Transforming a Patient Registry Into a Customized Data Set for the Advanced Statistical Analysis of Health Risk Factors and for Medication-Related Hospitalization Research: Retrospective Hospital Patient Registry Study

Prescribing pattern insights from a longitudinal study of older adult inpatients with polypharmacy and chronic non-cancer pain

BACKGROUND

The present study sought to determine the prevalence of chronic non-cancer pain (CNCP) among older adult inpatients with polypharmacy. It also aimed to analyse prescription patterns and assess the therapy adequacy and patient complexity for those with and without CNCP.

METHODS

This 4-year longitudinal study examined data from an exhaustive acute care hospital register on home-dwelling older adult patients (≥65) with polypharmacy. Commonly known combinations of potentially inappropriate medications were used to estimate therapy adequacy. Patient complexity was evaluated by comparing number of comorbidities and investigating physical and cognitive deficits.

RESULTS

We determined a prevalence of CNCP of 9.7% among all older adult inpatients with polypharmacy, rising to 11.3% for those aged ≥85. Overall, CNCP patients were prescribed more drugs and had more comorbidities and physical and cognitive deficits than patients without CNCP. Older adult patients with CNCP received more analgesics, greater quantities of opioids, paracetamol and co-analgesics and elevated opioid dosages. Older adult patients with CNCP aged ≥85 received fewer analgesics, opioids, non-steroidal anti-inflammatory drugs and co-analgesics but more paracetamol. Older adult patients with CNCP were prescribed more potentially inappropriate medications involving opioids. In particular, 24.5% received an opioid and a hypnotic (benzodiazepine or Z-drug), and 8.6% received an opioid and a gabapentinoid.

CONCLUSION

Observed differences in medication use between older adult inpatients with or without CNCP may be relevant for clinical practice. Potentially inadequate co-prescribing (such as hypnotics and opioids) affects a higher proportion of patients with CNCP and may have serious unintended consequences.

SIGNIFICANCE STATEMENT

This study describes differences in prescription patterns between people with and without chronic non-cancer pain in a large dataset of 20,422 discharges. The differences found may be relevant to clinical practice. In particular, high co-prescribing of opioids and hypnotics may have serious unintended consequences. Greater physical and cognitive deficits may indicate greater patient complexity, and appropriate interventions need to be developed to improve the management of this vulnerable patient group.

Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants

Distinguishing clinical subgroups for patients suffering with diseases characterized by a wide phenotypic spectrum is essential for developing precision therapies. Patients with gain-of-function (GOF) variants in the SCN8A gene exhibit substantial clinical heterogeneity, viewed historically as a linear spectrum ranging from mild to severe. To test for hidden clinical subgroups, we applied two machine-learning algorithms to analyze a dataset of patient features collected by the International SCN8A Patient Registry. We used two research methodologies: a supervised approach that incorporated feature severity cutoffs based on clinical conventions, and an unsupervised approach employing an entirely data-driven strategy. Both approaches found statistical support for three distinct subgroups and were validated by correlation analyses using external variables. However, distinguishing features of the three subgroups within each approach were not concordant, suggesting a more complex phenotypic landscape. The unsupervised approach yielded strong support for a model involving three partially ordered subgroups rather than a linear spectrum. Application of these machine-learning approaches may lead to improved prognosis and clinical management of individuals with SCN8A GOF variants and provide insights into the underlying mechanisms of the disease.

Development of a patient-centred medication management model for polymedicated home-dwelling older adults after hospital discharge: results of a mixed methods study

OBJECTIVE

This study aimed to investigate medication management among polymedicated, home-dwelling older adults after discharge from a hospital centre in French-speaking Switzerland and then develop a model to optimise medication management and prevent adverse health outcomes associated with medication-related problems (MRPs).

DESIGN

Explanatory, sequential, mixed methods study based on detailed quantitative and qualitative findings reported previously.

SETTING

Hospital and community healthcare in the French-speaking part of Switzerland.

PARTICIPANTS

The quantitative strand retrospectively examined 3 years of hospital electronic patient records (n=53 690 hospitalisations of inpatients aged 65 years or older) to identify the different profiles of those at risk of 30-day hospital readmission and unplanned nursing home admission. The qualitative strand explored the perspectives of older adults (n=28), their informal caregivers (n=17) and healthcare professionals (n=13) on medication management after hospital discharge.

RESULTS

Quantitative results from older adults' profiles, affected by similar patient-related, medication-related and environment-related factors, were enhanced and supported by qualitative findings. The combined findings enabled us to design an interprofessional, collaborative medication management model to prevent MRPs among home-dwelling older adults after hospital discharge. The model comprised four interactive fields of action: listening to polymedicated home-dwelling older adults and their informal caregivers; involving older adults and their informal caregivers in shared, medication-related decision-making; empowering older adults and their informal caregivers for safe medication self-management; optimising collaborative medication management practices.

CONCLUSION

By linking the retrospective and prospective findings from our explanatory sequential study involving multiple stakeholders' perspectives, we created a deeper comprehension of the complexities and challenges of safe medication management among polymedicated, home-dwelling older adults after their discharge from hospital. We subsequently designed an innovative, collaborative, patient-centred model for optimising medication management and preventing MRPs in this population.

Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora

Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage, blindness, and other illnesses. Researchers are motivated to create a Machine Learning methodology that can predict diabetes in the future. Exploiting Machine Learning Algorithms (MLA) is essential if healthcare professionals are able to identify diseases more effectively. In order to improve the medical diagnosis of diabetes this research explored and contrasts various MLA that can identify diabetes risk early. The research includes the analysis on real datasets such as a clinical dataset gathered from a doctor in the Indian district of Bandipora in the years April 2021–Feb2022. MLA are currently important in the healthcare sector due to their prediction abilities. Researchers are using MLA to improve disease prediction and reduce cost. In this Paper author developed a methodology using Machine Learning Algorithms for Diabetes Disease Risk Prediction in North Kashmir. Six MLA have been successfully used in the experimental study such as Random Forest (RF), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boost (GB), Decision Tree (DT), and Logistic Regression (LR). RF is the most accurate classifier with the uppermost accuracy rate of 98 percent followed by MLP (90.99%), SVM (92%), GBC (97%), DT (96%), and LR (69%), respectively, with the balanced data set. Lastly, this study enables us to effectively identify the prevalence and prediction of diabetes.

Unplanned nursing home admission among discharged polymedicated older inpatients: a single-centre, registry-based study in Switzerland

OBJECTIVE

To investigate patient characteristics and the available health and drug data associated with unplanned nursing home admission following an acute hospital admission or readmission.

DESIGN

A population-based hospital registry study.

SETTING

A public hospital in southern Switzerland (Valais Hospital).

PARTICIPANTS

We explored a population-based longitudinal dataset of 14 705 hospital admissions from 2015 to 2018.

OUTCOME MEASURES

Sociodemographic, health and drug data, and their interactions predicting the risk of unplanned nursing home admission.

RESULTS

The mean prevalence of unplanned nursing home admission after hospital discharge was 6.1% (n=903/N=14 705). Our predictive analysis revealed that the oldest adults (OR=1.07 for each additional year of age; 95% CI 1.05 to 1.08) presenting with impaired functional mobility (OR=3.22; 95% CI 2.67 to 3.87), dependency in the activities of daily living (OR=4.62; 95% CI 3.76 to 5.67), cognitive impairment (OR=3.75; 95% CI 3.06 to 4.59) and traumatic injuries (OR=1.58; 95% CI 1.25 to 2.01) had a higher probability of unplanned nursing home admission. The number of International Classification of Diseases, 10th version diagnoses had no significant impact on nursing home admissions, contrarily to the number of prescribed drugs (OR=1.17; 95% CI 1.15 to 1.19). Antiemetics/antinauseants (OR=2.53; 95% CI 1.21 to 5.30), digestives (OR=1.78; 95% CI 1.09 to 2.90), psycholeptics (OR=1.76; 95% CI 1.60 to 1.93), antiepileptics (OR=1.49; 95% CI 1.25 to 1.79) and anti-Parkinson's drugs (OR=1.40; 95% CI 1.12 to 1.75) were strongly linked to unplanned nursing home admission.

CONCLUSIONS

Numerous risk factors for unplanned nursing home admission were identified. To prevent the adverse health outcomes that precipitate acute hospitalisations and unplanned nursing home admissions, ambulatory care providers should consider these risk factors in their care planning for older adults before they reach a state requiring hospitalisation.

Risk of 30-day hospital readmission associated with medical conditions and drug regimens of polymedicated, older inpatients discharged home: a registry-based cohort study

OBJECTIVES

The present study analysed 4 years of a hospital register (2015-2018) to determine the risk of 30-day hospital readmission associated with the medical conditions and drug regimens of polymedicated, older inpatients discharged home.

DESIGN

Registry-based cohort study.

SETTING

Valais Hospital-a public general hospital centre in the French-speaking part of Switzerland.

PARTICIPANTS

We explored the electronic records of 20 422 inpatient stays by polymedicated, home-dwelling older adults held in the hospital's patient register. We identified 13 802 hospital readmissions involving 8878 separate patients over 64 years old.

OUTCOME MEASURES

Sociodemographic characteristics, medical conditions and drug regimen data associated with risk of readmission within 30 days of discharge.

RESULTS

The overall 30-day hospital readmission rate was 7.8%. Adjusted multivariate analyses revealed increased risk of hospital readmission for patients with longer hospital length of stay (OR=1.014 per additional day; 95% CI 1.006 to 1.021), impaired mobility (OR=1.218; 95% CI 1.039 to 1.427), multimorbidity (OR=1.419 per additional International Classification of Diseases, 10th Revision condition; 95% CI 1.282 to 1.572), tumorous disease (OR=2.538; 95% CI 2.089 to 3.082), polypharmacy (OR=1.043 per additional drug prescribed; 95% CI 1.028 to 1.058), and certain specific drugs, including antiemetics and antinauseants (OR=3.216 per additional drug unit taken; 95% CI 1.842 to 5.617), antihypertensives (OR=1.771; 95% CI 1.287 to 2.438), drugs for functional gastrointestinal disorders (OR=1.424; 95% CI 1.166 to 1.739), systemic hormonal preparations (OR=1.207; 95% CI 1.052 to 1.385) and vitamins (OR=1.201; 95% CI 1.049 to 1.374), as well as concurrent use of beta-blocking agents and drugs for acid-related disorders (OR=1.367; 95% CI 1.046 to 1.788).

CONCLUSIONS

Thirty-day hospital readmission risk was associated with longer hospital length of stay, health disorders, polypharmacy and drug regimens. The drug regimen patterns increasing the risk of hospital readmission were very heterogeneous. Further research is needed to explore hospital readmissions caused solely by specific drugs and drug-drug interactions.