Veterans Affairs patient database (VAPD 2014-2017): building nationwide granular data for clinical discovery

Heterogeneity of Benefit from Earlier Time-to-Antibiotics for Sepsis

Rationale: Shorter time-to-antibiotics improves survival from sepsis, particularly among patients in shock. There may be other subgroups for whom faster antibiotics are particularly beneficial.Objectives: Identify patient characteristics associated with greater benefit from shorter time-to-antibiotics.Methods: Observational cohort study of patients hospitalized with community-onset sepsis at 173 hospitals and treated with antimicrobials within 12 hours. We used three approaches to evaluate heterogeneity of benefit from shorter time-to-antibiotics: 1) conditional average treatment effects of shorter (⩽3 h) versus longer (>3-12 h) time-to-antibiotics on 30-day mortality using multivariable Poisson regression; 2) causal forest to identify characteristics associated with greatest benefit from shorter time-to-antibiotics; and 3) logistic regression with time-to-antibiotics modeled as a spline.Measurements and Main Results: Among 273,255 patients with community-onset sepsis, 131,094 (48.0%) received antibiotics within 3 hours. In Poisson models, shorter time-to-antibiotics was associated with greater absolute mortality reduction among patients with metastatic cancer (5.0% [95% confidence interval; CI: 4.3-5.7] vs. 0.4% [95% CI: 0.2-0.6] for patients without cancer, P < 0.001); patients with shock (7.0% [95% CI: 5.8-8.2%] vs. 2.8% [95% CI: 2.7-3.5%] for patients without shock, P = 0.005); and patients with more acute organ dysfunctions (4.8% [95% CI: 3.9-5.6%] for three or more dysfunctions vs. 0.5% [95% CI: 0.3-0.8] for one dysfunction, P < 0.001). In causal forest, metastatic cancer and shock were associated with greatest benefit from shorter time-to-antibiotics. Spline analysis confirmed differential nonlinear associations of time-to-antibiotics with mortality in patients with metastatic cancer and shock.Conclusions: In patients with community-onset sepsis, the mortality benefit of shorter time-to-antibiotics varied by patient characteristics. These findings suggest that shorter time-to-antibiotics for sepsis is particularly important among patients with cancer and/or shock.

Impact of Reducing Time-to-Antibiotics on Sepsis Mortality, Antibiotic Use, and Adverse Events

Rationale: Shorter time-to-antibiotics is lifesaving in sepsis, but programs to hasten antibiotic delivery may increase unnecessary antibiotic use and adverse events. Objectives: We sought to estimate both the benefits and harms of shortening time-to-antibiotics for sepsis. Methods: We conducted a simulation study using a cohort of 1,559,523 hospitalized patients admitted through the emergency department with meeting two or more systemic inflammatory response syndrome criteria (2013-2018). Reasons for hospitalization were classified as septic shock, sepsis, infection, antibiotics stopped early, and never treated (no antibiotics within 48 h). We simulated the impact of a 50% reduction in time-to-antibiotics for sepsis across 12 hospital scenarios defined by sepsis prevalence (low, medium, or high) and magnitude of "spillover" antibiotic prescribing to patients without infection (low, medium, high, or very high). Outcomes included mortality and adverse events potentially attributable to antibiotics (e.g., allergy, organ dysfunction, Clostridiodes difficile infection, and culture with multidrug-resistant organism). Results: A total of 933,458 (59.9%) hospitalized patients received antimicrobial therapy within 48 hours of presentation, including 38,572 (2.5%) with septic shock, 276,082 (17.7%) with sepsis, 370,705 (23.8%) with infection, and 248,099 (15.9%) with antibiotics stopped early. A total of 199,937 (12.8%) hospitalized patients experienced an adverse event; most commonly, acute liver injury (5.6%), new MDRO (3.5%), and Clostridiodes difficile infection (1.7%). Across the scenarios, a 50% reduction in time-to-antibiotics for sepsis was associated with a median of 1 to 180 additional antibiotic-treated patients and zero to seven additional adverse events per death averted from sepsis. Conclusions: The impacts of faster time-to-antibiotics for sepsis vary markedly across simulated hospital types. However, even in the worst-case scenario, new antibiotic-associated adverse events were rare.

Comparison of Administrative versus Electronic Health Record-based Methods for Identifying Sepsis Hospitalizations

Rationale: Despite the importance of sepsis surveillance, no optimal approach for identifying sepsis hospitalizations exists. The Centers for Disease Control and Prevention Adult Sepsis Event Definition (CDC-ASE) is an electronic medical record-based algorithm that yields more stable estimates over time than diagnostic coding-based approaches but may still result in misclassification. Objectives: We sought to assess three approaches to identifying sepsis hospitalizations, including a modified CDC-ASE. Methods: This cross-sectional study included patients in the Veterans Affairs Ann Arbor Healthcare System admitted via the emergency department (February 2021 to February 2022) with at least one episode of acute organ dysfunction within 48 hours of emergency department presentation. Patients were assessed for community-onset sepsis using three methods: 1) explicit diagnosis codes, 2) the CDC-ASE, and 3) a modified CDC-ASE. The modified CDC-ASE required at least two systemic inflammatory response syndrome criteria instead of blood culture collection and had a more sensitive definition of respiratory dysfunction. Each method was compared with a reference standard of physician adjudication via medical record review. Patients were considered to have sepsis if they had at least one episode of acute organ dysfunction graded as "definitely" or "probably" infection related on physician review. Results: Of 821 eligible hospitalizations, 449 were selected for physician review. Of these, 98 (21.8%) were classified as sepsis by medical record review, 103 (22.9%) by the CDC-ASE, 132 (29.4%) by the modified CDC-ASE, and 37 (8.2%) by diagnostic codes. Accuracy was similar across the three methods of interest (80.6% for the CDC-ASE, 79.6% for the modified CDC-ADE, and 84.2% for diagnostic codes), but sensitivity and specificity varied. The CDC-ASE algorithm had sensitivity of 58.2% (95% confidence interval [CI], 47.2-68.1%) and specificity of 86.9% (95% CI, 82.9-90.2%). The modified CDC-ASE algorithm had greater sensitivity (69.4% [95% CI, 59.3-78.3%]) but lower specificity (81.8% [95% CI, 77.3-85.7%]). Diagnostic codes had lower sensitivity (32.7% [95% CI, 23.5-42.9%]) but greater specificity (98.6% [95% CI, 96.7-99.55%]). Conclusions: There are several approaches to identifying sepsis hospitalizations for surveillance that have acceptable accuracy. These approaches yield varying sensitivity and specificity, so investigators should carefully consider the test characteristics of each method before determining an appropriate method for their intended use.

Blood count derangements after sepsis and association with post-hospital outcomes

Rationale

Predicting long-term outcomes in sepsis survivors remains a difficult task. Persistent inflammation post-sepsis is associated with increased risk for rehospitalization and death. As surrogate markers of inflammation, complete blood count parameters measured at hospital discharge may have prognostic value for sepsis survivors.

Objective

To determine the incremental value of complete blood count parameters over clinical characteristics for predicting 90-day outcomes in sepsis survivors.

Methods

Electronic health record data was used to identify sepsis hospitalizations at United States Veterans Affairs hospitals with live discharge and relevant laboratory data (2013 to 2018). We measured the association of eight complete blood count parameters with 90-day outcomes (mortality, rehospitalization, cause-specific rehospitalizations) using multivariable logistic regression models.

Measurements and main results

We identified 155,988 eligible hospitalizations for sepsis. Anemia (93.6%, N=142,162) and lymphopenia (28.1%, N=29,365) were the most common blood count abnormalities at discharge. In multivariable models, all parameters were associated with the primary outcome of 90-day mortality or rehospitalization and improved model discrimination above clinical characteristics alone (likelihood ratio test, p<0.02 for all). A model including all eight parameters significantly improved discrimination (AUROC, 0.6929 v. 0.6756) and reduced calibration error for the primary outcome. Hemoglobin had the greatest prognostic separation with a 1.5 fold increased incidence of the primary outcome in the lowest quintile (7.2-8.9 g/dL) versus highest quintile (12.70-15.80 g/dL). Hemoglobin and neutrophil lymphocyte ratio provided the most added value in predicting the primary outcome and 90-day mortality alone, respectively. Absolute lymphocyte count added little value in predicting 90-day outcomes.

Conclusions

The incorporation of discharge complete blood count parameters into prognostic scoring systems could improve prediction of 90-day outcomes. Hemoglobin had the greatest prognostic value for the primary composite outcome of 90-day rehospitalization or mortality. Absolute lymphocyte count provided little added value in multivariable model comparisons, including for infection- or sepsis-related rehospitalization.

Review of the research databases on population-based Registries of Unified electronic Healthcare system of Kazakhstan (UNEHS): Possibilities and limitations for epidemiological research and Real-World Evidence

INTRODUCTION

A 'learning healthcare system', based on electronic health records and other routinely collected healthcare data, would allow Real World Data (RWD) to be continuously fed into the system, ensuring that with every new patient treated, we know more overall about the practice of medicine. A judicious use of RWD would complement the traditional evidence from clinical research, for the benefit of all stakeholders involved in healthcare. Lack of data on disease epidemiology in Kazakhstan resonates with lower life expectancy and poorer health indicators compared to countries with analogous income per capita. Usage of primary data collection methods to fill these gaps require additional financial and human resources. Usage of big data, which is routinely collected though healthcare information systems, is considered as a competitive alternative in described circumstances.

OBJECTIVE

Development of the Unified National Electronic Healthcare System (UNEHS) in Kazakhstan allowed the creation of research databases to investigate epidemiology of numerous diseases. UNEHS research databases endorse extensive research activities due to a prospective follow-up, coverage of the whole Kazakhstani population and relatively lower expenses to conduct epidemiological studies. This review paper aims to introduce the content and descriptive data on research databases on population-based registries of UNEHS and to discuss opportunities and limitations of its usage.

RESULTS AND DISCUSSION

UNEHS databases include medical data on 36.4% of an adult population of Kazakhstan. Research databases presented in this paper contain critical variables that can be utilized for investigation of disease epidemiology, effectiveness of provided medical procedures and infectious disease epidemiology. A few examples accompany a detailed elaboration on the possibilities of research database utilization in epidemiological research.

CONCLUSION

Considering numerous advantages, the UNEHS research databases are expected to greatly contribute to healthcare in Kazakhstan by providing critical data on disease epidemiology. To warrant long-term usage and high research output several concerns and limitations should be addressed as well.

Opioid use and opioid use disorder in mono and dual-system users of veteran affairs medical centers

Introduction

Efforts to achieve opioid guideline concordant care may be undermined when patients access multiple opioid prescription sources. Limited data are available on the impact of dual-system sources of care on receipt of opioid medications.

Objective

We examined whether dual-system use was associated with increased rates of new opioid prescriptions, continued opioid prescriptions and diagnoses of opioid use disorder (OUD). We hypothesized that dual-system use would be associated with increased odds for each outcome.

Methods

This retrospective cohort study was conducted using Veterans Administration (VA) data from two facilities from 2015 to 2019, and included active patients, defined as Veterans who had at least one encounter in a calendar year (2015-2019). Dual-system use was defined as receipt of VA care as well as VA payment for community care (non-VA) services. Mono users were defined as those who only received VA services. There were 77,225 dual-system users, and 442,824 mono users. Outcomes were three binary measures: new opioid prescription, continued opioid prescription (i.e., received an additional opioid prescription), and OUD diagnosis (during the calendar year). We conducted a multivariate logistic regression accounting for the repeated observations on patient and intra-class correlations within patients.

Results

Dual-system users were significantly younger than mono users, more likely to be women, and less likely to report white race. In adjusted models, dual-system users were significantly more likely to receive a new opioid prescription during the observation period [Odds ratio (OR) = 1.85, 95% confidence interval (CI) 1.76-1.93], continue prescriptions (OR = 1.24, CI 1.22-1.27), and to receive an OUD diagnosis (OR = 1.20, CI 1.14-1.27).

Discussion

The prevalence of opioid prescriptions has been declining in the US healthcare systems including VA, yet the prevalence of OUD has not been declining at the same rate. One potential problem is that detailed notes from non-VA visits are not immediately available to VA clinicians, and information about VA care is not readily available to non-VA sources. One implication of our findings is that better health system coordination is needed. Even though care was paid for by the VA and presumably closely monitored, dual-system users were more likely to have new and continued opioid prescriptions.

Antimicrobial Resistance and Mortality in Hospitalized Patients with Bacteremia in the Greater Paris Area from 2016 to 2019

PURPOSE

Antibiotic-resistant bacteremia is a leading global cause of infectious disease morbidity and mortality. Clinical data warehouses (CDWs) allow for the secure, real-time coupling of diverse data sources from real-world clinical settings, including care-based medical-administrative data and laboratory-based microbiological data. The main purpose of this study was to assess the contribution of CDWs in the epidemiological study of antibiotic resistance by constructing a database of bacteremia patients, BactHub, and describing their main clinico-microbiological features and outcomes.

PATIENTS AND METHODS

Adult patients with bacteremia hospitalized between January 1, 2016 and December 31, 2019 in 14 acute care university hospitals from the Greater Paris area were identified; their first bacteremia episode was included. Data describing patients, episodes of bacteremia, bacterial isolates, and antimicrobial resistance were structured.

RESULTS

Among 29,228 patients with bacteremia, 41% of episodes were community-onset (CO) and 59% were hospital-acquired (HA). Thirty-day and ninety-day mortality rates were 15% and 20% in CO episodes, and 18% and 36% in HA episodes. Overall resistance rates were high, including third-generation cephalosporin resistance among Klebsiella pneumoniae (CO 21%, HA 37%) and Escherichia coli (CO 13%, HA 17%), and methicillin resistance among Staphylococcus aureus (CO 11%, HA 14%). Annual incidence rates increased significantly from 2017 to 2019, from 20.0 to 20.9 to 22.1 stays with bacteremia per 1000 stays (p < 0.0001).

CONCLUSION

The Bacthub database provides accurate clinico-microbiological data describing bacteremia across France's largest hospital group. Data from Bacthub may inform surveillance and the clinical decision-making process for bacteremia patients, including choice of antimicrobial therapy. The database also offers opportunities for research, including analysis of hospital care pathways and significant patient outcomes such as mortality and recurrence of infection.

High resolution data modifies intensive care unit dialysis outcome predictions as compared with low resolution administrative data set

High resolution clinical databases from electronic health records are increasingly being used in the field of health data science. Compared to traditional administrative databases and disease registries, these newer highly granular clinical datasets offer several advantages, including availability of detailed clinical information for machine learning and the ability to adjust for potential confounders in statistical models. The purpose of this study is to compare the analysis of the same clinical research question using an administrative database and an electronic health record database. The Nationwide Inpatient Sample (NIS) was used for the low-resolution model, and the eICU Collaborative Research Database (eICU) was used for the high-resolution model. A parallel cohort of patients admitted to the intensive care unit (ICU) with sepsis and requiring mechanical ventilation was extracted from each database. The primary outcome was mortality and the exposure of interest was the use of dialysis. In the low resolution model, after controlling for the covariates that are available, dialysis use was associated with an increased mortality (eICU: OR 2.07, 95% CI 1.75-2.44, p<0.01; NIS: OR 1.40, 95% CI 1.36-1.45, p<0.01). In the high-resolution model, after the addition of the clinical covariates, the harmful effect of dialysis on mortality was no longer significant (OR 1.04, 95% 0.85-1.28, p = 0.64). The results of this experiment show that the addition of high resolution clinical variables to statistical models significantly improves the ability to control for important confounders that are not available in administrative datasets. This suggests that the results from prior studies using low resolution data may be inaccurate and may need to be repeated using detailed clinical data.

Do all Patients Get the Same Care Across Hospitals?

There is extensive research demonstrating significant variation in the utilization of surgery and outcomes from surgery, including differences in mortality, complications, readmission, and failure to rescue. Literature suggests that these variations exist across as well as within small area geographies in the United States. There is also significant evidence of variation in access and outcomes from surgery that is attributable to race. Emerging research is demonstrating that there may be some variation attributable to a patient's social determinants of health and their lived averment. Those affected must work together to determine rate of utilization and how much variation is acceptable.

Temporal Trends in Antimicrobial Prescribing During Hospitalization for Potential Infection and Sepsis

Importance

Some experts have cautioned that national and health system emphasis on rapid administration of antimicrobials for sepsis may increase overall antimicrobial use even among patients without sepsis.

Objective

To assess whether temporal changes in antimicrobial timing for sepsis are associated with increasing antimicrobial use, days of therapy, or broadness of antimicrobial coverage among all hospitalized patients at risk for sepsis.

Design, Setting, and Participants

This is an observational cohort study of hospitalized patients at 152 hospitals in 2 health care systems during 2013 to 2018, admitted via the emergency department with 2 or more systemic inflammatory response syndrome (SIRS) criteria. Data analysis was performed from June 10, 2021, to March 22, 2022.

Exposures

Hospital-level temporal trends in time to first antimicrobial administration.

Outcomes

Antimicrobial outcomes included antimicrobial use, days of therapy, and broadness of antibacterial coverage. Clinical outcomes included in-hospital mortality, 30-day mortality, length of hospitalization, and new multidrug-resistant (MDR) organism culture positivity.

Results

Among 1 559 523 patients admitted to the hospital via the emergency department with 2 or more SIRS criteria (1 269 998 male patients [81.4%]; median [IQR] age, 67 [59-77] years), 273 255 (17.5%) met objective criteria for sepsis. In multivariable models adjusted for patient characteristics, the adjusted median (IQR) time to first antimicrobial administration to patients with sepsis decreased by 37 minutes, from 4.7 (4.1-5.3) hours in 2013 to 3.9 (3.6-4.4) hours in 2018, although the slope of decrease varied across hospitals. During the same period, antimicrobial use within 48 hours, days of antimicrobial therapy, and receipt of broad-spectrum coverage decreased among the broader cohort of patients with SIRS. In-hospital mortality, 30-day mortality, length of hospitalization, new MDR culture positivity, and new MDR blood culture positivity decreased over the study period among both patients with sepsis and those with SIRS. When examining hospital-specific trends, decreases in antimicrobial use, days of therapy, and broadness of antibacterial coverage for patients with SIRS did not differ by hospital antimicrobial timing trend for sepsis. Overall, there was no evidence that accelerating antimicrobial timing for sepsis was associated with increasing antimicrobial use or impaired antimicrobial stewardship.

Conclusions and Relevance

In this multihospital cohort study, the time to first antimicrobial for sepsis decreased over time, but this trend was not associated with increasing antimicrobial use, days of therapy, or broadness of antimicrobial coverage among the broader population at-risk for sepsis, which suggests that shortening the time to antibiotics for sepsis is feasible without leading to indiscriminate antimicrobial use.

Acquisition of new medical devices among the persistently critically ill: A retrospective cohort study in the Veterans Affairs

Patients who develop persistent critical illness remain in the ICU predominately because they develop new late-onset organ failure(s), which may render them at risk of acquiring a new medical device. The epidemiology and short-term outcomes of patients with persistent critical illness who acquire a new medical device are unknown. We retrospectively studied a cohort admitted to the Veterans Affairs (VA) ICUs from 2014 to 2019. Persistent critical illness was defined as an ICU length of stay of at least 14 days. Receipt of new devices was defined as acquisition of a new tracheostomy, feeding tube (including gastrostomy and jejunostomy tubes), implantable cardiac device, or ostomy. Logistic regression models were fit to identify patient factors associated with the acquisition of each new medical device. Among hospitalized survivors, 90-day posthospitalization discharge location and mortality were identified. From 2014 to 2019, there were 13,184 ICU hospitalizations in the VA which developed persistent critical illness. In total, 30.4% of patients (N = 3998/13,184) acquired at least 1 medical device during their persistent critical illness period. Patients with an initial higher severity of illness and prolonged hospital stay preICU admission had higher odds of acquiring each medical device. Among patients who survived their hospitalization, discharge location and mortality did not significantly differ among those who acquired a new medical device as compared to those who did not. Less than one-third of patients with persistent critical illness acquire a new medical device and no significant difference in short-term outcomes was identified. Future work is needed to understand if the acquisition of new medical devices is contributing to the development of persistent critical illness.

Racial bias and reproducibility in pulse oximetry among medical and surgical inpatients in general care in the Veterans Health Administration 2013-19: multicenter, retrospective cohort study

OBJECTIVES

To evaluate measurement discrepancies by race between pulse oximetry and arterial oxygen saturation (as measured in arterial blood gas) among inpatients not in intensive care.

DESIGN

Multicenter, retrospective cohort study using electronic medical records from general care medical and surgical inpatients.

SETTING

Veteran Health Administration, a national and racially diverse integrated health system in the United States, from 2013 to 2019.

PARTICIPANTS

Adult inpatients in general care (medical and surgical), in Veteran Health Administration medical centers.

MAIN OUTCOMES MEASURES

Occult hypoxemia (defined as arterial blood oxygen saturation (SaO2) of <88% despite a pulse oximetry (SpO2) reading of ≥92%), and whether rates of occult hypoxemia varied by race and ethnic origin.

RESULTS

A total of 30 039 pairs of SpO2-SaO2 readings made within 10 minutes of each other were identified during the study. These pairs were predominantly among non-Hispanic white (21 918 (73.0%)) patients; non-Hispanic black patients and Hispanic or Latino patients accounted for 6498 (21.6%) and 1623 (5.4%) pairs in the sample, respectively. Among SpO2 values greater or equal to 92%, unadjusted probabilities of occult hypoxemia were 15.6% (95% confidence interval 15.0% to 16.1%) in white patients, 19.6% (18.6% to 20.6%) in black patients (P<0.001 v white patients, with similar P values in adjusted models), and 16.2% (14.4% to 18.1%) in Hispanic or Latino patients (P=0.53 v white patients, P<0.05 in adjusted models). This result was consistent in SpO2-SaO2 pairs restricted to occur within 5 minutes and 2 minutes. In white patients, an initial SpO2-SaO2 pair with little difference in saturation was associated with a 2.7% (95% confidence interval -0.1% to 5.5%) probability of SaO2 <88% on a later paired SpO2-SaO2 reading showing an SpO2 of 92%, but black patients had a higher probability (12.9% (-3.3% to 29.0%)).

CONCLUSIONS

In general care inpatient settings across the Veterans Health Administration where paired readings of arterial blood gas (SaO2) and pulse oximetry (SpO2) were obtained, black patients had higher odds than white patients of having occult hypoxemia noted on arterial blood gas but not detected by pulse oximetry. This difference could limit access to supplemental oxygen and other more intensive support and treatments for black patients.

Pulse oximetry and supplemental oxygen use in nationwide Veterans Health Administration hospitals, 2013-2017: a Veterans Affairs Patient Database validation study

OBJECTIVE

Extraction and standardisation of pulse oximetry and supplemental oxygen data from electronic health records has the potential to improve risk-adjustment, quality assessment and prognostication. We develop an approach to standardisation and report on its use for benchmarking purposes.

MATERIALS AND METHODS

Using electronic health record data from the nationwide Veteran's Affairs healthcare system (2013-2017), we extracted, standardised and validated pulse oximetry and supplemental oxygen data for 2 765 446 hospitalisations in the Veteran's Affairs Patient Database (VAPD) cohort study. We assessed face, concurrent and predictive validities using the following approaches, respectively: (1) evaluating the stability of patients' pulse oximetry values during a 24-hour period, (2) testing for greater amounts of supplemental oxygen use in patients likely to need oxygen therapy and (3) examining the association between supplemental oxygen and subsequent mortality.

RESULTS

We found that 2 700 922 (98%) hospitalisations had at least one pulse oximetry reading, and 864 605 (31%) hospitalisations received oxygen therapy. Patients monitored by pulse oximetry had a reading on average every 6 hours (median 4; IQR 3-7). Patients on supplemental oxygen were older, white and male compared with patients not receiving oxygen therapy (p<0.001) and were more likely to have diagnoses of heart failure and chronic pulmonary diseases (p<0.001). The amount of supplemental oxygen for patients with at least three consecutive values recorded during a 24-hour period fluctuated by median 2 L/min (IQR: 2-3), and 81% of such triplets showed the same level of oxygen receipt.

CONCLUSION

Our approach to standardising pulse oximetry and supplemental oxygen data shows face, concurrent and predictive validities as the following: supplemental oxygen clusters in the range consistent with hospital wall-dispensed oxygen supplies (face validity); there are greater amounts of supplemental oxygen for certain clinical conditions (concurrent validity) and there is an association of supplemental oxygen with in-hospital and postdischarge mortality (predictive validity).

Test Volume Ratio Benchmarking to Identify and Reduce Low-Value Laboratory Utilization

OBJECTIVES

We analyzed test volume data to identify low-value test utilization. We subsequently tracked the efficacy of interventions to improve test utilization by decreasing low-value testing.

METHODS

Test volume data for analytes included in the Choosing Wisely guidelines were analyzed to identify population outliers. Outliers were defined by test volume ratios of either analyte to sodium or paired analytes to correct for variation in patient volumes at each site. Interventions to improve test utilization were targeted to outlier sites. Relative efficacy in reducing low-value testing was tracked at those sites.

RESULTS

After appropriate data cleaning, test volume ratios for 17 analytes paired with sodium and 8 pairs of analytes were acquired from 108 national sites. A site with abnormally high Clostridium difficile/sodium ratio was selected for intervention, leading to a 71% decrease in C difficile tests. Two different interventions to decrease creatine kinase MB isoform (CKMB) testing were performed at two unique sites with abnormally high CKMB/troponin ratios. These interventions decreased CKMB by 11% and 98% at the different sites, showing the efficacy of the different kinds of interventions.

CONCLUSIONS

Test volume ratio analysis and benchmarking enable identification of low-value test utilization.

Temporal Trends and Hospital Variation in Time-to-Antibiotics Among Veterans Hospitalized With Sepsis

Importance

It is unclear whether antimicrobial timing for sepsis has changed outside of performance incentive initiatives.

Objective

To examine temporal trends and variation in time-to-antibiotics for sepsis in the US Department of Veterans Affairs (VA) health care system.

Design, Setting, and Participants

This observational cohort study included 130 VA hospitals from 2013 to 2018. Participants included all patients admitted to the hospital via the emergency department with sepsis from 2013 to 2018, using a definition adapted from the Centers for Disease Control and Prevention Adult Sepsis Event definition, which requires evidence of suspected infection, acute organ dysfunction, and systemic antimicrobial therapy within 12 hours of presentation. Data were analyzed from October 6, 2020, to July 1, 2021.

Exposures

Time from presentation to antibiotic administration.

Main Outcomes and Measures

The main outcome was differences in time-to-antibiotics across study periods, hospitals, and patient subgroups defined by presenting temperature and blood pressure. Temporal trends in time-to-antibiotics were measured overall and by subgroups. Hospital-level variation in time-to-antibiotics was quantified after adjusting for differences in patient characteristics using multilevel linear regression models.

Results

A total of 111 385 hospitalizations for sepsis were identified, including 107 547 men (96.6%) men and 3838 women (3.4%) with a median (interquartile range [IQR]) age of 68 (62-77) years. A total of 7574 patients (6.8%) died in the hospital, and 13 855 patients (12.4%) died within 30 days. Median (IQR) time-to-antibiotics was 3.9 (2.4-6.5) hours but differed by presenting characteristics. Unadjusted median (IQR) time-to-antibiotics decreased over time, from 4.5 (2.7-7.1) hours during 2013 to 2014 to 3.5 (2.2-5.9) hours during 2017 to 2018 (P < .001). In multilevel models adjusted for patient characteristics, median time-to-antibiotics declined by 9.0 (95% CI, 8.8-9.2) minutes per calendar year. Temporal trends in time-to-antibiotics were similar across patient subgroups, but hospitals with faster baseline time-to-antibiotics had less change over time, with hospitals in the slowest tertile decreasing time-to-antibiotics by 16.6 minutes (23.1%) per year, while hospitals in the fastest tertile decreased time-to-antibiotics by 7.2 minutes (13.1%) per year. In the most recent years (2017-2018), median time-to-antibiotics ranged from 3.1 to 6.7 hours across hospitals (after adjustment for patient characteristics), 6.8% of variation in time-to-antibiotics was explained at the hospital level, and odds of receiving antibiotics within 3 hours increased by 65% (95% CI, 56%-77%) for the median patient if moving to a hospital with faster time-to-antibiotics.

Conclusions and Relevance

This cohort study across nationwide VA hospitals found that time-to-antibiotics for sepsis has declined over time. However, there remains significant variability in time-to-antibiotics not explained by patient characteristics, suggesting potential unwarranted practice variation in sepsis treatment. Efforts to further accelerate time-to-antibiotics must be weighed against risks of overtreatment.

Variation in model performance by data cleanliness and classification methods in the prediction of 30-day ICU mortality, a US nationwide retrospective cohort and simulation study

OBJECTIVE

There has been a proliferation of approaches to statistical methods and missing data imputation as electronic health records become more plentiful; however, the relative performance on real-world problems is unclear.

MATERIALS AND METHODS

Using 355 823 intensive care unit (ICU) hospitalisations at over 100 hospitals in the nationwide Veterans Health Administration system (2014-2017), we systematically varied three approaches: how we extracted and cleaned physiologic variables; how we handled missing data (using mean value imputation, random forest, extremely randomised trees (extra-trees regression), ridge regression, normal value imputation and case-wise deletion) and how we computed risk (using logistic regression, random forest and neural networks). We applied these approaches in a 70% development sample and tested the results in an independent 30% testing sample. Area under the receiver operating characteristic curve (AUROC) was used to quantify model discrimination.

RESULTS

In 355 823 ICU stays, there were 34 867 deaths (9.8%) within 30 days of admission. The highest AUROCs obtained for each primary classification method were very similar: 0.83 (95% CI 0.83 to 0.83) to 0.85 (95% CI 0.84 to 0.85). Likewise, there was relatively little variation within classification method by the missing value imputation method used-except when casewise deletion was applied for missing data.

CONCLUSION

Variation in discrimination was seen as a function of data cleanliness, with logistic regression suffering the most loss of discrimination in the least clean data. Losses in discrimination were not present in random forest and neural networks even in naively extracted data. Data from a large nationwide health system revealed interactions between missing data imputation techniques, data cleanliness and classification methods for predicting 30-day mortality.

Late Vasopressor Administration in Patients in the ICU: A Retrospective Cohort Study

BACKGROUND

Little is known about the prevalence, predictors, and outcomes of late vasopressor administration which evolves after admission to the ICU.

RESEARCH QUESTION

What is the epidemiology of late vasopressor administration in the ICU?

STUDY DESIGN AND METHODS

We retrospectively studied a cohort of veterans admitted to the Veterans Administration ICUs for ≥ 4 days from 2014 to 2017. The timing of vasopressor administration was categorized as early (only within the initial 3 days), late (on day 4 or later and none on day 3), and continuous (within the initial 2 days through at least day 4). Regressions were performed to identify patient factors associated with late vasopressor administration and the timing of vasopressor administration with posthospitalization discharge mortality.

RESULTS

Among the 62,206 hospitalizations with at least 4 ICU days, late vasopressor administration occurred in 5.5% (3,429 of 62,206). Patients with more comorbidities (adjusted OR [aOR], 1.02 per van Walraven point; 95% CI, 1.02-1.03) and worse severity of illness on admission (aOR, 1.01 per percentage point risk of death; 95% CI, 1.01-1.02) were more likely to receive late vasopressor therapy. Nearly 50% of patients started a new antibiotic within 24 h of receiving late vasopressor therapy. One-year mortality after survival to discharge was higher for patients with continuous (adjusted hazard ratio [aHR], 1.48; 95% CI, 1.33-1.65) and late vasopressor administration (aHR, 1.26; 95% CI, 1.15-1.38) compared with only early vasopressor administration.

INTERPRETATION

Late vasopressor administration was modestly associated with comorbidities and admission illness severity. One-year mortality was higher among those who received late vasopressor administration compared with only early vasopressor administration. Research to understand optimization of late vasopressor therapy administration may improve long-term mortality.

Developing a template matching algorithm for benchmarking hospital performance in a diverse, integrated healthcare system

Template matching is a proposed approach for hospital benchmarking, which measures performance based on matching a subset of comparable patient hospitalizations from each hospital. We assessed the ability to create the required matched samples and thus the feasibility of template matching to benchmark hospital performance in a diverse healthcare system.Nationwide Veterans Affairs (VA) hospitals, 2017.Observational cohort study.We used administrative and clinical data from 668,592 hospitalizations at 134 VA hospitals in 2017. A standardized template of 300 hospitalizations was selected, and then 300 hospitalizations were matched to the template from each hospital.There was substantial case-mix variation across VA hospitals, which persisted after excluding small hospitals, hospitals with primarily psychiatric admissions, and hospitalizations for rare diagnoses. Median age ranged from 57 to 75 years across hospitals; percent surgical admissions ranged from 0.0% to 21.0%; percent of admissions through the emergency department, 0.1% to 98.7%; and percent Hispanic patients, 0.2% to 93.3%. Characteristics for which there was substantial variation across hospitals could not be balanced with any matching algorithm tested. Although most other variables could be balanced, we were unable to identify a matching algorithm that balanced more than ∼20 variables simultaneously.We were unable to identify a template matching approach that could balance hospitals on all measured characteristics potentially important to benchmarking. Given the magnitude of case-mix variation across VA hospitals, a single template is likely not feasible for general hospital benchmarking.

Hospital-level variation in the development of persistent critical illness

PURPOSE

Patients with persistent critical illness may account for up to half of all intensive care unit (ICU) bed-days. It is unknown if there is hospital variation in the development of persistent critical illness and if hospital performance affects the incidence of persistent critical illness.

METHODS

This is a retrospective analysis of Veterans admitted to the Veterans Administration (VA) ICUs from 2015 to 2017. Hospital performance was defined by the risk- and reliability-adjusted 30-day mortality. Persistent critical illness was defined as an ICU length of stay of at least 11 days. We used 2-level multilevel logistic regression models to assess variation in risk- and reliability-adjusted probabilities in the development of persistent critical illness.

RESULTS

In the analysis of 100 hospitals which encompassed 153,512 hospitalizations, 4.9% (N = 7640/153,512) developed persistent critical illness. There was variation in the development of persistent critical illness despite controlling for patient characteristics (intraclass correlation: 0.067, 95% CI 0.049-0.091). Hospitals with higher risk- and reliability-adjusted 30-day mortality had higher probabilities of developing persistent critical illness (predicted probability: 0.057, 95% CI 0.051-0.063, p < 0.01) compared to those with lower risk- and reliability-adjusted 30-day mortality (predicted probability: 0.046, 95% CI 0.041-0.051, p < 0.01). The median odds ratio was 1.4 (95% CI 1.33-1.49) implying that, for two patients with the same physiology on admission at two different VA hospitals, the patient admitted to the hospital with higher adjusted mortality would have 40% greater odds of developing persistent critical illness.

CONCLUSION

Hospitals with higher risk- and reliability-adjusted 30-day mortality have a higher probability of developing persistent critical illness. Understanding the drivers of this variation may identify modifiable factors contributing to the development of persistent critical illness.

Statin discontinuation and new antipsychotic use after an acute hospital stay vary by hospital

Introduction

Patients are at risk for medication problems after hospital admissions, particularly those with critical illness. Medication problems include continuation of acute medications and discontinuation of chronic medications after discharge. Little is known across a national integrated health care system about the extent of these two medication problems.

Objective

To examine the extent of statin medication discontinuation and new antipsychotic medication use after hospital discharge.

Design

Retrospective cohort study.

Setting

Veterans Affairs healthcare system.

Participants

Veterans with an inpatient hospitalization from January 1, 2014-December 31, 2016, survived at least 180 days post-discharge, and received at least one medication through the VA outpatient pharmacy within one year around admission were included. Hospitalizations were grouped into: 1) direct admission to the intensive care unit (ICU) and a diagnosis of sepsis, 2) direct admission to the ICU without sepsis diagnosis, and 3) no ICU stay during the hospitalization.

Main outcome measures

Statin medication discontinuation and new antipsychotic use at six months post-hospital discharge.

Results

A total of 520,187 participants were included in the statin medication and 910,629 in the antipsychotic medication cohorts. Statin discontinuation ranged from 10–15% and new antipsychotic prescription fills from 2–4% across the three hospitalization groups, with highest rates in the ICU admission and sepsis diagnosis group. Statin discontinuation and new antipsychotic use after a hospitalization varied by hospital, with worse performing hospitals having 11% higher odds of discontinuing a statin (median odds ratio at hospital-level, adjusted for patient differences, aMOR: 1.11 (95% CI: 1.09, 1.13)) and 29% higher odds of new antipsychotic use (aMOR, 1.29 (95% CI: 1.24, 1.34)). Risk-adjusted hospital rates of these two medication changes were not correlated (p = 0.49).

Conclusions

Systemic variation in the rates of statin medication continuation and new antipsychotic use were found.

Inpatient and Discharge Fluoroquinolone Prescribing in Veterans Affairs Hospitals Between 2014 and 2017

Background

Between 2007 and 2015, inpatient fluoroquinolone use declined in US Veterans Affairs (VA) hospitals. Whether fluoroquinolone use at discharge also declined, in particular since antibiotic stewardship programs became mandated at VA hospitals in 2014, is unknown.

Methods

In this retrospective cohort study of hospitalizations with infection between January 1, 2014, and December 31, 2017, at 125 VA hospitals, we assessed inpatient and discharge fluoroquinolone (ciprofloxacin, levofloxacin, moxifloxacin) use as (a) proportion of hospitalizations with a fluoroquinolone prescribed and (b) fluoroquinolone-days per 1000 hospitalizations. After adjusting for illness severity, comorbidities, and age, we used multilevel logit and negative binomial models to assess for hospital-level variation and longitudinal prescribing trends.

Results

Of 560219 hospitalizations meeting inclusion criteria as hospitalizations with infection, 37.4% (209602/560219) had a fluoroquinolone prescribed either during hospitalization (32.5%, 182337/560219) or at discharge (19.6%, 110003/560219). Hospitals varied appreciably in inpatient, discharge, and total fluoroquinolone use, with 71% of hospitals in the highest prescribing quartile located in the Southern United States. Nearly all measures of fluoroquinolone use decreased between 2014 and 2017, with the largest decreases found in inpatient fluoroquinolone and ciprofloxacin use. In contrast, there was minimal decline in fluoroquinolone use at discharge, which accounted for a growing percentage of hospitalization-related fluoroquinolone-days (52.0% in 2014; 61.3% by 2017).

Conclusions

Between 2014 and 2017, fluoroquinolone use decreased in VA hospitals, largely driven by decreased inpatient fluoroquinolone (especially ciprofloxacin) use. Fluoroquinolone prescribing at discharge, as well as levofloxacin prescribing overall, is a growing target for stewardship.

A 20-Year Evaluation of LOINC in the United States' Largest Integrated Health System

CONTEXT.—

Clinical laboratories are obligated to implement Logical Observation Identifier Names and Codes (LOINC), an informatics standard used to uniquely identify laboratory tests. The historical progress of laboratories in achieving this goal is unknown.

OBJECTIVE.—

To evaluate the implementation of LOINC by clinical laboratories with attention to LOINC's adoption, diversity, and correctness over time.

DESIGN.—

We aggregated data from 130 facilities within the Veterans Health Administration (VA), an early adopter of LOINC, during a 20-year period (1999-2018). To assess the adoption of LOINC, we calculated the annual proportion of tests and results without a LOINC. To assess the diversity of LOINC, we counted the yearly number of distinct LOINCs in active use. To assess the correctness of LOINC over time, we compared the assigned LOINCs to a manually reviewed gold standard for each year.

RESULTS.—

We reviewed a total of 586 000 tests and 9.162 billion results. LOINC adoption, measured as a proportion of both tests and results, improved over time (P < .001). In the final year reviewed, 85% (172 142 of 202 125) of laboratory tests and 99% (547 229 066 of 551 205 087) of results had LOINCs. The number of distinct LOINCs in active use from 1999 to 2018 increased 2.78-fold from 4502 to 12 503 (P < .001). Correctness generally improved but varied considerably by test and across time.

CONCLUSIONS.—

The adoption of LOINC has improved during the past 2 decades. More diverse LOINCs were associated with increased adoption and were a challenge to keep up-to-date. The correctness of LOINCs has improved but remains an issue that likely necessitates supplemental review for most applications.