Risk and temporal order of disease diagnosis of comorbidities in patients with COPD: a population health perspective

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study

BACKGROUND

Enhanced 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.

OBJECTIVE

The 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.

METHODS

Gradient 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.

RESULTS

Performance (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%).

CONCLUSIONS

The 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.

Prevalence and Newly Diagnosed Rates of Multimorbidity in Older Medicare Beneficiaries with COPD

Few studies have quantified the multimorbidity burden in older adults with chronic obstructive pulmonary disease (COPD) using large and generalizable data. Such evidence is essential to inform evidence-based research, clinical care, and resource allocation. This retrospective cohort study used a nationally representative sample of Medicare beneficiaries aged 65 years or older with COPD and 1:1 matched (on age, sex, and race) non-COPD beneficiaries to: (1) quantify the prevalence of multimorbidity at COPD onset and one-year later; (2) quantify the rates [per 100 person-years (PY)] of newly diagnosed multimorbidity during in the year prior to and in the year following COPD onset; and (3) compare multimorbidity prevalence in beneficiaries with and without COPD. Among 739,118 eligible beneficiaries with and without COPD, the average number of multimorbidity was 10.0 (SD = 4.7) and 1.0 (SD = 3.3), respectively. The most prevalent multimorbidity at COPD onset and at one-year after, respectively, were hypertension (70.8% and 80.2%), hyperlipidemia (52.2% and 64.8%), anemia (42.1% and 52.0%), arthritis (39.8% and 47.7%), and congestive heart failure (CHF) (31.3% and 38.8%). Conditions with the highest newly diagnosed rates before and following COPD onset, respectively, included hypertension (39.8 and 32.3 per 100 PY), hyperlipidemia (22.8 and 27.6), anemia (17.8 and 20.3), CHF (16.2 and 13.2), and arthritis (12.9 and 13.2). COPD was significantly associated with increased odds of all measured conditions relative to non-COPD controls. This study updates existing literature with more current, generalizable findings of the substantial multimorbidity burden in medically complex older adults with COPD-necessary to inform patient-centered, multidimensional care.Supplemental data for this article is available online at https://doi.org/10.1080/15412555.2021.1968815 .

Towards Rational Prescription of Common Inhaler Medication in the Multimorbid COPD Patient

COPD is a chronic disease, typically accompanied by multiple comorbid conditions. The need to apply several, and sometimes conflicting, disease-specific treatment guidelines, complicates the management of individual patients. Moreover, national and international recommendations evolve rapidly but provide limited guidance on the integrated approach in the multimorbid patient. Particularly bothersome is the fact that the presence of comorbidities may deteriorate the course of COPD, and inversely COPD may affect the outcome of the comorbid diseases. In addition, some effects of commonly prescribed COPD inhaler medications, including beta2-agonists, long-acting antimuscarinics and especially inhaled corticosteroids, mimic or worsen COPD-related comorbidities. Therefore, the authors combined their perspectives to formulate advice that may help physicians to improve COPD patient care in daily practice when comorbidities are present. Diabetes, atrial fibrillation, osteoporosis/fractures, infections (pneumonia and tuberculosis) and asthma were identified as areas where practicing clinicians should give special attention to the risk-benefit ratio of the inhaled medication. Overall, the presence of multimorbidity in a COPD patient should act as a signal to carefully reconsider the treatment choices.

Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach

Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healthy veterans undergoing screening colonoscopy. The Veterans Affairs Cooperative Studies Program #380 was a prospective longitudinal study of screening and surveillance colonoscopy. We identified initial instances of three-digit ICD-9 diagnoses for participants with at least 5 years of linked EHR history (October 1999 to December 2015). For diagnoses affecting at least 10% of patients, we calculated pairwise chronological relative risk (RR). iGraph was used to produce directed graphs of comorbidities with RR > 1, as well as summary statistics, key diseases, and communities. A directed graph based on 2210 patients visualized longitudinal development of comorbidities. Top hub (preceding) diseases included ischemic heart disease, inflammatory and toxic neuropathy, and diabetes. Top authority (subsequent) diagnoses were acute kidney failure and hypertensive chronic kidney failure. Four communities of correlated comorbidities were identified. Close analysis of top hub and authority diagnoses demonstrated known relationships, correlated sequelae, and novel hypotheses. Directed network graphs portray chronologic comorbidity relationships. We identified relationships between comorbid diagnoses in this aging veteran cohort. This may direct healthcare prioritization and personalized care.

Comorbidity phenotypes and risk of mortality in patients with ischaemic heart disease in the UK

Objectives

The objective of this study is to use latent class analysis of up to 20 comorbidities in patients with a diagnosis of ischaemic heart disease (IHD) to identify clusters of comorbidities and to examine the associations between these clusters and mortality.

Methods

Longitudinal analysis of electronic health records in the health improvement network (THIN), a UK primary care database including 92 186 men and women aged ≥18 years with IHD and a median of 2 (IQR 1–3) comorbidities.

Results

Latent class analysis revealed five clusters with half categorised as a low-burden comorbidity group. After a median follow-up of 3.2 (IQR 1.4–5.8) years, 17 645 patients died. Compared with the low-burden comorbidity group, two groups of patients with a high-burden of comorbidities had the highest adjusted HR for mortality: those with vascular and musculoskeletal conditions, HR 2.38 (95% CI 2.28 to 2.49) and those with respiratory and musculoskeletal conditions, HR 2.62 (95% CI 2.45 to 2.79). Hazards of mortality in two other groups of patients characterised by cardiometabolic and mental health comorbidities were also higher than the low-burden comorbidity group; HR 1.46 (95% CI 1.39 to 1.52) and 1.55 (95% CI 1.46 to 1.64), respectively.

Conclusions

This analysis has identified five distinct comorbidity clusters in patients with IHD that were differentially associated with risk of mortality. These analyses should be replicated in other large datasets, and this may help shape the development of future interventions or health services that take into account the impact of these comorbidity clusters.

ERS Presidential Summit 2018: multimorbidities and the ageing population

As the average age of the population increases, so will the prevalence of chronic respiratory diseases and associated multimorbidity. This will result in a more complex clinical environment. Part of the solution will be to allow patients to be co-creators in the design of their care. It will also require clinicians to shift in their current approaches to care, step out of the disease- or pathology-oriented approach and embrace new ideas. In an effort to prepare the respiratory community for the challenge, we reflect on concepts to empower patients via multidisciplinary systems, new technologies and transition from end-of-life care to advanced care planning.