Dataset Information

Does higher quality of diabetes management in family practice reduce unplanned hospital admissions?

ABSTRACT:

Objective

To investigate the association between indicators of quality of diabetic management in English family practices and emergency hospital admissions for short-term complications of diabetes.

Study setting

A total of 8,223 English family practices from 2001/2002 to 2006/2007.

Study design

Multiple regression analyses of associations between admissions and proportions of practice diabetic patients with good (glycated hemoglobin [HbA1c] ?7.4 percent) and moderate (7.4 percent DataPractice quality measures extracted from practice records linked with practice-level hospital admissions data and practice-level covariates data.

Principal findings

Practices with 1 percent more patients with moderate rather than poor glycemic control on average had 1.9 percent (95 percent CI: 1.1-2.6 percent) lower rates of emergency admissions for acute hyperglycemic complications. Having more patients with good rather than moderate control was not associated with lower admissions. There was no association of moderate or good control with hypoglycemic admissions.

Conclusion

Cross-sectionally, family practices with better quality of diabetes care had fewer emergency admissions for short-term complications of diabetes. Over time, after controlling for national trends in admissions, improvements in quality in a family practice were associated with a reduction in its admissions.

SUBMITTER: Dusheiko M

PROVIDER: S-EPMC3034260 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Publications

Does higher quality of diabetes management in family practice reduce unplanned hospital admissions?

Dusheiko Mark M Doran Tim T Gravelle Hugh H Fullwood Catherine C Roland Martin M

Health services research 20100928 1 Pt 1

<h4>Objective</h4>To investigate the association between indicators of quality of diabetic management in English family practices and emergency hospital admissions for short-term complications of diabetes.<h4>Study setting</h4>A total of 8,223 English family practices from 2001/2002 to 2006/2007.<h4>Study design</h4>Multiple regression analyses of associations between admissions and proportions of practice diabetic patients with good (glycated hemoglobin [HbA1c] ≤7.4 percent) and moderate (7.4 p ...[more]

PMID: 20880046

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Project description:BackgroundEnhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection.ObjectiveThe study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes.MethodsGradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles.ResultsPerformance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%).ConclusionsThe results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation.

Project description:BackgroundIdentification of community-dwelling older adults at risk of unplanned hospitalizations is of importance to facilitate preventive interventions. Our objective was to review and appraise the methodological quality and predictive performance of prediction models for predicting unplanned hospitalizations in community-dwelling older adults.Methods and findingsWe searched MEDLINE, EMBASE and CINAHL from August 2013 to January 2021. Additionally, we checked references of the identified articles for the inclusion of relevant publications and added studies from two previous reviews that fulfilled the eligibility criteria. We included prospective and retrospective studies with any follow-up period that recruited adults aged 65 and over and developed a prediction model predicting unplanned hospitalizations. We included models with at least one (internal or external) validation cohort. The models had to be intended to be used in a primary care setting. Two authors independently assessed studies for inclusion and undertook data extraction following recommendations of the CHARMS checklist, while quality assessment was performed using the PROBAST tool. A total of 19 studies met the inclusion criteria. Prediction horizon ranged from 4.5 months to 4 years. Most frequently included variables were specific medical diagnoses (n = 11), previous hospital admission (n = 11), age (n = 11), and sex or gender (n = 8). Predictive performance in terms of area under the curve ranged from 0.61 to 0.78. Models developed to predict potentially preventable hospitalizations tended to have better predictive performance than models predicting hospitalizations in general. Overall, risk of bias was high, predominantly in the analysis domain.ConclusionsModels developed to predict preventable hospitalizations tended to have better predictive performance than models to predict all-cause hospitalizations. There is however substantial room for improvement on the reporting and analysis of studies. We recommend better adherence to the TRIPOD guidelines.

Dataset Information

Does higher quality of diabetes management in family practice reduce unplanned hospital admissions?

Objective

Study setting

Study design

Principal findings

Conclusion

Publications

Does higher quality of diabetes management in family practice reduce unplanned hospital admissions?

Similar Datasets

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