuminemia (aHR, 1.55; 95% CI, 1.07-2.26), hyperbilirubinemia (aHR, 1.67; 95% CI, 1.23-2.29), elevated lactate dehydrogenase (aHR, 1.58; 95% CI, 1.11-2.26), high neutrophil-lymphocyte ratio (aHR, 1.49; 95% CI, 1.09-2.04), and low PLR (aHR, 1.46; 95% CI, 1.08-1.97).

CONCLUSION

Although elevated PLR indicates worse outcomes in patients with solid tumors, decreased PLR was an independent prognostic factor for poor survival in cancer patients at the end of life.

Targeting Cancer Cells using 3-bromopyruvate for Selective Cancer Treatment

Cancer treatment deserves more research efforts despite intensive conventional treatment modalities for many types of malignancies. Metastasis and resistance to chemotherapy and radiotherapy receive a lot of global research efforts. The current advances in cancer biology may improve targeting the critical metabolic differences that distinguish cancer cells from normal cells. Cancer cells are highly glycolytic for energy production, exhibit the Warburg effect, establish aggressive acidic microenvironment, maintain cancer stem cells, exhibit resistance to chemotherapy, have low antioxidant systems but different ΔΨm (delta psi, mitochondrial transmembrane potential), express P-glycoprotein for multidrug resistance, upregulate glucose transporters and monocarboxylate transporters and are under high steady-state reactive oxygen species conditions. Normal cells differ in all these aspects. Lactate produced through the Warburg effect helps cancer metastasis. Targeting glycolysis reactions for energy production in cancer cells seems promising in decreasing the proliferation and metastasis of cancer cells. 3-bromopyruvate makes use of cancer biology in treating cancer cells, cancer stem cells and preventing metastasis in human cancer as discussed in this review. Updated advances are analyzed here, which include research analysis of background, experience, readings in the field of cancer biology, oncology and biochemistry.

Prospective Validation of the Objective Prognostic Score for Advanced Cancer Patients in Diverse Palliative Settings

CONTEXT

Prognostication is an essential part of palliative care to aid decision making and negotiate goals of care. The Objective Prognostic Score (OPS) is an easy-to-use prognosticating tool to predict survival among far-advanced cancer patients in palliative care units (PCUs) in Korea.

OBJECTIVES

This study aimed to prospectively validate the OPS for advanced cancer patients in the palliative care teams (PCTs), PCUs, and home-based palliative care (HPC) in Japan.

METHODS

This was a substudy of a multicenter prospective cohort study that was conducted to validate and compare prognostic tools among advanced cancer patients in Japan. Participants' survival was calculated according to OPS 3 as a cutoff for predicting survival of less than three weeks. Overall accuracy and area under the receiver operator characteristic curves of OPS 3 were calculated for PCT, PCU, and HPC, respectively.

RESULTS

A total of 1146 cases (PCTs 441, PCUs 519, and HPCs 186 cases) were included in final analyses. The overall accuracy of OPS 3 for predicting three-week survival ranged from 0.70 to 0.78 across diverse palliative care settings. The c-statistics ranged from 0.742 to 0.808 across three settings. Participants in the PCT showed the highest overall accuracy and c-statistics for OPS.

CONCLUSION

The OPS can be used for prognostication among advanced cancer patients in PCT, PCU, and HPC settings.

Overall survival of cancer patients with serum lactate dehydrogenase greater than 1000 IU/L

High level of serum lactate dehydrogenase (LDH) is a well-known poor prognostic factor in patients with malignancies. However, there was no data on overall survival (OS) in cancer patients with serum LDH level > 1000 IU/L, and the prognostic value of the changes in LDH over time for OS had not been reported. Clinical data of 311 cancer patients with metastatic disease with serum LDH >1000 IU/L (four times upper limit of normal) admitted consecutively to a single center were reviewed in this retrospective study. LDH level ranged from 1002 to 8235 U/L with a mean of 1689 U/L. The median OS was 1.7 months (95 % CI: 1.4–2.0). About half of patients (n = 163, 52 %) died within 2 months with the median OS of 0.5 months (95 % CI: 0.3–0.7). Only 173 patients were indicated for salvage treatment. Fifty-one patients’ serum LDH level decreased to normal at 2 months following chemotherapy; OS was significantly longer in these patients (22.6 months, 95 % CI: 10.9–34.3, p < 0.001) compared to those with persistently abnormal serum LDH at 2 months (4.0 months, 95 % CI: 3.4–4.6). The independent factors that increased the death risk were ECOG performance status 3–4 (HR: 2.05, 95 % CI: 1.42–2.97, p < 0.001), supportive care only (HR: 2.91, 95 % CI: 2.06–4.10, p < 0.001), and persistently abnormal serum LDH at 2 months (HR: 2.72, 95 % CI: 1.67–4.42, p < 0.001). In conclusion, serum LDH level > 1000 IU/L predicted a terminal stage in metastatic cancer patients. OS was significantly prolonged in patients indicated for effective palliative treatment and LDH level decreased to normal at 2 months.

The prognostic value of lactate dehydrogenase levels in colorectal cancer: a meta-analysis

BACKGROUND

The prognostic value of lactate dehydrogenase levels in the prognosis of colorectal cancer patients has been assessed for years, although the results remain controversial and heterogeneous. Thus, we comprehensively reviewed the evidence from studies that evaluated lactate dehydrogenase levels in colorectal cancer patients to determine their effect.

METHODS

The following databases were searched in September 2014 to identify studies that evaluated the prognostic value of lactate dehydrogenase levels in colorectal cancer: PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials. We extracted hazard ratios (HRs) and the associated 95% confidence intervals (CIs) from the identified studies, and performed random-effects model meta-analyses on the overall survival (OS) and progression-free survival (PFS). Thirty-two studies with a cumulative sample size of 8,261 patients were included in our analysis.

RESULTS

Our meta-analyses revealed that high levels of lactate dehydrogenase were associated with poor OS (HR, 1.75; 95% CI, 1.52-2.02) in colorectal cancer patients. However, this effect was not obvious in the OS of non-metastatic colorectal cancer patients (HR, 1.21; 95% CI, 0.79-1.86). The prognostic value of lactate dehydrogenase levels on PFS was also not confirmed (HR, 1.36; 95% CI, 0.98-1.87). Subgroup analyses revealed that the prognostic significance of lactate dehydrogenase was independent of study location, patient age, number of patients, metastasis, chemotherapy with anti-angiogenesis drugs, study type, or risk of bias.

CONCLUSIONS

Our results indicate that high lactate dehydrogenase levels are associated with poor OS among colorectal cancer patients, although these levels are not significant predictors of PFS.

Neutrophil-Lymphocyte Ratio Predicts Survival in Terminal Cancer Patients

BACKGROUND

Neutrophil-lymphocyte count ratio (NLR) is a prognostic factor in cancer, although its prognostic significance in terminally ill cancer patients is not clear.

OBJECTIVE

We investigated the association of NLR with survival in patients with terminal cancer.

METHODS

We retrospectively investigated 205 consecutive patients with terminal cancer admitted to a palliative care unit during 2014.

RESULTS

The median value of NLR was 9.7. In univariable analysis, NLR of 10 or more was significantly associated with shorter survival and NLR further discriminated survival of each palliative prognostic index (PPI) group. In multivariable analysis, increased NLR (≥10) was associated with shorter survival (hazards ratio [HR] 1.54, p<0.005), along with poor performance status (HR 1.55, p < 0.011), high PPI score (HR 1.59, p<0.003), and hyperbilirubinemia (HR 1.84, p < 0.001).

CONCLUSION

Our results suggest that elevated NLR was an independent prognostic factor for poor survival in patients with terminal cancer.

Equating salivary lactate dehydrogenase (LDH) with LDH-5 expression in patients with oral squamous cell carcinoma: An insight into metabolic reprogramming of cancer cell as a predictor of aggressive phenotype

Oral squamous cell carcinoma (OSCC) is the sixth most common human malignancy. According to World Health Organization, oral cancer has been reported to have the highest morbidity and mortality and a survival rate of approximately 50 % at 5 years from diagnosis. This is attributed to the subjectivity in TNM staging and histological grading which may result in less than optimum treatment outcomes including tumour recurrence. One of the hallmarks of cancer is aerobic glycolysis also known as the Warburg effect. This glycolytic phenotype (hypoxic state) not only confers immortality to cancer cells, but also correlates with the belligerent behaviour of various malignancies and is reflected as an increase in the expression of lactate dehydrogenase 5 (LDH-5), the main isoform of LDH catalysing the conversion of pyruvate to lactate during glycolysis. The diagnostic role of salivary LDH in assessing the metabolic phenotype of oral cancer has not been studied. Since salivary LDH is mainly sourced from oral epithelial cells, any pathological changes in the epithelium should reflect diagnostically in saliva. Thus in our current research, we made an attempt to ascertain the biological behaviour and aggressiveness of OSCC by appraising its metabolic phenotype as indirectly reflected in salivary LDH activity. We found that salivary LDH can be used to assess the aggressiveness of different histological grades of OSCC. For the first time, an evidence of differing metabolic behaviour in similar histologic tumour grade is presented. Taken together, our study examines the inclusion of salivary LDH as potential diagnostic parameter and therapeutic index in OSCC.

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.

Objective palliative prognostic score among patients with advanced cancer

CONTEXT

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Medical Manuscript

The role of total serum testosterone in the prognosis of terminal cancer is unclear. We retrospectively investigated the total serum testosterone level in 69 male patients with terminal cancer in a palliative care unit. The association between the serum testosterone level and survival was assessed using Cox proportional hazard model. The median value of serum total testosterone was 44.5 ng/dL, far lower than previously reported in patients with advanced cancer. Multivariate analysis revealed thrombocytopenia (adjusted hazard ratio [aHR], 2.68), hypoalbuminemia (aHR, 2.02), azotemia (aHR, 2.67), and lower serum testosterone level (aHR, 2.03) were significantly negatively prognostic of survival. Lower serum testosterone level was an independent unfavorable prognostic factor for life expectancy in male patients with terminal cancer.

Abnormal levels of heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) in tumour tissue and blood samples from patients diagnosed with lung cancer

Lung cancer is the second most common type of cancer in the world and is the most common cause of cancer-related death in both men and women. Research into causes, prevention and treatment of lung cancer is ongoing and much progress has been made recently in these areas, however survival rates have not significantly improved. Therefore, it is essential to develop biomarkers for early diagnosis of lung cancer, prediction of metastasis and evaluation of treatment efficiency, as well as using these molecules to provide some understanding about tumour biology and translate highly promising findings in basic science research to clinical application. In this investigation, two-dimensional difference gel electrophoresis and mass spectrometry were initially used to analyse conditioned media from a panel of lung cancer and normal bronchial epithelial cell lines. Significant proteins were identified with heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1), pyruvate kinase M2 isoform (PKM2), Hsc-70 interacting protein and lactate dehydrogenase A (LDHA) selected for analysis in serum from healthy individuals and lung cancer patients. hnRNPA2B1, PKM2 and LDHA were found to be statistically significant in all comparisons. Tissue analysis and knockdown of hnRNPA2B1 using siRNA subsequently demonstrated both the overexpression and potential role for this molecule in lung tumorigenesis. The data presented highlights a number of in vitro derived candidate biomarkers subsequently verified in patient samples and also provides some insight into their roles in the complex intracellular mechanisms associated with tumour progression.

A systemic inflammation-based prognostic scores (mGPS) predicts overall survival of patients with small-cell lung cancer

Recent studies have shown the combination of C-reactive protein (CRP) and albumin (The modified Glasgow Prognostic Score, mGPS) had prognostic value in some solid tumors. However, no studies have examined its prognostic role in small-cell lung cancer (SCLC) patients. In this retrospective study, 460 consecutive SCLC patients were screened. Eligible patient was assigned a mGPS of 0, 1, or 2 based on pre-treatment plasma CRP and albumin (0: CRP ≤ 10 mg/L; 1: CRP >10 mg/L and albumin ≥ 35 g/L; 2: CRP > 10 mg/L and albumin < 35 g/L). Univariate and multivariate analyses were performed to assess the prognostic value of relevant factors for SCLC. A total of 359 patients were analyzed. The mGPS of 0, 1, and 2 was assigned to 66.3, 30.6, and 3.1 % of total patients. For patients with mGPS of 0, 1, and 2, median overall survival (OS) was 30.4, 28.2, and 14.3 months, respectively (P < 0.001). Performance status (P < 0.001), disease stage (P < 0.001) and pre-treatment LDH (P < 0.001) also significantly predicted OS. Multivariate analyses showed mGPS was an independent prognostic factor (P < 0.001). This study demonstrated that higher mGPS independently predicts worse OS for SCLC patients. The assessment of mGPS could assist the identification of patients with poor prognosis and be a hierarchical factor in the future SCLC clinical trials.

New symptom-based predictive tool for survival at seven and thirty days developed by palliative home care teams

AIM

This study sought to develop models to predict survival at 7 and 30 days based on symptoms detected by palliative home care teams (PHCTs).

MATERIALS AND METHODS

This prospective analytic study included a 6-month recruitment period with patient monitoring until death or 180 days after recruitment. The inclusion criteria consisted of age greater than 18 years, advanced cancer, and treatment provided by participating PHCTs between April and July 2009. The study variables included death at 7 or 30 days, survival time, age, gender, place of residence, type of tumor and extension, presence of 11 signs and symptoms measured with a 0-3 Likert scale, functional and cognitive status, and use of a subcutaneous butterfly needle. The statistics applied included a descriptive analysis according to the percentage or mean±standard deviation. For symptom comparison between surviving and nonsurviving patients, the χ(2) test was used. Classification and regression tree (CART) methodology was used for model development. An internal validation system (cross-validation with 10 partitions) was used to ensure generalization of the models. The area under the receiver operating characteristics (ROC) curve was calculated (with a 95% confidence interval) to assess the validation of the models.

RESULTS

A total of 698 patients were included. The mean age of the patients was 73.7±12 years, and 60.3% were male. The most frequent type of neoplasm was digestive (37.6%). The mean Karnofsky score was 51.8±14, the patients' cognitive status according to the Pfeiffer test was 2.6±4 errors, and 8.3% of patients required a subcutaneous butterfly needle. Each model provided 8 decision rules with a probability assignment range between 2.2% and 99.1%. The model used to predict the probability of death at 7 days included the presence of anorexia and dysphagia and the level of consciousness, and this model produced areas under the curve (AUCs) of 0.88 (0.86-0.90) and 0.81 (0.79-0.83). The model used to predict the probability of death at 30 days included the presence of asthenia and anorexia and the level of consciousness, and this model produced AUCs of 0.78 (0.77-0.80) and 0.77 (0.75-0.79).

CONCLUSION

For patients with advanced cancer treated by PHCTs, the use of classification schemes and decision trees based on specific symptoms can help clinicians predict survival at 7 and 30 days.

Characteristics, treatment and prognostic factors of patients with gynaecological malignancies treated in a palliative care unit at a university hospital

BACKGROUND

Limited clinical data have been published on patients suffering from advanced gynaecological malignancies treated in palliative care units, and little is known about prognostic factors.

METHODS

In a retrospective study, the data of 225 patients with breast, ovarian and cervical cancer treated in the palliative care unit of a university hospital between 1998 and 2009 were assembled. Clinical aspects and baseline symptoms, laboratory parameters, the clinical course, and outcome were evaluated.

RESULTS

225 patients (497 cases; cancer diagnoses: breast 79%, ovarian 13%, and cervix 8%) were included in the analysis. The main symptoms were weakness/fatigue (71%), pain (65%), anorexia/nausea (62%), and dyspnea (46%). Pain control was achieved in 85% of all cases, satisfying control of other symptoms in 80%. The median overall survival (OS) was 59 days. 53% of the patients died at the palliative care unit. In the Cox proportional hazards model, 8 parameters indicated an unfavourable outcome: anorexia/nausea, disordered mental status, elevated lactate dehydrogenase, γ-glutamyltransferase, leukocyte count, hypoalbuminaemia, anaemia and hypercalcaemia. Based on these parameters 3 risk groups were defined: low risk (0-2 factors), intermediate risk (3-5 factors), and high risk (6-8 factors). Median survival for high-risk group was 13 days, for intermediate group 61 days, and for low-risk patients 554 days (p < 0.0001).

CONCLUSION

Weakness/fatigue, pain and anorexia were the main symptoms leading to the hospitalisation of patients with gynaecological malignancies. Symptom and pain control was accomplished in 80% of cases. 8 parameters were identified as indicating a poor outcome, and patients showing at least 6 or more of these factors had a very limited prognosis. Although studied retrospectively, these results may be helpful for individual treatment decisions in patients with advanced gynaecological malignancies. Prospective data and the introduction of documentation systems could help to gain more powerful knowledge about the quality of palliative care.

Lactate dehydrogenase A in cancer: a promising target for diagnosis and therapy

One of the principal biochemical characteristics of malignant cells compared to normal cells is a metabolic switch from oxidative phosphorylation to increased glycolysis, even under hypoxic conditions, and is termed the Warburg effect. Lactate dehydrogenase A (LDHA) catalyzes the conversion of pyruvate to lactate and is considered to be a key checkpoint of anaerobic glycolysis. It is elevated in many types of cancers and has been linked to tumor growth, maintenance, and invasion; therefore, its inhibition may restrict the energy supply in tumors and thereby reduce the metastatic and invasive potential of cancer cells. This enzyme is receiving a great deal of attention as a potential diagnostic marker or a predictive biomarker for many types of cancer and as a therapeutic target for new anticancer treatments. In this review, we summarize the role of LDHA in cancer, discuss its potential significance in clinical diagnosis and prognosis of cancer, and propose LDHA as a novel target for the inhibition of tumor growth and invasiveness.

A prediction rule for the development of delirium among patients in medical wards: Chi-Square Automatic Interaction Detector (CHAID) decision tree analysis model

OBJECTIVE

To predict development of delirium among patients in medical wards by a Chi-Square Automatic Interaction Detector (CHAID) decision tree model.

METHODS

This was a retrospective cohort study of all adult patients admitted to medical wards at a large community hospital. The subject patients were randomly assigned to either a derivation or validation group (2:1) by computed random number generation. Baseline data and clinically relevant factors were collected from the electronic chart. Primary outcome was the development of delirium during hospitalization. All potential predictors were included in a forward stepwise logistic regression model. CHAID decision tree analysis was also performed to make another prediction model with the same group of patients. Receiver operating characteristic curves were drawn, and the area under the curves (AUCs) were calculated for both models. In the validation group, these receiver operating characteristic curves and AUCs were calculated based on the rules from derivation.

RESULTS

A total of 3,570 patients were admitted: 2,400 patients assigned to the derivation group and 1,170 to the validation group. A total of 91 and 51 patients, respectively, developed delirium. Statistically significant predictors were delirium history, age, underlying malignancy, and activities of daily living impairment in CHAID decision tree model, resulting in six distinctive groups by the level of risk. AUC was 0.82 in derivation and 0.82 in validation with CHAID model and 0.78 in derivation and 0.79 in validation with logistic model.

CONCLUSION

We propose a validated CHAID decision tree prediction model to predict the development of delirium among medical patients.

Retrospective Assessment of Objective Prognostic Score in Terminally Ill Korean Patients With Cancer

We conducted a retrospective study by reviewing the medical records of 104 patients to assess the usefulness of the objective prognostic score (OPS) in an independent population of Korea. The median survival time (±standard error) of the high OPS group (≥3) was 9.0 ± 1.31 days and that of the low OPS group (<3) was 26.0 ± 3.3 days. The former was significantly shorter than the latter ( P < .001). Only delirium (hazard ratio 1.751, P = .032) was related to shorter survival time independent of the OPS. This study demonstrates that the OPS is a valid and useful prognostic tool for predicting survival in terminally ill Korean patients with cancer, and that inclusion of delirium into the OPS may improve its prognostic value.

Prognostication in advanced cancer: a study examining an inflammation-based score

CONTEXT

Prognostication in advanced cancer is challenging. Biomarkers of systemic inflammation (C-reactive protein and albumin) combined in the modified Glasgow Prognostic Score (mGPS) have been used to assist prognostication in various cancer types.

OBJECTIVES

The aim of this study was to examine whether an inflammation-based prognostic score (mGPS) is useful in prognostication in advanced cancer patients.

METHODS

Cancer patients who had biomarkers (C-reactive protein and albumin) recorded were allocated an mGPS ranging from 0-2. Groups were compared using Jonckheere-Terpstra and Chi-squared tests. Survival analyses were carried out using Kaplan-Meier and multivariate Cox regression models.

RESULTS

A total of 296 patients were included, and a representative subgroup of 102 had biomarkers recorded. The mGPS was predictive of death (P=0.014) adjusted for sex, cancer site, age, hemoglobin, and white cell count. Patients with an mGPS of 2 had 2.7 times the risk of death of those with an mGPS of 0 (P=0.011). Patients with an mGPS less than 2 had an 86.1% and 74.3% chance of being alive at two and four weeks, respectively.

CONCLUSION

A role for the mGPS in prognostication near the end of life is suggested. Biomarkers (e.g., mGPS) may assist clinical decisions as to whether intensive treatments are appropriate and may facilitate end-of-life care planning.

Development and validation of a prognostic nomogram for terminally ill cancer patients

BACKGROUND

Determining life expectancy in terminally ill cancer patients is a difficult task. We aimed to develop and validate a nomogram to predict the length of survival in patients with terminal disease.

METHODS

From February 1, 2003, to December 31, 2005, 406 consecutive terminally ill patients were entered into the study. We analyzed 38 features prognostic of life expectancy among terminally ill patients by multivariable Cox regression and identified the most accurate and parsimonious model by backward variable elimination according to the Akaike information criterion. Five clinical and laboratory variables were built into a nomogram to estimate the probability of patient survival at 15, 30, and 60 days. We validated and calibrated the nomogram with an external validation cohort of 474 patients who were treated from June 1, 2006, through December 31, 2007.

RESULTS

The median overall survival was 29.1 days for the training set and 18.3 days for the validation set. Eastern Cooperative Oncology Group performance status, lactate dehydrogenase levels, lymphocyte levels, albumin levels, and time from initial diagnosis to diagnosis of terminal disease were retained in the multivariable Cox proportional hazards model as independent prognostic factors of survival and formed the basis of the nomogram. The nomogram had high predictive performance, with a bootstrapped corrected concordance index of 0.70, and it showed good calibration. External independent validation revealed 68% predictive accuracy.

CONCLUSIONS

We developed a highly accurate tool that uses basic clinical and analytical information to predict the probability of survival at 15, 30, and 60 days in terminally ill cancer patients. This tool can help physicians making decisions on clinical care at the end of life.

Palliative gastrectomy and other factors affecting overall survival in stage IV gastric adenocarcinoma patients receiving chemotherapy: a retrospective analysis

OBJECTIVE

Most patients with gastric cancer present with locally advanced or metastatic disease and usually receive palliative therapy. We sought to identify factors influencing overall survival in patients with stage IV gastric cancer receiving palliative chemotherapy.

PATIENTS AND METHODS

The records of 311 patients with histological diagnosis of gastric adenocarcinoma were retrospectively reviewed and 17 clinicopathological and therapeutic parameters were evaluated for their influence on overall survival.

RESULTS

In multivariate analysis nine factors were found to independently influence survival: no previous palliative gastrectomy [Hazard ratio (HR, 12; CI 7.969-18.099)], single agent chemotherapy instead of combination chemotherapy (HR, 1.35; CI 1.068-1.721), histological grade III (HR, 1.39; 95% CI 1.098-1.782), the presence of hepatic (HR, 1.6; 95% CI 1.246-2.073) and abdominal metastasis (HR, 1.33; 95% CI 1.039-1.715), CA 72-4 > 7 U/L (HR, 1.39; 95% CI 1.026-1.887), LDH > 225 U/L (HR, 1.72; 95% CI 1.336-2.236], need for blood transfusions (HR, 1.58; 95% CI 1.213-2.082), and weight loss > 5% (HR, 1.96; 95% CI 1.352-2.853) at the time of initial diagnosis. Patients were stratified as low (0-2 factors), intermediate (3-6 factors) and high (7-9 factors) risk and the median survival was 76, 40 and 11 weeks, respectively.

CONCLUSION

Nine clinical and laboratory factors that adversely affect survival in patients with stage IV gastric cancer who receive chemotherapy were identified. Their concurrent presence seems to have an additive effect as patients with seven to nine factors have the worse prognosis. Palliative gastrectomy and combination chemotherapy appear to be associated with improved survival.

Validation of prognostic scores for survival in cancer patients beyond first-line therapy

Background

We aimed to validate prognostic scores for survival in patients undergoing chemotherapy for advanced or metastatic cancer after first-line treatment.

Methods

We previously described two models with good prognostic value based on a combination of Performance Status (PS) and either lactate dehydrogenase (LDH) level or lymphocyte count. These factors were evaluated for their ability to predict overall survival (OS) in a prospective cohort of 299 patients. Clinical and blood parameters were prospectively recorded. Candidate prognostic factors for OS with 0.05 significance level in univariate analysis were included in a multivariate Cox model.

Results

Median age was 59 years (range: 26-85). Primary tumor sites were breast (45%), lung (15%), ovaries (11%) and others (29%). The number of metastatic sites was 1 (29%), 2 (48%), >2 (23%). Median follow-up and median OS were 12 and 6 months, respectively. Multiple regression analysis confirmed that PS >1, lymphocyte count ≤700/μL and LDH >600 UI/L were independent predictors of short OS, as well as interleukin 6 (IL-6) level, serum albumin concentration and platelet count.

Conclusions

Prognostic scores using PS plus LDH level or PS plus lymphocyte count were validated for predicting survival in metastatic cancer patients in relapse beyond first-line treatment. A score combining PS, LDH, lymphocyte and platelet count, serum albumin and IL-6 level was superior in determining patients' prognosis.

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

OBJECTIVE

We aimed to develop a prognostic prediction model for 2-week survival among patients with terminal cancer in a palliative care unit (PCU).

METHODS

A prospective cohort study was conducted on terminal cancer patients in the PCU for 11 months at a general hospital in Tokyo, Japan. We collected data regarding demographics, treatment history, performance status, symptoms, and laboratory results. Patients who survived more than 2 weeks were labeled 'long survivors' and those who died within 2 weeks were grouped as 'short survivors'. Stepwise logistic regression model was constructed for the model development and bootstrapping was used for the internal model validation.

RESULTS

In 158 subjects whose data were available for the analysis, 109 (69%) subjects were categorized as long survivors and 49 (31%) subjects as short survivors. A prognostic prediction model with a total score of 8 points was constructed as follows: 2 points each for anorexia, dyspnea, and edema; 1 point each for blood urea nitrogen >25 mg/dl and platelets <260,000/mm(3). Area under the receiver operating characteristic (ROC) curve of this model was 83.2% (95% CI: 75.3-91.0%). Bootstrapped validation beta coefficients of the predictors were similar to the original cohort beta coefficients.

CONCLUSION

Our prognostic prediction model for estimating 14-day survival for patients with terminal cancer on the PCU ward included five clinical predictors that are readily available in the clinical setting and showed a relatively high accuracy. External validation is needed to confirm the model's generalizability.

Determining prognosis and predicting survival in end-of-life care

PURPOSE OF REVIEW

The purpose of this article is to review some recent studies referred to prognosis factors and our capacity to predict death during the final stages of disease.

RECENT FINDINGS

Recent studies show significant approaches through the use of some instruments (especially self-assessment tools), which we will analyze herein.

SUMMARY

In many cases of clinical practice, this prediction capacity based on solid elements may be key to determine or contraindicate certain therapeutic resources. Such is the case, for example, when it is necessary to define the transfer of a patient to a proper specialized center, under family or legal circumstances demanding a clarification on this point, or for handling the dialog with the patients and/or their relatives. According to recent findings, we should include some elements of self-assessment in our instruments to predict death in palliative care.

Upregulation of glycolytic enzymes in proteins secreted from human colon cancer cells with 5-fluorouracil resistance

5-Fluorouracil (5-FU) is the most commonly used chemotherapeutic agent for colorectal cancer (CRC). However, resistance to this drug is a major obstacle in CRC chemotherapy. Accurate prediction of response to 5-FU would avoid unnecessary chemotherapy and allow the selection of other effective drugs. To identify a candidate predictor of 5-FU resistance, we isolated secreted proteins that were up- or downregulated in a 5-FU-resistant cancer cell line, compared with the parent cell line (SNU-C4), using a stable isotope-coded labeling protocol. For validating the clinical applicability of this method, levels of the identified proteins were determined in the sera of 46 patients treated with 5-FU. In total, 238 proteins with molecular weights ranging from 50 to 75 kDa were identified. Among these, 45 and 35 secreted proteins were up- and downregulated in the 5-FU-resistant cell line, respectively. We observed significant upregulation of glycolytic enzymes, including glyceraldehyde-3-phosphate dehydrogenase, pyruvate kinase M2 (PK-M2), transketolase, and NADP(+)-dependent malic enzyme 1. In particular, the level of PK-M2, a key enzyme in the glycolytic pathway, showed an increasing tendency in both sera and tissues from CRC patients displaying no response to 5-FU-based chemotherapy (progressive and stable disease cases), compared with that in complete or partial responders to 5-FU-based chemotherapy; however, it did not reach the statistical significance. In conclusion, increasing pattern of PK-M2 observed with 5-FU resistance induced in vitro and in sera and tissues from CRC patients displaying poor response to 5-FU-based chemotherapy suggest the relevance of dysregulated glycolysis and 5-FU-resistant CRC.

Development and validation of a bedside score to predict early death in cancer of unknown primary patients

BACKGROUND

We have investigated predictors of 90-day-mortality in a large cohort of non-specific cancer of unknown primary patients.

METHODS

Predictors have been identified by univariate and then logistic regression analysis in a single-center cohort comprising 429 patients (development cohort). We identified four predictors that produced a predictive score that has been applied to an independent multi-institutional cohort of 409 patients (validation cohort). The score was the sum of predictors for each patient (0 to 4).

RESULTS

The 90-day-mortality-rate was 33 and 26% in both cohorts. Multivariate analysis has identified 4 predictors for 90-day-mortality: performance status>1 (OR = 3.03, p = 0.001), at least one co-morbidity requiring treatment (OR = 2.68, p = 0.004), LDH>1.5 x the upper limit of normal (OR = 2.88, p = 0.007) and low albumin or protein levels (OR = 3.05, p = 0.007). In the development cohort, 90-day-mortality-rates were 12.5%, 32% and 64% when the score was [0-1], 2 and [3]-[4], respectively. In the validation cohort, risks were 13%, 25% and 62% according to the same score values.

CONCLUSIONS

We have validated a score that is easily calculated at the beside that estimates the 90-days mortality rate in non-specific CUP patients. This could be helpful to identify patients who would be better served with palliative care rather than aggressive chemotherapy.

Construction of a new, objective prognostic score for terminally ill cancer patients: a multicenter study

GOALS OF WORK

The goal of this study was to develop a new, objective prognostic score (OPS) for terminally ill cancer patients based on an integrated model that includes novel objective prognostic factors.

MATERIALS AND METHODS

A multicenter study of 209 terminally ill cancer patients from six training hospitals in Korea were prospectively followed until death. The Cox proportional hazard model was used to adjust for the influence of clinical and laboratory variables on survival time. The OPS was calculated from the sum of partial scores obtained from seven significant predictors determined by the final model. The partial score was based on the hazard ratio of each predictor. The accuracy of the OPS was evaluated.

MAIN RESULTS

The overall median survival was 26 days. On the multivariate analysis, reduced oral intake, resting dyspnea, low performance status, leukocytosis, elevated bilirubin, elevated creatinine, and elevated lactate dehydrogenase (LDH) were identified as poor prognostic factors. The range of OPS was from 0.0 to 7.0. For the above cutoff point of 3.0, the 3-week prediction sensitivity was 74.7%, the specificity was 76.5%, and the overall accuracy was 75.5%.

CONCLUSIONS

We developed the new OPS, without clinician's survival estimates but including a new prognostic factor (LDH). This new instrument demonstrated accurate prediction of the 3-week survival. The OPS had acceptable accuracy in this study population (training set). Further validation is required on an independent population (testing set).