Predictive models for identifying risk of readmission after index hospitalization for heart failure: A systematic review

Mahajan, Satish M.; Heidenreich, Paul A.; Abbott, Bruce T.; Newton, Ana; Ward, Deborah

doi:10.1177/1474515118799059

Cited by 51 publications

(42 citation statements)

References 39 publications

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“…We did not find any significant differences in demographic and clinical characteristics between single and readmitted patients in contrast to other studies showing that older age, LV function <45% and an increase in the number of risk factors do increase the risk of rehospitalizations. 41 , 42 , 43 Our results may be explained by the nature of the database (no actual follow-up of patients nor does the database has out-patient monitoring data) and the patient population (all HF patients included in the study period older than 18 years, compared to newly diagnosed patients). This also permits us from doing any robust statistics including follow-up analyses such as a survival analysis.…”

Section: Resultsmentioning

confidence: 89%

Heart Failure Hospitalizations and Risk Factors among the Multi-Ethnic Population from a Middle Income Country: The Suriname Heart Failure Studies

Sairras

Baldew

Hilst

et al. 2021

Journal of the National Medical Association

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Introduction Heart failure (HF) is an emerging epidemic with poor disease outcomes and differences in its prevalence, etiology and management between and within world regions. Hypertension (HT) and ischemic heart disease (IHD) are the leading causes of HF. In Suriname, South-America, data on HF burden are lacking. The aim of this Suriname Heart Failure I (SUHF–I) study, is to assess baseline characteristics of HF admitted patients in order to set up the prospective interventional SUHF-II study to longitudinally determine the effectiveness of a comprehensive HF management program in HF patients. Methods A cross-sectional analysis was conducted of Thorax Center Paramaribo (TCP) discharge data from January 2013-December 2015. The analysis included all admissions with primary or secondary discharge of HF ICD-10 codes I50-I50.9 and I11.0 and the following variables: patient demographics (age, sex, and ethnicity), # of readmissions, risk factors (RF) for HF: HT, diabetes mellitus (DM), smoking, and left ventricle (LV) function. T-tests were used to analyze continuous variables and Chi-square test for categorical variables. Differences were considered statistically significant when a p-value <0.05 is obtained. Results 895 patients (1:1 sex ratio) with either a primary (80%) or secondary HF diagnosis were admitted. Female patients were significantly older (66.2 ± 14.8 years, p < 0.01) at first admission compared to male patients (63.5 ± 13.7 years) and the majority of admissions were of Hindustani and Creole descent. HT, DM and smoking were highly prevalent respectively 62.6%, 38.9 and 17.3%. There were 379 readmissions (29.1%) and 7% of all admissions were readmissions within 30 days and 16% were readmissions for 31-365 day. IHD is more prevalent in patients from Asian descendant (52.2%) compared to African descendant (11.7%). Whereas, HT (39.3%) is more prevalent in African descendants compared to Asian descendants (12.7%). There were no statistically significant differences in age, sex, ethnicity, LV function and RFs between single admitted and readmitted patients. Conclusion RF prevalence, ethnic differences and readmission rates in Surinamese HF patients are in line with reports from other Caribbean and Latin American countries. These results are the basis for the SUHF-II study which will aid in identifying the country specific and clinical factors for the successful development of a multidisciplinary HF management program.

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Section: Resultsmentioning

confidence: 89%

Heart Failure Hospitalizations and Risk Factors among the Multi-Ethnic Population from a Middle Income Country: The Suriname Heart Failure Studies

Sairras

Baldew

Hilst

et al. 2021

Journal of the National Medical Association

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“…Risk factors related to chronic diseases are consistently high, among readmission rates; especially those for heart failure, determining the need for strategies to systematically resolve this problem. Databased care models that incorporate risk predictions can be used at the point of care to optimize interventions and provide patient-centered care 28 .…”

Section: Discussionmentioning

confidence: 99%

Risk of hospitalization of elderly rural workers in the state of Rio Grande do Sul

Trindade

Schukes

Moraes

et al. 2019

Rev. bras. geriatr. gerontol.

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Objective : To analyze the risk of hospitalization of elderly rural workers in the state of Rio Grande do Sul, Brazil. Method : A cross-sectional, population-based study was carried out of retired rural workers (N=604), over 60 years of age, of both genders, selected by clusters. In order to evaluate the risk of hospitalization, the Probability of Repeated Hospitalization (or PIR) instrument validated and evaluated for Brazil was used. Risk of hospitalization was calculated through logistic regression analysis, and was classified into the following strata: low (<0.300); medium (0.300-0.399); medium-high (0.400-0.499) and high (≥0.500). Results : The rural elderly persons surveyed had a low risk of hospitalization (n=553; 91.6%). There was a predominance of men among the medium to high risk categories (n=42; 82.3%), distributed mainly in the Santa Maria, Sul and Camaquã regions. Conclusion: The results of the present study suggest a low risk of hospitalization among this population, however, there is a need for improved, more profound and robust research into the identification of factors associated with the health specificities of this population.

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“…Programs give great emphasis to education and the promotion of patient self-care (13). In HF, self-management involves the self-care skills and behaviours: of symptom recognition, weight monitoring, salt dietary restriction, exercise, medication adherence and steps to follow in case of exacerbation (14). In international (1,9) and local guidelines (15), health professionals recommendations include providing education and counselling on: aetiology, definition, symptoms and signs of HF, pharmacological treatment, modification of the risk factors, dietary restrictions, exercise, sexual activity, immunisation, sleep and respiratory pattern disorders, adherence to treatment, psychological aspects and prognosis.…”

Section: Introductionmentioning

confidence: 99%

Nursing knowledge of the principles of self‐care of heart failure in primary care: a multicentre study

Pibernat

Duran

Garín

et al. 2019

Scandinavian Caring Sciences

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Introduction Nurses play an important part in the education of patients with HF. To prepare patients with HF for self‐care maintenance behaviours, nurses must have knowledge of basic self‐care maintenance principles. Aim study The aim of this study was to determine the degree of knowledge of primary care (PC) nurses on the principles of self‐management of HF and variables associated with this. Methodology This is an observational, cross‐sectional descriptive study, carried out in 2014, in the city of Barcelona (Catalonia). Nurses’ Knowledge of Heart Failure Education Principles questionnaire (NKHFEP) was used to assess the principles of HF self‐care education. Instrument items assess knowledge of nurses on 5 themes: diet, liquids/weight, worsening signs or symptoms, medication and exercise. Factors related to adequate knowledge were evaluated. Results Of 216 PC nurses, who completed the questionnaire, the average score was 15.6 (SD: 2.2). Only 36 (16.7%) obtained an adequate level of knowledge and defined as a score ≥ 18 points. In multivariate logistic regression, nurse factors associated with an adequate knowledge of principles of self‐care of HF were having achieved a PhD degree (OR: 36.4, 95% CI: 2.8–468.2, p = 0.006) and previous specific training in HF (OR: 19.8, 95% CI: 1.4–279.3, p = 0.026). Conclusions The degree of knowledge of PC nurses in the principles of self‐care in HF was higher among nurses who had completed the doctorate and in nurses who had received specific training in HF.

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Predictive models for identifying risk of readmission after index hospitalization for heart failure: A systematic review

Cited by 51 publications

References 39 publications

Heart Failure Hospitalizations and Risk Factors among the Multi-Ethnic Population from a Middle Income Country: The Suriname Heart Failure Studies

Heart Failure Hospitalizations and Risk Factors among the Multi-Ethnic Population from a Middle Income Country: The Suriname Heart Failure Studies

Risk of hospitalization of elderly rural workers in the state of Rio Grande do Sul

Nursing knowledge of the principles of self‐care of heart failure in primary care: a multicentre study

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