Determining the Most Valuable Clinical Variables : a Stepwise Multiple Logistic Regression Program

Coles, L. Stephen; Brown, Barry W.; Engelhard, Chalee; Halpern, Jerry; Fries, James F.

doi:10.1055/s-0038-1635158

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…The minimum Akaike’s information criterion (AIC) was used to select the optimal model parameters and construct a nomogram for assessing the risk of LNM (Arunajadai 2009 ; Coles et al 1980 ; Wang et al 2004 ; Zhang 2016 ), and a total of six predictors including age at diagnosis, race, primary site, grade, histology, and T-stage were integrated to construct the nomogram (Fig. 2 ).…”

Section: Resultsmentioning

confidence: 99%

Nomogram for predicting the preoperative lymph node metastasis in resectable pancreatic cancer

Cheng

Kang

et al. 2023

J Cancer Res Clin Oncol

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Background Lymph node metastasis (LNM) is a critical prognostic factor in resectable pancreatic cancer (PC) patients, determining treatment strategies. This study aimed to develop a clinical model to adequately and accurately predict the risk of LNM in PC patients. Methods 13,200 resectable PC patients were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database, and randomly divided into a training group and an internal validation group at a ratio of 7:3. An independent group (n = 62) obtained from The First Affiliated Hospital of Xinxiang Medical University was enrolled as the external validation group. The univariate and multivariate logistic regression analyses were used to screen independent risk factors for LNM. The minimum Akaike’s information criterion (AIC) was performed to select the optimal model parameters and construct a nomogram for assessing the risk of LNM. The performance of the nomogram was assessed by the receiver operating characteristics (ROC) curve, calibration plot, and decision curve analysis (DCA). In addition, an online web calculator was designed to assess the risk of LNM. Result A total of six risk predictors (including age at diagnosis, race, primary site, grade, histology, and T-stage) were identified and included in the nomogram. The areas under the curves (AUCs) [95% confidential interval (CI)] were 0.711 (95%CI: 0.700–0.722), 0.700 (95%CI: 0.683–0.717), and 0.845 (95%CI: 0.749–0.942) in the training, internal validation and external validation groups, respectively. The calibration curves showed satisfied consistency between nomogram-predicted LNM and actual observed LNM. The concordance indexes (C-indexes) in the training, internal, and external validation sets were 0.689, 0.686, and 0.752, respectively. The DCA curves of the nomogram demonstrated good clinical utility. Conclusion We constructed a nomogram model for predicting LNM in pancreatic cancer patients, which may help oncologists and surgeons to choose more individualized clinical treatment strategies and make better clinical decisions.

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Section: Resultsmentioning

confidence: 99%

Nomogram for predicting the preoperative lymph node metastasis in resectable pancreatic cancer

Cheng

Kang

et al. 2023

J Cancer Res Clin Oncol

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“…The minimum Akaike's information criterion (AIC) was used to select the optimal model parameters and construct a nomogram for assessing the risk of LNM (Arunajadai, 2009;Coles et al, 1980;Wang et al, 2004;Zhang, 2016), and a total of six predictors including age at diagnosis, race, primary site, grade, histology, and T-stage were integrated to construct the nomogram (Fig. 2).…”

Section: Construction and Validation Of The Nomogram Based On Predict...mentioning

confidence: 99%

Nomogram for predicting the preoperative lymph nodes metastasis in resectable pancreatic cancer

Cheng

Xu²,

Kang

et al. 2023

Preprint

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Background Lymph node metastasis (LNM) is a critical prognostic factor in resectable pancreatic cancer (PC) patients, determining treatment strategies. This study aimed to develop a clinical model to adequately and accurately predict the risk of LNM in PC patients. Methods 13200 resectable PC patients were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database, and randomly divided into a training group and an internal validation group at a ratio of 7:3. An independent group (n = 62) obtained from The First Affiliated Hospital of Xinxiang Medical University was enrolled as the external validation group. The univariate and multivariate logistic regression analyses were used to screen independent risk factors for LNM. The minimum Akaike’s information criterion (AIC) was performed to select the optimal model parameters and construct a nomogram for assessing the risk of LNM. The performance of the nomogram was assessed by the receiver operating characteristics (ROC) curve, calibration plot, and decision curve analysis (DCA). In addition, an online web calculator was designed to assess the risk of LNM. Result A total of six risk predictors (including age at diagnosis, race, primary site, grade, histology, and T-stage) were identified and included in the nomogram. The areas under the curves (AUCs) [95% confidential interval (CI)] were 0.711 (95%CI: 0.700-0.722), 0.700 (95%CI: 0.683–0.717), and 0.845 (95%CI: 0.749–0.942) in the training, internal validation and external validation groups, respectively. The calibration curves showed satisfied consistency between nomogram-predicted LNM and actual observed LNM. The concordance indexes (C-indexes) in the training, internal, and external validation sets were 0.689, 0.686, and 0.752, respectively. The DCA curves of the nomogram demonstrated good clinical utility. Conclusion We constructed a nomogram model for predicting LNM in pancreatic cancer patients, which may help oncologists and surgeons to choose more individualized clinical treatment strategies and make better clinical decisions.

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Toward an Understanding of Patient Outcome Measurement

Fries

1983

Arthritis & Rheumatism

155

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It is difficult to objectively measure the effects of arthritis or its treatment, and agreement on satisfactory endpoints has not been reached. Each proposed measure, from ring size to 50-foot walking time to articular index, has had its several proponents and its many critics. Now, new questionnaire-based instruments attempting to measure patient "outcome" have appeared (1-7), couched in an unfamiliar jargon and using suspicious terms such as "quality of life." These questionnaires, it is implied, measure the truly important things. Yet the supporting literature is often dull and hard to understand, and it is difficult to tell what is science and what is self-serving. As new articles appear in the literature and more investigators include such measures in their studies, an evaluation of outcome measurement appears timely. This report attempts to describe the present state of the art, the future directions, and the emerging problems, and to suggest potential applications.

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Determining the Most Valuable Clinical Variables : a Stepwise Multiple Logistic Regression Program

Cited by 8 publications

References 21 publications

Nomogram for predicting the preoperative lymph node metastasis in resectable pancreatic cancer

Nomogram for predicting the preoperative lymph node metastasis in resectable pancreatic cancer

Nomogram for predicting the preoperative lymph nodes metastasis in resectable pancreatic cancer

Toward an Understanding of Patient Outcome Measurement

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