scholarly journals Validation of the Preoperative Score to Predict Postoperative Mortality (POSPOM) in Germany

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245841
Author(s):  
Yannik C. Layer ◽  
Jan Menzenbach ◽  
Yonah L. Layer ◽  
Andreas Mayr ◽  
Tobias Hilbert ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Postoperative Mortality ◽  
University Hospital ◽  
Brier Score ◽  
Calibration Plot ◽  
Coding System ◽  
C Statistic ◽  
Using Data ◽  
Previous Demonstration ◽  
Preoperative Score

Background The Preoperative Score to Predict Postoperative Mortality (POSPOM) based on preoperatively available data was presented by Le Manach et al. in 2016. This prognostic model considers the kind of surgical procedure, patients' age and 15 defined comorbidities to predict the risk of postoperative in-hospital mortality. Objective of the present study was to validate POSPOM for the German healthcare coding system (G-POSPOM). Methods and findings All cases involving anaesthesia performed at the University Hospital Bonn between 2006 and 2017 were analysed retrospectively. Procedures codified according to the French Groupes Homogènes de Malades (GHM) were translated and adapted to the German Operationen- und Prozedurenschlüssel (OPS). Comorbidities were identified by the documented International Statistical Classification of Diseases (ICD-10) coding. POSPOM was calculated for the analysed patient collective using these data according to the method described by Le Manach et al. Performance of thereby adapted POSPOM was tested using c-statistic, Brier score and a calibration plot. Validation was performed using data from 199,780 surgical cases. With a mean age of 56.33 years (SD 18.59) and a proportion of 49.24% females, the overall cohort had a mean POSPOM value of 18.18 (SD 8.11). There were 4,066 in-hospital deaths, corresponding to an in-hospital mortality rate of 2.04% (95% CI 1.97 to 2.09%) in our sample. POSPOM showed a good performance with a c-statistic of 0.771 and a Brier score of 0.021. Conclusions After adapting POSPOM to the German coding system, we were able to validate the score using patient data of a German university hospital. According to previous demonstration for French patient cohorts, we observed a good correlation of POSPOM with in-hospital mortality. Therefore, further adjustments of POSPOM considering also multicentre and transnational validation should be pursued based on this proof of concept.

scholarly journals Development and Validation of Risk Prediction Model for In-hospital Mortality Among Patients Hospitalized With Acute Exacerbation Chronic Obstructive Pulmonary Disease Between 2015 and 2019

2021 ◽  
Vol 8 ◽  
Author(s):  
Fen Dong ◽  
Xiaoxia Ren ◽  
Ke Huang ◽  
Yanyan Wang ◽  
Jianjun Jiao ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Respiratory Failure ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Pulmonary Disease ◽  
Calibration Plot ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
C Statistic ◽  
Acute Exacerbations

Background: In patients with chronic obstructive pulmonary disease (COPD), acute exacerbations affect patients' health and can lead to death. This study was aimed to develop a prediction model for in-hospital mortality in patients with acute exacerbations of COPD (AECOPD).Method: A retrospective study was performed in patients hospitalized for AECOPD between 2015 and 2019. Patients admitted between 2015 and 2017 were included to develop model and individuals admitted in the following 2 years were included for external validation. We analyzed variables that were readily available in clinical practice. Given that death was a rare outcome in this study, we fitted Firth penalized logistic regression. C statistic and calibration plot quantified the model performance. Optimism-corrected C statistic and slope were estimated by bootstrapping. Accordingly, the prediction model was adjusted and then transformed into risk score.Result: Between 2015 and 2017, 1,096 eligible patients were analyzed, with a mean age of 73 years and 67.8% male. The in-hospital mortality was 2.6%. Compared to survivors, non-survivors were older, more admitted from emergency, more frequently concomitant with respiratory failure, pneumothorax, hypoxic-hypercarbic encephalopathy, and had longer length of stay (LOS). Four variables were included into the final model: age, respiratory failure, pneumothorax, and LOS. In internal validation, C statistic was 0.9147, and the calibration slope was 1.0254. Their optimism-corrected values were 0.90887 and 0.9282, respectively, indicating satisfactory discrimination and calibration. When externally validated in 700 AECOPD patients during 2018 and 2019, the model demonstrated good discrimination with a C statistic of 0.8176. Calibration plot illustrated a varying discordance between predicted and observed mortality. It demonstrated good calibration in low-risk patients with predicted mortality rate ≤10% (P = 0.3253) but overestimated mortality in patients with predicted rate >10% (P < 0.0001). The risk score of 20 was regarded as a threshold with an optimal Youden index of 0.7154.Conclusion: A simple prediction model for AECOPD in-hospital mortality has been developed and externally validated. Based on available data in clinical setting, the model could serve as an easily used instrument for clinical decision-making. Complications emerged as strong predictors, underscoring an important role of disease management in improving patients' prognoses during exacerbation episodes.

scholarly journals Cirrhosis aggravates the ninety-day mortality after liver resection for hepatocellular carcinoma

2019 ◽  
Vol 6 (8) ◽  
pp. 2869
Author(s):  
Hosam Farouk Abdelhameed ◽  
Ashraf M. El-Badry
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Resection ◽  
Hospital Mortality ◽  
Postoperative Mortality ◽  
University Hospital ◽  
Control Group ◽  
Complication Rates ◽  
Hospital Stays ◽  
Cirrhotic Patients ◽  
Prolonged Hospital

Background: Ninety-day postoperative mortality (90-D POM) measures accurately the liver resection-related mortality. In cirrhotic patients, reporting post-hepatectomy-related death only as in-hospital or thirty-day postoperative mortality (30-D POM) may underestimate cirrhosis-related death after liver resection.Methods: Medical records of adult cirrhotic (cirrhosis group) and matched non-cirrhotic (control group) patients, who underwent elective liver resection at Sohag University Hospital (April 2014- March 2018), were analyzed. The 90-D POM versus in-hospital mortality and 30-D POM were compared in both groups.Results: Forty-six patients (23 per group) were eligible for the study. Liver resection was carried out in all cirrhosis group patients for hepatocellular carcinoma (HCC). In the control group, liver resection was indicated for colorectal metastasis (13), benign masses (7) and intrahepatic cholangiocarcinoma (3). Compared with the control group, cirrhotic patients exhibited significantly higher complication rates (p<0.05), prolonged hospital stays (p<0.05), increased postoperative levels of serum bilirubin and reduced prothrombin concentration (p<0.05). In the control group, in-hospital mortality and 30-D POM were zero while 90-D POM was 4%. In the cirrhosis group, the in-hospital mortality and 30-D POM were identical (8.7%), however the 90-D POM was significantly higher and almost doubled (17%). Conclusion: Liver cirrhosis triggers significant mortality that may extend for ninety days postoperatively. In cirrhotic patients, post-hepatectomy death should be reported as 90-D POM rather than the obviously misleading in-hospital mortality or 30-D POM.

scholarly journals Machine Learning Models for Prediction of Adverse Events after Percutaneous Coronary Intervention

2021 ◽  
Author(s):  
Nozomi Niimi ◽  
Yasuyuki Shiraishi ◽  
Mitsuaki Sawano ◽  
Nobuhiro Ikemura ◽  
Taku Inohara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Percutaneous Coronary Intervention ◽  
Adverse Events ◽  
Hospital Mortality ◽  
Kidney Injury ◽  
Coronary Intervention ◽  
Risk Scores ◽  
Calibration Plot ◽  
Percutaneous Coronary ◽  
Using Data

Abstract An accurate prediction of major adverse events after percutaneous coronary intervention (PCI) improves clinical decisions and specific interventions. To determine whether machine learning (ML) techniques predict peri-PCI adverse events (acute kidney injury [AKI], bleeding, and in-hospital mortality) with better discrimination or calibration than the National Cardiovascular Data Registry (NCDR-CathPCI) risk scores, we developed logistic regression (LR) and gradient descent boosting (XGBoost) models for each outcome using data from a prospective, all-comer, multicenter registry that enrolled consecutive coronary artery disease patients undergoing PCI in Japan between 2008 and 2020. The NCDR-CathPCI risk scores demonstrated good discrimination for each outcome (C-statistics of 0.82, 0.76, and 0.95 for AKI, bleeding, and in-hospital mortality) with considerable calibration. Compared with the NCDR-CathPCI risk scores, the XGBoost models modestly improved discrimination for AKI and bleeding (C-statistics of 0.84 in AKI, and 0.79 in bleeding) but not for in-hospital mortality (C-statistics of 0.96). The calibration plot demonstrated that the XGBoost model overestimated the risk for in-hospital mortality in low-risk patients. All of the original NCDR-CathPCI risk scores for adverse periprocedural events showed adequate discrimination and calibration within our cohort. When using the ML-based technique, however, the improvement in the overall risk prediction was minimal.

Preoperative Score to Predict Postoperative Mortality (POSPOM)

2016 ◽  
Vol 124 (3) ◽  
pp. 570-579 ◽  
Author(s):  
Yannick Le Manach ◽  
Gary Collins ◽  
Reitze Rodseth ◽  
Christine Le Bihan-Benjamin ◽  
Bruce Biccard ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Risk Score ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Postoperative Mortality ◽  
Clinical Decision ◽  
Derivation Cohort ◽  
International Statistical Classification ◽  
Hospital Deaths ◽  
Preoperative Score

Abstract Background An accurate risk score able to predict in-hospital mortality in patients undergoing surgery may improve both risk communication and clinical decision making. The aim of the study was to develop and validate a surgical risk score based solely on preoperative information, for predicting in-hospital mortality. Methods From January 1, 2010, to December 31, 2010, data related to all surgeries requiring anesthesia were collected from all centers (single hospital or hospitals group) in France performing more than 500 operations in the year on patients aged 18 yr or older (n = 5,507,834). International Statistical Classification of Diseases, 10th revision codes were used to summarize the medical history of patients. From these data, the authors developed a risk score by examining 29 preoperative factors (age, comorbidities, and surgery type) in 2,717,902 patients, and then validated the risk score in a separate cohort of 2,789,932 patients. Results In the derivation cohort, there were 12,786 in-hospital deaths (0.47%; 95% CI, 0.46 to 0.48%), whereas in the validation cohort there were 14,933 in-hospital deaths (0.54%; 95% CI, 0.53 to 0.55%). Seventeen predictors were identified and included in the PreOperative Score to predict PostOperative Mortality (POSPOM). POSPOM showed good calibration and excellent discrimination for in-hospital mortality, with a c-statistic of 0.944 (95% CI, 0.943 to 0.945) in the development cohort and 0.929 (95% CI, 0.928 to 0.931) in the validation cohort. Conclusion The authors have developed and validated POSPOM, a simple risk score for the prediction of in-hospital mortality in surgical patients.

scholarly journals Prognostic dynamic nomogram integrated with metabolic acidosis for in-hospital mortality and organ malperfusion in acute type B aortic dissection patients undergoing thoracic endovascular aortic repair

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jitao Liu ◽  
Weijie Liu ◽  
Wentao Ma ◽  
Lyufan Chen ◽  
Hong Liang ◽  
...  
Keyword(s):  
Metabolic Acidosis ◽  
Hospital Mortality ◽  
Base Excess ◽  
Thoracic Endovascular Aortic Repair ◽  
Brier Score ◽  
Acute Type ◽  
Type B ◽  
Type B Aortic Dissection ◽  
Predictive Values ◽  
C Statistic

Abstract Background Organ malperfusion is a lethal complication in acute type B aortic dissection (ATBAD). The aim of present study is to develop a nomogram integrated with metabolic acidosis to predict in-hospital mortality and organ malperfusion in patients with ATBAD undergoing thoracic endovascular aortic repair (TEVAR). Methods The nomogram was derived from a retrospectively study of 286 ATBAD patients who underwent TEVAR from 2010 to 2017 at a single medical center. Model performance was evaluated from discrimination and calibration capacities, as well as clinical effectiveness. The results were validated using a prospective study on 77 patients from 2018 to 2019 at the same center. Results In the multivariate analysis of the derivation cohort, the independent predictors of in-hospital mortality and organ malperfusion identified were base excess, maximum aortic diameter ≥ 5.5 cm, renal dysfunction, D-dimer level ≥ 5.44 μg/mL and albumin amount ≤ 30 g/L. The penalized model was internally validated by bootstrapping and showed excellent discriminatory (bias-corrected c-statistic, 0.85) and calibration capacities (Hosmer–Lemeshow P value, 0.471; Brier Score, 0.072; Calibration intercept, − 0.02; Slope, 0.98). After being applied to the external validation cohort, the model yielded a c-statistic of 0.86 and Brier Score of 0.097. The model had high negative predictive values (0.93–0.94) and moderate positive predictive values (0.60–0.71) for in-hospital mortality and organ malperfusion in both cohorts. Conclusions A predictive nomogram combined with base excess has been established that can be used to identify high risk ATBAD patients of developing in-hospital mortality or organ malperfusion when undergoing TEVAR.

Validation of the Hospital Frailty Risk Score among older adults receiving mechanical ventilation

2021 ◽  
Author(s):  
Eric Sy ◽  
Sandy Kassir ◽  
Jonathan F Mailman ◽  
Sarah Lauren Sy
Keyword(s):  
Older Adults ◽  
Mechanical Ventilation ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Hospital Readmissions ◽  
Brier Score ◽  
Emergency Hospital ◽  
Prolonged Hospitalization ◽  
C Statistic

Abstract Background:Older adults are increasingly being admitted to intensive care units, with frailty recognized as a risk factor for worse outcomes. The Hospital Frailty Risk Score (HFRS) was developed for use in administrative databases of older adults, but it has not yet been well-validated for critically ill patients. The objective of this study was to validate the HFRS to predict prolonged hospitalization, in-hospital mortality, and 30-day emergency hospital readmissions in critically ill patients.Methods:We selected index hospitalizations of older adults (≥75 years old) receiving mechanical ventilation, using the United States Nationwide Readmissions Database from January 1, 2016 to November 30, 2018. Frailty risk was determined by the HFRS using International Classification of Diseases, Tenth Revision Clinical Modification (ICD-10-CM) codes, and further subcategorized into low (score <5), intermediate (score 5-15), and high (score >15) risk for frailty. We evaluated the HFRS to predict prolonged hospitalization, in-hospital mortality, and 30-day emergency hospital readmissions, using multivariable logistic regression after adjustment for patient and hospital characteristics. Model performance was assessed using the c-statistic, Brier score, and calibration plots.Results:Among the 649,330 weighted index hospitalizations in the cohort, 50.0% were female, the median (interquartile range [IQR]) age was 81 (78-86) years old, and the median (IQR) HFRS was 10.8 (7.7-14.5). Among the cohort, 9.5%, 68.3%, and 22.2% were subcategorized as low, intermediate, and high risk for frailty, respectively. After adjustment, patient hospitalizations with high frailty risk were associated with increased risks of prolonged hospitalization (adjusted odds ratio [aOR] 5.59 [95% confidence interval [CI] 5.24-5.97], c-statistic 0.694, Brier score 0.216) and 30-day emergency hospital readmissions (aOR 1.20 [95% CI 1.13-1.27], c-statistic 0.595, Brier score 0.162), compared to low frailty risks. Conversely, high frailty risk using the HFRS was inversely associated with in-hospital mortality (aOR 0.46 [95% CI 0.45-0.48], c-statistic 0.712, Brier score 0.214). Calibration plots demonstrated good calibration for the adjusted analyses.Conclusions:The HFRS is associated with prolonged hospitalization and 30-day readmission in older adults receiving mechanical ventilation. Further research is necessary to develop frailty scores that accurately and intuitively predict mortality in critically ill patients.

scholarly journals The level of postoperative care influences mortality prediction by the POSPOM score: A retrospective cohort analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257829
Author(s):  
Jan Menzenbach ◽  
Yannik C. Layer ◽  
Yonah L. Layer ◽  
Andreas Mayr ◽  
Mark Coburn ◽  
...  
Keyword(s):  
Postoperative Care ◽  
Cohort Analysis ◽  
Postoperative Mortality ◽  
Scoring Systems ◽  
Mortality Prediction ◽  
University Hospital ◽  
Study Endpoint ◽  
Individual Risk ◽  
Coding System ◽  
Icu Admission

Background The Preoperative Score to Predict Postoperative Mortality (POSPOM) assesses the patients’ individual risk for postsurgical intrahospital death based on preoperative parameters. We hypothesized that mortality predicted by the POSPOM varies depending on the level of postoperative care. Methods All patients age over 18 years undergoing inpatient surgery or interventions involving anesthesia at a German university hospital between January 2006, and December 2017, were assessed for eligibility for this retrospective study. Endpoint was death in hospital following surgery. Adaptation of the POSPOM to the German coding system was performed as previously described. The whole cohort was divided according to the level of postoperative care (normal ward vs. intensive care unit (ICU) admission within 24 h vs. later than 24 h, respectively). Results 199,258 patients were finally included. Observed intrahospital mortality was 2.0% (4,053 deaths). 9.6% of patients were transferred to ICU following surgery, and mortality of those patients was increased already at low POSPOM values of 15. 17,165 patients were admitted to ICU within 24 h, and these patients were older, had more comorbidities, or underwent more invasive surgery, reflected by a higher median POSPOM score compared to the normal-ward group (29 vs. 17, p <0.001). Mortality in that cohort was significantly increased to 8.7% (p <0.001). 2,043 patients were admitted to ICU later than 24 h following surgery (therefore denoted unscheduled admission), and the median POSPOM value of that group was 23. Observed mortality in this cohort was highest (13.5%, p <0.001 vs. ICU admission <24 h cohort). Conclusion Increased mortality in patients transferred to high-care wards reflects the significance of, e.g., intra- or early postoperative events for the patients’ outcome. Therefore, scoring systems considering only preoperative variables such as the POSPOM reveal limitations to predict the individual benefit of postoperative ICU admission.

scholarly journals Simplified Acute Physiology Score 3 Performance in Austrian COVID-19 Patients with and Without Diabetes

2021 ◽  
Author(s):  
Faisal Aziz ◽  
Alexander Christian Reisinger ◽  
Felix Aberer ◽  
Caren Sourij ◽  
Norbert Tripolt ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Acute Physiology Score ◽  
Brier Score ◽  
Calibration Plot ◽  
Standardized Mortality Ratio ◽  
Regression Equations ◽  
Central European ◽  
Saps 3 ◽  
National Public Health Institute ◽  
Diabetes Patients

Abstract Background: TheSimplified Acute Physiology Score 3 (SAPS 3) is routinely used in intensive care units (ICUs) to predict in-hospital mortality. However, its predictive performance has not been widely evaluated in Coronavirus disease 19 (COVID-19) patients.This studyevaluated and comparedthe performance of SAPS 3for predicting in-hospital mortalityinCOVID-19patients with and without diabetesin Austria.Methods: This study analyzed the Austrian national public health institute (GÖG) data ofCOVID-19patients admitted to ICUs (N=5,850)fromMarch 2020 to March 2021.The SAPS 3 score was calculated and the predicted in-hospital mortality was estimatedusingthreelogit regression equations: standard equation, Central European equation, and Austrian equation recalibrated for COVID-19 patients. Concordance between observed and predicted mortalities was assessed using the standardized mortality ratio (SMR). Discrimination was assessed using the C-statistic. The DeLong test was applied to compare discrimination between diabetes and non-diabetes patients. Accuracy was assessed using the Brier score andcalibration using the calibration plot and Hosmer-Lemeshow test. Results: Theobservedin-hospital mortality was 38.9% in all patients, 42.9% in diabetes, and 37.3% innon-diabetes patients. Themean ±SD SAPS 3 score was 57.4 ±13.2 in all patients,58.8 ±12.9 in diabetes, and 56.8 ±13.2 in non-diabetes patients.The SMR was significantly greater than 1 for standard and Central European equations, while it was close to 1 for the Austrian equation in all, diabetes, and non-diabetes patients. TheC-statistics was 0.69 with aninsignificant (P=0.193) difference between diabetes (0.70)and non-diabetes (0.68)patients. The Brier score was >0.20 for all SAPS 3 equations. Calibration was unsatisfactory for both standard and Central European equations in all cohorts, whereas it was satisfactory for the Austrian equation in diabetes patients.Conclusions:The SAPS 3 score demonstratedlow discrimination and accuracy in COVID-19 patients in Austria with aninsignificant difference between diabetes and non-diabetes patients. All three equations of SAPS 3 were miscalibrated particularly in non-diabetes patients, while the Austrian equation demonstrated satisfactory calibration in diabetes patients. These findingssuggest that both uncalibrated and calibrated versions ofSAPS 3 should be used with caution in COVID-19 patients.

scholarly journals Validation of the BATT score for prehospital risk stratification of traumatic haemorrhagic death: usefulness for tranexamic acid treatment criteria

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Francois-Xavier Ageron ◽  
Timothy J. Coats ◽  
Vincent Darioli ◽  
Ian Roberts
Keyword(s):  
Tranexamic Acid ◽  
Acid Treatment ◽  
Injury Severity ◽  
External Validation ◽  
Research Network ◽  
Brier Score ◽  
Trauma Patients ◽  
Using Data ◽  
The Uk ◽  
The Impact

Abstract Background Tranexamic acid reduces surgical blood loss and reduces deaths from bleeding in trauma patients. Tranexamic acid must be given urgently, preferably by paramedics at the scene of the injury or in the ambulance. We developed a simple score (Bleeding Audit Triage Trauma score) to predict death from bleeding. Methods We conducted an external validation of the BATT score using data from the UK Trauma Audit Research Network (TARN) from 1st January 2017 to 31st December 2018. We evaluated the impact of tranexamic acid treatment thresholds in trauma patients. Results We included 104,862 trauma patients with an injury severity score of 9 or above. Tranexamic acid was administered to 9915 (9%) patients. Of these 5185 (52%) received prehospital tranexamic acid. The BATT score had good accuracy (Brier score = 6%) and good discrimination (C-statistic 0.90; 95% CI 0.89–0.91). Calibration in the large showed no substantial difference between predicted and observed death due to bleeding (1.15% versus 1.16%, P = 0.81). Pre-hospital tranexamic acid treatment of trauma patients with a BATT score of 2 or more would avoid 210 bleeding deaths by treating 61,598 patients instead of avoiding 55 deaths by treating 9915 as currently. Conclusion The BATT score identifies trauma patient at risk of significant haemorrhage. A score of 2 or more would be an appropriate threshold for pre-hospital tranexamic acid treatment.

scholarly journals CHARGE-AF in a national routine primary care electronic health records database in the Netherlands: validation for 5-year risk of atrial fibrillation and implications for patient selection in atrial fibrillation screening

Open Heart ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. e001459
Author(s):  
Jelle C L Himmelreich ◽  
Wim A M Lucassen ◽  
Ralf E Harskamp ◽  
Claire Aussems ◽  
Henk C P M van Weert ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Primary Care ◽  
High Risk ◽  
The Netherlands ◽  
Patient Selection ◽  
Low Cost ◽  
Calibration Plot ◽  
Primary Care Data ◽  
C Statistic ◽  
Kaplan Meier

AimsTo validate a multivariable risk prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation (CHARGE-AF)) for 5-year risk of atrial fibrillation (AF) in routinely collected primary care data and to assess CHARGE-AF’s potential for automated, low-cost selection of patients at high risk for AF based on routine primary care data.MethodsWe included patients aged ≥40 years, free of AF and with complete CHARGE-AF variables at baseline, 1 January 2014, in a representative, nationwide routine primary care database in the Netherlands (Nivel-PCD). We validated CHARGE-AF for 5-year observed AF incidence using the C-statistic for discrimination, and calibration plot and stratified Kaplan-Meier plot for calibration. We compared CHARGE-AF with other predictors and assessed implications of using different CHARGE-AF cut-offs to select high-risk patients.ResultsAmong 111 475 patients free of AF and with complete CHARGE-AF variables at baseline (17.2% of all patients aged ≥40 years and free of AF), mean age was 65.5 years, and 53% were female. Complete CHARGE-AF cases were older and had higher AF incidence and cardiovascular comorbidity rate than incomplete cases. There were 5264 (4.7%) new AF cases during 5-year follow-up among complete cases. CHARGE-AF’s C-statistic for new AF was 0.74 (95% CI 0.73 to 0.74). The calibration plot showed slight risk underestimation in low-risk deciles and overestimation of absolute AF risk in those with highest predicted risk. The Kaplan-Meier plot with categories <2.5%, 2.5%–5% and >5% predicted 5-year risk was highly accurate. CHARGE-AF outperformed CHA2DS2-VASc (Cardiac failure or dysfunction, Hypertension, Age >=75 [Doubled], Diabetes, Stroke [Doubled]-Vascular disease, Age 65-74, and Sex category [Female]) and age alone as predictors for AF. Dichotomisation at cut-offs of 2.5%, 5% and 10% baseline CHARGE-AF risk all showed merits for patient selection in AF screening efforts.ConclusionIn patients with complete baseline CHARGE-AF data through routine Dutch primary care, CHARGE-AF accurately assessed AF risk among older primary care patients, outperformed both CHA2DS2-VASc and age alone as predictors for AF and showed potential for automated, low-cost patient selection in AF screening.

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