Development and Validation of the Summary Elixhauser Comorbidity Score for Use With ICD-10-CM-Coded Data Among Older Adults

Laparoscopic common bile duct exploration is an effective, safe, and less-costly method of treating choledocholithiasis

BACKGROUND

Advancements in laparoscopic techniques led to the adoption of laparoscopic common bile duct exploration (LCBDE) as an alternative to endoscopic retrograde cholangiopancreatography (ERCP) for management of choledocholithiasis (CD). The goal of this study was to describe the initial experience at a safety net hospital with acute care surgeons performing LCBDE for suspected CD. We hypothesized LCBDE would reduce length of stay and hospital costs compared to laparoscopic cholecystectomy (LC) and ERCP performed in the same hospital admission.

METHODS

This was a retrospective case-control study from 2019 to 2023 comparing LCBDE to LC/ERCP among patients diagnosed with CD. Statistical analyses were performed using Mann-Whitney U tests for continuous variables and Chi-square tests for categorical variables. Data reported as median [interquartile range] or research subjects with condition (percentage).

RESULTS

A total of 110 LCBDE were performed, while 121 subjects underwent LC and ERCP. Patients in the LCBDE group were more likely to be female with a total of 87 female subjects (77.6%) compared to 76 male subjects (62.8%) (95% CI 1.14-3.74). Initial WBC was lower in the LCBDE group at 8.4 [6.9-11.8] compared to the LC/ERCP group at 10.9 [7.9-13.5] (p = 0.0013). Remaining demographics and lab values were similar between the two groups. Patients who underwent LCBDE had a significantly shorter length of stay at 2 days [1-3] compared to those in the LC/ERCP group at 4 days [3-6] (p < 0.001). Hospital charges for the LCBDE group were $46,685 [$38,687-$56,703] compared to $60,537 [$47,527-$71,739] for the LC/ERCP group (p < 0.001).

CONCLUSION

LCBDE is associated with significantly lower hospital costs and shorter length of stay with similar post-operative complication and 30-day readmission rates. Our results show that LCBDE is safe and should be considered as a first-line approach in the management of CD.

Incidence and predictive factors of double crush syndrome in patients undergoing cervical surgery: a database study

BACKGROUND

Double crush syndrome (DCS) is characterized by multiple compression sites along a single peripheral nerve. It commonly presents with persistent distal symptoms despite surgical treatment for cervical radiculopathy. Management typically involves nerve release of the most symptomatic site. However, due to overlapping symptoms with cervical radiculopathy, patients may undergo cervical surgery prior to DCS diagnosis. Due to its rarity and frequent misdiagnosis, the authors aim to utilize a large national database to investigate the incidence and associations of DCS.

METHODS

The Pearldiver database was utilized to identify patients undergoing cervical surgery for the management of cervical radiculopathy. Patients were stratified into three cohorts based on their clinical course before and after cervical surgery. The primary outcome was the prevalence of DCS, and secondary outcomes included an evaluation of predictive factors for each Group, using a significance level of P < 0.05.

RESULTS

Among 195,271 patients undergoing cervical surgery for cervical radiculomyelopathy, 97.95% were appropriately managed, 1.42% had potentially mids-diagnosed DCS, and 0.63% were treatment-resistant. Diabetes and obesity were significant predictors of potentially misdiagnosed DCS (P < 0.05).

CONCLUSION

This study presents data indicating that 1.42% of patients who receive cervical surgery may have underlying DCS and potentially benefit from nerve release prior to undergoing surgery. A concurrent diagnosis of diabetes and obesity may predict an underlying DCS.

Multi-year population-based analysis of Asian patients with acute decompensated heart failure and advanced chronic kidney disease

BACKGROUND

Data on disparities in outcomes and risk factors in Asian patients with advanced chronic kidney disease admitted for heart failure are scare.

METHODS

This was a retrospective cohort study that utilized data from the National Inpatient Sample between January 2016 and December 2019. Patients who had a primary diagnosis of acute decompensated heart failure and a concomitant diagnosis of advanced CKD were included. The primary outcome of interest was in-hospital mortality. Secondary outcomes include hospital cost, length of stay, and other clinical outcomes. Weighted multivariable logistic regression was used to adjust for comorbidities.

RESULTS

There were 251,578 cases of ADHF with advanced CKD, out of which 2.6 % were from individuals of Asian ethnicity. Asian patients exhibited a higher burden of comorbidities in comparison to other UREM patients, but a lower burden than White patients. Regardless of differences in comorbidity burden, Asian patients exhibited a higher likelihood of experiencing severe consequences. After adjusting for comorbidies, White (OR:1.11; 95 % CI 1.03-1.20;0.009) patients had higher odds of mortality than Asian patients. However, Blacks (OR: 0.58; 95 % CI 0.53 to 0.63; p < 0.001) and Hispanics (OR: 0.69; 95 % CI 0.62 to 0.78; p < 0.001) had lower odds of mortality.

CONCLUSION

This first population-based studies shows that Asian patients with advanced CKD admitted for ADHF have greater comorbidity burden and poorer outcomes Black and Hispanic patients. This data underscores the importance of comprehensive approaches in phenotyping, and ethnic specific interventions.

Home Health Care Use and Outcomes After Coronary Artery Bypass Grafting Among Medicare Beneficiaries

BACKGROUND

Home health care (HHC) has been increasingly used to improve care transitions and avoid poor outcomes, but there is limited data on its use and efficacy following coronary artery bypass grafting. The purpose of this study was to describe HHC use and its association with outcomes among Medicare beneficiaries undergoing coronary artery bypass grafting.

METHODS

Retrospective analysis of 100% of Medicare fee-for-service files identified 77 331 beneficiaries undergoing coronary artery bypass grafting and discharged to home between July 2016 and December 2018. The primary exposure of HHC use was defined as the presence of paid HHC claims within 30 days of discharge. Hierarchical logistic regression identified predictors of HHC use and the percentage of variation in HHC use attributed to the hospital. Propensity-matched logistic regression compared mortality, readmissions, emergency department visits, and cardiac rehabilitation enrollment at 30 and 90 days after discharge between HHC users and nonusers.

RESULTS

A total of 26 751 (34.6%) of beneficiaries used HHC within 30 days of discharge, which was more common among beneficiaries who were older (72.9 versus 72.5 years), male (79.4% versus 77.4%), White (90.2% versus 89.2%), and not Medicare-Medicaid dual eligible (6.7% versus 8.8%). The median hospital-level rate of HHC use was 31.0% (interquartile range, 13.7%-54.5%) and ranged from 0% to 94.2%. Nearly 30% of the interhospital variation in HHC use was attributed to the discharging hospital (intraclass correlation coefficient, 0.296 [95% CI, 0.275-0.318]). Compared with non-HHC users, those using HHC were less likely to have a readmission or emergency department visit, were more likely to enroll in cardiac rehabilitation, and had modestly higher mortality within 30 or 90 days of discharge.

CONCLUSIONS

A third of Medicare beneficiaries undergoing coronary artery bypass grafting used HHC within 30 days of discharge, with wide interhospital variation in use and mixed associations with clinical outcomes and health care utilization.

Associations between prior COVID-19 infection and venous thromboembolism following common plastic surgery operations

INTRODUCTION

SARS-CoV-2 (COVID-19) infection has been described as a cause of systemic hypercoagulability and a risk factor for the development of venous thromboembolism (VTE). Whereas some multispecialty studies have proposed a link between COVID-19 and postoperative thrombosis, other single-specialty studies have found no such association. We utilized a large national database to determine whether prior COVID-19 infection was associated with the incidence of VTE following common plastic surgery operations.

METHODS

The Merative™ MarketScan® Research Databases were used to identify female patients who underwent index abdominal panniculectomy, breast reduction, autologous breast reconstruction, or implant-based breast reconstruction procedures between 2020 and 2021. International Classification of Disease, tenth edition (ICD-10) codes were used to identify patients diagnosed with COVID-19 preoperatively and those who experienced a VTE in the 90 days postoperatively. Propensity score matching and multivariable logistic regression were used to determine any independent association between COVID-19 and postoperative VTE.

RESULTS

Twenty-four thousand two hundred and twenty-eight patients met inclusion criteria. Mean age at time of surgery was 44 years. Six percent carried a preoperative COVID-19 diagnosis, and postoperative VTE occurred in 1.3%. In a propensity-score-matched analysis of 2754 patients, COVID-19 did not significantly correlate with incidence of postoperative VTE (P = 0.463). Compared with a matched prepandemic cohort (14,151 patients), the incidence of VTE did not increase following any of the four studied procedures during the COVID-19 pandemic.

CONCLUSION

This analysis of a national insurance claims database provides evidence against a link between resolved COVID-19 infection and VTE within 90 days of four common plastic surgery operations.

Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder

BACKGROUND

Buprenorphine for opioid use disorder (B-MOUD) is essential to improving patient outcomes; however, retention is essential.

OBJECTIVE

To develop and validate machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans initiating B-MOUD.

METHODS

Veterans initiating B-MOUD from fiscal years 2006-2020 were identified. Veterans' B-MOUD episodes were randomly divided into training (80%;n = 45,238) and testing samples (20%;n = 11,309). Candidate algorithms [multiple logistic regression, least absolute shrinkage and selection operator regression, random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN)] were used to build and validate classification models to predict six binary outcomes: 1) B-MOUD retention, 2) any overdose, 3) opioid-related overdose, 4) overdose death, 5) opioid overdose death, and 6) all-cause mortality. Model performance was assessed using standard classification statistics [e.g., area under the receiver operating characteristic curve (AUC-ROC)].

RESULTS

Episodes in the training sample were 93.0% male, 78.0% White, 72.3% unemployed, and 48.3% had a concurrent drug use disorder. The GBM model slightly outperformed others in predicting B-MOUD retention (AUC-ROC = 0.72). RF models outperformed others in predicting any overdose (AUC-ROC = 0.77) and opioid overdose (AUC-ROC = 0.77). RF and GBM outperformed other models for overdose death (AUC-ROC = 0.74 for both), and RF and DNN outperformed other models for opioid overdose death (RF AUC-ROC = 0.79; DNN AUC-ROC = 0.78). RF and GBM also outperformed other models for all-cause mortality (AUC-ROC = 0.76 for both). No single predictor accounted for >3% of the model's variance.

CONCLUSIONS

Machine-learning algorithms can accurately predict OUD-related outcomes with moderate predictive performance; however, prediction of these outcomes is driven by many characteristics.

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.

Cannabis use associated with lower mortality among hospitalized Covid-19 patients using the national inpatient sample: an epidemiological study

BACKGROUND

Prior reports indicate that modulation of the endocannabinoid system (ECS) may have a protective benefit for Covid-19 patients. However, associations between cannabis use (CU) or CU not in remission (active cannabis use (ACU)), and Covid-19-related outcomes among hospitalized patients is unknown.

METHODS

In this multicenter retrospective observational cohort analysis of adults (≥ 18 years-old) identified from 2020 National Inpatient Sample database, we utilize multivariable regression analyses and propensity score matching analysis (PSM) to analyze trends and outcomes among Covid-19-related hospitalizations with CU and without CU (N-CU) for primary outcome of interest: Covid-19-related mortality; and secondary outcomes: Covid-19-related hospitalization, mechanical ventilation (MV), and acute pulmonary embolism (PE) compared to all-cause admissions; for CU vs N-CU; and for ACU vs N-ACU.

RESULTS

There were 1,698,560 Covid-19-related hospitalizations which were associated with higher mortality (13.44% vs 2.53%, p ≤ 0.001) and worse secondary outcomes generally. Among all-cause hospitalizations, 1.56% of CU and 6.29% of N-CU were hospitalized with Covid-19 (p ≤ 0.001). ACU was associated with lower odds of MV, PE, and death among the Covid-19 population. On PSM, ACU(N(unweighted) = 2,382) was associated with 83.97% lower odds of death compared to others(N(unweighted) = 282,085) (2.77% vs 3.95%, respectively; aOR:0.16, [0.10-0.25], p ≤ 0.001).

CONCLUSIONS

These findings suggest that the ECS may represent a viable target for modulation of Covid-19. Additional studies are needed to further explore these findings.

The Use of Telemedicine by US Nephrologists for In-Center Hemodialysis Care During the Pandemic: An Analysis of National Medicare Claims

Rationale & Objective

Because of coronavirus disease 2019 (COVID-19), the US government issued emergency waivers in March 2020 that removed regulatory barriers around the use of telemedicine. For the first time, nephrologists were reimbursed for telemedicine care delivered during in-center hemodialysis. We examined the use of telemedicine for in-center hemodialysis during the first 16 months of the pandemic.

Study Design

We ascertained telemedicine modifiers on nephrologist claims. We used multivariable regression to examine time trends and patient, dialysis facility, and geographic correlates of telemedicine use. We also examined whether the estimated effects of predictors of telemedicine use changed over time.

Setting & Participants

US Medicare beneficiaries receiving in-center hemodialysis between March 1, 2020, and June 30, 2021.

Exposures

Patient, geographic, and dialysis facility characteristics.

Outcomes

The use of telehealth for in-center hemodialysis care.

Analytic Approach

Retrospective cohort analysis.

Results

Among 267,434 Medicare beneficiaries identified, the reported use of telemedicine peaked at 9% of patient-months in April 2020 and declined to 2% of patient-months by June 2021. Telemedicine use varied geographically and was more common in areas that were remote and socioeconomically disadvantaged. Patients were more likely to receive care by telemedicine in areas with higher incidence of COVID-19, although the predictive value of COVID-19 diminished later in the pandemic. Patients were more likely to receive care using telemedicine if they were at facilities with more staff, and the use of telemedicine varied by facility ownership type.

Limitations

Limited reporting of telemedicine on claims could lead to underestimation of its use. Reported telemedicine use was higher in an analysis designed to address this limitation by focusing on patients whose physicians used telemedicine at least once during the pandemic.

Conclusions

Some US nephrologists continued to use telemedicine for in-center hemodialysis throughout the pandemic, even as the association between COVID-19 incidence and telemedicine use diminished over time. These findings highlight unique challenges and opportunities to the future use of telemedicine in dialysis care.

Building Learning Healthcare Systems for Critical Care Medicine

Learning healthcare systems are an evolving way of integrating informatics, analytics, and continuous improvement into daily practice in healthcare. This article discusses strategies to build learning healthcare systems for critical care medicine.

Performance of models for predicting 1-year to 3-year mortality in older adults: a systematic review of externally validated models

Mortality prediction models support identifying older adults with short life expectancy for whom clinical care might need modifications. We systematically reviewed external validations of mortality prediction models in older adults (ie, aged 65 years and older) with up to 3 years of follow-up. In March, 2023, we conducted a literature search resulting in 36 studies reporting 74 validations of 64 unique models. Model applicability was fair but validation risk of bias was mostly high, with 50 (68%) of 74 validations not reporting calibration. Morbidities (most commonly cardiovascular diseases) were used as predictors by 45 (70%) of 64 of models. For 1-year prediction, 31 (67%) of 46 models had acceptable discrimination, but only one had excellent performance. Models with more than 20 predictors were more likely to have acceptable discrimination (risk ratio [RR] vs <10 predictors 1·68, 95% CI 1·06-2·66), as were models including sex (RR 1·75, 95% CI 1·12-2·73) or predicting risk during comprehensive geriatric assessment (RR 1·86, 95% CI 1·12-3·07). Development and validation of better-performing mortality prediction models in older people are needed.

Hospitalization and readmission after single-level fall: a population-based sample

BACKGROUND

Single-level falls (SLFs) in the older US population is a leading cause of hospital admission and rates are increasing. Unscheduled hospital readmission is regarded as a quality-of-care indication and a preventable burden on healthcare systems. We aimed to characterize the predictors of 30-day readmission following admission for SLF injuries among patients 65 years and older.

METHODS

We conducted a retrospective cohort study using the Nationwide Readmission Database from 2018 to 2019. Included patients were 65 and older, admitted emergently following a SLF with a primary injury diagnosis. Hierarchical logit regression was used to model factors associated with readmission within 30 days of discharge.

RESULTS

Of 1,338,905 trauma patients, 65 years or older, 61.3% had a single-level fall as the mechanism of injury. Among fallers, the average age was 81.1 years and 68.5% were female. SLF patients underwent more major therapeutic procedures (56.3% vs. 48.2%), spent over 2 million days in the hospital and incurred total charges of over $28 billion annually. Over 11% of SLF patients were readmitted within 30 days of discharge. Increasing income had a modest effect, where the highest zip code quartile was 9% less likely to be readmitted. Decreasing population density had a protective effect of readmission of 16%, comparing Non-Urban to Large Metropolitan. Transfer to short-term hospital, brain and vascular injuries were independent predictors of 30-day readmission in multivariable analysis (OR 2.50, 1.31, and 1.42, respectively). Palliative care consultation was protective (OR 0.41). The subsequent hospitalizations among those 30-day readmissions were primarily emergent (92.9%), consumed 260,876 hospital days and a total of $2.75 billion annually.

CONCLUSIONS

SLFs exact costs to patients, health systems, and society. Transfer to short-term hospitals at discharge, along with brain and vascular injuries were strong predictors of 30-day readmission and warrant mitigation strategy development with consideration of expanded palliative care consultation.

The Impact of Type 2 Myocardial Infarction in Acute Pancreatitis: Analysis of 1.1 Million Hospitalizations and Review of the Literature

Introduction  Acute pancreatitis (AP) is a common inflammatory disorder with acute onset and rapid progression. Studies have reported cardiac injury in patients with AP. It is often thought that stress cardiomyopathy can induce these changes leading to type 2 myocardial infarction (type 2 MI) in AP. Our study aims to assess the prevalence as well as the impact of type 2 MI on outcomes in patients with AP.  Methods National Inpatient Sample (NIS) 2016-2020 was used to identify adult patients (age>18) with acute pancreatitis. We excluded patients with STEMI, NSTEMI, pancreatic cancer, or chronic pancreatitis. Patients with missing demographics and mortality were also excluded. Patients were stratified into two groups, based on the presence of type 2 MI. Multivariate logistic regression analysis was performed to assess the impact of concomitant type 2 MI on mortality, sepsis, acute kidney injury (AKI), ICU admission, deep venous thrombosis (DVT), and pulmonary embolism (PE) after adjusting for patient demographics, hospital characteristics, etiology of AP and the Elixhauser comorbidities.  Results Of the 1.1 million patients in the study population, only 2315 patients had type 2 MI. The majority of the patients in the type 2 MI group were aged >65 years (49.2%, p<0.001), males (54.6%, p=0.63), White (67.6%, p=0.19), had Medicare insurance (55.5%, p<0.001), and were in the lowest income quartile (34.8%, p=0.12). Patients in the type 2 MI group had a higher incidence of mortality (5.4% vs 0.6%, p<0.001), sepsis (7.1% vs 3.7%, p<0.001), shock (9.3% vs 0.9%, p<0.001), AKI (42.9% vs. 11.8%, p<0.001) and ICU admission (12.1% vs 1.4%, p<0.001). After adjusting for confounding factors, patients in the type 2 MI group were noted to be at higher odds of mortality (aOR=2.4; 95% CI 1.5-3.8, p<0.001). Patients in the type 2 MI group had a longer length of stay (adjusted coefficient=2.1 days; 95% CI 1.4-2.8; p<0.001) and higher total hospitalization charges (adjusted coefficient=$45,088; 95% CI $30,224-$59,952; p<0.001).  Conclusion Although the prevalence of type 2 MI in AP is low, the presence of type 2 MI is associated with increased mortality and worse outcomes. Physicians should be aware of this association and these patients should be monitored carefully to prevent worse outcomes.