Evaluation of the Predictive Performance of Nutritional Indicators by Receiver‐Operating Characteristic Curve Analysis

Braga, Marco; Gianotti, Luca; Radaelli, Giovanni; Cristallo, M.; Baccari, Paolo; Cin, S. Dal

doi:10.1177/0148607191015006619

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…12,13 Biohumoral prognostic factors, on the other hand, have been investigated to a lesser degree, even though some have confirmed predictive value. [14][15][16][17][18][19][20][21][22][23] Prognostic scores have been described for postsurgical, 24,25 advanced, 26 and geriatric 27 cancer patients; moreover, "intensity" and "acuity" scores have been elaborated for different stages of the disease. 28,29 Our group planned 30 a prospective multicenter study on more than 500 terminally ill cancer patients (training set) to identify clinical 13 and biological 23 prognostic factors.…”

Section: Introductionmentioning

confidence: 99%

A New Palliative Prognostic Score

Pirovano

Maltoni

Nanni

et al. 1999

Journal of Pain and Symptom Management

373

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In recent years, extensive research has been performed to identify prognostic factors that predict survival in terminally ill cancer patients. This study describes the construction of a simple prognostic score based on factors identified in a prospective multicenter study of 519 patients with a median survival of 32 days. An exponential multiple regression model was adopted to evaluate the joint effect of some clinico-biological variables on survival. From an initial model containing 36 variables, a final parsimonious model was obtained by means of a backward selection procedure. The Palliative Prognostic Score (PaP Score) is based on the final model and includes the following variables: Clinical Prediction of Survival (CPS), Karnofsky Performance Status (KPS), anorexia, dyspnea, total white blood count (WBC) and lymphocyte percentage. A numerical score was given to each variable, based on the relative weight of the independent prognostic significance shown by each single category in the multivariate analysis. The sum of the single scores gives the overall PaP Score for each patient and was used to subdivide the study population into three groups, each with a different probability of survival at 30 days: (1) group A: probability of survival at 30 days > 70%, with patient score < or = 5.5; (2) group B: probability of survival at 30 days 30-70%, with patient score 5.6-11.0; and (3) group C: probability of survival at 30 days < 30%, with patient score > 11.0. Using this method, 178/519 (34.3%) patients were classified in risk group A, 205 (39.5%) patients were in risk group B, and 136 (26.2%) patients were in risk group C. The patients classified in the three risk groups had a very different survival experience (logrank = 294.8, P < 0.001), with a median survival of 64 days for group A, 32 days for group B, and 11 days for group C. The PaP Score based on simple clinical and biohumoral variables proved to be statistically significant in a multivariate analysis. The score is valid in this population (training set). An independent validation on another patient series (testing set) is required and is the object of a companion paper.

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Section: Introductionmentioning

confidence: 99%

A New Palliative Prognostic Score

Pirovano

Maltoni

Nanni

et al. 1999

Journal of Pain and Symptom Management

373

View full text Add to dashboard Cite

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“…These observations were further confirmed by comparing CD3zeta levels in the serum of breast cancer patients and healthy controls by ELISA and ROC curve analysis. Analysis of ROC curves provides a useful parameter for evaluating the clinical usefulness of a respective biomarker, and an AUC of 0.5 signifies that detection of a respective biomarker would correctly identify disease or health states 50% of the time (Braga et al, 1991;Fluss et al, 2005;Perkins et al, 2005;Schisterman et al, 2005;Akobeng, 2007;Hao et al, 2008). The AUC of CD3zeta was 0.837, meaning that identification of CD3zeta can be used CD3zeta is a breast cancer biomarker as an adjuvant serum biomarker for breast cancer detection.…”

Section: Discussionmentioning

confidence: 99%

Down-regulation of CD3zeta is a breast cancer biomarker associated with immune suppression

Zhang

2011

Cell. Biol. Int.

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This work was done to establish a correlation between serum protein expression profiles and breast cancer. A high-density antibody microarray was used to identify serum protein expression profiles. Proteins up- or down-regulated by 2-fold compared with normal controls were used for hierarchical clustering analysis. ELISA and immunohistochemistry were used for validation of protein array data. The CD3zeta chain was one of the down-regulated molecules identified by the antibody array analysis and confirmed by ELISA and immunohistochemistry. The mean CD3zeta levels in breast cancer and in normal control were 119±23 and 156±35 ng/ml, respectively. The AUC (area under the curve) of CD3zeta was 0.837±0.057. Forty-one out of 65 breast cancer cases (63.08%) showed reduced CD3zeta expression on tumour-infiltrating lymphocytes by immunohistochemistry. This is the first report describing down-regulation of CD3zeta expression on breast cancer for the Chinese population and suggests that an immunosuppressive status plays an important role in breast cancer progression.

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“…A multiple logistic analysis and the receiver operating characteristics (ROC) curve analysis were used to address these issues. This approach was chosen because previous experiences showed that ROC analysis was a reliable statistical method to identify threshold values of other indices related to postoperative infections [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Single Unit of Transfused Allogeneic Blood Increases Postoperative Infections

et al. 1996

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The aim of this prospective study was to evaluate if there was a statistical correlation between allogeneic blood transfusion and postoperative infections, and if this could have a dose-dependent pattern. The evaluation was based on multiple logistic and receiver operating characteristics (ROC) curve analyses. On hospital admission the following parameters were determined in 267 consecutive patients with colorectal cancer: hemoglobin, serum albumin, serum cholinesterase activity, total iron binding capacity and weight loss. Duration of operation, operative blood loss, amount of transfused blood, Dukes' cancer stage and occurrence of postoperative infections were also recorded. One hundred and thirty-two patients (49.4%) were given perioperatively allogeneic blood. Postoperative infections developed in 47 (17.6%) patients. Multivariate analysis identified allogeneic blood transfusion as the only variable related to the occurrence of postoperative infections (p < 0.05). ROC curve analysis showed that the risk for the occurrence of infection was significantly higher in patients transfused one unit of blood (p < 0.01). Moreover, a significant trend between increasing number of transfused blood units and susceptibility to infection was found (p < 0.00019).

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Evaluation of the Predictive Performance of Nutritional Indicators by Receiver‐Operating Characteristic Curve Analysis

Cited by 9 publications

References 34 publications

A New Palliative Prognostic Score

A New Palliative Prognostic Score

Down-regulation of CD3zeta is a breast cancer biomarker associated with immune suppression

A Single Unit of Transfused Allogeneic Blood Increases Postoperative Infections

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