scholarly journals The diagnosis of tuberculous meningitis in adults and adolescents: protocol for a systematic review and individual patient data meta-analysis to inform a multivariable prediction model

2019 ◽  
Vol 4 ◽  
pp. 19
Author(s):  
Tom Boyles ◽  
Anna Stadelman ◽  
Jayne P. Ellis ◽  
Fiona V. Cresswell ◽  
Vittoria Lutje ◽  
...  
Keyword(s):  
Systematic Review ◽  
Prediction Model ◽  
Tuberculous Meningitis ◽  
Latent Variables ◽  
Individual Patient Data ◽  
External Validation ◽  
Patient Data ◽  
Nucleic Acid Amplification ◽  
Validation Data ◽  
Data Set

Background: Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnosis and treatment, which is a risk factor for poor outcome, is caused in part by lack of availability of diagnostic tests that are both rapid and accurate. Several attempts have been made to develop clinical scoring systems to fill this gap, but none have performed sufficiently well to be broadly implemented. We aim to identify and validate a set of clinical predictors that accurately classify TBM using individual patient data (IPD) from published studies. Methods: We will perform a systematic review and obtain IPD from studies published from the year 1990 which undertook diagnostic testing for TBM in adolescents or adults using at least one of, microscopy for acid-fast bacilli, commercial nucleic acid amplification test for Mycobacterium tuberculosis or mycobacterial culture of cerebrospinal fluid.  Clinical data that have previously been shown to be associated with TBM, and can inform the final diagnosis, will be requested. The data-set will be divided into training and test/validation data-sets for model building. A predictive logistic model will be built using a training set with patients with definite TBM and no TBM. Should it be warranted, factor analysis may be employed, depending on evidence for multicollinearity or the case for including latent variables in the model. Discussion: We will systematically identify and extract key clinical parameters associated with TBM from published studies and use a ‘big data’ approach to develop and validate a clinical prediction model with enhanced generalisability. The final model will be made available through a smartphone application. Further work will be external validation of the model and test of efficacy in a randomised controlled trial.

scholarly journals The diagnosis of tuberculous meningitis in adults and adolescents: protocol for a systematic review and individual patient data meta-analysis to inform a multivariable prediction model

2019 ◽  
Vol 4 ◽  
pp. 19 ◽  
Author(s):  
Tom Boyles ◽  
Anna Stadelman ◽  
Jayne P. Ellis ◽  
Fiona V. Cresswell ◽  
Vittoria Lutje ◽  
...  
Keyword(s):  
Systematic Review ◽  
Prediction Model ◽  
Tuberculous Meningitis ◽  
Latent Variables ◽  
Individual Patient Data ◽  
External Validation ◽  
Patient Data ◽  
Nucleic Acid Amplification ◽  
Validation Data ◽  
Data Set

Background: Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnosis and treatment, which is a risk factor for poor outcome, is caused in part by lack of availability of diagnostic tests that are both rapid and accurate. Several attempts have been made to develop clinical scoring systems to fill this gap, but none have performed sufficiently well to be broadly implemented. We aim to identify and validate a set of clinical predictors that accurately classify TBM using individual patient data (IPD) from published studies. Methods: We will perform a systematic review and obtain IPD from studies published from the year 1990 which undertook diagnostic testing for TBM in adolescents or adults using at least one of, microscopy for acid-fast bacilli, commercial nucleic acid amplification test for Mycobacterium tuberculosis or mycobacterial culture of cerebrospinal fluid.  Clinical data that have previously been shown to be associated with TBM, and can inform the final diagnosis, will be requested. The data-set will be divided into training and test/validation data-sets for model building. A predictive logistic model will be built using a training set with patients with definite TBM and no TBM. Should it be warranted, factor analysis may be employed, depending on evidence for multicollinearity or the case for including latent variables in the model. Discussion: We will systematically identify and extract key clinical parameters associated with TBM from published studies and use a ‘big data’ approach to develop and validate a clinical prediction model with enhanced generalisability. The final model will be made available through a smartphone application. Further work will be external validation of the model and test of efficacy in a randomised controlled trial.

scholarly journals The diagnosis of tuberculous meningitis in adults and adolescents: protocol for a systematic review and individual patient data meta-analysis to inform a multivariable prediction model

2021 ◽  
Vol 4 ◽  
pp. 19
Author(s):  
Tom Boyles ◽  
Anna Stadelman ◽  
Jayne P. Ellis ◽  
Fiona V. Cresswell ◽  
Vittoria Lutje ◽  
...  
Keyword(s):  
Systematic Review ◽  
Prediction Model ◽  
Tuberculous Meningitis ◽  
Latent Variables ◽  
Individual Patient Data ◽  
External Validation ◽  
Patient Data ◽  
Nucleic Acid Amplification ◽  
Validation Data ◽  
Data Set

Background: Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnosis and treatment, which is a risk factor for poor outcome, is caused in part by lack of availability of diagnostic tests that are both rapid and accurate. Several attempts have been made to develop clinical scoring systems to fill this gap, but none have performed sufficiently well to be broadly implemented. We aim to identify and validate a set of clinical predictors that accurately classify TBM using individual patient data (IPD) from published studies. Methods: We will perform a systematic review and obtain IPD from studies published from the year 1990 which undertook diagnostic testing for TBM in adolescents or adults using at least one of, microscopy for acid-fast bacilli, commercial nucleic acid amplification test for Mycobacterium tuberculosis or mycobacterial culture of cerebrospinal fluid.  Clinical data that have previously been shown to be associated with TBM, and can inform the final diagnosis, will be requested. The data-set will be divided into training and test/validation data-sets for model building. A predictive logistic model will be built using a training set with patients with definite TBM and no TBM. Should it be warranted, factor analysis may be employed, depending on evidence for multicollinearity or the case for including latent variables in the model. Discussion: We will systematically identify and extract key clinical parameters associated with TBM from published studies and use a ‘big data’ approach to develop and validate a clinical prediction model with enhanced generalisability. The final model will be made available through a smartphone application. Further work will be external validation of the model and test of efficacy in a randomised controlled trial.

scholarly journals Prediction of individuals at high risk of chronic kidney disease during treatment with lithium for bipolar disorder

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Joseph F. Hayes ◽  
David P. J. Osborn ◽  
Emma Francis ◽  
Gareth Ambler ◽  
Laurie A. Tomlinson ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
High Risk ◽  
Prediction Model ◽  
Lithium Treatment ◽  
External Validation ◽  
Elastic Net ◽  
Validation Data ◽  
Data Set ◽  
Roc Area

Abstract Background Lithium is the most effective treatment in bipolar disorder. Its use is limited by concerns about risk of chronic kidney disease (CKD). We aimed to develop a model to predict risk of CKD following lithium treatment initiation, by identifying individuals with a high-risk trajectory of kidney function. Methods We used United Kingdom Clinical Practice Research Datalink (CPRD) electronic health records (EHRs) from 2000 to 2018. CPRD Aurum for prediction model development and CPRD Gold for external validation. We used elastic net regularised regression to generate a prediction model from potential features. We performed discrimination and calibration assessments in an external validation data set. We included all patients aged ≥ 16 with bipolar disorder prescribed lithium. To be included patients had to have ≥ 1 year of follow-up before lithium initiation, ≥ 3 estimated glomerular filtration rate (eGFR) measures after lithium initiation (to be able to determine a trajectory) and a normal (≥ 60 mL/min/1.73 m2) eGFR at lithium initiation (baseline). In the Aurum development cohort, 1609 fulfilled these criteria. The Gold external validation cohort included 934 patients. We included 44 potential baseline features in the prediction model, including sociodemographic, mental and physical health and drug treatment characteristics. We compared a full model with the 3-variable 5-year kidney failure risk equation (KFRE) and a 3-variable elastic net model. We used group-based trajectory modelling to identify latent trajectory groups for eGFR. We were interested in the group with deteriorating kidney function (the high-risk group). Results The high risk of deteriorating eGFR group included 191 (11.87%) of the Aurum cohort and 137 (14.67%) of the Gold cohort. Of these, 168 (87.96%) and 117 (85.40%) respectively developed CKD 3a or more severe during follow-up. The model, developed in Aurum, had a ROC area of 0.879 (95%CI 0.853–0.904) in the Gold external validation data set. At the empirical optimal cut-point defined in the development dataset, the model had a sensitivity of 0.91 (95%CI 0.84–0.97) and a specificity of 0.74 (95% CI 0.67–0.82). However, a 3-variable elastic net model (including only age, sex and baseline eGFR) performed similarly well (ROC area 0.888; 95%CI 0.864–0.912), as did the KFRE (ROC area 0.870; 95%CI 0.841–0.898). Conclusions Individuals at high risk of a poor eGFR trajectory can be identified before initiation of lithium treatment by a simple equation including age, sex and baseline eGFR. Risk was increased in individuals who were younger at commencement of lithium, female and had a lower baseline eGFR. We did not identify strong predicters of eGFR decline specific to lithium-treated patients. Notably, lithium duration and toxicity were not associated with high-risk trajectory.

scholarly journals Prediction of individuals at high-risk of chronic kidney disease during treatment with lithium for bipolar disorder

2021 ◽  
Author(s):  
Joseph F Hayes ◽  
David PJ Osborn ◽  
Emma Francis ◽  
Gareth Ambler ◽  
Laurie A Tomlinson ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Renal Function ◽  
High Risk ◽  
Prediction Model ◽  
Lithium Treatment ◽  
External Validation ◽  
Elastic Net ◽  
Validation Data ◽  
Data Set ◽  
Roc Area

AbstractBackgroundLithium is the most effective treatment in bipolar disorder. Its use is limited by concerns about risk of chronic kidney disease (CKD). We aimed to develop a model to predict risk of CKD following lithium treatment initiation, by identifying individuals with a high-risk trajectory of renal function.MethodsWe used United Kingdom Clinical Practice Research Datalink (CPRD) electronic heath records (EHRs) from 2000-2018. CPRD Aurum for prediction model development and CPRD Gold for external validation. We used elastic net to generate a prediction model from potential features. We performed discrimination and calibration assessments in an external validation data set.We included all patients aged ≥16 with bipolar disorder prescribed lithium. To be included patients had to have ≥1 year of follow-up before lithium initiation, ≥3 estimated glomerular filtration rate (eGFR) measures after lithium initiation (to be able to determine a trajectory) and a normal (≥60 mL/min/1.73m2) eGFR at lithium initiation (baseline). In the Aurum development cohort 1609 fulfilled these criteria. The Gold external validation cohort included 934 patients.We included 44 potential baseline features in the prediction model, including sociodemographic, mental and physical heath and drug treatment characteristics. We compared a full model with the 3-variable five-year kidney failure risk equation (KFRE) and a 3-variable elastic net model.We used group-based trajectory modelling to identify latent trajectory groups for eGFR. We were interested in the group with deteriorating renal function (the high-risk group).FindingsThe high-risk group included 191 (11.87%) of the Aurum cohort and 137 (14.67%) of the Gold cohort, of these 168 (87.96%) and 117 (85.40%) respectively developed CKD 3a or more severe during follow-up. The model, developed in Aurum, had a ROC area of 0.879 (95%CI 0.853-0.904) in the Gold external validation data set. At the empirical optimal cut-point defined in the development dataset, the model had a sensitivity of 0. 91 (95%CI 0.84-0.97) and a specificity of 0.74 (95% CI 0.67-0.82). However, a 3-variable elastic net model (including only age, sex and baseline eGFR) performed similarly well (ROC area 0.888; 95%CI 0.864-0.912), as did the KFRE (ROC area 0.870; 95%CI 0.841-0.898).ConclusionsIndividuals at high-risk of a poor trajectory of renal function can be identified before initiation of lithium treatment by a simple equation including age, sex and baseline eGFR. We did not identify strong predicters of renal impairment specific to lithium treated patients.

Sentinel Lymph Node Procedure in Colon Cancer: A Systematic Review and Meta-Analysis of Individual Patient Data

2020 ◽  
Author(s):  
Marjolein Ankersmit ◽  
Martijn W. Heymans ◽  
Otto Hoekstra ◽  
Stijn L. Vlek ◽  
Linda J. Schoonmade ◽  
...  
Keyword(s):  
Systematic Review ◽  
Colon Cancer ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data ◽  
Sentinel Lymph Node Procedure

scholarly journals Development and temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e040778
Author(s):  
Vineet Kumar Kamal ◽  
Ravindra Mohan Pandey ◽  
Deepak Agrawal
Keyword(s):  
Hospital Mortality ◽  
External Validation ◽  
Trauma Centre ◽  
Unfavourable Outcome ◽  
Motor Score ◽  
Validation Data ◽  
Data Set ◽  
Development Data ◽  
Level 1 ◽  
Pupillary Reactivity

ObjectiveTo develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).DesignRetrospective.SettingLevel-1, government-funded trauma centre, India.ParticipantsPatients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.Outcome(s)In-hospital mortality and unfavourable outcome at 6 months.ResultsA total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).ConclusionFor clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

scholarly journals Genome-wide copy number variations as molecular diagnostic tool for cutaneous intermediate melanocytic lesions: a systematic review and individual patient data meta-analysis

2021 ◽  
Author(s):  
Chiel F. Ebbelaar ◽  
Anne M. L. Jansen ◽  
Lourens T. Bloem ◽  
Willeke A. M. Blokx
Keyword(s):  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Copy Number ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Copy Number Variations ◽  
Patient Data ◽  
Melanocytic Lesions ◽  
Genome Wide ◽  
Malignant Lesions

AbstractCutaneous intermediate melanocytic neoplasms with ambiguous histopathological features are diagnostically challenging. Ancillary cytogenetic techniques to detect genome-wide copy number variations (CNVs) might provide a valuable tool to allow accurate classification as benign (nevus) or malignant (melanoma). However, the CNV cut-off value to distinguish intermediate lesions from melanoma is not well defined. We performed a systematic review and individual patient data meta-analysis to evaluate the use of CNVs to classify intermediate melanocytic lesions. A total of 31 studies and 431 individual lesions were included. The CNV number in intermediate lesions (median 1, interquartile range [IQR] 0–2) was significantly higher (p<0.001) compared to that in benign lesions (median 0, IQR 0–1) and lower (p<0.001) compared to that in malignant lesions (median 6, IQR 4–11). The CNV number displayed excellent ability to differentiate between intermediate and malignant lesions (0.90, 95% CI 0.86–0.94, p<0.001). Two CNV cut-off points demonstrated a sensitivity and specificity higher than 80%. A cut-off of ≥3 CNVs corresponded to 85% sensitivity and 84% specificity, and a cut-off of ≥4 CNVs corresponded to 81% sensitivity and 91% specificity, respectively. This individual patient data meta-analysis provides a comprehensive overview of CNVs in cutaneous intermediate melanocytic lesions, based on the largest pooled cohort of ambiguous melanocytic neoplasms to date. Our meta-analysis suggests that a cut-off of ≥3 CNVs might represent the optimal trade-off between sensitivity and specificity in clinical practice to differentiate intermediate lesions from melanoma.

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