Development and internal validation of prognostic models to predict negative health outcomes in older patients with multimorbidity and polypharmacy in general practice

Identifying Predictors of Nursing Home Admission by Using Electronic Health Records and Administrative Data: Scoping Review

Background

Among older adults, nursing home admissions (NHAs) are considered a significant adverse outcome and have been extensively studied. Although the volume and significance of electronic data sources are expanding, it is unclear what predictors of NHA have been systematically identified in the literature via electronic health records (EHRs) and administrative data.

Objective

This study synthesizes findings of recent literature on identifying predictors of NHA that are collected from administrative data or EHRs.

Methods

The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines were used for study selection. The PubMed and CINAHL databases were used to retrieve the studies. Articles published between January 1, 2012, and March 31, 2023, were included.

Results

A total of 34 papers were selected for final inclusion in this review. In addition to NHA, all-cause mortality, hospitalization, and rehospitalization were frequently used as outcome measures. The most frequently used models for predicting NHAs were Cox proportional hazards models (studies: n=12, 35%), logistic regression models (studies: n=9, 26%), and a combination of both (studies: n=6, 18%). Several predictors were used in the NHA prediction models, which were further categorized into sociodemographic, caregiver support, health status, health use, and social service use factors. Only 5 (15%) studies used a validated frailty measure in their NHA prediction models.

Conclusions

NHA prediction tools based on EHRs or administrative data may assist clinicians, patients, and policy makers in making informed decisions and allocating public health resources. More research is needed to assess the value of various predictors and data sources in predicting NHAs and validating NHA prediction models externally.

Comparing risk prediction models aimed at predicting hospitalizations for adverse drug events in community dwelling older adults: a protocol paper

Background

The objective of this paper is to describe the creation, validation, and comparison of two risk prediction modeling approaches for community-dwelling older adults to identify individuals at highest risk for adverse drug event-related hospitalizations. One approach will use traditional statistical methods, the second will use a machine learning approach.

Methods

We will construct medication, clinical, health care utilization, and other variables known to be associated with adverse drug event-related hospitalizations. To create the cohort, we will include older adults (≥ 65 years of age) empaneled to a primary care physician within the Cedars-Sinai Health System primary care clinics with polypharmacy (≥ 5 medications) or at least 1 medication commonly implicated in ADEs (certain oral hypoglycemics, anti-coagulants, anti-platelets, and insulins). We will use a Fine-Gray Cox proportional hazards model for one risk modeling approach and DataRobot, a data science and analytics platform, to run and compare several widely used supervised machine learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting (XGBoost), Decision Tree, Naïve Bayes, and K-Nearest Neighbors. We will use a variety of metrics to compare model performance and to assess the risk of algorithmic bias.

Discussion

In conclusion, we hope to develop a pragmatic model that can be implemented in the primary care setting to risk stratify older adults to further optimize medication management.

The association of six autoimmune bullous diseases with thyroid disorders: A population-based study

BACKGROUND

The association of autoimmune bullous diseases (AIBDs) with thyroid disorders remains to be profoundly investigated.

OBJECTIVE

To evaluate the epidemiological association between six AIBDs and thyroid disorders.

METHODS

A population-based cross-sectional study enrolled patients with bullous pemphigoid (BP), mucous membrane pemphigoid (MMP), epidermolysis bullosa acquisita (EBA), pemphigoid gestationis (PG), pemphigus vulgaris (PV), and pemphigus foliaceus (PF). Patients with these six AIBDs were compared with six age- and sex-matched control groups regarding the prevalence of thyroiditis and hyperthyroidism. Logistic regression was used to calculate the odds ratio (OR) and 95% confidence interval (CI) for thyroid disorders.

RESULTS

The study population included 1,743, 251, 106, 126, 860, and 103 patients with BP, MMP, EBA, PG, PV, and PF, respectively. The corresponding control groups consisted of 10,141, 1,386, 606, 933, 5,142, and 588 matched controls, respectively. A significant association was found between thyroiditis and BP (OR, 1.98; 95% CI, 1.18-3.35; P=0.010), MMP (OR, 7.02; 95% CI, 1.87-26.33; P=0.004), and PV (OR, 2.73; 95% CI, 1.45-5.15; P=0.002). With regard to hyperthyroidism, PF was the only AIBD to demonstrate significant comorbidity (OR, 2.42; 95% CI, 1.13-5.21; P=0.024). EBA and PG were not found to cluster with any of the investigated thyroid conditions.

CONCLUSION

Patients with BP, MMP, PV, and PF experience an elevated burden of thyroid disorders. Patients with these AIBDs presenting with suggestive symptoms may be carefully screened for comorbid thyroid disorders.

How to Improve Healthcare for Patients with Multimorbidity and Polypharmacy in Primary Care: A Pragmatic Cluster-Randomized Clinical Trial of the MULTIPAP Intervention

(1) Purpose: To investigate a complex MULTIPAP intervention that implements the Ariadne principles in a primary care population of young-elderly patients with multimorbidity and polypharmacy and to evaluate its effectiveness for improving the appropriateness of prescriptions. (2) Methods: A pragmatic cluster-randomized clinical trial was conducted involving 38 family practices in Spain. Patients aged 65–74 years with multimorbidity and polypharmacy were recruited. Family physicians (FPs) were randomly allocated to continue usual care or to provide the MULTIPAP intervention based on the Ariadne principles with two components: FP training (eMULTIPAP) and FP patient interviews. The primary outcome was the appropriateness of prescribing, measured as the between-group difference in the mean Medication Appropriateness Index (MAI) score change from the baseline to the 6-month follow-up. The secondary outcomes were quality of life (EQ-5D-5 L), patient perceptions of shared decision making (collaboRATE), use of health services, treatment adherence, and incidence of drug adverse events (all at 1 year), using multi-level regression models, with FP as a random effect. (3) Results: We recruited 117 FPs and 593 of their patients. In the intention-to-treat analysis, the between-group difference for the mean MAI score change after a 6-month follow-up was −2.42 (95% CI from −4.27 to −0.59) and, between baseline and a 12-month follow-up was −3.40 (95% CI from −5.45 to −1.34). There were no significant differences in any other secondary outcomes. (4) Conclusions: The MULTIPAP intervention improved medication appropriateness sustainably over the follow-up time. The small magnitude of the effect, however, advises caution in the interpretation of the results given the paucity of evidence for the clinical benefit of the observed change in the MAI. Trial registration: Clinicaltrials.gov NCT02866799.

Examining the predictability and prognostication of multimorbidity among older Delayed-Discharge Patients: A Machine learning analytics

BACKGROUND

Patient complexity among older delayed-discharge patients complicates discharge planning, resulting in a higher rate of adverse outcomes, such as readmission and mortality. Early prediction of multimorbidity, as a common indicator of patient complexity, can support proactive discharge planning by prioritizing complex patients and reducing healthcare inefficiencies.

OBJECTIVE

We set out to accomplish the following two objectives: 1) to examine the predictability of three common multimorbidity indices, including Charlson-Deyo Comorbidity Index (CDCI), the Elixhauser Comorbidity Index (ECI), and the Functional Comorbidity Index (FCI) using machine learning (ML), and 2) to assess the prognostic power of these indices in predicting 30-day readmission and mortality.

MATERIALS AND METHODS

We used data including 163,983 observations of patients aged 65 and older who experienced discharge delay in Ontario, Canada, during 2004 - 2017. First, we utilized various classification ML algorithms, including classification and regression trees, random forests, bagging trees, extreme gradient boosting, and logistic regression, to predict the multimorbidity status based on CDCI, ECI, and FCI. Second, we used adjusted multinomial logistic regression to assess the association between multimorbidity indices and the patient-important outcomes, including 30-day mortality and readmission.

RESULTS

For all ML algorithms and regardless of the predictive performance criteria, better predictions were established for the CDCI compared with the ECI and FCI. Remarkably, the most predictable multimorbidity index (i.e., CDCI with Area Under the Receiver Operating Characteristic Curve = 0.80, 95% CI = 0.79 - 0.81) also offered the highest prognostications regarding adverse events (RRR_mortality = 3.44, 95% CI = 3.21 - 3.68 and RRR_readmission = 1.36, 95% CI = 1.31 - 1.40).

CONCLUSIONS

Our findings highlight the feasibility and utility of predicting multimorbidity status using ML algorithms, resulting in the early detection of patients at risk of mortality and readmission. This can support proactive triage and decision-making about staffing and resource allocation, with the goal of optimizing patient outcomes and facilitating an upstream and informed discharge process through prioritizing complex patients for discharge and providing patient-centered care.

The burden of neurological comorbidities in six autoimmune bullous diseases: a population-based study

BACKGROUND

Apart from bullous pemphigoid (BP), the association of other autoimmune bullous diseases (AIBDs) with neurological conditions is poorly understood.

OBJECTIVE

To estimate the association between a wide array of AIBDs and neurological conditions.

METHODS

A retrospective cross-sectional study recruited patients with BP, mucous membrane pemphigoid (MMP), epidermolysis bullosa acquisita (EBA), pemphigoid gestationis (PG), pemphigus vulgaris (PV) and pemphigus foliaceus (PF). These patients were compared with their age- and sex-matched control subjects with regard to the lifetime prevalence of Parkinson's disease (PD), Alzheimer's disease (AD), stroke, epilepsy and multiple sclerosis (MS). Logistic regression was used to calculate OR for specified neurological disorders.

RESULTS

The current study included 1743, 251, 106, 126, 860 and 103 patients diagnosed with BP, MMP, EBA, PG, PV and PF, respectively. These patients were compared with 10 141, 1386, 606, 933, 5142 and 588 matched controls, respectively. Out of the investigated neurological conditions, PD associated with BP (OR, 2.71; 95% CI, 2.19-3.35); AD with BP (OR, 2.11; 95% CI, 1.73-2.57), MMP (OR, 2.37; 95% CI, 1.03-5.47), EBA (OR, 6.00; 95% CI, 1.90-18.97) and PV (OR, 2.24; 95% CI, 1.40-3.60); stroke with BP (OR, 1.84; 95% CI, 1.55-2.19) and EBA (OR, 2.79; 95% CI, 1.11-7.01); and epilepsy with BP (OR, 2.18; 95% CI, 1.72-2.77) and PV (OR, 1.80; 95% CI, 1.19-2.73). MS did not significantly cluster with any of the six AIBDs.

CONCLUSION

In addition to BP, EBA and PV were found to cluster with neurological comorbidities. Patients with these AIBDs with compatible symptoms may be carefully assessed for comorbid neurological disorders.