Predicting hospitalisation for heart failure and death in patients with, or at risk of, heart failure before first hospitalisation: a retrospective model development and external validation study

Bradley, Joshua; Schelbert, Erik B.; Bonnett, Laura; Lewis, Gavin; Lagan, Jakub; Orsborne, Christopher; Brown, Pamela Frances; Naish, Josephine H.; Williams, Simon G.; McDonagh, Theresa A.; Schmitt, Matthias; Miller, Christopher A.

doi:10.1016/s2589-7500(22)00045-0

Cited by 19 publications

(18 citation statements)

References 38 publications

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“…Concordance gain is demonstrated using several models of adjustment incorporating various clinical variables and demographic information including the BMI‐based Framingham risk score (FRS) components, 47 that is, age, sex, total cholesterol, HDL cholesterol, systolic blood pressure, smoking status and BMI, plus T2D as well as CAC, TAC and prior history of CHD. We used the FRS, as it is a well‐established risk model for CHD and was originally developed for both CHD and HF, 48 and also because no single clinical risk model of HF has been fully validated and approved for use in the clinic 49,50 . Furthermore, adding a CAC score to the traditional FRS prediction model improved risk classification for future CHD events significantly 51 .…”

Section: Resultsmentioning

confidence: 99%

“…We used the FRS, as it is a well-established risk model for CHD and was originally developed for both CHD and HF, 48 and also because no single clinical risk model of HF has been fully validated and approved for use in the clinic. 49,50 Furthermore, adding a CAC score to the traditional FRS prediction model improved risk classification for future CHD events significantly. 51 The clinical characteristics that were most likely to predict all incident HF events using the LASSO model and 500 bootstrap iterations are shown in online supplementary Figure S2.…”

Section: Bootstrap 500 Timesmentioning

confidence: 99%

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Proteomic prediction of incident heart failure and its main subtypes

Emilsson,

Jonsson,

Austin

et al. 2023

European J of Heart Fail

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AimTo examine the ability of serum proteins in predicting future heart failure (HF) events, including HF with reduced or preserved ejection fraction (HFrEF or HFpEF), in relation to event time, and with or without considering established HF‐associated clinical variables.Methods and resultsIn the prospective population‐based Age, Gene/Environment Susceptibility Reykjavik Study (AGES‐RS), 440 individuals developed HF after their first visit with a median follow‐up of 5.45 years. Among them, 167 were diagnosed with HFrEF and 188 with HFpEF. A least absolute shrinkage and selection operator regression model with non‐parametric bootstrap were used to select predictors from an analysis of 4782 serum proteins, and several pre‐established clinical parameters linked to HF. A subset of 8–10 distinct or overlapping serum proteins predicted different future HF outcomes, and C‐statistics were used to assess discrimination, revealing proteins combined with a C‐index of 0.80 for all incident HF, 0.78 and 0.80 for incident HFpEF or HFrEF, respectively. In the AGES‐RS, protein panels alone encompassed the risk contained in the clinical information and improved the performance characteristics of prediction models based on N‐terminal pro‐B‐type natriuretic peptide and clinical risk factors. Finally, the protein predictors performed particularly well close to the time of an HF event, an outcome that was replicated in the Cardiovascular Health Study.ConclusionA small number of circulating proteins accurately predicted future HF in the AGES‐RS cohort of older adults, and they alone encompass the risk information found in a collection of clinical data. Incident HF events were predicted up to 8 years, with predictor performance significantly improving for events occurring less than 1 year ahead, a finding replicated in an external cohort study.

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Section: Resultsmentioning

confidence: 99%

Section: Bootstrap 500 Timesmentioning

confidence: 99%

Proteomic prediction of incident heart failure and its main subtypes

Emilsson,

Jonsson,

Austin

et al. 2023

European J of Heart Fail

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“…Participants were identified from a prospective longitudinal cohort study (NCT02326324), which has been described previously 8 . Briefly, consecutive patients undergoing clinically indicated cardiovascular magnetic resonance imaging (CMR) at Manchester University NHS Foundation Trust, UK, between 1 April 2016 and 31 May 2018 were prospectively recruited.…”

Section: Methodsmentioning

confidence: 99%

Identification of heart failure hospitalization from NHS Digital data: comparison with expert adjudication

Soltani,

Bradley,

Bonandi

et al. 2024

ESC Heart Failure

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AimsPopulation‐wide, person‐level, linked electronic health record data are increasingly used to estimate epidemiology, guide resource allocation, and identify events in clinical trials. The accuracy of data from NHS Digital (now part of NHS England) for identifying hospitalization for heart failure (HHF), a key HF standard, is not clear. This study aimed to evaluate the accuracy of NHS Digital data for identifying HHF.Methods and resultsPatients experiencing at least one HHF, as determined by NHS Digital data, and age‐ and sex‐matched patients not experiencing HHF, were identified from a prospective cohort study and underwent expert adjudication. Three code sets commonly used to identify HHF were applied to the data and compared with expert adjudication (I50: International Classification of Diseases‐10 codes beginning I50; OIS: Clinical Commissioning Groups Outcomes Indicator Set; and NICOR: National Institute for Cardiovascular Outcomes Research, used as the basis for the National Heart Failure Audit in England and Wales). Five hundred four patients underwent expert adjudication, of which 10 (2%) were adjudicated to have experienced HHF. Specificity was high across all three code sets in the first diagnosis position {I50: 96.2% [95% confidence interval (CI) 94.1–97.7%]; NICOR: 93.3% [CI 90.8–95.4%]; OIS: 95.6% [CI 93.3–97.2%]} but decreased substantially as the number of diagnosis positions expanded. Sensitivity [40.0% (CI 12.2–73.8%)] and positive predictive value (PPV) [highest with I50: 17.4% (CI 8.1–33.6%)] were low in the first diagnosis position for all coding sets. PPV was higher for the National Heart Failure Audit criteria, albeit modestly [36.4% (CI 16.6–62.2%)].ConclusionsNHS Digital data were not able to accurately identify HHF and should not be used in isolation for this purpose.

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“…perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted March 15, 2023. ; https://doi.org/10.1101/2023.03.14.23287278 doi: medRxiv preprint predictive model had a c-statistic of 0.71 and 0.70 for HF hospitalization and death, respectively [11] ; while the c-statistics of Joshua's prediction model were 0.80 and 0.79, respectively [10][11][12] . Unfortunately, these prediction models almost all excluded or did not focus on patients receiving MHD, so they are not suitable for this specific population.…”

Section: Introductionmentioning

confidence: 99%

“…A meta-analysis reported that only 33% of the 117 published predictive models were validated in separate cohort studies, and most studies only reported moderate predictive performance [9,10] . Joanne’s predictive model had a c-statistic of 0.71 and 0.70 for HF hospitalization and death, respectively [11] ; while the c-statistics of Joshua’s prediction model were 0.80 and 0.79, respectively [10–12] . Unfortunately, these prediction models almost all excluded or did not focus on patients receiving MHD, so they are not suitable for this specific population.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis

tang

Yuan

Wang

et al. 2023

Preprint

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Background: Heart failure (HF) is a common cardiovascular disease in patients receiving maintenance hemodialysis (MHD). Once these patients on MHD exhibit HF, their hospitalization rate, mortality, and economic burden will be significantly increased. Early identification and prediction of hospitalization and death are of great importance for reducing hospitalization and mortality. This study used multicenter clinical data to develop and externally validate clinical risk models to predict expected mortality and HF hospitalization rates in MHD patients with HF. Materials and Methods: From January 2017 to October 2022, 348 patients receiving MHD from four participating centers were enrolled. Demographic data, MHD treatment modalities, laboratory tests, and echocardiography data were collected when the initial event occurred. Three centers were randomly assigned to the modeling dataset (n=258), and one center was assigned to the external validation set (n=90). Considering a composite outcome of HF hospitalization and death as the primary endpoint and hospitalization due to HF or death as the secondary endpoint, a COX clinical prediction model was constructed and verified using internal and external datasets. Results: The median age of patients in the modeling cohort was 63 years old, 41.5% of patients were women; 165 (61%) had a history of HF; 81 (31.4%) were hospitalized for HF; and 39 (15.1%) patients had died. The c-statistic values for composite outcome, hospitalization for HF, and mortality were 0.812, 0.808, and 0.811, respectively. The predictors of death and hospitalization outcomes caused by HF are significantly different. The strongest predictors of HF hospitalization outcomes were advanced age, multiple HF hospitalizations, hyponatremia, high levels of NT-proBNP and hs-cTnT, and larger MVe values. The strongest predictors of mortality were longer dialysis age, combined atrial fibrillation, calcification of the aortic or mitral valve (especially calcification, and in particular aortic valve calcification), pleural effusion, low serum sodium, and higher levels of hs-cTnT. The median age of the patients in the external validation cohort was 63 years old; 28.8% were female; 35 (38.1%) had a history of HF; 11 (12.2%) were hospitalized for HF; and 5 (5.6%) died. The c-statistic of the predictive models for composite outcome, hospitalisation for HF, and mortality was comparable to that of the modelling cohort. Conclusion: The model established in this study is stable and reliable and the included variables are easily obtained from the routine clinical environment. The model can provide useful risk factors and prognostic information for patients with MHD combined with HF.

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Predicting hospitalisation for heart failure and death in patients with, or at risk of, heart failure before first hospitalisation: a retrospective model development and external validation study

Cited by 19 publications

References 38 publications

Proteomic prediction of incident heart failure and its main subtypes

Proteomic prediction of incident heart failure and its main subtypes

Identification of heart failure hospitalization from NHS Digital data: comparison with expert adjudication

Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis

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