Karnofsky performance status and assessment of global health status

Comparison of the performance of the GLIM criteria, PG-SGA and mPG-SGA in diagnosing malnutrition and predicting survival among lung cancer patients: A multicenter study

BACKGROUND & AIMS

The present study aimed to compare the ability of the GLIM criteria, PG-SGA and mPG-SGA to diagnose malnutrition and predict survival among Chinese lung cancer (LC) patients.

METHODS

This was a secondary analysis of a multicenter, prospective, nationwide cohort study, 6697 LC inpatients were enrolled between July 2013 and June 2020. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC), and quadratic weighted Kappa coefficients were calculated to compare the ability to diagnose malnutrition. There were 754 patients who underwent follow-up for a median duration of 4.5 years. The associations between the nutritional status and survival were analyzed by the Kaplan-Meier method and multivariable Cox proportional hazard regression models.

RESULTS

The median age of LC patients was 60 (53, 66), and 4456 (66.5%) were male. There were 617 (9.2%), 752 (11.2%), 1866 (27.9%), and 3462 (51.7%) patients with clinical stage Ⅰ, Ⅱ, Ⅲ, and Ⅳ LC, respectively. Malnutrition was present in 36.1%-54.2% (as evaluated using different tools). Compared with the PG-SGA (used as the diagnostic reference), the sensitivity of the mPG-SGA and GLIM was 93.7% and 48.3%; the specificity was 99.8% and 78.4%; and the AUC was 0.989 and 0.633 (P < 0.001). The weighted Kappa coefficients were 0.41 for the PG-SGA vs. GLIM, 0.44 for the mPG-SGA vs. GLIM, and 0.94 for the mPG-SGA vs PG-SGA in patients with stage Ⅰ-Ⅱ LC. These values were respectively 0.38, 0.39, and 0.93 in patients with stage Ⅲ-Ⅳ of LC. In a multivariable Cox analysis, the mPG-SGA (HR = 1.661, 95%CI = 1.348-2.046, P < 0.001), PG-SGA (HR = 1.701, 95%CI = 1.379-2.097, P < 0.001) and GLIM (HR = 1.657, 95%CI = 1.347-2.038, P < 0.001) showed similar death hazard ratios.

CONCLUSIONS

The mPG-SGA provides nearly equivalent power to predict the survival of LC patients as the PG-SGA and the GLIM, indicating that all three tools are applicable for LC patients. The mPG-SGA has the potential to be an alternative replacement for quick nutritional assessment among LC patients.

Triceps skinfold-albumin index significantly predicts the prognosis of cancer cachexia: A multicentre cohort study

BACKGROUND

The fat mass and nutritional status play important roles in the onset and progression of cancer cachexia. The present study evaluated the joint prognostic value of the fat mass, as indicated by the triceps skinfold thickness (TSF), and the serum albumin level, for mortality in patients with cancer cachexia.

METHODS

We performed a multicentre cohort study including 5134 patients with cancer cachexia from January 2013 to April 2019. The sum of the TSF (mm) and serum albumin (g/L) was defined as the triceps skinfold-albumin index (TA). Harrell's C index, a time-dependent receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the prognostic performance of the TA and other indices. Optimal stratification was used to identify the thresholds to define a low TA, and the association of the TA with all-cause mortality was evaluated using Kaplan-Meier analysis and Cox proportional hazard regression models.

RESULTS

The study enrolled 2408 women and 2726 men with a median age of 58.6 years and a median follow-up of 44 months. A total of 607 women (TA < 49.9) and 817 men (TA < 45.6) were classified as having a low TA. The TA showed better discrimination performance (C index = 0.621, 95% confidence interval [CI] = 0.607-0.636) to predict mortality in patients with cancer cachexia than the handgrip strength, the nutritional risk index, the prognostic nutritional index, the controlling nutritional status index, the systemic immune-inflammation index, the modified Glasgow prognostic score, and the TSF or albumin alone in the study population (all P < 0.05). The 1-, 3- and 5-year time-dependent ROC analyses (AUC = 0.647, 0.625 and 0.630, respectively) showed that the TA had the highest prognostic value among all indices investigated (all P < 0.05). Univariate analysis showed that a lower TA was associated with an increased death hazard (hazard ratio [HR] = 1.859, 95% CI = 1.677-2.062), regardless of the sex and cancer type. Multivariable survival analysis showed that a lower TA was independently associated with an increased death hazard (HR = 1.381, 95% CI = 1.223-1.560). This association was significantly strengthened in patients who did not receive curative chemotherapy (HR = 1.491, 95% CI = 1.298-1.713), those who had higher serum total protein levels (HR = 1.469, 95% CI = 1.284-1.681) and those with better physical performance (HR = 1.453, 95% CI = 1.271-1.662).

CONCLUSIONS

This study defined and evaluated a new prognostic index, the TA, which may improve the selection of intervention strategies to optimize the survival of patients with cancer cachexia.

De novo Creation and Assessment of a Prognostic Fat-Age-Inflammation Index “FAIN” in Patients With Cancer: A Multicenter Cohort Study

Background and Aims

Malnutrition is highly prevalent and is related to multiple impaired clinical outcomes in cancer patients. This study aimed to de novo create an objective, nutrition-related index specially for prognostic purposes in oncology populations.

Methods

We performed a multicenter cohort study including 14,134 cancer patients. The prognostic impact for each baseline characteristic was estimated by calculating Harrell's C-index. The optimal parameters reflecting the nutritional and inflammatory impact on patients' overall survival were selected to develop the fat-age-inflammation (FAIN) index. The associations of the FAIN with the nutritional status, physical performance, quality of life, short-term outcomes and mortality of patients were comprehensively evaluated. Independent external validation was performed to further assess the prognostic value of the FAIN.

Results

The study enrolled 7,468 men and 6,666 women with a median age of 57 years and a median follow-up of 42 months. The FAIN index was defined as: (triceps skinfold thickness + albumin) / [age + 5 × (neutrophil count/lymphocyte count)]. There were significant associations of the FAIN with the nutritional status, physical performance, quality of life and short-term outcomes. The FAIN also showed better discrimination performance than the Nutritional Risk Index, the Prognostic Nutritional Index and the Controlling Nutritional Status index (all P < 0.05). In multivariable-adjusted models, the FAIN was independently associated with a reduced death hazard both as a continuous variable (HR = 0.57, 95%CI = 0.47–0.68) and per one standard deviation (HR = 0.83, 95%CI = 0.78–0.88). External validation in a multicenter lung cancer cohort (n = 227) further confirmed the prognostic value of the FAIN.

Conclusions

This study created and assessed the prognostic FAIN index, which might act as a feasible option to monitor the nutritional status and help develop intervention strategies to optimize the survival outcomes of cancer patients.

Multi-label, multi-domain learning identifies compounding effects of HIV and cognitive impairment

Older individuals infected by Human Immunodeficiency Virus (HIV) are at risk for developing HIV-Associated Neurocognitive Disorder (HAND), i.e., from reduced cognitive functioning similar to HIV-negative individuals with Mild Cognitive Impairment (MCI) or to Alzheimer's Disease (AD) if more severely affected. Incompletely understood is how brain structure can serve to differentiate cognitive impairment (CI) in the HIV-positive (i.e., HAND) from the HIV-negative cohort (i.e., MCI and AD). To that end, we designed a multi-label classifier that labels the structural magnetic resonance images (MRI) of individuals by their HIV and CI status via two binary variables. Proper training of such an approach traditionally requires well-curated datasets containing large number of samples for each of the corresponding four cohorts (healthy controls, CI HIV-negative adults a.k.a. CI-only, HIV-positive patients without CI a.k.a. HIV-only, and HAND). Because of the rarity of such datasets, we proposed to improve training of the multi-label classifier via a multi-domain learning scheme that also incorporates domain-specific classifiers on auxiliary single-label datasets specific to either binary label. Specifically, we complement the training dataset of MRIs of the four cohorts (Control: 156, CI-only: 335, HIV-only: 37, HAND: 145) acquired by the Memory and Aging Center at the University of California - San Francisco with a CI-specific dataset only containing MRIs of HIV-negative subjects (Controls: 229, CI-only: 397) from the Alzheimer's Disease Neuroimaging Initiative and an HIV-specific dataset (Controls: 75, HIV-only: 75) provided by SRI International. Based on cross-validation on the UCSF dataset, the multi-domain and multi-label learning strategy leads to superior classification accuracy compared with one-domain or multi-class learning approaches, specifically for the undersampled HIV-only cohort. The 'prediction logits' of CI computed by the multi-label formulation also successfully stratify motor performance among the HIV-positive subjects (including HAND). Finally, brain patterns driving the subject-level predictions across all four cohorts characterize the independent and compounding effects of HIV and CI in the HAND cohort.

Low fat mass index outperforms handgrip weakness and GLIM-defined malnutrition in predicting cancer survival: Derivation of cutoff values and joint analysis in an observational cohort

BACKGROUND & AIMS

The optimal thresholds to define a survival-related low fat mass index (FMI) in Asian oncology populations remains largely unknown. This study sought to derive the sex-specific FMI cutoffs and analyze the independent and joint associations of a low FMI, handgrip weakness, and the Global Leadership Initiative on Malnutrition (GLIM)-defined malnutrition with cancer survival.

METHODS

We performed a multicenter cohort study including 2376 patients with cancer. The FMI was measured by bioelectrical impedance analysis and the best thresholds were determined using an optimal stratification (OS) method. Low handgrip strength (HGS) and malnutrition were defined based on the Asian Working Group for Sarcopenia 2019 framework and the GLIM, respectively. The associations of a low FMI, handgrip weakness and malnutrition with survival were estimated independently and jointly by calculating multivariable-adjusted hazard ratios (HRs).

RESULTS

The study enrolled 1303 women and 1073 men with a mean age of 57.7 years and a median follow-up of 1267 days. The OS-defined FMI cutoffs were <5 kg/m² in women and <7.7 kg/m² in men. A low FMI, low HGS and malnutrition were identified in 1188 (50%), 1106 (46.5%) and 910 (38.3%) patients, respectively. A low FMI was adversely associated with the nutritional status, physical performance, quality of life and hospitalization costs. A low FMI (HR = 1.50, 95%CI = 1.16 to 1.92) and malnutrition (HR = 1.31, 95%CI = 1.08 to 1.59) were independently associated with mortality. Overall, the FMI plus GLIM-defined malnutrition showed the maximal joint prognostic impact, and patients with a combined low FMI and malnutrition had the worst survival (HR = 1.93, 95%CI = 1.48 to 2.52).

CONCLUSIONS

Low FMI-indicated fat depletion outperforms and strengthens the prognostic value of handgrip weakness and GLIM-defined malnutrition for cancer survival. These findings indicate the importance of including fat mass assessment during routine cancer care to help guide strategies to optimize survival outcomes.

Several anthropometric measurements and cancer mortality: predictor screening, threshold determination, and joint analysis in a multicenter cohort of 12138 adults

BACKGROUND

Anthropometric measurements (AMs) are cost-effective surrogates for evaluating body size. This study aimed to identify the optimal prognostic AMs, their thresholds, and their joint associations with cancer mortality.

METHODS

We performed an observational cohort study including 12138 patients with cancer at five institutions in China. Information on demographics, disease, nutritional status, and AMs, including the body mass index, mid-arm muscle circumference, mid-arm circumference, handgrip strength, calf circumference (CC), and triceps-skinfold thickness (TSF), was collected and screened as mortality predictors. The optimal stratification was used to determine the thresholds to categorize those prognostic AMs, and their associations with mortality were estimated independently and jointly by calculating multivariable-adjusted hazard ratios (HRs).

RESULTS

The study included 5744 females and 6394 males with a mean age of 56.9 years. The CC and TSF were identified as better mortality predictors than other AMs. The optimal thresholds were women 30 cm and men 32.8 cm for the CC, and women 21.8 mm and men 13.6 mm for the TSF. Patients in the low CC or low TSF group had a 13% (HR = 1.13, 95% CI = 1.03-1.23) and 22% (HR = 1.22, 95% CI = 1.12-1.32) greater mortality risk compared with their normal CC/TSF counterparties, respectively. Concurrent low CC and low TSF showed potential joint effect on mortality risk (HR = 1.39, 95% CI = 1.25-1.55).

CONCLUSIONS

These findings support the importance of assessing the CC and TSF simultaneously in hospitalized cancer patients to guide interventions to optimize their long-term outcomes.

Fat mass assessment using the triceps skinfold thickness enhances the prognostic value of the Global Leadership Initiative on Malnutrition criteria in patients with lung cancer

The present study evaluated whether fat mass assessment using the triceps skinfold (TSF) thickness provides additional prognostic value to the Global Leadership Initiative on Malnutrition (GLIM) framework in patients with lung cancer (LC). We performed an observational cohort study including 2672 LC patients in China. Comprehensive demographic, disease and nutritional characteristics were collected. Malnutrition was retrospectively defined using the GLIM criteria, and optimal stratification was used to determine the best thresholds for the TSF. The associations of malnutrition and TSF categories with survival were estimated independently and jointly by calculating multivariable-adjusted hazard ratios (HR). Malnutrition was identified in 808 (30·2 %) patients, and the best TSF thresholds were 9·5 mm in men and 12 mm in women. Accordingly, 496 (18·6 %) patients were identified as having a low TSF. Patients with concurrent malnutrition and a low TSF had a 54 % (HR = 1·54, 95 % CI = 1·25, 1·88) greater death hazard compared with well-nourished individuals, which was also greater compared with malnourished patients with a normal TSF (HR = 1·23, 95 % CI = 1·06, 1·43) or malnourished patients without TSF assessment (HR = 1·31, 95 % CI = 1·14, 1·50). These associations were concentrated among those patients with adequate muscle mass (as indicated by the calf circumference). Additional fat mass assessment using the TSF enhances the prognostic value of the GLIM criteria. Using the population-derived thresholds for the TSF may provide significant prognostic value when used in combination with the GLIM criteria to guide strategies to optimise the long-term outcomes in patients with LC.

A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data

BACKGROUND AND AIMS

Most nutritional assessment tools are based on pre-defined questionnaires or consensus guidelines. However, it has been postulated that population data can be used directly to develop a solution for assessing malnutrition. This study established a machine learning (ML)-based, individualized decision system to identify and grade malnutrition using large-scale data from cancer patients.

METHODS

This was an observational, nationwide, multicenter cohort study that included 14134 cancer patients from five institutions in four different geographic regions of China. Multi-stage K-means clustering was performed to isolate and grade malnutrition based on 17 core nutritional features. The effectiveness of the identified clusters for reflecting clinical characteristics, nutritional status and patient outcomes was comprehensively evaluated. The study population was randomly split for model derivation and validation. Multiple ML algorithms were developed, validated and compared to screen for optimal models to implement the cluster prediction.

RESULTS

A well-nourished cluster (n = 8193, 58.0%) and a malnourished cluster with three phenotype-specific severity levels (mild = 2195, 15.5%; moderate = 2491, 17.6%; severe = 1255, 8.9%) were identified. The clusters showed moderate agreement with the Patient-Generated Subjective Global Assessment and the Global Leadership Initiative on Malnutrition criteria. The severity of malnutrition was negatively associated with the nutritional status, physical status, quality of life, and short-term outcomes, and was monotonically correlated with reduced overall survival. A multinomial logistic regression was found to be the optimal ML algorithm, and models built based on this algorithm showed almost perfect performance to predict the clusters in the validation data.

CONCLUSIONS

This study developed a fusion decision system that can be used to facilitate the identification and severity grading of malnutrition in patients with cancer. Moreover, the study workflow is flexible, and might provide a generalizable solution for the artificial intelligence-based assessment of malnutrition in a wider variety of scenarios.

Association of Malnutrition, as Defined by the PG-SGA, ESPEN 2015, and GLIM Criteria, With Complications in Esophageal Cancer Patients After Esophagectomy

Background: There are several approaches that can be used for the pre-treatment identification of malnutrition in oncology populations including the Patient-Generated Subjective Global Assessment (PG-SGA), the 2015 consensus statement by the European Society for Clinical Nutrition and Metabolism (ESPEN 2015) and the Global Leadership Initiative on Malnutrition (GLIM).

Aims: This study aimed to evaluate whether malnutrition, as defined by these three methods, can be used to predict complications in esophageal cancer (EC) patients after esophagectomy.

Methods: We performed a single center, observational cohort study that included 360 EC patients undergoing esophagectomy from December 2014 to November 2019 at Daping Hospital in China. The prevalence of malnutrition in the study population was prospectively defined using the PG-SGA (≥9 defined malnutrition), and retrospectively defined using the ESPEN 2015 and the GLIM. The prevalence of malnutrition and association with postoperative complications were compared in parallel for the three methods.

Results: The prevalence of malnutrition before surgery was 23.1% (83/360), 12.2% (44/360), and 33.3% (120/360) in the study population, as determined by the PG-SGA, the ESPEN 2015 and the GLIM, respectively. The PG-SGA and GLIM had higher diagnostic concordance (Kappa = 0.519, P < 0.001) compared to the ESPEN 2015 vs. GLIM (Kappa = 0.361, P < 0.001) and PG-SGA vs. ESPEN 2015 (Kappa = 0.297, P < 0.001). The overall incidence of postoperative complications for the study population was 58.1% (209/360). GLIM- and ESPEN 2015-defined malnutrition were both associated with the total number of postoperative complications in multivariable analyses. Moreover, GLIM-defined malnutrition exhibited the highest power to identify the incidence of complications among all independent predictors in a pooled analysis.

Conclusion: Among the PG-SGA, the ESPEN 2015 and the GLIM, the GLIM framework defines the highest prevalence rate of malnutrition and appears to be the optimal method for predicting postoperative complications in EC patients undergoing esophagectomy. These results support the importance of preoperatively identifying malnutrition using appropriate assessment tools, because it can facilitate the selection of management strategies that will optimize the clinical outcomes of EC patients.

Comparison of the AWGS and optimal stratification-defined handgrip strength thresholds for predicting survival in patients with lung cancer

OBJECTIVES

Handgrip strength (HGS) is related to cancer mortality. The aim of this study was to compare the performance of the Asian Working Group for Sarcopenia 2019 (AWGS)- and optimal stratification (OS)-defined HGS thresholds for predicting the survival of patients with lung cancer (LC).

METHODS

We performed an observational cohort study including 3230 patients with LC admitted to five institutions in China from November 2011 to January 2019. Comprehensive baseline and follow-up information was documented. Sex-specific thresholds for identifying patients with a low HGS were defined based on the AWGS (<28 kg in men and <18 kg in women) and the OS. The associations of a low HGS with survival were estimated by calculating multivariable-adjusted hazard ratios (HRs), and the relationships were flexibly modeled using restricted cubic splines.

RESULTS

The study included 1041 women and 2189 men with a mean age of 60 y and a median follow-up time of 761 d. The OS-calculated HGS thresholds were <31.2 kg in men and <22.4 kg in women. There were significant associations between a low HGS defined by the AWGS (n = 1392; 43.1%) or the OS (n = 2034; 63%) and various nutritional characteristics. An AWGS-defined low HGS was associated with prolonged hospitalization. The OS-defined low HGS group was associated with a 23% greater death hazard than the normal HGS group (HR, 1.23; 95% confidence interval, 1.08-1.40). An n-shaped non-linear association was observed between the HGS and survival in women (P = 0.003).

CONCLUSIONS

The OS-defined HGS thresholds show better performance than the AWGS for predicting the survival of patients with LC. Additionally, the HGS had n-shaped associations with the overall mortality among female patients with LC.

Nutritional features-based clustering analysis as a feasible approach for early identification of malnutrition in patients with cancer

BACKGROUND

Malnutrition is prevalent that can impair multiple clinical outcomes in oncology populations. This study aimed to develop and utilize a tool to optimize the early identification of malnutrition in patients with cancer.

METHODS

We performed an observational cohort study including 3998 patients with cancer at two teaching hospitals in China. Hierarchical clustering was performed to classify the patients into well-nourished or malnourished clusters based on 17 features reflecting the phenotypic and etiologic dimensions of malnutrition. Associations between the identified clusters and patient characteristics were analyzed. A nomogram for predicting the malnutrition probability was constructed and independent validation was performed to explore its clinical significance.

RESULTS

The cluster analysis identified a well-nourished cluster (n = 2736, 68.4%) and a malnourished cluster (n = 1262, 31.6%) in the study population, which showed significant agreement with the Patient-Generated Subjective Global Assessment and the Global Leadership Initiative on Malnutrition criteria (both P < 0.001). The malnourished cluster was negatively associated with the nutritional status, physical status, quality of life, short-term outcomes and was an independent risk factor for survival (HR = 1.38, 95%CI = 1.22-1.55, P < 0.001). Sex, gastrointestinal symptoms, weight loss percentages (within and beyond 6 months), calf circumference, and body mass index were incorporated to develop the nomogram, which showed high performance to predict malnutrition (AUC = 0.972, 95%CI = 0.960-0.983). The decision curve analysis and independent external validation further demonstrated the effectiveness and clinical usefulness of the tool.

CONCLUSIONS

Nutritional features-based clustering analysis is a feasible approach to define malnutrition. The derived nomogram shows effectiveness for the early identification of malnutrition in patients with cancer.

Classification Tree-Based Machine Learning to Visualize and Validate a Decision Tool for Identifying Malnutrition in Cancer Patients

BACKGROUND

The newly proposed Global Leadership Initiative on Malnutrition (GLIM) framework is promising to gain global acceptance for diagnosing malnutrition. However, the role of machine learning in facilitating its application in clinical practice remains largely unknown.

METHODS

We performed a multicenter, observational cohort study including 3998 patients with cancer. Baseline malnutrition was defined using the GLIM criteria, and the study population was randomly divided into a derivation group (n = 2998) and a validation group (n = 1000). A classification and regression trees (CART) algorithm was used to develop a decision tree for classifying the severity of malnutrition in the derivation group. Model performance was evaluated in the validation group.

RESULTS

GLIM criteria diagnosed 588 patients (14.7%) with moderate malnutrition and 532 patients (13.3%) with severe malnutrition among the study population. The CART cross-validation identified 5 key predictors for the decision tree construction, including age, weight loss within 6 months, body mass index, calf circumference, and the Nutritional Risk Screening 2002 score. The decision tree showed high performance, with an area under the curve of 0.964 (κ = 0.898, P < .001, accuracy = 0.955) in the validation group. Subgroup analysis showed that the model had apparently good performance in different cancers. Among the 5 predictors constituting the tree, age contributed the least to the classification power.

CONCLUSION

Using the machine learning, we visualized and validated a decision tool based on the GLIM criteria that can be conveniently used to accelerate the pretreatment identification of malnutrition in patients with cancer.

Is hand grip strength a necessary supportive index in the phenotypic criteria of the GLIM-based diagnosis of malnutrition in patients with cancer?

BACKGROUND

The Global Leadership Initiative on Malnutrition (GLIM) has the potential to gain global acceptance for diagnosing malnutrition. Of which, calf circumference (CC) was proposed as an alternative to evaluate the reduced muscle mass (RMM). The present study aimed to evaluate whether including the hand grip strength (HGS) was helpful for diagnosing malnutrition under the GLIM framework.

METHODS

We performed a multicenter, observational cohort study including 3998 patients with cancer at two teaching hospitals. The RMM criterion was separately assessed using the calf circumference (CC), or the CC and HGS combined. Accordingly, two methods of GLIM diagnosis were independently developed to determine the nutritional status of the patients. The diagnostic concordance, baseline characteristics, and outcomes of patients were compared across the malnourished-CC-HGS, malnourished-CC+HGS, and well-nourished groups. The Patient-Generated Subjective Global Assessment (PG-SGA) was used as a comparator to identify the optimal method.

RESULTS

Malnutrition was identified in 1120 (28%) patients by the CC method and 1060 (26.5%) patients by the CC+HGS method. Compared to the well-nourished group, the malnourished-CC+HGS group (60 patients, 1.5%) had poorer nutritional characteristics, poorer Karnofsky Performance Status scores, poorer global quality of life scores, and higher Nutritional Risk Screening 2002 scores. The severity of malnutrition diagnosed using the CC method (Kappa = 0.136) showed higher agreement with the PG-SGA than the CC+HGS method (Kappa = 0.127).

CONCLUSION

Compared to CC+HGS, the CC alone appears to be adequate to evaluate RMM under the GLIM framework. A simpler method might facilitate the application of these criteria in clinical settings by increasing efficacy and minimizing missed diagnoses.

Patients' treatment preferences for potentially resectable tumors of the head of the pancreas

BACKGROUND

The primary aim of this study was to assess if patients with potentially resectable ductal adenocarcinoma (PDAC) of the head of the pancreas would choose a Whipple procedure versus palliative chemotherapy.

METHODS

A cohort of adults with radiological resectable PDAC was enrolled at a tertiary Canadian teaching hospital. Participants were informed about treatment options, expected outcomes, and adverse events using data from the most recent scientific literature. Probability trade-off (PTO) was used to elicit treatment preferences.

RESULTS

Surgery was preferred by all participants except one (96.7% vs. 3.3%; P = 0.0001). For 90% of participants preferring surgery, the main reason was the hope of being cured (P = 0.001). If the risk of perioperative mortality was higher than 57%, the risk of perioperative morbidity higher than 85% and the survival benefit was less than 4 months, half of the participants preferred palliative chemotherapy. The likelihood of needing blood transfusions, the length of hospital stay, and long-term consequences such as diabetes or pancreatic exocrine insufficiency were negligible concerns to participants.

CONCLUSIONS

Informed patients with early-stage PDAC prefer resection over palliative chemotherapy. The dominating factor influencing their decision is the hope of a cure that overshadow the risks of complications, mortality and recurrent disease.

Association of complementary and alternative medicines with HIV clinical disease among a cohort of women living with HIV/AIDS

To assess the association between the use of complementary and alternative medicine (CAM) and HIV clinical disease indicators, CD4+ T-cell counts, viral load, number of HIV-related infections, Centers for Disease Control and Prevention categories, and Karnofsky scores. Data were collected from 391 HIV-positive women aged 18 to 50 years in Alabama and Georgia. A survey examining CAM use and other sociodemographic variables was used. Multiple logistic regression analyses were used to identify predictors of CAM use. Approximately 60% of study participants used 1 or more type of CAM. Predictors of CAM use included higher educational level (odds ratio [OR] = 2.4; P = 0.0008), absence of health insurance (OR = 0.49; P = 0.0055), longer disease duration (OR = 2.21; P = 0.0006), and higher number of infections (OR = 0.58; P = 0.017). Vitamins were the most commonly used CAM ( approximately 36%). Sociodemographic variables associated with vitamin use included higher educational level (OR = 2.34; P = 0.0055), longer disease duration (OR = 1.87; P = 0.026), and higher use among white women than among African-American women (OR = 0.41; P = 0.017). The use of CAM is prevalent among HIV-positive women, and vitamins are the most commonly used CAM among our study population. Several sociodemographic and clinical factors predicted CAM use. These findings have implications for improvement of care for HIV-positive women.

The physiologic health care needs of HIV-infected black men on admission to an AIDS-dedicated nursing home

The purpose of this study is to describe the physiologic health care needs of HIV-infected Black men on admission to an AIDS-dedicated nursing home. Participants were 68 Black men aged 26 to 60 years who were admitted to an AIDS-dedicated nursing home in the southern New England area between 1995 and 1999. The participants were very ill and weak on their admission to the nursing home, with most having diagnoses of AIDS (n = 65), an average Karnofsky Performance Scale score of 44 (SD = 14.90), and some degree of mental impairment. The late-stage of disease of the participants was reflected in their multiple symptomatology and functional impairment in activities of daily living. With patients living longer in the chronic disease stages of HIV disease, the results of this study provide support for the further investigation of the most effective long-term care settings for Black men with HIV/AIDS. The results also have implications for the multiple clinical roles nursing can assume within HIV/AIDS long-term care settings.

Disease-related factors associated with health-related quality of life in people with nonadvanced HIV disease assessed using an Italian version of the MOS-HIV Health Survey

OBJECTIVE

This study was intended to present evidence for the reliability and validity of an Italian version of the MOS-HIV Health Survey and to identify important disease-related factors associated with health-related quality of life (HRQoL) in people with nonadvanced HIV.

DESIGN

In this cross-sectional study, HRQoL was measured using an Italian version of the MOS-HIV Health Survey questionnaire in 213 HIV-infected people without previous opportunistic infections or neoplasms attending an outpatient clinic in a university hospital. Distribution of scores, reliability, and validity were calculated, and presence and frequency of HIV-related symptoms were recorded and transformed into a score. The relation of HRQoL values to sociodemographic, epidemiologic, and clinical data was assessed.

RESULTS

The level of internal consistency of the Italian version of the MOS-HIV Health Survey was high (Cronbach's alpha, 0.80-0.93), and items demonstrated acceptable discrimination across scales. At linear regression analysis, all domains of HRQoL correlated with symptom score (r2 range, 0.07-0.41), but only the pain and physical-functioning scores showed a significant correlation with CD4 cell count. A weighted sum of single domains of HRQoL, TOTQoL, is also strongly correlated with symptom score (r2 = 0.57; p < .0001) but not with CD4 cell count (r2 = 0.01; p = .1). Using multivariate analysis, only symptom score (p < .0001) and total number of daily pills (p = .03) showed significant association with HRQoL. The same results were observed when analysis was performed only on people with CD4 levels <200/microl.

CONCLUSIONS

This study presents the first evidence for the reliability and validity of a HRQoL instrument in Italian for people with HIV. Results also suggest a strong impact of symptoms on all measured dimensions of health status. The number of pills required to be taken daily is the only other significant factor associated with a lower HRQoL, whereas no relations were found with CD4 cell count or Karnofsky performance status values. To improve HRQoL in persons with nonadvanced HIV disease, symptom control could be a crucial element of medical treatment.