BACKGROUND: Digital ulcers (DU) affect 50% of systemic sclerosis (SSc) patients, representing a challenging clinical problem. Despite a high negative predictive value, capillaroscopic scores proposed to select patients at risk for DU show an inadequate positive predictive value, especially in patients without previous DU. AIM OF THIS STUDY: To increase the predictive value for DU development of capillaroscopy, through a predictive risk chart taking into account capillaroscopic, demographic, and clinico-serological parameters. PATIENTS AND METHODS: Two hundred and nineteen unselected SSc patients from 8 Italian Rheumatology Centers were consecutively enrolled during a 6-month period. Demographic, clinical, serological and instrumental data and capillaroscopy skin ulcers risk index (CSURI) were collected. RESULTS: A multivariate logistic regression analysis showed a significant positive association between DU appearance and male gender, DU history, altered CSURI, and ESR. A prediction risk chart of the development of DU within 6 months were built on the basis of the above parameters. According to the risk level, four risk classes were identified: low (≤19.3%); medium (>19.3%, ≤58.6%); high (>58.6%, ≤89.2%), and very high risk (>89.2%). CONCLUSIONS: The systematic evaluation of the above parameters can be helpful to identify patients at risk to develop DU optimizing preventive vasoactive therapy.
Prediction risk chart for scleroderma digital ulcers: A composite predictive model based on capillaroscopic, demographic and clinico-serological parameters / Manfredi, Andreina Teresa; Sebastiani, Marco; Carraro, Valeria; Iudici, Michele; Bocci, Mario; Vukatana, Gentiana; Gerli, Roberto; De Angelis, Rossella; Del Medico, Patrizia; Praino, Emanuela; Lo Monaco, Andrea; D'Amico, Roberto; DEL GIOVANE, Cinzia; Mazzuca, Salvatore; Colaci, Michele; Giuggioli, Dilia; Ferri, Clodoveo. - In: CLINICAL HEMORHEOLOGY AND MICROCIRCULATION. - ISSN 1386-0291. - 59:2(2015), pp. 133-143. [10.3233/CH-141809]
Prediction risk chart for scleroderma digital ulcers: A composite predictive model based on capillaroscopic, demographic and clinico-serological parameters
MANFREDI, Andreina Teresa;SEBASTIANI, Marco;D'AMICO, Roberto;DEL GIOVANE, Cinzia;COLACI, Michele;Giuggioli, Dilia;FERRI, Clodoveo
2015
Abstract
BACKGROUND: Digital ulcers (DU) affect 50% of systemic sclerosis (SSc) patients, representing a challenging clinical problem. Despite a high negative predictive value, capillaroscopic scores proposed to select patients at risk for DU show an inadequate positive predictive value, especially in patients without previous DU. AIM OF THIS STUDY: To increase the predictive value for DU development of capillaroscopy, through a predictive risk chart taking into account capillaroscopic, demographic, and clinico-serological parameters. PATIENTS AND METHODS: Two hundred and nineteen unselected SSc patients from 8 Italian Rheumatology Centers were consecutively enrolled during a 6-month period. Demographic, clinical, serological and instrumental data and capillaroscopy skin ulcers risk index (CSURI) were collected. RESULTS: A multivariate logistic regression analysis showed a significant positive association between DU appearance and male gender, DU history, altered CSURI, and ESR. A prediction risk chart of the development of DU within 6 months were built on the basis of the above parameters. According to the risk level, four risk classes were identified: low (≤19.3%); medium (>19.3%, ≤58.6%); high (>58.6%, ≤89.2%), and very high risk (>89.2%). CONCLUSIONS: The systematic evaluation of the above parameters can be helpful to identify patients at risk to develop DU optimizing preventive vasoactive therapy.File | Dimensione | Formato | |
---|---|---|---|
MAnfredi risk chart.pdf
Accesso riservato
Tipologia:
Versione pubblicata dall'editore
Dimensione
204.4 kB
Formato
Adobe PDF
|
204.4 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris