External validation and comparison of two variants of the Elixhauser comorbidity measures for all-cause mortality

Assessing the impact of transitioning to 11th revision of the International Classification of Diseases (ICD-11) on comorbidity indices

OBJECTIVES

This study aimed to support the implementation of the 11th Revision of the International Classification of Diseases (ICD-11). We used common comorbidity indices as a case study for proactively assessing the impact of transitioning to ICD-11 for mortality and morbidity statistics (ICD-11-MMS) on real-world data analyses.

MATERIALS AND METHODS

Using the MIMIC IV database and a table of mappings between the clinical modification of previous versions of ICD and ICD-11-MMS, we assembled a population whose diagnosis can be represented in ICD-11-MMS. We assessed the impact of ICD version on cross-sectional analyses by comparing the populations' distribution of Charlson and Elixhauser comorbidity indices (CCI, ECI) across different ICD versions, along with the adjustment in comorbidity weighting.

RESULTS

We found that ICD versioning could lead to (1) alterations in the population distribution and (2) changes in the weight that can be assigned to a comorbidity category in a reweighting initiative. In addition, this study allowed the creation of the corresponding ICD-11-MMS codes list for each component of the CCI and the ECI.

DISCUSSION

In common with the implementations of previous versions of ICD, implementation of ICD-11-MMS potentially hinders comparability of comorbidity burden on health outcomes in research and clinical settings.

CONCLUSION

Further research is essential to enhance ICD-11-MMS usability, while mitigating, after identification, its adverse effects on comparability of analyses.

Outcomes Associated With Esophageal Perforation Management: Results From a French Nationwide Population-based Cohort Study

OBJECTIVE

To evaluate outcomes associated with esophageal perforation (EP) management at a national level and determine predictive factors of 90-day mortality (90dM), failure-to-rescue (FTR), and major morbidity (MM, Clavien-Dindo 3-4).

BACKGROUND

EP remains a challenging clinical emergency. Previous population-based studies showed rates of 90dM up to 38.8% but were outdated or small-sized.

METHODS

Data from patients admitted to hospitals with EP were extracted from the French medico-administrative database (2012-2021). Etiology, management strategies, and short and long-term outcomes were analyzed. A cutoff value of the annual EP management caseload affecting FTR was determined using the "Chi-squared Automatic Interaction Detector" method. Random effects logistic regression model was performed to assess independent predictors of 90dM, FTR, and MM.

RESULTS

Among 4765 patients with EP, 90dM and FTR rates were 28.0% and 19.4%, respectively. Both remained stable during the study period. EP was spontaneous in 68.2%, due to esophageal cancer in 19.7%, iatrogenic postendoscopy in 7.3%, and due to foreign body ingestion in 4.7%. Primary management consisted of surgery (n = 1447,30.4%), endoscopy (n = 590,12.4%), isolated drainage (n = 336,7.0%), and conservative management (n = 2392,50.2%). After multivariate analysis, besides age and comorbidity, esophageal cancer was predictive of both 90dM and FTR. An annual threshold of ≥8 EP managed annually was associated with a reduced 90dM and FTR rate. In France, only some university hospitals fulfilled this condition. Furthermore, primary surgery was associated with a lower 90dDM and FTR rate despite an increase in MM.

CONCLUSIONS

We provide evidence for the referral of EP to high-volume centers with multidisciplinary expertise. Surgery remains an effective treatment for EP.

Using the Elixhauser risk adjustment model to predict outcomes among patients hospitalized in internal medicine at a large, tertiary-care hospital in Israel

BACKGROUND

In Israel, internal medicine admissions are currently reimbursed without accounting for patient complexity. This is at odds with most other developed countries and has the potential to lead to market distortions such as avoiding sicker patients. Our objective was to apply a well-known, freely available risk adjustment model, the Elixhauser model, to predict relevant outcomes among patients hospitalized on the internal medicine service of a large, Israeli tertiary-care hospital.

METHODS

We used data from the Shaare Zedek Medical Center, a large tertiary referral hospital in Jerusalem. The study included 55,946 hospitalizations between 01.01.2016 and 31.12.2019. We modeled four patient outcomes: in-hospital mortality, escalation of care (intensive care unit (ICU) transfer, mechanical ventilation, daytime bi-level positive pressure ventilation, or vasopressors), 30-day readmission, and length of stay (LOS). We log-transformed LOS to address right skew. As is usual with the Elixhauser model, we identified 29 comorbid conditions using international classification of diseases codes, clinical modification, version 9. We derived and validated the coefficients for these 29 variables using split-sample derivation and validation. We checked model fit using c-statistics and R2, and model calibration using a Hosmer-Lemeshow test.

RESULTS

The Elixhauser model achieved acceptable prediction of the three binary outcomes, with c-statistics of 0.712, 0.681, and 0.605 to predict in-hospital mortality, escalation of care, and 30-day readmission respectively. The c-statistic did not decrease in the validation set (0.707, 0.687, and 0.603, respectively), suggesting that the models are not overfitted. The model to predict log length of stay achieved an R2 of 0.102 in the derivation set and 0.101 in the validation set. The Hosmer-Lemeshow test did not suggest issues with model calibration.

CONCLUSION

We demonstrated that a freely-available risk adjustment model can achieve acceptable prediction of important clinical outcomes in a dataset of patients admitted to a large, Israeli tertiary-care hospital. This model could potentially be used as a basis for differential payment by patient complexity.

A novel comorbidity index in Italy based on diseases detected by the surveillance system PASSI and the Global Burden of Diseases disability weights

BACKGROUND

Understanding comorbidity and its burden characteristics is essential for policymakers and healthcare providers to allocate resources accordingly. However, several definitions of comorbidity burden can be found in the literature. The main reason for these differences lies in the available information about the analyzed diseases (i.e., the target population studied), how to define the burden of diseases, and how to aggregate the occurrence of the detected health conditions.

METHODS

In this manuscript, we focus on data from the Italian surveillance system PASSI, proposing an index of comorbidity burden based on the disability weights from the Global Burden of Disease (GBD) project. We then analyzed the co-presence of ten non-communicable diseases, weighting their burden thanks to the GBD disability weights extracted by a multi-step procedure. The first step selects a set of GBD weights for each disease detected in PASSI using text mining. The second step utilizes an additional variable from PASSI (i.e., the perceived health variable) to associate a single disability weight for each disease detected in PASSI. Finally, the disability weights are combined to form the comorbidity burden index using three approaches common in the literature.

RESULTS

The comorbidity index (i.e., combined disability weights) proposed allows an exploration of the magnitude of the comorbidity burden in several Italian sub-populations characterized by different socioeconomic characteristics. Thanks to that, we noted that the level of comorbidity burden is greater in the sub-population characterized by low educational qualifications and economic difficulties than in the rich sub-population characterized by a high level of education. In addition, we found no substantial differences in terms of predictive values of comorbidity burden adopting different approaches in combining the disability weights (i.e., additive, maximum, and multiplicative approaches), making the Italian comorbidity index proposed quite robust and general.

Allostatic load and cardiovascular outcomes in males with prostate cancer

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death in men with prostate cancer (PC). Accumulated stress plays an important role in CVD development. The cumulative burden of chronic stress and life events can be measured using allostatic load (AL).

METHODS

The initial cohort included males aged 18 years and older diagnosed with PC (2005-2019). AL was modeled as an ordinal variable (0-11). Fine-Gray competing risk regressions measured the impact of precancer diagnosis AL and postdiagnosis AL in 2-year major cardiac events (MACE). The effect of AL changes over time on MACE development was calculated via piecewise Cox regression (before, and 2 months, 6 months, and 1 year after PC diagnosis).

RESULTS

We included 5261 PC patients of which 6.6% had a 2-year MACE. For every 1-point increase in AL before and within 60 days after PC diagnosis, the risk of MACE increased 25% (adjusted hazard ratio [aHR] =1.25, 95% confidence interval [CI] = 1.18 to 1.33) and 27% (aHR = 1.27, 95% CI = 1.20 to 1.35), respectively. Using AL as a time-varying exposure, the risk of MACE increased 19% (aHR = 1.19, 95% CI = 1.11 to 1.27), 22% (aHR = 1.22, 95% CI = 1.14 to 1.33), 28% (aHR = 1.28, 95% CI = 1.23 to 1.33), and 31% (aHR = 1.31, 95% CI = 1.27 to 1.35) for every 1-point increase in AL before, 2 months after, 6 months after, and 1 year after PC diagnosis, respectively.

CONCLUSION

AL and its changes over time are associated with MACE in PC patients, suggesting a role of a biological measure of stress as a marker of CVD risk among men with PC.

Comorbidities and ethnic health disparities in the UK biobank

Objective

The goal of this study was to investigate the relationship between comorbidities and ethnic health disparities in a diverse, cosmopolitan population.

Materials and Methods

We used the UK Biobank (UKB), a large progressive cohort study of the UK population. Study participants self-identified with 1 of 5 ethnic groups and participant comorbidities were characterized using the 31 disease categories captured by the Elixhauser Comorbidity Index. Ethnic disparities in comorbidities were quantified as the extent to which disease prevalence within categories varies across ethnic groups and the extent to which pairs of comorbidities co-occur within ethnic groups. Disease-risk factor comorbidity pairs were identified where one comorbidity is known to be a risk factor for a co-occurring comorbidity.

Results

The Asian ethnic group shows the greatest average number of comorbidities, followed by the Black and then White groups. The Chinese group shows the lowest average number of comorbidities. Comorbidity prevalence varies significantly among the ethnic groups for almost all disease categories, with diabetes and hypertension showing the largest differences across groups. Diabetes and hypertension both show ethnic-specific comorbidities that may contribute to the observed disease prevalence disparities.

Discussion

These results underscore the extent to which comorbidities vary among ethnic groups and reveal group-specific disease comorbidities that may underlie ethnic health disparities.

Conclusion

The study of comorbidity distributions across ethnic groups can be used to inform targeted group-specific interventions to reduce ethnic health disparities.

Demographic and institutional predictors of stroke hospitalization mortality among adults in the United States

Introduction

Stroke remains a primary source of functional disability and inpatient mortality in the United States (US). Recent evidence reveals declining mortality associated with stroke hospitalizations in the US. However, data updating trends in inpatient mortality is lacking. This study aims to provide a renewed inpatient stroke mortality rate in a national sample and identify common predictors of inpatient stroke mortality.

Methods

In this cross-sectional study, we analyzed data from a nationwide database between 2010 and 2017. We included patient encounters for both ischemic (ICD9 433–434, ICD10 I630–I639) and hemorrhagic stroke (ICD9 430–432, ICD10 I600–I629). We performed an annual comparison of in-hospital stroke mortality rates, and a cross-sectional analytic approach of multiple variables identified common predictors of inpatient stroke mortality.

Results

Between 2010 and 2017, we identified 518,185 total stroke admissions (86.6% ischemic stroke and 13.4% hemorrhagic strokes). Stroke admissions steadily increased during the studied period, whereas we observed a steady decline in in-hospital mortality during the same time. The inpatient stroke mortality rate gradually declined from 4.8% in 2010 (95% CI 4.6–5.1) to 2.1% in 2017 (95% CI 2.0–2.1). Predictors of higher odds of dying from ischemic stroke were female (OR 1.059, 95% CI 1.015–1.105, p = 0.008), older age (OR 1.028, 95% CI 1.026–1.029, p < 0.001), and sicker patients (OR 1.091, 95% CI 1.089–1.093, p < 0.001). Predictors of higher odds of dying from hemorrhagic stroke were Hispanic ethnicity (OR 1.459, 95% CI 1.084–1.926, p < 0.001), older age (OR 1.021, 95% CI 1.019–1.023, p < 0.001), and sicker patients (OR 1.042, 95% CI 1.039–1.045, p < 0.001). All census regions and hospital types demonstrated improvements in in-hospital mortality.

Conclusion

This study identified a continuous declining rate in in-hospital mortality due to stroke in the United States, and it also identified demographic and hospital predictors of inpatient stroke mortality.

•

Stroke remains a leading cause of morbidity and mortality in the United States

•

Stroke hospitalization mortality trends are important to guide efforts in acute stroke care

•

Vascular risk factors are still prevalent in the population admitted due to stroke and continue to be associated with higher odds of death

•

There are important regional disparities in stroke hospitalization deaths in the United States

•

Hospital characteristics influence odds of death from a stroke independent of stroke etiology

Assessing the downstream value of first-line cardiac positron emission tomography (PET) imaging using real world Medicare fee-for-service claims data

BACKGROUND

Higher imaging quality makes cardiac positron emission tomography (PET) desirable for evaluation of suspected coronary artery disease (CAD). High cost of PET imaging may be offset by reduced utilization and/or improved outcomes.

METHODS

This retrospective observational study utilized Medicare fee-for-service dataset. Study participants had no CAD diagnosis within 1 year prior to initial imaging. The PET group (PET imaging) and propensity score matched comparison group (single photon emission computed tomography or stress echocardiography) underwent index imaging between January 2014 and December 2016. Outcomes were analyzed using generalized linear models.

RESULTS

Among 144,503 study subjects, 4619 (3.2%) had PET and 139,884 (96.8%) had conventional imaging. After matching, each group had 4619 patients (mean age 74 years, 59% female). The PET group had lower radiation exposure (3.8 milliSievert less per year, 95% CI - 3.96 to - 3.64, P < .0001) and unstable coronary syndrome (incidence rate ratio (IRR) 0.77, 95% CI 0.64-0.94, P = .008). The PET group experienced more hospital admissions (IRR 1.10, 95% CI 1.06-1.15, P < .0001), more use of percutaneous coronary intervention (IRR 1.24, 95% CI 1.02-1.50, P = 0.03), while similar mortality rate (hazard ratio 0.95, 95% CI 0.78-1.14, P = 0.55). The PET group had higher medical spending ($2358.2 vs $1774.3, difference = $583.9 per patient per month, P < .0001).

CONCLUSIONS

First-line PET imaging was not associated with reduced levels of utilization and spending. Clinical outcomes were mostly similar.

Comorbidity indices in orthopaedic surgery: a narrative review focused on hip and knee arthroplasty

Comorbidity indices currently used to estimate negative postoperative outcomes in orthopaedic surgery were originally developed among non-orthopaedic patient populations.

While current indices were initially intended to predict short-term mortality, they have since been used for other purposes as well.

As the rate of hip and knee arthroplasty steadily rises, understanding the magnitude of the effect of comorbid disease on postoperative outcomes has become increasingly more important.

Currently, the ASA classification is the most commonly used comorbidity measure and is systematically recorded by the majority of national arthroplasty registries.

Consideration should be given to developing an updated, standardized approach for comorbidity assessment and reporting in orthopaedic surgery, especially within the setting of elective hip and knee arthroplasty.

Cite this article: EFORT Open Rev 2021;6:629-640. DOI: 10.1302/2058-5241.6.200124

Association Between Racial Disparities in Hospital Length of Stay and the Hospital Readmission Reduction Program

Background:

On average Black patients have longer LOS than comparable White patients. Longer hospital length of stay (LOS) may be associated with higher readmission risk. However, evidence suggests that the Hospital Readmission Reduction Program (HRRP) reduced overall racial differences in 30-day adjusted readmission risk. Yet, it is unclear whether the HRRP narrowed these LOS racial differences.

Objective:

We examined the relationship between Medicare-insured Black-White differences in average, adjusted LOS (ALOS) and the HRRP’s implementation and evaluation periods.

Methods:

Using 2009-2017 data from State Inpatient Dataset from New York, New Jersey, and Florida, we employed an interrupted time series analysis with multivariate generalized regression models controlling for patient, disease, and hospital characteristics. Results are reported per 100 admissions.

Results:

We found that for those discharged home, Black-White ALOS differences significantly widened by 4.15 days per 100 admissions (95% CI: 1.19 to 7.11, P < 0.001) for targeted conditions from before to after the HRRP implementation period, but narrowed in the HRRP evaluation period by 1.84 days per 100 admissions for every year-quarter (95% CI: −2.86 to −0.82, P < 0.001); for those discharged to non-home destinations, there was no significant change between HRRP periods, but ALOS differences widened over the study period. Black-White ALOS differences for non-targeted conditions remained unchanged regardless of HRRP phase and discharge destination.

Conclusion:

Increased LOS for Black patients may have played a role in reducing Black-White disparities in 30-day readmission risks for targeted conditions among patients discharged to home.

Physical Comorbidity According to Diagnoses and Sex among Psychiatric Inpatients in South Korea

People with mental disorders are susceptible to physical comorbidities. Mind-body interventions are important for improving health outcomes. We examined the prevalence of physical comorbidities and their differences by diagnoses and sex among psychiatric inpatients. The dataset, from National Health Insurance claims data, included 48,902 adult inpatients admitted to psychiatric wards for at least 2 days in 2016 treated for schizophrenia, schizotypal and delusional disorders, or mood disorders. We identified 26 physical comorbidities using the Elixhauser comorbidity measure. Among schizophrenia-related disorders, other neurological disorders were most common, then liver disease and chronic pulmonary disease. Among mood disorders, liver disease was most common, then uncomplicated hypertension and chronic pulmonary disease. Most comorbid physical diseases (except other neurological disorders) were more prevalent in mood disorders than schizophrenia-related disorders. Male and female patients with schizophrenia-related disorders showed similar comorbidity prevalence patterns by sex. Among patients with mood disorders, liver disease was most prevalent in males and third-most in females. In both diagnostic groups, liver disease and uncomplicated diabetes mellitus were more prevalent in males, and hypothyroidism in females. Mental health professionals should refer to a specialist to manage physical diseases via early assessments and optimal interventions for physical comorbidities in psychiatric patients.

Effects of angiotensin type 1 receptor antagonists on Parkinson's disease progression: An exploratory study in the PPMI database

INTRODUCTION

We explored the potential clinical effects of angiotensin-II AT1 receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs) in patients from the Parkinson's Progress Marker Initiative (PPMI) study database.

METHODS

We included 423 newly diagnosed PD patients, free from antiparkinsonian treatment, from the PPMI. We compared the proportion of patients starting on l-DOPA during the first year of follow-up, and the changes in MDS-UPDRS total score and sub-scores during the first five follow-up years for patients exposed or not to ARBs or ACEIs.

RESULTS

Treatment with ARBs did not affect the proportion of patients on l-DOPA during the first year (adjusted OR, 95% CI = 0.26, 0.03-2.18, N.S.) while reduced MDS-UPDRS total score (0.85, 0.76-0.95, p < 0.01). Patients treated with ACEIs experienced no changes in either measure.

CONCLUSIONS

These results show potential signals for a beneficial effect with ARBs. Further clinical trials are warranted.

Risk factors associated with 30-day hospital readmission after carotid endarterectomy

OBJECTIVE

The current study evaluated all-cause 30-day readmissions after carotid endarterectomy.

METHODS

Patients undergoing carotid endarterectomy were selected from the Cerner Health Facts® database using ICD-9-CM procedure codes from their index admission. Readmission within 30 days of discharge was determined. Chi-square analysis determined characteristics of the index admission (demographics, diagnoses, postoperative medications, and laboratory results) associated with readmission. Multivariate logistic regression models were used to identify characteristics independently associated with readmission.

RESULTS

In total, 5257 patients undergoing elective carotid endarterectomy were identified. Readmission within 30 days was 3.1%. After multivariable adjustment, readmission was associated with end-stage renal disease (OR: 3.21, 95% CI: 1.01-10.2), hemorrhage or hematoma (OR: 2.34, 95% CI: 1.15-4.77), procedural complications (OR: 3.07, 95% CI: 1.24-7.57), use of bronchodilators (OR: 1.48, 95% CI: 1.03-2.11), increased Charlson index scores (OR: 1.22, 95% CI: 1.08-1.38), and electrolyte abnormalities (hyponatremia < 135 mEq/L (OR: 1.69, 95% CI: 1.07-2.67) and hypokalemia less than 3.7 mEq/L (OR: 2.26, 95% CI: 1.03-4.98)).

CONCLUSIONS

Factors associated with readmission following carotid endarterectomy included younger age, increased comorbidity burden, end-stage renal disease, electrolyte disorders, the use of bronchodilators, and complications including bleeding (hemorrhage or hematoma). Of note, in this real-world study, only 40% of the patients received protamine, despite evidence-based literature demonstrating the reduced risk of bleeding complications. As healthcare moves towards quality of care-driven reimbursement, physician modifiable targets such as protamine utilization to reduce bleeding are greatly needed to reduce readmission, and failure to reduce preventable physician-driven complications after carotid interventions may be associated with decreased reimbursement.

Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

Background

Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals.

Methods

Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types.

Results

Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865–0.868) and van Walraven’s weights (0.863, 95% CI, 0.862–0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI, 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights.

Conclusions

All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-020-05999-5.

Comorbidity and severity-of-illness risk adjustment for hospital-onset Clostridioides difficile infection using data from the electronic medical record

OBJECTIVE

To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection (HO-CDI) across multiple institutions and whether they could be used to improve risk adjustment.

PATIENTS

All patients at least 18 years of age admitted to 3 hospitals in Maryland between January 1, 2016, and January 1, 2018.

METHODS

Comorbid conditions were assigned using the Elixhauser comorbidity index. Multivariable log-binomial regression was conducted for each hospital using significant covariates (P < .10) in a bivariate analysis. Standardized infection ratios (SIRs) were computed using current Centers for Disease Control and Prevention (CDC) risk adjustment methodology and with the addition of Elixhauser score and individual comorbidities.

RESULTS

At hospital 1, 314 of 48,057 patient admissions (0.65%) had a HO-CDI; 41 of 8,791 patient admissions (0.47%) at community hospital 2 had a HO-CDI; and 75 of 29,211 patient admissions (0.26%) at community hospital 3 had a HO-CDI. In multivariable regression, Elixhauser score was a significant risk factor for HO-CDI at all hospitals when controlling for age, antibiotic use, and antacid use. Abnormal leukocyte level at hospital admission was a significant risk factor at hospital 1 and hospital 2. When Elixhauser score was included in the risk adjustment model, it was statistically significant (P < .01). Compared with the current CDC SIR methodology, the SIR of hospital 1 decreased by 2%, whereas the SIRs of hospitals 2 and 3 increased by 2% and 6%, respectively, but the rankings did not change.

CONCLUSIONS

Electronically available patient comorbidities are important risk factors for HO-CDI and may improve risk-adjustment methodology.

Administrative and Claims Data Help Predict Patient Mortality in Intensive Care Units by Logistic Regression: A Nationwide Database Study

Background

Increasing attention has been paid to the predictive power of different prognostic scoring systems for decades. In this study, we compared the abilities of three commonly used scoring systems to predict short-term and long-term mortalities, with the intention of building a better prediction model for critically ill patients. We used the data from the National Health Insurance Research Database (NHIRD) in Taiwan, which included information on patient age, comorbidities, and presence of organ failure to build a new prediction model for short-term and long-term mortalities.

Methods

We retrospectively collected the medical records of patients in the intensive care unit of a regional hospital in 2012 and linked them to the claims data from the NHIRD. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Elixhauser Comorbidity Index (ECI), and Charlson Comorbidity Index (CCI) were compared for their predictive abilities. Multiple logistic regression tests were performed, and the results were presented as receiver operating characteristic curves and C-statistic.

Results

The APACHE II score has the best predictive power for inhospital mortality (0.79; C − statistic = 0.77 − 0.83) and 1-year mortality (0.77; C − statistic = 0.74 − 0.79). The ECI and CCI alone have poorer predictive power and need to be combined with other variables to be comparable to the APACHE II score, as predictive tools. Using CCI together with age, sex, and whether or not the patient required mechanical ventilation is estimated to have a C-statistic of 0.773 (95% CI 0.744-0.803) for inhospital mortality, 0.782 (95% CI 0.76-0.81) for 30-day mortality, and 0.78 (95% CI 0.75-0.80) for 1-year mortality.

Conclusions

We present a new prognostic model that combines CCI with age, sex, and mechanical ventilation status and can predict mortality, comparable to the APACHE II score.

Opioid Use Is Associated With Higher Health Care Costs and Emergency Encounters in Inflammatory Bowel Disease

BACKGROUND

We aimed to examine opioid use among adult patients with inflammatory bowel disease (IBD) in the United States and the impact of extended opioid use on emergency health care services and health care costs among patients.

METHODS

We conducted a retrospective cohort study using medical claims data from the Truven Health MarketScan research databases, consisting of patients across the United States with employer-based health insurance. Subjects with IBD were identified in 2009. The occurrence of an emergent encounter in 2010 and health care costs were assessed.

RESULTS

There were 76,171 subjects with 35,993 emergent encounters among the study population, for an overall rate of 0.47 per patient-year. However, these encounters were confined to 6.9% of patients overall. The median total charges per patient in 2010 were $5372. Extended opioid use in 2009 was associated with a higher odds of an emergent encounter in 2010 (odds ratio [OR], 1.82; 95% confidence interval [CI], 1.67-1.98), higher incidence rate of emergent encounters (incidence rate ratio, 2.07; 95% CI, 1.91-2.24), and higher odds of being in the top quartile of cost in 2010 (OR, 1.90; 95% CI, 1.79-2.02). Depression was a strong predictor of extended opioid use (OR, 2.64; 95% CI, 2.49-2.81; P < 0.001).

CONCLUSIONS

Extended opioid use among patients with IBD is an important predictor of emergent encounters and is associated with higher total health care costs. Psychosocial comorbidities are significant predictors of extended opioid use in patients with IBD.

Healthcare disparities among orthopedic trauma patients in the USA: socio-demographic factors influence the management of calcaneus fractures

Background

Socio-demographic factors have been suggested to contribute to differences in healthcare utilization for several elective orthopedic procedures. Reports on disparities in utilization of orthopedic trauma procedures remain limited. The purpose of our study is to assess the roles of clinical and socio-demographic variables in utilization of operative fixation of calcaneus fractures in the USA.

Methods

The National Inpatient Sample (NIS) dataset was used to analyze all patients from 2005 to 2014 with closed calcaneal fractures. Multivariate logistic regression analyses were performed to evaluate the impact of clinical and socio-demographic variables on the utilization of surgical versus non-surgical treatment.

Results

A total of 17,156 patients with closed calcaneus fractures were identified. Operative treatment was rendered in 7039 patients (41.03%). A multivariate logistic regression demonstrated multiple clinical and socio-demographic factors to significantly influence the utilization of surgical treatment including age, gender, insurance status, race/ethnicity, income, diabetes, peripheral vascular disease, psychosis, drug abuse, and alcohol abuse (p <  0.05). In addition, hospital size and hospital type (teaching versus non-teaching) showed a statistically significant difference (p <  0.05).

Conclusions

Besides different clinical variables, we identified several socio-demographic factors influencing the utilization of surgical treatment of calcaneus fractures in the US patient population. Further studies need to identify the specific patient-related, provider-related, and system-related factors leading to these disparities.

Heterogeneity introduced by EHR system implementation in a de-identified data resource from 100 non-affiliated organizations

Objectives

Electronic health record (EHR) data aggregated from multiple, non-affiliated, sources provide an important resource for biomedical research, including digital phenotyping. Unlike work with EHR data from a single organization, aggregate EHR data introduces a number of analysis challenges.

Materials and Methods

We used the Cerner Health Facts data, a de-identified aggregate EHR data resource populated by data from 100 independent health systems, to investigate the impact of EHR implementation factors on the aggregate data. These included use of ancillary modules, data continuity, International Classification of Disease (ICD) version and prompts for clinical documentation.

Results and Discussion

Health Facts includes six categories of data from ancillary modules. We found of the 664 facilities in Health Facts, 49 use all six categories while 88 facilities were not using any. We evaluated data contribution over time and found considerable variation at the health system and facility levels. We analyzed the transition from ICD-9 to ICD-10 and found that some organizations completed the shift in 2014 while others remained on ICD-9 in 2017, well after the 2015 deadline. We investigated the utilization of “discharge disposition” to document death and found inconsistent use of this field. We evaluated clinical events used to document travel status implemented in response to Ebola, height and smoking history. Smoking history documentation increased dramatically after Meaningful Use, but dropped in some organizations. These observations highlight the need for any research involving aggregate EHR data to consider implementation factors that contribute to variability in the data before attributing gaps to “missing data.”

Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories

OBJECTIVE

Many healthcare systems employ population-based risk scores to prospectively identify patients at high risk of poor outcomes, but it is unclear whether single point-in-time scores adequately represent future risk. We sought to identify and characterize latent subgroups of high-risk patients based on risk score trajectories.

STUDY DESIGN

Observational study of 7289 patients discharged from Veterans Health Administration (VA) hospitals during a 1-week period in November 2012 and categorized in the top 5th percentile of risk for hospitalization.

METHODS

Using VA administrative data, we calculated weekly risk scores using the validated Care Assessment Needs model, reflecting the predicted probability of hospitalization. We applied the non-parametric k-means algorithm to identify latent subgroups of patients based on the trajectory of patients' hospitalization probability over a 2-year period. We then compared baseline sociodemographic characteristics, comorbidities, health service use, and social instability markers between identified latent subgroups.

RESULTS

The best-fitting model identified two subgroups: moderately high and persistently high risk. The moderately high subgroup included 65% of patients and was characterized by moderate subgroup-level hospitalization probability decreasing from 0.22 to 0.10 between weeks 1 and 66, then remaining constant through the study end. The persistently high subgroup, comprising the remaining 35% of patients, had a subgroup-level probability increasing from 0.38 to 0.41 between weeks 1 and 52, and declining to 0.30 at study end. Persistently high-risk patients were older, had higher prevalence of social instability and comorbidities, and used more health services.

CONCLUSIONS

On average, one third of patients initially identified as high risk stayed at very high risk over a 2-year follow-up period, while risk for the other two thirds decreased to a moderately high level. This suggests that multiple approaches may be needed to address high-risk patient needs longitudinally or intermittently.

Inpatient care for stiff person syndrome in the United States: a nationwide readmission study

Background

Stiff person syndrome (SPS) is a progressive neurological disorder characterized by axial muscle rigidity and involuntary spasms. Autoimmune and neoplastic diseases are associated with SPS. Our study objectives were to describe inpatient care for SPS in the United States and characterize 30-day readmissions.

Methods

We queried the 2014 Nationwide Readmission Database for hospitalizations where a diagnosis of SPS was recorded. For readmission analyses, we excluded encounters with missing length of stay, hospitalization deaths, and out-of-state and December discharges. National estimates of index hospitalizations and 30-day readmissions were computed using survey weighting methods. Unconditional logistic regression was used to examine associations between demographic, clinical, and hospital characteristics and readmission.

Results

There were 836 patients with a recorded diagnosis of SPS during a 2014 hospitalization. After exclusions, 703 patients remained, 9.4% of which were readmitted within 30 days. Frequent reasons for index hospitalization were SPS (27.8%) and diabetes with complications (5.1%). Similarly, readmissions were predominantly for diabetes complications (24.2%) and SPS. Most readmissions attributed to diabetes complications (87.5%) were to different hospitals. Female sex (OR, 3.29; CI: 1.22–8.87) and routine discharge (OR, 0.26; CI: 0.10–0.64) were associated with readmission, while routine discharge (OR, 0.18; CI: 0.04–0.89) and care at for-profit hospitals (OR, 10.87; CI: 2.03–58.25) were associated with readmission to a different hospital.

Conclusions

Readmissions in SPS may result from disease complications or comorbid conditions. Readmissions to different hospitals may reflect specialty care, gaps in discharge planning, or medical emergencies. Studies are required to determine if readmissions in SPS are preventable.

Mapping of global scientific research in comorbidity and multimorbidity: A cross-sectional analysis

BACKGROUND

The management of comorbidity and multimorbidity poses major challenges to health services around the world. Analysis of scientific research in comorbidity and multimorbidity is limited in the biomedical literature. This study aimed to map global scientific research in comorbidity and multimorbidity to understand the maturity and growth of the area during the past decades.

METHODS AND FINDINGS

This was a cross-sectional analysis of the Web of Science. Searches were run from inception until November 8, 2016. We included research articles or reviews with no restrictions by language or publication date. Data abstraction was done by one researcher. A process of standardization was conducted by two researchers to unify different terms and grammatical variants and to remove typographical, transcription, and/or indexing errors. All potential discrepancies were resolved via discussion. Descriptive analyses were conducted (including the number of papers, citations, signatures, most prolific authors, countries, journals and keywords). Network analyses of collaborations between countries and co-words were presented. During the period 1970-2016, 85994 papers (64.0% in 2010-2016) were published in 3500 journals. There was wide diversity in the specialty of the journals, with psychiatry (16558 papers; 19.3%), surgery (9570 papers; 11.1%), clinical neurology (9275 papers; 10.8%), and general and internal medicine (7622 papers; 8.9%) the most common. PLOS One (1223 papers; 1.4%), the Journal of Affective Disorders (1154 papers; 1.3%), the Journal of Clinical Psychiatry (727 papers; 0.8%), the Journal of the American Geriatrics Society (634 papers; 0.7%) and Obesity Surgery (588 papers; 0.7%) published the largest number of papers. 168 countries were involved in the production of papers. The global productivity ranking was headed by the United States (37624 papers), followed by the United Kingdom (7355 papers), Germany (6899 papers) and Canada (5706 papers). Twenty authors who published 100 or more papers were identified; the most prolific authors were affiliated with Harvard Medical School, State University of New York Upstate Medical University, National Taiwan Normal University and China Medical University. The 50 most cited papers ("citation classics" with at least 1000 citations) were published in 20 journals, led by JAMA Psychiatry (11 papers) and JAMA (10 papers). The most cited papers provided contributions focusing on methodological aspects (e.g. Charlson Comorbidity Index, Elixhauser Comorbidity Index, APACHE prognostic system), but also important studies on chronic diseases (e.g. epidemiology of mental disorders and its correlates by the U.S. National Comorbidity Survey, Fried's frailty phenotype or the management of obesity).

CONCLUSIONS

Ours is the first analysis of global scientific research in comorbidity and multimorbidity. Scientific production in the field is increasing worldwide with research leadership of Western countries, most notably, the United States.