Assessment of retrospective collection of EQ-5D-5L in a US COVID-19 population

BACKGROUND

It is imperative to evaluate health related quality of life (HRQoL) pre-COVID-19, but there is currently no evidence of the retrospective application of the EuroQol 5-Dimension, 5 level version (EQ-5D-5L) for COVID-19 studies.

METHODS

Symptomatic patients with SARS-CoV-2 at CVS Health US test sites were recruited between 01/31/2022-04/30/2022. Consented participants completed the EQ-5D-5L questionnaire twice: a modified version where all the questions were past tense to retrospectively assess pre-COVID-19 baseline QoL, and the standard version in present tense to assess current HRQoL. Duncan's new multiple range test was adopted for post analysis of variance pairwise comparisons of EQ visual analog scale (EQ VAS) means between problem levels for each of 5 domains. A linear mixed model was applied to check whether the relationship between EQ VAS and utility index (UI) was consistent pre-COVID-19 and during COVID-19. Matching-adjusted indirect comparison was used to compare pre-COVID-19 UI and EQ VAS scores with those of the US population. Lastly, Cohen's d was used to quantify the magnitude of difference in means between two groups.

RESULTS

Of 676 participants, 10.2% were age 65 or more years old, 73.2% female and 71.9% white. Diabetes was reported by 4.7% participants and hypertension by 11.2%. The estimated coefficient for the interaction of UI-by-retrospective collection indicator (0 = standard prospective collection, 1 = retrospective for pre-COVID-19), -4.2 (SE: 3.2), P = 0.197, indicates that retrospective collection does not significantly alter the relationship between EQ VAS and UI. After adjusting for age, gender, diabetes, hypertension, and percent of mobility problems, the predicted means of pre-COVID-19 baseline EQ VAS and UI were 84.6 and 0.866, respectively. Both means were close to published US population norms (80.4 and 0.851) compared to those observed (87.4 and 0.924). After adjusting for age, gender, diabetes, and hypertension, the calculated ES between pre-COVID-19 and COVID-19 for UI and EQ VAS were 0.15 and 0.39, respectively. Without retrospectively collected EQ-5D-5L, using US population norms tended to underestimate the impact of COVID-19 on HRQoL.

CONCLUSION

At a group level the retrospectively collected pre-COVID-19 EQ-5D-5L is adequate and makes it possible to directly evaluate the impact of COVID-19 on HRQoL. ( ClinicalTrials.gov NCT05160636).

Impact of COVID-19 and effects of booster vaccination with BNT162b2 on six-month long COVID symptoms, quality of life, work productivity and activity impairment during Omicron

BACKGROUND

Longitudinal estimates of long COVID burden during Omicron remain limited. This study characterized long-term impacts of COVID-19 and booster vaccination on symptoms, Health-Related Quality of Life (HRQoL), and Work Productivity Activity Impairment (WPAI).

METHODS

Outpatients with ≥ 1 self-reported symptom and positive SARS-CoV-2 test at CVS Health United States test sites were recruited between 01/31 and 04/30/2022. Symptoms, EQ-5D and WPAI were collected via online surveys until 6 months following infection. Both observed and model-based estimates were analyzed. Effect sizes based on Cohen's d quantified the magnitude of outcome changes over time, within and between vaccination groups. Mixed models for repeated measures were conducted for multivariable analyses, adjusting for covariates. Logistic regression assessed odds ratio (OR) of long COVID between vaccination groups.

RESULTS

At long COVID start (Week 4), 328 participants included 87 (27%) Boosted with BNT162b2, 86 (26%) with a BNT162b2 primary series (Primed), and 155 (47%) Unvaccinated. Mean age was 42.0 years, 73.8% were female, 26.5% had ≥ 1 comorbidity, 36.9% prior infection, and 39.6% reported ≥ 3 symptoms (mean: 3.1 symptoms). At Month 6, among 260 participants, Boosted reported a mean of 1.1 symptoms versus 3.4 and 2.8 in Unvaccinated and Primed, respectively (p < 0.001). Boosted had reduced risks of ≥ 3 symptoms versus Unvaccinated (observed: OR 0.22, 95% CI 0.10-0.47, p < 0.001; model-based: OR 0.36, 95% CI 0.15-0.87, p = 0.019) and Primed (observed: OR 0.29, 95% CI 0.13-0.67, p = 0.003; model-based: OR 0.59, 95% CI 0.21-1.65, p = 0.459). Results were consistent using ≥ 2 symptoms. Regarding HRQoL, among those with long COVID, Boosted had higher EQ-5D Utility Index (UI) than Unvaccinated (observed: 0.922 vs. 0.731, p = 0.014; model-based: 0.910 vs. 0.758, p-value = 0.038) and Primed (0.922 vs. 0.648, p = 0.014; model-based: 0.910 vs. 0.708, p-value = 0.008). Observed and model-based estimates for EQ-VAS and UI among Boosted were comparable with pre-COVID since Month 3. Subjects vaccinated generally reported better WPAI scores.

CONCLUSIONS

Long COVID negatively impacted HRQoL and WPAI. The BNT162b2 booster could have a beneficial effect in reducing the risk and burden of long COVID. Boosted participants reported fewer and less durable symptoms, which contributed to improve HRQoL and maintain WPAI levels. Limitations included self-reported data and small sample size for WPAI.

Impact of COVID-19 and effects of BNT162b2 on patient-reported outcomes: quality of life, symptoms, and work productivity among US adult outpatients

BACKGROUND

Although there is extensive literature on the clinical benefits of COVID-19 vaccination, data on humanistic effects are limited. This study evaluated the impact of SARS-CoV-2 infection on symptoms, Health-Related Quality of Life (HRQoL) and Work Productivity and Impairment (WPAI) prior to and one month following infection between individuals vaccinated with BNT162b2 and those unvaccinated.

METHODS

Subjects with ≥ 1 self-reported symptom and positive RT-PCR for SARS-CoV-2 at CVS Health US test sites were recruited between 01/31/2022 and 04/30/2022. Socio-demographics, clinical characteristics and vaccination status were evaluated. Self-reported symptoms, HRQoL, and WPAI outcomes were assessed using questionnaires and validated instruments (EQ-5D-5L, WPAI-GH) across acute COVID time points from pre-COVID to Week 4, and between vaccination groups. Mixed models for repeated measures were conducted for multivariable analyses, adjusting for several covariates. Effect size (ES) of Cohen's d was calculated to quantify the magnitude of outcome changes within and between vaccination groups.

RESULTS

The study population included 430 subjects: 197 unvaccinated and 233 vaccinated with BNT162b2. Mean (SD) age was 42.4 years (14.3), 76.0% were female, 38.8% reported prior infection and 24.2% at least one comorbidity. Statistically significant differences in outcomes were observed compared with baseline and between groups. The EQ-Visual analogue scale scores and Utility Index dropped in both cohorts at Day 3 and increased by Week 4 but did not return to pre-COVID levels. The mean changes were statistically lower in the BNT162b2 cohort at Day 3 and Week 4. The BNT162b2 cohort reported lower prevalence and fewer symptoms at index date and Week 4. At Week 1, COVID-19 had a large impact on all WPAI-GH domains: the work productivity time loss among unvaccinated and vaccinated was 65.0% and 53.8%, and the mean activity impairment was 50.2% and 43.9%, respectively. Except for absenteeism at Week 4, the BNT162b2 cohort was associated with statistically significant less worsening in all WPAI-GH scores at both Week 1 and 4.

CONCLUSIONS

COVID-19 negatively impacted HRQoL and work productivity among mildly symptomatic outpatients. Compared with unvaccinated, those vaccinated with BNT162b2 were less impacted by COVID-19 infection and recovered faster.

Incidence and Estimated Vaccine Effectiveness Against Symptomatic SARS-CoV-2 Infection Among Persons Tested in US Retail Locations, May 1 to August 7, 2021

This case-control study evaluates the estimated vaccine effectiveness against infection changes over time to help inform public health policy and clinical practices.

Relationships between creatinine increase and mortality rates in patients given vancomycin in 76 hospitals: The increasing role of infectious disease pharmacists

PURPOSE

Vancomycin is a commonly used antimicrobial with the potential for renal toxicity. We evaluated vancomycin duration, changes in renal function after vancomycin initiation ("post-vancomycin" renal function changes), and associated mortality risk among hospitalized patients.

METHODS

We analyzed data from 76 hospitals and excluded patients with a baseline serum creatinine concentration (SCr) of >3.35 mg/dL. We estimated mortality risk relative to vancomycin duration and the magnitude of post-vancomycin SCr change, controlling for demographics, baseline SCr, underlying diseases, clinical acuity, and comorbidities.

RESULTS

Among 128,993 adult inpatients treated with vancomycin, 49.0% did not experience SCr elevation. Among the remaining patients, 26.0%, 11.4%, 8.8% and 4.8% experienced increases in post-vancomycin SCr of 1% to 20%, 21% to 40%, 41% to 100%, and greater than 100%, respectively. Compared to mortality risk among patients with a vancomycin therapy duration between 4 and 5 days (the lowest-mortality group), longer vancomycin therapy duration was not independently associated with higher mortality risk after adjusting for confounders. In contrast, there was a graded relationship between post-vancomycin SCr elevation and mortality. Multivariable adjusted mortality odds ratios ranged from 1.60 to 13.66, corresponding to SCr increases of 10% and greater than 200%, respectively.

CONCLUSION

Half of patients given vancomycin did not experience SCr elevation and had the lowest mortality, suggesting that vancomycin can be used safely if renal function is stabilized. In the large study cohort, vancomycin duration itself was not an independent predictor of mortality. Post-vancomycin SCr elevation appeared to be a driver of in-hospital mortality. Even a 10% SCr increase from baseline prior to vancomycin infusion was associated with increased mortality risk. This finding stresses the importance of closely monitoring renal function and may support the value of pharmacokinetic dosing.

590. Vancomycin Infusion: Algorithmic Analysis of Unstructured Real-World Data Captured from Automated Infusion Devices

Background

Large scale research on antimicrobial usage in real-world populations traditionally does not consist of infusion data. With automation, detailed infusion events are captured in device systems, providing opportunities to harness them for patient safety studies. However, due to the unstructured nature of infusion data, the scale-up of data ingestion, cleansing, and processing is challenging.

Figure 1. Illustration of dosing complexity

Methods

We applied algorithmic techniques to quantitate and visualize vancomycin administration data captured in real-time by automated infusion devices from 3 acute care hospitals. The device data included timestamped infusion events – infusion started, paused, restarted, alarmed, and stopped. We used time density-based segmentation algorithms to depict infusion sessions as bursts of event activity. We examined clinical interpretability of the cluster-defined sessions in defining infusion events, dosing intensity, and duration.

Results

The algorithms identified 13,339 vancomycin infusion sessions from 2,417 unique patients (mean = 5.5 sessions per patient). Clustering captured vancomycin infusion sessions consistently with correct event labels in >98% of cases. It disentangled ambiguity associated with unexpected events (e.g. multiple stopped/started events within a single infusion session). Segmentation of vancomycin infusion events on an example patient timeline is illustrated in Figure 1. The median duration of infusion sessions was 1.55 (1^st, 3^rd quartiles: 1.14, 2.02) hours, demonstrating clinical plausibility.

Conclusion

Passively captured vancomycin administration data from automated infusion device systems provide ramifications for real-time bed-side patient care practice. With large volume of data, temporal event segmentation can be an efficient approach to generate clinically interpretable insights. This method scales up accuracy and consistency in handling longitudinal dosing data. It can enable real-time population surveillance and patient-specific clinical decision support for large patient populations. Better understanding of infusion data may also have implications for vancomycin pharmacokinetic dosing.

Disclosures

David L. Bostick, PhD, Becton, Dickinson and Co. (Employee) Kalvin Yu, MD, Becton, Dickinson and Company (Employee)GlaxoSmithKline plc. (Other Financial or Material Support, Funding) Cynthia Yamaga, PharmD, BD (Employee) Ann Liu-Ferrara, PhD, Becton, Dickinson and Co. (Employee) Didier Morel, PhD, Becton, Dickinson and Co. (Employee) Ying P. Tabak, PhD, Becton, Dickinson and Co. (Employee)

210. Vancomycin Infusion Frequency and Intensity: Analysis of Real-World Data Generated from Automated Infusion Devices

Background

Automated infusion devices captures actual infused medication administration data in real-time. Vancomycin use is now recommended to be driven by AUC (area under the curve) dosing. We evaluated automated infusion device data to depict vancomycin administration practices in acute care hospitals.

Figure 1. Distribution of vancomycin infusion dosing

Figure 2. Distribution of time intervals between each vancomycin infusion session (mostly around 8 or 12 hours)

Methods

We analyzed archived vancomycin infusion data from 2,417 patients captured by automated infusion systems from 3 acute care hospitals. The infusion device informatics software recorded a variety of events during infusion – starting and stopping times, alarms and alerts, vancomycin dose, and other forms of timestamped usage information. We evaluated infusion session duration and dosing, using data-driven clustering algorithms.

Results

A total of 13,339 vancomycin infusion sessions from 2,417 unique adult patients were analyzed. Approximately 26.1% of patients had just one infusion of vancomycin. For the rest of the patients, the median number of infusion sessions per patient was 4; the interquartile range was 3 and 8. The most common dose was 1.0 gram (53.7%) or 1.5 gram (24.6%) (see Figure 1). The distribution of infusion session duration (hours) was 4.2% (≤1.0 hh); 40.1% (1.01–1.5 hh); 29.1% (1.51–2.0 hh); and 26.6% (>2.0 hh). The dosing frequency was 39.5% (q8 hh), 42.9% (q12 hh), 11.1% (q24 hh), and 6.5% (>q24 hh) (Figure 2), demonstrating clinical interpretability.

Conclusion

A considerable number of patients received just one vancomycin infusion during their hospital stay, suggesting a potential overuse of empiric vancomycin. The majority of infusion doses were between 1 to 1.5 grams and most infusion sessions were administered every 8 or 12 hours. The actual infusion duration for each dose often exceeds the prescribed 1- or 2-hour infusion orders, which may be due to known instances of infusion interruptions due to patient movement, procedures or IV access compromise. The data generated by infusion devices can augment insights on actual antimicrobial administration practices and duration. As vancomycin AUC dosing becomes more prevalent, real world infusion data may aid timely data-driven antimicrobial stewardship and patient safety interventions for vancomycin and other AUC dosed drugs.

Disclosures

Cynthia Yamaga, PharmD, BD (Employee) David L. Bostick, PhD, Becton, Dickinson and Co. (Employee) Ying P. Tabak, PhD, Becton, Dickinson and Co. (Employee) Ann Liu-Ferrara, PhD, Becton, Dickinson and Co. (Employee) Didier Morel, PhD, Becton, Dickinson and Co. (Employee) Kalvin Yu, MD, Becton, Dickinson and Company (Employee)GlaxoSmithKline plc. (Other Financial or Material Support, Funding)

Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach

OBJECTIVE

Mental or substance use disorders (M/SUD) are major contributors of disease burden with high risk for hospital readmissions. We sought to develop and evaluate a readmission model using a machine learning (ML) approach.

METHODS

We analyzed patients with continuous enrollment for three years and at least one episode of M/SUD as the primary reason for hospital admission. The outcome was readmission within 30-days from discharge. Model performance was evaluated using the Area under the Receiver Operating Characteristic (AUROC). We compared the AUROCs of an extreme gradient boosted tree (XGBoost) model to generalized linear model with elastic net regularization (GLMNet).

RESULTS

We analyzed 65,426 unique patients and 97,688 admissions. Patients with mental disorders accounted for 66 % (13.2 % readmission rate) and substance use disorders, 34 % (22.3 % readmission rate). Among all those who had readmissions, 70.7 %, 17.0 %, and 12.4 % had 1, 2, or 3+ readmissions, respectively. Previous hospitalizations, hospital utilization, discharge disposition, diagnosis category, and comorbidity were among the highest important features in the XGBoost model. The XGBoost model AUROC was 0.737 (95 % CI: 0.732 to 0.742) versus the GLMNet 0.697 (95 % CI: 0.690 to 0.703). The AUROC of the final XGBoost model on the testing set was 0.738 (95 % CI: 0.730 to 0.748), higher than published readmission models for mental health patients.

CONCLUSIONS

The XGBoost model has a better performance than GLMNet and previously published models in predicting readmissions in mental health patients. Our model may be further tested to aid targeted demographic initiatives to reduce M/SUDs readmissions and benchmarking.

Contributing Factors to the Clinical and Economic Burden of Patients with Laboratory-Confirmed Carbapenem-Nonsusceptible Gram-Negative Urinary Tract Infections

PURPOSE

We explored patient- and hospital-level predictor variables for worse clinical and economic outcomes in carbapenem-nonsusceptible urinary tract infections (UTIs).

PATIENTS AND METHODS

We used electronic data (January 2013-September 2015; 78 US hospitals) from a large multicenter clinical database. Nonduplicate gram-negative isolates were considered carbapenem-nonsusceptible if they had resistant/intermediate susceptibility. Potential predictors of outcomes (mortality, 30-day readmissions, length of stay [LOS], hospital total cost, and net gain/loss per case) were examined using generalized linear mixed models. Significant predictors were identified based on statistical significance and model goodness-of-fit criteria.

RESULTS

A total of 1439 carbapenem-nonsusceptible urine cases were identified. The mortality rate was 5.5%; the hospital readmission rate was 25.0%. Mean (standard deviation [SD]) LOS, total cost, and loss per case were 12 (14) days, $21,502 ($37,172), and $5828 ($26,540), respectively. Hospital-onset (vs community-onset) infection significantly impacted all outcomes: mortality (odds ratio [OR], 2.21; 95% confidence interval [CI], 1.19-4.11; P=.01), 30-day readmissions (OR, 2.35; 95% CI, 1.49-3.71; P<.001), LOS (25.7 vs 10.2 days; P<.001), hospital total cost ($67,810 vs $22,141; P<.001), and loss per case (-$28,054 vs -$10,809; P<.001). Mechanical ventilation/intensive care unit status, neoplasms, and other underlying diseases were also common predictors for worse outcomes overall; polymicrobial infection was significantly associated with worse economic outcomes. Other key predictors were >1 prior hospitalization for 30-day readmissions, high Acute Laboratory Risk of Mortality Score for mortality, LOS, cost, and hospital teaching status for cost.

CONCLUSION

Hospital-onset infections, polymicrobial infections, higher clinical severity, and underlying diseases are key predictors for worsened overall burden of carbapenem-nonsusceptible gram-negative UTIs.

Contributing Factors to the Clinical and Economic Burden of Patients with Laboratory-Confirmed Carbapenem-Nonsusceptible Gram-Negative Respiratory Infections

PURPOSE

This study examined patient- and hospital-level predictor variables that contribute to worse clinical and economic outcomes in patients with carbapenem-nonsusceptible respiratory infections.

PATIENTS AND METHODS

Electronic data (January 2013 to September 2015) were from 78 US hospitals. Nonduplicate, gram-negative respiratory isolates were considered carbapenem-nonsusceptible if they tested resistant/intermediate to imipenem, meropenem, doripenem, or ertapenem. Potential predictors of outcomes (in-hospital mortality, 30-day readmission, length of stay [LOS], hospital total cost, and net gain/loss per patient) were examined using univariate analysis and generalized linear mixed models. Statistical significance and model goodness-of-fit criteria were used to identify significant predictors.

RESULTS

A total of 1488 carbapenem-nonsusceptible respiratory patients were identified. Overall, the mortality rate was 13.7%, 30-day readmission rate was 20.6%, mean LOS was 20 days, mean total cost was $54,158, and mean net loss was $139 per patient. Our models showed that hospital-onset infection, higher clinical severity, mechanical ventilation/intensive care unit status, polymicrobial infection, and underlying diseases were all significant predictors for mortality, LOS, and total cost. Hospital-onset infections were also associated with a significantly greater net loss (P≤.01), and underlying disease significantly impacted readmissions (P=.03). The number of prior admissions, hospital characteristics, and payer type were also found to significantly impact measured outcomes.

CONCLUSION

Carbapenem-nonsusceptible respiratory infections are associated with a considerable clinical and economic burden. The impact of hospital-onset infections on both clinical and economic outcomes highlights the continued need for action on this modifiable risk factor through antimicrobial stewardship and optimal therapy, thereby reducing the burden in this patient population.

Attributable burden in patients with carbapenem-nonsusceptible gram-negative respiratory infections

Objective

We aimed to describe the clinical and economic burden attributable to carbapenem-nonsusceptible (C-NS) respiratory infections.

Methods

This retrospective matched cohort study assessed clinical and economic outcomes of adult patients (aged ≥18 years) who were admitted to one of 78 acute care hospitals in the United States with nonduplicate C-NS and carbapenem-susceptible (C-S) isolates from a respiratory source. A subset analysis of patients with principal diagnosis codes denoting bacterial pneumonia or other diagnoses was also conducted. Isolates were classified as community- or hospital-onset based on collection time. A generalized linear mixed model method was used to estimate the attributable burden for mortality, 30-day readmission, length of stay (LOS), cost, and net gain/loss (payment minus cost) using propensity score-matched C-NS versus C-S cohorts.

Results

For C-NS cases, mortality (25.7%), LOS (29.4 days), and costs ($81,574) were highest in the other principal diagnosis, hospital-onset subgroup; readmissions (19.4%) and net loss (-$9522) were greatest in the bacterial pneumonia, hospital-onset subgroup. Mortality and readmissions were not significantly higher for C-NS cases in any propensity score-matched subgroup. Significant C-NS–attributable burden was found for both other principal diagnosis subgroups for LOS (hospital-onset: 3.7 days, P = 0.006; community-onset: 1.5 days, P<0.001) and cost (hospital-onset: $12,777, P<0.01; community-onset: $2681, P<0.001).

Conclusions

Increased LOS and cost burden were observed in propensity score-matched patients with C-NS compared with C-S respiratory infections; the C-NS–attributable burden was significant only for patients with other principal diagnoses.

2161. Organism-Specific Turn Around Time Improvement in Urinary Specimens as a Result of Microbiological Laboratory Automation

Background

University Medical Center in Lubbock, TX is one of few medical centers using Becton Dickinson (BD) Kiestra Total Laboratory Automation (TLA) system since May 2015. The impact on organism-specific turn around time (TAT) in urinary specimens after implementation of TLA was evaluated.

Methods

After approval from the Quality Improvement Review Board, a retrospective analysis of microbiological data from urinary specimens in BD research database was performed. Before vs. after implementation (2013 vs. 2016) TAT was compared. Ten clinically relevant organisms were analyzed. Statistical analysis was performed with SAS software version 9.2. Data were analyzed using Chi-squared test. A P-value of < 0.05 was considered statistically significant.

Results

Overall, 2282 specimens from 2013 and 2306 specimens from 2016 were analyzed. Compared with before vs. after implementation of TLA, an overall improvement in TAT was observed (expressed as mean hours for each organism): Enterococcus faecalis (55.2 vs. 38.8), Enterococcus faecium (68.4 vs. 43.8), Escherichia coli (44.2 vs. 41.0), Klebsiella pneumoniae (45.0 vs. 44.0), Proteus mirabilis (44.8 vs. 38.6), Pseudomonas aeruginosa (58.9 vs. 37.7), Staphylococcus aureus (49.2 vs. 36.0), Streptococcus agalactiae (49.2 vs. 31.4), Streptococcus pneumoniae (51.7 vs. 61.8), Streptococcus pyogenes (62.6 vs. 26.6). It was also observed that improvement in TAT was more pronounced for Gram-positive organisms than Gram-negative organisms. P-value was < 0.01 for all organisms except Streptococcus pneumoniae (0.7985) and Streptococcus pyogenes (0.2562). The number of specimens with these two organisms was too small to be considered significant.

Conclusion

Automation of microbiology laboratory leads to significant TAT improvement in urinary specimens, making early data availability to clinicians. This improves efficiency as well as supporting earlier antibiotic switch, antimicrobial stewardship and optimal patient care in treating urinary tract infections.

Disclosures

All authors: No reported disclosures.

Incremental clinical and economic burden of suspected respiratory infections due to multi-drug-resistant Pseudomonas aeruginosa in the United States

BACKGROUND

Multi-drug resistant (MDR) Pseudomonas aeruginosa can negatively affect patients and hospitals.

AIM

To evaluate excess mortality and cost burden among patients hospitalized with suspected respiratory infections due to MDR P. aeruginosa vs patients with non-MDR P. aeruginosa in 78 United States (US) hospitals.

METHODS

This study analyzed electronically captured microbiological and outcomes data of patients hospitalized with non-duplicate P. aeruginosa isolates from respiratory sources collected ≥3 days after admission to identify hospital-onset MDR or non-MDR P. aeruginosa per the Centers for Disease Control and Prevention definition. The risk of multi-drug resistance was estimated on mortality, length of stay (LOS), cost, operation gain/loss, and 30-day readmission. A sensitivity analysis was conducted utilizing a cohort with pharmacy data available.

FINDINGS

Of 523 MDR and 1381 non-MDR P. aeruginosa cases, unadjusted mortality was 23.7% vs 18.0% and multi-variable-adjusted mortality was 20.0% (95% confidence interval (CI): 14.3-27.2%) vs 15.5% (95% CI: 11.2-20.9%; P=0.026), the average adjusted excess LOS was 6.7 days (P<0.001); excess cost per case was US$22,370 higher (P=0.002) and operational loss per case was US$10,661 (P=0.024) greater, and the multi-variable adjusted readmission rate was 16.2% (95% CI: 11.2-22.9%) vs 11.1% (95% CI: 7.8-15.6%; P=0.006). The sensitivity analysis yielded similar results.

CONCLUSIONS

Compared with suspected infections due to non-MDR P. aeruginosa, patients with MDR P. aeruginosa had higher risk of mortality, readmission, and longer LOS, as well as US$20,000 incremental cost and >US$10,000 incremental net loss per case after controlling for patient and hospital characteristics.

Blood Culture Turnaround Time in U.S. Acute Care Hospitals and Implications for Laboratory Process Optimization

The rapid identification of blood culture isolates and antimicrobial susceptibility test (AST) results play critical roles for the optimal treatment of patients with bloodstream infections. Whereas others have looked at the time to detection in automated culture systems, we examined the overall time from specimen collection to actionable test results. We examined four points of time, namely, blood specimen collection, Gram stain, organism identification (ID), and AST reports, from electronic data from 13 U.S. hospitals for the 11 most common, clinically significant organisms in septic patients. We compared the differences in turnaround times and the times from when specimens were collected and the results were reported in the 24-h spectrum. From January 2015 to June 2016, 165,593 blood specimens were collected, of which, 9.5% gave positive cultures. No matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry was used during the study period. Across the 10 common bacterial isolates (n = 6,412), the overall median (interquartile range) turnaround times were 0.80 (0.64 to 1.08), 1.81 (1.34 to 2.46), and 2.71 (2.46 to 2.99) days for Gram stain, organism ID, and AST, respectively. For all positive cultures, approximately 25% of the specimens were collected between 6:00 a.m. and 11:59 a.m. In contrast, more of the laboratory reporting times were concentrated between 6:00 a.m. and 11:59 a.m. for Gram stain (43%), organism ID (78%), and AST (82%), respectively (P < 0.001). The overall average turnaround times from specimen collection for Gram stain, organism ID, and AST were approximately 1, 2, and 3 days, respectively. The laboratory results were reported predominantly in the morning hours. Laboratory automation and work flow optimization may play important roles in reducing the microbiology result turnaround time.

Attributable clinical and economic burden of carbapenem-non-susceptible Gram-negative infections in patients hospitalized with complicated urinary tract infections

BACKGROUND

Gram-negative complicated urinary tract infections (cUTIs) can have serious consequences for patients and hospitals.

AIM

To examine the clinical and economic burden attributable to Gram-negative carbapenem-non-susceptible (C-NS; resistant/intermediate) infections compared with carbapenem-susceptible (C-S) infections in 78 US hospitals.

METHODS

All non-duplicate C-NS and C-S urine source isolates were analysed. A subset had principal diagnosis ICD-9-CM codes denoting cUTI. Collection time (<3 vs ≥3 days after admission) determined isolate classification as community or hospital onset. Mortality, 30-day re-admissions, length of stay (LOS), hospital cost and net gain/loss in US dollars were determined for C-NS and C-S cases, with the C-NS-attributable burden estimated through propensity score matching. Three subgroups with adequate patient numbers were analysed: cUTI principal diagnosis, community onset; other principal diagnosis, community onset; and other principal diagnosis, hospital onset.

FINDINGS

The C-NS-attributable mortality risk was significantly higher (58%) for the other principal diagnosis, hospital-onset subgroup alone (odds ratio 1.58, 95% confidence interval 1.14-2.20; P < 0.01). The C-NS-attributable risk for 30-day re-admission ranged from 29% to 55% (all P < 0.05). The average attributable economic impact of C-NS was 1.1-3.9 additional days LOS (all P < 0.05), US$1512-10,403 additional total cost (all P < 0.001) and US$1582-11,848 net loss (all P < 0.01); overall burden and C-NS-attributable burden were greatest in the other principal diagnosis, hospital-onset subgroup.

CONCLUSION

Greater clinical and economic burden was observed in propensity-score-matched patients with C-NS infections compared with C-S infections, regardless of whether cUTI was the principal diagnosis, and this burden was most severe in hospital-onset infections.

The Effect of a Piperacillin/Tazobactam Shortage on Antimicrobial Prescribing and Clostridium difficile Risk in 88 US Medical Centers

Background

Anti-infective shortages are a pervasive problem in the United States. The objective of this study was to identify any associations between changes in prescribing of antibiotics that have a high risk for CDI during a piperacillin/tazobactam (PIP/TAZO) shortage and hospital-onset Clostridium difficile infection (HO-CDI) risk in 88 US medical centers.

Methods

We analyzed electronically captured microbiology and antibiotic use data from a network of US hospitals from July 2014 through June 2016. The primary outcome was HO-CDI rate and the secondary outcome was changes in antibiotic usage. We fit a Poisson model to estimate the risk of HO-CDI associated with PIP/TAZO shortage that were associated with increased high-risk antibiotic use while controlling for hospital characteristics.

Results

A total of 88 hospitals experienced PIP/TAZO shortage and 72 of them experienced a shift toward increased use of high-risk antibiotics during the shortage period. The adjusted relative risk (RR) of HO-CDI for hospitals experiencing a PIP/TAZO shortage was 1.03 (95% confidence interval [CI], .85-1.26; P = .73). The adjusted RR of HO-CDI for hospitals that both experienced a shortage and also showed a shift toward increased use of high-risk antibiotics was 1.30 (95% CI, 1.03-1.64; P < .05).

Conclusions

Hospitals that experienced a PIP/TAZO shortage and responded to that shortage by shifting antibiotic usage toward antibiotics traditionally known to place patients at greater risk for CDI experienced greater HO-CDI rates; this highlights an important adverse effect of the PIP/TAZO shortage and the importance of antibiotic stewardship when mitigating drug shortages.

Predicting Readmission at Early Hospitalization Using Electronic Clinical Data: An Early Readmission Risk Score

BACKGROUND

Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission.

METHODS

We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score as an aggregated measure of clinical severity at admission and the number of hospital discharges in the previous 90 days as a measure of disease progression. We then evaluated the administrative data-enhanced model by adding principal and secondary diagnoses and other variables. We examined the c-statistic change when additional variables were added to the model.

RESULTS

There were 1,195,640 adult discharges from 70 hospitals with 39.8% male and the median age of 63 years (first and third quartile: 43, 78). The 30-day readmission rate was 11.9% (n=142,211). The early readmission model yielded a graded relationship of readmission and the Acute Laboratory Risk of Mortality Score and the number of previous discharges within 90 days. The model c-statistic was 0.697 with good calibration. When administrative variables were added to the model, the c-statistic increased to 0.722.

CONCLUSIONS

Automated clinical data can generate a readmission risk score early at hospitalization with fair discrimination. It may have applied value to aid early care transition. Adding administrative data increases predictive accuracy. The administrative data-enhanced model may be used for hospital comparison and outcome research.

Innovative Use of Existing Public and Private Data Sources for Postmarketing Surveillance of Central Line-Associated Bloodstream Infections Associated With Intravenous Needleless Connectors

The Centers for Medicare and Medicaid Services (CMS) Hospital Compare central line-associated bloodstream infection (CLABSI) data and private databases containing new-generation intravenous needleless connector (study NC) use at the hospital level were linked. The relative risk (RR) of CLABSI associated with the study NCs was estimated, adjusting for hospital characteristics. Among 3074 eligible hospitals in the 2013 CMS database, 758 (25%) hospitals used the study NCs. The study NC hospitals had a lower unadjusted CLABSI rate (1.03 vs 1.13 CLABSIs per 1000 central line days, P < .0001) compared with comparator hospitals. The adjusted RR for CLABSI was 0.94 (95% confidence interval: 0.86, 1.02; P = .11).

Performance characteristics and associated outcomes for an automated surveillance tool for bloodstream infection

BACKGROUND

The objective of this study was to evaluate performance metrics and associated patient outcomes of an automated surveillance system, the blood Nosocomial Infection Marker (NIM).

METHODS

We reviewed records of 237 patients with and 36,927 patients without blood NIM using the National Healthcare Safety Network (NHSN) definition for laboratory-confirmed bloodstream infection (BSI) as the gold standard. We matched cases with noncases by propensity score and estimated attributable mortality and cost of NHSN-reportable central line-associated bloodstream infections (CLABSIs) and non-NHSN-reportable BSIs.

RESULTS

For patients with central lines (CL), the blood NIM had 73.2% positive predictive value (PPV), 99.9% negative predictive value (NPV), 89.2% sensitivity, and 99.7% specificity. For all patients regardless of CL status, the blood NIM had 53.6% PPV, 99.9% NPV, 84.0% sensitivity, and 99.9% specificity. For CLABSI cases compared with noncases, mortality was 17.5% versus 9.4% (P = .098), and median charge was $143,935 (interquartile range [IQR], $89,794-$257,447) versus $115,267 (IQR, $74,937-$173,053) (P < .01). For non-NHSN-reportable BSI cases compared with noncases, mortality was 23.6% versus 6.7% (P < .0001), and median charge was $86,927 (IQR, $54,728-$156,669) versus $62,929 (IQR, $36,743-$115,693) (P < .0001).

CONCLUSIONS

The NIM is an effective screening tool for BSI. Both NHSN-reportable and nonreportable BSI cases were associated with increased mortality and cost.

Meta-analysis on central line-associated bloodstream infections associated with a needleless intravenous connector with a new engineering design

BACKGROUND

Intravenous needleless connectors (NCs) with a desired patient safety design may facilitate effective intravenous line care and reduce the risk for central line-associated bloodstream infection (CLA-BSI). We conducted a meta-analysis to determine the risk for CLA-BSI associated with the use of a new NC with an improved engineering design.

METHODS

We reviewed MEDLINE, Cochrane Database of Systematic Reviews, Embase, ClinicalTrials.gov, and studies presented in 2010-2012 at infection control and infectious diseases meetings. Studies reporting the CLA-BSIs in patients using the positive-displacement NC (study NC) compared with negative- or neutral-displacement NCs were analyzed. We estimated the relative risk of CLA-BSIs with the study NC for the pooled effect using the random effects method.

RESULTS

Seven studies met the inclusion criteria: 4 were conducted in intensive care units, 1 in a home health setting, and 2 in long-term acute care settings. In the comparator period, total central venous line (CL) days were 111,255; the CLA-BSI rate was 1.5 events per 1,000 CL days. In the study NC period, total CL days were 95,383; the CLA-BSI rate was 0.5 events per 1,000 CL days. The pooled CLA-BSI relative risk associated with the study NC was 0.37 (95% confidence interval, 0.16-0.90).

CONCLUSION

The NC with an improved engineering design is associated with lower CLA-BSI risk.

Using electronic health record data to develop inpatient mortality predictive model: Acute Laboratory Risk of Mortality Score (ALaRMS)

Objective

Using numeric laboratory data and administrative data from hospital electronic health record (EHR) systems, to develop an inpatient mortality predictive model.

Methods

Using EHR data of 1 428 824 adult discharges from 70 hospitals in 2006–2007, we developed the Acute Laboratory Risk of Mortality Score (ALaRMS) using age, gender, and initial laboratory values on admission as candidate variables. We then added administrative variables using the Agency for Healthcare Research and Quality (AHRQ)'s clinical classification software (CCS) and comorbidity software (CS) as disease classification tools. We validated the model using 770 523 discharges in 2008.

Results

Mortality predictors with ORs >2.00 included age, deranged albumin, arterial pH, bands, blood urea nitrogen, oxygen partial pressure, platelets, pro-brain natriuretic peptide, troponin I, and white blood cell counts. The ALaRMS model c-statistic was 0.87. Adding the CCS and CS variables increased the c-statistic to 0.91. The relative contributions were 69% (ALaRMS), 25% (CCS), and 6% (CS). Furthermore, the integrated discrimination improvement statistic demonstrated a 127% (95% CI 122% to 133%) overall improvement when ALaRMS was added to CCS and CS variables. In contrast, only a 22% (CI 19% to 25%) improvement was seen when CCS and CS variables were added to ALaRMS.

Conclusions

EHR data can generate clinically plausible mortality predictive models with excellent discrimination. ALaRMS uses automated laboratory data widely available on admission, providing opportunities to aid real-time decision support. Models that incorporate laboratory and AHRQ's CCS and CS variables have utility for risk adjustment in retrospective outcome studies.

Attributable burden of hospital-onset Clostridium difficile infection: a propensity score matching study

OBJECTIVE

 To determine the attributable in-hospital mortality, length of stay (LOS), and cost of hospital-onset Clostridium difficile infection (HO-CDI).

DESIGN

 Propensity score matching.

SETTING

 Six Pennsylvania hospitals (2 academic centers, 1 community teaching facility, and 3 community nonteaching facilities) contributing data to a clinical research database.

PATIENTS

 Adult inpatients between 2007 and 2008.

METHODS

 We defined HO-CDI in adult inpatients as a positive C. difficile toxin assay result from a specimen collected more than 48 hours after admission and more than 8 weeks following any previous positive result. We developed an HO-CDI propensity model and matched cases with noncases by propensity score at a 1∶3 ratio. We further restricted matching within the same hospital, within the same principal disease group, and within a similar length of lead time from admission to onset of HO-CDI.

RESULTS

 Among 77,257 discharges, 282 HO-CDI cases were identified. The propensity score-matched rate was 90%. Compared with matched noncases, HO-CDI patients had higher mortality (11.8% vs. 7.3%; P < .05), longer LOS (median [interquartile range (IQR)], 12 [9-21] vs. 11 [8-17] days; P < .01), and higher cost (median [IQR], $20,804 [$11,059-$38,429] vs. $16,634 [$9,413-$30,319]; P < .01). The attributable effect of HO-CDI was 4.5% (95% confidence interval [CI], 0.2%-8.7%; P < .05) for mortality, 2.3 days (95% CI, 0.9-3.8; P < .01) for LOS, and $6,117 (95% CI, $1,659-$10,574; P < .01) for cost.

CONCLUSIONS

 Patients with HO-CDI incur additional attributable mortality, LOS, and cost burden compared with patients with similar primary clinical condition, exposure risk, lead time of hospitalization, and baseline characteristics.

Severe acute hypertension among inpatients admitted from the emergency department

BACKGROUND

Hospitalists often treat patients with severe acute hypertension (AH) presenting to the hospital. Little is known about the epidemiology of this syndrome.

OBJECTIVE

To examine the prevalence of severe AH in patients admitted through the emergency department (ED) and its associated outcomes.

DESIGN

A cohort study using retrospectively collected vital signs and other clinical data.

PATIENTS

A total of 1,290,804 adults admitted between 2005 and 2007.

SETTING

One hundred fourteen acute-care hospitals.

MEASUREMENTS

Severe AH was defined as at least 1 systolic blood pressure (SBP) >180 mmHg. We used multivariable regression to estimate AH-attributable in-hospital mortality, need for mechanical ventilation (MV), and length of stay (LOS).

RESULTS

Severe AH occurred in 178,131 (13.8%) patients. Disease categories with the highest prevalence were nervous (29.0%), circulatory (16.0%), endocrine (14.7%), and kidney/urinary (13.5%). The overall in-hospital mortality was 3.6%. The relationship between severe AH strata and mortality was graded for nervous system diseases; mortality rates for each 10 mmHg increase in SBP from 180 to >220 mmHg were 6.5%, 8.1%, 9.9%, 12.0%, and 19.7%, respectively (P < 0.0001). The relationship between severe AH strata and need for MV was graded in the most pronounced way in respiratory and circulatory conditions (P < 0.0001). The relationship between severe AH strata and LOS was graded in most disease categories (P < 0.0001).

CONCLUSIONS

Severe AH appears common and its prevalence varies by underlying clinical condition. Severe AH is associated with excess in-hospital mortality for patients with nervous system diseases and, for most disease categories, prolongs hospitalization.

Using electronic health information to risk-stratify rates of Clostridium difficile infection in US hospitals

BACKGROUND

Expanding hospitalized patients' risk stratification for Clostridium difficile infection (CDI) is important for improving patient safety. We applied definitions for hospital-onset (HO) and community-onset (CO) CDI to electronic data from 85 hospitals between January 2007 and June 2008 to identify factors associated with higher HO CDI rates.

METHODS

Nonrecurrent CDI cases were identified among adult (≥ 18-year-old) inpatients by a positive C. difficile toxin assay result more than 8 weeks after any previous positive result. Case categories included HO, CO-hospital associated (CO-HA), CO-indeterminate hospital association (CO-IN), and CO-non-hospital associated (CO-NHA). C. difficile testing intensity (CDTI) was defined as the total number of C. difficile tests performed, normalized to the number of patients with at least 1 C. difficile toxin test recorded. We calculated both the incidence density and the prevalence of CDI where appropriate. We fitted a multivariable Poisson model to identify factors associated with higher HO CDI rates.

RESULTS

Among 1,351,156 unique patients with 2,022,213 admissions, 9,803 cases of CDI were identified; of these, 50.6% were HO, 17.4% were CO-HA, 9.0% were CO-IN, and 23.0% were CO-NHA. The incidence density of HO was 6.3 per 10,000 patient-days. The prevalence of CO CDI on admission was, per 10,000 admissions, 8.4 for CO-HA, 4.4 for CO-IN, and 11.1 for CO-NHA. Factors associated (P < .0001) with higher HO CDI rates included older age, higher CO-NHA prevalence on admission, and increased CDTI.

CONCLUSION

Electronic health information can be leveraged to risk-stratify HO CDI rates by patient age and CO-NHA prevalence on admission. Hospitals should optimize diagnostic testing to improve patient care and measured CDI rates.

Developing and validating a risk score for lower-extremity amputation in patients hospitalized for a diabetic foot infection

OBJECTIVE

Diabetic foot infection is the predominant predisposing factor to nontraumatic lower-extremity amputation (LEA), but few studies have investigated which specific risk factors are most associated with LEA. We sought to develop and validate a risk score to aid in the early identification of patients hospitalized for diabetic foot infection who are at highest risk of LEA.

RESEARCH DESIGN AND METHODS

Using a large, clinical research database (CareFusion), we identified patients hospitalized at 97 hospitals in the U.S. between 2003 and 2007 for culture-documented diabetic foot infection. Candidate risk factors for LEA included demographic data, clinical presentation, chronic diseases, and recent previous hospitalization. We fit a logistic regression model using 75% of the population and converted the model coefficients to a numeric risk score. We then validated the score using the remaining 25% of patients.

RESULTS

Among 3,018 eligible patients, 21.4% underwent an LEA. The risk factors most highly associated with LEA (P < 0.0001) were surgical site infection, vasculopathy, previous LEA, and a white blood cell count >11,000 per mm(3). The model showed good discrimination (c-statistic 0.76) and excellent calibration (Hosmer-Lemeshow, P = 0.63). The risk score stratified patients into five groups, demonstrating a graded relation to LEA risk (P < 0.0001). The LEA rates (derivation and validation cohorts) were 0% for patients with a score of 0 and ~50% for those with a score of ≥21.

CONCLUSIONS

Using a large, hospitalized population, we developed and validated a risk score that seems to accurately stratify the risk of LEA among patients hospitalized for a diabetic foot infection. This score may help to identify high-risk patients upon admission.

Burden of sodium abnormalities in patients hospitalized for heart failure

Hyponatremia presumably is associated with adverse clinical outcomes in patients with congestive heart failure (CHF), but risk thresholds and economic burden are less studied. The authors analyzed 115,969 patients hospitalized for CHF and grouped them by serum sodium levels (severe hyponatremia, ≤130 mEq/L; hyponatremia, 131-135 mEq/L; normonatremia, 136-145 mEq/L; hypernatremia, >145 mEq/L). Univariable and multivariable analyses on the associated clinical and economic outcomes were performed. The most common abnormality was hyponatremia (15.9%), followed by severe hyponatremia (5.3%) and hypernatremia (3.2%). Hospital mortality was highest for severe hyponatremia (7.6%), followed by hypernatremia (6.7%) and hyponatremia (4.9%) (P<.0001). Compared with normonatremia, risk-adjusted mortality was highest for severe hyponatremia (odds ratio [OR], 1.78; 95% confidence interval [CI], 1.59-1.99), followed by hypernatremia (OR, 1.55; 95% CI, 1.34-1.80) and hyponatremia (OR, 1.29; 95% CI, 1.19-1.40; all P<.0001). Risk-adjusted hospital prolongation was greater for each level of sodium abnormality than for normonatremia, ranging from 0.42 (CI, 0.26-0.60) days for hypernatremia to 1.28 (CI, 1.11-1.47) days for severe hyponatremia. Risk-adjusted attributable hospital cost increase was highest for severe hyponatremia ($1132; CI, $856-$1425; all (P<.0001). Sodium abnormalities were common in patients hospitalized for CHF. Adverse outcomes resulted not only from severe hyponatremia, but also from mild hyponatremia and hypernatremia.

Development and validation of a disease-specific risk adjustment system using automated clinical data

OBJECTIVE

To develop and validate a disease-specific automated inpatient mortality risk adjustment system primarily using computerized numerical laboratory data and supplementing them with administrative data. To assess the values of additional manually abstracted data.

METHODS

Using 1,271,663 discharges in 2000-2001, we derived 39 disease-specific automated clinical models with demographics, laboratory findings on admission, ICD-9 principal diagnosis subgroups, and secondary diagnosis-based chronic conditions. We then added manually abstracted clinical data to the automated clinical models (manual clinical models). We compared model discrimination, calibration, and relative contribution of each group of variables. We validated these 39 models using 1,178,561 discharges in 2004-2005.

RESULTS

The overall mortality was 4.6 percent (n = 58,300) and 4.0 percent (n = 47,279) for derivation and validation cohorts, respectively. Common mortality predictors included age, albumin, blood urea nitrogen or creatinine, arterial pH, white blood counts, glucose, sodium, hemoglobin, and metastatic cancer. The average c-statistic for the automated clinical models was 0.83. Adding manually abstracted variables increased the average c-statistic to 0.85 with better calibration. Laboratory results displayed the highest relative contribution in predicting mortality.

CONCLUSIONS

A small number of numerical laboratory results and administrative data provided excellent risk adjustment for inpatient mortality for a wide range of clinical conditions.

Predicting bacteremia among patients hospitalized for skin and skin-structure infections: derivation and validation of a risk score

OBJECTIVE

Bacteremia is relatively common in patients with skin and skin-structure infection (SSSI) severe enough to require hospitalization. We used selected demographic and clinical characteristics easily assessable at initial evaluation to develop a model for the early identification of patients with SSSI who are at higher risk for bacteremia.

PARTICIPANTS

A large database of adults hospitalized with SSSI at 97 hospitals in the United States during the period from 2003 through 2007 and from whom blood samples were obtained for culture at admission.

METHODS

We compared selected candidate predictor variables for patients shown to have bacteremia and patients with no demonstrated bacteremia. Using stepwise logistic regression to identify independent risk factors for bacteremia, we derived a model by using 75% of a randomly split cohort, converted the model coefficients into a risk score system, and then we validated it by using the remaining 25% of the cohort.

RESULTS

Bacteremia was documented in 1,021 (11.7%) of the 8,747 eligible patients. Independent predictors of bacteremia (P<.001) were infected device or prosthesis, respiratory rate less than 10 or more than 29 breaths per minute, pulse rate less than 49 or more than 125 beats per minute, temperature less than 35.6 degrees C or at least 38.0 degrees C, white blood cell band percentage of 7% or more, white blood cell count greater than 11x10(9)/L, healthcare-associated infection, male sex, and older age. The bacteremia rates ranged from 3.7% (lowest decile) to 30.6% (highest decile) (P<.001). The model C statistic was 0.71; the Hosmer-Lemeshow test P value was .36, indicating excellent model calibration.

CONCLUSIONS

Using data available at hospital admission, we developed a risk score that differentiated SSSI patients at low risk for bacteremia from patients at high risk. This score may help clinicians identify patients who require more intensive monitoring or antimicrobial regimens appropriate for treating bacteremia.

Skin and soft tissue infections in hospitalised patients with diabetes: culture isolates and risk factors associated with mortality, length of stay and cost

AIMS/HYPOTHESIS

Skin and soft tissue infections (SSTIs) cause substantial morbidity in persons with diabetes. There are few data on pathogens or risk factors associated with important outcomes in diabetic patients hospitalised with SSTIs.

METHODS

Using a clinical research database from CareFusion, we identified 3,030 hospitalised diabetic patients with positive culture isolates and a diagnosis of SSTI in 97 US hospitals between 2003 and 2007. We classified the culture isolates and analysed their association with the anatomic location of infection, mortality, length of stay and hospital costs.

RESULTS

The only culture isolate with a significantly increased prevalence was methicillin-resistant Staphylococcus aureus (MRSA); prevalence for infection of the foot was increased from 11.6 to 21.9% (p < 0.0001) and for non-foot locations from 14.0% to 24.6% (p = 0.006). Patients with non-foot (vs foot) infections were more severely ill at presentation and had higher mortality rates (2.2% vs 1.0%, p < 0.05). Significant independent risk factors associated with higher mortality rates included having a polymicrobial culture with Pseudomonas aeruginosa (OR 3.1), a monomicrobial culture with other gram-negatives (OR 8.9), greater illness severity (OR 1.9) and being transferred from another hospital (OR 5.1). These factors and need for major surgery were also independently associated with longer length of stay and higher costs.

CONCLUSIONS/INTERPRETATION

Among diabetic patients hospitalised with SSTI from 2003 to 2007, only MRSA increased in prevalence. Patients with non-foot (vs foot) infections were more severely ill. Independent risk factors for increased mortality rates, length of stay and costs included more severe illness, transfer from another hospital and wound cultures with Pseudomonas or other gram-negatives.

Surgical site infections: Causative pathogens and associated outcomes

BACKGROUND

Surgical site infections (SSIs) are associated with substantial morbidity, mortality, and cost. Few studies have examined the causative pathogens, mortality, and economic burden among patients rehospitalized for SSIs.

METHODS

From 2003 to 2007, 8302 patients were readmitted to 97 US hospitals with a culture-confirmed SSI. We analyzed the causative pathogens and their associations with in-hospital mortality, length of stay (LOS), and cost.

RESULTS

The proportion of methicillin-resistant Staphylococcus aureus (MRSA) significantly increased among culture-positive SSI patients during the study period (16.1% to 20.6%, respectively, P < .0001). MRSA (compared with other) infections had higher raw mortality rates (1.4% vs 0.8%, respectively, P=.03), longer LOS (median, 6 vs 5 days, respectively, P < .0001), and higher hospital costs ($7036 vs $6134, respectively, P < .0001). The MRSA infection risk-adjusted attributable LOS increase was 0.93 days (95% confidence interval [CI]: 0.65-1.21; P < .0001), and cost increase was $1157 (95% CI: $641-$1644; P < .0001). Other significant independent risk factors increasing cost and LOS included illness severity, transfer from another health care facility, previous admission (<30 days), and other polymicrobial infections (P < .05).

CONCLUSION

SSIs caused by MRSA increased significantly and were independently associated with economic burden. Admission illness severity, transfer from another health care setting, and recent hospitalization were associated with higher mortality, increased LOS, and cost.

Candidemia on presentation to the hospital: development and validation of a risk score

INTRODUCTION

Candidemia results in substantial morbidity and mortality, especially if initial antifungal therapy is delayed or is inappropriate; however, candidemia is difficult to diagnose because of its nonspecific presentation.

METHODS

To develop a risk score for identifying hospitalized patients with candidemia, we performed a retrospective analysis of a large database of 176 acute-care hospitals in the United States. We studied 64,019 patients with bloodstream infection (BSI) on presentation from 2000 through 2005 (derivation cohort) and 24,685 from 2006 to 2007 (validation cohort). We used recursive partitioning (RPART) to identify the best discriminators for Candida as the cause of BSI. We compared three sets of models (equal-weight, unequal-weight, vs full model with additional variables from logistic regression model) for sensitivity analysis.

RESULTS

The RPART identified 6 variables as the best discriminators: age < 65 years, temperature <or= 98 degrees F or severe altered mental status, cachexia, previous hospitalization within 30 days, admitted from other healthcare facility, and need for mechanical ventilation. The prevalence for patients presented with 0 through 6 risk factors in the derivation cohort was 28.7%, 38.8%, 21.8%, 8.3%, 2.1%, 0.3%, and < 0.1% respectively. The corresponding candidemia rates were 0.4% (69/18,355), 0.8% (196/24,811), 1.6% (229/13,984), 3.2% (168/5,330), 4.2% (58/1,371), 9.6% (15/157), and 27.3% (3/11) respectively (P < 0.0001). Findings were similar in the validation cohort (P < 0.0001). The equal-weight risk score model, which signed 1 point to each risk factor, yielded good discrimination in both cohorts with areas under the receiver operating curve (AUROCs) of 0.70 versus 0.71 (derivation versus validation). AUROC values were similar for the unequal-weight model, which signed different weight to each risk factor based on multivariable logistic regression coefficient, (AUROCs, 0.70-0.72). Both equal-weight and unequal-weight models were well calibrated (all Hosmer-Lemshow P > 0.10, indicating predicted and observed candidemia rates did not differ significant across the 7 risk stratus). The full model with 16 risk factors had slightly higher AUROCs (0.74 versus 0.73 for derivation versus validation); however, 7 variables were no longer significant in the recalibrated model for the validation cohort, indicating that the additional items did not materially enhance the model.

CONCLUSIONS

A simple equal-weight risk score differentiated patients' risk for candidemia in a graded fashion upon hospital presentation.

Mortality and need for mechanical ventilation in acute exacerbations of chronic obstructive pulmonary disease: development and validation of a simple risk score

BACKGROUND

Acute exacerbations of chronic obstructive pulmonary disease (AECOPDs) often require hospitalization, may necessitate mechanical ventilation, and can be fatal. We sought to develop a simple risk score to determine its severity.

METHODS

We analyzed 88,074 subjects admitted with an AECOPD between 2004 and 2006. We used recursive partition to create risk classifications for in-hospital mortality. Need for mechanical ventilation served as a secondary end point. We internally validated the model via 1000 bootstrapping on half of patients and externally validated it on the remaining patients. We assessed predictive ability using the area under the receiver operating curve (AUROC).

RESULTS

The in-hospital mortality rate was 2%. Three variables had high discrimination of outcomes: serum urea nitrogen level greater than 25 mg/dL (to convert to millimoles per liter, multiply by 0.357); acute mental status change, and pulse greater than 109/min. For those without any of the 3 factors, age 65 years or younger further differentiated the lowest-risk group. In those with all 3 factors, the mortality rates were 13.1% (131 in 1000) and 14.6% (146 in 1000) in the derivation and validation cohorts, respectively, compared with 0.3% (3 in 1000) in both cohorts among patients without any of the 3 factors and age 65 years or younger (P < .001). The AUROC for mortality in the 2 cohorts were 0.72 (95% confidence interval [CI], 0.70-0.74) and 0.71 (95% CI, 0.70-0.73), respectively. For mechanical ventilation, the AUROCs were 0.77 (95% CI, 0.75-0.79) for both cohorts.

CONCLUSIONS

A simple risk class based on clinical variables easily obtained at presentation predicts mortality and need for mechanical ventilation. It may facilitate the triage and care of patients with AECOPD.