Multicomorbidity of chronic diseases and substance use disorders and their association with hospitalization: Results from electronic health records data

Wu, Li‐Tzy; Zhu, He; Ghitza, Udi E.

doi:10.1016/j.drugalcdep.2018.08.013

Cited by 119 publications

(96 citation statements)

References 36 publications

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“…In bivariate analyses of high-risk opioid users, these calculations were performed separately for each of the two age groups. Characteristics included in these analyses were chosen from literature review, specifically based on (a) those reported by Hirschtritt et al as significant predictors of opioid-benzodiazepine use compared with opioid use alone, such as substance use disorder, anxiety, and depression, plus (b) risk factors (e.g., diabetes, hypertension) and diagnoses for cardiovascular or respiratory diseases, which are often chronically comorbid with substance use disorders (Wu et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

High-risk opioid prescribing trends in the outpatient setting prior to issuance of federal guidance

Peckham

Fairman

Awanis

et al. 2019

Preventive Medicine Reports

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Co-prescription of opioid and benzodiazepine products increases the risk of overdose-related mortality four-fold due to respiratory depression. Accordingly, prevention of high-risk opioid prescribing (HROP) has become a focus over the past two decades and was the subject of a black-box warning (BBW) issued by the U.S. Food and Drug Administration (FDA) on August 31, 2016. Because older patients are at increased risk for these outcomes, we compared rates of HROP for older (aged ≥65 years) and younger (aged 18–64 years) adults using a repeated cross-sectional cohort design. Data from the National Ambulatory Medical Care Survey of U.S. office-based physician visits were accessed for 2006–2016 August. From 2006 to 2016, the opioid-prescribing rate increased by 40% among those aged 18–64 years and by 54% among those aged ≥65 years. From 2012–2013 to 2014–2016, the HROP rate, expressed as a proportion of all opioid-prescribing visits, increased to 26.6% among those aged 18–64 years but declined to 21.0% among those aged ≥65 years, primarily because of changes for patients aged ≥75 years. Prior to the FDA-issued BBW, the HROP prescribing rate trended upward for all adults, except in 2014–2016 when it began to decline among older adults.

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Section: Methodsmentioning

confidence: 99%

High-risk opioid prescribing trends in the outpatient setting prior to issuance of federal guidance

Peckham

Fairman

Awanis

et al. 2019

Preventive Medicine Reports

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“…The variables included in this study were socio-demographic factors (sex [4], age, marital status, insurance type [2], patients' residency (urban/rural) (based on their postal codes and the city planning of Beijing)); primary diagnostic categories (ICD 10 codes: depressive disorder (F32-F33), bipolar disorder (F31), schizophrenia and related disorder (F20-F29), substance use disorder (F10-F19), and other psychiatric disorders); number of medical comorbidities [14] (31 comorbidities defined in the AHRQ Elixhauser comorbidity index [14,34,35] were identified using the Stata module "ELIXHAUSER" and counted); treatment-related factors (use of electroconvulsive therapy (ECT) [15] (identified by the ICD-9-CM-3 code for ECT, 94.27)); previous admission(s) 1 year prior to the index admissions [5] (none, 1 times, 2 times, 3 times or more) (matched using patient identifier across hospitals and years); length of stay (days); as well as institutional factors [5,15], including hospital level (tertiary or secondary) and hospital location (urban or rural). The comorbidities listed in the Elixhauser comorbidity index included cardiovascular conditions liver and renal diseases [36] (See Supplementary Table 1 in Additional File 1 for details). The Elixhauser comorbidity index has been used previously in riskadjustment methodologies and predictive models [34].…”

Section: Independent Variablesmentioning

confidence: 99%

Factors associated with 30-day and 1-year readmission among psychiatric inpatients in Beijing China: a retrospective, medical record-based analysis

et al. 2020

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Background: Psychiatric readmissions negatively impact patients and their families while increasing healthcare costs. This study aimed at investigating factors associated with psychiatric readmissions within 30 days and 1 year of the index admissions and exploring the possibilities of monitoring and improving psychiatric care quality in China. Methods: Data on index admission, subsequent admission(s), clinical and hospital-related factors were extracted in the inpatient medical record database covering 10 secondary and tertiary psychiatric hospitals in Beijing, China. Logistic regressions were used to examine the associations between 30-day and 1-year readmissions plus frequent readmissions (≥3 times/year), and clinical variables as well as hospital characteristics. Results: The 30-day and 1-year psychiatric readmission rates were 16.69% (1289/7724) and 33.79% (2492/7374) respectively. 746/2492 patients (29.34%) were readmitted 3 times or more within a year (frequent readmissions). Factors significantly associated with the risk of both 30-day and 1-year readmission were residing in an urban area, having medical comorbidities, previous psychiatric admission(s), length of stay > 60 days in the index admission and being treated in tertiary hospitals (p < 0.001). Male patients were more likely to have frequent readmissions (OR 1.30, 95%CI 1.04-1.64). Receiving electroconvulsive therapy (ECT) was significantly associated with a lower risk of 30-day readmission (OR 0.72, 95%CI 0.56-0.91) and frequent readmissions (OR 0.60, 95%CI 0.40-0.91). Conclusion: More than 30% of the psychiatric inpatients were readmitted within 1 year. Urban residents, those with medical comorbidities and previous psychiatric admission(s) or a longer length of stay were more likely to be readmitted, and men are more likely to be frequently readmitted. ECT treatment may reduce the likelihood of 30day readmission and frequent admissions. Targeted interventions should be designed and piloted to effectively monitor and reduce psychiatric readmissions.

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“…Cluster 10 (Substance addiction, n = 685) includes patients with substance dependence disorders: alcohol disorders (35%) and nicotine dependence (99.3%), both previously studied as diagnoses associated with depression [29,36].…”

Section: Cluster Analysismentioning

confidence: 99%

A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses

Sánchez‐Rico

Alvarado

2019

Behavioral Sciences

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The study of diagnostic associations entails a large number of methodological problems regarding the application of machine learning algorithms, collinearity and wide variability being some of the most prominent ones. To overcome these, we propose and tested the usage of uniform manifold approximation and projection (UMAP), a very recent, popular dimensionality reduction technique. We showed its effectiveness by using it on a large Spanish clinical database of patients diagnosed with depression, to whom we applied UMAP before grouping them using a hierarchical agglomerative cluster analysis. By extensively studying its behavior and results, validating them with purely unsupervised metrics, we show that they are consistent with well-known relationships, which validates the applicability of UMAP to advance the study of comorbidities.

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Multicomorbidity of chronic diseases and substance use disorders and their association with hospitalization: Results from electronic health records data

Cited by 119 publications

References 36 publications

High-risk opioid prescribing trends in the outpatient setting prior to issuance of federal guidance

High-risk opioid prescribing trends in the outpatient setting prior to issuance of federal guidance

Factors associated with 30-day and 1-year readmission among psychiatric inpatients in Beijing China: a retrospective, medical record-based analysis

A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses

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