Construction and validation of a model to predict nonadherence to guidelines for prescribing antiplatelet therapy to hypertensive patients

Palazón‐Bru, Antonio; Martínez-Orozco, M J; Perseguer-Torregrosa, Zeneida; Sepehri, Armina; Rosa, David Manuel Folgado-de la; Orozco-Beltrán, Domingo; Carratalá‐Munuera, Concepción; Gil-Guillén, Vicente Francisco

doi:10.1185/03007995.2015.1030377

Cited by 11 publications

(16 citation statements)

References 21 publications

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“…1. In the model we selected the three most clinically relevant variables, as we had 35 outcomes in our sample (Alasil et al, 2010; Buabbud, Al-latayfeh & Sun, 2010; Hermann et al, 2014; Varma et al, 2014; Palazón-Bru et al, 2015). …”

Section: Resultsmentioning

confidence: 99%

“…There are, too, non-significant variables in the model constructed. However, as in other studies, we have to consider that we are evaluating the model overall and not variable by variable, that is the goodness of fit (likelihood ratio test = 53.4, p < 0.001; Nagelkerke R 2 = 0.583) and the AUC in the validation sample (0.90) (Palazón-Bru et al, 2015). As this model was validated in diabetic patients who had already been referred to the specialist, further studies are needed in the general diabetic population to determine if it is applicable as a screening test for all diabetic patients when they attend their primary health care center (selection bias).…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, calculating the AUC indicates the discriminating power of our model. A similar methodology has been used in other studies (Palazón-Bru et al, 2015). …”

Section: Methodsmentioning

confidence: 99%

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A predictive screening tool to detect diabetic retinopathy or macular edema in primary health care: construction, validation and implementation on a mobile application

Azrak

Palazón‐Bru

Baeza-Díaz

et al. 2015

PeerJ

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The most described techniques used to detect diabetic retinopathy and diabetic macular edema have to be interpreted correctly, such that a person not specialized in ophthalmology, as is usually the case of a primary care physician, may experience difficulties with their interpretation; therefore we constructed, validated and implemented as a mobile app a new tool to detect diabetic retinopathy or diabetic macular edema (DRDME) using simple objective variables. We undertook a cross-sectional, observational study of a sample of 142 eyes from Spanish diabetic patients suspected of having DRDME in 2012–2013. Our outcome was DRDME and the secondary variables were: type of diabetes, gender, age, glycated hemoglobin (HbA1c), foveal thickness and visual acuity (best corrected). The sample was divided into two parts: 80% to construct the tool and 20% to validate it. A binary logistic regression model was used to predict DRDME. The resulting model was transformed into a scoring system. The area under the ROC curve (AUC) was calculated and risk groups established. The tool was validated by calculating the AUC and comparing expected events with observed events. The construction sample (n = 106) had 35 DRDME (95% CI [24.1–42.0]), and the validation sample (n = 36) had 12 DRDME (95% CI [17.9–48.7]). Factors associated with DRDME were: HbA1c (per 1%) (OR = 1.36, 95% CI [0.93–1.98], p = 0.113), foveal thickness (per 1 µm) (OR = 1.03, 95% CI [1.01–1.04], p < 0.001) and visual acuity (per unit) (OR = 0.14, 95% CI [0.00–0.16], p < 0.001). AUC for the validation: 0.90 (95% CI [0.75–1.00], p < 0.001). No significant differences were found between the expected and the observed outcomes (p = 0.422). In conclusion, we constructed and validated a simple rapid tool to determine whether a diabetic patient suspected of having DRDME really has it. This tool has been implemented on a mobile app. Further validation studies are required in the general diabetic population.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A predictive screening tool to detect diabetic retinopathy or macular edema in primary health care: construction, validation and implementation on a mobile application

Azrak

Palazón‐Bru

Baeza-Díaz

et al. 2015

PeerJ

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“…1. In the model we selected the three most clinically relevant variables, as we had 35 outcomes in our sample (Alasil et al, 2010;Buabbud, Al-latayfeh & Sun, 2010;Hermann et al, 2014;Varma et al, 2014;Palazón-Bru et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Peer Review #1 of "A predictive screening tool to detect diabetic retinopathy or macular edema in primary health care: construction, validation and implementation on a mobile application (v0.1)"

2015

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The most described techniques used to detect diabetic retinopathy and diabetic macular edema have to be interpreted correctly, such that a person not specialized in ophthalmology, as is usually the case of a primary care physician, may experience difficulties with their interpretation; therefore we constructed, validated and implemented as a mobile app a new tool to detect diabetic retinopathy or diabetic macular edema (DRDME) using simple objective variables. We undertook a cross-sectional, observational study of a sample of 142 eyes from Spanish diabetic patients suspected of having DRDME in 2012-2013. Our outcome was DRDME and the secondary variables were: type of diabetes, gender, age, HbA1c, foveal thickness and visual acuity (best corrected). The sample was divided into two parts: 80% to construct the tool and 20% to validate it. A binary logistic regression model was used to predict DRDME. The resulting model was transformed into a scoring system. The area under the ROC curve (AUC) was calculated and risk groups established. The tool was validated by calculating the AUC and comparing expected events with observed events. The construction sample (n=106) had 35 DRDME (95% CI: 24.1-42.0%), and the validation sample (n=36) had 12 DRDME (95% CI: 17.9-48.7%). Factors associated with DRDME were: HbA1c (per 1%) (OR=1.36, 95% CI: 0.93-1.98, p=0.113), foveal thickness (per 1 μm) (OR=1.03, 95% CI: 1.01-1.04, p<0.001) and visual acuity (per unit) (OR=0.14, 95% CI: 0.00-0.16, p<0.001). AUC for the validation: 0.90 (95% CI: 0.75-1.00, p<0.001). No significant differences were found between the expected and the observed outcomes (p=0.422). In conclusion, we constructed and validated a simple rapid tool to determine whether a diabetic patient suspected of having DRDME really has it. This tool has been implemented on a mobile app. Further validation studies are required in the general diabetic population.

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“…The model was adapted to a points system through the methodology of the Framingham study (Sullivan, Massaro & D’Agostino, 2004), which weights coefficients determining an associated score for each variable and thus can be used in routine clinical practice. This methodology has been applied to various areas of knowledge in the field of medicine (Artigao-Ródenas et al, 2015; Azrak et al, 2015; Gutiérrez-Gómez, Cortés & Palazón-Bru, 2015; López-Bru et al, 2015; Palazón-Bru et al, 2015; Ramírez-Prado et al, 2015a). After calculating the score associated with each patient, the ROC curve was calculated and the cut-off point was established as the one that minimized the square root of (1-Sensitivity) 2 +(1-Specificity) 2 (Hanley & McNeil, 1982).…”

Section: Methodsmentioning

confidence: 99%

Construction, internal validation and implementation in a mobile application of a scoring system to predict nonadherence to proton pump inhibitors

Mares-García

Palazón‐Bru

Rosa

et al. 2017

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BackgroundOther studies have assessed nonadherence to proton pump inhibitors (PPIs), but none has developed a screening test for its detection.ObjectivesTo construct and internally validate a predictive model for nonadherence to PPIs.MethodsThis prospective observational study with a one-month follow-up was carried out in 2013 in Spain, and included 302 patients with a prescription for PPIs. The primary variable was nonadherence to PPIs (pill count). Secondary variables were gender, age, antidepressants, type of PPI, non-guideline-recommended prescription (NGRP) of PPIs, and total number of drugs. With the secondary variables, a binary logistic regression model to predict nonadherence was constructed and adapted to a points system. The ROC curve, with its area (AUC), was calculated and the optimal cut-off point was established. The points system was internally validated through 1,000 bootstrap samples and implemented in a mobile application (Android).ResultsThe points system had three prognostic variables: total number of drugs, NGRP of PPIs, and antidepressants. The AUC was 0.87 (95% CI [0.83–0.91], p < 0.001). The test yielded a sensitivity of 0.80 (95% CI [0.70–0.87]) and a specificity of 0.82 (95% CI [0.76–0.87]). The three parameters were very similar in the bootstrap validation.ConclusionsA points system to predict nonadherence to PPIs has been constructed, internally validated and implemented in a mobile application. Provided similar results are obtained in external validation studies, we will have a screening tool to detect nonadherence to PPIs.

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Construction and validation of a model to predict nonadherence to guidelines for prescribing antiplatelet therapy to hypertensive patients

Abstract: A tool was constructed and validated to predict NGAT. The associated factors were related with a greater cardiovascular risk. The scoring system has to be validated in other areas.

Cited by 11 publications

References 21 publications

A predictive screening tool to detect diabetic retinopathy or macular edema in primary health care: construction, validation and implementation on a mobile application

A predictive screening tool to detect diabetic retinopathy or macular edema in primary health care: construction, validation and implementation on a mobile application

Peer Review #1 of "A predictive screening tool to detect diabetic retinopathy or macular edema in primary health care: construction, validation and implementation on a mobile application (v0.1)"

Construction, internal validation and implementation in a mobile application of a scoring system to predict nonadherence to proton pump inhibitors

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