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Clinical Prediction Rule (clinical + prediction_rule)
Selected AbstractsClinical prediction rule to diagnose post-infectious bronchiolitis obliterans in childrenPEDIATRIC PULMONOLOGY, Issue 11 2009Alejandro J. Colom Abstract Rationale Infant pulmonary function testing has a great value in the diagnosis of post-infectious bronchiolitis obliterans (BOs), because of characteristic patterns of severe and fixed airway obstruction. Unfortunately, infant pulmonary function testing is not available in most pediatric pulmonary centers. Objective To develop and validate a clinical prediction rule (BO-Score) to diagnose children under 2 years of age with BOs, using multiple objectively measured parameters readily available in most medical centers. Methods Study subjects, children under 2 years old with a chronic pulmonary disease assisted at R. Gutierrez Children's Hospital of Buenos Aires. Patients were randomly divided into a derivation (66%) and a validation (34%) set. ROC analyses and multivariable logistic regression included significant clinical, radiological, and laboratory predictors. The main outcome measure was a diagnosis of BOs. The performance of the BO-Score was tested on the validation set. Results Hundred twenty-five patients were included, 83 in the derivation set and 42 in the validation set. The BO-Score (area under ROC curve,=,0.96; 95% CI, 0.9,1.0%) was developed by assigning points to the following variables: typical clinical history (four points), adenovirus infection (three points), and high-resolution computed tomography with mosaic perfusion (four points). A Score ,7 predicted the diagnosis of BOs with a specificity of 100% (95% CI, 79,100%) and a sensitivity of 67% (95% CI, 47,80%). Conclusions The BO-Score is a simple-to-use clinical prediction rule, based on variables that are readily available. A BO-Score of 7 or more predicts a diagnosis of post-infectious BOs with high accuracy. Pediatr Pulmonol. 2009; 44:1065,1069. ©2009 Wiley-Liss, Inc. [source] Clinical prediction rules for bacteremia and in-hospital death based on clinical data at the time of blood withdrawal for culture: an evaluation of their development and useJOURNAL OF EVALUATION IN CLINICAL PRACTICE, Issue 6 2006Tsukasa Nakamura MD (Research Fellow) Abstract Rationale, aims and objectives, To develop clinical prediction rules for true bacteremia, blood culture positive for gram-negative rods, and in-hospital death using the data at the time of blood withdrawal for culture. Methods, Data on all hospitalized adults who underwent blood cultures at a tertiary care hospital in Japan were collected from an integrated medical computing system. Logistic regression was used for developing prediction rules followed by the jackknife cross validation. Results, Among 739 patients, 144 (19.5%) developed true bacteremia, 66 (8.9) were positive for gram-negative rods, and 203 (27.5%) died during hospitalization. Prediction rule based on the data at the time of blood withdrawal for culture stratified them into five groups with probabilities of true bacteremia 6.5, 9.6, 21.9, 30.1, and 59.6%. For blood culture positive for gram-negative rods, the probabilities were 0.6, 4.7, 8.6, and 31.7%, and for in-hospital death, those were 6.7, 15.5, 26.0, 35.5, and 56.1%. The area of receiver operating characteristic for true bacteremia, blood culture positive for gram-negative rods, and in-hospital death were 0.73, 0.64, and 0.64, respectively, in original cohort and 0.72, 0.64, and 0.64 in validation respectively. Conclusions, The clinical prediction rules are helpful for improved clinical decision making for bacteremia patients. [source] Can a spinal manipulation clinical prediction rule improve decision making for patients with low back pain?FOCUS ON ALTERNATIVE AND COMPLEMENTARY THERAPIES AN EVIDENCE-BASED APPROACH, Issue 4 2005Article first published online: 14 JUN 2010 [source] Emergency Physicians' Risk Attitudes in Acute Decompensated Heart Failure PatientsACADEMIC EMERGENCY MEDICINE, Issue 1 2010Julie B. McCausland MD Abstract Objectives:, Despite the existence of various clinical prediction rules, no data exist defining what frequency of death or serious nonfatal outcomes comprises a realistic "low-risk" group for clinicians. This exploratory study sought to identify emergency physicians' (EPs) definition of low-risk acute decompensated heart failure (ADHF) emergency department (ED) patients. Methods:, Surveys were mailed to full-time physicians (n = 88) in a multihospital EP group in southwestern Pennsylvania between December 2004 and February 2005. Participation was voluntary, and each EP was asked to define low risk (low risk of all-cause 30-day death and low risk of either hospital death or other serious medical complications) and choose a risk threshold at which they might consider outpatient management for those with ADHF. A range of choices was offered (<0.5, <1, <2, <3, <4, and <5%), and demographic data were collected. Results:, The response rate was 80%. Physicians defined low risk both for all-cause 30-day death and for hospital death or other serious complications, at <1% (38.8 and 40.3%, respectively). The decision threshold to consider outpatient therapy was <0.5% risk both for all-cause 30-day death (44.6%) and for hospital death or serious medical complications (44.4%). Conclusions:, Emergency physicians in this exploratory study define low-risk ADHF patients as having less than a 1% risk of 30-day death or inpatient death or complications. They state a desire to have and use an ADHF clinical prediction rule that can identify low-risk ADHF patients who have less than a 0.5% risk of 30-day death or inpatient death or complications. ACADEMIC EMERGENCY MEDICINE 2010; 17:108,110 © 2010 by the Society for Academic Emergency Medicine [source] Clinical prediction rule to diagnose post-infectious bronchiolitis obliterans in childrenPEDIATRIC PULMONOLOGY, Issue 11 2009Alejandro J. Colom Abstract Rationale Infant pulmonary function testing has a great value in the diagnosis of post-infectious bronchiolitis obliterans (BOs), because of characteristic patterns of severe and fixed airway obstruction. Unfortunately, infant pulmonary function testing is not available in most pediatric pulmonary centers. Objective To develop and validate a clinical prediction rule (BO-Score) to diagnose children under 2 years of age with BOs, using multiple objectively measured parameters readily available in most medical centers. Methods Study subjects, children under 2 years old with a chronic pulmonary disease assisted at R. Gutierrez Children's Hospital of Buenos Aires. Patients were randomly divided into a derivation (66%) and a validation (34%) set. ROC analyses and multivariable logistic regression included significant clinical, radiological, and laboratory predictors. The main outcome measure was a diagnosis of BOs. The performance of the BO-Score was tested on the validation set. Results Hundred twenty-five patients were included, 83 in the derivation set and 42 in the validation set. The BO-Score (area under ROC curve,=,0.96; 95% CI, 0.9,1.0%) was developed by assigning points to the following variables: typical clinical history (four points), adenovirus infection (three points), and high-resolution computed tomography with mosaic perfusion (four points). A Score ,7 predicted the diagnosis of BOs with a specificity of 100% (95% CI, 79,100%) and a sensitivity of 67% (95% CI, 47,80%). Conclusions The BO-Score is a simple-to-use clinical prediction rule, based on variables that are readily available. A BO-Score of 7 or more predicts a diagnosis of post-infectious BOs with high accuracy. Pediatr Pulmonol. 2009; 44:1065,1069. ©2009 Wiley-Liss, Inc. [source] Development and validation of a clinical prediction rule to distinguish bacterial from viral pneumonia in childrenPEDIATRIC PULMONOLOGY, Issue 5 2006Article first published online: 28 MAR 200 The original article to which this Erratum refers was published in Pediatric Pulmonology 2006; 41(4): 331,337. DOI 10.1002/ppul20364. [source] Role of ,atypical pathogens' among adult hospitalized patients with community-acquired pneumoniaRESPIROLOGY, Issue 8 2009Grace LUI ABSTRACT Background and objective: Agents such as Mycoplasma pneumoniae, Chlamydophila pneumoniae and Legionella pneumophila are recognized as important causes of community-acquired pneumonia (CAP) worldwide. This study examined the role of these ,atypical pathogens' (AP) among adult hospitalized patients with CAP. Methods: A prospective, observational study of consecutive adult CAP (clinico-radiological diagnosis) patients hospitalized during 2004,2005 was conducted. Causal organisms were determined using cultures, antigen testing and paired serology. Clinical/laboratory/radiological variables and outcomes were compared between different aetiologies, and a clinical prediction rule for AP was constructed. Results: There were 1193 patients studied (mean age 70.8 ± 18.0 years, men 59.3%). Causal organisms were identified in 468 (39.2%) patients: ,bacterial' (48.7%), ,viral' (26.9%), ,AP' (28.6%). The AP infections comprised Mycoplasma or Chlamydophila pneumoniae (97.8%) and co-infection with bacteria/virus (30.6%). The majority of AP infections involved elderly patients (63.4%) with comorbidities (41.8%), and more than one-third of patients were classified as ,intermediate' or ,high' risk CAP on presentation (pneumonia severity index IV,V (35.1%); CURB-65 2,5 (42.5%)). Patients with AP infections had disease severities and outcomes similar to patients with CAP due to other organisms (oxygen therapy 29.1% vs 29.8%; non-invasive ventilation 3.7% vs 3.3%; admission to the intensive care unit 4.5% vs 2.7%; length of hospitalization 6 day vs 7 day; 30-day mortality: 2.2% vs 6.0%; overall P > 0.05). Age <65 years, female gender, fever ,38.0°C, respiratory rate <25/min, pulse rate <100/min, serum sodium >130 mmol/L, leucocyte count <11 × 109/L and Hb < 11 g/dL were features associated with AP infection, but the derived prediction rule failed to reliably discriminate CAP caused by AP from bacterial CAP (area under the curve 0.75). Conclusions: M. pneumoniae and C. pneumoniae as single/co-pathogens are important causes of severe pneumonia among older adults. No reliable clinical indicators exist, so empirical antibiotic coverage for hospitalized CAP patients may need to be considered. [source] Estimating the risk of gestational diabetes mellitus: a clinical prediction model based on patient characteristics and medical historyBJOG : AN INTERNATIONAL JOURNAL OF OBSTETRICS & GYNAECOLOGY, Issue 1 2010M Van Leeuwen Objective, To develop a clinical prediction rule that can help the clinician to identify women at high and low risk for gestational diabetes mellitus (GDM) early in pregnancy in order to improve the efficiency of GDM screening. Design, We used data from a prospective cohort study to develop the clinical prediction rule. Setting, The original cohort study was conducted in a university hospital in the Netherlands. Population, Nine hundred and ninety-five consecutive pregnant women underwent screening for GDM. Methods, Using multiple logistic regression analysis, we constructed a model to estimate the probability of development of GDM from the medical history and patient characteristics. Receiver operating characteristics analysis and calibration were used to assess the accuracy of the model. Main outcome measure, The development of a clinical prediction rule for GDM. We also evaluated the potential of the prediction rule to improve the efficiency of GDM screening. Results, The probability of the development of GDM could be predicted from the ethnicity, family history, history of GDM and body mass index. The model had an area under the receiver operating characteristic curve of 0.77 (95% CI 0.69,0.85) and calibration was good (Hosmer and Lemeshow test statistic, P = 0.25). If an oral glucose tolerance test was performed in all women with a predicted probability of 2% or more, 43% of all women would be tested and 75% of the women with GDM would be identified. Conclusions, The use of a clinical prediction model is an accurate method to identify women at increased risk for GDM, and could be used to select women for additional testing for GDM. [source] Prediction rules for computed tomography in the dementia assessment: do they predict clinical utility of CT?INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Issue 4 2003Kelly A. Condefer Abstract Neuroimaging is widely employed in the dementia assessment in refining clinical decision-making. However, with rising interest in cost-effective medical practice, efforts have been made in the literature to define clinical prediction rules that select for a subgroup of patients who would most likely benefit from neuroimaging. This short study examined the ability of a group of published clinical predictors to identify patients whose diagnoses or management would be influenced by CT scan results. The study finds that none of the published predictors bears a significant relationship to actual influence of CT scans in a group of memory clinic patients, highlighting the need for the development of clinical predictors for neuroimaging that will impact patient care. Copyright © 2003 John Wiley & Sons, Ltd. [source] Clinical prediction rules for bacteremia and in-hospital death based on clinical data at the time of blood withdrawal for culture: an evaluation of their development and useJOURNAL OF EVALUATION IN CLINICAL PRACTICE, Issue 6 2006Tsukasa Nakamura MD (Research Fellow) Abstract Rationale, aims and objectives, To develop clinical prediction rules for true bacteremia, blood culture positive for gram-negative rods, and in-hospital death using the data at the time of blood withdrawal for culture. Methods, Data on all hospitalized adults who underwent blood cultures at a tertiary care hospital in Japan were collected from an integrated medical computing system. Logistic regression was used for developing prediction rules followed by the jackknife cross validation. Results, Among 739 patients, 144 (19.5%) developed true bacteremia, 66 (8.9) were positive for gram-negative rods, and 203 (27.5%) died during hospitalization. Prediction rule based on the data at the time of blood withdrawal for culture stratified them into five groups with probabilities of true bacteremia 6.5, 9.6, 21.9, 30.1, and 59.6%. For blood culture positive for gram-negative rods, the probabilities were 0.6, 4.7, 8.6, and 31.7%, and for in-hospital death, those were 6.7, 15.5, 26.0, 35.5, and 56.1%. The area of receiver operating characteristic for true bacteremia, blood culture positive for gram-negative rods, and in-hospital death were 0.73, 0.64, and 0.64, respectively, in original cohort and 0.72, 0.64, and 0.64 in validation respectively. Conclusions, The clinical prediction rules are helpful for improved clinical decision making for bacteremia patients. [source] Emergency Physicians' Risk Attitudes in Acute Decompensated Heart Failure PatientsACADEMIC EMERGENCY MEDICINE, Issue 1 2010Julie B. McCausland MD Abstract Objectives:, Despite the existence of various clinical prediction rules, no data exist defining what frequency of death or serious nonfatal outcomes comprises a realistic "low-risk" group for clinicians. This exploratory study sought to identify emergency physicians' (EPs) definition of low-risk acute decompensated heart failure (ADHF) emergency department (ED) patients. Methods:, Surveys were mailed to full-time physicians (n = 88) in a multihospital EP group in southwestern Pennsylvania between December 2004 and February 2005. Participation was voluntary, and each EP was asked to define low risk (low risk of all-cause 30-day death and low risk of either hospital death or other serious medical complications) and choose a risk threshold at which they might consider outpatient management for those with ADHF. A range of choices was offered (<0.5, <1, <2, <3, <4, and <5%), and demographic data were collected. Results:, The response rate was 80%. Physicians defined low risk both for all-cause 30-day death and for hospital death or other serious complications, at <1% (38.8 and 40.3%, respectively). The decision threshold to consider outpatient therapy was <0.5% risk both for all-cause 30-day death (44.6%) and for hospital death or serious medical complications (44.4%). Conclusions:, Emergency physicians in this exploratory study define low-risk ADHF patients as having less than a 1% risk of 30-day death or inpatient death or complications. They state a desire to have and use an ADHF clinical prediction rule that can identify low-risk ADHF patients who have less than a 0.5% risk of 30-day death or inpatient death or complications. ACADEMIC EMERGENCY MEDICINE 2010; 17:108,110 © 2010 by the Society for Academic Emergency Medicine [source] | ||||||||||