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

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.

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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

Wellcome Open Research ◽

10.12688/wellcomeopenres.15056.1 ◽

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.

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Predictive tool for intravenous immunoglobulin resistance of Kawasaki disease in Beijing

Archives of Disease in Childhood ◽

10.1136/archdischild-2017-314512 ◽

2018 ◽

Vol 104 (3) ◽

pp. 262-267 ◽

Cited By ~ 6

Author(s):

Shuai Yang ◽

Ruixia Song ◽

Junmei Zhang ◽

Xiaohui Li ◽

Caifeng Li

Keyword(s):

High Risk ◽

Kawasaki Disease ◽

Intravenous Immunoglobulin ◽

External Validation ◽

Predictive Tool ◽

Validation Data ◽

Data Set ◽

Children's Hospital ◽

Ivig Resistance ◽

Children’S Hospital

ObjectiveTo construct a predictive tool for the efficacy of intravenous immunoglobulin (IVIG) therapy in children with Kawasaki disease (KD) in Beijing, China.DesignThis was a cohort study. Data set (including clinical profiles and laboratory findings) of children with KD diagnosed between 1 January 2010 and 31 December 2015 was used to analyse the risk factors and construct a scoring system. Data set of children with KD diagnosed between 1 January 2016 and 1 December 2016 was used to validate this model.SettingChildren’s Hospital Capital Institute of Pediatrics and Beijing Children’s Hospital.Patients2102 children diagnosed with KD.InterventionsNo.Main outcome measuresResponsiveness to IVIG.ResultsThe predictive tool included C reactive protein ≥90 mg/L (3 points), neutrophil percentage ≥70% (2.5 points), sodium ion concentration <135 mmol/L (3 points), albumin <35 g/L (2.5 points) and total bilirubin >20 μmol/L (5 points), which generated an area under the the receiver operating characteristic curve of 0.77 (95% CI 0.71 to 0.82) for the internal validation data set, and 0.69 (95% CI 0.58 to 0.81) and 0.63 (95% CI 0.53 to 0.72) for two external validation data sets, respectively. If a total of ≥6 points were considered high-risk for IVIG resistance, sensitivity and specificity were 56% and 79% in the internal verification, and the predictive ability was similar in the external validation.ConclusionsThe predictive tool is helpful in early screening of high-risk IVIG resistance of KD in the Beijing area. Consequently, it will guide the clinician in selecting appropriate individualised regimens for the initial treatment of this disease, which is important for the prevention of coronary complications.

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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

Wellcome Open Research ◽

10.12688/wellcomeopenres.15056.2 ◽

2019 ◽

Vol 4 ◽

pp. 19 ◽

Cited By ~ 4

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.

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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

Wellcome Open Research ◽

10.12688/wellcomeopenres.15056.3 ◽

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.

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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 ◽

10.1136/bmjopen-2020-040778 ◽

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.

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External validation of a heart failure risk prediction model in a remote monitoring cohort submitted to cardiac resynchronization therapy

European Heart Journal ◽

10.1093/ehjci/ehaa946.0817 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

I Cardoso ◽

M Coutinho ◽

G Portugal ◽

A Valentim ◽

A.S Delgado ◽

...

Keyword(s):

Heart Failure ◽

Heart Rate ◽

High Risk ◽

Remote Monitoring ◽

Hospital Admissions ◽

External Validation ◽

Left Ventricular Ejection ◽

National Database ◽

Failure Risk

Abstract Background Patients (P) submitted to cardiac ressynchronization therapy (CRT) are at high risk of heart failure (HF) events during follow-up. Continuous analysis of various physiological parameters, as reported by remote monitoring (RM), can contribute to point out incident HF admissions. Tailored evaluation, including multi-parameter modelling, may further increase the accuracy of such algorithms. Purpose Independent external validation of a commercially available algorithm (“Heart Failure Risk Status” HFRS, Medtronic, MN USA) in a cohort submitted to CRT implantation in a tertiary center. Methods Consecutive P submitted to CRT implantation between January 2013 and September 2019 who had regular RM transmissions were included. The HFRS algorithm includes OptiVol (Medtronic Plc., MN, USA), patient activity, night heart rate (NHR), heart rate variability (HRV), percentage of CRT pacing, atrial tachycardia/atrial fibrillation (AT/AF) burden, ventricular rate during AT/AF (VRAF), and detected arrhythmia episodes/therapy delivered. P were classified as low, medium or high risk. Hospital admissions were systematically assessed by use of a national database (“Plataforma de Dados de Saúde”). Accuracy of the HFRS algorithm was evaluated by random effects logistic regression for the outcome of unplanned hospital admission for HF in the 30 days following each transmission episode. Results 1108 transmissions of 35 CRT P, corresponding to 94 patient-years were assessed. Mean follow-up was 2.7 yrs. At implant, age was 67.6±9.8 yrs, left ventricular ejection fraction 28±7.8%, BNP 156.6±292.8 and NYHA class >II in 46% of the P. Hospital admissions for HF were observed within 30 days in 9 transmissions. Stepwise increase in HFRS was significantly associated with higher risk of HF admission (odds ratio 12.7, CI 3.2–51.5). HFRS had good discrimination for HF events with receiving-operator curve AUC 0.812. Conclusions HFRS was significantly associated with incident HF admissions in a high-risk cohort. Prospective use of this algorithm may help guide HF therapy in CRT recipients. Funding Acknowledgement Type of funding source: None

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Critical Predictors for the Early Detection of Conversion From Unipolar Major Depressive Disorder to Bipolar Disorder: Nationwide Population-Based Retrospective Cohort Study (Preprint)

10.2196/preprints.14278 ◽

2019 ◽

Author(s):

Ya-Han Hu ◽

Kuanchin Chen ◽

I-Chiu Chang ◽

Cheng-Che Shen

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Cohort Study ◽

High Risk ◽

Retrospective Cohort ◽

Risk Group ◽

Population Based ◽

Major Depressive ◽

Risk Stratification Model

BACKGROUND Unipolar major depressive disorder (MDD) and bipolar disorder are two major mood disorders. The two disorders have different treatment strategies and prognoses. However, bipolar disorder may begin with depression and could be diagnosed as MDD in the initial stage, which may later contribute to treatment failure. Previous studies indicated that a high proportion of patients diagnosed with MDD will develop bipolar disorder over time. This kind of hidden bipolar disorder may contribute to the treatment resistance observed in patients with MDD. OBJECTIVE In this population-based study, our aim was to investigate the rate and risk factors of a diagnostic change from unipolar MDD to bipolar disorder during a 10-year follow-up. Furthermore, a risk stratification model was developed for MDD-to-bipolar disorder conversion. METHODS We conducted a retrospective cohort study involving patients who were newly diagnosed with MDD between January 1, 2000, and December 31, 2004, by using the Taiwan National Health Insurance Research Database. All patients with depression were observed until (1) diagnosis of bipolar disorder by a psychiatrist, (2) death, or (3) December 31, 2013. All patients with depression were divided into the following two groups, according to whether bipolar disorder was diagnosed during the follow-up period: converted group and nonconverted group. Six groups of variables within the first 6 months of enrollment, including personal characteristics, physical comorbidities, psychiatric comorbidities, health care usage behaviors, disorder severity, and psychotropic use, were extracted and were included in a classiﬁcation and regression tree (CART) analysis to generate a risk stratification model for MDD-to-bipolar disorder conversion. RESULTS Our study enrolled 2820 patients with MDD. During the follow-up period, 536 patients were diagnosed with bipolar disorder (conversion rate=19.0%). The CART method identified five variables (kinds of antipsychotics used within the first 6 months of enrollment, kinds of antidepressants used within the first 6 months of enrollment, total psychiatric outpatient visits, kinds of benzodiazepines used within one visit, and use of mood stabilizers) as significant predictors of the risk of bipolar disorder conversion. This risk CART was able to stratify patients into high-, medium-, and low-risk groups with regard to bipolar disorder conversion. In the high-risk group, 61.5%-100% of patients with depression eventually developed bipolar disorder. On the other hand, in the low-risk group, only 6.4%-14.3% of patients with depression developed bipolar disorder. CONCLUSIONS The CART method identified five variables as significant predictors of bipolar disorder conversion. In a simple two- to four-step process, these variables permit the identification of patients with low, intermediate, or high risk of bipolar disorder conversion. The developed model can be applied to routine clinical practice for the early diagnosis of bipolar disorder.

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INeo-Epp: A novel T-cell HLA class-I immunogenicity or neoantigenic epitope prediction method based on sequence related amino acid features

10.1101/697011 ◽

2019 ◽

Author(s):

Guangzhi Wang ◽

Huihui Wan ◽

Xingxing Jian ◽

Yuyu Li ◽

Jian Ouyang ◽

...

Keyword(s):

Amino Acid ◽

T Cell ◽

Antigen Processing ◽

External Validation ◽

Cell Epitope ◽

Epitope Prediction ◽

Validation Data ◽

Data Set ◽

Immunogenic Peptide ◽

Related Amino Acid

AbstractIn silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g. proteasomal cleavage, transporter associated with antigen processing (TAP) and major histocompatibility complex (MHC) combination. To date, however, the mechanism for immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including amino acid physicochemical property of epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score and frequency score for immunogenic peptide. Subsequently, a random forest classifier for T cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC=0.81, external validation data set AUC=0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called ‘neoantigens’. Based on mutation information and sequence related amino acid characteristics, a prediction model of neoantigen was established as well (the optimal AUC=0.78). Further, an easy-to-use web-based tool ‘INeo-Epp’ was developed (available at http://www.biostatistics.online/INeo-Epp/neoantigen.php)for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

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