The Multimorbidity Index: A Tool for Assessing the Prognosis of Patients from Their History of Illness

Chemokine CXCL9, a marker of inflammaging, is associated with changes of muscle strength and mortality in older men

Our study examined associations of the CXC motif chemokine ligand 9 (CXCL9), a pro-inflammatory protein implicated in age-related inflammation, with musculoskeletal function in elderly men. We found in certain outcomes both cross-sectional and longitudinal significant associations of CXCL9 with poorer musculoskeletal function and increased mortality in older men. This requires further investigation.

PURPOSE

We aim to determine the relationship of (CXCL9), a pro-inflammatory protein implicated in age-related inflammation, with both cross-sectional and longitudinal musculoskeletal outcomes and mortality in older men.

METHODS

A random sample from the Osteoporotic Fractures in Men (MrOS) Study cohort (N = 300) was chosen for study subjects that had attended the third and fourth clinic visits, and data was available for major musculoskeletal outcomes (6 m walking speed, chair stands), hip bone mineral density (BMD), major osteoporotic fracture, mortality, and serum inflammatory markers. Serum levels of CXCL9 were measured by ELISA, and the associations with musculoskeletal outcomes were assessed by linear regression and fractures and mortality with Cox proportional hazards models.

RESULTS

The mean CXCL9 level of study participants (79.1 ± 5.3 years) was 196.9 ± 135.2 pg/ml. There were significant differences for 6 m walking speed, chair stands, physical activity scores, and history of falls in the past year across the quartiles of CXCL9. However, higher CXCL9 was only significantly associated with changes in chair stands (β =  - 1.098, p < 0.001) even after adjustment for multiple covariates. No significant associations were observed between CXCL9 and major osteoporotic fracture or hip BMD changes. The risk of mortality increased with increasing CXCL9 (hazard ratio quartile (Q)4 vs Q1 1.98, 95% confidence interval 1.25-3.14; p for trend < 0.001).

CONCLUSIONS

Greater serum levels of CXCL9 were significantly associated with a decline in chair stands and increased mortality. Additional studies with a larger sample size are needed to confirm our findings.

Improving the Accuracy of Predictive Models for Outcomes of Antidepressants by Using an Ontological Adjustment Approach

For patients with rare comorbidities, there are insufficient observations to accurately estimate the effectiveness of treatment. At the same time, all diagnosis, including rare diagnosis, are part of the International Classification of Disease (ICD). Grouping ICD into broader concepts (i.e., ontology adjustment) can not only increase accuracy of estimating antidepressant effectiveness for patients with rare conditions but also prevent overfitting in big data analysis. In this study, 3,678,082 depressed patients treated with antidepressants were obtained from OptumLabs® Data Warehouse (OLDW). For rare diagnoses, adjustments were made by using the likelihood ratio of the immediate broader concept in the ICD hierarchies. The accuracy of models in training (90%) and test (10%) sets was examined using the area under the receiver operating curves (AROC). The gap in training and test AROC shows how much random noise was modeled. If the gap is large, then the parameters of the model, including the reported effectiveness of the antidepressant for patients with rare conditions, are suspect. There was, on average, a 9.0% reduction in the AROC gap after using the ontological adjustment. Therefore, ontology adjustment can reduce model overfitting, leading to better parameter estimates from the training set.

Health information technology to improve care for people with multiple chronic conditions

OBJECTIVE

To review evidence regarding the use of Health Information Technology (health IT) interventions aimed at improving care for people living with multiple chronic conditions (PLWMCC) in order to identify critical knowledge gaps.

DATA SOURCES

We searched MEDLINE, CINAHL, PsycINFO, EMBASE, Compendex, and IEEE Xplore databases for studies published in English between 2010 and 2020.

STUDY DESIGN

We identified studies of health IT interventions for PLWMCC across three domains as follows: self-management support, care coordination, and algorithms to support clinical decision making.

DATA COLLECTION/EXTRACTION METHODS

Structured search queries were created and validated. Abstracts were reviewed iteratively to refine inclusion and exclusion criteria. The search was supplemented by manually searching the bibliographic sections of the included studies. The search included a forward citation search of studies nested within a clinical trial to identify the clinical trial protocol and published clinical trial results. Data were extracted independently by two reviewers.

PRINCIPAL FINDINGS

The search yielded 1907 articles; 44 were included. Nine randomized controlled trials (RCTs) and 35 other studies including quasi-experimental, usability, feasibility, qualitative studies, or development/validation studies of analytic models were included. Five RCTs had positive results, and the remaining four RCTs showed that the interventions had no effect. The studies address individual patient engagement and assess patient-centered outcomes such as quality of life. Few RCTs assess outcomes such as disability and none assess mortality.

CONCLUSIONS

Despite a growing body of literature on health IT interventions or multicomponent interventions including a health IT component for chronic disease management, current evidence for applying health IT solutions to improve care for PLWMCC is limited. The body of literature included in this review provides critical information on the state of the science as well as the many gaps that need to be filled for digital health to fulfill its promise in supporting care delivery that meets the needs of PLWMCC.

Measuring Multimorbidity: Selecting the Right Instrument for the Purpose and the Data Source

BACKGROUND

Adults have a higher prevalence of multimorbidity-or having multiple chronic health conditions-than having a single condition in isolation. Researchers, health care providers, and health policymakers find it challenging to decide upon the most appropriate assessment tool from the many available multimorbidity measures.

OBJECTIVE

The objective of this study was to describe a broad range of instruments and data sources available to assess multimorbidity and offer guidance about selecting appropriate measures.

DESIGN

Instruments were reviewed and guidance developed during a special expert workshop sponsored by the National Institutes of Health on September 25-26, 2018.

RESULTS

Workshop participants identified 4 common purposes for multimorbidity measurement as well as the advantages and disadvantages of 5 major data sources: medical records/clinical assessments, administrative claims, public health surveys, patient reports, and electronic health records. Participants surveyed 15 instruments and 2 public health data systems and described characteristics of the measures, validity, and other features that inform tool selection. Guidance on instrument selection includes recommendations to match the purpose of multimorbidity measurement to the measurement approach and instrument, review available data sources, and consider contextual and other related constructs to enhance the overall measurement of multimorbidity.

CONCLUSIONS

The accuracy of multimorbidity measurement can be enhanced with appropriate measurement selection, combining data sources and special considerations for fully capturing multimorbidity burden in underrepresented racial/ethnic populations, children, individuals with multiple Adverse Childhood Events and older adults experiencing functional limitations, and other geriatric syndromes. The increased availability of comprehensive electronic health record systems offers new opportunities not available through other data sources.

US veterans administration diabetes risk (VADR) national cohort: cohort profile

PURPOSE

The veterans administration diabetes risk (VADR) cohort facilitates studies on temporal and geographic patterns of pre-diabetes and diabetes, as well as targeted studies of their predictors. The cohort provides an infrastructure for examination of novel individual and community-level risk factors for diabetes and their consequences among veterans. This cohort also establishes a baseline against which to assess the impact of national or regional strategies to prevent diabetes in veterans.

PARTICIPANTS

The VADR cohort includes all 6 082 018 veterans in the USA enrolled in the veteran administration (VA) for primary care who were diabetes-free as of 1 January 2008 and who had at least two diabetes-free visits to a VA primary care service at least 30 days apart within any 5-year period since 1 January 2003, or veterans subsequently enrolled and were diabetes-free at cohort entry through 31 December 2016. Cohort subjects were followed from the date of cohort entry until censure defined as date of incident diabetes, loss to follow-up of 2 years, death or until 31 December 2018.

FINDINGS TO DATE

The incidence rate of type 2 diabetes in this cohort of over 6 million veterans followed for a median of 5.5 years (over 35 million person-years (PY)) was 26 per 1000 PY. During the study period, 8.5% of the cohort were lost to follow-up and 17.7% died. Many demographic, comorbidity and other clinical variables were more prevalent among patients with incident diabetes.

FUTURE PLANS

This cohort will be used to study community-level risk factors for diabetes, such as attributes of the food environment and neighbourhood socioeconomic status via geospatial linkage to residence address information.

Relative accuracy of social and medical determinants of suicide in electronic health records

OBJECTIVE

This paper compares the accuracy of predicting suicide from Social Determinants of Health (SDoH) or history of illness.

POPULATION STUDIED

5 313 965 Veterans who at least had two primary care visits between 2008 and 2016.

STUDY DESIGN

The dependent variable was suicide or intentional self-injury. The independent variables were 10 495 International Classification of Disease (ICD) Version 9 codes, age, and gender. The ICD codes included 40 V-codes used for measuring SDoH, such as family disruption, family history of substance abuse, lack of education, legal impediments, social isolation, unemployment, and homelessness. The sample was randomly divided into training (90 percent) and validation (10 percent) sets. Area under the receiver operating characteristic (AROC) was used to measure accuracy of predictions in the validation set.

PRINCIPAL FINDINGS

Separate analyses were done for inpatient and outpatient codes; the results were similar. In the hospitalized group, the mean age was 67.2 years, and 92.1 percent were male. The mean number of medical diagnostic codes during the study period was 37; and 12.9 percent had at least one SDoH V-code. At least one episode of suicide or intentional self-injury occurred in 1.89 percent of cases. SDoH V-codes, on average, elevated the risk of suicide or intentional self-injury by 24-fold (ranging from 4- to 86-fold). An index of 40 SDoH codes predicted suicide or intentional self-injury with an AROC of 0.64. An index of 10 445 medical diagnoses, without SDoH V-codes, had AROC of 0.77. The combined SDoH and medical diagnoses codes also had AROC of 0.77.

CONCLUSION

In predicting suicide or intentional self-harm, SDoH V-codes add negligible information beyond what is already available in medical diagnosis codes.

IMPLICATIONS FOR PRACTICE

Policies that affect SDoH (eg, housing policies, resilience training) may not have an impact on suicide rates, if they do not change the underlying medical causes of SDoH.

Health Status and Burden in Caregivers of Patients With Multimorbidity

Caregivers of patients with multimorbidity are important for improving patient outcomes. This descriptive study examines health status and burden of 22 caregivers of patients with multimorbidity discharged from the hospital who were enrolled in a self-management intervention study. Caregivers did not receive an intervention. Factors that increased caregiver burden were financial issues, caring for others (e.g., family members), and home obligations. Caregivers averaged between 2 and 3 chronic conditions themselves. Perceived caregiver burden remained unchanged over time for the caregiver whether the patient was in the intervention or the usual care group. We recommend rigorous research with larger samples to better understand the caregiver role, needed resources and potential interventions to mitigate caregiver burden in the multimorbid population during and after care transitions. Longitudinal studies that include assessment and interventions for the caregivers of patients with multimorbidity are needed.

mHOMR: a feasibility study of an automated system for identifying inpatients having an elevated risk of 1-year mortality

OBJECTIVE

The need for clinical staff to reliably identify patients with a shortened life expectancy is an obstacle to improving palliative and end-of-life care. We developed and evaluated the feasibility of an automated tool to identify patients with a high risk of death in the next year to prompt treating physicians to consider a palliative approach and reduce the identification burden faced by clinical staff.

METHODS

Two-phase feasibility study conducted at two quaternary healthcare facilities in Toronto, Canada. We modified the Hospitalised-patient One-year Mortality Risk (HOMR) score, which identifies patients having an elevated 1-year mortality risk, to use only data available at the time of admission. An application prompted the admitting team when patients had an elevated mortality risk and suggested a palliative approach. The incidences of goals of care discussions and/or palliative care consultation were abstracted from medical records.

RESULTS

Our model (C-statistic=0.89) was found to be similarly accurate to the original HOMR score and identified 15.8% and 12.2% of admitted patients at Sites 1 and 2, respectively. Of 400 patients included, the most common indications for admission included a frailty condition (219, 55%), chronic organ failure (91, 23%) and cancer (78, 20%). At Site 1 (integrated notification), patients with the notification were significantly more likely to have a discussion about goals of care and/or palliative care consultation (35% vs 20%, p = 0.016). At Site 2 (electronic mail), there was no significant difference (45% vs 53%, p = 0.322).

CONCLUSIONS

Our application is an accurate, feasible and timely identification tool for patients at elevated risk of death in the next year and may be effective for improving palliative and end-of-life care.

Physiological markers and multimorbidity: A systematic review

Background:

Multimorbidity is the co-occurrence of two or more diseases in the same individual. One method to identify this condition at an early stage is the use of specific markers for various combinations of morbidities. Nonetheless, evidence related to physiological markers in multimorbidity is limited.

Objective:

The aim was to perform a systematic review to identify physiological markers associated with multimorbidity.

Design:

Articles available on PubMed, Register of Controlled Trials, Academic Search Premier, CINAHL, Scopus, SocINDEX, Web of Science, LILACS, and SciELO, from their inception to May 2018, were systematically searched and reviewed. The project was registered in PROSPERO under the number CRD42017055522.

Results:

The systematic search identified 922 papers. After evaluation, 18 articles were included in the full review reporting at least one physiological marker in coexisting diseases or which are strongly associated with the presence of multimorbidity in the future. Only five of these studies examined multimorbidity in general, identifying five physiological markers associated with multimorbidity, namely, dehydroepiandrosterone sulfate (DHEAS), interleukin 6 (IL-6), C-reactive protein (CRP), lipoprotein (Lp), and cystatin C (Cyst-C).

Conclusions:

There is a paucity of studies related to physiological markers in multimorbidity. DHEAS, IL-6, CRP, Lp, and Cyst-C could be the initial focus for further investigation of physiological markers related to multimorbidity.

Electronic Health Record-Based Screening for Substance Abuse

Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. We focused on 4,681,809 veterans who had at least two primary care visits; 829,827 of whom had a hospitalization. Data included 699 million outpatient and 17 million inpatient records. The dependent variable was substance abuse as identified from 89 diagnostic codes using the Agency for Healthcare Quality and Research classification of diseases. In addition, we included the diagnostic codes used for identification of prescription abuse. The independent variables were 10,292 inpatient and 13,512 outpatient diagnoses, plus 71 dummy variables measuring age at different years between 20 and 90 years. A modified naive Bayes model was used to aggregate the risk across predictors. The accuracy of the predictions was examined using area under the receiver operating characteristic (AROC) curve in 20% of data, randomly set aside for the evaluation. Many physical/mental illnesses were associated with substance abuse. These associations supported findings reported in the literature regarding the impact of substance abuse on various diseases and vice versa. In randomly set-aside validation data, the model accurately predicted substance abuse for inpatient (AROC = 0.884), outpatient (AROC = 0.825), and combined inpatient and outpatient (AROC = 0.840) data. If one excludes information available after substance abuse is known, the cross-validated AROC remained high, 0.822 for inpatient and 0.817 for outpatient data. Data within EHRs can be used to detect existing or predict potential future substance abuse.