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

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.

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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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A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol

Diagnostic and Prognostic Research ◽

10.1186/s41512-021-00107-5 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Mariella Gregorich ◽

Andreas Heinzel ◽

Michael Kammer ◽

Heike Meiselbach ◽

Carsten Böger ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Renal Function ◽

High Risk ◽

Kidney Disease ◽

Prediction Model ◽

External Validation ◽

Renal Function Decline

Abstract Background Chronic kidney disease (CKD) is a well-established complication in people with diabetes mellitus. Roughly one quarter of prevalent patients with diabetes exhibit a CKD stage of 3 or higher and the individual course of progression is highly variable. Therefore, there is a clear need to identify patients at high risk for fast progression and the implementation of preventative strategies. Existing prediction models of renal function decline, however, aim to assess the risk by artificially grouped patients prior to model building into risk strata defined by the categorization of the least-squares slope through the longitudinally fluctuating eGFR values, resulting in a loss of predictive precision and accuracy. Methods This study protocol describes the development and validation of a prediction model for the longitudinal progression of renal function decline in Caucasian patients with type 2 diabetes mellitus (DM2). For development and internal-external validation, two prospective multicenter observational studies will be used (PROVALID and GCKD). The estimated glomerular filtration rate (eGFR) obtained at baseline and at all planned follow-up visits will be the longitudinal outcome. Demographics, clinical information and laboratory measurements available at a baseline visit will be used as predictors in addition to random country-specific intercepts to account for the clustered data. A multivariable mixed-effects model including the main effects of the clinical variables and their interactions with time will be fitted. In application, this model can be used to obtain personalized predictions of an eGFR trajectory conditional on baseline eGFR values. The final model will then undergo external validation using a third prospective cohort (DIACORE). The final prediction model will be made publicly available through the implementation of an R shiny web application. Discussion Our proposed state-of-the-art methodology will be developed using multiple multicentre study cohorts of people with DM2 in various CKD stages at baseline, who have received modern therapeutic treatment strategies of diabetic kidney disease in contrast to previous models. Hence, we anticipate that the multivariable prediction model will aid as an additional informative tool to determine the patient-specific progression of renal function and provide a useful guide to early on identify individuals with DM2 at high risk for rapid progression.

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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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Renal function improves mortality prediction in acute pulmonary embolism: results of a multicentre cohort study with external validation in the RIETE registry

European Heart Journal ◽

10.1093/ehjci/ehaa946.2239 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

R Chopard ◽

D Jimenez ◽

G Serzian ◽

F Ecarnot ◽

N Falvo ◽

...

Keyword(s):

Pulmonary Embolism ◽

Renal Function ◽

High Risk ◽

Renal Dysfunction ◽

External Validation ◽

Mortality Prediction ◽

Model Fit ◽

Funding Source ◽

Risk Of Death ◽

Acute Pe

Abstract Background Renal dysfunction may influence outcomes after pulmonary embolism (PE). We determined the incremental value of adding renal function impairment (estimated glomerular filtration rate, eGFR <60 ml/min/1.73m2) on top of the 2019 ESC prognostic model, for the prediction of 30-day all-cause mortality in acute PE patients from a prospective, multicenter cohort. Methods and results We identified which of three eGFR formulae predicted death most accurately. Changes in global model fit, discrimination, calibration and net reclassification index (NRI) were evaluated with addition of eGFR. We prospectively included consecutive adult patients with acute PE diagnosed as per ESC guidelines. Among 1,943 patients, (mean age 67.3±17.1, 50.4% women), 107 (5.5% (95% CI 4.5–6.5%)) died during 30-day follow-up. The eGFRMDRD4 formula was the most accurate for prediction of death. The observed mortality rate was higher for intermediate-low risk (OR 1.8, 95% CI 1.1–3.4) and high-risk PE (OR 10.3, 95% CI 3.6–17.3), and 30-day bleeding was significantly higher (OR 2.1, 95% CI 1.3–3.5) in patients with vs without eGFRMDRD4 <60 ml/min/1.73m2. The addition of eGFRMDRD4 information improved model fit, discriminatory capacity, and calibration of the ESC models. NRI was significantly improved (p<0.001), with 18% reclassification of predicted mortality, specifically in intermediate and high-risk PE. External validation using data from the RIETE registry confirmed our findings (Table). Conclusion Addition of eGFRMDRD4-derived renal dysfunction on top of the ESC prognostic algorithm yields significant reclassification of risk of death in intermediate and high-risk PE. Impact on therapy remains to be determined. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): BMS-Pfizer Alliance, Bayer Healthcare

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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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Prediction model of bleeding after endoscopic submucosal dissection for early gastric cancer: BEST-J score

Gut ◽

10.1136/gutjnl-2019-319926 ◽

2020 ◽

pp. gutjnl-2019-319926 ◽

Cited By ~ 1

Author(s):

Waku Hatta ◽

Yosuke Tsuji ◽

Toshiyuki Yoshio ◽

Naomi Kakushima ◽

Shu Hoteya ◽

...

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Early Gastric Cancer ◽

Prediction Model ◽

Endoscopic Submucosal Dissection ◽

Clinical Decision Making ◽

Validation Cohort ◽

External Validation ◽

Derivation Cohort ◽

Submucosal Dissection

ObjectiveBleeding after endoscopic submucosal dissection (ESD) for early gastric cancer (EGC) is a frequent adverse event after ESD. We aimed to develop and externally validate a clinically useful prediction model (BEST-J score: Bleeding after ESD Trend from Japan) for bleeding after ESD for EGC.DesignThis retrospective study enrolled patients who underwent ESD for EGC. Patients in the derivation cohort (n=8291) were recruited from 25 institutions, and patients in the external validation cohort (n=2029) were recruited from eight institutions in other areas. In the derivation cohort, weighted points were assigned to predictors of bleeding determined in the multivariate logistic regression analysis and a prediction model was established. External validation of the model was conducted to analyse discrimination and calibration.ResultsA prediction model comprised 10 variables (warfarin, direct oral anticoagulant, chronic kidney disease with haemodialysis, P2Y12 receptor antagonist, aspirin, cilostazol, tumour size >30 mm, lower-third in tumour location, presence of multiple tumours and interruption of each kind of antithrombotic agents). The rates of bleeding after ESD at low-risk (0 to 1 points), intermediate-risk (2 points), high-risk (3 to 4 points) and very high-risk (≥5 points) were 2.8%, 6.1%, 11.4% and 29.7%, respectively. In the external validation cohort, the model showed moderately good discrimination, with a c-statistic of 0.70 (95% CI, 0.64 to 0.76), and good calibration (calibration-in-the-large, 0.05; calibration slope, 1.01).ConclusionsIn this nationwide multicentre study, we derived and externally validated a prediction model for bleeding after ESD. This model may be a good clinical decision-making support tool for ESD in patients with EGC.

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