Clinical and nutritional predictors of hospital readmission within 30 days

Cruz, Paula Luiza Menezes; Soares, Bruna Lúcia de Mendonça; Silva, Jacqueline Elineuza da; Silva, Renata Reis de Lima E

doi:10.1038/s41430-021-00937-y

Cited by 10 publications

(13 citation statements)

References 42 publications

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“…Findings in our report indicated that the risk of readmission for admission with edema at first were 3.5 (AHR = 3.5 = 95%CI; 1.92, 6.2, p = 0.018) times the increased risk of relapse as compared with non-edematous children admitted cases. This finding is similar to the report of a study conducted in Hadya, Ethiopia [ 13 , 23 ], and Metekl, Pawe hospitals [ 22 ]. Possibly early discharge form an inpatient treatment programs by using weight as discharge criteria from the treatment centers; they may have early weight gain as a remnant of nutritional edema-related weight.…”

Section: Discussionsupporting

confidence: 92%

“…Many children younger than 5 years in developing countries are exposed to multiple risks, including poverty, malnutrition, poor health, and not stimulating home environments [ 6 , 9 ]. These can detrimentally affect their cognitive, motor, and social-emotional development, leading to repeated bouts of SAM [ 13 ]. Nearly half of all deaths in under-five children are attributable to undernutrition through increasing the frequency and severity of infections and delaying recovery [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Time to readmission and associated factors after post treatment discharge of severe acute malnourished under-five children in Pawe General Hospital

Kebede

2022

J Health Popul Nutr

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Background Relapse or repeated episodes is the admission of a child with the diagnosis of severe acute malnutrition (SAM) after being discharged to a status of treated and cured from a stabilizing center. A child may experience more than one episode of SAM depending on the improvement of the underlying comorbidity. Thus, this study aimed to estimate the time to readmission of SAM and associated factors for under-five children in North West Ethiopia. Methods An institution-based retrospective cohort study was employed in 760 files of under-five children spanning from 2014/15 to 2019/20. The data extraction tool was developed from SAM treatment guidelines and medical history sheets. Epi Data version 3.2 and STATA version 14 were used for data entry and final analysis, respectively. After checking all assumptions, the multivariable Cox Proportional Hazard model was fitted to the isolated independent predictors for time to readmission. A categorical variable with p < 0.05 was considered a risk factor for the relapse of SAM. Result The mean (± SD) age of participant children was 27.8 (± 16.5) months with mean (± SD) time to relapse of SAM cases were 30.4(± 21.39) weeks posttreatment discharge. The overall incidence density rate of relapse was determined as 10.8% (95% CI 8.3; 12.6). The average time (± SD) for treatment recovery from the first admission of the SAM case was 28.8(± 18.7) days. Time of readmission was significantly associated with living in rural resident (AHR 5⋅3 = 95% CI, 2⋅95, 13⋅87, p = 0.021), having HIV infection (AHR6⋅8 = 95%CI; 4.1–11.9 p = 0.001), and first admission with edema (AHR = 3.5 = 95% CI; 1.92, 6.2, p = 0.018). Conclusion Nearly one in every ten severely acute malnourished under-five children relapsed within a mean time to relapse 30.4(± 21.39) weeks posttreatment discharge. Time to relapse was significantly associated with being a rural resident for children, having edema during the first admission, and being HIV-infected cases. A protocol ought to be drafted for extending Supplementary Nutrition in Acute Malnutrition management program following discharge is highly needed.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Time to readmission and associated factors after post treatment discharge of severe acute malnourished under-five children in Pawe General Hospital

Kebede

2022

J Health Popul Nutr

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“…14 They observed that low unadjusted calf circumference at ICU admission increased the risk of ICU mortality by 2.63-fold in 249 patients with COVID-19. 14 However, their patient characteristics differed significantly from those in our sample, presenting higher severity with a mean SOFA score of 9.6 ± 3.5, median ICU stay of 14 days (range, [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24], and an ICU death rate of 48.6%. Notably, they examined unadjusted calf circumference in their analysis.…”

Section: Resultsmentioning

confidence: 73%

“…19,32 Its raw measurement requires interpretation in the context of an individual's body adiposity because it includes subcutaneous adipose tissue, leading to an overestimation of muscle mass. 16,33 However, most studies assessing calf circumference as a muscularity marker and its prognostic value in hospitalized patients 9,10,[12][13][14]22,27,28 and outpatients overlook BMI's impact on measurements and do not include BMI as a covariate in their multivariable analysis. [23][24][25] A further concern involves the values adopted to classify low calf circumference.…”

Section: Resultsmentioning

confidence: 99%

Absence of association between low calf circumference, adjusted or not for adiposity, and ICU mortality in critically ill adults: A secondary analysis of a cohort study

Bernardes,

Stello,

Milanez

et al. 2024

J Parenter Enteral Nutr

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Background and AimDespite its correlation with skeletal muscle mass and its predictive value for adverse outcomes in clinical settings, calf circumference is a metric underexplored in intensive care. We aimed to determine whether adjusting low calf circumference for adiposity provides superior prognostic value for intensive care unit (ICU) mortality and other clinical outcomes in critically ill patients than its unadjusted measurement.MethodsIn a secondary analysis of a cohort study across five ICUs, we assessed critically ill patients within 24 hours of ICU admission. We adjusted calf circumference for body mass index (BMI) (25–29.9, 30–39.9, and ≥ 40 kg/m2) by subtracting it in 3, 7, or 12 cm, respectively. Values ≤ 34 cm for males and ≤ 33 cm for females identified low calf circumference.ResultsWe analyzed 325 patients. In the primary risk‐adjusted analysis, the ICU death risk was similar between the low and preserved calf circumference (BMI‐adjusted) groups (HR 0.90, 95% CI 0.47–1.73). Low calf circumference (unadjusted) increased the odds of ICU readmission 2.91 times (95% CI, 1.40–6.05). Every 1 cm increase in calf circumference as a continuous variable reduced ICU readmission odds by 12%. Calf circumference showed no significant association with other clinical outcomes.ConclusionsCalf circumference BMI‐adjusted did not exhibit independent associations with ICU and in‐hospital death, nor did ICU and in‐hospital LOS compared to its unadjusted measurement. However, low calf circumference (unadjusted and BMI‐adjusted) was independently associated with ICU readmission, mainly when analyzed as a continuous variable.This article is protected by copyright. All rights reserved.

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“…(3) Thirdly, hospital readmission prediction is essentially data driven, where features and samples are the key to ensure model performance. While many methods have been using a wide variety of patient treatment data, such as patient blood tests, nutritional factors [ 12 ], treatment etc, the data privacy and the Health Insurance Portability and Accountability Act (HIPAA) [ 13 ] limit sensitive features to be used in general readmission prediction setting. Creating features strictly complying to the HIPAA and privacy regulation, and also effective and informative for learning, is crucial for hospital readmission prediction.…”

Section: Introductionmentioning

confidence: 99%

Nationwide hospital admission data statistics and disease-specific 30-day readmission prediction

Wang

Zhu

2022

Health Inf Sci Syst

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Purpose Hospital readmission prediction uses historical patient visit data to train machine learning models to predict risk of patients being readmitted after the discharge. Data used to train models, such as patient demographics, disease types, localized distributions etc., play significant roles in the model performance. To date, many methods exist for hospital readmission prediction, but answers to some important questions still remain open. For example, how will demographics, such as gender, age, geographic, impact on readmission prediction? Do patients suffering from different diseases vary significantly in their readmission rates? What are the nationwide hospital admission data characteristics? and how do hospital speciality, ownership, and locations impact on their readmission rates? In this study, we carry systematic investigations to answer the above questions, and propose a predictive modeling framework to predict disease-specific 30-day hospital readmission. Methods We first implement statistics analysis by using National Readmission Databases (NRD) with over 15 million hospital visits. After that, we create features and disease-specific readmission datasets. An ensemble learning framework is proposed to conduct hospital readmission prediction and Friedman test and Nemenyi post-hoc test is used to validate our proposed method. Results Using National Readmission Databases (NRD), with over 15 million hospital visits, as our testbed, we summarize nationwide patient admission data statistics, in related to demographic, disease types, and hospital factors. We use feature engineering to design 526 representative features to model each patient visit. Our studies found that readmission rates vary significantly from diseases to diseases. For six diseases studied in our research, their readmission rates vary from 1.832 (Pneumonia) to 8.761% (Diabetes). Using random sampling and voting approaches, our study shows that soft voting outperforms hard voting on majority results, especially for AUC and balanced accuracy which are the main measures for imbalanced data. Random under sampling using 1.1:1 for negative:positive ratio achieves the best performance for AUC, balanced accuracy, and F1-score. Conclusion This paper carries out systematic studies to understand US nationwide hospital readmission data statistics, and further designs a machine learning framework for disease-specific 30-day hospital readmission prediction. Our study shows that hospital readmission rates vary significantly with respect to different disease types, gender, age groups, any other factors. Gradient boosting achieves the best performance for disease specific hospital readmission prediction.

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Clinical and nutritional predictors of hospital readmission within 30 days

Cited by 10 publications

References 42 publications

Time to readmission and associated factors after post treatment discharge of severe acute malnourished under-five children in Pawe General Hospital

Time to readmission and associated factors after post treatment discharge of severe acute malnourished under-five children in Pawe General Hospital

Absence of association between low calf circumference, adjusted or not for adiposity, and ICU mortality in critically ill adults: A secondary analysis of a cohort study

Nationwide hospital admission data statistics and disease-specific 30-day readmission prediction

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