Incorporating patient-centered factors into heart failure readmission risk prediction: A mixed-methods study

Ahmad, Faraz; French, Benjamin; Bowles, Kathryn H.; Sevilla-Cazes, Jonathan; Jaskowiak‐Barr, Anne; Gallagher, Thomas R.; Kangovi, Shreya; Goldberg, Lee R.; Barg, Frances K.; Kimmel, Stephen E.

doi:10.1016/j.ahj.2018.03.002

Cited by 9 publications

(8 citation statements)

References 25 publications

(27 reference statements)

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“…small studies looking to incorporate questionnaire derived markers have failed to demonstrate improved prognostication [37,38] despite the apparent use of 'unstructured' free-text data in another study [39]. Perioperatively it is well known that clinician judgement improves the performance of commonly utilised risk tools such as SORT (Surgical Outcome Risk Tool) [40].…”

Section: Discussionmentioning

confidence: 99%

“…However, conflicting findings have been shown in other settings. Although qualitative data derived from patient interviews has suggested that social support and psychological state are influences on individual readmissions with heart failure[36], small studies looking to incorporate questionnaire derived markers have failed to demonstrate improved prognostication [37,38] despite the apparent use of ‘unstructured’ free-text data in another study [39]. Perioperatively it is well known that clinician judgement improves the performance of commonly utilised risk tools such as SORT (Surgical Outcome Risk Tool) [40].…”

Section: Discussionmentioning

confidence: 99%

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Can process mapping and a multi-site Delphi of perioperative professionals inform our understanding of system-wide factors that may impact operative risk?

Stubbs

Bashford

Gilder

et al. 2022

Preprint

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ObjectivesTo examine whether the use of process mapping and a multidisciplinary Delphi can identify potential contributors to perioperative risk. We hypothesised that this approach may identify factors not represented in common perioperative risk tools and give insights of use to future research in this area.DesignMultidisciplinary modified Delphi studySettingTwo centres (one tertiary, one secondary) in the United Kingdom during 2020 amidst coronavirus pressures.Participants91 stakeholders from 23 professional groups involved in the perioperative care of older patients. Key stakeholder groups were identified through the use of process mapping of local perioperative care pathways.ResultsResponse rate ranged from 51% in round one to 19% in round three. After round one, free text suggestions from the panel were combined with variables identified from perioperative risk scores. This yielded a total of 410 variables that were voted on in subsequent rounds. Including new suggestions from round two, 468/519 (90%) of the statements presented to the panel reached a consensus decision by the end of round three. Identified risk factors included patient level factors (such as ethnicity and socio-economic status); and organisational or process factors related to the individual hospital (such as policies, staffing, and organisational culture). 66/160 (41%) of the new suggestions did not feature in systematic reviews of perioperative risk scores or key process indicators. No factor categorised as ‘organisational’ is currently present in any perioperative risk score.ConclusionsThrough process mapping and a modified Delphi we gained insights into additional factors that may contribute to perioperative risk. Many were absent from currently used risk stratification scores. These results enable an appreciation of the contextual limitations of currently used risk tools and could support future research into the generation of more holistic datasets for the development of perioperative risk assessment tools.Strengths and Weaknesses-Novel use of process mapping to identify key perioperative stakeholders-Multidisciplinary Delphi panel to gain breadth of perspective-Performed across two sites-Comprehensive results may be of use to other researchers designing perioperative research databases-Results may be limited by low response rate in final round (although majority of consensus decisions made in round two)

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Can process mapping and a multi-site Delphi of perioperative professionals inform our understanding of system-wide factors that may impact operative risk?

Stubbs

Bashford

Gilder

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…The discrimination of six models was evaluated in multiple independent cohorts and was pooled in metaanalyses (figure 3, online supplemental figures 1-6): the CMS AMI (Centers for Medicare and Medicaid Services Acute Myocardial Infarction) administrative model 19 20 (0.65, 95% CI 0.56 to 0.73); the CMS HF (Heart Failure) administrative model [21][22][23][24][25][26][27][28][29] (0.60, 95% CI 0.58 to 0.62); the CMS HF medical model 24 27 30-32 (0.60, 95% CI 0.58 to 0.62); the HOSPITAL (Hemoglobin level, discharged from Oncology, Sodium level, Procedure during admission, Index admission Type, Admission, Length of stay) score [33][34][35] (0.64, 95% CI 0.58 to 0.70); the GRACE (Global Registration of Acute Coronary Events) score 36 37 (0.78, 95% CI 0.63 to 0.86); and the LACE (Length of stay, acuity of the Admission, Comorbidity of the patient and Emergency department use in the duration of 6 months before admission) score 23 28 29 34 38 (0.62, 95% CI 0.53 to 0.70).…”

Section: Prediction Modelsmentioning

confidence: 99%

Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis

et al. 2021

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ObjectiveTo describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.DesignSystematic review and meta-analysis.Data sourceMedline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.Eligibility criteria for selecting studiesStudies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months.Primary and secondary outcome measuresModel discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.ResultsSixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled.ConclusionSome promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.PROSPERO registration numberCRD42020159839.

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“…Although prior studies have suggested that patients are readmitted disproportionately early, no study has rigorously distinguished the weight of risk factors between the precise timing of 30 day and 1 year readmission or death. In addition, prior prediction models using complex methods were only modest discriminative ability 5,6,16 . Hence, these limitations emphasize the need for a user‐friendly model to accurately target elderly patients with multimorbidity.…”

Section: Introductionmentioning

confidence: 99%

A novel nomogram to predict all‐cause readmission or death risk in Chinese elderly patients with heart failure

Yang

Tao

et al. 2020

ESC Heart Failure

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Aims Elderly patients with heart failure (HF) are associated with frequent all-cause readmission or death. The present study sought to develop an accurate and easy-to-use model to predict all-cause readmission or death risk in Chinese elderly patients with HF. Methods and resultsThis was a prospective cohort study in patients with HF aged 65 or older. Demographic, comorbidity, laboratory, and medication data were collected. A Cox regression model was used to identify factors for the prediction of readmission or death at 30 days and 1 year. A nomogram was developed with bootstrap validation. Of the included 854 patients, the cumulative all-cause readmission and mortality rates were 10.5% and 11.6% at 30 days and 34.9% and 19.7% at 1 year, respectively. The independent risk factors associated with both 30 day and 1 year readmission or death were older age, stroke, diastolic blood pressure < 60 mmHg, body mass index ≤ 18.5 kg/m 2 , lower estimated glomerular filtration rate, and BNP > 400 pg/mL (all P < 0.05). Anaemia, abnormal neutrophils, and admission without angiotensin-converting enzyme inhibitors/angiotensin receptor blockers were the specific independent risk factors of 30 day all-cause readmission or death (all P < 0.05), whereas serum sodium ≤ 140 mmol/L and admission without beta-blockers were the specific independent risk factors of 1 year all-cause readmission or death (all P < 0.05). The Cindex of the 30 day and 1 year diagnosis prediction model was 0.778 [95% confidence interval (CI) 0.693-0.862] and 0.738 (95% CI 0.640-0.836), respectively. Conclusions We developed accurate and easy-to-use nomograms to predict all-cause readmission or death in Chinese elderly patients with HF. The nomograms will assist in reducing the all-cause readmission and mortality rates.

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Incorporating patient-centered factors into heart failure readmission risk prediction: A mixed-methods study

Cited by 9 publications

References 25 publications

Can process mapping and a multi-site Delphi of perioperative professionals inform our understanding of system-wide factors that may impact operative risk?

Can process mapping and a multi-site Delphi of perioperative professionals inform our understanding of system-wide factors that may impact operative risk?

Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis

A novel nomogram to predict all‐cause readmission or death risk in Chinese elderly patients with heart failure

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