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.

Development and External Validation of Diagnostic Model for in-Hospital Mortality in Patient with Acute ST Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Myocardial Infarction ◽  
Hospital Mortality ◽  
External Validation ◽  
Hospitalized Patients ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
Acute Stemi ◽  
Development Data ◽  
St Elevation

BACKGROUND Coronary heart disease, including ST elevation myocardial infarction(STEMI), was still the leading cause of mortality. OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of in-hospital mortality in the patients with acute STEMI . METHODS Design: Multivariable logistic regression of a cohort of hospitalized patients with acute STEMI. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: A total of 2,183 hospitalized patients with acute STEMI from January 2002 to December 2011.External validation: A total of 7,485 hospitalized patients with acute STEMI from January 2012 to August 2019. Outcomes: In-hospital mortality. All cause in-hospital mortality was defined as cardiac or non-cardiac death during hospitalization. We used logistic regression analysis to analyze the risk factors of in-hospital mortality in the development data set. We developed a diagnostic model of in-hospital mortality and constructed a nomogram.We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS In-hospital mortality occurred in 61out of 2,183 participants (2.8%) in the development data set. The strongest predictors of in-hospital mortality were age and Killip classification. We developed a diagnostic model of in-hospital mortality .The area under the receiver operating characteristic (ROC) curve (AUC) was .9126±.0166, 95% confidence interval(CI)= .88015 ~ .94504 in the development set .We constructed a nomogram based on age and Killip classification. In-hospital mortality occurred in 127 out of 7,485 participants(1.7%) in the validation data set. The AUC was .9305±.0113, 95% CI= .90827 ~ .95264 in the validation set . Discrimination, calibration ,and DCA were satisfactory. Date of approved by ethic committee:25 October 2019. Date of data collection start: 6 November 2019. Numbers recruited as of submission of the manuscript:9,668. CONCLUSIONS Conclusions: We developed and externally validated a diagnostic model of in-hospital mortality in patient with acute STEMI . CLINICALTRIAL We registered this study with WHO International Clinical Trials Registry Platform (ICTRP) (registration number: ChiCTR1900027129; registered date: 1 November 2019). http://www.chictr.org.cn/edit.aspx?pid=44888&htm=4.

scholarly journals The Stunting Tool for Early Prevention: development and external validation of a novel tool to predict risk of stunting in children at 3 years of age

2019 ◽  
Vol 4 (6) ◽  
pp. e001801
Author(s):  
Sarah Hanieh ◽  
Sabine Braat ◽  
Julie A Simpson ◽  
Tran Thi Thu Ha ◽  
Thach D Tran ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Validation Data ◽  
Data Set ◽  
Growth Faltering ◽  
Development Data ◽  
Gestational Age At Birth ◽  
Development And Validation ◽  
The Impact

IntroductionGlobally, an estimated 151 million children under 5 years of age still suffer from the adverse effects of stunting. We sought to develop and externally validate an early life predictive model that could be applied in infancy to accurately predict risk of stunting in preschool children.MethodsWe conducted two separate prospective cohort studies in Vietnam that intensively monitored children from early pregnancy until 3 years of age. They included 1168 and 475 live-born infants for model development and validation, respectively. Logistic regression on child stunting at 3 years of age was performed for model development, and the predicted probabilities for stunting were used to evaluate the performance of this model in the validation data set.ResultsStunting prevalence was 16.9% (172 of 1015) in the development data set and 16.4% (70 of 426) in the validation data set. Key predictors included in the final model were paternal and maternal height, maternal weekly weight gain during pregnancy, infant sex, gestational age at birth, and infant weight and length at 6 months of age. The area under the receiver operating characteristic curve in the validation data set was 0.85 (95% Confidence Interval, 0.80–0.90).ConclusionThis tool applied to infants at 6 months of age provided valid prediction of risk of stunting at 3 years of age using a readily available set of parental and infant measures. Further research is required to examine the impact of preventive measures introduced at 6 months of age on those identified as being at risk of growth faltering at 3 years of age.

Diagnostic Model of in-Hospital Mortality in Patient with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Logistic Regression ◽  
Hospital Mortality ◽  
Hospitalized Patients ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
St Segment Elevation ◽  
Acute Stemi ◽  
St Segment ◽  
Development Data

BACKGROUND Coronary heart disease, including ST-segment elevation myocardial infarction (STEMI), is still the leading cause of death. OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of in-hospital mortality in the patients with acute STEMI . METHODS We performed multivariate logistic regression of a cohort for hospitalized patients with acute STEMI in the emergency department of a university hospital. Participants: The model development data set was obtained from 2,183 hospitalized patients with acute STEMI from January 2002 to December 2011. A set of 7,485 hospitalized patients with acute STEMI from January 2012 to August 2019 were used for external validation. We used logistic regression analysis to analyze the risk factors of in-hospital mortality in the development data set. We developed a diagnostic model of in-hospital mortality and constructed a nomogram. We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS In-hospital mortality occurred in 61 of 2,183 participants (2.8%) in the development data set. The strongest predictors of in-hospital mortality were advanced age and high Killip classification. Logistic regression analysis showed differences between the groups with and without in-hospital mortality in age (odds ratio [OR] 1.058, 95% CI 1.029-1.088; P<.001), Killip III (OR 8.249, 95% CI 3.502-19.433; P<.001), and Killip IV (OR 39.234, 95% CI 18.178-84.679; P<.001). We developed a diagnostic model of in-hospital mortality. The area under the receiver operating characteristic curve (AUC) was 0. 9126 (SD 0. 0166, 95% CI 0. 88015-0. 94504). We constructed a nomogram based on age and Killip classification. In-hospital mortality occurred in 127 of 7,485 participants (1.7%) in the validation data set. The AUC was 0 .9305 (SD 0.0113, 95% CI 0. 90827-0. 95264). CONCLUSIONS We developed and externally validated a diagnostic model of in-hospital mortality in patient with acute STEMI . The discrimination, calibration, and DCA of the model were found to be satisfactory. CLINICALTRIAL Trial Registration: ChiCTR.org ChiCTR1900027129; http://www.chictr.org.cn/edit.aspx?pid=44888&htm=4.

Diagnostic Model of in-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Hospital Mortality ◽  
Logistic Regression Analysis ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
St Segment Elevation ◽  
Acute Stemi ◽  
Development Data

BACKGROUND Coronary heart disease, including ST-segment elevation myocardial infarction (STEMI), remains the main cause of death. OBJECTIVE The objective of our research was to develop and externally validate a diagnostic model of in-hospital mortality in acute STEMI patients. METHODS We performed multiple logistic regression analysis on a cohort of hospitalized acute STEMI patients. Participants: From January 2002 to December 2011, a total of 2,183 inpatients with acute STEMI were admitted for development.The external validation data set of this model comes from 7,485 hospitalized patients with acute STEMI from January 2012 to August 2019.We used logistic regression analysis to analyze the risk factors of in-hospital mortality in the development data set.We developed a diagnostic model of in-hospital mortality and constructed a nomogram. We evaluated the predictive performance of the diagnostic model in the validation data set by examining the measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS In the development data set, 61 of the 2,183 participants (2.8%) experienced in-hospital mortality. The strongest predictors of in-hospital mortality were advanced age and high Killip classification. Logistic regression analysis showed the difference between the two groups with and without in-hospital mortality (odds ratio [OR] 1.058, 95% CI 1.029-1.088; P <.001), Killip III (OR 8.249, 95% CI 3.502-19.433; P <.001) and Killip IV (OR 39.234, 95% CI 18.178-84.679; P <.001). We had developed a diagnostic model of in-hospital mortality. The area under the receiver operating characteristic curve (AUC) was 0.9126 (SD 0.0166, 95% CI 0.88015-0.94504). We constructed a nomogram based on age and Killip classification. In-hospital mortality occurred in 127 of 7,485 participants(1.7%) in the validation data set. The AUC was 0 .9305(SD 0.0113, 95% CI 0. 90827-0. 95264). CONCLUSIONS We had developed and externally validated a diagnostic model of in-hospital mortality in acute STEMI patients. It was found that the discrimination, calibration and DCA of this model were satisfactory. CLINICALTRIAL ChiCTR.org ChiCTR1900027129; http://www.chictr.org.cn/edit.aspx?pid=44888&htm=4.

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

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.

Development and External Validation of Diagnostic Model for Coronary Microvascular Obstruction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li ◽  
Shuzheng Lyu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Microvascular Obstruction ◽  
External Validation ◽  
Diagnostic Model ◽  
Total Occlusion ◽  
Validation Data ◽  
Data Set ◽  
Acute Stemi

BACKGROUND Prevention of coronary microvascular obstruction /no-reflow phenomenon(CMVO/NR) is a crucial step in improving prognosis of patients with acute ST segment elevation myocardial infarction (STEMI )during primary percutaneous coronary intervention (PPCI). OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of CMVO/NR in patients with acute STEMI underwent PPCI. METHODS Design: Multivariate logistic regression of a cohort of acute STEMI patients. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: Totally 1232 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2013. External validation: Totally 1301 acute STEMI patients who were treated with PPCI from January 2014 to June 2018. Outcomes: CMVO/NR during PPCI. We used logistic regression analysis to analyze the risk factors of CMVO/NR in the development data set. We developed a diagnostic model of CMVO/NR and constructed a nomogram.We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS A total of 147 out of 1,232 participants (11.9%) presented CMVO/NR in the development dataset.The strongest predictors of CMVO/NR were age, periprocedural bradycardia, using thrombus aspiration devices during procedure and total occlusion of culprit vessel. Logistic regression analysis showed that the differences between two group with and without CMVO/NR in age( odds ratios (OR)1.031; 95% confidence interval(CI), 1.015 ~1.048 ; P <.001), periprocedural bradycardia (OR 2.151;95% CI,1.472~ 3.143 ; P <.001) , total occlusion of the culprit vessel (OR 1.842;95% CI, 1.095~ 3.1 ; P =.021) , and using thrombus aspirationdevices during procedure (OR 1.631; 95% CI, 1.029~ 2.584 ; P =.037).We developed a diagnostic model of CMVO/NR. The area under the receiver operating characteristic curve (AUC) was .6833±.023. We constructed a nomogram. CMVO/NR occurred in 120 out of 1,301 participants (9.2%) in the validation data set. The AUC was .6547±.025. Discrimination, calibration, and DCA were satisfactory. Date of approved by ethic committee:16 May 2019. Date of data collection start: 1 June 2019. Numbers recruited as of submission of the manuscript:2,533. CONCLUSIONS We developed and externally validated a diagnostic model of CMVO/NR during PPCI. CLINICALTRIAL We registered this study with WHO International Clinical Trials Registry Platform on 16 May 2019. Registration number: ChiCTR1900023213. http://www.chictr.org.cn/edit.aspx?pid=39057&htm=4.

Reclassification of BRCA1 and BRCA2 variants of uncertain significance: a multifactorial analysis of multicentre prospective cohort

2018 ◽  
Vol 55 (12) ◽  
pp. 794-802 ◽  
Author(s):  
Jee-Soo Lee ◽  
Sohee Oh ◽  
Sue Kyung Park ◽  
Min-Hyuk Lee ◽  
Jong Won Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
External Validation ◽  
Brca1 And Brca2 ◽  
Clinical Genetic ◽  
Validation Data ◽  
Data Set ◽  
Variants Of Uncertain Significance ◽  
Pathogenic Variants ◽  
Uncertain Significance ◽  
Highly Correlated

BackgroundBRCA1 and BRCA2 (BRCA1/2) variants classified ambiguously as variants of uncertain significance (VUS) are a major challenge for clinical genetic testing in breast cancer; their relevance to the cancer risk is unclear and the association with the response to specific BRCA1/2-targeted agents is uncertain. To minimise the proportion of VUS in BRCA1/2, we performed the multifactorial likelihood analysis and validated this method using an independent cohort of patients with breast cancer.MethodsWe used a data set of 2115 patients with breast cancer from the nationwide multicentre prospective Korean Hereditary Breast Cancer study. In total, 83 BRCA1/2 VUSs (BRCA1, n=26; BRCA2, n=57) were analysed. The multifactorial probability was estimated by combining the prior probability with the overall likelihood ratio derived from co-occurrence of each VUS with pathogenic variants, personal and family history, and tumour characteristics. The classification was compared with the interpretation according to the American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG/AMP) guidelines. An external validation was conducted using independent data set of 810 patients.ResultsWe were able to redefine 38 VUSs (BRCA1, n=10; BRCA2, n=28). The revised classification was highly correlated with the ACMG/AMP guideline-based interpretation (BRCA1, p for trend=0.015; BRCA2, p=0.001). Our approach reduced the proportion of VUS from 19% (154/810) to 8.9% (72/810) in the retrospective validation data set.ConclusionThe classification in this study would minimise the ‘uncertainty’ in clinical interpretation, and this validated multifactorial model can be used for the reliable annotation of BRCA1/2 VUSs.

scholarly journals Development and External Validation of a Nomogram Predicting the Probability of Significant Gleason Sum Upgrading among Japanese Patients with Localized Prostate Cancer

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Takashi Imamoto ◽  
Takanobu Utsumi ◽  
Makoto Takano ◽  
Atsushi Komaru ◽  
Satoshi Fukasawa ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Model ◽  
Japanese Patient ◽  
External Validation ◽  
Specific Antigen ◽  
Japanese Patients ◽  
Study Cohort ◽  
Validation Data ◽  
Data Set ◽  
Pathological Assessment

Objective. The aim of this study is to develop a prognostic model capable of predicting the probability of significant upgrading among Japanese patients.Methods. The study cohort comprised 508 men treated with RP, with available prostate-specific antigen levels, biopsy, and RP Gleason sum values. Clinical and pathological data from 258 patients were obtained from another Japanese institution for validation.Results. Significant Gleason sum upgrading was recorded in 92 patients (18.1%) at RP. The accuracy of the nomogram predicting the probability of significant Gleason sum upgrading between biopsy and RP specimens was 88.9%. Overall AUC was 0.872 when applied to the validation data set. Nomogram predictions of significant upgrading were within 7.5% of an ideal nomogram.Conclusions. Nearly one-fifth of Japanese patients with prostate cancer will be significantly upgraded. Our nomogram seems to provide considerably accurate predictions regardless of minor variations in pathological assessment when applied to Japanese patient populations.

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 Diagnostic Model for In-Hospital Bleeding in Patients with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Logistic Regression ◽  
Hospitalized Patients ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
St Segment Elevation ◽  
Acute Stemi ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment ◽  
Development Data

BACKGROUND Bleeding complications in patients with acute ST-segment elevation myocardial infarction (STEMI) have been associated with increased risk of subsequent adverse consequences. OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of in-hospital bleeding. METHODS We performed multivariate logistic regression of a cohort for hospitalized patients with acute STEMI in the emergency department of a university hospital. Participants: The model development data set was obtained from 4262 hospitalized patients with acute STEMI from January 2002 to December 2013. A set of 6015 hospitalized patients with acute STEMI from January 2014 to August 2019 were used for external validation. We used logistic regression analysis to analyze the risk factors of in-hospital bleeding in the development data set. We developed a diagnostic model of in-hospital bleeding and constructed a nomogram. We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS In-hospital bleeding occurred in 112 of 4262 participants (2.6%) in the development data set. The strongest predictors of in-hospital bleeding were advanced age and high Killip classification. Logistic regression analysis showed differences between the groups with and without in-hospital bleeding in age (odds ratio [OR] 1.047, 95% CI 1.029-1.066; <i>P</i>&lt;.001), Killip III (OR 3.265, 95% CI 2.008-5.31; <i>P</i>&lt;.001), and Killip IV (OR 5.133, 95% CI 3.196-8.242; <i>P</i>&lt;.001). We developed a diagnostic model of in-hospital bleeding. The area under the receiver operating characteristic curve (AUC) was 0.777 (SD 0.021, 95% CI 0.73576-0.81823). We constructed a nomogram based on age and Killip classification. In-hospital bleeding occurred in 117 of 6015 participants (1.9%) in the validation data set. The AUC was 0.7234 (SD 0.0252, 95% CI 0.67392-0.77289). CONCLUSIONS We developed and externally validated a diagnostic model of in-hospital bleeding in patients with acute STEMI. The discrimination, calibration, and DCA of the model were found to be satisfactory. CLINICALTRIAL ChiCTR.org ChiCTR1900027578; http://www.chictr.org.cn/showprojen.aspx?proj=45926

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