Administrative database research infrequently used validated diagnostic or procedural codes

Measuring the Direct Medical Costs of Hospital-Onset Infections Using an Analogy Costing Framework

BACKGROUND

The majority of recent estimates on the direct medical cost attributable to hospital-onset infections (HOIs) has focused on device- or procedure-associated HOIs. The attributable costs of HOIs that are not associated with device use or procedures have not been extensively studied.

OBJECTIVE

We developed simulation models of attributable cost for 16 HOIs and estimated the total direct medical cost, including nondevice-related HOIs in the USA for 2011 and 2015.

DATA AND METHODS

We used total discharge costs associated with HOI-related hospitalization from the National Inpatient Sample and applied an analogy costing methodology to develop simulation models of the costs attributable to HOIs. The mean attributable cost estimate from the simulation analysis was then multiplied by previously published estimates of the number of HOIs for 2011 and 2015 to generate national estimates of direct medical costs.

RESULTS

After adjusting all estimates to 2017 US dollars, attributable cost estimates for select nondevice-related infections attributable cost estimates ranged from $7661 for ear, eye, nose, throat, and mouth (EENTM) infections to $27,709 for cardiovascular system infections in 2011; and from $8394 for EENTM to $26,445 for central nervous system infections in 2016 (based on 2015 incidence data). The national direct medical costs for all HOIs were $14.6 billion in 2011 and $12.1 billion in 2016. Nondevice- and nonprocedure-associated HOIs comprise approximately 26-28% of total HOI costs.

CONCLUSION

Results suggest that nondevice- and nonprocedure-related HOIs result in considerable costs to the healthcare system.

Development and validation of a claims-based algorithm to identify patients with Neuromyelitis Optica Spectrum disorder

INTRODUCTION

No validated algorithm exists to identify patients with neuromyelitis optica spectrum disorder (NMOSD) in healthcare claims data. We developed and tested the performance of a healthcare claims-based algorithm to identify patients with NMOSD.

METHODS

Using medical record data of 101 adults with NMOSD, multiple sclerosis (MS), or myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), we tested the sensitivity and specificity of claims-based algorithms developed through interviews with neurologists. We tested the best-performing algorithm's face validity using 2016-2019 data from IBM MarketScan Commercial and Medicare Supplemental databases. Demographics and clinical characteristics were reported.

RESULTS

Algorithm inclusion criteria were age ≥ 18 years and (≥1 NMO diagnosis [or ≥ 1 transverse myelitis (TM) and ≥ 1 optic neuritis (ON) diagnosis] and ≥ 1 NMOSD drug) or (≥2 NMO diagnoses ≥90 days apart). Exclusion criteria were MS diagnosis or use of MS-specific drug after last NMO diagnosis or NMOSD drug; sarcoidosis diagnosis after last NMO diagnosis; or use of ≥1 immune checkpoint inhibitor. In medical record billing data of 50 patients with NMOSD, 30 with MS, and 21 with MOGAD, the algorithm had 82.0% sensitivity and 70.6% specificity. When applied to healthcare claims data, demographic and clinical features of the identified cohort were similar to known demographics of NMOSD.

CONCLUSIONS

This clinically derived algorithm performed well in medical records. When tested in healthcare claims, demographics and clinical characteristics were consistent with previous clinical findings. This algorithm will enable a more accurate estimation of NMOSD disease burden using insurance claims datasets.

Effect of Paxlovid Treatment During Acute COVID-19 on Long COVID Onset: An EHR-Based Target Trial Emulation from the N3C and RECOVER Consortia

Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,352 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation (TTE) framework to estimate the effect of Paxlovid treatment on PASC incidence. We estimated overall PASC incidence using a computable phenotype. We also measured the onset of novel cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment did not have a significant effect on overall PASC incidence (relative risk [RR] = 0.98, 95% confidence interval [CI] 0.95-1.01). However, it had a protective effect on cognitive (RR = 0.90, 95% CI 0.84-0.96) and fatigue (RR = 0.95, 95% CI 0.91-0.98) symptom clusters, which suggests that the etiology of these symptoms may be more closely related to viral load than that of respiratory symptoms.

Microbiological findings in patients with community-acquired pneumonia: An analysis using the National Hospital Organization Clinical Data Archives

Although large-scale administrative databases may be useful for studies of infectious diseases, conventional databases lack microbiological data. To illustrate the applicability of the National Hospital Organization Clinical Data Archives, a novel database of electronic medical records in Japan, we conducted a descriptive study of the microbiological findings in patients with community-acquired pneumonia using the database. We identified patients aged ≥18 years who were hospitalized for community-acquired bacterial pneumonia between April 2016 and March 2019. We evaluated the results of bacterial culture and antibacterial susceptibility of specimens obtained on the first day of hospitalization, in addition to patient characteristics, diagnosis codes, and intravenous antibiotics administered. The analysis identified 2200 eligible patients from 15 hospitals. Sulbactam-ampicillin was the most frequently used initial antibiotic (32 %), followed by ceftriaxone (22 %) and tazobactam-piperacillin (19 %). Overall, 56 %, 95 %, 56 %, and 73 % of patients with pathogen-specific diagnosis codes in the database for Streptococcus pneumoniae, Haemophilus influenzae, Klebsiella pneumoniae, and Pseudomonas aeruginosa, respectively, also tested positive for the corresponding pathogen in their sputum or blood cultures. Antibacterial susceptibilities were consistent with a previous report from Japan; 81 % of S. pneumoniae cases were resistant to azithromycin, and 48 % of H. influenzae cases were resistant to ampicillin. These microbiological characteristics warrant the future use of this database for detailed real-world research on infectious diseases.

Revision Surgery Risk After Open Reduction and Internal Fixation Versus Acute Total Hip Arthroplasty in Geriatric Acetabular Fractures: A Nationwide Study

BACKGROUND

The aging population has contributed to a rising incidence of acetabular fractures in older patients, yet current evidence guiding surgical treatment is limited by small sample sizes. This study used a nationwide database to investigate outcomes in older patients undergoing open reduction and internal fixation (ORIF) versus acute total hip arthroplasty (THA).

METHODS

The PearlDiver database was queried for patients aged 60 years and older with an acute acetabular fracture who underwent ORIF or acute THA (2010 to 2021). ORIF patients were matched 1:1 to THA patients based on age, sex, US region, insurance plan, and Charlson Comorbidity Index score. Patients with less than 2 years of follow-up were excluded. The primary outcome was revision surgery.

RESULTS

Of 120,032 patients with an acetabular fracture, 3,768 (3.1%) underwent surgical treatment: 1,482 (39.3%) THA and 2,286 (60.7%) ORIF. Mean age was 71.0 ± 6.51 years. Mean follow-up duration was 5.2 ± 2.1 years. THA patients were slightly older (72.4 versus 70.2 years), had higher Charlson Comorbidity Index scores (3.3 versus 2.7), and had a higher percentage of female patients (62.5% versus 32.2%). After matching, 962 ORIF and 962 THA patients were included. ORIF patients had longer LOS (10.7 versus 8.1 days). ORIF patients were less likely to experience joint infections and periprosthetic fractures, but more likely to experience transfusions. The overall revision surgery rate within 10 years was 14.8% in the ORIF cohort and 6.2% in the THA cohort. In the ORIF cohort, 13.5% of patients had a delayed conversion to THA.

CONCLUSIONS

In this large national database, acute with or without internal fixation for geriatric acetabular fractures was associated with lower rates of revision surgery within 10 years when compared with a matched cohort undergoing ORIF. ORIF was associated with increased LOS, increased transfusion risk, and lower risk of joint infection and periprosthetic fracture.

LEVEL OF EVIDENCE

III.

Incidence of pneumococcal disease in children ≤48 months old in the United States: 1998-2019

BACKGROUND

Pneumococcal disease (PD) is a major cause of morbidity and mortality among children, particularly in the youngest age groups. This study aimed to assess the incidence of PD over time by age group in young children with commercial or Medicaid coverage in the US.

METHODS

Episodes of invasive pneumococcal disease (IPD), all-cause pneumonia (ACP), and acute otitis media (AOM) were identified in the MarketScan® Commercial and Medicaid claims databases using diagnosis codes among children aged ≤ 48 months with confirmed date of birth (DoB), at any time during the study period (1998-2019). DoB was assigned using diagnosis codes for birth or delivery using the child's or mother's medical claims to ensure accurate age determination. Annual incidence rates (IRs) were calculated as number of disease episodes/100,000 person-years (PY) for IPD and ACP and episodes/1,000 PY for AOM, for children aged 0-6, 7-12, 12-24, and 25-48 months.

RESULTS

Annual IPD IRs declined from 53 to 7 episodes/100,000 PY between 1998 and 2019 in commercially-insured and 58 to 9 episodes/100,000 PY between 2001 and 2019 in Medicaid-insured children. Annual ACP IRs declined from 5,600 to 3,952 episodes/100,000 PY, and from 6,706 to 4,521 episodes/100,000 PY, respectively, over these periods. In both populations, children aged 0-6 months had the highest incidence of IPD and inpatient ACP. Annual AOM IRs declined from 1,177 to 738 episodes/1,000 PY (commercially-insured) and 633 to 624 episodes/1,000 PY (Medicaid-insured), over these periods. IRs were higher in rural vs. urban areas for all disease manifestations.

CONCLUSIONS

Incidence rates of IPD, ACP, and AOM decreased in children with commercial insurance and Medicaid coverage from 1998 to 2019. However, burden of disease remained substantial, with higher annual IRs for IPD and ACP for Medicaid-insured vs. commercially-insured children. IPD and inpatient ACP were most common in the youngest children 0-6 months old, followed by the 7-12-month age group.

Development of the multivariate administrative data cystectomy model and its impact on misclassification bias

BACKGROUND

Misclassification bias (MB) is the deviation of measured from true values due to incorrect case assignment. This study compared MB when cystectomy status was determined using administrative database codes vs. predicted cystectomy probability.

METHODS

We identified every primary cystectomy-diversion type at a single hospital 2009-2019. We linked to claims data to measure true association of cystectomy with 30 patient and hospitalization factors. Associations were also measured when cystectomy status was assigned using billing codes and by cystectomy probability from multivariate logistic regression model with covariates from administrative data. MB was the difference between measured and true associations.

RESULTS

500 people underwent cystectomy (0.12% of 428 677 hospitalizations). Sensitivity and positive predictive values for cystectomy codes were 97.1% and 58.6% for incontinent diversions and 100.0% and 48.4% for continent diversions, respectively. The model accurately predicted cystectomy-incontinent diversion (c-statistic [C] 0.999, Integrated Calibration Index [ICI] 0.000) and cystectomy-continent diversion (C:1.000, ICI 0.000) probabilities. MB was significantly lower when model-based predictions was used to impute cystectomy-diversion type status using for both incontinent cystectomy (F = 12.75; p < .0001) and continent cystectomy (F = 11.25; p < .0001).

CONCLUSIONS

A model using administrative data accurately returned the probability that cystectomy by diversion type occurred during a hospitalization. Using this model to impute cystectomy status minimized MB. Accuracy of administrative database research can be increased by using probabilistic imputation to determine case status instead of individual codes.

Assessing cardiovascular disease risk in women with a history of hypertensive disorders of pregnancy: A guidance paper for studies using administrative data

BACKGROUND

Hypertensive disorders of pregnancy (HDP) are a major cause of maternal morbidity and mortality, and their association with increased cardiovascular disease (CVD) risk represents a major public health concern. However, assessing CVD risk in women with a history of these conditions presents unique challenges, especially when studies are carried out using routinely collected data.

OBJECTIVES

To summarise and describe key challenges related to the design and conduct of administrative studies assessing CVD risk in women with a history of HDP and provide concrete recommendations for addressing them in future research.

METHODS

This is a methodological guidance paper.

RESULTS

Several conceptual and methodological factors related to the data-generating mechanism and study conceptualisation, design/data management and analysis, as well as the interpretation and reporting of study findings should be considered and addressed when designing and carrying out administrative studies on this topic. Researchers should develop an a priori conceptual framework within which the research question is articulated, important study variables are identified and their interrelationships are carefully considered.

CONCLUSIONS

To advance our understanding of CVD risk in women with a history of HDP, future studies should carefully consider and address the conceptual and methodological considerations outlined in this guidance paper. In highlighting these challenges, and providing specific recommendations for how to address them, our goal is to improve the quality of research carried out on this topic.

Disparate potential for readmission prevention exists among inpatient and outpatient procedures in a minimally invasive surgery practice

BACKGROUND

Administrators have focused on decreasing postoperative readmissions for cost reduction without fully understanding their preventability. This study describes the development and implementation of a surgeon-led readmission review process that assessed preventability.

METHODS

A gastrointestinal surgical group at a tertiary referral hospital developed and implemented a template to analyze inpatient and outpatient readmissions. Monthly stakeholder assessments reviewed and categorized readmissions as potentially preventable or not preventable. Continuous variables were examined by the Student's t test and reported as means and standard deviations. Categorical variables were examined by the Pearson χ2 statistic and Fisher's exact test.

RESULTS

There were 61 readmission events after 849 inpatient operations (7.2%) and 16 after 856 outpatient operations (1.9%), the latter of which were all classified as potentially preventable. Colorectal procedures represented 65.6% of readmissions despite being only 37.2% of all cases. The majority (67.2%) of readmission events were not preventable. Compared to the not-preventable group, the potentially preventable group experienced more dehydration (30.0% vs 9.8%, P = .045) and ileostomy creation (78.6% vs 33.3%, P = .017). The potential for outpatient management to prevent readmission was significantly higher in the potentially preventable group (40.0% vs 0.0%, P < .001), as was premature discharge prevention (35.0% vs 0.0%, P < .001).

CONCLUSION

The use of the standardized template developed for analyzing readmission events after inpatient and outpatient procedures identified a disparate potential for readmission prevention. This finding suggests that a singular focus on readmission reduction is misguided, with further work needed to evaluate and implement appropriate quality-based strategies.

Validation and impact of algorithms for identifying variables in observational studies of routinely collected data

BACKGROUND AND OBJECTIVES

Among observational studies of routinely collected health data (RCD) for exploring treatment effects, algorithms are used to identify study variables. However, the extent to which algorithms are reliable and impact the credibility of effect estimates is far from clear. This study aimed to investigate the validation of algorithms for identifying study variables from RCD, and examine the impact of alternative algorithms on treatment effects.

METHODS

We searched PubMed for observational studies published in 2018 that used RCD to explore drug treatment effects. Information regarding the reporting, validation, and interpretation of algorithms was extracted. We summarized the reporting and methodological characteristics of algorithms and validation. We also assessed the divergence in effect estimates given alternative algorithms by calculating the ratio of estimates of the primary vs. alternative analyses.

RESULTS

A total of 222 studies were included, of which 93 (41.9%) provided a complete list of algorithms for identifying participants, 36 (16.2%) for exposure, and 132 (59.5%) for outcomes, and 15 (6.8%) for all study variables including population, exposure, and outcomes. Fifty-nine (26.6%) studies stated that the algorithms were validated, and 54 (24.3%) studies reported methodological characteristics of 66 validations, among which 61 validations in 49 studies were from the cross-referenced validation studies. Of those 66 validations, 22 (33.3%) reported sensitivity and 16 (24.2%) reported specificity. A total of 63.6% of studies reporting sensitivity and 56.3% reporting specificity used test-result-based sampling, an approach that potentially biases effect estimates. Twenty-eight (12.6%) studies used alternative algorithms to identify study variables, and 24 reported the effects estimated by primary analyses and sensitivity analyses. Of these, 20% had differential effect estimates when using alternative algorithms for identifying population, 18.2% for identifying exposure, and 45.5% for classifying outcomes. Only 32 (14.4%) studies discussed how the algorithms may affect treatment estimates.

CONCLUSION

In observational studies of RCD, the algorithms for variable identification were not regularly validated, and-even if validated-the methodological approach and performance of the validation were often poor. More seriously, different algorithms may yield differential treatment effects, but their impact is often ignored by researchers. Strong efforts, including recommendations, are warranted to improve good practice.

Impact of San Francisco's New Street crisis response Team on Service use among people experiencing homelessness with mental and substance use disorders: A mixed methods study protocol

Mobile crisis services for people experiencing distress related to mental health or substance use are expanding rapidly across the US, yet there is little evidence to support these specific models of care. These new programs present a unique opportunity to expand the literature by utilizing implementation science methods to inform the future design of crisis systems. This mixed methods study will examine the effectiveness and acceptability of the Street Crisis Response Team (SCRT), a new 911-dispatched multidisciplinary mobile crisis intervention piloted in San Francisco, California. First, using quantitative data from electronic health records, we will conduct an interrupted time series analysis to quantitatively examine the impacts of the SCRT on people experiencing homelessness who utilized public behavioral health crisis services in San Francisco between November 2019 and August 2022, across four main outcomes within 30 days of the crisis episode: routine care utilization, crisis care reutilization, assessment for housing services, and jail entry. Second, to understand its impact on health equity, we will analyze racial and ethnic disparities in these outcomes prior to and after implementation of the SCRT. For the qualitative component, we will conduct semi-structured interviews with recipients of the SCRT's services to understand their experiences of the intervention and to identify how the SCRT influenced their health-related trajectories after the crisis encounter. Once complete, the quantitative and qualitative findings will be further analyzed in tandem to assist with more nuanced understanding of the effectiveness of the SCRT program. This evaluation of a novel mobile crisis response program will advance the field, while also providing a model for how real-world program implementation can be achieved in crisis service settings.

Identification of complications requiring interventions after gastrointestinal cancer surgery from real-world data: An external validation study

Background

Recently, real-world data have been recognized to have a significant role for research and quality improvement worldwide. The decision on the existence or nonexistence of postoperative complications is complex in clinical practice. This multicenter validation study aimed to evaluate the accuracy of identification of patients who underwent gastrointestinal (GI) cancer surgery and extraction of postoperative complications from Japanese administrative claims data.

Methods

We compared data extracted from both the Diagnosis Procedure Combination (DPC) and chart review of patients who underwent GI cancer surgery from April 2016 to March 2019. Using data of 658 patients at Kyoto University Hospital, we developed algorithms for the extraction of patients and postoperative complications requiring interventions, which included an invasive procedure, reoperation, mechanical ventilation, hemodialysis, intensive care unit management, and in-hospital mortality. The accuracy of the algorithms was externally validated using the data of 1708 patients at two other hospitals.

Results

In the overall validation set, 1694 of 1708 eligible patients were correctly extracted by DPC (sensitivity 0.992 and positive predictive value 0.992). All postoperative complications requiring interventions had a sensitivity of >0.798 and a specificity of almost 1.000. The overall sensitivity and specificity of Clavien-Dindo ≥grade IIIb complications was 1.000 and 0.995, respectively.

Conclusion

Patients undergoing GI cancer surgery and postoperative complications requiring interventions can be accurately identified using the real-world data. This multicenter external validation study may contribute to future research on hospital quality improvement or to a large-scale comparison study among nationwide hospitals using real-world data.

Annual Increase of Acute Inpatients with Both Cancer and Cardiovascular Diseases in Japan 2011-2015: Analysis From National Database of Health Insurance Claims and Specific Health Checkups of Japan

BACKGROUND

Patients with cancer were able to live longer due to improvements in cancer treatment. Additionally, cardiovascular disease (CVD) is the second leading cause of mortality in cancer survivors. However, epidemiological data on onco-CVD have not been sufficiently provided. We aimed to investigate the clinical characteristics of cancer in CVD patients using the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB).

METHOD AND RESULTS

The NDB sampling dataset used in this study was randomly sampled 10% from the whole Diagnosis Procedure Combination (DPC) records from every January, April, July, and October from 2011 to 2015. The significance of the increase trend in the percentage of records in each disease group to the total number of all DPC records from 2011 to 2015 was checked with Chi-square test with a Bonferroni correction. The percentage of records in cancer with the CVD group to the total number of all DPC records significantly increased over time, and their average age also increased since 2011. Their proportion over 75 years was approximately 56 % in 2015. There was no difference in the cancer sites. However, the prevalence of heart failure dramatically elevated.

CONCLUSION

We were able to assess the increase in cancer among CVD patients using DPC inpatient records obtained from the NDB. Both cardiologists and oncologists should be more aware of this phenomenon.

Protecting patient safety and privacy in the era of artificial intelligence

The promise of artificial intelligence (AI) in health care has propelled a significant uptrend in the number of clinical trials in AI and global market spending in this novel technology. In vascular surgery, this technology has the ability to diagnose disease, predict disease outcomes, and assist with image-guided surgery. As we enter an era of rapid change, it is critical to evaluate the ethical concerns of AI, particularly as it may impact patient safety and privacy. This is particularly important to discuss in the early stages of AI, as technology frequently outpaces the policies and ethical guidelines regulating it. Issues at the forefront include patient privacy and confidentiality, protection of patient autonomy and informed consent, accuracy and applicability of this technology, and propagation of health care disparities. Vascular surgeons should be equipped to work with AI, as well as discuss its novel risks to patient safety and privacy.

Incidence and prevalence of traumatic spinal cord injury in Canada using health administrative data

INTRODUCTION

Incidence and prevalence data are needed for the planning, funding, delivery and evaluation of injury prevention and health care programs. The objective of this study was to estimate the Canadian traumatic spinal cord injury (TSCI) incidence, prevalence and trends over time using national-level health administrative data.

METHODS

ICD-10 CA codes were used to identify the cases for the hospital admission and discharge incidence rates of TSCI in Canada from 2005 to 2016. Provincial estimates were calculated using the location of the admitting facility. Age and sex-specific incidence rates were set to the 2015/2016 rates for the 2017 to 2019 estimates. Annual incidence rates were used as input for the prevalence model that applied annual survivorship rates derived from life expectancy data.

RESULTS

For 2019, it was estimated that there were 1,199 cases (32.0 per million) of TSCI admitted to hospitals, with 123 (10% of admissions) in-hospital deaths and 1,076 people with TSCI (28.7 per million) were discharged in Canada. The estimated number of people living with TSCI was 30,239 (804/million); 15,533 (52%) with paraplegia and 14,706 (48%) with tetraplegia. Trends included an increase in the number of people injured each year from 874 to 1,199 incident cases (37%), an older average age at injury rising from 46.6 years to 54.3 years and a larger proportion over the age of 65 changing from 22 to 38%, during the 15-year time frame.

CONCLUSION

This study provides a standard method for calculating the incidence and prevalence of TSCI in Canada using national-level health administrative data. The estimates are conservative based on the limitations of the data but represent a large Canadian sample over 15 years, which highlight national trends. An increasing number of TSCI cases among the elderly population due to falls reported in this study can inform health care planning, prevention strategies, and future research.

Validation studies of Japanese administrative health care data: A scoping review

PURPOSE

Large-scale administrative health care databases are increasingly being utilized for research. However, there has not been much literature that validated administrative data in Japan; a previous review identified six validation studies published between 2011 and 2017. We conducted a literature review of studies that assessed the validity of Japanese administrative health care data.

METHODS

We searched for studies published by March 2022 that compared individual-level administrative data with a reference standard from another data source, as well as studies that validated administrative data using other data within the same database. The eligible studies were also summarized based on characteristics which included data types, settings, reference standard used, numbers of patients, and conditions validated.

RESULTS

There were 36 eligible studies, including 29 that used external reference standard and seven that validated administrative data using other data within the same database. Chart review was the reference standard in 21 studies (range of the numbers of patients, 72-1674; 11 studies conducted in single institutions and nine studies in 2-5 institutions). Five studies used a disease registry as the reference standard. Diagnoses of cardiovascular diseases, cancer, and diabetes were frequently evaluated.

CONCLUSIONS

Validation studies are being conducted at an increasing rate in Japan, although most of them are small scale. Further large-scale comprehensive validation studies are necessary to effectively utilize the databases for research.

Effect of Nirmatrelvir/Ritonavir (Paxlovid) on Hospitalization among Adults with COVID-19: an EHR-based Target Trial Emulation from N3C

This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid's real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.

Decline in oral antimicrobial prescription in the outpatient setting after nationwide implementation of financial incentives and provider education: An interrupted time-series analysis

OBJECTIVES

To assess the impact of nationwide outpatient antimicrobial stewardship interventions in the form of financial incentives for providers and provider education when antimicrobials are deemed unnecessary for uncomplicated respiratory infections and acute diarrhea.

METHODS

We collected data from a large claims database from April 2013 through March 2020 and performed a quasi-experimental, interrupted time-series analysis. The outcome of interest was oral antimicrobial prescription rate defined as the number of monthly antimicrobial prescriptions divided by the number of outpatient visits each month. We examined the effects of financial incentive to providers (ie, targeted prescriptions for those aged ≤2 years) and provider education (ie, targeted prescriptions for those aged ≥6 years) on the overall antimicrobial prescription rates and how these interventions affected different age groups before and after their implementation.

RESULTS

In total, 21,647,080 oral antimicrobials were prescribed to 2,920,381 unique outpatients during the study period. At baseline, prescription rates for all age groups followed a downward trend throughout the study period. Immediately after the financial incentive implementation, substantial reductions in prescription rates were observed among only those aged 0-2 years (-47.5 prescriptions per 1,000 clinic visits each month; 95% confidence interval, -77.3 to -17.6; P = .003), whereas provider education immediately reduced prescription rates in all age groups uniformly. These interventions did not affect the long-term trend for any age group.

CONCLUSION

These results suggest that the nationwide implementation of financial incentives and provider education had an immediate effect on the antimicrobial prescription but no long-term effect.

Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data

Many administrative health data-based studies define patient cohorts using procedure and diagnosis codes. The impact these criteria have on a study's final cohort is not always transparent to co-investigators or other audiences if access to the research data is restricted. We developed a SAS and R Shiny interactive research support tool which generates and displays the diagnosis code summaries associated with a selected medical service or procedure. This allows non-analyst users to interrogate claims data and groupings of reported diagnosis codes. The SAS program uses a tree classifier to find associated diagnosis codes with the service claims compared against a matched, random sample of claims without the service. Claims are grouped based on the overlap of these associated diagnosis codes. The Health Services Research (HSR) Definition Builder Shiny application uses this input to create interactive table and graphics, which updates estimated claim counts of the selected service as users select inclusion and exclusion criteria. This tool can help researchers develop preliminary and shareable definitions for cohorts for administrative health data research. It allows an additional validation step of examining frequency of all diagnosis codes associated with a service, reducing the risk of incorrect included or omitted codes from the final definition. In our results, we explore use of the application on three example services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at https://kelsey209.shinyapps.io/hsrdefbuilder/ and the code at https://github.com/kelsey209/hsrdefbuilder.

Core concepts in pharmacoepidemiology: Validation of health outcomes of interest within real-world healthcare databases

Real-world healthcare data, including administrative and electronic medical record databases, provide a rich source of data for the conduct of pharmacoepidemiologic studies but carry the potential for misclassification of health outcomes of interest (HOIs). Validation studies are important ways to quantify the degree of error associated with case-identifying algorithms for HOIs and are crucial for interpreting study findings within real-world data. This review provides a rationale, framework, and step-by-step approach to validating case-identifying algorithms for HOIs within healthcare databases. Key steps in validating a case-identifying algorithm within a healthcare database include: (1) selecting the appropriate health outcome; (2) determining the reference standard against which to validate the algorithm; (3) developing the algorithm using diagnosis codes, diagnostic tests or their results, procedures, drug therapies, patient-reported symptoms or diagnoses, or some combinations of these parameters; (4) selection of patients and sample sizes for validation; (5) collecting data to confirm the HOI; (6) confirming the HOI; and (7) assessing the algorithm's performance. Additional strategies for algorithm refinement and methods to correct for bias due to misclassification of outcomes are discussed. The review concludes by discussing factors affecting the transportability of case-identifying algorithms and the need for ongoing validation as data elements within healthcare databases, such as diagnosis codes, change over time or new variables, such as patient-generated health data, are included in these data sources.

Validation study on definition of cause of death in Japanese claims data

Identifying the cause of death is important for the study of end-of-life patients using claims data in Japan. However, the validity of how cause of death is identified using claims data remains unknown. Therefore, this study aimed to verify the validity of the method used to identify the cause of death based on Japanese claims data. Our study population included patients who died at two institutions between January 1, 2018 and December 31, 2019. Claims data consisted of medical data and Diagnosis Procedure Combination (DPC) data, and five definitions developed from disease classification in each dataset were compared with death certificates. Nine causes of death, including cancer, were included in the study. The definition with the highest positive predictive values (PPVs) and sensitivities in this study was the combination of "main disease" in both medical and DPC data. For cancer, these definitions had PPVs and sensitivities of > 90%. For heart disease, these definitions had PPVs of > 50% and sensitivities of > 70%. For cerebrovascular disease, these definitions had PPVs of > 80% and sensitivities of> 70%. For other causes of death, PPVs and sensitivities were < 50% for most definitions. Based on these results, we recommend definitions with a combination of "main disease" in both medical and DPC data for cancer and cerebrovascular disease. However, a clear argument cannot be made for other causes of death because of the small sample size. Therefore, the results of this study can be used with confidence for cancer and cerebrovascular disease but should be used with caution for other causes of death.

Administrative codes may have limited utility in diagnosing biliary colic in emergency department visits: A validation study

Backgrounds/Aims

Biliary colic is a common cause of emergency department (ED) visits; however, the natural history of the disease and thus the indications for urgent or scheduled surgery remain unclear. Limitations of previous attempts to elucidate this natural history at a population level are based on the reliance on the identification of biliary colic via administrative codes in isolation. The purpose of our study was to validate the use of International Statistical Classification of Diseases and Related Health Problems codes, 10th Revision, Canadian modification (ICD-10-CA) from ED visits in adequately differentiating patients with biliary colic from those with other biliary diagnoses such as cholecystitis or common bile duct stones.

Methods

We performed a retrospective validation study using administrative data from two large academic hospitals in Toronto. We assessed all the patients presenting to the ED between January 1, 2012 and December 31, 2018, assigned ICD-10-CA codes in keeping with uncomplicated biliary colic. The codes were compared to the individually abstracted charts to assess diagnostic agreement.

Results

Among the 991 patient charts abstracted, 26.5% were misclassified, corresponding to a positive predictive value of 73% (95% confidence interval 73%–74%). The most frequent reasons for inaccurate diagnoses were a lack of gallstones (49.8%) and acute cholecystitis (27.8%).

Conclusions

Our findings suggest that the use of ICD-10 codes as the sole means of identifying biliary colic to the exclusion of other biliary pathologies is prone to moderate inaccuracy. Previous investigations of biliary colic utilizing administrative codes for diagnosis may therefore be prone to unforeseen bias.

Comparison of algorithms for identifying people with HIV from electronic medical records in a large, multi-site database

Objective

As electronic medical record (EMR) data are increasingly used in HIV clinical and epidemiologic research, accurately identifying people with HIV (PWH) from EMR data is paramount. We sought to evaluate EMR data types and compare EMR algorithms for identifying PWH in a multicenter EMR database.

Materials and Methods

We collected EMR data from 7 healthcare systems in the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) including diagnosis codes, anti-retroviral therapy (ART), and laboratory test results.

Results

In total, 13 935 patients had a positive laboratory test for HIV; 33 412 patients had a diagnosis code for HIV; and 17 725 patients were on ART. Only 8576 patients had evidence of HIV-positive status for all 3 data types (laboratory results, diagnosis code, and ART). A previously validated combination algorithm identified 22 411 patients as PWH.

Conclusion

EMR algorithms that combine laboratory results, administrative data, and ART can be applied to multicenter EMR data to identify PWH.

Validity of operative information in Japanese administrative data: a chart review-based analysis of 1221 cases at a single institution

PURPOSE

To evaluate the validity of operative information recorded in the Diagnosis Procedure Combination (DPC) database, a national inpatient database including administrative claims data.

METHODS

We reviewed the medical charts of 1221 patients who underwent one of six surgeries (breast, esophageal, gastric, thyroid cancer surgery, appendectomy, or inguinal hernia repair) at a surgery department of a university hospital from April 2016 to March 2019. We compared operative information (type, date, laterality of procedure; type of anesthesia; transfusion; and duration of anesthesia) recorded in the DPC database with the information recorded in the medical charts.

RESULTS

The DPC data for type, date, laterality of surgery, and type of anesthesia were accurate in 99% of the reviewed patients. The sensitivity and specificity for identifying whether a patient received a transfusion procedure were 97.5% and 99.6%, respectively. Data regarding the duration of anesthesia in the DPC database were identical to those in medical chart records in 1114 of 1216 cases that received general or spinal anesthesia (91.5%). The duration of anesthesia in the DPC data was 53 min longer on average than the recorded operative time in the medical charts.

CONCLUSION

The DPC database had high validity for operative information.

Epidemiology of traumatic brain injuries at a rural-serving Level II trauma center, 2004 - 2016

OBJECTIVE

To describe the epidemiology of traumatic brain injury (TBI) and quantify rural and urban differences.

METHODS

Patient characteristics, injury characteristics, imaging, and outcomes were extracted from the trauma registry of the level II trauma center at Essentia Health-St. Mary's Medical Center, Duluth, MN, for patients admitted for a TBI from January 1, 2004, through December 31, 2016. Estimated relative risk (RR) per year, Wald 95% confidence intervals, and p-values were calculated.

RESULTS

Of the 5,079 TBI admissions during the study period, just under half (2,510, 49.4%) resided in rural areas at the time of admission. Overall, there was a 3.8% unadjusted annual increase in TBI risk rom 2004-2016, with 2.9% and 4.7% annual increases among rural and urban U.S. residents, respectively. Rural residents had significant annual increases in risk of TBI admission resulting in 30-day post-discharge emergency department readmission and 30-day post-discharge combined inpatient/emergency department readmission of 35.2% and 22.4%, respectively.

CONCLUSIONS

We found that risk of rural resident TBI admission due to MVC was significantly greater than that for urban residents. Public health and medical interventions to decrease the rural/urban disparity are warranted, including public health campaigns to increase seat belt use, and supportive care post-discharge into rural communities.

Tracing all patients who received insured dialysis treatment in Japan and the present situation of their number of deaths

Background

The survival rate of chronic dialysis patients in Japan remains the highest worldwide, so there is value in presenting Japan’s situation internationally. We examined whether aggregate figures on dialysis patients in the National Database of Health Insurance Claims and Special Health Checkups of Japan (NDB), which contains data on insured procedures of approximately 100 million Japanese residents, complement corresponding figures in the Japanese Society for Dialysis Therapy Renal Data Registry (JRDR).

Methods

Subjects were patients with medical fee points for dialysis recorded in the NDB during 2014–2018. We analyzed annual numbers of dialysis cases, newly initiated dialysis cases– and deaths.

Results

Compared with the JRDR, the NDB had about 6–7% fewer dialysis cases but a similar number of newly initiated dialysis cases. In the NDB, the number of deaths was about 6–10% lower, and the number of hemodialysis cases was lower, while that of peritoneal dialysis cases was higher. The cumulative survival rate at dialysis initiation was approximately 6 percentage points lower in the NDB than in the JRDR, indicating that some patients die at dialysis initiation. Cumulative survival rate by age group was roughly the same between the NDB and JRDR in both sexes.

Conclusion

The use of the NDB enabled us to aggregate data of dialysis patients. With the definition of dialysis patients used in this study, analyses of concomitant medications, comorbidities, surgeries, and therapies will become possible, which will be useful in many future studies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10157-021-02163-z.

OUP accepted manuscript

Background

This study describes leading causes of hospitalization, including respiratory syncytial virus (RSV), in United States infants (<1 year) from 2009 through 2019.

Methods

Within the National (Nationwide) Inpatient Sample (NIS) data, hospitalizations were determined by primary diagnosis using International Classification of Diseases, Ninth or Tenth Revision codes. RSV was defined as 079.6, 466.11, 480.1, B97.4, J12.1, J20.5, or J21.0. Bronchiolitis was defined as 466.19, J21.8, or J21.9. Leading causes overall and by sociodemographic variables were identified. The Kids’ Inpatient Database (KID) was used for confirmatory analyses.

Results

Acute bronchiolitis due to RSV (code 466.11 or J21.0) was the leading primary diagnosis, accounting for 9.6% (95% confidence interval [CI], 9.4%–9.9%) and 9.3% (95% CI, 9.0%–9.6%) of total infant hospitalizations from January 2009 through September 2015 and October 2015 through December 2019, respectively; it was the leading primary diagnosis in every year accounting for >10% of total infant hospitalizations from December through March, reaching >15% in January–February. From 2009 through 2011, acute bronchiolitis due to RSV was the leading primary diagnosis in every birth month. Acute bronchiolitis due to RSV was the leading cause among all races/ethnicities, except Asian/Pacific Islanders, and all insurance payer groups. KID analyses confirmed these results.

Conclusions

Acute bronchiolitis due to RSV is the leading cause of US infant hospitalizations.

Validation of algorithms for identifying outpatient infections in MS patients using electronic medical records

Background Our multiple sclerosis (MS) stakeholder groups expressed concerns about whether MS disease-modifying therapies (DMTs) increase the risk of specific outpatient infections. Validated methods for identifying the risk of these selected outpatient infections in the general population either do not exist, exclude the clinically important possibility of recurrent infections, or are inaccurate, largely because existing studies relied primarily on International Classification of Diseases (ICD) codes to identify infectious outcomes. Additionally, no studies have validated methods among the MS population, where some MS symptoms can be mistaken for infections (e.g., urinary tract infections (UTIs)). Objective To utilize multiple data elements in the electronic health record (EHR) to improve accurate identification of selected outpatient infections in an MS cohort and general population controls. Methods We searched Kaiser Permanente Southern California's EHR based on ICD-9/10 codes for specified outpatient infections from 1/1/2008-12/31/2018 among our MS cohort (n=6000) and 5:1 general population controls matched on age, sex, and race/ethnicity (n=30,010). Random sample chart abstractions from each group were used to identify common coding errors for outpatient pneumonia, upper and lower respiratory tract infection, UTIs, herpetic infections (herpes zoster (HZ), herpes simplex virus (HSV)), fungal infections, otitis media, cellulitis, and influenza. This information was used to define discrete infectious episodes and to identify the algorithm with the highest positive predictive value (PPV) after supplementing the ICD-coded episodes with radiology, laboratory and/or pharmacy data. Results PPVs relying on ICD codes alone were inaccurate, particularly for identifying recurrent herpetic infections (HZ (42%) and HSV (60%)), UTIs (42%) and outpatient pneumonia (20%) in MS patients. Defining and validating episodes improved the PPVs for all the selected infections. The final algorithms' PPVs were 80-100% in MS and 75-100% in the general population, after including dispensed treatments (UTI, herpetic infections and yeast vaginitis), timing of dispensed treatments (UTI, herpetic infections and yeast vaginitis), removal of prophylactic antiviral use (herpetic infections), and inclusion of selected laboratory (UTIs) and imaging results (pneumonia). The only exception was outpatient pneumonia, where PPVs improved but remained ≤70%. There were no significant differences in the PPVs for the final algorithms between the MS and general population. Conclusions Provided herein are accurate and validated algorithms that can be used to improve our understanding of how the risk of recurrent outpatient infections are influenced by MS treatments, MS-related disability, and co-morbidities. Findings from such studies will be important in helping patients and clinicians engage in shared decision-making and in developing strategies to mitigate risks of recurrent infections.

Validity of a model using routinely collected data for identifying infections following gastric, colorectal, and liver cancer surgeries

PURPOSE

Validating outcome measures is a prerequisite for using administrative databases for comparative effectiveness research. Although the Japanese Diagnosis Procedure Combination database is widely used in surgical studies, the outcome measure for postsurgical infection has not been validated. We developed a model to identify postsurgical infections using the routinely-collected Diagnosis Procedure Combination data.

METHODS

We retrospectively identified inpatients who underwent surgery for gastric, colorectal, or liver cancer between April 2016 and March 2018 at four hospitals. Chart reviews were conducted to identify postsurgical infections. We used bootstrap analysis with backwards variable elimination to select independent variables from routinely-collected diagnosis and procedure data. Selected variables were used to create a score predicting the chart review-identified infections, and the performance of the score was tested.

RESULTS

Among the 756 eligible patients, 102 patients (13%) had postoperative infections. Three variables were identified as predictors: diagnosis of infectious disease recorded as a complication arising after admission, addition of an intravenous antibiotic, and bacterial microscopy or culture. The prediction model had a C-statistic of 0.891 and pseudo-R² of 0.380. A cut-off of 1 point of the score showed a sensitivity of 92% and specificity of 71%, and a cut-off of 2 points showed a sensitivity of 77% and specificity of 91%.

CONCLUSIONS

Our model using routinely-collected administrative data accurately identified postoperative infections. Further external validation would lead to the application of the model for research using administrative databases. This article is protected by copyright. All rights reserved.

Common Combinations of Pregestational Diagnosis and Pregnancy Complications

Objective Single pregestational diagnoses have been demonstrated to be associated with pregnancy-related complications. But, the effect of multiple diagnoses is understudied. The objective of this study is to determine the most common combinations of pregestational diagnoses and to determine if specific combinations increase the risk of pregnancy-related complications. Study design We performed a cross-sectional study of the 2016 Healthcare Cost and Utilization Project's National Inpatient Sample (HCUP NIS) database. Inclusion criteria were: Diagnosis-related groups assumed to be associated with delivery, and three or fewer International Classification of Diseases, Tenth Revision (ICD-10), clinical modification codes with a prevalence greater than or equal to 0.5%, or clinically important risk factors in Bateman's co-morbidity index. Chi-squared analysis of combinations of pregestational diagnoses was performed to assess the relative risk of pregnancy-related complications. Results The 2016 database included 255,233 delivered pregnancies. The most common combinations of pregestational diagnoses involved advanced maternal age, prior cesarean delivery, obesity, and tobacco use. Most combinations did not demonstrate an increased risk for complications greater than the risk with a single diagnosis. In those with statistically significant risk, all were 3-fold or less except we noted a 4.4-fold higher risk (95% CI: 3.16-6.15) of preeclampsia in obese patients of advanced maternal age compared to patients who were only of advanced maternal age. Conclusion Our results revealed that common combinations of pregestational diagnoses, in general, do not increase the risk for common pregnancy-related complications greater than the risk with a single diagnosis. This is reassuring, given that women entering pregnancy with multiple co-morbidities are becoming more common.

Increase in Achilles Tendon Rupture Surgery in Japan: Results From a Nationwide Health Care Database

Background:

Nationwide epidemiologic studies in Scandinavian countries have shown that the incidence of Achilles tendon ruptures (ATRs) has increased, and the rate of surgical treatment has declined markedly in the past decade. However, there is a lack of national-level data on the trend of ATRs and surgical procedures in other regions.

Purpose:

To clarify the trend in the incidence of ATRs and the proportion of surgery using the nationwide health care database in Japan.

Study Design:

Descriptive epidemiology study.

Methods:

Age- and sex-stratified data on the annual number of ATRs and surgical procedures between 2010 and 2017 were obtained from the Japanese national health care database, which includes almost all inpatient and outpatient medical claims nationwide. The Japanese population data were also obtained from the population census. The change in the annual incidence of ATRs per 100,000 people was assessed using a Poisson regression analysis. The trend in the annual proportion of surgeries relative to the occurrence of tendon ruptures was determined using a linear regression analysis.

Results:

A total of 112,601 ATRs, with men accounting for 67%, were identified over 8 years. Patients aged ≥60 years accounted for 27,106 (24%), while those aged 20 to 39 years and 40 to 59 years accounted for 36,164 (32%) and 49,331 (44%), respectively. The annual incidence of ATR ranged from 12.8/100,000 to 13.9/100,000 (women, 8.2-8.9/100,000; men, 17.2-19.5/100,000), which did not change over the study period (P = .82). Moreover, the annual incidences did not change across sexes and age categories. The annual proportion of surgery increased significantly, from 67% in 2010 to 72% in 2017 (P = .003). The annual proportions increased across sexes and age categories except for women aged 40 to 59 years.

Conclusion:

The incidence of ATR did not change between 2010 and 2017, according to the Japanese nationwide health care database. Furthermore, the proportion of surgical treatment increased during the study period. Overall, 70% of patients underwent surgical treatment. This study suggested that the trend in ATR and surgery differed across regions.

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods

Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized for target conditions. We investigated whether machine learning methods can supplement researchers' knowledge of target conditions in building CBAs. Retrospective cohort study using a claims database combined with annual health check-up results of employees' health insurance programs for fiscal year 2016-17 in Japan (study population for hypertension, N = 631,289; diabetes, N = 152,368; dyslipidemia, N = 614,434). We constructed CBAs with logistic regression, k-nearest neighbor, support vector machine, penalized logistic regression, tree-based model, and neural network for identifying patients with three common chronic conditions: hypertension, diabetes, and dyslipidemia. We then compared their association measures using a completely hold-out test set (25% of the study population). Among the test cohorts of 157,822, 38,092, and 153,608 enrollees for hypertension, diabetes, and dyslipidemia, 25.4%, 8.4%, and 38.7% of them had a diagnosis of the corresponding condition. The areas under the receiver operating characteristic curve (AUCs) of the logistic regression with/without subject-matter knowledge about the target condition were .923/.921 for hypertension, .957/.938 for diabetes, and .739/.747 for dyslipidemia. The logistic lasso, logistic elastic-net, and tree-based methods yielded AUCs comparable to those of the logistic regression with subject-matter knowledge: .923-.931 for hypertension; .958-.966 for diabetes; .747-.773 for dyslipidemia. We found that machine learning methods can attain AUCs comparable to the conventional knowledge-based method in building CBAs.

Preventing Fragility Fractures: A 3-Month Critical Window of Opportunity

Introduction:

Low-energy falls are the leading cause of injury-related morbidity and mortality in the elderly. In the past, physicians focused on treating fractures resulting from falls rather than preventing them. The purpose of this study is to identify patients with a hospital encounter for fall prior to a fracture as an opportunity for pre-injury intervention when patients might be motivated to engage in falls prevention.

Materials & Methods:

A retrospective analysis of all emergency room and inpatient encounters in 2016 with an ICD10 diagnosis code including “fall” across a tri-state health system was performed. Subsequent encounters with diagnosis of fracture within 2 years were then identified. Data was collected for time to subsequent fracture, fracture type and location, and length of stay of initial encounter.

Results:

There were 12,382 encounters for falls among 10,589 patients. Of those patients, 1,040 (9.8%) sustained a subsequent fracture. Fractures were most commonly lower extremity fractures (661 fractures; 63.5%), including hip fractures (447 fractures; 45.87%). Median time from fall to fracture was 105 days (IQR 16-359 days).

Discussion:

Falls are an important, modifiable risk factor for fragility fracture. This study demonstrates that patients are presenting to the hospital with one of the main modifiable risk factors for fracture within a time window that allows for intervention.

Conclusions:

Presentation to the hospital for a fall is a vital opportunity to intervene and prevent subsequent fracture in a high-risk population.

d-dimer and Death in Critically Ill Patients With Coronavirus Disease 2019

OBJECTIVES

Hypercoagulability may be a key mechanism for acute organ injury and death in patients with severe coronavirus disease 2019, but the relationship between elevated plasma levels of d-dimer, a biomarker of coagulation activation, and mortality has not been rigorously studied. We examined the independent association between d-dimer and death in critically ill patients with coronavirus disease 2019.

DESIGN

Multicenter cohort study.

SETTING

ICUs at 68 hospitals across the United States.

PATIENTS

Critically ill adults with coronavirus disease 2019 admitted to ICUs between March 4, 2020, and May 25, 2020, with a measured d-dimer concentration on ICU day 1 or 2.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The primary exposure was the highest normalized d-dimer level (assessed in four categories: < 2×, 2-3.9×, 4-7.9×, and ≥ 8× the upper limit of normal) on ICU day 1 or 2. The primary endpoint was 28-day mortality. Multivariable logistic regression was used to adjust for confounders. Among 3,418 patients (63.1% male; median age 62 yr [interquartile range, 52-71 yr]), 3,352 (93.6%) had a d-dimer concentration above the upper limit of normal. A total of 1,180 patients (34.5%) died within 28 days. Patients in the highest compared with lowest d-dimer category had a 3.11-fold higher odds of death (95% CI, 2.56-3.77) in univariate analyses, decreasing to a 1.81-fold increased odds of death (95% CI, 1.43-2.28) after multivariable adjustment for demographics, comorbidities, and illness severity. Further adjustment for therapeutic anticoagulation did not meaningfully attenuate this relationship (odds ratio, 1.73; 95% CI, 1.36-2.19).

CONCLUSIONS

In a large multicenter cohort study of critically ill patients with coronavirus disease 2019, higher d-dimer levels were independently associated with a greater risk of death.

Thrombosis, Bleeding, and the Observational Effect of Early Therapeutic Anticoagulation on Survival in Critically Ill Patients With COVID-19

BACKGROUND

Hypercoagulability may be a key mechanism of death in patients with coronavirus disease 2019 (COVID-19).

OBJECTIVE

To evaluate the incidence of venous thromboembolism (VTE) and major bleeding in critically ill patients with COVID-19 and examine the observational effect of early therapeutic anticoagulation on survival.

DESIGN

In a multicenter cohort study of 3239 critically ill adults with COVID-19, the incidence of VTE and major bleeding within 14 days after intensive care unit (ICU) admission was evaluated. A target trial emulation in which patients were categorized according to receipt or no receipt of therapeutic anticoagulation in the first 2 days of ICU admission was done to examine the observational effect of early therapeutic anticoagulation on survival. A Cox model with inverse probability weighting to adjust for confounding was used.

SETTING

67 hospitals in the United States.

PARTICIPANTS

Adults with COVID-19 admitted to a participating ICU.

MEASUREMENTS

Time to death, censored at hospital discharge, or date of last follow-up.

RESULTS

Among the 3239 patients included, the median age was 61 years (interquartile range, 53 to 71 years), and 2088 (64.5%) were men. A total of 204 patients (6.3%) developed VTE, and 90 patients (2.8%) developed a major bleeding event. Independent predictors of VTE were male sex and higher D-dimer level on ICU admission. Among the 2809 patients included in the target trial emulation, 384 (11.9%) received early therapeutic anticoagulation. In the primary analysis, during a median follow-up of 27 days, patients who received early therapeutic anticoagulation had a similar risk for death as those who did not (hazard ratio, 1.12 [95% CI, 0.92 to 1.35]).

LIMITATION

Observational design.

CONCLUSION

Among critically ill adults with COVID-19, early therapeutic anticoagulation did not affect survival in the target trial emulation.

PRIMARY FUNDING SOURCE

None.

Intra-database validation of case-identifying algorithms using reconstituted electronic health records from healthcare claims data

BACKGROUND

Diagnosis performances of case-identifying algorithms developed in healthcare database are usually assessed by comparing identified cases with an external data source. When this is not feasible, intra-database validation can present an appropriate alternative.

OBJECTIVES

To illustrate through two practical examples how to perform intra-database validations of case-identifying algorithms using reconstituted Electronic Health Records (rEHRs).

METHODS

Patients with 1) multiple sclerosis (MS) relapses and 2) metastatic castration-resistant prostate cancer (mCRPC) were identified in the French nationwide healthcare database (SNDS) using two case-identifying algorithms. A validation study was then conducted to estimate diagnostic performances of these algorithms through the calculation of their positive predictive value (PPV) and negative predictive value (NPV). To that end, anonymized rEHRs were generated based on the overall information captured in the SNDS over time (e.g. procedure, hospital stays, drug dispensing, medical visits) for a random selection of patients identified as cases or non-cases according to the predefined algorithms. For each disease, an independent validation committee reviewed the rEHRs of 100 cases and 100 non-cases in order to adjudicate on the status of the selected patients (true case/ true non-case), blinded with respect to the result of the corresponding algorithm.

RESULTS

Algorithm for relapses identification in MS showed a 95% PPV and 100% NPV. Algorithm for mCRPC identification showed a 97% PPV and 99% NPV.

CONCLUSION

The use of rEHRs to conduct an intra-database validation appears to be a valuable tool to estimate the performances of a case-identifying algorithm and assess its validity, in the absence of alternative.

Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts

Background

There have been few studies describing how production EMR systems can be systematically queried to identify clinically-defined populations and limited studies utilising free-text in this process. The aim of this study is to provide a generalisable methodology for constructing clinically-defined EMR-derived patient cohorts using structured and unstructured data in EMRs.

Methods

Patients with possible acute coronary syndrome (ACS) were used as an exemplar. Cardiologists defined clinical criteria for patients presenting with possible ACS. These were mapped to data tables within the production EMR system creating seven inclusion criteria comprised of structured data fields (orders and investigations, procedures, scanned electrocardiogram (ECG) images, and diagnostic codes) and unstructured clinical documentation. Data were extracted from two local health districts (LHD) in Sydney, Australia. Outcome measures included examination of the relative contribution of individual inclusion criteria to the identification of eligible encounters, comparisons between inclusion criterion and evaluation of consistency of data extracts across years and LHDs.

Results

Among 802,742 encounters in a 5 year dataset (1/1/13–30/12/17), the presence of an ECG image (54.8% of encounters) and symptoms and keywords in clinical documentation (41.4–64.0%) were used most often to identify presentations of possible ACS. Orders and investigations (27.3%) and procedures (1.4%), were less often present for identified presentations. Relevant ICD-10/SNOMED CT codes were present for 3.7% of identified encounters. Similar trends were seen when the two LHDs were examined separately, and across years.

Conclusions

Clinically-defined EMR-derived cohorts combining structured and unstructured data during cohort identification is a necessary prerequisite for critical validation work required for development of real-time clinical decision support and learning health systems.

Accuracy of Administrative Database Algorithms for Hospitalized Pneumonia in Adults: a Systematic Review

BACKGROUND

Administrative data algorithms (ADAs) to identify pneumonia cases are commonly used in the analysis of pneumonia burden, trends, etiology, processes of care, outcomes, health care utilization, cost, and response to preventative and therapeutic interventions. However, without a good understanding of the validity of ADAs for pneumonia case identification, an adequate appreciation of this literature is difficult. We systematically reviewed the quality and accuracy of published ADAs to identify adult hospitalized pneumonia cases.

METHODS

We reviewed the Medline, EMBase, and Cochrane Central databases through May 2020. All studies describing ADAs for adult hospitalized pneumonia and at least one accuracy statistic were included. Investigators independently extracted information about the sampling frame, reference standard, ADA composition, and ADA accuracy.

RESULTS

Thirteen studies involving 24 ADAs were analyzed. Compliance with a 38-item study-quality assessment tool ranged from 17 to 29 (median, 23; interquartile range [IQR], 20 to 25). Study setting, design, and ADA composition varied extensively. Inclusion criteria of most studies selected for high-risk populations and/or increased pneumonia likelihood. Reference standards with explicit criteria (clinical, laboratorial, and/or radiographic) were used in only 4 ADAs. Only 2 ADAs were validated (one internally and one externally). ADA positive predictive values ranged from 35.0 to 96.5% (median, 84.8%; IQR, 65.3 to 89.1%). However, these values are exaggerated for an unselected patient population because pneumonia prevalences in the study cohorts were very high (median, 66%; IQR, 46 to 86%). ADA sensitivities ranged from 31.3 to 97.8% (median, 65.1%; IQR 52.5-72.4).

DISCUSSION

ADAs for identification of adult pneumonia hospitalizations are highly heterogeneous, poorly validated, and at risk for misclassification bias. Greater standardization in reporting ADA accuracy is required in studies using pneumonia ADA for case identification so that results can be properly interpreted.

A primer on quantitative bias analysis with positive predictive values in research using electronic health data

OBJECTIVE

In health informatics, there have been concerns with reuse of electronic health data for research, including potential bias from incorrect or incomplete outcome ascertainment. In this tutorial, we provide a concise review of predictive value-based quantitative bias analysis (QBA), which comprises epidemiologic methods that use estimates of data quality accuracy to quantify the bias caused by outcome misclassification.

TARGET AUDIENCE

Health informaticians and investigators reusing large, electronic health data sources for research.

SCOPE

When electronic health data are reused for research, validation of outcome case definitions is recommended, and positive predictive values (PPVs) are the most commonly reported measure. Typically, case definitions with high PPVs are considered to be appropriate for use in research. However, in some studies, even small amounts of misclassification can cause bias. In this tutorial, we introduce methods for quantifying this bias that use predictive values as inputs. Using epidemiologic principles and examples, we first describe how multiple factors influence misclassification bias, including outcome misclassification levels, outcome prevalence, and whether outcome misclassification levels are the same or different by exposure. We then review 2 predictive value-based QBA methods and why outcome PPVs should be stratified by exposure for bias assessment. Using simulations, we apply and evaluate the methods in hypothetical electronic health record-based immunization schedule safety studies. By providing an overview of predictive value-based QBA, we hope to bridge the disciplines of health informatics and epidemiology to inform how the impact of data quality issues can be quantified in research using electronic health data sources.

Extracorporeal membrane oxygenation in patients with severe respiratory failure from COVID-19

PURPOSE

Limited data are available on venovenous extracorporeal membrane oxygenation (ECMO) in patients with severe hypoxemic respiratory failure from coronavirus disease 2019 (COVID-19).

METHODS

We examined the clinical features and outcomes of 190 patients treated with ECMO within 14 days of ICU admission, using data from a multicenter cohort study of 5122 critically ill adults with COVID-19 admitted to 68 hospitals across the United States. To estimate the effect of ECMO on mortality, we emulated a target trial of ECMO receipt versus no ECMO receipt within 7 days of ICU admission among mechanically ventilated patients with severe hypoxemia (PaO₂/FiO₂ < 100). Patients were followed until hospital discharge, death, or a minimum of 60 days. We adjusted for confounding using a multivariable Cox model.

RESULTS

Among the 190 patients treated with ECMO, the median age was 49 years (IQR 41-58), 137 (72.1%) were men, and the median PaO₂/FiO₂ prior to ECMO initiation was 72 (IQR 61-90). At 60 days, 63 patients (33.2%) had died, 94 (49.5%) were discharged, and 33 (17.4%) remained hospitalized. Among the 1297 patients eligible for the target trial emulation, 45 of the 130 (34.6%) who received ECMO died, and 553 of the 1167 (47.4%) who did not receive ECMO died. In the primary analysis, patients who received ECMO had lower mortality than those who did not (HR 0.55; 95% CI 0.41-0.74). Results were similar in a secondary analysis limited to patients with PaO₂/FiO₂ < 80 (HR 0.55; 95% CI 0.40-0.77).

CONCLUSION

In select patients with severe respiratory failure from COVID-19, ECMO may reduce mortality.

Validation of Administrative Codes for Palliative Care Consultation Among Critically Ill Children

OBJECTIVES

To determine the validity of palliative care (PC) administrative codes (V66.7 and Z51.5) among critically ill pediatric patients.

METHODS

In this single-center retrospective cohort study, we included all hospitalizations with a PICU admission between March 2016 and March 2018. Sensitivity, specificity, and positive and negative predictive values of the relevant codes were estimated by using a gold standard of a local PC registry.

RESULTS

During the study period, 4670 hospitalizations were included. The median admission age was 5 years (interquartile range 1.5-12.9) with 55% having at least 1 complex chronic condition. The median length of PICU stay was 1.8 days (interquartile range 1-3.4) and mortality was low (1.3%). A total 182 (3.9%) hospitalizations had evidence of a PC consultation. Administrative codes for PC had a sensitivity of 11% (95% confidence interval [CI] 6.8%-16.5%) and a specificity of 99.8% (95% CI 99.6%-99.9%). The positive and negative predictive values were 66.7% (95% CI 47.2%-82.7%) and 96.5% (95% CI 95.9%-97.0%), respectively.

CONCLUSIONS

Among critically ill children, PC administrative codes had high specificity but poor sensitivity. The potential for underascertainment of this resource should be considered in future research using administrative data.

Narrative Review: The Process of Expanding the Manual of Antimicrobial Stewardship by the Government of Japan

The Ministry of Health, Labour and Welfare has published the Manual of Antimicrobial Stewardship (1st edition) in June 2017 to improve the prescribing practice of antimicrobials for immunocompetent adult and pediatric (both school-aged and older children) patients. Due to the increasing demand for further promoting outpatient antimicrobial stewardship, we conducted a literature and national guideline review to identify the area of need. The results of our review revealed a high antimicrobial prescription rate in the Japanese pediatric population. Furthermore, although the Japanese clinical guidelines/guidance covered the fields of almost all infectious diseases, no system exists to estimate the incidence and treatment patterns of important infectious diseases such as asymptomatic bacteriuria, skin and soft tissue infections, and dental practices in Japan. Therefore, addressing the issues of both establishing surveillance systems and the implementation of guidelines/guidance can be the next step to promote further outpatient antimicrobial stewardship.

Association Between Early Treatment With Tocilizumab and Mortality Among Critically Ill Patients With COVID-19

Importance

Therapies that improve survival in critically ill patients with coronavirus disease 2019 (COVID-19) are needed. Tocilizumab, a monoclonal antibody against the interleukin 6 receptor, may counteract the inflammatory cytokine release syndrome in patients with severe COVID-19 illness.

Objective

To test whether tocilizumab decreases mortality in this population.

Design, Setting, and Participants

The data for this study were derived from a multicenter cohort study of 4485 adults with COVID-19 admitted to participating intensive care units (ICUs) at 68 hospitals across the US from March 4 to May 10, 2020. Critically ill adults with COVID-19 were categorized according to whether they received or did not receive tocilizumab in the first 2 days of admission to the ICU. Data were collected retrospectively until June 12, 2020. A Cox regression model with inverse probability weighting was used to adjust for confounding.

Exposures

Treatment with tocilizumab in the first 2 days of ICU admission.

Main Outcomes and Measures

Time to death, compared via hazard ratios (HRs), and 30-day mortality, compared via risk differences.

Results

Among the 3924 patients included in the analysis (2464 male [62.8%]; median age, 62 [interquartile range {IQR}, 52-71] years), 433 (11.0%) received tocilizumab in the first 2 days of ICU admission. Patients treated with tocilizumab were younger (median age, 58 [IQR, 48-65] vs 63 [IQR, 52-72] years) and had a higher prevalence of hypoxemia on ICU admission (205 of 433 [47.3%] vs 1322 of 3491 [37.9%] with mechanical ventilation and a ratio of partial pressure of arterial oxygen to fraction of inspired oxygen of <200 mm Hg) than patients not treated with tocilizumab. After applying inverse probability weighting, baseline and severity-of-illness characteristics were well balanced between groups. A total of 1544 patients (39.3%) died, including 125 (28.9%) treated with tocilizumab and 1419 (40.6%) not treated with tocilizumab. In the primary analysis, during a median follow-up of 27 (IQR, 14-37) days, patients treated with tocilizumab had a lower risk of death compared with those not treated with tocilizumab (HR, 0.71; 95% CI, 0.56-0.92). The estimated 30-day mortality was 27.5% (95% CI, 21.2%-33.8%) in the tocilizumab-treated patients and 37.1% (95% CI, 35.5%-38.7%) in the non-tocilizumab-treated patients (risk difference, 9.6%; 95% CI, 3.1%-16.0%).

Conclusions and Relevance

Among critically ill patients with COVID-19 in this cohort study, the risk of in-hospital mortality in this study was lower in patients treated with tocilizumab in the first 2 days of ICU admission compared with patients whose treatment did not include early use of tocilizumab. However, the findings may be susceptible to unmeasured confounding, and further research from randomized clinical trials is needed.

Health claims databases used for kidney research around the world

Health claims databases offer opportunities for studies on large populations of patients with kidney disease and health outcomes in a non-experimental setting. Among others, their unique features enable studies on healthcare costs or on longitudinal, epidemiological data with nationwide coverage. However, health claims databases also have several limitations. Because clinical data and information on renal function are often lacking, the identification of patients with kidney disease depends on the actual presence of diagnosis codes only. Investigating the validity of these data is therefore crucial to assess whether outcomes derived from health claims data are truly meaningful. Also, one should take into account the coverage and content of a health claims database, especially when making international comparisons. In this article, an overview is provided of international health claims databases and their main publications in the area of nephrology. The structure and contents of the Dutch health claims database will be described, as well as an initiative to use the outcomes for research and the development of the Dutch Kidney Atlas. Finally, we will discuss to what extent one might be able to identify patients with kidney disease using health claims databases, as well as their strengths and limitations.

Validation of the International Classification of Disease 10th Revision Codes for Kidney Transplant Rejection and Failure

BACKGROUND

Clinical research requires that diagnostic codes captured from routinely collected health administrative data accurately identify individuals with a disease.

OBJECTIVE

In this study, we validated the International Classification of Disease 10th Revision (ICD-10) definition for kidney transplant rejection (T86.100) and for kidney transplant failure (T86.101).

DESIGN

Retrospective cohort study.

SETTING

A large, regional transplantation center in Ontario, Canada.

PATIENTS

All adult kidney transplant recipients from 2002 to 2018.

MEASUREMENTS

Chart review was undertaken to identify the first occurrence of biopsy-confirmed rejection and graft loss for all participants. For each observation, we determined the first date a single ICD-10 code T86.100 or T86.101 was recorded as a hospital encounter discharge diagnosis.

METHODS

Using chart review as the gold standard, we determined the sensitivity, specificity, and positive predictive value (PPV) for the ICD-10 codes T86.100 and T86.101.

RESULTS

Our study population comprised of 1,258 kidney transplant recipients. The prevalence of rejection and death-censored graft loss were 15.6 and 9.1%, respectively. For the ICD-10 rejection code (T86.100), sensitivity was 72.9% (95% confidence interval [CI], 66.6-79.2), specificity 97.5% (96.5-98.4), and PPV 83.8% (78.3-89.4). For the ICD-10 graft loss code (T86.101), sensitivity was 21.2% (95% CI, 13.2-29.3), specificity 86.3% (84.3-88.3), and PPV 11.7% (7.0-16.4).

LIMITATIONS

Single-center study which may limit generalizability of our findings.

CONCLUSIONS

A single ICD-10 code for kidney transplant rejection (T86.100) was present in 84% of true kidney transplant rejections and is an accurate way of identifying kidney transplant recipients with rejection using administrative health data. The ICD-10 code for graft failure (T86.101) performed poorly and should not be used for administrative health research.

Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US

Importance

The US is currently an epicenter of the coronavirus disease 2019 (COVID-19) pandemic, yet few national data are available on patient characteristics, treatment, and outcomes of critical illness from COVID-19.

Objectives

To assess factors associated with death and to examine interhospital variation in treatment and outcomes for patients with COVID-19.

Design, Setting, and Participants

This multicenter cohort study assessed 2215 adults with laboratory-confirmed COVID-19 who were admitted to intensive care units (ICUs) at 65 hospitals across the US from March 4 to April 4, 2020.

Exposures

Patient-level data, including demographics, comorbidities, and organ dysfunction, and hospital characteristics, including number of ICU beds.

Main Outcomes and Measures

The primary outcome was 28-day in-hospital mortality. Multilevel logistic regression was used to evaluate factors associated with death and to examine interhospital variation in treatment and outcomes.

Results

A total of 2215 patients (mean [SD] age, 60.5 [14.5] years; 1436 [64.8%] male; 1738 [78.5%] with at least 1 chronic comorbidity) were included in the study. At 28 days after ICU admission, 784 patients (35.4%) had died, 824 (37.2%) were discharged, and 607 (27.4%) remained hospitalized. At the end of study follow-up (median, 16 days; interquartile range, 8-28 days), 875 patients (39.5%) had died, 1203 (54.3%) were discharged, and 137 (6.2%) remained hospitalized. Factors independently associated with death included older age (≥80 vs <40 years of age: odds ratio [OR], 11.15; 95% CI, 6.19-20.06), male sex (OR, 1.50; 95% CI, 1.19-1.90), higher body mass index (≥40 vs <25: OR, 1.51; 95% CI, 1.01-2.25), coronary artery disease (OR, 1.47; 95% CI, 1.07-2.02), active cancer (OR, 2.15; 95% CI, 1.35-3.43), and the presence of hypoxemia (Pao2:Fio2<100 vs ≥300 mm Hg: OR, 2.94; 95% CI, 2.11-4.08), liver dysfunction (liver Sequential Organ Failure Assessment score of 2-4 vs 0: OR, 2.61; 95% CI, 1.30-5.25), and kidney dysfunction (renal Sequential Organ Failure Assessment score of 4 vs 0: OR, 2.43; 95% CI, 1.46-4.05) at ICU admission. Patients admitted to hospitals with fewer ICU beds had a higher risk of death (<50 vs ≥100 ICU beds: OR, 3.28; 95% CI, 2.16-4.99). Hospitals varied considerably in the risk-adjusted proportion of patients who died (range, 6.6%-80.8%) and in the percentage of patients who received hydroxychloroquine, tocilizumab, and other treatments and supportive therapies.

Conclusions and Relevance

This study identified demographic, clinical, and hospital-level risk factors that may be associated with death in critically ill patients with COVID-19 and can facilitate the identification of medications and supportive therapies to improve outcomes.

Characterization of the rate, predictors, and thrombotic complications of thrombocytosis in iron deficiency anemia

The association of thrombocytosis with iron deficiency anemia (IDA) is well-recognized, but data describing the rate, predictors, and risk of thrombotic complications associated with IDA-related thrombocytosis are limited. We queried an institutional patient data repository containing comprehensive chart data for over 6 million patients to identify IDA patients with and without thrombocytosis and thrombotic events over a 40-year time period (1979 to 2019). Demographics, hematological parameters, thrombosis history, and other medical history were collected. Fidelity of query data was assessed via detailed manual chart review of 700 patients, including confirmation of ferritin and hematologic parameters in addition to temporal and clinical association of thrombocytosis. Our queries identified 36 327 cases of IDA of which 15 022 had thrombocytosis. Following assessment for data integrity, we observed a thrombocytosis rate of 32.6% in patients with IDA. The rate of thrombosis was calculated to be 7.8% in patients with IDA and 15.8% in patients with IDA and thrombocytosis. Platelet mass index at time of peak thrombocytosis was significantly higher than at baseline and was strongly negatively correlated with hemoglobin at peak thrombocytosis. A multivariable model demonstrated a significant predictive relationship between decreasing hemoglobin and increasing platelet count at peak thrombocytosis. In conclusion, we observed reactive thrombocytosis in one-third of IDA patients, and a 2-fold thrombosis risk in patients with IDA and thrombocytosis compared with patients with IDA alone. Given the global burden of untreated and undertreated IDA, adequate IDA treatment may reduce thrombotic complications and associated morbidity and mortality.

Use of ICD-10 Codes for Identification of Injection Drug Use-Associated Infective Endocarditis Is Nonspecific and Obscures Critical Findings on Impact of Medications for Opioid Use Disorder

Background

No International Classification of Diseases, 10th revision (ICD-10), diagnosis code exists for injection drug use–associated infective endocarditis (IDU-IE). Instead, public health researchers regularly use combinations of nonspecific ICD-10 codes to identify IDU-IE; however, the accuracy of these codes has not been evaluated.

Methods

We compared commonly used ICD-10 diagnosis codes for IDU-IE with a prospectively collected patient cohort diagnosed with IDU-IE at Barnes-Jewish Hospital to determine the accuracy of ICD-10 diagnosis codes used in IDU-IE research.

Results

ICD-10 diagnosis codes historically used to identify IDU-IE were inaccurate, missing 36.0% and misclassifying 56.4% of patients prospectively identified in this cohort. Use of these nonspecific ICD-10 diagnosis codes resulted in substantial biases against the benefit of medications for opioid use disorder (MOUD) with relation to both AMA discharge and all-cause mortality. Specifically, when data from all patients with ICD-10 code combinations suggestive of IDU-IE were used, MOUD was associated with an increased risk of AMA discharge (relative risk [RR], 1.12; 95% CI, 0.48–2.64). In contrast, when only patients confirmed by chart review as having IDU-IE were analyzed, MOUD was protective (RR, 0.49; 95% CI, 0.19–1.22). Use of MOUD was associated with a protective effect in time to all-cause mortality in Kaplan-Meier analysis only when confirmed IDU-IE cases were analyzed (P = .007).

Conclusions

Studies using nonspecific ICD-10 diagnosis codes for IDU-IE should be interpreted with caution. In the setting of an ongoing overdose crisis and a syndemic of infectious complications, a specific ICD-10 diagnosis code for IDU-IE is urgently needed.