Development and validation of a predicting model of all-cause mortality in patients with type 2 diabetes

Salvatore De Cosmo, Massimiliano Copetti, Olga Lamacchia, Andrea Fontana, Michela Massa, Eleonora Morini, Antonio Pacilli, Stefania Fariello, Antonio Palena, Anna Rauseo, Rafaella Viti, Rosa Di Paola, Claudia Menzaghi, Mauro Cignarelli, Fabio Pellegrini, Vincenzo Trischitta

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

OBJECTIVE-To develop and validate a parsimonious model for predicting short-term allcause mortality in patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS-Two cohorts of patients with T2DM were investigated. The Gargano Mortality Study (GMS, n = 679 patients) was the training set and the Foggia Mortality Study (FMS, n = 936 patients) represented the validation sample.GMS and FMS cohorts were prospectively followed up for 7.40±2.15 and 4.51±1.69 years, respectively, and all-cause mortality was registered. A new forward variable selection within a multivariate Cox regression was implemented. Starting from the empty model, each step selected the predictor that, once included into the multivariate Cox model, yielded the maximum continuous net reclassification improvement (cNRI). The selection procedure stopped when no further statistically significant cNRI increase was detected. RESULTS-Nine variables (age, BMI, diastolic blood pressure, LDL cholesterol, triglycerides, HDL cholesterol, urine albumin-to-creatinine ratio, and antihypertensive and insulin therapy) were included in the final predictive model with a C statistic of 0.88 (95% CI 0.82-0.94) in the GMS and 0.82 (0.76-0.87) in the FMS. Finally, we used a recursive partition and amalgamation algorithm to identify patients at intermediate and high mortality risk (hazard ratio 7.0 and 24.4, respectively, as compared with those at low risk). A web-based risk calculator was also developed. CONCLUSIONS-We developed and validated a parsimonious all-cause mortality equation in T2DM, providing also a user-friendly web-based risk calculator. Our model may help prioritize the use of available resources for targeting aggressive preventive and treatment strategies in a subset of very high-risk individuals.

Original languageEnglish
Pages (from-to)2830-2835
Number of pages6
JournalDiabetes Care
Volume36
Issue number9
DOIs
Publication statusPublished - Sep 2013

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Type 2 Diabetes Mellitus
Mortality
Blood Pressure
Proportional Hazards Models
LDL Cholesterol
HDL Cholesterol
Antihypertensive Agents
Albumins
Creatinine
Research Design
Odds Ratio
Urine
Insulin
Therapeutics

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism
  • Advanced and Specialised Nursing

Cite this

Development and validation of a predicting model of all-cause mortality in patients with type 2 diabetes. / De Cosmo, Salvatore; Copetti, Massimiliano; Lamacchia, Olga; Fontana, Andrea; Massa, Michela; Morini, Eleonora; Pacilli, Antonio; Fariello, Stefania; Palena, Antonio; Rauseo, Anna; Viti, Rafaella; Di Paola, Rosa; Menzaghi, Claudia; Cignarelli, Mauro; Pellegrini, Fabio; Trischitta, Vincenzo.

In: Diabetes Care, Vol. 36, No. 9, 09.2013, p. 2830-2835.

Research output: Contribution to journalArticle

De Cosmo, S, Copetti, M, Lamacchia, O, Fontana, A, Massa, M, Morini, E, Pacilli, A, Fariello, S, Palena, A, Rauseo, A, Viti, R, Di Paola, R, Menzaghi, C, Cignarelli, M, Pellegrini, F & Trischitta, V 2013, 'Development and validation of a predicting model of all-cause mortality in patients with type 2 diabetes', Diabetes Care, vol. 36, no. 9, pp. 2830-2835. https://doi.org/10.2337/dc12-1906
De Cosmo, Salvatore ; Copetti, Massimiliano ; Lamacchia, Olga ; Fontana, Andrea ; Massa, Michela ; Morini, Eleonora ; Pacilli, Antonio ; Fariello, Stefania ; Palena, Antonio ; Rauseo, Anna ; Viti, Rafaella ; Di Paola, Rosa ; Menzaghi, Claudia ; Cignarelli, Mauro ; Pellegrini, Fabio ; Trischitta, Vincenzo. / Development and validation of a predicting model of all-cause mortality in patients with type 2 diabetes. In: Diabetes Care. 2013 ; Vol. 36, No. 9. pp. 2830-2835.
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AU - De Cosmo, Salvatore

AU - Copetti, Massimiliano

AU - Lamacchia, Olga

AU - Fontana, Andrea

AU - Massa, Michela

AU - Morini, Eleonora

AU - Pacilli, Antonio

AU - Fariello, Stefania

AU - Palena, Antonio

AU - Rauseo, Anna

AU - Viti, Rafaella

AU - Di Paola, Rosa

AU - Menzaghi, Claudia

AU - Cignarelli, Mauro

AU - Pellegrini, Fabio

AU - Trischitta, Vincenzo

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N2 - OBJECTIVE-To develop and validate a parsimonious model for predicting short-term allcause mortality in patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS-Two cohorts of patients with T2DM were investigated. The Gargano Mortality Study (GMS, n = 679 patients) was the training set and the Foggia Mortality Study (FMS, n = 936 patients) represented the validation sample.GMS and FMS cohorts were prospectively followed up for 7.40±2.15 and 4.51±1.69 years, respectively, and all-cause mortality was registered. A new forward variable selection within a multivariate Cox regression was implemented. Starting from the empty model, each step selected the predictor that, once included into the multivariate Cox model, yielded the maximum continuous net reclassification improvement (cNRI). The selection procedure stopped when no further statistically significant cNRI increase was detected. RESULTS-Nine variables (age, BMI, diastolic blood pressure, LDL cholesterol, triglycerides, HDL cholesterol, urine albumin-to-creatinine ratio, and antihypertensive and insulin therapy) were included in the final predictive model with a C statistic of 0.88 (95% CI 0.82-0.94) in the GMS and 0.82 (0.76-0.87) in the FMS. Finally, we used a recursive partition and amalgamation algorithm to identify patients at intermediate and high mortality risk (hazard ratio 7.0 and 24.4, respectively, as compared with those at low risk). A web-based risk calculator was also developed. CONCLUSIONS-We developed and validated a parsimonious all-cause mortality equation in T2DM, providing also a user-friendly web-based risk calculator. Our model may help prioritize the use of available resources for targeting aggressive preventive and treatment strategies in a subset of very high-risk individuals.

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