The Experts below are selected from a list of 31761 Experts worldwide ranked by ideXlab platform
Yvonne Vergouwe - One of the best experts on this subject based on the ideXlab platform.
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progression to microalbuminuria in type 1 diabetes development and validation of a Prediction Rule
Diabetologia, 2010Co-Authors: Yvonne Vergouwe, Janice C. Zgibor, Nish Chaturvedi, Carol Forsblom, David M. Maahs, Per-henrik Groop, Marian Rewers, S S Soedamahmuthu, Janet K Snellbergeon, Trevor J. OrchardAbstract:Aims/hypothesis Microalbuminuria is common in type 1 diabetes and is associated with an increased risk of renal and cardiovascular disease. We aimed to develop and validate a clinical Prediction Rule that estimates the absolute risk of microalbuminuria.
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Progression to microalbuminuria in type 1 diabetes: development and validation of a Prediction Rule.
Diabetologia, 2009Co-Authors: Yvonne Vergouwe, Sabita S. Soedamah-muthu, Janice C. Zgibor, Nish Chaturvedi, Carol Forsblom, Janet K. Snell-bergeon, David M. Maahs, Per-henrik Groop, Marian Rewers, Trevor J. OrchardAbstract:Microalbuminuria is common in type 1 diabetes and is associated with an increased risk of renal and cardiovascular disease. We aimed to develop and validate a clinical Prediction Rule that estimates the absolute risk of microalbuminuria. Data from the European Diabetes Prospective Complications Study (n = 1115) were used to develop the Prediction Rule (development set). Multivariable logistic regression analysis was used to assess the association between potential predictors and progression to microalbuminuria within 7 years. The performance of the Prediction Rule was assessed with calibration and discrimination (concordance statistic [c-statistic]) measures. The Rule was validated in three other diabetes studies (Pittsburgh Epidemiology of Diabetes Complications [EDC] study, Finnish Diabetic Nephropathy [FinnDiane] study and Coronary Artery Calcification in Type 1 Diabetes [CACTI] study). Of patients in the development set, 13% were microalbuminuric after 7 years. Glycosylated haemoglobin, AER, WHR, BMI and ever smoking were found to be the most important predictors. A high-risk group (n = 87 [8%]) was identified with a risk of progression to microalbuminuria of 32%. Predictions showed reasonable discriminative ability, with c-statistic of 0.71. The Rule showed good calibration and discrimination in EDC, FinnDiane and CACTI (c-statistic 0.71, 0.79 and 0.79, respectively). We developed and validated a clinical Prediction Rule that uses relatively easily obtainable patient characteristics to predict microalbuminuria in patients with type 1 diabetes. This Rule can help clinicians to decide on more frequent check-ups for patients at high risk of microalbuminuria in order to prevent long-term chronic complications.
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the risk of severe postoperative pain modification and validation of a clinical Prediction Rule
Anesthesia & Analgesia, 2008Co-Authors: Kristel J.m. Janssen, Diederick E. Grobbee, Karel G.m. Moons, Cor J. Kalkman, Gouke J. Bonsel, Yvonne VergouweAbstract:risk). RESULTS: Modification of the original Rule to enhance Prediction in outpatients included reclassification of the predictor “type of surgery,” addition of the predictor “surgical setting” (ambulatory surgery: yes/no) and addition of interaction terms between surgical setting and the other predictors. One-third of the patients in the Utrecht cohort reported severe postoperative pain (36%), compared to 62% of the patients in the Amsterdam cohort. The distribution of most predictors was similar in the two cohorts, although the patients in the Utrecht cohort were slightly older, more often underwent ambulatory surgery and had large expected incision sizes less often than patients in the Amsterdam cohort. The modified Prediction Rule showed good calibration, when an adjusted intercept was used for the lower incidence in the Utrecht cohort. The discrimination was reasonable (area under the Receiver Operating Characteristic curve 0.65 [95% confidence interval 0.57–0.73]).
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The risk of severe postoperative pain: modification and validation of a clinical Prediction Rule.
Anesthesia and analgesia, 2008Co-Authors: Kristel J.m. Janssen, Diederick E. Grobbee, Karel G.m. Moons, Cor J. Kalkman, Gouke J. Bonsel, Yvonne VergouweAbstract:Recently, a Prediction Rule was developed to preoperatively predict the risk of severe pain in the first postoperative hour in surgical inpatients. We aimed to modify the Rule to enhance its use in both surgical inpatients and outpatients (ambulatory patients). Subsequently, we prospectively tested the modified Rule in patients who underwent surgery later in time and in another hospital (external validation). The Rule was originally developed from the data of 1395 adult inpatients. We modified the Rule with the data of 549 outpatients who underwent surgery between 1997 and 1999 in the same center (Academic Medical Center Amsterdam, The Netherlands). Furthermore, we tested the performance of the modified Rule in 1035 in- and outpatients who underwent surgery in 2004, in the University Medical Center Utrecht, The Netherlands (external validation). Performance was quantified by the Rule's calibration (agreement between observed frequencies and predicted risks) and discrimination (ability to distinguish between patients at high and low risk). Modification of the original Rule to enhance Prediction in outpatients included reclassification of the predictor "type of surgery," addition of the predictor "surgical setting" (ambulatory surgery: yes/no) and addition of interaction terms between surgical setting and the other predictors. One-third of the patients in the Utrecht cohort reported severe postoperative pain (36%), compared to 62% of the patients in the Amsterdam cohort. The distribution of most predictors was similar in the two cohorts, although the patients in the Utrecht cohort were slightly older, more often underwent ambulatory surgery and had large expected incision sizes less often than patients in the Amsterdam cohort. The modified Prediction Rule showed good calibration, when an adjusted intercept was used for the lower incidence in the Utrecht cohort. The discrimination was reasonable (area under the Receiver Operating Characteristic curve 0.65 [95% confidence interval 0.57-0.73]). A previously developed Prediction Rule to predict severe postoperative pain was modified to allow use in both inpatients and outpatients. By validating the Rule in patients who underwent surgery several years later in another hospital, it was shown that the Rule could be generalized in time and place. We demonstrated that, instead of deriving new Prediction Rules for new populations, a simple adjustment may be enough to recalibrate Prediction Rules for new populations. This is in line with the perception that external validation and updating of Prediction Rules is a continuing and multistage process.
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Good generalizability of a Prediction Rule for Prediction of persistent shoulder pain in the short term.
Journal of clinical epidemiology, 2007Co-Authors: Ton Kuijpers, Yvonne Vergouwe, Geert J. M. G. Van Der Heijden, Jos W. R. Twisk, Lex M. Bouter, A. Joan P. Boeke, Daniëlle A.w.m. Van Der WindtAbstract:To evaluate the generalizability of recently developed clinical Prediction Rules for the prognosis of shoulder pain in general practice. A large research program, consisting of a prognostic cohort study and three randomized controlled trials with 6 months follow-up, was carried out in The Netherlands. The clinical Prediction Rules were derived from the results of the prognostic cohort study (n=587). The main outcome measure was persistent symptoms at 6 weeks or 6 months. The control groups of the trials who received usual care were merged (n=212), and used to validate the Prediction Rules by studying calibration and discrimination. The Prediction Rule for short-term outcome showed reasonable calibration and discriminative ability in this validation cohort. The area under the receiver operating characteristic curve (AUC) was 0.72 compared to 0.74 in the derivation cohort. The Prediction Rule for long-term outcome performed less well. Discriminative ability (AUC) decreased to 0.56 in the validation cohort compared to 0.67 in the derivation cohort. The Prediction Rule for the short-term (6 weeks) prognosis showed good generalizability. The Prediction Rule for the long-term prognosis showed poor generalizability.
Dirkjan Van Schaardenburg - One of the best experts on this subject based on the ideXlab platform.
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a Prediction Rule for the development of arthritis in seropositive arthralgia patients
Annals of the Rheumatic Diseases, 2013Co-Authors: Lotte A Van De Stadt, Birgit I. Witte, Wouter H Bos, Dirkjan Van SchaardenburgAbstract:Objective To predict the development of arthritis in anticyclic citrullinated peptide antibodies and/or IgM rheumatoid factor positive (seropositive) arthralgia patients. Methods A Prediction Rule was developed using a prospective cohort of 374 seropositive arthralgia patients, followed for the development of arthritis. The model was created with backward stepwise Cox regression with 18 variables. Results 131 patients (35%) developed arthritis after a median of 12 months. The Prediction model consisted of nine variables: Rheumatoid Arthritis in a first degree family member, alcohol non-use, duration of symptoms Conclusions In patients presenting with seropositive arthralgia, the risk of developing arthritis can be predicted. The Prediction Rule that was made in this patient group can help (1) to inform patients and (2) to select high-risk patients for intervention studies before clinical arthritis occurs.
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A Prediction Rule for the development of arthritis in seropositive arthralgia patients
Annals of the rheumatic diseases, 2012Co-Authors: Lotte A Van De Stadt, Birgit I. Witte, Wouter H Bos, Dirkjan Van SchaardenburgAbstract:To predict the development of arthritis in anticyclic citrullinated peptide antibodies and/or IgM rheumatoid factor positive (seropositive) arthralgia patients. A Prediction Rule was developed using a prospective cohort of 374 seropositive arthralgia patients, followed for the development of arthritis. The model was created with backward stepwise Cox regression with 18 variables. 131 patients (35%) developed arthritis after a median of 12 months. The Prediction model consisted of nine variables: Rheumatoid Arthritis in a first degree family member, alcohol non-use, duration of symptoms <12 months, presence of intermittent symptoms, arthralgia in upper and lower extremities, visual analogue scale pain ≥50, presence of morning stiffness ≥1 h, history of swollen joints as reported by the patient and antibody status. A simplified Prediction Rule was made ranging from 0 to 13 points. The area under the curve value (95% CI) of this Prediction Rule was 0.82 (0.75-0.89) after 5 years. Harrell's C (95% CI) was 0.78 (0.73-0.84). Patients could be categorised in three risk groups: low (0-4 points), intermediate (5-6 points) and high risk (7-13 points). With the low risk group as a reference, the intermediate risk group had a hazard ratio (HR; 95% CI) of 4.52 (2.42-8.77) and the high risk group had a HR of 14.86 (8.40-28.32). In patients presenting with seropositive arthralgia, the risk of developing arthritis can be predicted. The Prediction Rule that was made in this patient group can help (1) to inform patients and (2) to select high-risk patients for intervention studies before clinical arthritis occurs.
Trevor J. Orchard - One of the best experts on this subject based on the ideXlab platform.
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progression to microalbuminuria in type 1 diabetes development and validation of a Prediction Rule
Diabetologia, 2010Co-Authors: Yvonne Vergouwe, Janice C. Zgibor, Nish Chaturvedi, Carol Forsblom, David M. Maahs, Per-henrik Groop, Marian Rewers, S S Soedamahmuthu, Janet K Snellbergeon, Trevor J. OrchardAbstract:Aims/hypothesis Microalbuminuria is common in type 1 diabetes and is associated with an increased risk of renal and cardiovascular disease. We aimed to develop and validate a clinical Prediction Rule that estimates the absolute risk of microalbuminuria.
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Progression to microalbuminuria in type 1 diabetes: development and validation of a Prediction Rule.
Diabetologia, 2009Co-Authors: Yvonne Vergouwe, Sabita S. Soedamah-muthu, Janice C. Zgibor, Nish Chaturvedi, Carol Forsblom, Janet K. Snell-bergeon, David M. Maahs, Per-henrik Groop, Marian Rewers, Trevor J. OrchardAbstract:Microalbuminuria is common in type 1 diabetes and is associated with an increased risk of renal and cardiovascular disease. We aimed to develop and validate a clinical Prediction Rule that estimates the absolute risk of microalbuminuria. Data from the European Diabetes Prospective Complications Study (n = 1115) were used to develop the Prediction Rule (development set). Multivariable logistic regression analysis was used to assess the association between potential predictors and progression to microalbuminuria within 7 years. The performance of the Prediction Rule was assessed with calibration and discrimination (concordance statistic [c-statistic]) measures. The Rule was validated in three other diabetes studies (Pittsburgh Epidemiology of Diabetes Complications [EDC] study, Finnish Diabetic Nephropathy [FinnDiane] study and Coronary Artery Calcification in Type 1 Diabetes [CACTI] study). Of patients in the development set, 13% were microalbuminuric after 7 years. Glycosylated haemoglobin, AER, WHR, BMI and ever smoking were found to be the most important predictors. A high-risk group (n = 87 [8%]) was identified with a risk of progression to microalbuminuria of 32%. Predictions showed reasonable discriminative ability, with c-statistic of 0.71. The Rule showed good calibration and discrimination in EDC, FinnDiane and CACTI (c-statistic 0.71, 0.79 and 0.79, respectively). We developed and validated a clinical Prediction Rule that uses relatively easily obtainable patient characteristics to predict microalbuminuria in patients with type 1 diabetes. This Rule can help clinicians to decide on more frequent check-ups for patients at high risk of microalbuminuria in order to prevent long-term chronic complications.
Kristel J.m. Janssen - One of the best experts on this subject based on the ideXlab platform.
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the risk of severe postoperative pain modification and validation of a clinical Prediction Rule
Anesthesia & Analgesia, 2008Co-Authors: Kristel J.m. Janssen, Diederick E. Grobbee, Karel G.m. Moons, Cor J. Kalkman, Gouke J. Bonsel, Yvonne VergouweAbstract:risk). RESULTS: Modification of the original Rule to enhance Prediction in outpatients included reclassification of the predictor “type of surgery,” addition of the predictor “surgical setting” (ambulatory surgery: yes/no) and addition of interaction terms between surgical setting and the other predictors. One-third of the patients in the Utrecht cohort reported severe postoperative pain (36%), compared to 62% of the patients in the Amsterdam cohort. The distribution of most predictors was similar in the two cohorts, although the patients in the Utrecht cohort were slightly older, more often underwent ambulatory surgery and had large expected incision sizes less often than patients in the Amsterdam cohort. The modified Prediction Rule showed good calibration, when an adjusted intercept was used for the lower incidence in the Utrecht cohort. The discrimination was reasonable (area under the Receiver Operating Characteristic curve 0.65 [95% confidence interval 0.57–0.73]).
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The risk of severe postoperative pain: modification and validation of a clinical Prediction Rule.
Anesthesia and analgesia, 2008Co-Authors: Kristel J.m. Janssen, Diederick E. Grobbee, Karel G.m. Moons, Cor J. Kalkman, Gouke J. Bonsel, Yvonne VergouweAbstract:Recently, a Prediction Rule was developed to preoperatively predict the risk of severe pain in the first postoperative hour in surgical inpatients. We aimed to modify the Rule to enhance its use in both surgical inpatients and outpatients (ambulatory patients). Subsequently, we prospectively tested the modified Rule in patients who underwent surgery later in time and in another hospital (external validation). The Rule was originally developed from the data of 1395 adult inpatients. We modified the Rule with the data of 549 outpatients who underwent surgery between 1997 and 1999 in the same center (Academic Medical Center Amsterdam, The Netherlands). Furthermore, we tested the performance of the modified Rule in 1035 in- and outpatients who underwent surgery in 2004, in the University Medical Center Utrecht, The Netherlands (external validation). Performance was quantified by the Rule's calibration (agreement between observed frequencies and predicted risks) and discrimination (ability to distinguish between patients at high and low risk). Modification of the original Rule to enhance Prediction in outpatients included reclassification of the predictor "type of surgery," addition of the predictor "surgical setting" (ambulatory surgery: yes/no) and addition of interaction terms between surgical setting and the other predictors. One-third of the patients in the Utrecht cohort reported severe postoperative pain (36%), compared to 62% of the patients in the Amsterdam cohort. The distribution of most predictors was similar in the two cohorts, although the patients in the Utrecht cohort were slightly older, more often underwent ambulatory surgery and had large expected incision sizes less often than patients in the Amsterdam cohort. The modified Prediction Rule showed good calibration, when an adjusted intercept was used for the lower incidence in the Utrecht cohort. The discrimination was reasonable (area under the Receiver Operating Characteristic curve 0.65 [95% confidence interval 0.57-0.73]). A previously developed Prediction Rule to predict severe postoperative pain was modified to allow use in both inpatients and outpatients. By validating the Rule in patients who underwent surgery several years later in another hospital, it was shown that the Rule could be generalized in time and place. We demonstrated that, instead of deriving new Prediction Rules for new populations, a simple adjustment may be enough to recalibrate Prediction Rules for new populations. This is in line with the perception that external validation and updating of Prediction Rules is a continuing and multistage process.
Pedro Pablo España - One of the best experts on this subject based on the ideXlab platform.
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development and validation of a clinical Prediction Rule for severe community acquired pneumonia
American Journal of Respiratory and Critical Care Medicine, 2006Co-Authors: Pedro Pablo España, Alberto Capelastegui, Inmaculada Gorordo, Amaia Bilbao, Cristobal Esteban, Mikel Oribe, Miguel Ortega, J M QuintanaAbstract:Rationale: Objective strategies are needed to improve the diagnosis of severe community-acquired pneumonia in the emergency department setting. Objectives: To develop and validate a clinical Prediction Rule for identifying patients with severe community-acquired pneumonia, comparing it with other prognostic Rules. Methods: Data collected from clinical information and physical examination of 1,057 patients visiting the emergency department of a hospital were used to derive a clinical Prediction Rule, which was then validated in two different populations: 719 patients from the same center and 1,121 patients from four other hospitals. Measurements and Main Results: Inthemultivariateanalyses,eightindependent predictive factors were correlated with severe communityacquired pneumonia: arterial pH 7.30, systolic blood pressure 90mmHg,respiratoryrate30breaths/min,alteredmentalstatus, blood urea nitrogen 30 mg/dl, oxygen arterial pressure 54 mm Hg or ratio of arterial oxygen tension to fraction of inspired oxygen 250 mm Hg, age 80 yr, and multilobar/bilateral lung affectation. From the parameter obtained in the multivariate model, a score was assigned to each predictive variable. The model shows an area under the curve of 0.92. This Rule proved better at identifying patients evolving toward severe community-acquired
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A Prediction Rule to identify allocation of inpatient care in community-acquired pneumonia.
The European respiratory journal, 2003Co-Authors: Pedro Pablo España, Alberto Capelastegui, José M. Quintana, A. Soto, Inmaculada Gorordo, M. García-urbaneja, Amaia BilbaoAbstract:The current authors developed a new Prediction Rule based on the five risk classes defined by the Pneumonia Severity Index to identify allocation of inpatient care in community-acquired pneumonia. The decision to hospitalise in low-risk classes (I−III) was unquestionable, if the presence of one or more of the following were evident: arterial oxygen tension The results at 18 months after implementation of this new Prediction Rule are reported in a series of 616 patients. The mortality rate was 0.5% in 221 patients treated as outpatients versus 8.9% in 395 patients treated as inpatients. Specific additional criteria for hospitalisation included in the Prediction Rule were present in 106 of the 178 low-risk patients treated as inpatients, whereas in the remaining 72, the decision to hospitalise was apparently unjustified by the Prediction Rule. These 72 patients showed a better outcome (significantly shorter hospitalisation, days on intravenous antibiotics, mortality, and complicated course) than high-risk patients and low-risk patients who met the additional specific criteria for deciding hospital admission. Therefore, admission in these low-risk patients might have been avoided by strict adherence to the new Prediction Rule. Another relevant finding was that the Pneumonia Severity Index alone did not identify all patients who needed to be admitted to the hospital.