Derivation and validation of a predictive model for the readmission of patients with diabetes mellitus treated in internal medicine departments

Ena, Javier; Gómez‐Huelgas, Ricardo; Gracia-Tello, Borja; Vázquez-Rodríguez, Patricia; Alcalá-Pedrajas, J.N.; Carrasco-Sánchez, Francisco Javier; Murcia-Casas, Belén; Romero-Sánchez, M.; Heras, José Vicente Segura; Carretero, J.

doi:10.1016/j.rceng.2018.03.018

Cited by 3 publications

(2 citation statements)

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“…With our selected criteria, model performance was as satisfactory as expected from published 90-day all-cause readmission models. [28][29][30] Specific readmission models within 90 days are rare 31 with only one publication reporting a c-statistic of 0.65 (for a psychiatric population). 32 Regardless of the practical relevance of PIP, other well-known predictors contributed significantly to the predictive performance, including comorbidities [33][34][35] or the number of prior hospitalizations.…”

Section: Discussionmentioning

confidence: 99%

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

et al. 2022

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Background Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks. Objectives To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data. Methods In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined (“stacked”) to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics. Results While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66–0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64–0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63–0.66]), HF (c-statistics: 0.61 [95% CI: 0.60–0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56–0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47–0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63–0.64]. Conclusion PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.

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Section: Discussionmentioning

confidence: 99%

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

et al. 2022

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“…Importantly, however, there has been considerably more research looking at the impact of inpatient hypoglycaemia on readmission patterns for patients discharged from hospital with diabetes. [19][20][21][22][23] These studies all considered generalised populations of people with diabetes admitted to hospital, rather than specific subsets of patients. It is likely that this focus on hypoglycaemia and readmission patterns is driven by an awareness of hypoglycaemia as a major driver of hospital admission and, therefore, cost in diabetes management.…”

Section: Introductionmentioning

confidence: 99%

Association between glycosylated haemoglobin and outcomes for patients discharged from hospital with diabetes: A health informatics approach

et al. 2021

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Aims/Objectives Extensive research considers associations between inpatient glycaemic control and outcomes during hospital admission; this cautions against overly tight glycaemic targets. Little research considers glycaemic control following hospital discharge. This is despite a clear understanding that people with diabetes are at increased risk of negative outcomes, following discharge. We evaluate absolute and relative Hba1c values, and frequency of Hba1c monitoring, on readmission and mortality rates for people discharged from hospital with diabetes. Methods All discharges (n = 46,357) with diabetes from a major tertiary referral centre over 3 years were extracted, including biochemistry data. We conducted an evaluation of association between Hba1c, mortality and readmission, statistical significance and standardised Cohen’s D effect size calculations. Results 399 patients had a Hba1c performed during their admission. 3,138 patients had a Hba1c within 1 year of discharge. Mean average Hba1c for readmissions was 57.82 vs 60.39 for not readmitted (p = 0.009, Cohen’s D 0.28). Mean average number of days to Hba1c testing in readmitted was 97 vs 113 for those not readmitted (p = 0.00006, Cohen’s D 0.39). Further evaluation of mortality outcomes, cohorts of T1DM and T2DM and association of relative change in Hba1c was performed. Conclusions Lower Hba1c values following discharge from hospital are significantly associated with increased risk of readmission, as is a shorter duration until testing. Similar patterns observed for mortality. Findings particularly prominent for T1DM. Further research needed to consider underlying causation and design of appropriate risk stratification models.

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Machine learning methods for hospital readmission prediction: systematic analysis of literature

Chen

Madanian

Airehrour

et al. 2022

J Reliable Intell Environ

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Derivation and validation of a predictive model for the readmission of patients with diabetes mellitus treated in internal medicine departments

Cited by 3 publications

References 36 publications

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

Association between glycosylated haemoglobin and outcomes for patients discharged from hospital with diabetes: A health informatics approach

Machine learning methods for hospital readmission prediction: systematic analysis of literature

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